Ijetcas vol3 print 1 by iasir journals

ISSN (ONLINE): 2279-0055 ISSN (PRINT): 2279-0047

Issue 9, Volume 1, 2 & 3 June-August, 2014

International Journal of Emerging Technologies in Computational and Applied Sciences

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijetcas@gmail.com

PREFACE We are delighted to welcome you to the ninth issue of the International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS). In recent years, advances in science, technology, engineering, and mathematics have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJETCAS is publishing high-quality, peer-reviewed papers covering topics such as computer science, artificial intelligence, pattern recognition, knowledge engineering, process control theory and applications, distributed systems, computer networks and software engineering, electrical engineering, electric machines modeling and design, control of electric drive systems, non-conventional energy conversion, sensors, electronics, communications, data transmission, energy converters, transducers modeling and design, electro-physics, nanotechnology, and quantum mechanics.

The editorial board of IJETCAS is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including computer science, cognitive science, learning sciences, artificial intelligence, electronics, soft computing, genetic

algorithms,

technology

management,

manufacturing

technology,

electrical

technology, applied mathematics, automatic control , nuclear engineering, computational physics, computational chemistry and other related disciplines of computational and applied sciences. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to computational and applied sciences. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic Search, INSPEC, ProQuest, ArnetMiner, Base, ChemXSeer, citebase, OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library,

arXiv,

ERIC,

EasyBib,

Infotopia,

WorldCat,

.docstoc

JURN,

Mendeley,

ResearchGate, cogprints, OCLC, iSEEK, Scribd, LOCKSS, CASSI, E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJETCAS for entrusting us with the important job. We are thankful to the members of the IJETCAS editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this ninth issue, we received 154 research papers and out of which only 55 research papers are published in three volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of computational and applied sciences.

This issue of the IJETCAS has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in computational and applied sciences and may open new area for research and development. We hope you will enjoy this ninth issue of the International Journal of Emerging Technologies in Computational and Applied Sciences and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), ISSN (Online): 2279-0055, ISSN (Print): 2279-0047 (June-August, 2014, Issue 9, Volume 1, 2 & 3). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Social networks and intelligence  Social science simulation  Information retrieval systems  Technology management  Digital libraries for e-learning  Web-based learning, wikis and blogs  Operational research  Ontologies and meta-data standards  Engineering problems and emerging application  Agent based modeling and systems  Ubiquitous computing  Wired and wireless data communication networks  Mobile Ad Hoc, sensor and mesh networks  Natural language processing and expert systems  Monte Carlo methods and applications  Fuzzy logic and soft computing  Data mining and warehousing  Software and web engineering  Distributed AI systems and architectures  Neural networks and applications  Search and meta-heuristics  Bioinformatics and scientific computing  Genetic network modeling and inference  Knowledge and information management techniques  Aspect-oriented programming  Formal and visual specification languages  Informatics and statistics research  Quantum computing  Automata and formal languages  Computer graphics and image processing  Web 3D and applications  Grid computing and cloud computing  Algorithms design  Genetic algorithms  Compilers and interpreters  Computer architecture & VLSI  Advanced database systems  Digital signal and image processing  Distributed and parallel processing  Information retrieval systems  Technology management  Automation and mobile robots  Manufacturing technology  Electrical technology  Applied mathematics  Automatic control  Nuclear engineering  Computational physics  Computational chemistry

TABLE OF CONTENTS (June-August, 2014, Issue 9, Volume 1, 2 & 3) Issue 9, Volume 1 Paper Code

Paper Title

Page No.

IJETCAS 14-504

Image Compression using Hybrid Slant Wavelet where Slant is Base Transform and Sinusoidal Transforms are Local Transforms H. B. Kekre, Tanuja Sarode, Prachi Natu

01-10

IJETCAS 14-507

Error Propagation of Quantitative Analysis Based on Ratio Spectra Prof. J. Dubrovkin

11-20

IJETCAS 14-508

Thermal and Moisture Behavior of Premise Exposed to Real Climate Condition Nour LAJIMI, Noureddine BOUKADIDA

21-28

IJETCAS 14-509

Influence of notch parameters on fracture behavior of notched component M. Moussaoui, S. Meziani

29-37

IJETCAS 14-510

Modeling Lipase Production From Co-cultures of Lactic Acid Bacteria Using Neural Networks and Support Vector Machine with Genetic Algorithm Optimization Sita Ramyasree Uppada, Aditya Balu, Amit Kumar Gupta, Jayati Ray Dutta

38-43

IJETCAS 14-515

Numerical investigation of absorption dose distribution of onion powder in electron irradiation system by MCNPX code T. Taherkhani, Gh. Alahyarizadeh

44-49

IJETCAS 14-516

Predicting Crack Width in Circular Ground Supported Reservoir Subject to Seismic Loading Using Radial Basis Neural Networks: RC & FRC Wall Tulesh.N.Patel, S.A. Vasanwala, C.D. Modhera

50-55

IJETCAS 14-518

Impact of Various Channel Coding Schemes on Performance Analysis of Subcarrier IntensityModulated Free Space Optical Communication System Joarder Jafor Sadique, Shaikh Enayet Ullah and Md. Mahbubar Rahman

56-60

IJETCAS 14-523

Glaucomatous Image Classification Based On Wavelet Features Shafan Salam, Jobins George

61-65

IJETCAS 14-524

Comparative Analysis of EDFA based 32 channels WDM system for bidirectional and counter pumping techniques Mishal Singla, Preeti, Sanjiv Kumar

66-70

IJETCAS 14-525

Appraising Water Quality Aspects for an Expanse of River Cauvery alongside Srirangapatna Ramya, R. and Ananthu, K. M.

71-75

IJETCAS 14-527

An Improved Image Steganography Technique Using Discrete Wavelet Transform Richika Mahajan, B.V. Kranthi

76-82

IJETCAS 14-528

Robust Watermarking in Mid-Frequency Band in Transform Domain using Different Transforms with Full, Row and Column Version and Varying Embedding Energy Dr. H. B. Kekre, Dr. Tanuja Sarode, Shachi Natu

83-93

IJETCAS 14-533

Optimizing Pair Programming Practice through PPPA Smitha Madhukar

94-98

IJETCAS 14-536

Estimation of Inputs for a Desired Output of a Cooperative and Supportive Neural Network P. Raja Sekhara Rao, K. Venkata Ratnam and P.Lalitha

99-105

IJETCAS 14-537

Effect of High Temperature Pre-Annealing on Thermal Donors in N-Doped CZ-Silicon Vikash Dubey and Mahipal Singh

106-109

Issue 9, Volume 2 Paper Code

Paper Title

Page No.

IJETCAS 14-538

Sybil Attack Detection through TDOA-Based Localization Method in Wireless Sensor Network Sweety Saxena, Vikas Sejwar

110-114

IJETCAS 14-539

Study of the Maxwell-Boltzmann Distribution Asymmetry J. Dubrovkin

115-118

IJETCAS 14-542

Design and Analysis of High Speed SRAM Cell at 45nm Technology PN Vamsi Kiran, Anurag Mondal

119-123

IJETCAS 14-545

An Empirical Study of Impact of Software Development Model on Software Delivery Dr. Rajinder Singh

124-127

IJETCAS 14-546

On finding nth root of m leading to Newton Raphson’s improved method Nitin Jain, Kushal D Murthy and Hamsapriye

128-136

IJETCAS 14-548

Analysis of compression and elasticity of the nanocrystalline cubic silicon nitride (γ- Si3N4) under high pressure Monika Goyal & B.R.K.Gupta

137-140

IJETCAS 14-549

Efficacy of GGBS Stabilized Soil Cushions With and Without Lime in Pavements Sridevi G and Sreerama Rao A

141-147

IJETCAS 14-550

Evaluation of UV/H2O2 advanced oxidation process (AOP) for the degradation of acid orange7 and basic violet 14 dye in aqueous solution P. Manikandan, P. N. Palanisamy, R.Ramya, and D. Nalini

148-151

IJETCAS 14-555

Modified Error Data Normalized Step Size algorithm Applied to Adaptive Noise Canceller Shelly Garg and Ranjit Kaur

152-158

IJETCAS 14-559

Design and Analysis of Solar Power Switched Inductor and Switched Capacitor for DC Distribution System Mr. D.Saravanakumar, Mrs. G.Gaayathri

159-165

IJETCAS 14-562

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Issue 9, Volume 3 Paper Code

Paper Title

Page No.

IJETCAS 14-578

Mass media Interventions and Technology transfer among Banana Growers: Experiences from Tamil Nadu, India P. Ravichamy, S. Nandakumar, K.C.Siva balan

204-209

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319-325

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Mass media Interventions and Technology transfer among Banana Growers: Experiences from Tamil nadu, India P. Ravichamy** S. Nandakumar* K.C.Siva balan*** * Technical Officer- Journalism, National Research Centre for Banana (ICAR), Thogamalai Road, Tiruchirapalli - 620 102, Tamil Nadu and P-T Research scholar, Dept. of Journalism and Mass Communication, Periyar University,Salem-636011,Tamil Nadu. ** Associate Professor, Dept. of Journalism and Mass Communication, Periyar University, Salem - 636 011, Tamil Nadu. *** PhD scholar Agricultural Extension, AD AC & RI, Trichy-620009, Tamil Nadu. ________________________________________________________________________________________ Abstract: The world has become a global village due to the revolution happened in technology and communication domain. In the advent of globalization, the new communication technologies have played vital role in dissemination of information for the development of society. The mass media acts as a powerful communication tool to disseminate technological transfer by sharing of information and helps the agricultural farmers for more agricultural production. The objective of this study is to analyse the role of the media as a catalyst in disseminating information, reach and awareness created by mass media, behaviour pattern of banana growers to adopting technology transfer information in Tiruchirapalli district. The paper aims to research how media plays an important role in spreading information and create awareness to accept and adopt various technological methods of banana cultivation. The present investigation carried out in Tiruchirapalli district of Tamil Nadu indicated that the progressive farmers were the most credible source of information for the Banana growers. A quantitative analysis was employed to check the role of media and create awareness among banana farmers. A random sample survey was conducted among banana farmers in 10 villages from the chosen four blocks. Critical aspects of banana cultivation like soil type, planting materials, fertilizer, and irrigation practice, number of irrigation and method of manures applications were tested through structured interview schedule. Results of the study show that, various technological developments adopted in banana cultivation and efficient use media for technological transfer by the banana growers were also brought out in this study. Keywords: Communication Technologies, Media technologies Dissemination of Information, behaviour pattern, Banana growers, Technological Transfer

__________________________________________________________________________________ I. Introduction A. Mass Media and Development The needs of the mankind opened the way for new inventions. In the same way the process of globalisation opened new avenues of development in Indian social architecture after the introduction of new economic and liberalisation policy in the end of 1990â&#x20AC;&#x2122;s. The foreign direct investments, Global trade and International collaborations paved a platform to develop media as international, national and mass media. The media developments in contents and technologies have brought the world as global village. The www (World Wide Web) technology have converge all media in the Global village. The credibility of the message, Timely delivery of technology, Information seeking behaviour of the clientele are very important for the strengthening the role of any media in agricultural technology transfer (Escalada et al.,1999). Mass media play an important role in the society. It should reflect the structure and content the various sociopolitico-economic and cultural activities of the society. In conventionally media can be classified as Print, electronic and New media. As per RNI 2013 (Registrar of Newspapers of India) 94,067 (12,511 newspapers and 81,556 periodicals) were registered. According to www.allinidaradio.gov.in, about 450 government supported All India Radio (AIR) stations, 266 private FM Channels under Phase I and phase II (http://www.becil.com) licensed to operationalise by the Government, 795 private television channels (www.mib.nic.in) are beaming their signals from Indian territory. After globalisation, classifications of print and electronic media have further extended as rural, urban, cosmo media. The new media computer-internet-website dominates all the media and converged into it. The international and national media contents are influencing mass media. Media provide information to its audience and to develop their knowledge and attitude. In general, media disseminate news or information to get attention of the people towards on it. The Nature of media is to instruct, educate and entertain its readers/Target groups/audience/users. Also the audience should get satisfied with the contents which disseminate by the media. The Uses and Gratification theory suggests that media play an active role using and choosing media. The audience are expecting that media. Therefore it has become an important communication tool for raising various issues in the society.

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Mass media act as a vehicle to communicate or transferring information. During the post World War-II, many countries have faced problems and to find solutions for the social needs of the people: the eradication of poverty, illiteracy, unemployment etc., in this period ‘Development Communication’ has been emerged in the field of Mass Communication. The famous social scientists Willbur Schramm, Lerner and Rogers have argued the development through Mass media. This development was termed by Schramm as magic multipliers. After the introduction of globalization, the primitive farming has no longer has its value. To meet out present day challenges in farming sector , a dynamic technology generation and transfer of technology system is needed. Hence it is imperative to keep the farmers with profitable and remunerative agriculture through latest communication gadgets. (Siva Balan, K.C et.al, 2013). The farmer should become an Agripreneur for meeting the market challenges. The farm income can be doubled which certainly based the technology usage of the famers. For the techno transfer, the mass media channels are playing the pivotal role. As for as extension functionaries are concerned, their preference for delivery of technologies were towards Radio and Television , since the reach of mass media is more than the print media.( Suganya,2000) B. National and International Scenario of Banana Production India is the largest producer of banana with an annual production of 29.78 million tones from 830.50 thousand ha and accounts to 19% of the total world production. Predominantly banana production in India is polyclonal and under small farming system. The banana production in India is hampered by various biotic and abiotic stresses and production has been seriously threatened by decreasing soil fertility and yield in most of the varieties. The present study is conducted to overcome these constraints and to improve the production and productivity of banana. Area a n d Pro d uct io n o f B a na na W o rld : 1 2 1 .8 5 mil lio n to n s/ 1 0 . 1 0 mil lio n h ec tare s Ind ia : 2 9 .7 8 mil lio n to n s fro m 8 .3 0 la k h h ect are s T a mi l N ad u : 8 .2 5 mil lio n to n s fro m 1 2 5 .5 t ho u sa nd he ct are s T hir uc h irap a ll i : 0 .5 5 mil lio n to n s fro m 1 5 ,1 3 2 h ect are s Source: National Horticultural Board Report 2012 Moreover the post harvest losses accounts for 22-30% with a tune of Rs. 300 crores annually. National Research Centre for Banana (NRCB) and State Agricultural Universities (SAUs) have developed many usable and fruitful technologies for the benefit of farming community. However, people are lack in awareness to adopt some of the technologies. II. Technology Adoption The word ‘adopt’ has the meaning ‘to take up and practice as one’s own, to accept formally and put into effect’. Adoption of a particular message or production recommendation practice by a farmer implies the voluntary acceptance of the message and its practice. The adoption of decision to act has a series of actions and thought action. Adoption behaviour is differing from one individual to another based on their characteristics, familiarity with the techniques and availability of the resources (Bhople S.R, 1998). Though the plant protection methods are recommended scientifically by the field and extension functionaries to obtain maximum benefit, all the farmers are not adopting the practices uniformly (Alagesan. V, 1989) Wide differences exist among farmers in the level or extent of adoption. Differential adoption of plant protection technologies have been a growing concern amongst researchers as well as extension functionaries (Phusel etal., 2007). It is an outcome of multifaceted factors, operating in the field situation besides various other factors. Generally adoption behaviour would be specific to particular innovations, individuals and environments (Heong KL and Escalada MM, 1997). The productivity of banana depends mainly on the technical know-how possessed and the extent of its use in production by the banana growers. Also extension programmes on various technologies are being transferred with help of different mass media channels, gaps exists in production by the farmer on his farm ( Somasundaram, D. and Singh, S.N ,1978). Therefore, there is a need to study the information seeking behavior, Adoption of recommended banana production practices by the banana growers. The mass media plays a crucial role in reaching/ transferring the technologies from lab to the field (Puthirapradap, 2003) In recent time, globalization plays greater role in marketing the produces across the globe. The present investigation was under taken with this background. The inference of the study can be utilized in the selection of mass media channels and for better dissemination of farm technologies in the mass media channels. III. Methodology A sample of 100 banana growers were selected from 10 villages from Thottium, Musiri, Lalgudi, Andanallur blocks in Tiruchirapalli district. The 10 respondents’ (banana growers) from each village were randomly selected. The sample includes both male and female respondents. A semi-structured interview schedule was constructed to collect data. The schedule contains the various critical aspects of banana cultivation with adopting

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new technology like soil type, planting materials, fertilizer, and irrigation practices, and method of manures applications, use of media dissemination of information were tested through structured interview schedule. Pilot study was conducted to fine tune the schedule before the actual data collection had taken place. The data collected by personal interviewing of sample respondents. The data were checked for completeness, classified, tabulated and analyzed with the help of surveys and interpretation. Banana farmer field visit by Extension functionaries

A. Impact Analysis The Research centers/organizations have developed new technologies on crop improvement, production, protection, post-harvest technology etc. These technologies have been disseminated to the farmers and entrepreneurs through available mass media channels. According to NRCB 2012 annual report, the banana cultivation in India has been increased from 3.5 lakh hectares to 4.5 lakh hectares and total annual production increased from 16.9 million tons to 29.8 million tons. The communication effort through media is one of the main factors for this tremendous growth of banana cultivation in this area and as well national level. The impact assessment depends on the economic, social and environmental aspects on adoption of new technologies. Banana is long duration crop (11 to 15 months) depending upon varieties, the lag period for adoption of new technologies would be approximately 3-4 years. So, this is the apt time to initiate ex-post impact assessment of new technologies developed by research organizations and SAUs over a period of time. During the impact assessment, the economic impacts (adoption aspects, economic studies i.e., returns to investment etc.,) the social impacts (studying the effects of new technologies on poverty, gender issues, food security, employment, cash flow, income, rural-urban migration etc.) and the environmental impacts (including studies on pollution, sustainability, natural resources etc.) were carried out. B. Garrettâ&#x20AC;&#x2122;s Ranking Technique To find out the credible sources of information in the adoption of banana cultivation Garret Ranking Technique (Garret, 1981) has been used. The respondents were asked to rank the factors as their own. The respondents ranking were converted into as score value with the help of Garrettâ&#x20AC;&#x2122;s ranking techniques.

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Per cent position = 100(Rij - 0.5) Nj Where, Rij- Rank given for the ith factor by the jth respondents Nj - Number of factors ranked by the j th respondents By referring the Garrettâ&#x20AC;&#x2122;s table, the estimated percent position was converted into scores. Then for each factor the scores of each individual were added and the mean values obtained were considered to be most important and then were ranked accordingly. III. Results and Discussion Table: 1. Sources of information for Banana growers of Tiruchirapalli District No.

Sources of information

Garatte Score 25.00

rank

Personal contact with subject specialist (at SAUs/Research organizations)

2. 3. 4.

Progressive farmers of mass village Plant protection dealers Agriculture extension officer

15.00 13.00 10.00

II III IV

5. 6. 7. 8.

Newspaper Television Radio broadcasting Books\Magazines

9.00 7.00 6.00 5.00

V IV VII VIII

9. 10. 11. 12.

Telephone Progressive farmers of the other villages Exhibition Internet

4.00 3.00 2.00 1.00

IX X XI XII

It is evident from Table-1, that the farmers seek the improved agro- technologies from various sources available in the massity. About 40% of information was received through personal visit by farmers, where as para extension workers (21%) Fellow farmers (11%), Agriculture extension officer (10%), Mass media (9.5%) and Village level workers (8.5%) were the other sources of information. Apart from the research organizations, the banana farmers preferred more towards the progressive traditional farmers of the village, plant protection dealers, Agriculture extension officer, Newspaper release, Television and Radio broad casting as a credible source of information in a descending order. Source of credibility According to the above table-1, that the personal contact with subject specialist at State agricultural University and Research organizations are considered as the most credible sources for the respondents to collect the information. The locational advantage of the farmers towards Research organizations in the study area may be the reason behind the ranking on source of information. About 40% of information is disseminated through personal visit by farmers themselves to research organization, attending the conferences, seminars, workshops, meetings, group discussions, kissan melas, exhibitions, trainings, field/ frontline demonstration etc. Therefore the research Institutes should scale up more extension activities in surrounding villages. Table: 2. Practice wise adoption of Banana through mass media channels ------------------------------------------------------------------------------------------------------------------Sl.No.

Practices

Full Adoption Partial Adoption No Adoption No. % No. % ------------------------------------------------------------------------------------------------------------------1. Soil (i) Soil testing 15 15.00 30 30.00

No.

55.00

2. Seedling

(i) Sucker selection

26.00

30.00

44.00

3. Varieties

(i) Udhayam

10.00

80.00

15.00

25.00

60.00

36 46

36.00 46.00

50 17

50.00 17.00

14 37

14.00 37.00

42.00

20.00

38.00

43.00

42.00

15.50

4. High density planting 5. Application of fertilizer (i) Doses (ii) Time of application (iii) Method of application

6. Application of manures

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P. Ravichamy et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 9(3), June-August, 2014, pp. 204-209 7. Application of micronutrient (i) Banana shakti

30.00

15.00

55.00

8. Bunch cover

8.00

12.00

80.00

9. Drip irrigation

25.00

20.00

65.00

10. Inter crop

35.00

10.00

55.00

11. Plant protection (i) IPM schedule (ii) Serious diseases

16 21

16.00 21.00

33 43

33.00 43.00

51 36

51.00 36.00

12. Harvesting (i) Time of harvesting

42.00

16.00

10 40

15.00 40.00

35 30

35.00 35.00

60 30

60.00 30.00

44.00

35.00

25.00

15. Export 12 12.00 33 33.00 -------------------------------------------------------------------------------------------------------------------

45.00

13. Post-harvest technology (i) Training (ii) Importance of Products 14. Marketing

The data with regard to the adoption of 15 improved banana cultivation practices by the growers. It reveal (table2) that complete adoption means the practices which were adopted by majority of the respondents i.e. time of application of fertilizer (46%), application of manures (43%), method of application of fertilizer & harvesting time (42%) and marketing knowledge (40%). The full adoption of the crop management practices may be due to frequent technology support of Mass traders, Para extension workers, since they are the most benefitted persons during marketing of the harvest. At the same time the farmers were unaware of new improved varieties which fetches bumper yield for the farmers. It was inferred that majority of the respondents did not adopt the important improved cultivation practices such as sucker selection, high density planting, intercropping, postharvest technologies due to lack of awareness. Though the research organizations such as State Agricultural Universities, KVKs, and Central Research stations of ICAR are taking earnest efforts to transfer the new and latest technologies to farmers, coherent group efforts are the need of the hour. The participation of community also required for reducing the technology divide. National Research Centre for Banana (NRCB), Trichy was organizing â&#x20AC;&#x2DC;Banana Farmers Clustersâ&#x20AC;&#x2122; from 2003 onwards which ensures people participation in the technology dissemination. Thereby the selected community cluster is better informed in the Post harvest practices and marketing, Value addition and Banana fiber extraction domains. All the technical know how are disseminated to the farmers, self help groups via the mass media channels. The farm profitability can be increased only be proper market linkage with the banana growers. It was found that sixty percent of the respondents are not fully aware about the technologies pertaining to the value addition in banana and incurring heavy loss in profit during glut seasons. The mass media channels viz., Radio, TV, Newspapers, Technical bulletins, Handouts, and Extension publications can better potential to link the farmers with the markets. The better informed farmers are better decision makers; they can decide when to sell, where to sell, and how to sell. The Agritech portal, DEMIC services of TNAU, Coimbatore are contributing worth mentioned services for rendering market related information to farmers. IV. Conclusion The adoption of Banana growers noted in this study is supported by the findings made by Bhople et.al. (1996), Gomase et al., 1998) and Bhople et.al., 1998). Similar findings were also reported by Adhikarya R and Posamentier H (1987), Ingle and Bhagawat (1998) and Deshmukh et.al., (1998),). From this study, it is concluded that majority of the technology transfer takes place through the personal contacts with central research organizations/state agricultural research centres/KVKs etc., followed by other communication media like farm TV programmes, radio programmes, news papers, books/magazines and journals. Though the mass media channels like electronic and print media makes wide publicity, they are underutilized by the farming community. So, the programmes of these media should be reoriented to reach grass root level of the farming community. The seasonal agricultural information should be highlighted during broadcasting/telecasting of current affairs. To overcome these constraints, the effectiveness of these media has to be improved through quality farm programmes, increasing credibility of media and for vast globalization. Thus mass media channels are the viable sources of information delivery and technology transfer in the Agricultural sector. The burgeoning population needs the best out of crop production. The doubling the farm income and tripling the crop production will be achieved only by the efficient use of mass media channels in the years to come.

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References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.

Adhikarya R and Posamentier H (1987) Motivating Farmers for Action: How Strategic Multi-Media, Campaigns Can Help. GTZ, Eschborn, Germany. Alagesan V (1989) Diagnostic study on communication behaviours of grape. Ph.D. thesis(unpublished) Tamil Nadu Agricultural University, Coimbatore. Bhople P.P., P.S. Shinde and S.R.Bhople (1998) Pattern of information management by orange growers. Maharashatra Journal of Extension Education. XVII. pp –184. Bhople R.S, P.S.Shinde and V.R.Nimje (1996) Determinates of knowledge and adoption behavior of orange growers. Journal of Maharashatra Agric. univ. 21 (1): 94-97. Deshmukh P.R., S.D. Wangikar and P.K.Wakle (1998) Knowledge and adoption of recommended cultivation practices of custard apple. Maharashatra J. Extension Education XVII. Escalada MM, Heong KL, Huan NH and Mai V (1999) Communication and behavior change in ricefarmers’ pest management: the case of using mass media in Vietnam. Journal of Applied Communications. Vol. 83 (1): 7-26. Gomase A.S., R.L. Patil and V.R. Kubde (1998) Factors influencing adoption of kagzi lime production technology. Maharashatra J. Extension Education XVII. Heong KL and Escalada MM (1997). Perception change in rice pest management: A case study of farmers’ evaluation of conflict information. Journal of Applied Communications, 81 (2), 3 – 17. http://www.becil.com/Private-FM-Broadcast-Services?tamp_id=MTExMTExMTE2 http://www.mib.nic.in/ShowhomeDocs.aspx Ingle P.O.and Bhagat P.R. (1998). Concurrent evaluation of Mango cultivation in Akola District, PKV Res. (J). 22 (1). pp. 152153. Phusel A. P., A. K. Vitonde and C.D.Thipse (2007). Adoption of Recommended Mandarin Orange Production Practices Indian Res. J. Ext. Edu. 7 (2&3. Pp. 98-100 Puthirapradap (2003) Relative effectiveness of farm communication through mass media. Unpub. Ph.D. thesis, TNAU, Coimbatore. Siva Balan, K.C, Swaminathan, B, Dr.P.Muthiah manoharan, Agricultural knowledge Transfer and Role of ICT Tools. Madras Agricultural Student Union Journal. 2-3 May, 2013.ISBN: 978-81-8424-828-9. Somasundaram, D. and Singh, S.N. (1978). "Communication gap between extension workers and paddy growing small farmers". Ind. Jr. of Ext.edu., 14(3&4) : 26-33. Suganya P (2000). Mass media utilization behavior of extension personnel. Unpub. M.Sc. thesis, TNAU, Coimbatore. www.allindiaradio.giv.in www.rni.nic.in

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net On Classifying Sentiments and Mining Opinions Jasleen Kaur1, Dr. Jatinderkumar R. Saini 2 Assistant Professor, 2Associate Professor & Director I/C 1 Shroff SR Rotary Institute of Chemical Technology, Ankleshwar 2 Narmada College of Computer Application, Bharuch 1,2 Gujarat, INDIA 1

Abstract: Due to presence of large volume of opinionated text on web in form of review sites, social media, blogs, discussion forums, People are intended to develop a system that can identify and classify opinion or sentiment expressed in opinionated text. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining involves building a system to collect and examine opinions about the product made in blog posts, comments, reviews or tweets. Opinion mining can be useful in several ways in marketing, it helps to judge the success of a launch of new product, determine which versions of a product or service are popular and even identify which demographics like or dislike particular features. This paper tries to cover some techniques and approaches that are used in this area. This paper presents a survey covering the techniques and methods in sentiment analysis. Keywords: Opinion mining, sentiment classification, supervised, unsupervised I.

Introduction

Emotions and opinions plays an important role in human being’s life. Every person’s life is filled up with a lot of emotions and opinions. We cannot imagine the world without them. Emotions and opinions influence the way humans think, what they do, how they act and how they share .Opinions have a relevant impact on our everyday life. Opinions give us information about other person’s experience. In today‘s world, people can express their views /opinions freely and these views/opinions become very useful source of information for making various policies /decisions. With advent of World Wide Web, social media become good source containing opinionated text and these opinions/emotions can be extracted from the text available online. This field of computer science is known as Sentiment classification, Opinion mining, Sentiment Analysis or Subjectivity Analysis. It is the study of person’s views, emotions or opinions associated with some events, some products and their features, some places etc. This task is very challenging and practically very useful. Opinion Mining and Sentiment Classification is a field of study at the crossroad of Information Retrieval (IR) and Natural Language Processing (NLP) and share some characteristics with other disciplines such as text mining and information Extraction. II.

Basic Terminology

Textual information in the world can be broadly categorized into two main types: facts and opinions.Facts ar e objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals or feelings toward entities, events and their properties. Opinion can be expressed in quintuple [4] (feature, sentiment, polarity, holder, time) Opinion feature is the target of the opinion. In general an opinion is either had on a specific object or on a feature of such object. An opinion sentiment is the word or set of words that conjointly describe the opinion given on the feature. That is, these are the descriptive terms or opinionated words used to transmit the opinion. The orientation or polarity of an opinion can be either positive, negative or neutral. The holder of an opinion is the person or organization that expresses the opinion. In the case of product reviews and blogs, opinion holders are usually the authors of the posts. An opinion will be held by an opinion holder over some object feature at some point in time. III.

Data Source

User’s opinion is a major criterion for the improvement of the quality of services rendered and enhancement of the deliverables. Blogs, review sites, data and micro blogs provide a good understanding of the reception level of the products and services [8]. A. Blogs As internet usage is increasing day by day, blogging and blog pages are growing rapidly. Expressing ones personal opinions through blogs/ blog pages is becoming popular day by day. Bloggers express their opinions,

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feelings, and emotions in a blog [10]. Many of these blogs contain reviews on many products, issues, events topics etc. B. Review sites The important factor considered for making a decision by a purchaser before purchasing is to know the experience of previous buyer regarding that product .For that a large number of user-generated reviews are available on the Internet. The reviewerâ&#x20AC;&#x2122;s data used in most of the sentiment classification studies are collected from the e-commerce websites like www.amazon.com (product reviews), www.yelp.com (restaurant reviews), www.CNET download.com (product reviews) and www.reviewcentre.com [16][22]. C. Datasetâ&#x20AC;&#x2122;s Most of the work in the field uses movie reviews data for classification. Movie review data are available as dataset (http:// www.cs.cornell.edu/People/pabo/movie-review-data) [7]. Other dataset which is available online is multi-domain sentiment (MDS) dataset. (http:// www.cs.jhu.edu/mdredze/datasets/sentiment), (http://www.cs.uic.edu/liub/FBS/CustomerReviewData.zip which consists of reviews of five electronics products downloaded from Amazon and CNET [31] [21]. D. Micro-blogging Most popular micro blogging service like twitter, facebook where information is represented in short messages like tweets or status. These short messages may contain Opinions about different topics which can be used for task of sentiment analysis task [32]. E. Others Opinion Mining can also be done on Legal Weblogs or Legal Documents [17].Person can express their emotion/views on politics, fashion trends, about any product, any movie. Discussion forums are also gaining popularity in Medical world. So Opinion mining can be applicable to any of above specified data. Literary Pieces, lyrics and unsolicited bulk mails [18] can also be mined. IV.

Granularity of Opinion Mining task

A. Mining Opinion at Document level Document level opinion mining is about classifying the overall opinion presented by the authors in the entire document as positive, negative or neutral about a certain object [3]. The assumption is taken at document level is that each document focus on single object and contains opinion from a single opinion holder. The challenge in the document level classification is that the entire sentence in a document may not be relevant in expressing the opinion about an entity. Therefore subjectivity/objectivity classification is very important in this type of classification. Both supervised and unsupervised learning methods can be used for the document level classification. Turney [21] present a work based on distance measure of adjectives found in whole document with known polarity i.e. excellent or poor. The author presents a three step algorithm i.e. in the first step; the adjectives are extracted along with a word that provides appropriate information. Second step, the semantic orientation is captured by measuring the distance from words of known polarity. Third step, the algorithm counts the average semantic orientation for all word pairs and classifies a review as recommended or not. In contrast, Pang et al. [7] present a work based on classic topic classification techniques. The proposed approach aims to test whether a selected group of machine learning algorithms can produce good result when opinion mining is perceived as document level, associated with two topics: positive and negative. He present the results using naive bayes, maximum entropy and support vector machine algorithms and shown the good results as comparable to other ranging from 71 to 85% depending on the method and test data sets. Jatinder Saini [18] tried to find the polarity of unsolicited bulk mails through sentiment analysis. In this experiment, Sentiment-depicting words in the whole document are analyzed, scaled and extremes of positive and negative opinions are identified. It has been found that for almost 50% of cases, the opinions expressed through such UBE have positive polarity; almost 30% cases are negatively opined whereas almost 20% cases contain neutral opinion. B. Mining Opinion at Sentence level In the sentence level sentiment analysis, the polarity of each sentence is calculated. The same document level classification methods can be applied to the sentence level classification problem. Objective and subjective sentences must be found out. The subjective sentences contain opinion words which help in determining the sentiment about the entity. After which the polarity classification is done into positive and negative classes. In case of simple sentences, single sentence bears a single opinion about an entity. But there will be complex sentences also in the opinionated text. The sentence level opinion mining is associated with two tasks [4] [6]. First one is to identify whether the given sentence is subjective (opinionated) or objective. The second one is to find opinion of an opinionated sentence as positive, negative or neutral. The assumption is taken at sentence level is that a sentence contain only one opinion. Riloff and Wiebe [12] use a method called bootstrap approach to identify the subjective sentences and achieve the result around 90% accuracy during their tests. In contrast, Yu and Hatzivassiloglou [14] talk

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about sentence classification and orientation. For the sentence classification, authors present three different algorithms: (1) sentence similarity detection, (2) naïve Bayens classification and (3) multiple naïve Bayens classification. For opinion orientation authors use a technique similar to the one used by Turney [21] for document level. Wilson et al. [28] pointed out that not only a single sentence may contain multiple opinions, but they also have both subjective and factual clauses. Like the document-level opinion mining, the sentence-level opinion mining does not consider about object features that have been commented in a sentence. For this the feature level opinion mining is discuss in the next sub-section. C. Mining Opinion at Feature level The task of opinion mining at feature level is to extracting the features of object that have been commented and after that determine the opinion of the object i.e. positive or negative and then group the feature synonyms and produce the summary report. Liu [5] used supervised pattern learning method to extract the object features for identification of opinion orientation. To identify the orientation of opinion he used lexicon based approach. This approach basically uses opinion words and phrase in a sentence to determine the opinion. The working of lexicon based approach [31] is explained in 3 steps: Identification of opinion words, Role of Negation words and Butclauses. In contrast, Hu and Liu do customer review analysis [20] through opinion mining based on feature frequency, in which the most frequent features is accepted by processing many reviews that are taken during summary generation. In opposite to Hu and Liu, Popescu and Etzioni [1], improved the frequency based approach by introducing the part-of relationship and remove the frequent occurring of noun phrases that may not be features. V.

Approaches for Classifying Sentiments and mining Opinions

In relation to sentiment analysis, the literature survey done indicates two types of techniques –Supervised and Unsupervised learning. A. Supervised learning In a machine learning based classification, two sets of data are required: training and a test set. A training set is used by an automatic classifier to learn the differentiating characteristics of data, and a test set is used to validate the performance of the automatic classifier. A number of machine learning techniques have been adopted to classify the reviews. Machine learning techniques like Naive Bayes (NB), maximum entropy (ME), and support vector machines (SVM) have achieved great success in text categorization. The other most well-known machine learning methods in the natural language processing area are K-Nearest neighborhood, ID3, C5, centroid classifier, winnow classifier, and the N-gram model. Naive Bayes classifier is a simple probabilistic classifier based on Bayes' theorem. The basic idea is to estimate the probabilities of categories given a test document by using the joint probabilities of words and categories. The Naive Bayes algorithm is widely used algorithm for document classification [22][24][26][33].The support vector machine is a statistical classification method proposed by Vapnik [30].SVM seeks a decision surface to separate the training data points into two classes and makes decisions based on the support vectors that are selected as the only effective elements in the training set .Support vector machines (SVM), a discriminative classifier is considered the best text classification method [17][24][26][33].The idea behind the centroid classification algorithm is extremely simple and straightforward [26]. Initially the prototype vector or centroid vector for each training class is calculated, then the similarity between a testing document to all centroid is computed, finally based on these similarities, document is assigned to the class corresponding to the most similar centroid. The K-nearest neighbor (KNN) is a typical example based classifier that does not build an explicit, declarative representation of the category, but relies on the category labels attached to the training documents similar to the test document. Given a test document d, the system finds the k nearest neighbors among training documents. The similarity score of each nearest neighbor document to the test document is used as the weight of the classes of the neighbor document [26]. Winnow is a well-known online mistaken-driven method. It works by updating its weights in a sequence of trials. On each trial, it first makes a prediction for one document and then receives feedback; if a mistake is made, it updates its weight vector using the document. During the training phase, with a collection of training data, this process is repeated several times by iterating on the data [26]. Besides these classifiers other classifiers like ID3 and C5 are also investigated [23]. In all supervised approaches, reasonably high accuracy can be obtained subject only to the requirement that test data should be similar to training data. To move a supervised sentiment classifier to another domain would require collecting annotated data in the new domain and retraining the classifier. This dependency on annotated training data is one major shortcoming of all supervised methods. B. Unsupervised Learning: All approaches previously described build upon a set of fully annotated data, which is used to train a classiﬁer with one technique or another. This classiﬁer is then used to classify novel incoming text. Unsupervised approaches to sentiment classification can solve the problem of domain dependency and reduce the need for annotated training data. This approach is also known as semantic orientation approach to Sentiment analysis b’coz it measures how far a word is inclined towards positive and negative, Instead of prior training in order to mine data. Much of the research in unsupervised sentiment classification makes use of lexical resources available.

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An unsupervised learning algorithm was proposed by Turney [21], which uses semantic orientation of phrases. Zagibalov and Carroll [27] describe a method of automatic seed word selection for unsupervised sentiment classification of product reviews in Chinese. The results obtained are close to those of supervised classifiers and sometimes better, up to an F1 score of 92%.Kamps et al [19] focused on the use of lexical relations in sentiment classification. Andrea Esuli and Fabrizio Sebastiani [2] proposed semi-supervised learning method started from expanding an initial seed set using WordNet. They determined the expanded seed term’s semantic orientation through gloss classification by statistical technique. Takamura et al. [15] determine term orientation according to a “spin model”. An approach was proposed by Chunxu Wu [11] to handle the reviews for which contextual information to determine the orientation of opinion is not enough. An unsupervised learning algorithm by extracting the sentiment phrases of each review by rules of part-ofspeech (POS) patterns was investigated by Ting-Chun Peng and Chia-Chun Shih [29]. They consider only opinionated sentences containing at least one detected sentiment phrase for opinion extraction. Gang Li and Fei Liu [13] developed an approach based on the k-means clustering algorithm. The technique of TF-IDF (term frequency – inverse document frequency) weighting is applied on the raw data. The result is obtained based on multiple implementations of the clustering process. Finally, the term score is used to further enhance the clustering result. Chaovalit and Zhou in [9] compared the Semantic Orientation approach with the N-gram model machine learning approach by applying to movie reviews. They confirmed from the results that the machine learning approach is more accurate but requires a significant amount of time to train the model. In comparison, the semantic orientation approach is slightly less accurate but is more efficient to use in real-time applications. The performance of semantic orientation also relies on the performance of the underlying POS tagger. VI.

Evaluation Metrics

The performance of sentiment classification system can be evaluated by using four metrics: Accuracy, Precision, Recall and F1 measure. Precision measures the exactness of a classifier. A higher precision means less false positives, while a lower precision means more false positives.

Recall measures the completeness, or sensitivity, of a classifier. Higher recall means less false negatives, while lower recall means more false negatives.

Precision and recall can be combined to produce a single metric known as F-measure, which is the weighted harmonic mean of precision and recall. The main advantage of using F-measure is it is able to rate a system with one unique rating.

Accuracy measures the overall degree to which instances have been correctly classified, using the formula as defined below.

VII. Tools used in Opinion Mining The tools which are used to track the opinion or polarity from text are [25]: WEKA (Weka is a collection of machine learning algorithms for data mining tasks), Rapidminer (provides software, solutions, and services in the fields of predictive analytics, data mining, and text mining), GATE(General Architecture for Text Engineering is NLP and language engineering tool) ,NLTK (Natural Language Toolkit ), Red Opal, Opinion observer, Review Seer tool, Ling Pipe,OpenNLP and Stanford Parser and Part-of-Speech (POS) Tagger . VIII. Conclusion Sentiment classification has a wide variety of applications in information systems, including classifying reviews, summarizing review and other real time applications. This paper focuses on the frame work on opinion mining and survey on some of the tasks which have been done in each phases. It is observed that performances of sentiment classifiers are severely dependent on domains or topics. This study shows that supervised approach works well for sentiment classification analysis of movie domain, product, and hotel etc., where as lexicon based approach is appropriate for web content mining such as short text in micro-blogs, tweets, and Facebook status. IX.

References

[1]

A.M..Popescu,, O. Etzioni,” Extracting Product Features And Opinions From Reviews” , In Proc. Conf. Human Language Technology And Empirical Methods In Natural Language Processing, Vancouver, British Columbia, Pp. 339–346, 2005.

