Ijetcas vol3 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

166-169

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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 ENHANCED METHOD TO CONTROL REAL AND REACTIVE POWER VARIATIONS IN WECS S.Pragaspathy M.E., (Ph.D)1, M.Mano raja paul M.E., (Ph.D)2, Assistant Professor/EEE, Nehru Institute of Engineering and Technology, Thirumalayampalayam, Coimbatore-641105, Tamilnadu, India. ________________________________________________________________________________________ Abstract: This project presents an advanced control strategy for the operation of a direct-drive IPM synchronous generator- based stand-alone variable-speed wind turbine. The control strategy for the generatorside converter with maximum power extraction is presented. The stand-alone control featured is constant output voltage and frequency that is capable of delivering to variable load. The main attention is dc link voltage control deals with the chopper control for various load condition.And also a battery storage system with converter and inverter has to be used to deliver continuous power at the time of fluctuated wind.The simulation results show this control strategy gives better regulating voltage and frequency under sudden varying load conditions. Dynamic representation of dc bus and small signal analysis are presented. The dynamic controller shows very good performance. Keywords: PMSM, boost converter, inverter, driver circuit and PIC/DSP _________________________________________________________________________________________ 1,2

I. INTRODUCTION In this paper to design advance control techniques in variable speed to give continuous Supply to load. Variablespeed wind turbines have many advantages over fixed-speed generation such as increased energy capture, operation at maximum power point, improved efficiency, and power quality. However, the presence of a gearbox that couples the wind turbine to the generator causes problems. The gearbox suffers from faults and requires regular maintenance. The reliability of the variable-speed wind turbine can be improved significantly by using a direct-drive synchronous generator. Synchronous machine has received much attention in windenergy application because of their property of a high power factor and high efficiency. To extract maximum power from the fluctuating wind, variable-speed operation of the wind-turbine generator is necessary. This requires a sophisticated control strategy for the generator. A control strategy for the generator-side converter with output maximization of a PMSG-based small-scale wind turbine is developed. It is simple and a low-cost solution for a small-scale wind turbine. For a stand-alone system, the output voltage of the load side converter has to be controlled in terms of amplitude and frequency and also a battery storage system with converter and inverter has to be used to deliver continuous power at the time of fluctuated wind. II. BLOCK DIAGRAM

Fig. 1: Block diagram of the project A. Block Diagram Description Generator converts the variable speed mechanical power produced by the wind turbine into electrical power. The power produced in the generator having variable frequency and voltage AC power. This Ac power converted into DC power with the help of uncontrolled rectifier. The dc power will be have variable voltage. This variable voltage is boostered to rated level with the help of boosted converter. Boosted dc power is converted into fixed frequency AC power and it is delivered to load. Between load and inverter as storage system with converter and inverter is used to store the energy. This storage system will store the energy at the time of load lesser than maximum level. Also this storage system is used to deliver power to load when the

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boost converter unable to boost up the voltage. Microcontroller is used to control boost converter and inverter to get fixed frequency and voltage. B Synchronous Generator Synchronous generators are the primary source of all electrical energy. Commonly used to convert the mechanical power output of steam turbines, gas turbines, reciprocating engines hydro turbines into electrical power for the grid can be extremely large â&#x20AC;&#x201C; power ratings. Are known as synchronous machines because they operate at synchronous speed (speed of rotor always matches supply frequency).

Fig. 2: Synchronous Generator The rotor is mounted on a shaft driven by mechanical prime mover. A field winding (rotating or stationary) carries a DC current to produce a constant magnetic field. An AC voltage is induced in the 3-phase armature winding (stationary or rotating) to produce electrical power. The electrical frequency of the 3-phase output depends upon the mechanical speed and the number of poles. C. Power Diode A power diode is a two terminal p-n junction device and a p-n junction normally formed by allowing diffusion and epitaxial growth structure of a power diode and symbol are shown in figure below. High power diodes are silicon-rectifiers that can operate at high junction temperatures. Power diodes have larger Power, Voltage and Current handling capabilities than ordinary signal diodes. In addition, the switching frequencies of power diodes are low as compared to signal diodes. The voltage current characteristics of power diodes are shown in figure below.when the anode potential is positive with respect to cathode, the diode is said to be forward biased, the diode conducts and behaves essentially as a closed switch. A conducting diode has a relatively small forward voltage drop across it and the magnitude of the drop would depend on the manufacturing process and temperature. When cathode potential is positive with respect to anode, the diode is said to be reversed. It behaves essentially as an open circuit. D. Insulated-gate bipolar transistor The IGBT is used in medium- to high-power applications such as switched-mode power supply, traction motor control and induction heating. Large IGBT modules typically consist of many devices in parallel and can have very high current handling capabilities in the order of hundreds of amperes with blocking voltages of 6000 V, equating to hundreds of kilowatts. The boundary within the crystal between these two regions, called a PN junction, is where the action of the diode takes place. The crystal conducts conventional current in a direction from the p-type side to the n-type side, but not in the opposite direction. Another type of semiconductor diode, the Scotty diode, is formed from the contact between a metal and a semiconductor rather than by a p-n junction.

Fig. 3: the Recovery Characteristics of Conventional and Fast Recovery Diodes

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IC PIC16F877A PIC is a family of Harvard architecture microcontrollers made by Microchip Technology. It is the controller IC for controlling purpose and it convert the given voice signal into the digital signal and it will send the appropriate signal to receiver side. F Features 1. Pin out compatible to the PIC16C73B/74B/76/77 2. Interrupt capability (up to 14 sources) 3. Eight level deep hardware stack 4. Direct, indirect and relative addressing modes 5. Power-on Reset (POR) 6. Power-up Timer (PWRT) and 7. Oscillator Start-up Timer (OST) G. Hall Effect Sensor A Hall effect sensor is a transducer that varies its output voltage in response to changes in magnetic field. Hall sensors are used for proximity switching, positioning, speed detection, and current sensing applications. In its simplest form, the sensor operates as an analogue transducer directly returning a voltage. With a known magnetic field, its distance from the Hall plate can be determined. Using groups of sensors, the relative position of the magnet can be deduced. Electricity carried through a conductor will produce a magnetic field that varies with current, and a Hall sensor can be used to measure the current without interrupting the circuit. Frequently, a Hall sensor is combined with circuitry that allows the device to act in a digital (on/off) mode, and may be called a switch in this configuration.

Fig. 4: Hall sensor Hall sensors are commonly used to time the speed of wheels and shafts, such as for internal combustion engine ignition timing, tachometers and anti-lock braking systems. They are used in brushless DC electric motors to detect the position of the permanent magnet. In the pictured wheel with two equally spaced magnets, the voltage from the sensor will peak twice for each revolution. This arrangement is commonly used to regulate the speed of disc drives. A hall probe contains an indium compound crystal such as indium antimonite, mounted on an aluminum backing plate, and encapsulated in the probe head. I. Boost Converter Boost Converter control strategy

Fig. 5: driver circuit The power circuit is a dc-dc boost converter. The command circuit is the one, in which the analogue controller was replaced with a Fuzzy one. The output of the Fuzzy controller is vc. In average current control method, an input voltage sensing is required to obtain a sinusoidal reference, an analogue multiplier to combine this reference with the output information, and an error amplifier in current loop to extract the difference between the input current and the reference to generate the control signal for modulating the input current. There are a lot of very sophisticated researches of boost converter dynamics. The most of PFC is based on boost converter, because of its input inductor which reduces the total harmonics distortion and avoids the transient impulse from power net, the voltage of semiconductor device below output voltage, the zero potential of Qâ&#x20AC;&#x2122;s source side which makes it easy to drive Q and its simple structure. Therefore, satisfied teaching of advanced power electronics should be introduced by unity power factor and high efficiency by Dc-dc boost converter.

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In this section one inductor and an IGBT are used to boost up the voltage. When the Dc voltage is lesser than the rated level, it will boost up the voltage. IGBT is used to charging and discharging the inductor. This IGBT is control by micro controller. J. Inverter This is a controlled inverter which is control by v/f control method. This is used to convert Dc into AC.This is control by the help of micro controller. If will produce 1 to 50 Hz frequency Ac output. K. Battery Storage This is the section to store energy. This section has a battery with rectifier and inverter and a step down transformer. Transformer will reduce the voltage to 15v.This 15v Ac is converted into Dc in rectifier. This is 15v c is stored in battery. This is 15v Dc is converted into 230v Ac in inverter. This inverter is control by microcontroller. III. V, F, P.F & I Measurement Current Transformer A current transformer (CT) is used for measurement of electric currents. Current transformers, together with potential transformers (PT), are known as instrument transformers. When current in a circuit is too high to directly apply for measuring instruments, a current transformer produces a reduced current accurately proportional to the current in the circuit, which can be conveniently connected to measuring and recording instruments. A current transformer also isolates the measuring instruments from very high voltage in the monitored circuit. Current transformers are commonly used in metering and protective relays. B. Potential Transformer A transformer is a device that transfers electrical energy from one circuit to another through inductively coupled conductors—the transformer's coils. A varying current in the first or primary winding creates a varying magnetic flux in the transformer's core, and thus varying field through the secondary winding. This varying magnetic field induces a varying electromotive force (EMF) or "voltage" in the secondary winding. This effect is called mutual induction. C. Shunt Resistor Current shunt resistors are low resistance precision resistors used to measure AC or DC electrical currents by the voltage drop those currents create across the resistance. Ohm’s law states that the Voltage (V in Volts) across a resistance (R in Ohms) is the product of the resistance and the current (I in Amps) flowing through the resistance. V = I × R. (1) For example: A current shunt whose resistance is 0.001 Ohms ,having a current of 50 Amps flowing through it will produce a voltage of 0.001 ×50 = 0.05 Volts or 50 mV (millivolts). So by inserting a current shunt into a circuit you can find the current by measuring the voltage drop across the shunt. Then knowing the resistance of the current shunt you can calculate the current using Ohm's law arranged as I=V÷R (2) A zero crossing detector literally detects the transition of a signal waveform from positive and negative, ideally providing a narrow pulse that D. XOR Gate The XOR gate is a special case in logic circuit. It will output a 1 only when the inputs are different (i.e. one input must be at logic high (1) and the other at logic low (0v). The resistor and cap form a delay so that when an edge is presented (either rising or falling), the delayed input holds its previous value for a short time. In the example shown, the pulse width is 50ns. The signal is delayed by the propagation time of the pulse A.

Fig. 6: Simulation circuit for PMSG based standalone variable speed wind turbine

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Fig. 7. Comparator Zero Crossing Detectors

Fig. 8: Simulation result for PMSG based stand alone variable speed wind turbine E.

Driver Circuit This circuit is used to drive all IGBT’s .This circuit is used to isolate the IGBT from microcontroller. This is having two transistors as shown in the circuit diagram. IV. CONCLUSION A control strategy for a direct-drive stand-alone variable speed wind turbine with a synchronous generator has been presented in this project. The controller is capable of maximizing output of the variable-speed wind turbine under fluctuating wind. The generating system with the proposed control strategy is suitable for a small-scale stand alone variable-speed wind-turbine installation for remote-area power supply the simulation results has proves that Regulating the o/p voltage & frequency under sudden load variations and typical wind movement. REFERENCES [1]. [2]. [3]. [4].

T. F. Chan and L. L. Lai, “Permanent-magnet machines for distributed generation: A review,” in Proc. IEEE Power Eng. Annu. Meeting, 2007,pp. 1–6. H. Polinder, F. F. A. Van der Pijl, G. J. de Vilder, and P. J. Tavner, “Comparison of direct-drive and geared generator concepts for wind turbines, ”IEEE Trans. Energy Convers, vol. 3, no. 21, pp. 725–733, Sep. 2006. M. Chinchilla, S. Arnaltes, and J. C. Burgos, “Control of permanent magnet generators applied to variable-speed wind-energy systems connected to the grid,” IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 130–135, Mar. 2006. K. Tan and S. Islam, “Optimal control strategies in energy conversion of PMSG wind turbine system without mechanical sensors,” IEEE Trans. Energy Covers., vol. 19, no. 2, pp. 392–399, Jun. 2004.

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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 TLC profiling and assessment of antibacterial activity of essential oils from three Lantana spp 1

Sadaf Kalam, 2Ashish Saraf, 1Toshi Mishra, 1Rupal Singh, 1Abin Mani, 1Jaswant Patel and 3Adarsh Pandey 1 Centre for Scientific Research and Development, Peopleâ&#x20AC;&#x2122;s Group, Bhopal, M.P., India 2 Faculty of Life Sciences, MATS University, Raipur- 492001(C.G.), India 3 Lab of Mycology, Department of Botany, SS (P.G.) College, Shahjahanpur, UP, India. ____________________________________________________________________________________ Abstract: Medicinal plants are important constituents of indigenous traditional medicinal system in India. Various species of Lantana have been traditionally used as a medicinal herb to treat various ailments like tetanus, rheumatism, malaria, cough, fevers, cold, rheumatisms and asthma. In this study focus has been made on TLC profiling and evaluation of antibacterial activity of volatile fraction of essential oils, extracted from three different Lantana species viz. L. involucrata, L. microphylla and L. montevidensis, using hydrodistillation method. The results of antibacterial activity by gaseous contact showed that out of the six bacterial strains tested against the volatile fraction of essential oil of three species of Lantana, maximum antibacterial activity was exhibited by L. involucrata oil against S. pyogenes with minimum inhibitory concentration of 0.26mg/l air and 0.33mg/l air respectively. P. aeruginosa was slightly susceptible to essential oil of L. involucrata and L. microphylla. These results drive new researches with all the three Lantana species in order to isolate the constituents responsible for the activity leading to the discovery of novel antibiotics. Key Words: Lantana, essential oil, TLC, gaseous contact, antibacterial activity ___________________________________________________________________________________ 1. Introduction The use of medicinal plants with therapeutic purposes represents a secular tradition in different cultures. Approximately 80% of all established natural products originated from plants and have a significant role in the production of new valuable pharmaceuticals (Phillipson and Wright, 1991; Owolabi et al., 2007). There has been an increasing interest in essential oil research during recent years as an alternative to new antimicrobials due to antibiotic resistance. Lantana camara Linn. (Verbenaceae) is a straggling aromatic shrub native of tropical America and Africa. It is cultivated world-wide as an ornamental and decorative plant but now has been regarded as an antagonistic weed. Lantana is a genus of about 150 species of perennial flowering plants popularly used as antirheumatic, stimulant, antibacterial and as ornamental plant (Ghisalberti, 2000). The plant has also been shown to have fungitoxic (Saxena and Sharma, 1999), autotoxic (Arora and Kohli, 1993) and antioxidant activity (Romero and Saavedra, 2005) but is also poisonous to grazing animals (Morton, 1994) Lantana genus is also known worldwide for its bioactive secondary metabolites and essential oils. The chemical composition and aroma of essential oils from Lantana species can provide valuable psychological and therapeutical benefits. The leaf oil of L. camara exhibits antimicrobial activities (Saxena and Sharma, 1999). The oil is reported to possess insecticidal (Patil et al., 1997) repellent activities towards bees, mosquitoes and cattle fly (Attri, 1978). Scientists have also studied the chemical composition of essential oil extracted from leaves and flowers of Indian Lantana (Khan et al., 2002). Present study has been focused with TLC profiling and evaluation of antibacterial activity of volatile fraction of essential oils, extracted from three different Lantana species viz. L. involucrata, L. microphylla and L. montevidensis, using hydrodistillation method. For studying interspecies variation of their antibacterial activity a set of three gram positive and gram negative bacteria have been employed. II. Materials and methods A. Plant sample Fresh leaves of three different varieties of Lantana (L. involucrata (L1), L. microphylla (L2) and L. montevidensis (L3)) were collected from Bhopal city (M. P., India) in August, 2010. The plant materials were further identified by Prof. A.K. Pandey, Botanist, Dept. of Biological Sciences, R. D. University, Jabalpur. A voucher specimen was deposited in the Herbarium of Dept. of Biological Sciences, Jabalpur (India). B. TLC profiling of pigments Fresh Lantana leaves of each species (0.5 g) were combined with 0.5 g of anhydrous magnesium sulphate and 1.0 g of sand. The mixture was grinded using a mortar and pestle, until it becomes fine, light green powder. To this, 2 ml of acetone was thereby added and the solution was stirred using a stirring bar for 2 minutes. The

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mixture was then allowed to sit for 10 minutes. The solid settled to the bottom, leaving a green liquid layer on top. The top green layer was collected and subsequently subjected to TLC. C. TLC analysis Silica Gel-G (Sigma) powder was used for preparation of TLC plate. The samples (3-5 µl) were spotted with a Hamilton syringe on pre-activated TLC plate and subsequently, the plate was developed by means of solvent system consisting of: Petroleum ether: Cyclohexane: Ethyl acetate: Acetone: Methanol (6:1.6:1:1:0.4). After complete run, Rf values of different pigments were calculated. D. Extraction of Essential Oils About 100g leaves of each species of Lantana were subjected to hydrodistillation for 3-4 hours using Clevenger-type apparatus. The extracted oils were dried over anhydrous sodium sulfate (Na2SO4) and were refrigerated in sealed vials prior to analysis (Shibamoto, 1987). E. TLC profiling of essential oils To determine the presence of alcohols, esters and carbonyl compounds, the extracted oils were subjected to TLC. Different solvent systems employed were as follows: Alcohols -Benzene: Methanol (10:1); EstersBenzene (neat); Carbonyl Compounds-n-Hexane: Ether (20:3) (Nigam et al., 1965). The separated compounds were localized by placing the TLC plate in a chamber saturated with Iodine vapors. Consequently, Rf values were calculated and the spots were identified on the basis of standard values (Nigam et al., 1965). F. Determination of Antibacterial Activity Antimicrobial activities of different Lantana sp. plants extract individually as well as their combined effect were estimated (equal ratio) by adopting volatile evaluation method (Bocher, 1938). This assay measures the activity of volatile part of a sample extract in a close micro atmosphere between the agar medium and the Petri dish cover in comparison to a control culture. Minimum inhibitory Concentration (MIC) was also determined. Known aliquot of appropriate nutrient medium (NAM for bacteria) was poured into 90mm diameter Petri dish. After the gelation of medium, agar disc of 6mm (dia.) from pre-grown organism (18-24h old bacterial culture) was inoculated in the center of the dish using a sterile cutter and a glass rod. The Petri dishes were turned upside down and a sterile 6mm Whatman filter paper soaked with known concentration of plants extract was placed in the center of the cover. The Petri dishes were then sealed with paraffin tape. In control set, the extract was replaced by sterilized double distilled water or the respective solvents used for extraction purposes. After desired period of incubation, percent inhibitions of growth were calculated by using the following formula. dc – dt Inhibition % = x 100 dc Where: dc: diameter of radial growth in control plates dt: diameter of radial growth in test plates G. Test Bacterial Strains The following bacterial strains were used as test organisms: B. subtilis MTCC 1789, Staphylococcus aureus MTCC 87, S. pyogenes MTCC, Escherichia coli MTCC 443, Klebsiella pneumoniae MTCC 2405 and Pseudomonas aeruginosa MTCC 934. All the bacterial strains were obtained from Microbial Type Culture Collection Centre, Chandigarh, India. The microorganisms were maintained at 4˚C on nutrient agar slants. III. Results A. Yield of essential oils Essential oils were extracted from leaves of different species of Lantana using hydrodistillation. The yields of extracted volatile oil were: 1.2 % for L. involucrata, 1 % for L. microphylla and 0.5 % for L. montevidensis. B. TLC analysis of pigments The TLC plate after the separation of a number of colorful pigments from leaf extracts is depicted in Figure1 and their respective Rf values are enlisted in Table 1. The solvent system employed was- Petroleum ether: Cyclohexane: Ethyl acetate: Acetone: Methanol (6:1.6:1:1:0.4) which yielded four pigments viz. β-carotene (Rf = 0.95), chlorophyll a (Rf = 0.44), chlorophyll b (Rf = 0.32), and xanthophylls (Rf = 0.16). C. TLC analysis of essential oils TLC has been applied successfully to characterize essential oils for the presence of alcohols, esters and carbonyl compounds. Such classes of compounds with different polarity necessitate the use of different developing systems (Nigam et al., 1965). TLC analysis of extracted essential oils of Lantana species reveals the presence of different chemical compounds. D. Detection of Alcoholic components Alcoholic compounds were separated and identified on the basis of Rf value obtained when compared with standards. Table 2 represents Rf values and their corresponding compounds identified from essential oils of Lantana species (Figure 2).

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E. Detection of Carbonyl compounds Different carbonyl compounds isolated from Lantana species along with their Rf values are illustrated in Table 3 (Figure 3). F. Detection of Ester compounds Major compound geranyl acetate was present uniformly in all the three species of Lantana and the results are shown in Table 4 (Figure 4). G. Results of antibacterial activity The results of antibacterial activity by gaseous contact showed that out of the six bacterial strains tested against the volatile fraction of essential oil of three species of Lantana, maximum antibacterial activity was exhibited by L. involucrata oil. The most susceptible strain was S. pyogenes followed by S. aureus with minimum inhibitory concentration of 0.26mg/l air and 0.33mg/l air respectively. P. aeruginosa was slightly susceptible to essential oil of L. involucrata (L1), L. microphylla (L2). Rest of the volatile fraction and their combination failed to inhibit the growth of P. aeruginosa. IV. Discussion The Rf values are in agreement with those reported in literature (Stahl, 1965). An advanced experimental method that focuses on extraction and thin layer chromatography of pigments from spinach was developed which clearly resolves chlorophyll a and b from spinach leaves while minimizing the appearance of chlorophyll degradation products (Quach et al., 2004). The essential oils of some other varieties of Lantana have been extracted and studied by researchers. Essential oil was extracted from L. trifolia with an oil yield of 0.2% (Juliao et al., 2009). Similarly, hydrodistillation of leaves of L. camara Linn afforded pale yellow oil with yield of 0.25% (v/w) based on the dry weight of the plant (Saikia and Sahoo, 2011) whereas, L. salvifolia dried leaves gives an average essential oil yield of 0.38% (Ouamba, 2006). From studies on essential oils and their constituents, Rf data has been calculated for various compounds viz., terpene alcohols, esters and ketones (Nigam et al., 1965). The different polar characteristics of terpenes, alcohols, esters, aldehydes and ketones require the use of different development system. Benzene is found to be a suitable solvent for esters, while a mixture of n-Hexane and ether (20:3) was applied to resolve carbonyl compounds. Alcohols were developed with a benzene-methanol solution (10:1). Iodine vapors were used as location reagent in a non-destructive method. The bioactive properties of compounds isolated from essential oils can were evaluated for therapeutic use. The variability in foliar essential oils among different morphotypes of Lantana species complexes and its taxonomic and ecological significance has been also investigated (Love et al., 2009). Earlier reports show that S. aureus was more susceptible (MIC 0.25 mg/L air) to the volatile constituents of the essential oil of L. montevidensis Briq. (Sousa et al., 2011). Previous reports verify the antibacterial activity of L. camara essential oil against S. aureus by direct contact method (Kurade et al., 2010; Costa et al., 2009; Hernandez et al., 2005), but there is no previous report regarding the antibacterial activity by indirect contact. In another study, essential oil of L. camara showed antibacterial activity by direct contact against Arthrobacter protophormiae, Micrococcus luteus, Rhodococcus rhodochrous and S. aureus with minimal bactericidal concentrations of 50, 25, 12.5 and 200 μg/mL, respectively (Kurade et al., 2010). Various species of Lantana has been used worldwide to treat a wide variety of disorders and are known for their potential therapeutic values. Due to bacterial resistance to various commercially available antibiotics, therapeutic action of plant essential oils has been widely explored. Investigation of antimicrobial spectrum of bioactive compounds from these three Lantana species has to be done more deeply to confirm the correlation between the chemical content of the essential oils and their antibacterial activities. These results are interesting as commercially available antibiotics are poorly active or inactive towards Gram- negative bacteria and in the present research work majority of test bacteria are Gram-negative. Present study indicates the presence of alcoholic, carbonyl and ester compounds in essential oil of Lantana species. Thus the remedial action of these compounds could be further investigated to unfold its antibacterial property. Acknowledgements The authors are thankful to Shri S.N. Vijaywargia and Capt. Ruchi Vijaywargia for providing laboratory facilities and Sarvajanik Jankalyan Parmarthik Nyas (SJPN), Peoples’ Group, Bhopal, for granting financial assistance to carry out the present research work. References 1. 2. 3. 4.

Arora RK, Kohli RK (1993). Autotoxic effect of decomposed leaf and inflorescence of Lantana camara var. camara on its seed germination parameters. Indian J Ecol 20: 109–112. Attri BS, Singh RP (1978). A note on the biological activity of the oil of Lantana camara L. Indian J Entomol 39: 384–385. Bocher OE (1938). Antibiotics. In: Peach K, Tracey MV, editors. Modern methods of plant analysis. Volume III. Berlin: Springer Verlag, pp. 651. Costa JG, Sousa EO, Rodrigues FFG, Lima SG, Braz-Filho R (2009). Chemical composition, evaluation of antibacterial activity and toxicity of the essential oils from Lantana camara L. and Lantana sp. Braz J Pharmacogn 19: 710–714.

