Ijetcas vol2 print by iasir journals

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

Issue 8, Volume 1, 2, 3, 4, 5 & 6 March-May, 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 eighth 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 eighth issue, we received 198 research papers and out of which only 100 research papers are published in six 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 eighth 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 (March-May, 2014, Issue 8, Volume 1, 2, 3, 4, 5 & 6). ---------------------------------------------------------------------------------------------------------------------------

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 (March-May, 2014, Issue 8, Volume 1, 2, 3, 4, 5 & 6) Issue 8 Volume 1 Paper Code

Paper Title

Page No.

IJETCAS 14-306

Laser Field Characteristics Investigation in the Chemisorption Process for the System Na/W (111) I. Q. Taha, J. M. Al-Mukh and S. I. Easa

01-12

IJETCAS 14-307

Optical Sensors for Control in Textile Industry T. Iliev, P. Danailov

13-16

IJETCAS 14-308

Generation of maps using a Pioneer 2DX mobile robot in a simulated environment Player/Stage Guillermo Ceme, Michel Garcia, Cinhtia González, Sergio González

17-22

IJETCAS 14-309

Borocarburizing of Construction Powder Metallurgy Materials of Fe - C - Cu System K.Popov

23-26

IJETCAS 14-312

Mathematical Analysis of Asymmetrical Spectral Lines J. Dubrovkin

27-36

IJETCAS 14-313

An automatic brain tumor detection and Segmentation scheme for clinical brain images Balakumar .B, Muthukumar Subramanyam, P.Raviraj, Gayathri Devi .S

37-42

IJETCAS 14-314

A Review on Graph-based Image Classification Mrs Snehal N. Amrutkar, Prof.J.V.Shinde

43-51

IJETCAS 14-315

Novel Method to Localize the Pupil in Eye Gaze Tracking Systems Mahesh R. Yadav, Sunil S. Shivdas

52-57

IJETCAS 14-316

Parallelizing Frequent Itemset Mining Process using High Performance Computing Sheetal Rathi, Dr.Chandrashekhar.Dhote

58-63

IJETCAS 14-317

X(3) measurements and optical limiting in Bismarck Brown Y dye Ketamm Abd AL-Adel and Hussain A. Badran

64-68

IJETCAS 14-318

Numerical Analysis of Wave Function Controlled by OLTP and Harmonic Oscillator in BEC Experiments Noori.H.N. Al-Hashimi; Waleed H Abid; Khalid M. Jiad

69-73

IJETCAS 14-319

Identifying Communication Intelligence for Drug Adoption in India Dipanjan Goswami, Neera Jain, Gour C. Saha, D. R. Agarwal,

74-82

IJETCAS 14-322

Design of Biosensor based on PH as a electrochemical transducer for early detection of Cancer Rajesh Laik, A.S.Vidyarthi, Vijay Nath, R.N.Gupta

83-93

IJETCAS 14-323

Design of ECG and EEG Hardware for Abnormality Identification using LabVIEW Ashok Kumar, Sekhar Kumar Patra,N Natarajan, S. Aparna,Sam Jeba Kumar

94-98

IJETCAS 14-325

SPGUP – Sequence analysis, Phylogenetic trees, Genome Diagrams Using Python - a Phylogenetic Tool Prof.Dr. P. K. Srimani and Mrs. Kumudavalli M.V

99-103

IJETCAS 14-326

Economic and Financial Feasibility Risks of Power Generation through Municipal Solid Wastes to Reduce Environmental Impacts, A Case Study based on Western Province in Sri Lanka Prof. S.W.S.B.Dasanayaka, Dr Gayan Wedawatta

104-117

IJETCAS 14-327

Mass Classification Method in Mammogram Using Fuzzy K-Nearest Neighbour Equality Laurence Aroquiaraj. I, Thangavel. K

118-122

Issue 8 Volume 2 Paper Code

Paper Title

Page No.

IJETCAS 14-329

Performance Comparison of Cosine, Walsh, Haar, Kekre and Hartley Transforms for Iris Recognition using Fractional Energies of the Transformed Iris Images Dr. Sudeep Thepade, Pushpa R. Mandal

123-127

IJETCAS 14-330

AODV ROUTING PROTOCOL MODIFICATION WITH STACK FOR VANET IN CITY SCENARIOS Arijit Modak, Soumen Saha, Palash Ray, Dr. Utpal Roy, Dr.D.D. Sinha

128-133

IJETCAS 14-335

A Steganography Technique for Hiding Information in Image Kamred Udham Singh

134-137

IJETCAS 14-336

A New Encryption Scheme Based on Enhanced RSA and ElGamal Mini Malhotra

138-142

IJETCAS 14-337

An Offline Signature Verification System: An Approach Based On Intensity Profile Charu Jain, Priti Singh, Aarti Chugh

143-146

IJETCAS 14-338

Classification of data using New Enhanced Decision Tree Algorithm (NEDTA) Hardeep Kaur Harpreet Kaur

147-152

IJETCAS 14-340

Impact of Aspect Oriented Programming on Software Maintainability - A Descriptive Study Sarita Rani, Puneet Jai Kaur

153-157

IJETCAS 14-344

A Critical Analysis of Pilot and Blind channel Estimation Techniques for OFDM systems Mr. Sivanagaraju.V, Dr. Siddaiah.P

158-165

IJETCAS 14-345

Cluster Performance Calculator for High-Performance Distributed Web Crawler Shishir Sarkar , Prateeksha Pandey

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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 Performance Comparison of Cosine, Walsh, Haar, Kekre and Hartley Transforms for Iris Recognition using Fractional Energies of the Transformed Iris Images Dr. Sudeep Thepade1,*, Pushpa R. Mandal2 Head of Department and Dean (R&D), Department of Information Technology, Pimpri Chinchwad College of Engineering Pune, India. 2 M.E Student, Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune, India.

Abstract: This paper presents a novel Iris feature extraction technique using fractional energies of transformed iris image. To generate image transforms various transforms like Cosine, Walsh, Haar, Kekre and Hartley transforms are used. The above transforms are applied on the iris images to obtain transformed iris images. From these transformed Iris images, feature vectors are extracted by taking the advantage of energy compaction of transforms in higher coefficients. Due to this the size of feature vector reduces greatly. Feature vectors are extracted in 5 different ways from the transformed iris images. First way considers all the higher energy coefficients of the transformed iris image while the rest considers 99%, 98%, 97%, and 96% of the higher energy coefficients for generating the feature vector. Considering fractional energies lowers the computations and gives better performance. Performance comparison among various proposed techniques of feature extraction is done using Genuine Acceptance Rate (GAR). Better Performance in terms of Speed and Accuracy is obtained by considering Fractional Energies. Among all the Transforms, Cosine and Walsh Transform gives good GAR value of 85% by considering 99% of Fractional Energy. Thus, using Fractional Energy gives better performance as compared to using 100% energies. The proposed technique is tested on Palacky University Dataset. Keywords: Discrete Cosine Transform, Feature vector, Haar Transform, Iris Recognition, Walsh Transform, Kekre Transform, Hartley transform. I. Introduction Iris Recognition is a biometric that uses a person’s iris patterns to uniquely identify an individual. It comes under biometrics because it makes use of person’s irises, which is a bio-logical characteristic of a person. Human iris has advantage that it is unique, stable and non-invasive in nature and hence it is the most reliable biometric. Iris Recognition has many biometrics based applications. It is growing very fast and has become a very challenging and interesting area in real life applications. An Iris recognition system firstly gathers the person’s one or more detailed eye image and then it generates a feature vector for that eye image and compares the generated feature vector with the feature vectors in the database. If a corresponding match is found, then that person is accepted else the person is rejected. Iris recognition has a wide range of security-related applications like access control, secure online transactions, time and attendance management system, government and law enforcement, passport-free automated bordercrossings, national ID systems, secure access to bank accounts at cash machines, internet security, antiterrorism, computer login, cell phones and other wireless-device based authentication [15]etc. There are many advantages of Iris recognition technology. The most important advantage of Iris Recognition technology is that irises are stable, so one enrolment can last a lifetime. Even for a single person his irises are same. Also, identical twins have different iris patterns and the left and right eye of the same person are also different. Moreover, from the age of two the iris pattern doesn’t change for a person. Also, it has the highest accuracy in comparison with other biometrics. II. Image Transforms A.

Discrete Cosine Transform(DCT)

DCT is a lossy image compression technique. When discrete cosine transform (DCT) is applied to an image, it separates the image into parts of differing importance. The DCT and discrete Fourier transform are both similar [3] since they both transform a signal or image from the spatial domain to the frequency domain. When DCT is applied to NXM image or matrix, all the low frequencies gets desegregated at upper left corner of the image

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([2], [4], [5]). These low frequencies represent the higher energies. These low frequencies represent much of the image information. These high energy values can be used to form a feature vector. B. Walsh Transform Joseph Leonardo Walsh proposed Walsh transform in the year 1923. Walsh transform contains only the entries +1 and -1([2], [6]-[9]). Each row of a Walsh matrix corresponds to a Walsh Basis function. The property of Walsh transform matrix is that the dot product of any two distinct rows or any two distinct columns is zero [5]. The sequence ordering of the rows of the Walsh matrix can be derived from the ordering of the Hadamard matrix by first applying the bit reversal permutation and then the Gray code permutation [7]. The Walsh matrix (and Walsh functions) are used in computing the Walsh transform and have applications in the efficient implementation of certain signal processing operations [8]. C. Haar Transform Haar transform is an orthogonal transform. The elements of Haar transform are derived from Haar matrix whose elements are either +1, 0, -1 multiplied by integer powers of . Haar transform has the advantage that it is fast, memory efficient and computationally simple [5]. D. Kekre Transform Kekre transform matrix can be of any size NXN. N need not be integer power of 2. All upper diagonal and diagonal elements of Kekreâ&#x20AC;&#x2122;s transform matrix are 1, while the lower diagonal part except the elements just below diagonal is zero [2]. E. Hartley Transform The Discrete Cosine Transform (DCT) utilizes cosine basis functions, while Discrete Sine Transform (DST) uses sine basis function. The Hartley transform utilizes both sine and cosine basis functions. III. Proposed Iris Recognition Technique The proposed method includes two modules. First is Feature Extraction module and second is Query Execution module. Following figure represents the block diagram of proposed method.

Results

Figure 1. Architecture of system

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The architecture consists of two modules: A. Feature vector Database creation The input is image of size NXN. Feature vector database is formed using following steps: 1. Separate the image into R, G and B components. 2. Apply transform on each to get transformed iris images. 3. Form feature vector depending upon the fractional energies considered. So, feature vector database is formed for various transforms and by considering varied percentages as 100%, 99%, 98%, 97%, and 96% of fractional energies. Also, for NXN image the size of feature vector is as follows: Size of Feature vector= mX3 Where, m= number of coefficients considered to form feature vector. B. Query Execution Above steps are repeated for query image and the generated query image feature vector is compared with all the feature vectors in database to find a match. Comparison between the query image feature vector and the feature vectors in database is done using the Mean squared error as similarity measurement criteria. IV. Implementation A. Platform Experiments are performed on Matlab R2008a version 7.6.0.324, Intel core 3 processor (4GB RAM and 2.24 GHz). B. Database The proposed method is tested on Palacky University Dataset. This database contains total 384 eye images. Images are of total 64 persons including images of both males and females. Total six images are taken per person i.e. 3 for left eye and 3 for right eye. The size of image is 768X576 pixels. All the images were taken in a single session [16]. Following are the sample images from the Palacky database. Person 1:

Person 3:

Person 5:

Left eye Right eye Figure 2. Sample images from Palacky Database

C. Similarity Measurement criteria The feature vectors are matched using Mean squared error. It is a similarity measurement criterion for matching the feature vectors. Mean squared error between two feature vectors x and y is calculated as follows,

1 MSE  N

(x  y ) i 1

(1)

Where, N is the size of the vectors to be compared. Low MSE indicates higher similarity between the feature vectors x and y. D. Performance Comparison metric Genuine Acceptance Rate (GAR) is used as a performance comparison metric to evaluate the performance of proposed iris recognition system.

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GAR is defined by following equation, (2)

V. Results and Discussions

GAR

To test the performance of the proposed method, total 384 queries were fired on the database containing 384 iris images. Matching between query feature vector and the feature vector in database is done using Mean Squared Error. Following figure 3 represents the GAR values using Cosine, Walsh, Haar, Kekre and Hartley Transforms. 90 85 80 75 70 65 60 55 50

100% 99% 98% 97% 96%

Figure 3. Performance comparison of transforms for respective percentage of fractional energies.

Following table shows the performance comparison between the proposed methods.

Transforms

Cosine

Walsh

Haar

Kekre

Hartley

Energy considered to form feature vector 100% 99% 98% 97% 96% 100% 99% 98% 97% 96% 100% 99% 98% 97% 96% 100% 99% 98% 97% 96% 100% 99% 98% 97% 96%

No. Of Transform Domain coefficient considered

Reduction in size of feature vector

256X256X3 0 5X3 196593 4X3 196596 4X3 196596 4X3 196596 256X256X3 0 10X3 196578 6X3 196590 5X3 196593 5X3 196593 256X256X3 0 20X3 196548 11X3 196575 8X3 196584 7X3 196587 256X256X3 0 3920X3 184848 2645X3 188673 2169X3 190101 1887X3 190947 256X256X3 0 9X3 196581 7X3 196587 6X3 196590 6X3 196590 Table1. Comparison of Methods

GAR

75% 85% 83% 83% 83% 75% 85% 85% 84% 84% 68% 75% 75% 75% 74% 56% 56% 56% 55% 55% 66% 68% 68% 67% 67%

Percentage improvement in GAR 0% 10% 8% 8% 8% 0% 10% 10% 9% 9% 0% 7% 7% 7% 6% 0% 0% 0% -1% -1% 0% 2% 2% 1% 1%

Results have shown that by considering fractional energies gives better results as compared to considering 100% energies. Also, the retrieval speed and computations are reduced greatly. Finally, Cosine and Walsh Transform gives better performance as compared to other transform.

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VI. Conclusion and Future Scope Since the iris patterns are unique for every individual, iris recognition is a reliable biometric. In this paper an attempt is being made to achieve good performance and higher accuracy. Better feature extraction techniques are proposed using various transforms and by considering fractional energies of the transformed iris image. Future scope will be to achieve much higher accuracy, improve the performance and achieve fast computational speed. References [1]

[2]

[3] [4] [5]

[6]

[7]

[8]

[9]

[10]

[11]

[12] [13]

[14]

[15] [16] [17]

[18]

Dr. Sudeep Thepade, Pushpa R. Mandal, “Novel Iris Recognition Technique using Fractional Energies of Transformed Iris Images using Haar and Kekre Transforms”, International Journal Of Scientific & Engineering Research Volume 5, Issue 4, April-2014. Dr. Sudeep D. Thepade, Pooja Bidwai, “Iris Recognition using Fractional Coefficients of Cosine, Walsh, Haar, Slant, Kekre Transforms and Wavelet Transforms”, International Journal of Emerging Technologies in Computational and Applied Sciences, June- August, 2013, pp. 141-146. M. Mani Roja, Dr. Sudhir Sawarkar, “Iris Recognition using Orthogonal Transforms”, M. Mani Roja et al. /International journal of Engineering and Technology(IJET). M. Sarhan, "Iris recognition using discrete cosine transform and artificial neural networks", Journal of Computer Science, vol. 5, no. 5, pp. 369-373, 2009. Dr. H. B. Kekre, Dr. Tanuja K., Pratik Bhatia, Sandhya N., “Iris Recognition using Partial Coefficients by applying Discrete Cosine Transform, Haar Wavelet and DCT Wavelet Transform”, International Journal of Computer Applications (0975-8887) Volume 32-No.6, October 2011. Dr. H. B. Kekre, Sudeep D. Thepade, Juhi Jain, Naman Agrawal, “Iris Recognition using Texture Features Extracted from Walshlet Pyramid”, ACM-International Conference and Workshop on Emerging Trends in Technology (ICWET 2011). Thakur College of Engg. And Tech., Mumbai, 26-27 Feb 2011. Dr.H.B.Kekre, Sudeep D. Thepade, Akshay Maloo,"Face Recognition using Texture Features Extracted form Walshlet Pyramid", ACEEE International Journal on Recent Trends in Engineering and Technology (IJRTET), Volume 5, Issue 1, www.searchdl.org/journal/IJRTET2010 . Dr. H. B.Kekre, Dr. Tanuja K. Sarode, Sudeep D. Thepade and Ms. Sonal Shroff, "Instigation of Orthogonal Wavelet Transforms using walsh, Cosine, Hartley, Kekre Transforms and their use in Image Compression", (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 6, 2011. Dr.H.B.Kekre, Sudeep D. Thepade, Juhi Jain, Naman Agrawal, "Performance Comparison of IRIS Recognition Techniques using Wavelet Pyramids of Walsh, Haar and Kekre Wavelet Transforms", International Journal of Computer Applications (IJCA), Number2, Article4,March2011. Dr. H. B. Kekre, Sudeep D. Thepade, Akshay Maloo, “Performance Comparison of Image Retrieval Using Fractional Coefficients of Transformed Image Using DCT, Walsh, Haar and Kekre’s Transform”, International Journal of Image Processing (IJIP) Volume (4): Issue (2). Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Akshay Maloo,” Comprehensive Performance Comparison of Cosine, Walsh, Haar, Kekre, Sine, Slant and Hartley Transforms for CBIR with Fractional Coefficients of Transformed Image”, International Journal of Image Processing (IJIP), Volume (5) : Issue (3) : 2011. Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Saurabh Gupta, “Content Based Video Retrieval in Transformed Domain using Fractional Coefficients”, International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013. Dr. H. B. Kekre, Dr. Sudeep D. Thepade, Varun K. Banura, Ankit Khandelwal, “Augmentation of Image Retrieval using Fractional Coefficients of Hybrid Wavelet Transformed Images with Seven Image Transforms”, International Journal of Computer Sci ence And Technology Vol. 3, Issue 1, Jan. - March 2012. H. B. Kekre, Sudeep D. Thepade, Ratnesh N. Chaturvedi, “ NOVEL TRANSFORMED BLOCK BASED INFORMATION HIDING USING COSINE, SINE, HARTLEY, WALSH AND HAAR TRANSFORMS”, International Journal of Advances in Engineering & Technology, Mar. 2013. http://www.cl.cam.ac.uk/~jgd1000/applics.html. Palacky University iris database, http://www.advancesourcecode.com/irisdatabase.asp. (Last referred on 10 August 2013). John Daugman, "How Iris Recognition works",IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 1, JANUARY 2004.R.P. Wildes, "Iris recognition: an emerging biometrics technology", Proc. IEEE 85 (1997) 13481363. KevinW. Bowyer, Karen P. Hollingsworth, and Patrick J. Flynn, "A Survey of Iris Biometrics Research: 20082010",M.J. Burge and K.W. Bowyer (eds.), Handbook of Iris Recognition, Advances in Computer Vision and Pattern Recognition, SpringerVerlag London 2013.

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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 AODV ROUTING PROTOCOL MODIFICATION WITH STACK FOR VANET IN CITY SCENARIOS Arijit Modak1, Soumen Saha2, Palash Ray3, Dr. Utpal Roy4, Dr.D.D. Sinha5 Department of Computer Science and Engineering, Haldia Institute of Technology, Haldia, West Bengal, India 2 Department of Computer Science and Technology, ICV Polytechnic, Jhargram, West Bengal, India 3 Dept of Computer Science and Engineering, Haldia Institute of Technology, Haldia, West Bengal, India 4 Department of Computer & System Sciences, Siksha-Bhavana, Visva-Bharati, West Bengal, India 5 Department of CSE, University of Calcutta, Kolkata, West Bengal, India __________________________________________________________________________________________ Abstract— Vehicular ad hoc network (VANET) is the sub part of mobile ad hoc network (MANET). It provides Intelligent Transport System i.e., provides wireless communication among vehicles and vehicle to roadside equipments. Based on this communication road network classified into two types 1) vehicle to vehicle communication, 2) vehicle to infrastructure communication. According to today’s traffic condition VANET technique is very useful technique for achieving safe and secure transport system. For introducing this technique, there are several types of routing protocols are developed. But all these routing protocol does not work efficiently in VANET. In this paper, we proposed a modified AODV routing protocol with the help of stack introduction into the RREQ header. It is based on packet delivery, packet drop and throughput. It shows less information of packet transmission compare to original AODV. Hence our proposal has less overhead routing algorithm compared to original AODV. Key words —VANET, stack, AODV, NCTUns-6.0 __________________________________________________________________________________________ 1

I. INTRODUCTION In today’s world the traffic system is very much complicated because a huge congestion due to heavy vehicle and human beings. For giving them secure and safety traffic control system , VANET technology is introduced. There are three types of VANET communications those are shown in below,

VANET is the one kind of Mobile Ad-hoc Network (MANET) but it is autonomous and self organizing wireless communication network. For creating autonomous wireless communication, among vehicles on the road for the help of traffic condition, safety, unique identity of every vehicle etc. For this autonomous wireless, every vehicle act as a node and involve themselves as a server or client for delivering information among themselves. For implementing VANET in traffic system , there are various type of routing protocol developed.

Proactive routing protocol: In proactive routing protocol, routing information i.e., next hop forwarding technique is maintained by back ground irrespective of communication request. As this routing information scheduled by routing table, so there is no need to route discovery process. It is the advantage of proactive routing protocol and the great disadvantage is that it provides low latency in real life application. The various type of proactive routing protocols are FSR, DSDV, OLSR. Reactive routing protocol: In Reactive routing protocol, routing information i.e., next hop forwarding techniques handle dynamically. When it necessary to communicate with other nodes then it needs to create rote discovery by broadcasting a message and this process continues until the destination is found. The various types of reactive routing protocols are AODV, DSR, and TORA.

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AODV Ad hoc On-demand Distance Vector (AODV) routing protocol is one of the important routing protocol used in VANET system. It is known that AODV is a reactive routing protocol which is based on topology based routing protocol. This AODV routing algorithm enables dynamic, self starting, multi-hop routing between participating mobile nodes wishing to establish and maintain an ad-hoc network [1]. As AODV routing algorithm is dynamic so it also allows highly mobile nodes to create a routes very fast for getting new destination, and the nodes which are not connected is not necessary to maintain the routes. As AODV used in VANET system ,so it allows the nodes to break a linkage from a network and can join this node to another network. But during packet delivery time AODV does not allows the loop(closed path) and the shortest path is measured by Bellman-Ford “counting to infinity problem”

Fig:1 AODV working mechanism AODV CONTROL MESSAGES AODV routing protocol defines three types of control messages for discovering and maintaining the routes, those are Route Request(REREQ) , Route Reply(RREP) and Route Error(RRER) packets[2]. RREQ: The first operation of AODV is route discovery process for delivering packet from source to destination. For that source node broadcast RREQ message to its all neighbor nodes and those nodes again broadcasts the same packet to its neighbors. This process is continued until the destination node is found. The RREQ packet format[1] shown in below,

Fig 2: RREQ packet format RREP: The node which is either destination node valid node to the destination unicast the RREP message to its adjacent previous node from which it got the RREQ message first. In this way source to destination path is created. This RREP packet format[1] shown in below,

Fig3: RREP packet format RRER: When any node detects a breakage in active route , then error message is generated. The node where the breakage occurs ,then this node send the RRER message to the next node in the way of destination.

Fig4: RRER packet format

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That node will take the ip address from the routing table and RREP message resend in the new way. The RRER packet format shown in above. Throughput Throughput is the average number of successfully delivered data packets on a communication network or network node. In other words throughput describes as the total number of received packets at the destination out of total transmitted packets [6]. Throughput is calculated in bytes/sec or data packets per second. The simulation result for throughput in NCTUns6.0 shows the total received packets at destination in KB/Sec, mathematically throughput is shown as follows: Total number of received packets at destination* packet size Throughput (kb/sec) = ----------------------------------------------------------------------------------------------kb/sec Total simulation time II. RELATED WORK Dharmendra Sutariya et. Al[3] in 3012, proposed a routing protocol AODVLSR(AODV Limited Source Routing) that ensures giving timely and accurate information to drivers in V2V communication compare to AODV protocols in city scenarios of vehicular ad-hoc networks and AODV is defined as limited source routing up to two hops for network nodes. The performance of proposed AODVLSR protocol is compare with basic AODV protocol in terms of Packet Delivery Ratio , Avg. End-to-End delay, Dropped TCP packets and normalized routing load. Annu Mor[4] in 2013, proposed cross layer technique that find channel security at link layer to AODV routing protocol to improved the communication in vehicles for safety. Gulhane S.P. et. al[5] in 2012, proposed the vehicular ad-hoc networks and the typical routing protocol. The adhoc on demand routing protocol(AODV) in mobile ad-hoc networks and the optimized protocol AODV-OBD for protocol AODV. Aswathy M C et. al [2] in 2012,proposed at improving the performance of AODV by enhancing the existing protocol by creating table clusters and perform coming by clusters nodes and gateway nodes. Uma Nagaraj et. al[6] proposed the advantages/disadvantages and the applications of various routing protocols for vehicular ad-hoc networks. It explores the motivation behind the designed, and trace the evolution between routing protocols. Uma Nagaraj et. al[7] in 2012 , proposed to compare four well-known protocols AODV,DSR,OSLR and DSDV by using three performance metrics packet delivery ratio, average end to end delay and routing overhead. V.P.Patil [8] in 2012, proposed an innovative approach to deal with the problem of traffic congestion using the characteristics of vehicular ad-hoc networks. The system is developed and tested using AODV protocol of ad-hoc mobile network to deal with the problem of vehicle traffic congestions in vehicular ad-hoc networks. The performance is measured in terms of no. of packet broadcasted, percentage of packet delivery and percentage of packet diverted and overhead to manage the problem of data traffic congestion in computer networks. Rakesh Kumar et. al [9] in 2011, an extended AODV routing protocol proposed for AODV networks which typically suits to resolve the realistic model problems. This propose protocol may improve the performance of regular AODV routing protocol. It has all features of AODV routing protocol, at is follows all the steps of the discovery algorithm of AODV routing protocol. Neeraj Sharma et. al[10] in 2013 performed analysis the AODV and GPSR routing protocol used in VANET and concluded them. III. PROPOSED WORK Proposed modified AODV routing protocol by implementing a stack on the basis of packet collision, packet drop and in-out throughput. The fact is that when a intermediate node between source and destination gets some packet from the another node which had got from any other node will push the nodeâ&#x20AC;&#x2122;s IP to the generated stack instead od discarded. During unicast the RREP packet if any link is breakage, then this node pop the another node from the created stack and re-unicast the RREP packet . For implementing the modified AODV protocol the proposed algorithm is, Algorithm of sAODV: Step 1: Until destination is not found continue the broadcast procedure. Step 2: If node n is the destination then Step 2.1: Reply RREP packet. Step 3: else Step 3.1: If node (n-m) already received the packet, Step 3.1.1: Push the packet into stack. Step 3.2: else Step3.2.1: The intermediate node rebroadcast the packet. Step 4: Until source node get the RREP message, the destination and the intermediate node unicast the RREP packet.

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Step 4.1: If any node does not get acknowledgement with in the time, Step 4.1.1: Pop the element from the stack and update the sequence number and IP address the re-unicast RREP packet. Step 4.2: else Step 4.2.1: If the node is Source node then in can ready to send data packet. Step 4.2.2: Otherwise unicast the RREP packet.

Fig5: on error Complexity analysis: As the conventional AODV followed the bellman-ford algorithm , so the complexity should be O(VE) where V is the number of vertices and E is the number of edges. As we implementing a stack in this protocol so the complexity of sAODV will be [O(VE)+O(V)] =O(V2E) ] which is more than conventional AODV but the great advantage of this protocol is that , in unicasting RREP packet if the previous adjacent node does not exist it follows the another path choosing the previous node that is pop out from the stack and it will continue the communication between the sender and destination rather than destroy the graph. IV. Simulation Result and analysis In this study, we used NCTUns-6.0 for simulation. We have chosen this simulator because[12], 1. Highly integrated and professional GUI environment. 2. Support for various network protocols. 3. Support for various important network. 4. Same configuration and operations as for real life networks. 5. High simulation speed and repeatable simulation result. 6. High fidelity simulation results. Simulation scenario: We have taken a mess scenario(city scenario) as our proposed algorithm for routing based on AODV ,for dense traffic.