[2]

Andrea Esuli And Fabrizio Sebastiani, “Determining The Semantic Orientation Of Terms Through Gloss Classification”, Proceedings Of 14th ACM International Conference On Information And Knowledge Management, Bremen, Germany, Pp. 617-624, 2005.

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[3]

B. Liu. Opinion Mining And Sentiment Analysis, In proc. Of AAAI, San Francisco, USA. 2011.

[4]

B. Liu, “Sentiment Analysis and Subjectivity.” A Chapter In Handbook Of Natural Language Processing, 2nd Edition, 2009.

[5]

B. Liu, And J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web”, Proceedings of International conference on World Wide Web, pp 342-351, 2005.

[6]

B. Liu. “Sentiment Analysis: A Multifaceted Problem”, Invited Paper, IEEE Intelligent Systems,pp 76-80, 2010.

[7]

B. Pang, L. Lee, And S. Vaithyanathan, Thumbs Up? Sentiment Classification Using Machine Learning Techniques,” Proceedings Of The Conference On Empirical Methods In Natural Language Processing, pp. 79–86, 2002.

[8]

B. Liu, Robert Grossman,Yahnhong Zhai,“Mining Data records in web pages”, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining,pp 601-606,2003

[9]

Chaovalit,Lina Zhou,” Movie Review Mining: A Comparison Between Supervised And Unsupervised Classification Approaches”, Proceedings Of The 38th Hawaii International Conference On System Sciences – 2005. [24]

[10] Chau, M., & Xu, J. Mining Communities And Their Relationships In Blogs: A Study Of Online Hate Groups. International Journal Of Human – Computer Studies,pp 57–70, 2007. [11] Chunxu Wu, Lingfeng Shen , “A New Method Of Using Contextual Information To Infer The Semantic Orientations Of Context Dependent Opinions” , International Conference On Artificial Intelligence And Computational Intelligence, 2009. [12] E. Riloff, And J. Wiebe, “Learning Extraction Patterns For Subjective Expressions”, Proceedings Of The Conference On Empirical Methods In Natural Language Processing, Japan, Sapporo, 2003. [13] Gang Li, Fei Liu, “A Clustering-Based Approach On Sentiment Analysis”, 978-1-4244-6793-8/10 ,IEEE,2010. [14] H. Yu, And V. Hatzivassiloglou, “Towards Answering Opinion Questions: Separating Facts From Opinions And Identifying The Polarity Of Opinion Sentences”, Proceedings Of The Conference On Empirical Methods In Natural Language Processing, Japan, Sapporos, 2003. [15] Hiroya Takamura, Takashi Inui, and Manabu Okumura. “Extracting Emotional Polarity Of Words Using Spin Model” In Proceedings Of ACL, 43rd Annual Meeting Of The Association For Computational Linguistics, Ann Arbor, US,2005. [16] Hu, And Liu, “Opinion Extraction And Summarization On The Web”,Proc. of AAAI, Pp. 1621-1624, 2006. [17] Jack G. Conrad Frank Schilder”Opinion Mining in Legal Weblogs” ICAIL ’, Palo Alto, California USA,2007. [18] Jatinderkumar R. Saini, "Polarity Determination using Opinion Mining in Stocks and Shares-advertising Unsolicited Bulk e-mails", published in International Journal of Engineering Innovations & Research; ISSN: 2277–5668; vol. 1, issue 2, pp. 86-92, March 2012. [19] Kamps, Maarten Marx, Robert J. Mokken And Maarten De Rijke, “Using Wordnet To Measure Semantic Orientation Of Adjectives”, Proceedings Of 4th International Conference On Language Resources And Evaluation, Pp. 1115-1118, Lisbon, Portugal, 2004. [20] M. Hu And B. Liu “ Mining And Summarizing Customer Reviews”, Proceedings Of ACM SIGKDD Conference On Knowledge Discovery And Data Mining , Pp. 168–177, 2004. [21] P.Turney “Thumbs Up or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews”, In: Proceeding Of Association For Computational Linguistics, p. 417—424, 2004. [22] Qiang Ye, Ziqiong Zhang, Rob Law, “Sentiment Classification Of Online Reviews To Travel Destinations By Supervised Machine Learning Approaches”, Expert Systems With Applications, pp 6527–6535, 2004. [23] Rudy Prabowo, Mike Thelwall, “Sentiment Analysis: A Combined Approach”, Journal of Informetrics, pp 143–157, 2009. [24] Rui Xia, Chengqing Zong, Shoushan Li, “Ensemble Of Feature Sets And Classification Algorithms For Sentiment Classification”, Information Sciences, pp 1138–1152, 2011. [25] Software accessed from http://en.wikipedia.org/wiki/Data_mining#Software on June 2013. [26] Songho Tan, Jin Zhang, “An Empirical Study of Sentiment Analysis for Chinese Documents”, Expert Systems with Applications, pp 2622–2629, 2008. [27] T. Zagibalov and J. Carroll. “Automatic Seed Word Selection For Unsupervised Sentiment Classification Of Chinese Text”, In Proceedings Of The 22nd International Conference On Computational Linguistics Volume 1, pp 1073-1080, 2008. [28] T.Wilson, J. Wiebe, R. Hwa,” Just How Mad Are You? Finding Strong and Weak Opinion Clauses”, In: The Association for The Advancement Of Artificial Intelligence, Pp. 761—769, 2004. [29] Ting-Chun Peng And Chia-Chun Shih, “An Unsupervised Snippet-Based Sentiment Classification Method For Chinese Unknown Phrases Without Using Reference Word Pairs”, International Conference On Web Intelligence And INtelligent Agent Technology Journal Of Computing, Volume 2, Issue 8, August 2010. [30] Vapnik, V. N. The Nature of Statistical Learning Theory. New York: Springer, 1995. [31] X. Ding, B”. Liu, and P. S. Yu,” A Holistic Lexicon-Based Approach To Opinion Mining, Proceedings Of The Conference On Web Search And Web Data Mining, 2008. [32] XiaoZhou , Fang LI “Mining Aspects and Opinions from Micro blog events” Journal of computational Information System, pp9-17, 2013. [33] Ziqiong Zhang, Qiang Ye, Zili Zhang, Yijun Li, “Sentiment Classification Of Internet Restaurant Reviews Written In Cantonese”, Expert Systems With Applications,pp 7674-7682,2011.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Security Design Issues in Distributed Databases Pakanati.Raja Sekhar Reddy@, Dr Syed Umar*, Narra.Sriram# B.Tech. Student, ECM dept.,KL University,Vaddeswaram, Guntur, AP, India * Assoc.Professor, ECM dept., K L University, Vaddeswaram, Guntur, AP, India # B. Tech. Student, CSE dept., K L University, Vaddeswaram, Guntur, AP, India __________________________________________________________________________________________ Abstract: In the rapid growth of networking and information technology expands the business worldwide. As distributed database became more popular, the need for improvement in distributed database management system become even more important. Database provides inbuilt security to manage different levels of data but if we apply overall security from accessing the different levels of userâ&#x20AC;&#x2122;s data to different levels of users, it will raise the performance complication and also slow down the normal function of the database server. The most important issue is security that may arise and possibly compromise the access control and the integrity of the system. In this paper, we review the most common as well as emerging security mechanism used in distributed database system. Keywords: Distributed database, distributed database security, distributed database architecture, distributed database retrieval problems, Concurrency control. ______________________________________________________________________________________ @

I. Introduction A Distributed database is a collection of databases which are distributed and then stored on multiple computers within a network. An application is able to access simultaneously and modify the data in several databases in a network. The database, link connection allows local users to access data on a remote database. In distributed database system, the major issue is security on data to be accessed at different levels of hierarchy. In a distributed database system, the database is stored on a number of computers. The computers in a distributed system communicate with each other through various communication media such as high-speed networks or telephone lines [1]. A database link connection allow local users to access data on a remote database for establishing these connections, each database in the distributed system must have a unique global database name in the network domain. The Distributed database management system (DDBMS) is a software that permits the management of the distributed database and makes the distribution transparent to the user. The main difference between centralized and distributed database is that the distributed databases are typically geographically separated and are separately administrated between local & global transactions. In a local transaction it access the data only from sites where the transaction originated, whereas in a global transaction on the other hand is one that either access data in a different site from the one at which the transaction was initiated or, accessed data in several different site [2]. II. Distributed Database Security The databases have been protected from external connections by firewalls or routers on the network outer limits with the database environment existing on the internal network opposed to being located within a demilitarized zone. Additional network security devices that detect and alert on malicious database protocol traffic include intrusion detection systems along with host-based intrusion detection systems [3]. Databases provide many layers and types of information security, typically specified in the data dictionary, including: a) Access control: Access control is a system which enables an authority to control access to areas and resources in a given physical facility or computer based information system. An access control system, within the field of physical security, is generally seen as the second layer in the security. b) Authentication: Authentication is the act of establishing or confirming something (or someone) as authentic, that is, the claims made by or about the subject are true. c) Encryption: In cryptography, encryption is the process of transforming information (referred to as plaintext) using an algorithm (called cipher) to make it unreadable to anyone except those possessing special knowledge, usually referred to as a key Integrity. III. Distributed Database System The theory of distributed database came into reality during mid 1970. It was felt that many applications would be distributed in future and therefore the database had to be distributed also. Essentially a distributed database system (DDBS) is a collection of several logically related databases which are physically distributed in different computers or sites over a computer network [4].

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While the users of distributed database have the impression that the whole database is local excepted for the possible communication delay between the sites. This is because a distributed database is a logical union of all the sites and the distribution is hidden from the users. DDBS is preferred over a non-distributed or centralized database system for various reasons. The design of responsible distributed database system is a key concern for information system. In high band-width network, latency and local processing are the most significant factors in query and update response time. Parallel processing can be used to minimize their effects, particularly if it is considered at design time. It is the judicious replication that enables parallelism to be effectively used. Distributed database design can thus be seen as an optimization problem requiring solutions to various interrelated problems: data fragmentation, data allocation and local optimization. Concurrency Control (CC) is another issue among database system. It permits user to access a distributed database in a multi-programmed fashion which preserving the illusion that each user is executing alone on a dedicated system. Another activity of Concurrency Control (CC) is to “Co-ordinating [5], concurrent accesses to a database in a multi user database management system (DDBMS). There are numbers of algorithms that provides Concurrency control, such as two phase locking, Time stamping, Multi-version timestamp, and Optimistic non- locking mechanism. Various methods provide better concurrency control than other depending on the systems [6]. IV. Distributed database Architecture The distributed database management system (DDBMS) involves a collection of sites interconnected by a network. Every site run one or, more of the following software modules: a Transaction Manager(TM), a Data Manager (DM) and a concurrency control scheduler or simply scheduler. A site can function as a client, a server or, both in client–server model. A client run only the TM module and a server run only the DM and scheduler module. All server stores a portion of the database. Each data item may be stored at any server or, redundantly at several servers. Figure -1 shows the system architecture for the client server model. The user’s interact with the DDBMS by executing transactions, which are on line queries or, application programmes. TMs supervise interaction between transaction and database. The TM at the site where the transaction originated is called the initiating TM. The indicating TM receives operations issued by a transaction, and forwards them to the appropriate schedulers. The goal of a scheduler is to order operations so that the resulting execution is correct. DMS manages the actual database by executing operations, and are responsible for recovery from failures. Transactions communicate with TMs which communicate with schedulers, DMs and it manages data. Architecturally, a DDBS consists of a set of query sites and a non-empty set of data sites. Data sites have data storage capability which the query sites do not. The latter only run the user interface in order to facilitate data access at data site. The problem of distributed query processing is to decide on a strategy for executing each query over the network in the most cost- effective way [7].

Figure 1: Architecture of a Distributed Database (DM Data manager TM Transaction manager) V. Security tools emerging in Distributed Database System: The emerging security tools used in distributed database system are data warehouses and data mining system, collaborative computing system, distributed object system and the web. The major issues here are ensuring that security is maintained in building a data ware house from the backend database systems and also enforcing appropriate access control technique when retrieving the data from warehouse. For example , security policies of the different data sources that from the warehouse have to be integrated to form a policy for the warehouse .This is not a straight forward task, as one has to maintain security rules during the transformation , then the warehouse security policy has to be enforced . In addition, the warehouse has to be audited. Ultimately, the

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retrieval problem also becomes as issue here, For example the warehouse may store average salaries. A user can access average salaries and then deduce the individual salaries in the data sources, which may be sensitive and therefore, the inference problem could become an issue for the warehouse. To date, little work has been reported on security for the data warehouse as well as the retrieval problem for the warehouse. This is an area that needs much research intension. In data mining system it is being extended to function in a distributed environment. This system is called distributed data mining system & has received very little attention. Other emerging technologies that have evolved in some way from distributed database are called collaborative computing system, distributed object management system and the web. Much of the work on securing distributed database can be applied to securing collaborative computing system, through distributed object systems security, there is a lot of work by the object management group’s security special interest group. Presently there has been much work on securing the web as well. The main issue here is ensuring that the databases, the operating systems , the applications, web servers, the client and the network are not only secure, but are also securely integrated [8] . VI. Conclusion The distributed database systems are getting popular day by day. Several organizations are now deploying distributed database system. In this paper we introduce the different aspects related to distributed database such as database system concept, distributed database architecture, design of distributed database and also some security issues including multi level security in distributed database system. We also, describe the most common mechanism of discretionary security and stated the emerging security used in distributed system tools. We also believe that there is much scope for further research and experimentation on these issues. References [1] [2] [3] [4] [5] [6] [7] [8]

Swati Gupta, Kuntal Saroba, Bhawna, “Fundamental Research of Distributed Database”, International Journal of Computer Science and Management Studies, vol. 11, 2011, pp. 138-146. Charles P. Pfleeger and Shari Lawrence Pfleeger, Security in Computing, Prentice Hall Professional Technical Reference, Upper Saddle River, New Jersey, 2003. Zhang Xing Hao Wei, “The structure design of database security monitoring system based on IDS”, IEEE Conference on Computer Engineering and Technology, 2010, vol. 3, pp. 450-453. A.A.Akintola,G.A.Aderounmu and A.U.Osakwe,” Performing Modeling of an Enhanced Optimistic Locking Architecture for Concurrency Control in a Distributed Database System”, ACM vol.37, No.4, November 2005 Simon Wiseman, DERA, Database Security: Retrospective and Way Forward, 2001. Pfleeger, Charles P., (1989) Security in Computing. New Jersey: Prentice Hall. 1989. Security Issues in Distributed Database System Model, MD.Tabrez quasim, Computer Science Department Faculty of Science T.M. Bhagalpur University tabrezquasim@gmail.com A.A.Akintola,G.A.Aderounmu and A.U.Osakwe,” Performing Modeling of an Enhanced Optimistic Locking Architecture for Concurrency Control in a Distributed Database System”, ACM vol.37, No.4, November 2005

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Groundwater Chemistry of South Karaikal and Nagapattinam Districts, Tamilnadu, India M.Chandramouli1 and T J Renuka Prasad2 Research Scholar, Department of Geology, Bangalore University, Bangalore, Karnataka, India 2 Professor, Department of Geology, Bangalore University, Bangalore, Karnataka, India. _______________________________________________________________________________________ Abstract: This study was carried out with the objective to find the ground water quality for the samples of south Karaikal and Nagapattinam. Ground water samples were collected from 14 wells located in this area and was analyzed for major ion concentration. The spatial variations of the ion concentrations have been studied. The suitability of water has been tested for different purposes specially for irrigation by groundwater by calculating the values of SAR, RSC & %NA. The Hill piper and Durov plots have been obtained to check the hardness of water.From overall analysis, it was observed that there was a slight fluctuation in the physico-chemical parameters among the water samples studied. Comparison of the physico-chemical parameters of the water sample with WHO and IS limits showed that the groundwater is not suitable for drinking purpose in the study area with few exceptions. Keywords: physico-chemical parameters, spatial variation maps, Hill piper, Durov plots. __________________________________________________________________________________________ 1

I. Introduction Water resources are mainly divided into, surface water sources and sub surface water sources. All water that occurs naturally below the Earthâ&#x20AC;&#x2122;s surface is called subsurface water, whether it occurs in the saturated or unsaturated zones. Ground water referred to without further specification is commonly understood to mean water occupying all the voids with in a geologic stratum. This saturated zone is to be distinguished from an unsaturated, or aeration zone where voids are filled with water and air. The objectives of the present study is to carry out a preliminary investigation of the ground water quality in south Karaikal and Nagapattinam and to demarcate the regions where the ground water is suitable or unsuitable for both domestic and agricultural purpose based on geochemical and GIS approach. An attempt has been made to study the behavior of water level in south karaikal and south nagapattinam. For this purpose water level data of CGWB was collected. II. STUDY AREA The study area south karaikal & nagapattinam is situated towards the east coast of India (Fig.1). The study area is part of the Toposheet number- 58 N/13, 58 N/14 and covers an area of 1082 sqkm. The study area has a plain terrain of alluvial soil consisting of sand, slit & clay. The rivers in the study area are the main branches of Cauvery below grand anicut are the kodmurutti, arasalar, virasolanar and the vikramanar. Vettar, the tributaries of river Cauvery are the major water bodies around the area. Since the districts are underlained by sedimentary formations the major landforms that occur are natural levee near mayiladuthurai. Coastal plain covers almost the entire district with beaches. Beach ridges, mud flats, swamps and back water along the coastal stretch. The delta plains are found near the confluence of river kollidam with sea in the east and also in the south. Flood plain deposits are observed along the river courses. Being situated on east coast, coastal geomorphological units like sand dunes, tidal inlet, spit bars, coastal beach with swamps and marshes are common.

Figure 1: Location Map of study area

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III. GEOLOGY The region is underlain by formations of quaternary to recent. Ground water occurs in these formations under water table and confined condition. The ground water is extracted by filter point wells, tube wells, shallow bore wells, and infiltration wells especially in the sandy aquifers. The aquifers are represented by sand, gravel, and clay. The aquifer is more clayley towards east and south eastern part of the district except the coastal stretch where the beach and sand occur. The east of the study area is fluvial and the north-western part is fluvio marine. The alluvial formations of fluvial origin occupy a major part of the Region. In addition, coastal sands and aeolian sands occur along the coast on the east. The Alluvium mainly consists of clays and sands. The alluvial thickness ranges between 26m in the west to 43m in the east of which sands constitute 40 â&#x20AC;&#x201C; 90% of the total thickness of the alluvium. A prominent granular zone ranging in thickness between 9m in the west to about 40m in the east occurs beneath a clay bed. There is a general increase in clay content towards east that is the coast wards occurring as lenses. IV. METHODOLOGY Fourteen samples of the study area have been collected and analysed.(Fig.1). All the anions, cations along with TDS, TH, EC were estimated. Spatial variation maps have been generated. Piper and Durov diagrams have been generated.Chemical concentration of groundwater like Ca, Mg, Cl, HCO3, CO3, Na, K, SO4, NO3, EC, Ph have been obtained in ppm (Table 1 ) and the same has been estimated in epm (Table 2). Figure 2: well location map.

Table 1: chemical constituents of the study area Sodium (Na+)

SN 1 2 3 4 5 6 7 8 9 10 11 12 Max Min Avg Stdev

Potassium (K+)

64 67 598 19 585 14 313 125 71 6.7 355 5 193 78 414 4.7 280 3 268 6 304 6.2 234 4.6 598 125 64 3 306.5833 28.26667 168.1668 39.73037

Calcium (Ca++)

Magnesium Chloride (Mg++) (Cl)

Bicarbonate Carbonate Sulphate (HCo3) (Co3) (So4)

Nitrate (No3)

Fluoride (F) Total hardness as CaCo3

EC PH 96 18 142 275 12 38 58 0.12 315 1070 8.3 52 51 922 317 30 0 0 0 340 3200 8.35 24 19.2 140 244 30 0 0 0 140 2215 9 192 63 525 592 0 173 206 0.16 741 3000 8.1 108 13 104 342 18 19 2.8 0.21 325 940 8.4 8 10 390 439 0 4 0.517 0.7 60 1845 8.2 44 57 273 317 0 96 136 0.13 345 1750 8 8 7.3 358 464 24 19 3 0.24 50 1970 8.5 5 3 248 311 15 3 0.411 0.528 25 1380 8.34 12 4.8 191.4 317.2 9 88 0 0 50 1290 8.3 14 4.8 272.9 317.2 9 58 0 0 55 1440 8.4 10 8.4 163.1 414.8 0 10 0 0 60 1150 8.2 192 63 922 592 30 173 206 0.7 741 3200 9 5 3 104 244 0 0 0 0 25 940 8 47.75 21.625 310.7833 362.5167 12.25 42.33333 33.894 0.174 208.8333 1770.833 8.340833 57.3603 22.04929 227.8877 97.50325 11.40275 53.11965 67.77479 0.226449 213.3434 729.6663 0.24938

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Table 2: chemical constituents (epm) & SAR, RSC & %Na S. Location Month/Ye PH No ar

Total Calcium Magnesiu Sodium Potassium Carbonate Bicarbona Chloride Sulphate SAR RSA %NA hardne (Ca++) m (Mg++) (Na+) (K+) (Co3) te (HCo3) (Cl) (So4) ss as CaCo3 1 Kilvalur 10°47'00'';79°45'00'' 8.3 1070 315 4.7904 1.4796 2.784 16.32 0.3996 4.51 1.0754 0.7904 1.183856 -1.3604 75.28967 2 vedaranniyam 10°21'00'';79°51'45'' 8.35 3200 340 2.5948 4.1922 26.013 152.49 0.999 5.1988 5.5468 4.0768 12.01054 -0.5892 96.33709 3 Pratapara 10°40'30"; 9 2215 mapuram 79°49'30" 140 1.1976 1.57824 25.4475 149.175 0.999 4.0016 2.2923 1.6848 18.05414 2.22476 98.43525 4 Nagappatinam 10°50'32'';79°49'30'' 8.1 3000 741 9.5808 5.1786 13.6155 79.815 0 9.7088 4.8959 3.5984 3.902892 -5.0506 86.35788 5 Tagattur 10°24'30'';79°41'00'' 8.4 940 325 5.3892 1.0686 3.0885 18.105 0.5994 5.6088 0.5377 0.3952 1.268991 -0.2496 76.64558 6 Melvanjur 10°49'45"; 8.2 1845 e 79°49'45" 60 0.3992 0.822 15.4425 90.525 0 7.1996 0.1132 0.0832 17.15622 5.9784 98.8607 7 Velanganni10°41'30'';79°51'30'' 8 1750 345 2.1956 4.6854 8.3955 49.215 0 5.1988 2.7168 1.9968 3.940941 -1.6822 89.33038 8 Tevur 10°43'00'';79°44'00'' 8.5 1910 50 0.3992 0.60006 18.009 105.57 0.7992 7.6096 0.5377 0.3952 21.53672 7.40954 99.19789 9 Valmanga 10°52'30";79°48'30" 8.34 1380 lam 25 0.2495 0.2466 12.18 71.4 0.4995 5.1004 0.0849 0.0624 19.94846 5.1038 99.40994 50 0.5988 0.39456 11.658 68.34 0.2997 5.20208 2.4904 1.8304 13.06609 4.50842 98.7735 10 Sellur 10°55'03'';79°46'20'' 8.3 1290 55 0.6986 0.39456 13.224 77.52 0.2997 5.20208 1.6414 1.2064 13.97134 4.40862 98.80968 11 Sorakudi 10°56'21'';79°46'27'' 8.4 1440 60 0.499 0.69048 10.179 59.67 0 6.80272 0.283 0.208 11.07828 5.61324 98.32558 12 Sethur 10°56'14'';79°44'12'' 8.2 1150

SPATIAL VARIATION MAPS:

Figure 3: Ca map

Figure 5: Co3 map

Figure 6: HCo3 map

Figure 4: Cl map

Figure 7: Mg map

Figure 8: K map

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Figure 9: Na map

Figure 10: pH map

Figure 11: SO4, TH maps, Piper & Durov diagram

Figure 12: SAR, RSC & %NA maps V. Results and Discussion Chemical concentration of groundwater like Ca, Mg, Cl, HCO3, CO3, Na, K, SO4, NO3, EC, Ph have been obtained (Table 1&2). The iso-concentration map of Calcium (fig 2) shows that a small portion towards south is high in Calcium. The south-western part, Calcium is medium and remaining portion is very low.The chloride map (fig 3) indicates that in the south-east part of the study area Chloride is high. It is low in the western part,

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and the remaining portion is medium. From the Carbonate map(fig 4) it is clear that southern part and some portion in the central area CO3 is high. Northern portion shows low and remaining part is medium. The iso-concentration map of HCO3 (fig 5) shows that almost whole area is rich in HCO3 and some portion in the north is low and medium. From the iso-concentration map (fig 9) it shows that Ph is high in north and south portion of the study area and it is moderate around the central portion. It is low in some part of area. The potassium map (fig 7) shows that the central part and south-east part of the study area is high in potassium. North-western and south-western part has low potassium and the remaining portion is moderate. The isoconcentration map shows that Magnesium (fig 6) is high in south-eastern part and some portion in north-east. It is low towards western part of the study area. From the iso-concentration map (fig 8) it is clear that Na is high in the south-east portion. It is low in the south-west and north-west of the study area and medium in the remaining parts. Ph map (fig 9) shows that Ph is high in north and south portion of the study area and it is moderate around the central portion. It is low in some part of area. From the iso-concentration map of SO4 (fig 10) it shows that southern area is high in SO4, and almost it is low in remaining area except some area is medium.The iso-concentration map of total hardness(fig 10) shows that it is high in the north of the study area, and low in almost whole area. The Hill Piper Diagram (Piper,1994) is one way of comparing quality of water . The Hill piper and Durov plots(fig 10) indicates the presence of mixture of both temporary and permanent hardness of water.From the piper diagram in the figure it shows that all the samples falls in the second group. Its constituent is sodium bicarbonate {Na, K, -CO3, HCO3}. The four groups have been further classified into nine groups. In that the samples fall in 7th & 8th groups. It indicates that Non-Carbonate alkali exceeds 50% i.e chemical properties are dominated by alkalies and strong acids that ocean water and many brine plot near the right hand vertex of the sub area. Carbonate alkali exceeds 50%. Here the plot of water which are in ordinarily soft in properties to their content of dissolved solids. SODIUM ABSORPTION RATIO (SAR): Sodium hazard is expressed as sodium adsorption ratio (SAR). The SAR is calculated from the ratio of sodium to calcium and magnesium. Water condition is divided into Excellent, Good and Fair according to the SAR values.In the SAR map (fig 11) it is observed that, the south-west part of the study area (Tagattur) shows Sodium Absorption Ratio is below 10epm, in which water condition is excellent. In the northern part of Tevur, Prataparamapuram, Melvanjiyur, Sorakudi and Valamangalam are showing fair condition. The remaining part of Vedaraniyam shows good condition of water. RESIDUAL SODIUM CARBONATE (RSC): RSC gives an account of calcium and magnesium in the water sample as compared to carbonate and bicarbonate ions (Eaton, 1950).In the study area, southern part of Tagattur & Vedaranyam, and in the north of Velanganni & Kilvelur shows safe in the classification of water. And in the central part of the area â&#x20AC;&#x201C; Prataparamapuram shows marginal. The remaining areas Tevur, Sellur, Sorakudi, & Sethur shows unsuitable condition from the RSC map (fig 10). PERCENTAGE OF SODIUM (%NA): In the percentage of sodium map (fig 11) , it is observed that the water condition is unsuitable for irrigation in major portion of the study area. The western side of areas around Tagattur and Kilvelur also shows doubtful condition of water. It indicates that permeability of the soil in the majority of the area is very less and causes water logging. S. No 1 2 3

Location Kilvalur Vedaranniyam Prataparamapuram

4 5 6 7 8 9

Nagappatinam Tagattur Melvanjure Velanganni Tevur Valmangalam

10 11 12

Sellur Sorakudi Sethur

SAR

RSC

%NA

1.183856 12.01054

-1.3604 -0.5892

75.28967 96.33709

18.05414 3.902892 1.268991 17.15622 3.940941 21.53672

2.22476 -5.0506 -0.2496 5.9784 -1.6822 7.40954

98.43525 86.35788 76.64558 98.8607 89.33038 99.19789

19.94846 13.06609 13.97134 11.07828

5.1038 4.50842 4.40862 5.61324

99.40994 98.7735 98.80968 98.32558

VI. Conclusion Ground water samples were collected from 14 wells analyzed for major ion concentration. The spatial variation in the concentration of EC, Mg, Cl, So4, Na, K, Co3, Hco3, pH, TH, SAR, & RSC in ground water of this region using GIS. The pH indicates water is alkaline in nature and it is beyond the permissible limit (WHO, 1984). The total hardness is within the permissible limit in all the wells except well no. 4 (Tagattur). The nitrate is

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exceeding the permissible limit in well no. 1(Kilvalur) and 4(Tagattur) and may be attributed to the paddy cultivation with high usage of fertilizers. All the other ions are well within the permissible limit. Water is not suitable for drinking purpose in the study area with few exceptions. The values of SAR, RSC & %NA are indicative of non-suitability of Groundwater for irrigation purpose. The Hill piper and Durov plots indicates the presence of mixture of both temporary and permanent hardness of water. References [1] [2] [3] [4] [5] [6]

[7]

[8] [9]

WHO, 1984. Guidelines for the drinking water quality. Recommendation WHO, Geneva, Vol: 1. Eaton, F.M.: Significance of bicarbonate in irrigation water. Soil Sci., 69, 121-133 (1950). Piper, A.M. (1994). A geographic procedure in the geochemical interpretation of water analysis. Trans. Am. Geophysics Union. v. 25.pp.914-928, Washington D. C. P.G. Smitha1 , K. Byrappa*2 and S.N. Ramaswamy3 Physico-chemical characteristics of water samples of Bantwal Taluk, south-western Karnataka, India H.C. Vajrappa, N. Rajdhan Singh and J. M. Neelakantarama, Hydrochemical Studies of Suvarnamukhi Sub-Basin of Arkavathi river, Bangalore District, Karnataka. Journal of Applied Geochemistry Vol.9 No.2, 2007 pp 224-233. K. S. Kshetrimayum and V. N. Bajpai, Assessment of Ground Water Quality for Irrigation Use and Evolution of Hydrochemical Facies in the Markanda River Basin, Northwestern India. Journal Geological Society of India. Vol. 79, February 2012, pp. 189198. K. Ashok, V. Sudarshan, R. Sundaraiah, Madhusudhan Nalla and A. Ravi Kumar, Geochemistry of Ground Water in and around Mangampeta Barite Deposit, Cuddapah District, Andhra Pradesh, India. Journal of Applied Geochemistry. Vol. 15, No.1, 2013. pp 98-110. Panduranga Reddy, Hydrogeochemistry of Groundwater of Rangapur, Mahabubnagar District, Andhra Pradesh, India. Journal of Applied Geochemistry. Vol. 15, No.3, 2013.pp 361-371. Rosalin Das, Madhumita Das and Shreerup Goswami, Groundwater Quality Assessment for Irrigation Uses of Banki SubDivision, Athgarh Basin, Orissa, India. Journal of Applied Geochemistry. Vol. 15, No.1, 2013.pp 88-97.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences(IJETCAS) www.iasir.net Bit Error Rate vs Signal to Noise Ratio Analysis of M-ary QAM for Implementation of OFDM Mrs. Jasbir Kaur1, AnantShekhar Vashistha2 Assistant Professor1, Student ME Electronics (VLSI)2 E&EC Department PEC University of Technology Chandigarh 160012, India ______________________________________________________________________________________ Abstract: Orthogonal frequency division multiplexing (OFDM) is a multi-carrier system where data bits are encoded to multiple sub-carriers, while being transmitted simultaneously. OFDM modulation can reduce the influence of inter-symbol interference (ISI) and enables high-quality communication, and is increasingly being used in environments that exhibit severe multipath. Although OFDM in theory has been in existence for a long time, recent developments in digital signal processing (DSP) and field programmable gate array (FPGA) technologies have made it a feasible option. In this paper, an implementation of an OFDM transceiver on FPGA by instantiating parameter able signal processing intellectual property (IP) functions is presented. The FPGA resource requirements of the various sub-systems are reported and the design methodology employed IP design, verification and FPGA implementation is described. Recent theoretical studies show much interest on high-level modulation, such as M-ary quadrature amplitude modulation (M-QAM), and most related works are based on the assumption of phase synchrony. The possible presence of synchronization error and channel estimation error highlight the demand of analyzing the bit error rate (BER) performance under different phase errors. Assuming synchronization and a general constellation mapping method, which maps the superposed signal into a set of M coded symbols ,this paper, analytically derive the BER for M-QAM We obtain an approximation of BER for general M-QAM modulations, as well as exact BER for quadrature phase-shift keying (QPSK), i.e. 4QAM.The simulation is done on MATLAB 2013 environment. Keywords: QAM, SDR, FPGA, OFDM __________________________________________________________________________________________ I. Introduction If the information signal is digital and the amplitude lV of the carrier is varied proportional to the information signal, a digitally modulated signal called amplitude shift keying (ASK) is produced. If the frequency (f) is varied proportional to the information signal, frequency shift keying (FSK) is produced, and if the phase of the carrier (0) is varied proportional to the information signal, phase shift keying (PSK) is produced. If both the amplitude and the phase are varied proportional to the information signal, quadrature amplitude modulation (QAM) results. ASK, FSK, PSK, and QAM are all forms of digital modulation.

. Figure 1: A Simplified Block Diagram for a Digital Modulation System Square M-ary QAM involves the amplitude modulation of two carriers in quadrature expressed as S(t)=Accos2πfct-Assin2πfct 0≤t<T (1) where Ac and As are the signal amplitudes of the in-phase and quadrature components respectively. T is the symbol duration and fc is the carrier frequency [1]. Ac and As in (1) are represented by log2M level amplitudes which take values of either – (√M-1)d,-( (√M-3)d,….. (√M-1)d,( (√M-3)d where d is half of the minimum distance between two symbols. For the discussion of this paper, a perfect 2 dimensional Gray code [2] is assumed to be used in assigning bits to each point in the QAM constellation. This assures that each symbol differs to its nearest neighbors by the minimum number of bits possible. It is also assumed that all the symbols are equiprobable. In addition, the noise to be considered in this paper is zero mean Additive White Gaussian Noise (AWGN) with variance 345. Finally, it is assumed that there is no error contributed by carrier recovery and symbol synchronization.

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II. M-ary Encoding M-aryis a term derived from the word binary. M simply represents a digit that corresponds to the number of conditions, levels, or combinations possible for a given number of binary variables. For example, a digital signal with four possible conditions (voltage levels, frequencies, phases, and so on) is an M-ary system where M = 4. If there are eight possible conditions, M= 8 and so forth. The number of bits necessary to produce a given number of conditions is expressed mathematically as N=log2M. Where: N= number of bits necessary and M = number of conditions, levels, or combinations possible with N bits It can be simplified and rearranged to express the number of conditions possible with N bits as 2N=M. M-QAM is a well known modulation technique use in wireless communication. In wireless communication fading phenomenon is a boundary condition. So the practice for combating fading in wireless communication over such a time varying channel is to use diversity technique. Due to the high spectral efficiency M-QAM is an attractive modulation technique for wireless communication. For a large number of signal points (i.e., M-ary systems greater than 4), QAM outperforms PSK. This is because the distance between signaling points in a PSK system is smaller than the distance between points in a comparable QAM system. As the number of bits in each symbol is increased i.e. increase in M value in M-QAM the speed of communication is increased which results increase in bandwidth but at the same time symbol error rate is increased due to decrease in bit distance. 16-QAM is mainly used technique for implementation of OFDM with less probability of error(BER) in comparison of higher order QAM . With increase in order of QAM size of IFFT/IDFT blocks are also increased which results increase in complexity.. But in future work and in order to ensure the correct functionality of the OFDM system, frame synchronization would need to be implemented. In addition, the OFDM transceiver will be further improved to allow a high order modulation scheme such as 256-QAM. Equalization techniques will also be utilized to mitigate the effect of multipath fading, particularly over the 60 GHz wireless radio channel.[1]. But this increase in order of QAM to implement OFDM results in increase in BER for the same SNR in comparison of error of lower order QAM because symbol distance is decreased with increase in M value. III. Fading In wireless communications, fading is deviation or the attenuation that a telecommunication signal experiences over certain propagation media. The fading may vary with time, geographical position and/or radio frequency, and is often modeled as a random process [9]. Slow Fading Slow fading arises when the coherence time of the channel is large relative to the delay constraint of the channel. So the amplitude and phase change imposed by the channel can be considered roughly constant over the period of use. Flat Fading Flat fading attenuates or fades all frequencies in a communications in the same amount. In this fading, the coherence bandwidth of the channel is larger than the bandwidth of the signal. Rayleigh fading Rayleigh fading is a statistical model which assumes that the magnitude of a signal that has passed through a transmission medium will vary randomly, or fade, according to a Rayleigh distribution. It is most applicable when there is no dominant propagation along a line of sight between the transmitter and receiver. Ricianfading Rician fading is a stochastic model for radio propagation anomaly caused when the signal arrives at the receiver by two different paths, and at least one of the paths is changing.Rician fading occurs when one of the paths, typically a line of sight signal, is much stronger than the others. IV. QAM Error Performance For a large number of signal points (i.e., M-ary systems greater than 4), QAM outperforms PSK. This is because the distance between signaling points in a PSK system is smaller than the distance between points in a comparable QAM system. The general expression for the distance between adjacent signaling points for a QAM system with L levels on each axis is

whered = error distance

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L = number of levels on each axis D = peak signal amplitude The general expression for the bit error probability of an L-level QAM system is

M-ary Quadrature Amplitude Modulation In M-QAM modulation scheme, the in-phase and quadrature components are both in-dependently PAM Modulated. The signal constellation for MQAM consists of a square lattice of message points. The error probability as a function of K, and N of the system can be calculated by averaging the conditional probability of error over the pdf of γ,

Where, PS (E /γ) is the conditional probability of symbol error. The probability of symbol error for QAM over a Gaussian channel is given as [10]

Figure 2: Theoretical Behavior for SNR vs BER of M-QAM Physical-layer network coding (PNC) [2] is considered as a promising technology to improve the throughput performance of wireless relaying networks. It employs both the broadcast nature of wireless channels and the natural network coding ability introduced by the superposition of electromagnetic waves. Between the two methods of PNC, i.e. amplify-and forward [3] and de noise-and-forward (DNF), the DNF method shows more performance advantages because it avoids noise amplification [4]. Hence, DNF has attracted much interest in recent research, and we also focus on DNF in this paper. Recently, PNC (using the DNF method) with highlevel modulations or nested lattice code attracts much interest [5]– [8], but these are generally based on the assumption of perfect synchronization. Although there is also some work focusing on asynchronous PNC [8]– [9], synchronous PNC still has advantages because it allows more efficient constellation design [4] and can make use of capacity-approaching channel codes [7]. The capacity region of the Gaussian two-way relay channel can also be reached with synchronous PNC [8]. In this paper, the analysis of the SER for M-QAM modulated PNC with arbitrary phase error is done. We consider a general constellation mapping, which maps the superposed (2√M-1) by (2√M-1) constellation into a set of M coded symbols. By projecting the 2-dimensional signal onto the in-phase and quadrature axes, we derive an approximation of the SER for M-QAM analytically. For an M-ary PSK system with 64 output phases (n = 6), the angular separation between adjacent phases is only 5.6° (180 / 32). This is an obvious limitation in the level of encoding (and bit rates) possible with PSK, as a point is eventually reached where receivers cannot discern the phase of the received signaling element. In addition, phase impairments inherent on communications lines have a tendency to shift the phase of the PSK signal, destroying its integrity and producing errors.

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V. Probability of Error and Bit Error Rate Probability of error P(e) and bit error rate (BER) are often used interchangeably. Probability of error is a function of the carrier-to-noise power ratio (or, more specifically, the average energy per bit-to-noise power density ratio) and the number of possible encoding conditions used (M-ary). Carrier-to-noise power ratio is the ratio of the average carrier power (the combined power of the carrier and its associated sidebands) to the thermal noise power Carrier power can be stated in watts or dBm. Where C(dBm) = 10 log [C(watts) / 0.001] VI. Simulation Model With increase in number of bits there is increment in number of symbols to be transmitted. It results in decrement in distance between symbols so increase in probability of error. The constellation diagram for 32 QAM is simulated in MATLAB 2013 and shown in fig. 3.

Figure 3: Constellation Diagram of 32-QAM In figure 4 the simulation results of SNR vs BER are shown using MATLAB 2013.

Figure 4: Simulated SNR vs BER for General M-QAM VI. Conclusion Theoretically with increase in order of M-ary QAM the BER must be increased with increasing the M value for a particular level of signal to noise ratio because and the simulation results also follows it. So it is concluded that to increase the rate of transmission in digital communication using OFDM if it is implemented using higher order QAM then it will result in increase in error probability because the distance between the transmitted symbols is decreased which cause the fading between these symbols. VII. References [1] [2] [3]

K.Jasbir and V.S.Anant , “Implementation and performance evaluation of OFDM Transceiver” IJSRD, pp. 908–912, vol.2, issue 4, june. 2014. S. Zhang, S. C. Liew, and P. P. Lam, “Hot topic: Physical-layer network coding,” in Proc. ACM MobiCom, Sep. 2006, pp. 358– 365. P. Popovski and H. Yomo, “The anti-packets can increase the achievable throughput of a wireless multi-hop network,” in Proc. IEEE ICC, Jun. 2006, pp. 3885–3890

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[4] [5]

K. Lee and L. Hanzo, “Resource-efficient wireless relaying protocols,” IEEE Wireless Commun. Mag., vol. 17, no. 2, pp. 66 72, Apr. 2010 . M. Noori and M. Ardakani, “On symbol mapping for binary physicallayer network coding with PSK modulation,” IEEE Trans. WirelessCommun., vol. 11, no. 1, pp. 21–26, Jan. 2012.