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Ghisalberti EL (2000). Lantana camara L. (Verbenaceae). Fitoterapia 71: 467–486. Hernández T, Canales M, Avila JG, García AM, Martínez A, Caballero J, de Vivar AR, Lira R (2005). Composition and antibacterial activity of essential oil of Lantana achyranthifolia Desf. (Verbenaceae). J Ethnopharmacol 96: 551–554. Juliao Lde S, Bizzo HR, Souza AM, Lourenço MC, Silva PE, Tavares ES, Rastrelli L, Leitão SG (2009). Essential oil from two Lantana species with antimycobacterial activity. Nat Prod Commun 4: 1733–1736. Khan M, Srivastva SK, Syamasundar KV, Singh M, Naqvi AA (2002). Chemical composition of leaf and flower essential oil of Lantana camara from India. Flav Fragr J 17: 75–77. Kurade NP, Jaitak V, Kaul VK, Sharma OP (2010). Chemical composition and antibacterial activity of essential oils of Lantana camara, Ageratum houstonianum and Eupatorium adenophorum. Pharm Biol 48: 539–544. Love A, Naik D, Basak SK, Babu S, Pathak N, Babu CR (2009). Variability in foliar essential oils among different morphotypes of Lantana species complexes and its taxonomic and ecological significance. Chem Biodivers 6: 2263–2274. Morton JF (1994). Lantana or red sage (Lantana camara L., Verbenaceae), notorious weed and popular garden flower: Some cases of poisoning in Florida. Econ Bot 48: 259–270. Nigam MC, Nigam IC, Levi L (1965). Essential oils and their constituents XXV. Thin Layer Chromatography. Some chemical and chemotaxonomic applications. J Soc Cosmetic Chemists 16: 155–168. Ouamba JM, Ouabonzi A, Ekouya A, Bessière JM, Menut C, Antoine Abena A, Banzouzi JT (2006). Volatile constituents of the essential oil leaf of Lantana salvifolia Jacq. (Verbenaceae). Flav Fragr J 21: 158–161. Owolabi OJ, Omogbai EKI, Obasuyi O (2007). Antifungal and antibacterial activities of the ethanolic and aqueous extract of Kigella africana (Bignoniaceae) stem bark. Afr J Biotechnol 6: 1677–1680. Patil RK, Rayar SG, Basappa H, Hiremath IG, Patil BR (1997). Insecticidal property of indigenous plants against Dactynotus carthamii, H.R.L. and its predator, Chrysoperla carnea L. J Oilseed Res 14: 71–73. Phillipson JD, Wright CW (1991). Medicinal plants in tropical medicine. 1. Medicinal plants against protozoal diseases. Trans R Soc Trop Med Hyg 85: 18-21. Quach HT, Steeper RL, Griffin GW (2004). An improved method for the extraction and thin-layer chromatography of chlorophyll a and b from spinach. J Chem Educ 81: 385–387. Rosas-Romero A, Saavedra G (2005). Screening Bolivian plants for antioxidant activity. Pharm Biol 43: 79–86. Saikia AK, Sahoo RK (2011). Chemical composition and antibacterial activity of essential oil of Lantana camara L. Middle-East J Sci Res 8: 599–602. Saxena VK, Sharma RN (1999). Antimicrobial activity of the essential oil of Lantana aculeata. Fitoterapia 70: 67–70. Shibamoto T (1987). Retention indices in essential oil analysis. In: Sandra P, Bicchi C, editors. Capillary gas chromatography in essential oil analysis. New York: Huethig Verlag, pp. 259–274. Sousa EO, Rodrigues FFG, Coutinho HDM, Campos AR, Lima SG, Costa JGM (2011). Chemical composition and aminoglycosides synergistic effect of Lantana montevidensis Briq. (Verbenaceae) essential oil. Rec Nat Prod 5: 60–64. Stahl E (1965). Thin layer chromatography. New York: Academic Press.

7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

Table 1: Rf values of different fractions from leaf extracts of Lantana species (Bottom to top) S. No.

Rf values (obtained) L. involucrata -

L. microphylla -

Rf values (Standard)

L. montevidensis -

Pigments

0.16

Xanthophylls

0.36

0.34

0.32

Chlorophyll-b

0.45

0.44

Chlorophyll-a

0.95

β- carotenes

0.64

0.58

0.66

0.61

Pheophytin-a

0.52

0.51

0.49

Pheophytin-b

Table 2: Alcoholic Compounds isolated from essential oil S. No. 1.

Plant Name

Rf value (Obtained)

Rf value (Standard)

Compound Name

L. involucrata

0.87

0.86

Citronellol

L. microphylla

0.71 0.32 0.97

0.72 0.31 0.98

Geraniol Limonenediol Isopiperitenol

L. montevidensis

0.91 0.83 0.97

0.95 0.82 0.98

Trans-Carveol Nerol Isopiperitenol

0.87 0.82

0.86 0.82

Citronellol Nerol

Table 3: Carbonyl Compound isolated from essential oil S. No. 1.

Plant Name

Rf value (Obtained)

Rf value (Standard)

0.87

0.92

γ-Ionone

0.77

Piperitone β-Ionone

L. involucrata

Compound Name

L. microphylla

0.78

L. montevidensis

0.87

0.82

Citral

0.83

0.86

Citronellol

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Table 4: Esters isolated from essential oil S. No. 1. 2. 3. 4.

Plant Name

Rf value (Standard)

Compound Name

L. involucrata L. microphylla

Rf value (Obtained) 0.89 0.90

1.00 1.00

Geranyl acetate Geranyl acetate

L. montevidensis

0.90

1.00

Geranyl acetate

Figure 1

Figure 2 1: L. involucrata; 2: L. microphylla: 3. L. montevidensis Figure 1: TLC of leaf pigments. Figure 2: TLC of Essential Oils for Detection of Alcohol Developing Solvent: Benzene : Methanol (10:1)

Figure 3 Figure 4 1: L. involucrata; 2: L. microphylla: 3. L. montevidensis Figure 3: TLC of Essential Oils for Detection of Carbonyl compounds Developing Solvent: n-Hexane: Ether (20:3) Figure 4: TLC of Essential Oils for Detection of Esters Developing Solvent: Benzene (neat)

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 

Data are multiple of the replicate Values are mean ± SEM (Standard Error of Mean)

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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 Isolation of three chemical constituents of Mangifera indica wood extract and their characterization by some spectroscopic techniques Kapil Madan1, D.S.Shukla1,Richa Tripathi1, Akanksha Tripathi2, Ruchi Singh3, Himanshu Dhar Dwivedi4 1 Botany Department, MLK PG College, Balrampur,271201 2 Zoology department, M.L.K.P.G. College, Balrampur,271201 3 Department of Oriental Studies, Dev Sanskrit Haridwar University, 249403 4 Botany Department, Shakti Smarak Sansthan, Balrampur ______________________________________________________________________________________ Abstract: Mangifera indica, commonly called Mango or Amra belongs to a family of Anacardiaceae. The plant is used as antiasthamatic, antiseptic, antiviral, emetic, expectorant and laxative. It is cultivated in the Indian subcontinent for thousands of years from where it travelled to East Asia between the 5th-4th century BC. Later by the 10th century AD it was transported to East Africa and subsequently to Brazil, West Indies and Mexico. It is an important medicinal plant used in various Ayurvedic preparations. Scientific investigations have shown that the mango triterpene, lupeol is an effective inhibitor in laboratory models of prostate and skin cancers. Extract of its branch bark in water contains numerous polyphenols with anti-oxidant properties. Mango wood is used in yagya as base fire through which medicated smoke is produced. Extract of mango leaves are astringent, cooling, antiemetic and useful in hyperacidity, burning sensation, dysentery and fever. The ash of leaves is used for wound healing properties in burns and scalds. The leaves of mango are kept in the kalash during puja rituals and are known as leaves of deity’s seat. Flowers of mango are acrid, and are useful in diarrhoea and anaemia. In Fiji, fresh mango kernels are consumed as a cure for dysentery and asthma, while mango juice is used as a nose drop for sinus trouble. In India, dry seed powder is applied to the head to remove dandruff. It is also applied as an ant diarrheal agent. Kernel starch is eaten as a famine food, while hot water extracts of kernel are administered as antihelminthic, laxatives and tonics. We have isolated three new compounds from alcoholic and hexane extracts. 1,2-benzenedicarboxylic acid and mono (2-ethylhexyl)ester 9,12-tetradecadiene-1-ol-acetate were separated from the hexane extract of the stem bark of Mangifera indica. On the other hand alcoholic extract 3-chloro-N-(2-phenylethyl) propanamide. These were first identified by thin layer chromatography and later separated in a silica gel column. All the compounds gave characteristic infrared bands corresponding to functional groups. The structures were elucidated by GC-MS fragmentation pattern after comparing the data with NIST mass spectral data base. Key Words: Mango,Mangifera indica, amra, chemical constituents, GC-MS _______________________________________________________________________________________ I. Introduction Mango which belongs to family Anacardiaceae, order rutales, is one of the most important fruit marketed in the world with global production exceeding 26 million tons in 2004 1. It grown naturally or cultivated mainly in tropical and sub tropical regions and has been reported to be the second largest tropical fruit crop in the world. 2 Extract of Mangifera indica have been reported to possess antiviral, antibacterial, analgesic, antiinflammatory and immuno-modulatory activities3, in-vitro antamoebic activity4, interesting α-amylase and αglycosidase inhibitory activities5 and cardio toxic and diuretic properties6. The chemical constituents of the different organs of M.indica are reviewed in Ross (1999)7 and Scartezzine and Speroni (2000).The bark is reported to contain protocatechic acid, catechin,mangiferin, alanine, glycine, γamino-butyric acid, kinic acid, shikimic acid and the tetra cyclic triterpenoids cycloart-24-en-3p,26-diol,3-keto dammar-24(ES-en-2Os,26-di ol,C-24 epimers of cycloart-25 en 3β,24,27-triol and cycloartan- 3β, 24,27-triol.8 Present work was an attempt to isolate and identify new organic compound from the hexane and alcoholic extract of Mangifera indica L. These were identified by IR spectral and Gas Chromatography-Mass spectrometry. II. Experimental Sampling The dried stem of Mangifera indica were collected from the garden of Shantikunj. They were thoroughly washed with distilled water to remove any dirt and other surface contamination. Finally, they were dried 80°C for 24 hours in an oven and crushed to homogeneous powder (80 mesh). Separation and identification of organic constituents: 500 g air-dried powder were first extracted with n-hexane in a soxhlet for 48 hours the solvent was removed and the residue (15g) was kept aside. Now, the extracted powder were dried and re-extracted with methanol in soxhlet for 30 hours. Again, the solvent was removed and the residue (13g) was collected.

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The n-hexane extract was subjected to column chromatography and eluted with solvent of their increasing polarity order such as hexane, CCl4, benzene, CH2Cl2 ,CHCl3, EtOAc, and CH3OH respectively. The fractions were collected and the benzene fraction was chromatographed over silica gel-G eluting with benzene and increasing proportion of EtOAc in benzene .Elution with benzene-EtOAc (6:1) gave two distinct spots corresponding to Rf=.56 and Rf=.43 obtained that was separated by column chromatography. Again TLC was checked for methanolic extract in different solvent mixture but only Benzene:CCl4 mixture showed five distinct spots corresponding to Rf=.21,.36,.52,.77,.84 respectively. Finally, only one was separated through column chromatography. III. Results and discussion Three new compounds two from hexane extract and one from methanol extract were separated and identified by GC-MS fragmentation assignments after comparing the data with NIST mass spectral data base. The compounds are 1,2 benzenedicarboxylic acid,mono(2-ethylhexyl)ester (rs-1); 9,12-Tetradecadiene,1-ol,acetate (rs-11) and 3chloro-N-(2-phenylethyl) propanamide (rs-0) that is used as antimalarial9.It may be noted that rs-1 is an alleleopathic compound that reduces the need for weed management in other crops10 and already reported in curry leaves11. Rs-1 was identified as 1,2-benzenedicarboxylic acid, mono(2-ethylhexyl)ester from the IR and mass spectral bands. A band for hydroxyl group,-OH (3441.76) cm-1 can be observed indicating the presence of this functional group. The other prominent peaks are seen at 2930 cm-1(C-H stretch),1625 cm-1 and 1567 cm1 (C=Ocarboxylate), 1631cm-1(C-C stretch) and 1383 cm-1(C-O stretch).The mass spectrum indicated the molecular ion peak at m/z 278 and the base peak at m/z 149. The other prominent peaks are those at m/z 167,113 and 57. Rs-11 was identified as 9,12-Tetradecadiene-1-ol-acetate from IR and mass spectral bands. The typical diagnostic band for ester carbonyl group,C=O (1707 cm-1) was observed. The other prominent bands are seen at 2852.86 cm-1(-CH3 stretch),2920.59 cm-1(-CH2- stretch),1461cm-1(methylene C-H bend) and 1380 cm-1 (C-O stretch).The mass spectrum showed the molecular ion peak at m/z 252 and the base peak at 55.The other prominent peaks are those at m/z 67,81,95,107,121,149,163 and 192. IR spectrum for Rs-0 gave prominent bands at 3770, 2925 and 2845, 1680, 1620, 1545 and1480 cm-1 corresponding to the amide, -CH2 and carbonyl groups and a band for benzene ring respectively. A molecular ion peak was observed at m/z 211 and the base peak at m/z 104.The other prominent peaks are those at m/z 213,175,148,120,105,104,91,77,63,65,51 and 49. Recent reports by some scientists have investigated that an aqueous stem bark extract of Mangifera indica (Vimang) exhibits anthelminthic and anti-allergic activities12 which in turn may be correlated with their organic constituents that may help in developing an understanding for its pharmacological action. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12].

Barreto et al.Charecterization and qantitazation of Polyphenolic compounds in bark,kernel,leaves and peel of Mango.J.Agric.Food Chem.2008,56,5599-5610 Joseph,J.K.; Abolaji,J.Effects of replacing maize with graded leaves of cooked Nigerian mango seed kernels on the performance, carcass yield meat quality of broiler chicken.Bioresour.Techno.1997,61,99-102 Makare,N.; Bodhankar,s.; Rangari,V.Immunomodulatory activity of alcoholic extract of Mangifera indica L.in mice. J.Ethnopharmacol.2001,78(2-3),133-137 Tona,L.; Kambu,K.; Ngimbi,N.;Cimanga,K.; Vlietinek,A.J.Antiamoebica and phytochemical screening of some Congolese Medicinal Plants.J.Ethnopharmacol.1998 Prashanth,D.;Amit,A.;Samiulla,D.S.;Asha,M.K.;Padamaja,R.α-Glucosidase inhibitory activity of Mangifera indica bark.Fitoterapia.2001,72,686-688. Seartezzini,P.;Speroni,e.Review on some plants of Indian Traditional Medicine with anti oxidant activity.J.Ethnopharmacol.2000,71,23-43 Ross IA (1999).Medicinal plants of the World, Chemical Constituent, Traditional and modern medicinal uses,Humana Press,Totowa,pp 197-205 Wauthoz et al.Mangifera indica L. Bark and Mangiferin.Inter.J.of Biomed.and Pharm. Sc.2007,1(2),112-119 Marreo Ponce .et .al. Ligand-Based Virtual Screening and in Silico Design of New Antimalarial Compounds Using Nonstochastic and Stochastic Total and Atom-Type Quadratic Maps.J.of Chem.Infor.and Modelling.2005,45(4),1082-1100 Labrada R.(Ed.).Weed management for developing countries,Food and Agriculture Organization of the United States,Rome,2003 Choudhury,R.P.;Jain,G.;Garg A.N.,Short irradiation instrumental neutron activation analysis of essential and trace elements in curry leaves and their organic constituents by GC-MS.J.of Radio. Ana. and Nuclear Chemistry,2006,207,187- 195 Garrido G, Blanco-Molina M, Sancho R, Macho A, Delgado R, Muñoz E. An aqueous stem bark extract of Mangifera indica (Vimang) inhibits T cell proliferation and TNF-induced activation of nuclear transcription factor NF-kappaB. Phytother Res. 2005 Mar;19(3):211-5.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Framework for Domain Specific Software Agent Ontology Reuse Deepti Aggarwal1, Dr. Amandeep Verma2 University Institute of Computing, Chandigarh University, Gharuan, Mohali, India 2 Punjabi University Regional Centre of Information Technology & Management Mohali, India _____________________________________________________________________________________________ Abstract: A software agent is a piece of software that is invoked for accomplishment of particular task. Reusing of software agents helps in the development of agent based applications. The heterogeneity of the development environments used by software agents creates a problem in its reusability. Ontology is the standardized representation of knowledge/ structure, irrespective of the development environment. So, by representing the agents as ontology helps in reusing such agents even if they are heterogeneous with respect to their environment. The present study depicts a framework to reuse existing software agents using ontological engineering approach. Keywords: Software Agent, Reusability, ontology.

______________________________________________________________________________ I. INTRODUCTION A software agent is a piece of software that is invoked and is executed autonomously and continuously in a particular environment for accomplishment of a particular task. The use of collaboration of existing software agents rather than building a new agent to answer query that cannot otherwise, be answered by the existing agent individually, may lead to benefits related to development time and its cost as well. The application of reuse techniques on software agents may also lead to significant improvements in agent based application development [12]. The software agents belonging to same domain, that need to be reused in order to have new agent, may be heterogeneous in terms of their development environment. The reusability of such existing agents can be achieved by employing a mechanism that can ensure the exchange of information and data irrespective of their environment. The ontology is a means of knowledge/structure representation and it is independent of development environment. The ontological engineering approach helps in solving the problem of heterogeneity by using the common and standardized form of representation of the agents by an ontology . So, this ontology based approach provides the solution to the problem of heterogeneity when disjoint and diverse data sources are available. In this study, a framework for the reuse of existing software agents to answer a query has been presented. Section 2 gives a brief review of literature and presents the proofs of knowledge sharing via ontological approach. In third section the proposed framework is presented and the last section consists of the conclusion and future direction of this approach.

II.

LITERATURE REVIEW

Ghassan Beydoun et. al. argue that the ontologies should be used in the SDLC of MASs so that the already existing softwares can be reused. An agent framework based on ontology has been presented in [15] that can retrieve data from distributed heterogeneous data sources such as XML and RDF. With this the user is able to retrieve data sources by simple query as an input and this query will be reformed and sent to remote data sources for information retrieval. But this system suffers from the problem of query translation. In their research work, Awny Alnusair and Tian Zhao proposed an approach to identify the relevant components from the existing ontologies. A well defined ontology model has been proposed to ensure component reuse. The work[10] proposed ontology merging mechanism. This ontology can promise the reformulation of user queries in accordance with the user requirements. The researchers have outlined the methodology for semantic data retrieval based on distributed heterogeneous data sources and the challenging problem of query reformulation on the basis of merged ontology and data source descriptions has been identified. A study to evaluate various existing ontology based query systems has been presented by Hoang and Tjoa. The authors also presented the research directions in ontology based query research. Search strategies, query formulation, query refinement and user interaction techniques has been analyzed and compared.[8]

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The usage of ontologies for information retrieval can overcome the problems associated with syntactic search. J. Uma Maheswari suggests a conceptual framework that goes through five phases for information retrieval. To improve the relevancy of retrieval an improved matching algorithm is used. [12] A framework namely OBSERVER represents a highly independent system which has no concern with the diversity of data repositories/ontologies and it can deal with many types of heterogeneity at the structural, functional or semantic level [3]. III. PROPOSED FRAMEWORK In this section a framework to facilitate the reuse at ontological level is proposed. As per the figure 1, the architecture of the proposed framework consists of following components: User/ Interface Agent: The user/ interface agent is an entity who generates a query in the agent environment. The query comprises of the terms required by the user. The query is then parsed to the query processor.  Query Processor: The query processor is composed of following components:  Processing Agent:-The query processor is responsible for the reformation of query entered by the user. The QP can employ many techniques such as reformation, query stemming.  Mapping Agent:- . Mapping Agent looks for the suitable ontologies in the domain of ontologies. The outcome will be the list of ontological data sources that satisfy the requirements of the user/interface agent fully or partially.  Transfer Agent: The transfer agent is responsible for transferring the reformulated query to the remote agents.

Source1

Source2

Source3

Source4

Source n

Figure1. Mechanism for ontological reuse of software agents

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 Wrapper Agent: The wrappers come into picture when the remote agents exhibit their ontologies to be reused by user/interface agent. The simply serve as the mediators between the remote agent and the user/interface agent.  Remote Agent: The remote agents are the actual data sources which fall in the domain of the requirements of the user/interface agent. These can share their ontologies for satisfying the needs of user/interface agents. A. FUNCTIONAL SCENARIO Figure 2 depicts the functional scenario of the framework. The step by step processing of the query is as follows: 1. The user/interface agent will make a query using suitable keywords or terms. The query then be parsed to Query Processor.

2 1

5 4

10 Figure 2: Functional Flow of the Proposed Framework

Query Processor will reformulate the query by using query parsing and word stemming techniques. Query parsing phase results in the meaningful words. In this, the terms/keywords entered by the user are split into meaningful words and then Word Stemming process is applied to them. Stemming deduces the stem from the fully suffixed word according to its morphological rules. The unwanted words such as a, the, an etc. are also removed. For example if the user enters the keyword interesting or interested, then the system will stem it to interest. Now the mapping agent will look into its domain ontologies for the same keywords obtained after stemming. All the combination of words is taken for processing. Specific domain ontology is taken to verify whether the word is present in that ontology. If yes then the relationship of the words are taken into the consideration. This phase is called as Ontology Matching. The Ontology Matching may present more than one data sources as a result. For considering the best, Weight Assignment and Rank Calculation is done. The weight is assigned to each word with respect to other word according to the relationship in ontology like superclass, immediate subclass, subclass etc based on improved matching [6] algorithm. After the weights are assigned, the next step is to calculate the rank. The cumulative weight is calculated for each combination of words based on the improved matching algorithm. The best document gets the minimum score. Documents are arranged in ascending order according to their cumulative weight.

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Now the mapping agent will pass the query to the mobile agent called as Transfer Agent. The passed query will hold a module that holds the necessary mapping and merging instances, tables of mapping and common vocabulary. 7. The transfer agent accesses the remote agent sites. The local processing agents of remote agent sites will perform the necessary mapping of the information obtained from remote agents to its local data and then provide the required information. 8. The output of Step 7 will be the complete or partial ontologies extracted from remote sites. Then after this, the necessary ontology integration and/or merging, if required, will be performed. For the ontologies on the same subject, merge technique is used. A globally accepted framework for the merging of ontologies is available. But if the ontologies are exhibited on different subject, then Ontology Integration is performed. For this three operations namely assemble, include or extend are used depending upon the level of integration required. 9. The outputs of ontology integration and/or merging are passed to Wrapper Agent. It will present the data in the language understandable to the user/interface agent. 10. The results are then passed to User/interface agent via Wrapper Agent. IV.

CONCLUSION AND FUTURE SCOPE

The proposed model is under study. It is based on the observations from the papers quoted in the literature. The description of the model using a modeling/description language is the future scope of the study. REFERENCES [1] [2] [3]

[4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

Awny Alnusair, Tian Zhao, “Component Search and Reuse: An Ontology – based Approach” IEEE International Conference on Information Reuse and Integration, pp. 258-261, 4-6 Aug 2010. Bruno Antunes, Nuno Seco, Paulo Gomes, “Using Ontologies for Software Development Knowledge Reuse”, EPIA 2007, LNAI 4874, pp 357-368, 2007. Eduardo Mena, Arantza Illarramendi, Vipul Kashyap, Amit P. Sheth, “OBSERVER: An Approach for Query Processing in Global Information Systems based on Interoperation Across Pre-existing Ontologies”, International conference on Cooperative Information Systems, Pp. 14-25, 19-21 June 1996. Elena Paslaru, Bontas, Malgorzata Mochol, Robert Tolksdorf, “ Case Studies on Ontology Reuse”, Proceedings of I-Know ’05, pp 345-353, Jun 29-Jul 1, 2005. Gerd Stumme, Alexander Maedche, “Ontology Merging for Federated Ontologies on the Semantic Web”, In Proceeding of the International Workshop for Foundations of Models for Information Integration (FMII 2001), September 2001. Ghassan Beydoun, Brian Henderson-Sellers, Jun Shen, G. Low, “Reflecting on Ontologies towards Ontology-based Agent-Oriented Software Engineering”, Advances in Ontologies: 5th Australasian Ontology Workshop, pp 23-33, 2009 H. Sofia Pinto, Towards Ontology Reuse, AAAI Technical Report WS99-13, pp 67-73. Hanh Huu Hoang A Min Tjoa, “ The State of the Art of Ontology-based Query Systems: A Comparison of Existing Approaches”, IEEE, 2006 Helena Sofia Pinto, Joao P. Martins, “Ontology Integration: How to Perform the Process”, Proceedings of the IJCAI-01 Workshop on Ontologies and Information Sharing” pp 71-80, Aug 4-5, 2001. Hui Peng, Zhongzhi Shi1, Liang Chang, Wenjia Niu, Improving Grade Match to Value Match for Semantic Web Service Discovery, Fourth International Conference on Natural Computation IEEE,2008. J. Bermejo Alonso, R. Sanz, “ A Survey on Ontologies for Agents” 6 th Framework Programme – Cognitive Systems, April 2006. J. Uma Maheswari, “A Conceptual Framework for Ontology Based Information Retrieval” International Journal of Engineering Science and Technology, Vol. 2(10), 2010, 5679-5688 John F. Sowa, Building, Sharing and Merging Ontology”, 2009, www.jfsowa.com/ontology/ontoshar.htm K. Munir, M. Odeh, R. McClatchey, S. Khan, I. Habib, “Semantic Information Retrieval from Distributed Heterogeneous Data Sources”, 4th International Workshop on Information Technology – FIT 2006, Islamabad, Pakistan, December 2006. Najood Al-Ghamdi, Mostafa Saleh, Fathy Eassa, “Ontology-Based Query in Heterogeneous & Distributed Data Sources”, International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 10 No: 06, December 2010. Natalya F. Noy, Semantic Integration: A Survey of Ontology-Based Approaches”, SIGMOD Record, Volume 33, No. 10, pp 65-70, December 2004. Peddinti V. Gopalacharyulu, Erno Lindfors, Catherine Bounsaythip, Winnie Wefelmeyer, Matej Oresic, “Ontology Based Data Integration and context-based mining of life sciences”, W3C Workshop on Semantic Web for Life Sciences, pp 1-3, October 2004. Tom Holvoet and Elke Steegmans,” Application-Specific Reuse of Agent Roles”, SELMAS 2002, LNCS 2603, pp. 148–164, 2003. Springer-Verlag Berlin Heidelberg 2003

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Association between Requirement Engineering Processes and Risk Management Rajinder Singh Department of Electronics, S.D College, Ambala Cantt, Haryana, India ___________________________________________________________________________________ Abstract: The software industry has shown ballooning growth rate in last few years but still it is over burdened with failed and delayed projects. Most of these failed projects overrun their original budget. As per a report presented by The Standish Group, 72 percent of software projects are failed as those are completed after scheduled time and are over budget. More than 23 percent of software projects are cancelled before they ever get completed, and 49 percent of projects cost 145 percent of their original estimates. (Standish, 1995). In retrospection, many of these companies reported that their problems could have been avoided or drastically reduced if high-risk elements of the project could have been identified in advance. Although there are many risks involved in software development life cycle but the major risks are related to software cost, quality and scheduling which can be controlled and avoided if proper strategies are adopted in the initial stages. Risk management helps us to identify, analyze and control various risks associated with software development cycle. Theories say, all of these risks crepes in due to poor requirement engineering processes followed by software development team. Lack of understanding of client’s requirements, frequent changes in requirements, lack of user involvement. Lack of standards for requirement elicitation methods are some of the factors which leads to delayed, over budget and low quality software projects. If requirement engineering processes are followed by companies, risks can be managed properly. So requirement engineering processes and risk management are corelated and go side by side. The aim of this study is to check how much RE processes affects risk management by conducting a survey in nine different software development companies and taking evidence from the software developers who are actually using these practices practically in their jobs. Keywords: Requirement Engineering, RE Processes, Risk Management, Software Engineering ___________________________________________________________________________________ I. Introduction The software industry has shown ballooning growth rate in last few years but still it is over burdened with failed and delayed projects. Most of these failed projects overrun their original budget. As per a report presented by The Standish Group, 72 percent of software projects are failed as those are completed after scheduled time and are over budget. More than 23 percent of software projects are cancelled before they ever get completed, and 49 percent of projects cost 145 percent of their original estimates (Standish, 1995). In retrospection, many of these companies reported that their problems could have been avoided or drastically reduced if high-risk elements of the project could have been identified in advance. Although there are many risks involved in software development life cycle but the major risks are related to software cost, quality and scheduling which can be controlled and avoided if proper strategies are adopted in the initial stages. Risk management helps us to identify, analyze and control various risks associated with software development cycle. Theories say, all of these risks crepes in due to poor requirement engineering processes followed by software development team. Lack of understanding of client’s requirements, frequent changes in requirements, lack of user involvement. Lack of standards for requirement elicitation methods are some of the factors which leads to delayed, over budget and low quality software projects. If requirement engineering processes are followed by companies, risks can be managed properly. So requirement engineering processes and risk management are corelated and go side by side. The aim of this study is to check how much RE processes affects risk management by conducting a survey in nine different software development companies and taking evidence from the software developers who are actually using these practices practically in their jobs. For this study, I have asked the questions to 23 respondents from 9 software development companies of Ambala, Gurgaon, Pune, and Chandigarh. All these respondents are engaged in software development as developer or manager. This paper is divided in five sections. Section I gives introduction, Section II describes Requirement Engineering, Section III gives introduction of Risk Management, Section IV states the objective of study, and Section V gives details of Questions, results and their analysis followed by conclusion. II. Requirement Engineering: RE can be simply defined as identifying a problem’s context, locating the customer’s requirements within that context and delivering a specification that meets customer needs within that context. There are many

