Aijrstem vol3 print by iasir journals

ISSN (PRINT): 2328-3491 ISSN (ONLINE): 2328-3580 ISSN (CD-ROM): 2328-3629

Issue 6, Volume 1, 2 & 3 March-May, 2014

American International Journal of Research in Science, Technology, Engineering & Mathematics

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, aijrstem@gmail.com

PREFACE We are delighted to welcome you to the sixth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM). 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. AIJRSTEM is publishing high-quality, peer-reviewed papers covering topics such as Computer and computational sciences, Physics, Chemistry, Mathematics, Applied

mathematics,

Biochemistry,

Robotics,

Statistics,

Electrical

Electronics

engineering, Mechanical & Industrial engineering, Civil Engineering, Aerospace engineering, Chemical engineering, Astrophysics, Nanotechnology, Acoustical engineering, Atmospheric sciences, Biological sciences, Education and Human Resources, Environmental research and education, Geosciences, Social, Behavioral and Economic sciences, Geospatial technology, Cyber security, Transportation, Energy and Power, Healthcare, Hospitality, Medical and dental sciences, Marine sciences, Renewable sources of energy, Green technologies, Theory and models and other closely related fields in the discipline of Science, Technology, Engineering & Mathematics. The editorial board of AIJRSTEM is composed of members of the Teachers & Researchers community who have expertise in the fields of Science,

Technology,

Engineering

Mathematics

order

develop

and

implement widespread expansion of highâ&#x20AC;?quality common standards and assessments. These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. 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 science, technology, engineering & mathematics. 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 AIJRSTEM for entrusting us with the important job. We are thankful to the members of the AIJRSTEM 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 sixth issue, we received 126 research papers and out of which only 50 research papers are published in three volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of science, technology, engineering & mathematics.

This issue of the AIJRSTEM 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 science, technology, engineering & mathematics and may open new area for research and development. We hope you will enjoy this sixth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM), ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 (March-May, 2014, Issue 6, Volume 1, 2 & 3). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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

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

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

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

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

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

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

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

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

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Computer and computational sciences  Physics  Chemistry  Mathematics  Actuarial sciences  Applied mathematics  Biochemistry, Bioinformatics  Robotics  Computer engineering  Statistics  Electrical engineering & Electronics  Mechanical engineering  Industrial engineering  Information sciences  Civil Engineering  Aerospace engineering  Chemical engineering  Sports sciences  Military sciences  Astrophysics & Astronomy  Optics  Nanotechnology  Nuclear physics  Operations research  Neurobiology & Biomechanics  Acoustical engineering  Geographic information systems  Atmospheric sciences  Educational/Instructional technology  Biological sciences  Education and Human resource  Extreme engineering applications  Environmental research and education  Geosciences  Social, Behavioral and Economic sciences  Advanced manufacturing technology  Automotive & Construction  Geospatial technology  Cyber security  Transportation  Energy and Power  Healthcare & Hospitality  Medical and dental sciences  Pesticides  Marine and thermal sciences  Pollution  Renewable sources of energy  Industrial pollution control  Hazardous and e-waste management  Green technologies  Artificial/computational intelligence  Theory and models

TABLE OF CONTENTS (March-May, 2014, Issue 6, Volume 1, 2 & 3) Issue 6 Volume 1 Paper Code

Paper Title

Page No.

AIJRSTEM 14-306

Optimization of Model Parameters of Experimentally Studied Broadband Transmission Line Transformers with Ferrite Toroidal Cores Amidon FT82-43 and FT114-43 Boyan Karapenev

01-05

AIJRSTEM 14-307

Goertzel Algorithm based DTMF Detection S Nagakishore Bhavanam, Dr. P. Siddaiah, Dr. P. Ramana Reddy

06-12

AIJRSTEM 14-308

Optimization of sound transmission loss and prediction of insertion loss of single chamber perforated plug muffler with straight duct Shantanu V. Kanade, A. P Bhattu

13-19

AIJRSTEM 14-310

Analysis of GPON Downstream with 128 Users using EDFA In-Line Amplifier for Extended Reach To 80 Bentahar Attaouia, Kandouci Malika

20-27

AIJRSTEM 14-312

Comparative Modeling of Molten Salt Reactor (MSR) Passive Cooled Drain Tanks C.E.Okon, T. Abram

28-40

AIJRSTEM 14-313

COLLISION AVOIDANCE SCHEME USING EMBEDDED SYSTEM Prof. Sangram Keshari Swain, Anup Patnaik, Abhijeet Pradhan, Vinod Kumar Kurneni

41-51

AIJRSTEM 14-316

Influence of twist on tensile and abrasion Properties of DREF-II friction spun plied yarns Prof. Sunil Kumar Sett, Dr. Asis Mukherjee and Mr. N Kundu

52-56

AIJRSTEM 14-318

Distinguishing between Global Warming and Urban Warming for Bangalore with the aid of Statistical Analysis Rajesh Gopinath, Vijayalakshmi Akella and P. R. Bhanumurthy

57-60

AIJRSTEM 14-319

Smart Controller for Wind-Solar Hybrid System under Grid Connected Operations Kishore Ravi Hegde, Prajwal C P, Pramod Kumar H S, Shivakumar S, Prof. R. Jayapal

61-66

AIJRSTEM 14-320

Gaseous Emissions from MSW Dumpsites in Vijayawada Shaheda Niloufer, Dr.A.V.V.S.Swamy, K.Syamala Devi

67-73

AIJRSTEM 14-321

Equal Area Criterion Scheme to Reduce DC Bus Voltage Stress of Single Stage Single Switch Power Factor Corrected Converter Bindu S J and C A Babu

74-82

AIJRSTEM 14-325

Synchronization Response of an Indirectly Coupled Nonlinear Digital Resonator -A Simulation Study S Chakraborty and B C Sarkar

83-91

AIJRSTEM 14-329

Studies on Spectral and Antifungal Activity of Some Complexes of Chromium, Nickel and Copper Metals with p-phenylenedibiguanide [C10H16N10, Ph (BigH)2] Molecule R.K. Prasad, Bina Rani & Madhu Kumari Gupta

92-96

AIJRSTEM 14-338

PHYTOREMEDIATION OF CADMIUM AND CHROMIUM CONTAMINATED SOILS BY CYPERUS ROTUNDUS. L Subhashini, V and A. V. V.S. Swamy

97-101

AIJRSTEM 14-339

Fracture strength evaluation of AA 2219-T87 weldment using SINTAP and modified IFM procedures S.Rajakumar and N.Murugan

102-111

AIJRSTEM 14-340

Design and Analysis of Flexural Mechanism-A Short Review S.V.Deokar, S.M.Gaikwad, S.P.Deshmukh

112-114

Issue 6 Volume 2 Paper Code

Paper Title

Page No.

AIJRSTEM 14-342

Correlation between electrical resistivity and water content of sand â&#x20AC;&#x201C; a statistical approach Sudhir Bhatt, Pradeep K. Jain

115-121

AIJRSTEM 14-343

Kinetics of thermal decomposition of gadolinium alkanoates Suman Kumari, Mithlesh Shukla, and R.K Shukla

122-125

AIJRSTEM 14-353

A Novel Unequal Error Protection scheme employing binary BCH codes and Hierarchical 8PSK modulation for wireless channels Mussawir Ahmad Hosany

126-132

AIJRSTEM

A Study on Adsorption of Cd(II) from Aqueous Solution Using Fly Ash

133-139

14-356

Saroj Kumar, A.K.Mishra, M. Upadhyay

AIJRSTEM 14-360

Effect of FSP Multipass on Microstructure and Impact Strength of AL6063 Chandan Deep Singh, Ripandeep Singh, Naveen Kumar, Dr. Jaimal Singh Khamba

140-145

AIJRSTEM 14-361

Performance of Reinforced Concrete Beam under Point Impact Loading I.K .Khan

146-150

AIJRSTEM 14-367

n Empirical Influence of Classical Raaga on Face Mr.AshishA.Bardekar Dr.A.A.Gurjar

151-155

AIJRSTEM 14-370

Physico-mechanical Response of Acrylic-Viscose Ring Spun and Dref 2 Friction Spun Yarns Prof. Siddhartha Bandyopadhyay and Prof Sunil Kumar Sett

156-160

AIJRSTEM 14-373

Positivity Preserving Monotonic Quadratic Trigonometric Beta-Spline Mridula Dube and Meenal Priya Singh

161-165

AIJRSTEM 14-374

Shading Impact on Solar PV Module Savita Kumari

166-169

AIJRSTEM 14-375

Exposure to low-level ionizing radiations in some dwellings and Industrial units of Haryana Ajay Garg, R. P .Chauhan and Sushil Kumar

170-173

AIJRSTEM 14-376

Almost n-Duals of Some Difference Sequence Spaces K. B. Gupta and Ashfaque A. Ansari

174-180

AIJRSTEM 14-377

Proposed Maintainability Model for Software Development: Design Issues Anshul Mishra and Ajay Kumar Yadav

181-184

AIJRSTEM 14-378

ICT Tools for Precision Farming Mr.Vivek Parashar, Mrs. Amrita Parashar

185-188

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Robust Procedure for Estimating Multivariate Location and Scatter Muthukrishnan. R and K.Mahesh

189-195

AIJRSTEM 14-384

A Review of Criticality Safety Analysis for Under-Moderated Low Enriched Uranium (LEU) Dioxide, High Enriched Uranium (HEU) Nitrate & Plutonium Oil C.E.Okon, Y. E. Chad-Umoren

196-214

Issue 6 Volume 3 Paper Code

Paper Title

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AIJRSTEM 14-385

Data Security in Cloud Environment Using Cryptography Algorithm Prof. Y. N. Patil, Mangesh D. Namewar

215-218

AIJRSTEM 14-386

Scientific Computations of Black-Scholes-Merton Equation for Option Pricing Jigna Panchal, Dr. Sandeep Malhotra

219-223

AIJRSTEM 14-387

Design and Development of Automated Aero-Terrestrial Systems for Persistent Surveillance Missions Nithan Raj T N ,Rajani Katiyar

224-227

AIJRSTEM 14-388

Reliability Estimation of Component-Based Software through Interaction-Based Model Dimpal Tomar, Dr. Pradeep Tomar

228-231

AIJRSTEM 14-389

Bayesian Analysis of First Conception through a Probability Model Ashivani Kumar Yadav

232-236

AIJRSTEM 14-390

Investigation of Flexural behavior of hybrid natural fiber composite with recycled polymer matrix Ramanpreet Singh, Lakshya Aggarwal, Mohit Sood

237-240

AIJRSTEM 14-391

Mechanical Charactreisation of Typha Domingensis Natural Fiber Reinforced Polyester Composites Ponnukrishnan.P, Chithambara Thanu. M and Richard.S

241-244

AIJRSTEM 14-393

A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications Prof. Dr. P.K. Srimani, Prof. Rajasekharaiah K.M.

245-252

AIJRSTEM 14-394

Reactive Routing in MANETs: A Performance Evaluation Jaspreet Singh, C.S. Rai

253-257

AIJRSTEM 14-395

Study of UWB Low Noise Amplifier Somit Pandey, Prof. Puran Gour , Brij Bihari Soni

258-260

AIJRSTEM 14-396

SORPTION OF Ni (II) FROM AQUEOUS SOLUTION USING CHITOSAN Dhanesh Singh, Anjali Singh, Saroj Kumar

261-264

AIJRSTEM 14-398

Numerical Solution of One - Dimensional Time- Independent Problems Using FEM Vinay Saxena

265-268

AIJRSTEM 14-399

Discussion of Effective Speech Communication under Different Compression Approaches Amit, Sunita

269-272

AIJRSTEM 14-400

Study on Routing Protocols Classification in Sensor Network Jyoti, Sunita

273-277

AIJRSTEM 14-401

Study on Watermarking Approaches on Biometric Images Neeraj, Sunita

278-281

AIJRSTEM 14-405

Study on Communication Architectures in Sensor Network Rimpy, Sunita

282-285

AIJRSTEM 14-409

Microwave Assisted Synthesis, Characterisation and Antibacterial Study of Drug based Schiff Bases and their Zn(II) Complexes K.P.Srivastava, Anuradha Singh & Suresh Kumar Singh

286-292

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Scientific Computations of Black-Scholes-Merton Equation for Option Pricing Jigna Panchal1, Dr. Sandeep Malhotra2 Department of Science & Humanities, Indus University, Ahmadabad, Gujarat, India 2 Department of Science & Humanities, Nirma University, Ahmadabad, Gujarat, India 1

Abstract: Black-Scholes-Merton Partial differential equation represents the model for pricing an option. It is of second order parabolic type differential equation. It is a very useful application for the trading terminal. Using Black-Scholes-Merton option pricing model the trader can find the theoretical value of options (call/put). This model can also be used to price an option on a verity of assets including securities, commodities, currencies etc. It is thus important to solve Black-Scholes-Merton Partial differential equation. The solution provides fair price of an option (call/put). In the present paper several methods are discussed and we have proposed to apply Fourier transformation to solve the model with the due advantages. Key words: Black-Scholes-Merton model, Partial differential equation, call/put options, Fourier transform method. I. Introduction The pricing of option is very important problem in financial market. In option pricing theory, the Black-ScholesMerton equation is one of the most effective models for pricing options. If we consider the European call option, which gives the right to buy an asset on a specific future date, at a specific price, which depends on S-Spot price, X-Exercise Prices, t-Expiration date, r-risk free interest rate, and σ-Volatility. This model is very useful, since this requires five variables only in which four are easily available, those are S, X, t, r, and for the volatility we have to use historical data to estimate it. The formula was developed by three economists- Fisher Black, Myron Scholes and Robert Merton. They were awarded the 1997 Nobel Prize in economics for their work. II. Theoretical Analysis In Mathematical Finance, the Black-Scholes-Merton equation is a Partial differential equation to find the value of European Call/put option. Suppose C ( S , t ) is the call premium then the equation,

C 1 2 2  2C C   S  rS  rC  0 2 t 2 S S is a Black-Scholes-Merton Partial differential equation. Where S - Spot price, X – Exercise price, t  [0, T ] , C (0, t )  0 for all t. Consider the European call option whose final payoff at the expiry time T is given by a function f of the final spot price S.

lim C ( S , t )  f (S )

t T

which is continuous function need not be differentiable everywhere, as S  , C ( S , t )  S , r- risk free interest rate, σ-Volatility (both are constant). The common assumptions found to be made in the model are:  The options are European type  No dividends are paid  Movement of the market cannot be predicted  No Commission and no transaction cost  Interest rates are risk free and are constant  Volatility is constant  III. Solution of the problem To solve the above mathematical model various approaches and methods are proposed by researcher. Some of them having limitations and some having advantages over them. In this paper different types of available

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approaches are discussed and attempt is made to bring out the best suitable approach which fulfills the due advantages. Our goal is to find the function C (S , t ) : (0, )  [0, T ]  [0, ) which satisfy the given Partial differential , equation. This equation has many solutions corresponding to all the different functions that can be defined with S as the underlying variable. The particular value that is obtained when the equation is solved depends on the boundary condition. These specify the values of the call option at the boundaries of possible values of S and t. Here we will discuss several analytical methods like Laplace transform method, Separation of variable method, Fourier integral method, and Fourier transform method to solve the model. Method (I): Use of Laplace transformation

C 1 2 2  2 C C   S  rS  rC  0 2 t 2 S S With lim C ( S , t )  f ( S ) which is continuous function need not be differentiable everywhere.

(1)

t T

To convert the boundary condition problem into the initial condition problem, we let

y T t

With this equation (1) reduce to

1 2 2  2C C C  S  rS  rC  0 2 2 S y S With lim C ( S , y )  f ( S )

(2)

y 0

Applying Laplace transform to equation (2) with respect to y, we get

1 2 2 d 2C dC  S  rS  (r  q)C   f ( S ) 2 2 dS dS

(3)

This is an ordinary differential equation in S. We can find its complimentary function easily (by using solution of Cauchy-Euler equation) but it is very difficult to find particular integral, because it depends up on the unknown function f (S ) . Method (II): Use of Variable Separable method and Fourier Integral Second we thought to apply Variable Separable method to solve the equation. This differential equation is parabolic type differential equation so before solving this first we convert it in to one dimension Heat equation which is also of parabolic type. So in place of solving Black-Scholes-Merton Partial differential equation if we solve Heat equation then we are through. We can convert the Black-Scholes-Merton Partial differential equation to the heat equation by the following substitutions: y T t ,

S x  ln  X

  )(T  t ) ,   (r  2  2

D( x, y)  e r (T t ) C (S , t ) These substitutions also convert the boundary condition ( lim C ( S , t )  t T

f (S ) )

into the initial condition lim D( x, y )  f ( Xe ) . x

y 0

Using these substitutions, we get

D  2  2 D  y 2 x 2 x With, lim D( x, y )  f ( Xe )

(4)

y 0

By using Variable Separable method to solve equation (4), we get following three possible solutions

(i)

D( x, y)  (C1e  C2e px

 px

2

)C3e 2

p2 y

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(ii)

D( x, y)  (C1 cos px  C2 sin px)C3e

(iii)

D( x, y)  (C1 x  C2 )C3



2 2

p2 y

Among the three solutions only case (ii) will give the appropriate solution which is consistent with the initial condition of the problem. Accordingly,

D( x, y)  ( A cos px  B sin px)e



2

p2 y

(5)  D( x, y, p) Now, any separable solution of the linear second order partial differential equation can be written as a linear combination of the family of solutions. As any series of function (5), found in the usual manner by taking p as multiples of a fixed number, would lead to a function that is periodic in x when y=0. However, since f ( Xe x ) is not assumed to be periodic, it is natural to use Fourier integral instead of Fourier series. Therefore, let 2



D( x, y )   D( x, y, p) dp 0



  ( A cos px  B sin px)e



2 2

p y

(6)



D( x,0)   ( A cos px  B sin px) dp  f ( Xe x )

Now,

This is Fourier integral of function f. Where,

A( p) 





 f (v) cos pv dv and

B( p ) 







 f (v) sin pv dv



     1   D( x,0)   (  f (v) cos pv dv) cos px  (  f (v) sin pv dv) sin px) dp   0     

2 2      p y 1    D( x, y )   (  f (v) cos p( x  v)e 2 dv) dp    0      2     p 2 y   1   2    f (v )   e cos p( x  v) dp) dv      0     2 ( x v )  1  1  2 2 y      f (v ) e dv     2s       vx 1  Z 2 , D( x, y )  Taking, Z    f ( x  Z 2 y )e dZ   2y     

D( x, y) 

 f (Se

(r 

2 2

) (T t ) x T t )



x2 2

(7)



x   1    2 f ( x   x y ) e dx   2      2

Again taking,

x  2Z , we get D( x, y ) 

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

  x2     1   ( x x y ) )e 2 dx    f ( Xe 2       

Now substituting the values of x and y, we get

S   (ln( )  ( r  ) (T t ) x 1  X 2 D ( x, y )  f ( Xe  2  2

   (r    f ( Se 2 2   



T t )



) (T t ) x T t )

 x2 2

 dx 



x2 2

  dx   

This is the general solution of the model in the integral form. Method (III): Use of Fourier transformation Finally, we thought to use Fourier transform method to solve the model for completeness and for clarity. The Fourier transform can be viewed as an extension of the Fourier series to non-periodic functions. Applying Fourier transform on both the sides of the equation (4) , we get  22

y  2 2 F D   F D   0  F D   C1e 2 y 2 x Since, D( x,0)  f ( Xe )  F D( x,0)   G( ) Where, G is the Fourier transform of f.

 F D   G e

 22 y 2

Taking inverse Fourier transform on both the sides we get,   2 2   y  D( x, y )  F 1 G  e 2      

   Now, by using convolution theorem as, F G( )   f ( Xe ) and F  e 2   1



D ( x, y ) 





f (v )

 y

x    1 e 2 2 y   y  2

2 2

1

 ( x v ) 2

2 2 y

This is similar to what we get in equation (7). Therefore applying same steps, we get

C (S , t )  e



r (T t )

 f (Se

(r

2 ) (T t )x T t ) 2



x2 2



dx  e r (T t ) E f ( S  )







(r 

2 ) (T  t ) x T  t ) 2

Where, S  Se and x is the normal random variable with mean 0 and variance 1. This is the general solution of the model in the integral form. The solution is obtained by using the condition which provides fair price of an option (call/put). IV. Conclusion Several methods are proposed to solve the black Scholes Merton partial differential equation. Laplace Transformation method works when we use the payoff function as f ( S )  max{ S  X , 0} Fourier analysis . methods i.e., Fourier integral method and Fourier Transform method give the answer to the general payoff function f (S ) . We are required to solve integral equation to obtain the particular solution for the model in both

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the methods but general solution is obtained shortly with Fourier Transform method. Using this solution the trader can find the theoretical value of options (call/put) on a verity of assets including securities, commodities, currencies etc with different pay off functions. References [1] [2] [3] [4] [5]

BLACK, F. & SCHOLES, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy 81, 637-656. HYOSEOP LEE AND DONGWOO SHEEN, Laplace transform method for the Black -Scholes equation, International Journal of Numerical Analysis and Modeling, Volume 6, Number 4, Pages 642-658. AMBER HABIB, The calculus of finance, Universities Press. WILLMOT, P., HOWISON, S. & DEWYNNE, J. (1995). The Mathematics of Financial Derivatives. Cambridge University Press. ERWIN KRYSZIG, Advanced Engineering Mathematics 8 Edition ,Wiley India Pvt. Ltd. J C Hull, Options, Futures and other derivatives, Pearson Education, India

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Design and Development of Automated Aero-Terrestrial Systems for Persistent Surveillance Missions Nithan Raj T N1 ,Rajani Katiyar2 B.E student, 2Assistant Professor Department of Electronics and communication engineering RVCE Bangalore, Karnataka, INDIA 1

Abstract: Overhead unmanned aerial surveillance is currently being performed by fixed wing aircrafts. These vehicles require a constant flow of air over the wings and as such cannot perform stationary aerial surveillance of an area of land. Multi-Rotor Aerial Vehicles are used for by hovering at a fixed point in air. Since this vehicle uses multiple rotary motors to achieve the hovering condition, the endurance of the vehicle is inadvertently very low. Low endurance leads to short duration surveillance mission. Typical endurance of surveillance for a quadrotor system varies from 15mins to 20mins. In this paper Design and Development of automated Aeroterrestrial system for persistent surveillance mission, deals with improving the endurance of the Multi-rotor aerial vehicle by autonomously recharging the batteries. Keywords: Quad copter, docking station I. INTRODUCTION Over the past decade, the use of unmanned aerial vehicles (UAVs) in reconnaissance and search and rescue has greatly increased throughout the world. Remotely piloted unmanned aircraft provide tactical aid to the military in real-time. These vehicles have proved to be efficient first responders in a number of hostile mission scenarios. Teams of multiple such vehicles can also be used for persistence missions in hazardous or unknown environments reducing the risk to their human counterparts. However, the most significant constraint on the any aerial vehicle for such persistent missions is the depletion of its on-board power resource - electrical or fossil fuel. Several structure and configurations have been developed to execute aerial surveillance. Overhead unmanned aerial surveillance is currently being performed by fixed wing aircrafts. These vehicles require a constant flow of air over the wing, and as such cannot perform stationary aerial surveillance of an area of land. It can be solved by the design and development of a Multi-Rotor Aerial Vehicle which can perform surveillance by hovering at a fixed point in air. Since this vehicle uses multiple rotary wings to achieve the hovering condition, the endurance of the vehicle is inadvertently very low. II. DESIGN METHODOLOGY Development of a stable platform for aerial surveillance, involves designing and developing any Multi-Rotor Aerial Vehicle. For the convenience of design Quadcopter is chosen as the Multi-Rotor aerial vehicle. Here aim is to achieve the surveillance without the intervention of humans. There is a requirement of autonomous navigation i.e. Waypoint Navigation and Auto Landing onto a docking station. The docking station used for recharging of the Li-Po batteries should center and position the quadcopter for recharging its internal batteries. The docking station is built-in with embedded system involving Li-Po recharging circuit to recharge the batteries. It involves three main subsystems 1. Developing Multi Rotor Aerial Vehicle 2. Waypoint Navigation and Docking Station 3. Autonomous recharging of batteries The development of Quadcopter involves the study of the kinematics and dynamics helps to understand the physics of the platform and its behavior. Together with the modelling, the determination of the control algorithm structure is very important to achieve a better stabilization. Is tested on Matlab. III. SIMULATION Simulation of the control algorithm of the Quadcopter using Simulink and Matlab. Simulation of the Quadcopter for attitude performance i.e., Roll, Pitch and Yaw performance was conducted by considering the follow control design specifications [25]. i. Overshoot: 5 % ii. Settling time: 500 ms

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iii. Rise time: 120 ms iv. Steady State Error: 2% Using the technique of Root Locus, the values of Kp, Ki and Kd were found.

Figure 1. Simulink model of Quadcopter The sensor feedback block adds the sensor covariance after filtering the sensor values through Kalman filter [24]. The simulation block, plots the attitude and shows the 3D animation of the quadcopter .The PID controllerâ&#x20AC;&#x;s design constants Kp, Ki and Kd obtained from Root Locus design . The controller was followed by the Inverse Kinematics for the Servo to obtain the desired PWM. The input to the Controller is Error and its output is corresponding PWM . Simulink model of PID controller with the motor control (Inverse Kinematics) is shown in Figure 2.

Figure 2. Design of PID Controller The PID constants i.e. Kp, Ki and Kd obtained by simulation need to be tested with the actual quadcopter model. IV. TESTING Testing of the PID controller the values of Kp, Ki and Kd were used to obtain the corresponding Z-transform and it was implemented on APM controller. The desired angles for the Stabilized platform were maintained

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constant at 0 degrees [13] & [14]. The performance of the PID is measured in terms of overshoot and settling time. In Figure 3 we see set point at 0 degrees meaning the roll axis has to be maintained at zero degree.

Figure 3. Roll Axis Response (Desired Angle vs Time) This increase in overshoot and settling time for yaw is due to relative increase in moment of inertia of yaw axis.

Figure 4. Yaw Axis Response (Desired Angle vs Time) The above tested PID constant values were used for implementing the PID controller on hardware.