Fig 6: City Scenario Graph Parameter Transmission mode Lane Width Simulation time RTS threshold The car profile (Taken five) Number of lane The protocol standard used for each vehicular node cars are selected for three different scenarios Transmission power used

Settings TCP/IP 20m 100sec 3000bytes 18km/H, 36km/H, 50km/H, 60km/H, 80km/H 2 AODV,sAODV IEEE802.11b 5,10,15,20,25,30,35,40 15dbm

Table: input parameter for testing scenario

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V. Result X-axis sx-axis stands for time in sec and y-axis stands for throughput(kb/sec).

sAODV AODV AODV sAODV

Fig 7: Throughput vs Time for 5 cars

Fig 11: Throughput vs Time for 25 cars

sAODV AODV AODV sAODV

Fig 8: Throughput vs Time for 10 cars

Fig 12: Throughput vs Time for 30 cars

AODV

sAODV

Fig 9: Throughput vs Time for 15 cars

Fig 13: Throughput vs Time for 35 cars

sAODV

AODV

AODV sAODV

Fig 10: Throughput vs Time for 20 cars

Fig 14: Throughput vs Time for 40 cars

VI. ANALYSIS Here we found initially both protocol’s throughput are almost same but after some time sAODV much better than original AODV. Here we have taken low car density (5) then packet loss will be much more therefore our stack utilization is better here. Next when we increase our car density (10,15,20,25) ,we observe initially sAODV throughput is better than conventional AODV as initially packet loss is much more as synchronization is not done therefore our protocol works better. But as a progress original AODV works better compare to sAODV. Here packet loss is much less therefore stack utilization is less here. Therefore sAODV’s throughput is less than original AODV’s throughput. Finally we found in fig(12,13,14), almost similar performance where average throughput of sAODV is less compare to original AODV. Hence it proves when we need not to utilize stack or failure of node is less , more number of packet transmitted by original AODV. Whereas sAODV need to send less number of packet to reply. Therefore we observe sAODV is better than conventional AODV with respect of number of packet transmission to complete routing.

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VII. CONCLUSION AND FUTURE WORK As we have proposed a backup path(stack) information for reply ,AODV protocol works better. Though sAODV takes more time but it has a great preference that is the graph cannot disconnect due to breakage of any link. So that the packet delivery is continuously going on several paths between source to destination. Our future work is that we will modified this AODV protocol by using de-queue and compare with original AODV protocol as stack has much more time complex.

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

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

[10]. [11]. [12].

REFERENCES RFC 3561 Aswathy M C,Tripathi C. “a cluster based enhancement to AODV for inter-vehicular communication in VANET.” International Journal of Grid Computing and Applications(IJGCA). Vol no.3,september 2012,pp.41-49. Dharmendra Sutariya,Ronak Solanki and Pratik Mewada. “AODVLSR: AODV limited soyrce routing protocol for VANET in city scenarios”. International Journal of Computer Networking,Wireless and Mobile Communications (IJCNWMC). ISSN 22501568 Vol. 2 Issue 4 Dec – 2012,pp.7-16 Annu Mor. “ a study of improved AODV routing protocol in VANET”. International Journal of Computer Applications & Information Technology.Vol. II, Issue I, January 2013 (ISSN: 2278-7720),pp.1-3. Gulhane S.P.,Joshi A.A. and Chavan K.L. “optimized AODV routing protocol for vehicular ad hoc network”. International Journal of Networking, ISSN:2249-278X & E-ISSN 2249-2798, volume 2, Issue 1,2012,pp.55-59. Uma Nagaraj, Dr. M. U. Kharat, Poonam Dhamal. “Study of Various Routing Protocols in VANET”. IJCST Vol. 2, Isssue 4, ISSN : 0976-8491(Online) | ISSN : 2229-4333(Print), Oct . - Dec. 2011,pp.45-52 Uma Nagaraj, Poonam Dhamal. “performance evaluation of proactive and reactive routing protocol in VANET”. International Journal of Information and Education Technology, vol. 2,no. 5,October 2012,pp.434-438. Patil V.P. “VANET Based Traffic Management System Development And Testing Using AODV Routing Protocol.”International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue.5,Page 1682-1689. Rakesh kumar, Siddharth Kumar, Sumit Pratap Pradhan, Varun Yadav. “Modified route-maintenance in AODV Routing protocol using static nodes in realistic mobility model”. International Journal on Computer Science and Engineering (IJCSE). ISSN : 0975-3397, Vol. 3, No. 4 ,Apr 2011, pp.,1554-1562. Neeraj Sharma,et. al. “Performance analysis of AODV &GPSR routing protocol in VANET”. International Journal of Computer Science & Engineering Technology (IJCSET)ISSN : 2229-3345 ,Vol. 4 ,No. 02 ,Feb 2013 , pp.104-112. http://elearning.vtu.ac.in/15/E-Notes/NW%20prog%20lab/NCTUns%20Manual.pdf Shie-Yuan Wang, Chih-Che Lin, and Chao-Chan Huang.“nctuns tool for evaluating the performances of real-life p2p applications”. In: Peer-to-Peer Networks and Internet Policies,Editor: Diego Vegros and Jaime S´aenz, pp. 1-23, ISBN 978-160876-287-3,2010 Nova Science Publishers, Inc.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A Steganography Technique for Hiding Information in Image Kamred Udham Singh Department of Computer Science, Faculty of Science Banaras Hindu University, Varanasi, (U.P.), INDIA _______________________________________________________________________________________ Abstract: In the area of Information Security, hiding the facts and communication from third parties is the most important factor of information technology. Security is concerned with the protection and safeguard of that information which, in its various forms can be identified Information Assets. To protect the Information from the third party we use the message hiding technique called steganography. Steganography is art and science of invisible communication. This paper deals with image steganography. For hiding secret information in images, there are number of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Various steganographic algorithms like Least Significant Bit (LSB) algorithm, Jsteg and F5 algorithms, from all of these.We are going to use LSB algorithm. This paper gives a brief idea and overview of image steganography that make use of Least Significant Bit (LSB) algorithm for hiding the data into image. Keywords: Decryption, Encryption, LSB (Least Significant Bit, Steganography), Stego key __________________________________________________________________________________________ I. Introduction The most important factor of information technology and communication is the security of information. The idea of information hiding is not new in the history. Various methods have been developed to hide data in order to keep the message secret. Sometimes it is not enough to keep the contents of a message secret but it may also be necessary to keep the existence of the message secret. Steganography is the art and science of invisible communication of messages. It is implemented by hiding the existence of the communicated information in image, video and audio. The word steganography is derived from the Greek words “stegos” meaning “cover” and “grafia” meaning “writing”[1] defining it as “covered writing”. The graphical representation of Steganography system presented in fig: 1.

Figure: 1

The idea and practice of hiding information has a long history. In Histories the Greek historian Herodotus writes of a nobleman, Histaeus, who needed to communicate with his son-in-law in Greece. He shaved the head of one of his most trusted slaves and tattooed the message onto the slave’s scalp. When the slave’s hair grew back the slave was dispatched with the hidden message [2]. The difference between Steganography and Cryptography is that the cryptography focuses on keeping the contents of a message secret whereas steganography focuses on keeping the existence of a message secret [3]. Steganography and cryptography both techniques are protecting information from undesired parties. There are two other technologies which are related to steganography are watermarking and fingerprinting. There are three components in steganography structure: Carrier image,

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Message, Key. This paper describes the LSB algorithm used for image steganography to the security potential of steganography for business and personal use. In this paper, we describe the LSB algorithm used for image steganography to the security potential of steganography for business and personal use. Section II described basics of Encryption and Decryption techniques. Section III discuss about compression of image files i.e. lossy or lossless. In Section IV surveyed of least significant bit (LSB) algorithm and how it works on image files. Finally, Section V concludes the Paper. II. Encryption and Decryption In encryption the secret information is hiding in image file. In Decryption secret information is extract from image file. When a text message embedded with image a key is needed. This key is used to help in encryption and it decided that where the information should be hidden in the image. A short text can be use as a key.

III. Image Compressions Image compression is a process of minimizing the size in bytes of a graphics file without demeaning the quality of the image. Microsoft Document Imaging Format (MDI) or Tagged Image File Format (TIFF), image compression is used to reduce the size of the file. There are two types of image compression: lossy and lossless [4]. These both methods are used to save storage space, but the implementing procedures are distinct. Lossy image compression creates slighter files by dumping excess image data from the original image. And it also removes that details which are too tiny for the human eye to differentiate [6].On the other side Lossless image compression never removes any information from the original image, but instead represents data in mathematical formulas [6]. The original imageâ&#x20AC;&#x2122;s integrity is maintained and the decompressed image output is bit-by-bit identical to the original image input [4]. Lossless compression image formats are GIF (Graphical Interchange Format) and 8-bit and 24 bit BMP (Bitmap) [5]. Lossless image compression keeps the original digital image intact without the chance of lost. IV. Concepts of Image File An image is a collection of numbers of pixels. These pixels are displayed horizontally row by row. The number of bits in a color scheme, called the bit depth, refers to the number of bits used for each pixel. The smallest bit depth is 8, it means that there are 8 bits used to describe the color of each pixel. Grayscale images use 8 bits for each pixel and able to display 256 different colors. Digital color images are stored in 24-bit files and use the RGB color model that is known as true color. All colors of 24- bit image are most frequently represented as additive combinations of red (R), blue (B), and green (G), and each primary color is represented by 8 bits. In one given pixel, there can be 256 different quantities of red, green and blue [7]. We can represent over 16 million colors with 24 bits. The representation of RGB colors in a [R, G, B] format is [0, 0, 0] for black, and [255, 255, 255] for white. Grayscale colors will be represented by a single number for example black has a value of 0, white is 255 and light gray is 200 V. Images and Transform Domain Image steganography techniques are two type first is Image Domain and second is Transform Domain [8]. In transform domain, images are first transformed and then the message is embedded in the image [10]. Image domain techniques cover bit-wise methods that apply bit insertion and noise manipulation [11]. Lossless image compression formats are most suitable for image domain steganography techniques are usually dependent on the image format [12]. Steganography in the transform domain involves the manipulation of algorithms and image transforms [11].These methods hide messages in more significant areas of the cover image, making it more robust [9].

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VI. LEAST SIGNIFICANT BIT TECHNIQUE Least significant bit (LSB) insertion is a most popular and easy approach to hide information in carrier image file. In the LSB method byte is replaced with an M‟s bit. This technique is best for image steganography. To the human eye the stego image will look identical to the carrier image. An image file is simply a file which shows different colors on the different areas of an image. The most popular image formats that use lossless compression is 24 Bit BMP (Bitmap), use for hiding information. It is a easier to hide information inside in a high quality and resolution image. Due to their size 24 Bit images are best for hiding information. But you can also choose 8 Bit BMP‟s or another image format such as GIF [13]. The least significant bit is used to change with the bit of secret message. In 24-bit image, one pixel can store 3 bits by changing a bit of each of the red, green and blue color components. For example we have three adjacent pixels (9 bytes) with the RGB encoding [14] (figure 4)-

10010100 00001100 11001001 10010111 00001110 11001011 10011111 00010001 11001011

10010101 00001101 11001000 10010110 00001111 11001010 10011110 00010000 11001010

Figure: 4

Figure: 5 Table 1: Advantage of LSB algorithm

Binary representation of number 400 is 110010000 embedded into the least significant bits of this part of the image. If we overlay these 9 bits over the LSB of the 9 bytes above we get the following (where bits in bold and underline have been changed) (figure5) Number 400 was embedded into the grid and LSB have changed according to the embedded message. VII. LSB algorithm  Select an image of size M*N for an input.  The message to be hidden in RGB component only of an image.  Use a pixel selection filter to obtain the best areas to hide information in the cover image to obtain an enhanced rate. The filter is applied to Least Significant Bit (LSB) of every pixel to hide information, leaving most significant bits (MSB).  After that Message is hidden using Bit Replacement method.

Fig: 6 Algorithm of Least Significant Bit

Benefits of LSB Algoritm: There are two main advantage of Least Significant Bit algorithm which described in following table: Message Security A least significant bit technique uses 24 bit BMP images, because it is a lossless compression image formats. In a 24 bit BMP image for hiding the secret data , there is the need of large cover image. Before embedding it in digital image message data is converted in digital image in to byte. So this approach is secure, prevent from liability.

Reduce Distortion Rate Mostly images are used in steganography as a cover objects. 24 bit BMP (Bitmap) is use for hiding information because it is a lossless compression image formats. To hide the message in image, first it is converted into byte format and then stored in byte array. After that message is encrypted and then embeds each bit into the LSB position of each pixel position. This approach involves change in least significant bit (LSB) of each pixel byte. Hence reduces the distortion rate that is look of original image.

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VIII. Result and conclusion In this paper the existing Least Significant Bit Algorithm has been analyzed and found can effectively hide a message inside a digital image file. There are many applications of image steganography which are used for secretly and covertly communication. Main use of image steganography is, communication of high- level or top-secret documents between international governments. Image steganography has many illegal uses as it can be used by hackers to send viruses and Trojans. Image steganography increase the security of the message by hiding it in a less obvious location. IT also describes the benefits from approach like security of message increases and distortion rate has reduced. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

Moerland, T., “Steganography and Steganalysis”, Leiden Institute of Advanced Computing Science, www.liacs.nl/home/ tmoerl/privtech.pdf Silman, J., “Steganography and Steganalysis: An Overview”, SANS Institute, 2001 Wang, H & Wang, S, “Cyber warfare: Steganography vs. Steganalysis”, Communications of the ACM, October2004 Moerland, T., “Steganography and Steganalysis”, Leiden Institute of Advanced Computing Science, www.liacs.nl/home/ tmoerl/privtech.pdf Johnson, N.F. & Jajodia, S., “Exploring Steganography: Seeing the Unseen”, Computer Journal, February 1998 “Reference guide: Graphics Technical Options and Decisions”,http://www.devx.com/projectcool/Article/19997 Owens, M., “Adiscussion of covert channels and steganography”, SANS Institute, 2002 Silman, J., “Steganography and Steganalysis: An Overview”, SANS Institute, 2001 Wang, H & Wang, S, “Cyber warfare: Steganography vs. Steganalysis”, Communications of the ACM,47:10, October2004 Lee, Y.K. & Chen, L.H., “High capacity image steganographic model”, Visual Image Signal Processing, 147:03, June 2000 Johnson, N.F. & Jajodia, S., “Steganalysis of Images Created Using Current Steganography Software”, Proceedings of the 2nd Information Hiding Workshop, April1998 Venkatraman, S., Abraham, A. & Paprzycki, M., “Significance of Steganography on Data Security”, Proceedings of the International Conference on Information Technology: Coding and Computing, 2004 V. Lokeswara Reddy, Dr.A.Subramanyam, Dr.P. Chenna Reddy, “Implementation of LSB Steganography and its Evaluation for Various File Formats”, Int. J. Advanced Networking and Applications 868 Volume: 02, Issue: 05, Pages: 868-872 (2011) T. Morkel, JHP Eloff and MS Olivier, "An Overview of Image Steganography," in Proceeding of the Fifth Annual Information Security South Africa Conference (ISSA2005), Sand to South Africa, June/July 2005

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A New Encryption Scheme Based on Enhanced RSA and ElGamal Mini Malhotra Department of Computer Science, Lovely Professional University, Punjab, India

______________________________________________________________________________ Abstract: An encryption scheme based on the integration of Enhanced RSA and Elgamal algorithm is introduced. Enhanced RSA algorithm is based on Integer Factorization Problem (IFP). On the other hand, Elgamal algorithm is based on Discrete Logarithm Problem (DLP). A combination of IFP and DLP is proposed. A comparison has been conducted for different public key encryption algorithms at different data size. The encryption time and throughput of the naive scheme is computed and compared with the hybridized system of RSA and Elgamal algorithm. The aim of this paper is to make the novel algorithm efficient than the existing system as described above. As a result, the proposed algorithm holds an increased throughput and decreased encryption time as compared to the Elgamal and existing hybridized system of RSA-Elgamal. Keywords: Cryptography, RSA, Enhanced RSA, Elgamal, IFP, DLP, Encryption __________________________________________________________________________________________ I. Introduction Cryptography is an art of writing and reading the secret information. It uses mathematics in science to protect the information. It is a method of encrypting the original information into a form that is not easily interpreted by anyone. Original message can be revealed only after decrypting the encrypted message. Public and private keys are used for this purpose. Generally, the cryptographic systems can be classified into symmetric and asymmetric. In symmetric cryptography, same key is used for the encryption and decryption whereas in asymmetric cryptography separate keys are used for the encryption and decryption process [1]. This paper is based on asymmetric cryptosystem and introduces an algorithm based on Enhanced RSA and Elgamal cryptosystem. The Enhanced RSA is based on Integer Factorization Problem (IFP). Enhanced RSA uses three prime numbers to generate the public and the private keys. It enables faster encryption and decryption process and generates the public and the private key faster than the original RSA [3]. The Elgamal cryptosystem is based on Discrete Logarithm Problem (DLP) [2]. Now days there are many algorithms available to solve the DLP problem of small size numbers within a reasonable time span. To improve the strength of these algorithms, a combination of Enhanced RSA and Elgamal is used. This will provide a higher level of security. This paper is based on the combination of IFP and DLP to provide a more efficient and secure system than the existing Elgamal and RSA-Elgamal system. II. RSA and Enhanced RSA RSA is a public key encryption algorithm developed by Ron Rivest, Adi Shamir and Leonard Adleman [3]. For signing as well as encryption, RSA was the first known suitable algorithm. Three steps are involved in RSA: Key generation, Encryption and Decryption. The following are the shortcomings of RSA which are used to break the algorithm, when we use two prime numbers:  Small encryption exponent, sending the same message to different recipients using a small exponent like e=4.  Same key used for encryption and signing. Enhanced RSA is based on the RSA algorithm. The RSA algorithm is enhanced using an additional third prime number in the generation of the N. This speeds up the encryption and the decryption process and generates the public and private key faster than the original RSA [4]. The steps of Enhanced RSA algorithm are as follows: A. Generation of Public and Private keys Following are the steps for the generation of public and private keys:  Choose three distinct prime numbers p1, p2 and p3. Multiply them to get ‘n’.  Calculate (p1 -1) * (p2 -1) * (p3 -1) and mention it as ᴓ(n).  Select ‘e’ as a public key, such that e and ᴓ(n) are relatively prime.  Compute e*d = 1(mod ᴓ(n)) and consider ‘d’ as the private key.

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B. Encryption Scheme The message M is encrypted into cipher text C using the public key ‘e’ such that C = Me mod n. C. Decryption The cipher text C is decrypted back to its original form M with the help of the private key ‘d’ such that M = Cd mod n. The following are the advantages of Enhanced RSA algorithm over RSA algorithm:  Increased difficulty of analysis of variable N.  Faster generation of the keys.  Faster encryption and decryption process. III. ElGamal Elgamal is an asymmetric key algorithm developed by Taher Elgamal in the year 1984. It is based on DiffieHellman key exchange algorithm [5] and works over finite fields [6]. The security of this algorithm is based on Discrete Logarithm Problem (DLP). The steps involved in the Elgamal algorithm are as follows: A. Initialization Before the encryption and decryption process can start, the following initialization is done:  Choose a random prime p and a primitive root element ‘a’ ԑ Fa.  Private key ‘x’ is chosen as a random number such that ‘x’ ԑ ᴜ Fa-1.  Public key ‘y’ is computed using the private key ‘x’. Therefore, y = ax mod p. B. Encryption Scheme The sender chooses a random integer k ԑ ᴜ Fa-1 and computes one time key K = yk mod p. The message M is encrypted into two parts (C1 and C2) as ak mod p and K*M mod p respectively. C. Decryption The cipher text is decrypted as M = C2 K-1 mod p using one time key K = C1x mod p. IV. Proposed Method The proposed algorithm uses three large prime numbers to generate the public and private keys. The generated public and private keys are then passed to the Elgamal cryptosystem. In this method, we are integrating the IFP and DLP techniques. The proposed method is an integration of the Enhanced RSA and Elgamal cryptosystem. This method is more efficient than the provenance of RSA, Elgamal and merge between the RSA and Elgamal algorithm [7]. The working of proposed method is explained as follows: A. Generation of Public and Private keys The key generation involves the following steps: Choose primitive finite field Fa and a primitive root element ‘a’ ԑ Fa. Then choose 3 large prime  numbers and multiply them to get ‘n’. Calculate (p1 -1) * (p2 -1) * (p3 -1) and name it as ᴓ(n) .  Choose public key ‘e’: gcd(e, ᴓ(n)) = 1.  Compute private key ‘d’ = e-1 mod ᴓ (n).  B. Encryption Choose another large prime number q1. The message to be sent is M. Here, the message M will be converted into cipher text in two parts namely C1 and C2. The cipher text is calculated using the one time key K. The following computations are done at the encryption end:  Select random integer k such that 1< k < q1-1.  Compute K = ak mod q1.  Compute cipher text as C1 = (ke mod n) and C2 = (M * K mod q1).  Transmit the cipher text as (C1, C2). C. Decryption The cipher text C1 and C2 is converted back to its original form M. Decryption of the cipher text is done in the following way:  Compute k = C1d mod n.  Calculate (a-k mod q1) and consider it as K-1.  Calculate M = C 2 * K-1 mod q1.

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Figure 1. Working of the proposed method

V. Validity of the Proposed Method A. Encryption Scheme The message M is encrypted into cipher text C with the help of one time key K, and a primitive root element ‘a’. The cipher text C is computed as: C = M * K mod n (1) The one time key K = ak mod n. Therefore, on putting the value of K in (1) we get, C = M * ak mod n (2) B. Decryption Scheme The encrypted text C is decrypted into the original message M with the help of K-1 and C such that M = C * K-1 mod n (3) Putting the value of K-1 as M * ak in (3) we get, M = C * ak mod n (4) Putting the value of C as calculated in (2), in the above equation (3) we get, M = M * ak * a-k mod n On solving this, we will get M = M. This proves that the message M is encrypted into C and the encrypted message can then be successfully decrypted back to its original form M.

VI. Results and Discussion This section will discuss the encryption time and throughput of the proposed algorithm and will compare the results with the RSA, Elgamal and the existing hybridized system of RSA and Elgamal algorithm. All the required keys are generated by the main program. The naive scheme is implemented in Java platform. The encryption time and throughput of the RSA and Enhanced RSA is computed and compared. As a result, the encryption time of Enhanced RSA is less than the RSA. Also, the throughput of Enhanced RSA is better than the RSA. This proves that the Enhanced RSA is better than the RSA. Also, the encryption time and throughput of the proposed algorithm is compared with the existing hybridized system of RSA and Elgamal algorithm. Our proposed system holds better results than the Elgamal and existing merge between RSA and Elgamal. The algorithms are run on different data size. The throughput is an indication of the speed of encryption. To calculate the throughput of an encryption scheme, encryption time is required. The throughput is calculated by dividing the total plain text in megabytes on the average encryption time in seconds for each algorithm [8]. The power consumption will be decreased, as the throughput value will increase. Combination of Enhanced RSA and Elgamal is not only used for the generation of an efficient algorithm but also for the generation of a more secure cipher text. The following table shows the encryption time and throughput of each algorithm.

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TABLE I Encryption time and Throughout for each method Message Size 1 KB 2 KB 3 KB 4 KB 5 KB 10 KB 20 KB Average Time Throughput (Megabytes/sec)

RSA 0.00326 sec 0.00346 sec 0.00479 sec 0.00759 sec 0.00829 sec 0.01669 sec 0.03186 sec 0.01085 sec 4.05069

Enhanced RSA 0.00157 sec 0.00323 sec 0.00450 sec 0.00724 sec 0.00786 sec 0.01532 sec 0.03122 sec 0.01013 sec 4.33859

Elgamal 0.02697 sec 0.03959 sec 0.04763 sec 0.05606 sec 0.06758 sec 0.12194 sec 0.23498 sec 0.06908 sec 0.63622

RSA-Elgamal 0.00778 sec 0.01428 sec 0.02177 sec 0.02867 sec 0.03862 sec 0.07409 sec 0.16017 sec 0.04934 sec 0.89076

Proposed Method 0.00678 sec 0.03959 sec 0.02046 sec 0.02867 sec 0.03422 sec 0.07227 sec 0.15899 sec 0.04766 sec 0.92216

Figure 2. Encryption time of each algorithm

Figure 3. Throughput of each algorithm Figure 2 shows the average encryption time of each algorithm. It clearly shows that the encryption time of Enhanced RSA is better than RSA. Also, the encryption time of the proposed method is better than the encryption time of Elgamal and the integration of RSA and Elgamal. The throughput of each algorithm is depicted in figure 3. The throughput of Enhanced RSA is better than the throughput of simple RSA. Another point noticed is that the throughput of the integration of Enhanced RSA and Elgamal is higher than that of the Elgamal and the integration of RSA-Elgamal. This leads to high performance of the naive approach. Figure 4 and 5 depicts the encryption time for 1 KB and 5 KB data respectively.

Figure 4. Encryption time of each algorithm for 1KB (in nanoseconds)

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Figure 5. Encryption time of each algorithm for 5KB (in nanoseconds) VII. Complexity This section discusses the computational complexity of the proposed method in terms of the big O-notation. The complexity is computed as O (f (n)); in terms of some function f. The encryption of the naive approach consists of the following main steps:  Generation of one time key, K = ak mod q1.  Generation of cipher text into two parts C1 and C2; where C1 = ke mod n and C2 = M * K mod q1. The complexity of the first step is O (log n) 3. The computational complexity of the second step is O (log n) 3 and O (log n) + 2 O (log n) 3 for C1 and C2 respectively. The decryption of the naive approach consists of the following main steps:  Retrieval of random integer k (k = C1d mod n) with the help of C1.  Computation of K-1 =a-k mod q1.  Retrieval of original message M = C2 * K-1 mod q1. Therefore, the complexity of the first and the second step is O (log n) 3. And, the complexity of the last step i.e. original message M is O (log n) + 3 O (log n) 3. VIII. Conclusion and Future Work In this paper, Enhanced RSA cryptosystem is combined with the Elgamal cryptosystem. The public and the private keys are generated using the Enhanced RSA. These keys are then passed to the Elgamal. A few modifications are also done in the encryption and decryption process; leading to an increased efficiency of our system than the Elgamal and existing hybridized system of RSA-Elgamal. The encryption time and throughput of Enhanced RSA is improved than the RSA. Finally, the encryption time and throughput of the proposed system comes up to be larger than the Elgamal and existing integrated RSA-Elgamal system. We can sum up the conclusion in the following points:  Enhanced RSA is better than RSA in terms of encryption time and throughput.  The proposed algorithm is efficient than the existing Elgamal and integrated system of RSA-Elgamal system.  The throughput of the proposed system is more than the Elgamal and the existing hybridized system of RSA and Elgamal algorithm, which leads to less power consumption.  The proposed algorithm is of great use for secure data transmission.  Encryption time and complexity is a trade-off Presently, this system is working with the encryption, decryption and throughput. Future work can also be done for the generation of digital signature.

References [1] [2] [3] [4] [5] [6] [7] [8]

W. Mao, Modern cryptography: theory and practice: Prentice Hall Professional Technical Reference, 2003, pp. 294-296. William Stallings, Cryptography and Network Security-Principles and Practice, Fifth Edition, Pearson publication, pp. 259-262. Thomas H. Cormen. Charles E. Leiserson. Ronald L. Rivest. Clifford Stein; Introduction algorithms; second edition; 2003; Al-Hamami,A.H. ; Aldariesh,I.A., “Enhanced Method for RSA Cryptosystem Algorithm”, Proc. IEEE Advanced Computer Science Applications and Technologies (ACSAT), 2012, pp : 402 – 408. Al.Hasib,A. ; Haque, A.A.M.M , “A Comparative Study of the Performance and Security Issues of AES and RSA Cryptography”, Proc. IEEE, Convergence and Hybrid Information Technology, ICCIT’08, 2008, Volume 2, pp : 505 – 510. Rashmi Singh, Shiv Kumar, “Elgamal’s Algorithm in Cryptography”, International Journal of Scientific & Engineering Research,2012, Volume 3. Ahmed, J.M. ; Ali, Z.M, “ The Enhancement of Computation Technique By Combining RSA and ElGamal Cryptosystems”,IEEE Proc. Electrical Engineering and Informatics (ICEEI), 2011, pp : 1-5. Diaa Salama Abd Elminaam, Hatem Md Abdual Kader, Mohiy Md Hadhoud, “Evaluating the Performance of Symmetric Encryption Algorithms”, International Journal of Network Security, 2010, Volume 10, pp: 213-219.

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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 An Offline Signature Verification System: An Approach Based On Intensity Profile Charu Jain1, Priti Singh2, Aarti Chugh3 Department of Computer Science 1, 3, Department of Electronics and Communication 2, Amity University, Gurgaon, Haryana, India.