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requirements methodologies that purport to do this, for example, soft systems methodology [1], scenario analysis [2], and UML [3]. Sometimes they work, sometimes they do not. The implication of such requirements methodologies, if we can label at least aspects of them as such, is that the application of ‘x’ method will produce the right requirements irrespective of the problem’s characteristics. This is conventional wisdom and unsurprisingly, the creators and vendors of requirements methodologies claim, with one exception [4] that their approach is a hammer and all problems are nails. While there are many factors other than just application of a requirements methodology that influence the success or failure of software projects in practice, in this paper I focus only on requirements engineering. III. Risk Management: A risk is a potential future harm that may arise from some present action (Wikipedia, 2004), such as, a schedule slip or a cost overrun. The loss may be in terms of direct financial loss or indirect loss in terms of goodwill, fame, future business, or loss of property or life. Risk management is a series of steps whose objectives are to identify, address, and eliminate software risk items before they become either threats to successful software operation or a major source of expensive rework. (Boehm, 1989) In the software development cycle, risks can be software requirement risks, software cost risks, software scheduling risks and software quality risks. Due to these risks projects fail. Many projects fail either because simple problems were reported too late or because the wrong problem was addressed (Bruegge and Dutoit, 2000). Software development Teams can be reactive or proactive about these problems. Reactive teams are which take rapid action after the problem has occurred and find out the solution. On the other hand, Proactive teams anticipate the risks and take preventive measures to avoid those risks to happen. They plan in advance to avoid those risks to happen. A. Risk Management Process( Risk Management ,Laurie Williams 2004): A.1 Risk Identification The very first step in the risk management process is risk identification. In this step, the team identifies as many project risks as possible to make them explicit before they become problems. Risks can be of three types: i) Project Risks The risks which affect the project schedule or the personnel or budgets dedicated to the project are known as Project risks. ii) Product risks The risks which affect the quality or performance of the software being developed are known as Product risks. iii) Business risks: The risks which threaten the viability of the software are known as business risks , such as building an excellent product no one wants or building a product that no longer fits into the overall business strategy of the company. A.2 Analyze After the identification of risks, the next step is the analysis of risks. In the analysis phase, the identified risks are transformed into decision-making information. The probability and the seriousness of each identified risk is assessed. For each risk, the team must do the following: • Assess the probability of a loss occurring. Set numerical probability for each qualitative value (e.g. very improbable= 5 percent, improbable = 10 percent). • Assess the impact of the loss if the loss were to occur. Delineate the consequences of the risk, and estimate the impact of the risk on the project and the product. Similar to the probability discussion above, the team can choose to assign numerical monetary values to the magnitude of loss, such as Rs100,000 for a week delay in schedule. A.3 Prioritize The third step in risk management process is prioritizing the risks by ranking them. It is not feasible to take action on every identified risk as too much of cost is involved. Some of them have a very low impact or a very low probability of occurring – or both. By prioritization process, the team decides which risks it will take action on. The team sorts the list so that the high probability, high impact risks percolate to the top of the table and the low-probability, low impact risks drop to the bottom. A.4 Plan Risk management plans should be developed for each of the “above the line” prioritized risks so that proactive action can take place. A.5 Mitigate Related to risk planning, through risk mitigation, the team develops strategies to reduce the possibility or the loss impact of a risk. Risk mitigation produces a situation in which the risk items are eliminated or otherwise resolved. A.6 Monitor

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After risks are identified, analyzed, and prioritized, and actions are established, it is essential that the team regularly monitor the progress of the product and the resolution of the risk items, taking corrective action when necessary. This monitoring can be done as part of the team project management activities or via explicit risk management A.7 Communicate On-going and effective communication between management, the development team, marketing, and customer representatives about project risks is essential for effective risk management. IV. Objective of Study: Theories say that RE practices helps managing the risk which in turn leads to the success of any software project. The objective of this study is to explore the impact of requirement engineering practices on risk management. Software industry is devoting large amount of funds towards the development of software thus increasing the cost of final project. Cost incurred due to risks of rework or delayed projects or over budget projects can be eliminated if can be identified at the initial stage of project development cycle and RE processes if followed can reduce these risks drastically. Knowing the common underlying problems that cause risks and identification of RE practices that reduces scope of risks will help software development teams avoid making those same mistakes over and over and making use of those practices that have more success rate. Aim of this study is to take the evidence from the IT professionals about the impact of RE processes on Risk management. A. Research Methodologies The aim of this paper is to check whether the RE practices lead to effective risk management or not. If such practices are thoroughly studied, they could be used for enabling reduction of risks associated with project development. For this paper, I have conducted survey on some of Indian Companies and tried to find out the relation between RE practices and Risk management. To prepare the evidence to check the impact of Requirement engineering tools on risk management, a Questionnaire is prepared and is filled by the authorized employees of the companies such as Infosys, Cognizant Technology(Pune), Market RX (Gurgaon), One World Technology (Ambala), Ameotech Informatics (Chandigarh), GENPACT, GTech Informatics, Automatic Data Processing India Pvt. Ltd, Silex Softwares Pvt. Ltd. (Ambala). After collecting the data from these companies, analysis of the data is done using frequency tables and graphs tools of SPSS Software. The sample size used in this study involved 23 software development projects from nine companies of Pune, Gurgaon, Chandigarh and Ambala. Due to this reduced sample size, the use of qualitative research methods was preferred. Furthermore, the main aim of this study is to formulate a hypothesis about the relationship between RE process and the risk management. V. Questionnaire Results & Analysis: I received completed questionnaires from number of respondents, reporting on 23 distinct projects. As noted earlier, the majority of our respondents were developers or project managers from Pune, Gurgaon, Chandigarh and Ambala based companies.The Survey questionnaire had mixed type of questions. A. Questions & Responses: Q1 How important is the use of RE Processes in improving risk management?  Very Important  Important  Unsure  Not really Important  Not Important at All Q1

Valid

important very important Total

Frequency

Percent

Valid Percent

Cumulative Percent

13.6

86.4

100.0

important

very important

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Analysis: 13.6 % respondents believe that RE processes are very important in improving risk management and 86.4% believes it plays important role in improving risk management. No one selected unsure, not really important or no important at all. Q2 How the RE process affected risk assessment of the project?  Far More  More  Same  Less  Far Less Q2

Valid

far more more Total

Frequency

Percent

Valid Percent

Cumulative Percent

4.5

95.5

100.0

far mor e

Valid

far more more Total

Q3 Cumulative Percent

Valid Percent

Percent

Frequency

Analysis: 95.5% respondents have selected more option. 4.5% respondents selected far more option. That means 100% respondents believe that RE process affected risk assessment of the project Q3 How do you believe the communication inspired by the requirements Analysis sessions improved or deteriorated Risk management  Far More  More  Same  Less  Far Less

31.8

68.2

100.0

far mor e

Analysis: 68.2% respondents have selected more option. 31.8% respondents selected far more option. That means 100% respondents believe that communication inspired by the requirements Analysis sessions improved or deteriorated Risk management. Q4 To what extend did the requirements engineering process enable your organization to manage requirements:  Far More  More  Same

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 

Less Far Less Q4

Valid

far more more same Total

12 5 5 22

54.5 22.7 22.7 100.0

Cumulative Percent

Valid Percent

Percent

Frequency

same

far mor e

54.5 77.3 100.0

Analysis: 54.5% respondents have selected far more option. 22.7% respondents have selected more option and 22.7% respondents have selected same option. That means requirements can be managed effectively if RE processes are followed.

Valid

far more more same Total

Cumulative Percent

Valid Percent

Percent

Frequency

Q5 To what extend did the requirements engineering process enable your organization to assess impact of changing requirements?  Far More  More  Same  Less  Far Less

4.5

90.9

95.5

4.5 100. 0

4.5

100.0

same

far mor e

100.0 more

Valid Percent

Cumulative Percent

far more less

Percent

Valid

Frequency

Analysis: 90.9% respondents have selected more option. 4.5% respondents have selected far more option and 4.5% respondents have selected same option. That means approx 95% respondents believe that requirements engineering process enable organization to assess impact of changing requirements. Q6 To what extend did the requirements engineering process enable your organization to analyze risk  Far More  More  Same  Less  Far Less Q6

22.7

45.5

54.5

100.0

Total

100.0

far mor e

less

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Analysis: 54.5% respondents have selected more option. 22.7% respondents have selected far more option and 22.7% respondents have selected less option. That means 77.2% respondents believe that requirements engineering process enable organization to analyze risk. Q7 In your design, coding, testing or documentation activities, how important is it to understand the effect of RE processes on risk management. DESIGN

Valid

import ant very import ant Total

Cumulative Percent

Valid Percent

Percent

Frequency

DESIGN

13.6

86.4

100.0

important

very important

IMPLEMENTATION

Valid

important very important Total

Cumulative Percent

Valid Percent

Percent

Frequency

IMPLEMENTATION

13.6

86.4

100.0

TESTING

Vali d

important neutral not really important very important Total

Cumulative Percent

Valid Percent

Percent

Frequency

TESTING

9.1

18.2

4.5

22.7

17 22

77.3 100.0

100.0

important neutral

not really important

very important

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Valid

important neutral not really important very important Total

DOCUMENT

Cumulative Percent

Valid Percent

Percent

Frequency

DOCUMENT

6 3

27.3 13.6

27.3 40.9

9.1

50.0

100.0

important

very important

neutral

not really important

Analysis: In Design phase, 100% respondents considered RE processes Important and very important for risk management In Implementation phase, 100% respondents considered RE processes Important and very important for risk management. In Testing phase, 77.3% respondents considered RE processes very important, 9.1 % considered important, 9.1% were neutral and 4.5 % considered not important. In Documentation phase, 50% respondents considered RE processes very important, 27.3 % considered important, 13.6% were neutral and 9.1 % considered not really important. Conclusion: From above study, it is concluded that practically also most of the Software developers believe that if RE processes followed properly help manage the risk effectively thereby reducing the risk of cost, schedule, quality and requirements. There is a direct relationship between RE processes and Risk management. References: [1] [2] [3] [4] [5] [6] [7]

Checkland, P., Systems Thinking, Systems Practice, Wiley Publications, 1981. Carroll, J. (ed.), Scenario-Based Design: Envisioning Work and Technology in System Development, Wiley Publications, 1995. Booch, G., Rumbaugh, J., Jacobson, The UML User Guide, Addison Wesley, 1999. Jackson, M., Problem Frames, Addison Wesley, 2001. Boehm, B. (1989). Software Risk Management. Washington, DC, IEEE Computer Society Press. Boehm, B. (January 1991). "Software Risk Management: Principles and Practices." IEEE Software: 32-41. Boehm, B. and R. Turner (2003). Balancing Agility and Discipline: A Guide for the Perplexed. Boston, MA, Addison Wesley.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Offline Handwritten Devanagari Script Recognition Using Probabilistic Neural Network Abhay S. Lengare1, Suhas S. Patil2 Electronics Engineering Department K.B.Patil college of Engineering & Polytechnic, Satara Shivaji University, Kolhapur INDIA Abstract: In the last half century, the English character recognition was studied and the results were of such type that’s it can produce technology driven applications. But the same approach cannot be used in case of Indian languages due to the nature of complication in terms of structure and computation. “Hindi” the national language of India (written in Devanagri script) is world’s third most popular language after Chinese and English. Devanagri handwritten character recognition has got lot of application in different fields like postal address reading, cheques reading electronically. There are several Handwritten numeral recognition have been proposed and evolved during last few decades. But robustness and accuracy of such system is still a issue due to variety of writing patterns, size, slant, ink, and writing style. So In this paper, a novel approach for Devanagari handwritten numerals recognition based on global and local structural features is proposed. Probabilistic NeuralNetwork (PNN) Classifier is used to classify the Devanagari numerals separately. Keywords: Character Recognition, Off-line Handwriting Recognition, Segmentation, Feature Extraction, Training and Recognition and probabilistic Neural network. I. Introduction Handwritten numeral recognition is an integral part of the handwritten character recognition system. The problem of the handwritten numeral recognition is a complex task due to the variations among the writers such as style of writing, shape, stroke etc., and it has variety of applications in various fields like reading postal zip code, passport number, employee code, bank cheque, and form processing. For recognition of image object one need to extract the potential features. Thus, Feature extraction plays a vital role in Pattern Recognition and Image Processing systems in general and character recognition systems in particular. Ivind and Jain [1] present a survey of various feature extraction methods used in character recognition system. The problem of numeral recognition has been studied for decades and many methods have been proposed, e.g. template matching, dynamic programming, hidden Markov modeling, neural network, expert system and combinations of these techniques [2, 3, and 4]. Recognition of character/numeral in foreign languages like English, Chinese, Japanese, and Arabic reported many authors. The paper is organized as follows: Section 2 of the paper contains the Related Works .The Classification method and its algorithm is the subject matter of Section 3. Section 4 contains the conclusion part of the problem. Section 5 and 6 fallows Acknowledgment and References. II. Related Works Historically, handwritten character recognition systems have evolved in three ages: 1900-1980 Early ages-- The history of character recognition can be traced as early as 1900, when the Russian Scientist Trying attempted to develop an aid for visually handicapped. The first character recognizers appeared in the middle of the 1940s with the development of the digital computers. The early work on the automatic recognition of characters has been concentrated either upon machine printed text or upon small set of well distinguished handwritten text or symbols. Machine-printed CR systems in this period generally used template matching in which an image is compared to a library of images. For handwritten text, low level image processing techniques have been used on the binary image to extract feature vectors, which are then fed to statistical classifiers. Successful, but constrained algorithms have been implemented mostly for Latin characters and numerals. However, some studies on Japanese, Chinese, Hebrew, Indian, Cyrillic, Greek and Arabic characters and numerals in both machine-printed and handwritten cases were also initiated [5]. The commercial character recognizers were available in 1950s, when electronic tablets capturing the x-y coordinate data of pen-tip movement was first introduced. This innovation enabled the researchers to work on the on-line handwriting recognition problem. A good source of references for on-line recognition until 1980 can be found in [6].

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1980-1990 Developments-- The studies until 1980 suffered from the lack of powerful computer hardware and data acquisition devices. With the explosion on the information technology, the previously developed methodologies found a very fertile environment for rapid growth in many application areas, as well as CR system development [7]. Structural approaches were initiated in many systems in addition to the statistical methods. These systems broke the character image into a set of pattern primitives such as lines and curves. The rules were then determined which character most likely matched the extracted primitives. However, the CR research was focused on basically the shape recognition techniques without using any semantic information. This led to an upper limit in the recognition rate, which was not sufficient in many practical applications. Historical review of CR research and development during this period can be found in [8] and [9] for off-line and on-line case, respectively. After 1990 Advancements-- The real progress on CR systems is achieved during this period, using the new development tools and methodologies, which are empowered by the continuously growing information technologies. In the early nineties, Image Processing and Pattern Recognition techniques are efficiently combined with the Artificial Intelligence methodologies. Researchers developed complex CR algorithms, which receive high-resolution input data and require extensive number crunching in the implementation phase. Nowadays, in addition to the more powerful computers and more accurate electronic equipments such as scanners, cameras and electronic tablets, we have efficient, modern use of methodologies such as Neural Networks, Hidden Markov Models, Fuzzy Set Reasoning and Natural Language Processing. The recent systems for the machine-printed offline [10] and limited vocabulary, user dependent on-line handwritten characters [11] are quite satisfactory for restricted applications. However, there is still a long way to go in order to reach the ultimate goal of machine simulation of fluent human reading, especially for unconstrained on-line and off-line handwriting. III. Devanagari script India is a multi-lingual and multi-script country comprising of eighteen official languages. One of the defining aspects of Indian script is the repertoire of sounds it has to support. Because there is typically a letter for each of the phonemes in Indian languages, the alphabet set tends to be quite large. Most of the Indian languages originated from Bramhi script. These scripts are used for two distinct major linguistic groups, Indo-European languages in the north, and Dravidian languages in the south.

Figure 1: Devnagari character Devnagari is the most popular script in India. It has 11 vowels and 33 consonants. They are called basic characters. Vowels can be written as independent letters, or by using a variety of diacritical marks which are written above, below, before or after the consonant they belong to. When vowels are written in this way they are known as modifiers and the characters so formed are called conjuncts. Sometimes two or more consonants can combine and take new shapes. These new shape clusters are known as compound characters. IV. Therotical Methods Neural network : To develop an accurate OCR system is a complicated task and requires a lot of effort. Such types of systems are usually complicated and can hide a lot of logic behind the code. To achieve the good performance and improved quality of recognition, the artificial neural network in OCR applications performs the important function. Another benefit of using neural network in OCR is extensibility of the system ability to recognize more character sets than initially defined. There are the different types of neural networks which can be used for the character recognition. The artificial Neural Network is a computing architecture which consists of â&#x20AC;&#x2DC;neuralâ&#x20AC;&#x2122; processors connected in a parallel sequence.

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Figure 2: (a) Artificial Neural Network, (b) Multilayer ANN Artificial Neural Networks can perform the computation at a higher rate as compared to the classical techniques because it has the parallel nature. The output from one node goes to another node as the input and the final decision depends on the complex interaction of all nodes. The neural network can be categorized in to two types, feed forward and feedback network. The feedback network is also known as the recurrent network. In OCR system, the most common neural network is multilayer perceptron (MLP) network which is of type feed forward network. The interesting Feature of MLP is that it provides the confidence in the character classification. First of all MLP is proposed by U. Bhattacharya et al [12]. M. Egmont- Petersen has shown the comparison of various NN classifiers like Feed forward, Neuro-fuzzy system etc. for English language, K.Y Rajput et al[13] have also used classifier like back propagation type which is based on Genetic algorithm and also classification along with fusion of NN and Fuzzy logic. V. Proposed Methods A. System Design:

Figure 3: Block diagram of handwritten neural recognition system Preprocessing includes the steps that are necessary to bring the input data into an acceptable form for feature extraction. The raw data, depending on the data acquisition type, is subjected to a number of preliminary processing stages. Preprocessing stage involves noise reduction, slant correction, size normalization and thinning. Among these size normalization and thinning are very important. Normalization is required as the size of the numeral varies from person to person and even with the same person from time to time.

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Thinning provides a tremendous reduction in data size, thinning extracts the shape information of the characters. It can be considered as conversion of off-line handwriting to almost on-line data. Thinning is the process of reducing thickness of each line of pattern to just a single pixel. The reduced pattern is known as the skeleton and is close to the medial axes, which preserves the topology of the image. For classification and recognition nearest neighbor classifier and feed forward back propagation neural network classifiers are used. Steps involved in implementation of proposed method are given as following and Shown in Figure.2 Block diagram of handwritten neural recognition system. B. Algorithm: Step 1: Compute the input image centroid Step 2: Divide the input image in to n equal zones. Step 3: Compute the distance between the image centroid to each pixel present in the zone. Step 4: Repeat step 3 for the entire pixel present in the zone. Step 5: Compute average distance between these points. Step 6: Repeat this procedure sequentially for the entire zone. Step 7: Finally, n such features will be obtained for classification and recognition VI. Conclusion The important applications of pattern recognition include Character recognition technique. Character recognition of Indian scripts is in its preliminary stage and a lot of research is needed to handle the complexity and issues in Devanagari character recognition (DCR). The accurate recognition is directly depending on the nature of the material to be read and by its quality. The recognition process needs to be much efficient and accurate to recognize the characters written by different users. This system involves implementation of Devanagari Handwritten Numeral recognition which includes image acquisition, preprocessing, segmentation, feature extraction, classification and recognition and at last post-processing. References [1]

Ivind due trier, anil Jain, torfiinn Taxt, “A feature extraction method for character recognition-A survey “, pattern Recg, vol 29, No 4, pp-641-662, 1996

[2]

A.L.Koerich, R. Sabourin, C.Y.Suen, “Large offlineHandwritten Recognition: A survey”, Pattern Analysis Application, 97-121, 2003.

[3]

J.D. Tubes, A note on binary template matching. Pattern Recognition, 22(4):359-365, 1989.

[4]

B.V. Dhandra, R.G.Benne and Mallikargun Hangargi, “Isolated Handwritten Devnagari Numeral recognition based on Template matching”, IEEE-ACVIT -07, pp.1276-1282,Dec-2007.

[5]

S. Mori, H. Nishida, H. Yamada, Optical Character Recognition, Wiley, 1999.

[6]

S. Mori, K. Yamamoto, M. Yasuda, “Research on Machine Recognition Of Hand printed Characters”, IEEE Trans. Pattern Analysis, Machine Intelligence, vol.6, no.4, pp.386-404, 1984.

[7]

C. Y. Suen, C. C. Tappert, T. Wakahara, “The State of the Art in on-Line Handwriting Recognition”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 8, Pp.787-808, 1990.

[8]

V. K. Govindan, A. P. Shivaprasad, “Character ecognition- A review”, Pattern Recognition vol.23, no.7, Pp.671- 683, 1990.

[9]

T. Steinherz, E. Rivlin, N. Intrator, “Off-line Cursive script Word Recognition - A survey”, Int. Journal Document Analysis and Recognition, vol.2, no.2, pp.90- 110, 1999

[10]

C. Y. Suen, C. C. Tappert, T. Wakahara, “The State of the Art in On-Line Handwriting Recognition”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 8, pp.787- 808, 1990.

[11]

I. Bazzi, R. Schwartz, J. Makhoul, “An Omnifont Open Vocabulary OCR System for English and Arabic”, IEEE Pattern Analysis and Machine Intelligence, vol.21, no.6, pp.495-504, 1999.

[12]

Sandhya Arora et al., “Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, May 2010.

[13]

K. Y. Rajput and Sangeeta Mishra, “Recognition and Editing of Devnagari Handwriting Using Neural Network”, SPITIEEE Colloquium and Intl. Conference, Mumbai, India.

Trans.

Acknowledgements With all respect and gratitude, we would like to thank all people who have helped us directly for this article. We also thankful to Electronics Engineering Department of our Institute for providing lab and necessary tools.

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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 Binarization of Black/Green Board Data Captured by Mobile 1

Puneet, 2Naresh Kumar Garg Department of Computer Science & Engineering, GRDIET, Bathinda, Punjab, India 2 Department of Computer Science & Engineering, GZSPTU Campus, Bathinda, Punjab, India __________________________________________________________________________________________ Abstract: This paper deals with the binarization of mobile captured images from the black/green board images in which text is segment from the degraded images of the black/green board and get the 92.589% accuracy. In proposed technique first apply the enhancement technique to eliminate the distortion in background of given image. After enhancement image is segmented in 3x3 parts and computed locally threshold value. Binarization is the active area of research in academic because it is the important phase of the pre-processing in the field of pattern recognition and the rate of recognition is highly dependent on the accuracy of binarization. Keywords: Binarization, thresholding, Precision __________________________________________________________________________________________ 1

I. INTRODUCTION Binarization is the process of converting grey scale image to purely a black and white image which is known as digitized image. This field is mainly applied in the field of segmentation, biometrics pattern recognition and so on. In segmentation field the objects are located on the image are segmented from the image [1]. This segmentation concept is used for binarization of mobile captured images. The big challenge is to binarize the images under luminous intensity variation. To binarize these mobile captured images global thresholding method is not used because they binarize the one part more dark and lost the information. So resolve this binarization we briefly study the binarization techniques and literature survey. This paper is structured as follow: section two describes literature survey of various papers in the field of binarization, Section three describes the various binarization techniques, Section fourth describe the database, Section Fifth describe the proposed method and section Sixth describes the various results. II. LITERATURE SURVEY This section deals with the various works done by various authors in the field of binarization. Some piece of work is reviewed and analysed by me that is described as under: Anoop Mukhar[2] Develop the two novel algorithms for the thresholding. In his method the image histogram is used for computation of thresholding value. The optimal value of threshold is determined on the bases of average mean value. M.valizadeh, M.komeili[3] Proposed algorithm involves two stages. In the first stage extract the some part of the character and estimate the grey level foreground and background pixels. In the second stage, apply the binarization method based on the estimation.Bolan Su, Shijian Lu [4] proposed a selftraining learning algorithm for document image binarization. Based on reported binarization methods, the proposed framework first divides document image pixels into three categories, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then trained by learning from the document image pixels in the foreground and background categories. Finally, the uncertain pixels are classified using the learned pixel classifier. Yi Wang Bin Fang [5] proposed the adaptive binarization for the character segmentation of the license plate. In their proposed method first they image pass through the no of filters like average filter, Gaussian filter and median filter to eliminate the light intensity effect. At last covert the grey scale image into binary image by the local threshold value obtained from the convolved image. ChiMa [6] describes the multithreshold dynamic binarization algorithm for bill images. The algorithm can be used those bill images where intensity variation is too much. The algorithm is suitable for scanning small and large scales of text images or others. The experiment result shows that the improved algorithm has a good anti-noise capability. However, although the algorithm can much reduce the interference of noise, the noise pixels cannot be completely eliminated. Therefore, the algorithm still needs to be further improved. III. BINARIZATON TECHNIQUES From the literature survey we find that Binarization is the technique to digitize the image. The Binarization Method converts the grey scale image (0 up to 256 gray levels) in to black and white image (0 or 1). In every binarization method a particular threshold value is calculated by an algorithm and that threshold value is used to convert the grey scale image into binary image.

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In this equation g(x,y) is transformed image, f(x,y) is a transformation function and T is a threshold value. To more understand the binarization sees the block diagram fig no 2[7].