[6]

H. J. Yang, Y. Choi, and J. Chun, “Modified high-order PAMs for binary coded physical-layer network coding,” IEEE Commun. Lett., vol. 14, no. 8, pp. 689 –691, Aug. 2010.

[7]

M. P. Wilson, K. Narayanan, H. D. Pfister, and A. Sprintson, “Joint physical layer coding and network coding for bidirectional relaying,” IEEE Trans. Inf. Theory, vol. 56, no. 11, pp. 5641–5654, Nov. 2010.

[8]

W. Nam, S.-Y. Chung, and Y. H. Lee, “Capacity of the gaussian twoway relay channel to within 1/2 bit,” IEEE Trans. Inf. Theory, vol. 56, no. 11, pp. 5488–5494, Nov. 2010.

[9]

A. Goldsmith and S. G. Chua, “Variable-rate variable-power M-QAM for fading channels,”IEEE Trans. Commun., vol. 45, pp. 1218– 1230, Oct. 1997.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Physico-chemical analysis of groundwater covering the parts of Padmanabhanagar, Bangalore Urban District S Shruthi1 and T J Renuka Prasad2 Research Scholar & Professor, Department of Geology, Bangalore University, Bangalore, Karnataka, India

Abstract: The present study evaluated the ground water quality and physico-chemical characteristics of the water samples collected from 15 bore wells in the study area which is situated in Rangappa Layout which is located in the Ittamadgu Village of Uttarahalli Hobli which falls in the Bangalore South taluk of the Bangalore Urban district. Physico-chemical characteristics of the collected water samples, various parameters like pH, Temperature, Total Suspended Solids, Turbidity, Total Dissolved Solids, Total Hardness, Electrical Conductivity, Sodium, Potassium, Ca, Mg, Cl, HCO3, CO3, NO3, F, SO4, PO4, Cr+6, Fe, Cu, Pb, Ni, Zn are analyzed. Spatial variation maps are prepared to understand the distribution. The variation in elevation, overburden thickness, fractures, rate of discharge are determined. Hill piper, Wilcox and USSL have been obtained to determine the percentage of salinity and alkalinity of water. The quality of water found suitable for drinking purpose. Keywords: Physico-chemical characteristics, spatial variation maps, Hill Piper, Wilcox, USSL I. Introduction Water is the most essential and one of the prime necessities of life. No one can live without water (Khanna et al., 2007). Unplanned urban development has posed gigantic problems of environmental pollution. Due to this, water of natural bodies is getting polluted at an alarming rate (Shastri et al., 2008). Physico-chemical parameters play a vital role in determining the distributional pattern and quantitative abundance of organism’s inhabitating a particular aquatic ecosystem (Singh et al., 2009). Groundwater quality is being threatened by agricultural, urban & industrial activities, which leach or inject the polluted water directly into underlying aquifers. Quality of water is an important criterion for evaluating the suitability of water for irrigation and drinking. The study of underground contamination will be of immense help to researchers and environmental regulators to evolve and initiate mitigative measures. Long and sustained industrial activity in any given area can often lead to soil and ground water contamination. Improper waste disposal practices might contaminate the soils and gradually the entire ground water in the area, impairing ground water quality for many applications including drinking. The present investigation involves the analysis of water quality in relation to physico- chemical parameters. II. Study Area In order to study the ground water development and the quality of the ground water, a sample study area (Fig. 1) the Rangappa Layout which is located in the Ittamadgu Village of Uttarahalli Hobli which falls in the Bangalore South taluk of the Bangalore Urban district has been chosen. It falls between Longitude 77° 32’ 53” & 77° 32’ 58” and Latitude 12° 55’ 28” & 12° 55’ 32”. The area is spread approximately less than square kilometer which houses residential flats. The people in the layout depend mainly on bore well for their day to day water need. It is a Rocky upland Plateau and predominant geology is Granitic Gneisses. The Bangalore south taluk is categorized as Over Exploited with stage of development 175 % as on March 2011.

Fig: 1 Location of the study area III. Methodology Fieldwork was carried out in the study area and collected the GPS locations from various points within the layout for geo referencing the layout map. 15 numbers of Borewells were inventoried and water samples are

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collected for basic parameter analysis. A total of 20 GPS readings were recorded at different points Based on the inventoried data, thematic maps are prepared using GIS software. Check for the completeness has been done. The borewell details from which the water samples are collected are tabulated (Table 1).

12.9413 77.5488 12.9252 77.5487 12.9249 77.5485

TABLE 1 : DETAILS OF BORE WELLS INVENTORIED Fractured zone (mbgl) 876 200 80 0.75 870 200 90 0.75 888 400 150 2.45

12.9248 77.5482

884

190

0.75

1995

WORKING

12.9253 12.9253 12.9245 12.9255 12.9255 12.9247 12.9255 12.9247 12.9258 12.9250 12.9246

888 893 888 887 889 879 915 891 895 875 877

150 430 150 350 170 600 200 650 300 200 500

100 128 70 250 160 200 120 250 250 100 200

0.75 4.27 0.75 4.27 2.45 1.18 0.75 4.27 0.75 0.22 4.26

80 285

2001 2011 1990 2013 2008 1999 1995 2012 1993 1994 2012

WORKING WORKING WORKING WORKING WORKING WORKING WORKING WORKING WORKING WORKING WORKING

Sl. Depth of Depth of casing Discharge Location Latitude Longitude Altitude No well (m bgl) (mbgl) (lps) 1 2 3

2 22 47

4 5 6 7 8 9 10 11 12 13 14 15

45 11 16 44 26 4 12 8 18

77.5486 77.5489 77.5489 77.5486 77.5488 77.5489 77.5493 77.5483 77.5491 77.5483 77.5487

120 140 160

120

Year of drilling

Status of well

1996 1997 2009

WORKING WORKING WORKING

Pump lowered (mbgl) 170 170 300 HAND PUMP 100 300 100 250 160 350 160 400 250 190 300

Depth of Well: DEPTH OF WELLS 2020 2010 2000 1990 1980 1970 150 200 200 200 150 400 650 350 Fig 2: Depth of well

The above bar graph represents the depth of the wells drilled in the study area from 1990 – 2014 (Fig 2). The water availability in the ground has decreased which has resulted in increase in the depth of the wells. This indicates the decrease in the level of water table. In the recent past artificial recharge is introduced compulsory. It is expected artificial recharge scheme is enhances the level of water table in future scenario.  Elevation: The elevation in the study area ranges from 875 – 915 m bgl where as 60% of the area ranges from 875 – 885 m bgl. There is increase in elevation towards north east. About 15% of area ranges with an elevation between 885 – 895 m bgl and maximum elevated area lies in the north eastern region with values ranging from 905 – 915 m bgl (Fig 3).  Fractures: The fractures which are yielding water in the study area ranges from 20 – 100 m bgl. The western side of the study area the fractures are encountered within the range of 20 – 40 m bgl whereas the north eastern portion of the study area have deep seated aquifers with depth up to 100 mts (Fig 4).  Discharge: The yield from the bore wells ranges from < 1 lps to > 4 lps. Around 20 % of the study areas have borewells having yield less than 1 lps, where has borewells having yield more than 4 lps is located in isolated pockets. The common discharge from the borewells in the study area is between 1 to 3 lps (Fig 5).  Overburden thickness: In the Study area the likeliness of encountering the massive rock at greater depth is more on the south-western part and in northern part, whereas possibility of massive rock at the shallow depth is more in the central portion of the study area. The depth to massive rock increases gradually from central part towards North and south (Fig 6).

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Fig: 3 Elevation map

Fig: 4 Distribution of Fracture zone

Fig: 5 Discharge Map

Fig: 6 Overburden Thicknesses

IV. Chemical Analysis 15 Ground water samples were collected from the Bore wells in the Study area. The water samples thus collected were analyzed for Total hardness, calcium, magnesium, nitrate, fluoride, Sulphate & TDS. Whereas Electrical conductivity and pH were measured insitu at the sampling collection site itself. Standard methods were used for the analysis. The sample locations are given in the (Fig. 7). The result of the chemical analysis is given in Table No: 2.

Fig: 7 Location map of key well

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S Shruthi et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 9(3), June-August, 2014, pp. 229236 TABLE 2: CHEMICAL ANALYSIS DATA Sl. Parameter/Sample No

7.5

7.3

7.7

7.5

7.4

7.5

7.4

7.6

7.5

7.3

Temp

TSS

Nil

Turb

0.1

0.15

0.1

0.15

0.2

0.1

0.15

0.2

0.1

0.2

0.1

0.2

0.1

0.15

TDS

390

400

380

370

350

370

380

360

380

390

410

400

370

380

400

630

645

630

610

580

610

630

590

620

630

650

640

620

635

51.2

52.9

52.1

55.6

52.8

53.1

24.1

22.4

23.8

24.1

25.5

21.9

24.78

25.3

25.2

22.4

21.6

24.1

230

240

232

225

230

240

235

220

240

243

237

225

220

240

CO3

3.4

3.1

2.8

3.5

4.1

2.5

HCO3

160

170

165

150

140

165

170

155

170

165

168

160

155

160

0.31

0.39

0.31

0.35

0.39

0.28

0.31

0.26

0.35

0.32

0.39

0.37

0.35

0.34

0.35

NO3

6.14

5.56

4.12

5.23

6.19

5.63

5.12

6.83

5.01

3.41

7.79

5.94

3.49

5.28

5.56

PO4

0.01

SO4

23.1

Cr+6

0.02

0.03

0.01

0.03

0.01

0.02

0.03

0.019

0.03

0.02

0.03

0.001

0.01

0.002

0.01

pH: pH is the scale of intensity of acidity and alkalinity of water and measures the concentration of hydrogen ions. Normal range of pH in the irrigation water is 6.5 to 8.4 (Ayersand Westcot, 1985; KSPCBOA, 2000). The pH of the collected water samples were found by using pH meter (Electrometric method). It was found that pH in all the 15 samples are within the permissible limit of ISI i.e. 6.5-9. TEMPERATURE: Water temperature directly as well as indirectly influences many abiotic and biotic components of aquatic ecosystem. It also reflects to the dynamics of the living organisms such as metabolic and physiological behaviour of aquatic ecosystem. The temperature is one of the important factors in aquatic environment since it regulates physicochemical as well as biological activities (Kumar et al., 1996). The water temperature was recorded 25°C to 27°C. TOTAL SUSPENDED SOLIDS: The total suspended solids are composed of carbonates, bicarbonates, chlorides, phosphates and nitrates of Ca, Mg, Na, K, Mn organic matter, salt and other particles. The effect of presence of total suspended solids is the turbidity due to silt and organic matter. When the concentration of suspended solids is high it may be aesthetically unsatisfactory for bathing (APHA, 2002). In the present study TSS is not detected. TURBIDITY: Turbidity is an expression of optical property; wherein light is scattered by suspended particles present in water (Tyndall effect) and is measured using a nephelometer. Turbidity of groundwater samples obtained from 2.1 to 6.2 which showed limits under the CPCB. TOTAL DISSOLVED SOLIDS: The dissolved solids in water samples include all solid materials in solution. It does not include suspended sediments, colloids or dissolved gases. Different limits of TDS content are fixed for different purposes by various organizations and individuals. (Davis and Dewiest, 1967) TABLE 3 : PERMISSIBLE LIMITS FOR TDS TDS(ppm) CLASS < 500 Desirable for Drinking 500 - 1000 Permissible for Drinking 1000 - 3000 Slightly Saline 3000 – 10,000 Moderately Saline 10,000 – 35,000 Very Saline >35,000 Brine

From this classification the water samples collected in the study area are found suitable for drinking water with its value within 500ppm (Table 3).

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ELECTRICAL CONDUCTIVITY: Conductance is a function of water temperature; hence a standard temperature usually 25°C must be specified in reporting conductivity test. As the ion concentration increases conductance of solution also increases. Therefore conductance measurement indicates the ion concentration. The Electrical conductivity was measured using conductivity meter (Electrometric method) at the sampling site. EC ranged between 620 µs/cm to 670 µs/cm. SODIUM: Sodium content of bore well ranged from a minimum of 17 mg/L to a maximum of 30 mg/L. Spatial variation (Fig.8) has been studied and concentration of Na is found high in sample no 1 and it is found less in concentration in sample no 5. POTASSIUM: Although potassium is nearly as abundant as sodium in igneous rocks, its concentration in ground water is comparatively very less as compared to sodium (nearly one-tenth or one-hundred that of sodium). This is due to the fact that the potassium minerals are resistant to decomposition by weathering. Potassium content of bore well ranges from 2-6 mg/L. Spatial variation (Fig.9) has been studied and concentration of K is found high in sample no 12 and it is found less in concentration in sample nos 5, 8 and 4. CALCIUM: Calcium found in water samples was estimated by EDTA titrimetric method. It was found that all the calcium concentration in water samples are within in the permissible limits of 200ppm prescribed by ISI. Spatial variation (Fig.10) has been studied and concentration of Ca is found high in sample no 12 and it is found less in concentration in sample nos 4, 5 and 8. MAGNESIUM: Magnesium content in water samples was determined by using total hardness and calcium hardness value. It was found that the magnesium concentration in water samples is within the permissible limit of 30ppm as per WHO and ISI standards. Spatial variation (Fig.11) has been studied and concentration of Mg is found high in sample nos 8, 10, 11 and 12, it is found less in concentration in sample nos 5, 9, 13 and 14. TOTAL HARDNESS: Total hardness is the measure of Ca and Mg content and it is customarily expressed as the equivalent of CaCO3. Hardness results from the presence of divalent metallic cations of which Ca and Mg are most abundant on ground water. The total hardness was estimated by EDTA titrimetric method. The total hardness of all the 15 samples range from 150 – 300 which indicates that the given water samples are hard. CHLORIDE: The chloride concentration in water samples are within the permissible limit i.e, it ranges from 250-1000ppm, according to IS. Spatial variation (Fig.12) has been studied and concentration of Cl is found high in sample nos 1, 6, 7, 11, 12, and 15, it is found less in concentration in sample no 5 and 13. CARBONATE: Carbonate content of bore well ranges from 2-4.1 mg/L. Spatial variation (Fig.13) has been studied and concentration of CO3 is found high in sample nos 11 and 3, it is found less in concentration in sample nos 5 and 14. BI-CARBONATE: Bi-carbonate content of bore well ranges from 140-170 mg/L. Spatial variation (Fig.14) has been studied and concentration of HCO3 is found high in sample nos 2, 3, 6, 7, 10, 11 and 12, it is found less in concentration in sample no 5. FLUORIDE: The maximum permissible limit of fluoride in drinking water is recommended to be 1.5mg/l by WHO. Fluoride concentration in water samples were found by using the Fluorimeter. All the 15 samples are showing fluoride value within the permissible limit of less than 1.5mg/L. Spatial variation (Fig.15) has been studied and concentration of F is found high in sample nos 2, 5, 11and 12, it is found less in concentration in sample nos 6and 8. NITRATE: Nitrate concentrations in ground water ranges from 50 mg/L are influenced by excessive applications of nitrate fertilizer. Nitrate concentration in water samples were detected by phenol disulphonic acid method. It was found that the Nitrate concentration in water samples is within the permissible limit. Spatial variation (Fig.16) has been studied and concentration of NO3 is found high in sample no 11 and it is found less in concentration in sample no 3, 10 and 13. PHOSPHATE: Phosphate content of bore well ranges from 0.004-0.011mg/L. Spatial variation (Fig.17) has been studied and concentration of PO4 is found high in sample nos 4, 7, 8 and 11, it is found less in concentration in sample no 14. SULPHATES: The sulphates in water samples are estimated by Turbidimetric method. The sulphate concentration in water samples in the study area are within the permissible limit which ranges between 200-

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400ppm. Spatial variation (Fig.18) has been studied and concentration of SO 4 is found high in sample nos 2, 4 and 15, it is found less in concentration in sample no 4, 5 and 6. CHROMIUM: As per IS the chromium content in the water should not be greater than 0.05mg/L. From the analysis it is determined that the concentration of chromium in the water samples in the study area is well within the permissible limit. Spatial variation (Fig.19) has been studied and concentration of Cr+6 is found high in sample nos 5, 9, 11 and 12, it is found less in concentration in more than 12 samples. IRON: The standard of the BIS suggests that the Iron content of drinking water should not be greater than 0.3mg/L. The iron concentration in water samples in the study area are within the permissible limit. Spatial variation (Fig.20) has been studied and concentration of Fe is found high in sample nos 5, 8, 9 and 15, it is found less in concentration in sample nos 6 and 4. COPPER: The permissible limit of copper according to IS suggested that the concentration of copper should not exceed 0.05 mg/L. It is found that the concentration of copper in water samples is within the permissible limit. Spatial variation (Fig.21) has been studied and concentration of Cu is found high in sample nos 4, 6 and 11, it is found less in concentration in sample nos 3, 8, 13, 12 and 15. ZINC: Zinc concentrations in ground water ranges from 5-15ppm. It was found that the Zinc concentration in water samples is within the permissible limit. Spatial variation (Fig.22) has been studied and concentration of Zn is found high in sample nos 3, 5 and 7, it is found less in concentration in sample no 5. V. Hill Piper Diagram Piper diagram (Piper, 1994), is most useful to understand chemical relationships of ground water. The pipers trilinear diagram is one way of comparing quality of water. The first step is for determining the water facies for the purpose of studying the evolution of ground waters. The lower left ternary or cat ion ternary, compress the cation composition as on equivalent fraction (% epm) of calcium (total dissolved Ca), magnesium (total dissolved Mg) and the sum of sodium and potassium (Na+K). Similarly, the lower right ternary, or anion ternary contrasts the anion composition in terms of fraction of equivalents of sulphate ion SO 4-2 , chloride (Cl-), and the sum of bicarbonate and carbonate ions (HCO3 – + CO3-2). The central diamond is a combination of the cation and anion fractions. The groundwater nature is explained by the Piper trilinear diagram which is divided into 4 groups which in turn are further subdivided into 9 groups. Most of the groundwater samples of the study area fall in group 9 which indicates that none of the cation or anion pair exceeds 50% and Ca, Mg-Cl, SO4 are the dominating facies (Fig 23). VI. Wilcox’ Diagram Quality of water used for irrigation can also be assessed based on salinity as determined by electrical conductivity and soluble sodium percentage according to the method proposed by Wilcox (1948). Percent of sodium content in natural water is an imperative parameter to assess its suitability for agricultural use. A maximum of 60% sodium in ground water is allotted for agricultural purposes (Wilcox, 1948, 1967; USSL,1954). Sodium percentage can be defined in terms of epm of the common cations (Wilcox, 1948). %Na= (Na+ + K+) 100 Ca++ + Mg++ + Na+ + K+ The concentration of cations is in epm. The chemical quality of water samples are studied from %Na versus EC on the Wilcox diagram (Fig.24). Out of 15 samples, 4 samples fall under excellent to good category and the remaining 11 samples fall under good to permissiblecategory (Table 4). TABLE 4: WATER CLASSES FOR IRRIGATION ON THE BASIS OF %NA Water Class for Irrigation

% of Na

No of Samples

Excellent to Good

< 20

Good to Permissible

20-40

Permissible to Doubtful

40-60

Doubtful to Unsuitable

60-80

Unsuitable

>80

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VI.

USSL Classification

Water used for irrigation can be related to salinity and sodium hazards. This sentence is given by “USSR” in 1954. This classification can be plotted by taking SAR values and EC to consideration (Fig.25).

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SODIUM ADSORPTION RATIO: Sodium concentration in ground water is important since increase of sodium concentrations in water effect detritions of soil quality reducing permeability (Kelly, 1951 and Tijani, 1994). SAR is expressed as: SAR=

Na Sqrt (Ca+Mg)/2

All the 15 water samples collected from the study area falls under C 2S1 category which has medium salinity, low sodium water and are good for medium permeable soil (Table 5). Category C1S1 C2S1 C3S1 C2S2 C3S2 C3S3 C3S4

TABLE 5: USSL CLASSIFICATION FOR GROUND WATER No. of Samples Water Quality 0 Low salinity and lower alkali water Medium salinity and lower sodium water. Good for medium 15 permeable soil 0 Moderate to high salinity and less alkaline water Moderately alkaline and medium salinity. Good for medium 0 permeable and coarse grained permeable soil. 0 Moderate to high salinity and moderate alkaline. 0 Highly alkaline and have moderate to high salinity. 0 Highly alkaline and saline.

VII. Conclusion The water samples collected from the area are subjected to chemical analysis. Check for the completeness of the analysis is carried out. All the components were converted into epm. Sum of the cation and anion were obtained and percentage of error has been calculated, it ranges between 1.9-3.9. It is compared with the TDS. All the samples show completeness of analysis. Spatial variation maps have been done using GIS. Hill Piper Trilinear Diagram represents that most of the groundwater samples of the study area fall in group 9 which indicates that none of the cation or anion pair exceeds 50%. Wilcox Diagram is plotted which shows that, Out of 15 samples, 4 samples fall under excellent to good category and the remaining 11 samples fall under good to permissible category. USSL Classification graph shows that, all the 15 water samples collected from the study area falls under C2S1 category which has medium salinity, low sodium water and are good for medium permeable soil. when compared with ISI and WHO, for all the 15 samples standards are recorded the range within the permissible limit. VIII. [1] [2] [3] [4] [5] [6] [7]

[8]

[9] [10]

Reference

APHA. Standard methods for the examination of water and waste water, 19thed. American Public Health Association 1995 APHA. Standard methods for the examination of water and waste water, 17 th Edition; Prepared and published jointly by USA: American Public Health Association 1989. Hem JD. Study and interpretation of the chemical characteristics of natural water. US Geol Survey Water-Supply Paper 1959; 1473: 261-68. Wolf, L., Eiswirth, M., & Hötzl, H. (2006). Assess- ing sewer–groundwater interaction at the city scale based on individual sewer defects and marker species distributions. Environmental Geology, 49, 849–857. P. Ravikumar, K. Venkatesharaju, R. K. Somashekar. Major ion chemistry and hydrochemical studies of groundwater of Bangalore South Taluk, India. EnvironMonit Assess 2010; 163:643–653. H.C. Vajrappa, N. Rajdhan Singh and J. M. Neelakantarama, Hydrochemical Studies of Suvarnamukhi Sub-Basin of Arkavathi river, Bangalore District, Karnataka. Journal of Applied Geochemistry Vol.9 No.2, 2007 pp 224-233. K. S. Kshetrimayum and V. N. Bajpai, Assessment of Ground Water Quality for Irrigation Use and Evolution of Hydrochemical Facies in the Markanda River Basin, Northwestern India. Journal Geological Society of India. Vol. 79, February 2012, pp. 189198. K. Ashok, V. Sudarshan, R. Sundaraiah, Madhusudhan Nalla and A. Ravi Kumar, Geochemistry of Ground Water in and around Mangampeta Barite Deposit, Cuddapah District, Andhra Pradesh, India. Journal of Applied Geochemistry. Vol. 15, No.1, 2013. pp 98-110. Panduranga Reddy, Hydrogeochemistry of Groundwater of Rangapur, Mahabubnagar District, Andhra Pradesh, India. Journal of Applied Geochemistry. Vol. 15, No.3, 2013.pp 361-371. Rosalin Das, Madhumita Das and Shreerup Goswami, Groundwater Quality Assessment for Irrigation Uses of Banki SubDivision, Athgarh Basin, Orissa, India. Journal of Applied Geochemistry. Vol. 15, No.1, 2013.pp 88-97.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net

Advanced Energy Efficient Routing Protocol for Clustered Wireless Sensor Network: Survey Prof. N R Wankhade 1 Dr. D N Choudhari 2 Associate Professor1, Professor 2, Department of Computer Science Engineering, 1 GNSCOE,Pune University, Pune, Maharashtra, INDIA 2 Sant Gadge Baba Amravati University (SGBA), Amravati, Maharashtra, INDIA _________________________________________________________________________________________ Abstract: In wireless sensor network important issues are to gather sensed information, transforming the information data to the base station in an energy efficient manner. Clustering is one of the most popular approaches used in wireless sensor networks to conserve energy and increase network lifetime. LEACH is among the most popular clustering protocols proposed for wireless sensor networks. Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. These sensor nodes have some constraints due to their limited energy, storage capacity and computing power. There are a number of routing protocols for wireless sensor networks. In this paper we study routing problems for WSNs and analysis of clustering protocols. Keywords: Wireless sensors; protocols; routing; energy efficiency; clustering I.

Introduction

Wireless sensor network typically consists of a large number (tens to thousands) of low-cost, low-power [1] [2] and multifunctional sensor nodes that are deployed in a region of interest. These sensor nodes are small in size, but are equipped with embedded microprocessors, radio receivers, and power components to enable sensing, computing, communication, and actuation. These components are integrated on a single or multiple boards, and packaged in a few cubic inches. With state-of-the- art, low-power circuit and networking technologies. A wireless sensor network communicates over a short distance through wireless channels for information sharing and cooperative processing to accomplish a common task. Wireless sensor network can be deployed on a global scale for environmental monitoring and habitat study, over a battlefield for military surveillance and reconnaissance, in emergent environments for search and rescue, in factories for condition based maintenance and process control, in buildings for infrastructure health monitoring, in homes to realize smart homes. The basic philosophy behind Wireless sensor network is that, while the capability of each individual sensor node is limited, the aggregate power of the entire network is sufficient for the required mission. The fundamental goal of a wireless sensor network is to produce information from raw local data obtained (sensed data) by individual sensor mode by prolonging the life time of WSN as much as possible. The resource constrained nature of sensor nodes pose the unique challenges to the design of WSNs for their applications. The limited power of sensor nodes mandates the design of energy- efficient communication protocol. A routing protocol is required when a source node cannot send its packets directly to its destination node but has to rely on the assistance of intermediate nodes to forward these packets on its behalf. There are mainly two types of routing process: one is static routing and the other is dynamic routing. Dynamic routing [1] performs the same function as static routing except it is more robust. Static routing allows routing tables in specific routers to be set up in a static manner so network routes for packets are set. If a router on the route goes down, the destination may become unreachable. Dynamic routing allows routing tables in routers to change as the possible routes change. In case of wireless sensor networks dynamic routing is employed because nodes may frequently change their position and die at any moment. Routing [2] in WSNs is very challenging due to several inherent characteristics. First, it is not possible to build a global addressing scheme for the deployment of sheer number of sensor nodes. Therefore, classical IP-based protocols cannot be applied to sensor networks. Second, in contrast to typical communication networks almost all applications of sensor networks require the flow of sensed data from multiple regions (sources) to a particular sink. Third, the generated data traffic has significant redundancy in it since multiple sensors may generate same data within the vicinity of a phenomenon. Such redundancy needs to be exploited by the routing protocols to improve energy and bandwidth utilization. Fourth, sensor nodes are tightly constrained in terms of transmission power, on- board energy, processing capacity and storage. Thus, they require careful resource management. Due to such differences, many new algorithms have been proposed [1] [2] for the routing problem in WSNs. Based on the network structure adopted, routing protocols for WSNs can be classified into flat network

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routing, hierarchical network routing, and location based network routing. In hierarchical network routing divides the network into clusters to achieve energy-efficiency and scalability. Naturally, grouping sensor nodes into clusters has been widely adopted by the research community to satisfy the above scalability objective and generally achieve high energy efficiency and prolong network lifetime in large- scale WSN environments. One of the famous and attractive hierarchical network routing protocols is low-energy adaptive clustering hierarchy (LEACH), which has been widely accepted for its energy efficiency and simplicity. Most algorithms are heuristic in nature, and aim at generating the minimum number of clusters and minimum transmission distance. In this paper various energy-efficient hierarchical [4] cluster based routing protocols for wireless sensor network are discussed and proposed energy efficient routing protocol for clustered wireless sensor network in which node with more energy , near to center and near to base station will get more chance to become cluster head depends on location and centrality. The paper is organized in the following way. In Section II, the energy-efficient clustering structures in WSN are briefly explained. Sections III describes preprocessing work the energy- efficient cluster-based routing protocols are discussed. In Section IV, describe survey and analysis of existing protocol. Finally, Section VI presents conclusion. II.

Related Work

Routing Protocols in Wireless Sensor Networks Protocols defined for Ad Hoc Networks are generally not suitable for wireless sensor networks [3][4]. As aggregate sensor data for any event is more important than individual node data, the communication is more data-centric than address- centric. Energy and bandwidth conservation is the main concern in WSN protocol design since power resources of sensor nodes are very limited as well as computation, communication capabilities. Among the other design factors and challenges for wireless sensor networksâ&#x20AC;&#x2122; protocol are robustness to dynamic environment, and scalability to numerous number of sensor nodes. Some recommended solutions to these challenges are as follows: a minimization of data communications over the wireless channel and maximization of network life time (i.e. minimum energy routing) Scalability, on another hand; may be enhanced by organizing network in a hierarchical [2] manner (e.g., clustering) and utilizing localized algorithms with localized interactions among sensor nodes. A hierarchical protocol is an approach to the balance between scalability and performance. In hierarchical routing, energy consumption of sensor nodes is drastically minimized when the sensor nodes are involved in multi-hop communication in an area of cluster and performing data aggregation and fusion so as to reduce the number of transmitted information to the sink. The clusters formation is based on the energy reserve of sensor nodes and its proximity to the cluster head (Akkaya and Younis, 2005; Lin and Gerla, 1997). In hierarchical routing, data moves from a lower clustered layer to higher region, hopping from one node to another which covers larger distances, hence moving the data faster to the sink faster. Clustering provides inherent optimization capability at the cluster heads. Traditional (or flat) routing protocols for WSN may not be optimal in terms of energy consumption. Clustering can be used as an energyefficient [4] communication protocol. The objectives of clustering are to minimize the total transmission power aggregated over the nodes in the selected path, and to balance the load among the nodes for prolonging the network lifetime. Clustering is a sample of layered protocols in which a network is composed of several clumps (or clusters) of sensors. As shown in Figure 1, each cluster is managed by a special node or leader, called cluster Fig. 1 head (CH), which is responsible for coordinating the data transmission activities of all sensors in its clump. All sensors in a cluster communicate with a cluster head that acts as a local coordinator or sink for performing intra-transmission arrangement and data aggregation. Cluster heads [5] in tern transmits the sensed data to the global sink. The transmission distance over which the sensors send their data to their cluster head is smaller compared to their respective distances to the global sink. Since a network is characterized by its limited wireless channel bandwidth, it would be beneficial if the amount of data transmitted to the sink can be reduced. To achieve this goal, a local collaboration between the sensors in a cluster is required in order to reduce bandwidth demands. LEACH, TEEN, APTEEN [5][6] are cluster based routing protocols they have similar features and their architectures are to some extent similar. They have fixed infrastructure.. The performance of APTEEN lies between TEEN and LEACH with respect to energy consumption and longevity of the network. TEEN only transmits timecritical data, while APTEEN performs periodic data transmissions. In this respect APTEEN is also better than LEACH because APTEEN transmits data based on a threshold value whereas LEACH transmits data continuously.

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III.

Pre Processing work

As shown in Figure 2, a hierarchical approach breaks the network into clustered layers [7][8]. Nodes are grouped into clusters with a cluster head that has the responsibility of routing from the cluster to the other cluster heads or base stations. Data travel from a lower clustered [4] layer to a higher one. Although, it hops from one node to another, but as it hops from one layer to another it covers larger distances. This moves the data faster to the base station. Theoretically, the latency in such a model is much less than in the multi hop model. Clustering provides inherent optimization capabilities at the cluster heads. In the cluster-based hierarchical model, data is first aggregated in the cluster then sent to a higher-level cluster-head. As it moves from a lower level to a higher one, it travels greater distances, thus reducing the travel time and latency. This model is better than the one hop or multi-hop mode. A cluster-based hierarchy moves the data faster to the base station thus reducing latency than in the multi-hop model. Further, in cluster-based model only cluster-heads performs data aggregation [6] whereas in the multi-hop model every intermediate node performs data aggregation. As a result, the cluster-based model is more suitable for time-critical applications than the multi-hop model. However, it has one drawback, namely, as the distance between clustering level increases, the energy spent is proportional to the square of the distance. This increases energy expenditure. Despite this drawback, the benefits of this model far outweigh its drawback. Fig. 2 A. Probabilistic Clustering Approaches: As the need for efficient use of WSNs on large regions increased in the last decade dramatically, more specific clustering protocols were developed to meet the additional requirements (increased network lifetime, reduced and evenly distributed energy consumption, scalability, etc.). The most significant and widely used representatives of these focused on WSN clustering protocols (LEACH, EEHC, and HEED). [3][4]They are all probabilistic in nature and their main objective was to reduce the energy consumption and prolong the network lifetime. 1. Low Energy Adaptive Clustering Hierarchy (LEACH): LEACH [1][2] is a clustering based protocol. LEACH is organized in rounds, each of which consists of a setup phase and a steady state phase. In the setup phase, each sensor node randomly chooses a number between 0 and 1. If the chosen number is less than the value of the threshold denoted by T(n), the node n declares itself a CH.

Where p is the desired percentage of CHs (e.g.0.05); r represents the number of current round; and G refers to the set of nodes that have not served as the CH in the last 1/p rounds. Sensor nodes join the CHs that are closest to them based on the signal strength of the CHs, and thus, several clusters may be formed. The CH arranges a TDMA (Time Division Multiple Access) schedule for its cluster members and assigns different time slots to cluster members accordingly. In steady state phase, cluster members transmit the collected data in the allocated time slot, while the CH processes data aggregation before passing the obtained data to the BS via single-hop. The advantages of LEACH include the following: (1) CHs collect data forwarded by cluster members before passing the data to the BS, power consumption decreases; (2) any node that served as a CH in certain round cannot be selected as the CH again, so each node can equally share the load imposed upon CHs; (3) utilizing a TDMA schedule prevents CHs from Unnecessary collisions; and (4) cluster members can open or close communication interfaces in compliance with their allocated time slots to avoid excessive energy dissipation. IV.

Recent work

1. Energy Efficient Clustering Scheme (EECS) EECS is a clustering algorithm in which cluster head candidates compete for the ability to elevate to cluster head for a given round. This competition involves candidates broadcasting their residual energy to neighboring candidates. If a given node does not find a node with more residual energy, it becomes a cluster head. Cluster

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formation is different than that of LEACH. LEACH [1][2] forms clusters based on the minimum distance of nodes to their corresponding cluster head. EECS extends this algorithm by dynamic sizing of clusters based on cluster distance from the base station. The result is an algorithm that addresses the problem that clusters at a greater range from the base station requires more energy for transmission than those that are closer. Ultimately, this improves the distribution of energy throughout the network, resulting in better resource usage and extended network lifetime. 2. Hybrid energy- efficient distributed clustering (HEED) HEED (Younis and Fahmy, 2004) is an extension of LEACH which uses node density and residual energy as a metric for cluster selection so as to balance the network energy. Hybrid Energy-Efficient Distributed Clustering (or HEED) is a multi-hop clustering algorithm for wireless sensor networks, with a focus on efficient clustering by proper selection of cluster heads based on the physical distance between nodes. The main objectives of HEED [3][5] are to Distribute energy consumption to prolong network lifetime; Minimize energy during the cluster head selection phase;• Minimize the control overhead of the network. The most important aspect of HEED is the method of cluster head selection. Cluster heads are determined based on two important parameters: 1) The residual energy of each node is used to probabilistically choose the initial set of cluster heads. This Parameter is commonly used in many other clustering Schemes. 2) Intra-Cluster Communication Cost is used by nodes to determine the cluster to join. This is especially useful if a given node falls within the range of more than one cluster head. In HEED it is important to identify what the range of a node is in terms of its power levels as a given node will have multiple discrete transmission power levels. The power level used by a node for intra-cluster [6] announcements and during clustering is referred to as cluster power level. Low cluster power levels promote an increase in spatial reuse while high cluster power levels are required for inter cluster communication as they span two or more cluster areas. 3. Threshold sensitive energy efficient sensor network protocol (TEEN) TEEN (Akkaya andYounis, 2005; Lou, 2005; Manjeshwar and Agrawal, 2002) is a hierarchical protocol [6][7]whose main aim is to respond to sudden changes in the sensed attributes such as temperature. The protocol combines the hierarchical technique in line with a data-centric approach. It then involves the formation of clusters along with cluster leaders which broadcast two thresholds to the nodes; the hard and soft thresholds. Hard threshold have the minimum values of an attribute for sensor node to trigger to power on its transmitter so as to transmit to the cluster head. It is normally not suited in applications where continuous data is needed, since it is threshold dependent. 4. Adaptive threshold sensitive energy efficient sensor network protocol (APTEEN) APTEEN (Manjeshwar and Agrawal, 2002) is an improved version of TEEN, whose main function is not limited to the formation of clusters, but also aim at both capturing periodic data and reacting to time dependent events. In APTEEN, cluster leaders perform aggregation [4] as well as conserve energy. Three queries are supported in the protocol; historical for analysis of past information values, persistent for monitoring of events for some time duration, and one-time for snapshot view of the sensor network. Simulation results show that it outperforms LEACH, having the problem of overhead and complexity in clusters formation in multiple levels, and implementation of the threshold based functions. 4.1 PERFORMANCE BEASED ON NETWORK LIFETIME When analyzing the performance of the proposed clustering algorithms, there are two major areas that will be examined. Power, Energy and Network Lifetime. Due to the limited energy nature of the sensor nodes, network lifetime is dependent on the efficient use of this energy. The primary comparison measurement when looking at the efficiency of a

given algorithm is the network lifetime. A. Power, Energy and Network Lifetime 1) LEACH: It provides the following key areas of energy savings: • No overhead is wasted making the decision of which node becomes cluster head as each node decides independent of other nodes. • CDMA allows clusters to operate independently, as each cluster is assigned a different code. • Each node calculates the minimum transmission energy to communicate with its cluster head and only transmits with that power level. LEACH provides the following improvements over conventional networks • LEACH reduces transmission energy by a factor of 8 versus MTE and direct-transmission. • The first death occurs in LEACH 8 times later than that of MTE, direct-transmission and static clustering. In addition the final death of a node occurs more than 3 times later than that of the other listed protocols. 2) TL-LEACH: The energy improvements are achieved from smaller transmission distance for the majority of nodes. This network configuration requires that merely a few nodes transmit large distances. Simulations have shown that the addition of the two-level hierarchical algorithm TL-LEACH results in an improvement of network lifetime by approximately 30% versus its basis algorithm LEACH [1][2].

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3) PEGASIS: The minimization of energy in this algorithm is achieved from four areas. •During a given round, only 1 node in the network is transmitting data to the base station. Since the transmission range to the base station is large, this can result in an improvement with regards to energy savings. • Since each node communicates with its nearest neighbor, the energy utilized by each node is also minimized. • Each node performs data-fusion, effectively distributing the energy required for this task across the network. • The overhead associated with dynamic cluster formation during each round is eliminated. Simulations in C have shown that PEGASIS [7] can result in a 100% to 300% improvement over LEACH for a variety of different network sizes and configurations. 4) EECS: Minimization of energy consumption in EECS is accomplished in a similar manner to that of LEACH, however the algorithm attempts to improve on LEACH. This is accomplished by creating dynamic cluster sizes which are a function of the distance from the base station to the cluster. This addresses the larger transmission power requirements for nodes at a greater range from the base station. It is the ratio of the total energy consumed in the network at the time the first node dies, to the total initial energy. This measurement is related to the efficient spread of energy in the network. EECS was found to be approximately 93% while LEACH had only of 53% .The EECS [6] protocol has shown a 35% improvement in network lifetime versus the original LEACH in a simulation environment.. 5) HEED: In this algorithm, network life time is prolonged through: • Reducing the number of nodes that compete for channel access; • Cluster head updates, regarding cluster topology; and Routing through an overlay among cluster heads, which has a small network diameter. HEED improves network lifetime over generalized LEACH because generalized LEACH randomly selects cluster heads, thus resulting in a faster death of some nodes. HEED avoids this by well distributing cluster heads across the network. B. Quality and Reliability of Links 1) LEACH & TL-LEACH: When examining the reliability of both the LEACH and TL-LEACH protocols, we can observe the several key features that have been built into the protocol to improve the reliability of transmission • The CSMA mechanism is used to avoid collisions.• CDMA is utilized between clusters to eliminate the interference from neighboring clusters. • Periodic rotation of cluster heads extend the network lifetime, guaranteeing full connectivity in the network for longer periods than conventional algorithms. The TL-LEACH extension of a two-level hierarchy offers no direct reliability improvements over standard LEACH. 2) PEGASIS: It offers promising improvements with relation to network lifetime; however reliability may not be as promising. In PEGASIS, each node communicates with its nearest neighbor. This implementation may be more susceptible to failure due to gaps in the network. 3) EECS: It extends on the capability of LEACH [1][2] by utilizing dynamic cluster sizing. In terms of recovery mechanisms, EECS offers similar reliability as that of LEACH. However, since EECS offers improved energy utilization throughout the network [21], full connectivity can be achieved for a longer duration. This results in reliable sensing capabilities at the range extremes of a network for a longer period of time. 4) HEED: This algorithm produces balanced clusters compared to GC, where it has a higher percentage of nonsingle node clusters than GC. HEED also reduces the likelihood that cluster heads are neighbors within the cluster range. This is because HEED uses intra-cluster communication cost in selecting its cluster heads. Therefore the node distribution does not impact the quality of communication. V. Parameter Expansion Role of the Protocol Objective Designed for Algorithm used Clustering Process – Methodology Clustering techniques Hopping Communication with base station

Data gathering Method

Observation

LEACH Low energy adaptive clustering hierarchy Relaying

PEGASIS Power Efficient Gathering in Sensor Information Systems Relaying

To save energy For Homogeneous wireless sensor network Distributed clustering formation algorithm Distributed

To save Power For Homogeneous wireless sensor Greedy networkalgorithm for chain formation Distributed

Clustering approach Single hop clustering Cluster heads can communicate with base station

Tree based Approach Multi hop clustering Only one node (the node which is very close to the base station) can communicate with base station Non aggregation method

Aggregation method

HEED Hybrid Energy Efficient Distributed Protocol

EECS Energy Efficient Clustering Scheme

Aggregation and Relaying To save energy For Heterogeneous wireless sensor Distributed network clustering formation algorithm Distributed

Aggregation and Relaying To save energy For Heterogeneous wireless sensor Distributed network randomized clustering algorithm Distributed

Clustering approach Single hop clustering Cluster heads can communicate with base station.

