International Journal of Emerging Technologies in Computational and Applied Sciences issue 11 vol2

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

Issue 11, Volume 1, 2 & 3 December-2014 to February-2015

International Journal of Emerging Technologies in Computational and Applied Sciences

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

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States.

Offices Overseas: Germany, Australia, India, Netherlands, Canada.

Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijetcas@gmail.com

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

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

algorithms,

technology

management,

manufacturing

technology,

electrical

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

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

arXiv,

ERIC,

EasyBib,

Infotopia,

WorldCat,

.docstoc

JURN,

Mendeley,

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

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJETCAS for entrusting us with the important job. We are thankful to the members of the IJETCAS editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions.

We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this eleventh issue, we received 157 research papers and out of which only 48 research papers are published in three volumes as per the reviewers’ recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of computational and applied sciences.

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

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), ISSN(Online): 2279-0055, ISSN(Print): 2279-0047 (December-2014 to February-2015, Issue 11, Volume 1, 2 & 3).

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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.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, 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.

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.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India.

Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India.

Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India.

Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India.

Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India.

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, India.

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, India.

Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India.

Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India.

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.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India.

Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India.

Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt.

Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines.

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, India.

Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India.

Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India.

Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India.

Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India.

Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India.

Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India.

Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India.

Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.

Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India.

Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India.

Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt.

Arash Shaghaghi, University College London, University of London, Great Britain.

Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India.

Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia.

Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India.

Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001.

Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India.

Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India.

Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor.

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,Tamil Nadu, 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 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.

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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.

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 Engg., 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.

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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 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.

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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.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 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 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 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India.

Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036.

Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura.

Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India.

Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria.

NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur.

Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India.

Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India.

Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India.

Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India.

Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh.

Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India.

Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India.

Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India.

Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India.

Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212.

Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India.

Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic.

Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan.

Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014.

Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal.

Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India.

Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India.

Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria.

Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran.

Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India.

Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India.

Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India.

Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria.

Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, 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, India 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

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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 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 Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, 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. .

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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), India.

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, India.

Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India.

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.

Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India.

Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India.

Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India.

Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India.

Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.

Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India.

Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India.

Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich – 271801 UP, 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 (PB) 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.

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Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India.

Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India.

Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India.

Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India.

Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India.

Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India.

Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India.

Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India.

Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India.

Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India.

Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India.

Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India.

Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India.

Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana.

Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany.

Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany.

Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India.

Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra.

Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India.

Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India.

Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India.

Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India.

Sumit Sharma, Team Lead, American Express, New Delhi, India.

Vivek Gautam, Senior Software Engineer, Wipro, Noida, India.

Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India.

Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India.

Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India.

Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India.

Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India.

Naresh Setia, Senior Software Engineer, Infogain, Noida, India.

Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India.

Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India.

Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India.

Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India.

Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India.

Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India.

Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

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

TABLE OF CONTENTS International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) ISSN (Print): 2279-0047, ISSN (Online): 2279-0055 (December-2014 to February-2015, Issue 11, Volume 1, 2 & 3) Issue 11, Volume 1 Paper Code

Paper Title

Page No.

IJETCAS 15-109

Mathematical Methods for Separation of Overlapping Asymmetrical Peaks in Spectroscopy and Chromatography. Case study: One-Dimensional Signals J. Dubrovkin

01-08

IJETCAS 15-111

Content Based Image Retrieval System with Feature Extraction and Recently Retrieved Image Library Seema H. Jadhav, Dr. Sunita Singh, Dr. Hari Singh

09-16

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A Dual Security Approach for Medical Images using Encryption and Watermarking Optimized by Differential Evolution Algorithm Mr. CH.Venu Gopal Reddy, Dr. Siddaiah.P

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Energy Efficient Routing Protocol for MANET: A Survey GANESH GUPTA, MADNESH KUMAR GUPTA, ASHOK K. RAGHAV

30-37

IJETCAS 15-121

Solving Generalized Assignment Problem with Genetic Algorithm and Lower Bound Theory Mr. VikasThada, Mr. Utpal Shrivastava, Ms. Meenu Vijarania

38-42

IJETCAS 15-123

Priority Based Scheduling in a Federated Cloud Environment A. Stanislas, L. Arockiam

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IJETCAS 15-125

Swarm Intelligence Techniques for Optimization in Data Clustering Ms. Dipali Kharche, Prof. A.D.Thakare

49-53

IJETCAS 15-129

Blind digital watermarking using AES technique for colour images Rahul Saxena, Nirupma Tiwari, Manoj Kumar Ramaiya

54-57

IJETCAS 15-130

Mapping and Partitioning of Task Graphs Using Kernighan-Lin/Fiduccia-Mattheyses Algorithm Ashish Mishra, Raja Jimit, Abhijit Rameshwar Asati, Kota Solomon Raju

58-61

IJETCAS 15-132

Ultrasonic and Pyroelectric Sensory Fusion System for Indoor Human/ Robot Localization and Monitoring Azhar K H

62-66

IJETCAS 15-134

Rough Set Techniques for Text Classification and Sentiment Analysis in Social Media G. K. Panda, Jayanta Mondal

67-74

IJETCAS 15-135

OFDM Channel Analysis between FFT and Wavelet Transform Techniques Quosay Jalil, S Nagakishore Bhavanam

75-79

IJETCAS 15-138

Bianchi Type IX Cosmological Model with Varying LambdaTerm R.K. Tiwari, D.K. Tiwari, C.Chauhan

80-83

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Thermo Physical Properties of Nano Ferro fluids L.S.V Prasad, Paul Douglas Sanasi, V.Srinivas

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A Novel Efficient and Accurate Detection Model to Detect Emerging Attacks in Network Supriya Gupta, Ankur Goyel

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Analysis of 5-Iodo-Uracil for its Infrared Spectra, Laser Raman Spectra and its Thermodynamic Functions Dr. Pradeep Kumar Sharma

94-101

Issue 11, Volume 2 Paper Code

Paper Title

Page No.

IJETCAS 15-152

Evaluation of the Peak Location Uncertainty in Spectra. Case Study: Exponentially Modified Asymmetrical Gaussian Doublets J. Dubrovkin

102-109

IJETCAS 15-155

LEBESGUE CONVERGENCE OF MODIFIED COMPLEX TRIGONOMETRIC SUMS JATINDERDEEP KAUR, SANDEEP KAUR AND S.S. BHATIA

110-114

IJETCAS 15-158

MULTIPURPOSE SMART CARD USING ADVANCED ENCRYPTION STANDARD ALGORITHM Nusrath A

115-118

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A Study on the Academic Performance of the Students by Applying Multiple Linear Regression Analysis using the method of Least Squares G. Narasinga Rao

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Analysis of Incompressible fluid flow over wedge with different angles Dr. Deepak Sharma, Mr. Swapnil Jain, Ankush Kumar

122-126

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Pairwise Key Distribution Mechanism for Heterogeneous Sensor Network Kanwalinderjit Gagneja

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Influence and Role of Technology on Stress: A Mathematical Analysis Dr. M.S.Saleem Basha, Dr. Esam Al Lawati, Mrs. Gargi Bhattacharya and Dr. Nasir Ahmed Khan

134-139

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Design of Unified Power Quality Conditioner (UPQC) To Improve the Power Quality Problems by Using Instantaneous Real & Reactive Power Theory S.Natarajan, Dr.M.AntoBennet, M. Manimaraboopathy,S.Sankararnarayan,N.Srinivasan

140-147

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Enhanced More Secure Trust Based Routing Scheme for DSR Protocol in MANETs Ankita Sahu, Vikas Sejwar

148-151

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Recognising Natural Emotions Using Artificial Neural Network In A Classroom Setting Rhea Mahajan, Remia Langer

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An Approach to Solve Single Machine Job Scheduling Problem using Heuristic Algorithm Satyasundara Mahapatra, Dr. Rati Ranjan Dash, Dr. Sateesh Kumar Pradhan

157-163

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Performance Comparison of Unicast and Multicast Routing Protocol in MANET using NS2 Mandeep Singh, Prakash Rao Ragiri

164-168

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A Study on Mining Approach under Cyber Crime Analysis Priyanka Maan, Meghna Sharma

169-173

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Design of Universal Gates Based on Reversible Logic Deepali Samtani, Naman Kumar Patel, Aditya Gupta, Shruti Jain

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IJETCAS 15-198

Turbulent Heat Transfer and Pressure Drop in an Internally Ribbed Rectangular Duct Sohil Akahter, Ishwar Singh

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Brain Image Segmentation Using Chan-Vese algorithm using Active Contours and Level Set Functions Payal, Satinderjeet Singh

183-187

IJETCAS 15-203

Thermoluminescence of nanocrystalline Eu doped BAM Phosphor Vinit Kumar, M. K. Dhasmana, R.B.S. Rawat

188-189

Issue 11, Volume 3 Paper Code

Paper Title

Page No.