Fig. 1: Block Diagram of Binarization For the binarization the several method are used which can classify into categories local thresholding method and global thresholding method. The global thresholding method consider the whole image to compute the thresholding value and local thresholding method divide the image into several regions and compute the threshold value for each region. The global thresholding method are Otsu, Kilter and Illingworth Method, Yanni and Horne Method etc and the local thresholding method are Niblack, Sauvola, adaptive and Bernsen etc. A. Otsu Method In image processing, Otsuâ&#x20AC;&#x2122;s thresholding method is used for automatic binarization level decision, based on the shape of the histogram. Otsu's thresholding method involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either falls in foreground or background. The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum [8]. B. Kilter and Illingworth Method The kilter method [5] is used mixture of Gaussian distribution to find threshold value. In kilter Method the t is threshold that is used to segment the image into two parts background and foreground, both of the parts modelled by Gaussian distribution, and , the mixture of these two Gaussian distribution. Where is determined by the portions of background and foreground in the image. C. Yanni and Horne Method Yanni and Horne method [9] initializes the midpoint of two peaks of image histogram which is defined as: gmid =(gmax + gmin)/2 gmid is the midpoint of the highest and lowest peak point. The midpoint is updated using the mean of the two peaks on the right and left sides of the initial midpoint which can be written as: g_mid = (gpeak1 + gpeak2)/2 Where g_mid is updated midpoint and gpeak1 and gpeak2 are the mean values of left and right. D. Niblack method [5] the threshold value for the local area under the window is calculated pixel wise. The calculation of the threshold value is depending upon the local mean and standard deviation of window area. The threshold value is finding using following equation.

E. Sauvola Method The Sauvola algorithm [9] is a modified form of Niblack algorithm. It gives more performance than Niblack under such conditions as light variation on document image, light texture etc. In the Sauvola modification, the binarization is given by:

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IV. DATABASE Database of grey scale images is prepared by capturing the images from various black boards with different orientation by using mobile camera. In this data I have collected different 50 images. Some part of database is shown in fig no1.

(c)

(a)

(d)

(b) Fig. 2: Some Part of Database

V. PROPOSED WORK In this method, we take variable sized images from different black/green boards which are captured by mobile camera. First of all image enhancement technique is applied on input image and then image is divided into 3x3 parts. After that threshold value is computed for each part by using OTSU method and binarized all these segmented parts by using corresponding threshold values. After that the parts of image are joined. Algorithm Step 1: Capture the image of black board using mobile camera. Step 2: Apply image enhancement technique on input image. Step 3: Apply 3x3 partition method on enhanced image. Step4: Now apply OTSU method by using following steps 4.1 Separate the pixels into two clusters according to the threshold.

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P represents the image histogram. 4.2 Find the mean of each cluster.

4.3

Calculate the individual class variance. and

4.4 Square the difference between the means.

Finally, this expression can safely be maximized and the solution is t that is maximizing Step5: Repeat the step 4 for each part of the image. Step6: Join each and every part of image. Flow chart of the proposed work: flow chart of the proposed work represents the flow of data from input to output through various stages of the algorithm.

Fig. 3: Flow chart of proposed work VI. RESULTS AND DISCUSSION Results of some part of database used by proposed method is illustrated by following figures.

(a)

(b)

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(d)

Table 1: Comparison of Result with Other Algorithm

The binarize images obtained by proposed method are shown in the section 5. To measure the performance of the algorithm the different matrix are used such as precision, Recall, F1-measure etc [10]. The true positive pixels are those pixels which are positive in truth image as well in obtained image. The false positive pixels are those pixels which are black in truth image but white in the obtained image. Similarly true negative pixels are those pixels are which are negative in true image as well in obtained image. The false negative pixels are those pixels which are white in truth image and black in obtained image. True Positive Recall= -----------------------------------(False Negative + True Positive)

Precision=

F-Measure=

True Positive --------------------------------True Positive+ True Positive 2. Recall. Precision --------------------------Recall + Precision

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Table 2: Comparison of Visual Inspected Result on Some More Database Images

VII. CONCLUSION This paper has presented binarization of the blackboard images by mobile camera from different orientations and shows the better results as compared to the results shown by Otsu, Niblack and Sauvola when applied on the same black/green board images visually as well measured by using the evaluation metrics algorithms by measuring their performance by evaluation metrics. The accuracy obtained by using proposed algorithm is 92.589%. From the experimental result, we can infer that proposed method shows good result when compared with other methods. According to the results, proposed method had the best overall performance. This research paper will also act as a guide for the students, researchers in the field of binarization and the further scope can be imagined that the data written on walls, white boards number plates etc can be binarized. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Gonzalez and Woods, Digital image processing, 2ndEdition, prentice hall, 2002 Aroop Mukherjee, ”Enhancement of image resolution by binarization” International Journal of Computer Applications (0975 – 8887), Volume 10– No.10, November 2010 M.valizadeh,” A Contrast Independent Algorithm for Adaptive Binarization of Degraded Document Images” Proceedings of the 14th International CSI Computer Conference (CSICC'09) ©2009 IEEE Bolan Su and Shijian Lu,” A Self-training Learning Document Binarization Framework” International Conference on Pattern Recognition, 2010. Yi Wang and Bin Fang, “Adaptive Binarization: A New Approach to License Plate Characters Segmentation”, Proceedings of the 2012 International Conference on Wavelet Analysis and Pattern Recognition, Xian, 15-17 July, 2012 ChiMa and Qiuying Bai,” The New Improvement of Multi-threshold Dynamic Binarization for Bill Images” ©2012 IEEE Puneet and Naresh Garg,” Binarization Techniques used for Grey Scale Images” International Journal of Computer Applications (0975 – 8887) Volume 71– No.1, June 2013 Otsu thresholding methodwww.codesnap.com M. Yanni and E. Horne, “A new aproach to dynamic thresholding,” in 9th EuropeanConference on Signal Processing, pp. 34–44, pp: 30, 31, 33, 2010. P.Subashini, N.Sridevi,” An Optimal Binarization Algorithm Based on Particle Swarm Optimization”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-1, Issue-4, September 2011

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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 WIRELESS IMAGE DIFFUSION USING LED TO LED COMMUNICATION 1

Solanki Yogeshkumar J. 2Kantipudi MVV Prasad 1 PG Research Scholar, 2Assistant Professor Dept. of ECE, RK University Rajkot, India ____________________________________________________________________ _______________________ Abstract: Light Emitting Diodes (LEDs) are considered to represent the next generation of lighting and Communication technology. In this paper we proposed a system for wireless transmission of an image using LED to LED visible light Communication. In proposed methodology, we represent the LED as a both transmitter and receiver and free space as a transmission medium. This paper highlight the ability of LED to act as a light sensor and achieved very low data rate approximately 200 bps. Keywords: LED to LED Communication, Visible Light Communication, Serial Communication, LOS, LED

__________________________________________________________________________________________ . I. INTRODUCTION At the present visible light communication is a rapidly emerging area so researchers are paying attention towards it. The visible light communication defines as the technology which consists of the visible light source as a signal transmitter, air as a transmission intermediate, and photodiode or LED as a receiving element [1]. Visible light is easily available in our routine life and it can easily distinguish by human eye. The bandwidth of visible light communication system is high with respect to radio frequency. Visible light is most trustworthy for the indoor communication because it is cost efficient, power efficient and reliable communication system [2]. The scheme for Visible light communication is shown in figures 1.

Figure 1: Visible Light Communication Model We have developed a model in which we use one 3W Red LED light as source and receiving element. We have transmitted an image in terms of binary data using serial communication (RS-232) cable from pc to microcontroller. We use Atmega16 AVR controller to toggle LEDs at both transmitter and receiver side. To establish communication through LEDs we can turns it ON and OFF. When LEDs turns ON, “1” will be transmitted and when it turns OFF, “0” will be transmitted. This technique is known as “ON-OFF- Keying”. OOK, OFDM, PCM, PWM, etc are most popular modulation schemes for visible light communication. II.

LED AS A PHOTODIODE

After reading so many literatures we came to know that we can use LED as a light receiver or photodiode. By changing the polarity of LED like connecting voltage probe of multi meter to LED’s negative and ground probe to the LED’s positive. After throwing some light on LED we will achieve voltage in mV.

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Figure 2: Various Arrangements to use LED as a sensor [2] Here we introduce three techniques which can be used for LED sensing [2]. These techniques have their own advantages so we can use these techniques for different purposes. As shown in figure 2, In first technique LED is connecting to Transistor because it can use as a level converter. For simple application this technique is easy and very effective. In second technique LED is connecting to Op-Amp which is very popular and useful to achieve high amplification. In third technique led is connecting to LED for bi-directional microcontroller at where it will act as a capacitor. When light falls on the LED it will discharge faster so we can calculate the intensity of light. Every color LEDs sensing ability is different because of wavelength emitted by them. Red LED has higher wavelength so it can sense every color. Its wavelength is closer to infrared spectrum. Whereas Blue LED can sense only Blue and White color. Here we are using third technique to use Led as a light sensor. Due to some limitations here we do one way communication.

III.

IMPLEMENTATION

Here we use 5mm Red LED as a both transmitter and receiver because it can sense every color as discussed earlier [3]. In the transmitter section image was converted into a binary data using MATLAB tool box. If one have color image then first it will be converted into the black and white image and after that binary data will be produced from that black and white image. Here we can use any format of an image like .png, .jpg, .tiff etc. The information about Colum and row size of an image is being calculated using MATLAB code. Binary data is going to feed to the microcontroller using serial port MAX 232.Max 232 is 16 pin IC which convert the signals from RS-232 serial port. Here we use AVR microcontroller which is high performance, low power and 8-bit microcontroller. Binary data will be stored in the memory of microcontroller and it will transmit it one by one using Red LED. The code for the AVR microcontroller is prepared in Micro C Pro for AVR. At the receiver side, AVR microcontroller with NPN transistor is used. Red LED receives the binary data transmitted by the LED. First it receives the row and Colum size of an image from that it calculates the size of data. Received data will be stored in the memory of AVR microcontroller and it will given to the PC through Max 232 serial port. At where image will be generated based on the received data. We kept 1 inch distance between transmitter and receiver. IV.

RESULT

After series of experiments we come to know that LEDs can be used as a sensor or photodiode. Different color LEDs has different color sensing capabilities. Red color LED can sense all the colors. At the receiver end we successfully reproduced an image for the shorter distance approximately 1 inch. We achieved lower data rate approximately 200 bps. V.

APPLICATION

The applications for this technology are infinite due to the fact that light is most likely the safest source of energy. ď&#x201A;ˇ It will be used in Vehicle to Vehicle communication. Every car have LED lamps so with the use of this traffic update, weather information will be transmitted [1].

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   

It can be used in under water communication where RF does not work under water but visible light can support high speed data transmission over short distances in this environment. This could enable divers and underwater vehicles to talk to each other.   It can be used in petroleum or chemical plants where other transmission or frequencies could be hazardous.   It has ability to send data speedily and in a safe way so it will be used in defense and Security. Because of the fact that visible light cannot be detected on  the other side of a wall.  It can be used for data transmission in hospitals and health care premises where mobile phones and WiFi are not allowed.   VI. CONCLUSION

 We have developed a system which transmit and receive an image using LED both as a transmitter and receiver. After series of experiments, we identify property of different color of LEDs that they are able to sense certain colors only. In this technique we have achieved Very low data rate which is nearer to 200 bits/sec for a distance of 1 inch. At the end of experiment we achieved successful reproduction of an image for 1 inch distance. VII.

FUTURE WORK

After so many test and experiments finally we successfully transmit and receive an image using LEDs for shorter distances due to sensing limitation of LEDs. Here we use 8-bit AVR microcontroller. One can use higher end microcontroller or DSP processor to achieve better result. High power LEDs can be used to improve range for the communication. To achieve higher data rate photodiode or photo transistor can be used. Here we established system only for one way communication in future it can be extended for bidirectional communication. VIII. [1] [2]

[3] [4] [5] [6]

[7] [8] [9] [10] [11]

[12] [13] [14] [15] [16]

REFERANCES

H. Parikh, J. Chokshi, N. Gala,” Wirelessly Transmitting a Grayscale Image using Visible Light ” IJCA (0975 – 8887) Volume 58 No.14, November 2012. Devendra J.Varanva, Kantipudi MVV Prasad, “Various Aspects of Visible Light Communication and its applications”, eDCSECT- 2013, International Journal of Electronics and Communication Technology (IJECT) Volume 4, Spl – 2 / Jan - March 2013,105-107. Devendra J. Varanva, Kantipudi MVV Prasad ― LED to LED Communication using WDM‖, International Journal of Computer Applications, Volume 72 –Number19, June 2013. Dominic O’Brien, Hoa Le Minh, LubinZeng and Grahame Foulkner, Kyungwoo Lee, Daekwang Jung, YungJe Oh, Eun Tae Won, “Indoor Visible Light Communication: challenges and prospects”, proc. of SPIE vol.7091 709106. H. Elgala, R. Mesleh and H. Haas “Practical Considerations for Indoor wireless Optical System Implementation using OFDM “, Downladable content from www.see.edu.ac.uk Dominic C. O'Brien, LubinZeng, Hoa Le-Minh, Grahame Faulkner, Joachim W. Walewski, SebastianRandel, “Visible Light Communications: challenges and Possibilities”, Personal, Indoor and Mobile Radio Communications, 2008. PIMRC 2008. IEEE 19th International Symposium on 15-18 Sept. 2008. J Nagdev, D Sher, R Nathani, G Kalwani, “Wireless Data Transfer Using Light Fidelity”, International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064. A.L. Intini “Orthogonal frequency division multiplexing for wireless networks”, Graduate student UCSB, Dec, 2000. H. Le Minh , D. O. ;Brien , G. Faulkner , L. B. Zeng , K. Lee , D. Jung , Y. Oh and E. T. Won "100-Mb/s NRZ visible light communication using a post equalized white LED", IEEE Photon. Technol. Lett., vol. 21, no. 15, pp.1063 -1065 2009. P. Dietz, W. Yerazunis, and D. Leigh, “Very low-cost sensing and communication using bidirectional leds,” in UbiComp 2003: Ubiquitous Computing. Springer, 2003, pp. 175–191. L. Zeng, D. O'Brien, H. Minh, G. Faulkner, K. Lee, D. Jung, Y.Oh, and E. Won, “High data rate multiple input multiple output (MIMO) optical wireless communications using white LED lighting,” IEEE Journal on Selected Areas in Communications: Special Issue on Optical Wireless Communicatiosns, vol. 27, no. 9, pp. 1654-1662, December 2009. Christian Pohlmann, ”Visible Light Communication”, publication from Seminar Communications standard in der Medizintechniks. A.Burton, H.Le Minh, Z.Ghassemlooy, S.Rajbhandari and P.A.Haigh, ”Smart Receiver for Visible Light Communication: Design and Analysis”, 978-1-4577-1719-2/12, CROWN 2012. Cen Liu, Bahareh Sadeghi, Edward W.Knightly,” Enabling Vehicular Visible Light Communication”,VANET‘11,September 23,2011.Las Vegas, Nevada, USA.ACM 978-1-4503-0869-4/11/09. Mitsunori Miki, Emi Asayam, Tomoyuki Hiroyasu, “Intelligent Lighting System using Visible-Light Communication Technology”, IEEE Conference on Cybernetics and Intelligent Systems 2006, 1-6, June 2006. J. Grubor, S. Randel, K. Langer, and J. Walewski, “Bandwidth Efficient Indoor Optical Wireless Communications with White Light emitting Diodes,” in Proc. of the 6th International Symposium on Communication Systems, Networks and Digital Signal Processing vol. 1, Graz, Austria, Jun. 23–25, 2008, pp 165-169.

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S. Arai, T. Yamazato, T. Endo, T. Fujii, M. Tanimoto, K. Kidono, Y. Kimura, Y. Ninomiya, ― Experimental on Hierarchical Transmission Scheme for Visible Light Communication using LED Traffic Light and High-Speed Camera‖, IEEE 66th Vehicular Technology Conference, 2007. VTC-2007 Fall. 2007, 2174 - 2178, Sept. 30 2007-Oct. 3 2007.

ACKNOWLEDGEMENTS The author would like to thank guide of this project Kantipudi M V V Prasad and Devendra J.Varanva for providing their valuable guidance. The author also likes to thank friends and faculty members of school of engineering, Department of Electronics and Communication Engineering, Rk University for their support .

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Performance Analysis of Combining Techniques Used In MIMO Wireless Communication System Using MATLAB 1

Prabhat Kumar, 2Manisha Manoj Chahande Amity Institute of Telecom Engineering and Management Amity University, sector-125, Noida-201301, (U.P), INDIA Abstract: In wireless communication diversity techniques are very powerful to mitigate the fading impairment. In presence of receiver side how do we use effectively the information from the entire antenna to demodulate the data? This paper presents the equal gain combining and maximal ratio combining techniques used in MIMO wireless communication system. The analysis of the SNR and BER after these two combining techniques is made in Rayleigh fading channels and modulation which is taking into count is BPSK modulation. Keywords: Maximal ratio combining, Equal gain combining, Effective Eb /N0, Bit Error Rate. I. Introduction Transmit/receive diversity can improve the performance of mobile radio system by weighting and combining the receiving signals strength from all branches of antenna to minimize the fading and co-channel interference (CCI) [1]. Receive diversity are including the algorithm maximal ratio combining and equal gain combining [2]. In equal gain combining, the signals from all the branches are first co-phased then equally weighted by their amplitudes. In other words the branch weights are set to unity. The possibility of producing acceptable signals from number of unacceptable signals input is still retained. The equal gain combining has optimal performance close to maximal ratio combining but its implementations are very simple. This paper focuses the impact of fading and co-channel interference on equal gain combining, since these are the major factor in wireless communication. In maximal ratio combining the signal from each branch is first co-phased and then phase distributions are canceled out, the signals in each branch is weighted by weighting factor proportional to the ratio of career amplitude to the noise power of ith branch [7]. In particular, we focus on the outage probability of unacceptable repletion in the proposed coverage area [3]. II. Maximal Ratio Combining On the ith receive antenna, the received signal is yi= hi x + ni Where, yi is the received signal strength at ith received antenna. hi is the channel on ith received antenna x is the transmitted symbol ni is the ith received antenna Received signal in matrix form is represented by, y=hx + n y= [y1 y2 y3……yN]T is the received symbol from all the received antenna h= [h1 h2 h3……hN]T channel on all the received antenna x is the transmitted symbol n= [n1 n2 n3….nN]T is noise on all the receive antenna The equalized symbol is,

It is intuitive to note that the term,

i.e. sum of channel power at receive antenna. A. Effective Eb/N0 with maximal ratio combining In presence of channel hi , the signal-to-noise ratio at any time instant at i th received antenna is

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Since we are equalizing the channel hH with N receive antenna. So the effective bit energy to noise ratio is

In case of N receive antenna effective bit energy to noise ratio is N times the bit energy to noise ratio for single antenna case. B. Error rate with maximal ratio combining In chi-square random variable, we know that if, hi is the rayleigh distributed random variable, then h i2 is the chisquared random variable with two degrees of freedom. The probability denesity function of ᵞi is

Effective bit energy to noise ratio ᵞ is the sum of N random variables, then the probability density function of ᵞ is chi-random variable with 2N degrees of freedom. The pdf ᵞ is defined as

Effective bit energy to noise ratio with MRC is ᵞ, total bit error rate is the integral of the conditional BER integrated over all possible values of ᵞ [4].

The equation reduces as [5]

III. Equal Gain Combining Equalization is performed at the receiver on the ith receive antenna by dividing the received symbol yi by the apriori known phase of hi [4]. The hi is represented in polar form as:

The decoded symbol is the sum of the phase compensated channel from all the received antenna.

Where,

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is the aditive noise scaled by the phase of the channel cofficient. For demodulation, we use classical definition i.e. and A. Effective Eb/N0 with Equal gain combining In precence of channel hi, the instantaneous bit energy to noise ratio at ith receive antenna is, For the notational convenience, we can defined it is as:

The effective Eb/N0 with equal gain combining is the channel power accumulated over all receive chains, i.e.

The first term is chi-square random variable with 2N degrees of freedom having mean value of Hence the first term reduces and written as:

The second term is the product of two Rayleigh random variables. The mean of Rayleigh random variable with variance

. Hence the second term is,

Simplifying, the effective Eb/N0 with equal gain combining is,

B. Bit Error Rate with Equal Gain Combining With two receive antennas, BER with equal gain combining is [6],

IV. Simulation and Results We are using MATLAB to simulate these combining techniques. Here the channel is flat Rayleigh and modulation is BPSK. Here we have taken three parameters SNR, BER and No. of receive antenna for simulation. In figure-1 and 3 we have plot the graph between number of receive antenna and SNR with Maximal Ratio Combining. We analysis that if, we are increasing the number of antenna than SNR is improved. So if SNR will be high then BER will be less. In figure-2 and 4 we have plot the graph between SNR and BER with Equal Gain Combining. Here in this case we see that if SNR is high then BER got reduced.

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Figure 1: SNR improvement with MRC

Figure 2: BER plot for BPSK in Rayleigh channel with MRC

Figure 3: SNR improvement with EGC

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Figure 4: BER plot for BPSK in Rayleigh channel with MRC

V. Comclusions MIMO systems are high in demand because of their versatility features. They offer high data rates, throughput, different frequency of operability as per demand and many more. In case of EGC nRx=1 gives better theoretical result and nRx=2 simulated result is fluctuated after 16 dB of SNR. In case of MRC nRx=1, theoretical result is better than simulated result. While nRx=2 theoretical result is good but simulated result is fluctuated after 20dB of SNR. so, Maximal Ratio combining techniques performs better in all above cases. REFRENCES [1] [2] [3] [4] [5] [6] [7]

G. L. Stüber, Principle of Mobile Communication. Norwell, MA: Kluwer, 1996. D. G. Brennan, “Linear diversity combining techniques,” Proc. IRE, vol. 47, pp. 1075–1102, June 1959. A. A. Abu-Dayya and N. C. Beaulieu, “Outage probabilities of diversity cellular systems with cochannel interference in Nakagami fading,” IEEE Trans. Veh. Technol., vol. 41, pp. 343–355, Nov. 1992. Receive diversity by Prof. RaviRaj Adve. Equation 11.12 and Equation 11.13 in Section 11.3.1 Performance with Maximal Ratio Combining in [DIG-COMM-BARRYLEE MESSERSCHMITT]. [ZHANG97] Probability of error for equal-gain combiners over Rayleigh channel some closed-form solutions Zhang, Q.T. Communications, IEEE Transactions on Volume 45, Issue 3, Date: Mar 1997, Pages: 270 – 273 Kahn, .L., “Ratio Squarer”,M Proceedings of IRE (Correspondence), Vol. 42, pp.1074, Nov. 1954

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Analysis of Heat Transfer Augmentation and Flow Characteristics due to Rib Roughness of a Solar Air Heater Manoj Kumar, Ashish Agarwal Mechanical Engineering Department Technocrats Institute of Technology, Bhopal, MP, INDIA. Abstract: This paper presents the study of fluid flow and heat transfer in a plain rectangular duct of a solar air heater by using Computational Fluid Dynamics (CFD). The effect of Reynolds number on heat transfer coefficient and friction factor was investigated. A commercial finite volume package ANSYS FLUENT 12.1 is used to analyze and visualize the nature of the flow across the duct of a solar air heater. CFD simulation results were found to be in good agreement with experimental results and with the standard theoretical approaches. It has been found that the Nusselt number increases with increase in Reynolds number and friction factor decreases with increase in Reynolds number. Keywords: Energy, Solar Air Heater, Heat transfer, Pressure Drop, CFD

Introduction

A conventional solar air heater generally consists of an absorber plate, a rear plate, insulation below the rear plate, transparent cover on the exposed side, and the air flows between the absorbing plate and rear plate. A solar air heater is simple in design and requires little maintenance. However, the value of the heat transfer coefficient between the absorber plate and air is low and this results in a lower efficiency. For this reason, the surfaces are sometimes roughened in the air flow passage. The main application of solar air heaters are space heating, seasoning of timber, curing of industrial products and these can also be effectively used for curing/drying of concrete/clay building components [1]. Figure 1: Solar air heater

A conventional solar air heater generally consists of an absorber plate with a parallel plate below forming a small passage through which the air is to be heated and flows as shown in Fig. 1. A solar air heater is simple in design and requires little maintenance [2]. Performance of any system represents the degree of utilization of input to the system. It is required to analyze thermal and hydraulic performance of a solar air heater for making an efficient design of such type of a system. Thermal performance concerns with heat transfer process within the collector and hydraulic performance concerns with pressure drop in the duct. A conventional solar air heater (Fig. 1) is considered for brief analysis of thermal and hydraulic performance in the following sub-sections [3-6]. Thermal performance of solar air heater can be expressed in terms of the fraction of incident solar radiation utilized to increase the temperature of air. In other words, Thermal efficiency is a measure of thermal

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performance of a solar air heater. Thermal performance of solar air heater can be computed with the help of Hottel–Whillier–Bliss equation reported by Duffie and Beckman.

The rate of valuable energy gain by flowing air in the course of duct of a solar air heater can be intended as follows equation:

The value of heat transfer coefficient (h) can be increased by various active and passive augmentation techniques. It can be represented in non-dimensional form of Nusselt number (Nu).

Further, thermal efficiency of a solar air heater can be expressed by the following equation;

Hydraulic performance of a solar air heater concerns with pressure drop (ΔP) in the duct. Pressure drop accounts for energy consumption by blower to propel air through the duct. The pressure drop for fully developed turbulent flow through duct with Re< 50, 000 is given as

II.

CFD Modeling and Analysis

Computational Fluid Dynamics (CFD) is the science of determining numerical solution of governing equation for the fluid flow whilst advancing the solution through space or time to obtain a numerical description of the complete flow field of interest. The equation can represent steady or unsteady, Compressible or Incompressible, and in viscid or viscous flows, including non-ideal and reacting fluid behavior. The particular form chosen depends on intended application. The state of the art is characterized by the complexity of the geometry, the flow physics, and the computing time required obtaining a solution. The 2-D computational domain used for CFD analysis having the height (H) of 20 mm and width (W) 100 mm as shown in Fig. 2. In the present analysis, a 2dimensional computational domain of artificially roughened solar air heater has been adopted which is similar to computational domain of Chaube et al. [7]. Figure 2: 2-D computational domain

After defining the computational domain, non-uniform mesh is generated. In creating this mesh, it is desirable to have more cells near the plate because we want to resolve the turbulent boundary layer, which is very thin compared to the height of the flow field. After generating mesh, boundary conditions have been specified. We will first specify that the left edge is the duct inlet and right edge is the duct outlet. Top edge is top surface and

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bottom edges are inlet length, outlet length and solar plate. All internal edges of rectangle 2D duct are defined as turbulator wall. Meshing of the domain is done using ANSYS ICEM CFD V12.1 software. Since low-Reynoldsnumber turbulence models are employed, the grids are generated so as to be very fine. Present non-uniform quadrilateral mesh contained 161,568 quad cells with non-uniform quad grid of 0.22 mm cell size. This size is suitable to resolve the laminar sub-layer. For grid independence test, the number of cells is varied from 103,231 to 197,977 in five steps. It is found that after 161,568 cells, further increase in cells has less than 1% variation in Nusselt number and friction factor value which is taken as criterion for grid independence. To select the turbulence model, the previous experimental study is simulated using different low Reynolds number models such as Standard k-ω model, Renormalization-group kε model, Realizable kε model and Shear stress transport kω model. The results of different models are compared with experimental results. The RNG k-ε model is selected on the basis of its closer results to the experimental results. The working fluid, air is assumed to be incompressible for the operating range of duct since variation is very less. The mean inlet velocity of the flow was calculated using Reynolds number. Velocity boundary condition has been considered as inlet boundary condition and outflow at outlet. Second order upwind and SIMPLE algorithm were used to discretize the governing equations. The FLUENT software solves the following mathematical equations which governs fluid flow, heat transfer and related phenomena for a given physical problem [9-10]. III.