Aggregation Method

Aggregation method

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Indirect type

Direct type

Setup phase, steady state phase.

Chain formation phase, broadcasting phase

Life time

When compared to the conventional method of clustering,, the life time of LEACH gives 8 times better results in terms of first node death. 53%

PEGASIS provides 100% to 300 % increase in lifetime when compared with LEACH

Initialization phase, setup phase, steady phase Better lifetime when compared with LEACH protocol

Cluster head election phase, cluster head formation phase lifetime when Better compared with LEACH protocol

Energy utilization in HEED is less when compared to EECS

93%

In environmental monitoring applications

In Homogeneous and Heterogeneous Scenarios.

Energy Utilization rate

Applications

For continuous monitoring and conveying the information to the base station like weather forecasting

The performance of the PEGASIS is improved due to the energy saving parameter at several stages In Disaster management Scenarios

VI.

Conclusion

In this paper we have examined the hierarchical cluster based routing protocols, specifically with respect to their power and reliability requirements. Selection of a routing protocol for a wireless sensor network depends on various factors like network lifetime, and stability period. In my work, first I have gone through a comprehensive survey of Energy efficient protocol for clustered routing techniques in wireless sensor networks. We have also examined the current state of proposed clustering protocols, specifically with respect to their power and reliability requirements. In wireless sensor networks, the energy limitations of nodes play a crucial role in designing any protocol for implementation. Future perspectives of this survey are focused towards modifying one of the above routing protocols such that the modified protocol could minimize more energy for the entire system VII. References [1]

K. Padmanabhan, Dr. P. Kamalakkannan “Energy Efficient Adaptive Protocol for Clustered Wireless Sensor Networks” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, September 2012.

[2]

Muhammad Haneef, Zhou Wenxun,”MG-LEACH :Multi Group Based LEACH an Energy Efficient Routing Algorithm For Wireless Sensor Network” ICACT, Volume 2, Issue 1, Feb 2012.

[3]

A.B.M. Alim Al Islam,Chaudhary Sayeed” Finding the optimal percentage of cluster heads from a new and complete mathematical model on LEACH” Wireless Sensor Network, Volume 3, Issue 2, Feb 2010.

[4]

Tanuja Khurana, Sukhvir Singh, Nitin Goyal “An Evaluation of Ad-hoc Routing Protocols for Wireless Sensor Networks “International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 1, Issue 1, July 2012.

[5]

Min Liu n, ShijunXu,SiyiSun,” An agent-assisted QoS-based routing algorithm for wireless sensor networks “ Journal of Network and Computer Applications, Volume 4, Issue 2,July 2012.

[6]

Eduardo Canete, Manuel Diaz, Luis Llopis, Bartolome Rubio, ”HERO: A hierarchical, efficient and reliable routing protocol for wireless sensor and actor networks” Computer Communications, Vol 5, Issue 3,June 2012.

[7]

Adamu Murtalau, Li-MinnAng,”Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison” Journal of Network and Computer Applications, Volume 3, Issue 2, May 2012.

[8]

Muhammad Saleem, Israr Ullah, Muddassar Farooq,” BeeSensor: An energy-efficient and scalable routing protocol for wireless sensor networks” Information Sciences, May 2012.

[9]

Tao Liu, Qingrui Li, Ping Liang,”An energy-balancing clustering approach for gradient-based routing in wireless sensor networks” Computer Communications, Vol 3, Issue 3, May 2012.

VIII. Acknowledgments The First author would like to thank Dr . D N Choudhari for suggestion and guidance

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Design of a digital PLL with divide by 4/5 prescaler employing ring oscillator TDC and accumulator type DCO Jayalekshmi1, Vipin Thomas2 Department of Electronics and Communication Ilahia College of Engineering and Technology Muvattupuzha, Ernakulam INDIA ______________________________________________________________________________________ Abstract: A digital PLL compares the circuit frequency with a reference frequency and adjusts the output using the feedback loop. A divide by 4/5 prescaler is used. The PLL locks when both the frequencies become equal. Analog PLLs are susceptible to noise and temperature variations. To deal with the problem of power dissipation and increased jitter, a digital PLL is prescribed. For reduced lock time and jitter, an accumulator type DCO and ring oscillator TDC is used. It attains fast lock time. Keywords: Digital PLL, Prescaler, TDC,DCO, frequency divider. __________________________________________________________________________________________ I.

Introduction

Clock signals are particular signals which oscillate between high and low states. It changes its value from zero to one in every period. Clock signals are the heart beat of any processor. Processor executes one thing at a time and it is given by the clock. Clock signals are generated by the oscillators and all circuits in a digital system should get the clock at the same time. When the circuits get clock signals at different time, the effect may be due to clock skew. Clocking is essential for all synchronous circuits. Clock skew and jitter are the two phenomena which affects clocking. Clock skew can be caused by the static mismatches in clock path and differences in clock load. Clock jitter is the deviation in periodicity of actual clock with that of a reference clock. It is the temporal variation of the clock period. A PLL can be used to reduce these effects. It is a common part of high performance micro processors. PLLs are used to generate high performance micro processors in the GHZ range. Traditionally, a PLL is made to function as an analog building block, but integrating an analog PLL on a digital chip is difficult. Analog PLLs are also more susceptible to noise and process variations. Digital PLLs allow a faster lock time to be achieved and are attractive for clock generation on high performance microprocessors [1]. II. Digital PLL Digital PLL is designed by four major components. These components can be analog or digital. Analog PLL composed of PFD, charge pump, loop filter, VCO and frequency divider. The analog components such as charge pump, loop filter and VCO are replaced in digital PLL. Charge pump and loop filter is replaced by TDC (Time to digital converter).VCO is replaced by DCO and the other two components are same in both analog and digital PLL.

Fig. 1- Digital PLL- Basic block diagram The phase and frequency mismatch of reference clock and divided DCO clock are compared on PFD. The condition of ‘Lock’ is attained when Ref clock matches divided DCO clock. The PFD output is given to TDC.PFD generates up enable and down enable signals to generate the control word. The control word is generated by the thermometric decoder which controls the DCO. III.

Design of Components

A. Digital Phase Frequency Detector It compares the reference frequency and divided DCO frequency to generate the output. It consists of D flipflops, OR, XOR and NOT gate. During clock flip flop catches D input and the captured value becomes Q output until the next clock. The edge triggered D flip flop is used here. It is sensitive to edges and corrective action can be

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occurred instantly. The output of the modified D Flip-flops enters a two input NOR gate that resets the Flip-flops if both clocks are high [1]. The up and down signals indicate if the DCO clock needs to be increased (up is true) or decreased (down is true). The event and direction signal are necessary to create the up and down enable signals for the T2D converter.

Fig. 2- Digital phase frequency detector

Fig. 3- Output of PFD when ref clk and divided DCO clock varies with different frequencies

B.Time to Digital Converter It consists of up counter, down counter and carry ripple adder. Both counters are of 7 bit and ripple carry adder is of 8 bit. Both counters are controlled by phase detector. Initial state of up counter is 0000000 and down counter is 1111111.Both counters are connected to 8bit ripple carry adder and produces the control word for the DCO [1].

Fig. 4 ime to Digital Converter C. Thermometric Decoder Decoders are devices which do the reverse operation of encoders. Thermometric code is the one in which each digit has some pace value. It generates the control word that controls the DCO [4]. It was obtained by modifying the general decoder to obtain the required functionality. The decoder generates a 128 bit output, of which only 126 bits are used by the DCO. These 126 bits controls the DCO [5]. ď&#x201A;ˇ

Fig. 5- Binary to thermometric conversion D. Digitally controlled oscillator DCO generates the output of the PLL. Also a feedback loop is provided from DCO to the PFD through frequency divider.DCO is designed to overcome the tuning stability limitation of VCO designs. The following logic is used to generate the DCO output.

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Fig. 6 - DCO logic

Fig. 7-DCO output

E. Frequency Divider It is also called clock divider. It keeps input and output phase in lock step, keeping input and output frequency same. DCO output is given to the frequency divider. The output of DCO needs to be matched to the reference frequency. When the divided frequency and reference frequency matches the PLL locks [2].

Fig. 8- Frequency divider block diagram The frequency divider simply divides the DCO output to matched to the reference frequency. It consists of a 5 bit counter,3 bit swallow counter ,modulus control and a divide by 4/5 dual modulus prescaler. (1) Divide by 4/5 Dual Modulus prescaler: It is also called divide by 4/5 counter. It consists of D flip flops and NOT gates. The input frequency Fin is given to all the flip flops. The selection of the counter depends on the Mode control. If MC=0,one output clock cycle is equal to 4 input clock cycles else if MC=1. One output clock cycle is equal to 5 input clock cycles.

Fig. 9- Divide by 4/5 dual modulus prescaler . (2) Integrated swallow counter and 5 bit counter: MC is also used to control 5 bit counter and swallow counter so that it is integrated. It consists of divide by two counters, XOR, AND, NAND, NOT gates. Output of divide by two and C input is given to the XOR gates. The five XOR gate outputs are combined in AND gate and again the out is given to two NAND gates each along with MC and MC bar [2].

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Fig. 10- Integrated swallow counter and 5 bit counter IV.

Final system

Fig. 12 shows the proposed PLL. It consists of PFD, TDC, Thermometric Decoder, DCO, Divide by 4/5 dual modulus prescaler and integrated swallow counter and 5 bit counter. The divisional ratio is 4 to 5.The final system runs until the lock is obtained. A phase or frequency variation will cause the PLL to be activated again.

Fig. 12-Final system IV. Proposed System In the proposed system an accumulator type DCO is used. Also, a ring oscillator type TDC is used. The proposed system has an improved jitter and lock time.

Fig. 14 â&#x20AC;&#x201C; Proposed system Block diagram A. Accumulator type DCO It is basically an N bit adder which adds a K value to itself.The accumulator type DCO is shown in the figure 15.The N bit accumulator module determines the ability to lock at a desired frequency and also the jitter can be settled. It consists of a D register and an N bit accumulator in which one input is the feedback from output of itself and other is the K value.

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Fig. 16 – Ring oscillator type TDC

Fig. 15- Accumulator type DCO

B. Ring oscillator type TDC TDC is used for the digitization of the time intervals. The existing system works simply on the reference clock and divided DCO clock and generates the digital word. There is no more digital control on the delay. But in the proposed system, we can modify the frequency by digital control on the line. Also it is possible to target a specific time interval between the oscillators so that the path of the signal in the chain is modified. V. Conclusion A phase locked loop ensures that the clock frequencies seen at the clock inputs of various registers and flip-flops match the frequency generated by the oscillator. A divide by 4/5 dual modulus prescaler is used in the frequency divider section to obtain high resolution .A ring oscillator type TDC is used to attain fast locking. An accumulator type DCO is used for settling the jitter. The proposed PLL attains fast locking in the zeroth time itself. References [1] [2] [3] [4] [5] [6] [7]

Anitha Babu,Bhavya Daya,Banu Nagasundaram, Niveditha Veluchamy, “All Digital Phase Locked Loop Design and Implementation. Thomas Olsson and Peter Nilsson, “A Digitally Controlled PLL for SoC Applications”, vol. 39,NO.5 May 2004 751. Jim Dunning,Gerald Garcia, “All Digital Phase Locked Loop with50-Cycle Lock Time Suitable for High Perfomance Microprocessors,” vol. 30,NO.4,April 1995. T.Olsson and P. Nilsson, “A fully integrated standard-cell digital PLL”,vol.37,pp.211-212, feb. 2001. I-Ting Lee,Yun-Ta Tsai and Shen-Iuan Liu, “A Wide-Range PLL Using Self-Healing Prescaler/VCO in 65-nm CMOS”,vol 21,NO:2, feb. 2013. L. L. Lewyn, T. Ytterdal, C. Wulff, and K. Martin, “Analog circuit design in nanoscale CMOS technologies,” Proc. IEEE, vol. 18, no. 11, pp. 1687–1714, Oct. 2009. J.M.Wang, Y. Cao, M. Chen, J. Sun, and A. Mitev, “Capturing device mismatch in analog and mixed-signal designs,” IEEE Circuits Syst. Mag., vol. 8, no. 4, pp. 137–144, Dec. 2008.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Evaluation of the Peak Location Uncertainty in Second-Order Derivative Spectra. Case Study: Symmetrical Lines J. Dubrovkin Computer Department, Western Galilee College 2421 Acre, Israel Abstract: The dependences of the relative peak shifts of the second-order derivatives of spectral doublets on the peak separation were evaluated numerically. Doublets were composed of symmetrical Gaussian and Lorentzian lines with widely ranging relative intensities and widths. Qualitative shift patterns were obtained and some abnormal phenomena in derivative spectra were revealed. Keywords: derivative spectroscopy; peak identification; peak location uncertainty; spectral line profiles. I. Introduction Location of the spectral peaks is one of the most important quantitative parameters, which is widely employed in theoretical and applied spectroscopy [1]. For example, classical methods of identifying unknown elements and chemical compounds are usually based on comparing the experimentally measured peak positions with those found in standard libraries. However, overlapping of adjacent spectral contours and uncompensated background often cause apparent peak shifts, which lead to errors in spectrum interpretation. There exist numerous physicochemical, instrumental, and mathematical methods of improving spectral resolution of overlapping lines and bands [2]. Physicochemical and instrumental methods are very specific and usually cannot be implemented in practice without special consideration of each particular case. The mathematical methods which are most commonly used in practice can be divided into two groups: a) decomposition of a composite spectrum or the matrix of spectra into elementary components (multivariate curve resolution [3] and independent component analysis [4]) and b) artificial improvement of spectral resolution (deconvolution [5] and its particular version, derivative spectroscopy [6, 7]). Deconvolution is usually performed by solving the integral equation that describes the undistorted spectrum convolution by the response function of the spectral instrument [8, 9]. Another approach to the deconvolution problem is based on digital filtering in spatial or frequency domains [5, 10]. The above methods have mathematically rigorous definition in the framework of the Inverse Problem [11]. However, they need a priory information, which can sometimes have fuzzy features (see, e.g., [12]). In addition, the mathematical tools of these methods, which are based on the regularization procedure, are often too complicated for practitioners. In contrast to this, derivative spectroscopy has been very popular among analytical chemists for over half a century. Derivative spectra are very simple for visual inspection and can be readily obtained using polynomial or Fourier digital filters or wavelength modulation. The fingerprint interpretation of spectra is usually performed using the second-order derivative spectrum (SDS) because it is similar to the original one, but has improved resolution. From the theoretical point of view, the derivative method is based on sequential data treatment (derivation) [13], which is, actually, linear transform of the spectrum coordinates [14]. The main drawbacks of this method are a) decreased signal-no-noise ratio and b) additional artifacts (satellites). The peak positions of the resolved maxima of SDS are often assumed to be accurate values. However, the peaks may be shifted from their correct positions. Quantitative evaluation of the apparent shifts of peak positions in the derivative spectra of Gaussian and Lorentzian doublets was performed only in certain particular cases by means of computer modeling [6, 7]. The shift dependence on the parameters of the overlapping lines was briefly discussed only qualitatively. The goal of the present study was evaluating the peak position uncertainty caused by overlapping lines for the second-order derivatives of Gaussian and Lorentzian doublets in a wide range of their spectral parameters. In what follows, for the sake of simplicity, term “line” is used instead of term “line and band”. The standard algebraic notations are used throughout the article. All calculations were performed and the plots were built using the MATLAB program. II Theory a. Models Consider the second-order derivative of a doublet with the maxima located at and , respectively: where

is the doublet line; is the line shape parameter; is the full line width at half maximum; βδ/2 and

are the relative and the

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absolute separation of the doublet components, respectively; is the line maximum position in the scale; are the relative intensity and the relative width of the second doublet line, respectively. We studied symmetrical Gaussian and Lorentzian functions [1] :

where The second-order derivatives of a Gaussian and of a Lorentzian doublets (1) are represented by Eqs. 4 and 5, respectively:

where . b. Relative shift The relative shift of the line position is usually measured relative to the line width. However, in practice, the widths can be evaluated very approximately because the lines are overlapping [6, 15]. Therefore, we chose to calculate the shifts with respect to the separation of the doublet components, which estimation can be readily obtained visually. In this case, the relative shift of the resolved doublet peak for the component has the form [16]: where

and

is the point at which the derivative of Eq. 1 is zero:

changes sign when it passes through point of the resolved peak. The third-order derivatives of doublets (2) and (3) are readily obtained by differentiating Eqs. 4 and 5, respectively. Since, for a symmetrical doublet , absolute shift of the merged derivatives of the doublet lines is the same at and below the resolution limit, the value of the relative shift, does not depend on the separation of the doublet components. For separations larger than the resolution limit, the maxima of the first and the second lines are located at and , respectively. Since the analytical solution of Eq. 7 is too complicated, it was obtained numerically. It must be pointed out that the shifts of a non-ideal derivative peak depend also on the derivation procedure parameters [6]. Smoothing a noisy spectrum causes broadening of the derivative spectra, which results in decreased resolution and the shifts different from those obtained for ideal derivatives. However, these effects are significant for the doublet separation close to the resolution limit, where the shifts are very large [6]. Using such poorly-resolved derivatives for precise peak identification leads to erroneous results. II. Results of Computer Modeling and Discussion a. Equal-width lines ( ) 1. The dependences of the relative shifts of the second-order derivative peaks on for Gaussian and Lorentzian doublets are presented in Fig. 1. On the strength of symmetry, 2. The plots of the shift dependences for the first component of Gaussian and Lorentzian lines (and also for the second component, according to the rule of symmetry (8)) pass through the intersection points (0.612 and 0.500, respectively). For the rest values, the corresponding plots are cut off at the resolution limit and do not reach these points. The shifts are zero at the intersection points, where the second-order derivative peaks are located at the satellite maxima of the interfering line (Fig. 2). The signs of the shifts are opposite on the left and on the right from the intersection point since the sign of the interfering line slope changes when the line passes this point. On the right of the intersection point, the shift values for the Lorentzian derivatives are smaller than those for the Gaussian derivatives. On the left, the Gaussian lines are not resolved, except for the case . 3. Although the more intense second line (larger R values) causes larger shifts of the first line, its own shifts decrease. Thus, if ,

4. The dependences of the shifts on the separation of the doublet lines are well approximated by a high-degree polynomial in variable [16]:

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the behaviour of coefficients being presented in Fig. 3. For Lorentzian lines, the positive first-component odd-degree coefficients ( ) increase, while the negative even-degree coefficients decrease. The dependences for the second component obey the symmetry rule (8). In contrast to this, for Gaussian lines, in the case of , the dependences for even-degree coefficients are increasing, while those for the odd-degree coefficients are decreasing. So, for large values, the shifts increase inversely proportional to the two largest degrees of , namely, 10 and 8 for Lorentzian and 8 and 4 for Gaussian lines. Fortunately, for large values, the absolute shifts of the Lorentzian peaks are small. This is not true in the case of Gaussian derivative spectra, where the steep slopes of function may cause noticeable sensitivity of the measured peak locations to the spectral noise. Figure 1. Dependences of the relative shifts on

for doublets consisting of equal-width lines

(a), (b) Gaussian lines, (c), (d) Lorentzian lines; 1 st line - blue curves, 2nd line – red curves.

values are shown next to the curves.

Figure 2. Location of the zero-shift point in the second-order derivative spectrum

(a) Gaussian lines, (b) Lorentzian lines. Figure 3. Dependences of the polynomial coefficients on the relative intensity of the doublets

1th lines and 2nd lines of Gaussian (a) and (b) and Lorentzian (c) and (d) doublets, respectively. Coefficients: (●, red), (■, red), (●, green ), (■, green ) , (●, blue ) and (■, blue).

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b. Non-equal-width lines ( ) 1. If the shifts of the second line are very large, especially for Gaussian doublets (Figs. 4 and 5). The shifts significantly increase with the increase of (see Figs. 8-14, ) because the intensity of the second-order derivative spectrum is inversely proportional to the squared line width [6]. The shift of the second line is less than 0.1 only if its intensity is large enough ( ) and the doublet components are well separated (see Table). The shifts of the first line increase with the growth of . 2. If the negative shifts of the first line become very large because the intensity of the second-order derivative spectra increases (Figs. 6 and 7). The shifts of the first line increase with the growth of . 3. The second component plots for the Gaussian and Lorentzian lines pass through the intersection points (0.612 and 0.500, respectively) (Figs. 6 and 7, panels c and d). It has been pointed out above that the shifts are zero at the intersection points, where the narrow second-order derivative peaks are located at the satellite maxima (Fig. 2). If the wide second-order derivative peaks are shifted to the region outside the satellite and thus no intersection points can appear. 4. For , while grows from 1/3 to 1, the absolute values of the first line negative shifts increase for Gaussian doublets (Figs. 8-14, panels a). Further broadening of the second line ( ) results in changing the sign of the shifts to positive and in decreasing the shift values. For Lorentzian lines, the signs and the ordinates of the plots (Figs. 8-14, panels b) change in a complicated manner depending on the location of the intersection points. 5. The apparent resolution limit of the doublet second-order derivative may be observed even at very low separation of the doublet lines due to the effect of overlapping with the satellite of the second-order derivative of the first line (Fig. 15a). Near this limit the shifts of the weak second doublet component grow very quickly and may be more than ten times as large as the separation for Gaussian lines (Fig. 5a). The symmetrical wrong “resolved” line (denoted by an arrow on the left of Fig. 15a) may indicate that the right-hand peak is wrong. The correct peak is located at same point on – axis as the wrong one only for (Fig. 15b). Such “super-resolution” gives rise to great errors in analysis. 6. Acceptable relative shifts ( ) at and for the first and the second lines, respectively, are sometimes observed for smaller line separations ( ) of Gaussian doublets than those of Lorentzian doublets (marked in bold in the Table). In other words, for a given separation value, the peak of the narrow strong second-order derivative of a Gaussian line may be less shifted from its actual position than that of a Lorentzian line. These different shifts are accounted for by different slopes of the interfering derivatives of Gaussian and Lorentzian lines [6]. In conclusion, we have shown that the correct peak location in the second-order derivative spectrum, in each particular case, should be evaluated by computer modeling of the overlapping lines. The shifts connected with changes of the physico-chemical parameters of the sample under study must be differentiated from apparent shifts, which may be caused by changes of the line form, width, and the degree of overlapping. For this reason, correlating the peak shifts in the second-order derivative spectrum with the physicochemical parameters may lead to erroneous conclusions. Figure 4. Dependences of the relative shift on

for doublets consisting of non-equal-width lines (

)

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J. Dubrovkin, International Journal of Emerging Technologies in Computational and Applied Sciences, 9(3), June-August, 2014, pp. 248255 (a), (b) Gaussian lines, (c), (d) Lorentzian lines; 1 st line - blue curves, 2nd line – red curves. values are shown next to the curves. Figure 5. Dependences of the relative shift on for doublets consisting of non-equal-width lines ( )

(a), (b) Gaussian lines, (c), (d) Lorentzian lines; 1 st line - blue curves, 2nd line – red curves. values are shown next to the curves. Figure 6. Dependences of the relative shift on for doublets consisting of non-equal-width lines ( )

(a), (b) Gaussian lines, (c), (d) Lorentzian lines; 1 st line - blue curves, 2nd line – red curves. values are shown next to the curves. Figure 7. Dependences of the relative shift on for doublets consisting of non-equal-width lines ( )

(a), (b) Gaussian lines, (c), (d) Lorentzian lines; 1 st line - blue curves, 2nd line – red curves.

values are shown next to the curves.

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Figure 8. Dependences of the relative shift on

for doublets consisting of lines of equal intensity (

)

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves. values are given in the legend (panel a). Figure 9. Dependences of the relative shift on for doublets consisting of lines of unequal intensity ( )

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves; values are given in the legend (panel a). The 1st line plots are shifted down by 0.5 (a) and 0.2 (b). Figure 10. Dependences of the relative shift on for doublets consisting of lines of unequal intensity ( )

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves; values are given in the legend (panel a). The 1st line plots are shifted down by 0.2. Figure 11. Dependences of the relative shift on for doublets consisting of lines of unequal intensity ( )

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves; values are given in the legend (panel a).

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J. Dubrovkin, International Journal of Emerging Technologies in Computational and Applied Sciences, 9(3), June-August, 2014, pp. 248255 The 1st line plots are shifted down by 0.2. Figure 12. Dependences of the relative shift on for doublets consisting of lines of unequal intensity (

)

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves; values are given in the legend (panel a). The 1st line plots are shifted down by 0.5 (a) and 0.2 (b). Figure 13. Dependences of the relative shift on for doublets consisting of lines of unequal intensity ( )

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves; values are given in the legend (panel a). The 1st line plots are shifted down by 0.5 (a) and 0.2 (b). Figure 14. Dependences of the relative shift on for doublets consisting of lines of unequal intensity ( )

(a) Gaussian lines, (b) Lorentzian lines; 1st line - blue curves, 2nd line – red curves; values are given in the legend (panel a). The 1st line plots are shifted down by 0.5 (a) and 0.2 (b). Figure 15. Wrong and correct peaks in the second-order derivative of Gaussian doublets

(b) 1.

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0.2 1/3 0.5 1 2 3 5

Minimum relative separation values for which relative shifts do not exceed 0.1 1/ 3 0.5 1 2 3 G L G L G L G L G L 0.46 0.30 0.56 0.24 0.80 0.50 0.68 0.82 0.40 1.1 0.20 0.16 0.56 0.27 >1 0.50 >1 >1 >1 >1.2 0.48 0.34 0.61 0.25 0.74 0.48 0.62 0.66 0.30 0.84 0.22 0.14 0.38 0.25 >1 0.48 >1 >1 >1 >1.2 0.50 0.37 0.64 0.26 0.74 0.44 0.50 0.52 0.20 0.72 0.20 0.14 0.74 0.44 >1 >1 >1 >1.2 0.18 0.26 0.53 0.43 0.69 0.44 0.68 0.39 0.34 0.48 0.18 0.48 0.20 0.20 0.68 0.39 >1 0.88 >1 >1.2 0.17 0.25 0.55 0.50 0.73 0.53 0.74 0.44 0.40 0.52 0.12 0.30 0.74 0.44 >1 0.52 >1 1.1 0.20 0.26 0.24 0.28 0.56 0.54 0.76 0.58 >1 0.48 0.75 0.50 0.10 0.42 0.74 0.48 >1 0.50 >1 1.0 0.20 0.30 0.28 0.36 0.58 0.59 0.79 0.65 >1 0.50 >1 0.54 0.10 0.48 0.82 0.50 >1 0.48 >1 0.88 0.20 0.40 0.40 0.48

The shifts of the first and the second lines of Gaussian (G) and Lorentzian (L) doublets are listed in rows 1 and 2, respectively. Values in bold correspond to the case of .

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

B. K. Sharma, Spectroscopy. 19th Ed. India, Meerut-Delhy: Goel Publishing House, 2007. I.Ya. Bernstein and Yu.L. Kaminsky, Spectrophotometric Analysis in Organic Chemistry. Leningrad: Science, 1986. S.C. Rutan, A. de Juan, R. Tauler. Introduction to Multivariate Curve Resolution, in Comprehensive Chemometrics.,Oxford: Elsevier, 2009, vol. 2, pp. 249-259. Y.B. Monakhova, S.A. Astakhov, A. Kraskova and S. P. Mushtakova, ” Independent components in spectroscopic analysis of complex mixtures”, Chem. Intell. Lab. Syst., vol. 103, 2010, pp. 108– 115. P. A. Jansson, Deconvolution: with applications in spectroscopy. Academic Press, 1984. J. M. Dubrovkin and V. G. Belikov, Derivative Spectroscopy. Theory,Technics, Application.Russia: Rostov University, 1988. G. Talsky. Derivative Spectrophotometry. Low and Higher Order.Germany, Weinheim: VCH Verlagsgesellschaft, 1994. L. Szczecinski, R.Z. Morawski and A. Barwicz, “Original-domain Tikhonov regularization and non-negativity constraint improve resolution of spectrophotometric analyses”, Measurement, vol. 18,1996, pp.151–157. N.Zorina, G.Revalde and R.Disch, ”Deconvolution of the mercury 253.7 nm spectral line shape for the use in absorption spectroscopy”, Proc. of SPIE, Vol. 7142, 2008, 71420J-1. L. Szczecinski, R.Z. Morawski and A. Barwicz, “Numerical correction of spectrometric data using a rational filter”, J. Chemom. vol 12, 1998, pp. 379-395. R. Aster,B. Borchers and C. Thurber. Parameter Estimation and Inverse Problems, 2nd Ed., Elsevier, 2012. M. Sawall and K. Neymeyr ,”On the area of feasible solutions and its reduction by the complementarity theorem”, Anal.Chim. Acta, vol. 828, 2014, pp. 17-26. R. G. Brereton, Applied Chemometrics for Scientisis. England, Chichester: Wiley & Sons, 2008. J. M. Dubrovkin, ”Effectiveness of spectral coordinate transformation method in evaluation the unknown spectral parameters”, J. Appl. Spectr., vol. 38, 1983, pp. 191-194. V. A. Lóenz-Fonfría and E. Padrós, “Method for the estimation of the mean lorentzian bandwidth in spectra composed of an unknown number of highly overlapped bands ", Appl. Spectr., 2008, vol. 62, pp. 689-700. J. Dubrovkin, “Evaluation of the peak location uncertainty in spectra. Case study: symmetrical lines”, Journal of Emerging Technologies in Computational and Applied Sciences, vol. 1-7, 2014 ,pp. 45-53.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Comparison of Various Biometric Methods Dr. Rajinder Singh1, Shakti Kumar2 Department of Electronics & IT S.D.College, Ambala Cantt Haryana Abstract: Biometrics is the process of automated recognition of individuals based on their behavioral and physiological characteristics. Biometrical authentication is the process of making sure that the person is who he claims to be. Physiological biometric traits include face and retina structures, fingerprint (whorls minutia), hand geometry, iris (pattern), ear (structure of the cartilaginous tissue of the pinna), palm vein and DNA structures etc where as Behavioral biometric traits include gait patterns, signatures, keystroke and odor etc. Biometric is a very accurate and reliable method as compared to traditional knowledge based (e.g., passwords) and token based (e.g., ID cards) mechanisms. Various biometrics are used on the basis of the scope of the testing medium, the accuracy required and speed required. Every medium of authentication has its own advantages and shortcomings. Keywords: Biometric System, Enrollment, Identification, Verification, Biometric traits. I. Introduction The term biometrics is derived from the Greek words bio (life) and metric (to measure). Biometrics implies life measurement, the term is associated with the utilization of unique physiological or behavioral characteristics to distinguish an individual. There are two phases in a biometric system a learning phase (enrolment) and a recognition phase (verification or identification).

Fig.1 Various modules of a biometric system Various modules used in a biometric system are: Sensor- This module is used for signal acquisition. Sensors are used to capture the data. For example, a facial recognition system might employ multiple cameras to capture different angles on a face. Feature Extraction- This module is used to extract features to be stored in database. For example in finger print recognition we extract minute points. Matcher Module- This module is used to compare the claimed identity with the stored template. And then decision is carried out whether the clamed identity is true or not. Template Database-This module is used to store templates which are actually the output of feature extraction module. Biometric system can recognize a person by using different physiological or behavioral characteristics of that person. Depending on the application context, a biometric system may operate either in verification mode or identification mode. In verification mode, the system validates a person’s identity by comparing the captured biometric characteristic with the individual’s biometric template, which is pre-stored in the system database and system conducts a one-to-one comparison to determine whether the claim is true or not. In identification mode, the system recognizes an individual by searching the entire template database for a match.

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Fig.2 Enrollment, Verification, & Identification tasks.[4] The system conducts one-to-many comparison to establish an individual’s identity (or fails if the subject is not enrolled in the system database).Enrollment creates an association between an identity and its biometric characteristics. In a verification task, an enrolled user claims an identity and the system verifies the authenticity of the claim based on his biometric feature. An identification system identifies an enrolled user based on his biometric characteristics without the user having to claim an identity. II. BIOMETRICS HISTORY Bertillonage is the first type of biometrics came into form in 1890, created by an anthropologist named Alphonse Bertillon. He based his system on the claim that measurement of adult bones does not change after the age of 20. The method consisted of identifying people by taking various body measurements like a person’s height, arm length, length and breadth of the head, the length of different fingers, the length of forearms, etc. using calipers. However, the methodology was unreliable as non-unique measurements allowed multiple people to have same results, decreasing the accuracy and hence is no longer used.[5] III. TYPES OF BIOMETRICS A. Fingerprint Recognition It involves taking an image of a person's fingertips and records its characteristics like whorls, arches, and loops along with the patterns of ridges, furrows, and minutiae. Fingerprint matching can be achieved in three ways • Minutae based matching stores minutiae as a set of points in a plane and the points are matched in the template and the input minutiae. • Correlation based matching superimposes two fingerprint images and correlation between corresponding pixels is computed. • Ridge feature based matching is an advanced method that captures ridges, as minutiae capturing are difficult in low quality fingerprint images.

Fig.3 Minutiae of a Fingerprint To capture the fingerprints, current techniques employ optical sensors that use a CCD or CMOS image sensor; solid state sensors that work on the transducer technology using capacitive, thermal, electric field or piezoelectric sensors; or ultrasound sensors that work on echography in which the sensor sends acoustic signals through the transmitter toward the finger and captures the echo signals with the receiver.[5] B. Face Recognition Face recognition involves an evaluation of facial features. It is a computer system application for automatically determining or verifying an individual from a digital image or a video framework from a video source. One of

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the techniques to do this is simply by evaluating selected facial features from the image as well as from facial database[1]. This technique records face images through a digital video camera and analyses facial characteristics like the distance between eyes, nose, mouth, and jaw edges. These measurements are broken into facial planes and retained in a database, further used for comparison. Face recognition can be done in two ways: • Face appearance employs Fourier transformation of the face image into its fundamental frequencies and formation of eigenfaces, consisting of eigen vectors of the covariance matrix of a set of training images. The distinctiveness of the face is captured without being oversensitive to noise such as lighting variations. • Face geometry models a human face created in terms of particular facial features like eyes, mouth, etc. and layout of geometry of these features is computed. Face recognition is then a matter of matching constellations. Another face identification technology, Facial thermograms, uses infrared heat scans to identify facial characteristics. This non-intrusive technique is light-independent and not vulnerable to disguises. Even plastic surgery, cannot hinder the technique. This technique delivers enhanced accuracy, speed and reliability with minimal storage requirements. This technique is gaining support as a potential tool for averting terrorism, law enforcement areas and also in networks and automated bank tellers.[5] C. Voice Recognition It combines physiological and behavioral factors to produce speech patterns that can be captured by speech processing technology. Inherent properties of the speaker like fundamental frequency, nasal tone, cadence, inflection, etc. are used for speech authentication.

Fig.4 Image sample of Voice Recognition Voice recognition techniques can be divided into categories depending on the type of authentication domain. • Fixed text method is a technique where the speaker is required to say a predetermined word that is recorded during registration on the system. • In the text dependent method the system prompts the user to say a specific word or phrase, which is then computed on the basis of the user’s fundamental voice pattern. • The text independent method is an advanced technique where the user need not articulate any specific word or phrase. The matching is done by the system on the basis of the fundamental voice patterns irrespective of the language and the text used. • Conversational technique verifies identity of the speaker by inquiring about the knowledge that is secret or unlikely to be known or guessed by a fraud. This technique is inexpensive but is sensitive to background noise and it can be duplicated.Also, it is not always reliable as voice is subject to change during bouts of illness, hoarseness, or other common throat problems. Applications of this technique include voice-controlled computer system, telephone banking, m-commerce and audio and video indexing.[5] D. Iris recognition It analyzes features like rings, furrows, and freckles existing in the colored tissue surrounding the pupil.

Fig. 5 Outer structure of iris The scans use a regular video camera and works through glasses and contact lenses. The image of the iris can be directly taken by making the user position his eye within the field of a single narrow-angle camera. This is done by observing a visual feedback via a mirror. The isolated iris pattern obtained is then demodulated to extract its phase information. Iris image acquisition can be done in two ways:

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• Daugman System that uses an LED based point light source in conjuction with a standard video camera. The system captures images with the iris diameter typically between 100-200 pixels from a distance of 15-46 cm using 330mm lens. • Wildes System in comparison results in an illumination rig that is more complex. The system images the iris with approximately 256 pixels across the diameter from 20cm using an 80mm lens.[5] E. Hand geometry As the name suggests, involves the measurement and analysis of the human hand. Features like length and width of the fingers, aspect ratio of the palm or fingers, width of the palm, thickness of the palm, etc are computed

. Fig.6 Features of Hand The user places the palm on a metal surface, which has guidance pegs on it to properly align the palm, so that the device can read the hand attributes. Hand Vascular Pattern Identification It uses a non-harmful near infrared light to produce an image of one's vein pattern in their face, wrist, or hand, as veins are relatively stable through one's life. It is a non-invasive, computerized comparison of shape and size of subcutaneous blood vessel structures in the back of a hand. The vein "tree" pattern, picked up by a video camera, is sufficiently idiosyncratic to function as a personal code that is extremely difficult to duplicate or discover. The sensor requires no physical contact, providing excellent convenience and no performance degradation even with scars or hand contamination.[5] F. Retina Recognition This technology uses infrared scanning and compares images of the blood vessels in the back of the eye, the choroidal vasculature. The eye’s inherent isolation and protection from the external environment as an internal organ of the body is a

Fig.7 Retina Image benefit. Retina scan is used in high-end security applications like military installations and power plants.[5] G. Signature recognition It is an instance of writer recognition, which has been accepted as irrefutable evidence in courts of laws. The way a person signs his name is known to be a characteristic of that individual. Approach to signature verification is based on features like number of interior contours and number of vertical slope components. Signatures are behavioral biometric that can change with time, influenced by physical and emotional conditions of the signatories. Furthermore, professional forgers can reproduce signatures to fool an unskilled eye and hence is not the preferred choice. [5] Different biometric and their trait is listed in the Table 1.

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Table 1: List of Biometric Features [6] Biometric Fingerprint Signature (dynamic) Facial geometry Iris Retina Hnad Geometry Finger Geometry Vein structure of back of hand Ear Form Voice

Biometric Trait Finger lines ,pore structure Writing with pressure and speed Differentials Distance of specific facial features(eyes, nose, mouth) Iris pattern Eye background (pattern of the vein structure) Measurements of fingers and palm Finger measurement Vein structure of the back of the hand Dimensions of the visible ear Tone or timbre

COMPARISON OF BIOMETRIC TYPES We can compare various technologies on the basis of Accuracy, Cost, Size of Template, Long term stability, & Security level. Various Biometric features are used for authentication some of them form a large template size and offer high security level Table 2: Comparison table of all biometrics[1] Biometrics Finger print Facial recognition Voice recognition Iris scan Finger vein Hand Geometry Retina Signature

Accuracy

Cost

Size of template

Stability

Security level

Medium

Low

small

Low

High

Large

Low

Medium

Small

Low

High

Small

High

Medium

High

Medium

High

Medium

small

Medium

High Medium

High Low

small Medium

Low Low

medium Low

. Comparison for various Biometric on the basis of accuracy, reliability, security & stability is given below.[5]. We have plotted these results using MATLAB with different values of reliability, security & accuracy with a maximum value 4 and minimum value 0 Above graph show that accuracy of Iris, Retina & Finger Biometric is very high. Graph given below shows that Finger, Iris & Retina Biometric are highly reliable.