IJETCAS 15-204

Calculation of the Probability for CR-39 Alpha Particles Detectors Ali Farhan Nadir, Noori H.N. Al-Hashimi, Abdul Ridha.H.Subber

190-193

IJETCAS 15-208

Mathematical Analysis of Asymmetrical Spectral Lines.Case study: Exponentially Modified Functions J. Dubrovkin

194-201

IJETCAS 15-213

Bezier Surface Reconstruction using Artificial Neural Networks Kavita, Navin Rajpal

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IJETCAS 15-219

A Dual Mode DNA Cryptography: Bio Inspired Approach for Information Security Fakhrayh Al Harrasi, Dr. M.S.Saleem Basha, Dr. A. Mohamed Abbas and Mohamed Jameel Hashmi

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Optimization of land and water resource in Nakatiya Minor Canal command area in Udham Singh Nagar, Uttarakhand, India

213-216

G.S.Yurembam and Vinod Kumar IJETCAS 15-229

Interactive Animations to Present Academic Subjects to Elementary School Children Cristian Javier Cauich Valle, Lizzie Narváez Díaz, Cinhtia M. González Segura

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Citizen Charter Validation A K Tripathy, M R Patra and S Pradhan

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Type 2 Diabetes, Therapeutic Targets and Potential Drugs: A Review Neha Verma and Usha Chouhan

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Database Management - Inline Queries vs. Stored Procedures–An Extended Analysis Er. Shobhit Gupta

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An Analytical Study of Weakly Nonlinear Convection in a Horizontal Mushy Layer Prof.Dr.P.K.Srimani, Mr. R.Parthasarathi

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ANALYSIS OF BANK’S UNSTRUCTURED DATA USING MAP REDUCE TECHNIQUE Jeny George, A. Karthika, Dr. Ulagamuthalvi V

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SOCIAL AWARENESS THROUGH NEW MEDIA Niket Mehta, Dr. Suparna Dutta, Dr. Asit Bandyopadhyay

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IDENTIFYING GENETIC MUTATION RARE GENETIC DISORDER BY ANALYZING CHARACTERISTICS OF GENOTYPE-PHENOTYPE BY IMPLEMENTING APRIORI ALGORITHM Bipin nair B J ,Ratheesh A, Koushik K S

262-265

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Mining Frequent and Similar Patterns with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) Data Mining Technique Spits Warnars

266-276

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

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Evaluation of the Peak Location Uncertainty in Spectra. Case Study: Exponentially Modified Asymmetrical Gaussian Doublets J. Dubrovkin Computer Department, Western Galilee College 2421 Acre, Israel Abstract: The dependences of the relative peak shifts of the exponentially modified asymmetrical Gaussian doublets on the peak separation were evaluated numerically in a wide range of varied parameters of doublet lines. The "wing effect" of asymmetrical lines on the relative peak shifts was discussed. Qualitative shift patterns were obtained.

Keywords: modified asymmetrical Gaussian doublets; peak identification; peak location uncertainty; spectral line profiles.

I. Introduction Location of the spectral peaks (line and bands) is the most important quantitative parameters widely employed in theoretical and applied spectroscopy [1]. For example, classical methods of identifying unknown elements and chemical compounds are usually based on comparing the experimentally measured peak positions with those found in the spectral libraries. However, overlapping of adjacent spectral contours and uncorrected background often cause apparent peak shifts, which lead to errors in spectral interpretation and establishing relationships between peak position and physicochemical properties of the sample under study. Broadening of the elementary spectral components and disturbing their symmetry due to the intra- and inter-atomic and molecular interactions produce additional problems. In this connection, a priory evaluation of the uncertainty in peak location measurements using synthesized overlapping components is very important. These models should be build using superposition of the spectral components of known form (e.g., Gaussian, Lorentzian and Voigt) which parameters are varied over a wide range of values. Comparison of experimentally observed and calculated peak shifts allows distinguishing between the apparent and the true shifts.

Evaluation of the peak location uncertainty in spectra, in the second-order derivative spectra and in Tikhonov deconvoluted spectra which are consisted of symmetrical lines of different shapes, has been performed in our previous papers [2-4]. The goal of the present study was error evaluation in determining peak positions of overlapping asymmetrical lines. Asymmetrical line shapes have been approximated by numerous mathematical functions [5]. We chose new version of the exponentially modified Gaussian peak which parameters allowed us to control the line asymmetry in a flexible manner [6].

In what follows, for the sake of simplicity, term “line” is used instead of term “line and band”. The standard algebraic notations are used throughout the article. All calculations were performed and the plots were built using the MATLAB program.

II. Theory a. Model Consider the doublet which maxima are located at the points and , respectively: where

is the doublet line; is the parameter of the line shape; is the full line width at half-maximum; βδ/2 is the relative separation of the doublet components; is the absolute separation; is the position of the line maximum; are the relative intensity and the relative width of the second doublet line, respectively.

The exponentially modified Gaussian line (EMGL) was studied in this work. If position of the Gaussian line maximum is zero then EMGL can be represented as the sum of symmetrical and asymmetrical parts [7]: where

tilde is the sign of Fourier transform (FT), is the angular frequency, is the asymmetry parameter, and For simplicity the line intensity is taken as unity.

According to Eqs. 3 and 4 Eq. 2 is the weighted sum of symmetrical part and its derivative:

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New version of the integrand (3) for

is [6]:

where may be a fraction.

If the maximum of Gaussian line is located in the point

then the correcting multiplier of Eq. 7 is equal to

where .

We studied lines which have right asymmetry .

It was found [6] that for and does not depend on . For slightly increases with increasing .

EMGL line is broadened if and increase (Fig. 1); the right half-width becomes wider than the left halfwidth (Fig. 1c). b. Relative shift The relative shift of the line position is usually measured relative to the line width. However, in practice, the widths can be evaluated very approximately because the lines are overlapping [8, 9]. Therefore, we chose to calculate the shifts with respect to the separation of the doublet components, which estimation can be readily obtained visually. In this case, the relative shift of the resolved doublet peak for the component has the form: where , and are the measured points of -peak maximum of the doublet and of the isolated peak, respectively. The term eliminates peak shifts which appear due to the dependence of the peak maximum position on and . Thus depends only on the degree of the doublet line overlap.

The minimum calculation errors of depend on the sampling interval ( ) of spectra along x-axis [10]. E. g., for and minimum absolute value of the uncertainty of The uncertainty decreases for the smaller sampling interval. However, it is unpractical to further decrease the -value since the number of the spectral points will be very large. It was shown that the measurement error of the peak maximum location may be made smaller than the sampling interval using polynomial approximation of the spectrum which signal-to-noise ratio is high [10]. Therefore the spectral curve in the vicinity of the peak maximum was approximated by the 3rd-order polynomial. Number and location of the polynomial points were selected according to the best-fit criterion. The first derivative of this polynomial is equal to zero in the point .

II. Results of Computer Modelling and Discussion a. Noise impact on peak shifts Random normal noise with zero mean was added to the doublet spectrum. Noisy spectrum has been smoothed using multiply three-point moving average filter. Obtained results (Fig. 2a) show that the smoothing slightly reduces doublet resolution for and increases peak shifts. However, the noise impact is not significant even for the noise standard deviation (signal-to-noise ratio is less than 50).

Figure 1. Broadening of exponentially modified Gaussian lines

(a, b) =0, 0.2 and 0.5 (black, blue and red curves, respectively); peaks are shifted to =1 (a), =1.5 (b). (c) Full line width, left half-width and right-half width (black, blue and red curves, respectively). n=1, 1.2,‌, 2 from the bottom to the top plots, respectively. b. Equal-width lines (r=1) 1. The dependences of the relative shifts on are represented in Figs. 2, 4 and 5. Projections of the plots (Figs. 2a and b) onto the y-axis (dependence ) are given in Fig. 3.The rule of symmetry [2]:

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is valid only for symmetrical lines. If τ and increase then doublet resolution significantly decreases, and for a given the shifts increase. With increasing the long right wing of the 1st line of asymmetrical doublets overlaps the 2nd line (the wing effect). Therefore the maximum of the 2nd line is shifted more than the maximum of the 1st line. The wing effect is most pronounced when and (Figs. 2d and 3b).