Results And Discussion

Study of the heat transfer and flow friction characteristics of the artificially roughened ducts shows that an enhancement in heat transfer is accompanied with friction power penalty due to a corresponding increase in the friction factor. The present CFD investigation shows that the roughened duct with relative roughness height (e/d) of 0.06 yields the maximum value of average Nusselt number in the order of 2.78 times that of the smooth duct at higher Reynolds number (18,000) whereas similar roughened duct with similar relative roughness height (e/d) and roughness pitch results in the maximum value of friction factor in the order of 4.24 times that of the smooth duct at lower Reynolds number (3800). Therefore, it is essential to determine the optimal rib dimension and arrangement that will result in maximum enhancement in heat transfer with minimum friction power penalty. A value of thermal enhancement factor higher than unity ensures the effectiveness of using an enhancement device and can be used to compare the performance of number of arrangements to decide the best among these Figure 3: Thermal enhancement factor vs Reynolds number

Fig. 3 shows the variation of the thermal enhancement factor with Reynolds number for all cases. It is found that the thermal enhancement factor values vary between 1.12 and 1.61 for the range of parameters investigated. It is observed that roughened duct having square transverse wire rib with e = 1.5 mm and P = 10 mm (i.e. e/D = 0.045) gives better thermal enhancement factor (TEF=1.61) at a Reynolds number of 15,000. The heat transfer phenomenon can be observed and described by the contour plot of turbulence kinetic energy. The contour plot of turbulence intensity is shown in Fig. 4. The intensities of turbulence are reduced at

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the flow field near the rib and wall and a high turbulence intensity region is found between the adjacent ribs close to the main flow which yields the strong influence of turbulence intensity on heat transfer enhancement. Figure 4: Contour plot of turbulence intensity

IV.

Conclusion

A 2-dimensional CFD analysis has been carried out to study heat transfer and fluid flow behavior in a rectangular duct of a solar air heater with one roughened wall having square transverse wire rib roughness. The effect of Reynolds number and relative roughness pitch on the heat transfer coefficient and friction factor have been studied. In order to validate the present numerical model, results have been compared with available experimental results under similar flow conditions. CFD Investigation has been carried out in medium Reynolds number flow (Re = 3800–18,000). The following conclusions are drawn from present analysis: 1. The Renormalization-group (RNG) k-ε turbulence model predicted very close results to the experimental results, which yields confidence in the predictions done by CFD analysis in the present study. RNG k-ε turbulence model has been validated for smooth duct and grid independence test has also been conducted to check the variation with increasing number of cells. 2. It is found that the square transverse wire rib roughness with rib height e=1.7mm, pitch P=10mm and e/D=0.051 provides the better thermal enhancement factor (TEF=1.66) for the studied range of Reynolds number and hence can be employed for heat transfer augmentation. 3. The discrepancy between available experimental data and present computational results is less than ±10%. It can therefore be concluded that the present computational results are reasonably satisfactory. V. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

References

J.L. Bhagoria , J.S. Saini , S.C. Solanki. Heat transfer coefficient and friction factor correlations for rectangular solar air heater duct having transverse wedge shaped rib roughness on the absorber plate. Renewable Energy 25 (2002) 341–369. R.P. Saini , Jitendra Verma. Heat transfer and friction factor correlations for a duct having dimple-shape artificial roughness for solar air heaters. Energy 33 (2008) 1277– 1287. Gupta Dhananjay, Solanki SC, Saini JS. Heat and fluid flow in rectangular solar Air heater ducts having transverse rib roughness on absorber plate. Solar Energy 1993; 51:31–7. J.C. Han, J.S. Park, C.K. Lei. Heat transfer enhancement in channels with turbulence promoters. J. Eng. Gas Turb.Power 107 (1985) 628–635. Y.M. Zhang, W.Z. Gu, J.C. Han, Heat transfer and friction in Rectangular channel with ribbed or ribbed-grooved walls. ASME/J.Heat Transfer 116 (1994) 58–65. Liou TM, Hwang JJ. Effect of ridge shapes on turbulent heat transfer and Friction in a rectangular channel. International Journal of Heat and Mass Transfer 1993; 36:931–40. Alok Chaube, P.K. Sahoo, S.C. Solanki. Analysis of heat transfer augmentation and flow characteristics due to rib roughness over absorber plate of a solar air heater, Renewable Energy 31 (2006) 317–331. R. Kamali, A.R. Binesh . The importance of rib shape effects on the local heat transfer and flow friction characteristics of square ducts with ribbed internal Surfaces. International Communications in Heat and Mass Transfer 35 (2008) 1032–1040. Han, J.C., Chandra, P.R., Lau, S.C., 1988. Local heat/mass transfer distributions around sharp 180 deg turns in two-pass smooth and rib roughened channels. J. Heat Transfer 110 (February), 91– 98. Taslim, M.E., Li, T., Spring, S.D. Measurements of heat transfer coefficients and friction factors in passages rib-roughened on all walls. ASME J. Turbomach.1998; 120, 564–570.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net CRC AND LOOK-UP TABLE ASSISTED ERROR CORRECTION IN A CONVOLUTIONAL CODED SYSTEM ON DSP S. V. Viraktamath1, Dr. Girish V. Attimarad2 Assistant Professor, Department of E&CE, SDMCET, Dharwad, Karnataka, India. 2 Professor, HOD Department of E&CE, Dayanand Sagar College of Engineering, Bangalore, Karnataka, India __________________________________________________________________________________________ Abstract: Cyclic Redundancy Codes (CRC) code provides a simple, yet powerful, method for the detection of errors during digital data transmission and storage. Among Forward Error Correction (FEC) schemes, convolutional encoding and Viterbi decoding are the most popular because of their powerful coding-gain performances. In this paper the implementation of CRC and Viterbi decoder on DSP is presented. CRC-32 and Viterbi hard decision decoding algorithm for rate 1/2 and for different generator polynomials is simulated and also implemented on DSP-TMS320C5416. Also the for higher SNR receivers concept of serially concatenated CRC- Convolutional Coding (CC) with lookup table at the decoder side is proposed. Key Words: CRC, DSP, Viterbi, Trellis, Constraint length. __________________________________________________________________________________________ 1

I. INTRODUCTION The evolving world of telecommunications requires increasing reliability and speed in communications. Reliability in information storage and transmission is provided by coding techniques. Information is usually coded in bit streams and transmitted over the communication medium, channel. The communication media is prone to errors due to noise present in the analog portion of the channel. Therefore errors have to be detected and corrected while decoding. CRC has the advantages of easy coding and decoding as well as strong abilities of checking errors and correcting errors. Therefore, it was widely used in the field of communications. Reliability in information storage and transmission is provided by coding techniques. CRC is an error-detecting code designed to detect accidental changes to raw computer data, and is commonly used in digital networks and storage devices such as hard disk drives. Blocks of data entering these systems get a short check value attached, derived from the remainder of a polynomial division of their contents; on retrieval the calculation is repeated, and corrective action can be taken against presumed data corruption if the check values do not match. The CRC was invented by W. Wesley Peterson in 1961. CRC is an error detecting code that is widely used to detect corruption in blocks of data that have been transmitted or stored. The Error Control Coding techniques (ECC) rely on the systematic addition of redundant bits at the transmitting side. The task of channel coding is to encode information sent over a communication channel in such a way that in the presence of channel noise, errors can be detected and possibly corrected. There are two coding methods - backward error correction codes and forward error correction codes. Backward error correction codes requires only error detection, if an error is found then the transmitter is requested to retransmit the message. Forward error correction codes require the decoder to be capable of correcting errors. There are several error correcting codes and these are classified under two basic categories namely block codes and convolutional codes. Convolutional codes [1] differ from block codes [2] in the sense that bit streams are not partitioned into binary words instead redundancy is added continuously to the whole stream. Convolutional codes are widely used error control coding technique in channel coding because of low complexity and error controlling capability. Viterbi decoding algorithm [3, 4] is the simplest and best algorithm for decoding of convolutional codes. The Viterbi algorithm first appeared in the coding literature in a paper written by Andrew J. Viterbi in 1967 [5]. Since then, due to its easiness in implementation, it has been applied to many different areas related to decoding problems. The 8-bit parallel CRC-32 is proposed in [6] to meet the high throughput of USB3.0. Exhaustive survey of all CRC polynomials from 3 bits to 15 bits is presented in [7]. A set of 35 new polynomials in addition to 13 previously published polynomials are also described. The method that realizes the ability of multiple bits error correction using cyclic redundancy check codes is presented in [8]. The structures of 8-bit CRC are presented in [9]. The joint decoding scheme of serially concatenated CRC and convolutional code (CC) has been investigated in [10]. This paper is organized as follows. Section 2 gives the proposed work. Section 3 gives the CRC coding. The Viterbi decoder is discussed in section 4. Processor TMS 320VC5416 is discussed in section 5. The results of the proposed model are discussed in section 6. Next section concludes the paper. II. PROPOSED WORK The basic block diagram of a system used for the simulation is shown in Fig.1. The output of the source encoder is given to the convolutional encoder. The output of the convolutional encoder is given to the CRC encoding;

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the output of which will be modulated and sent on the channel. At the receiver the reverse process is done. The CRC encoding, decoding, convolution encoding and decoding blocks are simulated and implemented on DSP. Convolution Encoder

CRC Encoding

Source Encoder Out put

Channel Source Decoder Viterbi Decoder

CRC Decoding

Fig.1 Block diagram of an proposed system

Fig. 2 Block diagram of CRC

III. CRC CODING CRC are specifically designed to protect against common types of errors on communication channels, where they can provide quick and reasonable assurance of the integrity of messages delivered as shown in Fig.2. However, they are not suitable for protecting against intentional alteration of data. The selection of generator polynomial is the most important part of implementing the CRC algorithm. The polynomial must be chosen to maximize the error-detecting capabilities while minimizing overall collision probabilities. CRC is divided into the following types: Code CRC-12, code CRC-16, code CRC-CCITT, and code CRC-32. Code CRC-12 is usually used to send 6-bit string. Code CRC-16 and code CRCCCITT is used to send 8-bit string, and code CRC-16 is mainly used in America, however, code CRC-CCITT is often used in European countries. Code CRC-32 is often used in a kind of synchronous transfer which is called Point to-Point transfer. In the proposed system CRC-32 is simulated and implemented. The Convolutional encoder described in this system is of rate 1/2, and of constraint length -3. Programming is done in ‘C’ language for both coding and decoding and the same is implemented on DSP-5416 kit. Input to encoder is entered by the user. The output of the encoder is stored in the matrix form, and the same is fed to the decoder part. IV. VITERBI DECODER For the implementation of Viterbi decoder algorithm, there are two techniques available: the soft decision decoding and hard decision decoding method. In case of hard decision, the decoder makes a firm or hard decision as to whether one or zero is transmitted and provides no other information regarding how reliable the decision is, hence, its output is only either zero or one (the output is quantized only to two levels) which are called hard-bits. The soft decoder provides the system with some side information together with the decision. The side information provides the decoder with a measure of confidence for the decision. The decoder outputs, which are called soft-bits, are quantized to more than two levels. In this paper the simulation and implementation of Viterbi decoder algorithm is discussed for hard decision decoding method. The input to and output from the system are bit streams. The bits entered by the user are used as message/input bit stream to convolutional encoder. A (n, k, m) convolutional encoder accepts k-bit blocks of input sequence and produces n-bit blocks of output sequence. It consists of ‘m’ k-stage shift registers and ‘n’ modulo-2 adders. The outputs of ‘n’ modulo-2 adders are sequentially sampled to produce the encoded sequence. A (2, 1, 2) convolutional encoder is considered for simulation and implementation. While decoding the proposed code generates output table and next state table depending on generator polynomial and stores them. Basically, decoding of convolutional codes is comparison of different paths in trellis. The trellis diagram for (2, 1, 2) convolutional encoder is as shown in Fig.3.

Fig.3. Trellis for convolutional encoder (2, 1, 2). Fig. 4 The TMS320VC5416 kit

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In hard decision decoding technique, the Hamming distance is computed by simply counting how many bits are different between the received encoded bits and the actual output bits. The Hamming distance values are computed at each time instant for the paths between the states at the previous time instant and the states at the current time instant are called ‘branch metrics’. For the first time instant these results are saved as “Accumulated Error Metric” values, associated with the states. For the second time instant on, the accumulated error metrics will be computed by adding the previous accumulated error metrics to the current branch metrics. When two paths enter the same state, the one having the best metric (i.e. lower branch metric) is chosen, this path is called the ‘surviving path’. Then by seeing the path and using output table and next state table decoding is done. This procedure is repeated for all encoded bits and at every comparison makes a hard decision as to whether one or zero is transmitted. The output of hard decision decoder is compared with original message bits for verification. V. PROCESSOR TMS 320VC5416 The TMS320VC5416 fixed-point, digital signal processor (DSP) is based on an advanced modified Harvard architecture that has one program memory bus and three data memory buses. This processor provides an arithmetic logic unit (ALU) with a high degree of parallelism, application-specific hardware logic, on-chip memory, and additional on-chip peripherals. The basis of the operational flexibility and speed of this DSP is a highly specialized instruction set. Separate program and data spaces allow simultaneous access to program instructions and data, providing a high degree of parallelism. Two read operations and one write operation can be performed in a single cycle. Instructions with parallel store and application-specific instructions can fully utilize this architecture. In addition, data can be transferred between data and program spaces. Such parallelism supports a powerful set of arithmetic, logic, and bit-manipulation operations that can all be performed in a single machine cycle. The device also includes the control mechanisms to manage interrupts, repeated operations, and function calls. The TMS320VC5416 DSP starter kit (DSK) is a low-cost development platform designed to speed the development of power-efficient applications based on TI's TMS320VC54x DSPs. The kit, which provides new performance-enhancing features such as USB communications and true plug-and-play functionality, gives both experienced and novice designers an easy way to get started immediately with innovative product designs. The C5416 DSK offers the ability to detect, diagnose and correct DSK communications issues, download and step through code faster and get a higher throughput with Real Time Data Exchange (RTDX™). The contents of the kit include: C5416 DSK Code Composer Studio™ v2.1 IDE, Quick Start Guide, Technical Reference, Customer Support Guide, USB Cable, Universal Power Supply and AC Power Cord(s). The Fig.4 shows the view of TMS320VC5416 kit. VI. RESULTS AND DISCUSSIONS In this section, simulation results are presented. Different types of CRC are simulated from CRC-8 to CRC-32. The simulation results of CRC-8 and CRC 32 are shown in the paper. Fig 5 shows the simulation environment completely.

Fig. 5 Simulation Environment of DSP kit The bottom left window called ‘stdout’ shows the input and output of the program. The simulation result of CRC 8 is shown in Fig 6 when received message has an error. Fig 7 shows the output when the received message has an error. Fig 8 and Fig 9 shows the input and output of the CRC -32 with errors as well as without errors.

Fig. 6 CRC-8 with error in the received message

Fig. 7 CRC-8 with correct received message

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Fig. 8 CRC-32 with error in the received message Fig. 9 CRC-32 with correct received message The code has been verified for all possible lengths of message polynomial and generator polynomial. The CRC8 to CRC-32 are successfully implemented on TMS320VC5416 fixed-point DSP. The simulation and implementation of convolutional encoder and Viterbi decoder is carried out for different message bits and different generator polynomials. The processor implementation results for rate Â˝ convolutional encoder and Viterbi decoder with generator polynomials g1= {1 1 1} and g2= {1 0 1} and for message bits (1 1 1 0) are as shown in Fig.10 and Fig.11 respectively.

Fig.10. Implementation result of convolutional encoder for message stream (1 1 1 0)

Fig.11. Implementation result of Viterbi decoder with one bit error for message stream (1 1 1 0) Similarly the results are verified for generator polynomials by giving different message bits as input. The simulation results of Viterbi decoder for generator polynomials g1= {1 1 1}, g2= { 1 1 0} and g1={1 1 0}, g2={1 0 1} are as shown in Fig.12 and Fig.13 respectively.

Fig.12.Implementation result of Viterbi decoder with two bits error for message stream (1 0 0 1 1 0) for Generator polynomial g1={1 1 1} and g2={1 1 0}

Fig.13. Implementation result of Viterbi decoder with two bits error for message stream (1 1 0 1 0 1) for Generator polynomial g1={1 1 0} and g2={1 0 1}

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Also different generator polynomials are given as input to the encoder block. The simulation and implementation results exactly matched with the theoretical results. The proposed algorithm is working for all message bits and delivering the expected results. In this paper separate results for CRC and Viterbi decoder are shown. Many researchers suggested ideas how to reduce the computations by selecting only few branch metric [10, 11] based on the hamming distances. The investigation on joint decoding schemes for serially concatenated cyclic redundancy check (CRC) and convolutional code (CC) is done in [12]. A soft decoding algorithm for CC decoding is used with CRC to check the hard decision output from the soft CC decoding module for errors. After Viterbi decoding to check the errors CRC was used. The use of CRC was after decoding [12]. It is possible to use CRC before the Viterbi decoding for the errors; if no errors found just using the lookup table decoding can be done without going for the online computation using trellis. The lookup table will be having the decoded output offline. This saves the time as well as computations. If the errors were found then classical decoding of Viterbi decoder may be used. VII. CONCLUSIONS The procedure of calculation of remainder or redundant bits at the transmitter and checking the errors at the receiver is presented in the paper. The CRC code has been verified for all possible lengths of message polynomial and generator polynomial. The CRC-8 to CRC-32 are successfully implemented on TMS320VC5416 fixed-point DSP. The simulation results are presented in the paper. Convolutional Encoder and Viterbi decoder for the rate ½ is simulated for different constraint lengths. From the simulation it is found that the error recovery capability of the Viterbi decoder varies from generator polynomials as well as for different constraint lengths. Convolutional encoder and Viterbi decoder are successfully implemented on DSPTMS320C5416. The usage of look-up table may help in fast decoding. ACKNOWLEDGEMENTS The authors thank the authorities of Sri Dharmasthala Manjunatheshwara College of Engineering and Technology, Dhavalagiri, Dharwad, and authorities of TEQIP 1.2 for encouraging us for this research work and providing financial support. REFERENCES J. Viterbi, “Convolutional codes and their performance in communication systems”, IEEE Transaction Communication Technology,cVol.19, pp. 751-772, Oct. 1971. [2] B. Sklar, Digital Communications: “Fundamentals and Applications”, 2nd edition, Prentice-Hall, Upper Saddle River, N J, 2001 [3] G. C. Clark Jr. and J. B. Cain, “Error-Correction Coding for Digital Communications”, Plenum Press, NY, 1981. [4] A. J. Viterbi and J. K. Omura, “Principles of Digital Communications and Coding”, McGraw-Hill, NY, 1979. [5] A. J. Viterbi, “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm”, IEEE. Transaction of Information Theory, vol. IT-13, pp. 260-269, Apr. 1967. [6] Julian Satran, Dafna Sheinwald and Ilan Shimony “Brief Contributions- Out of Order Incremental CRC Computation”, IEEE Transactions on computers, VOL. 54, NO. 9, SEPTEMBER 2005 [7] Ma Youjie, Zhang Haitao, Zhou Xuesong, Qi Ming, Xu Lijin, “The Realization of the CRC Arithmetic which is based on DSP”, 2009 [8] Philip Koopman, Tridib Chakravarty, “Cyclic Redundancy Code (CRC) Polynomial Selection For Embedded Networks”, The International Conference on Dependable Systems and Networks, DSN-2004. [9] Sunil Shukla, Neil W. Bergmann, “Single bit error correction implementation in CRC-16 on FPGA”, ICFPT 2004, 0-7803-86523/04/$20.00 0 2004 IEEE. [10] K. S. Arunlal1 and Dr. S. A. Hariprasad2 “An Efficient Viterbi Decoder”, International Journal of Advanced Information Technology (IJAIT) Vol. 2, No.1, February 2012. DOI : 10.5121/ijcsea.2012.2109 95 [11] Renqiu Wang, Wanlun Zhao , and Georgios B. Giannakis, “CRC-Assisted Error Correction in a Convolutionally Coded System”, IEEE Transactions On Communications. Vol 56, No 11, November 2008. Pages -1807-1815. [1]

BIOGRAPHIES Mr. S. V. Viraktamath is with SDMCET, Dharwa. He has received a gold medal from VTU Belgaum for securing first rank in M.Tech (DC&N). He is the Life Member of IETE, IE and ISTE. He has served as a reviewer for many International conferences and journals. His research interests include Error control coding, Wireless communication and Networking.

Dr. G. V. Attimarad is with Dayanand Sagar College of Engineering Bangalore. His research interest includes area of waveguides and wireless communications. He has published many papers in reputed National and International journals. He is the Life Member of IETE, IE and ISTE. He has served as a reviewer for many International conferences.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Robust regression model for prediction of Soybean crop price based on various factors 1

K.Karthikeyan, 2Akshay Harlalka Associate professor, SAS, Mathematics Division, VIT University, Vellore-14, Tamil Nadu, India, 2 SMBS, VIT University, Vellore-14, Tamil Nadu, India. __________________________________________________________________________________________ Abstract: Prediction of future food prices involves taking a lot of critical factors like temperature, precipitation and crop yield in to consideration. Most regression models consider only climatic and scientific factors in to consideration which give only one side of the picture. This article gives a more balanced overview as it considers a host of other factors which indirectly play a significant role in crop prices especially the economic factors. Multi-linear regression model is used to predict the price of Soybean in USA during the 11 year period from 1995 to 2005 and to compare the factors affecting food price. This model explain more than 90% of the variation in the crop price based on just four major selected factors and shown that there is a very strong relationship between observed values and model predicted values with a multiple correlation coefficient of 0.949 for USA. Also we use the F-test to test the significance of the regression relationship between crop prices and selected factors. Keywords: Crop prices, Regression model, Correlation, Partial regression ___________________________________________________________________________________ 1

I. INTRODUCTION The recent crisis in food prices, which has affected thousands of families throughout the developing world, has once again underscored the urgent need for governments to strengthen their safety net systems to ensure that the rise in the price of basic commodities does not trigger an increase in poverty rates. Jordan Schwartz, World Bank lead economist for sustainable development in Latin America and the Caribbean, mentioned several factors that are driving the price increase: speculation in commodity markets, the booming demand from Asia for feed grains and land use switching out from food crops to biofuels, among others. There is growing consensus that food prices have increased due to fundamental shifts in global supply and demand. A variety of forces contribute to rising food prices: high energy prices, increased income, climate change and the increased production of biofuel. Income and per capita consumption in developing countries has increased; consequently, demand has also risen. Changes in food supply and demand have been accompanied by predictable effects in terms of pricing and have been further affected by the rise in the cost of non-renewable resources[1]. There are many systematic studies being done in various countries on the prediction model of different crops. But a majority of studies have taken only the influence of climate change on crop prices into consideration. Nicholls[2] estimated the contribution of climate trends in Australia to the substantial increase in Australian wheat yields since 1952. Non-climatic influences such as new cultivars and changes in crop management practices were removed by detrending the wheat yield and climate variables and using the residuals to calculate quantitative relationships between variations in climate and yield. Lobell[3] used a combination of mechanistic and statistical models to show that much of this increase in wheat yields in Mexico can be attributed to climatic trends in Northwest states, in particular cooling of growing season nighttime temperatures.Despite the complexity of global food supply, Field[4] showed that simple measures of growing season temperatures and precipitation spatial averages based on the locations of each crop explain30% or more of year-to-year variations in global averageyields for the worldâ&#x20AC;&#x2122;s six most widely grown crops. For wheat, maize and barley, there is clearly negative response of global yields to increased temperatures. Burke[5] used a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. Kaufmann[6]estimated a model that accounts for both climatic and social determinants of corn yield in the United States. Climate variables are specified for periods that correspond to phonological stages of development. Social determinants include market conditions, technical factors, scale of production, and the policy environment. Bonfils[7] concluded that climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO2 fertilization or adaptation measures, projected losses range from 0 to greater than 40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Lobell[8] seeks to improve quantitative understanding of price spikes in general and the potential effects of climate change on these spikes in particular. Naylor[9] provided an insight into the causes and consequences of the volatile events like the 2008 food price run up. Naylor mentioned that price variability, particularly spikes, has enormous impacts on the rural poor who spend a majority of their income on food and have minimal

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savings. Impacts at the local level have not been well measured, yet are the key to improving food security globally. To see how the rural poor were impacted on a local scale, Naylor and Falcon looked at Ghana, Uganda, Malawi, Guatemala, and India. Price changes at the local level during the 2008 price spike were frequently half that of international prices, primarily as a consequence of domestic food and trade policies. Additionally, domestic self-sufficiency polices tended to have long-term negative impacts on the international market when governments lacked the resources to defend a targeted price or were large actors with significant shares of global production or consumption. II. PRELIMINERIES Linear regression is used to model the value of a dependent scale variable based on its linear relationship to one or more predictors. The linear regression model assumes that there is a linear or straight line relationship between the dependent variable and each predictor. This relationship is described in the following formula. where yi the value of the ith case of the dependent scale variable pthe number of predictors bj the value of the jth coefficient, j = 0,...,p xij - the value of the ith case of the jth predictor ei the error in the observed value for the ith case The model is linear because increasing the value of the jth predictor by 1 unit increases the value of the dependent by bj units. Note that b0 is the intercept the model-predicted value of the dependent variable when the value of every predictor is equal to 0. For the purpose of testing hypotheses about the values of model parameters, the linear regression model also assumes the following: • The error term has a normal distribution with a mean of 0. • The variance of the error term is constant across cases and independent of the variables in the model. An error term with non-constant variance is said to be heteroscedastic. • The value of the error term for a given case is independent of the values of the variables in the model and of the values of the error term for other cases. Before, using linear regression, the correlation between each of the independent variables and the dependent variable must be studied to determine whether a linear model is suitable for those variables. This study uses the multiple regressions modeling to predict the crop prices of Soybean crop in USA. The relationship between the variables and crop prices is studied systematically using the above procedure and a final regression model with four most important variables is created for each case. III. DATA SOURCES Eight major factors were identified and the corresponding information was collected from reliable sources. The following data on the crop prices, import and export quantity, total population, crop yield, and food supply were obtained from the Food and Agriculture Organization of the United Nations[11]. The average annual temperature and precipitation data of United States of America was obtained from the National Climatic Data Center of NOAA (National Oceanic and Atmospheric Administration)[12]. The crude oil prices were obtained from the Energy Information Administration of US Department of Energy[13]. The data table explains the variation of crop prices with regard to eight different variables. USD/bbl refers to US Dollar per barrel. Hg/ha refers to hectograms per hectare. The representation of the symbols is as given below: CP refers to Crop Price (USD/tonne); COP refers to Crude Oil Price (USD/bbl) Total P refers to Total Population (in thousands); Temp refers to Temperature (in Fahrenheit) Ppt refers to Precipitation (in inches); ExQ refers to Export Quantity (tonnes) ImQ refers to Import Quanity (tonnes); FS refers to Food Supply (tonnes); CY refers to Crop yield (Hg/ha) DATA TABLE CP

COP

Total P

Temp

Ppt

ExQ

ImQ

Year

247

14.62

266324

52.71

31.69

2.28E+07

134644

9780

23758.93

1995

247

18.46

269394

51.89

32.59

2.60E+07

93847

10006

25269.93

1996

238

17.23

272643

52.26

31.29

2.64E+07

272900

10256

26165.48

1997

181

10.78

275986

54.32

32.97

2.04E+07

171757

10393

26168.84

1998

170

15.56

279300

53.93

27.84

2.32E+07

105397

10851

24634.35

1999

167

26.72

282496

53.27

27.73

2.72E+07

132025

10386

25613.18

2000

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21.84

285545

53.68

28.58

2.89E+07

112128

10992

26638.6

2001

203

22.51

288467

53.21

28.66

2.74E+07

109517

10582

25566.31

2002

270

27.56

291291

53.29

29.95

3.10E+07

181673

11415

22768.74

2003

211

36.77

294063

53.12

32.88

2.56E+07

130032

11552

28404.15

2004

208

50.28

296820

53.64

29.84

2.57E+07

159197

11698

28959.96

2005

IV. RESULTS AND DISCUSSION A. Strength of relationship between variables The variables involved in the following scatter plots were excluded from the prediction model since their correlation with the crop price was insignificant.