Fig. 8 Accuracy for various biometric

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Fig. 9 Reliability for various biometric

Fig.10 Security for various biometric Above figure shows that Iris, Retina & Finger Biometric are more secure as compared to Face & Voice Recognition systems. Various Biometric & their trais are also given below in the table. IV. CONCLUSION Current electronic security systems, which rely primarily on personal identification to ensure that a client is an authorized user of a system, have a common vulnerability; this can be eliminated using biometrics. Biometric authentication refers to automated methods of identifying or verifying the identity of a person. Using this technology, we can easily identify a person in a crowd and by so we can verify their identity [1]. Biometrics can be used by various organizations to increase security levels and protect their data and patents. It is clear that each trait has different parameters in the field of accuracy, efficiency, speed cost and security. REFERENCES [1] [2] [3] [4] [5] [6]

Rupinder Saini ,Narinder Rana, “Comparison of Various Biometric Methods,” (IJAST) Vol 2 Issue I (March 2014) Alina Klokova, “Comparison of Various Biometric Methods”(2011) Alastair Cummings, Mark Nixon and John Carter “A Novel Ray Analogy for Enrolment of Ear Biometrics.” In: IEEE Fourth Conference on Biometrics: Theory, Applications and Systems, September 2010, Washington DC, USA. S. Prabhakar, S. Pankanti, Anil K. Jain “Biometric Recognition: Security and Privacy Concerns” In: IEEE Security & Privacy,( 2003) Siddhesh Angle, Reema Bhagtani, Hemali Chheda “Biometrics : A Further Echelon of Security”,(2005) K.P.Tripathi “A Comparative Study of Biometric Technologies with Reference to Human Interface” International Journal of Computer Applications (0975 – 8887) Volume 14– No.5, January (2011)

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Determination of Diffusion Constants in Boronation Powder Metallurgy Samples of the System Fe-C-Cu

I. Mitev K.Popov Department of Machinery and Equipment Department of Machinery and Equipment Technical University of Gabrovo Technical University of Gabrovo 4, H. Dimiter str. 4, H. Dimiter str. Bulgaria Bulgaria Abstract: In this present work are calculated values of activation energy - Q and before exponential multiplier Do, in boronation of powder metallurgy samples of the system Fe-C-Cu. The subject of this study are powdered samples based on iron powders ASC100.29, towards them is added 0,1%C and 1,0÷3,0%Cu. The influence of the amount of copper, the density of samples and the saturation temperature on the Q and Do is traced. Equations for determining the diffusion of boron in the studied samples depending on their density and strength of the copper are presented. Keywords: powder metallurgy, boronizing, diffusion, iron powder, electrolytic copper __________________________________________________________________________________________ I. Introduction Diffusion is the process that had a significant influence over the crystallization and phase transformations in the solid condition. Since it depends on the implementation of one or the other phase as well as in the dense and in the structure of the powder metallurgical materials. The dependence of the formation of the diffusion coating of density, porosity and chemical composition of powder metallurgy materials, and from the saturation temperature, the duration of the process and the type of saturated area, makes it necessary for each case, to determine the kinetics of growth of the diffusion layers, and specifying diffusion constants. When calculating diffusion constants is using the second law of Fick – 1. [1,2] (1) To solve the equation 1 and the determination of the diffusion constant is necessary to comply two conditions:  The concentration of difoundation element on the surface of saturated samples to be permanent – Со= constanta;  Presence of atomic diffusion of difoundation element. In practice, a pure atomic diffusion occurs rarely. If you the amendment of the thickness of the diffusion layer – δ, over time – t, becomes by parabolic law or in coordinates δ2/t, the dependence is straightforward with sufficient accuracy that can be calculated diffusion coefficient – D.For cases where the diffusion coefficient regardless of the concentration equation 1 takes the form 2. (2) 2

, cm /s where: Do - before exponential multiplier; Q – activation energy; R – gas constant; T – saturation temperature - °К. On conditions that Со= constanta follows that: = const

(3)

(4) (5)

Substituting in equation 5 the values for D from equation 3 we get: (6) At a certain temperature the saturation expression (7) Therefore equation 6 yields type δ2= 2Р.t.The values of 2R is experimentally determined and then calculate the values of Do and Q. The graphical interpretation of equation 7 in coordinates lnD; 1/T is a straight line. Tangent of the angle dependence determines the value of activation energy - Q. tg β = Q/ (RT) (8) Q = R . tgβ (9) Knowing the values of activation energy can be calculated from the diffusion coefficient in equality 4. In the equation before exponential multiplier Do depends on the number of technological factors. Considering the influence of the parameters of the crystal lattice, it may be determined by formula 10.

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(10) -1

where: N – Avogadro`s number - mol ; h - constant of Planck -J.s; а - parameter of crystal lattice. II. Technical requirements The subject of this study are powder metallurgical samples based on iron powders ASC100.29. Towards them is added 0,1%C and 1,0÷3,0% electrolytic copper with particle size of 63µm. For this study are used powder metallurgical samples based on iron dust ASC 100.29. To this powder is added 0,1%С и 1,0÷3,0% electrolytic copper with particles 63µm. After mixing, the powdered samples is extruded with a load of 200÷800MPa, which allows to obtain samples having a density in the range of 5,80÷7,00g/cm3. Sintering is conducted at a temperature of 1150°C for 60minutes in a protective environment of dissociated ammonia. The diffusion constants - Do and Q, are determined in after boronizing in liquid environment [2] at temperatures of 920, 950 and 980°C for 2, 3 and 4 hours. The thickness of the formed boride coatings is determined by a micrometer screw of metallographic microscope with an accuracy of 1 μm - Table № 1. Table 1: Thickness of the boride layer № обр.

ρ, g/cm3

δ, µm

Cu, % 3

920°С 3h

950°С 3h

980°С 3h

5,80

6,20

6,60

7,00

5,80

105

6,20

6,60

7,00

1,0

5,80

106

100

116

6,20

108

6,60

101

7,00

5,80

100

106

6,20

71`

103

6,60

7,00

2,0

3,0

Figure 1: Dependence δ2/t at a temperature of saturation

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c. Using the results in Table № 1 are drawn graph dependence δ 2/t depending on the saturation temperature – T, Fig.1. From the graph dependence in Figure 1, experimentally and using formulas 7 and 8 are defined values ln2P and tgβ. This allows us by formulas 9 and 10 to calculate the values of activation energy - Q, and diffusion constants – Do , Table № 2. Saturation of powder metallurgical samples with different density regardless of the concentration of copper in them, carrying difoundation elements going through the surface, boundary and volume diffusion. [4,5]. From conducted results - table № 2, it is seen that with increasing density of 5,80 to 7,00g/cm3, the values of activation energy of samples with no copper doped rose from 18,80÷20,40 kkal/mol.[3]. Table 2: Diffusion constants under boration of powdered samples from the system Fe-C-Cu ln2P ρ, Cu, Q. Do. tgβ 2 g/cm3 % cal/mol cm /s 920°С 950°С 980°С

№ обр. 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

5,80 6,20 6,60 7,00 5,80 6,20 6,60 7,00 5,80 6,20 6,60 7,00 5,80 6,20 6,60 7,00

1,0

2,0

3,0

-0,841 -0,867 -0,950 -1,083 -0,511 -0,892 -0,916 -1,055 -0,292 -0,587 -0,756 -0,835 -0,694 -0,762 -0,852 -1,228

-0,6401 -0,5390 -0,6667 -0,8998 -0,2916 -0,5624 -0,6372 -0,7562 -0,1736 -0,4680 --0,5298 -0,4711 -0,4647 -0,5816 -0,6965 -0,8362

-0,4620 -0,4711 -0,5241 -0,7726 -0,2091 -0,5486 -0,5258 -0,6372 -0,0202 -0,2548 -0,4212 -0,4376 -0,3346 -0,3986 -0,4498 -0,7801

9475,5 9910,0 10650,0 10260,0 7537,5 8609,5 9762,3 10445,0 6784,5 8305,0 8370,0 9935,0 8975,0 9085,0 10055,0 11197,5

18837,29 19701,10 21172,20 20396,90 14984,60 17115,70 19407,40 20764,70 13487,59 16510,34 16639,56 19750,80 17842,30 18061,00 19989,30 22260,60

0,2618 0,2738 0,2943 0,2835 0,2083 0,2379 0,2698 0,2886 0,1875 0,2295 0,2313 0,2745 0,2480 0,2610 0,2779 0,3094

Saturation of powder metallurgical samples with different density regardless of the concentration of copper in them, carrying difoundation elements going through the surface, boundary and volume diffusion. [4,5]. From conducted results - table № 2, it is seen that with increasing density of 5,80 to 7,00g/cm3, the values of activation energy of samples with no copper doped rose from 18,80÷20,40 kkal/mol.[3]. The increased rate of diffusion in powder samples were explains the increased circumference of the flow of diffusion processes due to the presence of an open porosity especially in samples with a lower density – 5,80g/cm3 Adding up to 2,0%Cu to Fe-C samples reduces the values of Q. For samples with a density of 5,80g/cm3 and concentration of 1,0%Cu, this reduction is about 15%, while samples with same density but containing 2,0%Cu is 20% and results in a substantial increase in the diffusion process of boron in the Fe-C-Cu matrix. This could be explained by the fact that the sintering Cu is dissolved in the crystal lattice of the austenite does not obstacles the diffusion of boron in the samples. With increasing copper concentrations above 2.0%, however,

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notice an increase in the values of Q, and samples containing 3,0%Cu, Q values are close to those of pure iron samples with the same thickness. This is explained by the fact that at a copper concentration of more than 2.0% it can not be dissolved in austenite, and are separated by boundaries in the form of a supersaturated solid solution - ε phase. [4,6]. This leads to obstruction of diffusion processes, and limits the diffusion of boron in the samples. Table №3 presents the expressions for calculating the diffusion of boron in our study samples.

№ обр.

Table 3: Expressions for determining the diffusion in austenite doped with copper. ρ, Cu, D, ρ, Cu, D, № обр. g/cm3 % cm2 / s g/cm3 % cm2 / s

1 2 3 4 5 6 7 8

5,80 6,20 6,60 7,00 5,80 6,20 6,60 7,00

4 -18840/RT

1,0

0,2618e 0,2738e-19700/RT 0,2944e-21170/RT 0,2835e-20400/RT 0,2083e-14990/RT 0,2380e-17115/RT 0,2698e-19410/RT 0,2890e-20765/RT

9 10 11 12 13 14 15 16

5,80 6,20 6,60 7,00 5,80 6,20 6,60 7,00

2,0

3,0

0,1875e-13490/RT 0,2295e-16510/RT 0,2310e-16640RT 0,2745e-19750/RT 0,2480e-17840/RT 0,2510e-18060/RT 0,2780e-19990/RT 0,3095e-22260/RT

ІІІ. Conclusion From the conducted researches and obtained results could be presented the following conclusions:  Kinetics of growth of the diffusion layer in boration of powder samples of the system Fe-C-Cu at temperatures of saturation of 920, 950 and 980°C for 2, 3 and 4 hours is monitored;  The diffusion constants - Q and Do are defined, depending on the density of the studied sample and the concentration of copper in them; 3  It is proved that by increasing the density of samples from 5,80 up to 7,00g/cm , the values for the 3 activation energy increases. For samples with density 7,00g/cm , not containing copper calculated values are close to those of samples of solid iron - 21,00 kkal/mol. In all other samples the values of activation energy are significantly lower - 13÷20 kkal/mol. This explains the presence of an open porosity - especially in samples having a density of 5,80g/cm3, which increases the surrounding surface and the rate of diffusion.  It is proved that the addition up to 2,0%Cu to the Fe-C matrix leads to a reduction of the Q value to 20%, and activation of the diffusion processes;  It is proved that the increase in the copper concentration above 2.0% is observed increasing value of Q, and on samples containing 3,0%Cu, the Q values are close to those of the samples of the pure iron with the same density. This is the result of separate at the borders of iron grains ε phase, which prevent the diffusion of boron in austenite.  Expressions for theoretical calculation of diffusion in powder metallurgical samples of the system FeC-Cu, depending on their density - 5,80÷7,00 g/cm³, and the concentration of the copper within them are presented. IV References [1] [2] [3] [4] [5] [6] [7] [8]

Анчев, В., Физика на металите, Техника, София, 1981 Atanasova, J., I.Mitev, Boronizing of Structural Metal-Ceramic Fe-Cu Base, Journal of Engineering, vol.11÷12, 1996, p.315÷316 Глухов, В., Борированные покрытия на железа и сталлях, Науковая думка, Киев, 1980. May, I., L. Schetky, Cooper in iron and steel, John Wiley and sons. Toronto, 1988, p.307, Митев, И., Получаване на праховометалургични материали, УИ”В.Априлов”, Габрово, 2004, ISBN 954-683-233-2 Mitev,I., R.Maimarev, Sintering of Binary Powder Structural Materials in the Pressence of Liquid Phasse, Mechanical Engineering and Mechanmics, vol.№17, 2012, p.70÷73, ISSN 1312-8612 Митев, И., Структурен анализ, „ЕКС-ПРЕС”, Габрово, 2013, ISBN 978-954-490-363-3 Митев, И., Изследване параметъра на кристалната решетка на желязото след спичане и стареене на Fe-Cu металокерамика, ТУ-Варна, 2001, с.98÷103, ISSN 1311-896Х

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ALMOST NORLUND SUMMABILITY OF CONJUGATE SERIES OF A FOURIER SERIES V. S. Chaubey Department of Mathematics, B R D P G College, Deoria (274001), U.P., India __________________________________________________________________________________________ Abstract: In this paper a more general result than those of Pati, T (1961), U.N Singh and V.S Singh (1993) has been obtained so that their results come out as particular cases. U.N Singh and V.N Singh has been given some interesting result on this in 1993. Keywords: Almost Norlund Summability, Fourier Series ______________________________________________________________________________________ I. INTRODUCTION Let n be an infinite series with {sn} as the sequence of its n-th partial sums. Lorentz (1948) has given the following definition: Definition: A bounded sequence { } is said to be almost convergent to a limit s if (1.1) v = s uniformly with respect to m. Let { Pn} be a sequence of non – zero real constants and Pn= p0 + p1 + p2 …………….pn , pn-1= Pn-1 =0

(1.2)

We define that the series an or sequence {sn} is said to be almost (N,Pn) summable to s if tn,m = n –v ,Sv,m tends to s

(1.3)

As n Sv,m

(1.4)

, uniformly with respect to m k

Let the fourier series of a 2 -periodic and Lebesgue Integrable function f in ( – F (t) a0 + cosnt + bn sin nt)= n (t) And then the conjugate series of (1.5) is ( bn cos nt – an sin nt ) = n (t) Let (x) denote the n- th partial sum of the series Bn (x) . Then we write v,m= k Let us write (t) = f(x + t) + f(x-t) – 2f(x ) (t) = f(x +t ) – f(x – t) (t) = (u) | du (t) = = =

(u) | du n-v n-v

) be given by (1.5) (1.6) (1.7) (1.8) (1.9) (1.10) (1.11) (1.12) (1.13)

T = [ ] = the integral part of II. MAIN THEOREM Pati (1961) has established the following theorem on the norlund summability of a Fourier series: Theorem: If (N, Pn ) be a regular norlund method defined by real , non – negative monotonic non –increasing sequence of coefficient {po} such that pn as n Pn = as n and log n = O (Pn) , as n (2.1) v Then if (t) = (u) | du = O[ ] as t to (2.2) The series (1.5) is summable (N,Pn) to f(x) at the point t = x

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Theorem: Let {pn} be a real non – negative monotonic , non increasing sequence of coefficient such that pn ,h if

(t)=

where o

(u)| du = O [

] as t

0. and

du = O (1), as n

uniformly with respect to m, then the series (1.6 ) is almost (N, pn) summable to t dt at every point where this integral exist is provided

(n) log n = O (pn) , as n

III. LEMMA The following lemma is essential for the proof of our theorem. Lemma: if (t) is given by (1.13 ) Then (t) = { O (n+ m) , for 0 = { O ( ), Proof of the lemma:We have

(t) =

for

n-v

(

=O (n+m), for Similarly, on expanding sine, cosine in powers of t, we get (t) = o( ) = for

Let

IV. PROOF OF MAIN THEOREM (x) denote the n- th partial sum of the series n (x) .Then we have –

(x) = (x) -

)t dt

t dt =

So that – f(x) }

= ==

dt (t)

Now by (1.12) we have =

n-v

sin

=( t) dt =0(1) = R( say) In order to prove the theorem we have to show that under our assumptions (t) dt = o (1) as n For o (t) dt = o [ =R1 + R2 + R3 Say Let us first consider R1 Now |R1| = o[

(t)

(4.1) (t) dt]

=O(n+m) [ =O (n+m) [ =0(1) as n |R2| =0 [

(t) dt

(lemma) ] as t

Next considering R2 we have ]=0 (1) as n

by (1.2)

Lastly we have

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R3 =

n-v

= I3.1 â&#x20AC;&#x201C; I3.2 ( say ) Now using second mean value theorm I3.1 n-v Where = O (1) as n uniformly with respect to m. Similarly I3.2 =

(4.2) n-v

= O(1). As n uniformly with respect to m. Collecting from (1.4) to (1.5) we get the required result This completes the proof of our main theorem.

(4.3)

REFERENCES [1]. [2]. [3].

Lorentz G.G (1948) . Acta Mathematics 80,167. Pati , T(1961) : Indian journal of Mathematics , 3, 85. Singh V.N.and Singh V.S. (1993) : Bull .cal Math. Soc 87,57- 62.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net On a New Weighted Average Interpolation Vignesh Pai B H1 and Hamsapriye2 B.Tech. 7 Semester Student, Department of Mechanical engineering, R V College of Engineering, R.V. Vidyaniketan post, Mysore Road, Bangalore- 560059, INDIA. 2 Professor, Department of Mathematics, R V College of Engineering, R.V. Vidyaniketan post, Mysore Road, Bangalore- 560059, INDIA.

Abstract: A new interpolation technique called the Weighted Average Interpolation (WAI) is discussed. A new concept named the effect is explained, for both even and odd number of points, along with associated correction factors. The procedure of deriving the formula is discussed in detail, under different cases. These ideas are also extended to extrapolation of data. The relation between the WAI and Lagrange’s interpolation formula is analyzed. Further, the advantages and disadvantages of the WAI with reference to the Lagrange’s formula are examined. Numerical examples are worked out for clarity. Keywords: Weighted Average Interpolation, Effect, Odd points, Even points, Correction factor, Pascal’s triangle. I.

Introduction

Interpolation is a technique of constructing new data points, based on the existing data points obtained by sampling or experimentation. It is often required to estimate the values at intermediate points. The well-known Lagrange method of interpolation is such that, the number of arithmetic operations increase rapidly, whenever the number of data points is increased. This is a limitation and therefore there is a need to reduce the number or operations without compromising on the accuracy. The new method discussed herein overcomes this limitation and thus the number of operations are significantly reduced. Further, the formulae are derived based on logical reasoning. The method is simple compared to other methods. II.

The Concept of Positive and Negative Effect in Interpolation

Let ( , ) be an intermediate point between two points ( formula [1] is given by

) and (

). The Lagrange’s interpolation .

(1)

This formula can be rewritten in the form as

(2)

We see that is the weighted average of and and the weights are observed to be ratios of distances. We set a reference distance as d( , ) = |( - )|. The weight associated with is the ratio of the reference distance and d( , ). Similarly, the weight associated with is the ratio of the reference distance with itself. Refer Figure 1. Figure 1: The concept of Effect

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From figure 1 we can rewrite equation (2) as. (3) The above expression can be recast into a different form, by using the concept of “Effect”. This effect is defined as the ratio of the reference distance with either d( , ) or d( , ). The effect of ( , ) on ( , ) is the ratio and the effect of ( , ) on ( , ) is . Thus, equation (3) takes the form (4) The reference distance can as well be d( , ). In fact, the reference distance can be taken to be unity. In this case the formula (4) can be written as (5) The concept of effect can be extended to many number of points. Taking the reference distance to be unity, we can similarly write the weighted average formula for n points ( , ), ( , ), …, ( , ) as (6) Initially, we have considered the effects of the n points on the interpolating point ( , ) with positive signs, which may not be correct. Figure 2 explains the possible negative effects clearly. Figure 2: Negative effect of even points. 6

0 0

Sequence 1

Sequence 2

Sequence 1

Sequence 2

0 0

2 Sequence 1

4 Sequence 2

2 Sequence 1

Sequence 2

Consider the point (2.5, ). The idea of interpolation is to fit a smooth curve passing through the given points. If the given data points are (1, 4), (2, 4), (3, 4), (4, 4) then = 4 for 2.5. Suppose the point (1, 4) is changed to (1, 5) then the point (2.5, ) slides down below 4. Similarly, if the point (4, 4) is changed to (4, 5) then the point (2.5, ) again slides below = 4. If simultaneously the two points (1, 4) and (4, 4) are varied to (1, 5) and

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(4, 5), then the effect piles-up and the effect is clearly visible, as shown in figure 2, as sequence 1. If (2.5, ) is the reference point, then (3, 4) (on the right) and (2, 4) (on the left) are defined to be odd points. Also, (4, 4) (on the right) and (1, 4) (on the left) are defined to be even points. Therefore, the observation is that whenever the value of “even points” increases, the interpolated value decreases. For the same reference point (2.5, ) and for the same data points, if the point (1, 4) is changed to (1, 3) then the point (2.5, ) increases above 4. Similarly, if the point (4, 4) is changed to (4, 3) then the point (2.5, ) again increases above 4. If simultaneously the two points (1, 4) and (4, 4) are changed to (1, 3) and (4, 3), then the effect piles-up and the effect is clearly visible, as shown in figure 2, as sequence 2. Therefore, the observation is that whenever the value of “even points” decreases, the interpolated value increases. In a nut-shell, we say that the “even points” (“odd points”) exert a “negative effect” (“positive effect”) on the point to be interpolated. It is to be noted from equation (2) that the immediate points or the “first points” or the “odd points” exert positive effect. With all these observations the formula for interpolation can be modified to be

, whenever four data points are given. Formula (7) is true when ( , ) lies between ( , Extending these ideas we can obtain the formula for eight points to be

(7) ) and ( ,

(8) for

. The formula for any general case would have alternate signs. III.

Correction Factors

At this stage we have only considered the effects, without their magnitudes. Incorporating these magnitudes leads us to the “correction factors. As an illustration we consider the below data. Table I: Data Points. Sl. no. 1

100

144

196

256

Let the point of interpolation be (9, ). Equation (8) takes the form ,

(9)

where = 0.142857, = 0.2, = 0.333333, = 1, = 1, = 0.333333, = 0.2 and = 0.142857 are the weights. The estimated y = 75.47, whereas the exact value is 81. The Lagrange’s interpolation gives the exact value 81. We now compare with the coefficients of Lagrange’s interpolation formula, written in the form as .

(10)

Here = – 0.00244, = 0.023926, = – 0.11963, = 0.598145, = 0.598145, = – 0.11963, = 0.023926 and = – 0.00244. On comparing the weights with the above coefficients, we impose the following condition that any two weight ratios must equal the corresponding coefficient ratios. That is,

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Since the ratio is important and not their individual values, we may equate the numerators and denominators separately. Thus we obtain and . In the above example, = 0.017089844, = 0.119628906, = 0.358886719, = 0.598144531, = 0.598144531, = 0.358886719, = 0.119628906 and = 0.017089844. Now if we use the weights ’s we obtain y = 81.The advantage is that these correction factors can be computed just once, irrespective of the interval in which ( , ) lies. Also these weights are independent of the function that is interpolated. These correction factors rectify the end result obtained from the formula (9), in such a way that the final result coincides with that of the Lagrange’s. The weights are not actually the coefficients of the ordinates in WAI formula. For instance, the coefficient of in WAI formula is This is compared with c1 of Lagrange interpolation. It is to be remarked that in the weighted average interpolation, we are just interested in the relative importance of the given ’s with reference to each other. This simplifies the computations to a greater extent. It is found that these correction factors can be obtained from the Pascal’s triangle. Since their ratios are of importance, dividing all of them by the smallest, we obtain the correction factors to be = 1, = 7, = 21, = 35, = 35, = 21, = 7 and = 1. Thus, the correction factors for “n” points are obtained from the n th line of the Pascal’s triangle. The formula with correction factors for 4 points: ( , ), ( , ), ( , ), ( , ) is tabulated below Table 2: List of formulae to be used for 4 points. Interval Formula to be used

1 to 2

2 to 3

3 to 4

IV.

Extrapolation Using Weighted Average Method

We extend the idea of weighted average interpolation to extrapolation as well. Initially, few “virtual intervals” are created beyond the given range. Suppose that ( , ), ( , ) and ( , ) are the given points. If < < , then the interpolation formula is (11) and if

the interpolation formula is (12)

Suppose < < . Then we are extrapolating on the right. We include a virtual interval ( , ) and use the interpolation ideas, as explained in earlier sections. For instance, consider = 2, = 4 and = 6. For 6 < < 8, we include the virtual interval (6, 8). Using the ideas discussed in the earlier sections, the extrapolation formula can be written in the form as (13)

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It is observed that (6, ) is an “odd point”, which therefore exerts positive effect on ( , ). Similarly, if 8 < 10, then the extrapolation formula in the virtual interval (8, 10) is

(14) It is to be noted that (6, ) is an “even point”, which now exerts a negative effect on ( , ). This pattern continues for every additional virtual interval. It is also observed that there is absolutely no difference between the expressions (13) and (14), except that the numerator and the denominator both are multiplied by – 1. Therefore there is exactly one formula for extrapolation. Similar ideas are used while extrapolating on the left. V.

A Comparative Study of Lagrange Interpolation and WAI

In this section we shall confirm that the end results of WAI and Lagrange’s interpolation coincide. Let us consider three data points. If ( , ) lies between the first two data points, the WAI formula is (15) The Lagrange’s interpolation formula is (16) Suppose the points are equally spaced, then equation (16) simplifies to (17) and formula (17) simplifies to (18) Dividing equation (18) throughout by

and multiplying by two we obtain (19)

This is the numerator of the WAI formula. Also, it is easily proved that (20) Therefore equation (19) reduces to (15). These ideas can be easily generalized to any number of points. VI.

Unequally Spaced Points

The above study is based on equally spaced points. The extension of these ideas to unequally spaced points is a tedious task. Nevertheless, unequally spaced points, following a pattern is of special interest. Therefore, we have considered three such cases, as stated below: 1) Unequally spaced points, whose consecutive differences are in geometric progression (UGP) 2) Unequally spaced points placed in harmonic progression (UHP) 3) Unequally spaced points, whose consecutive differences are in arithmatic progression (UAP) 1) UGP: As an illustration, we fix the common ratio r = 2. The correction factors can be computed on similar lines as in the case of equally spaced points. Let the data points be a + s, a + s r, a + s r2. The correction factors are found to be = 2, = 3 and = 1, which can be viewed as , and . Again, with four data points, the correction factors are , , and and with 5 data points the correction factors are given to be , , , and . In general, for n points and for any r, we can compute , , , … The p’s follow the special pattern close to the Pascal’s triangle as given below.

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Table 3: 1 POINT 2 POINTS 3 POINTS 4 POINTS 5 POINTS 6 POINTS 7 POINTS 8 POINTS 9 POINTS 10 POINTS

’s for points in UGP for

1 1 1 1

155

651

127 10795

511

155

11811 97155

43435

1 15

788035

651 11811

200787 3309747

1 31

1395

2667

255

1 7

1 3

63 2667

97155 3309747

1 127

10795 788035

255 43435

1 511

The pattern for the ’s is explained as follows. The fourth line consists of the numbers 1, 7, 7 and 1. The fifth line is computed as 1, , , and 1. So, in general, if the numbers in the th line is th 1, 1, 2, 3, …, 1, then the ( +1) line can be computed to be 1, , , , ... , 1. The ’s for any in UGP is tabulated below: Table 3:

’s for points in UGP for any .

1 POINT

1+r+r^2

2 POINTS

3 POINTS

4 POINTS

5 POINTS

6 POINTS

So, in general, if the numbers in the th line is 1, 1, 2, 3, …, 1, then the ( +1)th line can be computed to be 1, , , , ... , 1. 2) UHP: We consider a general harmonic progression in the form , , …. The correction factors can be computed on similar lines as in the case of equally spaced points. The correction factors are computed in table 4. Table 4: List of Correction factors for points in UHP. 1 POINT

2 POINTS

3 POINTS

4 POINTS

5 POINTS

6 POINTS

7 POINTS 8 POINTS

192

9 POINTS

10 POINTS

The

pattern

448

2304

for

3645

61236 236196

the

810

15309

1024

162

256 2560

20480 143360

917504

46875 546875

5468750

5505024

49218750

corrections

factors

125 3125

979776 15676416

211631616

1296 46656

23059204 484243284

nth

the

16807 823543

262144 16777216

603979776

line

4782969 387420489

given

100000000

3) UAP: The general form of the sequence in this case is considered to be a, a+d, a+3 d, a+6 d, a+ 10 d, … The correction factors for the above choice of values is tabulated below. Table 5: List of Correction factors for points in UAP. 1 POINT 2 POINTS 3 POINTS 4 POINTS 5 POINTS 6 POINTS 7 POINTS 8 POINTS 9 POINTS 10 POINTS 4862

1 1 2 5 14 42 132 429 1430 11934

1001 3432

1001

637

1 9

54 273

1260 5508

1 7

154

2548 9996

1 5

275

3640 13260

9 28

297

1 3

77 440

2244

1 11

1 13

104 663

1 15

135

1 17

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If the numbers in the th line are computed to be ,

…,

=1, then the corrections factors in the ( +1)th line can be ,... , 1.

VII. Error Analysis In section V, we have shown that the end results of Lagrange interpolation and the WAI methods are equal. Hence the error estimate of WAI coincides with that of Lagrange’s method. Thus the error term in the WAI is estimated to be [1]: (21) is a polynomial of degree

. VIII. Advantages and Disadvantages

The major advantage of WAI over Lagrange interpolation is that fewer arithmetic operations are required. As an illustration, with eight data points, it can be easily verified that WAI and Lagrange interpolation requires 47 and 183 distinct arithmetic operations, respectively. In general, with n points WAI performs 6 n – 1 distinct arithmetic operations, whereas Lagrange interpolation performs 3 n2 – n – 1 arithmetic operations. Remarkably, it is possible to obtain a polynomial approximation in Lagrange interpolation, whereas this is difficult in the case of WAI. This is a disadvantage. IX.

Numerical Examples

In this section, we have worked out an example under UGP. The following data points , , , , , and satisfy the function . The problem is to estimate at . For these four data points the correction factors are , , , , , , and or , , , , , , and . Plugging in these correction factors in the WAI formula and for

we arrive at (22)

Thus 1.467758441which is the same value as Lagrange interpolation, whereas the actual value is 1.479425539. X.

Conclusions

A new interpolation technique called the Weighted Average Interpolation (WAI) has been discussed. A new concept called effect has been introduced, for both odd and even number of points, along with the respective correction factors. Also, the procedure of deriving the formula has been discussed in a greater detail, under different cases. Further, these ideas have been extended to extrapolation of data. Furthermore, the relation between the WAI and Lagrange interpolation formula has been studied. The merits and demerits of the WAI and the Lagrange’s interpolation have also been explained. Finally, several illustrations and numerical examples are worked out for clarity. References [1]

Kendall E. Atkinson, “An Introduction to Numerical Analysis”, 2nd Edition, John Wiley & sons, 1988.

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A Review on Bandwidth Enhancement Methods of Microstrip Patch Antenna Tanvir Singh Buttar1, Narinder Sharma2 M.Tech. Scholar, Department of ECE, AICTE, PTU, Amritsar College of Engg. & Tech., AmritsarJalandhar GT road, Manawala, Amritsar, India, tanvirbuttar88@gmail.com 2 Associate Professor, Department of EEE, AICTE, PTU, Amritsar College of Engg. & Tech., AmritsarJalandhar GT road, Manawala, Amritsar, India, narinder.acet@gmail.com 1

Abstract: Microstrip patch antenna (MPA) plays significant role in modern communication devices. And a large part of daily communication is done by using MPA. The study of MPA has made great progress in recent years. The study of literature work shows that the major work is concentrated on designing small sized broadband MPA. As the antennas are build smaller, the operating bandwidth decreases. Hence to improve bandwidth different techniques are used. This review paper delivers various bandwidth enhancement techniques since last few years. Keywords: Microstrip patch antenna (MPA), Bandwidth enhancement, Dielectric constant, Antenna design, substrate. I. Introduction Physically, MPA is made up of dielectric substrate which is sandwiched between a radiating flat rectangular sheet or patch of metal and a larger sheet of metal called ground plane. The radiating patch is made up of conducting material like gold or copper and can have any shape. The feed lines and radiating materials are photo etched on the dielectric substrate as shown in figure 1.

Fig. 1. Microstrip Antenna configuration

The height (h) of the dielectric substrate usually ranges from 0.003 λ o ≤ h ≤0.05 λo, where λo is the free space wavelength and the dielectric constant of the substrate (εr) ranges from 2.19 to 12. The MPA becomes popular in the world of wireless communication system because of its low profile, easy and low cost fabrication, light weight non-planar and planar geometries easy integration of components, mechanical robustness, easy association in arrays. II. Excitation Techniques The MPA can be excited by different methods. These feeding techniques are majorly classified into two categories: contacting and non-contacting feed technique. In contacting feeding technique the power is fed directly to the radiating patch by using a connecting element such as microstrip line. In non-contacting feed technique an electromagnetic field coupling is done to transfer power between radiating patch and microstrip line. The four most common feed techniques are microstrip line and coaxial probe (both contacting techniques); aperture coupling and proximity coupling (both non-contacting techniques). III. Methods of Analysis of Microstrip Patch Antenna The analysis mechanism for MPA are divided into two parts. First part is based on equivalent magnetic current distribution around the patch edges. There are three analytical techniques:The transmission line model,The cavity model, The full-wave model. The second part is based on the electric current distribution on patch conductor and the ground plane. The numerical methods are as:The finite element method (FEM), The spectral domain technique (SDT), The method of moments (MOM), The finite difference time domain (FDTD) method. The analytical methods are less accurate, gives good physical insight and require less computation where as the numerical methods are more accurate, gives less physical insight and requires more computation.

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IV. Need of Higher Bandwidth Generally, the MPA has a narrow bandwidth besides that today wireless communication system demands higher operating bandwidth. These communication devices need higher bandwidth so as to work in the broader band in order to shoulder high speed internet, multimedia communication like 9.5% for a digital communication system (1710-1880 MHz), 12% for universal mobile telecommunication system (1920-2170 MHz), 7% for global system for mobile communication (890-960)[1]. In order to fulfil the demands of the bandwidth various techniques are employed and some of them are described in this review paper. V. Literature Survey The concept of MPA revert back about 26 years to work by Deschamps in USA[2], by Gutton and Baissinot in France[3]. Afterwards Lewin explored radiation from stripline discontinuities. Further studies were assumed in 1960 by kaloi, who considered the rectangular and square configurations. But the report of Deschamps work was not announced until the 1970’s, when the conducting strip radiator ( half wavelength wide and a few wavelengths long fed by coaxial connections) separated by a dielectric substrate from a ground plane was explained by Byron. This concept was used in Project Camel. After that, the microstrip element was manifested by Munson[4] and data on circular and rectangular microstrip patched was reported by Howell. Weinschel evolved various microstrip geometries for use with cylindrical S band arrays on rockets. Further work on basic microstrip patch elements was announced in 1975 by Garvin et al, Weinschel, Janes, Howell and Wilson. The development of microstrip antennas by Munson, for use as low profile flush mounted antennas on missiles and rockets reveals that this was a practical concept which can be used in solving various antenna problems and hence invented the new antenna industry. Parminder singh et. al [5], highlighted the bandwidth enhancement of probe fed MPA. Chen and Zhang [6] explained the bandwidth enhancement of a microstrip of a microstrip-linefed printed wide slot antenna with a fractal shaped slot. Shubam Gupta et al[7], described the bandwidth enhancement in multi patch microstrip antenna array. M. Gujral et al[8], introduces the bandwidth improvement of microstrip antenna array using dummy EBG pattern on feed line. Song and Zhang [9], desigend a novel momopole antenna with a self similar slot for wideband applications. R.C Hadarig M.E. de Cos and F. Las-Heras [10], have presented microstrip patch antenna with enhanced bandwidth using AMC/EBG structures. Sandeep Kumar et. Al[11], proposed a design of microstrip square shaped patch antenna for improvement of bandwidth and directivity gain. Parmar, and Makwana [12], introduces the bandwidth enhancement of microstrip patch antenna using parasitic patch configuration. Jia-Yi Sze and Kin-Lu Wong [13], shows the slotted rectangular microstrip antenna for bandwidth enhancement. Qu, D. ; Shafai, L. ; Foroozesh, A. [14], highlights the Improvement of microstrip patch antenna performance using EBG substrates. D. C. Chang, J. X. Zheng [15], describes the Wide-Band microstrip antenna using two triangular patches. Parminder singh, Anjali Chandel, Divya Naina[16], explains the bandwidth enhancement of probe fed microstrip patch antenna. Juhua Liu, Quan Xue, HangWong, Hau Wah Lai, and Yunliang Long [17], describes thedesign and analysis of a low- profile and broadband microstrip monopolar patch antenna. Y. Sung [18], introduces the bandwidth enhancement of a microstrip line-fed printed wide-slot antenna with a parasitic center patch. Aliakbar Dastranj and Habibollah Abiri [19], highlights the bandwidth enhancement of printed e-shaped slot antennas fed by cpw and microstrip line. VI. Techniques To Enhance Bandwidth Of Microstrip Patch Antenna The techniques used to uplift the bandwidth of the microstrip patch antenna are described below. A. Broadband Mcrostrip Patch Antennas Having Modified Patch Shapes Bandwidth is enhanced by using this technique by simply modifying i.e changing the radiating patch’s shape. In other words by changing the shape of rectangular and circular patches into rectangular ring and circular ring so as to enhance the Bandwidth. By modifying the shapes of the patches the bandwidth improves because the quality factor reduces, as the less energy will be stored under the patch and produces higher radiation. B. Broadband Mcrostrip Patch Antennas Having Multilayer Configuration The patches are planted over the various dielectric substrates and are stacked on each other, in case of multilayer configuration. The coupling in the multilayer configuration can be done in two ways either by electromagnetic coupling or aperture coupling.. Ground plane have an aperture slot in aperture coupling and to avoid the radiation losses the substrate is made up of high dielectric constant and the top patch is made up of thick dielectric substrate with low dielectric constant. In electromagnetic coupled MPA one or more patches are placed over different dielectric layers. In two layered configuration then any one of them is fed and other is electromagnetically coupled.

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Although the dielectric constant and dimension of the patch may varies but the resonant frequency is near to each other in order to have high bandwidth [20]. Broad bandwidth approximately 70% can be obtained by making use of multilayer configuration. C. Broadband Microstrip Patch Antenna Having Planar Multiresonator Configuration In the planar multi-resonator configuration the multiple resonators are placed close to each other and only one is fed and others are parasitically coupled also called as gap coupled. This configuration can also be fed by directly connecting the patches through microstrip line. In certain cases both direct and gap coupling is used and is called as hybrid coupling. The major disadvantages of this configuration is that these configuration is not suitable for an array configuration, as their size is too large and there is changes in the radiation pattern over the impedance matching. D. Broadband Microstrip Patch Antenna Having Stacked Multiresonator Configuration This configuration is evolved by the combination of stacked configuration and multi-resonator configuration in order to further enhance the bandwidth. Although the size of the stacked multi-resonator microstrp patch antenna is not small but it provides very high bandwidth and gain. S. No.

Broadband Techniques

Modified patch shapes

Table1: Comparison of different broadband techniques Configuration Remarks T-slot rectangular patch[21] Impedance Bandwidth of 25.23% with average gain of 7.43 dBi is obtained. E-H shaped patch[22] Delivered bandwidth is about 27% U-shaped slot with single-layer single- patch[23]

Air substrate of 12 mm is used to provide bandwidth of 27.5%.

Multilayered patches[24]

Nearly 70% of bandwidth can be otained by making use of multilayered configuration of radiating patches

Gap-coupled multi resonator and stacked configuration[25]

delivers bandwidth of 25.7% with more than 10 dB gain.

Shifted parasitically multiresonator[26]

coupled

Improves the impedance bandwidth from 65 MHz to 251 MHz

Directly coupled and parasitic patches[27]

Impedance bandwidth of 12.7% (365 MHz) is yeilded, which is 6.35 times when compared with the simple patch i.e. 2% (54 MHz) at same center frequency of 2879 MHz bandwidth of 25.7% with more than 10 dB gain

configuration

Multilayered Technique

Multi resonator Technique

Stacked Technique

Multilayered

Gap-coupled planar multiresonator and stacked configuration[25] Stacked U-slot microstrip antenna incorporating E-shape and modified half-E shape radiating patch configuration[28]

Maximum impedance bandwidth of 60.2% can be obtained

E. Impact of Varrious Feeding Techniques on Bandwidth of Microstrip Patch Antenna. There are variety of feeding techniques which can be used to feed the microstrip patch antenna. Feeding techniques are broadly categorised into two categories: contact feeding and non-contact feeding. These techniques are mentioned above in this paper. A better impedance matching between a patch and the feed line without using any extra matching element depends on the type of feeding technique used. As shown in table the proximity coupled feed technique provides the highest bandwidth. Characteristics Bandwidth

Table 2: Comparison of Feeding Techniques Microstrip Line Feed Coaxial Feed Aperture Coupled Feed 2-5%

2-5%

Proximity Coupled Feed 13%

VII. CONCLUSION Low bandwidth is always the limitation of MPA. The impact of different broadband microstrip patch antenna configurations and feeding techniques on the bandwidth of the microstrip patch antenna has been presented in this paper so as to increase the operating bandwidth. After an exhaustive study of literature work it is perceived that in case of feeding techniques, the proximity coupled feeding technique delivers the maximal bandwidth. And among all techniques multilayer structures provide maximum bandwidth but its size increases with the increase in number of layers. Slot loaded techniques can be used to further enhance the bandwidth as they have inherent advantage that the size of the antenna remains small.