2. Although the more intense 2nd line (larger R values) causes larger shifts of the 1st line, its own shifts decrease (Figs. 4 and 5, plot a-c). Thus, if , then Asymmetrical wings have significant impact on the measured position of the maximum of the weak 2nd line (Figs. 4 and 5, plots d-f).

3. Dependences rapidly increase near resolution limit. c. Non-equal-width lines ( ) 1. As pointed out above, with increasing and resolution of doublet lines worsens due to the line broadening [6]. The maximum of the 2nd line is shifted to the right wing of the 1 st line which is very long for . This wing decays more slowly than the pure Gaussian line; therefore, the relative shifts of the 2nd line increase as compared with those of symmetrical lines (the wing effect) (Figs. 6-17).

2. If and then the shifts of the wide 2nd line and 3) are less than those of the 1st line.

However, for strongly asymmetrical lines ( ) (Figs. 6 and 9).

3. The shifts of the intense ( ) and wide 2nd line ( and 3) is significantly less than of the 1st line (Figs.

7, 8, 10 and 11, plots a-c). There are exceptions near resolution limit ( ) (Figs. 10a and 11a) [2].

4. The shifts of the 1st intense line overlapped by wide weak line are significantly less than those of the 2 nd line for (Figs. 7, 8, 10 and 11, plots d-f).

5. For the shifts of the narrow 2 nd line and 1/3) are larger than those of the 1st line (Figs. 12 and 15). Some exceptions are seen near resolution limit. For , and 1/3 and are less than 4% (Figs. 13 and 16, plots a-c).

6. The shifts of the 1st line overlapped by narrow weak 2nd line are significantly less than those of the last line (Figs. 13, 14, 16 and 17, plots d-f).

Figure 2. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves. =1(a) and 1.5 (b), and 0.02 (···) (c) and 0.5 (d), , (●), ▲).

(●),

Figure 3. Dependences of the relative peak shifts on the asymmetry parameter (

)

▲)

( )

)

1st line - red curves, 2nd line – blue curves. =1(a) and 1.5 (b). values are given in the table from the top to the bottom plots for the 1st line and vice versa for the 2nd line. n=1

n=1.5

0

0.45 0.50 0.55 0.60 0.65

0.45 0.50 0.55 0.60 0.65

0.2

0.50 0.55 0.60 0.65 0.70

0.52 0.57 0.62 0.67 0.72

0.5

0.58 0.63 0.68 0.73 0.78

0.68 0.73 0.78 0.83 0.88

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Figure 4. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

(●), respectively.

▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f,

Figure 5. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

(●), respectively.

)

)

▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f,

Figure 6. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

IJETCAS 15-152; © 2015, IJETCAS All Rights Reserved

(●),

▲), =1 (a) and 1.5 (b).

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Figure 7. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

(●), respectively.

)

▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f,

Figure 8. Dependences of the relative peak shifts on the line separation (

)

1st line - red curves, 2nd line – blue curves.

(●), ▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f, respectively.

Figure 9. Dependences of the relative peak shifts on the line separation ( )

1st line - red curves, 2nd line – blue curves.

IJETCAS 15-152; © 2015, IJETCAS All Rights Reserved

(●),

▲), =1 (a) and 1.5 (b).

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Figure 10. Dependences of the relative peak shifts on the line separation (

)

1st line - red curves, 2nd line – blue curves.

(●), ▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f, respectively.

Figure 11. Dependences of the relative peak shifts on the line separation ( )

1st line - red curves, 2nd line – blue curves.

(●), ▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f, respectively.

Figure 12. Dependences of the relative peak shifts on the line separation ( )

1st line - red curves, 2nd line – blue curves.

IJETCAS 15-152; © 2015, IJETCAS All Rights Reserved

(●),

▲), =1 (a) and 1.5 (b).

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Figure 13. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

(●), respectively.

)

▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f,

Figure 14. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

(●), respectively.

▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f,

Figure 15. Dependences of the relative peak shifts on the line separation (

1st line - red curves, 2nd line – blue curves.

IJETCAS 15-152; © 2015, IJETCAS All Rights Reserved

(●),

)

)

▲), =1 (a) and 1.5 (b).

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Figure 16. Dependences of the relative peak shifts on the line separation of the modified asymmetrical Gaussian doublets ( )

1st line - red curves, 2nd line – blue curves.

(●), ▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f, respectively.

Figure 17. Dependences of the relative peak shifts on the line separation ( )

1st line - red curves, 2nd line – blue curves.

(●), ▲), =2, 3, 5, 0.5, 1/3, 0.2 for the plots a-f, respectively.

7. Obtained results showed that peak asymmetry results in additional increase in peak shifts caused by peak overlap. These shifts must be taken into account in performing spectrochemical analysis. E. g., the measured position of the asymmetrical peak maximum must be corrected if it is used as an initial guess for decomposition of a complex spectral contour into its elementary components.

References [1].

[2].

[3].

[4].

[5].

[6].

[7].

[8].

[9].

[10].

B. K. Sharma, Spectroscopy. 19th Ed. India, Meerut-Delhy: Goel Publishing House, 2007.

J. Dubrovkin, "Evaluation of the peak location uncertainty in spectra. Case study: symmetrical lines", Journal of Emerging Technologies in Computational and Applied Sciences, vol. 1-7, 2014, pp. 45-53.

J. Dubrovkin, "Evaluation of the peak location uncertainty in second-order derivative spectra. Case study: Symmetrical lines", Journal of Emerging Technologies in Computational and Applied Sciences, vol. 3-9 , 2014, pp. 248-255 .

J. Dubrovkin ," Identification of peak positions using second-order derivative spectra and Tikhonov deconvolution method.

Comparison study", Journal of Emerging Technologies in Computational and Applied Sciences, vol. 2-10, 2014, pp.192-197 .

J. Dubrovkin ,"Mathematical Analysis of Asymmetrical Spectral Lines", Journal of Emerging Technologies in Computational and Applied Sciences, vol.1-8, 2014, pp. 27-36.

J. Dubrovkin, " Mathematical analysis of asymmetrical spectral lines.Case study: exponentially modified functions", Journal of Emerging Technologies in Computational and Applied Sciences, 2015, in press .

Felinger, Data analysis and signal procesing in chromatography. Elsevier, 1998.

J. M. Dubrovkin and V. G. Belikov, Derivative Spectroscopy. Theory,Technics, Application.Russia: Rostov University, 1988.

V. A. Lóenz-Fonfría and E. Padrós, “Method for the estimation of the mean lorentzian bandwidth in spectra composed of an unknown number of highly overlapped bands ", Appl. Spectr., 2008, vol. 62, pp. 689-700.

L. Smeller, How precise are the positions of computer-determined peaks? Appl. Spectr., 1998, vol. 52, pp. 1623-1626.

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Lebesgue Convergence of Modified Complex Trigonometric Sums Jatinderdeep Kaur, Sandeep Kaur and S.S. Bhatia School of Mathematics and Computer Applications, Thapar University, Patiala (Punjab)-147004, INDIA. Abstract: In this paper, some estimates for Dirichlet kernel has been proved. Also, we have obtained the complex form of modified trigonometric cosine and sine sums of X.Z. Krasniqi [10] and have proved the convergence of complex trigonometric series under class J* of complex coefficients in metric space Keywords: .

L1 .

L1 -convergence, Dirichlet Kernel, Fejer Kernel.

I.

Introduction

It is well known that if a trigonometric series converges in L1 -norm to a function fˆ  L1 , then it is the Fourier series of the function fˆ . But in 1932, Riesz { [5], Vol. II, Ch.VIII } gave an example



 log n

cos nx

which shows

n 2

that it is a Fourier series but it does not converges in L1 -metric. This enhance many authors to study the Lebesgue convergence of trigonometric series. During literature survey, one can found that many authors introduced modified cosine and sine sums and use these modified sums as a tool to study the Lebesgue convergence of trigonometric series as these sums approximate their limits better than the classical trigonometric series.