Figure 5: Docking Station

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V. CONCLUSION Automated persistent Quadcopter serves a system that has great potential to ensure jitter free imaging for long duration missions. Aerial surveillance being very crucial in the modern day applications, this serve enhances the capabilities of aerial surveillance by improving the endurance of the multi-rotor aerial vehicle. Autonomous intelligent aerial surveillance for longer duration is possible. Good and reliable attitude stabilization was achieved with PID controller with overshoot of 5% and settling time of 500ms. Roll response of 5.3% overshoot and settling time of 490ms, pitch response of 4.8% overshoot and settling time of 457ms and yaw response of 8% overshoot and settling time 621ms. The roll, pitch and yaw errors from the setpoint never exceeded more than a degree. Auto landing is an important feature, this was achieved with combination of GPS and Visual system which provided an accuracy of 30cms. Autonomous alignment of Quadcopter and recharging of batteries was achieved in the docking station. The recharging took 15 minutes to get the battery back to 12V. VI. FUTURE SCOPE Implementation of automated aerial vehicle can be improved by making it more accurate and reactive. Accuracy can be achieved by reducing motor overshoot. This can be further improved by incorporating state space control algorithm. The accuracy of the system is very limited due to the use of classical control algorithms; there is a definite scope for improvement by using advanced techniques like Fuzzy Logic Controller and Artificial neural network. Obstacle recognition and avoidance is a very important and necessary feature for this class of vehicles. The intelligence of obstacle detection and avoidance can be implemented in future. REFERENCES [1]

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

Salih, A.L.,Centre for Res. in Appl. Electron. (CRAE), Univ. of Malaya, Kuala Lumpur, Malaysia, Moghavvemi, M. ; Mohamed, H.A.F.; Gaeid, K.S, “Modelling and PID controller design for a quadrotor unmanned air vehicle”, Proceedings of IEEE International Conference on Aerial Robotics Hong Kong, vol 122, 2010, pp 46-326. Gaponov, I, Cheonan and Razinkova, “Quadcopter design and implementation as a multidisciplinary engineering course”, Proceedingsof IEEE International Conference on Robotics and Control Frankfurt, vol 34, 2013, pp 39-85. Sima Mitra, Bruce Land, “Autonomous Quadcopter Docking System”, Cornell University, Spring 2013 Qingbo Geng, Huan Shuai, Qiong Hu, “Obstacle avoidance approaches for quadcopter UAV based on backstepping technique”, Proceedings of IEEE International Conference on Robotics and Biometrics Abu Dhabi, vol 57, 2013, pp 404-409 Olfati-Saber. R, “Nonlinear Control of Underactuated Mechanical Systems with Application to Robotics and Aerospace Vehicles”, PHD Thesis in Electrical Engineering and Computer Science, Massachusetts Institute Of Technology, Feb 2001. Tommaso Breasciani, “Modelling, Identification and Control of Quadcopter Helicopter”, Conference of Automation and Control , University, 2012 Yash Mulgaonkar, “Automated Recharging for persistence missions with Multiple micro Aerial vehicles”, Robotics and Controls, University of Pennsylvania, 2012. L. Beji K. M. Zemalache and H. Marref. Control of an under-actuated system: Application to a four rotors rotorcraft. Proceedings IEEE International Conference on Robotics and Biomimetics, vol 5, 2009, pp 404 - 409. A. Abichou L. Beji and K. M. Zemalache, “Smooth control of an x4 bidirec- tional rotors flying robot”, Fifth International Workshop on Robot Motion and Control, 2005, pp 181 - 186. G. Fay, “Derivation of the aerodynamic forces for the mesicopter simulation”, University of Michigan, 2001. P. McKerrow,”Modelling the drganflyer four-rotor helicopter”, Proceedings of the IEEE International Conference on Robotics and Automation, vol 35,(4), 2004, pp 3596 –3601. M. Achtelik K, M. Doth G, Hirzinger D. Gurdan, J. Stumpf and D. Rus, “Energy-efficient autonomous four-rotor flying robot controlled at 1 khz”, Proceedings of IEEE International Conference on Robotics and Automation, 2007, pages 361 - 366. A. Tayebi and S. McGilvray, “Attitude stabilization of a four-rotor aerial robot”, Proceedings of 43rd IEEE Conference on Decision and Control, 2004, pp 1216 - 1221. A. Tayebi and S. McGilvray, “Attitude stabilization of a vtol quadrotor aircraft”, IEEE Transaction on Control System Technology, 14(3), May 2006, pp 562 - 571.

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Reliability Estimation of Component-Based Software through InteractionBased Model Dimpal Tomar1, Dr. Pradeep Tomar2 School of Information and Communication Technology Gautam Buddha University Greater Noida, Uttar Pradesh INDIA Abstract: Software component technology has a great impact on the evolution of software development. The benefit of this technology, such as reusability, complexity management, time-effort reduction, and increased productivity, has been key drivers of its adoption by industry. One of the main issues in building componentbased systems is to maintain quality in which reliability play a vital role. This paper proposes a methodology based on Interaction-Based Model to assess the reliability of overall Component-Based Software System (CBSS). This estimation of CBSS reliability from the reliabilities of individual components and average interaction reliability is a matter of concern. Keywords: Reliability; CBSS; component; CIG; average interaction reliability, interaction ratio. I. Introduction The software reliability is defined as the probability that software will perform its purpose adequately for the period of time intended under the operating and environmental conditions encountered. Software reliability stresses four elements: Probability, Adequate performance, Time and Operating and environmental conditions. The purpose of the software reliability engineering is to model the failure behavior of software systems to estimate and measure the quality but traditional approach of reliability to measure the quality of complex software is not appropriate. To measure the reliability of complex software’s require a new approach to measure the failure behavior of software system. Researcher & practitioner develop new method and methodology to estimate reliability of component-based software (CBS). A large number of reliability models have also been proposed to specify the problem of quantifying the system reliability, which is one of the most important attribute of quality. Therefore, the goals of this study is also to propose a methodology for CBS reliability model which mainly focus on to measure the reliability of individual component and average interaction reliability. The rest of the paper is organized as follows. The section II discusses about CBS reliability. Section III describes related work. Section IV presents theoretical understanding of the proposed methodology for reliability estimation model. Section V presents an application of the proposed model to a case study. Section VI describes the conclusion and future work. II. Component-Based Software Reliability Component-Based Software Development (CBSD) approach is based on the idea to develop software systems by selecting appropriate components and then to assemble them with a well-defined software architecture. Currently, Component-Based Software Engineering (CBSE) is being popular among both researchers & practitioners due to its several advantages over Object Oriented Approach. Councill and Heineman [1] define a component as a software element that conforms to a component model and can be independently deployed and composed without modification according to a composition standard. The approach of composition of component to develop component-based software is a complex process where the reliability of component is not on before integration. According to author [2], [3], larger scale use of components has raised questions on the component’s reliability and the reliability of aggregate systems derived out of these components. To quantify the failure behavior of software system, software reliability is an important measure to facilitate developers to arrange sufficient test activities. There are three estimation methods to measure software reliability: profile- based, state-based and path-based. Achieving a highly reliable software application is a difficult task, even when high quality, pre-tested, and trusted software components are composed together [4]. As a result, several techniques have emerged to analyze the reliability of CBS. These can be categorized as: system-level reliability estimation and component-based reliability estimation [5].

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System-level reliability estimation- Reliability is estimated for the application as a whole. Component-based reliability estimation- The application reliability is estimated using the reliability of the individual components and their interconnection mechanisms. The first approach is not the most suitable for CBS because it does not consider compositional properties of systems, and does not adapt the reliability growth of individual components. Reliability prediction of CBS is getting a lot of attention with the emergence of CBSD approaches which focus on scrutinize the reliability of software application based on its components behavior and software architecture. CBS reliability can be evaluated using individual component reliability and their interconnection mechanisms. III. Related Study With the growing emphasis on reuse, a great deal of research effort has been done to propose a variety of reliability models and estimation techniques to assess the reliability of CBSS. Sherif Yacoub et al. [5], introduces a reliability model, and a reliability analysis technique for CBS. The technique is named Scenario- Based Reliability Analysis (SBRA). Using scenarios of component interactions, they construct a probabilistic model named Component-Dependency Graph (CDG). Based on CDG, a reliability analysis algorithm is developed to analyze the reliability of the system as a function of reliabilities of its architectural constituents. This is particularly useful when the system is built partially or fully from existing off-the-shelf components. Gokhale et al. [6] discuss the flexibility offered by discrete-event simulation to analyze component-based applications. Their approach relies on random generation of faults in components using a programmatic procedure which returns the inter-failure arrival time of a given component. The total number of failures is calculated for the application under simulation, and its reliability is estimated. This approach assumes the existence of a control flow graph of a program. The simulation approach assumes failure and repair rates for components, and uses them to generate failures in executing the application. It also assumes constant execution time per component interaction, and ignores failures in component interfaces and links (transition reliabilities). Krishnamurthy et al. [7] assess the reliability of CBS using a technique called Component Based Reliability Estimation (CBRE). The approach is based on test information and test cases. For each test case, the execution path is identified. The path reliability is calculated using the reliability of the components assuming a series connection (using the independent failure assumption and perfect interfaces between components). This approach does not consider component interface faults, although they are considerable factors in reliability analysis of CBS. Goseva-Popstojanova and Trivedi [8] present a classification of architecture-based approaches to reliability assessment of CBSS. They identify three classes based on the methods used to describe the architecture, and aggregate the failure behaviour of components and connectors. These classes are: a) State-based where software architectures and failure behaviour are represented as a Markov chain or a semi-Markov process; b) Path-based where reliability is estimated for set of execution scenarios; and c) Additive models which focus on estimating the time-dependent failure intensity of the system using components failure data. The approach we present in this paper is a path-based approach. Our approach is based on considering interaction failures among components within a system for estimating reliability of CBSS. IV. Proposed Interaction-Based Model Proposed methodology for reliability estimation model is based on the following assumption:  This study assumes that system is designed based on the concept of CBS methodology.  This study assumes that transfer of control among components is assumed to be based on Markov property that means current component behavior is conditionally independent of past behavior. This assumption is not always true for systems designed at large scale because they are closely converse to Markov process after a long time interval [9] [10].  All components are assumed to be independent of one another that mean every component fails independently because presence of dependence, a fault in one component could mask out faults in other component. When the fault in first component is discovered and corrected, the second component could then exhibit a higher failure rate [11].  Probability of failure of interaction among components is assumed in advance. The proposed methodology for reliability estimation through interaction-based model is as follows: Step 1 Interaction-based model for reliability estimation of CBSS is a function of two important parameters i.e. component reliability and average number of interaction failure. For reliability estimation, the CBSS is used as input to create the Component Interaction Graph (CIG). The CIG is defined by the tuples <N, E> where N is a set of nodes assigned with component reliabilities and E is the set of directed edges which represents interaction between Ni and Nj. This study assumes that individual component reliability is known in advance. The node reliability is (0<CRi<1).

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Step 2 Component’s interface marks out how it interacts with another component interface. Hence, average interaction reliability of CBSS “AIRs” is the probability that information sent among components within a system is delivered error-free. This probability includes average number of interaction failures. Step 3 To estimate the average interaction failures “Fi” in CBSS, two factors are taken into consideration: Interaction Ratio and Probability of Failure of Interaction among components. The interaction ratio of a component “Θ” represents the ratio of total number of actual interaction over total number of available/maximum interaction possible. The value of interaction ratio lies in between 0 and 1 and is estimated as in (1). Θk = (1) The average interaction reliability “AIRs” for CBSS is estimated as (2) with interaction ratio and probability of failure of interaction U(n). AIRs = 1- [ ]/N (2) where N is the total number of components within a system. Step 4 The reliability “RCBSS” for CBSS is estimated as (3) with components reliabilities, CR i average interaction reliability, AIRs. RCBSS = AIRs * (3) In summary the prior condition for estimating the overall reliability of CBSS depends upon existence of analytical approaches, total number of components in a system and their prior information, data obtained after execution. V. Case Study This study assumes that all reliabilities of components are taken from the case study of given by [2] and the interaction ratio of each component assumed as per equation (1). Consider an example illustrates a componentbased software system consist of 5 components. After system execution, the data contained in Table 1 is obtained. It is assumed that all five components involved in interaction. Table 1 Example Data Set Component

Component Reliability (CRi)

Θn

0.992

0.04

0.5

0.989

0.02

1.0

0.9988

0.001

0.5

0.994

0.002

0.33

0.996

0.02

1.0

As per the equation (2), average interaction reliability of a system is calculated as AIRs = 1 - [ ]/N = 1 - [(0.04*0.5) + (0.02*1.0) + (0.001*0.5) + (0.002*0.33) + (0.02*1.0)]/5 = 1 - [0.02 + 0.02 + 0.0005 + 0.00066 + 0.02]/5 = 1 - 0.06116/5 = 1 - 0.01223 = 0.98777 And using equation (3), the CBSS reliability RCBSS is equal to RCBSS = AIRs * = 0.98777 * [0.992 * 0.989 * 0.9988 * 0.994 * 0.996] = 0.95827 VI. Conclusion Reliability of CBSS relies not only on individual component reliability but also interaction failures are also considerable factors. So here, for this purpose, a new approach is proposed to analysis the reliability of CBSS, introduces an interaction-based model in order to measure the quality of CBS. The proposed methodology presented an approach to analyze the reliability of CBSS. The reliability estimation can be performed after system integration. The proposed methodology can be used to explore the quality measures of different system configurations. Our ongoing research involves implementing the proposed methodology on any language to estimate the CBS reliability. The other parameters like usage ratio, impact analysis of component and glue code reliability can also be considered for reliability estimation as future work.

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References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

R. S. Pressman, “Software Engineering: A Practitioner’s Approach”, 7th ed McGraw Hill, (2009). A. P. Singh And P. Tomar, “A New Model for Reliability Estimation of Component-Based Software”, in proceeding of 3rd IEEE International Advance Computing Conference,(2013). X. Mao and Y. Deng, “A General Model for Component-Based Software Reliability”, in proceeding of Conference on Software Engineering and Advanced Applications, pp. 395-398, 2003. H. Jin and P. Santhanam, “An approach to higher reliability using software components”, in 12th IEEE Int. Symp. Software Reliability Engineering (ISSRE ’01), Hong Kong, Nov. 2001, pp. 1–11. S. Yacoub, B. Cukic, and H. Ammar., “A Scenario-Based Reliability Analysis Approach for Component-Based Software”, in IEEE Transactions on Reliability, Vol. 28, No. 6, Dec. 2004, pp. 529-54. S. Gokhale et al., “Reliability Simulation of Component-Based Software Systems”, in 9th International. Symposium on Software Reliability Engineering (ISSRE Nov 98), Germany, Nov. 1998, pp. 192–201. S. Kirshnamurthy and A.P Mathur, “On the Estimation of Reliability of a Software System Using Reliabilities of its Components” in 8th International Symposium Software Reliability Engineering (ISSRE), New Mexico, Nov. 1997, pp 146-155. K. Goseva-Popstojanova and K. Trivedi, “Architecture-based approach to reliability assessment of software systems,” in Performance Evaluation, an International Journal, vol. 45, pp. 179–204, 2001. B. Littlewood, “Software Reliability Model for Modular Program Structure”, in IEEE Transactions on Reliability, Vol. R-28, No. 3, pp. 241-246, 1979. W. L. Wang, D. Pan and M.H. Chen, “Architecture-Based Software Reliability Modeling”, in Journal of Systems and Software, Vol. 79, No. 1, pp. 132-146, 2006. J. Dolbec and T. Shepard, “A Component Based Software Reliability Model”, in proceeding of conference of the Centre for Advanced Studies on C, Citeseer, 1995.

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Bayesian Analysis of First Conception through a Probability Model Ashivani Kumar Yadav Department of Mathematics and Statistics D.D.U. Gorakhpur University Gorakhpur, U.P., India, 277123 Abstract: Probability models are widely used in different disciplinary fields. The present paper is an attempt to develop probability model of waiting time of first conception and also analyze under the Bayesian environment of waiting time of first conception under Entropy Loss Function loss function. Keywords: Probability model, waiting time, conception, Entropy Loss Function . I. Introduction At the time of marriage a woman is susceptible to conception and the time elapsed before a conception is a random variable determined by fecundability, which is defined as the monthly chance of a conception. It is important here to note the time of first conception after the marriage because the analysis of waiting time of first conception signifies couple’s fertility at early stages of married life. This variable is widely used to study fertility characteristic of a woman, since it is independent of effect of amenorrhea period and generally, a woman does not like to use contraceptives to postpone first birth. There is little chance of recall lapse in reporting the time of first birth from the date of first marriage of first birth. Treating the first conception as the random phenomenon the probabilistic models can be developed. And this variable can be treated as discrete as well as continuous variable depending upon the situation and assumptions made for the study. For the first time this variable was considered as a discrete variable and Gini (1924) derived the geometric distribution for the waiting time of first conception. He defined the term ‘Fecundability’ as the monthly chance of conception for women living in the married, fecund and exposed state. Later the same variable was considered as a continuous variable and hence treating the time elapsed from the marriage or from the beginning of the reproductive process to first conception as continuous makes mathematical treatments more convenient and easy. Singh (1964), Henry (1953) and Vincent (1961) developed some models treating the waiting time of first conception as continuous. The negative exponential distribution plays the role of geometric distribution for studying the waiting time of conceives after marriage. Thus if X denotes the time of first conception then the density function, say, f(x) is given by f (x) = ; x > 0, > 0 Where is instantaneous fecundability A number of authors made modifications on the above simple distribution to study realistic situations. In the present model of waiting time of first conception the time elapsed is defined over the range (0, ). But in practical problems the upper limit may be considered as finite; as a woman can conceive up to an age limit. So, there is a need of introducing a new continuous model with finite range. Keeping this in view, an attempt has been made to characterize an existing model derive by mukeherjee-Islam (1983), defined over a finite range for the purpose of life testing analysis but it suits in realistic or real life situations. II. A FINITE RANGE CONTINUOUS MODEL A new probability distribution has been considered in the section as a continuous model, introduced by Mukherjee-Islam (1983) for the purpose of studying waiting time f (x, θ, p) = (p/θ p, θ > 0 x 0 The above model is monotonic decreasing and highly skewed to the right. The Graph is J-shaped thereby the unimodel future. Let us consider the reparametrized finite range distribution whose p.d.f. is given by f (x;

θ) =

where ‘θ’ is instantaneous fecundability and conceive. The c.d.f. of the above model is given by

;

θ > 0, > 0, (2.1.1) 0, x is considered as age limit beyond which a married woman cannot

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F (x) = P ( X =

= The survival function at time x, say S(x) = P (X>x) is given by S (x) = P (X > x) = 1- P (X x)

(2.1.2)

= 1Also, the conception rate function, say, w(x) at time is then given by W(x) =

(2.1.3)

W (x) =

θ θ

(2.1.4) θ

= E(X) =

(2.1.5)

and =

therefore θ

=V(X) =

(2.1.6)

Maximum Likelihood Estimator: If having a common pdf as given in (2.1.1), where density function is given by f ( ‫׀‬θ

is known, then the joint probability (2.1.7)

Where z= Thus the maximum likelihood estimator (MLE) θ of θ is given as θ

(2.1.8)

Bayesian Analysis of The model: The fundamental problems in Bayesian Analysis are that of the choice of prior distribution g(θ) and a loss function L (θ θ). Let us consider three prior distribution of θ to obtain the Bayes estimators which are as follows:

(i)

Quasi-Prior : For the situation where the experimenter has no prior information about the parameter θ, one may use the quasi density as given by (θ) = ; θ > 0, d > 0 (2.1.9) θ

Here d = 0 leads to a diffuse prior and d = 1, a non informative prior.

(ii)

Natural Conjugate Prior of θ: The most widely used prior distribution of θ is the inverted gamma distribution with parameters α and β ( 0) with p.d.f. given by βα

(θ) =

β θ

αβ

(2.1.10) The main reason for general acceptability is the mathematical tractability resulting from the fact that inverted gamma distribution is conjugate prior for θ.

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(iii)

Uniform Prior: It frequently happens that the life tester knows in advance that the probable values of θ lies over a finite range [α, β] but he does not have any strong opinion about any subset of values over this range. In such a case uniform distribution over [α, β] may be a good approximation. (θ) =

β α

(2.1.11)

Loss Function: The Bayes estimator θ of θ is of course, optimal relative to the loss function chosen. A commonly used loss function is the squared error loss function (SELF) L (θ,θ) = θ θ (2.1.12) which is a symmetrical loss function and assigns equal losses to over estimation and underestimation. Canfield (1970) points out that the use of symmetric loss function may be inappropriate in the estimation of reliability function. Over estimation of reliability function or average lifetime is usually much more serious than under estimation of reliability function or mean failure time. Also, an under estimate of the failure rate results in more serious consequence than an overestimation of the failure rate. This leads to statistician to think about asymmetrical loss functions which have been proposed in statistical literature. It is well known that the Bayes estimator under the above loss function, say θs, is the posterior mean. The squared error loss function (SELF) is often used also because it does not lead to extensive numerical computation but several authors {Ferguson (1967), Varian (1975), Berger (1980), Zellner (1986) and Basu and Ebrahimi (1991)} have recognized the inappropriateness of using symmetric loss function in several estimation problems. These have proposed different asymmetric loss function. Entropy Loss Function θ

In many practical situations, it appears to be more realistic to express the loss in terms of the ratio In this case, θ Calabria and and pulcini (1994) points out that a useful asymmetric loss function is the entropy loss L( ) Where θ θ

And whose minimum occurs at when p > 0, a positive error ( ) causes more serious consequences than a negative error, and vice-versa. For small value, the function is almost symmetric when both and measured in a logarithmic scale, and approximately L( ) θ θ Also, the loss function L ( ) has been used in Dey et al. (1987) and Dey and Liu (1992), in the original form having p = 1. Thus L ( ) can be written as =b ; b>0 (2.1.13) where θ θ

The Posterior expectation of loss function in (2.1.13) is θ

(2.1.14)

The value of θ that minimise (2.1.14), denoted by θ , Bayes estimater of θ under entropy loss function is obtained by solving the following equation θ θ

⇨b ⇨

θ θ

⇨θ θ 2. Bays Estimator under (θ): Under (θ), the posterior distribution is defined by f θ Substituting the values of simplification, as

(2.1.15)

(2.2.1)

θ from equations (2.1.9) and (2.1.7) in (2.2.1) we get, after

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f θ

θ θ

(2.2.2) θ

The Bayes estimator under squared error loss function is the posterior mean given by θ = θf θ θ Substituting the values of f θ from equation (2.2.2) in equation (2.2.3) and on solving we get θ =

(2.2.3)

θ θ

= θ

; n+d>2 .

(2.2.4)

The Bayes estimator under entropy loss function, θ using the value of f θ solution of equation (2.1.15) given by θ

from equation (2.2.2) is the

θ θ

whis, on simplification, leads to θ

(2.2.5)

3. Bayes Estimator Under : Under θ , the posterior distribution is defind by f θ Substituting the values of get

(2.3.1)

θ from equations (2.1.10) and (2.1.7) in (2.3.1) and simplifying, we

f θ

β α

βα α

β θ

β θ θ

(2.3.2)

The Bayes estimator under squared error loss function is the posterior mean given by θ = θf θ θ Substituting the values of θ from equation (2.3.2) in equation (2.3.3) and on solving, we get α

(2.3.4)

θ =

The Bayes estimator under entropy loss function, solution of equation (2.1.15) given by

θ =

(2.3.3)

β α

, using the value of f

from equation (2.3.2) is the

θ θ

α β β α

(2.3.5)

Bayes Estimator Under Under θ , the posterior distribution is defined by

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f θ Substituting the values of simplifying, we get

f θ

(2.4.1.)

from equations (2.1.11) and (2.1.7) in (2.4.1) we get, after θ

θ β α

β α

, θ

β α

(2.4.2)

Where = dt is the incomplete gama function The Bayes estimator under squared error loss function is the posterior mean given by β θ = α θf θ θ Substituting the values of f θ from equation (2.4.2) in equation (2.4.3) , we get θ =

β θ α

(2.4.3)

dθ

which on simplification leads to θ

(2.4.4)

The Bayes estimator under entropy loss function, using the value of of equation (2.1.15) given by θ =

from equation (2.4.2) is the solution

β α θ

θ θ

θ α

β α

(2.4.5)

The equation (2.4.4) and (2.4.5) can be solved numerically.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

[10]

Gini, c. (1924) : “ Premiers recherchers surla econlabilite de laemme”, Proceedings of the international Mathematic Congress, Toronto, pp 889-892. Singh S.N. (1964 a) : “ On the time of first birth” , Sankhya, vol26(B) , pp 95-102. Henary, L (1973) : “Fundements theoriques des measures de la feconolite naturelle,” Reveve del’ Institute international de statistique, vol 21, pp 135-151. Vincent, P. (1961): “Researches sua la Fecodite- Biologique Institute National ‘D’ Etudes Dempgraphic Paris”,Farnce, Press Universitaries De France. Mukheerji, S. P. and Islam, R. (1983). A finite range distribution of failures times, Naval Research Logistics Quarterly, Vol. 30, p. 487 – 491. Ferguson, T.S.(1967): Mathematical Statistics: A Decision Theoretic Approach. New York: Academic Press. Varian Hal R. Two Problems in the Theory of Fairness. Berger, J.O.(1998): ‘Bayes Factors” . in Encyclopaedia of Statistical Science, Vol.3 (update), Eds S. Kotz et al., New York: Wiley. Zellner, A. On assessing prior distributions and Bayesian regression analysis with g-prior distributions. In Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti, (eds. P. K. Goel and A. Zellner), pp. 233-243. NorthHolland/Elsevier(1986). Basu, A.P. and Ebrahimi, N. (1991): Bayesian approach to life testing and reliability estimation using asymmetric loss function. J. Statist. Plan. Inf. 29, pp 21-31.

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Investigation of Flexural behavior of hybrid natural fiber composite with recycled polymer matrix Ramanpreet Singh 1, Lakshya Aggarwal2, Mohit Sood3 Research Scholar, Department of Mechanical Engineering, MMU, Sadopur, Ambala, India 2 Associate Professor, Department of Mechanical Engineering, MMU, Sadopur, Ambala, India 3 Research Scholar, Department of Mechanical Engineering, Punjabi University, Patiala, India House No. 896, Street No. 8, Guru Nanak Nagar, Patiala, Punjab, India 1

Abstract: Natural fiber reinforced composites (NFRC) is an emerging area in polymer science. Environmental awareness of the general public resulted in an increasing use of natural materials, recycled polymers & their composites. The present experimental study aims at learning the flexural behavior of hybrid natural fiber reinforced composite. In this work Composites based on recycled high density polyethylene (RHDPE) and natural fibers were made. Samples are prepared with HDPE (50% virgin & 50% post consumer) as polymer matrix. Sisal and hemp are used as reinforcing fibers. The weight fraction of fiber and matrix is kept up to 30%. Specimens are manufactured by using injection molding according to ASTM standards. The results showed that the maximum Flexural Strength is obtained from the sample having 15% Sisal + 15% hemp used as reinforcement with HDPE as a matrix. While maximum specific strength is obtained from the sample having 5% sisal +5% hemp as reinforcement with HDPE as a matrix. It is also evident that by the addition of fibre and using recycled HDPE the flexural strength of material increases in the range of 13% to 40% using different fibre ratio. Keywords: NFRC, Recycled, Hybrid composite, RHDPE, Flexural strength. I. Introduction Nowadays, ecological concern has resulted in a renewed interest in natural materials and issues such as recyclability and environmental safety are becoming increasingly important for the introduction of new materials and products. Lignocellulosic fibres have many advantages as they are biodegradable, renewable and environmentally friendly.(Arbelaiz et al [1] ) Although natural fibres have many advantages which offer greater opportunities to develop a new class of environmental friendly structural composites – the drawbacks for the application of cellulosic fibres are the strong hydrophilic nature of their surface, which causes poor adhesion and non-homogeneous dispersion in most non-polar matrices, as well as water absorbance, causing a decrease of the mechanical performances. Indeed, good dispersion of fibres in the polymer matrix and good adhesion is an important prerequisite for obtaining good mechanical properties of the resulting composites .To improve the filler–matrix interactions and thus the composite properties, either surface modification of fibre and/or matrix are necessary. (haque et al. [2]) Lignocellulosic materials are lighter, much less abrasive, and renewable compared to other inorganic fillers (glass, clays, minerals, etc.), and have improved thermal stability over products made with unfilled material. Thermoplastics used with lingo cellulosics, including polypropylene, polystyrene, polyvinylchloride (PVC), and low- and high-density Polyethylene, must melt or soften at or below the degradation point of the lignocellulosic component, normally 200–220 °C (yemele et al. [3]) During the last decade many works have been carried out on natural fibre based polymer composites with polyolefin matrices (PP, PE, PS, etc.) and it have been reported that composites with modified fibre and/or modified matrix showed improved properties as compared to composites containing non-modified fibres and matrix. ([4], [5] ,[6]) Several of the major areas of interest behind the development of natural composites are the environmental impacts, potential economic impacts and the ability of natural composite to meet economic, social and material needs worldwide. The aim of our study is to determine the Flexural strength of Recycled natural fiber reinforced Hybrid composite (RNFRHC). II. Experimental procedure Material The matrix (HDPE & RHDPE) was purchased from Goyal Polymer, Industrial Area Phase-2, Chandigarh. The fibres were provided by Chandra Prakash & co from Jaipur (Rajasthan).