From the analysis, it was concluded that temperature is the best predictor of food prices in USA, followed by precipitation and crop yield. All other factors have insignificant influence on the crop prices and hence they shall be excluded from the prediction model. B. Regression Model for Prediction

The prediction model is CP(t) = b0 + b1Temp(t) + b2Ppt(t) + b3CY(t) + b4COP(t) ___(I) where CP- Crop Price (USD/tonne), COP- Crude Oil Price (USD/bbl), Temp- Temperature (in Fahrenheit), PptPrecipitation (in inches), CY- Crop yield (Hg/ha) C. Robustness of Prediction model The following is the model summary and ANOVA table (obtained by the SPSS software): ANOVA(TABLE 3) Model 1

Sum of Squares Regression Residual Total

Mean Square

12480.231

3120.058

1382.315

230.386

13862.545

F 13.543

Sig. .004a

a. Predictors: (Constant), Crude Oil Price (USD/bbl), Temperature (farhenheit), Precipitation (inches), Crop Yield (Hg/ha) b. Dependent Variable: Food Price (USD/tonne)

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F_model for Regression Suppose that the regression assumption hold and that the linear regression model has k+1parameters and consider testing H0: β1= β2= β3= β4 = 0; Ha; Atleast one of β1, β2, β3, β4 does not equal to zero. = 13.543 where n is the number of observations and k is the number of independent variables. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. This analysis of variance table gives us a first-hand proof that the model considered is suitable for multi- linear regression. Model summary (TABLE 4) Change Statistics Model 1

R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change a

.949

.900

.834

15.17846

.900

13.543

.004

a. Predictors: (Constant), Crude Oil Price (USD/bbl), Temperature (farhenheit), Precipitation (inches), Crop Yield (Hg/ha) b. Dependent Variable: Food Price (USD/tonne)

Multiple coefficient determination (R2) and Adjusted multiple coefficient of determination (Adjusted R2) R2 = Explained variation/ Total variation = 12480.231/13862.545 = 0.9002843 Adjusted R2 = (R2 – (k/(n –1)) * ((n –1)/(n – (k+1)) = 0.834 R, the multiple correlation coefficient tells us how much accurate a model is in predicting the crop prices. R has a value of 0.949 which indicates a very strong relationship between observed and the model predicted values of the dependent variable. R Square, the coefficient of determination indicates that this model explain about 90% variation in the crop prices. This shows that the model accounts for a majority of the variation in the crop prices. As a further measure of strength of the model fit, the standard error of the estimate of the model (15.17846) is considerably lower than the standard deviation of the crop price. (37.23244). This also measures the accuracy of the model in predicting the crop prices. D. Regression and Correlation Coefficients Regression Coefficients (TABLE 6) Unstandardized Coefficients Standardized Coefficients Model

Constant (b0)

b0 = 1340.257

Temperature (farhenheit)

Std. Error

Beta

Collinearity Statistics T

Sig.

Tolerance

VIF

418.054

3.206 .018

b1= -20.069

7.477

-.384 -2.684 .036

.813

1.230

Precipitation (inches)

b2 = 10.809

2.757

.584 3.920 .008

.748

1.338

Crop Yield (Hg/ha)

b3 = 0 .017

.004

-.835 -4.634 .004

.512

1.954

.570

.662 3.803 .009

.549

1.822

Crude Oil Price (USD/bbl) b4 = 2.169 a. Dependent Variable: Food Price (USD/tonne)

Refer Equation no. I The Sig. and Standardized Coefficients column show that all the factors are quite significant predictors of the food/crop price. This shows that the crop yield (Hg/ha) contributes the most to the model as it has the highest standardized coefficient. The tolerance values are significant indicating that there is low multi-collinearity and the more than 50% of the variance in a predictor cannot be explained by other variables. This shows that the regression model is very much accurate with low standard error of the regression coefficients. The Variance Inflation Factor of all the variables is less than 2 which confirms the previous statement. E. Partial regression plots A partial regression plot is a scatterplot of the partial correlation of each independent variable with the dependent variable after removing the linear effects of the other independent variables in the model.Each plot is considered to see if it shows a linear or nonlinear pattern. If the specific independent variable shows a linear relationship to the dependent variable, it meets the linearity assumption of multiple regression[16].These partial

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regression plots show a linear relationship between the dependent and specific independent variables. Hence, this satisfies the linearity assumption of the multiple regression.

Summary of Findings Soybean Crop Prices USA

Findings 1. Climatic factors play a major role in the prediction of prices 2. Role of economic factors like Import Quantity, Export Quantity not significant Reason: Soybean is a widely grown crop in USA and one of the largest exporters of the crop. Fluctuations in climate can cause a huge shift in the supply and demand causing price variations.

V. CONCLUSION Temperature is the most important predictor of soybean crop prices for USA. Climatic factors accounted for much of the variation in the food prices in USA. The most of the food price model do not take economic factors into account thus compromising the accuracy and robustness of the model. The prediction model account for more than 90% variation in crop prices and a strong relationship has been established between the observed and model predicted values with a multiple correlation coefficient close to 0.95. ACKNOWLEDGEMENTS We would like to thank the Food and Agriculture Organization of the United Nations, National Climatic Data Center of NOAA (National Oceanic and Atmospheric Administration) and Energy Information Administration of US Department of Energy for providing the relevant information for research and analysis. REFERENCES [1]. [2]. [3] [4].

World Bank News, Feature Story, September 13, 2012 What are the facts about rising food prices and their effect on the region? Nicholls N 1997 Increased Australian wheat yield due to recent climate trends, Nature, Vol.No. 387, pp. 484–485. Lobell D B, Ortiz-Monasterio J , Asner G P, Matson P A, Naylor R L and Falcon W P 2005 Analysis of wheat yield and climatic trends in Mexico Field Crops Res. 94 250–6 David B Lobell and Christopher B Field, Global scale climate–crop yield relationships and the impacts of recent warming, Environ. Res. Lett. 2 (2007) 014002 (7pp)

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David B.Lobella, MarshallB.Burke, On the use of statistical models to predict crop yield responses to climate change, Article in press, 2010 Robert K. Kaufmann, A Biophysical Model of Corn Yield: Integrating Climatic and Social Determinants, American Journal of Agricultural Economics, Volume 79, Issue 1, 178-190. Lobell D B, Field C B, Cahill K N and Bonfils C 2006 Impacts of future climate change on California perennial crop yields: model projections with climate and crop uncertainties Agric Meteorol. 141 208â&#x20AC;&#x201C;18 David Lobell, Food Price Spikes in a Warming World, FSI Stanford, FSE Project 2010-Present. (http://foodsecurity.stanford.edu/research/food_price_spikes_in_a_warming_world/) .Rosamond L. Naylor,Food price volatility and the world's poor, January 3, 2011 - FSI Stanford, FSE News, (http://foodsecurity.stanford.edu/news/food_price_volatility_and_the_worlds_poor_20110103/) Nomura Global Economics, Global Economics and Strategy, September 2010 FAO (Food and Agriculture Organization of the United Nations) FAO Statistical Databases available from: http://faostat.fao.org National Climatic Data Center of NOAA (National Oceanic and Atmospheric Administration) Database available from: http://www.ncdc.noaa.gov/cag/ US Department of Energy, Energy Information Administration, Petroleum Marketing Annual 2009, DOE/EIA-0487(2009) Multi collinearity www.chsbs.cmich.edu/fattah/courses/empirical/multicollinearity.html University of Texas, Illustration of Regression analysis, http://www.utexas.edu/courses /schwab/sw388r7 /Tutorials/IllustrationofRegressionAnalysis_doc_html/045_Linearity_of_Independent_Variables_Partial_Plots.html

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Review on Automatic Red Blood Cell Counting 1

Sumeet Chourasiya, 2AshishGoud Purushotham 1 M. Tech. Scholar, 2Asst. Prof. Department of Electronics and Communication Engineering Jagadguru Dattatray College of Technology, Indore (M.P.), India ______________________________________________________________________________________ Abstract: Blood cell segmentation and identification is a vital in the study of blood as a health indicator. A complete blood count is used to determine the state of a person’s health based on the contents of the blood in particular the white blood cells and the red blood cells. The main problem arises when massive amounts of blood samples are required to be processed by the hematologist or Medical Laboratory Technicians. The time and skill required for the task limits the speed and accuracy with which the blood sample can be processed. This paper considers image processing for counting of blood cells. Image processing technique involved five basic components which are image acquisition, image preprocessing, image segmentation, image post-processing and image analysis. The most critical step in image processing is the segmentation of the image. In this paper, we review some of the general segmentation methods that have found application in classification in biomedicalimage processing especially in blood cell image processing. Basically, segmentation of the image divides the whole image into some unique disjoint regions. The fact that the segmented image should retain maximum useful information and discard unwanted information makes the whole process critical. Keywords: Blood Cells, Image Segmentation, Image Enhancement, Hough Transform, Cells Count ______________________________________________________________________________________ I. INTRODUCTION Content-based image indexing and retrieval has been an important research area in computer science for the last few decades. Many digital images are being captured and stored such as medical images, architectural, advertising , design and fashion images, etc. As a result large image databases are being created and being used in many applications. In this work, the focus of our study is on medical images. A large number of medical images in digital format are generated by hospitals and medical institutions every day. Consequently, how to make use of this huge amount of images effectively becomes a challenging problem [1]. In the field of biomedicine, because of cell’s complex nature, it still remains a challenging task to segment cells from its background and count them automatically [2-5]. Among all of the body’s tissues, blood is unique due to its existence as the only fluid tissue. A blood cell can be any type of cell normally found in blood which falls into four categories which are red blood cell (RBC), white blood cell (WBC), platelet and plasma [6]. The differences between these groups lie on the texture, color, size and morphology of nucleus and cytoplasm. In blood smear, number of red cells is many more than white blood cells. For example an image may contain up to 100 red cells and only 1 to 3 white cells. Platelets are small particles and are not clinically important [7]. Blood cells form in the bone marrow, the soft material in the center of most bones. Leukocytes or WBC are cells involved in defending the body against infective organisms and foreign substances. Leukocytes cells containing granules are called granulocytes (composed by neutrophil, basophil, eosiphil). Cells without granules are called agranulocytes (lymphocyte and monocyte) [6]. These cells provide major defense against infections in organisms and their specific concentrations can help specialists to discriminate the presence or the absence of very important families of pathologies [8]. When infection occurs, the production of WBCs increases [6]. Abnormal high or low counts may indicate the presence of many form of disease, since blood counts are amongst the most commonly performed blood test in medicine. Current research is doing on blood counting application in the image segmentation. It is an implementation of automated counting for blood cell which manually done by hematocytometer by using counting chamber. Blood counting is synonym with the complete blood count or CBC which refers to compilation test of red blood cell (RBC), white blood cell (WBC), platelet, hemoglobin and hematocrit. Each of them has their role in the body system and the counting result is important to determine the capability or deficiency of the body system. In short, any abnormal reading of CBC can give a sign of infection or disease. For example, the present of bacterial infection is diagnosed from increasing WBC count. Plus, specific low vitamin may come from a decreased RBC and thrombocytopenia is referring to low platelet count. The result can influence physician to make the best response and monitor the drug effectiveness from the blood count [14]. CBC consists of several counting of the main component in the blood cell. Each of them has a standard quantity range as a reference for a healthy women and man. Any counting value out of the range is considered abnormal and physician will interpret the result for further action. In addition, differential count also include in the

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measurement of CBC as a division of WBC count for five different types of WBC. They are neutrophils, lymphocytes, monocytes, eosinophils and basophils. The standard count for them is 60%, 30%, 5%, 4% and below 1% respectively from the total WBC counts. Table 1 shows the standard CBC for the healthy person divided by gender. Table 1: Normal Blood Count Differentiate by Gender Blood Cell Types RBX WBC Platelet Hematocrit Hemoglobin

Gender Men 5.4-6.0 Million/micro liter 4.5-11 Thousand/micro liter 150-450 Thousand/micro liter 42%-50% 14-17 Grams/100 milli liters

Women 4.0-5.0 Million/micro liter 4.5-11 Thousand/micro liter 15.0-450 Thousand/micro liter 36%-45% 12-15 Grams/10 milli liters

II. IMAGE SEGMENTATION In the segmentation process, morphological technique is major used because the mathematical morphology offers a powerful tool for segmenting images and useful to describe the region shape, such as boundaries, skeletons and texture. The first method in this process divides saturation, S image into two images output by applying the thresholding process. Thresholding is one of the methods to extract and segment the object from the background by selecting any point, T [15]. Any point for which is called an object point, otherwise the point is called background point. Thresholding normally results in binary image and the mathematically; the operation can be expressed as;

where the pixels labeled 1 is corresponded to object whereas the pixels labeled 0 are corresponding to the background.

Figure 2(a)

Figure 2(b) (a) Morphological area closing on lower pixel value image. (b) Morphological dilation and area closing on higher pixel value image. Jianhua et al. [21] stated that in the case of cell segmentation for blood, edge detection performs poorly on cell images because not all boundaries are sharp and it is difficult to get all edge information and locate cells accurately. They developed an iterative Otsu’s approach based on circular histogram for the leukocyte segmentation. Otsu’s approach is generalized on the base of least square method. R. Sukesh Kumar et al. [22] discussed about an approach for color image segmentation using higher order entropy as a textural feature for determination of thresholds over a two dimensional image histogram. Two basic models for color images are the RGB (Red, Green, Blue) color model and the HIS (Hue, intensity, saturation) color model. Two methods of color image segmentation used RGB space as the standard processing space. These techniques might be used in blood cell image segmentation. Color images are very rich source of information, because they provide a better description of a scene as compared to grayscale images. Hence, color segmentation becomes a very important issue [22]. Khoo Boon et al. [23] performed comparisons between nine image segmentation which is gray level thresholding, pattern matching, morphological operators, filtering operators, gradient-in method, edge detection operators, RGB color thresholding, color matching and HSL (hue, saturation, lightness) and color thresholding techniques on RBC and concluded that there is no single method can be considered good for RBC segmentation [23]. In image enhancement process, there are two common image processing techniques used in order to reduce the noise and at the same time to enhance the image. Figure 3 shows the flow process in enhancement processing which are analyses in hue-saturation value color space (HSV) and the green component image. For HSV, we

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proceed with analyses in saturation component, S, because this S image show clearly the bright objects such as white blood cell and parasites, therefore, it's easy to distinguish the red blood cells with another cell.

Figure 3: Image Enhancement III. COUNTING METHODS Roy A. Dimayuga et. al [13] used the histogram thresholding to distinguish the nucleus of the leukocyte or white blood cells from the rest of the cells in the image. Ramin Soltanzadeh [9] has purposed feature extraction technique based on morphology in his three blood cell’s experiments. Based on morphology of the cells, the mass center of each cell in the images and then find the distance of each pixel on an edge from the center. Heidi Berge [10] has purposed the segmentation red blood cells in a thin blood smear image which is based on the Zack’s Method [11]. This method is one of the approached to determine the red blood cells tresholding where a line is drawn between the two peaks and between these two peaks, they used the point which is furthest from the drawn line as a threshold for red blood cell. In the conclusion for this technique, the segmentation result is better to the blood smear which in case red blood cells is sparse and in the image. However, in images with high Red blood cell concentrations, large clumps may result and this method is less accurate. Guitao et. al [12] purposed the Hough transform in detecting and extracting the red blood cells in the urine micrograph. Based on Hough transform, Guitao has used the geometrical feature to detect the circle center in the image. Haider Adnan Khan et al. presented a framework for cell segmentation and counting by detection of cell centroids in microscopic images [16]. Preprocessing is done with Contrast-Limited Adaptive Histogram Equalization to get enhanced image. Next, cells are separated from background using global thresholding. Then, distance transform of binary image is computed which converts binary image into distance map indicating distance of every cell pixel from its nearest background pixel. In order to perform template matching, the template image is generated from the distance transform of circular disk. Distance map is used to identify the cell centroids. The template matching is done using normalized cross-correlation between template and distance map. Finally, the similarity matrix is complemented and all background pixels are set to -∞. The watershed transform is then applied on this complemented similarity matrix. This splits the similarity matrix into separate disjoint regions. Each region is labeled and counted to get the count. The experimental results show excellent accuracy of 92 % for cell counting even at very high 60 % probability. Watcharin et al. proposed an algorithm to count blood cells in urine sediment using ANN and hough transform [17]. First step of algorithm is the segmentation between background and blood cells by using feedforward backpropagation algorithm. For training neural network, the input is Hue, Saturation, Value and standard deviation. After deriving output from feedforward backpropagation, salt and pepper noise is eliminated by using morphological opening and closing method. Last step is blood cell counting using circular hough transform. Experimental results show the average percentage of error of RBCs and WBCs detection 5.28 and 8.35 respectively. 6. J. G. A. Barbedo presented a method for counting of microorganisms that use a series of morphological operations to create a representation in which objects of interest are easily isolated and counted [18]. First step of this method is RGB to gray conversion. After that, two-dimensional median filter is applied, in order to eliminate noise and other artifacts. Ideal size of the neighborhood over which filter should be applied depends on three main factors: size of objects of interest, size of spurious artifacts and resolution of the image. The program has two approaches for deciding neighborhood. In the first approach, user enters estimate of diameter of objects and artifacts. In the second approach, estimation using multiple counts is done. Then, contrast is adjusted in such a way the brightest pixel assumes the full-scale value 255 and darkest pixel equal to zero. In following, the algorithm verifies if the background is brighter or darker than the objects. If the background is brighter, a complement operation is performed. The image is then submitted to top-hat morphological filtering. Image is binarized with threshold in 128. After that object counting becomes trivial. By observing results, it can be seen that, except for the case of merged objects, the method identifies the objects correctly in more than 90 %

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of the cases, and the number of false positives is always low. The overall deviation was 8 %; such a number falls to 2.5 % if the images with merged objects are not taken into account. IV.

RESUTLS AND DISCUSSION

Figure 4: Original Image of Blood for 40X object When the operation of masking is applied, the masked image has diminished the WBC nucleus morphological view. After morphological operation involving binary erosion and filling holes, the RBC can be viewed accordingly. In this study, masking has been used to remove WBC and platelet is substracted by morphological operators. The left one will be RBC which represent the RBC segmentation. Figure 5 shows the result of the RBC segmentation from the elimination of WBC nucleus and small particles including platelets.

Figure 5: a) Segmented WBC nucleus b) RBC segmentation from the elimination of WBC nucleus and small particles. To handle overlapping RBC, it involves Laplace of Gaussion (LoG) edge detection, morphological operation, gradient magnitude and marker controlled watershed algorithm. The result from erosion on RBC segmentation result and gradient magnitude has being used together as mask with watershed algorithm to form marker controlled watershed algorithm. This could avoid oversegmentation which often occurs for watershed algorithm. After dilation, it being used together with LoG edge detection on the Ycbcr second component of the image as mask and segmented RBC as marker. Lastly, it being superimpose to the original image. The result of [19] is given in figure 6.

Figure 6: a) Separation of overlapping cell b) Superimpose on original image. The circular hough transform is applied to the contrast adjusted image by some of researchers.

Figure 7(a) Microscopic Image and (b) Green Plane Extraction - I

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Figure 7(c) Contrast adjustment-1 and (d) Accumulation array-1

Figure 7(e) Detected blood cells-1 and (f) Binary image-1

Figure (g) Holes filled-1 and (h) Borders cleared-1

Figure 7(i) Labelled Image The results of the image can be used as good input in determining the number of red blood cells by using Hough transform technique. By using the MATLAB, all the importanceâ&#x20AC;&#x2122;s aspects like correct algorithm and system has been successfully produced. With correct algorithm, the red blood cells can be detected and segmented as well as estimated the number of the red blood cells. Through system created using MATLAB, it also enable the study of the morphological features of the red blood cells image, thus, can determine whether the person is normal or otherwise by referring amount of red blood cells in human blood [15].

Figure 8 (a) and (b)

Figure 8(c) and (d)

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Figure 8(e) and (f)

Figure 8 (g) and (h) Figure 8: results of 8 samples of red blood cells after performing Hough transform technique. Object counting using image processing has huge applications where automation is to be introduced and time of counting is to be reduced. Some of the main applications of object counting in industrial systems are packaging, quality control, and so on. It is helpful in the research areas where objects are of very small size. Object counting algorithm can be also used to track and identify objects. The present methods can be extended to have counting system based on userselected attributes. V. CONCLUSION Image processing techniques are helpful for object counting and reduce the time of counting effectively. Proper recognition of the object is important for object counting. The accuracy of the algorithm depends on camera used, size of objects, whether or not objects touching and illumination conditions. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

[14] [15] [16] [17]

[18] [19]

Lehmann T.M., Wein B., Dahmen J., Bredno J., Vogelsang F. & Kohnen M. : Content based image retrieval in medical applications : a novel multi step approach. International Society for Optical Engineering (SPIE), 3972, pp.312-320.(2000) Dwi Anoragaingrum : Cell segmentation with median filter and mathematical morphology operation, proceeding of the IEEE 10th International Conference on Image Analysis and Processing (ICIAP), pp. 1043-1046 (1999). Keng Wu et al.: Live cell image segmentation, IEEE Trans on Biomedical Engineering, 42(1), pp.1-12.(1995). Mark B. Jeacocke, Brian C. Lovell : A Multi-resolution algorithm for Cytological image segmentation, The second Australian and New Zealand conference on intelligent information systems, 322-326 (1994). Choi H, Baraniuk R., Multiscale : Image segmentation using wavelet-domain hidden Markov models, IEEE Transaction on image processing, 10(9), pp.1309-1321 (2001). H Elaine N. Marieb (2006): Essentials of Human Anatomy & Physiology, 8th ed. Pearson Benjamin Cummings Fatemeh Zamani, Reza Safabakhhsh: “An Unsupervised GVF Snake Approach for White Blood Cell Segmentation Based on Nucleus”, Signal Processing, The 8th International Conference on Volume 2, 2006. Vincenzo Piuri, Fabio Scotti: “Morphology Classification of Blood Leucocytes by Microscope Images”, IEEE International Conference on Computitional Intelligence for Measurement Systems and Applications, Boston, MA, USA, 14-16 July 2004. Ramin Soltanzadeh. “Classification of Three Types of Red Blood Cells in Peripheral Blood Smear Based on Morphology. Proceedings of ICSP, 2010. Heidi Berge, Dale Taylor, Sriram Krishnan, and Tania S. Douglas. Improved Red Blood Cell Counting in thin Blood Smears. Proceedings of ISBI, 2011. pp.204-207. Zack G.W., Rogers W.E. and Latt S.A. “Automatic-measurement of sister chromatid exchange frequency.” Journal of Histochemistry & Cytochemistry 25, 1977, 741-753. Guitao Cao, Cai Zhong,Ling Li and Jun Dong. “Detection of Red Blood Cell in Urine Micrograph”. The 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE). 2009. Roy A. Dimayuga, Gerwin T. Ong, Rainier Carlo S. Perez, Gefferson O. Siy, Saman C. Sohrabi Langroudi and Miguel O.Gutierrez. “Leukemia Detection Using Digital Image Processing in Matlab”. ECE Student Forum, De La Salle University, Manila. March 26, 2010. Miswan, M. F., et al. "An Overview: Segmentation Method for Blood Cell Disorders" 5th Kuala Lumpur International Conference on Biomedical Engineering 2011 Springer Berlin Heidelberg, 2011 Mahmood, Nasrul Humaimi, and Muhammad Asraf Mansor. "Red blood cells estimation using Hough transform technique." Signal & Image Processing: An International Journal (SIPIJ) 3.2 (2012): 53-64. Haider Adnan Khan and Golam Morshed Maruf, “Counting Clustered Cells using Distance Mapping,” 2013 International Conference on Informatics, Electronics and Vision (ICIEV), May 2013. pp. 1-6. Watcharin Tangsuksant, Chuchart Pintavirooj, Somchart Taertulakarn, Somsri Daochai, “Development Algorithm to Count Blood Cells in Urine Sediment using ANN and Hough Transform,” The 2013 Biomedical Engineering International conference, Oct. 2013. pp. 1-4 Jayme Garcia Arnal Barbedo, “Method for Counting Microorganisms and Colonies in Microscopic Images,” 12th Int. Conf. Computer Science and Its Applications, June 2012. pp. 84-87. Sharif, J. MISWAN, et al. "Red blood cell segmentation using masking and watershed algorithm: A preliminary study." Biomedical Engineering (ICoBE), 2012 International Conference on. IEEE, 2012.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A study on the effect of different organic supplements on the ascorbic acid and riboflavin content of French bean ( Phaseolus vulgaris) P. Uma amareswari and P. Sujathamma Department of Sericulture Sri Padmavathi Mahila Visvavidyalayam (Womenâ&#x20AC;&#x2122;s University) Tirupati. Chittoor Dist. A.P.INDIA _________________________________________________________________________________________ Abstract: Vegetables are a rich source of nutrients. Fresh vegetables like French beans (Phaseolus vulgaris) are known for their high protein content, fibre, water and mineral composition. They are also good source of vitamins like Ascorbic acid and Riboflavin and minerals like calcium and phosphorus. In view of the growing awareness regarding health the French beans are recommended for regular diet as vegetable proteins. However the demand for these vegetables is high when they are produced organically. An experiment to study the role of organic supplements like kitchen waste vermicompost, jeevamrutha, panchagavya and mulching on the ascorbic acid and riboflavin content of French beans was conducted at Kothavaripalli village, Madanapalle mandal of Chittoor district. Household kitchen waste was converted to vermicompost by employing Eisenia foetida variety of earthworms. Jeevamrutha was prepared by the procedure laid by Subash Palekar (2007) and Panchagavya by Natarajan (2007). Cotton mill waste along with paddy straw was used as mulch. Various growth and yield parameters both qualitative and quantitative were studied by using different combinations of the organics. Highest percentage of ascorbic acid (10.57%) was observed in the fresh pods with treatment T-11 (10.27) using straw mulch, vermicompost and panchagavya together followed by T-12 using straw mulch, jeevamrutha and panchagavya. For riboflavin content highest values were recorded with T-11 (0.41) and T-10 (0.40) using straw mulch, NPK (recommended dose of fertilizers) and panchagavya. The results confirm the positive role of panchagavya in increasing the vitamin content of the fresh pods. Key words: Ascorbic acid, French beans, Jeevamrutha, Mulching, Panchagavya, Vermicompost _________________________________________________________________________________________ I. INDRODUCTION French bean (Phaseolus vulgaris) is mainly grown for its pod yield rather than Rajmas in Southern parts of India including Andhra Pradesh.Different bush and pole varieties are cultivated in Andhra Pradesh for regular supply of this protein vegetable. As the small farmerâ&#x20AC;&#x2122;s affordability for chemical fertilizers and pesticides has decreased due to high prices and low subsidies on these chemicals cultivation of vegetables by organic methods is gaining importance day by day. Also growing awareness regarding healthy food has laid stress on vegetable production by natural or organic methods. Many consumers are in search of organic markets for their vegetable needs as organically produced vegetables are not only free from harmful chemicals but also proved to have better nutritive value than chemically produced ones. As French beans is a good source of crude fibre, water, vitamin and mineral content apart from being a protein source growing French beans using different organic supplements is studied to determine the best combination for increasing vitamin content of the pods. The quantitative and qualitative yield of French beans were tested by using different combinations of kitchen waste vermicompost, jeevamrutha, panchagavya and mulch in comparison to recommended dose of fertilizers. Many studies have proved that vegetables and fruits produced naturally without using chemical fertilizers show higher levels of organic acids and vitamins like ascorbic acid and riboflavin. Aboderin (2000) observed that ascorbic acid content increased in Abelmoschus esculentus and Celosia argentia grown in unfertilized soils when compared to those grown with chemical fertilizers. The ascorbic acid content was high in organic potatoes than in potatoes produced conventionally (Rembialkowska, 2003). Similarly vitamin B2 or riboflavin was found to be 6.7% higher in organic rice (Nandhasri, 2008). A slight increase in vitamin C content was observed in Lactuca sativa and Allium sepa when grown on natural soil without chemical fertilizers (Puia et al., 2009). A high percentage of ascorbic acid was recorded in organic vegetables and fruit juices of apple, beetroot, carrot and pear when compared to those produced by conventional method (Gastol, et al.2011). The effect of organic methods on ascorbic acid and riboflavin content of French beans is studied in the present experiment in comparison to chemical method. II. METHODS AND MATERIALS The experiment with Anupam variety of French beans (Phaseolus vulgaris) was carried out in the agricultural field of Kothavaripalli village situated in Madanapalle Mandal of Chittoor District, Andhra Pradesh. The soil was a light red loamy soil with pH of 8.3. Kitchen wastes were converted into vermicompost by using cowdung slurry and Eisenia foetida variety of earthworms and applied in 4 split doses, 10DAS, 30DAS, 50DAS