Lebesgue convergence of complex trigonometric series was studied by C.V. Stanojevic and V.B. Stanojevic [2] Sheng Shu Yun [6], F. Moricz [3], C.P Chen [1] and S.S. Bhatia and B. Ram [8] for various classes of complex sequences by considering additional conditions to control the sine and mixed part of  complex trigonometric series. Some of authors like S.S. Bhatia and B. Ram [7], Z Tomovski [9], J. Kaur and S.S. Bhatia [4] have obtained the complex form of modified cosine and sine sums and obtained necessary and sufficient condition for Lebesgue convergence of complex trigonometric series.

Xhevat Z. Krasniqi [10] introduced new modified cosine and sine sums as

Hˆ n ( x) 

1 2 sin x

n

n

 [(aˆ

j 1

 aˆ j 1 ) cos jx]

(1.1)

j 1

 aˆ j 1 ) sin jx]

(1.2)

k 0 j  k

and

Gˆ n ( x) 

1 2 sin x

n

n

 [(aˆ k 1 j  k

and studied their Lebesgue convergence with semi-convex coefficients.

Keeping this idea, we have obtained the complex form of modified sums (1.1) and (1.2) as: gˆ n (Cˆ , t )  Sˆ n (Cˆ , t ) 

and studied its

i 2 sin t

 int cˆ e i (n1)t  cˆ e i (n1)t  cˆ e int  cˆ  (n  1)(cˆ n  cˆ n2 )e i (n1)t  n n1 ( n1) e  n     (n  1) (cˆ (n2)  cˆ n ) e i (n1)t  

L1 -convergence under a class J* of complex coefficients.

II.

Basic notations and background

Let the partial sums of the complex trigonometric series

 cˆ e n

i nt

be denoted by

n 

Sˆn (Cˆ , t ) 

 cˆ e k

ikt

, t  T  R / 2Z .

k n

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If a trigonometric series is the Fourier series of some function fˆ  L1 , we shall write cˆn  fˆ (n) for all n and Sˆ (Cˆ , t )  Sˆ ( fˆ , t )  Sˆ ( fˆ ) . Let n

n

n

1  sin n  t 1 2 Dn (t )   cos t  cos 2t  ...  cos nt   t 2 2 sin 2 t 1  cos  cos n  t ~ 2 2  Dn (t )  sin t  sin 2t  ...  sin nt  t 2 sin 2 and

1 1 sin(n  1)t  ~ ~  K n (t )  D j (t )  sin t   t n  1 j 0 n  1  4 sin 2  2 denote the Dirichlet's kernel, the conjugate Dirichlet's kernel and the conjugate Fejer's kernel respectively. Let n



n

En (t ) 

e

ikt

~ ~ , then the first differentials Dn' (t ) and Dn' (t ) of Dn (t ) and Dn (t ) can be written as

k 0

2Dn' (t )  En' (t )  E' n (t ) ~ 2i Dn' (t )  En' (t )  E' n (t )

where En' (t ) denotes the first differential of En (t ) .

Definition. [4] A null sequence {cˆn } of complex numbers belongs to class J* if there exists a sequence { Aˆ n } such that Aˆ n  0, as n   (2.1) 

 n Aˆ

n



(2.2)

n1

 cˆ  cˆn  Aˆ n  n ,  n n  

for all n  1,2,3,...

III.

(2.3)

Lemmas

The proof of main result is based upon the following lemmas.

Lemma 3.1. [8] Let r be a non-negative integer and 0   . Then there exists M r  0 such that for all

 t   and for all n  1 , r

(i)

Enr (t )  Arn

(ii)

Er n (t )  Arn

(iii)

Dnr (t )  Arn

(iv)

r ~ Dnr  Arn

t

,

t r

,

t

r

t

,

.

Lemma 3.2. For n  1 , we have En (t ) (i)  o(n), n   2 sin t (ii)

En (t )  o(n), n   2 sin t

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e in t  o(log n), n   2 sin t

(iii)

Proof. For t  0 , we note that En (t )  2 sin t



 0

En (t ) dt  2 sin t

En (t )  2 sin t

Therefore,

 /2

 0

M t2

sin t 2  in (0, / 2) and using Lemma 1, we get t  

 0

 /2

M dt  2 2 | t sin t |

  M  dt  lim   n  t 

 0

M dt 2 | t || sin t |

 /2

 o(n), n  ,  /n

Similarly, En (t )  o( n) , 2 sin t and to prove (iii), we consider,

e in t  2 sin t



 0

 /2

ein t dt  2 2 sin t

 0

1 dt  2 sin t

 /2

 0

1 dt t

Therefore

e in t  lim[log t ] // 2n  o(log n), n   .

2 sin t n IV.

Main Result

The main result of this paper read as follows: Theorem 4.1. Let cˆn  J * . Then there exists fˆ (t ) such that lim gˆ (Cˆ , t )  fˆ (t ) for | t | (0, ] , (i) n

n

(ii)

fˆ (t )  L1 (0, ] and gˆ n (Cˆ , t )  fˆ (t )

(iii)

Sˆn ( fˆ , t )  fˆ (t )

L1

L1

 o(1) as n   ,

 o(1) as | n |  .

Proof. Consider, gˆ n (Cˆ , t )  Sˆ n (Cˆ , t ) 

i ( n1)t ˆ   c n e i (n1)t  cˆ n1e in t  cˆ (n1) e in t  (n  1)(cˆ n  cˆ n2 ) e i(n1)t  i cˆ n e  i ( n1)t  2 sin t  ˆ ˆ  ( n  1 )( c  c ) e ( n 2) n  

e in t is bounded in (0, ] . Also {cˆn } is a null sequence. Therefore, we can write 2 sin t iA lim gˆ n (Cˆ , t )  lim Sˆn (Cˆ , t )  lim  (n  1)(cˆn  cˆn )  (n  1)(cˆ( n2)  cˆn2 ) n n n 2 But for all n  1 , we note that Since





 n(n  1)(cˆn  cˆn )  cˆ  cˆ   n(n  1)  k k  n k  k n 







Aˆ k

 k (k  1) k k n

 o(1) , By hypothesis of the theorem. Therefore, lim gˆ n (Cˆ , t )  lim Sˆn (Cˆ , t )  fˆ (t ) n

n

Now, we show that fˆ (t ) exists in

(0,  ] .

Consider,

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n

 cˆ e

Sˆn (Cˆ , t ) 

n

ikt

k

 cˆ0 

k  n

 cˆk

  k ke

ikt



k 1

cˆk ikt  ke  k 

By the use of Abel's transformation, we get Sˆ n (Cˆ , t )  cˆ0 

n1

 cˆ 

  kk  (iE k' (t ))  k 1 n1 

 cˆ0 

n1  cˆ cˆ n (iE n' (t ))   k n  k

 cˆ 



k 1

 cˆ k k

  kk  (iE k' (t ))   k 1

 ' cˆ (iE ' (t ))  (iE k (t ))  n n n 

 cˆ (iE ' (t ))  cˆ n (iE ' n (t ))  '  (iE k (t ))  n n n  

By using Lemma 3.1 and (2.3), we get 

 cˆ 

 cˆ k k

  kk  (iE k' (t ))   k 1

A   '  cˆ  cˆ k   (iE k (t ))     k  k    k t    k 1 



 A   kAˆ k    t    k 1 k



A     Aˆ k   ,  t    k 1



Since Aˆ n  nAˆ n for all n  1 and

 Aˆ n

Therefore

 nAˆ n converges, by hypothesis of the theorem.

is also convergent.