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Preparation of fibre The length of fibres available is usually long so before treatment it is necessary to cut them into desired length. I took fibre of 4 to 5 mm (approx) length. The fibres obtained were then soaked in distill water for 1 hour at 80C. The water was then drained off and fibres were placed on filter paper to soak the extra amount of water present in the fibres. The fibres so obtained were oven dried for 5 hours at 100C in universal hot air oven. Chemical treatment. Sisal mercerization was conducted by keeping it in 10 wt% NaOH solution at room temperature for 3 h. After mercerization, the sisal was rinsed with water to remove the soda excess until pH ~7 was reached and dried in oven at 100°C for 3 hour.(favao et al;2010) For alkali treatment, the pre-dried hemp fibres were soaked in an aqueous solution of sodium hydroxide (10wt. % solution of NaOH) for two hours at room temperature(20 °C). Afterwards, the fibres were rinsed with distilled water until the filtrate reached pH = 7. Next, the fibres were dried again at 105 °C, until a constant mass was reached. Maleic anhydride modification was carried out via soaking the fibres in a solution of Maleic anhydride (10 wt. %) in acetone for two hours at elevated temperatures (55 °C). Afterwards, the fibres were rinsed to obtain pH = 7 and dried at 105 °C until constant mass was observed. (kaczmar et al;2011) At last, fibres were placed in air tight container with silica gel so as to prevent the absorption of moisture from the atmosphere by the fibres. III. Manufacturing of composite Table-1 presents the detail of composite prepared. The table was constructed on the basis of sisal fibre density 1.45g/cc & hemp fibre density1.45 g/cc and the density of HDPE matrix 0.94 g/cc. of flexural specimen. The volume of flexural specimen from the drawing came was 5.16 . Table1- Weight of different constituents used to make composite SAMPLE

HDPE%

SISAL%

HEMP%

DENSITY OF COMPOSITE (g/cc)

1 2 3 4 5 6 7 8

70 75 75 80 90 70 70 85

15 20 5 10 5 30 0 15

15 5 20 10 5 0 30 0

1.093 1.068 1.068 1.042 0.991 1.093 1.093 1.017

FLEXURAL WEIGHT OF COMPOSITE (gram) 5.640 5.508 5.508 5.377 5.114 5.640 5.640 5.245

WEIGHT OF HDPE (PER SAMPLE) 3.948 4.131 4.131 4.301 4.602 3.948 3.948 4.458

WEIGHT OF SISAL (PER SAMPLE) 0.846 1.102 0.275 0.538 0.256 1.692 0.000 0.787

WEIGHT OF HEMP (PER SAMPLE) 0.846 0.275 1.102 0.538 0.256 0.000 1.692 0.000

Injection molding machine (model no BH 100) was used to manufacture the composites in J. B. Industries Pvt. Ltd., Ambala.

Chemical treatment of fibre

c) Drawing of flexural specimen

b) PH checking

d) Die to prepare flexural specimen

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e) Injection molding machine (BH100)

f) Manufactured specimen

IV. Flexural Testing This was performed according to ASTM D 790 test method. The slot used for testing consisted of five samples for different constitutions. The specimens were kept according to ASTM standard at 23C for 40 hours in Environmental Test Chamber at Central Institute of Plastics Engineering Technology, Amritsar (CIPET).The samples were then examined using the three-point bending test. The whole procedure was carried out using UTM with cross head speed 2mm/min and load used for test was 10KN at Central Institute of Plastics Engineering Technology, Amritsar (CIPET).

g) UTM machine for flexural testing

V. Observation Table I have taken the five readings as an average value from samples belonging to different percentage of specimen i.e. 10%, 20%, 25% and 30% for flexural strength test. Table- 2, Flexural and Specific Flexural strength of different composites prepared Sample no.

Constituent

HDPE 70%, SISAL 15%, HEMP 15%

HDPE 75%, SISAL 20%, HEMP 5%

HDPE 75%, SISAL 5%, HEMP 20%

HDPE 80%, SISAL 10%, HEMP 10%

HDPE 90%, SISAL 5%, HEMP 5%

HDPE 70%, SISAL 30%, HEMP 0%

HDPE 70%, SISAL 0%, HEMP 30%

HDPE 100%, SISAL 0%, HEMP 0%

Density

1.093

1.068

1.042

0.991

1.093

0.94

Flexural Strength(Mpa)

17.22

16.81

16.17

16.87

16.74

13.93

15.09

12.27

Specific Flexural Strength (Nm/kg)

15.75

15.74

15.14

16.19

16.89

12.74

13.81

13.05

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Graphs:

Graph 1: Showing Flexural strength of different samples

Graph 2: showing specific flexural strength of different samples

VI. Results and Discussions Graph 1 that has been drawn was constructed from the observations obtained after performing testing on composites. In graph abscissa represents the samples having different fibre percentage in HDPE (recycled + fresh) while the ordinate depicts Flexural Strength (MPa). After careful analysis of the graph it is clear that by the addition of fibre and using recycled HDPE the flexural strength increases (13% to 40%). The maximum flexural strength is 17.22 MPa which is obtained from sample containing 30% fibre (15% sisal+ 15% hemp) in 70% HDPE (fresh + recycled), which is more than sample containing 30% of single fibre (i.e. sisal or hemp separately). So it is evident that the Flexural strength of hybrid composite is more than that of composite having single fibre as reinforcement. Graph 2 gives the Specific Flexural strength of different materials. Specific flexural strength is the ratio of flexural strength and material density. From the graph it is evident that Specific Flexural strength is Maximum for sample no.5 (which contains 5% sisal, 5% hemp and 90% HDPE). Sample no. 5 having density 0.991 g/cc and flexural strength16.74 gives Specific Flexural strength 16.89 Nm/Kg, while the sample no. 1 which is having Maximum Flexural strength 17.22 MPa and having density 1.093g/cc gives Specific Flexural strength 15.75 Nm/Kg. VII. Acknowledgments Authors thankfully acknowledge Head of the Department of Mechanical Engineering and Applied sciences, MMU, for their encouragement and support in carrying out this work. Special thanks to Itender Singh of J. B. Industries Pvt. Ltd., Ambala to provide machinery in the manufacturing of composite. VIII. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Arbelaiz et al,” Influence of matrix/fibre modification, fibre content, water uptake and recycling”, Composites Science and Technology, vol. 65 (2005); p 1582–1592 Haque et al.,”Reactive compatibilization of composites of ethylene–vinyl acetate copolymers with cellulose fibres”, Composites: Part A 41 (2010); p 1545–1550 Yemele et al.“Effect of bark fiber content and size on the mechanical properties of bark/HDPE composites”, Composites: Part A 41 (2010); p 131–137 Arrakhiz et al.,” Mechanical and thermal properties of polypropylene reinforced with Alfa fiber under different chemical treatment”, Materials and Design, vol. 35 (2012) ; p 318–322 Favaro et al.,” Chemical, morphological, and mechanical analysis of rice husk/post-consumer polyethylene composites”, Composites: Part A 41 (2010); p 154–160 Kaczmar et al.,” The chemically treated hemp fibres to reinforce polymers”, Polimery ( 2011) ,p 817-822 Dittender et al.,”Critical review of recent publications on use of natural composites in infrastructure”, Composites: Part A 43 (2012); p 1419–1429 Deka et al.,” Renewable resource based “all green composites” from kenaf biofiber and poly(furfuryl alcohol) bioresin”, Industrial Crops and Products, vol.; 41 (2013) ; p 94– 101 Xie et al.,”Silane coupling agents used for natural fiber/polymer composites: A review”Composites: Part A 41 (2010) ; p 806– 819.

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MECHANICAL CHARACTREISATION OF TYPHA DOMINGENSIS NATURAL FIBER REINFORCED POLYESTER COMPOSITES Ponnukrishnan.P a , Chithambara Thanu. M a and Richard.S a a Department of Mechanical Engineering, Dr.Sivanthi Aditanar College of Engineering, Tiruchendur, Tamil Nadu, India - 628215

Abstract: Partially biodegradable Typha domingensis natural fiber–reinforced polyester composites were prepared. Samples were fabricated by the hand lay-up process (30:70 fibre and matrix ratio by volume) and the properties were evaluated by using the INSTRON material testing system. The mechanical properties were tested and showed that has the maximum tensile strength of 49.72 MPa, and the impact strength of 121 J/m. Keywords: Impact strength; Natural fiber composites; Tensile properties, Hand layup process.

I. INTRODUCTION The importance of natural fibers has been recognized since their use by the Egyptians 3,000 years ago, but since the 1970s a number of high-tech synthetic fibers such as glass, aramid, and carbon have entered and dominated the composite market because of their superior mechanical and thermal properties. With increasing environmental concerns, natural fibers are once again being considered as reinforcements for polymer composites. The use of natural fibers to make low-cost and eco-friendly composite materials is a subject of green importance. The development and applications of several natural fiber polymer composites have been comprehensively reviewed.[1–5] Unsaturated polyester has extremely multipurpose properties and applications and is a popular matrix for making composites. This matrix has been used for many years in broad technology fields such as shipping construction, offshore applications, waterlines, and building construction.[6]The mechanical properties of natural fiber–reinforced composites can be further improved by chemically promoting good adhesion between the matrix and the fiber. The tensile properties of elephant grass fiber–reinforced polyester composites with and without chemical treatments were reported.[12] The extraction of rice straw fibers and their incorporation in a polyester resin matrix to prepare composites was also explained.[13] Natural fibre-reinforced composites have been increasingly utilized in quite widespread applications. Natural fibres are obtained from different parts of the plants, to name a few, for example jute, flax, kenaf, coconut, hemp, ukam, sisal, banana, pineapple fibres from the leaf; cotton and kapok from seed; coir and coconut from the fruit. For example hemp, jute, flax and sisal fibres are already used in automotive industry II. EXPERIMENTAL METHOD A. FIBER Typha Domingensis It is mostly found in regions where there is a stock of water, sewage water in open places, and also in waste lands. These plants are helpful in some cases, such as chemical wastewater from industry where they absorb harmful water. It is a perennial growing 1 m to 3 meters long with spongy strap-like leaves. Typha domingensis is available abundantly in nature and is renewable. It is flower from May to August, and the seeds ripen from Jun to September. The flowers are monoecious (individual flowers are either male or female, but both sexes can be found on the same plant) and are pollinated by wind .It is suitable for light (sandy), medium (loamy) and heavy (clay) soils. Suitable pH: acid, neutral and basic (alkaline) soils and can grow in saline soils. It cannot grow in the shade.

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Resins

Unsaturated polyester with density (0.909) g/cm3 was obtained from BORNEO INDAH SDN BHD with the properties such as appearance (pink), density (1.12) g/cm3, and stability in the dark below 25ÂşC. C. Curing Agents Methyl ethyl ketone Peroxide (MEKP) and Dimethyl Aniline (DMA) were obtained from the same supplier for unsaturated polyester resin and used as a catalyst and accelerator, respectively. The properties of DMA were as follows: the density at (20-24) ÂşC temperature - (0.955-0.960) g/cm3, molecular weight - 121.18 g/mol, D. Extraction of Fibers The extraction of fibers involves the retting process followed by decortication. The stems of Typha domingensis were cut at their base and immersed in a water tank for three weeks. Then they were removed; the fibers were stripped from their stalks by hand, washed, and dried in the sun. After drying, any unwanted matter that might still be adhering to them was removed by washing it with NaOH. The extracted fibers were used for composite making. E. Fabrication of composites For the fabrication of the randomly oriented Typha domingensis fiber-reinforced composite. A releasing plastic sheet is spread over the wooden mold. Heavy duty silicon spray is applied to the plastic sheet for easy removal of the composite plate. The fibers are cut into 30 mm length and distributed uniformly at the bottom of the mold which is prepared before. Thirty volume percentage of the fiber is used for the fabrication of the composite. A load of 10 metric tons was applied on the plates by hydraulic compression to form a single sheet. Then resin and curing agent are mixed together on a volume percentage of 10:1 to form a matrix. The matrix is poured over the fibers in an evenly manner and left for curing for 24 h .Composite samples were prepared with five different percentage volumes of Typha domingensis fibers. The picnometric procedure was adopted for measuring the density of the composite. III. TESTING A. Tensile Testing The tensile properties of the composites were calculated as per the standard test method, ASTM D 3039 M. Test specimens 150mm long, 12.5mm width, and 3mm thickness were prepared. The sample of 10 cm length was clamped into the two jaws of the machine. Each end of the jaws covered 2 cm length of the sample.Reading of the tensile strength test instrument for Newton force and extension were initially set at zero. The test was conducted at the constant strain rate of the order of 10 mm/min. Five specimens of each sample have been used for the measurement of the above mechanical properties at ambient laboratory environment and average results are reported. B. Impact Testing The impact strength of notched specimen was determined by using an impact tester according to ASTM D 25605 standards. The specimens were 62.5mm long, 12.5mm width, and 3mm thickness. The samples were fractured in a plastic impact testing machine and the impact toughness was calculated from the energy absorbed and the sample width. In each case three specimens were tested to obtain average value. IV. RESULTS AND DISCUSSION A. Tensile Test The variation of mean tensile strength with varying fiber content is shown in Figure 1. It is clearly seen that increasing the fiber content in the polyester matrix, increases the tensile strength .This is due to the fact that the polyester resin transmits and distributes the applied stress to the Typha domingensis fibers, resulting in higher strength. The tensile modulus also increases as shown in the Table I. Mean tensile properties of Typha domingensis polyester composites increases as the volume fraction of fiber increases in the composites and is 1.041GPa at 0.387 volume fraction of fiber loading (Figure 2).

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Table I. Mean tensile properties of Typha domingensis polyester composites Volume fraction of fiber

Density of composite (kg/m3)

0.000 0.192 0.227 0.278 0.312 0.387

Mean Tensile strength (M Pa)

Mean Tensile Modulus (G Pa)

32.34 34.20 37.45 40.43 43.71 49.72

0.712 0.896 0.914 0.947 0.988 1.041

1343 1289 1146 1097 1078 1041

Mean tensile strength(Mpa)

60 50 40 30 20 10 0 0

0.1

0.2

0.3

0.4

0.5

volume fraction of fibers

Mean Tensile Modulus (G Pa)

Figure 1. Effect of volume fraction of fiber on mean tensile strength of the composite. 1.5 1 0.5 0 0

0.1

0.2

0.3

0.4

0.5

Volume fraction of fiber . Figure 2. Effect of volume fraction of fiber on mean tensile modulus of the composite.

Impact strength(J/m)

120 100 80 60 40 20 0 0

0.1

0.2

0.3

0.4

volume fraction of fiber

Figure 3. Variation of impact strength of fiber-reinforced composite with volume fraction of fiber. B. Impact Test The results of the impact test are shown in Figure 3. As the volume fraction of Typha domingensis fiber increases, the impact strength increases. The composite has impact strength of 121 J/m, at maximum volume fraction (0.387). This is understandable as most natural fibers impart better load-bearing capacity

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V. CONCLUSION In this work, Typha domingensis fiber–reinforced polyester composites were prepared. Typha domingensis is available abundantly in nature and has low density. The tensile and impact properties of the composites with these fibers were found to be higher than those of the matrix and increased with fiber content. Thus, the composites of Typha domingensis fiber-polyester composites were found to be light in weight and possessed better mechanical and insulating properties. Hence, the newly developed composite material can be used for applications such as electronic packages, insulation boards, automobile parts, building construction, and other uses. REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

Mohanty, A. K., M. Misra, and G. Hinrichsen. 2000. Biofibres, biodegradable polymers and biocomposites: An overview. Macromol. Mater. Eng. 276–277: 1–24. Garkhail, S. K., R. W. H. Heijenrath, and T. Peijs. 2000. Mechanical properties of natural-fibre-mat-reinforced thermoplastics based on flax fibers and polypropylene. Appl.Compos. Mater. 7: 351–372. Joshia, S. V., L. T. Drzal, K. Mohanty, and S. Arora. 2004. Are natural fiber compositesenvironmentally superior to glass fiber reinforced composites? Composites Part A. 35: 371–376. Schneider, J. P., G. E. Myers, C. M. Clemons, and B. W. English. 1995. Biofibers as reinforcing fillers in thermoplastic composites. J. Vinyl Addit. Technol. 1(2): 103–108. Varada Rajulu, A., G. Ramachandra Reddy, and K. Narasimha Chari. 1998. Chemical resistance and tensile properties of styrenated polyester-coated bamboo fibers. Indian J.Fibre Text. Res. 23: 49–51. Aziz, S. H., M. P. Ansell, S. J. Clarke, and S. R. Panteny. 2005. Modified polyester resins for natural fibre composites. Compos. Sci. Technol. 65: 525–535. Manfredi, L. B., E. S. Rodrı´guez, M. Wladyka-Przyblak, and A. Va´squez. 2006. Thermal degradation and fire resistance of unsaturated polyester, modified acrylic resins and theircomposites with natural fibres. Polym. Degradation Stab. 91: 255–261. Mark C. Symington, W. M. Banks, Opukuro David West, and R. A. Pethrick. 2009. Tensile testing of cellulose based natural fibres for structural composite applications. J.Comp. Mater. 43: 1083–1106. Brahim, S. B., and R. B. Cheikh. 2000. Influence of fiber orientation and volume fraction on the tensile properties of unidirectional Alfa polyester composite. Compos. Sci. Technol. 67: 140–147. Wang, W., and G. Huang. 2009. Characterisation and utilization of natural coconut fibres composites. Mater. Des. 30: 2741– 2744. Satyanarayana, K. G., J. L. Guimara˜es, and F. Wypych. 2007. Studies on lignocellulosic fibers of Brazil. Part I: Source, production, morphology, properties and applications. Compososites Part A: Appl. Sci. Manuf. 38: 1694 1708. Murali Mohan Rao, K., A. V. Ratna Prasad, M. N. V. Ranga Babu, K. Mohan Rao, and A. V. S. S. K. S. Gupta. 2007. Tensile properties of elephant grass fiber reinforced polyester composites. Mater. Sci. 42: 3266–3272. Ratna Prasad A. V., K. Mohan Rao, and A. V. S. S. K. S. Gupta. 2007. Tensile and impact behaviour of rice straw-polyester composites. Indian J. Fibre Text. Res. 32: 399–403. Indicula, M., A. Boudenne, L. Umadevi, L. Ibos, Y. Candau, and S. Thomas. 2006. Thermophysical properties of natural fibre reinforced polyester composites. Compos. Sci. Technol. 66: 2719–2715. Sgriccia, N., and M. C. Hawley. 2007. Thermal, morphological and electrical characterization of microwave processed natural fiber composites. Compos. Sci. Technol. 67: 1986–1991. R. Karnani, M. Krishnan and R. Narayan, “Biofiber-Re- inforced Polypropylene Composites,” Polymer Engineer-ing and Science, Vol. 37, No. 2, 1997, pp. 476-483. A. M. Mohd Edeerozey, M. A. Harizan, A. B. Azhar and M. I. Zainal Ariffin, “Chemical Modification of Kenaf Fibers,” Materials Letters, Vol. 61, No. 10, 2007, pp. 2023-2025. doi:10.1016/j.matlet.2006.08.006 E. Robson, “Surface Treatment of Natural Fibre,” EC/ 4316/92, 1993. H. A. Sharifah and P. A. Martin, “The Effect of Alkaliza-tion and Fibre Alignment on the Mechanical and Thermal Properties of Kenaf and Hemp Bast Fibre Composites: Part 1—Polyester Resin Matrix,” Composites Science and Technology, Vol. 64, No. 9, 2004, pp. 1219-1230.

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A Data Centric Privacy Preserved Mining Model for Business Intelligence Applications 1

Prof. Dr. P.K. Srimani, F.N.A.Sc. 2.Prof. Rajasekharaiah K.M. Former Chairman, Dept. of Computer Science & Maths, Bangalore University Director, R & D, Bangalore University, Bangalore, India. 2 Professor & HOD, Department of Computer Science and Engineering, JnanaVikas Institute of Technology, Bangalore Mysore High Way, Bidadi, Bangalore, Visvesvaraya Technological University (VTU), Belgaum, Karnataka India 1

Abstract: In present day competitive scenario, the techniques such as data warehouse and on-line analytical process (OLAP) have become a very significant approach for decision support in data centric applications and industries. In fact the decision support mechanism puts certain moderately varied needs on database technology as compared to OLAP based applications. Data centric decision support schemes (DSS) like data warehouse might play a significant role in extracting details from various areas and for standardizing data throughout the organization to achieve a singular way of detail presentation. Such efficient data presentation facilitates information for decision making in business intelligence (BI) applications in various industrial services. In order to enhance the effectiveness and robust computation in BI applications, the optimization in data mining and its processing is must. On the other hand, being in a multiuser scenario, the security of data on warehouse is also a critical issue, which is not explored till date. In this paper a data centric and service oriented privacy preserved model for BI applications has been proposed. The optimization in data mining has been accomplished by means of C5.0 classification algorithm and comparative study has been done with C4.5 algorithm. The implementation of enhanced C5.0 algorithm with BI model would provide much better performance with privacy preservation facility for Business Intelligence applications. Keywords: Business intelligence, Data warehouse, OLAP, data mining, privacy preservation, C5.0, rule generation I. INTRODUCTION Numerous organizations and business houses need certain system model that can effectively maintain huge data collections and can perform its processing for further analysis. The information retrieved from its local databases might not be efficient to accomplish the expected results for getting certain decision making solution. Therefore, in order to accomplish such objectives, it is required to have a framework that can collect huge datasets from diverse areas and can perform mining to achieve the expected help in service oriented organizational decision making process. In present day scenario the intricacies existing in business environment needs certain potential facilities that could make organizations swift and proactive for management oriented optimistic decision-making. Industry needs higher production and it can be facilitated by an effective resource planning that can retain its enterprise survival in competitive markets. In recent era each complicated and longterm information approach is suffered with the issues related to its business rules [1]. Then while, majority of business rules management approaches are emphasized on data collection, processing and its ultimate detail reporting. The detailed reporting can be a significant achievement for optimum decision making rather than to have good knowledge only [2]. The service oriented industries need highly intelligent production and resource management schemes for predicting the organizational performance and for providing short-term arrangement and control substitutes. Data warehouse is nothing else but a â&#x20AC;&#x153;subject-oriented, integrated, time -varying, nonvolatile collection of data that can be employed predominantly for effective organizational decision making processes and business intelligence (BI)â&#x20AC;? [3]. With the help of BI approaches the organizations can effectively integrate certain potential analytical tools for standardized data reporting, monitoring and analysis while taking into account of numerous metrics in certain service-oriented architecture [4], [5] which is must for any kind of management and strategic planning. The data analysis and its revelation might detect evolutionary data ignored in the past. Ultimately employing strategic measures an alignment can be done with certain tactical schemes while incorporating stringent objectives to be accomplished every moment, permitting permission on simplified and highly robust approach

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for correction in reports divergences. Such feasibility in historical analysis can be accomplished in case the organization possesses certain data bases or data from internal as well as external resources in data warehouse, facilitating investigative data analyses while implementing Business Intelligence (BI) approaches [6]. This integration of data warehouse and OLAP frameworks with BI tools can facilitate an optimistic strategic decisions support system which is significant in the course of exploratory schemes with huge data analyses with unknown patterns. Figure 1 illustrates management framework for performance which exhibits that data mining enhancement can play a very vital role in overall Business intelligence optimization. Functions · Data cleansing · Data integration · Data transformation · Data extraction

Service management and Management database

Operational Data Store

Closed-Loop Business Intelligence Applications

Conformed centralized data marts

Decision Center Analytic Application Advance query & Reporting OLAP/Data warehouse Performance Analytical Modules

Closed-Loop Decision makers

· · · ·

Data Mining Dashboards Scorecards Predicative analysis

Figure 1: Performance management framework For strengthening the performance of data warehouse and BI techniques, it is always advocated to enhance the data mining scheme. The effective data processing and information reporting can be accomplished only with better mining and classification approaches. Meanwhile, data security is a vital issue in data warehouse applications and business application tool development. The privacy preservation of data in this entire data mining, processing and analysis process might play a significant role [7][8], therefore in this paper, a highly robust privacy preservation enriched data mining algorithm has been developed for BI applications. Researchers always focus on employing certain optimum decision tree classification algorithm for BI frameworks. The C5.0 algorithm facilitates optimum data mining for further data analysis. For BI process and data warehouse applications C5.0 algorithm has been employed for performing analysis of data sets associated with various classified classes or parties. Since for classification accuracy and optimized rule generation of C5.0 can perform better as compared to C4.5 [9][10][11][12], in this paper, C5.0 data mining algorithm has been implemented for performing rules generation and data classification. The optimized data mining approach can be implemented with any data warehouse or online analytical process (OLAP) frameworks and business intelligence tools. The optimized mining process with enriched privacy preservation would provide better results for data centric and service oriented BI utilities. Other section of this paper is arranged in this way: Section II presents related works; in Section III proposed framework has been discussed. In Section IV the business intelligence oriented data analysis using C5.0 algorithm has been presented. The integration of OLAP with proposed mining module has been presented in Section V. Section VI present the results obtained which is followed by conclusion in Section VII. II. RELETED WORKS A number of systems have been proposed for enhancements in BI or decision support and mining modules are found as a potential solution for it [13]. Work [14] advocated a hybrid approach of data mining and analytical scheme for decision support in BI. They also developed a unified presentation for BI architecture using stream mining concept. In [15] a BI driven data mining (BidDM) model was proposed for decision support in ecommerce, comprising four layered framework and mining model. Chang et al in [16] developed a BI system with web information while performing data integration with mining model. In data integration they employed Oracle database with Cognos BI tool and in mining module they implemented multimedia data analysis using Monago database with Pentaho BI tool and performed comparative analysis, which states that data mining can exhibit better for BI. In [17] a BI model was developed for bankruptcy prediction and its prediction using qualitative as well as quantitative factors using Genetic algorithm [18]. A similar work was done in [19] with a mining module for rule generation for fraud patterns. Precise decision making by means of correct and reliable data in data warehouse was explored in [20] [21]. In [21] data sets from UCI machine learning repository was processed for data clustering and its visualization for significant decision making. In data mining for security reasons, in [22] a k-anonymity based privacy preservation scheme was developed while in [23] system was enhanced by classification and weighted attributes. In [24][26] a privacy preservation model for distributed decision tree mining module was developed on the basis of homomorphic encryption scheme. For OLAP application work [25] presented an approach of privacy preservation for OLAP multi-dimensional data cubes that enhanced the visualization of data sets in various perspectives in BI utilities. This is the matter of fact that

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these works have come up with certain enhanced outcomes, but considering an optimized solution for BI application and data warehouse, still there exist gap for further enhancements, such as highly efficient rule set generation, accuracy in classification and swift processing for huge data sets. Majority of existing systems employ ID3 or C4.5 algorithm for mining but it can further be enhanced by means of C5.0 algorithm which can provide better results for privacy preserved data mining for business intelligence (BI) applications. In this paper, we have considered C5.0 algorithm to accomplish this goal. III. PROPOSED FRAMEWORK An optimized framework might be an effective approach to perform validation for various needs of certain project, in the form of a scheme to detect any irregularity and discrepancy against the specific needs allied with the project. Considering the need of precise, privacy preserved and accurate data processing system for BI in this paper we intend to develop a data centric and service oriented, privacy preserved mining system that can provide optimally classified and accurate data classification. Such enhancements in mining and classification would strengthen the effectiveness and performance of BI model. The optimized mining approach would be efficient for its implementation in OLAP or data warehouse applications for BI tools. In this paper we have proposed C5.0 classification algorithm enriched with data privacy preservation approach. Privacy preservation scheme with C5.0 would make system highly efficient in terms of data classification, accuracy and anonymity in entire data warehouse. The mining of huge datasets from various resources in BI function would provide optimistic decision making while exhibiting data extraction, its processing and ultimate visualization of data retrieved from huge data from unknown patterns. The proposed framework has been illustrated in Figure 2.