__________________________________________________________________________ Abstract: Image Intensities have been processed traditionally without much regard to how they arise. Typically they are used only to segment an image into regions or to find edge-fragments. Image intensities do carry a great deal of useful information about three-dimensional aspects of objects and some initial attempts are made here to exploit this. Here we propose an algorithm which uses the inveterate characteristic features to recognize signatures with perceptive accuracy by utilizing the intensity variations in the way in which they may be written. Keywords: Intensity Profile (IP), False Acceptance Rate (FAR), False Rejection Rate (FRR). __________________________________________________________________________________________ I. Introduction Signature verification is an important research area in the field of authentication of a person as well as documents. The importance of signature verification arises from the fact that it has long been accepted in government, legal, and commercial transactions as an acceptable method of verification [1] [12]. The problem of offline signature verification [4] has been faced by taking into account three different types of forgeries: random forgeries, Simple forgeries and skilled forgeries [6]. There are two major methods of signature verification systems. One is an on-line method to measure the sequential data such as handwriting speed and pen pressure with a special device. Off-line[7] [8] data is a 2-D image of the signature and processing off-line is complex due to the absence of stable dynamic characteristics of the individual [5]. Difficulty also lies in the fact that it is hard to segment signature strokes due to highly stylish and unconventional writing styles. The non-repetitive nature of variation of the signatures, because of age, illness, geographic location and perhaps to some extent the emotional state of the person, accentuates the problem. All these coupled together cause large intra-personal variation. A robust system [13] [14] [15] has to be designed which should not only be able to consider these factors but also detect various types of forgeries. The system should neither be too sensitive nor too coarse. It should have an acceptable trade-off between a low False Acceptance Rate (FAR) and a low False Rejection Rate (FRR). The false rejection rate (FRR) and the false acceptance rate (FAR) are used as quality performance measures. The FRR is the ratio of the number of genuine test signatures rejected to the total number of genuine test signatures submitted. The FAR is the ratio of the number of forgeries accepted to the total number of forgeries submitted. The offline method, therefore, needs to apply complex image processing techniques to segment and analyze signature shape for feature extraction [2], [3]. Here, we propose an experimental method for the extraction of intensity profiles of offline signatures. The intensity profile of an image is the set of intensity values taken from regularly spaced points along a line segment or multiline path in an image. For points that do not fall on the center of a pixel, the intensity values are interpolated. Our work can be further expanded by merging more quantitative measures to provide better accuracy. II. Methodology The overall architecture of our signature recognition system follows: Signature acquisition, Preprocessing, Feature extraction, and Classification. Offline signatures are the signatures made on papers. This requires specifying the resolution, image type and format to be used in scanning each image. In any offline signature verification system, the first step is to extract these signatures from paper using scanner. A. Data Acquisition and pre-processing The system has been tested for its accuracy and effectiveness on data from 25 users with 10 specimens of each making up a total of 250 signatures. The proposed verification algorithm is tested on both genuine and forged signature sample counterparts. So we developed a signature database which consists of signatures from all the age groups. Our database is also language independent and also it consists of signatures done with different pens with different colors. 10 users were asked to provide genuine signatures, 5 were asked to do skilled forgeries, 5 provide casual forgeries and 5 did random forgeries. A scanner is set to 300-dpi resolution in 256 grey levels and then signatures are digitized.

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Charu Jain et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 8(2), March-May, 2014, pp. 143-146 Figure 1 Methodology of Signature Verification System Input Signature

Processing Stage

Feature Extraction Stage

Training Stage

Analysis & Matching Stage

Result

For further working we cut and pasted scanned images to rectangular area of 3 x 10 cm or 400 x 1,000 pixels and were each saved separately in files. The signature samples from the data base are shown in Figure 2. Figure 2 Sample Signatures from Database

Original Signature

Skilled Forgery

The scanned signature may contain spurious noise. Hence we started with preprocessing. In preprocessing stage, the RGB image of the signature is converted into grayscale and then to binary image. Thinning is applied to make the signature lines as single thickness lines and any noise present in scanned images are removed thus making the signature image ready to extract features. B. Feature Extraction Feature extraction process [9] [10] [11] is an important step in developing any signature verification system since it is the key to identifying and differentiating a user’s signature from another. Features available to extract in offline signatures can be either global features i.e. features extracted from whole images or local features i.e. features extracted from local region or part of the signature. In this system, the features extracted are intensity and intensity profile. An ‘intensity profile’ gives a one-dimensional view of a single cross-section of the data. It is a popular technique in photographic analysis as well, where a ‘density profile’ is constructed. These are used to train the system. The mean value of these profiles is obtained. In order to keep the problem under control, we use only pairs of points (defining line segments) and keep only interest points that show very high stability with respect to scale and rotation change of the image [16]. Figure 2 (a) Intensity Profile of Trained Sample for genuine signature

Table 1 Statistics for Sample Signature

300 data 1 x min x max x mean x median x std y min y max y mean y median y std data 2 data 3

250

200

150

100

150 200 250 300 Distance along profile

350

400

Minimum Value

Maximum Value

424.1

255

Mean

212.1

188.1

Median

212.1

229

Standard Deviation

122.9

77.36

Range

424.1

249

450

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The intensity profiles of a signature are extracted from a sample group of signature images of different persons. The values derived from each sample group are used in deriving a mean intensity profile for each group of samples. The mean values and standard deviations of all the profiles are computed and used for final verification. A sample plot for genuine signature is shown in figure 2 (a). Table 1 provides values computed from this plot. Figure 2 (b) Intensity Profile of Test Signature

Table 2 Statistics for Test Signature

300 data 1 x min x max x mean x median x std y min y max y mean y median y std data 2 data 3

250

200

150

100

150 200 250 300 Distance along profile

350

400

Minimum Value

Maximum Value

424

251

Mean

208

199.1

Median

208

230

Standard Deviation

120.6

75.71

Range

424.1

247

450

C. Verification Phase In the next step the scanned signature image to be verified is fed to the system. It is preprocessed to be suitable for extracting features. It is fed to the system and its intensity profile is extracted. These values are then compared with the mean features that were used to train the system. Depending on whether the input signature satisfies the condition the system either accepts or rejects the signature. The intensity profile (IP) extracted from database are compared with the intensity profile (IP) extracted from test signatures and based on the classification criteria the signatures are classified either genuine or forged. Figure 2(c) Difference of Intensity Profiles of Sample Signature and Test Signature

Table 3 Statistics for Difference of Intensity Profile

300 data 1 x min x max x mean x median x std y min y max y mean y median y std data 2 data 3

250

200

150

100

Minimum Value

Maximum Value

0.1

Mean

4.1

-11

Median

4.1

-1

Standard Deviation

2.3

1.65

Range

100

150 200 250 300 Distance along profile

350

400

450

Figure 3(c) Difference between intensity profiles of forged signatures 300

250 data 3 x mean x median x std

200

150 100

100 Y difference

data 1

200

50 0

200

400

600

Intensity Profile of Signature(a) 400 data 2 300

-100

200 -200 100 -300

40 60 X difference

100

200

300

400

Intensity Profile Of Signature (b)

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III. Results False Acceptance Rate (FAR) and False Rejection Rate (FRR) are the two parameters used for measuring performance of any signature verification method. The results of our simulation for forged and genuine signatures are as shown in the table 4. The system is robust; it rejected all the casual forgeries. Out of all the genuine signatures that were fed in, 4 were rejected as forgeries. FAR and FRR are calculated by given equations. FAR = (number of forgeries accepted/number of forgeries tested) * 100 FRR = (number of originals rejected/number of originals accepted) *100 This yielded a False Rejection Rate (FRR) of 5.26%. Also out of 50 skilled forgeries fed into the system, 5 signatures were accepted. This gave us a False Acceptance Rate (FAR) of 10%. Nature Of Signature Original Casual Forgery Skilled Forgery

Table 4: Results for genuine and forged Signatures False Acceptance Rate False Rejection Rate -----5.26% 0% -----10% ------

IV. Conclusions The methodology followed by us uses various geometric features to characterize signatures that effectively serve to distinguish signatures of different persons. We can see that the best performance was given by dominant intensities in a signature. The system is robust and can detect random, simple and semi-skilled forgeries but the performance deteriorates in case of skilled forgeries. By observing the individual performance of each signature, we found that the complexity of the signature, and the character of the signature do not affect the performance of the intensity profile method. We are further going to enhance our work by including correlation coefficient for signature classification. Using a higher dimensional feature space and also incorporating dynamic information gathered during the time of signature can also improve the performance. V. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

References

A.K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4–20, January 2004. Diana Kalenova, “Personal Authentication Using Signature Recognition”, Department of Information Technology, Laboratory of Information Processing, Lappeenranta University of Technology. J. Fierrez-Aguilar et al., “An off-line signature verification system based on fusion of local and global information,” in Proc. BIOAW,LNCS-3087, pp. 295–306, 2004. H B kekre, V A Bharadi, "Specialized Global Features for Off-line Signature Recognition", 7th Annual National Conference on Biometrics RFID and Emerging Technologies for Automatic Identification, VPM Polytechnic, Thane, January 2009. Faundez-Zanuy, M., 2005. Biometric recognition: why not massively adopted yet?, s. Syst. Mag., 20(8): 25-28. Hemanta Saikia,KC Sarma, 2012, “Approaches and Issues in Offline Signature Verification System ”International Journal of Computer Applications (0975 – 8887) Volume 42– No.16, March 2012. Batista, L., Rivard D., Sabourin R., Granger E., Maupin P. 2007. “State of the art in off-line signature verification” In: Verma B., Blumenstein M. (eds.), Pattern Recognition Technologies and Applications: Recent Advances, (1e). IGI Global, Hershey (2007). Arya M S and Inamdar V S. (2010). “A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches”. 2010 International Journal of Computer Applications. Volume 1, No. 9 (pp 0975 – 8887) Ramachandra , Ravi, Raja, Venugopal and Patnaik, Signature Verification using Graph Matching and Cross-Validation Principle, Int. J. of Recent Trends in Engineering (IJRTE),Vol. 1 (1), May 2009,Page(s): 57-61. Samaneh and Moghaddam, Off-Line Persian Signature Identification and Verification Based on Image Registration and Fusion", Journal of Multimedia, Vol 4, No 3 (2009). Larkins and Mayo, “Adaptive Feature Thresholding for offline signature verification”, 23rd International Conference In Image and Vision Computing New Zealand (2008), pp. 1-6. A. K. Jain, A. Ross, S. Prabhakar, “An Introduction to Biometric Recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, January 2004 Bence Kovari. "The development of off-line signature verification methods, comparative study," 2007. microCAD 2007 International Scientific Conference. "Pattern Recognition, special issue on automatic signature verification," June 1994, Vol. 8, no. 3. K. Anil Jain. “Handwritten Signature Recognition” Michigan State University - Biometrics. [Online] http://www.cse.msu.edu/~cse891/Sect601/SignatureRcg.pdf. J. Matas, J. Buri´anek, J. Kittler “Object Recognition using the Invariant Pixel–Set Signature” BMVC, British Machine Vision Association, (2000).

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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 Classification of data using New Enhanced Decision Tree Algorithm (NEDTA) Hardeep Kaur 1 Harpreet Kaur 2 Department of Computer Science and Engineering SBBSIET, Jalandhar, Punjab, India __________________________________________________________________________________________ Abstract: Data mining is method of maintaining a large amount of data stored in the database. Decision tree is a technique of data mining which classify the data and produces valuable results. These results are used in analysis and future prediction. The prime objective of this research work is to present an enhanced decision tree algorithm that classifies the data more efficiently and effectively than existing decision tree classifiers. We apply existing decision tree classifiers ID3, J48, NBTree on a large amount of data. Then the efficiency and performance of existing algorithms is examined and compared with new enhanced decision tree algorithm (NEDTA). Our enhanced decision tree algorithm produces better results as compared to other decision tree algorithms. Keywords: Data Mining; Decision Tree; ID3; J48; NBTree __________________________________________________________________________________________ I.

Introduction

Data mining is a knowledge discovery process in which analysis of the data is done. The analysis is based on historical activities stored in very large repositories and results are used to obtain useful information. Data mining is method to find the hidden patterns in a large amount of data. There are various applications of data mining such as banking, insurance, medicine, real estate etc. Data mining concept is applied in insurance and banking field [1] for fraud detection, identification of loyal customers, sales promotion and enhanced research. The process of data mining is iterative and also known as Knowledge Discovery process. It consists of following phases: 1. Problem Definition: This phase consists of data mining experts, business experts and domain experts, who understand the problem, define objectives. 2. Data Exploration: In this phase data is explored and metadata is defined by domain experts. 3. Data Preparation: A data model is formed in this phase from collected data. 4. Modeling: Data mining functions from data model are selected and applied on data. 5. Evaluation: The results obtained by modeling are evaluated. If the results are not according to expectations then model is rebuild until required results are obtained. 6. Deployment: In deployment process, the mining results are deployed into applications There are various data mining techniques are available such as Clustering, Classification, Association. Clustering is the process of grouping the similar objects into one class. Therefore multiple classes are formed which comprises similar objects. Association is the process in which association rules are created. These association rules analyze unrelated data and produces association between them. In this paper, Section I describes introduction of data mining and the process of knowledge discovery. Section II describes about classification and their techniques. Section III gives information about decision tree classification technique. In this section some decision tree algorithms are explained. In Section IV the objectives of research work are discussed. In Section V Weka data mining tool is discussed briefly. Weka tool is used in research work. Section VI describes about data used in research work. The proposed work is implemented in Section VII. The results are evaluated and compared in section VIII. The comparison of algorithms is based on execution time, accuracy and error rate (Mean absolute error (MSE), Root mean squared error (RMSE), Relative absolute error (RAE), Root relative squared error (RRSE)). In section IX a new enhanced decision tree algorithm (NEDTA) is proposed. In section X the results of NEDTA are evaluated and compared with ID3, J48 and NBTree. Section XI contains conclusion. Section XII contains references that are used in this research work. II.

Classification

Classification is data mining technique which classifies data with the help of certain classification rules and valuable results are formed. There are two areas of classification: Decision Tree Induction and Neural Induction.

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Decision tree induction is a classification method in which a set of rules are applied recursively on a dataset and a tree is generated. III.

Decision Tree

A decision tree consists of nodes [2]. Each node represents some information. Decision tree learning is started from root node and discrete values are produced at each node by testing the values of attribute. These discrete values acts as target function. Then by using target function, value of attribute for next node is evaluated. This process is repeated for each new node. The learned tree is represented by if-then rules. Decision tree algorithms [3] such as ID3, C4.5, J48 [4] NBTree can be applied on large amount of data and valuable predictions can be produced. These predictions evaluate future behavior of problem. Decision tree are preferred because they can evaluate information more accurately than other methods. In this research work following decision tree algorithms are used: (1) ID3: ID3 means Iterative Dichotomiser 3. It is a decision tree algorithm which is developed by Ross Quinlan. The steps of ID3 algorithm are as following: (a) ID3 is a greedy algorithm in which the tree created from top to bottom. (b) At each node, the appropriate attribute is selected which best classifies the data. Data is in the form of training examples. (c) The above process is repeated until the complete tree is generated or until all the attributes used. (2) J48: J48 is the open source Java implementation [5] of C4.5 decision tree algorithm in Weka data mining tool. Following are the steps of J48 algorithm: (a) This algorithm uses basic algorithm which create trees by using recursive top down divide and conquer approach. (b) First of all, the training examples are at the root node. (c) Test attribute is selected based on some measures such as information gain, entropy etc. (d) Examples are divided repeatedly by using test attribute. (e) The process continued until no sample leaf is leaf. (3) NBTree: NBTree(Naive Bayesian tree) consists of [6] naïve Bayesian classification and decision tree learning. An NBTree classification sorts the example to a leaf and then assigns a class label by applying a naïve bayes on that leaf. The steps of NBTree algorithm are: (a) At each leaf node of a tree, a naive bayes is applied. (b) By using naive bayes for each leaf node, the instances are classified. (c) As the tree grows, for each leaf a naive bayes is constructed. (d) This process repeated until no example is left. IV.

Objectives of Research Work

The objectives of this research work are as following: (1) To apply Decision tree algorithms ID3, J48 and NBTree on banking dataset. (2) Evaluation of results produced. (3) Comparative analysis of results using parameters accuracy, execution time and error rate for ID3, J48 and NBTree. (4) To build a new enhanced method for classification of data. V.

Tool Used

In this research work, an open source tool named Weka is used. Weka is free open source data mining software which is based on a Java data mining library. Weka consists of various machine learning algorithms for different data mining applications. The algorithms are directly applied to dataset and results are generated in the form of tree. Weka contains various classifiers for classification [7], clustering, association, regression, pre-processing and visualization. Weka is also used for development of new machine learning schemes. VI.

Data Set Used

In our research work, we have used banking dataset [8]. The main focus of this research is performance and evaluation of decision tree algorithms. There are many decision tree algorithms in data mining but we focus mainly on ID3, J48 and NBTree. The data set contains 5264 rows and 13 columns. VII.

Implementation of Work

To implement the objectives of research work, Firstly, we have applied the ID3, J48 and NBtree algorithms on banking [9] dataset using data mining tool Weka 3.4 Figure 1 shows implementation of ID3 decision tree

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algorithm on Weka data mining tool. Figure 2 shows implementation of J48 on Weka data mining tool. Figure 4 shows implementation of NBTree decision tree algorithm on Weka data mining tool Figure 1: ID3 Algorithm

Figure 2: J48 Algorithm

Figure 3: J48 Algorithm Visualization Tree in Weka Explorer

VIII.

Figure 4: NBTree Algorithm

Comparative Analysis of ID3, J48 and NBTree

The experiments have been conducted and different decision tree algorithms are applied on banking dataset in Weka Framework. The results of Decision tree classifiers ID3, J48 and NBTree are compared. In our experiment, parameters such as error rate, execution time and accuracy are evaluated and compared. Table I shows the accuracy of decision tree classifiers ID3, J48 and NBTree. Table II shows the performance of different decision tree classifiers. The table shows execution time of various classifiers. Table I: Classifier Accuracy

Algorithm

Table II: Performance of Classifiers

ID3

Correctly Classified instances 84.8784 %

Incorrectly Classified Instances 15.1216 %

J48

87.9179 % 85.6003 %

NBTree

Algorithm

ID3

Time Taken to build model (in seconds) 0.08

12.0821 %

J48

0.1

14.3997 %

NBTree

7.96

Table III shows the error rate of decision tree classifiers. Error rate is shown as Mean absolute error (MSE), Root mean squared error (RMSE), Relative absolute error (RAE), Root relative squared error (RRSE) Table III: Error rate of Classifiers

Algorithm ID3 J48

MSE 0.0991 0.0787

RMSE 0.2237 0.1983

RAE 28.3881 % 22.4945 %

RRSE 53.4855 % 47.4297 %

NBTree

0.1065

0.2296

30.4526 %

54.9118 %

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Following graphs shows accuracy, error rate and execution comparison of ID3, J48 and NBTree algorithms. Figure 5: This graph shows accuracy comparison of ID3, J48 and NBTree algorithms

100 90 80 70 60 50 40 30 20 10 0

Figure 6: This graph shows Execution Time of ID3, J48 and NBTree algorithms

8 7 6 5 4 3 2 1 0

Correctly Classified Instances Incorrectly Classified Instances

Execution Time

Figure 7: This graph shows Error Rate (MSE, RMSE, RAE and RRSE) comparison of ID3, J48 and NBTree algorithms

XI. New Enhanced Decision Tree Algorithm We have proposed a new decision tree algorithm which classifies a large amount of data. Existing decision tree algorithms have some drawbacks. But our enhanced algorithm produces better results as compared to ID3, J48 and NBTree [10]. Following are the steps of proposed algorithm: 1. A decision tree DT built from the training examples, with a collection S of m source leaf nodes and a collection D of n destination leaf nodes. 2. A pre specified constant k (kâ&#x2030;¤ m), where m is the total number of source leaf nodes, 3. Construct the branches according to different values of attribute Pi so that the samples are partitioned accordingly. 4. If samples in a certain value are all of the same class, then generate a leaf node and is labeled with that class. 5. Otherwise use the same process repeated recursively to form a decision tree for the samples at each partition. X.

Results

NEDTA is applied on banking dataset and results are compared with ID3, J48 and NBTree algorithms. Figure 9 shows the evaluation of NEDTA on Weka data mining tool. NEDTA produces better results as compared to ID3, J48 and NBTree in terms of execution time, accuracy and error rate.

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Figure 8: Implementation of New Enhanced Decision Tree Algorithm (NEDTA) on Weka Explorer

Table IV shows accuracy comparison of ID3, J48 sand NBTree with NEDTA. The percentage of correctly classified instances of our algorithm is better than other algorithms. Table V shows the performance comparison of ID3, J48 and NBTree with NEDTA. The execution time of our algorithm is better than other algorithms. Table VI shows error rate comparison of ID3, J48 and NBTree with NEDTA. Table IV: Comparison of accuracy of NEDTA with ID3, J48 and NBTree Algorithm

ID3 J48 NBTree NEDTA

Correctly Classified instances 84.8784 % 87.9179 % 85.6003 % 88.3549 %

Incorrectly Classified Instances 15.1216 % 12.0821 % 14.3997 % 11.6451 %

Table V: Performance comparison of NEDTA with ID3, J48 and NBTree Algorithm ID3

Time Taken to build model (in seconds) 0.08

J48

0.1

NBTree

7.96

NEDTA

0.06

Table VI: Comparison of error rate of NEDTA with ID3, J48 and NBTree Algorithm MSE RMSE RAE RRSE ID3 0.0991 0.2237 28.3881 % 53.4855 % J48

0.0787

0.1983

22.4945 %

47.4297 %

NBTree

0.1065

0.2296

30.4526 %

54.9118 %

NEDTA

0.0675

0.1838

19.3113 %

43.9458 %

Figure 9: This graph shows accuracy comparison of ID3, J48 and NBTree algorithms with NEDTA 100 80 60 40 20 0

Correctly Classified Instances Incorrectly Classified Instances

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Figure 10: This graph shows performance comparison of ID3, J48 and NBTree algorithms with NEDTA

Figure 11: This graph shows Error rate (MSE, RMSE, RAE and RRSE) comparison of ID3, J48 and NBTree algorithms with NEDTA

8 7 6 5 4 3

Execution time

2 1 0

XI. Conclusion Data mining plays an important role in knowledge discovery. There are various decision tree algorithms which are used to classify a larger amount of data. Each algorithm has different performance for different data set. While classifying a large amount of data, the performance of some algorithms decreases. Our algorithm removes this problem. The results of NEDTA show better performance in terms of execution time, error rate and accuracy than other algorithms. References [1] [2] [3] [4] [5] [6]

[7] [8]

[9] [10]

Kazi Imran Moin and Dr. Qazi Baseer Ahmed, ‘‘Use of Data Mining in Baking’’, International Journal of Engineering Research and Applications (IJERA),Vol. 2, Issue 2, pp.738-742, 2012. J. R. Quinlan, ‘Introduction of decision tree’, Journal of Machine learning. Mrs. Swati .V. Kulkarni, ‘‘Mining knowledge using Decision Tree Algorithm’’, International Journal of Scientific & Engineering Research, Volume 2, Issue 5. Youvrajsinh Chauhan, Jignesh Vania, “J48 Classifier Approach to Detect Characteristic of Bt Cotton base on Soil Micro Nutrient”, International Journal of Computer Trends and Technology (IJCTT), volume 5 number, 2013. Bangsuk Jantawan and Cheng-Fa Tsai, “The Application of Data Mining to Build Classification Model for Predicting Graduate Employment”, “International Journal of Computer Science and Information Security, Vol. 11, No. 10, October 2013 Yumin Zhao, Zhendong Niu_ and Xueping Peng, “Research on Data Mining Technologies for Complicated Attributes Relationship in Digital Library Collections”,“Applied Mathematics & Information Sciences, An International Journal”, Appl. Math. Inf. Sci. 8, No. 3, 1173-1178 (2014) Aman Kumar Sharma and Suruchi Sahni, ‘‘A Comparative Study of Classification Algorithms for Spam Email Data Analysis’’, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 5, pp. 1890-1895, 2011. Pardeep Kumar, Nitin, Vivek Kumar Sehgal and Durg Singh Chauhan, ‘‘A BENCHMARK TO SELECT DATA MINING BASED CLASSIFICATION ALGORITHMS FOR BUSINESS INTELLIGENCE AND DECISION SUPPORT SYSTEMS’’, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.2, No.5, pp. 25-42, 2012. Vivek Bhambri, ‘‘Role of Data Mining in Banking Sector’’, International Indexed & Referred Research Journal, VoL.III, ISSUE-33, pp. 70-71, 2012. Milija Suknovic, Boris Delibasic, Milos Jovanovic, Milan Vukicevic, Dragana Becejski-Vujaklija and Zoran Obradovic, ‘‘Reusable components in decision tree induction algorithms’’, Springer, 2011.

Acknowledgment I express my sincere gratitude to Er. Harpreet Kaur, Assistant Professor in department of computer science engineering at SBBSIET, Jalandhar, Punjab for her stimulating guidance, continuous encouragement and supervision.

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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 Impact of Aspect Oriented Programming on Software Maintainability - A Descriptive Study Sarita Rani1, Puneet Jai Kaur2 University Institute of Engineering and Technology, Panjab University, Sector 25, Chandigarh INDIA Abstract: Software maintenance is a term of major interest and it is a valuable part of software development cycle. One of the main aspects of software quality in software products is maintainability. Further, there are four sub categories of maintainability: analyzability, changeability, stability, and testability. Maintainability plays a vital role in improving software quality as software changes/updates are frequently required in this process. Object oriented programming has contributed in improving software maintainability, but crosscutting concerns affects the maintainability of object oriented software. In these days, Aspect oriented programming (AOP) is rising as a new methodology which provides more modularization of crosscutting concerns that help the programmers to reuse the code. This paper presents the various software maintainability metrics for an AOP and also discusses the various case studies which were conducted to assess the maintainability of software. Keywords: Aspect Oriented Programming (AOP), Object Oriented Programming (OOP), AO system, OO system, Maintainability, Changeability. I.

Introduction

The major objective of software developer is to deliver highest software quality. ISO 9126 standard illustrated six main attributes of software quality: maintainability, functionality, efficiency, reliability, usability, and portability [1, 2]. Maintainability has further four forms: adoptive maintainability, corrective maintainability, preventive and perfective maintainability. Among these types a huge amount of effort in terms of cost is spent on the enhancements of composing elements of component-based software systems [3]. The sub-traits of maintainability are: testability, analyzability, changeability, and stability. Changeability is the most important attribute from organization’s viewpoint, as most organizations operate other organization’s software. Software maintenance is a very important and costly activity that expends software development cost up to 5070 percent [4]. Due to this reason, developers have designed methodologies for development that can lessen the effects of change, ease the interpretation of the program, and promote the initial detections of fault and can be preferred. OOP contributed in improving software maintainability by applying the approaches of object and encapsulation, but crosscutting concerns affect the maintainability of object oriented software. Crosscutting concerns can be described as the traits of software system whose operation is disseminated into many modules, which leads to code tangling and code scattering problem. This limitation is overcome by AOP. AOP is a new emerging methodology which provides more modularization of crosscutting concerns that helps the programmers to reuse the code. A concern is defined as a feature which includes all the functional and non functional requisites and the design constraints in the system, that a system should implement [5]. The remaining paper is formulated as follows. Section 2 represents a concise outline of aspect-orientation and AspectJ explaining what aspects are. Section 3 presents a brief introduction to maintainability and discusses the several surveys that were accompanied to figure out the resourcefulness of maintainability metrics. Section 3 also lists the various maintainability metrics. Section 4 provides conclusion of the study. II.

Aspect-Orientation & Software Metrics

AO allows a developer to specify, modularize and encapsulate the crosscutting concerns in modules instead of having them tangled within the system’s components. For AO to achieve its objective of increasing software reuse and providing better software designs, software metrics are required. Software metrics are also needed for AO to determine the correct design practices when designing AO systems. AO System can also be poorly designed as one can write poor object-oriented software. Aspects introduction in the object-oriented software might increase the system complexity and reduce its understandability. Due to these reasons metrics are required for measuring how efficient, understandable, and reusable an aspect-oriented design is. A. AspectJ AspectJ [6] is a slightly modified form of Java, called JCore that yields, from the definition of new constructors, support for modular employment of crosscutting concerns. AspectJ is used for expressing the core functionality of a program. Various crosscutting concerns such as synchronization, consistency checking, protocol

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management and others, has been successfully modularized by AspectJ. AspectJ supports the following definitions:  Aspect: An aspect is the new modular unit design to implement a crosscutting concern and its definition contain when, where and how to invoke a concern.  Join points: A joint point is well defined point in the code at which concern crosscut the application. There can be many join points. For example, method call, access to class members and the execution of exception handler blocks.  Pointcuts: On the basis of specified criteria, a pointcut chooses a set of join points.  Advice: An advice is used to put behavior before, after, or around the elected join points. The three types of advice is executed as follows: Before Advice: before the join point After Advice: after the join point Around Advice: executes the join point zero or more times and surrounds its execution.  Introduction (Inter-type declaration): Introduction adds variables and new methods to a class, notifies that an interface is implemented by a class, and also permits aspects to customize the static structure of a program. III.