Also,

cˆn (iEn' (t ))  cˆn (iE' n (t )) An cˆn  cˆn   o(1), as n   . n t n Hence, fˆ (t )  lim Sˆn (Cˆ , t ) exists and thus (i) follows. n

Further, for t  0 , Consider i ( n1)t  cˆn e i ( n1)t  cˆn1ein t  cˆ( n1) e in t  (n  1)(cˆn  cˆn2 )ei ( n1)t  i cˆn e   i ( n1)t 2 sin t   ˆ ˆ  ( n  1 )( c  c ) e k n ( n 2) n   Using Abel's transformation, we get

fˆ (t )  gˆ n (Cˆ , t ) 



 cˆk eikt 

fˆ (t )  gˆ n (Cˆ , t ) 



  cˆ k  k k n1  





L1

 A n cˆn1  cˆ( n1)  cˆ  cˆk  A k  k dt    dt  k t n   t 0 k n1 0













(cˆn1  cˆ( n1) )  (n  2)( cˆn  cˆn )  (n  1)( cˆn 2  cˆ( n2) )

Therefore,  1

L





 

0 k n1

k

dt

sin t

0

fˆ (t )  gˆ n (Cˆ , t )

 cˆ (iE n' (t )) cˆ ( n1) (iE ' n (t ))  '  (iE k (t ))  n1  n 1 n 1  

i ( n 1)t ˆ    c n e i ( n1)t  cˆ n 1e in t  cˆ ( n1) e in t  (n  1)(cˆ n  cˆ n 2 )e i ( n1)t i cˆ n e     i ( n  1 ) t 2 sin t  (n  1)(cˆ (n 2)  cˆ n )e  



fˆ (t )  gˆ n (Cˆ , t )

  cˆ  (iE k' (t ))   k   k





  ˆ cn  cˆn   cˆn1  cˆ(n1) Aˆ k A A dt  cˆ n1  cˆ (n1) dt  dt k t t sin t







0

 0





 0





(n  1) cˆ n  cˆ n   (n  1) cˆ n 2  cˆ ( n 2) dt sin t

    O  Aˆ k log k   o log n cˆn  cˆn  o log n cˆn  cˆn  o log n cˆn  cˆn   k n1  cˆ  cˆn  But, for all n  1 , we note that log n cˆn  cˆn   n 2 n n

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 cˆk  cˆk   k   k n   Aˆ   k 2 k   k  k n    o(1) 

Thus, fˆ (t )  gˆ n (Cˆ , t )

L1

(4.2)

 o(1), n   and since gˆ n (Cˆ , t ) is a polynomial, it follows that fˆ  L1 (0, ] , which

proves the assertion (ii).

Further, we notice that fˆ  Sˆn 1  fˆ  gˆ n  gˆ n  Sˆn L

 fˆ  gˆ n  fˆ  gˆ n

 k 2 

L1

L1



L1

 gˆ n  Sˆn  i cˆ n e 2 sin t  

L1

i ( n1)t

 cˆ n e i (n1)t  cˆ n1e in t  cˆ (n1) e in t  (n  1) cˆ n  cˆ n2 e i (n1)t     (n  1) cˆ (n2)  cˆ n e i (n1)t 





Using (4.1), (4.2) and assertion (ii), we get the conclusion of assertion (iii). .

References 1 -convergence of multiple trigonmetric series, Bull. Austral. Math. Soc., 49(1994), 333-339.

[1]

C.P. Chen, Integrability and L

[2]

C.V. Stanojevic and V. B. Stanojevic, Generalizations of the Sidon- Teljakovskii theorem, Proc. Amer. Math. Soc., 101(4)(1987), 679-684.

[3]

F. Moricz, On the Integrability and 64.

[4]

J. Kaur and S.S. Bhatia, Convergence of new modified comlex trigonometric sums in the metric space of Mathematics, 31(3) (2010), 290-294.

[5]

N. K. Bary, A treatise on trigonometric series, Vol I and Vol II, Pergamon Press, London (1964).

[6]

Shuyun Sheng, The Extension of the theorems of C.V. Stanojevic and V.B. Stanojevic, Proc. Amer. Math. Soc., 110(1990), 895904.

[7]

S.S. Bhatia and B. Ram, On 105(2)(1995), 193-199.

[8]

S.S. Bhatia and B. Ram, The Extensions of the F. Moricz theorems, Proc. Amer. Math. Soc., 6(124)(1996), 1821-1829.  Z . Tomovski, On a Theorem of S.S. Bhatia and B. Ram, Math. Comm., 8(2003),187-193.

[9] [10]

L1 -Convergence of Complex Trigonometric series, Proc. Amer. Math. Soc., 113(1991), 53L , Lobachevskii Journal

L1 -convergence of modified complex trigonometric sums, Proc. Indian Acad. Sci. Math. Sci.,

1

X. Z. Krasniqi, A Note on L -convergence of the sine and cosine trigonometric series with Semi-Convex coefficients, Int. J. Open Problems Comput. Sci. Math, 2(2),(2009), 231-239.

Acknowledgment This work is the part of UGC sponsored Major Research Project entitled "On L1 -convergence of trigonometric series with special coefficients

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

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net MULTIPURPOSE SMART CARD USING ADVANCED ENCRYPTION STANDARD ALGORITHM Nusrath A Embedded System Technologies, Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Anna University, Coimbatore - 641105, Tamil Nadu, INDIA Abstract: Smart card is one of the greatest achievements in the world of information technology. Smart cards have many applications such as health, ID verification and access control, electronic purse card, banking card, payphone card, passport card and license card. Since, there are many kinds of smart cards, it is difficult to carry and protect them. Losing one card means losing a lot of important information. As everyone knows, carrying and protecting one smart card is easier and more comfortable than carrying two or more smart cards.

Multipurpose smart card is a smart card with manzy different applications in one smart card. Multipurpose smart card would be giving all services and making data processing and data transfer more efficient and secure. We choose three most important cards such as health card, aadhaar cards and passport cards to be combined in one smart card and find an encryption method to make it enough secure. It should also be efficient in transferring information. The advanced encryption standard algorithm should be effective encryption system for these three applications in a multipurpose smart card.

Keywords: AES, multipurpose smart card I. Introduction Smart card is also called a cheap card or integrated circuit card. It is the youngest and cleverest member of the family of identification cards. The smart card is defined as a device, which includes an embedded integrated circuit that can be either a secure microcontroller or equivalent intelligence with internal memory or memory chip alone. The card interacts with a reader through direct physical contact or with a remote contactless radio frequency interface. The smart cards play a vital role in today’s life. There are different smart cards for different purposes. For example- credit cards, debit cards, voting cards, Aadhar cards (in INDIA), etc. These different cards are to be carried by the people wherever they go, for different purposes. Smart cards are used for data storage, transaction and processing with strong security authentication. Smart cards do not have component such as power resource, display area or keyboard. As mentioned above, smart cards are very useful these days, but there are problems with them. Since, there are many kinds of smart cards, it is difficult to carry many cards and protect them. You may lose, leave or forget one of them and it means that you lose important personal information. Another problem with smart cards is limitation of storage in smart cards. Smart cards need to transfer data in a short time and encrypt them very fast. So, having an efficient encryption system is very important. Therefore, we have decided to design a cryptosystem for multipurpose smart cards. Multipurpose smart card is a smart card with many different applications in one smart card. The main aim of the project is to develop a multipurpose smart card for various applications such as: health, passport and aadhaar card and find an encryption method to make it enough secure.

II. Multipurpose smart card In the multipurpose smart card three applications are implemented. The applications are health, passport and aadhaar card. a) Health system: Healthcare organizations worldwide are implementing smart health cards supporting a wide variety of features and applications. Smart health cards can improve the security and privacy of patient information, provide the secure carrier for portable medical records, reduce healthcare fraud, support new processes for portable medical records, provide secure access to emergency medical information, enable compliance with government initiatives and mandates, and provide the platform to implement other applications as needed by the healthcare organization. b) Passport system: The ePassport or a digital passport is a combined paper and electronic passport that contains biometric information that can be used to authenticate the identity of travelers. It uses contactless smart card technology, including a microprocessor chip (computer chip) and antenna (for both power to the chip and communication) embedded in the front or back cover, or center page, of the passport. Document and chip characteristics are documented in the International Civil Aviation Organization's (ICAO) Doc 9303. The passport's critical information is both printed on the data page of the passport and stored in the chip. Public Key

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Infrastructure (PKI) is used to authenticate the data stored electronically in the passport chip making it expensive and difficult to forge when all security mechanisms are fully and correctly implemented.