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REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

[17] [18] [19] [20] [21] [22]

[23] [24] [25] [26]

[27] [28]

Kin-Lu Wong, “Compact and Broadband Microstrip Antennas”, John Wiley & Sons, page number: 12-14, ISBNs: 0-471-417173, 2002. G. A.Deschamps, “Microstrip microwave antennas”, presented at the 3rd USAF Symp. On antennas, 1953. H. Gutton and G. Baissinot, “Flat aerial for ultra high frequencies”, French patent No. 703113, 1955 R. E. Munson, “Single slot cavity antennas assembly”, U.S. patent No. 3713 162, January 23, 1973. Parminder singh et. Al,” bandwidth enhancement of probe fed MPA” International journal of electronics communication and computer technology (IJECCT), Vol. 3, No. 1, pp. 368-371, 1992. Chen, W. L., G. M. Wang, and C. X. Zhang, “Bandwidth enhancement of a microstrip-linefed printed wideslot antenna with a fractalshaped slot” IEEE Transactions on Antennas Propagation, Vol. 57, No. 7, 2176-2179,2009. Shubam Gupta et al, “bandwidth enhancement in multi patch microstrip antenna array”, International Journal of Application or Innovation in Engineering and Technology (IJAIEM), Vol. 2, No. 2, pp. 353-358, 2013. M. Gujral et al, “ bandwidth improvement of microstrip antenna array using dummy EBG pattern on feed line”, Progress in electromagnetic research Symposium (PIERS) Proceedings, Vol. 127, pp. 79-92, 2012. Kim, H. and C.-W. Jung, “Bandwidth enhancement of CPW fed tapered slot antenna with multitransformation characteristics” Electronics Letters, Vol. 46, No. 15, pp.1050-1051, 2010. ] R.C Hadarig M.E. de Cos and F. Las-Heras, “microstrip patch antenna with enhanced bandwidth using AMC/EBG structures”, Hindawi Publishing Corporation International Journal of Antennas and Propogation, Article ID 843754, 6 pages, 2012. Sandeep Kumar et. Al, “design of microstrip square shaped patch antenna for improvement of bandwidth and directivity gain”, International Journal of Engineering Research a and Applications (IJERA), Vol. 2, no. 2, pp. 441-444, 2012. Parmar, and Makwana ,“Bandwidth Enhancement of Microstrip Patch Antenna Using Parasitic Patch Configuration” IEEE conference published in Communication Systems and Network Technologies (CSNT), Rajkot,pp. 53 - 57 ,11-13 May 2012 Jia-Yi Sze and Kin-Lu Wong, “Slotted rectangular microstrip antenna for bandwidth enhancement” IEEE Transactions on Antennas and Propagation, vol. 48 , issue no. 8, pp. 1149 – 1152, Aug 2000. Qu, D. ; Shafai, L. ; Foroozesh, A. , “Improving microstrip patch antenna performance using EBG substrates” IEE Proc. Microwave Antennas Propag., Vol. 153, Issue No. 6, pp. 558-563, December 2006. D. C. Chang, J. X. Zheng, "A Wide-Band microstrip antenna using two triangular patches", IEEE Tran. Antennas Propagation, vol. 51, Issue No. 9, pp. 2218-2216, Sept 2003. Parminder singh, Anjali Chandel, Divya Naina, “Bandwidth Enhancement of Probe Fed Microstrip Patch Antenna,”, International Journal of Electronics Communication and Computer Technology (IJECCT), Volume 3, Issue No. 1, pp. 368-371, January 2013. Juhua Liu, Quan Xue, HangWong, Hau Wah Lai, and Yunliang Long, “Design and Analysis of a Low- Profile and Broadband Microstrip Monopolar Patch Antenna,” IEEE Transactions on Antennas and Propagation, VOL. 61, NO. 1, pp. 11-18, Jan. 2013. Y. Sung, “Bandwidth Enhancement of a Microstrip Line-Fed Printed Wide-Slot Antenna With a Parasitic Center Patch,” IEEE Transactions on Antennas and Propagation, VOL. 60, NO. 4, pp. 1712-1716, Apr. 2012. Aliakbar Dastranj and Habibollah Abiri, “Bandwidth Enhancement of Printed E-Shaped Slot Antennas Fed by CPW and Microstrip Line,” IEEE Transactions On Antennas And Propagation,” Vol. 58,No. 4, pp. 1402-1407, 2010. Girish Kumar and K.P. Ray, “Broadband microstrip antennas”, Artech House antennas and propagation library, page number: 15-17, ISBN 1-58053-244-6, 2003. Lolit Kumar Singh, Bhaskar Gupta and Partha P. Sarkar, “T-slot Broadband Rectangular Patch Antenna”, International Journal of Electronic and Electrical Engineering Vol. 4, page number: 43-47, ISSN 0974-2174, 2011. Mohammad Tariqul Islam, Mohammed Nazmus Shakib and et al., “Modified E-H Shaped Microstrip Antenna for wireless Systems”, IEEE International Conference on Networking, Proceedings, ISBN 978-1-4244-3492-3, page number: 794-796, Japan, March 26-29, 2009 T. Huynh and K. F. Lee, “Single-layer single-patch wideband microstrip antenna”, Electron. Lett. 31, page number: 1310–1311, Aug. 3, 1995 Girish Kumar and K.P. Ray, “Broadband microstrip antennas”, Artech House antennas and propagation library, page number: 132-138, ISBN 1-58053-244-6, 2003 Girish Kumar and K.P. Ray, “Stacked gap-coupled multi-resonator rectangular microstrip antennas”, Antennas and Propagation Society International Symposium, IEEE Vol. 3, ISBN 0-7803-7070-8, page number: 514 – 517, Aug. 7, 2002. K. Chattopadhyay, S.K. Parui, S. Das and S.R. Bhadra Chaudhuri, “Bandwidth Enhancement of Microstrip Antennas Using Shifted Parasitically Coupled Planar Multiresonators”, Applied Electromagnetics Conference(AEMC), IEEE, ISBN 978-1-42444819-7, 2009. C. K. Wu and K. L. Wong, “Broadband microstrip antenna with directly coupled and gap-coupled parasitic patches”, Microwave Opt. Technol. Lett. 22, 348–349, Sept. 5, 1999. M. A. Matin, B. S. Sharif and C. C. Tsimenidis, “Broadband Stacked Microstrip Antennas with Different Radiating Patch”, Springer Science Business Media- Wireless Pers Commun, page number: 637-648, 11277-009-9836-7, Oct. 9, 2009.

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A Script Recognizer Independent Bi-lingual Character Recognition System for Printed English and Kannada Documents N. Shobha Rani1, Deepika B.D.2, Pavan Kumar S.3 Department of Computer Science Amrita Vishwa Vidyapeetham, Mysore Campus Bogadi, Mysore INDIA _____________________________________________________________________________________ Abstract: Recognition of text document images is the inclination of any optical character recognition systems. This paper aims at extending the functionality of optical character recognition system to recognize more than one language. At present optical character recognition technologies are able to recognize and translate only one language, however multi-lingual recognition capabilities for OCR are accomplished through incorporation of script recognizer. This paper eliminates the need of identifying the script type and achieves the automatic recognition of two different scripts with single optical character recognition system, which we are representing as bilingual OCR. Bilingual OCR recognizes the text document images composed of both English and Kannada scripts. The construction of bilingual OCR for English and Kannada is achieved by employing efficient constructs like multiple projection profiles, connected component analysis and principal component analysis. The devised system is proved to be effective and reliable by claiming around 95%-100% accuracy. Keywords: Bilingual document, script recognizer, principal component analysis (PCA), Optical Character Recognition (OCR), bilingual OCR. ______________________________________________________________________________________ I. Introduction OCR is software that recognizes characters by exploiting its structural/visual characteristics on the basis of script and represents the same in readable character format. The development of an efficient OCR is an interesting and challenging research area in the field of Pattern Recognition and Image processing for its usefulness since 1950. During 1960s and 1970s numerous OCRs are developed and sprang up in retail businesses, banks, hospitals, post offices, insurance, railways and aircraft companies, news paper publishers and many other industries to meet needs of different regional linguistic individuals. In a multi-lingual country like India there are numerous instances to design documents containing printed English characters as well as regional languages of many different states. In a multi-lingual country like India the existence of documents containing two different scripts are very popular and highly used. Some of the application requirements are creation of language learning books in digital libraries, processing of invoices, applications, forms, bank cheques, sorting of mails and magazines etc which are related to any Govt/Private organizations. The above factors implies that there is an increasing demand for recognition of bilingual documents through an efficient bilingual OCR Processing of documents plays very significant role in the country. Since eighteen official languages are in use, every government office uses at least two languages, English and the official language of the corresponding state respectively. The official language of the state Karnataka is Kannada. This system can interpret the Kannada and English words in question papers, Newspapers, Magazines, Books, Application forms, Railway Reservation forms, many national organizations such as Banks. However, most of the documents in the government offices of the state Karnataka adapt the languages English and Kannada. The proposed system considerably cuts down the efforts and saves time needed to process document images via Bi-Lingual OCR instead of using two different OCR’s. II. Literature Survey Researches in the area of uni-lingual optical character recognition system are considerably wide and almost successful. Some of the experimentations in the area of script recognition and optical character recognition systems are has reviewed below. Sanghamitra Mohanty et. al[1] has proposed an approach for the processing of printed documents containing both English and Oriya texts. The method works by taking into consideration the paragraph wise or line wise features of text. Sanghamitra Mohanty et. al [2] has propped an approach of distinguishing script for bilingual OCR for Oriya and Roman by employing horizontal projection profile features. Even though the method can efficiently distinguishes two types of scripts, it still requires the use of different OCR’s for each language to process the data. Rahiman M.A. et. al [3] had presented a bilingual OCR system for printed Malayalam and English text using

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bitmap obtained through segmentation of document line wise and character wise. The comparison is done using pixel-match algorithm. The matched character is displayed in the notepad. An efficiency of 87.25% is obtained using this approach. Still the method requires many performance issues to be experimented further. D. Dhanya et al. [4] had devised a optimal feature extraction techniques for distinguishing Tamil and Roman by incorporating techniques like structural and geometrical features, DCT based features, wavelet transform based features etc. S. Basava Raju Patil [5] had implemented Neural Network based Bilingual OCR system which can read printed document images, written in two scripts of English and Kannada languages. Dynamic feature extractor extracts distinctive equal number of features from each separated word irrespective of size of the word. These features are accepted by probabilistic neural classifier and are sorted by script, Kannada and Roman. All the above approaches discussed has been contributing towards script recognition and the experimentation by [5] based on neural networks proves to be effective in processing bilingual documents with bilingual OCR, but still the approach encourage the need of script recognition. The proposed system focuses on efficient feature extraction technique that can distinguish each and every component of image and processes the bilingual documents through bilingual OCR. III. Proposed Methodology The proposed bilingual OCR system for English and Kannada is composed of six phases like Pre Processing, Segmentation, Feature extraction, Classification, Recognition and Post-processing. Any OCR system begins by reading a scanned document image as input and produces an output which is in editable document format. The architecture of the bilingual OCR is depicted in the Figure 1.

Figure 1: Architecture of Bi-Lingual OCR In general any bi-lingual OCR systems are incorporated with the script recognizer in order to distinguish the types of scripts and then script wise processing will be performed with separate OCRâ&#x20AC;&#x2122;s of that particular script. In the proposed system the computational complexities involved in script identification is eliminated and an effective feature extraction, classification and post-processing is performed by employing techniques like principal component analysis and template matching techniques. The document processing initiates with preprocessing, since the performance of any recognition system (OCR systems) depends on the detailed analysis of pre-processing and segmentation operations. Bi-lingual OCR system acquires a scanned image as an input of any legal image formats such as JPEG, BMP, PIX etc. A. Pre-processing Data pre-processing describes any type of processing performed on raw data to prepare it for another processing procedure. Hence, pre-processing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Pre-processing activity involves representation, noise reduction, binarization, skew estimation/detection, zoning, character segmentation. Therefore, the main task in pre-processing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, Pre-processing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, pre-processing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages. The stages in a pattern recognition system are in a pipeline fashion meaning that each stage depends on the success of the previous stage in order to produce optimal/valid results.

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B. Segmentation The segmentation is considered to be one of the very important as well as crucial phases of any optical character recognition system. In particular for South Indian language like Kannada the segmentation is a non trivial aspect. The language like Kannada complicates the process of segmentation due to its typical structure and its consonant as well as vowel modifier group may gives rise to widest collection of compound or connected characters. In the proposed methodology a hybrid approach is devised by incorporating traditional constructs of segmentation like projection profiles, XY-cut analysis and connected component analysis. Segmentation in the proposed system is comprised of two phases. In the first stage, mathematical morphology technique is used for constructing bridge between the components. The morphological operations avoid the intrusions that ensue during the recognition of a character. The second stage is the core process of the segmentation stage. The hybrid approach designed can handle the isolated text, connected components, overlapping lines/characters, broken characters and touching characters. Initially the pre-processed image from phase one of segmentation is subject to the line segmentation process using connected component analysis in line wise. The result of segmentation from original image is as represented in figure 2a and figure 2b.

Figure 2a: Binarized Image

Figure 2b: Line Images Extracted

The figure 2a depicts the original image and figure 2b indicates the lines extracted from the binarized image. The segmentation algorithm automatically extracts all the line segments and stores each line as separate image. The line images extracted are normalized to a fixed size using interpolation techniques. Then the vertical projection profile of each line image is analyzed to perform the word segmentation. The proposed system is also able to deal with touching characters upto some extent. The document considered for touching character segmentation is shown in figure 3a figure 3b and figure 3c.

Figure 3a: Original Image with few touching components

Figure 3b: A Binarized Image corresponding to Figure 3a

Figure 3c: Lines Extracted from figure 3b C. Character Segmentation The character segmentation is concerned with extraction of individual character components from segmented word images. The extracted word is divided into two zones as upper zone and bottom zone as depicted in figure 4a.

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Figure 4a: The upper zone and bottom zone of a segmented word block In the proposed methodology the segmented words are subject to connected component analysis [10] initially. The bounding boxes are used to enclose the connected characters with respect to the assumed width of various characters. The width of characters is defined from a knowledge base. The bounding boxes are assigned separately for upper zone and bottom zone. First the characters which come in the upper zone are assigned with bounding boxes and the maximum character width in the upper zone is 13, which is equal to number of columns in the character segment considered. The bounding box of length greater than 13 is considered to be the touching characters and which will be handled through water reservoir principle [14]. The maximum character width in the bottom zone is fixed to 7, which is inferred from knowledge base. Since, the proposed system is concentrating on the printed characters; the width of printed characters determined from knowledge base tends to be accurate and reliable. The printed English text blocks extracted will also be segmented in the same procedure as the Kannada character segmentation is carried out, but the bottom zone of English word block in more than 98% of cases does not contain any connected components, since it is the printed text considered here. In a similar fashion rest of the segmented word blocks will be processed iteratively and vice versa. The results obtained through segmentation algorithm are presented in figure 4b.

Figure 4b: Few Segmented character Images corresponding to line 1 and line 2 D. Feature Extraction Feature extraction is an integral part of any recognition system. The aim of feature extraction is to describe the pattern by means of a minimum number of features or attributes that are effective in discriminating among pattern classes. The accuracy of feature extraction is depending upon the way we segment the characters in the document. In the proposed system, Principal components analysis (PCA) [15] features are extracted from each segmented character block to uniquely identify the characters in both English and Kannada. PCA is a linear transformation that chooses a new coordinate system for the data set such that the greatest variance by any projection of the data set comes to lie on the first axis (then called the first principal component), the second greatest variance on the second axis, and so on. PCA can be used for reducing dimensionality in a dataset while retaining those characteristics of the dataset that contribute most to its variance by eliminating the later principal components (by a more or less heuristic decision). These characteristics may be the "most important", but this is not necessarily the case, depending on the application. A covariance matrix for the matrix M is defined as (x - x')*(y - y'), and can be represented formally as C = M*MT. The covariance matrix C is then used to compute the matrix eigenvectors. Assuming zero empirical mean (the empirical mean of the distribution has been subtracted from the data set), the principal component wi of a dataset x can be calculate by finding the eigen values and eigenvectors of the covariance matrix of x, we find that the eigenvectors with the largest eigen values correspond to the dimensions that have the strongest correlation in the dataset. The original measurements are finally projected onto the reduced vector space. The variance features of PCA makes it possible to deal with both the type of scripts to identify the various alphabetic sets uniquely. Thus PCA is good alternative for differentiating the two types of scripts in the proposed system. E. Classification and Post-Processing The classification stage is the decision making stage of the recognition system. The extracted features are given as the input to the classification process. In the proposed system the PCA features of 2346 character samples are trained including with both English and Kannada font styles and font sizes. The covariance features of extracted

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in feature extraction process are compared with the trained set of features in 2346 classes defined. Once a class matched with test character features, It is immediately post processed. In post processing the Unicode of the corresponding character class matched will printed onto the Microsoft word processor as represented in figure 5e and figure 5f. IV. Experimental Results and Discussion An user friendly graphical user interface has been designed using MATLAB and made accessible to all types of users in a simple and comprehensive way. We have conducted three different experiments. The first one is to test only the printed English document images. The second one is to test only the printed Kannada document images. Finally, a bilingual printed document image containing both English and Kannada are subject to experimentation process. The graphical user interface and the results of experimentations are as represented in figure 5a, figure 5b, figure 5c, figure 5d, figure 5e and figure 5f as follows.

Figure 5a: The GUI loaded with English document image

Figure 5b: The editable document output of fig. 5a

Figure 5c: The Kannada Document image loaded

Figure 5d: The editable document output of fig. 5c

Figure 5e: The Bilingual document input

Figure 5f: The editable document output of fig. 5e

The figures indicate that the results obtained are quite good and encouraging. The Table 1 shows recognition accuracy for independent input of printed English and Kannada documents and for the mixed printed Kannada and English documents respectively.

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The data set of more than 2000 character samples images are collected and trained to our database using template matching method with respect to principal component analysis features. The recognition accuracy of 95.25% - 97.05% was achieved and the results appeared to be encouraging in most of the cases with respect to both printed bilingual document images of English and Kannada respectively. The Table 1 shows recognition accuracy for independent input of printed English and Kannada documents and for the mixed printed Kannada and English documents respectively. Document Type English only Kannada only English and Kannada

Recognition Accuracy 100% 98% 95-97%

V. Conclusion and Future Scope By employing the concepts of image processing and MAT LAB it’s possible to design a system which could identify the different scripts used in a document which contain different scripts. In general, when a Bilingual script document is to be processed, the respective language OCR’s are to be used. But the proposed system effectively eliminates the need of using the script recognizer and produces reliable results for certain types of font styles and sizes. The output in post processing of our proposed system is displayed in Microsoft word processor, which quite interesting and challenging to test all the unicodes of more than 2000 classes including with English and Kannada. Along with the reasonable set of advantages, even there are some limitations that are associated with our proposed system. The first limitation is the complete system works for only certain font sizes and font styles that we have trained to the system, however the future work focuses on extending the same to font style/ size independent bilingual OCR system. The second limitation to be focused in future is errors of the segmentation process in case of many number of touching components, if document contains only touching characters the proposed system fails to work. VI. [1]

[2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

[12] [13]

[14] [15]

References

Sanghamitra Mohanty, Himadri Nandini Dasbebartta, Tarun Kumar Behera, "An Efficient Bilingual Optical Character Recognition (English-Oriya) System for Printed Documents," icapr, pp.398-401, 2009 Seventh International Conference on Advances in Pattern Recognition, 2009. Sanghamitra Mohanty, Himadri Nandini Das Bebartta, “A Novel Approach for Bilingual (English - Oriya) Script Identification and Recognition in a Printed Document” International Journal of Image Processing (IJIP), Volume (4): Issue 2. Rahiman, M.A, Adheena, C.V., Anitha, R., Deepa, N., “Bilingual OCR system for printed documents in Malayalam and English”, Electronics Computer Technology (ICECT), 2011 3rd International Conference Volume 3. D. Dhanya, A. G. Ramakrishnan, “An Optimal feature extractor for bilingual OCR”, department of Electrical engineering, Indian Institute of Science, Springer, pp 25-36, 2002. S. Basava Raju Patil, “Neural Network based Bilingual OCR System: Experiment with English and Kannada Bilingual Documents”, International Journal of Computer Applications (0975 – 8887) Volume 13– No.8, January 2011. U. Pal, B. B. Choudhuri, “Script line separation from Indian multi-Script documents,” Proc. of fifth Intl. Conf. on Document Analysis and Recognition (IEEE computer society press), pp. 406-409, 1999. Santanu Choudhury, Gaurav Harit, Shekar Madnani, R.B. Shet, “Identification of Scripts of Indian Languages by Combining Trainable Classifiers” ICVGIP, Bangalore, India, Dec.20-22, 2000. S. Chanda, U. Pal, “English, Devanagari and Urdu Text Identification,”Proc. Intl. Conf. on Document Analysis and Recognition, pp. 538-545. B.V. Dhandral, Mallikarjun Hangarge', Ravindra Hegadil and V.S. Malemathl IEEE “Word Level Script Identification in Bilingual Documents through Discriminating Features”, ICSCN 2007 Chennai,India, pp.630-635, Feb. 2007. P. A. Vijaya, M. C. Padma, “Text line identification from a multilingual document,” Proc. of Intl.Conf. on digital image processing (ICDIP2009) Bangkok, pp. 302-305, March 2009. B.V.Dhandra, H.Mallikarjun, Ravindra Hegadi V.S Malemath. “Word-wise Script Identification from Bilingual Documents Based on Morphological Reconstruction” PG department of study and research in computer science Gulbarg University, Gulbarg, INDIA. Prakash K. Aithal, Rajesh G., Dinesh U. Acharya, Krishnamoorthi M. Subbareddy N. V. “Text Line Script Identification for Trilingual Document” Manipal Institute of Technology Manipal, Karnataka,INDIA. [13] M.C. Padma and P. A. Vijaya, “Identification and separation of Text words of Kannada, Telugu, Tamil, Hindi and English languages through visual discriminating features,” Proc. of Intl. conf. on Advances in Computer Vision and Information Technology(ACVIT-2007),Aurangabad, India, pp. U. Pal1, A. Belaïd, C. Choisy, “Water Reservoir Based Approach for Touching Numeral Segmentation”, Indian Statistical Institute, 203 B. T. Road, Calcutta-35, India. Mirosław Miciak, “Character Recognition Using Radon Transformation and Principal Component Analysis in Postal Applications”, Proceedings of the International Multi conference on Computer Science and Information Technology, pp. 495 – 500 ISBN 978-83-60810-14-9, ISSN 1896-7094. D. Kornack and P. Rakic, “Cell Proliferation without Neurogenesis in Adult Primate Neocortex,” Science, vol. 294, Dec. 2001, pp. 2127-2130, doi:10.1126/science.1065467.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A Survey on String Similarity Matching Search Techniques S.Balan1, Dr. P.Ponmuthuramalingam2 Ph.D. Research Scholar, 2Associate Professor & Head, Department of Computer Science, Government Arts College (Autonomous), Coimbatore, Tamilnadu, INDIA. 1

Abstract: String similarity matching search Problem is mainly used to find text which is present in the documents. In thousands of years many features are available in the modern world but yet people not realized to find the information correctly. Because of huge amount of information’s stored in the World Wide Web. The field of information retrieval was born in the year 1950 and H.P. Luhun in the year of 1957 find the basic idea of searching text with computer. The problem of string matching is to find errors .for example in online searching, user faces different problems and irrelevant information’s. The goal of this survey is to present overview of string similarity matching and comparison of different algorithms to conclude the better performance on searching the text. There are many areas where this problem appears and one of the most demanding is information retrieval to find relevant information in text collection and the important tool is named as string matching. Keywords: Information retrieval, String Matching, Similarity Search, Approximate String Match I. Introduction In recent years the problem is growing communities of information retrieval and computational biology. The field of information retrieval problem can be addressed into different views. A string is a sequence of characters over a finite set of alphabet. Similarity search provides a list of input data similar to an input query. In the context of search engines such as Google or yahoo search is based on document similarity and query similarity. Document similarity is nothing but overall similarity of an entire document to the given query. Query similarity suggests many query strings while searching is based on machine learning. [Thomas Bocek, et al., 1997]. At first 1992, text retrieval conference or TREC [Harman 1993] sponsored by US government which aims to encouraging research in information retrieval from large text collections. In that many old techniques are modified and many new techniques are identified to retrieve over large number of text collections. The first algorithms developed in information retrieval for searching the World Wide Web during the year 1996 to 1998. Early there are various models and implementations are available for information retrieval system. Boolean system is used to specify the user information based on combination of And, Or, Not’s. Using this system they are not overcome to produce the relevant information. Several models are proposed for these process in that three most models are vector space model, the probabilistic models, and inference network model [Amit Singhal 2001]. Vector space model is represented by a vector of terms [Gerard Salton, 1975]. Terms are typically words or phrases. Any text can be represented by a vector in high dimensional space. Text belongs to non-zero value. Most vector term processed in a positive value to assign a numeric score to a document for a query. In the year of 1960 maron and kuhun proposed many Probabilistic model and it is based on the general principle that document in a collection should be ranked by decreasing probability of their relevance to a query [Amit Singhal 2001]. Estimation is the key part of this model. Inference network model is a document retrieval model as an inference process in an inference network. [Van Rijsbergen1979] Most techniques implemented under this model. Similarity search is important for timesensitive applications. The increasing amounts of electronic information available on the web in order to improve data quality or find all information based on the user request. To provide a similarity search in the dictionary size may be too slow for many applications. There are various existing methods are available for fast similarity search for example English dictionary and a randomly generated dictionary and compared search performance for dynamic programming, a keyword tree, neighborhood generation and n-grams with index lookup extraction [Amit Chandel, 2006]. The extraction of structured and unstructured text is a challenging problem in many applications such as data warehousing, web data integration and bio-informatics. For example, to identify book author from html pages, match of text string with book author is displayed and found the accuracy of the string extraction [Amit Chandel, 2006]. This paper categorized into four sections. Section-1 contain the introduction to information retrieval and string similarity search, Section-2 contain the literature survey, Section-3 contain Analysis of string similarity search Section-4 includes conclusion while references mentioned in the last section. II. Literature Survey It is defined as a finite state pattern matching machine from the keywords to process the text string in a single pass. To improve the speed of a library bibliographic search program by factor of 5 to 10. The main purpose of

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this technique is to allow a bibliographer to find in a citation index all titles and satisfying some Boolean function of keywords and phrases. If m is a program which takes as input the text string s and produces as output the locations in p at which keywords y appear as substrings. It consists of a set of states and it is represented by a number. The behavior of the pattern matching machine is carried out by three functions named as go to function go, a failure function fa and an output function out [Alfred V. Aho, et al.,1975]. Edit distance [Levenstein V.I, 1966] is the minimum number of operations required to transform one string into another with operations being a deletion, an insertion or a replacement. Navarroâ&#x20AC;&#x2122;s NR-grep [Navarro.G, 2000] is an exhaustive online similarity search algorithm. NR stands for non-deterministic reverse pattern matching. It uses bit-parallelism and forward and backward searching. An n-gram is created by sliding a window of length g over the data and noting the content and position of all such windows. An extension of this approach for large text collections uses cosine similarity [Koudas, et al., 2004], t is a global measure to represent a vector of their frequencies. Approximate similarity search based on hashing is to hash the points from the database from the probability of higher objects that close to another. It is based on hierarchical tree decomposition for large number of dimensions. There are various algorithms such as locality-sensitive hashing, analysis of locality-sensitive hashing and nearest neighbor search. Approximate string matching is about finding a pattern in a text where one or both of them have suffered some kind of undesirable corruption. The classification and the existing schemes in context of data structure are suffix tree, suffix array, Q-grams, Q-samples. Search approach method is classified into two ways namely partitioning into exact searching and intermediate partitioning based on text and patterns [Kaushik Chakrabartie, et al., 2000]. The existing algorithms are hamming distance, reversals, block distance, Q-gram distance, allowing swaps, approximate searching in multidimensional texts, in graphs, multi pattern approximate matching , non standard algorithms such as approximate or parallel algorithms, indexed searching, these are the other surveys on string similarity matching. There are various string matching types namely multiple string match, extended string matching, regular expression matching and approximate matching. The approximate matching contains various algorithms to find the similarity of given string such as dynamic programming algorithms, computing edit distance, text searching, improving the average case, other algorithm based on dynamic programming, algorithms based on automata, bit-parallel algorithms, parallelizing the NFA, parallelizing the DP matrix, algorithm for fast filtering the text, partitioning into k + 1 pieces, approximate BNDM, other filtration algorithms, multi pattern approximate searching, a hashing based algorithm for one error, searching for extended strings and regular expressions. III. Analysis of String Similarity Matching Techniques Sno 1

Author Name Alfred V. Aho and Margaret J. Corasick

Title Efficient String Matching An Aid to Bibliographic Search

Methods Pattern matching algorithm

Advantages Locates keyword in a text string

Construction of go to, output and failure functions

Directed graph begins at the state 0

Time complexity of algorithms 2

Arvind Arasu, Venkatesh Ganti, et al.;

Efficient Exact-Set Similarity Joins

Threshold SSJoin

based

Time complexity is large Threshold parameter is high

Failure function stored in one dimensional array Different similarity sets

Vector representation between two sets

Jaccard SSJoin

Thomas Bocek, Burkhard Stiller, et al.,

Partially computed output function

Dimension is differ Hamming SSJoin

Dis Advantages Substrings may overlap with one another

Fast Similarity Search in Large Dictionaries

Common elements

NR|-Grep

Similarity value is 0 or 1. Minimum operations required from one string to one string to another

N-grams and Cosine Similarity

Reverse matching

Pruning condition

Offline approach Three similarity measures are identified

Edit distance

pattern

Dictionary size is low Avoids number of searching words in NRgrep method Similarity is shared

Kaushik Chakrabarti, Dong Xin, et al.,

An Efficient Filter for Approximate Membership Checking

Filtering by ISH Weighted signatures

Sub string search is quick Weighted signature is in decreasing order

Lower bound value is not identified String similarity is less Different signatures

number

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Amit Chandel, P.C.Nagesh, et al.,

Efficient Batch Top-k for Dictionary-based Entity Recognition

Batch Top-K

Aristides Gionis, Piotr Indyk, et al.,

Increasing run time for threshold values

Decreasing IDF Values

Upper bound scoreless is removed

Simple Top-K Segmented Algorithm

Finding the most top-k score

Similarity Search in High Dimensions via Hashing

A token of a the sub query is strong or weak

Locality Sensitive Hashing

Better run time

Color Histograms

Dependence size

data

Existing tight features is not unique Value is small and there is resort needed One index sufficient

not

Texture Features

Daniel Karch,Dennis Luxen,etal.,

Improved Fast Similarity Search in Dictionaries

Preprocessing Space Preprocessing Time

Amit Singhal

Modern Retrieval: Overview

Query Performance Vector Space Model

Information A Brief

To measure the performance String Split Parameter based on query time

Compare with SR-tree is low Speed is low

Ten Times Faster

Does not Store information’s

Maximum calculated

any

Distance

Calculate using Term Weighting

the

Probabilistic Model

Relevance feedback based on user queries

Inference Network Model

Retrieval effectiveness

Query time and search space size is average. Boolean systems are less effective Poor stemming Style of phrase generation is not critical

IV. Conclusion In this paper, survey focus on various algorithms for string similarity matching based on search techniques. Some of the algorithm for set similarity with its property value is 0 or 1. It indicates the previous algorithms matches more than in many cases. The performance of the algorithm is analyzed and stated in a table manner. Additionally it focuses on information retrieval and search engine in World Wide Web. To improve the quality of a word search similarity, next the exact similarity is finer based on semantic relationship of a word. This further reduces the time size for a large database. V. [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14].

References

Alfred V. Aho and Margaret J. Corasick Bell Laboratories, Efficient String Matching An Aid to Bibliographic Search, communications of the ACM, Vol. 18 No.6, June 1975. Amit Chandel, P.C.Nagesh, Suita Sarawagi, Efficient Batch Top-k for Dictionary-based Entity Recognition, Proc. 22nd International Conference Data Engineering., pp.28, 2006. Amit Singhal, Modern Information Retrieval: A Brief Overview, IEEE Computer Society Technical Committee on Data Engineering, pp 1-9, 2001. Aristides Gionis, Piotr Indyk, Rajeev Motwani, Similarity Search in High Dimensions via Hashing, Proceedings of the 25th VLDB Conference,Edinburgh, Scotland, pp 518, 1999. Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, Efficient Exact-Set Similarity Joins, VLDB ’06, September 12-15, 2006, Seoul, Korea,VLDB Endowment, ACM 1-59593-385-9/06/09. Daniel Karch,Dennis Luxen, Peter Sanders, Improved Fast Similarity Search in Dictionaries, presented at the 17th Symposium on String Processing and Information Retrieval, 2010. Gerard Salton, A.Wong, and C. S. Yang. A vector space model for information retrieval. Communications of the ACM, 18(11):613–620, November 1975. Harman D.K, Overview of the first Text Retrieval Conference (TREC-1). In Proceedings of the First Text REtrieval Conference (TREC-1), pages 1–20. NIST Special Publication 500-207, March 1993. Kaushik Chakrabarti, Dong Xin, et al., An Efficient Filter for Approximate Membership Checking, SIGMOD’08, June 9–12, 2008, Vancouver, BC, Canada, 2008 ACM 9781605581026/08/06. Koudas D.S.N, A. Marathe. Flexible String Matching Against Large Databases in Practice. In VLDB, pages 1078–1086, 2004. Levenstein V.I, Binary codes capable of correcting insertions and reversals. Sov. Phys. Dokl., 10:707–101966. Navarro.G, NR-grep: A Fast and Flexible Pattern Matching Tool, Technical Report TR/DCC-2000-3 Technical report, University of Chile, Departmento de Ciencias de la Computacion, Santiago, 2000, http://www.dcc.uchile.cl/gnavarro. Thomas Bocek, Burkhard Stiller, et al., Fast Similarity Search in Large Dictionaries, University of Zurich, Department of Informatics (IFI), Binzmühl estrasse 14, CH-8050 Zürich, Switzerland, 2007. Van Rijsbergen C.J, Information Retrieval. Butter worths, London, 1979.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

(An Association Unifying the Sciences, Engineering, and Applied Research)

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net INDEPENDENT GATE FINFET SRAM CELL USING LEAKAGE REDUCTION TECHNIQUES Anshul Jain1, Dr. Minal Saxena2 and Virendra Singh2 Research Scholar of Sagar Institute of Research & Technology, Bhopal, Madhya Pradesh, India 2 Professor, Dept. of Electronics and Communication, Sagar Institute of Research & Technology, Bhopal, Madhya Pradesh, India. __________________________________________________________________________________________ Abstract: Scaling of devices in bulk CMOS technology contributes to short channel effects and increase in leakage. Static random access memory (SRAM) is needed to occupy 90% of the area of SoC. Since leakage becomes the important factor in SRAM cell, it is implemented using FinFET. FinFET devices became better alternative for deep submicron technologies. In this paper, 6T SRAM cell is implemented using independent gate FinFET in which both the opposite side of gates are operated independently which provides better scalability to the SRAM cell. The device is implemented using different leakage reduction techniques such as Multi threshold voltage, and Gated-VDD technique to reduce leakage current, power consumption in the SRAM cell and provides better performance. The Proposed FinFET based 6T SRAM cell has been designed using Cadence Virtuoso Tool, all the simulation results has been generated by Cadence SPECTRE simulator at 45nm Technology. Keywords: SRAM, FinFET, Leakage Current, Leakage Power, Independent Gate ______________________________________________________________________________________ 1

I. INTRODUCTION CMOS scaling has lead to advance in performance of digital circuits however faces important challenges due to process technology limits. Sub-threshold leakage, short channel effects, gate dielectric leakage and device-todevice changes are the challenges lead in additional leakage current [1]. Scaling to nanometer regime develops a major short channel effect which develops from several geometrical effects in which the channel length becomes equal to the depletion layer [2]. Drain Induced Barrier Lowering (DIBL) is the major effect produced by SCE, in which high electric fields from the drain can lower that barrier that is supposedly only controlled by the gate. As technology scales down, while dealing with short-channels (SCEs), not only very ultra-thin to keep the current drive is required but also very low VTH is required to maintain the device speed and VTH variations under control [3] as this effect can degrade the devices sub-threshold slope and cause changes in the threshold Voltage (VTH). Therefore, multi-gate FETs such as planar double gate FETs and FinFETs have been proposed for low power digital CMOS technologies to reduce SCE. FinFET is a double gate device in which second gate is aligned vertically opposite to the first gate [4]. FinFET can be designed as tied gate and independent gate FinFET. In the tied gate type, both the opposite gates are tied together giving short channel effect immunity and in the independent gate, one gate is used to switch ON/OFF and threshold voltage of the FinFET is adjusted by the other gate gives better VTH control [5]. Use of independent gate FinFET reduces leakage and hence reduces power consumption to improve performance. Many circuit and architectural level techniques have been introduced to reduce leakage. In this paper, some circuit level techniques are introduced instead of architectural level techniques, because architectural level techniques degrade the performance with the reduction of power. There are different types of leakage reduction techniques. One is multi threshold leakage reduction technique [6] which uses high threshold PMOS and NMOS acting as a switch to disconnect power supply during standby mode thereby reducing leakage. This technique provides increased operating speed by low threshold MOSFET and reduced leakage by high threshold voltage. This technique has the disadvantage of increased overall circuit area and introduces extra parasitic capacitance and delay during MOSFET fabrication. Other technique is gated VDD [7] in which a NMOS transistor with gated voltage supply is connected to the SRAM cell. This technique maintains the lower supply and threshold voltages although reducing leakage and leakage power dissipation. Stacking effect is produced by additional transistor in combination with the SRAM cell transistors when the gated-VDD transistor is turned OFF [8]. These techniques have also been used to reduce leakage in independent gate mode of FinFET based 6T SRAM cell. II. FINFET BASED SRAM CELL The FinFET SRAM cell structure is better choice due to the self-alignment of opposite side of gates and the fabrication compatibility with the existing standard CMOS fabrication technology. The supply voltage (V DD), threshold voltage (VTH) and Fin height can be used for reducing leakage in SRAM cell by increasing Fin-height which allows reduction in VDD but reduction in VDD leaves strong impact on the stability of the SRAM cell under the parametric variations [9]. FinFET provides effective control of the short-channel effects without

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vigorously scaling down the gate-oxide thickness and increasing the channel doping density [10-11]. The FinFET SRAM cell works in different four modes: shorted or tied gate (SG) mode, independent gate (IG) mode, low power (LP) mode and IG/LP mode. In the independent gate mode, both the gates are controlled independently for low power consumption. Independent Gate FinFET SRAM Cell Opposite side of gates are restrained independent to each other in the independent gate FinFET. In this device, multi-threshold voltages are provided by independent gate biasing which can be exploited to reduce number of transistors. Independent gate SRAM cell is designed which reduces leakage current and increase data stability hence performance of the cell. Fins have been provided with minimum width. In cross coupled inverters, both the opposite side gates in pull up transistors are controlled independently to provide multi-threshold voltages and opposite gates in pull down transistors are tied to each other. Direct-data-access mechanism causes interference in read cycle [12] which can be minimized within the latch without increasing the transistor size. Therefore independent gate FinFET gives better stability in SRAM cell. WL

VDD

BLB

VD PM1

PM2

NM4

NM3 Q NM2

NM1

GND

Figure 1: Schematic design of SRAM cell using independent gate FinFET During the word line (WL) is kept at low, the access transistors cut off to make the SRAM in standby mode. The stored bit is sustained by the latch of the SRAM cell. For a read operation, WL becomes high after the bit lines (BL and BLB) are pre-charged to VDD and VSS. Node Q stores “0”, BL is discharged through NM3 and NM1. Alternatively, when node QB stores “0”, BLB is discharged through NM4 and NM2. The access transistors NM3 and NM4 act as high-VTH devices with weaker current conducting capability as compared to tied-gate FinFET SRAM so that the current produced by access transistors is reduced. During a write operation, the WL is high. The access transistors NM3 and NM4 act as weak high-threshold-voltage devices. To write “0” at node Q, BL is discharged and BLB is charged so that transistor NM3 conducts and “0” is passed to IG-FinFET SRAM cell through NM3. Alternatively, to write “0” at node QB, BL is charged and BLB is discharged so that transistor NM4 conducts and “0” is passed to IG FinFET SRAM cell through NM4 as shown in Figure 1. III. LEAKAGE REDUCTION TECHNIQUES (A) Multi Threshold Voltage Leakage Reduction Technique In multi threshold voltage (MVT) technique [13], a high threshold sleep control is connected in series with low VTH circuit. In active mode, sleep transistor must be ON to provide the standard circuit functionality of SRAM cell. In standby mode, sleep transistor must be OFF to provide the improved leakage control. Sleep transistor of high threshold must be used otherwise leakage current will increase making this technique less effective so that low threshold FinFET transistors are used in the SRAM cell logic and high threshold transistor as sleepy transistor. High VTH transistors are used for low sub-threshold current and low VTH transistors are used to improve the performance of the cell. These two different types of threshold can be developed by changing the channel length. Figure 2 and 3 shows the MVT technique using PMOS and NMOS respectively. VDD Sleep

High VTH

Virtual VDD

Low VTH Logic

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In Figure 2, PMOS high threshold voltage transistor is connected above the low threshold voltage device providing the virtual VDD, whereas in Figure 3, NMOS high threshold voltage transistor is connected below the low threshold voltage device to provide virtual ground. VDD

Low VTH Logic

Virtual ground High VTH

Sleep

Figure 3: NMOS HVT (B) Gated VDD Technique Leakage associated with SRAM cell creates major problem in chip designing because of large area. The leakage in cell can be reduced using gated VDD technique by contributing an extra transistor producing stacking effect. This extra NMOS transistor produces greater impact on leakage current in conjunction with SRAM cell transistors. WL

VDD

PM1

BLB

PM2 QB

NM4

NM3 NM2

NM1

Gated VDD control GND

Figure 4: Conventional 6T SRAM cell using gated VDD technique It happens because gated transistor becomes ON in used portions and becomes OFF during unused portions in SRAM cell. Here NMOS transistor is connected between ground and source region of NMOS transistors of the cell. Similarly, PMOS transistor can also be connected between the VDD and source region of PMOS transistors in SRAM cell. Gated VDD transistor becomes ON during active mode and switches to OFF state during standby mode. Leakage current due to this technique can be reduced by stacking effect produced which arises because of the three transistors present between the ground terminal and the bit lines (BL and BLB). Transistor of adequate width must be used to provide isolation from leakage current during read cycle. During PMOS gated VDD, insignificant area overhead and transistor width reduces because of no concern during read operation. So that NMOS gated VDD provide better control over the leakage than PMOS gated VDD. The gated VDD technique is shown in Figure 4. IV. PERFORMANCE ANALYSIS OF DOUBLE GATE FINFET SRAM CELL USING MVT AND GATED VDD TECHNIQUES The 6T FinFET SRAM cell using independent gate mode is simulated using Cadence virtuoso tool at 27o C in 45 nm technology. In the SRAM cell, the output Q depends on bit line (BL) and QB depends on bit line bar (BLB) when write line (WL) is kept high. Leakage current and leakage power in independent gate FinFET SRAM cell are shown in Figure 5 and 6 respectively in which transient analysis between 0 ns and 100 ns has been done and leakage current is calculated approximately equal to 120.3 pA and leakage power becomes 21.46 nW. The leakage current and leakage power in Independent gate FinFET is reduced using multi threshold voltages. Figure 7 and 8 shows leakage current and leakage power during standby mode in IG FinFET SRAM cell using PMOS HVT technique. According to this waveform, when elevated threshold PMOS transistor is connected serially between VDD and low threshold SRAM cell logic circuit, sub-threshold leakage and leakage power reduces allowing the low threshold circuit to amplify the performance of the device.