AO Maintainability Metrics

A. Background & Literature Review In software development methodology, a crucial role is carried out by maintainability. Software maintainability is an essential attribute of software quality. Software maintainability is different from software maintenance. Software maintainability allows software system or component to be altered, fix the defects, enhances performance, or other attributes, whereas software maintenance reorganizes a software system or component after delivery to fix the defects for better performance [7]. Changeability is a sub attribute of software maintainability and it defines the amount of effort to change a system. Various factors that affect the maintainability of AO are size, coupling, cohesion and separation of concerns. The case studies and the metrics that are exercised to estimate the maintainability of AOP software are summarized below in Table I. Table I Summary of Maintainability Metrics Author

Parameters

Metrics Tested

Mguni et al. [8]

Changeability

San’t Anna metrics and all Ceccato and Tonella metrics

Shen et al. [10]

Changeability (coupling and maintenance tasks)

Ceccato and Tonella metrics for coupling

Kumar et al. [11]

Changeability

WOM

Result  



Correlation model within system maintainability and coupling metrics



Evaluated the correlation between change impact factor and WOM. WOM cannot be adopted as a stable pointer of changeability.



 Kulesza et al. [12]

Coupling, cohesion, separation of concerns

Concern level metrics (Sant’Anna metrics) are good indicators of changeability. Structural complexity metrics (Ceccato and Tonella metrics) are poor indicators of changeability.

Sant’Anna metrics 

AOP attained an upgraded separation of concerns, exhibited components with weaker coupling and internal complexity as it required fewer LOC. LCOO metric unsatisfactory for measuring maintainability.

Mguni et al. [8] computed the maintainability of COTS-based system developed using AOP. By using the programming language AspectJ, an identical implementation in AOP is generated by refactoring the OpenBravoPos and Jasppereports. The metrics were classified into two categories: concern level metrics and structural complexity metrics. An AOP based implementation is well maintainable than OO system and the concern level metrics are more acceptable indicators of changeability as compared to structural complexity metrics.

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Mohanta [9] presented prototyping techniques and the modeling work for perfective maintainability that focuses on the relevance of project analysis. A set of exercises were proposed by using criterion such as time, quality, and efficiency which are mandatory for process quality analysis of perfective maintainability. This approach aids in accomplishing better performance of perfective maintainability with the concepts of AOP in case of any modification. Shen et al. [10] undertook a study to measure software variations during system development. Their study represents a correlation model between coupling metrics and system maintainability and also a fine-grained coupling metrics suite for AO. AJMetrics, a coupling metrics tool, was implemented and an experimental study was carried out on eight aspect benchmarks. The correlation model is used for assessing the maintainability of AO systems and offers suitable information for this purpose. Kumar et al. [11] studied change impact in AOP systems. The refactored AOP systems, from their OO versions, were used and 149 OO modules were refactored into 129 modules of AOP systems. Average change impact in OO is greater than in AO systems, which indicates that OO systems are less maintainable than AO systems. The change impact will be higher for these modules, if crosscutting concerns are not mined to aspects and others do. Kulseza et al. [12] estimated the amount to which each solution produces maintainable software decomposition by comparing the OO and AO versions of typical web-based information system with reference to essential maintainability characteristics. At the system and component levels, AO systems are somewhat preferable over OO systems. The inter-related behaviors were not accumulated by some aspects and it leads to lower cohesion in AO system. Ceccato et al. [13] explored the trade-off between merits and demerits attained by AOP approach through a measuring routine. The routine is based on a metrics suite that expands the conventional metrics applicable with the OO paradigm. The valid properties, such as the ratio of the system influenced by an aspect and the amount of familiarity an aspect has of the modules it crosscuts, are acquired by the proposed metrics. Sant’Anna et al. [14] presented a schema, to facilitate the computation of aspect-oriented software with regards to reusability and maintainability depends on a quality model and a suite of metrics. The proposed metrics fulfils essential pre-requisites in order to accomplish successful measurements in the AOSD context. B. Metrics Different researchers have proposed different metric suite. Sant’Anna et al. [14] proposed following metrics: 1. SoC Metrics: It is the capability to analyze, encapsulate, and modify that part of software which is related to a specific concern. In following we describe the SoC metrics:  Concern Diffusion over Components (CDC): CDC computes the number of aspects and classes responsible for the implementation of a concern. The design metric also computes the number of other classes as well as the aspects that access them.  Concern Diffusion over Operations (CDO): CDO computes the number of primary operations such as methods and advices responsible for the implementation of a concern.  Concern Diffusion over LOC (CDLOC): CDLOC for each concern computes the number of transition points with the help of code lines. Transition points are the points in the code where shadowed area converts into non- shadowed area and vice-versa. 2. Coupling Metrics Coupling is defined as a degree of dependence among components. In following we describe the coupling metrics:  Coupling Between Components (CBC): CBC counts the number of other aspects and classes to which an aspect or class is coupled.  Depth of Inheritance Tree (DIT): DIT measures the maximum distance from a given module to the aspect/class hierarchy. 3. Cohesion Metric The cohesion of a component refers to the degree to which its internal components are related. High cohesion generally correlates with low coupling. In following we describe the cohesion metric:  Lack of Cohesion in Operations (LCOO): LCOO computes the lack of cohesion of an aspect or a class, or it measures the relationship between the methods in a given module. 4. Size Metrics The extent of a software system’s design and code is software size. In following we describe the size metrics:

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   

Vocabulary Size (VS): VS computes the number of aspects and classes into the system. Lines of Code (LOC) : LOC computes the number of lines of code. Number of Attributes (NOA): NOA computes the number of attributes of each aspect or class. Weighted Operations per Components (WOC): WOC computes the number of operations such as advices or methods of each aspect or class.

Ceccato et al. [13] proposed following metrics:         

Weighted Operations per Module (WOM): WOM counts the number of advices or methods in a module. Number of Children (NOC): NOC counts the number of immediate sub- classes or sub- aspects of a given module. Coupling on Field Access (CFA): CFA is the number of modules or interfaces defining fields which are accessed by a given module and it also measures the dependences of a given module on other modules. Coupling on Method Call (CMC): CMC is the number of interfaces or modules defining operations which are likely called by a given module. Coupling Between Modules (CBM): CBM is the number of fields and operations which can be represented by the number of outward arrows from a given module and are accessed by a given class. Crosscutting Degree of an Aspect (CDA): CDA is the number of modules in a given aspect influenced by pointcuts and introductions. Coupling on Advice Execution (CAE): CAE is the number of aspects consists of advices invoked in a given module by the execution of operations. Response for Module (RFM): RFM is the number of advices and methods possibly accomplished by a given module in response to a received message. Lack of Cohesion of Operations (LCO): LCO computes the interconnection in between the methods of a given module. IV.

Conclusions

This paper presented the various relevant metrics which are used to assess the maintainability. This paper also discusses several surveys that were accompanied to figure out the resourcefulness of maintainability metrics. WOM cannot be adopted as a stable pointer of changeability because the correlation factor between the change impact and WOM are found to be weak. LCOO metric are found to be uncertain for measuring maintainability. The metrics suggested by Sant’ Anna et al. [14] are good indicators of changeability. This paper concludes that AO systems are more adaptable to changes in comparison to OO systems and therefore AOP systems are conveniently maintainable than OOP systems. V.

References

[1] J. Boegh, S. Depanfilis, B. Kitchenham, and A. Pasquini, “A method for software quality planning, control, and evaluation” in IEEE Software, vol 16, pp. 69-77, April 1999.

[2] H. Jung, S. Kim, and C. Chung, “Measuring software product quality: A survey of ISO/IEC 9126” in IEEE Software, vol 21, no.5, pp. 88-92, September 2004.

[3] cimitile@unisannio.it University of Sannio, Faculty of Engineering at Benevento Italy 29 November, 2000. [4] P. Jalote, An Integrated Approach to Software Engineering, Springer, Berlin, Germany, 3rd edition, 2005. [5] I. Jacobson and P. -W. Ng, Aspect-Oriented Software Development with Use Cases, Addison-Wesley Object Technology Series, Addison-Wesley Professional, Reading, Mass, USA, 2004.

[6] Aspectj project http://eclipse.org/asectj/. [7] IEEE, "IEEE Standard Glossary of Software Engineering Terminology," IEEE Std 610.12-1990, 1990. [8] K. Mguni, and Y. Ayalew, “An Assessment of Maintainability of an Aspect-Oriented System” in ISRN Software Engineering, vol. 2013, 2013.

[9] V.K.K Mohanta, “Maintainability of software using aspect oriented programming for process quality analysis” International Journal of Engineering and Computer Science, vol 1, no. 3, pp. 137-142, December 2012.

[10] H. Shen, S. Zhang, and J. Zhao, “An empirical study of maintainability in aspect oriented system evolution using coupling metrics” in

Proceedings of the 2nd IFIP/IEEE International Symposium on Theoretical Aspects of Software Engineering (TASE’08), pp. 233-236. June 2008.

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[11] A. Kumar, R. Kumar, and P.S. Grover, “An evaluation of maintainability of aspect-oriented system: a practical approach” International Journal of Engineering and Computer Science, vol 1, no. 2, pp. 1-9, 2007.

[12] U. Kulesza et al., “Quantifying the effects of aspect-oriented programming: a maintenance study” in Proceedings of the 22nd IEEE International Conference on Software Maintenance (ICSM ’06), pp. 223–232, September 2006.

[13] M. Ceccato and P. Tonella, “Measuring the effects of software aspectization,” in Proceedings of the 1st Workshop on Aspect Reverse Engineering (WARE ’04), 2004.

[14] C. Sant’Anna, A. Garcia, C. Chavez, C. Lucena, and A. V.von Staa, “On the reuse and maintenance of aspect-oriented software: an assessment framework,” in Proceedings of the 17th Brazilian Symposium on Software Engineering, pp. 19–34, 2003.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A Critical Analysis of Pilot and Blind channel Estimation Techniques for OFDM systems Mr. Sivanagaraju.V1, Dr. Siddaiah.P2 Associate Prof, Dept of ECE, S.S.N College of Engineering and Technology, Ongole, A.P, INDIA 2Professor and Dean, Dept of ECE, University College of Engineering and Technology, Acharya Nagarjuna University, Guntur, A.P, INDIA

Abstract: The channel estimation techniques for OFDM systems based on pilot arrangement and blind estimation are investigated in the proposed work .Various channel estimation techniques are employed in order to judge the physical effects of the medium present. In this proposed work, we have analyzed and implemented various estimation techniques for MIMO OFDM Systems such as Least Squares (LS), Minimum Mean Square Error (MMSE), Constant Modulus Algorithm (CMA) and linear Pre-coding. These techniques are therefore compared to effectively estimate the channel in MIMO OFDM Systems. The objective of the proposed work is to further aid in the development of NDA based channel estimation methods by serving as an analytical tool for comparison between the existing methods and new methods being developed. Keywords: OFDM, CMA, linear precoding, minimum mean square error I.

Introduction

The increasing require for high-bit-rate digital mobile communications has incited the appearance of Orthogonal Frequency-Division Multiplexing (OFDM) for achieving good performance in high rate data transmission [1] [2]. It is also an effective technique that produces a high spectral efficiency and a good scheme to combat frequency-selective fading channels in wireless communication systems without forgetting the major property that is subcarrier orthogonality. Hence, the symbol duration must be significantly larger than the channel delay spread. In orthogonal frequency division multiplexing (OFDM), the entire channel is divided into many narrow sub channels. Splitting the high-rate serial data stream into many low-rate parallel streams, each parallel stream modulates orthogonal subcarriers by means of the inverse fast Fourier transform (IFFT). If the bandwidth of each subcarrier is much less than the channel coherence bandwidth, a frequency flat channel model can be assumed for each subcarrier. Moreover, inserting a cyclic prefix (or guard interval) results in an inter-symbol interference (ISI) free channel assuming that the length of the guard interval is greater than the delay spread of the channel. Therefore, the effect of the multipath channel on each subcarrier can be represented by a single complex multiplier, affecting the amplitude and phase of each subcarrier. Hence, the equalizer at the receiver can be implemented by a set of complex multipliers, one for each subcarrier. Under multi path spread situation, a dynamic estimation of channel is necessary before the demodulation of OFDM signals to ensure a coherent detection and since the radio channel is frequency selective and time-varying for wideband mobile communication systems [2]. In the literature, many channel estimation schemes are found and depends on if the channel is constant, slowly or fast time varying. Traditionally, channel estimation is achieved by sending training sequences through the channel. However, when the channel is varying, even slowly, the training sequence needs to be sent periodically in order to update the channel estimates. Hence, the transmission efficiency is reduced [2]. The increasing demand for high-bit-rate digital mobile communications makes blind channel identification and equalization very attractive, since they do not require the transmission of a training sequence. This paper investigates and compares both pilot based and blind channel based estimators for OFDM systems. The primary objective of the proposed work is to aid in further development of blind channel estimation techniques by providing a critical review of the existing systems. Such analysis will be of great help in comparing and analyzing the performance of new techniques in related to the techniques existing in the literature. II. System Model The baseband OFDM system is practically the same for all the schemes of channel estimations and differs just from the block of the channel but some schemes can add another block used especially for interpolation or for equalization.

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Figure 1: A Base Band OFDM System. III. OFDM System for Channel Estimation based on Pilot Arrangement The OFDM system model used for training sequence (pilot signal) consists of mainly of a mapper block forwarded by a S/P conversion, then there is an insertion pilot block follow up of an IDFT calculation of the information data [4]. After that we find the guard insertion block and a P/S conversion before reaching the channel which is affected by an AWGN noise. The data stream will be converted on a parallel stream, and then the guard interval is removed and will sail towards the frequency domain. Channel estimation is afterward performed before carrying out a P/S conversion and attainment of the demapper block to restore back the data stream. After crossing the S/P block, the pilot used here will be inserted in all sub carriers of one OFDM symbol with a specific period or uniformly between data sequence for a block pilot type estimator or in some specific sub carriers for the comb pilot type. Then, the data sequence will pass up the IDFT block for the transformation to time domain and the expression of x(n) (N being the DFT length) is given as follow:

(1) After that, the cyclic prefix will be inserted to preserve orthogonality of the sub carriers on the one hand and to evade inter symbol interference between adjacent OFDM symbols on the other hand. The guard time which contains this cyclic prefix having a length N g is chosen to be greater than the delay spread [5]. Then the resulting symbol is:

(2) After a P/S conversion, the OFDM symbol will cross the channel expected to be frequency selective and time varying fading channel with an additive noise and will be given by

(3) h(n) is the channel impulse response represented by

(4)

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Where r is the total number of propagation paths, hi is the complex impulse response of the i th path, fDi is the ith path Doppler frequency shift is the delay spread index, T is the sample period and ĆŽi is the ith path delay normalized by the sampling time.

(5) The frequency form of this resulting signal will be expressed as follow:

(6) By supposing a transmission without an inter symbol interference ISI, the relation between the frequency components is (7) Where H(k)= DFT {h(n)}, I(k) is the inter-carrier interference because of the Doppler frequency and W(k)=DFT{w(n)} The pilot signals are then extracted and cross the channel estimation block, after that the estimated channel He(k) for the data sub-channel is obtained and the transmitted data is estimated. At last, the binary data sequence is recovered by the signal demapper block IV. OFDM System for Blind Estimation In OFDM systems, the serial data are converted into M parallel streams. Each parallel data stream modulates a different carrier [3][6]. The frequency separation between the adjacent carriers is 1/T, where T is the symbol duration for the parallel data that is M times of the symbol duration for the serial data. Let us consider an OFDM signal in the interval (nT, (n+1)T) as

(8) Where am(n) are symbols resulting from a modulation constellation like QAM. wm is the frequency of mth carrier

(9) From this equation, the M samples can be seen as the inverse discrete Fourier transform (IDFT) of a block for M input symbols. Theoretically speaking, when the number of carriers is large enough, symbol duration T is much larger than the duration of FIR channel; IS1 is negligible. However, for the high-bit-rate communications, it is impractical to choose very large M to make ISI negligible. Therefore, a cyclic prefix of length P is added into each block of IDFT output at the transmitter. The length of the prefix is chosen to be longer than the length of the channel impulse response in order to avoid inter-block interference (IBI). That results with total cancellation of IS1 and inter carrier interference (ICI). The input data will be as follow

(10)

(11)

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Where H(.) is the frequency response of the channel. It is evident from the equation above that the ISI is completely cancelled and the effect of the channel at the receiver is simply a complex gain and AWGN

V. Channel Estimation Techniques Channel Estimation is the process of characterizing the effect of the physical medium on the input sequence. It is an important and necessary function for wireless systems [3]. Even with a limited knowledge of the wireless channel properties, a receiver can gain insight into the data sent over by the transmitter. The main goal of Channel Estimation is to measure the effects of the channel on known or partially known set of transmissions [3]. Orthogonal Frequency division multiplexing (OFDM) Systems are especially suited for channel estimation. The sub carriers are closely spaced. While the system is generally used in high speed applications that are capable of computing channel estimates with minimum delay. There are primarily two major classification of channel estimation techniques in to Pilot based channel Estimation and Blind Channel estimation [3] [8]. Pilot based Channel estimation is based on the training sequence which is known to both transmitters and receiver. The receiver can utilize the known training bits and the corresponding received samples for estimating the Channel. Some of the major approaches in this technique include Least Squares (LS) and Minimum Mean Squares (MMSE) among others. In the Least Squares Error (LSE) estimation method can be used to estimate the system h[m] by minimizing the squared error between estimation and detection. In Minimum Mean Square Error (MMSE) the estimator minimizes the mean-square error [7]. Although ISI can be avoided, via the use of cyclic prefix in OFDM modulation, the phase and gain of each sub channel is needed for coherent symbol detection. An estimate of these parameters can be obtained with pilot/training symbols, at the expense of bandwidth. Blind channel estimation methods avoid the use of pilot symbols, which makes them good candidates for achieving high spectral-efficiency[3][8]. Existing blind channel estimation methods for OFDM systems can be classified as: 1. Statistical. 2. Deterministic. The statistical methods explore the cyclo-stationarity that the cyclic prefix induces to the transmitted signal [9]. They recover the channel using cyclic statistics of the received signal, or subspace decomposition of the correlation matrix of the pre-DFT received blocks. The deterministic methods process the post DFT received fblocks, and exploit the finite alphabet property of the information bearing symbols. Maximum likelihood and iterative Bayesian methods are two examples taking into account, specific properties of M-PSK or QAM signals, while utilizing an exhaustive search. In comparison to the statistical methods, the deterministic ones converge much faster; however, they involve high complexity, which becomes even higher as the constellation order increases [3] [9].Equalization technique employed is from the deterministic class of blind channel estimation. It involves the use of equalizers. An equalizer removes the channel effects on a transmitted signal and reduces the Intersymbol Interference (ISI). The type of equalization, capable of tracking a slowly time-varying channel response is known as adaptive equalization. It can be implemented to perform tap-weight adjustments periodically or continually. Periodic adjustments are accomplished by periodically transmitting a preamble or short training sequence of digital data that is known to the receiver in advance. The receiver also uses the preamble to detect start of transmission, to set the automatic gain control (AGC) level. Continual adjustments are accomplished by replacing the known training sequence with a sequence of data. These algorithms adjust filter co-efficients in response to sample statistics rather than in response to sample decisions [9].

Figure 2: Adaptive equalization. In Blind channel equalizer, the channel input is reconstructed as accurately as possible by using an adaptive filter to cancel the adverse effects of the channel, particularly the presence of Intersymbol Interference (ISI) and additive noise. Many blind equalization algorithms are available in the literature with different cost functions

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namely, Bussgang algorithm, Stato algorithm, Constant Modulus algorithm and Godard algorithm among others [9].In the proposed work we have implemented and analyzed one deterministic type method namely the Constant Modulus algorithm and one Statistical type method in the form of Linear Precoding. VI. Simulation, Results, and Discussion The proposed system was coded in MATLAB environment. We have coded the system for both MIMO and SISO based OFDM systems. In order to illustrate the effect of Carrier Frequency Offset (CFO) and Symbol Timing Offset in OFDM in regard to ISI we have also coded their effects in OFDM signal. The following figures and discussion summarizes the simulation results of the proposed work. The following figure demonstrates the effect of CFO and STO in OFDM. It is demonstrated for QAM type Modulation

Figure 3: A 16 QAM Constellation under the effect of CFO.

Figure 4: A 16 QAM Constellation under the effect of STO. From the above figures it can be inferred that the pilots are not static but are rotating around the centre. During such a situation the phase estimation of the signal can not be perfect because of the presence of a time varying phase which is not constant over a symbol period. Higher the offset more the phase changes over one OFDM symbol and estimation becomes much more difficult The following figures demonstrate the results of simulation of channel estimation using the pilot based approach of LSE and MMSE.

Figure 5: The Plot of Actual Channel.

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Figure 6: The Plot of Channel Estimated Using LS.

Figure 7: The Plot of Channel Estimated Using MMSE.

Figure 8: The Plot of SNR and Mean Squared Error for LS and MMSE based Estimator. Figure (6) and Figure (7) depicts the estimation using LS and MMSE methods in comparison to the original signal as depicted in Figure (5). The above pilot based approaches are effective as long as the training sequence is available to the receiver. Figure (8) demonstrates the mean square error of channel estimation at different SNR in dB as SNR increases mean square error decreases for both LSE and MMSE. It can also be observed that for a given SNR, MMSE estimator shows better performance than LSE estimator. The complexity of MMSE estimators will be larger than LSE estimators but gives a better performance in comparison to LSE. The below mentioned figures demonstrate the implementation of estimation using blind channel estimation techniques.

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Figure 9: Blind Channel Estimation Based on CMA QAM based frequency response.

Figure 10: Blind Channel Estimation Based on Precoding for QAM based frequency response.

Figure 11: Symbol or Bit error For Blind Channel Estimation.

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Figure 12: MSE Estimated using CMA over 8000 Data Points. Figure (9) and Figure (10) blind channel approaches exhibits better performance as compared to the trainingbased one for the case of fast varying channels. The BER result is shown in Figure (11) and by using the MSE estimate in Figure (12) one can see that these methods achieves much better performance that the training based one. But these methods are too computationally intensive to be used with higher N and constellation orders. VII. Conclusions A MATLAB based schema for performance analysis of pilot based channel estimation techniques and blind channel estimation techniques is implemented. In CMA type Blind channel Estimation BPSK type modulation was considered over 8000 data Points. The results for different techniques are compared and observed. The obtained results show that blind channel estimation can be used in future wireless communications especially, when the spectrum efficiency, low complexity and low level of received signal powers are considered. In an embedded transceiver design, the blind techniques can easily be employed with bearable performance degradation. The performance of LSE with MMSE estimator is also investigated. It is observed that MMSE estimation is better that LSE estimator in low SNRs; whereas at high SNRs, performance of LSE estimator is comparable to that of the MMSE estimator. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

IEEE standard for wireless LAN: Medium Access Control and Physical Layer Specification, P802.11, January 1999. “Mobile WiMAX – Part I A Technical Overview and Performance Evaluation”,2006 WiMAX Forum. Socheleau, F., Abdeldjalil Aἳssa-El Bey and Houcke, S. “Non Data-Aided SNR Estimation of OFDM Signals”, IEEE Communications Letters, Vol. 12, No. 11, November 2008. Pauluzzi D.R. and Norman C.B. “A Comparison of SNR Estimation techniques for the AWGN Channel”, IEEE Transactions on Communications, Vol.48 no. 10, 2000. Y. Linn, “A Carrier-Independent Non-Data-Aided Real-Time SNR Estimator for M-PSK and D-MPSK Suitable for FPGAs and ASICs,” IEEE Trans. on Circuits and Systems, Vol. 56, No. 7, pp. 1525-1538, July 2009. J. Hua et al, “Novel Scheme for Joint Estimation of SNR, Doppler, and Carrier Frequency Offset in Double-Selective Wireless Channels,” IEEE Trans. on Vehicular Tech., Vol. 58, No. 3, pp. 1204-1217, Mar. 2009. S. Kim, H. Yu, J. Lee and D. Hong, “Low Bias Frequency Domain SNR Estimator Using DCT in Mobile Fading Channels,” IEEE Trans. On Wireless Commun., Vol. 8, No. 1, pp. 45-50, Jan. 2009. J.-J. van de Beek, O. Edfors, M. Sandell, S.K. Wilson, and P.O. B¨orjesson, “OFDM channel estimation by singular value decomposition”, IEEE Trans. Commun., vol. 46, no. 7, pp. 931-936, July 1998. E. Panayirci and H. A. Cirpan, ”Channel estimation for space-time block coded OFDM systems in the presence of multipath fading,” IEEE Globecom 2002, Nov. 17-21 2002, Taiwan.

Acknowledgements The author’s would like to acknowledge the SSN College of Engineering and Technology, Ongole and University College of Engineering and Technology, Acharya Nagarjuna University, Guntur for providing a platform for doing this research work.