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and 70DAS @8MT/ha. Chemical fertilizers were applied in three split doses 15DAS, 35DAS and 60DAS. Jeevamrutha was used as 5% spray applied to the soil as well as foliar spray on 15DAS, 30DAS, @ 6.5% on 45DAS and 60DAS, @ 10% on day 75. Panchagavya @3% was given as foliar spray on 20,35,50,65 and 80DAS. The plants were irrigated once in 4 days through drip irrigation. Straw mulch was laid with rice straw and cotton mill waste after the plants attained a height of 25 cms. The fertilizers and manures were applied to the crop as per the following treatments: 1. T-0 Control - Soil only 2. T-1 - NPK @ 25:75:50 3. T-2 - Vermicompost @ 8MT/ha 4. T-3 - Jeevamrutha @ 500lts/ha 5. T-4 - Panchagavya @ 3% as foliar spray 6. T-5 - Straw mulch @ 15 cms above soil 7. T-6 - NPK + Panchagavya 8. T-7 - Vermicompost + Panchagavya 9. T-8 - Jeevamrutha + Panchagavya 10. T-9 - Straw mulch + Panchagavya 11. T-10 – Straw mulch + NPK + Panchagavya 12. T-11 – Straw mulch + Vermicompost + Panchagavya 13. T-12 – Straw mulch + Jeevamrutha + Panchagavya The fresh pods were collected at regular intervals and ascorbic acid content was determined by the method given by Plummer and Jayaraman. Riboflavin content of the pods was determined by modified AACC method (1976). TABLE – 1

ASCORBIC ACID CONTENT MEAN VALUE FOR THREE YEARS (2010-2012) OVER 4 REPLICATIONS EACH Treatment 1st season 2nd season 3rd season Average T0 (Control) 5.42 6.35 6.59 6.12 T1 NPK @ 8:25:18 kgs/ acre 6.84 7.57 7.35 7.25 T2 VC 4 tons/acre 6.78 8.27 9.15 8.06 T3 JV @ 200lts /acre 6.13 7.8 7.64 7.19 T4 PG @ 3% FS 7.42 8.78 10.06 8.75 T5 SM @ 15cms height 6.23 7.29 8.41 7.31 T6 NPK + PG 8.25 9.05 10.17 9.15 T7 VC + PG 9.2 9.17 10.74 9.7 T8 JV + PG 8.72 9.48 9.89 9.36 T9 SM + PG 9.06 9.21 9.82 9.36 T10 SM + NPK + PG 9.6 10.06 9.82 9.82 T11 SM + VC + PG 10.9 10.33 10.49 10.57 T12 SM + JV + PG 10.6 10.21 10 10.27 STATISTICAL ANALYSIS (3X13 ANOVA) Source Type III Sum of Squares df Mean square F p value Sig Seasons 34.693 2 17.347 99.464 0.000 ** Treatments 272.587 12 22.716 130.249 0.000 ** Seasons*Treatments 31.079 24 1.295 7.425 0.000 ** Error 20.405 117 0.174 Corrected Total 358.764 155 A R Squared = .943(Adjusted R Squared = .925)

Note: ** = Significant at 0.01 level TABLE – 2 RIBOFLAVIN CONTENT AVERAGE FOR THREE YEARS (2010-2012) OVER 4 REPLICATIONS EACH Treatment 1st season 2nd season 3rd season Average T0 (Control) 0.10 0.10 0.09 0.096 T1 NPK @ 8:25:18 kgs/ acre 0.29 0.32 0.31 0.30 T2 VC 4 tons/acre 0.31 0.35 0.35 0.33 T3 JV @ 200lts /acre 0.18 0.28 0.30 0.25 T4 PG @ 3% FS 0.32 0.31 0.34 0.32 T5 SM @ 15cms height 0.20 0.24 0.24 0.22 T6 NPK + PG 0.30 0.30 0.34 0.31 T7 VC + PG 0.34 0.36 0.40 0.36 T8 JV + PG 0.30 0.32 0.33 0.31 T9 SM + PG 0.32 0.32 0.34 0.32 T10 SM + NPK + PG 0.38 0.41 0.43 0.40 T11 SM + VC + PG 0.38 0.42 0.43 0.41 T12 SM + JV + PG 0.34 0.37 0.41 0.37 Source Seasons Treatments

Type III Sum of Squares 0.049 1.016

df 2 12

Mean square 0.025 0.085

F 61.195 211.003

p value

Sig

0.000 0.000

** **

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P. Uma amareswari et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 8(3), March-May, 2014, pp. 269-271 Seasons*Treatments 0.035 24 Error 0.047 117 Corrected Total 1.147 155 A R Squared = .959(Adjusted R Squared = .946)

0.001 0.000

3.626

0.000

Note: ** = Significant at 0.01 level III. RESULTS AND DISCUSSION Vitamins are organic compounds that are required for various biological functions. In general, vitamins are not synthesized in the human body, but deficiency of them may lead to certain diseases. Both Ascorbic acid and Riboflavin (vitamin B2) are water soluble vitamins found abundantly in fresh fruits, vegetables and green leaves. Riboflavin also known as vitamin B2 or G is widely distributed in plants, soybeans, green leaves and fresh vegetables. Milk and eggs are good source of this vitamin. It is an important constituent of FMN (Flavin Mono Nucleotide) and FAD (Flavin Adenine Dinucleotide) which acts as coenzyme in cellular oxidation reduction reactions. Chemically riboflavin is a yellow nitrogen containing alcohol. It is found in two forms in animals, riboflavin phosphate and Flavin. Riboflavin deficiency leads to retardation of body growth and abnormal vascularization of cornea of the eye. Ascorbic acid or Vitamin C is the first chemically identified vitamin abundant in citrus fruits, berries, melons, tomatoes and leafy vegetables. It is an important anti-oxidant vitamin also found sufficiently in green and fresh vegetables like French beans. It has strong reducing property and is oxidized to de hydro ascorbic acid. Its deficiency causes symptoms of scurvy such as falling of teeth, cracking of bones, bleeding etc. It is also found to prevent infections by accelerating phagocytosis. However it is quickly destroyed by oxidation during cooking. It is evident from table – 1 that regarding the ascorbic acid content the effect of straw mulch along with vermicompost and foliar spray of panchagavya (T-11) was more significant with a highest value of 10.57 mg/100 gm. of fresh pods. T-12 using straw mulch, jeevamrutha and foliar spray of panchagavya showed a value of 10.27mg/100 gm. In all other treatments where Panchagavya was used there was a significant increase in ascorbic acid level. The treatments where vermicompost and panchagavya were used separately showed considerably greater levels of ascorbic acid and when they were used together with straw mulch exerted more influence on increasing the levels of ascorbic acid. Though Jeevamrutha could not influence to produce more ascorbic acid content it could yield more ascorbic acid when used along with straw mulch and panchagavya. It is evident from the data that panchagavya was more efficient in increasing the ascorbic acid levels compared to vermicompost, jeevamrutha or straw mulch. The T-11 recorded a huge increase of 72.71% of ascorbic acid when compared to the control and an increase of 45.79% over T-1 (NPK). The T-12 showed a 67.81% increase over control and 41.65% over T-1 which indicates that producing French beans through organic farming improves the nutrient content to a maximum extent. Table – 2 shows that highest Riboflavin content (0.41µg/100gm.) was obtained from pods produced by T-11 using straw mulch, vermicompost and foliar spray of panchagavya. T-10 using straw mulch, NPK (recommended dose of fertilizers) and foliar spray of panchagavya recorded 0.40µg/100gm of Riboflavin. Vermicompost along with panchagavya was found to show better effect on this parameter. It is also evident from the table that NPK coupled with panchagavya could produce more riboflavin whereas the treatments where jeevamrutha was used could not influence this parameter significantly. The role of vermicompost, panchagavya and NPK showed greater influence on riboflavin content independently as well as used together with straw mulch. The T-11 recorded 42.70% higher riboflavin content than control and 13.6 % increase over T-1 followed by T-10 which recorded 41.6% increase in riboflavin content over control and 13.3 % increase over T1. Hence it is evident that organic methods increase the vitamin content of the vegetable produce. By the two tables it is evident that T-11 using vermicompost along with panchagavya and straw mulch influenced both ascorbic acid and riboflavin levels in the pods. However Jeevamrutha along with straw mulch and panchagavya was second in increasing the ascorbic acid, while NPK along with straw mulch and panchagavya had equally good effect on riboflavin content. In this experiment also there is an increase in ascorbic acid content using organic supplements. This study emphasizes the role of organics in increasing the vitamin content of French bean pods. References [1].

[2]. [3]. [4]. [5].

Aboderin, O.A., (2000) – Variation in levels of moisture, total protein, ascorbic acid, sodium, zinc, potassium in some green leafy vegetables eaten by most Nigerians with age and the effect of organic fertilizers on these parameters – Journal of tropical forest resources – 16(1) – 152-159. Gastol, M. DomagalaSwiatkiewnicg, I. – 2011 - Organic versus Conventional – a comparative study on quality and nutritive value of fruit and vegetable juices – Biological Agriculture and Horticulture – 27(3/4) – 310-319. Nandhasri, P. Sattaponpun, C. Ritusso, W. Parawach, W. Uechiewchankit, K (2008.) - From organic rice bran to natural vitamin B-complex –Acta Horticulturae - (786):147-152. Puia, L. Oancea, S. Ruiz, I. (2009) – The effect of pre harvest factors on L-Ascorbic Acid content of Lactuca sativa, Spinaciaoleracea and Allium cepa – Acta Universitalis Cibiniensis – Series E Food Technology – 13(1) – 13-18. Rembialkowska, E. (2003) – Organic farming as a better system to provide better quality – Acta Horticulture – 604 – 473-479.

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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 A comparative study of resistance offered by different stainless steel alloys against corrosion and pitting by determination and study of corrosion resistance parameter Rita Khare Department of Chemistry Government Womenâ&#x20AC;&#x2122;s College, Gardanibagh, Patna, India __________________________________________________________________________________________ Abstract: Corrosion resistance parameter (Rp) is determined and studied in case of stainless steels bearing different alloying elements at 298K. Corrosion resistance parameter is calculated on the basis of anodic parameters obtained from potentiodynamic polarization curves. R p is found to vary depending on the type of minor alloying element present as constituent of steel. There is found to be direct relationship between R p and occurrence of pitting attack on alloy surface. Minimum value of Rp below which pitting occurs is found to be dependent on the minor alloying element present, being lowest for 316SS (molybdenum bearing stainless steel). At a fixed value of HCl concentration, R p is found to show dependence on alloy composition reflecting that corrosion resistance offered by steels follows the order, 316SS > 321SS > 304SS. Key words: Stainless steel, phosphoric acid, alloying element, corrosion resistance, anodic parameters. __________________________________________________________________________________________ I. Introduction Chromium and nickel alloyed stainless steels are known to offer good corrosion resistance in pure phosphoric acid making them most suitable alloy to be utilized as construction material of containers during production, storage and transport of phosphoric acid. The ability of alloy steels to form passive film is the basis of corrosion resistance offered by these steels. However problems are often faced as in practical situation phosphoric has some amount of aggressive ions present in it which enter into phosphoric acid during its production. It may be realized from the results presented previously [1] that corrosion resistance parameter shows a decrease with increase in content of HCl in 14M phosphoric acid reflecting that 304SS loses corrosion resistance and becomes more susceptible to corrosion and pitting attack with increase in amount of aggressive ion in the medium. This limits the utilization of this material in the studied medium. Since pitting is basically due to imperfect state of passivity at various locations on the alloy surface the type of elements and the amount of elements constituting the alloy play a decisive role as far as deleterious attack on alloy in the form of pitting is concerned. It has been noticed that steels alloyed with chromium [2] and molybdenum [3] show a better corrosion resistance in chloride containing solutions. Passive metals such as Ti and Ti bearing alloys have been found to be stable in many environments but in certain environments such as acid solutions containing chlorides, pitting has been witnessed. Therefore much attention was given by earlier investigators to the study of pitting of passive metals and alloys [4], [5], [6], [7]. However a systematic research on corrosion and pitting of stainless steels containing Ti as a minor alloying element has not been conducted. In the present work evaluation of resistance offered by stainless steel alloyed with less than 1% Ti and the other with 3% Mo against corrosion and pitting is done. Present study is aimed at performing comparative study of resistance offered by stainless steels against corrosion and pitting under the influence of minor alloying elements Mo and Ti in terms of variation of corrosion resistance parameter. II. Experimental The alloys tested in this study were 304SS, 321SS and 316 SS. Composition of alloys was: 304SS 18%Cr 10%Ni balance Fe 321SS 18%Cr 8% Ni < 1%Ti balance Fe 316SS 18%Cr 8% Ni 3%Mo balance Fe The electrode system consisted of austenitic stainless steel working electrode, a counter electrode of platinum and saturated calomel electrode with KNO3 salt bridge. Electrochemical experiments were conducted in an air thermostat maintained at 298K under still condition. The solution contained 14M phosphoric acid with different concentrations of HCl present in it. Potentials were impressed on the working electrode of area 1 cm2 by a fast rise power potentioscan Wenking Model POS 73. Potentiodynamic polarization curves were recorded starting from open circuit potential with a scan rate of 1mv.sec-1.

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Anodic polarization curves were of a similar nature independent of composition of alloy and concentration of HCl [8]. General nature of curves is an active zone followed by a passive region which enters into transpassive region at nobler potentials. Various anodic parameters i.e. passivation potential (Ep), passive current density (ip) [8] and breakdown potential (Eb) are found out from anodic polarization curves and are shown in Table 2 and 3 for 321SS and 316SS respectively. Corrosion resistance parameter (Rp) is also calculated at various concentrations of HCl in 14M phosphoric acid medium. Various anodic parameters in case of 304SS determined earlier [1] are shown in Table 1 for comparison. Table 4 shows corrosion resistance parameter (Rp) at 298K with various concentrations of HCl in case of 304SS, 321SS and 316SS. III. Result and discussion It was noticed from anodic polarization curves that nature of curves was similar irrespective of concentration of HCl [8] and alloy composition of stainless steels [8], [9]. General feature of anodic polarization curves with increase in potential from steady state potential is an active zone followed by a passive region which enters into transpassive region at Eb. Passive region starts at Ep and ends at Eb. Current remains constant in the range Ep to Eb and is denoted by ip. As introduced and discussed earlier [1] corrosion resistance parameter Rp depends on Ep, Eb and ip following the equation, Rp = K (Eb –Ep)/ ip Where Eb-Ep indicates the range of potential in which the rate of formation of vacancies is compensated by the rate of their disappearance in the bulk [1]. Ip is the current flowing through the passive film which depends on concentration of HCl present in the medium at a constant temperature. As discussed elsewhere [1] ip depends on the compactness of passive film which in turn is decided by concentration of HCl in the medium. The values of corrosion resistance parameter (Rp) at 298K for 304SS, 321SS and 316SS are shown in Tables 1, 2 and 3 respectively. Corrosion resistance parameter is found to decrease with increase in HCl content in the medium irrespective of the minor alloying element Mo or Ti present in stainless steel. However the extent up to which Rp is affected by same increase in HCl content shows dependence on alloy composition. Both Ep and Eb attain nobler values when 304SS is replaced by 321SS. A greater shift in Ep and Eb towards nobler values is observed on replacing 304SS with 316SS. Passive range for the three stainless steels at any concentration of HCl is found to follows the order, (Eb-Ep) for 304SS < (Eb-Ep) for 321SS < (Eb-Ep) for 316SS Passive current density (ip) shows a decrease when 304 SS is replaced with 321SS. On replacing 304SS with 316SS a decrease by a greater amount is observed. Variation in i p at any concentration of HCl follows the order, Ip for 304SS > ip for 321SS > ip for 321SS Rp is dependent on Eb-Ep and ip. It is observed from table 1, 2 and 3 that at a fixed value of HCl concentration, Rp shows an increase when 304SS is replaced with 321SS in the medium H 3PO4-HCl. Although Rp increases up to some extent, increase in Rp is not remarkable. An increase by a greater amount is observed in Rp when 304SS is replaced with 316SS keeping content of HCl in the electrolyte constant. Rp almost doubles when 304SS is replaced with 316SS in the studied medium at any fixed concentration of HCl. Critical concentration of HCl beyond which pitting starts also shows a greater shift (7600ppm to 14800ppm) on replacing 304SS with 316SS compared to a shift (7600ppm- 7800ppm) observed on replacing 304SS with 321SS. Table -4 shows the variation of Rp with concentration of HCl for 304SS, 321SS and 316SS at 298K for comparison. It is observed in case of 304SS that when Rp attains a value below 7.3Ω pitting starts. The minimum value of corrosion resistance parameter Rp below which pitting starts is found to be 3.4Ω and 2.7Ω for alloy 321SS and 316SS respectively. It is inferred from the results that the minimum value of resistance below which pitting starts shows a decrease by 3.9Ω when 304SS is replaced with 321SS. It may be explained on the basis of reason given earlier by researchers that Ti present in 321SS binds carbon and nitrogen and hence prevents the formation of chromium carbides and nitrides. It minimizes the probability of creation of sites favourable for pitting attack [10]. In case of 304SS, minimum value of Rp below which pitting is witnessed is 7.3Ω which corresponds to 7600ppm HCl concentration. The corrosion resistance parameter in case of 316SS under similar environmental conditions almost doubles and is found to be 13.8Ω. Further no pitting is observed at this value of R p in case of 316SS. At 10000ppm HCl concentration also Rp in case of 316SS is almost double of that in case of 304SS. No pitting is observed to take place on 316SS till the concentration of HCl is less than 14800ppm in the medium. Rp attains a value as low as 2.7Ω at 14800ppm HCl concentration and pitting is witnessed. Increased corrosion resistance in terms of increase in corrosion resistance parameter Rp when 304SS is replaced with 316SS may be explained on the basis of beneficial effect of Mo as an alloying element [11], [12]. When molybdenum is present as minor alloying element, molybdate ions take part in passive film formation by their adsorption on the film. This makes passive film less porous hence minimizing the chances of pitting attack [13]. Increased compactness of the film results in decreased value of ip and increased value of corrosion resistance parameter Rp. Highly charged Mo6+ species interact with cation vacancies (defects) present in the film reducing cation vacancy

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flux from film-solution interface to metal -film interface where the vacancies condense resulting in pit formation[14]. Under similar environmental conditions, value of Rp is increased to almost two times on replacing 304SS with 316SS. This may be explained by considering the following mechanism, Cr2O3  2Cr3+ + 3O26+ Mo + 3O2-  MoO3 Two Cr3+ ions are replaced by one Mo6+ showing that one Mo6+ is sufficient for providing passivity as compared to two Cr3+. Hence Mo bearing steel 316SS is twice as efficient as 304SS in offering resistance against pitting corrosion. IV. Conclusion It is found that susceptibility of all the stainless steel alloys to pitting corrosion increases with increase in concentration of HCl as is observed experimentally [9], [13] and from determination of corrosion resistance parameter Rp. The susceptibility of the stainless steels towards pitting corrosion depends on the presence of minor alloying element. Corrosion resistance offered by steels in terms of corrosion resistance parameter follows the order Rp (316SS) > Rp (321SS) > Rp (304SS) At a fixed concentration of HCl corrosion resistance parameter Rp shows a greater increase when 304SS is replaced with 316SS as compared to increase in Rp observed on replacing 304SS with 321SS. This indicates that Mo has greater efficiency to offer resistance against corrosion and pitting in comparison to that offered by Ti if present as a minor alloying element. Critical concentration of HCl beyond which pitting corrosion is observed and minimum value of Rp below which pitting starts show a greater change on replacing 304SS with 316SS as compared to the change observed on replacing 304SS with 321SS. Corrosion resistance evaluated mathematically using relation, Rp = K (Eb –Ep)/ ip in case of 304SS, 321SS and 316SS is a measure of resistance offered by stainless steels as is found from agreement between experimental observations and theoretical calculation. Table-1 Anodic parameters of AISI 304SS in 14M phosphoric acid at 298 K having different concentrations of HCl. Concentration of HCl (ppm) Blank solution 1000 2000 3000 4000 5000 10000 15000 20000

Ip (mA.m-2)x104 0.004 0.006 0.008 0.008 0.010 0.012 0.020 0.060 0.150

Ep (mV) -180 -120 -100 -60 -30 +30 +80 +100 +120

Eb (mV) 1130 950 940 920 910 900 690 680 680

Eb-Ep (mV) 1310 1070 1040 980 940 870 610 580 560

Rp=(Eb-EP)/ip Ω.m-2 32.8 17.8 13.0 12.3 9.4 7.3 3.1 0.9 0.4

Table-2 Anodic parameters of AISI 321SS in 14M phosphoric acid at 298 K having different concentrations of HCl. Concentration of HCl (ppm) Blank solution 1000 2000 3000 4000 5000 10000 15000 20000

Ip (mA.m-2) x104 0.004 0.006 0.006 0.008 0.008 0.010 0.018 0.050 0.090

Ep (mV) -170 -100 -80 0 +80 +90 +120 +120 +180

Eb (mV) 1160 980 980 980 960 950 730 720 690

Eb-Ep (mV) 1330 1080 1060 980 880 860 610 600 510

Rp=(Eb-EP)/ip Ω.m-2 33.3 18.0 17.7 12.3 11.0 8.6 3.4 1.2 0.6

Table-3 Anodic parameters of AISI 316SS in 14M phosphoric acid at 298 K having different concentrations of HCl. Concentration of HCl (ppm) Blank solution 1000 2000 3000 4000 5000 10000 15000 20000

Ip (mA.m-2 x104) 0.001 0.002 0.004 0.006 0.006 0.008 0.020 0.024 0.030

Ep (mV) -70 -70 -20 +30 +30 +30 +40 +80 +90

Eb (mV) 1250 1190 1180 1170 1150 1130 1100 730 720

Eb-Ep (mV) 1320 1260 1200 1140 1120 1100 1060 650 630

Rp=(Eb-EP)/ip Ω.m-2 132.0 63.0 30.0 19.0 18.7 13.8 5.3 2.7 2.1

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Table-4 Variation of corrosion resistance parameter (RP) in Ω.m-2 with concentration of HCl at 298K. Concentration of HCl (ppm) 1000 2000 3000 4000 5000 10000 15000 20000

Corrosion resistance parameter (Rp) for 304SS 17.8 13.0 12.3 9.4 7.3 3.1 0.9 0.4

321SS 18.0 17.7 12.3 11.0 8.6 3.4 1.2 0.6

316SS 63.0 30.0 19.0 18.7 13.8 5.3 2.7 2.1

References [1]

[2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

Rita Khare, “Evaluation of resistance offered by 304SS in H3PO4-HCl medium by determination and study of corrosion resistance parameter”, American International Journal of Research in Science, Technology, Engineering and Mathematics, vol 2,issue 5, (2013) pp 149-152. E.A. Lizlovs and A.P.Bond, “Anodic polarization behaviour of high purity 13 and 18 % Cr stainless steel”, J.Electrochem. Soc.,vol 122, (6), (1975) p 719. H.H.Uhlig, P.Bond and H.Feller, “Corrosion and passivity of molybdenum nickel alloys in hydrochloric acid”, J. Electrochem. Soc, vol 110, (6), (1963), PP 650-653. Z.Szklarska.Smialowska, “Review of literature on pitting corrosion published since 1960”, Corrosion, vol 27 (1971) p 223. M.Janik Czachor, G.C.Wood and G.E.Thompson, “Assessment of processes leading to pit nucleation”, British Corrosion Journal, vol 15,(4), (1980) p 154. E A Ebd El Meguid and A.A.El Latiff, “Electrochemical and SEM study on type 254 SMO stainless steel in chloride solutions”, Corrosion Science, vol 46, issue 12 (2004) pp 2431-2444. E A Ebd El Meguid, N.A. Mahmood and V.K. Gouda, “Pitting corrosion behaviour of AISI 316L steel in chloride containing solutions”, British Corrosion Journal, vol 33, ( 1), (1997) pp 42-48. Rita Khare, M.M.Singh and A.K.Mukherjee, “The effect of addition of HCl on the corrosion behaviour of 304SS in concentrated H3PO4 at different temperatures”, Bulletin of Electrochemistry, vol11, (10) (1995), pp 457- 461. Rita Khare, M.M.Singh and A.K.Mukherjee, “The effect of temperature on pitting corrosion of 316SS in concentrated phosphoric acid containing hydrochloric acid”, Indian Journal of Chemical Technology, vol 9, no. 5 (2002), PP 407-410. Mirzam Bajt Leban and Robert Tisu “The effect of TiN inclusions and deformation induced martensite on the corrosion properties of AISI 321 stainless steel”, Engineering Failure Analysis, vol 33, October 2013, pp 430-438. H.A.El Dahan, “Pitting corrosion inhibition of 316 stainless steel in phosphoric acid chloride solutions Part I, Potentiodynamic and potentiostatic polarization studies”, Journal of Material Science, vol 34, issue 4 (1999) pp 851-857. H.A.El Dahan, “ Pitting corrosion inhibition of 316 stainless steel in phosphoric acid-chloride solutions part II AES investigation”, Journal of Material Science, vol 34, issue 4, (1999) pp 859-868. Rita Khare, “Surface analysis of steel samples polarized anodically in H 3PO4-HCl mixtures by SEM and EDAX techniques”, International Journal Manthan, vol 13 (2012), pp 22-25. D.D.Macdonald and M.Urquidi Macdonald, “Deterministic model for passivity breakdown”, Corrosion Science, vol 31 (1990), p 425.