Data Preparation Construct OLAP Decision Tree preparation C5.0/C4.5 Figure 2: Proposed BI Framework The mining enriched model can be integrated with data warehouse of OLAP to yield better results and decision supports in BI applications. A. System Model In data warehouse or OLAP frameworks, the collected datasets might encompass various kinds of integration anomalies. On the other hand being in multiuser scenario, there might be certain possibilities of data misplacement or even breaching. Therefore the datasets available are required to be cleaned and preserved for its privacy or anonymity. Once the data has been preserved for its security then it is succeeded by construction of OLAP multidimensional cubes which is followed by decision tree preparation using optimized C5.0 algorithm. The data mining can be exhibited on any dimension of the cube generated in second step. Once the model is prepared, it is then stored in OLAP cube where each dimension of cube represents the rules associated with certain node in the decision tree mining model. A sequential flow for the developed system has been presented in Figure 3 as follows:

Data

Data preparation and Information retrieval

Data Mining

Knowledge based Decision process

Figure 3: BI functional flow chart A.1 Data Preparation: The overall process of data preparation is classified into three sequential phases. These are, selection of data, data cleaning and formatting of data. In first step, the data can be selected on the basis of certain criteria, such as its data completeness and accuracy. Similarly, data selection also depends on its technical constraints like maximum data limits or data type, which is in fact associated with mining tools that we have planed earlier to operate with. The second step performs data cleaning that encompasses process such as data normalization and either the decimal scaling into range (0,1) or standard deviation normalization. Data cleaning can also have processes such as data smoothing, rectification for missing parameters, reduction in data volume in case of ineffective or even imperfect. In our work we have employed the discretization of numeric attributes for exhibiting data smoothing. For rectifying the issue of missing values, we have employed a common way called replacement of missing value with a single global constant. The data dimension reduction has been done by analyzing predictive potential of the specific data to be employed for decision making. This is accomplished

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using schemes of attribute selection with mean and standard variation, principal component analysis (PCA) or feature merging using linear transform. In this work we have considered attribute selection process while taking into account of mean and variances. Once the data has been cleaned then it is followed by new data construction that performs constructive functions such as deviation of certain attribute form two or more attributes in the data sets, generating new data sets, its transformation (data normalization and smoothing) and privacy preserved data aggregation from different tables. In this work, the data formatting is achieved by syntactic modification to the specific data sets which do not change its significances. It encompasses re-arrangement of data attributes, variation in constraints as per tools, removal of any unwanted entities like comma, special characters, tabs etc. It can also do certain trimming for higher data size. A.2 OLAP Cube Construction In business intelligence (BI) applications the significance of OLAP is to exhibit materialization of certain vital but expensive computations which is inquired frequently. These expensive computations encompass processes such as aggregate function, counting, summation, averaging, max estimation etc and stores the processed datasets on certain multidimensional databases (Cubes) for supporting decision making in BI applications. Here it must be noted that “cube’ states for data base. In OLAP formation the data aggregation operation might be a function that can be pre-estimated as per classification accomplished by various datasets and attributes. The data values in individual attribute could be categorized into certain lattice architecture of hierarchies. The process such as specialization and generalization can be exhibited on multi-dimensional databases by employing paradigms called “roll-up” or “roll-down”. The roll-up process minimizes the number of data dimensions and performs generalization on attributes values to the higher level approaches. On the other hand, the drill-down approach exhibits the reverse of the roll-up process. As, a number of aggregate functions might be required to be estimated iteratively for data analysis and BI applications, the processed data storage (pre-estimated results) can be done in multi-dimensional cubes that would ensure high speed computation and thus the response time would be enhanced. This also enhances flexibilities in viewing data originating from various angles and with numerous level of abstraction. A.3 Decision Tree Construction In this paper, we have implemented C5.0 algorithm for data classification and decision tree generation that can be employed for achieving most precise and accurate data information. In this work we have emphasized on the implementation of C5.0 algorithm and a performance oriented comparison has been done with C4.5 decision tree classification algorithm. (a) C5.0 Data Mining Algorithm The decision tree classification algorithm C5.0 is the successor of C4.5 algorithm. The C5.0 algorithm exhibits better efficiency and accuracy as compared to its descendant C4.5 algorithms, CART or ID3 algorithm. OLAP framework considered C5.0 algorithm to provide optimum classification and rule set generation which is must for BI tools. In this proposed framework, C5.0 algorithm has been used for creating rule sets which is further enhanced for classifying data sets from different sources or attributes of organization. The information available with attributes, called as Entropy, plays vital role in justifying effectiveness of decision tree classification learning process. For a random variable , as per information theory, entropy can be defined as , Where states attributes and signifies for the value of the class attributes. Meanwhile, for another attribute the conditional entropy is given by

. For a better system

model, the conditional entropy is needed to be made lower than entropy for . In uncertain scenario this type of minimization is stated as mutual information existing between attributes given by . Primarily, for making each data partition the attribute is optimized that could give maximum information about other attribute and thus the overall information gain is enhanced. As this process considers the employment of a parallel approach to assist , resulting into multiple results, the classification algorithm C5.0 enhances the relation that provides . Additionally, to avoid the assortment of lower entropy by any attribute, factor can be optimized by an adaptive weighting process. The dynamic weight update is done by incrementing . A predominant problem in decision tree classification, rule set/rule generation schemes is the compliance of training data sets which is in general stated as outfitting. For eliminating this kind of limitation, the proposed C5.0 algorithm advocates a scheme stated as . Here, the reason behind using C5.0 algorithm with data warehouse applications are its characteristics; adaptive boosting, pruning and enhanced rule set generation that can optimize business intelligence tools for precise decision making. A brief of these features have been given in next section. (b) Adaptive Boosting The reason behind the implementation of adaptive boosting in C5.0 is the generation of a number of classifiers on the data existing in different cubes (consider individual party). In case of any unique data occurrence for

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classification, the predicted data class is adaptively weighted with the help of independently trained data classifiers. C5.0 generates multiple data classifiers by inducing a single data classifier. The induced classifier is further re-trained on the data samples considered for forming the initial classifier. Final data sets are generated using second classifier. The significant algorithmic approaches behind the realization of adaptive boosting are: · Step-1: Choose data samples from existing datasets, where each datasets provides a likelihood of to train certain data classifier. · Step-2: Classification of data sets with the trained classifier. · Replace the data present by multiplying the likelihood of the imperfectly-classified datasets by certain adaptive weight . · Step-3: Go on with earlier steps times with the generated likelihoods. · Step-4: Blend the classifier by introducing a weight factor on independently trained data classifier. (c) Pruning The proposed C5.0 algorithm exhibits two consecutive steps for generating decision trees. Initially it constructs a data classifier for available data sets and in ascending phase it performs pruning on the data classifiers formed in initial phase. This is done only to prevent the probability of over-fitting on available data in individual datasets or party (cube). In this entire process two significant influensive alternatives can be considered to pruning. The initial phase functions for characterizing the degree in which the decision tree generated is adaptable with training datasets. To accomplish it, this approach finds out minimum number of datasets which might have minimum two branches at any of the generated nodes in decision tree. Here, the predominant goal is to avoid data over-fitting by ending training. Another option for pruning is that its security features which do influence this algorithm states for the process of post pruning on the generated decision trees and generated rule sets. On the other hand, pruning process is also performed by removing some generated decision trees that do not have more significance due to higher error rate. The post pruning is exhibited by substituting a branch of classified tree by a leaf in the case when the predicted errors for the last step is lower than one of the branch in decision tree. (d) Rule Sets Generation One of the predominant functions of C5.0 algorithm is the conversion of decision trees into optimal rule sets. Here in proposed mining model the algorithm has been optimized in such a way so that it can generate rule sets for classifying data. Our proposed system model has exhibited optimized results in terms of flexible rule generation and the generated rule sets are easy to understand for decision making applications in BI models. The rule generation plays very significant role in optimizing performance of Data warehouses, OLAP and the ultimate BI tools. This is the matter of fact, that in data warehouse, there are huge data sets and attributes which might be operated by multiple users or operational attributes, therefore security or precisely anonymity of data is an important need. Considering this requirement of privacy preservation in mining or data warehouse systems, in this paper we have developed a privacy preservation approach that ensures authenticity and anonymity of data elements across data warehouse infrastructure. IV. BI ORIENTED DATA ANALYSIS with C5.0 ALGORITHM Considering the significance of a robust decision tree classification system with privacy preserved security features for data mining in data warehouse and business intelligence applications, in this paper the C5.0 algorithm [26] has been enhanced with privacy preservation. The efficiency of C5.0 can be visualized in terms of higher accuracy in classification, rule generation and swift processing with huge data sets. These all aforementioned factors are must for effective business intelligence (BI) or decision support applications. In this paper, C5.0 algorithm is optimized for its function for data analysis with raw as well as unclassified data available with every data set or cubes (in terms of OLAP). Once the rules generated by individual classifier are collected then these are combined together to exhibit higher accuracy and swift data processing. Let, an entity represents a set of row or unclassified data available with datasets where , and for analysis process the mingled rules generated with collected classification rules from each party then the combined rule set generated is expressed by , where represents local rule set formed on the basis of the data and that refers C5.0 enriched rule set generation function. ; Here represents rule set formed. ; Where represents the maximum number of rules generated by certain cube or party. Now, observing these expressions it can be realized that the final combined rule sets generated by our proposed C5.0 algorithm ( ) may encompass every rule generated by to . This would strengthen maximum utilization of data sets available for decision making. The utmost feasible number of rules generated by a party is referred by . The Represents for the function of overall combined rule sets generation. The expression is the function for exhibiting classification by means of C5.0 algorithm. Here, depicts a function which considers two factors; classification rule

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and input row data available Figure 4 as below:

datasets or cubes in OLAP. The pseudo code for data analysis is given in

Business Intelligence oriented data mining and analysis using C5.0 Algorithm Input: Combined rule sets generated Output: Analyzed and mined data results 1. Initializing combined rule set 2. Initialize 3. For each 4. For each 5. Update 6. Update 7. End For 8. Update 9. End For 10. Estimate final result

Figure 4: BI oriented data analysis and mining using C5.0 decision tree classification algorithm For accomplishing the data analysis the ultimate classification function can be further integrated with data warehouse or OLAP application that would result into optimized performance for accurate and precise decision making process in Business intelligence (BI) tools. V. OLAP AND DATA MINING INTEGRATION The process of effective data mining and information exploration from datasets or warehouse relate to the phenomenon of exploring certain genuine, unique, till unknown and potentially significant presentations of information from collected unprocessed datasets available in huge cubes or databases. The resemblance of the process called â&#x20AC;&#x153;Data miningâ&#x20AC;? advocates the sifting in the course of huge raw data so as to achieve certain valuable information. In fact, it presents a multi- phase, recursive inductive phenomenon that encompasses processes like analysis of issues, extraction and exploitation of huge data, data preparation and its cleaning, reduction of unwanted data, rule generation, results analysis and prediction or decision making. Data warehouse or Online Analytical Processing (OLAP) represents an approach of presenting expected information from certain data bases or multidimensional cubes formed. OLAP scheme can be employed for visualizing multidimensional data cubes and identifying the expected patterns, data orientation a relationship can be built. The process of visualization may encompass slicing and dicing among data where the authority or analytic may chose the significant data for accomplishing certain specific decisive information. It may re-orientation of data view, increment or decrease in abstraction level and filtering of data available. Hence, the multidimensional privacy preserved cubes, would facilitate data warehouse or OLAP to exhibit data analysis and decision making. In our work, once the privacy preserved and enhanced decision tree mining framework has been developed, the concept hierarchies has been employed to perform generalization of individual node in decision tree that can be further accessed by data warehouse applications or OLAP functions. Thus, such unique system implementation makes the overall business intelligence (BI) process more robust and efficient for service oriented applications for varied industries or organizations. VI. RESULTS AND ANALYSIS In this paper, the proposed system model was developed with robust C5.0 decision tree classification algorithm so as to achieve optimum performance results in mining process that may further enhance the overall efficiency of certain Business intelligence application. The proposed system model has been developed on Visual Studio platform and C#, .net programming language has been employed for it. Since, data mining is one of the most predominant factors that optimize business intelligence (BI) optimization. For optimizing classification accuracy and rule generation, C5.0 has been developed which was further enhanced with its privacy preservation features. In order to justify the robustness of proposed C5.0 algorithm based framework, we have developed a parallel reference model using C4.5 decision tree algorithm. In comparative study, on the basis of results analysis for rule set generation, testing accuracy, training accuracy and classification aspects, the proposed C5.0 algorithm based model has over performed its descendant C4.5 classification based mining model or system. The implementation of proposed system model and its comparative study has been done in terms of privacy preserved rule set size and rule generation effectiveness, accuracy analysis for classification rule based training process and similarly, accuracy analysis for testing and area covered in computation. The retrieved data illustrates that the proposed C5.0 based decision tree classification performs better with data warehouse/OLAP frameworks or business intelligence tools is given in Figure 5 & 6 as below:

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ANALYSIS OF RULE GENERATION

SIZE OF SECURE RULE SET

C5.0 based Mining Model

C4.5 based Mining Model SIZE (Kb)

TIME (S)

C5.0 based mining model

4 3 2

C4.5 based Mining Model

15 10 5

0 2

DATASET or CUBE ID

Figure 5: Analysis for rule generation Figure 6: Analysis for rule set generation As depicted in Figure7 to 10 as below, the factor area under curve for the framework analysis might be considered as the accuracy approximation toward positive instances. This characteristic exhibits that the probability of classifier may accomplish higher positive occurrence in comparison with the randomly selected negative instances. This also depicts that proposed C5.0 encompasses minimum computational overheads and cost that might strengthen the OLAP or data warehouse frameworks for business intelligence applications or tools. CLASSIFICATION RULE BASED TRAINING ACCURACY

100 ACCURACY (%)

ACCURACY (%)

100 95 90

C5.0 based Mining… C4.5 based Mining…

CLASSIFICATION RULE BASED TESTING ACCURACY

90 80

C5.0 based Mining… C4.5 based Mining…

Figure 7: Classification training accuracy analysis

AREA COVERED BY ROC CURVE

AREA COVERED BY ROC

1 0.9 0.8 C5.0 based Mining Model C5.0 based Mining Model

0.6 2

Figure 8: Classification testing accuracy AREA COVERED BY ROC CURVE IN TESTING

AREA COVERED BY ROC IN TRAINING

0.7

DATASET or CUBE ID

1 0.9 0.8 0.7

C5.0 based Mining Model C4.5 based Mining Model

0.6 2

DATASET or CUBE ID

Figure 9: Comparative analysis for area covered by Figure 10: Comparative analysis for area covered by C4.5 and C5.0 ROC for data set testing This enhanced performance can enrich decision support system (DSS) with higher accuracy and minimum complexity even with the huge data set’s processing. VII. CONCLUSION Because of the huge data volume in certain organization or institutions, business intelligence (BI) becomes very significant process for decision making and optimistic strategic planning. Similarly data warehouse and online analytical processing (OLAP) also have vital significances for business intelligence (BI) applications and decision support systems (DSS). Data mining is one of the key components of data warehouse and BI models. The optimization of decision tree classification in data mining model can optimize the overall performance of business intelligence (BI) model. In this paper, a highly robust and privacy preserved, data centric mining module has been developed for BI applications. Considering the need of efficient classification and rule generation approach in data mining, in this work C5.0 decision tree classification algorithm has been employed

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to perform mining which ultimately enhances the performance of integrated BI framework. The developed mining model has exhibited better results in terms of rule set generation, data classification, learning/training accuracy with enhanced optimized area covered by receiver operating characteristics. The proposed C5.0 algorithm has outperformed C4.5 based mining model. The implementation of proposed mining model with OLAP/data warehouse applications and BI frameworks would result into highly efficient, privacy preserved and most precise decision making process for BI applications. REFERENCES Z. Michalewicz; M. Schmidt; M. Michalewicz, C. Chiriac; “Adaptive Business Intelligence”; Springer, 2006 L. Nemuraite; L. Ceponiene; G. Vedrickas; “Representation of Business Rules in UML&OCL Models for Developing Information Systems” Lecture Notes in Business Information Processing in 2009, 182-196. [3] nmon, WH; Building the data warehouse; John Wiley in 1992. [4] W. W. Eckerson; “Performance Dashboards: Measuring, Monitoring and Managing Your Business”; Wiley, 2010. [5] J. Ranjan; “Business justification with business intelligence”;‖ Vine, pp. 461-475, 2008. [6] U. Sekaran; “Research Methods for Business”; John Wiley & Sons in 2006. [7] O. Goldreich; “Secure multi-party computation”; Final draft, version 1. 4 in 2002. [8] C. Dwork; K. Kenthapadi; F. McSherry; I. Mironov; and M. Naor; “Our data, ourselves: Privacy via distributed noise generation”; Advances in Cryptology-EUROCRYPT; pages 486–503, 2006. [9] Xindong Wu; Vipin Kumar; J. Ross Quinlan; Joydeep Ghosh; Qiang Yang; Hiroshi Motoda; Geoffrey J. McLachlan; Angus Ng; Bing Liu and Philip S. Yu, et al. "Top 10 algorithms in data mining"; Springer; Knowledge and Information Systems Volume 14, Number 1 in 2008. [10] Tomasz Bujlow; Tahir Riaz; Jens Myrup Pedersen; “A method for classification of network traffic based on C5.0 Machine Learning Algorithm" in workshop on Computing, Networking and Communications in 2012 IEEE pp 237-241. [11] Po-Hsun. Sung; Jyh-Dong Lin; Shih-Huang Chen; Shun-Hsing Chen; and Jr-Hung Peng; “Utilization of Data Mining on Asset Management of Freeway Flexible Pavement"; Proceedings of IEEE IEEM; Pp 977 – 979 in 2010. [12] R. Agrawal and R. Srikant; “Privacy preserving data mining”; in Proc. ACM SIGMOD Int’l Conf. on Management of Data in 2000. [13] Guarda, T.; Santos, M.F.; Augusto, M.F.; Silva, C.; Pinto, F.; "Process Mining: A framework proposal for Pervasive Business Intelligence," Information Systems and Technologies (CISTI), 8th Iberian Conference on 19-22 June 2013, pp.1-4. [14] Yang Hang; Fong, S., "Real-time business intelligence system architecture with stream mining," Digital Information Management (ICDIM), 2010 Fifth International Conference on5-8 July 2010, pp.29-34. [15] Yang Hang; Fong, S., "A Framework of Business Intelligence-Driven Data Mining for E-business," INC, IMS and IDC, Fifth International Joint Conference on 25-27 Aug. 2009, pp.1964-1970. [16] Ping-Tsai Chung; Chung, S.H., "On data integration and data mining for developing business intelligence," Systems, Applications and Technology Conference (LISAT on 3-3 May 2013, pp.1-6. [17] Martin, A.; Lakshmi, T.M.; Prasanna Venkatesan, V., "An analysis on business intelligence models to improve business performance," Advances in Engineering, Science and Management, International Conference on 30-31 March 2012, pp.503-508. [18] Martin, A.; Miranda Lakshmi, T.; Venkatesan, V.Prasanna, "A business intelligence framework for business performance using data mining techniques," Emerging Trends in Science, Engineering and Technology (INCOSET), 2012 International Conference on 13-14 Dec. 2012, pp.373-380. [19] Ma Jun, "Research of detecting e-business fraud based on data mining," Computer Science and Network Technology, 2011 International Conference on 24-26 Dec. 2011, vol. no. 4, pp.2201-2204. [20] Ali, K.; Warraich, M.A., "A framework to implement data cleaning in enterprise data warehouse for robust data quality," Information and Emerging Technologies (ICIET), 2010 International Conference on 14-16 June 2010, pp.1-6. [21] Usman, M.; Pears, R., "A methodology for integrating and exploiting data mining techniques in the design of data warehouses," Advanced Information Management and Service (IMS), 2010 6th International Conference on Nov. 30 -Dec. 2 2010, pp.361-367. [22] Zare-Mirakabad, M.-R.; Kaveh-Yazdy, F.; Tahmasebi, M., "Privacy preservation by k-anonymizing Ngrams of time series," Information Security and Cryptology (ISCISC), 2013 10th International ISC Conference on 29-30 Aug. 2013, pp.1-6. [23] Jiandang Wu; Jiyi Wang; Jianmin Han; Hao Peng; Jianfeng Lu, "An anonymized method for classification with weighted attributes," Signal Processing, Communication and Computing (ICSPCC), 2013 IEEE International Conference on 5-8 Aug. 2013, pp.1-5. [24] Weiwei Fang; Bingru Yang; DingLi Song; Zhigang Tang, "A New Scheme on Privacy-Preserving Distributed Decision-Tree Mining," Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on 7-8 March 2009, vol. no. 2, pp.517-520. [25] Mumtaz, S.; Rauf, A.; Khusro, S., "A distortion based technique for preserving privacy in OLAP data cube," Computer Networks and Information Technology (ICCNIT), 2011 International Conference on 11-13 July 2011, pp.185-189. [26] KumaraSwamy, S; Manjula S H; Venugopal, K R; Iyengar S S; Patnaik, L M, "Association rule sharing model for privacy preservation and collaborative data mining efficiency," Engineering and Computational Sciences (RAECS) on 6-8 March 2014;pp.1-6. ACKNOWLEDGMENT One of the author’s Mr. Rajasekharaiah K.M. thanks Ms. Chhaya Dule, Asst.Prof. Jyothy Institute of Technology, Bangalore for her valuable suggestions. AUTHOR: Presently Mr. Rajasekharaiah K.M. is working as Professor & HOD Department of Computer Science & Engineering, Jnana Vikas Institute of Technology, Bangalore. He has done M.Tech. in Computer Science & Engg. M.Sc. Information Technology, M.Phil. in Computer Science, and PGDIT from reputed Universities, India. He is having 30+ years of total experience including 16 years of Industrial experiences. He is a Life fellow Member of Indian Society for Technical Education (ISTE), New Delhi. He is presently pursuing the doctoral degree in the Branch of Computer Science & Engineering, in the domain area of Data Mining & Warehousing. He has research publications in reputed national and international journals. His other area of interests are DBMS, Software Engg., Software Architecture, Computer Networks, Programming Languages, Data Structures and Mobile Computing. He is also a resource scholar for other Engineering Colleges/University. [1] [2]

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Available online at http://www.iasir.net

Reactive Routing in MANETs: A Performance Evaluation Jaspreet Singh1, C.S. Rai2 Research Scholar-USICT, Professor and Director- USICT and Research & Consultancy University School of Information Technology, Guru Gobind Singh Indraprastha University Dwarka Sector-14 C, New Delhi, India 1

Abstract: An ad-hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing infrastructure or centralized administration. Routing is the process of communication established for exchange of messages. The process of routing is central to any application for ad hoc networks. This paper brings about a performance comparison between three existing reactive routing protocols: AODV, DSR and LAR1. These three protocols exhibit different levels of processing requirements and overheads. Our study is different from existing studies as we are concentrating only on reactive routing methods, while most other studies compare reactive with proactive methods. Also, we are comparing the behavior of protocols vis-à-vis different mobility patterns, which we define as a combination of varying three parameters: pause time, minimum speed and maximum speed of movement. High mobility is marked by rapid movement and constantly changing topology, which has its own challenges. The goal of this study is to bring out adaptability of existing routing solutions with respect to varying network characteristics and see their suitability. Keywords: QoS, MANET, routing protocols, Comparison, Performance Evaluation I. Introduction Mobile Ad-hoc networks (MANETs) are self organizing, infrastructure-less and multi-hop packet forwarding networks. There is no concept of fixed base station. So, each node in the network acts as a router to forward the packets to the next node. Ad-hoc networks are self-adaptable and capable of handling topology changes and malfunctions in nodes [6]. Routing is the process of communication established for exchange of messages. The process of routing is central to any application for ad hoc networks. Since the path is continuously changing, routing is the first process for any activity. Ad hoc networks are multi-hop networks. The key challenge in mobile ad hoc networks is to route the packets with low overheads even in dynamic conditions of node mobility, limited channel bandwidth and limited battery life of nodes. In general routing algorithms fall into one of two categories: Reactive algorithms: Here, routes are established, only when they are required, they are not readily available and Proactive algorithms: Here, regular information is exchanged between the neighbors. This information is maintained in form of tables and routes are readily available to all destinations. This also calls for more overheads, as many of these routes may not be used at all times. The rest of this paper is organized as follows: Section II presents the simulation environment and performance metrics, Section III presents simulation results and its analysis and finally Section IV concludes the paper. II. RELATED STUDY Manickam et al. [2] compares three MANET Routing protocols: DSDV, AODV and DSR. It concludes that DSR is preferable for moderate traffic with moderate mobility while DSDV produces low end-to-end delay compared to other protocols, due to its proactive nature. Ehsan et al. [3] compares four different types of routing protocols: DSDV, DSR, AODV and TORA. While DSDV is a proactive protocol, others are reactive in nature. It concludes that DSR outperforms all other three. DSR generates less routing load then AODV. While AODV suffers from higher end to end delays, TORA has very high routing overhead and DSDV has a low packet delivery fraction at high mobility. Bouhorma et al. [4] compares two routing protocols: AODV and DSR and concludes that while DSR scales well in small networks with low node speeds, AODV performs better and exhibits higher packet delivery ratio even at higher mobility. Baraković et al. [5], compares performances of three routing protocols: DSDV DSR and AODV. It concludes that under low mobility scenarios, all three protocols show similar results, while under high load conditions or high mobility, AODV performs pretty well.

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DSDV depicts a low packet delivery fraction while DSR shows overall low performance of higher Normalized Routing Load and higher delay. III. SIMULATION ENVIRONMENT AND PERFORMANCE METRICS We have used Glomosim 2.03 simulator for studying the behavior of these routing protocols. Global Mobile Information System Simulator (GloMoSim) is a parallel discrete-event simulator which provides a scalable network environment [1]. For our comparison purpose, we simulate the running behavior of three existing reactive routing protocols: AODV, DSR and LAR1. A. Fixed Parameters Field Size: A field of size 800m X 1000 m has been selected for node movement. Traffic Type: We have used Constant Bit Rate traffic. CBR offers a fixed traffic pattern, generating it continuously at designated rate, unlike TCP. Such constant traffic rate may be generated by applications such as audio, video or other fixed forms of data. For our study, the traffic generation rate has been fixed at four packets per second. The size of each data packet is 512 Bytes. Mobility Model: We use the Random Waypoint Model for movement of nodes. In Random Waypoint model, a node randomly selects a target point and moves in that direction with a speed chosen randomly between a minimum and a maximum speed. After reaching the target, node waits at that point for certain specified time called the pause-time and then moves again. These parameters are summarized as below: Table 1: Parameters used for the simulation Parameter Name Environment Size

Value 800m X 1000m

Mobility Model

Random Waypoint

Traffic type

Constant Bit Rate

Packet size

512 Bytes

Packet Rate

4 packet /s

MAC-Protocol

802.11

Radio Transmission Power

15 dB

Simulation Time

100 sec

B. Variable Parameters We compare the performance of above protocols vis-Ă -vis type of mobility and also number of nodes. We take into account three different mobility patterns: low, medium and high mobility. A low mobility is characterized with fairly static network topology, while a high mobility is marked by constant changing topology. In our experimental setup, we define mobility by varying speed of movement and also pause time between two movements. We define mobility as follows: Low Mobility: Speed between 0 and 5 m/sec and pause time of 25 sec. Medium Mobility: Speed between 5 and 15 m/sec and pause time of 10 sec. High Mobility: Speed between 15 and 25 m/sec and pause time of 1sec. Number of nodes: We repeat the simulation by varying number of nodes as 10, 25, 50 and 100. The node density directly affects the routing behavior. The more the number of nodes, more will be the number of packets to be sent and received. The behaviour of a protocol will vary as the number of nodes increases. C. Performance Metrics The metrics being used to evaluate the performance of routing protocols in this paper are: average end-to-end delay, Packet Delivery Fraction and throughput of the network. Average end-to-end delay: The end-to-end delay of a packet represents the time it takes to route a packet from source to the destination. This delay consists of propagation delay, queuing delay, and transmission delay introduced by network components. The average end-to-end delay is the average of end-to-end delay of all the packets which are successfully delivered. The end to end delay is important because most applications need a small latency to deliver usable results. It shows the suitability of the protocol for these applications. Packet delivery fraction: The packet delivery ratio is defined as the ratio between the number of packets received by the destination to the number of packets sent by the source. It describes percentage of the packets which reach the destination. A high percentage is desirable and reflects the reliability of the network.