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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 Cluster Performance Calculator for High-Performance Distributed Web Crawler 1

Shishir Sarkar , 2Prateeksha Pandey 1 M.Tech. Scholar, 2Asst. Professor CSE Dept., CSIT, Durg (C.G), India _________________________________________________________________________________________ Abstract: Broad web search engines as well as many more specialized search tools rely on web crawlers to acquire large collections of pages for indexing and analysis. Such a WebCrawler may interact with millions of hosts over a period of weeks or months, and thus issues of robustness, flexibility, and manageability are of major importance. In addition, I/O performance, network resources, and OS limits must be taken into account in order to achieve high performance at a reasonable cost. In this paper, we describe the algorithm through which we can calculate the threshold value of each CPU Node in cluster and then calculate the cumulative threshold value whenever cluster will restart or configuration will change. Using this threshold value we can calculate the performance of cluster. . Keywords: web search engines, crawling, monitor, cumulative threshold. _________________________________________________________________________________________ I. Introduction Web crawlers, programs that automatically find and download web pages, have become essential to the fabric of modern society. This strong claim is the result of a chain of reasons: the importance of the web for publishing and finding information; the necessity of using search engines like Google to find information on the web; and the reliance of search engines on web crawlers for the majority of their raw data, as shown in Figure 1 (Brin & Page, 1998; search engines is emphasized by Van Couvering (2004), who argues that they alone, and not the rest of the web, form a genuinely new mass media. Web users do not normally notice crawlers and other programs that automatically download information over the Internet. Yet, in addition to the owners of commercial search engines, they are increasingly used by a widening section of society including casual web users, the creators of email spam lists and others looking for information of commercial value. In addition, many new types of information science research rely upon web crawlers or automatically downloading pages (e.g., Björneborn, 2004; Faba-Perez, Guerrero-Bote, & De Moya-Anegon, 2003; Mechanism for stopping crawlers from visiting some or all of the pages in their site. Suggestions have also been published governing crawling speed and ethics (e.g., Koster, 1993, 1996), but these have not been formally or widely adopted, with the partial exception of the 1993 suggestions. Nevertheless, since network speeds and computing power have increased exponentially, Koster’s 1993 guidelines need reappraisal in the current context. Moreover, one of the biggest relevant changes between the early years of the web and 2005 is in the availability of web crawlers. The first crawlers must have been written and used exclusively by computer scientists who would be aware of network characteristics, and could easily understand crawling impact. Today, in contrast, free crawlers are available online. In fact there are site downloaders or offline browsers that are specifically designed for general users to crawl individual sites, (there were 31 free or shareware downloaders listed in tucows.com on March 4, 2005, most of which were also crawlers). A key new problem, then, is the lack of network knowledge by crawler owners. This is compounded by the complexity of the Internet, having broken out of its academic roots, and the difficulty to obtain relevant cost information (see below). In this paper, we review new and established moral issues in order to provide a new set of guidelines for web crawler owners. This is preceded by a wider discussion of ethics, including both computer and research ethics, in order to provide theoretical guidance and examples of II. Introduction to Ethics The word ‘ethical’ means, ‘relating to, or in accord with, approved moral behaviors’ (Chambers, 1991). The word ‘approved’ places this definition firmly in a social context. Behavior can be said to be ethical relative to a particular social group if that group would approve of it. In practice, although humans tend to operate within their own internal moral code, various types of social sanction can be applied to those employing problematic behavior. Formal ethical procedures can be set up to ensure that particular types of recurrent activity are systematically governed and assessed, for example in research using human subjects. Seeking formal ethical approval may then become a legal or professional requirement. In other situations ethical reflection may take place without a formal process, perhaps because the possible outcomes of the activity might not be directly harmful, although problematic in other ways. In such cases it is common to have an agreed written or unwritten ethical framework, sometimes

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called a code of practice or a set of guidelines for professional conduct. When ethical frameworks or formal procedures fail to protect society from a certain type of behavior, it has the option to enshrine them in law and apply sanctions to offenders. The founding of ethical philosophy in Western civilization is normally attributed to ancient Greece and Socrates (Arrington, 1998). Many philosophical theories, such as utilitarianism and situation ethics, are relativistic: what is ethical for one person may be unethical for another (Vardy & Grosch, 1999). Others, such as deontological ethics, are based upon absolute right and wrong. Utilitarianism is a system of making ethical decisions, the essence of which is that “an act is right if and only if it brings about at least as much net happiness as any other action the agent could have performed; otherwise it is wrong.” (Shaw, 1999, p.10). Different ethical systems can reach opposite conclusions about what is acceptable: from a utilitarian point of view car driving may be considered ethical despite the deaths that car crashes cause but from a deontological point of view it could be considered unethical. The study of ethics and ethical issues is a branch of philosophy that provides guidance rather than easy answers. III. Computer Ethics The philosophical field of computer ethics deals primarily with professional issues. One important approach in this field is to use social contract theory to argue that the behavior of computer professionals is self-regulated by their representative organizations, which effectively form a contract with society to use this control for the social good (Johnson, 2004), although the actual debate over moral values seems to take place almost exclusively between the professionals themselves (Davis, 1991). A visible manifestation of self-regulation is the production of a code of conduct, such as that of the Association for Computing Machines (ACM, 1992). The difficulty in giving a highly prescriptive guide for ethical computing can be seen in the following very general important advice, “One way to avoid unintentional harm is to carefully consider potential impacts on all those affected by decisions made during design and implementation” (ACM, 1992). There seems to be broad agreement that computing technology has spawned genuinely new moral problems that lack clear solutions using exiting frameworks, and require considerable intellectual effort to unravel (Johnson, 2004). Problematic areas include: content control including libel and pornography (Buell, 2000); copyright (Borrull & Oppenheim, 2004); deep linking (Fausett, 2002); privacy and data protection (Carey, 2004; Reiman, 1995; Schneier, 2004); piracy (Calluzzo & Cante, 2004); new social relationships (Rooksby, 2002); and search engine ranking Technology is never inherently good or bad; its impact depends upon the uses to which it is put as it is assimilated into society (du Gay, Hall, Janes, Mackay, & Negus, 1997). Some technologies, such as medical innovations, may find themselves surrounded at birth by a developed ethical and/or legal framework. Other technologies, like web crawlers, emerge into an unregulated world in which users feel free to experiment and explore their potential, with ethical and/or legal frameworks later evolving to catch up with persistent socially undesirable uses. Two examples below give developed illustrations of the latter case. The fax machine, which took off in the eighties as a method for document exchange between businesses (Negroponte, 1995), was later used for mass marketing. This practice cost the recipient paper and ink, and was beyond their control. Advertising faxes are now widely viewed as unethical but their use has probably died down not only because of legislation which restricted its use (HMSO, 1999), but because they are counterproductive; as an unethical practice they give the sender a bad reputation. Email is also used for sending unwanted advertising, known as spam (Wronkiewicz, 1997). Spam may fill a limited inbox, consume the recipient’s time, or be offensive (Casey, 2000). Spam is widely considered unethical but has persisted in the hands of criminals and maverick salespeople. Rogue salespeople do not have a reputation to lose nor a need to build a new one and so their main disincentives would presumably be personal morals, campaign failure or legal action. It is the relative ease and ultra-low cost of bulk emailing that allows spam to persist, in contrast to advertising faxes. The persistence of email spam (Stitt, 2004) has forced the hands of legislators in order to protect email as a viable means of communication (www.spamlaws.com). The details of the first successful criminal prosecution for Internet spam show the potential rewards on offer, with the defendant amassing a 24 million dollar fortune (BBCNews, 4/11/2004). The need to resort to legislation may be seen as a failure of both ethical frameworks and technological solutions, although the lack of national boundaries on the Internet is a problem: actions that do not contravene laws in one country may break those of another. IV. Research ethics Research ethics are relevant to a discussion of the use of crawlers, to give ideas about what issues may need to be considered, and how guidelines may be implemented. The main considerations for social science ethics tend to be honesty in reporting results and the privacy and well-being of subjects (e.g., Penslar, 1995). In general, it seems to be agreed that researchers should take responsibility for the social consequences of their actions, including the uses to which their research may be put (Holdsworth, 1995). Other methodological-ethical considerations also arise in the way in which the research should be conducted and interpreted, such as the influence of power relationships (Williamson, & Smyth, 2004; Penslar, 1995, ch. 14).

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Although many of the ethical issues relating to information technology are of interest to information scientists, it has been argued that the focus has been predominately on professional codes of practice, the teaching of ethics, and professional dilemmas, as opposed to research ethics (Carlin, 2003). The sociology-inspired emerging field of Internet research (Rall, 2004a) has developed guidelines, however, although they are not all relevant since its research methods are typically qualitative (Rall, 2004b). The fact that there are so many different environments (e.g., web pages, chatrooms, email) and that new ones are constantly emerging means that explicit rules are not possible, instead broad guidelines that help researchers to appreciate the potential problems are a practical alternative. The Association of Internet Researchers has put forward a broad set of questions to help researchers come to conclusions about the most ethical way to carry out Internet research (Ess & Committee, 2002), following an earlier similar report from the American Association for the Advancement of Science (Frankel & Siang, 1999). The content of the former mainly relates to privacy and disclosure issues and is based upon considerations of the specific research project and any ethical or legal restrictions in place that may already cover the research. Neither allude to automatic data collection. Although important aspects of research are discipline-based, often including the expertise to devise ethical frameworks, the ultimate responsibility for ethical research often lies with universities or other employers of researchers. This manifests itself in the form of university ethics committees (e.g., Jankowski & van Selm, 2001),

i. ii. iii. iv. v.

V. Problem Identification Mostly web crawler doesn’t have any distribute cluster performance system. Doesn’t have any implemented algorithm. Doesn’t have any threshold value which help to divert the traffic. It’s very difficult to implement load balancer. Doesn’t have ay dynamic generation technique

VI. Web crawling issues Having contextualized ethics from general, computing and research perspectives, web crawling can now be discussed. A web crawler is a computer program that is able to download a web page, extract the hyperlinks from that page and add them to its list of URLs to be crawled (Chakrabarti, 2003). This process is recursive, so a web crawler may start with a web site home page URL and then download all of the site’s pages by repeatedly fetching pages and following links. Crawling has been put into practice in many different ways and in different forms. For example, commercial search engines run many crawling software processes simultaneously, with a central coordination function to ensure effective web coverage (Chakrabarti, 2003; Brin & Page, 1998). In contrast to the large-scale commercial crawlers, a personal crawler may be a single crawling process or a small number, perhaps tasked to crawl a single web site rather than the ‘whole web’. It is not appropriate to discuss the software engineering and architecture of web crawlers here (see Chakrabarti, 2003; Arasu, Cho, Garcia-Molina, Paepcke, & Raghavan, 2001), but some basic points are important. As computer programs, many crawler operations are under the control of programmers. For example, a programmer may decide to insert code to ensure that the number of URLs visited per second does not exceed a given threshold. Other aspects of a crawler are outside of the programmer’s control. For example, the crawler will be constrained by network bandwidth, affecting the maximum speed at which pages can be downloaded. Since crawlers are no longer the preserve of computer science researchers but are now used by a wider segment of the population, which affects the kinds of issues that are relevant. Table 1 records some user types and the key issues that particularly apply to them, although all of the issues apply to some extent to all users. Note that social contract theory could be applied to the academic and commercial computing users, but perhaps not to non-computing commercial users and not to individuals. These latter two user types would be therefore more difficult to control through informal means. There are four types of issue that web crawlers may raise for society or individuals: denial of service, cost, privacy and copyright. These are defined and discussed separately below. VII. Algorithms 1. Start fetching the code details of each CPU when the cluster is restart or configuration is changed. 2. Assign value in each attribute {CPU Clock Speed, Cores per Processor and Operations per Cycle} 3. Putting this value in calculation using below formula {CPU Clock Speed * Cores per Processor * Operations per Cycle} 4. Threshold value of CPU Node = ------------------------------------------------------------------------------------{CPU Clock Speed * Cores per Processor}/Cores per Processor 5. Calculate threshold value of each CPU Node 6. Add all the Threshold value of each node

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7. Cumulative Threshold value(CTV) = {Threshold value of CPU Node 1+Threshold value of CPU Node 2+...........+Threshold value of CPU Node N}. 8. Calculate the Response time of each CPU node using below formula Calculate Start time - Calculate End Time 9. Response time = ------------------------------------------------------Operations per Cycle 10. IF (Response time > Cumulative Threshold value) 11. Check the threshold value of another cluster if response time if less than Cumulative Threshold Value divert the traffic to that cluster. Else 12. Continue to same cluster

IX. Benefits of Proposed Architecture Able to calculate the cumulative threshold value Efficiently able to divert the traffic. Down time of cluster is very less. Efficiently utilize the entire cluster. Runtime ability to calculate the cumulative threshold if server configuration will change or adding new server in cluster vi. Reduced the hardware cost. vi. Very less required human interference i. ii. iii. iv. v.

X. Conclusion This kind of mechanism should be used in large scale of crawler kind of system, so that we can avoid the system shutdown, fail kind of problem generally we can have a scanner system to scan the complete system but doesn’t have a System or mechanism which will take a action according. References [1] [2] [3] [4] [5] [6] [7].

Shishir Sarkar and Prateeksha Pandey “Design t he framework for distributed and high performance web crawler” S. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for digital communications channel equalization using radial basis function networks,” IEEE Trans. on Neural Networks, vol. 4, pp. 570-578, July 1993. J. U. Duncombe, “Infrared navigation—Part I: An assessment of feasibility,” IEEE Trans. Electron Devices, vol. ED-11, pp. 34-39, Jan. 1959. C. Y. Lin, M. Wu, J. A. Bloom, I. J. Cox, and M. Miller, “Rotation, scale, and translation resilient public watermarking for images,” IEEE Trans. Image Process., vol. 10, no. 5, pp. 767-782, May 2001. K. Bharat, A. Broder, M. Henzinger, P. Kumar, and S. Venkatasubramanian. The connectivity server: Fast Access to linkage information on the web. In 7th Int.world Wide Web Conference, May 1998. S. Brin and L. Page. The anatomy of a large-scale Hypertextual web search engine. In Proc. of the Seventh world wide. M. Burner. Crawling towards eternity: Building an archive of the world wide web. Webtechniques, 1997.

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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 ECONOMIC PRINTING OF BRAILLE DOCUMENTS Padmavathi.S, Nivedita.V,Sankari.G,RaamPrashanth.N.S,Rajat Bohra, Department of Information Technology, Amrita School of Engineering, Ettimadai, Coimbatore, Tamil Nadu, INDIA __________________________________________________________________________________________ Abstract: The objective of the paper is to explain the process of printing Braille documents using a dot matrix printer. Braille language has been the only medium of communication for the blind without the help of other people. Hence this project was attempted to help them and create an efficient and economical solution for the same. The Braille documents were printed using a dot matrix printer after removing the ink ribbon. Due consideration was given to the standard braille size and the accuracy of the impression made on paper. Various trials were done by changing paper quality and the best out of them was chosen. Keywords: Braille, aksharas, maatras, brailler _______________________________________________________________________________________________________________

I. INTRODUCTION Information is spread majorly through written documents where reading and comprehending are the sources of knowledge gathering. However, an alternative has to be developed for the visually impaired. Braille solves this problem by providing a means of communication for the blind. It makes them independent and confident as they do not require any assistance. This paper focuses on developing an economic method to print Braille documents using a dot matrix printer. According to recent statistics taken by WHO, there are 285 million blind people all over the world. Majority of them use Braille as their only source of communication. Hence by providing an affordable method to print the Braille documents, this paper would significantly contribute to the Blind society. Considering the English language, there are 26 Braille images corresponding to the 26 English alphabets. It is a one to one mapping where each alphabet can be uniquely mapped to its Braille image. However in case of the Tamil and Hindi language, the mapping is not unique. The Tamil language has 12 vowels, 18 consonants and an additional character. The complete script contains 31 letters in its independent form and an additional 216 in the combined form representing a total of 247 combinations. There are Braille images mapped to the vowels and constants which are to be combined in order to get these 247 combinations. The Hindi language has 10 vowels and 36 consonants. The Hindi script is represented as a combination of the consonants, aksharas and maatras through which vowel phonetics are added to the consonants. There is a one to one mapping for each of the consonants, aksharas and maatras with the Braille images. The converted text to Braille is to be converted into printed format in the form of impressions that can be felt by the naked hand. The dot matrix printer the closest relative to the typewriter which works in a similar by leaving impressions. The typical dot matrix printer contains 9 or 24 pins which impact on an ink ribbon whic h in turn casts a visible print on the paper. By removing the ink ribbon we make the pins strike the paper directly. Care had to be taken to make sure that the pins were not damaged in the process. The Braille so generated was done in such a way that it was in accordance with the standard Braille dot size and spacing. Being an experimental setup with a modified printer the paper chosen to be extremely thin so that the impressions could be felt . Also as the impressions would be felt on the other side of the paper where the impression was made so the conversion of the Braille to its mirror image is made. The following sections explain the printing process in detail. Section II gives a summary of the literature survey made in study of existing printing techniq ues, Section III talks in brief about the proposed methodology, Section IV explains the printing process in detail, Section V gives suggestion for future improvements and Section VI gives the conclusion. II. LITERATURE SURVEY We have researched and analyzed several Braille printing devices which have been developed earlier and that has helped significantly towards the printing of Braille documents. The list of devices based on chronological order is as follows:

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A. First Generation: Slate and Stylus The slate and stylus is the oldest device used to produce braille, an invention of Charles Barbier. A simple device, its main advantage is its portability. Slates come in basically two sizes: 27 and 41 cell width. Slates are basically two pieces of metal, connected by a hinge. The top metal piece serves as a guide for the stylus, a sharp metal awl held by a wooden handle. The back metal plate contains indented braille cells, which further serve to guide the stylus in the embossing of braille dots. The main disadvantage of this type of model is its orientation. Since you are embossing the dots into the paper, it stands to reason that the dots need to be made inverted. Similarly, when writing, one needs to write from right to left, rather than from left to right. This is because when the paper is turned over to expose the upward dots, the braille is in a left to right order. B. Second Generation: Braille writers Perkins Brailler: The standard Perkins braille writer has six keys (one for each dot in a braille cell), a space bar, a backspace key, a carriage return, and a line feed key. Braille writers use heavyweight paper. There are also uni - manual braille writers, for individuals using only one hand, electric braille writers, and "Next Generation" braille writers. It costs around $700 - $3000. This printer turns out to be very costly and portability is difficult. Next came the Mountbatten Braille printer which is an electronic braille writer, note taker and embosser. This provided multiple functions and proved to be versatile. It integrates modern computer technology and has applications to support embossing, reading and file storage - and it has audio support for all its operations. It is adaptive technology that has been designed to meet the needs of blind students in today's environment, especially in early braille instruction, as a foundation tool for literacy. However, the quality of output and the impressions made on the paper was not very prominent. After that came â&#x20AC;&#x2DC;The Perkins SMART Braillerâ&#x20AC;&#x2122; that has a small video screen attached to the front of the braille writer which displays SimBraille and large print, combined with audio feedback. It allows users to edit, save and transfer electronic documents via USB, and it also features built-in software with lessons for braille beginners. The main disadvantages of Perkins braillers are that they are very expensive and difficult to afford by a normal person. They are very heavy with an average weight of 10 pounds which makes it very difficult to carry. C. Jot a Dot Jot a Dot is the newest innovation in Braille writing, available at a fraction of the cost of a traditional Brailler. Until now, the choice has either been a slate and stylus or a standard Braille Writer. Jot a Dot gives you the best of both worlds, combining portability with functionality. Jot a Dot enables regular Braille writing from the left hand side of the page to the right, a major advance in simple manual Braille writing. It has both line and cell indicators. The cell indicator shows the position of the embossing head on the line. The line indicator gives instant feedback on which line you are writing. Weighing less than a pound, Jot a Dot is easily carried as a personal item by both children and adults. One piece construction means there are no parts that can be lost. Again this kind of model turns out to be very expensive and not affordable for normal users. D. Tatrapoint Tatrapoint is another invention that came into existence which is a six-key braille writer that is lightweight, robust and easy to use. It is suitable for students and adults of any age, Tatrapoint features an adjustable keyboard. Simply slide the width adjuster to increase or decrease the spacing between keys. Bright, contrasting colors make Tatrapoint's key features easily identifiable, as well as make the brailler appealing to younger students. All the above mentioned printers do not show promising results in areas of portability and quality whereas the cost effective printer that we have designed by modifying the dot matrix printer proves to be very economical and productive. The method which we adopted to help achieving satisfactory results is explained in the next section. This shows why our Braille printer is very much cost effective and economical to everyone compared to the other generation printers explained previously. III. PROPOSED METHODOLOGY Dot matrix printer works by injection of ink on paper which is done by pins hitting an ink ribbon which falls on the paper and the document is printed as a pattern of dots. Printing braille with special braille printers is expensive. Dot matrix printer that makes use of pins was used in our project to create impressions for braille

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dots on paper. Changing the print head of a dot matrix printer will also cost more than a new printer. The ink ribbon could be removed and the pins will directly hit the paper, thus forming impressions. The pin specification and the paper had to be decided in accordance with the braille unit size, impression quality and accuracy. IV.EXPERIMENTAL SETUP The braille images in the braille documents are to be converted to its standard size which was done using CAD. Each braille character is designed in standard size as already mentioned. Every major braille-producing country has standards for the size and spacing of braille embossed on paper as a setting of uniqueness to their language. The nominal height of braille dots shall be 0.019 inches [0.48 mm] and shall be uniform within any given transcription. The nominal base diameter of braille dots shall be 0.057 inches [1.44 mm]. Cell spacing of dots shall conform to the following: The nominal distance from center to center of adjacent dots (horizontally or vertically, but not diagonally) in the same cell shall be 0.092 inches [2.340 mm].The nominal distance from center to center of corresponding dots in adjacent cells shall be 0.245 inches [6.2 mm]. The nominal line spacing of braille cells from center to center of nearest corresponding dots in adjacent lines shall be 0.400 inches [1.000 cm]. Most braille embossers support between 34 and 37 cells per line, and between 25 and 28 lines per page. These specifications were followed by using CAD. Once the braille units were got as images in the standard size, printing had to be done. The ink ribbon was removed from the dot matrix printer so that the pins directly hit the paper. The impression was dependent on the pins and also the paper quality. Standard dot matrix printers have 9X21 print head. Since changing the print head was not the idea, different types of paper with varied thickness was used as trials. Thinner the paper, more accurate were the impressions. These Braille documents will be read by the feel of the impression of braille dots. The printing should be done on the backside of the page, so that the braille is projected in the front side of the paper. After a series of experiments we found that printing mirror image of the output on the back side of the paper results in printed braille on the front side suiting the reader to feel the dots. The mirror image of the output is obtained using CAD, which is then sent to the printer for printing process. In the dot matrix printer, the pins hit the paper to make projections on the opposite side, mirror image is fed to the printer and the printer prints the projections on the paper.

Braille for hello world

Mirror image of the braille The printer which was used for experimentation is as shown in Figure 1. Various textures and types of paper were taken as samples for testing and the results and accuracy were studied accordingly.

Figure 1: Dot Matrix printer

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As a first trial, bond papers were used. The thickness of this paper is high hence the impressions of the pins were hardly visible. It was not even close to original braille documents which on comparison would give a very less accuracy of 20% approximately. Next a4 sheets were used to print the braille dots. Though it was better than the case of bond papers, the impression was very mild. On a comparison with original braille documents they gave an accuracy of 35%. As a next trial, in order to reduce the thickness of the printing paper, butter papers were used. The pins hit the paper harder in this case, and the impressions were better visually measuring the accuracy it was close to 50%.As a final trial, papers used for billing purposes were used, in which impressions were felt properly. They seemed very close to the original braille documents and gave accuracy up to 84%. The final output that we achieved is as shown in Figure 2. Accuracy percentage were measured by giving the printed documents to blind people organizations and checking how many of the printed words were read correctly by the reader. The printed Braille document was tested against 100 visually impaired people out of which 70-80 people could decipher the text correctly.

Figure 2: Printed Braille document Since the billing paper gave the best results from the trials, it was chosen to be the correct choice of paper to be used along with a dot matrix printer for braille printing. V. FUTURE IMPROVEMENTS Braille printing need not be costly and can be made cheap by jus modifying the print head of a dot matrix printer at the manufacturing stage itself .It benefits the blind and also by adding a ink ribbon one can use it as a multipurpose printer. A modified printer head can be made with pins capable of leaving impressions on thicker papers thus making the Braille documents generated to be of higher quality and thus can last longer. Braille documents can be made cheap and available as easily as a normal printed document. VI.CONCLUSION The main purpose of economic braille printing is to provide a cost effective solution for the blind and the needy. This printer can also be integrated to be used as a normal purpose printer hence the necessity for purchasing this printer separately is not required. By providing this dual purpose in a dot matrix printer we will be able to benefit a significant amount of blind people by helping them to read and interpret letters and words. REFERENCES 1. 2. 3. 4. 5. 6. 7.

8. 9.

Methods for Braille Writing and Braille Note-taking. March 6 2002 . http://www.dotlessbraille.org/braillewritingmethods.htm Gately, Rosemary and Gotwals . BRL: Braille through Remote Learning http://www.brl.org/intro/session02/perkins.html ,July 1997 Evan, Monte BRL: Braille through Remote Learning http://www.brl.org/intro/session02/slate.html ,July 1997 Miller, Cyral Betsy McGinnity .For paths to literacy for the visually impaired http://www.pathstoliteracy.org/tools-writingbraille DeafblindTelecommunications ,18 February 2011. http://www.dbt.org.au/Accessibility/Braille%20Devices/Braille%20writers.html Bleach,Kelly , American Foundation for the Blind , http://www.afb.org/afbpress/pub.asp?DocID=aw060106 Bleach,Kelly , Braille Writing Tools and Tools for Tactile Graphics http://www.afb.org/info/living-with-visionloss/usingtechnology/reading-and-writing/braille-writing-tools-and-tools-for-tactilegraphics/1235 INDEX BASIC-D , http://www.synapseadaptive.com/braille/basic.htm Single Sided Braille Embossers from Enabling Technologies .April 1998,. http://www.brailler.com/single.htm

ACKNOWLEDGEMENTS Sincere gratitude is hereby extended to the following who never ceased in helping until this paper is structured. Prof.S.Padmavati, Project guide Assistant professor at the Department of Information Technology, Amrita University, Coimbatore. Finally to the unwavering financial support of family and friends.

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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 Improved BSP Clustering Algorithm for Social Network Analysis 1

T.B.Saranya Preetha, 2G.T.Prabavathi Research Scholar, 2Assistant Professor in Computer science Department of Computer Science Gobi Arts and Science College Gobichettipalayam, Tamil Nadu 638453, India _________________________________________________________________________________________ Abstract: A social network is a social structure of individuals, who are linked (directly or indirectly to each other) depending on a common relation of interest, e.g. friendship, trust, etc. Social network analysis is the study of social networks to recognize the structure and behavior of social actor. Social network analysis has gained importance due to its usage in different applications - from product marketing to search engines and organizational dynamics. The conventional clustering approaches group objects based only on objectsâ&#x20AC;&#x2122; similarity which does not suite for social network data. Social network objects should be grouped into classes depending on their links as well as their attributes. In this paper, a clustering algorithm based on BSP (Business System Planning) clustering with Principal Component Analysis (PCA) technique is proposed. This algorithm produces significant improvement in clusters, as it groups objects in a social network into different classes based on their links and identify relation among classes. Keywords: Social Network Analysis, BSP Clustering, PCA __________________________________________________________________________________________ 1

I. INTRODUCTION Social Networks are graph structures whose nodes or vertices represent people or other entities embedded in a social context, and whose edges represent interaction or collaboration between these entities [10]. Social networks are highly dynamic, evolving relationships among people or other entities. This dynamic property of social networks makes studying these graphs a challenging task. A lot of research has been done recently to study different properties of these networks. Such complex analysis of large, heterogeneous, multi-relational social networks has led to an interesting field of study known as Social Network Analysis (SNA). Social network analysis, which can be applied to analyze the structure and the property of personal relationship, web page links, and the spread of messages, is a research field in sociology. Recently social network analysis has attracted increasing attention in the data mining [1] research community. From the viewpoint of data mining, a social network is a heterogeneous and multi-relational dataset represented by graph [3].Research on social network analysis in the data mining community includes following areas: clustering analysis [1], classification [8], link prediction [7], Page Rank [9] and Hub-Authority [4] in web search engine. This paper deals with the social network cluster analysis. In the second section, a social network clustering based on Business System Planning (BSP) clustering algorithm is given. The algorithm can group objects in a social network into different classes based on their links, and it can also identify the relations among classes. In the third section, importance of Principle Component Analysis technique is dealt. Fourth section describes the methodology of BSP clustering. Section five shows how BSP clustering is improved by applying the PCA reduction technique. II. BSP Clustering in Social Networks There has been extensive research work on clustering in data mining. Traditional clustering algorithms [1] divide objects into classes based on their similarity. Objects in a class are similar to each other and are very dissimilar from objects in different classes. Social network clustering analysis, which is different from traditional clustering problem, divides objects into classes based on their links as well as their attributes. The biggest challenge of social network clustering analysis is how to divide objects into classes based on objectsâ&#x20AC;&#x2122; links, and to find algorithms that can meet this challenge. The BSP clustering algorithm [11] is proposed by IBM. It is designed to define information architecture for the firm in business system planning. This algorithm analyses business process and their data classes, cluster business process into subsystems, and define the relationship of these sub-systems. Basically BSP clustering algorithm uses objects (business processes) and links among objects (data classes) to make clustering analysis. Similarly social network also includes objects and links among these objects. In view of the same pre-condition, the BSP clustering algorithm can be used in social network clustering analysis. According to graph theory, social network is a directed graph composed by objects and their relationship. Figure 1 shows a sample of social network, the circle in the figure represents an object; the line with arrow is an edge

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of the graph, and it represents directed link between two objects, so a social network is a directed graph. In figure 1, Let Oi be an object in social network (i = 1...m ), let Ej which means directed link between two objects, be a directed edge of the graph ( j = 1...n ).After definition of objects and directed edges, reachable relation between two objects is also defined. O2 O1

O5 E5

E7 E8

E2 E3 E6 E4

E9 E10

Figure 1: A Sample of Social Network There are two kinds of reachable relation among objects, shown as following: 1) One-step reachable relation: if there is directed link from Oi to Oj through one and only one directed edge, then Oi to Oj is a one-step reachable relation. For instance in Figure 1 there is a directed link from O1 to O2 through the directed edge E1, O1 to O2 is one-step reachable relation. 2) Multi-steps reachable relation: if there is directed link from O i to O j through two or more directed edges, then O i to O j is a multi-steps reachable relation. There is a directed link from O1 to O4 through directed edges E1 and E5, then O1 to O4 is a 2-steps reachable relation. After these relations, BSP analyses a social network by generating edge pointed matrix and calculates one-step and multi-step reachable matrix between objects. III. PRINCIPAL COMPONENT ANALYSIS Principal component analysis (PCA) [13] in multivariate statistics is widely adopted as an effective unsupervised dimension reduction method and is extended in many different directions. The main justification of dimension reduction is that PCA uses singular value decomposition (SVD) which gives the best low rank approximation to original data. However, this essentially noise reduction perspective alone is inadequate to explain the effectiveness of PCA. In this paper, we provide a new perspective of PCA based on its close relationship with the BSP clustering algorithm. We show that the principal components are actually relaxed cluster membership indicators. The central idea of Principal Component Analysis (PCA) is to reduce the dimensionality of the data set while retaining as much as possible the variation in the data set. Principal components (PC’s) are linear transformations of the original set of variables. PC’s are uncorrelated and ordered so that the first few PC’s contain most of the variations in the original data set [Jolliffe, 1986]. PCA is a classic tool for analyzing large scale multivariate data. It seeks linear combinations of the data variables (often called factors or principal components) that capture a maximum amount of variance. Numerically, PCA only amounts to computing a few leading eigenvectors of the data’s covariance matrix, so it can be applied to very large scale data sets. One of the key shortcomings of PCA however is that these factors are linear combinations of all variables; that is, all factor coefficients (or loadings) are non-zero. This means that while PCA facilitates model interpretation and visualization by concentrating the information in a few key factors, the factors themselves are still constructed using all observed variables. In many applications of PCA, the coordinate axes have a direct physical interpretation; in finance or biology for example, each axis might correspond to a specific financial asset or gene. In such cases, having only a few nonzero coefficients in the principal components would greatly improve the relevance and interpretability of the factors. In sparse PCA, there is a trade-off between the two goals of expressive power (explaining most of the variance or information in the data) and interpretability (making sure that the factors involve only a few coordinate axes or variables). When PCA is used as a clustering tool, sparse actors identify the clusters with the action of only a few variables. The original n data points in m-dimensional space are contained in the data matrix In general data is not centered around the origin. The centered data matrix , where and . The covarance matrix is given by

The principal eigenvectors o are the principal directions of the data Y. The principal eigenvectors of the Gram matrix are the principal components; entries of each are the projected values of data points on the principal direction . and are related via: : where is the eigenvalue of the covarance matrix . Thus after applying PCA to the data, the principal components are identified from the data to be clustered. Thus only the important and the vital data are going to be clustered. In this proposed approach, the social network data is analyzed based on the BSP and PCA.