The currently standardized biometrics used for this type of identification system are facial recognition, fingerprint recognition, and iris recognition. These were adopted after assessment of several different kinds of biometrics including retinal scan. The ICAO defines the biometric file formats and communication protocols to be used in passports. Only the digital image (usually in JPEG or JPEG2000 format) of each biometric feature is actually stored in the chip. The comparison of biometric features is performed outside the passport chip by electronic border control systems (e-borders). To store biometric data on the contactless chip, it includes a minimum of 32 kilobytes of EEPROM storage memory, and runs on an interface in accordance with the ISO/IEC 14443 international standard, amongst others. These standards intend interoperability between different countries and different manufacturers of passport books. c) Aadhaar card: We all know in this web based world our security, our performance and most importantly our identification holds high priority. Keeping in mind all these factors aadhaar card has been launched by the government which is also called as UID i.e. unique identification card or number as it was issued by unique identification authority of India (uidai) to all citizens above five. In this the unique number of an individual is linked with his biometric data so as to check his identity. Since one uid is allotted to only one person, it enables to verify ones identity universally. It is based on the biometric and demographic information of an individual which helps to alienate the risk of all the fictitious activities which are very much prevalent in today’s world.

These smart cards allow the devoid and needy persons in approaching various services. It helps them to prove their identity at multiple places. A person’s aadhaar number can be used at various places like for booking tickets online, for opening bank accounts, for permitting admissions in universities , applying for passport and at many other places where one need to give their identity proof. This number could be used to enjoy any of the beneficiary schemes allotted by the government. Due to the increasing terrorism in our country it has become very important to record each and every movement of an individual in companies and industries and other expected places.

III. Block diagram Smart Cards are secure portable storage devices. Reader provides a path for an application to send and receive commands from the card. Middleware are smart card software, they are communication link between application on the computer and code located on smart card.The card used for health, passport and aadhaar card.

Figure 1: Block diagram of card end

Figure 2: Block diagram of smart card receiver end

III Algorithm AES (Advanced Encryption Standard) is used in the multipurpose smart card. The AES is now the industry standard for encryption. The NSA employs it for protecting secret information and industry uses the algorithm for creating commercially available encryption products. File encryption and email encryption are two common applications for AES. File encryption protects the information on your hard disk or thumb drive. With encryption, your data will be secure even if your computer is hacked or your USB drive stolen. Email

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encryption protects your messages as they journey through the cloud and keeps them from being read by unintended recipients. The design of AES algorithm consist of following steps Cipher Text Creation -ORing Operation rmutation Figure 3: Basic Block Diagram of Implementation of Encryption in AES

IV. Simulation Analysis Multipurpose smart card is a smart card with many different applications in one smartcard. Multipurpose smart card would be giving all services and making data process and data transfer more efficient and secure. The multipurpose smart card in implemented by using the proteus and arduino software. The input part consists of three buttons that indicate the three applications. User cannot run two applications at the same time. Access to each application is possible only after exiting other applications. It means it is not possible to run more than one application at the same time. The AES algorithm is used for this card. The AES algorithm is done by using embedded c language.

Figure 4: Proteus implementation of the multipurpose smart card

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Here we can see the ATMEGA328 microcontroller in encryption and decryption side. The three buttons in the encryption side indicate the three applications such as: health, passport and aadhaar card. The MAX232 is act as the level shifter.

Figure 5: The simulated output by selecting Aadhaar card

V. Conclusion AES (advance encryption standard) algorithm is one of the useful and efficient algorithms for high level data transfer. Multipurpose smart card is much better than carrying different card. In this part we design and implemented the multipurpose smart card by using software. This card can be used for three applications such as passport system, aadhaar card and health card. The multipurpose smart card done by re-programming the microcontroller embedded in the single application smart card. The ATMEGA328 microcontroller used in encryption and decryption part of the card. This microcontroller provides the high data storage. In future the one smart card can be used for all the application. .

VI. References [1].

[2] [3].

[4].

[5].

[6].

[7].

[8] [9] [10].

D. R. Hankerson, S. A. Vanstone, A. J. Menezes.(2003), “Guide to Elliptic Curve Cryptography,”.

W. Rankl, W. Effing, “Smart card handbook,” (2004)3rd ed., John Wiley and Sons Ltd., Baffins Lane, England: Spring Verlag.

J. A .Menezes, C.Paul, V.Oorschot, S. A. Vanstone (2001)“Handbook of Applied Cryptography,” 5the ed.

T. Abdurahmonov, M. H. Helmi, Y. E. Thiam, (2010) “The Implementation of Elliptic Curve Binary Finite Field for the Global Smart Card,” Proceedings of 2010 IEEE Student Conference on Research and Development (SCOReD 2010.

T. Abdurahmonov, Y. E. Thiam, M. H. Helmi,(2010) “Improving Smart Card Security Using Elliptic Curve Cryptography over Prime Field”.

S.Arora, (2007)“Information Security-Siddhartha Arora Final,” Available:http://www.porvoo12.net/MSc_Information_Security. pdf.

C. Sanchez-Avilaf, R. Sanchez-Reillo, (2001)“The Rijndael Block Cipher (AES Proposal): A Comparison with DES,” 0-78036636-0/01/ 02001 IEEE, pp. 229-234.

S. Omar, H. Djuhari, (2004)“Multi-purpose student card system using smart card technology. Information technology based higher education and training,” ITHET 2004. Proceedings of the Fifth Internationalconference on, pp. 527-532.

A. B. Mohamed, A. A. Hamid, K. Y, Mohamed (2009) “Implementation of an Improved Secure System Detection for E-passport by using EPCRFIDTags,” A. Kleijhorst, van der Velde.E T, Baljon.M H, Gerritsen.M J G M,Oon.H,(1997) “Secure and Cost-Effective Exchange of Cardiac Images over the Electronic Highway in the Netherlands,” 0276-6547/97 1997 IEEE,pp. 191-194.

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A Study on the Academic Performance of the Students by Applying Multiple Linear Regression Analysis using the method of Least Squares G. Narasinga Rao, Ch.Sreenu Babu Computer Science Engineering Department, GMR Institute of Technology, GMR Nagar, Rajam, Srikakulam District, Andhra Pradesh, INDIA. Abstract: Many Educational Institutions want their students to be placed in reputed companies and hence they focus on the academic performance of their students. If they could able to know about the student’s academic performance, then they can focus more on those students whose academic performance is poor. They can take special care on those students whose academic performance is poor and make them prepare for the placement.

In this paper, we present a study on the academic performance of the students of a reputed college by applying multiple linear regression models on the data set consisting of 23 students and evaluate the dependent variable for two given predictor variables.

Keywords: Data mining, Data Set, Data objects, Classification, Multiple linear regressions.

I. Introduction Data Mining is the process of analyzing data available in large data sets and obtains knowledge out of it. The knowledge so obtained is known as the Knowledge Discovery in Databases (KDD). This knowledge helps us in different ways. A data set is a collection of data objects. Each data object represents a record. This data object consists of any number of attributes which specifies the characteristics of the data object. Any Educational Institution consists of students with different categories. We can categorize students based on several properties like attendance, percentage of marks secured, attitude, intelligence etc., to judge their academic performance. In this paper, we present a study on the academic performance of the students based on their aggregate percentage of marks, attendance percentage and number of backlogs. Hence we use multiple linear regression models on the data set of 23 students and evaluate the dependent variable for two given predictor variables.

II. Methodology[7] A linear regression model that contains more than one predictor variable is called a multiple linear regression model. This is of the form Y=β0+ β1X1+ β2X2 The above model is linear in the parameters β0, β1, β2 that describes a plane in three dimensional space of Y, X1 and X2. β0 is the intercept of the plane and β1, β2 are considered as partial regression coefficients.

Parameter

represents the change in the mean response corresponding to a unit change in

when

is

held constant. Parameter represents the change in the mean response corresponding to a unit change in when is held constant. The above model that we consider here is a first order multiple linear regression models. The true regression model is generally never known. It can be estimated by calculating the parameters of the model for an observed data set.

The first order regression model applicable to the data set having two predictor variables is Y=β0+ β1X1+ β2X2 Where the dependent variable Y represents the aggregate percentage of marks and the predictor variables X1 and X2 represents the attendance% and number of backlogs.