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Throughput: Throughput of a network is defined as the number of packets received by destination per unit time. A higher throughput reflects a higher efficiency of the network. However, a higher throughput may result in longer end to end delays. IV. SIMULATION RESULTS AND ANALYSIS A. Comparative Analysis of MANET protocols as a function of mobility 1) Average end to end delay: The trend shows that end to end delay decreases, as mobility increases from low to medium, and then increases again, as mobility increases again from medium to high. AODV is least affected by mobility pattern, while DSR is most affected. This proves that moderate mobility is better than a static network and it reduces end to end delay. However, high mobility leads to frequent topology changes and greater delays. AODV shows a fairly constant delay, irrespective of speed of movement and the pause time. DSR, in contrast, depicts a high delay when mobility is less. This delay decreases as mobility increases from low to medium and then again increases, as mobility increases from medium to high. We attribute the reason for this to the fact that at low mobility, if nodes are far apart, then end to end delay is more. However, as mobility increases, the probability of nodes coming within transmission range increases, during the transmission interval. This leads to lower delays and higher delivery ratio. However, as mobility increases further, the network topology changes very fast. As a result, the available routes obsolete out very fast, leading to frequent route discoveries. Hence the overheads increase and therefore, average end to end delay increases very fast. Fig 1: Average end to end delay versus mobility

Fig 2: Packet delivery fraction versus mobility

DSR LAR1 AODV

DSR LAR1 AODV Throughput (Kbps)

PDF (%)

Delay (sec)

80 2 1

75 70 65 60

0 Low

Medium Mobility

high

Fig 3: Average throughput figures versus mobility

Low

Medium Mobility

high

DSR LAR1 AODV

350 300 250 200 150 100 50 0 Low

Medium

high

Mobility

2) Packet delivery fraction: The trend clearly shows that packet delivery fraction decreases with higher mobility. This result is as per expectation. Higher mobility leads to frequent topology changes and routes become obsolete. Hence many packets are dropped in the process. LAR1 shows better packet delivery fraction than other two. This is probably due to accurate prediction of destination location. This expected location is helpful in targeted broadcast. A noted exception is this behavior is AODV at low mobility. Packet delivery fraction for AODV is particularly less at low mobility. This fraction however improves and becomes comparable to DSR at medium mobility and then it decreases again at high mobility, which is natural. This lower packet delivery fraction for AODV at low mobility may be attributed to the fact that network is quite static. Hence two nodes, which are unable to send packets across to each other, will keep dropping packets, as the network is static. However, when the mobility increases, chances are that such nodes may move closer to each other, within the transmission interval and paths may get established. But when mobility increases further, from medium to high, the network becomes highly dynamic and frequent path breaks occur. This leads to high packet dropout ratio. The reason why DSR and LAR1 are able to have high packet delivery ratio for low mobility is due to their path optimization features. DSR runs in promiscuous mode and listens to all Route Replies and Route Requests, to update its path information. LAR1 uses GPS information to find approximate location of destination. For fairly static network, there is hardly any change and therefore, packet delivery ratio is high. 3) Average Throughput: Throughput is the number of packets received by destination per unit time. The graph shows that throughput of LAR1 and AODV exhibits a fairly constant rate, irrespective of mobility. The effect of mobility on their throughput is minimum. Because the traffic pattern and traffic density remains the same between low and high mobility, there is not much variation in packets received per unit time. A noted exception to this trend is the behavior of DSR at low mobility. However, this throughput improves significantly and becomes equal for DSR, AODV and LAR1, at medium and high mobility.

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B. Comparative Analysis of MANET protocols as a function of number of nodes

Fig 4: Average end to end delay Vs No. of nodes

100

600

500

PDF (%)

Delay (sec)

3 2

60 40

0 10

25 50 100 No of nodes

DSR LAR1 AODV

5 4

Fig 6: Avg. throughput Vs No. of nodes

Throughput (Kbps)

DSR LAR1 AODV

Fig 5: PDF Vs No. of nodes

25 50 No of nodes

100

400 300 200 100 0 10 25 50 No of nodes

100

1) Average end to end delay: The end to end delay as a function of number of nodes, clearly shows that there is a non-linear increase in the delay with increase in nodes. As the number of nodes increase, the number of nodes in the transmission range also increases. This increases overall traffic and number of packets handled by a node per unit time increases manifold. Therefore, there is a significant increase in Route Requests, Route Replies and data packets generated. This leads to appreciable increase in queuing and transmission delays, as more requests are lined up at each node. This in turn automatically pushes the end to end delay, as nodes increase. The trend shows that end to end delay decreases slightly, as number of nodes increase from 10 to 25. This is due to the fact that for 10 nodes, nodes are sparse and far spread out. Hence many packets time-out and reach their destination late. However, as nodes increase to 25 and node density becomes desirable, paths become easily available and queuing delays are also manageable. Here the network exhibits best end to end delays. With further increase in number of nodes, the number of messages increases non-linearly. This affects the end to end delays and it increases beyond this point. It increases moderately from 25 to 50 nodes and then significantly, as nodes increase from 25 to 50. From the data we can find that average delay is almost same for all three protocols, when number of nodes are 25. However, for 50 nodes, AODV clearly outperforms DSR and LAR1. LAR1 has the highest average end to end delay for 50 nodes. This performance is maintained, as nodes increase from 50 to 100 and DSR exhibits highest end to end delay. The graph clearly shows that AODV clearly outperforms DSR and LAR1. AODV being a simple algorithm, the delay here is minimum. However, LAR1 and DSR have significant overheads, which cannot be overlooked. These overheads are due to more processing required, to optimize the routes. 2) Packet delivery fraction: The trend here is that PDF decreases with increasing number of nodes. Initially, when nodes increase from 10 to 25, PDF increases slightly, due to easy availability of routes between source and destination. However, as nodes increase futher to 50 and 100, there is a decrease in PDF. This decrease is quite natural. With more nodes in the transmission range, there is an explosion in number of Route Requests, Route Replies and even data messages. Many of these messages time out, due to increased queuing and processing delays. Hence, it results in overall reduced PDF. This decrease is more marked as nodes increase from 50 to 100 than from 25 to 50. It clearly means that network degradation starts with increasing node density and it becomes significant when network size increases to 100 and beyond. Of the tree protocols, LAR1 exhibits highest PDF, when nodes are 10, but becomes comparable to other two when nodes increase to 25 and 50. At 100 nodes, DSR has slightly better PDF than AODV and LAR1. 3) Average Throughput: The trend clearly shows that average throughput grow linearly with increasing number of nodes. This is depicted by all the three protocols. It can be attributed to the fact that as the nodes increase, so does the number of packets generated by them. When overall volume of traffic increases, more traffic is handled by every node and every link per unit time. This pushes up the throughput of the system. Therefore, we can say that bigger network is good, if throughput is the only parameter. However, higher throughput has to be seen in context of higher PDF and end to end delay, and not in isolation. Both LAR1 and AODV exhibit comparable throughput and DSR exhibits lowest throughput, of the three. V. CONCLUSION The metrics like end to end delay, throughput and packet delivery fraction are not mutually exclusive, but are overlapping. The improvement of one may lead to degradation of another. For example, though throughput of

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the system may improve, the end to end delay might actually increase. Therefore, the selection of protocol should be on the basis of actual requirement. AODV has lowest end to end delay, than its counterparts. This is due to its simplicity and low overheads. DSR, on the other hand, has highest delays. This is due to its promiscuous mode of operation. LAR1 exhibits highest PDF than other two. This actually means that if we can calculate approximate location of target, it might actually help in increasing PDF. In terms of throughput, both AODV and LAR1exhibit fairly better throughput than their counterparts. From above results, we can easily conclude that no single solution is apt for all situations and all types of networks. Each solution has its own strengths and weaknesses. These results are summarized in the table below. Table 2: A Comparison of various aspects of AODV, DSR and LAR1

Nature

AODV Reactive

DSR Reactive

LAR1 Reactive

No. of Paths

Single path

Multi-path

Single path

Address field in Data packets Multiple paths information

Packet contains only next-hop information Only single path

Packet contains entire path

Load Balancing

Only source has multiple paths to destination No

Packet contains only next-hop information Only single path

Adaptability to network load Categorization of paths

Not adaptable

Network signal monitoring End to End Delay PDF Throughput

Not present Lowest Moderate Comparable to LAR1

Not present Highest Low for Low Mobility Low for Low Mobility

Not present Moderate Highest Highest

VI.References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Jorge Nuevo, “A Comprehensible GloMoSim Tutorial”, March 2004. P. Manickam, T. Guru Baskar, M.Girija, D.Manimegalai, “Performance Comparisons Of Routing Protocols In Mobile Ad Hoc Networks “, IJWMN Vol. 3(1), 2011. Humaira Ehsan' and Zartash Afzal Uzmi, “Performance Comparison Of Ad Hoc Wireless Network Routing Protocols”, IEEE INMIC 2004. Mohammed Bouhorma, H. Bentaouit and A.Boudhir, “Performance Comparison of Ad-hoc Routing Protocols AODV and DSR”, IEEE Multimedia Computing and Systems, 2009. Sabina Baraković, Suad Kasapović, and Jasmina Baraković , “Comparison of MANET Routing Protocols in Different Traffic and Mobility Models”, Telfor Journal, Vol. 2, No. 1, 2010. C.Sivaram murthy, B.S.Manoj, Adhoc wireless networks:Architectures, and protocols, Pearson Education, 2004. S. Corson and J. Macker, “Mobile Ad-hoc networking: Routing protocol performances and evaluation considerations”, In RFC 2501, 1999. C. Toh, “Challenges in MANET”, IEEE Communication, 2001. E. M. Royer and C. K. Toh, “A review of current routing protocols for Ad-hoc mobile wireless networks”, IEEE Personal Communications, 6(2), Apr. 1999. Subir Kumar Sarkar, T. G. Basavaraju, C. Puttamadappa , “Ad Hoc Mobile Wireless Networks: Principles, Protocols, and Applications”, CRC Press, 2007.

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Study of UWB Low Noise Amplifier Somit Pandey1, Prof. Puran Gour2 , Brij Bihari Soni3 1 M.Tech Scholar, NIIST, Bhopal (M.P.), India 2 Head, Dept. Of Electronics & Communication Engineering, NIIST, Bhopal (M.P.), India 3 Asst. Prof. NIIST Bhopal (M.P.), India Abstract: Low Noise Amplifier is proposed in this paper. Proposed LNA studied between frequency band of 3.1 GHz – 10 GHz. This LNA has been studied in a 0.18μm CMOS process. The measured noise figure is calculated over 3GHz –10 GHz along with good gain and noise figure, good linearity is also required for the LNA to operate properly. Keywords: band-pass filter, CMOS , LNA. I. INTRODUCTION The demand for high-speed wireless communication systems is growing during the last few years with a frequency spectrum allocated from 3-10GHz ,ultra-wideband (UWB) is emerging as a very attractive solution for short-distance and high data rate wireless communications. Two possible approaches have been proposed to implement an UWB system. One uses the multi-band OFDM modulation, while the other transmits short pulses with position or polarity modulation. Although the standard has not been completed, a front-end wideband low noise amplifier is indispensable regardless of the receiver architecture. The amplifier must meet several stringent requirements. Those include broadband input matching to minimize return loss, sufficient gain to suppress the noise of a mixer, low noise figure (NF) to enhance receiver sensitivity, low power consumption to increase battery life, and small die area to reduce the cost. There are several existing solutions for high frequency wideband amplifiers in CMOS technology. Distributed amplifiers can bring the gain-bandwidth-product (GBW) to a value close to device fT, but consume large power and area [1]. Amplifiers employing shunt-shunt feedback are well-known for their wideband matching capability, but require high power consumption to obtain reasonable noise figure [2]. A band pass filter is used to provide good gain and noise figure [3].However, the rapid growth of noise figure at high frequencies decreases the receiver sensitivity when operating at upper bands. Besides, the loss of inductors in the matching network contributes substantial noise, and this makes it difficult to realize them in a small area. In this work, the concept of noise canceling is re-exploited [4]. By using inductive series and shunt peaking techniques and the design methodology described in this paper, broadband noise canceling effectively lowers the noise figure over the target band under reasonable power consumption and small die area. II. CIRCUIT DISCRIPTION The proposed schematic is shown in Figure 1. A Band-Pass filter is used to achieve resonance in the reactive part of the input impedance over the whole frequency range of 3 to 10 GHz. Typically the Band-Pass filter consists of three capacitors and three inductors. The Band-Pass filter works as a passband filter if the sizes of L1, C1, L2 and C2 are selected correctly.

Figure 1: Proposed circuit diagram.

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The proposed solution expands the basic inductively degenerated common source amplifier by inserting an input multi section reactive network, so that the overall reactance can be resonated over a wider bandwidth. This input matching network is shown in the Figure 1. by a dotted square. An inductor (L5) is placed in series with a capacitor (C3) to add flexibility to the design. Different values of L5 and C3 would give different matching conditions. The cascade connection of M1 and M2 improves the input output reverse isolation and the frequency response of the amplifiers. III. CIRCUIT ANALYSIS A. Gain Analysis The input network impedance is equal to Rs/W(s) where W(s) is the Band-Pass filter transfer function given by: W(s)= wL1 + (1/wCi) + wL2 (1) Note that W(s) is approximately unity in the in-band and tends to zero at out-of-band. The impedance looking into the amplifier is therefore equal to Rs in the in-band, and it is very high out-of-band. At high frequency the MOS transistor acts as a current amplifier because of the channel length modulation effect. The current gain is given by n(s) = gm/(sCt) . The current flowing into M1 is [Vi W(s)]/R, and therefore the output current is VIW(s)/(sCtR,). The load of the LNA is a shunt peaking transistor used as a resistor. The overall gain is: Vout = {GmW(s)}{RL(1+sL/RL)}

(2) where, RL is the load resistance, L is the load inductance, and Cout is the total capacitance between the drain of M2 and ground. That means Cout = Cdb1+Cgd2, where Cdb2 is the drain and bulk capacitance and Cgd3 is the gate and drain capacitance of transistor M2. Equation (2) shows that the voltage gain roll is compensated by L because it is directly connected to the drain of transistor M2. Moreover, it shows that Cout introduces a spurious resonance with L, which must be kept out of the band. IV. SIMULATION AND RESULTS m1 freq=3.660GHz dBm(Pout/Pin)=-36.555

m2 freq=4.610GHz dBm(Pout/Pin)=-45.484

m3 freq=5.700GHz dBm(Pout/Pin)=-59.886

m4 freq=6.570GHz dBm(Pout/Pin)=-56.320

0 -10

dBm(Pout/Pin)

-20 -30

m1 m2

-40 -50

-60 -70 -80 1

freq, GHz

Figure 2: Power Gain in dB m1 freq=3.510GHz dB(SNR1/SNR2)=3.086

m2 freq=4.600GHz dB(SNR1/SNR2)=3.709

m4 freq=6.660GHz dB(SNR1/SNR2)=4.458

m3 freq=5.580GHz dB(SNR1/SNR2)=4.031

5.5 5.0

dB(SNR1/SNR2)

4.5

m3 4.0

3.5

3.0 2.5 2.0 1.5 1

freq, GHz

Figure 3 : Noice factor

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m1 freq=3.590GHz dB(Vout/Vin)=-46.942

m2 freq=4.530GHz dB(Vout/Vin)=-59.970

m3 freq=5.530GHz dB(Vout/Vin)=-66.633

m4 freq=6.630GHz dB(Vout/Vin)=-63.469

-20

dB(Vout/Vin)

-30

-40

-50

m2 -60

-70

-80 1

freq, GHz

Figure 4: Voltage Gain TABLE-1 3 GHz - 4 GHz

4 GHz - 5 GHz

5GHz - 6GHz

6 GHz - 7 GHz

Noice Factor

3.081

3.709

4.03

4.45

Power Gain

-36.55dB = 14.8mW -46.942dB =4.5mV

-45.48dB =5.32mW

-59.886 dB =1.01mW -66.63dB =0.4mV

-56.320dB =1.52mW -63.64 dB =066mV

Voltage Gain

-59.97 dB =1.0mV

V. CONCLUSIONS Wireless high rate communications leverage the ultra-wide unlicensed spectrum around 60 GHz. The combination of wide bandwidths and high frequency carriers makes the design of a transceiver CMOS IC challenging. Conventional narrowband RF implementations circumvent the problem of delivering high gain at low power levels by extensive use of tuned LC circuits. This work introduces that is we use a Band-Pass filter at input stage than we can reduce the noise considerable amount. References 1. 2. 3.

4. 5. 6. 7.

8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

Ke-Hou Chen , Jian-Hao Lu et.al “An Ultra-Wide-Band 0.4–10-GHz LNA in 0.18-_m CMOS”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—II: EXPRESS BRIEFS, VOL. 54, NO. 3, MARCH 2007 PP 217-221. A Wideband Receiver for Multi-Gbit/s Communications in 65 nm CMOS Federico Vecchi, et.al IEEE JOURNAL OF SOLIDSTATE CIRCUITS, VOL. 46, NO. 3, MARCH 2011 PP 551-561. Anuj Madan , Michael J. MC3artlin et.al “A 5 GHz 0.95 dB NF Highly Linear Cascode Floating-Body LNA in 180 nm SOI CMOS Technology” IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, VOL. 22, NO. 4, APRIL 2012 PP 200-202. J. Borremans, K. Raczkowski, and P.Wambacq, “A digitally controlled compact 57-to-66 GHz front-end in 45 nm digital CMOS,” in 2009 IEEE Int. Solid-State Circuits Conf. (ISSCC) Dig. Tech. Papers, Feb 2009, pp. 429–493. [5] High Rate 60 GHz Phy, MAC and HDMI PAL,1st ed. ECMA International,Dec. 2008, Standard ECMA-387 [Online]. Available:http://www.ecma-nternational.org/publications/files/ECMA-ST/Ecma-387.pdf Wireless Medium Access Control (MAC) and Physical Layer (Phy)Specifications for High Rate Wireless Area Network, Amendment 2,IEEE 802.15.3c, IEEE, New York, USA, 2009. F.Vecchi, S. Bozzola, M. Pozzoni, D. Guermandi, E. Temporiti, M. Repossi,U. Decanis, A. Mazzanti, and F. Svelto, “A wideband mm-wave CMOS receiver for Gb/s communications employing inter-stage coupled resonators,” in 2010 IEEE Int. Solid-State Circuits Conf. (ISSCC)Dig. Tech. Papers, Feb. 2010, pp. 220–221. T. H. Lee, The Design of CMOS Radio-Frequency Integrated Circuits. Cambridge, U.K.: Cambridge University Press, 1998.[9] B. Analui and A. Hajimiri, “Bandwidth enhancement for transimpedance amplifiers,” IEEE J. Solid-State Circuits, vol. 39, no. 8,pp. 1263–1270, Aug. 2004. G. L. Matthaei, L. Young, and E. M. T. Jones, Microwave Filters,Impedance-Matching Networks, and Coupling Structures. Norwood,MA: Artech House, 1980. J. R. Nelson, “A theoretical comparison of coupled amplifiers with staggered circuits,” Proc. Inst. Radio Eng., vol. 20, no. 7, pp.1203–1220, Jul. 1932. C. H. Doan, S. Emami, A. M. Niknejad, and R. W. Brodersen, Millimeter-wave CMOS design,” IEEE J. Solid-State Circuits, vol. 40, no.1, pp. 144–144, Jan. 2005. S. Bozzola, D. Guermandi, F.Vecchi, M. Repossi, M. Pozzoni, A. Mazzanti,and F. Svelto, “A sliding IF receiver for mm-wave WLANs in 65 nm CMOS,” in Proc. 2009 IEEE Custom Integrated Circuits Conf.(CICC), Sep. 2009, pp. 669–669. C. K. Liang and B. Razavi, “Systematic transistor and inductor modeling for millimeter-wave design,” IEEE J. Solid-State Circuits, vol. 44, no. 2, pp. 450–457, Feb. 2009. I. Zverev, Handbook of Filter Synthesis. New York: Wiley, 1967. M. El-Nozahi, E. Sánchez-Sinencio, and K. Entesari, “A millimeterwave (23–32 GHz) wideband BiCMOS low-noise amplifier,” IEEE J.Solid-State Circuits, vol. 45, no. 2, pp. 289–299, Feb. 2010. R. A. Shafik, M. Rahman, and A. Islam, “On the extended relationship among EVM, BER and SNR as performance metrics,” in Proc.4th Int. Conf. Electrical and Computer Engineering, Dec. 2006, pp. 408–411.

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SORPTION OF Ni (II) FROM AQUEOUS SOLUTION USING CHITOSAN Dhanesh Singh1, Anjali Singh2, Saroj Kumar3. Deptt. of Chemistry, K.G. Arts & Science College, Raigarh (C.G)- INDIA 2 Deptt. of Applied Sciences, Singhania University, Jhunjhunu (Rajasthan)- INDIA 3 Deptt. of Chemistry, K.G. Arts & Science College, Raigarh (C.G)- INDIA

Abstract: The sorption of nickel (II) on chitosan has been found to be dependent on contact time, concentration, temperature, and pH of the solution. The process of removal follows first order kinetics and absorption of heat. Keywords: chitosan, bioabsorbent, nickel (II), heavy metal adsorption, Chitin. I. INTRODUCTION The general methods of treating wastewater having Nickel follow precipitation and ion exchange. Recently, much interest has been exhibited in the use of sorption technique for the removal of nickel from wastewater using chitosan[1]. The present investigation aims at using chitosan, a low cost and highly effective sorbent for the removal of nickel from waste water. Chitosan is a biopolymer, which is extracted from crustacean shells or from fungal biomass. The structure of chitosan is presented schematically in figure 1.

Fig. 1: Structure of chitosan II. EXPERIMENTAL PROCEDURE Chitosan was obtained from India sea foods, cochi (India). Batch sorption experiments were carried out in temperature controlled shaking machine by agitating 25ml aqueous solutions of sorbates with 1.0 g sorbent in different glass bottles at different conditions of concentrations, temperatures and pH. The pH of different solutions were adjusted with 0.05 M NaOH or HCl by pH meter, systronic 335. The speed of agitation was maintained at 1000 rpm to ensure equal mixing. The progress of sorption was noted after each 20 min. till saturation. At the end of predetermined time interval each 20 min, the sorbate and sorbent were separated by centrifugation at 16,000 rpm and the supernatant liquid analyzed by atomic absorption spectrophotometer. [2] III. RESULT AND DISCUSSION EFFECT OF CONTACT TIME AND CONCENTRATION The removal of Ni (II) by sorption on chitosan from aqueous solution increase with time (fig. 2) till equilibrium is attained in 140 min. The fig. show that time of saturation is independent of concentration. It is further noted that the amount of Ni (II) sorbed increases from 2.190 mg.g-1(87.60%) to 5.680 mg.g-1(89.54%) by increasing Ni (II) concentration from 100 mg/l to 250 mg/l. the time-amount sorbed curve is single, smooth and continuous indicating monolayer coverage of Ni (II) on the outer surface of chitosan. [3]

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Fig. 2: effect of concentration for the sorption of nickel (II) on chitosan; ●100 mg/L, ▪ 150 mg/ L, ◄ 200 mg/L, * 250 mg/L SORPTION KINETICS The kinetics of sorption of Ni (II) on chitosan was studied using Lagergren equation (yadav et. al. 1987) [4] Log (qe –q) = log qe- kt / 2.3

………………….. (1)

Where qe and q are the amount sorbed (mg.g-1) of Ni (II) at equilibrium and at time‘t’ respectively and k is sorption constant. The straight lines obtained from the plots of log (qe-q) against‘t’ (fig. 4) and different concentrations indicate that the sorption process follows first order kinetics . [5] Effect of temperature The amount of Ni (II) sorbed on chitosan increases from 2.190 mg.g-1(87.60%) to 2.3336 mg.g-1 (96.13%) by increasing temperature from 30oc to 40oc indicating the process to be endothermic (fig. 5). [6] Langmuir isotherm The equilibrium data at the different temperatures follow Langmuir equation. Ce/qe = 1/φ.b + Ce/φ ……………………… (2) Where Ce mg..L-1 is equilibrium concentration of Ni (II) and φ and b are Langmuir constants related to sorption capacity and sorption energy respectively. The value of φ and b (table 4) were determined from the slope and intercept of linear plots Fig. 6. The sorption capacity also increases with temperature suggesting that the active centers available for sorption have increased with temperature. [7]

Fig. 3: Langergren plot for the sorption of Ni (II) on chitosan; ●100 mg/L, ▪ 150 mg/ L, ◄ 200 mg/L, * 250 mg/L, pH 5, temp 30oc The change in free energy (∆GO), enthalpy (∆HO), and entropy (∆SO) of sorption have been calculated using following equations. [8]

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∆G○= -RT lnK

(3)

∆H○ = RT 1T 2(T1 – T2) ln k2 /k 1

(4)

∆So= ∆H○ - ∆GO / T1

(5)

Where K1 and K2 are equilibrium constants at temperature T1 and T2 respectively. The negative values of ∆Go (Table 2) indicate the spontaneous nature of the sorption process. The positive values of ∆Ho at different temperature support the endothermic nature of the process. [9]

Fig. 4: Effect of temperature on the sorption of Ni (II) on Chitosan ● 30oc, ▪ 40oc, ◄ 50 oc Temperature (°C) 30 40 50

Ø mg.g-1 0.7543 1.1865 1.3656

pH 2 4 6.5

Ø mg.g-1 0.3891 0.6880 0.7543

TABLE 1: Ø values at different temp. and pH

Fig. 5: Langmuir isotherm for the sorption of Ni (II) on chitosan; ● 30oc, ▪ 40oc, ◄ 50 oc. Effect of pH The amount of Ni (II) sorbed on chitosan increases from 1.640mg.g-1 (65.60 %) to 2.190 mg.g-1 (87.60 %) by increasing pH of the solution from 2.0 to to 6.5 (Fig.6). The Sorption capacity Φ, also increase with the increase of pH. [10]

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Fig. 6: Effect of pH on the sorption of Ni (II) on chitosan; ● 2.0, ▪ 4.0, ◄ 6.5; temp: 30 o c, conc. 100 mg/l.

Temperature (°C)

∆Go (kcal.mol-1)

∆Ho (kcal.mol-1)

30 40 50

-5.40 -6.85 -8.24

12.41 24.86

∆So (kcal.mol-1) 20.02 35.16

Table 2: Thermodynamic parameters at different temperatures IV. CONCLUSION From the above discussion it is clear that due to chemical composition, structure, more adsorption sites, cheap availability in plenty etc., this substance will provide to be efficient adsorbent. [11] V. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

Yadav, K.P., Tyagi, B.S.Pandey, K.K and. Singh., Flyash for the treatment of Ni (II) rich effluents. Env. Tech. Letter, (8), 225234 (1989) Singh, V.N., Singh, I.S. and Singh, N.P., Removal of Ni (II) from aqueous solution by fly ash. Indian Journal of Technology, 22(2), 22-27 (1984) Vishwakarma, P.P. and Singh, V.N., Removal of Ni (II) by China Clay. Asian Environment, 11(3): 49-64 (1984) Pandey. K.K. Prasad, G. Singh, V.N., Fly ash China Clay for the removal of Cr (VI) from aqueous solution. Indian Journal of Chemistry, 23( A), 514-515 (1984) Namasivayam C., and Yamuna R T., Environ Pollut., 9 (1). 1- 4 (1985) Sekeran G., Shanmugasundaram K A.., Mariappan M and Raghavan K V., Indian J Chemical Technol., 2, (311), 71-75 (1995) Findon A., Mckay G., Blair HS., Transport studies for the sorption of nickel ions by chitosan. J. Environ. Sci. Health, A2 8(1), 173-185 (1993) Gotoh T., Matsushima K., Kikuchi KI., Preparation of alginatechitosan hybrid gel beads and adsorption of divalent metal ions. Chemosphere, 5.5 (1), 135-140 (2004) Singh Dhanesh and Singh Anjali. ,Chitosan for the Removal of Chromium from Waste Water, International Research Journal of Environment Sciences, Vol. 1(3), 1-7, October (2012) Singh Dhanesh and Singh Anjali. ,Chitosan for the Removal of Cadmium from Waste Water, International Research Journal of Environment Sciences, Vol. 1(4), 1-7, November (2012) Removal of Lead from Waste Water Using Low Cost Adsorbent., Singh Dhanesh, Mishra M., Mishra A.K. and Singh Anjali, International Research Journal of Environment Sciences, Vol. 2(9), 23-26, September (2013)

VI. ACKNOWLEDGEMENTS The authors are thankful to Dr. Nagesh Gaveli, Director, Sudarshan College of Science, Management & Research, Pune (Maharashtra), for providing research facilities, co-operation and constant encouragement to carry out the work.