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IV METHODOLOGY a) Generate edge creation matrix and edge pointed matrix First according to the objects and edges in the graph, define two matrixes Lc(m× n matrix) and Lp(m× n matrix). Lc means the creation of edges and Lp denote the pointed relations of edges. In the matrix, Lc (i, j) =1 denotes object Oi connects with the tail of edge Ej, which means that object Oi creates the directed edge Ej . Lc (i, j)=0 denotes object Oi has no connection with the tail of edge Ej, which means Ej isn’t created by object Oi. In the matrix, Lp(i, j) =1 denotes object Oi connects with the head of edge Ej, which means object Oi is pointed to by the directed edge Ej. Lp (i, j)=0 denotes Oi doesn’t connect with the head of edge Ej, which means Ej doesn’t point to Oi. After defining the matrix reachable matrices are calculated. b) Calculate one-step reachable matrix between objects After the definition of Lc and Lp, one-step reachable matrix between objects is calculated through the following equation.

V is Boolean sum and ^is Boolean product. G(i, j)=1 means Oi to Oj is a one-step reachable relation, G(i, j) = 0 means there is no one-step reachable relation from Oi to Oj . All one-step reachable relation between objects is calculated. c) Calculate multi-steps reachable matrix between objects Besides one-step reachable relation, there are multi-steps reachable relations between objects. According to graph theory and the BSP clustering algorithm, multi-steps reachable matrix G2, G3, G4,…., Gm−1 is calculated. Following equations show the calculation of multi-steps reachable matrix:

These matrices include 2-steps, 3-steps… m-1-steps reachable relations between objects. Now n-steps reachable relation between two objects through G2G3G4...Gm−1 is calculated. d) Calculate reachable matrix The algorithm considers whether reachable relations exist between two objects, but do not care these relations are one-step or multi-steps, so reachable matrix R based on G,G2 ,G3 ,G4 ,...,Gm−1 is calculated as R=IVGVG2...VGm−1 where V is Boolean sum and I is unit matrix. R(i, j) = 1 means reachable relation exists from Oi to Oj, but the reachable relations existing in matrix R is not mutual, for instance R(i, j) = 1 means reachable relation exists from Oi to Oj, but it doesn’t mean reachable relation exists from Oj to Oi. Mutual reachable relations between two objects are important in a social network, so mutual reachable matrix based on R is calculated. e) Mutual reachable matrix and cluster generation The mutual reachable matrix can be calculated through Q=R^RT where ^means Boolean product, RT means Reachable Transpose matrix. In the matrix Q(i, j) = 1 indicates there are mutual reachable relation between Oi and Oj . In a social network if two objects that have mutual reachable relation, they should belong to the same class, thus cluster based on Q is generated. Thus according to mutual reachable matrix Q, a social network is divided into classes based on strong sub matrices in Q or adjusted Q. If all elements in a sub-matrix of Q are 1, then that matrix is a strong sub matrix. f) Identify relationships among classes After clustering social network nodes, there is a need to identify relationship among clusters. This can be done through generated clusters and one-step reachable matrix G. If there is one-step reachable relation between two objects in different classes, directed links exist between classes. Through G all relations among classes is identifed. After performing these steps, a social network is divided into classes. Social network clustering analysis algorithm can be given as Q− > Ck means generating clusters through mutual reachable matrix Q, and (C k ,Q )- >Relation(Ck) means identifying relationships among clusters based on clusters and one-step reachable matrix G. V. PROPOSED ALGORITHM BSP clusters divide a social network into different classes according to objects in the social network and links between objects, and it also can identify relations among clusters. Main disadvantage of this algorithm is that it uses matrices to store edges and reachable relations. In a real social network these matrices will be very huge,

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which can’t be loaded into main memory. But these matrices are normally sparse, so this feature is used in our work to reduce the size of the matrix. In this paper we propose a modification of existing BSP algorithm using Link list data structure. Using this data structure we have overcome the shortcomings of BSP. Following procedure is required for converting this sparse matrix in to link list: A matrix is a two-dimensional data object made of m rows and n columns, therefore having m, n values. When m=n, it is a square matrix. The most natural representation is to use two-dimensional array A[m][n] and access the element of ith row and jth column as A[i][j]. If large number of elements of the matrix are zero elements, then it is called a sparse matrix. Representing a sparse matrix by using a two-dimensional array leads to the wastage of a substantial amount of space. Therefore, an alternative representation must be used for sparse matrices. One such representation is to store only non- zero elements along with their row positions and column positions. That means representing every non-zero element by using triples(i,j,value), where i is a row position and j is a column position, and store these triples in a linear list. It is possible to arrange these triples in the increasing order of row indices, and for the same row index in the increasing order of column indices. Each triple can be represented using a node having four fields as shown: Struct snode{Int row,col,val; Struct snode *next;} The algorithm to reduce the dimensionality of the matrix is given below: Input Edge creation Lists Edge pointed List Begin Swap(Lu) For k=3 to m do Gk-1=Gk-2*G Edge pointed Lists Begin Swap(Lu) For k=3 to m do Gk-1=Gk-2*G R=IVGVG2……Gm-1 Swap(R) Qk ->C ( Ck,Q)->Relation(Ck) End End 1.

In order to add two sparse matrices represented using the sorted linked lists, the lists are traversed until the end of one of the lists is reached. 2. In the process of traversal, the row indices stored in the nodes of these lists are compared. If they don't match, a new node is created and inserted into the resultant list by copying the contents of a node with a lower value of row index. The pointer in the list containing a node with a lower value of row index is advanced to make it point to the next node. 3. If the row indices match, column indices for the corresponding row positions are compared. If they don't match, a new node is created and inserted into the resultant list by copying the contents of a node with a lower value of column index. The pointer in the list containing a node with a lower value of column index is advanced to make it point to the next node. 4. If the column indices match, a new node is created and inserted into the resultant list by copying the row and column indices from any of the nodes and the value equal to the sum of the values in the two nodes. 5. After this, the pointers in both the lists are advanced to make them point to the next nodes in the respective lists. This process is repeated in each iteration. After reaching the end of any one of the lists, the iterations come to an end and the remaining nodes in the list whose end has not been reached are copied, as it is in the resultant list. We performed the above mentioned steps to reduce the dimensionality of the matrix representation, so that, the input to the BSP clustering is reduced in large number and improves the efficiency of the clusters.

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Accuracy (%)

VI. EXPERIMENTAL RESULTS The performance of the proposed social network using the PCA and BSP clustering is evaluated on the Zachary's Karate Club dataset. This data set is a social network of friendships links between 34 members of a karate club at a US university in the 1970. The values ‘1’ in the data set denotes that there is relationship between the two members in the data set. Alternatively, the values ‘0’ denotes that there is no relationship among the members in the dataset. The proposed PCA and BSP clustering is compared with the existing BSP clustering algorithm. From the dataset, 1156 links were extracted. But after applying PCA technique it was reduced to 700 links which was provided as data to the BSP clustering algorithm. The reachable matrix is obtained from the data set. G34 reachable matrix is computed. Then, clusters are obtained. Cluster 1 and cluster 2 is obtained. If, there is relation between the two clusters, then a relation is formed between them. The clustering accuracy of the data sets is obtained. Moreover, the time taken by the proposed approach for clustering the data set is also calculated and is displayed. The proposed PCA and BSP clustering is compared with the BSP clustering algorithm. The performance of the proposed system is evaluated based on the parameters like clustering accuracy and classification time. The accuracy in clusters between BSP and PCA with BSP is shown in Figure.1. It is observed from the graph that the classification accuracy of the proposed approach using PCA and BSP approach is high when compared to the BSP approach. Moreover the time taken by proposed approach using PCA and BSP approach is very less when compared to the existing BSP approach due to the reduction in matrix size. 88 86 84 82 80 78 76 74

Accuracy

BSP clustering PCA and BSP Clustering Clustering Approaches

Figure 1. Cluster Comparison

The data set taken for this experiment is small in size. Our future work is to check whether the same efficiency and time reduction exists for a large data set and to combine other reduction techniques with BSP. VII CONCLUSION This paper proposes an improved social network clustering technique based on PCA and BSP clustering algorithm. The main idea of this approach is introducing the PCA approach before applying BSP clustering. The main advantage of the PCA technique is that the main and the principal components are identified. Thus after the PCA technique is applied, the important components are given to the BSP clustering. The performance of the proposed approach is evaluated using the Zachary’s karate club dataset. The parameters used for the evaluation are clustering accuracy and the time taken for clustering. From the experimental observation, it is clearly observed that the proposed BSP clustering with PCA shows significant performance in terms of accuracy and classification time. Thus the proposed approach is very significant in providing better social network analysis. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Gao X, Wu S, Yu B. “Management Information System. Beijing: Economy and Management Press (in Chinese)”, 2002. Han J, Kamber M. “Data Mining: Concepts and Techniques 2nd edition San Francisco: The Morgan Kaufmann Publishers, 2006. W. W. Zachary, “An information flow model for conflict and fission in small groups”, Journal of Anthropological Research 33, 452-473 (1977). Krebs V. “Mapping networks of terrorist cells”, Connections, Vol. 24, pages 43-52, 2002. Kubica J, Moore A and Schneider J. “Tractable Group Detection on Large Link Data Sets”, Proceeding 3rd IEEE international conference on data mining, Melbourne, FL, pages 573-576, 2003. Liben-Nowell D and Kleinberg J. “The Link prediction problem for social networks”, Proceeding 2003 international conference on information and knowledge management, New Orleans, LA, pages 556-559, 2003. Lu Q and Getoor L. “Link-based classification” Proceeding 2003 international conference on machine learning, Washington DC, pages 496-503, 2003. Page L, Brin S, Motwani R and Winograd T. “The PageRank citation ranking: Bring order to the web. Technical report, Stanford University”, 1998. The Link Prediction Problem for Social Networks (2003) David Liben-Nowell, Jon Kleinberg, 556 – 559. Communications of the IIMA (2007) Volume 7 Issue 4 “Social Network Analysis Based on BSP Clustering Algorithm” Gong Yu School of Business Administration China University of Petroleum. Algorithms by thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Prentice-Hall India (1999). Yeung, K. Y. and Ruzzo, W. L., “An empirical study of Principal Component Analysis for clustering gene expression data”, 2001. Jiangtao Qiu, Zhangxi Lin, Changjie Tang and Shaojie Qiao, “Discovering Organizational Structure in Dynamic Social Network”, Ninth IEEE International Conference on Data Mining, 2009.

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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 Estimation of seasonal variation in Radon, thoron and their progeny levels in some dwellings A. K. Garga Sushil Kumarb and R. P. Chauhanc Department of Physics, Arya P.G.College, Panipat, Haryana, India b Department of Physics, Choudhary Devi Lal University Sirsa, Haryana, India c Department of Physics, National Institute of Technology, Kurukshetra, Haryana, India _________________________________________________________________________________________ Abstract: Radon and its progeny are the major contributors in the radiation dose received by general population of the world. Keeping this in mind the environmental radon, thoron and their progeny measurements have been carried out in some dwellings. The radon thoron twin dosimeter cups designed by environmental assessment division of Bhabha Atomic Research Centre (BARC) Mumbai, India have been used for this study. Three pieces of LR-115 type II solid-state nuclear track detectors are fixed in the dosimeters. The dosimeters were suspended in the dwellings for three months during a season. The specially designed twin cup dosimeter used in present study consists of two chambers of cylindrical geometry separated by a wall in the middle with each having a length of 4.5 cm and radius of 3.1 cm. This dosimeter employs three SSNTDs out of which two detectors were placed in each chamber and a third one was placed on the outer surface of the dosimeter. One chamber is fitted with glass fiber filter so that radon and thoron both can diffuse into the chamber while in other chamber, a semi permeable membrane made of latex or cellulose nitrate, having a thickness of 25 Âľm is used. The membrane mode measures the radon concentration alone as it can diffuse through the membrane but suppresses the thoron. The twin cup dosimeter also has a provision for bare mode enabling it to register tracks due to radon, thoron and their progeny in total. To observe the effect of environmental conditions the measurements have been carried out during different seasons of the year. The radon-thoron progeny levels and annual dose received by the inhabitants in the dwellings under study have also been calculated. The levels are found to be higher during winter season as compare to other seasons of the year. Keywords: Radon, Annual effective dose, LR-115 type II, Radioactivity. __________________________________________________________________________________________ a

I. Introduction About 90% of radiation exposure to human arises from natural sources such as cosmic radiation, terrestrial radiation and exposure to radon, thoron and their progeny etc. Various studies have been made on exposure to many of the forms of natural radiation [1]. These studies have shown that more than 50% of annual exposure to humans is from radon and its daughter products. Also it is a well-known that the radiations from the naturally occurring radioactive material originating from the earthâ&#x20AC;&#x2122;s crust are the major contributors to the total background exposures to the human populations which includes external gamma radiations exposures and inhalation exposures, the latter being due to radon, thoron and their progeny [2]. The major inhalation does is contributed by the radon progeny nuclides. All building materials shows various amounts of radioactivity as most of these are derived from rocks and soil which contains uranium-238 and thorium-232 series and the radioactive isotope of potassium-40. All these can be sources of both internal and external radiation exposure. Internal exposure takes place through the inhalation of radon gas and external exposure occurs through the emission of penetrating gamma radiations [3]. The problem of radon is an important global problem of radiation hygiene particularly in homes. Radon is a radioactive gas of natural origin, is produced by the disintegration of uranium. In general, there are three main mechanisms of radon entry into a building4; convection via utility access points, cracks and openings, diffusion from soil via the pore space of the building material and emanation from building materials. Indoor high radon concentration is usually due to penetration of radon from the surrounding soil. Radon levels in a home can fluctuate from day to day, depending upon the level of radon in the soil, type of soil, airflow through the soil, openings to building and ventilation. Outdoor radon concentrations are low but in case of indoors, this gas may accumulate in high concentrations emitted from the soil and from building materials when the room is not properly ventilated. Radon emanation from the soil depends upon its radium content but also upon mineralogy, porosity, grain size, moisture content and permeability of host rock and soil [4-5]. Radon gas decays overtime into radioactive particles that can be inhaled and trapped in the lungs as these daughter products remain air borne for a long time. When radon decays it forms its progeny 218Po and 214Po, which are electrically charged and can attach themselves to tiny dust particles, water vapours, oxygen, trace gases in indoor air and other solid surfaces. These daughter products remain air-borne for a long time and can easily be inhaled into the lung and can adhere to the epithelial lining of the lung, thereby irradiating the tissue.

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Bronchial stem cells and secretion cells in airways are considered to be the main target cells for the induction of lung cancer resulting from radon exposure. The exposure of population to high concentrations of radon and its daughters for a long period lead to pathological effects like the respiratory functional changes and the occurrence of lung cancer [6]. Based upon current knowledge about health effects of inhaled radon and its progeny, ICRP has made recommendations for the control of this exposure in dwellings and work place [7]. Keeping this in mind the environmental monitoring of radon, thoron and their progeny in some dwellings of eastern Haryana has been carried out using radon-thoron dosimeter cups. The climate of Haryana is of pronounced character, very hot in summer and very cold in winter. The locations under study are the parts of the districts of Panipat and Sonepat. II. EXPERIMENTAL TECHNIQUES For the measurement of concentration of radon, radon and thoron both, and total sum of radon, thoron and their progeny in the dwellings, the radon-thoron mixed field dosimeter popularly known as, ‘Twin Chamber RadonThoron Dosimeter, developed by Bhabha Atomic Research Centre (BARC) has been employed. The specially designed twin cup dosimeter used in present study consists of two chambers of cylindrical geometry separated by a wall in the middle with each having a length of 4.5 cm and radius of 3.1 cm. This dosimeter employs three SSNTDs out of which two detectors were placed in each chamber and a third one was placed on the outer surface of the dosimeter. One chamber is fitted with glass fiber filter so that radon and thoron both can diffuse into the chamber while in other chamber, a semi permeable membrane made of latex or cellulose nitrate, having a thickness of 25 µm is used. The membrane mode measures the radon concentration alone as it can diffuse through the membrane but suppresses the thoron. The twin cup dosimeter also has a provision for bare mode enabling it to register tracks due to radon, thoron and their progeny in total. Therefore, using this dosimeter we can measure the individual concentration of radon, thoron, and their progeny at the same time (Fig.-1).

Fig.-1 Twin Chamber Radon-thoron dosimeter cups used in the present study The dosimeters were suspended at a height a height of about 1.5 m in order to evaluate the annual average indoor radon levels. At the end of the exposure time, the detectors were removed and subjected to a chemical etching process in 2.5N NaOH solution at 600C for 90 minutes. The detectors were washed and dried and the tracks produced by the alpha particles were observed and counted under an optical Olympus microscope at 600X. A large number of graticular fields of the detectors were scanned to reduce statistical errors. The measured track density (Track/cm2/day) was converted into radon and thoron concentration using calibration factors [2]. Radon and thoron progeny levels in mWL has also been calculated using indoor equilibrium factor as 0.4 for radon and 0.1 for thoron from UNSCEAR [3]. Annual dose received by the inhabitants in the dwellings under study in mSv was estimated using the relation [4-5]: D=[(0.17+ 9 FR) CR +(0.11+32 FT) CT] 7000 10-6 Where, FR =equilibrium factor for radon; CR = radon concentration; FT = equilibrium factor for thoron and CT = thoron concentration. III. RESULTS AND DISCUSSION In Rainy season in Panipat, the concentration of radon has been found to vary from 46 to 61 Bqm-3 with an average of 52 ± 2 Bqm-3 Annual effective dose received during Rainy season varied from 1.5 to 1.8 mSv with an average of 1.7±0.1mSv.In Winter season in Panipat, the concentration of radon has been found to vary from 53 to 76 Bqm-3 with an average of 64 ± 3 Bqm-3. Annual effective dose received during winter season varied from 1.7 to 2.4 mSv with an average of 2.1 ± 0.3 mSv .In Summer season in Panipat, the concentration of radon and

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thoron has been found to vary from 38 to 91 Bqm-3 with an average of 52 ± 4 Bqm-3 Annual effective dose received during summer season varied from 1.3 to 2.7 mSv with an average of 1.7 ± 0.1 mSv In Rainy season in Sonipat, the concentration of radon has been found to vary from 30 to 46 Bqm-3 with an average of 37 ± 2 Bqm-3. Annual effective dose received during Rainy season varied from 1.2 to 1.5 mSv with an average of 1.4 ± 0.2 mSv. In winter season in Sonipat, the concentration of radon has been found to vary from 38 to 61 Bqm-3 with an average of 51 ± 3 Bqm-3. Annual effective dose received during winter season varied from 1.3 to 1.9 mSv with an average of 1.7 ± 0.1 mSv. In summer season in Sonipat, the concentration of radon has been found to vary from 23 to 53 Bqm-3 with an average of 41 ± 3 Bqm-3.Annual effective dose received during summer season varied from 1.3 to 1.9 mSv with an average of 1.7 ± 0.1 mSv. There is a variation in the concentration of radon, thoron, and their progeny from one location to another in the Study area during the same season which may be due to the composition of the soil beneath the dwellings and the type of construction material used. The levels are found to be higher during winter season as compare to other seasons of the year which may be due to poor ventilation conditions in winter. References 1 2

3 4 5 6 7

BEIR VI Report of the Committee on the Biological effects of Ionizing Radiation. Natl. Res. Council. Natl. Acad. Press, Washington, DC (1999). K.P. Eappen and Y.S. Mayya, Radiation Measurements, 38, p5 (2004). UNSCEAR (United Nations Scientific Committee on the Effects of Atomic Radiation), Exposures from Natural Sources of Radiation, A/Ac., 82/R. (1992) 511. J.Sannappa et al., Radiation Measurement 37, p55 (2003). Y.S. Mayya , K.P. Eappen and K. S. V. Nambi. Radiat. Prot. Dosim. 77(3), p177 (1998). ICRP (International Commission on Radiological Protection). Oxford: Pergamon Press, ICRP Publication No. 65 (1993). J. Somali, M. Horvath, B. Kanyar, Z. Lendvai and C.S. Nemeth, Health Phys 75, p648 (1998).

Location (Samples)

Radon conc. (Bqm-3)

Thoron conc. (Bqm-3)

Radon progeny levels (mWL)

Thoron progeny levels(mWL)

Annual Effective dose(mSv)

Panipat (9) (Rainy season) Panipat (9) (Winter season)

52±2

12±1

5.6±0.2

0.3±0.1

1.7±0.1

64±3

15±1

6.9±0.3

0.4±0.1

2.1±0.3

Panipat (12) (Summer season)

52±4

10±1

5.0±0.3

0.3±0.1

1.7±0.1

Radon conc. (Bqm-3)

Thoron conc. (Bqm-3)

Radon progeny levels (mWL)

Thoron progeny levels(mWL)

Annual Effective dose(mSv)

Sonepat (9) (Rainy season) Sonepat (9) (Winter season)

37±2

16±1

3.8±0.2

0.5±0.1

1.4±0.2

51±3

13±1

5.6±0.3

0.4±0.1

1. 7±0.1

Sonepat (12) (Summer season)

41±3

15±3

4.5±0.3

0.4±0.1

1.6±0.1

Location (Samples)

AM (arithmetic mean); * SE (statistical error)

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Age Group Estimation using Facial Features C. Arun Kumar1, Praveen Kumar R V, Sai Arvind R. Assistant Professor (Sr.Gr)1, Department of Information Technology, Amrita School of Engineering, Amrita Viswa Vidyapeetham University, Amrita Nagar Post, Ettimadai, Coimbatore-641112, Tamil Nadu, India. Abstract: Human face is the non-verbal portal into the person. A personâ&#x20AC;&#x2122;s gender, mood, country of origin, age category, and identity can be identified from the face. There has been a growing interest in automatic age estimation from facial images due to a variety of potential applications in law enforcement, security control, and human computer interaction (HCI). This paper presents a method to improve the accuracy of the estimated age. Apart from geometric shape features, wrinkle analysis is also incorporated in classifying the age. Multiple algorithms are applied for different phases like feature extraction, illumination correction, image fitting and edge detection etc. The main objective of this paper is to present a working model of an age classifier that is more efficient that the existing models. The experimental results show that 93.01% recognition rate can be achieved when applying the proposed system on the images. Keywords: Edge detection, Age classifier, geometric shape features, wrinkle analysis, feature extraction, illumination correction. I. Introduction The research in the field of image processing has undergone a rapid growth with the ability to solve the real world problems. Those researches include applications in tracking down a vehicle in an image, facial recognition, authentication purposes such as debit/credit cards, passports, voterâ&#x20AC;&#x2122;s identification cards etc. Among these there are several applications that use the image of the user as the input and perform various operations on them. This paper presents an overview of the prior works in age estimation (determination) and a novel approach based on a hierarchical model, which infuses a classification system with multiple age estimator functions to create an industry age-estimation algorithm. It is a well-known fact that the biodynamic factors of facial aging are quite different for the two stages of aging: growth and development and adult. During the latter, the major changes in facial complex are due to lengthening and widening factors of cranial complex. The aging factor for adults does include some cranial changes, but the primary drivers are the development of wrinkles, lines, creases, and sagging of the skin. There are various methods used in this paper for age group estimation techniques that are currently deployed in areas like features extraction, age classification and texture analysis and the suitable algorithms are selected that efficiently suits the current needs. II. Earlier Works The active appearance model (AAM) is used to estimate age as facial global features. The AAM is a generative parametric model that contains both the shape and appearance of a human face, which it models using the principal component analysis (PCA), and is able to generate various instances using only a small number of parameters. Therefore, an AAM has been widely used for face modelling and facial feature point extraction. Active Appearance Model, which is the extension of Active Shape Model, finds the feature points using the improved Least Mean Square Method. Then Support Vector Machine method is applied to create hyper planes that will act as the classifiers. Using the result, the person is classified as young or adult. Two separate aging functions are developed and used to find the age as proposed by K. Luu et al. [4] and Choi et al. [9]. The method proposed by K. Ricanek et al. [5] can be considered as the extension of K.Luu et al. [4], with the exception that Least Angle Regression (LAR) method is used to increase the accuracy of finding the feature points in the image using AAM. In LAR method, all the coefficients are initially assigned 0. Then from feature point x1, LAR moves continuously towards least mean square value until it reaches the efficiency. Global features such as distance, angle and ratio are also considered for classification of age group. Merve Kilinc et al. [6] use a new method of having overlapped age groups and a classifier that combines geometric and textural features. The classifier scoring results are interpolated to produce the estimated age. Comparative experiments show that the best performance is obtained using the fusion of Local Gabor Binary patterns and Geometric features. From the geometric features, the cross-ratio is found out, which the ratio of distance between the facial features like nose ends, chin, head, jaw. The role of geometric attributes of faces is considered, as described by a set of landmark points on the face, in the perception of age. The affine transformations used to approximate change in the pose of the subject. Subspaces can be identified as points on

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a Grassmann manifold. The warping of an average face to a given face is quantified as a velocity vector that transforms the average to a given image in unit-time. Then Euclidean space regression method is applied. This paper concerns with providing a methodology to estimate age groups using face features. This method is based on the face triangle which has three coordinate points between left eyeball, right eyeball and mouth point. The face angle between left eyeball, mouth point and right eyeball estimates the age of a human. On human trial, it works well for human ages to 18 to 60 as discussed by P. Turaga et al. [7] and R. Jana et al.[8]. Choi et al. [9] discusses about the age detection using age feature classification combined in order to improve the overall performance. In feature extraction, they discussed about the local, global and hierarchical features. In local features such as wrinkles, skin, hair and geometrical features are extracted using Sobel filter method. In global features AAM method, Gabor Wavelet transform methods are used. Hierarchical is the combination of both the local and global features. In proposed model they use Gabor filter to extract the wrinkles and LBP method for skin detection. This improves the age estimation performance of local features. C. T. Lin et.al[11], estimated the age by global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. The feature selection is based on Harr features and Adaboost method for strong classifier. The Gabor wavelet transformation is used to increase efficiency of SVM construction. Hu Han et.al[12] discussed about the face pre-processing, facial component localization, feature extraction and hierarchical age estimation. They use SVM-BDT (Binary Decision Tree) to perform age group classification. A separate SVM age repressor is trained to predict the final age. III. Proposed method A. Architecture Diagram The architecture diagram of the proposed model for age group estimation using facial features is shown in Fig 1.

Fig. 1 Architecture diagram for the proposed method B. Pre processing 1. Illumination The contrast of the input image is enhanced using its histogram. The input image is transformed into frequency domain using Discrete Cosine Transform and is used to get the areas affected by illumination. Those areas are normalized using power law transformation, using the gamma value as proposed by C.Arun Kumar et al.[1] shown in the Fig. 2. The pre processed image is now sent for feature extraction of both global and local features.

Fig. 2 Histogram Equalization (Left: Original Image, Right: Histogram equalized image)

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2. Global features 2.1 Feature Extraction using Active Appearance Model Active Appearance Model (AAM) is particularly suited to the task of interpreting faces in images. The shape of a face is represented by a vector consisting of the positions of the landmarks. All shape vectors of faces are normalized into a common coordinate system. The shape of an independent AAM is constructed by a mesh of 68 points. AAMs are normally computed from hand labelled training images. This approach is to apply Principal Component Analysis (PCA) to the training meshes of 68 points. AAM analyzes the gray level of the particular feature point. The gray level of each and every 68 feature points are analyzed in the same way and these 68 feature points are used mainly for collecting the attributes of all the distance, ratio and angle classification. .

Fig. 3 Feature extraction using Active Appearance model The AAM applied image is now classified using distance, angle and ratio computation. 2.2 Classification using Distance Computation The 18 facial features points are identified from the AAM applied image and then 15 facial features distance between selected feature points are calculated as shown in Fig. 4. Most of the selected feature points are related to the mouth, nose, eye and eyebrow. There are eight distances respect to horizontal axis and seven distances respect to vertical axis on the facial image. Those distances are (a) Eye length (b) Eye inner cornet distance (c) Width of the mouth (d) Eyebrow length (e) Left to Right Eyebrow (f) Width of the face (g) Width of nose (h) Bottom width (i) Mouth to bottom (j) Nose to bottom (k) Top to bottom (l) Eye height (m) Top to nose (n) Lip height (o) Top to lip.