Acknowledgements Author acknowledges the junior and senior research fellowships awarded in the Indian Institute of Technology, Banaras Hindu University, Varanasi, during conduction of experimental part of this work. Valuable suggestions from Prof. M.M.Singh and Prof. A.K.Mukherjee, Institute of Technology, B.H.U., have been providing impetus towards performing comparative study of corrosion resistance offered by the alloys on the basis of variation of corrosion resistance parameter.

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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 JAVA TO C COMPILER (J2CC) Nair Saarika Bhasi, J. Shylaja, Arun.V, T.S.Niveda Department of Information Technology, Amrita School of Engineering, Amrita Viswa Vidyapeetham University, Amrita Nagar post, Ettimadai, Coimbatore-641112, Tamil Nadu,India. _______________________________________________________________________________________ Abstract: To design a flexible compiler to convert a java program to a C program and compile it with the aim of reducing the time complexity. The main objective of the project is to combine the robustness of Java and speed factor of C. Though the implementation seems to be from object oriented programming language to procedural language, the project concentrates in reducing the execution time of the program and utilizing the rich libraries of Java. This project can be used as a interface for many real time projects that are time-dependent. Keywords: Java, Compiler, OOPS, procedural , C, C++

___________________________________________________________________________ I. INTRODUCTION C is an imperative (procedural) language. It was designed to be compiled using a relatively straightforward compiler, to provide low-level access to memory, to provide language constructs that map efficiently to machine instructions, and to require minimal run-time support. C is therefore useful for many applications that had formerly been coded in assembly language, such as in- system programming. Despite its low-level capabilities, the language was designed to encourage cross-platform programming. A standards-compliant and portably written C program can be compiled for a very wide variety of computer platforms and operating systems with few changes to its source code. The language has become available on a very wide range of platforms, from embedded microcontrollers to supercomputers. Java is a computer programming language that is concurrent, class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible. It is intended to let application developers "write once, run anywhere" (WORA), meaning that code that runs on one platform does not need to be recompiled to run on another. Java applications are typically compiled to byte code (class file) that can run on any Java virtual machine (JVM) regardless of computer architecture. With the advent of Java, most of the development activities in the recent times have experienced a shift from C based languages to Java based languages. While this is advantageous in a way, it has its disadvantage too. As programs written in Java language runs on a virtual machine, it runs somewhat slowly compared to other programs. A rather worse problem is that the programs do not always work correctly even if they are written correctly, because a Java virtual machine may be written incorrectly. It is difficult to write a perfect program, especially if it is as big as a virtual machine, so that programs written in Java language tend to suffer from slightly different problems depending on the platforms on which the Java virtual machines run. In C, itâ&#x20AC;&#x2122;s easier to work with hardware directly. This is the reason why embedded C is still widely in use than embedded JAVA. C programs can definitely be faster than Java Programs since Java is partially interpreted. On the other hand, Java is a strong, platform independent language with powerful features such as garbage collection, error handling and the powerful library framework. Developing in Java provides us with various options and scope of improvement. The deployment in Java is what seems to be less advantageous compared to deployment in C. This study aims to provide a solution to this particular problem. The objective of the study is to devise a method that can be used to practice developing in Java and deploying the program in C. This allows us to harness the rich features of the Java programming language while developing and at the same time, eliminate the need for a JVM to deploy the code. This way, the advantages of both the languages are brought forward, eliminating their drawbacks. The proposed plan is to develop a Java-to-C translator which performs the task of syntactic conversion of the Java language to C language.

II.

ANALYSIS OF JAVA AND C

The first step of the approach starts with understanding and comparison between the both the languages. On addressing the way of compilation of two programming language and according to [1], compared to C oriented languages, Java has an overhead in terms of execution time and memory that it uses. To prove this fact we undertook an analysis over execution time and memory of C and Java programs from an online programming

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competition forum from [3]. The execution time and memory values that are recorded for a particular problem from [4] were plotted in a graph. The result showed that Java is costlier in aspect of memory and speed and the result will be in agreement with solutions in Java and C to all kinds of problems.

Figure:1.1Analysis on Java and C II. ARCHITECTURAL DIAGRAM The first step for constructing the J2CC is to analyze and understand the work flow and design the Architectural diagram. Figure 1.2 shows the Architectural diagram of J2CC. The first phase is Lexical Analysis (to emit tokens), second phase is syntax analysis (writing CUP grammar for the Java specification), third phase is semantic analysis( writing Abstract syntax definition) and finally Target code generation( output i.e. a C program). The detail description of each phase will be described in the section III

spec.cup

spec.lex

CUPS

JLex

Parser.java

Java Program

Tokens pec.lex

Abstract Syntax

Target code generation

C Program

Figure1.2 Architectural diagram

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The work flow starts with writing spec.lex, considering all the data types, operators and writing regular expression of Java here a input is a Java program and output will be a stream of tokens used in the input program. Then writing spec.cup, here we write cup specification for the entire Java language after identifying the syntax. The parser.java is the output of the Cup specification i.e. Syntax analysis phase and tokens are output of Lexical analysis. Then we write Abstract syntax for the grammar. The abstract classes are written based on the usability of the language specification in C. According to [6], the current embedded implementations of Java impose tight constraints on functionality and require significant storage space. It demonstrates a Java-to-C compilation strategy that enables the use of Java on a wide range of embedded platforms by removing many of the constraints on functionality and code size and is also applicable to embedded systems without a JVM. The generated executables were found to be over 25 times smaller than those generated by a Java-to-native code compiler. The method aims to eliminate the use of a JVM by converting a Java code to a C code which is then compiled by a C compiler. In this method, all Java library classes are compiled into a library and the required ones are loaded in the runtime. The code is analyzed and unnecessary classes, methods and fields are discarded. This leads to generation of highly optimized C code. III.

TOOLS

Lexical analyzer reads input characters and produces a sequence of tokens as output (nexttoken()). A program or function that performs lexical analysis is called a lexical analyzer. A lexer is generally combined with a parser, which together analyze the syntax of programming languages. A lexer is itself a kind of parser – the (contextfree) syntax of the language is divided into two pieces: the lexical syntax (word structure), which is processed by the lexer; and the phrase structure, which is processed by the (phrase-level) parser. The lexical syntax is usually a regular language, whose atoms are individual characters, while the phrase syntax is usually a contextfree language, whose atoms are words (tokens produced by the lexer).A lexical analyzer breaks an input stream of characters into tokens. Lex is a lexical analyzer generator for the UNIX operating system, targeted to the C programming language. Lex takes a specially-formatted specification file containing the details of a lexical analyzer. This tool then creates a C source file for the lexer. The JLex utility is based upon the Lex lexical analyzer generator model. JLex takes a specification file then creates a Java source file for the corresponding lexical analyzer. Format of JLex specification has three parts, first is User code-It have the Java source code to be copied into the generated Java source file. It contains utility classes or return type classes you need. Second is JLex directive-have the macro definition and third is Regular Expression-These rules specify how to divide up the input into tokens. Each rule includes an optional state list, a regular expression, and an associated action. The format can be like User Code %% JLex Directives %% Regular Expression rules The J2CC compiler does the parsing action with the help of LALR type parser. This section gives a brief idea of LALR parsers and working of the parser. LALR parsers are based on a finite-state-automata concept, from which they derive their speed. The data structure used by an LALR parser is a Stack with pushdown automaton (PDA). A deterministic PDA is a deterministic-finite automaton (DFA) that uses a stack for a memory, indicating which states the parser has passed through to arrive at the current state. Because of the stack, a PDA can recognize grammars that would be impossible with a DFA; for example, a PDA can determine whether an expression has any unmatched parentheses, whereas an automaton with no stack would require an infinite number of states due to unlimited nesting of parentheses. LALR parsers are driven by a parser table in a finite-state machine (FSM) format. An FSM is very difficult for humans to construct and therefore an LALR parser generator is used to create the parser table automaticallyfrom a grammar in Back–Naur Form which defines the syntax of the computer language the parser will process. The parser table is often generated in source code format in a computer language (such as C++ or Java). When the parser (with parser table) is compiled and/or executed, it will recognize text files written in the language defined by the BNF grammar. LALR parsers are generated from LALR grammars, which are capable of defining a larger class of languages than SLR grammars, but not as large a class as LR grammars. Real computer languages can often be expressed as LALR(1) grammars, and in cases where a LALR(1) grammar is insufficient, usually an LALR(2) grammar is adequate. If the parser generator handles only LALR(1) grammars, then the LALR parser will have to interface with some hand-written code when it encounters the special LALR(2) situation in the input language. As in [2], the Cups does the syntax analysis of the grammar. A parser specification for CUPs consists of a set grammar rules. The CUP-generated parser implements semantic values by keeping a stack of them parallel to

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the state stack. Where each symbol would be on a simple parsing stack, now there is a semantic value. When the parser performs a reduction, it must execute a Java – language semantic action; it satisfies each reference to the right-hand-side semantic value by a reference to one of top k elements of the stack (for a rule with k right-handside symbols). When the parser pops the top k elements from the symbol stack and pushes a non terminal symbol, it also pops k from the semantic value stack and pushes the value obtained by executing the Java semantic action code. The J2CC compiler's Parsing phase with the CUPS (LALR) parser firstly adds the CUPS (Common Utility Parser) package following the lexical analysis. The lexicons are made to emit from spex.lex. Then a separate spec.cup file is written such a way that the file gets the lexicons emitted from spec.lex in the form of command line argument. With the input file specified (i.e. Input java file for which the transition of language happens) is accepted by separate Java file. With the Java specifications written in the spec.cup whole project is built and made to generate the parse. The generation of the parser is accepted only if the sym.java and parser.java is created in the current working directory of the project. The sym.java file contains the enumeration of all terminal symbols specified in the project. The parser.java file turns the grammar into separate functions, Each function checks for the syntax and the semantic(This is a point where abstract syntax classes are called for the next phase).Now running the Java file that is written for accepting the Java file(which is used for transition) gives a parsing successful message if the constructs are maintained. Errors are flashed by CUPS if the constructs are not followed by the Java program. Hence the Error handling happens in the Parsing Phase of the project IV.

TRANSLATION SYNTAX DEFINITION

The Java classes are written for the grammar and also semantic action codes are included in the CUP specification. Wherever the generated parser reduces by rule, it will execute the corresponding semantic action fragment. The semantic actions are written only for the production that is being used in C. Now consider some examples, the production modifier which have list of modifiers like public, protected, private…etc, since the target code (C) does not have definition for modifiers in the language specification, the production modifier in the CUP specification does not have semantic action for it. Similarly consider the production package declaration, since package statements are not used C, the semantic action code for production package declaration is also not defined and the class declaration in Java is mapped to Struct in C. So the semantic actions are written after the comparative study on Java and C. An Abstract syntax does semantic check of the grammar. For Example: The import statement in cup specification is import_declaration ::= single_type_import_declaration | type_import_on_demand_declaration | static_single_type_import_declaration | static_type_import_on_demand_declaration ; single_type_import_declaration ::= IMPORT name SEMICOLON ; static_single_type_import_declaration ::= IMPORT STATIC name SEMICOLON ; type_import_on_demand_declaration ::= IMPORT name DOT MULT SEMICOLON ; static_type_import_on_demand_declaration ::= IMPORT STATIC name DOT MULT SEMICOLON ; Here, IMPORT STATIC name SEMICOLON and IMPORT name SEMICOLON is mapped as "#include<" + name + ">";and IMPORT name DOT MULT SEMICOLON and IMPORT STATIC name DOT MULT SEMICOLON as return "#include<" + name + ".*.h>"; V. TARGET CODE GENERATION Since the target is an algorithmic language, the syntax can directly be mapped to the target syntax. Our approach works not only for syntactically compatible language pairs but to the other category as well, where the source and target are syntactically non compatible. The generation can be carefully controlled for the most basic syntax elements, so as to get the perfect translation to the target language. The higher syntax structures such as classes and blocks simply delegates their translation to its constituent syntax elements eventually reaching the basic

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constructs such as keywords, operators, identifiers and their ordering in the target. This kind of explosion can even deal with nested and semi nested type syntax contracts such the virtual method overriding of java. The methods of certain classes can be individually overridden for each of its objects, its sub objects and so on. Although it is difficult to capture far relation between syntax constructs, we will be able to cater to all CFG based constructs. This is adequate for our task at hand for all programming languages, since they will never have more complex constructs. A few cases that we had to deal with the generation include templates and generics in Java and C++ pair. Consider the template class construct in C++. This has an explicit ‘template’ key word and followed by a list of arguments both type and value, enclosed in angle brackets (<>), e.g. template<class T> class A{….} . Where as in Java the same generic class is constructed with simple placeholders in angle brackets, e.g. class A<T> {….} . Although the Java to CPP translation is predominantly lossy in nature, this is an exceptional case where we had to insert the template key word and rearrange the later, to a more complex form. VI. CONCLUSION AND FUTURE WORKS As stated, J2CC compiler is flexible and is efficient. Since the grammar specification and the abstract syntax classes are written for the entire Java specification, the compiler can be generated for other languages too like pearl, Pascal, C++…etc. We just want to change representation in the specification for the desired target code language. It takes the time complexity for the LALR grammar. Our future works are Error handling, Garbage collection, Templates. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8]

Andrew W.Appel,”Modern Compiler Implementation in Java”Revised Ed. Cambridge University Press India Pvt.Ltd, New Delhi, 2010. CUPS [online].Available: http://www.cs.princeton.edu/~appel/modern/java/CUP/ CodeChef [online].Available:www.codechef.com CodeChef [online].Available:www.codechef.com/problems/H1 Dejan Jelovic.”Why Java Will Always Be Slower than C++”unpublished. Ankush Varma and Shuvra S. Bhattacharyya, “Java-through-C Compilation: An Enabling Technology for Java in Embedded Systems”, Java-through-C Compilation: An Enabling Technology for Java in Embedded Systems. Anders Nilsson, Torbjorn Ekman and Klas Nilsson, “Real Java for Real Time – Gain and Pain”, CASES 2002, October 8–11, 2002, Grenoble, France. Robert C. Martin.“Java C++ A critical comparison. “unpublished.

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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 SVM Based Speaker Independent Isolated Malayalam Word Recognition System Thushara P V1, Gopakumar C2 M.Tech Scholar, Department of Electronics, College of Engineering, Chengannur Chengannur, Alappuzha Dist., Kerala-689121, INDIA 2 Associate Professor, Department of Electronics, College of Engineering, Karunagapally Karunagapally, Kollam Dist., Kerala-690523, INDIA 1

Abstract: As technology advances man-machine interaction is becoming an unavoidable activity. So an effective method of communication with machines enhances the quality of life. If it is able to operate a system by simply commanding, then it will be a great blessing to the users. Speech is the most effective mode of communication used by humans. So by introducing voice user interfaces the interaction with the machines can be made more user friendly. This paper implements a speaker independent speech recognition system for limited vocabulary Malayalam Words in Raspberry Pi. Mel Frequency Cepstral Coefficients (MFCC) are the features for classification and this paper proposes Radial Basis Function (RBF) kernel in Support Vector Machine (SVM) classifier gives better accuracy in speech recognition than linear kernel. An overall accuracy of 91.8% is obtained with this work. Keywords: Speech Recognition; Feature Extraction; MFCC; Support Vector Machine; RBF kernel; Linear kernel; Raspberry Pi I.

Introduction

Automatic speech recognition can be defined as a technology which enables a system to recognize the input speech signals and interpret the meaning, after which the system should be able to generate some control signals [1]. In this paper, the speech recognition system recognizes Malayalam words with limited vocabulary. There are so many challenges in speech processing like acoustic and phonetic variability. Isolated speech input is used since continuous speech is difficult to process. In continuous speech, it is difficult to find the start and end points of words. Within all these considerations, system recognizes words with better accuracy and greater speed. Malayalam is recently being considered among one of the classic languages in India. Speech recognition system for Malayalam language helps people who are not conversant with English and unaware of using computer. Malayalam is one among the 22 languages spoken in India with classical status [2]. Malayalam belongs to the Dravidian family of languages and most of the Malayalam speakers live in the Kerala, one of the southern states of India. There are 37 consonants and 16 vowels in the language. For the proper functioning of the system, there should be distinct pauses between the words i.e. isolated words. Due to the memory constrains in the handheld device, the vocabulary supported by the system is limited i.e. it is a limited vocabulary speaker independent isolated word recognition system. II.

Literature Survey

Nowadays, innovation in scientific research is focused much more on the interactions between humans and technology and automatic speech recognition is a driving force in this process. Speech recognition technology is changing the way information is accessed, tasks are accomplished and business is done. There are two related speech tasks: speech understanding and speech recognition. Speech understanding is getting the meaning of an utterance such that one can respond properly whether or not one has correctly recognized all of the words. Speech recognition is simply transcribing the speech without necessarily knowing the meaning of the utterance. The two can be combined, but the task described here is purely recognition. Automatic speech recognition (ASR) is the ability of a machine to convert the words that is spoken in to the microphone to recognized words. A. Speech Production and Perception Five different elements are associated with speech production and perception. They are speech formulation, human vocal mechanism, acoustic air, perception of the ear, speech comprehension etc.

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B. Types of ASR Systems ASR can be classified in several ways: speaker dependent or independent, discrete or continuous, and small or large vocabulary. 1. Speaker Independent (SI) systems or Speaker Dependent (SD) system: System can recognize a variety of speakers, without any training. Such systems limit the number of words in a vocabulary. But speaker dependent system can only recognize the speech of users it is trained to understand [3]. 2. Discrete ASR or Continuous ASR: Discrete ASR recognizes isolated utterances. Here the user must speak unnaturally, leaving distinct pauses between each word. In Continuous ASR, the user can speak naturally, with normal conversational pauses, but it is more difficult for the system to detect the word boundaries. 3. Small vocabulary or large vocabulary system: In small vocabulary ASR, all the words in the vocabulary are trained at least once, whereas large vocabulary systems recognize sounds rather than whole words and are able of recognizing words that have never been in the training set. C. Factors Affecting Speech Recognition Performance Different speech recognition systems have different parameters and design methodology according to the application. But in most cases some factors are similar. For instance, Vocabulary size and variability of factors [3].These factors have significant roles for the accuracy of the system. According to how much vocabulary can be recognized, speech recognition can be divided into three different scales vocabulary speech recognition: Small vocabulary speech recognition, Medium vocabulary speech recognition, large vocabulary speech recognition. Small-scale can identify less than 100 vocabulary while medium-scale can identify more than 100 vocabulary and large-scale can identify more than 1000 vocabulary. Other factors that affect performance of speech recognition system, are variability in speakers, environments, transmission channels and microphones. The variability in speakers involves gender, speed of speaking, regional changes in language. Variability in environment means whether it is noisy or clean. The bandwidth of transmission channel also plays an important role in determining the accuracy of the system as number of samples transmitted changes when bandwidth changes. According to the type of microphone and distance of microphone from mouth, reliability of system may changes. And finally the performance of the system will be in the hands of experts behind the work. III.

Methodology

A speech recognition system is basically a pattern recognition system dedicated to recognize the words spoken into microphone. Proposed automatic speech recognition system (ASR) starts with a feature extraction module where, the input speech waveform is processed to extract the required acoustic feature vectors that are used to characterize the spectral properties of the time varying speech signal. Feature extraction is processing the input speech to extract compact and efficient set of parameters that uniquely represents the speech input .The second stage is the classifier. This stage evaluates the similarities between the input feature vector sequence and trained database to determine which words were most likely spoken. By feature extraction, we calculate Mel Frequency Cepstral Coefficients (MFCCs) [4].We use MFCCs as the feature for classification because it allows better processing of data, high accuracy for clean speech and it approximates the human auditory system's response. The classifier used for training and testing is Support Vector Machine (SVM). The block diagram of the proposed speech recognition system is shown in figure 1:

Figure 1. Block Diagram of Proposed System

A. Voice Activity Detection Voice Activity Detection is a preprocessing technique which detects the start and end point of a word. In the proposed method we use three different features per frame. The first feature is the widely used short-term

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energy (E) [5]. Energy is the most common feature for speech/silence detection. Another feature selected for separating speech and silence is zero crossing rate. It is observed that silence parts have high zero crossing rate whereas in speech, zero crossing is low. Besides these two features, it was observed that the most dominant frequency component of the speech frame spectrum can be very useful in discriminating between speech and silence frames. B. Mel Frequency Cepstral Coefficients (MFCCs)

Figure 2. Calculating MFCCs

C. Support Vector Machine (SVM) Classifier Pattern recognition algorithm used for the proposed system is Support Vector Machine [6]. MFCCs of each of the words is given to the classifier stage for training using SVM learning algorithm. Here 100 samples of each word from different speakers is used to train the classifier and testing is conducted with another different speakers in order to make the system speaker independent. Here we optimize the parameters of SVM by making a comparison between the kernel functions. In this work, we use simple linear kernel and radial basis function kernel for comparison. IV.

Results and Analysis

A. Database Used For conducting this experiment we choose six Malayalam words. The samples stored in the database are recorded by using a high quality studio-recording microphone at a sampling rate of 16 KHz. Malayalam numerals from one to six is chosen to create the database. Twelve speakers are selected to record the words. Each speaker utters six words with thirty samples each. We have used six male speakers and six female speakers for creating the database. Thus the database consists of a total of 2160 utterances of the spoken words. Speech database is shown in Table 1. Table 1. Speech Database

NO 1 2 3 4 5 6

WORDS ഒന്ന് (onnu) രണ്ട് (randu) മൂന്ന് (moonnu) നാല് (naalu) അഞ്ച് (anj) ആറു (aaru)

B. Voice Activity Detection This important front end processing detects the start and end point of words. The result obtained by voice activity detection of word ‘onnu’ is plotted in Figure 3. C. Simulation Parameters of Feature Extraction Block  Sampling frequency = 16000Hz  Frame duration = 10ms  Frame overlapping = 5ms  Number of DFT points = 256  Number of Mel filters = 24  Number of MFCC coefficients = 19

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Figure 3. Voice Activity Detection of word ‘ONNU’

D. Recognition Results There are 100 input samples for training and testing is conducted with samples of different speakers for each word. Training is performed with maximum iterations of 100. In SVM, it is possible to use RBF kernel and linear kernel. In this paper, a comparison of accuracy in test results is made for both the cases when RBF and Linear kernel are used. The accuracy obtained in both the experiments are shown in the table II: Table 2. Accuracy Test Results

Words onnu randu moonnu naalu anj aaru Average

Accuracy (%) Linear kernel RBF kernel 75 91.6 91.6 91.6 84 100 50 100 68 84 84 84 75.5 91.8

E. Comparison of Results

accuracy

COMPARISON BETWEEN KERNELS IN SVM 100 80 60 40 20 0

linear kernel RBF kernel

Words

Figure 4. Comparison of accuracies between linear and RBF kernel

The experimental results prove that training with RBF kernel gives better accuracy in recognition than with linear kernel.System trained with linear kernel has got an average accuracy of 75.5% whereas for RBF kernel it is 91.8%.

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F. Hardware Implementation Results The system consists of six Malayalam words and five different speakers for training and 5 speakers for testing. The experiment was conducted in Raspberry Pi and obtained 91.8% accuracy.

Figure 5. Hardware platform: Raspberry Pi

Conclusion

The paper proposes a speech recognition system using MFCCs and support vector machine. The proposed method improves the accuracy of recognition compared to existing methods such as Wavelet coefficients and ANN [7], MFCCs and k-means clustering, Formant frequencies and ANN [8] etc. Misclassification rate in support vector machine is analyzed by comparing the kernel functions for mapping input space to feature space. The generalization capability of SVM classifier improves when we are using RBF kernel compared to linear kernel. The system implemented in Raspberry Pi has got an accuracy of 91.8%. References [1] [2]

Shivanker Dev Dhingra , Geeta Nijhawan , Poonam Pandit , “Isolated speech recognition using mfcc and dtw”,International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8, August 2013 Sreejith C, “Isolated Spoken Word Identification in Malayalam using Mel-frequency Cepstral Coefficients and K-means clustering”, International Journal of Science and Research, Vol. 1, December 2012, pp.163-167

[3]

Nitin Trivedi et al, “Speech Recognition by Wavelet Analysis”, International Journal of Computer Application, Volume 15, February 2011, pp.27-32

[4]

Daniel Jurafsky & James H. Martin, “Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition”, Pearson Education, 2007, pp.327-335

[5]

M. H. Moattar, M. M. Homayounpour, “A simple but efficient real-time voice activity detection Algorithm”, 17th European Conference on Signal Processing, August 24-28, 2009, pp.2549-2553

[6]

M.A.Anusuya, S.K.Katti, “Classification Techniques used in Speech Recognition Applications: A Review”, International Journal of computer applications, Vol 2, AUGUST 2011, pp.910-954

[7]

Sonia Sunny et al, “Development of a Speech Recognition System for Speaker Independent Isolated Malayalam Words”, International Journal of Computer Science & Engineering Technology, Vol. 3, April 2012, pp.69-75

[8]

Dipanwita Paul, “Automated speech recognition of isolated words using neural networks”, International Journal of Engineering Science and Technology, Vol. 3, June 2011

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