The least square estimates,

, can now be obtained:

= (X1X)-1X1Y

Thus

=

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Then the estimated regression coefficients Y=β0+ β1X1+ β2X2

0,

1,

are calculated. Thus the final regression model is

III. Results We applied the multiple linear regression model on the data set of a reputed Institution consisting of 60 students by considering the aggregate percentage of marks(Y) secured and attendance %(X1), number of backlogs(X2) as attributes of each of the student data object. Then we identified those students with similar attribute values and reduced the total data set consisting of 23 students having unique attribute values. The reduced data set consisting of 23 students with their respective attribute values is shown below in Table 1.

Table 1 Serial No

Aggregate Percentage of Marks (y) 62 55.3 71.8 69.8 46.5 66.6 53.2 56.6 50.2 59.1 54.6 45.8 63.2 49.1 47.2 59.6 53 43.8 51.8 74.8 45.8 77.4 45.1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Attendance% (x1)

No. of backlogs (x2)

84.2 80.7 91.7 93 74.5 88.4 81.2 82.1 87.6 93 93.2 69.8 91.9 79.1 74.9 90.2 75.4 81.2 75.8 88.4 68.9 96.6 79.6

0 3 0 0 9 0 2 1 4 0 2 14 0 8 7 1 8 8 4 0 13 0 18

Thus the above student data set is expressed in the matrix form as: Y=β0+ β1X1+ β2X2 Now compute

= (X1X)-1X1Y

Clearly X1X =

And X1Y = Also (X1X)-1 =

Now

= (X1X)-1X1Y

= Hence the estimated regression coefficients are calculated and the final regression model is obtained as Y = 59.65904 + 0.73247055691X1 + 0.56003386358X2 Now after the final results, we can easily evaluate the dependent variable Y if we are given the predictor variables X1 and X2.

IV. Conclusion In this paper, we present a study on the academic performance of students by considering the aggregate percentage of marks secured by the students, attendance percentage and number of backlogs of the students as attributes of each of the student data object to monitor the performance of the students. We demonstrated this by applying multiple linear regressions on a student data set of a reputed institution for total number of 23 students.

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After applying the multiple linear regression model, we can easily predict the dependent variable Y for two given predictor variables X1 and X2.

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

Larose, D. T., “Discovering Knowledge in Data : An Introduction to Data Mining”, Hoboken, NJ, USA: Wiley, 2005.

S.Sujit Sansgiry, M.Bhosle and K. Sail, “Factors that affect academic performance among pharmacy students,” American Journal of Pharmaceutical Education, 2006 N. V. Anand Kumar and G. V. Uma, “Improving Academic Performance of Students by Applying Data Mining Technique,” R. Baker, “Data mining for education,” in International Encyclopedia of Education, B.McGaw, P. Peterson, and E. Baker, Eds., 3rd ed. Oxford, U.K.: Elsevier, 2010.

Garcia, E. P. I., & Mora, P. M., “Model Prediction of Academic Performance for First Year Students”, Paper presented at the Artificial Intelligence (MICAI), 10th Mexican International Conference on, Nov. 26 2011-Dec. 4 2011.

Sachin, R. B., & Vijay, M. S, “A Survey and Future Vision of Data Mining in Educational Field”, Paper presented at the Advanced Computing & Communication Technologies (ACCT), Second International Conference on 7-8 Jan. 2012. http://reliawiki.org/index.php/Multiple_Linear_Regression_Analysis

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Analysis of Incompressible fluid flow over wedge with different angles Dr. Deepak Sharma1, Mr. Swapnil Jain2, Ankush Kumar3 1,2,3 Department of Mechanical Engineering 1 Shri Shankaracharya Institute of Engineering and Technology,2,3 Shri Shankaracharya Engineering College Bhilai, Chhattisgarh, INDIA Abstract: The title of our research paper is “Analysis of Incompressible fluid flow over wedge with different angles”. The basic objective of this research work is to optimize the fluid flow over different wedges and it angles. The optimization is done by varying wedge angle, its parameters considering fluid flow parameters also.

The analysis of wedge is carried out in both 3D and 2D from which various results and graph plots are obtained. By looking to the results we can know the best wedge parameters to be used according to its application.

Keywords: incompressible; wedge; optimization; analysis I. Introduction Fluid Flow Motion of a fluid subjected to unbalanced forces or stresses. The motion continues as long as unbalanced forces are applied. A fluid may be a liquid, vapor, or gas. The term vapor denotes a gaseous substance interacting with its own liquid phase, for example, steam above water. If this phase interaction is not important, the vapor is simply termed a gas. The physical properties of a fluid are essential to formulating theories and developing designs for fluid flow. Especially important are pressure, density, and temperature. Since shear stresses cause motion in a fluid and result in differences in normal stresses at a point, it follows that a fluid at rest must have zero shear and uniform pressure at a point. This condition is known as hydrostatic condition.

II. Literature Review Chen and Kadambi (1994) have conducted experiments in a vertical pipe. In their study, pressure gradients have been measured for vertical flow of slurry of particles with diameters between 1.37 mm and 3.4 mm in pipes of diameter 26 mm and 40 mm.

Doron and Barnea (1996) presented different flow patterns that are observed in horizontal liquid-solid pipeline transportation.. One of the important correlations on deposition velocity was given by Durand (1963) considering the diameter of the pipe (D), density of solid (ρs), density of liquid (ρl), and Froude number (FL). The correlation is given as: VD= FL

This correlation does not consider the particle diameter (d) and initial solids concentration (Cv). Wasp et al.

(1970) modified Durand’s correlation and derived a deposition velocity equation for lower concentrations which is given as: VD= 1.87( )1/6

The above correlation describes the behavior of 1% dilute suspension. Later Wasp et al. (1977) derived a correlation describing the behavior of higher concentration slurries (up to 40%). It is given as: VD= 4.0( )1/6(Cv)1/5

The above three correlations are the most important correlations for deposition velocity.

Achim Daniela et al (1999) have studied the experimental and computational data of tube erosion in a fluidized bed and simulated the work with CFX code having computational model of hydrodynamics and finnie model of erosion.

Manickam M et al (1999) have modeled the flow in the bifurcation duct of a power generation boiler plant. The computational fluid dynamics code was customized to determine erosion rate cause by particles that hit the duct wall. .

P.S.V.S. Sridhar et al (2003) have studied dilute solid-fluid flow over a wedge in a stationary channel which is numerically solved using one-way coupling between fluid and solid particles.

Figure 1: Flow domain-wedge placed along the channel axis

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III. Problem Identification This impact wear is mainly occurred in the tongue area of a centrifugal pump. And the bends in the pipe flow where the velocity of fluid is very high.

Impact wear is also occurred at the curved vanes of centrifugal pump those are handing slurries of variable densities and heterogeneous fluid.

Proposed work plan (Problem Formation): i) Proposed research work is to prepare the CFD modeling for calculation of impact wear co-efficient [EI(α)] for dense slurry and multi size particle. ii) By calculation of [EI(α)] in CFD modeling for different size particle and in multiphase flow. We can predict the approx. accurate time of pipes, curved vanes, open channel bends for replacement.

Proposed experimental set-up: a) Computation of two phase flow field within the component. b) Correlation of local flow conditions (velocity and concentration) near the wear surface to the local wear rate via a suitable wear model. c) Empirical determination of wear co-efficient. d) Erosion wear mainly occurred due to sliding wear and impact wear. e) Impact wear rate is correlated to the K.E. flux of the particles as: WI = Where ρs is the solids density Cs is particle concentration Vs is particle impact velocity EI (α) is the impact wear coefficient as a function of the impact angle α.

Note that ρsCsVs is the mass flux.

For prediction of impact wear co-efficient of test rig shown in figure 4, in this figure there is open channel through which solid-liquid flow strikes the impact wear specimen and causes erosion wear. Solid liquid flow through a channel strikes the impact wear specimen & causes erosion of wear sample. By measuring the wear depth for a given test duration the impact wear Specific energy co-efficient for a given slurry/wear material combination is calculated.

Figure 2 In this research paper we will focus our effort on calculating velocity Vs and the velocity profile of the flow and we are doing this research with water as the fluid.