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NUMERICAL SOLUTION OF ONE - DIMENSIONAL TIMEINDEPENDENT PROBLEMS USING FEM Vinay Saxena Department of Mathematics, Kisan Post Graduate College Bahraich, 271801(U.P.), India Abstract: The Finite Element Method (FEM) is computational technique for numerical solution of differential equations in science and engineering. The discretization process using a finite element method is different from classical Finite Difference Method (FDM) where we replace derivatives by difference quotients. In FEM, we start from reformulation of given differential equation as an equivalent variational problem. In this paper, we focused our attention on the one - dimensional time - independent problems using FEM. The entire FEM including variational formulation, finite element formulation, choice of elements, choice of basis function, constructing stiff matrix, constructing load vector has been discussed. Numerical results for one-dimensional fourth order differential equation which arises in Euler-Bernoulli beam theory are given and discussed. Keywords: Finite Element Method, Time Independent Problem, Cantilever Beam Problem I. Introduction Basic idea of any numerical method for differential equation [1] is to discretize the given continuous problem with large degree of freedom to obtain a discrete problem or system of equation with only finite unknown that may be solved using a computer. Any continuous solution field such as stress, displacement, temperature, pressure, etc. can be approximated by a discrete model composed of a set of piecewise continuous functions defined over a finite number of subdomains. The Finite Element Method (FEM) is computational technique for numerical solution of diffrential equations in science and engineering. The technique has been used on problems [2]-[4] involving stress analysis, fluid mechanics, heat transfer, diffusion, vibrations, electrical and magnetic fields, etc. The fundamental concept involves dividing the body under study into a finite number of subdomains called elements. Particular assumptions are then made on the variation of the unknown dependent variables across each element using so-called interpolation or approximation functions. This approximated variation is quantified in terms of solution values at special element locations called nodes. The discretization process using a finite element method is diffrent from classical finite diffrence method, where we replace derivatives by difference quotients.Through this discretization process, the method sets up an algebraic system of equations for unknown nodal values which approximate the continuous solution [5]-[6]. In FEM we start from reformulation of given diffrential equation as an equivalent variational problem [7]. Because element size, shape and approximating scheme can be varied to suit the problem, the method can accurately simulate solutions to problems of complex geometry and loading and thus this technique has become a very useful and practical tool. In Section II, the proposed algortihm is briefly summarized in six steps. In Section III, we give numerical results for cantilever beam problem. The FEM solution are compared with exact solution and are given in Figure1. A short summary and outlook is given in Section IV. II. Proposed Algorithm Here we consider the finite element formulation of one-dimensional fourth order diffrential equation which arises in Euler-Bernoulli beam theory.Procedure is explained in the subsequent steps using the equation which governes the transeverse difflection ‘ w ’ of the beam.

d2 dx 2

 d 2w   EI dx 2  = f ( x)  

0< x<L

(1)

E := modulus of elasitity I := moment of inertia f := transversely distributed load Step[1]. Discretisation of Domain : The domian of the structure (length of beam) is divided toa set of N elements (line segments) each element have

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at least two nodes(end points). Step[2]. Weak form : The weak form of problem in soid mechanics can be developed by weighted residual method i.e. to multiply the equation by a test function v then integrating over the domain and finally equating to zero.

 d2 0  dx 2  L





 d 2w  EI  dx 2  

 f vdx = 0 

(2)

 d  d 2 w  dv L d 2 w d 2v d 2w   dx  v ( x ) f ( x ) dx  v EI  EI  =0 0  dx 2 dx 2 dx 2  dx dx 2   dx 

(3)

Variational form (3) requires that interpolation function w(x) be continuous with non zero derivative up to order two. The deformation of beam must have continuous slope ( = dw/dx) , as well as continuous deflection at any two neighbouring elements. In this case, any two neighbouring beam element have common deflection and slope at shared node points. The approximation of the primary variables w,  over finite element should be such that

w( xe ) = u1e , w( xe1 ) = u3e ,  ( xe1 ) = u2e ,  ( xe1 ) = u4e

(4)

where ui is FEM solution. Let w( x) = c1  c2 x  c3 x  c4 x then by using condition (4) 2

we ( x) = u1e1e  u2e2e  u3e3e  u4e4e

(5)

where

 x  xe  = 1  3  he e 1

  x  xe   2   he

  x  xe  , 2e = ( x  xe )1  he  

  

2 3  x  x  2  x  x   x  xe   x  xe  e e e   2  , 4 = ( x  xe )       = 3 h h h h  e   e   e   e  e 3

xe1 = xe  he , ie satisfies the all interpolating properties

with

Step[3].Finite Element Model : The finite element model of Euler Bernoulli beam is obtained by substituting the finite element interpolation for w and i for weight function v in weak form we get

 L d 2ie d 2 ej  L  dx u ej   ie f ( x)dx  Qie = 0  2 2   0 dx dx  0 j =1   d   d 2 w  d 2w  e e  EI    , Q = EI where Q1 =  2 2  2   dx dx dx     xe   xe 4

(6)

d   d 2 w  d 2w  e    Q3e =    EI , Q =  EI  4  dx 2  x dx 2  x   dx  e 1 e 1 so now we can rearrange (6) to system of equation for element e

`e as

[ K ][u ] = [ F ] e

(7) e

[ K ] is called stiffness matrix and [ F ] is called load vector. Step[4] Assembly of element matrix to global matrix : In deriving the element equations, we isolate a typical element (eth) from the mesh and formulated the

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variational problem (or weak form). The assembly of elements is carried out by imposi ng two conditions: e

e 1

1. Continuity of variable u (x) (primary) at connecting nodes ui = ui is same as the first node value of adjacent element. 2. Balance of natural variable (secondary) at connecting nodes

0 Qne  Q1e1 =  Q0

i.e., the last node value of element

if no external point source is applied, if an external point source Q0 is applied .

Local and global nodes are connected by a matrix called connectivity matrix which comes through discretization of domain. For a mesh of E linear elements (n = 2) we have

1  K1 K11 0 . 0  11  1 1 1 2 K12 . .  K 21 K 22  K11  2 2 3 K 21 K 22  K11 . .  0  3 . K 21 . .  .  . . . . .  E . 0 . K11  0   This is called the assembled system.

   U 1   2   U   .   .   .   E  U     

 f11   Q11   1  1 2 2  f 2  f1   Q2  Q1      . .     = . .       . .     E E  f 2   Q21         

(8)

Step[5] Imposition of boundary conditions : One particular problem differs from other in specification of data and boundary conditions. Here we have to impose the boundary condition on the assembled set of algebraic equations. Step[6] Post processing of solution : If we have 2 elements then solution will be represented as

 U 331  U 441 we =  3 2 4 2 5 2 6 2 U 3  U 4  U 3  U 4

0  x  h,

(9)

h  x  2h.

III. Numerical Results 2

Consider a cantilever beam problem with 2 elements L =3 m, E.I =5800 kNm ,

f 0 =24 kNm1 , F0 =60 kN, M0 =0 kNm. To express solution w as (9), we get FEM solution that is represented by dotted curve in Figure(1) however exact solution is represented by continuous curve.

Figure(1): FEM solution (dotted curve) Vs Exact solution (continuous curve)

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IV. Conclusions The finite element methods (FEM) are having less time complexity for the complicated geometry, genral boundary conditions, variable and non linear properties over finite diffrence method (FDM). In this case one meets unneccessary artificial complications with finite diffrence methodology. Further, the clear structure and versatility of finite element software makes it possible to construct genral purpose software for various applications. Acknowledgments I would like to thank Sudhir Srivastava for stimulating discussion on FEM and his warm encouragement of continued support during this work. References [1]. [2].

Richard L. Burden ,J.Douglas (2002) ,Numerical Analysis , Faires Thomson Asia Pvt. Ltd. J.N.Reddy , (2003), An introduction to the finite element method , Tata McGraw-Hill Edition.

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

Huebner, K.H., Thorton, E.A., ByromT.G.,(1994), The Finite Element Method for Engineers, 3 Ed., John Wiley Young W. Kwon, Hyochoong Bang , (2000), The finite element method using matlab, CRC Press P. Deuflhard, P. Leinen,H. Yserentant, (1989),Concepts of an Adaptive Hierarchical Finite Element Code. IMPACT Comput. Sci. Engg. 1,3. J.R.A.Collado, R.J. Buenker, (1992) ,On the numerical Solution of the Multidimensional Vibrational Timeâ&#x20AC;&#x201C;independent Schroedinger Equation, J. Comp. Chem. 13, 135. S. Srivastava,(2006) Finite Element Method Through Engineering Problems, Master's thesis, IIT Madras

[6]. [7].

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Discussion of Effective Speech Communication under Different Compression Approaches 1

Amit, 2Sunita Student, M.Tech , 2Asstt. Professor, Deptt. of Computer Sc. & App., College, Shri Baba Mastnath Engineering College, Rohtak, Haryana, India 1

Abstract: Speech is most interactive way of communication to interact with a person directly or distance person. The distance communication is low cost and does not require any specialized device. The only drawback of speech communication is the size of communicating data. As the data size increases, the communication criticality increases. To provide the effective network voice communication, some compression approach is required that will reduce the communication size without affecting the quality of voice. In this paper, a discussion of compression and encoding approaches is done. The paper also discussed some of the effective compression based techniques so that the effective and reliable communication will be drawn. Keywords: Speech Compression, Encoding, DWT, Noise Reduction. I. INTRODUCTION Speech is one of the most interactive communication medium to perform the distance mode communication. Lot of work is done under different applications of speech processing so that the efficient and reliable communication will be drawn based on speech. Speech processing comes under the signal processing. That requires the encoding of the signal along with different associated applications. Speech processing one of the fundamental as well as critical application area because of its real time involvement. There are many applications that uses the speech as the biometric feature to perform the person identification. The application areas associated with speech processing includes the speech recognition, speaker recognition etc. These application areas are shown in figure 1. Speech Synthesis

Speech Encoding

Speech Processing Speech Recognition

Speaker Recognition

Language Translation

Figure 1: Application Areas of Speech Processing As the raw speech is collected it is in the form of acoustic signals. To perform different operations and processing, this speech is transformed so that the effective communication as well as processing will be performed over it. The speech processing also depends on multiple communication vectors. These vectors are shown in figure 2. The communication reliability depends on these all factors so that to ensure the effective processing and decisions, the reliable communication vector is drawn. While performing the speech processing, the memory requirement and the processor requirements are high as compared to some traditional applications.

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These parameters affect the speech taken from the real time environment. As the speech is collected, the first factor affects the person or the speaker itself. The speech processing outcome different person to person. It is possible that the application is running on the person voice but not working for other. If a person record his voice multiple times, the results can differ. The difference in the speech can be obtained in terms of pitch or the speech. Pitch represents the speech frequency or the intensity and the speed criticality.

Background Noise

Instrument Noise

Speaker

Parameter Affect Speech Processing

Speed

Pitch

Figure 2: Parameters that affects Speech Processing In this paper, a study on different compression approaches on speech is discussed. In this section, a study on the speech processing its application and the criticality parameters is defined. In section II, the work done by the earlier authors is discussed in same application area. In section III, the basic compression techniques are discussed. In section IV, the conclusion obtained from the work is presented. II. RELATED WORK Lot of work is already done by different researchers in the area of speech processing and the compression techniques. Some of the work done by earlier researchers in same area is discussed in this section. A work on effective compression technique was done by H.B. Kekre[2] in year 2008. Author used the vector quantization method top compress the speech signal and to obtain the effective compression ratio. The work is simulated by the researcher and the obtained results shows the effective outcome is driven. The outcome is presented under different performance parameters. The parameters included in this research work are SNR evaluation, compression ration and absolute error identification. Another work on compression approaches is done using Fractional change analysis. This work is defined by S. Kumar[9] in year 2010. Author combined the vector quantization along with fractional analysis so that effective compression ratio for speech signal will be obtained. Author attain the effective compression ratio and maintained the quality of the signal. A DWT based work is defined by M.A Najih for speech compression. Author applied a series of wavelet filters so that the best filter for the speech processing will be elected and low bit rate transmission will be performed. Author[10][11] defined the work under different schemes so that the communication reliability will be obtained. The encoding techniques adapted by the author includes DWT, Thresholding, Quantization, Huffman Encoding, Speech signal reconstruction etc. The comparative analysis is performed to generate the signal and to perform the filtration. Author obtained the effective compression ratio as well as bit error rate over the signal is reduced. The parameters considered by the author include the frequency based analysis as well as time domain based analysis. This analysis provided the pitch prediction approach so that high quality speech signal will be composed. Author claims the effective compression ratio so that the signal reliability is attained. The quality signal obtained from the work also depend on the codec used in the signal. A work based on codec processing was defined by Jean-Marc Valin[13]. Author presented the work on different frequency ranges as well as low and high quality signals. Author gain the algebraic vector quantization so that the frequency domain oriented pitch prediction will be obtained as well as the signal process will be done effectively. Another DWT based compression was defined by H. Elavdi[14]. This compression technique is lossy compression that provided the high compression ratio but degraded the speech quality. The compression was performed using DWT under the bandwidth limits. Author defined the comparision using speech coder so that the effective coefficient vector based encoding will be performed. K L Neville [15] worked on DWT on speech with different coefficient vectors. Neville focused on analysis of wavelet compression effects. Two wavelet compression techniques, thresholding and low-subband filtering have been utilized. K T Talele in 2012 [16], proposed a simple speech compression algorithm using sub-band

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division and ADPCM algorithm. Although speech data are stored in a semiconductor memory device, its capacity and the available network capacity are limited. Therefore, it is necessary to compress the data as much as possible. S Vatsa did speech compression based on DCT and DWT. The main objective of work is to achieve the compression with encoding approach [17]. In this paper DWT and DCT (discrete cosine transform) based speech compression techniques are implemented with run length encoding, Huffman encoding and their combination M Suman in [18] dealt with multistage vector quantization technique used for coding of narrow band speech signals. The parameter used for coding of speech signals are the line spectral frequencies, so as to ensure filter stability after quantization. III. SPEECH COMPRESSION TECHNIQUES The Speech compression techniques are broadly classified as either lossless or lossy, depending, respectively, on whether or not an exact replica of the original Speech could be reconstructed using the compressed Speech. In addition to using the spatial and spectral redundancies, lossy techniques also take advantage of the way people see to discard data that are perceptually insignificant. Lossy schemes provide much higher compression ratios than lossless schemes. Lossless compression is used only for a few applications with stringent requirements such as medical imaging. Lossy schemes are widely used since the quality of the reconstructed Speechs is adequate for most applications. Taxonomy of Speech compression techniques is given here under A. Run length encoding This encoding technique replaces sequences of identical symbols (pixels), called runs by shorter symbols. This technique is usually used as a post-processing step after applying a lossy technique to the Speech and obtaining a set of data values that are suitably reordered to get long runs of similar values B. Huffman coding This is a general technique for coding symbols based on their statistical occurrence frequencies (probabilities). The pixels in the Speech are treated as symbols. The symbols that occur more frequently are assigned a smaller number of bits, while the symbols that occur less frequently are assigned a relatively larger number of bits. Huffman code is a prefix code. This means that the (binary) code of any symbol is not the prefix of the code of any other symbol. Most Speech coding standards use lossy techniques in the earlier stages of compression and use Huffman coding as the final step. C. Lempel-Ziv coding This approach is based on storing frequently occurring sequences of symbols (pixels) in a dictionary (table). Such frequently occurring sequences in the original data (Speech) are represented by just their indices into the dictionary. D. Bit-plane encoding This encoding approach is defined under the binary representations of the values of the pixels in the Speech are considered. The corresponding bits in each of the positions in the binary representation form a binary Speech of the same dimensions as the original Speech. This is called a bit plane. Each of the bit planes can then be efficiently coded using a lossless technique. The underlying principle is that (in most Speechs) the neighboring pixels are correlated. That means the values of the neighboring pixels differ by small amounts. They can be captured by the representation of pixel values in gray code so that the values of neighboring bits in the bit planes are similar. This makes the individual bit planes amenable for good compression. All known lossy Speech compression techniques take advantage of how we see things. We derive the spatial frequencies of an Speech and suitably allocate more bits for those frequency components that have more visual impact. We then allocate fewer bits, or even discard the insignificant components. The resulting Speech is represented with fewer bits and reconstructed with a better closeness to the original. D. Block truncation coding This approach uses the concept to divide an Speech is into non-overlapping blocks of pixels. For each block, threshold and reconstruction values are determined. The threshold is usually the mean of the pixel values in the block. Then a bitmap of the block is derived by replacing all pixels whose values are greater than or equal (less than) to the threshold by a 1 (0). Then for each segment (group of 1s and 0s) in the bitmap, the reconstruction value is determined. This is the average of the values of the corresponding pixels in the original block. The lossy DPCM is very similar to the lossless version. The major difference is that in lossy DPCM, the pixels are predicted based on the “reconstructed values” of certain neighboring pixels. The difference between the predicted value and the actual value of the pixels is the differential (residual) Speech. It is much less correlated than the original Speech. The differential Speech is then quantized and encoded.

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E. Subband coding This approach analyzes the Speech to produce the components containing frequencies in well defined bands, the subbands. Subsequently, quantization and coding is applied to each of the bands. The advantage of this scheme is that the quantization and coding well suited for each of the subbands can be designed separately. IV. CONCLUSIONS In this paper, a discussion on different encoding or the compression mechanism is defined for speech encoding. The paper has discussed the speech processing along with different limitations and dependencies. The paper also discussed different encoding mechanisms adapted by speech processing. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19]

H Hermansky, "Perceptual linear predictive (PLP) analysis of speech", Journal of the Acoustical Society of America, pp 17381752, 1990. H. B. Kekre, Tanuja K. Sarode, "Speech data compression using vector quantization", Int’l Journal of Computer and Information Engineering, Vol. 2, No.8, pp 535-538, 2008. M H Johnson, Abeer Alwan, "Speech coding: fundamentals and applications", Wiley Encyclopedia of Telecom., pp 1-20, 2003. David Suendermann, Harald Höge, and Alan Black, "Challenges in speech synthesis", Speech Technology, Springer ScienceBusiness Media, pp 19-32, 2010. Bhupinder Singh, Neha Kapur, Puneet Kaur, "Speech recognition with Hidden- Markov model: A review", Int’l Journal of Advanced Research in Computer Sc and Software Engineering, Vol. 2, No. 3, 2012. D A Reynolds, "An overview of automatic speaker recognition technology", Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference, Vol 4, pp 4072-4075, 2002. N.S. Bansod, S Kawathekar, S.B. Dabhade, "Review of different techniques for speaker recognition system", Advances in Computational Research, Vol. 4, No. 1, pp 57-60, 2012. (Journal) Y Zhang, "Survey of current speech translation research", Carnegie-Mellon Univ, 2003. (Paper) Chaudhari, V.K. , Srivastava, M. ; Singh, R.K. ; Kumar, S. "A new algorithm for voice signal compression and analysis suitable for limited storage devices using Matlab", Int’l Journal of Computer and Electrical Engg, Vol. 1, No. 5, pp 656-665, 2009. M A Najih, Ramli, A.R. ; Prakash, V. ; Syed, A.R. "Speech compression using discreet wavelet transform", Telecommunication Technology, 2003. NCTT 2003 Proceedings, pp 1-4, 2003. M A Najih, Ramli, A.R. ; Ibrahim, A. ; Syed, A.R., "Comparing speech compression using wavelets with other speech compression schemes", Research and Development, 2003. SCORED 2003., pp 55-58, 2003 V Radha, "Comparative analysis of compression techniques for Tamil speech datasets", IEEE-Int’l conference on recent trends in Information Technology, pp 712-716, Chennai, 2011. J M Valin, T.B. ; Montgomery, C. ; Maxwell, G., "A high-quality speech and audio codec with less than 10 ms delay", IEEE Trans. on Audio, Speech and Language Processing, Vol. 18, No. 1, pp 58-67, 2010. H Elaydi, "Speech compression using wavelets", Electrical & Computer Engineering department-Islamic University of Gaza, Palestine, 2010. (Report) Katrina L Neville, Zahir M Hussain, "Effects of wavelet compression of speech on its Mel-Cepstral coefficients", Int’l Conference on Communication, Computer and Power (ICCCP'09), pp 387-390, Muscat, 2009. KT Talele and S T Gandhe, "Speech compression using ADPCM", IJCA Proceedings on International Conference in Computational Intelligence, Vol 8, 2012 New York, USA. S Vatsa, O. P. Sahu, "Speech compression using discrete wavelet transform and discrete cosine transform", International Journal of Engineering Research & Technology, Vol. 1, No. 5, 2012 M.Suman,, Habibulla Khan,, M. Madhavi Latha, D. Aruna Kumari, "Dimensions of performance in compressed speech signals and its enhancement", International Journal of Engineering Sciences Research, pp 87-93,.2011. Preet Kaur, Pallavi Bah, "Comparative analysis between DWT and WPD techniques of speech compression", IOSR Journal of Engineering, Vol. 2, No. 8, pp 120-128, 2012.

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Study on Routing Protocols Classification in Sensor Network 1

Jyoti, 2Sunita Student, M.Tech, 2 Asstt. Professor Deptt. Of Computer Sc. & App., College, Shri Baba Mastnath Engineering College, Rohtak, Haryana, India 1

Abstract: A Sensor Network is one of congested network defined under the sensing range and energy limitations. To provide energy effective communication, there are different network architectures to control the network communication. These architectures are defined under the rules of relative protocols. In this paper, studies on different associated classes to the routing protocols in sensor network are defined. Paper explored these protocols along with their utility and application area. Keywords: WSN, Routing Protocols, LEACH, PEGASIS

I. INTRODUCTION Sensor network is one of the networks defined under energy constraints and range limitations. The sensor nodes can perform the low distance communication because of this, generally the network is congested and low distance communication is performed over the network. The communication performed in sensor network is a multi hop communication because of limited sensing range. These nodes are connected through the wireless medium such as infrared, optical medium or the radio communication. A sensor network is capable to perform the different kind of global communication on small scale and large scale network. In many critical applications, these kind of network architecture is adopted. The common application areas of sensor network are the scientific, medical and the industrial communication. There are different frequency bands at which, the communication is carried out in sensor network. In some cases, the sensor network is integrated with mobile network to enhance with the communication features. Another requirement of sensor network is the sensing devices that include low cost, energy specific transceivers. These devices are defined under the power constrained to attain the efficiency and to reduce the power consumption. These devices allow the communication at different frequency band according to the spectrum availability. The basic component based architecture of sensor network is shown in figure 1.

Figure 1: Component Oriented Architecture The main challenge in sensor network is to perform the power saving communication. To perform this communication, there are number of constraints and the limitations are defined in sensor network. These sensor networks are defined under different frequency bands to control the communication and to obtain the actual circuit design. The outcome of these communication networks depend on different factors such as the frequency range, kind of device, spectrum type etc. Another improvement to the sensor network is in terms of intelligent

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sensing devices. These devices are called smart sensor devices. The network composed from these sensing devices is called smart dust network. In critical application areas such as medical network, these kind of communication architectures are been used. In this paper, the communication architecture and the communication dependencies are discussed in sensor network. These architectures and protocols are discussed under different constraints and requirements. In section I, the basic requirement of communication sensor network is defined along with requirement specification. In section II, the work done by the earlier researchers is discussed. In section III, the available protocols in sensor network are discussed as well divided them in different classes. These protocols are divided in application area specific classes. II. EXISTING WORK In this section, the work done by the earlier researchers in the area of Data classification is discussed and presented. In year 2000, a work on energy effective sensor network was defined by W. Heinzelman. Author defined the clustering algorithm along with protocol specification. Author introduced the LEACH protocol. LEACH protocol includes the cluster formation based architecture so that the hierarchical communication will be carried on over the network. This proposed protocol divided the complete network in smaller segments called clusters and each cluster communication is control by the cluster head. The algorithmic approach is defined on the selection of this cluster head. This cluster head selection is performed under the energy and load based analysis. LEACH protocol is able to provide the effective communication in effective time frame. It also defined the communication under the assumptions and the constraints. The drawback of the protocol is the random selection of cluster head. It gives the equal probability of a node to set as cluster head. But this gives the unequal distribution of clusters over the network. There are the chances that a particular protocol is congested and some cluster is not having the enough nodes. It also gives the problem of orphan nodes i.e. the nodes that not covered by any cluster. Another drawback of this protocol is the election of cluster heads with each communicating round that gives the communication delay. Because of these reasons there was the requirement of certain improvement over the clustering architecture so that reliable and balanced communication will be performed over the network [1][2][3][4]. Different authors provided the improvement over the LEACH protocol and clustering architecture. One of these improvements includes the format energy LEACH and the multi hop LEACH. These improved protocols improve the process of cluster head selection. The cluster head is formed on each communication rounds based on the residual energy comparison on nodes. Another improvement to the protocol is done in terms of multi hop communication. It improves the network communication. Authors discussed the comparative analysis between the energy LEACH and multi-hop LEACH [5][6]. Some authors also provided the communication over the network under different routing protocols. A lot of work is done to identify the effective routes in network. Authors discussed different constraints while performing the route identification in sensor network. These parameters include the layered based communication, data centricity, path redundancy, location information analysis, QoS parameters evaluation etc. The type of network i.e. homogeneous or heterogeneous networks also affects the network architecture and the communication. The main objective behind the formation of this clustering architecture is to improve the network life time and to provide the energy effective communication. The work also controls the energy consumption and provides the dominated transmission and reception. The routing protocol designed here is energy effective as well as provide the network architecture so that the network life time is improved[7][8]. The energy criticality in case of LEACH protocol is discussed and resolved by many researchers under the energy restricted resources available in the sensor network. These sensor networks are defined under the energy source specification so that the effective design will be performed along with communication control. The author discussed the operative time span so that the restricted power supply over the network will be obtained. These protocols are dependent on the network layers. The communication in such network is based on the probabilistic estimation on the cluster head selection and to perform the communication based on network coordination. The network is defined with initial energy specification and to discuss the certainty in LEACH. Author discussed different aspects of these protocols so that the life time of the network will be improved. The communication is performed for N number of rounds and based on the energy effectiveness of network as well as the communication is measured. Simulated results shows the improvement to the network life upto 20% with the modification on the cluster head selection process[8][9]. An improvement to the clustering routing protocol is defined reduce and control the network deficiencies. Different authors discussed different ideas to provide the improvement to the network communication and the clustering process. These improvements are defined in the form of cluster selection process. One of such improvement is presented in the form of N-LEACH protocol. This improvement protocol has restricted the number of clusters over the network so that the equalize distribution of network nodes will be obtained. The root node collects data from the base station and improves the network energy effectiveness and the life time. N-

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LEACH algorithm is discussed to provide the energy balanced communication. This communication protocol is also effective to provide the long distance communication [9]. Another improvement to LEACH protocol was presented by Khamayesh in year 2009. This improvement is presented in the form of a new protocol called V-LEACH protocol. This protocol reduces the energy consumption over the network and provides the effectiveness of clustering architecture. According to this new protocol, each cluster has two cluster heads called, Main Cluster head and Vice Cluster head. As the main cluster head dies, the vice-cluster head takes its position so that the process of cluster selection is reduced. Author defined this protocol to reduce the communication between the cluster head and the base station. The obtained results from the system shows that the defined protocol reduced the network communication and improved the network life. In VLEACH protocol, the number of messages generated by the cluster head is lesser than the traditional LEACH protocol [10]. Some author modification to the existing protocols is done by different authors. These protocols provided the energy adaptive communication in sensor network as well as provided a balanced communication over the network. The balanced network communication is controlled under the residual energy based communication so that the network communication and performance will be improved. The comparative analysis over the network is performed to provide the reliable communication [11][12]. These available protocols in sensor network are effective under different parameters as well as the architecture. Based on the application areas these network architectures are applied as well as according to the type of application area, the respective protocols are applied. In next section, the classification of these protocols is defined. III. WSN PROTOCOL CLASSIFICATION Routing or the communication in sensor network is not same as of traditional adhoc network because of limited sensing range and the energy restriction. This network architecture requires more constraint adaptive communication so that the reliability of the communication is performed. A sensor network is applied in different application areas and based on the same discussion. The routing algorithm adapted by the communication includes the energy saving communication. There are number of protocols available that provide the communication under different categories or the parameters. These parameters are shown in figure 2. Data Centric

Qos Based

Location Based

Communication Adaptive Parameters

Hierarchical

MultiPath

Mobility based

Heterogeneity

Figure 2: Communication Adaptive Parameters These all categories are represents the different application areas as well as communication architectures. These protocols are discussed in this section. The protocols that come under these different categories are shown in table 1. A. Data Centric Protocols Data centric protocol is different from traditional data centric protocols. These protocols provide the effective network communication. The protocol defined in this category is based on the source and sink specification. The data centric communication is defined so that the network aggregation will be performed as well as the aggregative communication will be directed to the base station. The energy adaptive communication in such network also reduced the communication so that the effectiveness of network is improved. This communication architecture form reduces the network communication and provides the energy effective and reliable communication over the network. The data centric routing adapted in the communication is effective to increase the network life.