Fig. 4 Distance computation for input image 2.3 Classification using Face angles The facial features points are identified from the AAM applied image and then from the obtained facial features, four angles are found. The four angles that are computed for the points as follows:  angle from eyes to nose,  angle from eyes to mouth,  angle from eyebrows to mouth  angle between eyes, nose and mouth Each of the four angles is found by drawing a triangle for the respective points and slopes are calculated as m1 and m2. From the slopes m1 and m2, the face angle (A) is calculated using the formula A= (1) These four angles are taken as feature points combined with the attributes obtained from distances and ratios calculations to classify the images under four age groups. 2.4 Classification using Ratios Similar to the computation of the angles and distances, the facial features points are identified from the AAM applied image and then from the obtained facial features, the eight ratios are calculated using the image shown in Fig. 5.

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Fig. 5 Ratio calculation from the input image 3. Local features 3.1 Feature extraction using wrinkles Each type of wrinkle has their own orientation and it is good to apply gabor filter for that particular angle. The mean, variance, standard deviation will give the details of the feature in the wrinkle areas. In case of PCA, the maximum support vector will give the feature of the wrinkle. The facial parts to be considered are shown in Fig. 5.

Fig.6 Regions to be considered and Gabor filterâ&#x20AC;&#x2122;s angles To determine the wrinkle features, we use the mean and variance of the magnitude response of the Gabor filter in each wrinkle area, because the mean and the variance of the magnitude represent both the strength and quantity of wrinkles. 4. Classification Analysis All the attributes obtained from both the global and local features are integrated and loaded into the classifier in WEKA tool. The classifier used is NNge (Non Nested Generalized Exemplar) with loaded training set form the FG-Net database. This classifier is used because of reduced error rate and high accuracy level when compared to the other classifiers. This classifier gives accuracy of up to 93.01% as shown in the Fig. 6. Totally 216 out of 229 instances are classified into correct age groups 0-19, 19-29, 29-39 and 40 above.

Fig. 7 NNge Classifier analysis

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IV Conclusion and Future Work In this project the age group is estimated by computation of face angles, distances between facial feature points, ratios and wrinkle detection is demonstrated and how these components are used to classify the age is analyzed in detail. We included nearly 34 attributes calculated from these components if they vary linearly (or close to being linear) with age. The main challenge lies in identifying the best combination of these components (distance, ratio and angle). Future work involves in selection of best combination of features that will help in liberalizing the values which will automatically improve the efficiency of the classifier.

References [1]

[2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[14]

[15] [16]

C.Arunkumar, T.Raghuram, M.N.Sekharan “A Hybrid Approach to normalize the light illumination in facial images using DCT and Gamma Transformations”, The International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), ISSN (Print): 2279-0047, ISSN (Online): 2279-0055, Issue 7, Vol. 1,2,3 & 4, December 2013-February-2014. M.Mendonca, G.Denipote, A.S.Fernandes and V.Paiva, “Illumination Normalization Methods for Face Recognition”. S.Venkatesan and S.Srininvasa Rao Mandane, “Experimental study on illumination normalization methods for effective face identification by genetic algorithm”, Journal of Global Research in Computer Science, vol.3, no.2, February 2012. K. Luu, K. Ricanek , T. Bui, and C. Suen. “Age Estimation Using Active Appearance Models and Support Vector Machine Regression”. In IEEE BTAS, 2009 K.Ricanek, Y. Wang, C. Chen, and S. Simmons. “Generalized Multi-Ethnic Age Estimation. In IEEE BTAS”, 2009. Merve Kilinc, Yusuf Sinan Akgul, “Human Age Estimation via Geometric and Textural Features”, 2009. P. Turaga , S. Biswas and R. Chellappa"The role of geometry in ageestimation",Proc. 2010 IEEE Int. Conf. AcousticsSpeech and Signal Processing (ICASSP),pp.946 -949 2010 R. Jana, H. Pal, A.R.Chowdhury “Age group Estimation using Face Angle”,IOSR Journal of Computer Engineering, pp. 35-39, 2012. Choi, Youn Joo lee, Sung Joo Lee, Kang Ryoung Park, Jaihie Kim, “Age estimation using a hierarchical classifier based on global and local facial features”, ELSEVIER, pp.1262-1281, 2011. G. Guo , Y. Fu , T. S. Huang and C. Dyer"Locally adjusted robust regression for human age estimation",IEEE Workshop on Applications of Computer Vision,2008 C.T. Lin, D.L. Li, J.H. Lai, M.F. Han, J.Y. Chang, “Automatic Age Estimation System for Face Images”, International Journal of Advanced Robotic Systems, 2012. Hu Han, Charles Otto and Anil K. Jain “Age Estimation from Face Images: Human vs. Machine Performance”, IAPR International Conference on Biometrics, 2013. V.P. Vishwakarma, S. Pandey and M. N. Gupta, “A Novel Approach for Face Recognition using DCT Coefficients Re-scaling for Illumination Normalization”, in Proc. Of IEEE Int. Conf. on Advanced Computing & Communication(ADCON 2007), Dec. 2007, pp. 535-539. Weilong Chen, Meng Joo and Shiquian Wu, “Illumination Compensation and Normalization For Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain” IEEE Transactions on Systems, Man, And Cybernetics – Part B: Cybernetics, vol.36, No. 2, pp 458-466, Apr. 2006. G. Guo, Y. Fu, C. Dyer, and T. Huang, “Image-based human age estimation by manifold learning and locally adjusted robust regression,” IEEE Trans. Image Process., vol. 17, no. 7, pp. 1178–1188, Jul. 2008. G.Guo,G.Mu, Y. Fu, and T. Huang, “Human age estimation using bioinspired features,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit.,2009, pp. 112–119.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net An Information Security Technique Using DES-RSA Hybrid and LSB 1

Sandeep Singh, 2Aman Singh Department of Computer Science Engineering, Lovely Professional University, Phagwara, Punjab, India 2 Assistant Professor, Department of Computer Science Engineering, Lovely Professional University, Phagwara, Punjab, India ______________________________________________________________________________________ Abstract: Security plays a vital role while exchanging large amount of information from source to destination. Currently technology is increasing very rapidly from security point of view. Each and every person wants his communication must be confidential and protected from the access of the illegal users over internet. Cryptography and Steganography plays a very important role as security tools. Cryptography is the art of converting the readable information into an unreadable form. Steganography is a tool used to hide information inside a media files such as images, audio and video etc. In this paper a combined technique of both cryptography and steganography is proposed for the better security of the data. The message is initially encrypted with DES and the keys of DES are encrypted with RSA then the hybrid of both DES-RSA is embedded inside an image with help of LSB image steganography. Results of the technique provide a stronger security. The encryption time is also faster than the previous techniques as well as brute force attack to this technique is almost not possible. Keywords: DES; RSA; Hybridization; LSB; Steganography; ______________________________________________________________________________________ 1

I. Introduction The day by day development in the communication systems demands the very high level of information security in communication networks. As the transmission of information is increases on the internet so network security is getting very importance. Therefore the confidentiality and the reliability of the data must be protected from unauthorized access. It means there must be an explosive development in the field of information security along with the copyrights of the digital media. For the protection of secrete information cryptography and steganography are the two commonly used security tools. Confidentiality, accessibility and integrity are the three main concepts of information security. Opinions of the different people who make the use of such information are authentication, authorization and non-repudiation. Cryptography and Steganography are the security tools available for the protection of the secrete information. Cryptography is an encryption technique used to convert the readable information into an unreadable form. It is of two type’s symmetric and asymmetric key cryptography. In symmetric key cryptography same key is used for both encryption and decryption while in public key or asymmetric key cryptography different keys are used for both encryption and decryption i.e. public key for encryption and private key for decryption or vice versa. Steganography Steganography is an information security tool which provides privacy of text or images to protect from disputers. Steganography insert the data in a cover image and changes its properties. Steganography generates secret messages so that hacker cannot notice the occurrence of message. So Steganography is the key of ability to avoid messages from exposure. Steganography is a Greek word , which means “covered writing.” Steganography includes an immense collection of secret communications techniques which covers the message’s persistence. The high-quality method of image Steganography aims at three aspects. First one is capacity, which means that maximum data can be stored within cover image, second one is the imperceptibility that describes the visual quality of stego-image after hiding the information and the last is robustness. II. Various Encryption Algorithms A. DES (Data Encryption Standard) It was created in 1972 by IBM with the data encryption algorithm and was adopted by the US government as standard encryption technique for DES begins the encryption procedure by using a 64-bit key. DES wants two inputs - the plaintext to be encrypted and the secret key. The way in which the cleartext is received also the key agreement used for encryption and decryption, both confirm the type of cipher it is. DES is a symmetric key algorithm, 64 bit block cipher as it uses the similar key for both encryption and decryption and just operates on 64 bit blocks of information at a time 5 (be they plaintext or cipher text). The key size used is 56 bits, but a 64 bit (or eight-byte) key is actually input. The LSB of each byte is either used for equality (odd for DES) or set randomly and does not enlarge the security in any mode. All blocks are numbered from left to right which makes the eight bit of each one byte the parity bit.

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Ever since its acceptance as a central standard there have been concerns about the level of security provided by DES in two areas, Key size and nature of the algorithm.  56 bit key length (approx 7.2 × 1016) on early consideration brute-force attack seems unworkable. But with an extremely parallel machine of about 5000 nodes with every node able of achieving a key search rate of 50 million keys/sec, the time taken to perform a brute-force search is about 100 hrs which is far from extreme.  The characteristic of DES algorithm: of additional concern is that cryptanalysis is achievable by exploiting the features of DES. The centre of attention is the eight S-boxes used in each iteration. The plan criteria for the whole algorithm has certainly not available and there has been rumour that the boxes were constructed in such a manner that cryptanalysis is achievable by a challenger who knows the weak points in the S-boxes. B. RSA (Rivest, Shamir and Adleman) It is the most important algorithm of the asymmetric key cryptography; it is able to resist all most all the possible attacks on the password till now. RSA represents the name of its inventors, first algorithm which provide both encryption as well as digital signature. Its security is lies on the mathematical computation of the two large prime numbers. In RSA two prime numbers are used for the creation of public key and private key. It is very difficult to know the original message from signal key. It is very much secured from brute force attack as well [6]. RSA algorithm can be basically described as Follows: 1. Generating keys of RSA 2. Choose two prime number p and q a. n=p X q where n is a large integer whose factorization gives two large prime number p and q 3. ᶲ (n) = (p - 1) × (q-1) 4. Randomly select key encryption key where 1<e<ᶲ (n), gcd (e, ø (n))=1 5. Now solve the following equation to compute decryption key d e. d=1 mod ø(n) and 0≤d≤n 6. public key PU= e, n 7. private key PR= d, n C. Hybrid cryptography Symmetric key algorithms are facing a problem related to the security of the keys and asymmetric key algorithms are facing the problem of very slow speed as compared to symmetric key algorithms. Symmetric key algorithms can be used for both large and small message transmission but asymmetric key algorithms are only well suitable for small message transformation over the internet. Therefore, to reduce or overcome from the problems of both symmetric and asymmetric key algorithms a hybrid of both these algorithms are used which is known as PGP (Pretty Good Privacy). It will provide the protection for the symmetric keys as well as increases the speed of the asymmetric key cryptography. Advantages of hybrid cryptography: • No need to send DES keys secretly before communication. • Keys are sending by RSA, so it will also act as digital signature. • Having same speed of encryption and decryption as DES. D. LSB (Least significant bit) It is a simple, efficient and easy to use technique that embeds data in a cover image [4]. For example, it would be an easy way to put the information at the least significant bit (LSB) of an image at every pixel. The embedded information will create a distorted image called stego-image. For a computer system, an image refers to a file that signifies some colours and that too with different intensities of brightness on different regions of that image. Usually steganography is the technique to hide data behind an object and that object may be any text, image, audio or video file. Various techniques are there in steganography to perform such hiding mechanism. One of those techniques is LSB. In the LSB technique, out of the total size of file, 8 th bit of each byte gets replaced by one bit of the secret data. This technique is effective mostly in case of image files where substitution done creates least changes in the image so formed. The substitution done is performed from 0 to 1 or vice versa. The changes in the so formed image will be such that they cannot be significantly visible. The file created after LSB treatment hardly makes any change in the size of the data. But the output file may vary in its clarity or sometimes seems to be distorted. LSB image steganography steps 1. According to key choose n hidden pixels from the image.

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2. Determine kth bit of information. (0<k<=length of data). 3. Now choose the kth bit of hidden pixel and determine its pixel value. (0<k<=m). 4. If the LSB of the image pixel is same to information then left it unchanged otherwise proceed to next iteration. 5. Now replace the LSB of the image with the data bit and left the other 7 bits unchanged. 6. If all the information is embedded inside the image then end the method otherwise back to step 2 again. III. Related Study Sattar J Aboud, Mohammad A AL-Fayoumi, Mustafa Al-Fayoumi and Haidar S Jabbar (2008) purpose the enhanced version of RSA public key algorithm. It relies on the group of linear algebraic over ring of integers numbers which mod a composite modulus n that is the product of two prime numbers p and q. In this the original message and the encrypted message are in h x h matrices having parameters in Z n denoted by l(h, Zn), where as in original RSA it is denoted by (0, n-1) for modulus n. Brute force attack on RSA is almost very rear as it is based on mathematical factorization of prime numbers. RSA can be used for encryption and decryption as well as a digital signature scheme [8]. Sattar J Aboud purposed an algorithm to attack for RSA algorithm (2009). Main objective of the algorithm is to get the private key of RSA algorithm and then factoring the modulus based on the public key of RSA algorithm. Key generation algorithm was run 100 times to get the average key generation time that is only 93ms as compared to the RSA of small e method that is 42 seconds. For each loop the average numbers of iterations were 842 as compared to RSA small e method that is 37656. It not compute the complete prime factoring of p and q but it only tries to find the small factors until desired factoring. It is efficient key generation scheme. Decryption time is longer than a small e scheme RSA. Encryption is 2.6 times faster but decryption is 1.2 times slower. It is effective but having large number of iterations. It is more efficient algorithm for attack on RSA as it is faster and takes less time to run [7]. Tang Songsheng and Ma Xianzhen (2010) conducted the research on two block cipher algorithms (DES and AES). Weakness of the DES algorithm was shown on various aspects such as weak key and semi-weak keys, short key length and “Trap door”. As there are 16 rounds in DES each of which use a different encryption subkeys as an important feature for DES strength. Some of the keys generated overlap among themselves as the sub-key generation process is poorly designed, so the use of such weak keys can decrease DES security therefore these should be avoided in practical applications. DES keys are also short in length. Only 56 bit is valid and rest 8 bit is parity, so only 256 possible key combinations can be there for successful brute force attack. DES has only 8 S-boxes. The whole security of the algorithm lies in the non-linear component of the Sbox, as its design standards are not announced yet, therefore it may hide the chances of successful attacks from an intruder [10]. Wuling Ren and Zhiqian Miao (2010) proposed hybrid encryption algorithm of DES-RSA to enhance the security of blue tooth communication. A hybrid encryption is used in blue tooth communication instead of E0 encryption. As E0 stream cipher is facing a problem that it cannot restore the plaintext from cipher text in decryption if pseudo-random sequence create an error, therefore it will affect the whole cipher text. The security of E0 relies on its internal mechanism of secrete key stream generator, if its output become endless zero then cipher text is a plain text, so it is a worthless. To overcome from all these hybrid encryption was introduced. In hybrid encryption the message is transmitted through DES and the keys are transmitted through RSA. Using together, so there will be no need to send the keys separately. Speed of encryption and decryption is same as that of DES. RSA can also be used as a digital signature. It is relatively more secure and easier to achieve than E0 [11]. Misbha Rum, Aihab Khan, Malik Sikander Hayat Khiyal (2011) Analysis of different pairs of encryption and decryption algorithms such as RSA-DES, RSA-AES and RSA-3DES is done as hybrid encryption techniques. It has been seen that the message passing between two different parties is secured by using hybrid approach. Conclusion was carried out on basis of execution time and memory uses. Table 2.1 shows results of the research shows that RSA-DES hybrid takes less time and consumes less memory as compared to others [5]. Table 2.1 Time and Memory Comparison of Hybrid Techniques Message Size 56 bits 256 bits 512 bits

RSA-AES Time Consumed 273 ms 307 ms 311 ms

Memory Consumed 33114 kb 50467 kb 57344 kb

RSA-DES Time Consumed 216 ms 266 ms 278 ms

Memory Consumed 32768 kb 37683 kb 37683 kb

RSA-3DES Time Consumed 287 ms 290 ms 309 ms

Memory Consumed 37683 kb 37723 kb 40960 kb

Monica Adriana Dagadita, Emil Ioan Slusanschi and Razvan Dobre (2013) proposed a least significant bits (LSB) approach using BMP images. Hiding a secrete message inside a image is most widely used and popular technique, as image is a most frequently used media in a internet, so no one can easily identify it. Serial and

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parallel versions are proposed and performance is evaluated using images of size from 1.9 to 131 megapixels Information is hidden in an image is depends on the size of the images and number of LSB used for encryption. It provides better efficiency and capacity. Quality of stego images is also good. PSNR ratio is also better than the existed techniques [12]. Zhiyi Fang,Ying Wang, Zelin Deng Fan Yang (2013) proposed a secure key service mechanism in mobile network. The proposed technique was based on RSA-DES hybrid encryption technique. Analysis has been carried out among various algorithm such as DES, RSA and RSA-DES hybrid. It has been that response time of DES-RSA hybrid is fast than other two algorithms. Table 2.2 shows that DES-RSA provides more security than others also the scalability of the DES-RSA hybrid is very much stronger than the DES and RSA. Practicality of the DES-RSA hybrid is also stronger than the RSA and DES [13]. Table 2.2 Comparison between DES, RSA and DES-RSA Factors

Algorithmsď&#x192;

Response time Security Scalability Practicality

DES

RSA

Fast Weak Weak Weak

Slower High Weak Weak

DESRSA Fast High Strong Strong

The proposed scheme is implemented in C# platform using standard cryptography and steganography algorithm. DES-RSA hybrid cryptography is used along with LSB image steganography. IV. Proposed Methodology The proposed scheme is implemented in C# platform using standard cryptography and steganography algorithm. DES-RSA hybrid cryptography is used along with LSB image steganography. Figure 1 shows the working of proposed information security scheme. Figure 1 Proposed Information Security Scheme

For encoding steps are following: Step 1: Encrypt the message with DES algorithm Step 2: Choose any two prime numbers for the creation of RSA keys. Step 3: Encrypt the DES keys with the public key of RSA generated from two prime numbers. Step 4: Ciphertext of both DES and RSA is shuffled among themselves to represent a hybrid of ciphertexts. Step 5: Hybrid of ciphertexts is hidden inside a image using LSB steganography. Step 6: Stego image is generated for transmission. For decoding steps are following: Step 1: Stego image received from sender.

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Step2: Obtain the hybrid ciphertexts from the stego image by decoding it using keys of LSB. Step3: Extract the DES keys from the ciphertext of keys using private key of RSA. Step4: Now use the keys obtained from RSA to decrypt the ciphertext of DES. Step5: The real information is available now for the receiver . V. Results and Discussions Proposed model is stronger information security technique because without knowing the secrete behind the hybrid of DES-RSA ciphertext no one can easily able to identify that how to break it and generate two different ciphertexts from it. It has been seen that this scheme is far better than the previous scheme in which only DES and LSB is used. Brute force attack can be easily used on the ciphertext of DES to generate its keys but in proposed technique the brute attack is almost not possible until or unless the hybrid is not break. So for the protection of DES keys RSA is used for it and a hybrid of both cipertext is generated. Same plaintext and same key is given two both proposed and previous techniques. It has been clearly seen that to break the ciphertext of DES is far easier than to break hybrid cipertexts. Figure 2 Ciphertext of DES

Fig 3 Ciphertext of RSA

Fig 4 Ciphertext of DES-RSA Hybrid

Figure 2 shows the cipertext of DES, it can be very easily cracked by brute force attack by keep trying all possible key combinations. Figure 3 shows the ciphertext of RSA which encrypts the keys of DES so for the keys of DES RSA should be cracked first but it is not easy as it is looking. Figure 4 shows the hybrid ciphertext of both DES and RSA. It provides a very strong security to the system. No one can crack the system until or unless the two cipertexts are not separated from the hybrid. No doubt that it will never be break, it can be crack but it takes a very long time than a simple DES. So it is clear from Figure 4 that is very difficult to crack the hybrid therefore it provides a stronger security than the existed technique. Timing evaluation of the proposed technique is also better than the previous technique. PlaintextLovely Professional University Key Jalandharroad Figure 5 Encryption time of proposed technique Algorithm

Time in ms

DES

4ms

RSA

752ms

DES-RSA

50ms

Total Time

273ms

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Figure 6 Encryption time of previous technique Algorithm

Time in ms

DES

5ms

Total Time

412ms

From figure 5 and figure 6 is clear that the encryption time taken by the proposed technique is better than the previous technique. VI. Conclusion In the proposed method the strength of DES-RSA hybrid increase the level of security as compared to the existing technique where only DES is used. In this method the message is encrypted with a DES and the keys of DES are again encrypted with a RSA then the hybrid of both ciphertext is hidden inside an image using LSB image steganography. Steganography, specially shared with the cryptography is a stronger tool which allows exchanging information secretly. With the fast growth of digital technology and internet, steganography has highly developed a lot in a past few years. It will test the knowledge of the attacker about both cryptography and steganography. If an attacker is able to extract data from image then he has to crack the hybrid cryptography then only he will get the exact data. A result of proposed technique shows that the encryption time is better than the existing technique. It provides a more security compared to the existing one. Brute force attack on this technique is very difficult to use as there is use of RSA for DES key. In future other steganography techniques may be used with hybrid cryptography for more security. VII. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

[12]

[13]

References

Gutub A A, and Khan F. A. (2012) Hybrid crypto hardware utilizing symmetric-key & public-key cryptosystems, International Conference on Advanced Computer Science Applications and Technologies, page(s): 116-121. Harshali D Z and Prakash W W. (2012) Design and Implementation of Algorithm for DES Cryptanalysis, Hybrid Intelligent Systems (HIS), 2012 12th International Conference on 4-7 December 2012, page(s): 278-282. Hybrid Cryptography for Improving Data embedding Capacity, Communication, Information & Computing Technology (ICCICT), 2012 International Conference on 19-20 October 2012, page(s): 1-6. Monica A D, Emil I S and Razvan D. (2013) Data Hiding Using Steganography, Parallel and Distributed Computing (ISPDC), 2013 IEEE 12th International Symposium on 27-30 June 2013, page(s):159-166. Misbha I, Aihab K and Malik S H K. (2011) Confidentiality of Messages in a Cardless Electronic Payment System, Publication of Little Lion Scientific R & D, Islamabad Pakistan, page(s): 29-32. Ramaiya M K, Hemrajani N and Saxena A K (2013) Improvisation of security aspect in steganography applying DES,â&#x20AC;? Communication systems and network technologies (CSNT), International conference, page(s):431-436. Sattar J. (2009) An Efficient Method for Attack RSA Scheme, Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the 4-6 August 2009, page(s): 587-591. Sattar J, Mohammad A, Mustafa Al and Haidar S. (2008) An Efficient RSA Public Key Encryption Scheme, Information Technology: New Generations, 2008. ITNG 2008. Fifth International Conference on 7-9 April 2008, page(s): 127-130. Shand M and Vuillemin J. (1993) Fast Implementations of RSA Cryptography, Computer Arithmetic, 1993. Proceedings. 11th Symposium on 29 June- 2 july 1993, page(s): 252-259. Tang S and Ma X. (2010) Research of Typical Block Cipher Algorithms, Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010 International Conference on 24-26 August 2010, page(s): 319-321. Wuling R and Zhiqain M. (2010) A Hybrid Encryption Algorithm Based on DES and RSA in Bluetooth Communication, Modeling, Simulation and Visualization Methods (WMSVM), 2010 Second International Conference on 15-16 May 2010, page(s): 221-225. Yongzhen Z, Fenlin L, Xiangyang L and Chunfang Y. (2102) A Method Based on Feature Matching to Identify Steganography Software, Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on 2-4 November 2012, page(s): 989-994. Zhiyi F, Ying W, Zelin D and Fan Y. (2013) The Research on NSSC Key Service Mechanism in Mobile Network, 2nd International Conference on Science and Social Research (ICSSR 2013).

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Estimation of alpha radioactivity in some soil samples collected from eastern Haryana A. K. Garga, Sushil Kumarb,and R. P. Chauhanc Department of Physics, Arya P.G.College, Panipat, Haryana, India b Department of Physics, Choudhary Devi Lal University, Sirsa, Haryana, India c Department of Physics National Institute of Technology, Kurukshetra, Haryana, India __________________________________________________________________________________________ Abstract: All humans are constantly exposed to radiations spontaneously emitted by naturally occurring atomic elements ever since their existence on the earth. Radon (222Rn) has been identified as the largest single source of radiation exposure to world population. Indoor radon has been recognized as one of the health hazards for mankind. Common building materials used for construction of houses are considered as major sources of radon gas in indoor environment. In the present work, the radon exhalation rates were measured using ‘Canister’ technique. The alpha sensitive solid state nuclear track detector (LR-115 type-II) were used in the canisters for recording tracks produced by alpha particles from radon gas emanated from soil samples. The soil samples were collected from Sonipat ,Panipat and Karnal districts of eastern Haryana, India. The detectors were exposed in the canisters for 100 days. After the exposure, the detectors were etched using 2.5 N NaOH solution at 60˚ C for 1.5 hours. The track density was found using an optical microscope at a magnification 600X. The mass and surface exhalation rates are also calculated from the data. . The measurements indicate normal to some higher levels of natural radioactivity in soil samples. However, these samples satisfy the universal standards (UNSCEAR, 2000) limiting the radioactivity within the safe limits. Key words: Radon, exhalation rates, building materials, soil, LR-115. __________________________________________________________________________________________ a

I. Introduction Human population is always exposed to ionizing radiation from natural radiations arising from within and outside the earth [1] .Radon ,which is a topic of public health concern has been found to be ubiquitous indoor air pollutant to which all persons are exposed [2-3]. The exposure of population to high concentrations of radon and its daughter for a long period lead to pathological effects like the respiratory functional changes and the occurrences of lung cancer. Radon is derived from the radioactive decay of radium, a decay element in uranium series. It has a half life of 3.8 days, which is long enough, allowing a part of it to diffuse from the building materials in to the inside atmosphere of the dwelling. Building materials and the soil beneath the floor are the main sources of radon activity inside the dwellings. A large variation in radon activity is observed in dwellings as the uranium concentration in natural materials used as a building materials very in a wide range and from place to place. The building materials and the water used in the homes is a source of radon in indoor air[4]. Thus it is desirable to study the radon concentration and exhalation rules from building materials and soil used in different regions. Various researchers have reported that exposure to high levels of radon at the workplace and in other public sector indoor settings are important risk factors for lung cancer for workers [5]. The United States Environmental Protection Agency(US-EPA) has reported that inhalation of radon is the second killer from cancer after smoking. [6]. The health hazards caused by radon and thoron are not primarily due to Isotopes ,but due to their short-lived daughters that are inhaled. II. Experimental For the measurement of radon concentration and its exhalation rates in building materials canister technique was used [7]. Soil samples were collected from different sites . The sample dried in oven .the known amount of each sample was taken in plastic canister..LR-115 type –II plastic track detectors were fixed on the bottom of lid of each canister with tape such that sensitive side of the detector faced the sample . The cans were tightly closed from the top and sealed . The size of the detectors was 1cm x 1cm and LR-115 (type –II) detectors were exposed in closed plastic canisters. After 100days the detector were removed,washed and dried and subjected to a chemical etching process in 2.5N NaOH solution at 60 degree centigrade for 90 minutes.The tracks produced by the alpha particles were observed and counted under an optical microscope at 600X. The measured track density was converted in to radon concentration using a calibration factor (.021tracks/cm2/day = 1Bq/m3)as used by other workers.[7-8] The equations used for exhalation rates are: EM = CV/M______ (Bq Kg-1 h-1) for mass exhalation rate (1) T+1/(e-T-1)

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EA =

CV/A _ (Bq m-2 h-1) -T T+1/(e -1)

for surface exhalation rate

(2)

Where C = Integrated radon exposure (Bq m-3 h1) M = Mass of sample (Kg) V = Volume of air in can (m3) T = Time of exposure (hrs)  = Decay constant for radon (h-1) A = Area covered by the can or Surface area of the sample (m2) III. Results and Discussion The calculated values of radon concentration in soil samples collected from the Karnal District varied from 197 Bq m-3 to 1495 Bq m-3 with an average of 964  227 Bq m-3. The values of radon concentration in soil samples from Panipat District varied from 708 Bq m-3 to 2243 Bq m-3 with an average of 1346 151 Bq m-3. The values of radon concentration in soil samples from Sonipat District varied from 905 Bq m-3 to 2204 Bq m-3 with an average of 1175 142. Bq m-3. The mass and the surface exhalation rates of radon were also calculated for all types of samples under study as shown in tables 1-3. It can be seen from the results that the radon concentration varies appreciably in various soil samples. It is due to the fact that the soil collected from various sites may have different uranium contents which results in change of radon emanation rates8. References 1 BEIR VI (Report of the Committee on the Biological effects of Ionizing Radiation). Natl. Res. Council. Natl. Acad. Press, 2 3 4 5 6 7 8

Washington, DC (1999). Mazur J, Kozak K., HorwacikT, Haber,R & ZdziarskiT, In the proceedings of NORM IV conference Szczyrk, Poland, (2004)77. Abu-Jarad, F. Nucl. Tracks Radiat. Meas., 15 (1988) 525. Deka P C, Bhattachargee B K, Sharma B K. & Goswami T D, Indian J. Environmental Protection. 21 (2001) 24. Jojo, P.J, Rawat A & Prasad R, Nucl. Geo Phys, 8 (1) (994) . Abu-Jarad F, Fremlin J H & Bull R, Phys. Med. Biol, 25 (1980) 683. Khan J, Tyagi R K & Prasad R, Nucl. Tracks Radiat. Meas, 20 (1992) 609. El-Bahi,S M, Health Physics, 86(5) (2004) 517.