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Figure 5: Leakage current in independent gate FinFFET

Figure 6 Leakage current in independent gate FinFET Transient response of IG FinFET SRAM cell is observed using MVT technique and leakage current and power have been obtained which are predicted equal to 51.48 pA and 8.103 nW respectively.

Figure 7: Leakage current in IG FinFET SRAM using PMOS HVT Figure 9 and 10 shows leakage current and leakage power waveform of independent gate FinFET SRAM cell when sleep transistor is kept OFF throughout hold state using NMOS HVT transistor. When high threshold NMOS transistor is connected between ground and low threshold SRAM cell logic leakage current and leakage power reduces and thereby reducing power consumption and increased performance. Leakage current and leakage power have been observed approximately equal to 51.92 pA and 20.59 nW respectively. Simulated independent gate FinFET SRAM cell is connected with gated VDD transistor generating stacking effect. NMOS gated VDD transistor is used because it provides better insulation from leakage current and leakage power than PMOS gated VDD transistor. The extra gated VDD transistor becomes OFF during unused portions of the SRAM cell by providing virtual ground. The leakage current in gated VDD circuit is 58.09 pA moreover leakage power becomes 20.65 nW reducing the total power consumption as shown in Figure 11 and 12.

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Figure 8: Leakage power in IG FinFET SRAM cell using PMOS HVT

Figure 9: Leakage current in IG FinFET SRAM cell using NMOS HVT

Figure 10: Leakage power in IG FinFET SRAM cell using NMOS HVT

Figure 11: Leakage current in IG FinFET using gated VDD

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Figure 12: Leakage power in IG FinFET using gated VDD SRAM Cells Independent Gate PMOS HVT IG with HVT NMOS HVT IG with Gated VDD

Leakage Current (pA) 120.3 51.48

Leakage Power (nW) 21.46 8.103

51.92 58.09

20.59 20.65

Table 1: Leakage Current and leakage power in Independent gate FinFET SRAM Cell using MVT and Gated VDD V. CONCLUSION Since SRAM cell consumes larger cell area in embedded system designs it should have less leakage current and consume less power to offer better performance. Acording to the simulated results it is concluded that leakage current and leakage power have reduced to about 94 % using independent gate FinFET in comparison with conventional MOSFET. Leakage current in IG FinFET SRAM cell using NMOS HVT is reduced by approximately 95 %.Leakage current using PMOS HVT is reduced by 94 % and using gated V DD technique, it is reduced by 93 % Similarly leakage power is reduced by approximately 30 % using NMOS HVT and leakage power is reduced by 76 % using PMOS HVT in IG FinFET SRAM cell.Whereas leakage power is reduced by ~20 % - 25% using gated VDD technique in IG FinFET SRAM cell.Therefore it can be said that among these leakage reduction techniques, Multi threshold voltage technique offers better leakage control over the gated VDD technique by providing virtual ground and virtual V DD instead of direct supply voltage. MVT technique provide less power consumption and better performance making it suitable for IC design. REFERENCES [1] [2] [3] [4]

[5]

[6] [7] [8]

[9] [10]

[11]

[12] [13]

“Process Integration, Devices, and Structures,” International Technology Roadmap for Semiconductors, 2011 Edition. Marc Van Rossum, “MOS Device and Interconnects Scaling Physics,” Springer, 2009. David Michael Fried, “The Design, Fabrication and Characterization of Independent-Gate FinFETs,” Cornell University, pp 1184, 2004. Kazuhiro Nakajima, “Interface-State Density of Three Dimensional Silicon Channels Measured by Charge Pumping Method,” Department of Electronics and Applied Physics, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 2012. Paolo Magnone, Felice Crupi, Abdelkarim Mercha, Pietro Andricciola, Hans Tuinhout, and Robert J. P. Lander, “FinFET Mismatch in Sub-threshold Region: Theory and Experiments,” IEEE Transactions On Electron Devices, vol. 57, no. 11, pp. 2848-2856, November 2010. Masoud Rostami and Kartik Mohanram, “Novel Dual-VTH Independent-Gate FinFET Circuits,” IEEE Conference on Design Automation (ASP-DAC), pp. 867-872, 2010. Zhiyu Liu, V. kursun, “High Read Stability and Low Leakage SRAM Cell Based on Data/Bit line Decoupling,” IEEE Proceedings of SOC Conference, pp. 115 – 116, 2006. Rajlaxmi Belavadi, Pramod Kumar. T, Obaleppa. R. Dasar, Narmada. S, Rajani. H. P, “Design and Implementation of Low Leakage Power SRAM System Using Full Stack Asymmetric SRAM,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, issue 7, pp. 3083-3091, July 2013. Changhwan Shin, “Advanced MOSFET Designs and Implications for SRAM Scaling,” University of California, Berkeley, 2011. Zhiyu Liu, S. A. Tawfik, V. kursun, “Statistical Data Stability and Leakage Evaluation of FinFET SRAM Cells with Dynamic Threshold Voltage Tuning under Process Parameter Fluctuations,” IEEE Symposium on Quality electronic design, pp- 305 – 310, 2008. Garima Mittal and Nidhi Bajpayee, “Comparative Analysis of Leakage current and Ground Bounce Noise using MTCMOS combinational circuit,” Journal of Environmental Science, Computer Science and Engineering & Technology, vol.2, no.2, pp. 467-476, 2013. Bipin Gupta, Sangeeta Nakhate, “TRANSISTOR GATING: A Technique for Leakage Power Reduction in CMOS Circuits,” International Journal of Emerging Technology and Advanced Engineering, volume 2, issue 4, pp. 321-326, April 2012. Ehsan Pakbaznia, Massoud Pedram, “Coarse-Grain MTCMOS Sleep Transistor Sizing Using Delay Budgeting,” Proceedings of the conference on Design, automation and test in Europe, pp. 385-390, 2008.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Towards a new ontology matching system through a multi-agent architecture Jihad Chaker, Mohamed Khaldi and Souhaib Aammou LIROSA, Faculty of Sciences, Abdelmalek Essaadi University, B.P.2121, Mhanech II, Tetouan, MOROCCO Abstract: This paper presents a new method of ontology matching to improve semantic interoperability. This method takes as input ontologies described in XML, RDF Schema and OWL format. The proposed matching process involves several stages through the analysis of ontologies entities sources, calculates the terminological similarity with several matchers to maximize the discovery of many similar couples. Once the mapping hypotheses are generated, a filtering system is in place to ensure the quality of alignments. The system architecture is based on a multi agent system, each agent has its own behavior and communicates with the common environment to produce mappings between ontologies source. Keywords: Ontology; Ontology Matching; Semantic Interoperability; Multi-Agent System; Matchers; Mappings I.

Introduction

The notion of ontology is related to the field of philosophy, it comes from the greek word (Ontologia), meaning speaking (logia) about being (onto), Ontology refers to the theory of being as being. In the context of artificial intelligence and more specifically in knowledge engineering, ontology is rich in definitions, the most commonly cited, is that given by GRUBER [1] , an ontology is defined as an explicit specification of a conceptualization. Studer added to the generic definition of Gruber the sharing criterion: Ontologies are a formal, explicit specification of a shared conceptualization [2]. A more recent generic definition is given by ROCHE [3]: « Ontology is a conceptualization of a domain which is associated with one or more vocabularies of terms. The concepts are structured in a system and participate in the meaning of terms. Ontology is defined for a given purpose and expresses a view shared by a community. An ontology is expressed in language (representation) based on a theory (semantics) that guarantees the properties of the ontology in terms of consensus, coherence reuse and sharing». Ontology matching is a solution to the semantic heterogeneity problem. It finds correspondences between semantically related entities of ontologies. These correspondences can be used for various tasks, such as ontology merging, query answering, or data translation. Thus, matching ontologies enables the knowledge and data expressed with respect to the matched ontologies to interoperate [4]. L’objectif majeur est l’établissement de liens de correspondances entre les ontologies originales, précisément entre les concepts from the two ontologies ,the estimated similarity between the two concepts et le type des relations inter-ontologies. Also according EUZENAT [4], The matching process can be seen as a function f which, from a pair of ontologies to match o and o’, an input alignment A, a set of parameters p and a set of oracles and resources r, returns an alignment A’ between these ontologies: . This can be schematically represented as illustrated in Figure 1: Figure 1: The ontology matching process.

Several systems of ontology matching have implemented, we quote: SAMBO [5], Falcon [6], OLA [7], QOM [8], DSsim [9], RiMOM [10], ASMOV [11]. The increasing number of methods available for schema or ontology

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matching mandate consensus for evaluation of these methods. The Ontology Alignment Evaluation Initiative is a coordinated international initiative (OAEI) to forge this consensus. The paper is organized as follows. The second section discusses the agents and multi-agent systems. In the third section, the process of the new method for ontology matching is described. The fourth section shows the implementation of the system based on agents. II.

Agents and multi-agent systems

In the literature we find a multitude of definitions of agents. They all look alike, but they differ depending on the type of application for which the agent is designed. One of the first definitions of agent due to FERBER [12]: An agent is a real or abstract autonomous entity which is able to act on itself and its environment, which, in a multiagent universe, can communicate with other agents, and whose behavior is the result its observations, knowledge and interactions with other agents. RUSSELL added that the agent is an entity that senses its environment and acts upon it [13]. One of the most comprehensive definition of agents, that I particularly favor, is the one given by WOOLDRIDGE and JENNINGS [14].in which an agent is: “ a hardware or (more usually) a software-based computer system that enjoys the following properties: autonomy - agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state; social ability - agents interact with other agents (and possibly humans) via some kind of agent-communication language; reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it; pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behaviour by taking initiative.” The agent is capable of acting on its environment, act and control its own shares without the intervention of a third party (human or agent), take the initiative at the right time, respond in time and interact with other agents to perform tasks or help these agents to do theirs. Depending on the type of agent used in an application, there are systems of cognitive and reactive agents: The first is based on the cooperation of agents able alone to perform complex operations but the reactive agent systems have only a protocol and a very small communication language, respond only to the law "stimulus / response". Multi-agent systems have emerged with the advent of distributed artificial intelligence. Unlike the traditional artificial intelligence, which models the intelligent behavior of a single agent, distributed artificial intelligence is concerned with intelligent behavior are the products of cooperative activity several agents. A Multi-Agent System is a set of agents operating in a common environment, which means the real world or the virtual world. According to FERBER [12] A Multi-Agent System is a system consisting of:  E environment, a space with a generally metric.  A set of objects O. These objects are located, that is to say that, for any object, it is possible, at a given time, to associate a position in E. These objects are passive; they can be perceived, created, destroyed and modified by the agents.  A set A of agents, which are specific objects (A ⊆ O), which represent the active entities of the system.  A set of relations R that unite objects (and thus agents) to each other.  A set of operations Op allowing agents of A to perceive, produce, consume, transform and manipulate objects from O.  Operators responsible for representing the application of these operations and the world's reaction to this attempt to change, that the laws of the universe will be called. III.

Our approach to ontology matching

A. Process of matching Once the extraction of the basic concepts description languages (XML, RDF Schema, OWL) is made, ontologies target entities to make it usable for analysis during the calculation of similarity terminology, we analyze the target ontology entities to make them usable when calculating similarity terminology, it involves standardizing the entities. Preprocessing comments and labels as necessary to support the calculation of similarity, eliminating words that do not carry useful information. The purpose of calculating similarity terminology is to maximize the discovery of many similar couples and reduce the number of those who are dissimilar. Our system uses multiple matchers, including syntactic and lexical matchers. Syntactic matchers calculate the similarity or dis-similarity between two strings using functions and methods of comparison based on a sequence of characters. this system uses the following matchers:  N-GRAM [15] Many works have shown the efficacy of n-grams as a method for representing texts for their classification. This test takes as input two strings and calculates the number of common n-grams between them. Let ngram (s, n) be the set of substrings of s (augmented with n−1 irrelevant characters at the beginning and the end) of length n, the n-gram distance is a dissimilarity such that:

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The normalized version of this function is:

This function is quite efficient when characters are only missing.  Edit distance [15]: Given a set Op of string operations , and a cost function , such that for any pair of strings there exist a sequence of operations which transforms the first one into the second one (and vice versa), the edit distance is a dissimilarity , such that , is the cost of the less costly sequence of operations which transform s in t.



Jaccard similarity [15]: calculates the similarity between two sets of elements by comparing the number of common elements to the total number of elements belonging to both sets. A value between 0 and 1 is obtained, which corresponds to the identical assemblies 1. To find associations between entities or classes, linguistic matchers are used based on the external resource, mainly WordNet dictionary [16]. The results of individually previous matchers are combined to generate a single mapping between each pair of concepts. After a filter based on a similarity threshold is applied to reduce the number of false assumptions mapping After the filter similarity, structural and semantic matchers intervene to find new relations similarity. Structural methods determine the similarity between two entities based on structural information. Indeed, the entities are connected together by links of the semantic or syntactic, this process provides the use of:  Internal structural Methods: operate only the information describing the attributes of entities, more specifically, it uses the information contained in the internal structures of the entities for calculating similarity (eg, value interval, cardinality of attributes, etc.).  External structural methods: compare relationships with other entities The technical structural techniques implement various heuristics and are based on the hypothesis [17]: “if two entities both ontologies are similar, their neighbors are also somehow”, we propose the calculation of the structural similarity between entities in the ontologies, one inspired by the work of ABOLHASSANI [18]. Still with the aim of improving the quality of the matching, we thought of using both approaches semantic methods. The first approach is based on logic models, while the second approach includes methods of deduction to derive the similarity between two entities. The filter system and validation intervenes once again after the generation of mappings. B. Comparison of our system with other systems of ontology matching The following table compares our system with other existing systems, based on a set of key criteria of alignment methods such as input formats, the outputs of alignments between concepts and relationships, the validation system of the mappings generated, also the extensional methods used and semantic filtering. Table I: Comparison table between our system and other systems System DSsim

Input OWL, SKOS

Output 1 :1 alignments

Validation expert

Extensional -

Semantic yes

RiMOM

OWL

1 :1 alignments

expert

ASMOV

OWL

n :m alignments

expert

AgreementMaker

XML, RDFS , OWL and N3 XML, RDFS and OWL

n :m alignments

expert

Vector distance Object similarity -

n :m alignments

Expert and automatic (agent)

yes

Our system

IV.

yes -

Agents architecture of our system

Multi-agent systems are now a technology of choice for the design and implementation of distributed applications and cooperatives. The proposed architecture is based on four types of agents, namely: resource Agent (RA), the matchers Agent (MA), agent of generating the mappings (MGA), Agent Filtering hypothesis and Validation (FVA). The system is not centralized, and each agent has its own behavior with his entourage (which can be an agent or an external user). Transmitting and / or receiving results as messages. Figure 2 illustrates the general behavior of a multi-agent system, presented in the form of a agent interaction protocol (AIP) tell defined in AUML [19], and that the messages provides standardized communication, we chose the FIPA agent communication language (ACL).

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Figure 2: Interactions between agents.

Conclusion

We describe in this paper a new method of matching of anthologies. It is based on a process comprising the similarity calculation, and generation of mappings, filtering and validation. One of the highlights of our system is the ability to integrate several matchers, the filtering system and semi-automatic validation, which reflects positively in values of quality metrics alignment (precision measurements, recall Fallout and Fmesure) and subsequently ensure the quality of matching. The implementation as a system-based agents, obviously inherits the benefits of these systems such as robustness, flexibility and scalability. The next job is to evaluate the performance of our algorithm, through a series of tests it using a few basic tests provided in the Benchmark at the disposal of the international community by EON competition [20], as a comparison with other methods

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VI.

References

[1]

GRUBER, Thomas R. A translation approach to portable ontology specifications. Knowledge acquisition, 1993, vol. 5, no 2, p. 199220.

[2]

STUDER, Rudi, BENJAMINS, V. Richard, et FENSEL, Dieter. Knowledge engineering: principles and methods. Data & knowledge engineering, 1998, vol. 25, no 1, p. 161-197.

[3]

ROCHE, Christophe. Terminologie et ontologie. Langages, 2005, no 1, p. 48-62.

[4]

EUZENAT, Jérôme, SHVAIKO, Pavel, et al. Ontology matching. Heidelberg : Springer, 2007.

[5]

LAMBRIX, Patrick et TAN, He. Sambo—A system for aligning and merging biomedical ontologies. Web Semantics: Science, Services and Agents on the World Wide Web, 2006, vol. 4, no 3, p. 196-206.

[6]

HU, Wei, QU, Yuzhong, et CHENG, Gong. Matching large ontologies: A divide-and-conquer approach. Data & Knowledge Engineering, 2008, vol. 67, no 1, p. 140-160.

[7]

EUZENAT, Jérôme, LOUP, David, TOUZANI, Mohamed, et al. Ontology alignment with OLA. In : Proc. 3rd ISWC2004 workshop on Evaluation of Ontology-based tools (EON). 2004. p. 59-68.

[8]

EHRIG, Marc et STAAB, Steffen. QOM–quick ontology mapping. In : The Semantic Web–ISWC 2004. Springer Berlin Heidelberg, 2004. p. 683-697.

[9]

EUZENAT, Jérôme, FERRARA, Alfio, HOLLINK, Laura, et al. Results of the ontology alignment evaluation initiative 2009. In : Proc. 4th ISWC workshop on ontology matching (OM). 2009. p. 73-126.

[10] LI, Juanzi, TANG, Jie, LI, Yi, et al. Rimom: A dynamic multistrategy ontology alignment framework. Knowledge and Data Engineering, IEEE Transactions on, 2009, vol. 21, no 8, p. 1218-1232. [11] JEAN-MARY, Yves R., SHIRONOSHITA, E. Patrick, et KABUKA, Mansur R. Ontology matching with semantic verification. Web Semantics: Science, Services and Agents on the World Wide Web, 2009, vol. 7, no 3, p. 235-251. [12] FERBER, Jacques et PERROT, Jean-François. Les systèmes multi-agents: vers une intelligence collective. InterEditions, 1995. [13] RUSSELL, Stuart J. Rationality and intelligence. Artificial intelligence, 1997, vol. 94, no 1, p. 57-77. [14] WOOLDRIDGE, Michael et JENNINGS, Nicholas R. Agent theories, architectures, and languages: a survey. In : Intelligent agents. Springer Berlin Heidelberg, 1995. p. 1-39. [15] EUZENAT, Jérôme, LE BACH, Thanh, BARRASA, J., et al. State of the art on ontology alignment. Knowledge Web Deliverable D, 2004, vol. 2, p. 2.3. [16] MILLER, George A. WordNet: a lexical database for English. Communications of the ACM, 1995, vol. 38, no 11, p. 39-41. [17] EUZENAT, Jérôme, VALTCHEV, Petko, et al. Similarity-based ontology alignment in OWL-lite. In : ECAI. 2004. p. 333. [18] ABOLHASSANI, Hassan, HARIRI, Babak Bagheri, et HAERI, Seyed H. On ontology alignment experiments. Webology, 2006, vol. 3, no 3, p. 1-22. [19] BAUER, Bernhard, MÜLLER, Jörg P., et ODELL, James. Agent UML: A formalism for specifying multiagent interaction. In : Agentoriented software engineering. Springer, Berlin, 2001. p. 91-103. [20] EUZENAT, Jérôme, MEILICKE, Christian, STUCKENSCHMIDT, Heiner, et al.Ontology alignment evaluation initiative: Six years of experience. In : Journal on data semantics XV. Springer Berlin Heidelberg, 2011. p. 158-192.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) (An Association Unifying the Sciences, Engineering, and Applied Research)

www.iasir.net

Bridgeless SEPIC for AC to DC Kakkeri Roopa, Bagban Jasmine, Patil Vanita Department of Electronics and Telecommunications Engineering, S.A.E, Pune, Maharashtra, INDIA _________________________________________________________________________________________ Abstract: This paper presents a new bridgeless single phase AC-DC converter based on Single Ended Primary Inductance Converter (SEPIC). The proposed rectifier utilizes a bidirectional switch (MOSFET) and two fast diodes. The absence of an input diode bridge and the presence of only one diode in the flowing current path during each switching cycle result in less conduction loss compared to existing PFC rectifiers. In the proposed scheme, DSPIC30F2010 controller is used to produce signals. Experimental circuit of this converter is developed with universal input voltage capability for 20-30V DC output voltage connected to resistive load (incandescent lamp with different watts). Textronics TDS2024B storage oscilloscope is used to store the gate pulses and waveforms. Key words: Bridgeless rectifier, MOSFETs, AC-DC Converter, Voltage level sensor, Zero cross detector _______________________________________________________________________________________________________

I. Introduction The active power factor correction (PFC) circuits are widely used to effectively draw the energy from the mains via an AC to DC converter. These PFC circuits are normally consists of full bridge diode rectifier and DC-DC converter. If only one DC-DC converter is used, then it will be classified as a single-stage converter while two-stage converter utilizes two-DC-DC converter. On the other hand, some PFC circuits are realized without the full-bridge rectifier circuit, which is known as the bridgeless PFC topology. Actually, these bridgeless PFC circuit combines the operation of bridge rectifier and DC-DC converter into a single circuit. The bridgeless PFC topology has less number of components conduct at each switching cycle compared to the conventional Boost PFC circuit. Numerous works on bridgeless PFC which focus on several key issues such as higher power factor and higher efficiency capability compared to the conventional PFC converters.A new bridgeless PFC circuit based on single ended primary inductance converter (SEPIC) offer several advantages as a PFC circuit such as lower input current, simple control circuitry, reduced switch voltage stress, easily implemented as isolated converter and less electromagnetic inference (EMI). The demand for improving power quality of the ac system has become a great concern due to the rapidly increased number of electronic equipment. To reduce harmonic contamination in power lines and improve the transmission efficiency. In recent years , the demand for improving power quality of the ac system has become a great concern due to the rapidly increased number of electronic equipment. To reduce harmonic distortion in power lines and improve the transmission efficiency, power factor correction became an active topic in power electronics. II. Block Diagram and Its Explanation A. System overview The block diagram of the proposed AC-DC converter using bridgeless SEPIC is shown in fig 1. It has gate drive unit, control unit. Each MOSFET acts as a switch without any switching losses and facilitates the operation of the converter.The primary function of the gate drive circuit is to convert logic level control signals into the appropriate voltage and current for efficient, reliable, switching of the MOSFET module.In this work an optocoupler TLP50 is used to isolate the gate drive circuit and the MOSFET basedcircuit. The optocoupler consists of an infrared lightemitting diode and a silicon phototransistor. The input signal is applied to the IRLED and the output is taken from the phototransistor. A controller (DSPIC30F2010) is used to implement the core of the control function, which simplifies the hardware setup. B .Control circuit The control circuit of the proposed scheme consists of a Digital signal controller DSPIC30F2010. The microcontroller is operated at 10MHz crystal frequency. A control unit (CU) is, in general, a central (or sometimes distributed but clearly distinguishable) part of the machinery that controls its operation, provided that a piece of machinery is complex and organized enough to contain any such unit. The controller decides the instant timing of

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the gate signal to be given to the MOSFETs in order to avoid overlapping in conduction of incoming and outgoing MOSFETs. AC supply

MOSFET - A

Micro Controller

Driver Unit

DC Load

MOSFET – B

Fig. 1. Block diagram of the proposed system III. Converter System Description

(a)

(b)

(c) Fig. 2. (a) The proposed bridgeless SEPIC operated during (b) positive and (c) negative half line cycle The proposed circuit is much simpler in several aspects namely: (1) less number of components operated at each input-voltage cycle, (2) the minimum number of output capacitor (Co) required is one, (3) driving the MOSFETs gate terminal is simpler due to both ‘source’ terminals of the MOSFETs are connected to a common node and (4) no gate-driver circuit with isolation is required. In the operation of the converter the three conductor are working in DCM. Operating the SEPIC in DCM offers advantages over continuous-current-mode (CCM) operation, such as a near-unity power factor can be achieved naturally and without sensing the input line current. In DCM, both S1 and S2 are turned on at zero current , while diodes Do1 and Do2 are turned off at zero current. Thus, the loss due to the switching losses and the reverse recovery of the rectifier are considerably reduced. As the analysis goes deeper, it is found that the circuit analysis can be divided into two main parts which are the operation during positive half-line cycle and negative half-line cycle as shown in Fig 3(b) and (c). During positive half-line cycle, all components will conduct except Ds1, S2, C2, L3 and Do2. During negative cycle, the components that will not conduct are Ds2, S1, C1, L2 and Do1.

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IV. Circuit Operation The proposed converter will operate in Discontinuous Conduction Modes (DCM) since this type of mode offers several advantages namely capability to operate as PFC is inherent, suitable for low power applications and lower component stress. As depicted in Figure 3, the circuit operation of the proposed converter within each switching period, TS, can be divided into three subinterval modes, namely MODE 1 (d1TS), MODE 2 (d2TS) and MODE 3 (d3TS).

(a) In MODE1, equivalent circuit is shown in Figure 3(a). As can be seen, when the upper MOSFET, S1, is turned on, the current from the source, Vg, will flow through the input inductor and continue to S1 and Ds2 before completing the current path through Vg.

(b)

(c) Fig.3. Equivalent circuit during (a) MODE 1(d1Ts),(b) MODE 2(d2Ts) and MODE 3(d3Ts). Figure 3(b) shows the circuit in MODE 2. Obviously at this mode, S1 is turned off such that no current will flow through it, but now Do1 is forward-biased. At this point, L1 falls linearly due to the process of discharging its current to the load through iCb1 and iDo1 and create the return path through Ds2. At the same time, L2 will also discharge its current linearly to the load through iDo1. Finally, in MODE 3, both S1 and Do1 are turned off resulting only two closed current path which is at the input and the output side. L1 and L2 are equal while Vg is equal to VCb1. As a result, the input current is approximately equal to zero. However, an almost DC current exist at this mode and the amount of current at L1 and L2 are equal but on the opposite direction. V. Experimental Setup and Results Experimental results for Resistive load and incandescent lamp are as follws: Table.1 Table.2 Vin

70 80

Vout( Observed value of output voltage for set Vout=23V) Resistive load

60w

100w

23.2 23.2

22.5 22.3

20.8 20.7

23.6

23.4

20.9

100

23.8

23.6

21.5

110

23.9

23.2

21.6

120

23.9

23.8

21.8

Vin

70 80 90 100 110 120

V out( Observed value of output Voltage for set Vout=30V) Resistive load

60w

100w

28.2 28.3 28.5 29.4 29.6 29.8

29.2 29.4 30.6 30.8 30.7 30.9

25.6 25.8 26.9 26.5 26.7 26.9

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The new AC-DC converter using bridgeless SEPIC developed hardware is tested with load. The proposed control system is implemented by a DSPIC30F2010. C language is used to develop the program. The device is programmed using MPLAB Integrated Development Environment (IDE) tool. For execution of C-code, MPLAB compiler is used. In this work, I have used resistive load, 60W & 100W incandescent lamp. The hardware set is developed and tested in power electronics laboratory and the photograph of complete setup is shown in fig 4. The test is carried out on resistive load and bulb. DC voltages and DC output voltages for different loads are tabulated. In the complete experiment the oscilloscope used is Tektronix TDS2024B Digital Storage Oscilloscope (DSO) to store gate pulses and voltage waveforms. Tables 1 & 2 shows, the output voltages for resistive load and for incandescent lamps ofdifferent voltages. Fig 5.a-d & 6.a-d. shows the corresponding waveforms taken from the Digital Storage Oscilloscope.

Fig. 4. Photograph of the complete designed system Waveforms for resistive load and incandescent lamps of different wattages for set Vout =23v

Fig. 5(a). Gate pulse waveform

Fig. 5(c). Gate pulse waveform

Fig. 5(b). Gate pulse waveform

Fig. 5(d). Gate pulse waveform with respect to zero cross detector Waveforms for resistive load and incandescent lamps of different wattages for set Vout =30v

Fig. 6(a). Gate pulse waveform

Fig. 6(b). Gate pulse waveforms

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Fig. 6(d). Gate pulse waveform with respect to zero cross detector VI. Conclusion A new AC-DC converter using bridgeless SEPIC has been proposed and verified by experimental works.It is showed that the proposed circuit is capable to achieve high power factor under universal input voltage condition. The capability to reshape the input current is inherent when the circuit is operated in DCM. The main features of the proposed converters include high efficiency, low voltage stress on the semiconductor devices & simplicity of design. This circuit would be most suitable to be used as a switch mode power supply application for low power equipments especially those requiring high quality input power. In the proposed scheme, DSPIC30F2010 controller is used to produce signals. Experimental circuit of this converter is developed with universal input voltage capability for 20-30V DC output voltage and the developed hardware setup is tested on a resistive load and incandescent lamp(60w,100w) in power electronics laboratory. From the experimental setup and results chapter it is clear that the developed hardware satisfactory converts AC-DC,& can be used in switch mode power supply, equipments which require high quality input power, LED lightning DC motor etc. VII. Future Scope This paper has explored some good ideas and suitable solutions, but further investigation is necessary either for telecom and computer server applications or in related field of power management, which are suggested as follows: 1.Dynamic response in low power applications, 2.Design of PFC converter at very high switching frequency, 3.Unbalanced input voltage in modular approach. Appendix The following defines the nomenclature and system parameters used in this paper : Loads: 60W,100W incandescent lamps Inverter parameters : Vin :lnput voltage 230V Cb1,Cb2,Co capacitors S1,S2MOSFETs Fig. 6(c). Gate pulse waveform

References [1] [2] [3] [4] [5]

D.M. Mitchell, "AC-DC Converter having an improved power factor",U.S. Patent 4,412,277, Oct. 25, 1983. D. Tollik and A. Pietkiewicz, “Comparative analysis of 1-phase active power factor correction topologies,” in Proc. Int. Telecommunication Energy Conf., Oct. 1992, pp. 517–523. A. F. Souza and I. Barbi, “High power factor rectier with reduced conduction and commutation losses,” in Proc. Int. Telecommunication Energy Conf., Jun. 1999, pp. 8.1.1–8.1.5 J. Liu, W. Chen, J. Zhang, D. Xu, and F. C. Lee, “Evaluation of power losses in different CCM mode single-phase boost PFC converters via simulation tool,” in Rec. IEEE Industry Applications Conf., Sep. 2001, pp. 2455–2459. Huber, Laszlo; Jang, Yungtaek; Jovanovic, Milan M.,"Performance Evaluation of Bridgeless PFC Boost Rectifiers" IEEE Transactions on Power Electronics, vol. 23, no 3, pp.1381-1390, May 2008.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net The Effect of Temperatures on the Silicon Solar Cell Asif Javed Department of Physics GC University, Faisalabad â&#x20AC;&#x201C; 38000, Pakistan __________________________________________________________________________________________ Abstract: In this research work, described the effect of temperatures on the silicon solar cells parameters such as open circuit voltage, short circuit current, fill factor and efficiency. These all parameters are the function of temperature to understand the performances of silicon solar cells at temperature range (20-80)OC and estimated variation of silicon solar cells parameters such as short circuit current I sc increases so tedious with temperature but voltage Voc decreases regularly and shows linearly behavior on this temperature conditions. The fill factor and efficiency variations are directly proportional to the Isc and Voc respectively.. On this temperature range the maximum efficiency of (18.5%) is obtained in which I sc= 37.56 mA and Voc=667.3 mV. Keywords: silicon solar cell; open circuit voltage; short circuit current; temperature; linearly; efficiency. _________________________________________________________________________________________ I. Introduction To estimate the performances of silicon solar cells under different conditions such as irradiance and temperature [1].Solar radiation produces great effect on the performance of silicon solar cell in the form of temperature. There are so many parameters can effects on the performances of solar cells working in which most prominent is temperature. A standard solar cell conditions are solar radiation is equal 1kW/m2 and temperature usually 25OC [2].In this paper, we talk about the solar cells effects on the temperature variation. For that purpose only crystalline silicon solar cells were used. II. Silicon Solar Cells The temperature performance of crystalline silicon solar cells were studied because c-Si solar cells most promising substance in the field of photovoltaic application, specially used for the solar cell among low cost and a large area [3-4]. There are so many technologies that produce energy but all of these are harming full but the only solar energy is environmental friendly and non toxic. In photovoltaic industry different material are used in which silicon most popular candidate due to its easily availability and this silicon further divide into some categories such as amorphous, crystalline and multi-crystalline. The most silicon solar cells made by crystalline material because of excess of existing in the earth about 80% [5-6]. III. Simulated Parameters Details In this section describes the main parameters of the silicon solar cells base on different quantities. Short circuit and open circuit voltage is function of temperature.

A. Open Circuit Voltage Maximum voltage of the solar cell which can be delivering and open circuit voltage no flow of current exist. It calculates by the formula (VOC).Open circuit voltage depends on Jo Saturation current. Open Circuit Voltage decrease with increase the temperature in regular interval (1) Where, K, is Boltzmann constant, T, is the temperature, q is the electronic charge, J ph, is the photocurrent, and Jo, is the diode saturation current. B. Short Circuit Current Short circuit current is the maximum current of the solar in the circuit and no open circuit voltage no exist. It calculates by the formula (ISC). (2) Where, q is the electronic charge, G, Ln, Lp C. Fill Factor An optimal output power needs for electrical engineering through load resistor R a. The maximum power (Pm) is obtain from where Im and Vm locate points. FF Factor is ratio (Vm x Im) to (VOC x ISC) is given: Why we called

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fill factor because of graphically show the covered area under I-V curve are fill by two rectangles first one is Vmx Im and VOC x ISC). 0.75 to 0.85 this is range of normally fill Factor. (3) Where Vm, maximum voltage, Im, maximum current, Voc, open circuit voltage and Isc, short circuit current. D. Efficiency Efficiency of the solar cell is calculated by the ratio maximum power generated verus incoming power. The incoming solar intensity Pin is 1000W/m2 of spectrum 1.5AM (4) Where Vm, maximum voltage, Im, maximum current, Voc, open circuit voltage, Isc, short circuit current and Plight, Power light IV. Result and discursions The most important parameter of silicon solar cell efficiency is open circuit voltage (Voc). It is function of temperature which shown in equation [1]. For Temperature range 20 to 80 thickness =100µm. The Voc decreases as temperature increased as shown in table (1).

Table I: Open circuit voltage verse Temperature T(OC)

Vsc(mv)

663.9

654.9

645.4

635.7

625.9

615.9

606.0

595.9

585.8

575.7

565.6

555.4

545.2

Figure (1) shows the effect of temperature variation on the Voc. At 20oC° the Voc has it higher value of 663.9 mV and increased with temperature to achieve its minimum value of 545.2mV at 80C° and its regularly decreeing with temperature increased. Figure 1: Variation of VOC with Temperature at 100µm 680

Voc(mv)

660

Thickness = 100m Bulk Recombination= n= p=100s

640

Voc(mv)

620 600 580 560 540 10

Temp ( C) .

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he short circuit current Isc of the silicon solar cell minor depending on temperature as shown in equation [2].The short circuit current, Isc, versus temperature is shown in table below: Table II: Short circuit current versus temperature T(OC)

Isc(mA)

37.49

37.50

37.51

37.52

37.53

37.54

37.55

37.56

Figure (2) shows that the effect of temperature variation on I sc. The short circuit current Isc was minor increase with temperature which almost no change and tends to arrive at its maximum value of (37.56 mA) at temperature of (80°C). Isc start to almost no depends on temperature increase and minimum value 37.49 mA at T=20°C. Figure 2: Variation of ISC with Temperature at 100µm. .

37.56

Isc(mA)

37.54

37.52

ISC(mA) Thickness = 100m Bulk Recombination= n= p=100s

37.50

37.48 10

Temp( C)

The efficiency of Silicon solar cell is most important parameter which shows the performance on temperature and FF of Silicon Solar cell between 0.75 to 0.85 on standard solar irradiation 1kw/m2 in equation [4] The efficiency at different temperatures is shown in table below: Table III: The Temperature of Silicon solar cell versus External Parameters T(OC) 20 25 30 35 40 45 50 55 60 65 70 75 80

Isc(mA) 37.49 37.50 37.51 37.52 37.52 37.53 37.53 37.54 37.54 37.55 37.55 37.56 37.56

Voc(mv) 663.9 654.9 645.4 635.7 625.9 615.9 606.0 595.9 585.8 575.7 565.6 555.4 545.2

η(%) 18.34 17.97 17.57 17.17 16.78 16.55 16.16 15.76 15.35 14.94 14.53 14.12 13.71

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In Figure shows that maximum value of efficiency 18.34% at Temperature 20 oC and at Temperature 80 oC obtained minimum. The open circuit voltage increased with decreased Temperature. Figure 3: Variation of ƞ with Temperature at 100µm.



Thickness = 100m Bulk Recombination= n= p=100s



13 10

Temp ( C)

V. Conclusion In this study we have evaluated effect of temperature on the performance of thin film silicon solar cell made up materials like crystalline. For that purpose simulation of TF solar cells was performed by using PC1D. The temperature is varied from 20 – 80 C while solar cell thickness was varied from 100 micron down to 1 micron. It is observed that the Voc, FF and efficiency decreases with increasing temperature whereas Jsc shows almost no appreciable change and maximum value of 157 mV at 89° C is obtained. The short circuit current (Isc) increase with temperature increases until reaching a maximum value of 92 mA at 85°C and then decreases for highest temperatures. The efficiency follows the changes of open circuit voltage and short circuit current. Maximum Efficiency was achieved η=18.34% in PCID with Thickness 100µm at temperature 20 OC. Figure (4) show the changes of Voc, Isc and η in each case.

References [1] [2] [3] [4] [5]

Tsuno, Y., Hishikawa, Y., & Kurokawa, K. (2005). Temperature and irradiance dependence of the IV curves of various kinds of solar cells. In 15th international photovoltaic science & engineering conference (PVSEC-15) (No. 1). Singh, P. & Ravindra, N.M. (2012). Temperature dependence of solar cell performance an analysis. Solar Energy Materials & Solar Cells, 101, 36-45. Carlson DE. Monolithic amorphous silicon alloy solar modules. Solar Energy Materials and Solar Cells 2003;78:627–45. V. Fthenakis, Third Generation Photovoltaics, pp.202, InTech Publisher, Croatia 2012 Z. C. Liang, D. M. Chen, X. Q. Liang, Z. J. Yang, H. Shen, and J. Shi, “Crystalline Si solar cells based on solar grade silicon materials”, Renewable Energy, vol. 35, no.10, pp.2297-2300,Oct. 2010.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Performance Enhancement and Characterization of Junctionless VeSFET 1,2

Tarun Chaudhary1, Gargi Khanna2 Department of Electronics and Communication Engineering National Institute of Technology, Hamirpur (HP), INDIA

Abstract: The design of double gate n-channel transistor named as junctionless vertical slit field effect transistor (JL VeSFET) is demonstrated in this paper. JLVeSFET is novel twin gate device which turns on and off depending upon the extension of depletion region from two gates inside the channel. It is observed that it offers very low OFF current with ideal subthreshold slope. JLVeSFET is compared with bulk MOSFETs at 65nm technology node. Characteristics of JLVeSFET with high-k dielectric are demonstrated through simulations in this paper. The device shows optimized performance with OFF current (~10-18A/µm), high Ion/Ioff (~1012) and subthreshold slope of ~65mV/decade for a 50nm radius of simulated JLVeSFET Keywords: JLVeSFET, Slit width, Ion/Ioff , Ssub, DIBL, High –k dielectric. I. Introduction The junctionless Vertical Slit Field Effect Transistor is a novel concept which provides new manufacturing and design paradigm. This JLT (junctionless transistor) based on the ideas and vision of Wojciech Maly is a promising device for future technology as it reduces the fabrication complexity as well as cost of fabrication, with the maintenance of design flexibility and inbuilt properties of power optimization in JLVeSFET [1]. To optimise the performance of JLT and to reduce the short channel effects in submicron technologies, new design approaches, such as, SOI JLFET, bulk planar JLFET, nanowire junctionless transistors [2-3], gate all around FET [4] etc, have been investigated to obtain a better gate control and improved performance. For VGS=0V, junctionless transistor is in the OFF state with no current conduction path between source and drain, for this reason a very low OFF current is obtained. A study in [5] suggests that circuits implemented with JLVeSFET can have similar performance to CMOS circuits but occupy very small chip area. A junctionless transistor due to absence of sharp junctions provides better scalability and easy fabrication steps and offers many advantages which makes it suitable in ballistic transport at short channel lengths and as a future device for high speed memories [6].The simulation-based feasibility studies presented here are very promising and confirm many attractive properties of JLVeSFETs [7-8], such as very high Ion/Ioff ratio, low leakage currents and effective current control by twin gate configuration. It has been found that the threshold voltage can be modified by either independently biasing two gates or by changing the doping levels, gate oxide thickness and slit width In the paper the structure and operation of junctionless vertical slit field effect transistor (JLVeSFET) are explained, the voltages of two gates TGC (tied gate configuration) is varied from 0 to V DD to operate the device in ON and OFF mode. These devices exhibit high Ion/Ioff ratio and subthreshold slope of 65mV/decade at room temperature. The working principle of VeSFET is similar to MOSFET and JFET which is explained in Section2. In Section3 the optimization of device characteristics are performed by introducing high-k gate dielectric, the variation of device characteristics at different gate doping concentration and different substrate doping concentration are also presented in Section 3. Section 4 presents the conclusion of the paper.