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B. Location-Based Protocols The location oriented protocols are used in the application area where the nodes are distributed over different location in the network architecture. The communication is here performed based on the location of the network nodes. The distance effective communication is performed in such network. The energy consumption is estimated so that the energy effective communication is performed over the network. The long distance communication is location based communication. The location based criticality to the communication architecture is adopted so that the communication reliability is achieved. The prioritization based on the location identification is done. Some of these protocols requires the pre-specification of the node location and some perform the location identification to provide reliable communication. Table 1: Classification of Protocols Category

Representative Protocols

Data-centric Protocol

SPIN, Directed Diffusion, Rumor Routing, COUGAR, ACQUIRE, EAD, InformationDirected Routing, Gradient-Based Routing, Energy-aware Routing, Quorum-Based Information Dissemination, Home Agent Based Information Dissemination

Location-based Protocols

MECN, SMECN, GAF, GEAR, Span, TBF, BVGF, GeRaF

Hierarchical Protocols

LEACH, PEGASIS,HEED,TEEN,APTEEN

Mobility-based Protocols

SEAD, TTDD, Joint Mobility and Routing, Data MULES, Dynamic Proxy Tree-Base Data Dissemination

Multipath-based Protocols

Sensor-Disjoint Multipath, Braided Multipath, N-to-1 Multipath Discovery

Heterogeneity-based Protocols

IDSQ, CADR, CHR

QoS-based Protocols

SAR, SPEED, Energy-aware routing

C. Hierarchical Protocols In the clustered network architecture, the complete network is divided in small area segments called clusters and each cluster is controlled by cluster head. All the cluster nodes perform the communication with cluster heads and cluster head perform the communication with base station. These cluster head based communication can be hierarchical. This communication type is helpful for short range sensing devices as well as it reduces the network communication. As the communication is performed in different network areas simultaneously so that the collision over the communication will not be performed.

Figure 3: Hierarchical clustering in WSN D. Mobility-Based Protocols: The mobility in sensor network is a crucial communication form in sensor network. The type of mobility in network also affects the network communication. The mobility affects the communication type. Such as if the mobility is random, motion tracking is required but if the motion is linear, the motion can be expected. E. Multipath-Based Protocols: Data transmission in sensor network is performed under different routing approaches. These routing approaches include the single path and multi path communication. The multi-path communication in network is sink oriented and based on the path length. As the communication is performed in K path and divide the network load so that more effective communication will be drawn over the network. In some cases, different kind of data is also directed to different network path so that the effective load distribution will be obtained.

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F. Heterogeneity-Based Protocols Heterogeneity is sensor network is based on the node types in the sensor network. These nodes are different respective to the type of sensor or the type of energy supply attached with the network. The type of nodes also depends on the utility as well as the responsibility. These features also affect the energy consumption over that nodes and well as restrict the communication. G. QoS-Based Protocols QoS in sensor network is defined under different parameters such as delay, congestion, fault tolerence, reliability etc. Based on these parameters the protocol selection is performed. IV. CONCLUSION In this paper, a classification on routing protocols of Sensor Network is defined. The paper has disused different aspects of sensor network architecture under the requirements and limitations. Finally, the discussion on the routing protocols is done and these available protocols are divided in different classes and based on this classification the protocols are categorized. REFERENCES [1] [2] [3] [4] [5] [6 ]

[7] [8] [9] [10] [11] [12]

Shio Kumar Singh, M P Singh, and D K Singh: Routing Protocols in Wireless Sensor Networks A Survey, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.2, November 2010 Ajay Jangra, priyanka, Swati, richa Wireless Sensor Network (WSN): Architectural Design issues and Challenges, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 3089-309. I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci : Wireless sensor networks: a survey, Computer Networks 38 (2002) 393 Joohwan Kim, Xiaojun Lin, Ness B. Shroff and Prasun Sinha “Minimizing Delay and Maximizing Lifetime for Wireless Sensor Networks With Any cast” IEEE/ACM Transactions On Networking, Vol. 18, No. 2, April 2010 page:515-527 “Improvement on LEACH Protocol of Wireless Sensor Network”, M. Bani Yassein, A. Al-zou'bi, Y. Khamayseh, W. Mardini IEEE 2009. "Energy-Efficient Communication Protocol for Wireless Micro sensor Networks", W. Heinzelman , A.Chandrakasan and H. Balakrishnan , January 2000. IN 2000 IEEE. Published in the Proceedings of the Hawaii International Conference on System Sciences, January 4-7, 2000, Maui, Hawaii. “Improvement on LEACH Protocol of Wireless Sensor Network”, Fan Xiangning, Song Yulin IEEE 2007 International Conference on Sensor Technologies and Applications. “Routing Protocols in Wireless Sensor Networks”, Shio Kumar Singh1, M P Singh, and D K Singh ,A Survey, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.2, November 2010. “Improved LEACH Protocol for Wireless Sensor Networks”, Networks”, 978-1-4244-6252- 0/11/ ©2011 IEEE Naveen Kumar, Mrs. Jasbir Kaur. “Research about Improvement of LEACH Protocol”, Zhao Yulan, Jiang Chunfeng IEEE 2010, 978-1-4244- 7618-3 /10/©2010 IEEE “MECN: Minimum Energy Mobile Wireless Networks”, V. Rodoplu and T. H. Meng, IEEE Journal Selected Areas in Communications, vol. 17, no. 8, Aug. 1999, pp. 133344. “On Energy Efficient Routing for Wireless Sensor Networks”, Jamal N. Al-Karaki Islam T. Al-Malkawi ,978-1-4244-3397-1/08/ ©2008 IEEE

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Study on Watermarking Approaches on Biometric Images 1

Neeraj, 2Sunita Student, M.Tech, 2Asstt. Professor, Deptt. of Computer Sc., Baba Mast Nath Engg. College Rohtak, Haryana, India 1

Abstract: To obtain the information security and information authentication one of effective approach is watermarking. In this approach, the data is hide behind some multimedia objects. One of such effective watermarking concept is provided by Biometric Image watermarking. In this work, an analysis on DCT and DWT based watermarking approaches is defined to perform Biometric Image watermarking. In this paper, the issues and challenges associated with watermarking process are also discussed. Keywords: Biometric Image Watermarking, DWT, DCT, Noisy, Compression I. INTRODUCTION To protect the Biometric Image and the Copy Protection Technical Working Group (CPTWG), an ad hoc group consisting of the Motion Picture Association of America, the Consumer Electronics Manufacturers Association, and members of the computer industry, examine the digital Biometric Image protection in the form of Biometric Image watermarking. The system was designed basically for the DVD copyright protection. The Biometric Image watermarking provides the Biometric Image protection with better robustness and transparency. In this watermarking system, the watermark conveys the information about the copying authority like copy once, copy never, copy freely etc. The another advantage of this mechanism is the cost effectiveness. It provides the higher security and reliability in terms of low false positive rate. This Biometric Image watermarking was defined with MPEG decoder. The watermark is embedded within the drive that makes it sure that watermark will stay till the multimedia content is available in the DVD. But to include some more features such as cryptography and the authentication over the watermark contents, the application based watermarking come into the picture. This application oriented watermarking scheme gives more flexibility to extend the application as well as the methodology. The extension to this Biometric Image marking approach is the inclusion of second watermark. This second watermark scheme is inexpensive and basically used for the compressed Biometric Images. The another watermarking approach is based on tickets that represents a cryptographic counter defined by hash key and this counter is incremented each time the Biometric Image pass to the recorder. The another scheme adopted by Biometric Image watermarking is the scene based watermarking. This sceheme basically seprate the static and dynamic areas of the Biometric Image frames by using wavelet decomposition. Now these static areas are used to store the watermark objects [6]. The another issue associated with Biometric Image watermarking is the compressed Biometric Image formats. These compressed Biometric Image formats having the compressed bit streams so that full length decoding process cannot be implemented here. These kinds of Biometric Image formats increase the complexity as well as delay while performing the watermarking [6]. A. Requirements of Biometric Image Watermarking Some of the requirements of watermarking are discussed in earlier section. But along with this, the aditional requirements of Biometric Image watermarking is given as [7] (i) Compressed Data Procssing In normal case, the Biometric Image watermarking system must be operated under the compressed domain because most of the avaialble Biometric Image formats are itself in compressed forms. To achieve the robustness, it is required for a watermarking approach to be feasible in compressed, recompressed and decompressed forms. (ii) Fast Embedding/Detection Generally, the size of Biometric Image data is quite large so that a Biometric Image watermarking algorithm must be fast enought to process the Biometric Image and to minimize the process delay. (iii) Blind Detection

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In case of Biometric Image watermarking, there is no availability of origional Biometric Image while performing the watermark detection. Because of this it is important for Biometric Image watermarking to perform the detection procss blink by analyzing the Biometric Image sequences. But the detection process, can have the watermarked object to perform the detection.

B. Watermarking Perspectives The perceptivity of the watermark actually defines the need of the user or the actual requirement of user. These requirements can be crystal clear, if we get the features of these persepectives. The features of these two classes are listed in Table 2. Table 2: Perceptive based Classification Visible Watermarking Feature



  

Overwrite the Existing media contents Cannot remove watermarks completely Used for logos or the trademarks Direct Confirmation Comparatively Fast

 

Fragility to Attack Degrade Quality



Advantage Disadvantage

Invisible Watermarking Features

 

Advantages



Most Commonly used Nessessary for commercial authentication Not Degrade the contents

Disadvantages



Can be modified

II. LITERATURE REVIEW C. K. Chan et al. [1] presented a data hiding scheme by simple LSB substitution is proposed. By applying an optimal pixel adjustment process to the stego-Biometric Image obtained by the simple LSB substitution method, the Biometric Image quality of the stego-Biometric Image can be greatly improved with low extra computational complexity. The worst case mean-square-error between the stego-Biometric Image and the coverBiometric Image is derived. Experimental results show that the stego Biometric Image is visually indistinguishable from the original cover-Biometric Image. Sanjeev Manchanda et. al [2] stated that steganography is the science of hiding information in media based data. They present random numbers logic based steganographic methods and layout management schemes for hiding data/Biometric Image into Biometric Image(s). These methods and schemes can be customized according to the requirements of the users and the characteristics of data/Biometric Images. These methods are secure enough to meet the requirements of the users and user can play significant role in selection and development of these methods. Methods can be chosen randomly and implemented dynamically based on inputs, user choices as well as outputs. Experimental results are given to demonstrate the performance of the proposed methods. Xinpeng Zhang et. al [3] said that the pixelvalue differencing (PVD) steganography can embed a large amount of secret bits into a still Biometric Image with high imperceptibility as it makes use of the characteristics of human vision sensitivity. However, a loophole exists in the PVD method. Unusual steps in the histogram of pixel differences reveal the presence of a secret message. An analyst can even estimate the length of hidden bits from the histogram. To enhance security, a modified scheme is proposed which avoids occurrence of the above-mentioned steps in the pixel difference histogram while preserving the advantage of low visual distortion of the PVD. The histogram-based steganalysis is therefore defeated. H.-C. Wu, N.-I. Wu et. al [5] proposed a method in order to improve the capacity of the hidden secret data and to provide an imperceptible stego-Biometric Image quality, a novel steganographic method based on least-significant-bit (LSB) replacement and pixel-value differencing (PVD) method is presented. First, a different value from two consecutive pixels by utilising the PVD method is obtained. A small difference value can be located on a smooth area and the large one is located on an edged area. In the smooth areas, the secret data is hidden into the cover Biometric Image by LSB method while using the PVD method in the edged areas. Because the range width is variable, and the area in which the secret data is concealed by LSB or PVD method are hard to guess, the security level is the same as that of a single using the PVD method of the proposed method. From the experimental results, compared with the PVD method being used alone, the proposed method can hide a much larger information and maintains a good visual quality of stego-Biometric Image. Hwang M.S. et. al [6] proposed that in a t, n  threshold proxy signature scheme, the original signer delegates the power of signing messages to a designated proxy group of n members. Any t or more proxy

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signers of the group can cooperatively issue a proxy signature on behalf of the original signer, but t  1 or less proxy signers cannot. Previously, all of the proposed threshold proxy signature schemes have been based on the discrete logarithm problem and do not satisfy all proxy requirements. In this paper, they propose a practical, efficient, and secure t, n threshold proxy signature scheme based on the RSA cryptosystem. They scheme satisfies all proxy requirements and uses only a simple Lagrange formula to share the proxy signature key. Furthermore, their scheme requires only 5 percent of the computational overhead and 8 percent of the communicational overhead required in Kim’s scheme. Chen et. al [7] proposed a new Biometric Image cryptosystem to protect Biometric Image data. It encrypts the original Biometric Image into another virtual Biometric Image. Since both original and virtual Biometric Images are significant, this new cryptosystem can confuse illegal users. Besides the camouflage, this new cryptosystem has three other benefits. First, this cryptosystem is secure even if the illegal users know that the virtual Biometric Image is a camouflage. Second, this cryptosystem can compress Biometric Image data. Finally, this method is more efficient than a method that encrypts the entire Biometric Image directly. Fu, M.S et. al [8] said that in many printer and publishing applications, it is desirable to embed data in halftone Biometric Images. In this paper, they proposed some novel data hiding methods for halftone Biometric Images. For the situation in which only the halftone Biometric Image is available, they propose data hiding smart pair toggling (DHSPT) to hide data by forced complementary toggling at pseudo-random locations within a halftone Biometric Image. The complementary pixels are chosen to minimize the chance of forming visually undesirable clusters. Their experimental results suggest that DHSPT can hide a large amount of hidden data while maintaining good visual quality. For the situation in which the original multitone Biometric Image is available and the halftoning method is error diffusion, they propose the modified data hiding error diffusion (MDHED) that integrates the data hiding operation into the error diffusion process. In MDHED, the error due to the data hiding is diffused effectively to both past and future pixels. Their experimental results suggest that MDHED can give better visual quality than DHSPT. Both DHSPT and MDHED are computationally inexpensive.

 

III. WATERMARKING APPRAOCH In this present work, a DWT and DCT based approach for Biometric Image Watermarking approach is presented. These approaches are most effectively used to perform effective information storage in image objects. The discussion on these two approaches is given here under A. DCT DCT is one of the important approaches that first separate the video frames in smaller parts and assign the weightage to these parts under the quality analysis. The concept of DCT is similar to the Fourier transformation so that the signal is identified under the spatial domain and the frequency analysis on these frames will be done effectively.

Figure 1: DCT Encoding The basic process defined by DCT is given as under  As the video frames is accepted by the DCT, it split the frame in smaller windows of size nxm.  Extract the intensity from different window forms at position (I,j)  Define the coefficient for each row and column under the DCT coefficient matrix.  The extraction of low frequency areas from the frame that will be used as the key area for storing the data over the image  The compression will be performed effectively by neglecting the non visible areas so that no visible distortion will be done.  Define the DCT array of integer so that the gray level scaling will be done.  The matrix contents will be reterived under different formats such as horizontally, vertically, in zig-zag motion. B. DWT The DWT is one of the fundamental processes in the JPEG2000 image compression algorithm [4]. The DWT is a transform which can map a block of data in the spatial domain into the frequency domain. The DWT returns information about the localized frequencies in the data set. A two-dimensional (2D) DWT is used for images. The 2D DWT decomposes an image into four blocks, the approximation coefficients and three detail

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coefficients. The details include the horizontal, vertical, and diagonal coefficients. The lower frequency (approximation) portion of the image can be preserved, while the higher frequency portions may be approximated more loosely without much visible quality loss. The DWT can be applied once to the image and then again to the coefficients which the first DWT produced. It can be visualized as an inverted treelike structure. The original image sits at the top. The first level DWT decomposes the image into four parts or branches, as previously mentioned. Each of those four parts can then have the DWT applied to them individually, splitting each into four distinct parts or branches [5]. IV. CONCLUSION In this paper, an effective approach is defined to perform the Biometric Image watermarking using DCT and DWT based approaches. The paper has discussed the issues associated with watermarking along with approach specification. References [1] [2] [3] [4] [5]

[6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

C. K. Chan and L. M. Chen, “Hiding Data in Biometric Images by Simple LSB Substitution,” Pattern Recognition, Vol. 37, Issue (3), pp. 469–474, 2004. Sanjeev Manchanda, Mayank Dave and S. B. Singh, “Customized and Secure Biometric Image Steganography Through Random Numbers Logic”, Signal Processing: An International Journal, Vol. 1, Issue (1), 2007. Xinpeng Zhang, Shuozhong Wang, “Vulnerability of Pixel-Value Differencing Steganography to Histogram Analysis and Modification for Enhanced Security”, Pattern Recognition Letters , Vol.25, pp. 331–339, 2004. Rafel C. Gonzalez, Richard E. Woods, “Digital Biometric Image Processing” 2nd ed., 2002. H.-C. Wu, N.-I. Wu, C.-S. Tsai and M.-S. Hwang, M.S., “Biometric Image Steganographic Scheme Based on Pixel-Value Differencing and LSB Replacement Methods. IEE Proceedings – Vision Biometric Image and Signal Processing Vol. 152, pp. 611–615, 2005. Hwang M.S., Lu, E.J.L., and Lin, I.C., “A Practical (t, n) Threshold Proxy Signature Scheme Based on the RSA Cryptosystem”, IEEE Trans. Knowl. Data Eng., Vol. 15, Issue (6), pp. 1552–1560, 2003. Chen, T.S., Chang, C.C., and Hwang, M.S., “A Virtual Biometric Image Cryptosystem Based upon Vector Quantization”, IEEE Trans. Biometric Image Process., Vol. 7, Issue (10), pp. 1485–1488, 1998. Fu, M.S., and Au, O.C., “Data Hiding Watermarking for Halftone Biometric Images”, IEEE Trans. Biometric Image Process.,Vol. 11, Issue (4), pp. 477–484, 2002. Tseng, Y.C., Chen, Y.Y., and Pan, H.K., “A Secure Data Hiding Scheme for Binary Biometric Images”, IEEE Trans. Commun., Vol. 50, pp. 1227–1231, 2002. Tseng, Y.C., and Pan, H.K., “Data Hiding in 2-color Biometric Images”, IEEE Trans. Comput., Vol. 51, Issue (7), pp. 873–878, 2002. Cheng-Hsing Yang, Chi-Yao Weng, Shiuh-Jeng Wang, “Adaptive Data Hiding in Edge Areas of Biometric Images with Spatial LSB Domain Systems.” IEEE Transactions on. Information Forensics and Security, Vol. 3, Issue (3), September 2008. D. Artz, “Digital Steganographic: Hiding Data within Data,” IEEE Internet Comput., Vol. 5, Issue (3), pp. 75–80, May/Jun. 2001. R. R. Anderson and F. A. P. Peticolas, “On the Limits of Steganography”, IEEE J. Sel. Areas Commun., Vol. 16, Issue (4), pp. 474–481, May 1998. Chih-Chiang Lee, Hsien-ChuWu, Chwei-Shyong Tsai, Yen-Ping Chu, “Adaptive Lossless Steganographic Scheme with Centralized Difference Expansion”, Pattern Recognition Vol. 41, pp. 2097 – 2106, 2008. Martín Alvaro, Sapiro Guillermo and Seroussi Gadiel, “Is Biometric Image Steganography Natural?”, IEEE Transactions On Biometric Image Processing, Vol. 14, Issue(12), December, 2005. Provos, N. and Honeyman, “Hide and Seek: An Introduction Steganography. IEEE Magazine on Security & Privacy, pp. 32-44, 2003.

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Study on Communication Architectures in Sensor Network 1

Rimpy, 2Sunita Student, M.Tech, 2Asstt. Professor, Deptt. of Computer Sc., Baba Mast Nath Engg. College Rohtak, Haryana, India 1

Abstract: To obtain the information security and information authentication one of effective approach is watermarking. In this approach, the data is hiding behind some multimedia objects. One of such effective watermarking concept is provided by Biometric Image watermarking. In this work, an analysis on DCT and DWT based watermarking approaches is defined to perform Biometric Image watermarking. In this paper, the issues and challenges associated with watermarking process are also discussed. Keywords: Biometric Image Watermarking, DWT, DCT, Noisy, Compression I. INTRODUCTION A sensor network is restricted area network defined with tiny sensor nodes with energy specification and with sensing capability. These sensor networks are having its advantages in many environment specific applications, home applications, hospital application etc. The sensor node specification is defined with positional constraint and the nodes can be mobile or the fix position nodes. Each node is defined with fixed energy specification or with battery backup. The energy backup is defined to perform network communication with energy effective communication over the network. The network is having the capability to provide effective communication with energy restriction. As the communication is performed under the environmental, communication based restriction, sensor network is effective. Sensor network provides the communication in restricted scenario communication such as rescue area communication, class room based network etc. Sensor network architecture is defined with large number of sensor nodes with the specification in large communication area. This network type is defined with sensing network capability and with the energy node specification. The network is defined with the specification of receiver node at particular location. This kind of network is defined with the specification of the sensor nodes under the architectural restrictions. The architectural specification also includes the hardware specification, topological specification, operation system specification, communication parameter specification etc. The address of the network system is defined to perform reliable communication over the network. This architectural specification is shown in figure 1 A. Network Tracking This scenario commonly discussed for sensor networks is the tracking of a tagged object through a region of space monitored by a sensor network. There are many situations where one would like to track the location of valuable assets or personnel. Current inventory control systems attempt to track objects by recording the last checkpoint that an object passed through. However, with these systems it is not possible to determine the current location of an object. For example, UPS tracks every shipment by scanning it with a barcode whenever it passes through routing centres. The system breaks down when objects do not flow from checkpoint to checkpoint.

Sensor node Gateway

Figure 1: Architecture of Wireless Sensor Network

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In typical work environments it is impractical to expect objects to be continually passed through checkpoints. With wireless sensor networks, objects can be tracked by simply tagging them with a small sensor node. The sensor node will be tracked as it moves through a field of sensor nodes that are deployed in the environment at known locations. Instead of sensing environmental data, these nodes will be deployed to sense the RF messages of the nodes attached to various objects. The nodes can be used as active tags that announce the presence of a device. A database can be used to record the location of tracked objects relative to the set of nodes at known locations. With this system, it becomes possible to ask where an object is currently, not simply where it was last scanned . Unlike sensing or security networks, node tracking applications will continually have topology changes as nodes move through the network. While the connectivity between the nodes at fixed locations will remain relatively stable, the connectivity to mobile nodes will be continually changing. II. LITERATURE REVIEW In the year 2001, Xu Lin carried out a task named "Location Based Localized Alternate, Disjoint, Multi-path and Component Routing Schemes for Wireless Networks". Within this article, Xu Lin suggested four proposals that can improve the performance of greedy routing method. In the other method, the i-th copy of the message m received is forwarded to i-th best neighbour, according to the selected criterion (if the number of copies exceeds the number of neighbours, the proposal fails). In the disjoint approach, each intermediate node, upon receiving m, will forward it to its best neighbour amongst those who have never received the message. If no neighbours as such exists, it fails.[1] In 2006, Shekhar H M P carried out a task called " Mobile Agents based Framework for Routing and Congestion Control in Mobile Ad Hoc Networks". In this paper, Shekhar presented a Mobile Agents based Framework for Routing and Congestion control in Mobile Ad Hoc Networks (MAFRC). The structure uses a cross-layer design tactic where numerous smart agents presented across the network protocol stack work together with each other and help in discovering cost-effective routes between the source and destination and also assist in controlling the amount of congestion in the network. The mobile agents moving across the network slowly gather the network connectivity information based on the congestion state given by the static agent at the link layer [2]. In 2009, Nacer Hamani carried out a task called the "An ACO/MAS joint approach to manage communications in wireless sensor networks". This article demonstrates the initial development of the MWAC model using an ant colony optimization. Hamani also brings up the use of ant colony optimization in the context of multi-agent systems and wireless sensor networks. Ants are not simply deployed on the multi-agent society [3]. In 2007, Vincent Borrel carried out a task called the " Understanding the Wireless and Mobile Network Space: A Routing-Centered Classification". Within this article, Borrel looked at the problem of classifying mobile and wireless networks with the aim of getting knowledge about what form of routing is the utmost suitable and that too for which network. The author next goes about developing a routing-awareness classification that understands that the limits between network classes are not that tough and are very much rely on the routing protocol parameters that are provided [4]. In 2008, Alfredo Garcia performed a work," Rational Swarm Routing Protocol for Mobile Ad-hoc Wireless Networks". Wireless Mobile Ad-hoc networks (MANET) require dynamic routing schemes for adequate performance. In this paper, Author introduced a new dynamic routing scheme based upon energy. In a similar manner to ant-colony based dynamic routing protocols, Presented scheme is able to respond to link quality changes after a path is established [5]. In year 2011, Ashima Rout performed a work," Optimized Ant Based Routing Protocol for MANET". In this paper, Author introduce a new ant based routing protocol to optimize the route discovery and maximize the efficiency of routing in terms of packet delivery ratio (PDR) using the blocking expanding ring search (Blocking-ERS), third party route reply, local route repair and n-hop local ring techniques. These techniques control the overhead and minimize the end-to end delay with improvement of PDR[6]. In year 2004, Xuefei Li performed a work," Node-Disjointness-Based Multipath Routing for Mobile Ad Hoc Networks". AODV and DSR are the two most widely studied on-demand ad hoc routing protocols. Author modified and extended AODV to include the path accumulation feature of DSR in route request/reply packets so that lower route overhead is employed to discover multiple node-disjoint routing paths. The extended AODV is called Node-Disjointness-Based Multipath Routing Protocol (NDMR), which has two novel aspects compared to the other on-demand multipath protocols: it reduces routing overhead dramatically and achieves multiple node-disjoint routing paths [7]. In year 2010, Ying Lin performed a work," An Ant-colony-systembased Activity Scheduling Method for the Lifetime Maximization of Heterogeneous Wireless Sensor Networks". This paper proposes an antcolony- system-based scheduling method (ACS-SM) for maximizing the lifetime of a typical type of heterogeneous WSNs. First, the lifetime maximization problem is formulated as finding the maximum number of disjoint sets of devices, with each set fulfilling sensing coverage and network connectivity simultaneously [8]. In year 2002, Fabian Kuhn performed a work," Asymptotically Optimal Geometric Mobile Ad-Hoc Routing". In this paper Author present AFR, a new geometric mobile adhoc routing algorithm. The algorithm is completely distributed; nodes need to communicate only with direct neighbors in their transmission range. Author show that if a best route has cost c, AFR finds a route and terminates with cost

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O(c2) in the worst case. Author gave a non-geometric algorithm that also matches the lower bound, but needs some memory at each node. This establishes an intriguing trade-off between geometry and memory [9]. In year 2011, Chen Yu performed a work," A Behavior-Geography based Routing scheme in Mobile Ad hoc Networks". This paper devotes to adopt a behavior-geography based way to promote its performance. The core idea is to eschew the emergence of the redundant message duplicate which heads to errant directions of geography, when a message dispensed in the network, by the knowledge of the behavior type its terminus performing [10]. In year 2010, Wei-Jie Yu performed a work," Pheromone-Distribution-Based Adaptive Ant Colony System". The proposed AACS algorithm is applied to solve a series of benchmark traveling salesman problems (TSPs). The resulting solution quality and the convergence rate of AACS are favorably compared with the results by the ACS using fixed parameters values and two existing adaptive parameters control methods [11]. In year 2012, Ana Cristina B. Kochem Vendramin performed a work," CGrAnt: A Swarm Intelligencebased Routing Protocol for Delay Tolerant Networks". This paper presents a new routing protocol for Delay Tolerant Networks (DTNs), based on a distributed swarm intelligence approach. The protocol is called Cultural Greedy Ant (CGrAnt), as it uses a Cultural Algorithm (CA) and a greedy version of the Ant Colony Optimization (ACO) metaheuristic[12]. III. COMMUNICATION ARCHITECTURE Data aggregation is one of the major communication approach in which multiple sources are sending data to single sink. This communication approach is applicable to most of the network scenarios as each network is controlled by some controller node or the base station and all other nodes are agreed to report them regularly for different purpose such as for permissions, authentications, to connect to outer interface etc. In all such case we need to transfer data from multiple sources to the single sink. But as such kind of communication is performed individually between the each source and the sink. There will be heavy traffic or the communication over the network that can result data and energy loss. Because of this there is the requirement of Data aggregation. The basic idea behind the aggregation is to avoid the one to one communication to the sink node and build an aggregative path over the network. The aggregative path is the path that selects a distance node as the source node and all the network nodes are intermediate nodes. The main objective of the data aggregation is to reduce the network traffic and to save the energy loss over the communication. While working with sensor network or any adhoc network, there are many dedicated applications that are suitable to the data aggregation. The aggregation also defined under some constraints and the components. One of the components is the aggregation operator called aggregator. The aggregator can be union operator that just combine the dataset taken from the previous node, add a delimiter and node message to it, and pass the message to next node. But such kind of aggregation is applicable only for the smaller network. As the network size grows, the union cannot be used. In such case, other aggregative operator called summation will be used. According to this operator the numerical values extracted from all nodes are added as the communication transferred to the next node. But these all kind of aggregation also suffer from the problem of inclusion of some false or the bad information. In such case, the false data detection approach is used. The basic process of aggregation is shown in figure 1. In-network data aggregation reduces energy consumption in WSN. Specifically, the main problems to consider are how to create the converge cast tree, how to select the routes, and where to do the aggregation in this tree. There are number of different data aggregation processes in in-network data aggregation. For example: (a) Tree based in – network data. Aggregation (b) Application specific data aggregation. (c) Structure free in – network data aggregation. But the most important one is (d) Cluster based in – network data aggregation [5]. In cluster based in - network data aggregation partitioning the network into clusters and deploying cluster-heads to perform data aggregation. The job of these cluster-heads is specifically aggregating the data received from the sensors and transmitting them to the BS. In most scenarios, they do not perform any sensing. Low-energy adaptive clustering hierarchy (LEACH), Energy-Efficient and Secure Pattern-based data aggregation (ESPDA) and Power-Efficient Gathering in Sensor Information Systems (PEGASIS) are some of the example of Cluster based in – network data aggregation. They select a cluster-head among the sensor nodes that performs the data aggregation. Although LEACH considers clusters where each sensor can reach the cluster-head within one hop, PEGASIS creates chains of sensor nodes in which a leader is designated as the aggregator. ESPDA, on the other hand, considers clusters with multi-hop routes. In all of these approaches, sensor nodes take turns to be elected as the cluster-head so that the load of being cluster-head is distributed evenly among the sensor nodes in the network [5]. PEGASIS is a chain based protocol that transfer data to its nearest node and form a chain and in case of some node failure chain is re constructed. The proposed work is the improvement in PEGASIS protocol in terms to decide the next node in the chain. It will check the ratio of residual energy along with distance to perform the initial decision it not eligible the second level decision is taken by checking the response time. The work proposed here is the try to increase the network life time as well to increase the throughput over the network.