Table -1: Radon Concentration, Mass Surface Exhalation rates in soil samples collected from district Karnal (Haryana). Soil Samples Location

Radon Conc(C) (Bq/m3)

Mass Exhalation Rate (Em)(mBq kg-1Hr--1)

Surface Exhalation Rate (EA) (mBq m-2Hr-1)

KNL-1 KNL-2 KNL-3

1023 236 197

29 07 06

756 174 145

KNL-4 KNL-5

2125 1141

60 32

1570 843

KNL-6 KNL-7

1180 315

34 09

872 233

KNL-8 AM±SE*

1495 964±227

42 27±6

1105 712±168

AM (arithmetic mean);* SE (statistical error) Table -2: Radon Concentration, Mass and Surface Exhalation rates in soil samples collected from district Panipat (Haryana). Soil Samples Location PNP-1

Radon Conc(C) (Bq/m3)

PNP-2

2164

Mass Exhalation Rate (Em) (mBq kg-1Hr--1) 62

Surface Exhalation Rate (EA) (mBq m-2Hr-1) 1599

2243

1658

PNP-3

1259

931

PNP-4

1338

989

PNP-5

708

523

PNP-6

1220

901

PNP-7

708

523

PNP-8 PNP-9 PNP-10 AM±SE*

1220 1260 1338 1346±151

35 36 38 38±4

901 931 989 995±112

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AM (arithmetic mean);* SE (statistical error) Table -3: Radon Concentration, Mass and Surface Exhalation rates in soil samples collected from district Sonipat (Haryana). Soil Samples Location

Radon Conc(C) (Bq/m3)

Mass Exhalation Rate (Em) (mBq kg-1Hr--1)

Surface Exhalation Rate (EA) (mBq m-2Hr-1)

SP-1

1613

1192

SP-2

1180

872

SP-3

2164

1599

SP-4

2203

1628

SP-5

2125

1570

SP-6

1417

1047

SP-7

2164

1599

SP-8

2204

1628

SP-9

905

668

SP-10

1775

1311

AM±SE*

1175±142

50±4

1311±105

AM (arithmetic mean);* SE (statistical error)

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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 CLUSTER BASED HYBRID ROUTING TO IMPROVE QOS IN WIRELESS MESH NETWORK V.Shanthi1 , M.Selvi2, E.Muniyasamy Alias Anand3 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, TamilNadu INDIA 3 Department of Electronics and Communication Engineering, Vickram College of Engineering, TamilNadu INDIA _________________________________________________________________________________________ 1,2

Abstract: Maximizing the network throughput in a multichannel multiradio wireless mesh network various efforts have been committed.The recent solution are based on either static (or) dynamic approaches.In this approaches MMAC (MULTICHANNEL MAC) protocol is used which is optimized only for network throughput. A hybrid multichannel multiradio wireless mesh network is developed for channel allocation and routing.Here each mesh node has both static and dynamic interfaces. ADCA (ADAPTIVE DYNAMIC CHANNEL ALLOCATTION ) protocol is used in hybrid multichannel WMN,here ADCA optimizes both throughput and delay in the channel assignment, and also it results in reducing packet delay without degrading the network throughput.To balance the channel usage in the network ICAR (INTERFERENCE AND CONGESTION AWARE ROUTING PROTOCOL) is added. Simulation is done by NS2 (network simulator -2). The hybrid architecture achieves lower delay than the static and dynamic approaches and also it improves the QOS(quality of service) in the network architecture.Additionally we also compare with clustering approach is to improve the network throughput and QOS than the hybrid architecture. Keywords: WMN,hybrid channel allocation,clustering approach,routing ____________________________________________________________________________________ I. Introduction WIRELESS mesh networking has fascinated grand look into attention freshly. WMN has become a hopeful technology to facilitate that has the budding to smooth the progress of many useful applications. Capacity reduction problem is the one of the major problem facing in WMN due to wireless interference.The major challenge in multiradio multichannel WMN is the allocation of channel to interfaces with in mesh router as a result the network capacity can be maximized.The current two approaches of channel allocation are static and dynamic allocation. In static channel allocation, each one interface of all mesh router is assign a channel permanently. In dynamic channel allocation, an interface is formal to change from one channel to another channel frequently. Both approaches have their mertis and demerits. Static approach do not require interfaces to change channels and have lower overhead. Dynamic approach need frequent channel switching and thus have higher overhead than the static strategies.Due to the inflexibility of static channel allocation and the purely dynamic channel allocation,in this paper we suggest a hybrid architecture.Comparing to static and dynamic channel allocation it has serveral advantages. Here this architecture, has two interfaces,One interface from each router uses the dynamic channel allocation approach while the other interfaces use the static channel allocation approach. The working of links in static channels provide high throughput paths from end-users to the gateway while the dynamic channels links improve the network connectivity and the network’s adaptivity to the changing traffic. Hence, this hybrid architecture can achieve better adaptivity than the purely static architecture without much increase of overhead compared to the purely dynamic architecture.In this pape we converse several important issues in the hybrid wireless mesh network. 1) The system architecture: where each mesh node contains both static and dynamic interfaces, we converse on how to manage the channel assignment between both types of interfaces, so that the channel resources could be utilized efficiently. 2) The channel allocation for dynamic interfaces: Multichannel MAC protocol (MMAC) [6] is presently one of the most proficient dynamic channel allocation. The channel assignment in MMAC is obtained only for network throughput. We propose an Adaptive Dynamic Channel Allocation protocol (ADCA), which obtained for both throughput and delay in the channel assignment.Compared with MMAC, ADCA is better to reduce the packet delay without corrupting the network throughput. The rest of the paper is ordered as follows: We précis the previous work in II. In III,we introduce the network model. In IV protocol design in MMAC and V we present our dynamic channel allocation protocol and the routing algorithm in the hybrid wireless mesh network. We estimate our comparision result and clustering approach in VI, and at last we conclude our work in VII.

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II.

ROUTING AND PREVIOUS WORK.

In the hybrid structure, have static links and dynamic links, both can be used to transmit data. We added an (ICAR) Interference and Congestion Aware Routing protocol. Which provides and balancing the channel usage over the network and to improve the network throughput. Many studies have been devoted on how to allocate channel in multichannel WMN and how to minimize interference and maximize throughput.Two fundamental channel allocation strategies have been considered: 1) Static channel allocation, where the interfaces are assigning channels permanently(2).Dynamic channel allocation, where interfaces are authorized to change to different channels. The Raniwala et al [1] wished-for an iterative approach to work out the joint routing and channel assignment problem, each mesh router changes from time to time. Alicherry et al [2] and Kodialam and Nandagopal [11], they proposed an approximation algorithm for getting joint routing and channel assignment. In this paper, link level channel allocation algorithms on dynamic interfaces were spot lighted .Unlike previous approaches, we suggest a hybrid architecture in this paper, which uses dynamic channel allocation strategy on one way and static channel allocation strategy on the other way of each node. In our comparison, the hybrid wireless mesh network is capable to obtained the advantages of both channel allocation methodologies.The hybrid multichannel allocation protocol (HMCP) has been proposed in [11]. For each node, it assigns some channel interface as fixed channels, where as letting the remaining interfaces to switch channels.

III.

NETWORK MODEL

In this model, we suggest to use the hybrid architecture for achieving high throughput and network adaptivity to changing traffic and low channel switching overhead. Let G(V,E) be the network topology, Here represents a set of mesh routers and E represents a pairs of mesh routers that are with in radio communication range.FIGURE I, describes a hybrid multichannel multiradio wireless mesh network. Most mesh nodes including the gateway have 3 interfaces, and a few boundary nodes(c,g,i.f )have 2 interfaces. For each mesh node, one interface works as dynamic interface, and the others work as static interfaces. After the network topology has been constructed, each link can then be assigned channels. The links nearer to the gateways are specified superior priority to be allocated with less overcrowded channels. In FIGURE I, the link is shown in bold lines, are called as static link. Dynamic interfaces work in an on-demand fashion. Two dynamic interfaces that are within radio transmission range of each other are able to discuss a common channel and communicate when they have data to transmit. We call these links as dynamic links. Here we use TDMA-style dynamic multichannel MAC protocols such as MMAC [6]. In this the time is divided into fixed-length intervals, each one consists of control interval and data interval. In the control interval, all nodes communicate on a default channel (or control channel) to discuss the channels to use in the data interval. In the data interval, nodes transmit and receive data on the negotiated channels (or data channel).

FIGURE I: Hybrid WMN Architecture IV. PROTOCOL DESIGN IN MMAC In FIGURE II, consider A has some data to send to C. According to MMAC, in the interval t1 the packets are transmitted from A to B and then in the second interval t2, the packets are transmitted from B to C. Although the packets can be transmitted from A to C through B in one interval, MMAC actually requires two intervals. Because of this, B has to wait till the second interval to discuss a common channel with C in order to continue transmitting the data.

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FIGURE II: Unnessesary Delay Of MMAC V. ADAPTIVE DYNAMIC CHANNEL ALLOCATION ADCA uses the similar to frame work with MMAC. Yong ding et al [11] proposed about ADCA. It divides time into fixed length intervals. Each interval is additional split into control interval and data interval. Let T be the interval length.Tc be the control interval length, and Td be the data interval. In the control interval, all nodes switch to the same default channel and negotiate channels. In the data interval, the nodes working on the same channel transmit and receive data among each other. In this MMAC protocol ,interval length T is set to 100 ms, and control interval Tc is set to 20 ms, which is long enough for nodes to negotiate channels when network traffic is saturated. Our protocol uses the same parameter settings (T and Tc), but is different in the channel allocation scheme during control interval.

FIGURE III: Adaptive Dynamic Channel Allocation In ADCA, dynamic interface maintain many queues in the link layer with one queue for each neighbor.The data to be sent to each neighbor are uffered in the corresponding queue. The first step of channel negotiation in ADCA is similar with MMAC. For each dynamic interface, if it has data to transmit, it choose a neighbor that it wants to communicate and try to negotiate a common channel with the neighbor. There are many methods for selecting neighbors. If throughput is the only consideration, we may select the neighbor with the longest queue. However, this approach may cause malnourishment therefore, For that we supplement it with some justice consideration.Here we evaluate a neighborâ&#x20AC;&#x2122;s priority by considering both its queue length and how long the queue has not been served. As a result, during this step, pairs of nodes have discuss common channels with each other. such as the example in FIGURE III.Different from MMAC, ADCA enables further channel cooperation among nodes. VI. COMPARISION RESULT OF STATIC AND HYBRID ARCHITECTURE The results of the different simulation are summarized in the list that follows:

FIGURE IV: Demonstrates the hybrid architecture compared with the static architecture. In this packet jitter of hybrid architecture is lower than the static architecture. Here x axis denotes the data rates and y axis denotes the packet jitter. Green colour curve denotes hybrid architecture results and red colour curve denotes static architecture result.

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FIGURE V: Presents the hybrid architecture compared with the static architecture. In this throughput of hybrid architecture is higher than the static architecture. Here x axis denotes the ratio skewed and y axis denotes the throughput. Green colour curve denotes static architecture results and red colour curve denotes hybrid architecture results.

FIGURE VI: Shows the hybrid architecture compared with the static architecture. In this packet delay of hybrid architecture is lower than the static architecture Here x axis denotes the data rates and y axis denotes the packet delay. Green colour curve denotes hybrid architecture results and red colour curve denotes static architecture results. VII. CLUSTERING APPROACH RESULTS one achievable approach to accomplish such goal is to use Cluster-based Multipath Routing.The plan following this method assumes that nodes in the network enclose to be grouped in clusters, all cluster having a clusterhead. Cluster-heads from dissimilar clusters do not hold up with each other, so by selecting path that pass in this nodes, non-interfering paths are selected.Channel selection is based on dynamic ( based on Highest energy level in every nodes). To reduce the routing distance and increase the QOS through the clustering approaches packets transfer from CH to Sink node( receiver node) Throughput can be increased compare to the hybrid architecture and packet delay can be reduced compared to the hybrid architecture. Steps to create cluster and CH Step 1-choose number of nodes at random in wireless mesh network and create clusters of those nodes on the beginning of cut of frequency of the nodes. Step 2-In a cluster cut of frequency selected by the user. The frequency is divided into three levels, Higher, Middle and Lower levels. Step 3-choose the cluster head on the basis of battery power. choice of cluster head in energy efficient techniques normally depends on the initial energy, residual energy, and average energy of the network or energy consumption rate or combination of these. Step 4-After selection of cluster head, for the communiquĂŠ between two cluster heads or other nodes. Established the connection between new cluster head to other nearest cluster heads in wireless mesh network.

s FIGURE VII: Shows the throughput of clustering. Here x axis denotes the data rates and y axis denotes the throughput.

FIGURE VIII: Shows the average delay of clustering. Here x axis denotes the data rates and y axis denotes the average delay.

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FIGURE IX: Shows the average jitter of clustering. Here x axis denotes the data rates and y axis denotes the average jitter. VIII. CONCLUSION The hybrid wireless mesh network architecture, achieves high throughput and reduce packet delay than the static and dynamic interfaces. Have made two contributions. First, proposed an adaptive dynamic channel allocation protocol to be used on dynamic interfaces. Compared with MMAC, ADCA reduces the packet delivery delay without degrading the network throughput. In addition, proposed an interference and congestion aware routing algorithm in the hybrid network, which balances the channel usage in the network and therefore increases the network throughput. The simulation results have shown that, Hybrid architecture achieves better results compared to the purely static architecture, Hybrid approach is more adaptive to the changing traffic without significant increase in overhead. And additionally by comparing with the clustering approaches,throughput can be increased than the hybrid method.Finally we can improve our QOS of network in this clustering approaches REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]. [9] [10] [11] [12] [13] [14] [15] [16 ]

Raniwala, K. Gopalan, and T. Chiueh, “Centralized Channel Asignment and Routing Algorithms for Multi-Channel Wireless Mesh Networks,” ACM Mobile Computing and Comm. Rev., vol. 8, pp. 50-65, 2004. M. Alicherry, R. Bhatia, and L. Li, “Joint Channel Assignment and Routing for Throughput Optimization in Multi-Radio Wireless Mesh Networks,” Proc. ACM MobiCom, 2005. A.Raniwala and T.Chiueh, “Architecture and Algorithms for an IEEE 802.11-based Multi-Channel Wireless Mesh Network,” Proc. IEEE INFOCOM, 2005. J.Tang,G.Xue,andW.Zhang,“Interference-Aware Topology Control and QoS Routing in Multi-Channel Wireless Mesh Networks,” Proc. ACM MobiHoc, 2005. S.-L. Wu, C.-Y. Lin, Y.-C. Tseng, and J.-P. Sheu, “A New Multi- Channel Mac Protocol with On-Demand Channel Assignment for Multi-Hop Mobile Ad Hoc Networks,” Proc. Int’l Symp. Parallel Architectures, Algorithms, and Networks (ISPAN), 2000. J. So and N. Vaidya, “Multi-Channel Mac for Ad Hoc Networks: Handling Multi-Channel Hidden Terminals Using a Single Transceiver,” Proc. ACM MobiHoc, 2004. I.F. Akyildiz, X. Wang, and W. Wang, “Wireless Mesh Networks: A Survey,” Computer Networks, vol. 47, pp. 445-487, 2005. P. Gupta and P.R. Kumar, “The Capacity of Wireless Networks,” IEEE Trans. Infomation Theory, vol. 46, no. 2, pp. 388-404, Mar. 2000. J.Padhye,S.Agarwal V.N. Padmanabhan, L. Qiu, A. Rao, and B. Zill, “Estimation of Link Interference in Static Multi-Hop Wireless Networks,” Proc. Internet Measurement Conf., 2005. M.OE,“Advanced Internet Technology ii: Internet Operation Wireless Network Operation,”http://www.soi.wide.ad.jp/ class/20040013/slides/09, 2010. Yong ding ,kanthakumar pongaliur,and li xiao “ channel allocation and routing in hybrid multichannel multiradio wireless mesh networks” Martin Krebs, Andr´e Stein, M´onica Alejandra Lora “Topology Stability-based Clustering for Wireless Mesh Networks”. Cristina Neves Fonseca “Multipath Routing for Wireless Mesh Networks”. Abdessalam Elhabbash, Yousif Mansour “Location Enhanced Cluster Based Routing protocol”. Krishna N. H. Vaidya, M. Chatterjee, D. K. Pradhan “A Cluster-based Approach for Routing in Dynamic Networks” Sudarsanan.D, Sharanabasava, Megha.J “ A Study on Wireless Mesh Network with Hierarchical Cluster”. .

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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 Effect of Variable Thermal Conductivity & Heat Source/Sink near a Stagnation Point on a Linearly Stretching Sheet using HPM Vivek Kumar Sharma, Aisha Rafi, Chandresh Mathur Department of Mathematics, Jagan Nath University, Jaipur, Rajasthan, India Department of Mathematics, Jagan Nath University, Jaipur, Rajasthan, India Department of Mathematics, Jagan Nath Gupta Institute of Engineering and Technology, Jaipur, Rajasthan, India __________________________________________________________________________________________ Abstract: Aim of the paper is to investigate effects of variable thermal conductivity on flow of a viscous incompressible fluid in variable free stream near a stagnation point on a non-conducting stretching sheet. The equations of continuity, momentum and energy are transformed into ordinary differential equations and solved numerically using Similarity transformation and Homotopy Perturbation Method. The velocity and temperature distributions are discussed numerically and presented through graphs. Skin-friction coefficient and the Nusselt number at the sheet are derived, discussed numerically and their numerical values for various values of physical parameter are presented through Tables. Keywords: Homotopy Perturbation Method, Similarity transformation method, Steady, boundary layer, variable thermal conductivity, stretching sheet, skin-friction coefficient and Nusselt number. __________________________________________________________________________________________ I. INTRODUCTION Study of heat transfer in boundary layer find applications in extrusion of plastic sheets, polymer, spinning of fibers, cooling of elastic sheets etc. The quality of final product depends on the rate of heat transfer and therefore cooling procedure has to be controlled effectively. Liquid metals have small Prandtl number of order 0.01~ 0.1(e.g. Pr = 0.01 is for Bismuth, Pr = 0.023 for mercury etc.) and are generally used as coolants because of very large thermal conductivity. Aim of the present paper is to investigate effects of variable thermal conductivity, heat source/sink and variable free stream on flow of a viscous incompressible electrically conducting fluid and heat transfer on a nonconducting stretching sheet. Linear stretching of the sheet is considered because of its simplicity in modelling of the flow and heat transfer over stretching surface and further it permits the similarity solution, which are useful in understanding the interaction of flow field with temperature field. The heat source and sink is included in the work to understand the effect of internal heat generation and absorption [Chaim (1998)]. The Homotopy Perturbation Method is a combination of the classical perturbation technique and homotopy technique, which has eliminated the limitations of the traditional perturbation methods. This technique can have full advantage of the traditional perturbation techniques. J.H. He, Approximate analytical solution for seepage flow with fractional derivatives in porous media. J.H. He, A coupling method of homotopy technique and perturbation technique for nonlinear problems. To illustrate the basic idea of the Homotopy Perturbation Method for solving nonlinear differential equations, we consider the following nonlinear differential equation: A(u) –f(r) = 0, (1) Subject to boundary condition (2) Where A is a general differential operator, B is a boundary operator, f(r)is a known analytic function, and Γ is the boundary of the domain Ω. The operator A can, generally speaking, be divided into two parts: a linear part L and a nonlinear part N. Equation can be rewritten as follows: L(u) +N(u) –f(r) =0 (3) By the homotopy technique, we construct a homotopy V(r,p) : Ω*(0,1)→R which satisfy H(V,p) = (1-p)[L(v) – L(u0)] + p[A(v) –f(r)] =0 (4) H(V,p) = L(v) – L(u0) + p L(u0) + p[N(v) –f(r)] =0 (5) Where p ∈ [0, 1] is an embedding parameter and u0 is an initial approximation of which satisfies the boundary conditions. H(V,0) = L(v) – L(u0) H(V,1) = A(v) –f(r)] (6) Thus, the changing process of p from zero to unity is just that of v(r, p) from u0(r) to u(r). In Topology, this is called deformation and L(v) – L(u0), A(v) –f(r)] are called homotopic. According to the HPM, we can first use

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the embedding parameter p as a “small parameter,” and assume that the solution of can be written as a power series in p: V =V0+ pV1+p2 V2 + …………… (7) Setting p=1 results in the approximate solution of ……………… (8) p→1 The series is convergent for most cases; however, the convergent rate depends upon the Nonlinear operator A(V). The second derivative of N(V )with respect to V must be small because the parameter may be relatively large; that is, p → 1. In this paper is to investigate effects of variable thermal conductivity on flow of a viscous incompressible fluid in variable free stream near a stagnation point on a non-conducting stretching sheet. II. FORMULATION OF THE PROBLEM Consider steady two-dimensional flow of a viscous incompressible electrically conducting fluid of variable thermal conductivity in the vicinity of a stagnation point on a non-conducting stretching sheet It is assumed that external field is zero, the electric field owing to polarization of charges and Hall Effect are neglected. Stretching sheet is placed in the plane y = 0 and x-axis is taken along the sheet. The fluid occupies the upper half plane i.e. y> 0. The governing equations are: , (9) (10) ,

(11) ½

where ε-perturbation parameter, η-similarity parameter { = (c/ν) y}, η∞-value of η at which boundary conditions is achived, κ-uniform thermal conductivity, κ*-variable thermal conductivity, ν-kinematic viscosity, ρ-density of fluid, ψ-stream function, σ-electrical conductivity, θ-dimensionless temperature{ = ( T -T ) / ( Tw T )}, τw-shear stress, S-heat source/sink parameter {= Q/ρ Cp c }, T- fluid temperature. The second derivatives of u and T with respect to x have been eliminated on the basis of magnitude analysis considering that Reynolds number is high. Hence the Navier-Stokes equation modifies into Prandtl’s boundary layer equation. The boundary conditions are. (12) Introducing the stream function ψ (x, y) as defined by ψ ψ ,

(13)

1/2

the similarity variable η =(c / ν ) y and Ψ (x, y) = (c ν )1/2 x f (η), (14) into the equations (3) and (5), we get f ′′′ + ff ′′ − ( f ′)2 +λ2 = 0 , (15) And (1+ε) θ ′′ +ε (θ′) 2 + Prθ ′f + Pr Sθ = 0. (16) The governing boundary layer and thermal boundary layer equations (15) and (16) with the boundary conditions (12) are solved using Homotopy Perturbation Method. Equations (15) and (16) are non-linear coupled differential equation. To solve these equations, we introduce the following Homotopy. (17) (18) With the following assumption (19) (20) Using equation (19),(20) into equation (10) and (11) and on comparing the like powers of p, we get the zeoth order equation, (21)

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(22) with the corresponding boundary conditions are of zeroth order equations are: (23) λ

λ (24) (25)

With the corresponding boundary conditions are of first order equations are:

(26) Solving equations with corresponding boundary conditions, the following functions can be obtained successively, by summing up the results, and p → 1 we write the f( ) , profile as: (27) (28) where

SKIN-FRICTION: Skin-friction coefficient at the sheet is given by . NUSSELT NUMBER: The rate of heat transfer in terms of the Nusselt number at the sheet is given by

(29)

(30) (31)

Conclusion: It is observed from Table 1 as L increases, the numerical values of f ′′(0) also increase. It is noted from Table 2 that the numerical values of -θ ′(0) increase when λ increases and -θ ′(0) decreases when ε increases. The skin-friction coefficient and Nusselt number are presented by equations (30) and (31) and they are directly proportional to and respectively. The effects of ε, Pr and S on Nusselt number have been presented through Table 3 respectively. Table 1 f’’(0)

f’’(0)

0.0

-1

0.1

-1.0800

0.01

-1.0098

1.0

0.0004

0.05

-1.0450

2.0

2.0175

Table 2

0.1 0.5

.81235 .13629

0.0 0.05

0.223558 0.215792

2.0

.24133

0.1

0.204672

S 0 0.1 0 0.1

0 0 -0.1 -0.1

Table 3 Pr 0.5 0.5 1 1

Nu 2.010357 3.542315 2.565423 2.845633

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velocity distribution versus n

Temperature distribution when

1.8

0.9

1.6

0.8

=-.1 =-.05 =0 =1.1 =1.5

1.2 1

0.7 0.6 

1.4

0.5

0.8

0.4

0.6

0.3

0.4

0.2

0.2 0

=0 =.01 =.5 =1 =1.5

0.1 0

5 n

0.5

1.5

Fig 1

3.5

4.5

Temperature distribution when E=0, =0.1

Temperature distribution when =1,S=.01,Pr=.01 1

E=0 E=.05 E=.1

0.9 0.8

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.1 0

0.5

1.5

2.5 n

3.5

4.5

Pr=.01 Pr=.02 Pr=.023 Pr=.075 Pr=.1

0.9



2.5 n

Fig 2

0.5

1.5

2.5 n

3.5

4.5

Fig 4

Fig 3

Fig 5

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physicsl model

6 y

1 0 5

0 5

8 6

2 1

1 2

2 x

1.5 3 0.5 1

Fig 7

Fig 6

From figure 1, we observe that as λ increases, value of f’ also increases. From figure 2 it is observed that when λ increase simultaneously θ also increases. In figure3, λ, S and Pr are constant but when ε increases θ will also increased. It is observed in figure 4, s, ε and λ are constant, when Pr increases, θ will also increase. Figure 5 is a physical model which becomes clearer from figure, 6 and 7. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

Arunachalam, M. and N.R. Rajappa (1978). Forced convection in liquid metals with variable thermal conductivity and capacity. Acta Mechanica 31, 25-31. Bansal, J.L. (1977). Viscous Fluid Dynamics. Oxford & IBH Pub. Co., New Delhi. Chakrabarti, A. and A.S. Gupta (1979). Hydromagnetic flow and heat transfer over a stretching sheet. Quarterly Journal of Applied Mathematics 37, 73-78. Bansal, J.L. (1994). Magnetofluiddynamics of Viscous Fluids. Jaipur Pub. House, Jaipur, India. Chen, C.H. (1998). Laminar mixed convection adjacent to vertical, continuously stretching sheet. Heat and Mass Transfer 33, 471-476. J.H. He, Approximate analytical solution for seepage flow with fractional derivatives in porous media, Comput. Method Appl. Mech. Engrg., 167 (1998) 57-68. J.H. He, A coupling method of homotopy technique and perturbation technique for nonlinear problems, Int. J. Nonlinear Mech., 35 (2000) 37- 43. Chamka, A.J. and A.R.A. Khaled (2000). Similarity solution for hydromagnetic mixed convection and mass transfer for Hiemenz flow though porous media. Int. Journal of Numerical Methods for Heat and Fluid Flow 10, 94-115. Sharma, P.R and U. Mishra (2001). Steady MHD flow through horizontal channel: lower being a stretching sheet and upper being a permeable plate bounded by porous medium. Bull. Pure Appl. Sciences, India 20E, 175-181. Biazar J., Ayati Z. and Ebrahimi H.(2009) ”Homotopy Perturbation Method for General Form of Porous Medium Equation,” Journal of Porous Media, 12, 1121-1127. Rafei M., Vaseghi J. and Ganji D. (2007) ”Application of Homotopy-Perturbation Method for Systems of Nonlinear Momentum and Heat Transfer Equations,” Heat Transfer Research, 38, 361-379 Jafari H., Zabihi M. and Saidy M. (2008) ”Application of Homotopy Perturbation Method for Solving Gas Dynamics Equation,” Applied Mathematical Sciences, 2, 2393-2396 Ganj D. and Esmaeilpour M. (2008) ”A Study on Generalized Couette Flow by He’s Methods and Comparison with the Numerical Solution,” World Applied Sciences Journal, 4, 470-478

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