Fig. 1 Structure of n-channel double gate JLVeSFET with r=50nm and uniform channel doping of 1×10 17 cm-3

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II.

Device Structure and Operation

The JLVeSFET is a junctionless transistor with a gate controlled bulk current using either a p-type or n-type substrate for the complementary transistor types. The device is hybrid of JFET and MOSFET. The operation is JFET-like because it is based on transport of majority carriers in a bulk channel, whose effective width is controlled by depleted regions induced by two gates on both sides of this channel. The gates, however, are separated from the channel by a layer of oxide, like in a MOSFET [9]. Fig. 1 shows the structure of JLVeSFET that we use for simulation and the device comprises of source, drain and channel region with same dopants. It is a gated resistor in which the current is controlled by depletion regions created by the two gates on either side of the channel. In the OFF state, the depletion region, created due to the work function difference between the channel and the gate material, packs up the channel completely, leading to low OFF current. In the ON state, when a voltage is applied on the gate to counter the work function difference, the depletion region recedes and a path is created for the current to flow between source and drain. VeSFET exhibits majority carrier flow unlikely MOSFET that exhibit minority carrier flow [7]. All relevant parameters considered for simulations are listed in Table 1 Table 1: Parameters used for device simulation Parameters Value Radius of metal pillars 50nm Radius of STI fillers 50nm Thickness of gate dielectric (tox) 3nm to 4nm Slit width (Ws) 37.9nm Height of the device (h) 200nm Substrate doping (Nsub) 1×1016 cm-3 to 1×1018 cm-3 Polysilicon gate doping (NPoly) 1×1018cm-3 to 1×1020 cm-3 Gate voltage for G1 and G2(TGC) Drain bias (Vds) High- k dielectric (Si3N4) Low-k dielectric (SiO2)

1V 1V k=7.5 k=3.9

All simulations are carried out using Sentaurus TCAD [10]. The ID-VGS plot for JLVeSFET is shown in Fig. 2 for different values of VDS and it shows the effect of DIBL short channel effect on the device. The simulated result shows very less variation in ON current with increase in drain voltage. The simulated device structure is a vertical n-type JLT with two gates, named gate1 and gate2. The voltage is sweeped at both the gates simultaneously (TGC) from 0 to VDD for turning the device ON.

Fig. 2 ID-VGS plot for JLVeSFET with r=50nm, w s=37.9nm tox = 4nm and VDS=1.0V

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Fig. 3 Energy band diagram of OFF state of the JLVeSFET with VGS=0V

Fig. 4 Energy band diagram of JLVeSFET with V DS=0V and VGS=1V

Fig. 5 Energy band diagram of JLVeSFET with V DS=1V and VGS=1V In order to achieve low subthreshold swing, the device is made without any sharp junctions (ultra steep doping profile) [6] which reduces the need of high and different doping profiles. In order to get the better control of gate on output, material with high-k dielectric (Si3N4, k=7.5) is utilized. Through simulations it is obtained that use of high-k material results in optimized low subthreshold swing of 63mV/decade and low OFF current of ~10-18Amp at room temperature When positive gate voltage is applied to TGC JLVeSFET the device turns ON, with variation in the gate voltage from 0V to 1V, band bending occurs and continuous path for current to flow between source and drain of the device occurs as shown in Fig. 3, 4 and 5. All simulations are carried out by taking the gate doping concentration to be 1Ă&#x2014;10 19cm-3. The gate work function is 4.25eV for gate1 and

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gate2 with gate material as poly silicon. A key issue of JLVeSFET is to optimize the I on/Ioff ratio and to get minimized subthreshold swing comparable to subthreshold slope of MOSFET at 65nm technology node. The device JLVeSFET device demonstrates a subthreshold slope of 65mV/decade with SiO 2 as oxide, whereas it is between 70-100 mV/decade as reported in [12] for MOSFET. Fig. 6 represents the energy band diagram of both the oxide regions at two gates of TGC and the silicon channel in between, it is clear from the energy band diagram that due to very high band gap 8eV there is no current conduction through the oxide region, thus this leads to the reduction of gate leakage current which occurs due to tunneling through the oxide region and also inhibit the hot carrier injection in the JLVeSFET device and thus the device is suitable for low power applications.

Fig. 6 Energy band diagram of JLVeSFET considering two insulators and silicon channel in between

16 14

Ion/Ioff(×1011)

Subthreshold slope (mV/deacde)

III. Results and Discussion A. Simulation Parameters The n channel VeSFET device at radius of 50nm is simulated using Sentaurus TCAD device simulator. The height of the device is 200nm, gate oxide thickness is 4nm, the substrate doping concentration is 1×1017 cm-3 and boron concentration for twin gate structure is 1×1019 cm-3 . The device is operated at 1.0V for TGC structure. B. High-k gate dielectric with Low- k STI filler An improved double gate (DG) JLVeSFET with superior performance is observed by the use of high k dielectric (in this simulation Si3N4 with a dielectric constant, k=7.5), with low-k STI fillers (we use SiO2 with k=3.9). The simulation results show that by proper choice of suitable gate dielectric and fillers I on/Ioff ratio ~1011 and low subthreshold slope ~ 63mV/decade is achieved as shown in Fig. 7 .

8 Dielectric Constant [K]

Fig. 7 Subthreshold slope and Ion/Ioff dependence on gate dielectric constant for r=50nm, w s=37.9nm, tox=4nm and VDS=1V High-k dielectric is used to enhance gate control and STI filler are used to separate the three regions so that proper isolation can be maintained. In Fig. 7 both Ion/Ioff and subthreshold slope are plotted for two different dielectric constant materials. The simulation result shows that, with increase of gate dielectric constant, OFF current reduces considerably, where as ON current remains almost constant. Hence I on/Ioff increases by a factor of

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Ion/Ioff (×1011)

7 6

1 0

Subthreshold slope (mv/decade)

~10 with increase in dielectric constant from 3.9 to 7.5. Fig. 7 shows that Ion/Ioff increases from 3.72×1011 to 17.5×1011 with increase in gate dielectric from 3.9 (SiO2) to 7.5 (Si3N4) respectively. The JLVeSFET shows a significant improvement by lowering the subthreshold slope below 65mV/decade. When the gate dielectric constant increases, there is a drop in subthreshold slope from 65mV/decade at k=3.9 to 63mV/decade at k=7.5.The ID-VGS plot for different gate dielectric constants is shown in Fig. 8. The results show that the OFF current is greatly dependent on the dielectric constant. When the dielectric constant increases, the OFF current reduces while there is a negligible change in ON current. In our simulations, we use Si3N4 (k=7.5) as high-k dielectric and compare it with SiO2 whose dielectric constant is 3.9, the OFF current reduces by the factor of 4. C. Dependence of Device Charcteristics on Doping Variations Fig. 8 and 9 show the variation of device characteristics such as Ion, Ion/Ioff, subthreshold slope and DIBL with doping concentration. In this work gate doping is varied from 1×10 18cm-3 to 7×1019cm-3. The thickness of slit width is 37.9nm, besides with increase in channel doping the OFF current reduces whereas ON current reduces as well, subthreshold slope remains almost constant and DIBL value is reduced from 58mV/V to 40mV/V, which shows that the effect of drain bias voltage on the ON current reduces and gate control over the channel enhances as we increase gate doping concentration. The Ion/Ioff ration is increases from 2.34×1010 to 0.84×1012 with increase in gate doping concentration.

65 1E+18 3E+18 5E+18 7E+18 1E+19 3E+19 5E+19 7E+19 Gate doping concentration [cm-3]

4.0

3.8

60 50

3.6 40 3.4 30

DIBL (mV/V)

ON current (µAmp/µm)

Fig. 8 Ion/Ioff ratio and subthreshold slope dependence on gate doping variation for r=50nm, t ox=4nm channel doping=1×1017cm-3and ws=37.9nm

3.2 20 3.0

2.8

0 1E+18 3E+18 5E+18 7E+18 1E+19 3E+19 5E+19 7E+19 Gate doping concentratrion [cm-3]

Fig. 9 ON current and DIBL dependence on gate doping variation for r=50nm, t ox=4nm Fig.10 shows the effect of variation in channel doping concentration (1×10 16cm-3 to 1×1018 cm-3), on the device electrical parameters, such as Ion/Ioff ratio and subthreshold slope. Subthreshold slope has shown significant reduction from 300mV/decade to 65mV/decade, and remains almost constant with reduction from 5×10 17cm-3 to 1×1016cm-3 in channel doping concentration.

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Ion/Ioff (×1011)

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

350 300 250 200 150 100 50

Subthreshold slope (mV/decade)

Tarun Chaudhary et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 9(3), June-August, 2014, pp. 309-314

0 1.00E+16 5.00E+16 1.00E+17 5.00E+17 1.00E+18 Channel doping [cm-3]

ON current (pAmp/µm)

200 180 160 140 120 100 80 60 40 20 0

10 8 6 4 2 0 1.00E+16

5.00E+16

1.00E+17

5.00E+17

DIBL (mV/V)

Fig. 10 Ion/Ioff ratio and subthreshold slope dependence on channel doping variation for r=50nm, t ox=4nm gate doping=1×1019cm-3, ws=37.9nm and h=200nm

1.00E+18

Channel doping [cm-3]

Fig. 11 ON current and DIBL dependence on channel doping variation for r=50nm, t ox=4nm gate doping=1×1019cm-3, ws=37.9nm and h=200nm Fig. 11 describes the reduction of ON current and reduction in DIBL (drain induced barrier lowering) with decrease in channel doping concentration. Significant reduction has been seen from 170mV/V to 42mV/V with reduction in doping concentration from 1×10 18cm-3 to 1×1016cm-3. IV. Conclusion In this work, the proposed device is simulated with variation in different device parameters and it has been observed that the characteristics of JLVeSFET are comparable to conventional MOSFET without the need of any junction. The device shows minimum gate tunneling current and when observed with high-k dielectric material (Si3N4) and low –k STI fillers (SiO2) with optimised gate doping concentration and channel doping concentration it offers OFF current of ~10-18 A/µm, Ion/Ioff ratio of ~1012 order and a subthreshold slope as low as ~65mV/decade. Such low and optimised value signifies its importance both in addressing fabrication issues and low power applications V. References W. Maly, “ Integrated Circuits Fabrication and Associated Methods Devices and Systems”, U.S. Non-Provisional Patent Application serial Number CMU Docket 06-091; DMC Docket 06-001PCTCMU [2] Lee C.W., Ferain, I., Kranti, A., DehdashtiAkhavan, N., Razavi, McCarthy, B., Gheorghe, S., Murphy, R., Colinge, J.P Short-channel junctionless nanowire transistors. In: Proc. SSDM, pp. 1044–1045 (2010) [3] Kranti, A., Yan, R., Lee, C.-W., Ferain, I., Yu, R., DehdashtiAkhavan, N., Razavi, P., Colinge, J.P.: Junctionless nanowire transistor(JNT): properties and design guidelines. In: Proceedings of theEuropean Solid-State Device Research Conference (ESSDERC),April 2010, pp. 357–360 (2010) [4] Su, C.-J., Tsai, T.-I., Liou, Y.-L., Lin, Z.-M., Lin, H.-C., Chao,T.-S.: Gate-all-around junctionless transistors with heavily doped polysilicon nanowire channels. IEEE Electron Device Lett. 32 (4) (2011) [5] Y-W. Lin, M. Marek-Sadowska, W. Maly, A. Pfitzner and D.Kasprowicz, “Is There Always Performance Overhead for Regular Fabric?” Proc. ICCD, pp. 557-562, October 2008. [6] Ghosh B., Bal.P., M Partha., “ A junctionless tunnel field effect transistor with low subthreshold slope”, J Comput Electron (2013) 12: 428-436 [7] W. Maly and A. Pfitzner, "Complementary Vertical Slit Field Effect Transistors", Carnegie Mellon University, Techreport No. CSSI 08-02, 01/2008. [8] http://vestics.org [9] W Marcus., A Pfitzer., D Kasprowicz., R Emling., W Maly., D Schmitt., “Adder circuits with transistor using Independently controlled gates” IEEE 2009. [10] “Sentaurus Structure Editor User’s Manual”, Synopsys International [11] “Sentaurus Inspect User’s Manual”, Synopsys International [1]

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Review Paper on Comparative study of various PAPR Reduction Techniques Gagandeep Singh1 Ranjit Kaur2 PG Student, Electronics and Communication Engineering Department 2 Associate Professor, Electronics and Communication Engineering Department Punjabi University, Patiala, Punjab, INDIA 1

Abstract: Orthogonal Frequency Multiplexing (OFDM) has been increasingly used in modern wired and wireless communication systems due to its high data rate, immunity to noise and high frequency spectral efficiency. The major drawback in using OFDM technique is its high Peak to Average Power Ratio (PAPR). Within the existing PAPR reduction techniques, Selective Mapping is most popularly used Technique. In this paper, we discuss various PAPR reduction techniques. And find out that the signal scrambling techniques are having most advantages in terms of no out of band radiations and BER. But these techniques are more complex than others. Keywords: Multi Carrier Modulation (MCM), Orthogonal frequency division multiplexing (OFDM), Peak to average power ratio (PAPR), Partial Transmit Sequence (PTS), Selective Mapping (SLM).

Introduction

Orthogonal Frequency Division Multiplexing (OFDM) is most widely used technique due to its capability of supporting high data rates. In OFDM based systems, a high data rate stream is broken down into several lower speed streams which are orthogonal or parallel to each other([7][12]). Due to this property, OFDM is widely used in high speed systems like Digital Subscriber Line (DSL), Digital Video Broadcasting (DVB) and wireless networks([3][12]). The main concept behind OFDM system is that the available bandwidth is divided among the various subcarriers. Due to the formation of subcarriers, the data rate of each carrier is reduced but overall data rate of system increases as more number of subcarriers can be used[7]. Example, in DSL communication 64 subcarriers are used whereas in DVB, 2048 subcarriers are used for video transmission. The major disadvantage in using the OFDM is the high value of PAPR. This means the difference in Peak power and average power is more. This difference increases if we increase the number of subcarriers. This high PAPR reduces the performance of High Power Amplifier (HPA) used for transmission due to the fact that amplifier is driven into saturation region if input power is increased beyond its linear range. This causes inter modulation and out of band modulation. This causes the system efficiency to degrade. So before using HPA, these high peaks need to be suppressed. To reduce this PAPR, there are many techniques used such as Source Coding, Scrambling Technique and Signal Distortion Techniques etc.[8]. This paper is organized as follows: Section I describes OFDM system. Section II describes PAPR in OFDM system. Section III describes PAPR reduction techniques. Section IV describes overall analysis of different techniques. Section V describes conclusion. II.

Papr in OFDM Signal

Let the input data block of length K be represented by block AX  [ A1 , A2, A3 ... AK ] , Thus OFDM symbol can be written as: K

x (s) =

A k 1

e j 2kf0t

Where X(s) is the OFDM symbol, data block.

(1)

AX is the input data block and K is the number of symbols in input

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PAPR is defined as the ratio of Peak to Average Power. Mathematically PAPR can be defined as following: PAPR xs 

max xs 



E xs 



(2)



2



2  is the

Where x (s) is the original OFDM signal, max xs  is the peak signal power, E xs  average signal power and E[.] is the expectation operator. III.

PAPR Reduction Techniques

There are various techniques available for PAPR reduction in OFDM. These are described as follows: A. Signal Scrambling Techniques: Selected mapping (SLM) [2], Partial Transmit Sequence (PTS) [8], Tone Injection(TI) etc are Signal Scrambling Techniques. Signal Scrambling Techniques are described as follows: A.1. Pts Technique In the PTS Technique, the input symbol sequence is partitioned into a number of disjoint symbol subsequences. IFFT is then applied to each symbol subsequence and the resulting signal subsequences are summed after being multiplied by a set of distinct rotating vectors. Next the PAPR is computed for each resulting sequence and then the signal sequence with the minimum PAPR is transmitted. As the number of subcarriers and the order of modulation are increased, the system complexity is also increased to great extent thus making this technique more complex in implementation on hardware[8]. Figure 1 Block diagram of conventional PTS scheme [8]

A.2. Selective Mapping Selective Mapping is a useful technique to reduce PAPR in OFDM system, as this technique does not effects the system performance in terms of Bit Error Rate. The basic idea behind this technique is the phase rotation of the OFDM modulated data. After several rotations, the signal with low PAPR is selected[2].

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Gagandeep Singh et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 9(3), June-August, 2014, pp. 315-318 Figure 2 Block diagram of SLM technique[2]

Let the input data block be represented as

AK  [ A0 , A1, A2 ... AN 1 ]T

(6)

And independent phase sequences are given by

pU  [ p0U , p1U ..... pUN 1 ]T

(7)

Where P is the phase sequence, U is the total no. of Phase sequences and T is the length of input data block. After applying Phase rotation, IFFT is applied to obtain data block with different PAPR value and phase sequence [2].

d U  [d 0U  d1U  .....d NU 1 ]T

(8)

Where d is the OFDM symbol generated after IFFT operation. After this the stream with lowest PAPR is selected for transmission. CCDF is used for PAPR representation. CCDF of PAPR in SLM can be represented as

P( PAPR  PAPR 0)  (1  (1  e PAPR 0 ) . N )U

(9)

Where α is the oversampling factor, N is no. of sub-carrier, U is total no. of independent phase sequences and PAPR0 is the threshold value. A.3. Tone Injection Tone injection uses a set of equivalent constellation points for an original constellation to minimize the level of PAPR. It increases original constellation size and map several constellation points to extended constellation from each original constellation points. Since all elements mapped are useful for PAPR reduction. There is no need for additional operation and no side information is required to transmit along with the original signal. This technique is based on summing a data block and time domain signal[13]. B. Signal Distortion Techniques Clipping and Filtering ([2][10]), Peak cancellation, peak power suppression, companding etc. are signal distortion techniques. B.1. Peak Cancellation In this algorithm the amplitude and phase of peak is kept within constellation region which points to the data symbol to be transmitted. For example, to use this technique for QPSK constellation, it carries four regions to represent the four different value of QPSK symbol.

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B.2. Peak Windowing With this technique it is possible to remove larger peaks at the rate of a little amount of interference when large peaks arise infrequently. It mitigates PAPR at cost of increases BER (bit-error-rate) and out-of-bands radiation. In this method multiply large signal peak with a specific window such as Gaussian shaped window, Kaiser, cosine, and hamming window which results a spectrum of convolution of original OFDM spectrum with spectrum of window. The window size should be narrow otherwise it affects number of signal sample which cause increasing BER[9]. IV.

Overall Analysis Of Various Techniques

Table 1 Comparison of PAPR reduction techniques[11] NAME OF PARAMETERS NAME OF SCHEME

PTS SLM TI Peak Cancellation Companding PW

DISTOR TION LESS

POWER INCREASE

DATA RATE LOSS

YES

BER IMPRO VED YES YES YES NO NO NO

Conclusion

In this paper, OFDM is studied and various PAPR reduction techniques are studied. These techniques are compared for different parameters. Here SLM is analyzed and it is found that this technique is better for PAPR reduction because this technique does not cause out-of-band radiations or degrades BER performance. The only drawback in using this Technique is that system becomes complex when phase sequence increases or number of subcarriers increases. References [1]

Md. Mahmudul Hasanl, “An Overview of PAPR Reduction Techniques in OFDM Systems,” International Journal of Computer Applications, vol no. 60, issue no. 15, pp. 33-37, dec. 2012.

[2]

Aparna P. More, “The reduction of PAPR in OFDM system using SLM method”, IEEE , year 2010 , vol. no. 1.

[3]

Abhishek Arun Dash, “OFDM systems and papr reduction techniques in OFDM systems,” Phd Thesis, year 2010.

[4]

Gagandeep Kaur, “Compare SLM (selective mapping) and PTS (partial transmit sequence) technique for PAPR reduction of an MCCDMA (multi-carrier complex division multiple access),” IEEE, vol. no.2, issue no. 4, pg no. 779-784, jul-aug 2012.

[5]

Guangyue Lu1, “Partial Transmit Sequences Method for Reduction of PAPR in OFDM Systems,” Signals and Systems Division, Dept. of Engineering Sciences, Uppsala University Uppsala, Sweden.

[6]

Md. Ibhrahim Abdullah, “Comparative Study of PAPR Reduction Techniques in OFDM,” ARPN journal of system and software, vol. no. 1, issue no. 8, pp 263-269, nov 2011.

[7]

Komal Gupta, “PAPR reduction of OFDM using a new phase sequence in SLM technique,” IJAEEE, vol-2 ,issue-2, pp 125-109,2013.

[8]

Kamal Singh, “A modified PAPR reduction scheme based on SLM and PTS Techniques,” IEEE, 2012.

[9]

H. Sakran, “ An efficient technique for reducing PAPR of OFDM system in the presence of high power amplifier,” IEEE, vol. 4, no. 8, pp 1749-1751, 2008.

[10] Filbert H. Juwano, “PAPR reduction using Huffman coding combined with clipping and filtering for OFDM transmitter to mitigate PAPR (peakto-average power ratio) in OFDM,” (CITISIA 2009), vol. 8, PP. 344-347, year 2009. [11] Mohd. Zavid Parveez, Md. Abdullah Al Baki, ”PAPR reduction in OFDM based radio system,” PhD thesis, Belkinge Institute of technology, Sweden, may 2010. [12] Sang Woo Kim,“Peak to average ratio reduction for multicarrier modulation,” PhD thesis, University of Stanford, 2006. [13] J.Tellado, “Peak to average power ratio reduction for multicarrier modulation,” PhD thesis, University of Stanford, 1999.

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ISSN (Print): 2279-0047 ISSN (Online): 2279-0055

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Link Prediction-Based Topology Control and Adaptive Routing for Cognitive Radio Mobile Ad-Hoc Networks Kanchan Hadawale1, Sunita Barve2, Parag Kulkarni3 Computer Department, MIT Academy of Engineering, Pune University1 Research Scholar, Bharati Vidyapeeth Deemed University2 EKLaT Research3 Pune, Maharashatra, India Abstract: Cognitive Radio (CR is a promising technology which deals with using vacant spectrum of licensed frequency band opportunistically. In CR Network, route construction must not affect the transmission of Primary User activity. In CR technology challenge of maintaining optimal routes arises due to PU activity and mobility of spectrum resources i.e. CR users. Maintaining optimal routes in Ad-hoc CR network increases the overall network throughput and decreases end-to-end delay. This end-to-end network performance can be increased by providing cognition capability to routing in Cognitive Radio Mobile Ad-Hoc Network. We propose routing scheme with link-availability prediction and topology control. Link-availability prediction considers primary user activities and user mobility. Topology control constructs dynamic and reliable topology based on link prediction. This routing scheme reduces energy consumption, rerouting and thus enhances overall network performance and ensures least delay. Keywords: Cognitive Radio Network (CRN); Cognition capability; Link prediction; Topology control I.

Introduction

Cognitive Radio (CR) technology is one of the promising technologies that allow unlicensed users to access licensed spectrum bands opportunistically in a dynamic and non- interfering manner. Federal Communication Commission (FCC) highlights that many licensed spectrum bands are used only in specific geographical areas and over limited period of time average utilization of spectrum band varies between 15% and 85%. Cognitive radio technology is thus introduced to solve the problem of spectrum usage inefficiency. In CRN, most research is done on MAC layer issues such as opportunistic spectrum access and spectrum utilization. Whereas CR technology has more impact on upper layer performance issues in wireless network, specifically in Mobile Ad-Hoc Networks (MANETs). In routing issue, data should be routed via stable and reliable path to avoid rerouting and thus congestion in network. This degrades the performance of network such as throughput and delay. As compared to classical routing, routing in CRN is more unstable as it is affected not only by mobility of Cognitive Userâ&#x20AC;&#x2122;s (CU) but also by Primary User (PU) activities. Thus routing in CRMANETs should have the following characteristics to ensure stable and reliable paths. i. PU activity awareness: In CRN, path selection should be in such a way that it should not interfere to PU activity i.e. PU interference should be below the required threshold. ii. Link-availability: To avoid PU interference, cognitive routing should also be reactive. It should be aware of link-available periods. iii. Adaptive behavior: Cognitive routing should be adaptive to selected path based on some prediction to avoid rerouting frequency and to increase end-to-end throughput and to decrease end-to-end delay. There exists large numbers of routing protocols for MANETs which cannot be applied directly to CRMANETs because of its distinct characteristics of CR-MANETs. So here we proposed technique that provides cognition capability for routing through middleware mechanism like topology control. Topology control is used in almost all wireless networks to reduce energy consumption and interference. With topology control network connectivity is achieved through link information provided by MAC and physical layers. Thus topology control works as middleware that connects routing and lower layers. In CR-MANETs, topology control takes care of PU activities and spectrum-availability. In proposed scheme, prediction based topology control is considered to provide cognition capability for routing. This technique captures topology dynamically based on the link information provided by lower layers to provide opportunistic link management and routing in CR-MANETs. Link-prediction model is proposed to deal with cognitive user mobility and PU activities. This model predicts the link-availability duration as well as probability that duration remains till the end of this period. Based on this predicted links, reliable topology is constructed to reduce rerouting. For link prediction, local neighbor information is collected and network connectivity is preserved in distributed manner.

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II. Related Work A. Link Stability Routing Solutions Link availability in cognitive network is different from traditional wireless network. Link availability in cognitive network varies with time and space. Thus stable links is one of the routing solutions which can be achieved through following routing schemes. 1) Enhanced Path Recovery Functionalities In [2], authors proposed SPEctrum-Aware Routing (SPEAR) protocol to get link flexibility with spectrum heterogeneity. Spectrum availability depends on location and presence of primary user activities. SPEAR considers following concepts for link stability, a) To deal with spectrum heterogeneity, spectrum discovery is integrated with route discovery. b) By minimizing inter-flow interference, channel assignments are coordinated on per-flow basis. c) To achieve spectrum diversity and to reduce intra-flow interference, local spectrum heterogeneity is considered. SPEAR set-up route by broadcasting and AODV-style route discovery is used to get each node’s channel quality and availability information. Each Route Request (RREQ) contains node IDs, nodes spectrum availability and link quality. All these parameters are combined at destination to select optimal route. SPEAR discovers multiple paths with redundant paths that are sent to destination for best path selection. Selected route is reserved at destination by using RREP messages. In [3], collaboration between route selection and spectrum decision is considered. In this paper, authors proposed Spectrum Tree based On Demand routing Protocol (STOD-RA) which includes, a) Route metric based on PUs activities and SUs QoS requirements. b) Spectrum-tree structure of each available channel c) Spectrum Tree based On Demand Routing Algorithm Routing metric combines both link stability and spectrum availability. It predicts spectrum availability time from history of PU activities. 2) Targetting Route Stability In [4], link stability is associated to path connectivity through mathematical model. Degree of connectivity is considered while measuring paths, also PU behavior is also considered. Authors introduce novel metric to assign weight to routes which also captures path stability and availability over time. In this paper routing scheme is named as “Gymkhana”, which forwards information along paths to avoid unstable and low connectivity network zones. Gymkhana uses distributed protocol to collect information of candidate paths from source to destination. Gymkhana contributes following work in cognitive routing, a) Provision of re-elaboration of algebraic connectivity in cognitive context. b) Formulation of path connectivity and path length used in cognitive routing protocol. In [5], route stability is defined In terms of route maintenance cost and protocol is designed. Route maintenance cost represents efforts required for maintaining end-to-end connectivity in cognitive routing. It includes operations like link switching and channel switching on presence of primary user activity. In link switching link routes must be replaced by the links that are not affected by PU. In channel switching same link can be maintained but spectrum portion must be changed. Authors propose MILP formulation to minimize route maintenance cost with link capacity and flow balance constraints. Centralized algorithm is designed to compute minimum maintenance cost routes in cognitive routing. 3) Routing with Mobile SUs In [6], SEARCH routing protocol is designed on geographic forwarding principle. In cognitive radio network, route is constructed at network layer must not affect primary user’s transmission and thus must be aware of spectrum availability. The frequency changing PU activity and mobility of CR user make the problem of maintaining optimal routes in Ad-Hoc cognitive radio network challenging. SEARCH mainly works on following two concepts, a) PU activity awareness: In CR network, route must be constructed to avoid region affected by active PU. When PU activity affect region, SEARCH provides hybrid solution, it first uses greedy geographic routing on each channel to reach destination by identifying and circumventing PU activity region. The path information from different channels is combined at destination in series of optimization steps to decide on optimal end-to end route in a computationally efficient way.

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b) CR user mobility: Cognitive user mobility results into frequent route disconnections. Thus for each node, through periodic beacons, updates its one-hop neighbor about it current location. SEARCH ensures performance as well as less interference in cognitive radio network. B. Solutions with Link-Availability Prediction Model In [7], to reduce rerouting, path availability and reliability metric is concerned in routing of MANETs. For that prediction based link-availability estimation and path reliability estimation is used as routing metric. The basic idea of this estimation is nodes first predict that a continuous time period (Tp) that the link-availability remains from t0 with assumption that velocity both nodes of that link will be constant during Tp. Then the probability estimation L(Tp), that this link remains until t0 + Tp, with possible changes in velocities of nodes during t0 to t0 + Tp. Thus with these link-availability estimation they get two approaches, “unaffected Tp” with constant velocities and “affected Tp” with changes in velocities. This estimation improves the tendency of link availability to develop path selection metrics to improve network performance. In [8], authors proposed enhancement to the prediction based link availability estimation. Since, in original estimation L (Tp) all possible changes in velocities during Tp do not take into account. It only considers first change in velocity during time period Tp and link availability for these changes is estimated by ϵ. This is given as, 

( 2Tp ) 1   +ò  e 2  Tp  

L (Tp) ≈ 

  1    Tp    ò  2Tp   

Where, p = probability of two nodes of a link to get close to each other. For link prediction Tp, node can measure the link duration that remains till the end of Tp and link-availability measured as Lm (Tp) = (Tr) / (Tp) 1) Spectrum Sensing: Spectrum sensing allows cognitive user to detect spectrum holes by detecting primary users that are receiving data within its communication range. Spectrum sensing techniques are classified as follows, transmitter detection, cooperative detection, interference-based detection. 2) Spectrum Management: To meet QoS requirements of application cognitive radio should select best available spectrum band among all available spectrum bands. This is achieved through following spectrum management functions, Spectrum Analysis, Spectrum Decision. 3) Spectrum Mobility: Spectrum mobility occurs when primary user requires its licensed band. Such type of handoff is referred as spectrum handoff. Spectrum mobility ensures that transition of network protocol from one mode of operation to another is made smoothly and without degrading network performance. 4) Spectrum Sharing: Provides spectrum scheduling among all existing users. Spectrum sharing process is divided into following 5 steps, spectrum sensing, spectrum allocation, spectrum access, transmitter-receiver handshake and spectrum mobility. This paper presents current challenges of routing in Cognitive Radio Network (CRN) and different solutions with different routing metrics. We first explain differences between classical routing in wireless network and routing in cognitive radio network. Then we provide brief overview of spectrum management for cognitive radio network. We explain existing work and challenges in cognitive radio network. And finally different methodologies for different routing metrics are explained as solutions for different challenges of CRN. III.

System Model

Cognitive Radio Network (CRN) routing is different from classical wireless routing in terms of dynamic spectrum availability and PU activity interference. Spectrum availability is affected by following two factors, a) PU activities: As CUs are considered as secondary user due to low priority in accessing the spectrum band allocated to PU. CU should be aware of PU activities while sensing the spectrum. Spectrum sensing can be done either non-cooperatively i.e. individually each CU conducts radio detection and takes decision by itself or cooperatively where spectrum sensing is done by group of CUs in collaboration. b) CU mobility: Due to node mobility frequent disconnection occurs in established route. Route disconnection is detected when next-hope node in route does not reply to message and retry limit is exceeded. In CR-MANETs, due to node mobility in PU activity region spectrum also becomes unavailable. To solve the spectrum availability problem, above two factors should be consider for designing efficient topology control and routing schemes. In proposed system, focus is on spectrum availability affected by CU mobility. That means cognitive routing should select the links with long path survival time to improve path stability. This cognition capability is achieved through middleware technology such as topology control. Topology control works with cross-layer module that connects routing layer and CR module. In proposed scheme topology control is integrated with link-availability prediction. The proposed system is divided into 3 stages,

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Fig. 1 Proposed system architecture A. Link-Availability Predictions A reasonable link-availability prediction model is used for proposed system. The basic principle is to provide predicted time period Tp for link-availability between two nodes. L(Tp) parameter is used to estimate probability that this link may remain till the end of Tp by considering changes in velocities. It also estimates L(Tp) for a node that does not move at a constant velocity. In CRMANETs along with CU mobility PU activity region is considered for link prediction. To avoid PU interference distance between CU and PU is calculated. For linkavailability before node moves into PU activity region, another pair of parameters [Tp,L(Tp)] is calculated. is predicted time till CU is outside the PU activity region and L( ) is its corresponding probability. Final link prediction we get by combining [Tp, L(Tp)] and [ , L( )]. 1) [ , L ( )] Estimation As we consider that the velocities of nodes stay constant during random long period of times, so it is easy to get δ2d2/δ2T2=0. Where, d = distance between two nodes T = time interval Then d can be calculated as, d2 = αT2 + βT + γ (1) Where, α, β and γ = constant α, β and γ can be calculated by three points of measurements (t 0, d0), (t1 , d1) and (t2 , d2). Sample time is ti = t0 + Ti and di is measured distance between two nodes. Without loss of generality, Let t 0 = 0 then α, β and γ can be sorted as, α = [( t2 – t1) – (t2 – t1)] / [t1t2 (t1-t2)] β = [( – )– ( – )] / [t1t2 (t1-t2)] γ= When two nodes are in each other’s transmission range and their velocities are constant and different from each other than there is also possibility that they will travel out of this range then Tp can be considered for equation 1. Some nodes always remain outside the PU activity region then there is no solution for equation 1. Once CU is detected in PU activity region by CR module then we don’t need to predict link availability thus, is set to 0. This system consider situation where nodes are out of PU activity region. Let, ρ = interference radius ∆ = β2 + 4αρ2 - 4αγ To get one solution for equation 1 of we have ∆ >= 0. Consider >= 0, the available time period Tp if counted from t2 is, =

      

 2  4 2  4 if ∆ >= β2 t 2 2

(2)



Preciseness depends on ρ, we can be derived by propagation model and measured received signal strength by time-of arrivals based distance measurement for indoor environment and GPS system for outdoor environment. Similarly probability L ( ) to can be derived as, L(

) e

Tˆpe

  (1  e

Tˆp

Where, τ and ζ are obtained by measurements. Then the pair [ to PU interference.

)

,L(

(3) )] can predict link duration corresponding

1) Link-Availability Prediction Link is considered to be available if two nodes associated with link are within transmission range of each other and both are out of interference region of any PU. We consider Tp * L (Tp) routing metric to assist routing protocols in reliable path selection. But if Tp * L (Tp) > *L( ) then it suppress transmission. Thus, link available duration Ta should be set to,

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



ˆ j *L(T ˆj ) T  min T *L(Tp ),T pi pi a i 1,2, j{PUs} p

(4) Where, i = associated with 2 ends of link {PUs} = all PUs in network i, j = indicates boundary of link i.e. if any end moves in PU activity region then link becomes unavailable. Thus, Ta is cognitive feature enabled which considers node mobility and PU activity region. B. Distributed Topology Construction Based on link prediction, topology control and routing scheme is proposed. In wireless network, topology control is introduced to save energy consumption. In CR-MANETs due to CU mobility and PU activity region causes frequent rerouting and thus results into low end-to-end network performance. Thus to solve this problem more reliable topology is constructed in CR-MANETs. New link reliability metric is defined for topology control. In CR-MANETs rerouting penalty denoted by δ is the period that occurred by rerouting is one of the metric. Rerouting penalty reduces link availability to (Ta - δ). In CR-MANETs it is requires fewer rerouting if selected path consists links of longer Ta and higher quality. Link weight can be estimated as, w = r * (Ta - δ) Where, r = link data rate, δ = rerouting penalty Here δ is converted into capacity loss r* δ during available period. Traffic carrying ability of link is represented by link weight. Path weight can be defined as, W = min wi i ϵ L Where, L = all links along the path In proposed scheme, focus is on to transmit more data traffic before link failure. Topology construction is three step process: Neighbor collection, path search and neighbor selection. For CR-MANETs to preserve end-to-end reliable paths, distributed cognitive algorithm uses enhancement of Localized Dijkstra Topology Control (LDTC) algorithm. With this distributed algorithm, each node performs following functions as an initial node. a) Neighbor Collection: Exchange local information within all its neighbors and calculate edge weight as, w = r * (Ta - δ) b) Path Search: For initial node set path weight to infinity and to zero for neighbors. Mark initial node as current one and all neighbors as unvisited. Calculate path weight by using following equation, from initial node to unvisited nodes. According to change in path weights update the records. W = min wi iϵL Then mark the current node as visited, and set the unvisited node with largest path weight as the current node. Until all nodes get visited repeat from step b). c) Neighbor Selection: Once all reliable paths from initial node are found, select first hop neighbors from initial node along these paths. C. Routing on Resulting Topology With link prediction, routing in CR-MANET considers both CU mobility and PU activity region. It makes routing adaptive to mobile environments by considering reliable paths. For example, with DSR and AODV routing protocols, node sends RREQ packets to find path from source to destination. When RREQ reaches an intermediate node, it may drop if transmitter does not exist in neighborhood relationship. Otherwise this RREQ is disposed by intermediate node and rebroadcast. Thus as a result, links in PU activity region or poor quality are avoided. With predicted link durations, performance of topology control can be evaluated. IV.

Simulation Results

The proposed scheme is evaluated using computer network simulator tool. In this random wireless environment nodes are moving randomly in a 2-D space. IEEE 802.11 is used for MAC layer. The performance evaluation of PCTC is plotted in Fig.2. Simulation results are in area 600×500m2 with 45 mobile CUs. The original topology shows that the link between 2 nodes exists whenever they are in each other’s transmission range. In original topology PU awareness is not considered. Whereas, in Fig.2 (b) and Fig.2 (c) shows that with cognitive link duration Ta, links around PU are avoided for communication. Thus the resulting topology makes reliable paths available for communication. We run simulation 50 times. Fig. 3 (a) shows proposed topology has small average node degree and small maximum node degree as compared to without topology control mechanism. The small node degree simplifies route-discovery process by reducing no. of RREQs and at same time it also reduces contention in shared wireless medium. Fig. 3 (b) shows topology control algorithm also results into longer link-durations. Thus the proposed

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topology provides cognition capability to avoid interference to PUs activities, which in turn provides adaptive routing over proposed topology in CR-MANETs.

(a)

(b)

(c) Fig. 2 Topology comparison (a) Original Topology (b) Topology with cognitive link-prediction (c) Prediction Control Adaptive Topology Control resulting topology.

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50 Avg aft topology control

Node degree

40 35

Avg before topology control

30 25

Max aft topology control

20 15 10

Max before topology control

5 0 30

No. of nodes

(a) 80

Link duration (s)

Avg before topology control

60 50

Avg aft topology control

40 30

Min before topology control

20 10 0 30

40 50 No. of nodes

Min aft topology control

(b) Fig. 3 Properties of resulting prediction based topology (a) Node degree (b) Link duration V.

Conclusion

Thus proposed system provides cognition capability to routing protocols in CR-MANETs which takes care of PU activities as well as CU mobility. With last longer links, reliable topology is constructed which makes protocol adaptive to mobility environment. With resulting topology, rerouting is reduced which increases overall end-to-end performance. References [1]

I.F. Akyildiz, W.-Y. Lee, M.C. Vuran, S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey”, Computer Networks 50 (13) (2006) 2127–2159. doi: 10.1016/j.comnet.2006.05.001.

[2]

A. Sampath, L. Yang, L. Cao, H. Zheng, “High throughput spectrum-aware routing for cognitive radio based ad-hoc networks”, in: 3th International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Crwown-Com 2008 G.-M. Zhu, I. Akyildiz, G.-S. Kuo, “STOD-RP: a spectrum-tree based ondemand routing protocol for multi-hop cognitive radio networks”, in: IEEE Global Telecommunications Conference, GLOBECOM 2008. A. Abbagnale, F. Cuomo, “Gymkhana: a connectivity- based routing scheme for cognitive radio ad hoc networks”, in: IEEE Conference on Computer Communications, INFOCOM 2010, pp. 1U5, doi:10.1109/ INFCOMW.2010.5466618. I. Filippini, E. Ekici, M. Cesana, “Minimum maintenance cost routing in cognitive radio networks, in: Mobile Adhoc and Sensor Systems”,2009, MASS Š09. IEEE 6th International Conference on, 2009, pp. 284U293, doi: 10.1109/MOBHOC. 2009.5336987 K. Chowdhury, M. Felice, “Search: a routing protocol for mobile cognitive radio ad-hoc networks”, Computer Communications 32(18) (2009) 1983-1997 K. Yang and Y. Tsai, “Link stability prediction for mobile ad hoc networks in shadowed environments, T in Proc. IEEE GLOBECOM, San Francisco, CA, Nov. 2006, pp. 1U5 S. Jiang, “An enhanced prediction-based link availability estimation for MANETs,T IEEE Trans. Commun., vol. 52, no. 2, pp. 183U186, Feb. 2004. Quansheng Guan,Shengming Jiang,“Prediction Based Topology Control and Routing in Cognitive Radio Mobile Ad hoc Networks”, November 2010

[3] [4] [5] [6] [7] [8] [9]

[10] M. Cesana et al.,“Routing doi:10.1016/j.adhoc.2010.06.009

cognitive

radio

networks:

Challenges

and

solutions”,

Hoc

Netw.(2010),

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