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The key features of PEGASIS are:  The BS is fixed at a far distance from the sensor nodes.  The sensor nodes are homogeneous and energy constrained with uniform energy.  No mobility of sensor nodes. PEGASIS is based on two ideas; Chaining, and Data Fusion. In PEGASIS, each node can take turn of being a leader of the chain, where the chain can be constructed using greedy algorithms that are deployed by the sensor nodes. PEGASIS assumes that sensor nodes have a global knowledge of the network, nodes are stationary (no movement of sensor nodes), and nodes have location information about all other nodes. PEGASIS performs data fusion except the end nodes in the chain. PEGASIS outperforms LEACH by eliminating the overhead of dynamic cluster formation, minimizing the sum of distances that non leader-nodes must transmit, limiting the number of transmissions and receives among all nodes, and using only one transmission to the BS per round. PEGASIS has the same problems that LEACH suffers from. IV. CONCLUSION In this paper, an effective communication architecture in sensor network is defined using clustering and aggregative communication capabilities. The work is defined using LEACH and PEGASIS protocol. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Xu Lin," Location Based Localized Alternate, Disjoint, Multi-path and Component Routing Schemes for Wireless Networks", MobiHOC 2001, Long Beach, CA, USA© ACM 2001 1-58113-390-1/01/10 Shekhar H M P," Mobile Agents based Framework for Routing and Congestion Control in Mobile Ad Hoc Networks", CoNEXT’06, December 4–7, 2006, Lisboa, Portugal. © 2006 ACM 1-59593-456-1/ 06/ 0012 Nacer Hamani," An ACO/MAS joint approach to manage communications in wireless sensor networks", MEDES 2009 October 27-30, 2009, Lyon, France ACM 978-1-60558-829-2/09/0010 Vincent Borrel," Understanding the Wireless and Mobile Network Space: A Routing-Centered Classification", CHANTS’07, September 14, 2007, Montréal, Québec, Canada. ACM 978-1-59593-737-7/07/0009 Alfredo Garcia," Rational Swarm Routing Protocol for Mobile Ad-hoc Wireless Networks", ICPS’08, July 6–10, 2008, Sorrento, Italy. ACM 978-1-60558-135-4/08/07 Ashima Rout," Optimized Ant Based Routing Protocol for MANET", ICCCS’11, February 12–14, 2011, Rourkela, Odisha, India. ACM 978-1-4503-0464-1/11/02 Xuefei Li," Node-Disjointness-Based Multipath Routing for Mobile Ad Hoc Networks", PE-WASUN’04, October 7, 2004, Venezia, Italy. ACM 1-58113-959-4/04/0010 Ying Lin," An Ant-colony-system-based Activity Scheduling Method for the Lifetime Maximization of Heterogeneous Wireless Sensor Networks", GECCO’10, July 7–11, 2010, Portland, Oregon, USA. ACM 978-1-4503-0072-8/10/07 Fabian Kuhn," Asymptotically Optimal Geometric Mobile Ad-Hoc Routing", Dial-M’02, September 28, 2002, Atlanta, Georgia, USA. ACM 1-58113-587-4/02/0009 Chen Yu," A Behavior-Geography based Routing scheme in Mobile Ad hoc Networks", UAAII’11, September 18, 2011, Beijing, China. ACM 978-1-4503-0932-5/11/09 Wei-Jie Yu," Pheromone-Distribution-Based Adaptive Ant Colony System", GECCO’10, July 7–11, 2010, Portland, Oregon, USA. ACM 978-1-4503-0072-8/10/07 Ana Cristina B. Kochem Vendramin," CGrAnt: A Swarm Intelligence-based Routing Protocol for Delay Tolerant Networks", GECCO’12, July 7–11, 2012, Philadelphia, Pennsylvania, USA. ACM 978-1-4503-1177-9/12/07 Anuj K. Gupta," Analysis of various Swarm-based & Ant-based Algorithms", ACAI '11, July 21 - July 22 2011, Rajpura/Punjab, India ACM 978-1-4503-0635-5/11/10 Carlos M. Fernandes," Binary Ant Algorithm", GECCO’07, July 7–11, 2007, London, England, United Kingdom. ACM 978-159593-697-4/07/0007 Marcelo Portela Sousa," Ant Colony Optimization with Fuzzy Heuristic Information Designed for Cooperative Wireless Sensor Networks", MSWiM’11, October 31–November 4, 2011, Miami, Florida, USA. ACM 978-1-4503-0898-4/11/10

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Available online at http://www.iasir.net

Microwave Assisted Synthesis, Characterisation and Antibacterial Study of Drug based Schiff Bases and their Zn(II) Complexes K.P.Srivastava*, Anuradha Singh & Suresh Kumar Singh Department of Chemistry, Ganga Singh College, J.P.University, Chapra-841301, Bihar, INDIA Abstract: A rapid, efficient, clean and environmentally benign exclusive synthesis of Schiff bases as new ligands and their complexes with Zn(II) have been developed using condensation of salicylaldehyde with amoxicillin (L1), cephalexin (L2), sulphamethoxazole (L3) and trimethoprim (L4) efficiently in an alcoholic suspension medium using alkali catalyst with excellent yields under microwaves irradiation. This method provides several advantages such as environmental friendliness, simple work-up procedure, short reaction times, non-hazardous and excellent yield of products. The results are compared with conventional methods for their yield and reaction time. The Schiff base ligands and the complexes were characterized by microanalytical, thermo-gravimetric, magnetic and spectroscopic studies. All the Schiff bases were bidentate (NO donor) ligands. Salicylidenesulphamethoxazole-Zn(II) monohydrate was five co-ordinate whereas all other complexes were found to be six co-ordinate dihydrates and ML2 [1:2 (metal: ligand) ratio] type. The complexes are coloured and stable in air. All the complexes under investigation possess antibacterial activity. The antibacterial activity showed the following trend: Zn(II)-complexes ˃ Schiff base ligands ˃ parent drugs. Keywords: Microwave irradiation; Schiff bases; Coordination compounds; Antibacterial activity; Zinc I. INTRODUCTION As environmental consciousness in chemical research and industry has increased, efficient, economic and clean procedures have received increased attention in recent years. The development of a simple and effective method, using an environmentally friendly approach as well as an economical process is in great demand in coordination chemistry. Recent advances in technology have now made microwave energy a more efficient means of heating reactions. Chemical transformations that took hours, or even days, to complete their organic reaction, can now be accomplished in minutes. Microwave irradiation is well known to promote the synthesis of a variety of organic and inorganic compounds, where chemical reactions are accelerated because of selective absorption of microwave by polar molecules [1-5]. Schiff base ligands have been widely studied in the field of coordination chemistry mainly due to their facile syntheses, easily availability, electronic properties and good solubility in common solvents and they easily form stable complexes with most transition metal ions [6-11]. A large number of Schiff bases and their metal complexes have been found to possess important biological and catalytic activity. Due to their great flexibility and diverse structural aspects, a wide range of Schiff bases have been synthesized conventionally and their complexation behavior was studies. The development of the field of bioinorganic chemistry has increased the interest in Schiff base complexes, since it has been recognized that many of these complexes may serve as models for biologically important species and were investigated for antifungal, antimicrobial, antibacterial, anti-inflammatory, anti-convulsant, anticancer activities[12- 16]. Zinc is an essential trace element, necessary for plants, animals, and microorganisms. Zinc is found in nearly 100 specific enzymes (other sources say 300) [17-19], serves as structural ions in transcription factors and is stored and transferred in metallothioneins. It is "typically the second most abundant transition metal in organisms" after iron and it is the only metal which appears in all classes. There are 2-4 grams of zinc distributed throughout the human body. Most zinc is in the brain, muscle, bones, kidney, and liver, with the highest concentrations in the prostate and parts of the eye. Semen is particularly rich in zinc, which is a key factor in prostate gland function and reproductive organ growth. In humans, zinc plays "ubiquitous biological roles". It interacts with "a wide range of organic ligands", and has roles in the metabolism of RNA and DNA, signal transduction, and gene expression. It also regulates apoptosis. In the brain, zinc is stored in specific synaptic vesicles by glutamatergic neurons and can "modulate brain excitability". It plays a key role in synaptic plasticity and so in learning. However it has been called "the brain's dark horse" since it also can be a neurotoxin, suggesting zinc homeostasis plays a critical role in normal

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functioning of the brain and central nervous system. Thus, zinc is an essential mineral of "exceptional biologic and public health importance"[20-21]. Application of green and sustainable chemistry protocols has seen enormous surge in recent times for the development of novel and eco-friendly methodologies towards the synthesis of valuable synthetic scaffolds and drug intermediates. Microwave syntheses of coordination and organometallic compounds are presented by relatively a small number of reports in the available literature in comparison with inorganic and organic synthesis. Prompted by the above mentioned biological and multifunctional activities of Schiff bases and their metal complexes as well as the utility of microwave irradiation in inorganic syntheses, in continuation of our efforts to synthesize novel heterocyclic molecules of biological importance [22-24] , we envisaged a mild base mediated synthesis of some new Schiff bases of salicylaldehyde with amoxicillin (L 1H), cephalexin (L2H), sulphamethoxazole (L3H) and trimethoprim (L4H) using alcoholic medium as a recyclable eco-friendly solvent under microwave irradiation without using any non-ecofriendly organic solvents and the products were isolated simply by crystallization. We report the eco-friendly and efficient synthesis, structural characterization and antibacterial activities of synthesized new ligands and their complexes with Zn(II) ion in this research article

II. EXPERIMENTAL SECTION A. Materials & Methods All the used chemicals and solvents were of Anal R grade. All the reagents used for the preparation of the Schiff bases were obtained from Sigma Aldrich. Metal salts were obtained from Loba Chemie and original drugs were obtained from Glaxo SmithKline, India and were used without further purification. Melting points were determined on a Mel-Temp melting point apparatus and are uncorrected. All compounds were analysed satisfactorily for C, H and N using Carl-Ebra 1106 elemental analyser in micro analytical laboratory. Thin layer chromatography (TLC) was carried out on silica gel plates (Fluka-Kieselgel, 0.2 mm thickness) and the plates were scanned under 254 nm ultraviolet light. Magnetic susceptibility measurements of the metal complexes in the solid state were determined by a Gouy balance at room temperature. Electronic spectra (in DMSO solvent) in the UV-Visible range were recorded on Perkin Elmer Lambda-2B-spectrophotometer. Molar conductance measurements were conducted using 10-3 M solution of the complexes in DMSO on Elico-CM 82 Conductivity Bridge at room temperature. Magnetic susceptibility measurements were carried out on a Gouy balance at room temperature using mercuric tetrathiocyanato cobaltate(II) as the calibrant. Diamagnetic corrections were applied in compliance with Pascalâ&#x20AC;&#x2122;s constant [25]. FT-IR spectra were recorded in KBr medium and Nujol mull techniques on a Perkin Elmer RX1 spectrophotometer in wave number region 4000-400 cm-1. Thermogravimetric analysis was carried out under atmospheric condition with a heating rate 10 0C min-1 on TGA Q500 universal V4.5A TA instrument from ambient to 773K. The solid state electrical conductivity has been measured by impedance spectroscopic method using HIOKI 3532-50 LCR Hitester at fixed frequency 1 KHz in the temperature range of 298-473 K. Microwave assisted synthesis were carried out in open glass vessel on a modified microwave oven model 2001 ETB with rotating tray and a power source 230 V, microwave energy output 800 W and microwave frequency 2450 MHz. A thermocouple used to monitor the temperature inside the vessel of the microwave. The microwave reactions were performed using on/off cycling to control the temperature. Completion of reaction was monitored by performing TLC and melting point. B. Synthesis of Ligands The equimolar (1:1) ratio of methanolic solution of drug and methanolic solution of salicylaldehyde were mixed thoroughly and 0.1% methanolic KOH was added to adjust the pH of the solution within 7-8 and was then irradiated in the microwave oven by taking 3-4 ml solution. The reaction was completed in a short time (1-3 min) with higher yields showing clear coloured solution. The Schiff base ligands were isolated by crystallization after volume reduction by evaporation. The crystalline products were dried under vacuum or reduce pressure under anhydrous CaCl2 and kept in a desiccator till further use. The progress of the reaction and purity of the products were monitored by TLC using silica gel G (yield: 75-89%). H

COOH

H C

CONH H

CH 3

N HO

COONa O

H C

CONH

S H

CH OH

L1H = Salicylideneamoxicillin

L2H = Salicylidenecephalexin

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L3H = Salicylidenesulphamethoxazole

NH2

MeO

N MeO

CH2

N MeO

L4H = Salicylidenetrimethoprim Figure-1: Structure Formulae of the investigated ligands C. Synthesis of metal complexes The methanolic solution of ligand and the metal salt were mixed thoroughly in 1:2 (metal : ligand) ratio and 0.1% methanolic KOH was added to adjust the pH of the solution within 7-8 and was then irradiated in the microwave oven by taking 3-4 ml solution. The reaction was completed in a short time (2-5 min.) with higher yields. The resulting light yellow product was then recrystallized with methanol and ether and finally dried under reduced pressure over anhydrous CaCl2 in a desiccator. The progress of the reaction and purity of the product was monitored by TLC using silica gel G (yield: 76.6-89.7%). (Scheme-1) .

MWI Zn(CH3COO)2 H2O + 2LH ---------------------→ Zn[L2 .2H2O] 2-5 minutes Coloured complex Scheme-1: Synthesis of metal complexes D. Biological Evaluation The in vitro biological activity of the investigated Schiff base ligands (L 1H – L4H) and their metal complexes was tested against three bacteria Escherichia coli, P.aeruginosa and Staphylococcus aureus by disc diffusion method using nutrient agar as medium and streptomycin as control. The minimum inhibitory concentration was determined using the disc diffusion technique [26]. III. RESULTS AND DISCUSSION As a result of microwave assisted synthesis, it was observed that the reaction was completed in a short time with higher yields compared to the conventional method. In the microwave method homogeneity of reaction mixture was increased by the rotating of reaction platform tray. The confirmation of the results was also checked by the repeating of the synthesis process. Comparative study results obtained by microwave assisted synthesis; versus conventional heating method is that some reactions which required 2-3 h. by conventional method, was completed within 2-5 min. by the microwave irradiation technique, yields have been improved from 35-48% to 76-89%. All the metal complexes are coloured, solid and stable towards air and moisture at room temperature. They do not possess sharp melting points and decompose on heating at high temperature (at about ˃563K). The complexes are insoluble in common organic solvents but soluble in DMF and DMSO. The comparative results of conventional and microwave methods, analytical data of the compounds, together with their physical properties are consistent with proposed molecular formula are given in Table-1. The microanalytical data suggest that the composition of all the metal complexes corresponds to 1:2 (metal: ligand) stoichiometry and have one or two water molecules i.e. hydrated. The observed molar conductance values (2.9 – 21.55 ohm1 cm2mole-1) are too low to account for any dissociation of the complexes in DMF at room temperature, indicating non-electrolytic nature of the complexes [27]. A. IR Spectral Studies The data of the IR spectra of investigated Schiff base ligands and their metal complexes are listed in Table2.The IR spectra of the complexes were compared with those of the free ligand in order to determine the involvement of coordination sites in chelation. Characteristic peaks in the spectra of the ligand and complexes were considered and compared. The FT-IR spectra of the investigated complexes contained all the absorption bands from the ligands and some new absorption bands indicative of coordination of the ligands with metal ion

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through N and O. The spectra of these complexes exhibited a broad band around 3380-3500 cm-1 which is assigned to water molecules, ν(OH), associated with the complexes. The coordinated water exhibited, in addition to these modes, the ρr(H2O), rocking near 892, 840 cm-1, ρw(H2O), wagging near 530-550 cm-1[28]. The FT-IR spectra of all the ligands contained a band at 1619-1632 cm-1, ν(C=N), which shifted slightly to a higher value in all the complexes suggesting that the ligands are coordinated through -C=N- [29]. The absorption due to carboxylic group did not change in the spectra of complexes indicating that the carboxylic groups are not involved in coordination with the metal ion. New absorption bands, ν(MN) and ν(MO), appeared at 435-460 cm1 and 335-355 cm-1 respectively, in the spectra of the investigated complexes indicating coordination of the ligands through N and O. The important bands along with their assignments are listed in table-2.The assignments were made by comparison with related Schiff base complexes [30-31]. Table-1 The comparative results of conventional and microwave methods, analytical and physical data of the compounds Compounds (Colour)

Reaction Time CM (MM)

Yield (%) CM (MM)

Elemental analysis Found (Calculated) % C H N Zn

Decomposition Point (0C)

Conductance (ohm-1 cm2 mol1 )

L1H=Salicylidenea-moxicillin (Orange)

0.5 (1.5)

39 (75)

58.07 (58.9)

5.13 (5.05)

8.76 (9.05)

194

16.5

L2H=Salicylidenecephalexin (Yellow-orange)

0.5 (2.0)

45 (83)

60.84 (61.5)

4.92 (5.30)

9.19 (9.25)

193

21.5

L3H=Salicylidenesulphamethoxazole (Pale yellow)

0.5 (2.0)

42 (80)

57.04 (57.0)

4.26 (4.30)

11.12 (11.35)

197

4.0

L4H=Salicylidenetrimethoprim (Yellowish-green)

0.5 (3.0)

48 (89)

64.25 (64.0)

5.70 (5.60)

14.10 (14.4)

190

12.5

Zn-L12H2O complex (Light yellow)

2-3 (3.5)

35 (76)

53.02 (53.1)

4.43 (4.5)

7.96 (8.2)

6.22 (6.25)

200

10.8

Zn -L2 2H2O complex (Light yellow)

2-3 (4.0)

40 (79)

54.93 (55.2)

4.46 (4.6)

8.16 (8.25)

6.41 (6.35)

261

11.0

Zn -L3H2O complex (Light yellow) Zn -L4 2H2O complex (Light yellow)

2-3 (3.7)

45 (89)

51.16 (51.4)

3.83 (3.9)

10.69 (10.7)

8.11 (8.0)

292

6.3

2-3 (5.0)

41 (86)

56.64 (56.8)

5.29 (5.3)

12.66 (12.7)

7.32 (7.4)

250

4.0

CM = Conventional method, time in hours; MM = Microwave method, time in minutes Table-2 Observed IR bands (cm-1) of Schiff base ligands and their Zn-complexes Compound Salicylideneamoxicillin Salicylidenecephalexin Salicylidenesulphamethoxazole Salicylidenetrimethoprim Zn-L12H2O complex Zn-L2 2H2O complex Zn-L3H2O complex Zn-L4 2H2O complex

υ(O-H) 3540 3500 3520 3480 3490 3395 3385 3482

υ(C=N) 1615 1610 1625 1625 1635 1625 1630 1620

ρr(H2O) 892, 840 880, 845 885, 850 890, 845 880, 845 890, 847 865, 840 880, 845

ρw(H2O) 544 540 535 545 535 540 530 542

δ (CO) 750 740 ------755 753 -------

π (CO) 560 575 ------560 578 -------

υ(M-N) ------------435 460 445 450

υ(M-O) ------------340 350 330 345

B. Electronic Spectral Studies The electronic absorption spectra show that when drug substances and salicylaldehyde were mixed together, the imine formation occurred which was indicated by colouration of the solution and development of an absorption band in the visible region [32]. In these spectra, there is an intense band at 240-292 nm which is assigned to a ππ* transition originating in the phenyl ring [33]. The bands in the 325-370 nm range, by analogy with LM, are attributed to a π-π* transition originating in the –CH=N- chromophore. The important electronic spectral bands along with their assignments of the isolated ligands and the complexes under investigation are listed in table-3.

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C. Magnetic Moment Studies The magnetic moments of the complexes under investigation were observed as expected and found zero which is indicative of diamagnetic nature of these investigated complexes. Table-3 Electronic spectral bands, magnetic moments and proposed geometry of the Zn(II) complexes Compound Salicylideneamoxicillin Salicylidenecephalexin Salicylidenesulphamethoxazole Salicylidenetrimethoprim Zn-L12H2O complex Zn-L2 2H2O complex Zn-L3H2O complex Zn-L4 2H2O complex

nm (ε, cm2 mol-1) Ligand bands d-d bands 220(20469),240(18896), ---350(4284) 210(21760), 250(15756) ---210(7375), 270(4473) ---210(15261), 90(3261) ---220(7012),240(6437), ---350(1462) 210(7438), 251(5365) ---210(2555), 271(1538) ---210(2560), 292(1140) ----

Magnetic moments μ (BM) ----

Proposed geometry of complexes ----

---------00

---------Octahedral

00 00 00

Octahedral Square-pyramidal Octahedral

D. Thermo-analytical Studies The thermal decomposition reactions of investigated complexes have been studied in N 2 atmosphere using TG and DTA techniques. The TG weight loss data and DTA peak temperatures are presented in table-4. The complexes begin to lose weight around 353K show the presence of moisture and around 393K suggesting the presence of coordinated water; it is the dehydration stage which water molecules are eliminated. There was a weight loss equivalent to one water molecule in case of salicylidenesulphamethoxazole-Zn(II) and two water molecules in the case of other complexes around 393 K showing that the water is coordinated. From 473 to 773 K a sharp decrease in weight indicated a loss of one of the Schiff base ligands from the complexes. In general the thermal decomposition of complexes may be considered by the following equation [34]: ML2xH2O ------------------------------------------→ML2 + xH2O ML2 -------------→ML + L The DTA curves show different peaks in the range of 451-663K. The first (endothermic) peak in the range of 451-497K corresponds to the loss of water molecules. The second (exothermic) peak in these complexes in the range of 647-658K is assigned to the loss of one of the Schiff base ligands. Table-4 Thermal analysis data of the Zn(II) complexes Complexes

Temperature range (0C)

Stage

TG Wt. Loss, % Found

DTA Temp. Peak, (0C)

Zn-L1.2H2O complex Zn-L2.2H2O complex Zn-L3.H2O complex Zn-L4.2H2O complex

100-250 250-500 100-200 200-500 100-300 300-500 50-200 200-500

1 2 1 2 1 2 1 2

3.60 46.25 3.73 46.85 2.32 46.18 4.24 45.26

224.22(Endo) 375.82(Exo) 276.56(Exo) 385.16(Exo) 290.42(Exo) 383.59(Exo) 178.13(Endo) 390.00(Endo)

Evolved Moiety Formula Mass calculated %

2H2O Ligand 2H2O Ligand H2O Ligand 2H2O Ligand

3.78 46.73 3.71 46.61 2.42 45.79 4.29 46.16

E. Proposed Structures On the basis of the above observations, it is tentatively suggested that Cu(II) investigated complexes show an octahedral geometry [Figure-2, 3 & 4] in which the Schiff bases act as bidentate [N & O donor] ligands.

Figure-2: Proposed octahedral structure of Zn[L21.2H2O] and Zn[L22.2H2O] complexes

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Figure-3: Proposed square-pyramidal structure of Zn[L23.H2O] complex

Figure-4: Proposed octahedral structure of Zn[L24.2H2O] complex F. Anti-bacterial Activity The difference in anti-bacterial activities of the investigated complexes, ligands and their parent drugs were studied and the results are presented in table-6 The cursory view of the data indicates the following trend in antibacterial activity of the substances under investigation: Zn(II)-complexes ˃ Schiff base ligands ˃ parent drugs All the Zn(II)-complexes and Schiff base ligands under investigation were more active than the parent drugs against E. coli and S. aureus. All the Zn(II)-complexes with the Schiff bases derived from cephalexin showed substantially enhanced activity against P.aeruginosa as compared with the parent drug. Table-6 Minimum inhibitory concentrations of investigated ligands, their Zn-complexes and the parent drugs (in μgmL-1) MICs (in μgmL-1)

Compound

Amoxicillin Cephalexin Sodium Sulphamethoxazole Trimethoprim Salicylideneamoxicillin Salicylidenecephalexin Salicylidenesulphamethoxazole Salicylidenetrimethoprim Zn-L1.2H2O complex Zn-L2 .2H2O complex Zn-L3.H2O complex Zn-L4.2H2O complex

E.coli 95 99 100 0.5 73 51 76 0.4 9.5 6.8 8.9 0.06

S.aureus 13 10 65 0.7 10 5.5 46 0.5 5.7 3.5 5.8 0.15

P.aeruginosa >300 >300 >300 >300 >300 >300 >300 >300 >200 >200 >200 >200

IV. CONCLUSION In this report, we described coordination chemistry of unsymmetrical Schiff bases as new ligands and their complexes with Zn(II) which have been synthesized using condensation of salicylaldehyde and amoxicillin (L1), cephalexin (L2), sulphamethoxazole (L3) and trimethoprim (L4) efficiently in an alcoholic suspension medium using alkali catalyst with excellent yields under microwaves irradiation and characterized by various physicochemical and spectral analyses. In the result of microwave assisted synthesis, it has been observed that the reaction time decreased from hours to minutes and availability of the product within better yields compared

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