A. Mathematical Modelling 1) Newton’s Second Law a) For a solid mass: F = m.a  u  b) For a continuum:   t  u.u  .  f Where ρ is mass per volume(Density)   c) Expressed in terms of velocity field u(x,y,z,t). In this form the momentum equation is also called Cauchy’s law of motion. d) For an incompressible Newtonian fluid, this becomes:  u   u.u   p   2 u  t 



2)

Navier-Stokes and Bernoulli equations a) When: The flow is steady: u / t  0 The flow is irrotational: the vorticity     u  0 The flow is inviscid: μ = 0 1 b) and using: u.u  2 (u.u)  u    u c) We can rewrite the Navier-Stokes equation:

as the Bernoulli equation:

 u   u.u   p   2u  t 



 p u.u  0   2  

1) k - model At high Reynolds numbers the rate of dissipation of kinetic energy is equal to the viscosity multiplied by the fluctuating vorticity. An exact transport equation for the fluctuating vorticity, and thus the dissipation rate, can

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be derived from the Navier Stokes equation. The k - epsilon model consists of the turbulent kinetic energy equation and the dissipation rate equation

B. Setup & solution FLUENT requires inputs (solver settings) which tell it how to calculate the solution. By introducing the boundary condition we can command the software to calculate in its limits. Emphasis will be placed on convergence, which is critical for the CFD simulation. The sketch below shows the basic workflow for any simulation.

V. Results The Following results were obtained after performing the analysis under ANSYS FLUENT 14.0 when the wedges were constructed at different nose angle and we have also seen the effect when the wedges were made blunt with a nose radius of 0.25 mm. The data’s obtained are as follows: 2-D WEDGE WITH DIFFERENT NOSE ANGLES: The above shown analysis was also performed under 2-D within the same circumstances and different nose angle such as 300, 450, 600. The graphs obtained are: 2-D WEDGE WITH NOSE ANGLE OF 300:

Figure 3. Coefficient of lift

Figure 4. Scaled Residuals

Figure 6. Velocity profile Figure 5. Coefficient of drag 2-D WEDGE WITH NOSE ANGLE OF 450:

Figure 9. Scaled Residuals Figure 7. Coefficient of drag

Figure 10. Velocity profile Figure 8. Coefficient of lift

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2-D WEDGE WITH NOSE ANGLE OF 600:

Figure 11. Coefficient of lift

Figure 12. Scaled Residuals

Figure 13. Velocity profile

2-D WEDGE WITH NOSE ANGLE OF 300AND BLUNT WITH NOSE RADIUS OF 0.25 mm:

Figure 14. Coefficient of drag

Figure 15. Scaled Residuals

Figure 16. Coefficient of lift Figure 17. Velocity profile 2-D WEDGE WITH NOSE ANGLE OF 600 AND BLUNT WITH NOSE RADIUS OF 0.25 mm:

Figure 18. Coefficient of drag

Figure 19. Coefficient of lift Figure 20. Scaled Residuals VI. Conclusion At same velocity inlet and pressure outlet the inlet pressure is least at angle of 45 0 and inlet pressure increases with decrease and increase in angle.

Figure 21. 30 degree pressure

Figure 22. 45 degree pressure

Figure 23. degree pressure Figure 24. velocity vector 30 degree At sharp edges we get more pressure and eddies are forming and these losses can be minimized by making the edge blunt. Velocity at outlet increases with increase in wedge angle.

At sharp edges we get more pressure and eddies are forming and these losses can be minimized by making the edge blunt. Velocity at outlet increases with increase in wedge angle.

Figure 25: velocity 30 degree

Figure 26: 45 degree velocity

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Figure 27: 60 Degree velocity

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VII. Future Scope The Future works that can be implied through this research paper are as follows: A) The fundamental equation for calculation of impact wear rate is WI = For calculation of wear in any object requires the quantity .

B) We have carried out our simulation in a water medium; the experimental analysis for slurry having multiple density particles needs to be done.

C) The proposed experimental setup needs to be completed for the validation of our work.

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

Sridhar P.S.V.S., Pagalthivarthi Krishnan V. and SanghiSanjeev. 2003. Simulation of Particle Impact with a Wedge in Dilute Two-phase Flow. Defence Science Journal. 53(2):199-204 Pagalthivarthi K.V. and Gupta P.K.2009. Prediction of ErosionWear in Multi-Size Particulate Flow through a Rotating Channel.

Tech Science Press FDMP.5(1): 93-121 PagalthivarthiK.V.,Gupta P.K.,TyagiVipin and Ravi M.R.2011. CFD Predictions of Dense Slurry Flow in Centrifugal Pump Casings. World Academy of Science, Engineering and Technology.51 Patnika Amar, SatapathyAlok, chandNavin and Barkoula N.M. 2010.Solid particle erosion wear characteristics of fiber and particulate filled polymer composites: A Review.Elsevier.Wear 268: 249–263 PatilMilind S.,DeoreE.R.,Jahagirdar R.S. and Patil S.V. 2011.Study of the parameters Affecting Erosion Wear of Ductile Material in Solid-Liquid Mixture. World Congress on Engineering. 3: ISSN:2078-0958(Print) Kim Changhee, Lee Mansoo and Han Cheolheui 2008. Hydraulic Transport of Sand Water Mixture in Pipelines. Journal of Mechanical Science and Technology.22 :2534-2541 Yuliang Zhang, Baoling CUI and Huashu 2013. Numerical Simulation and Analysis of Solid liquid Two phase Flow in Centrifugal Pump. Chinese Journal of Mechnical Engineering.26 Clark H. McI. 2004.The influence of the squeeze film in slurry erosion. Journal of wear.256: 918-926.

Gandhi B.K. and BorseSatish V. 2004. Nominal particle size of multi- sized particulate slurries for evaluation of erosion wear and effect of fine particles. Journal of wear. 257: 73-79.

Tian Harry H., Graeme R. Addie .2005 .Experimental study on erosive wear of some metallic materials using coriolis wear testing approach. Journal of wear.258: 458-469.

Tian Harry H., Graeme R. Addie . 2005. Experimental study on erosive wear of some metallic materials using corioliswear testing approach. Journal of wear .258 : 458-469.

Tian Harry H., Addie Graeme R., Pagalthivarthi Krishnan V. 2005. Determination of wear coefficients for erosive wear prediction through coriolis weartesting.Journal of wear. 259:160-170.

Chen, R.C. and Kadambi, J.R. 1994. Experimental and Numerical Studies of Liquid-Solid Pipe Flow.ASME-FED.189: 123-135.l PagalthivarthiK.V.,Desai P.V. and Addie G.R.1990.Particulate motion and Concentration field in Centrifugal pump. Particulate Science and Technology.8:77-96 Brown, G. J. 2002.Erosion prediction in slurry pipeline tee-junctions.Applied Mathematical Modelling. 26:155-170.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Pairwise Key Distribution Mechanism for Heterogeneous Sensor Network Kanwalinderjit Gagneja Department of Computer Science Southern Oregon University Ashland, Oregon, USA Abstract: Generally the sensor nodes are deployed in hostile environment. Therefore, it becomes particularly significant that sensor nodes communicate data in a secure fashion. Since sensor nodes have limited resources, thus it is recommended by many researchers to use heterogeneous sensor nodes to increase the performance of the laid sensor network. In this paper, an efficient pairwise key distribution mechanism for heterogeneous sensor networks has been presented. The performance evaluation of the given mechanism shows optimal usage of sensor resources. The given mechanism assures the node connectivity in the network and each communication is made secure using cryptography. The nodes that get compromised do not disturb the functioning of rest of sensor network. However, it does affect the immediate neighbors of the compromised nodes. Moreover, if the network detects that a node is compromised the security breach could be effortlessly clogged. The security analysis of pairwise key distribution protocol (PKDP) shows that it is performing better than probabilistic key distribution techniques such as E-G while storing lesser number of keys and the communications are encrypted in a manner that increases its efficiency.

Keywords: Security; key distribution; pairwise; heterogeneous sensor networks I.

Introduction and Related Work

Nowadays, the use of sensor nodes has increased tremendously in our lives. In the coming ten to fifteen years these sensor nodes may possibly cover the world completely, and these tiny, little nodes could be accessed over the Internet. At present, these sensor nodes are used in various applications, for instance: traffic monitoring, nuclear power plant controlling and monitoring, global positioning system, environment monitoring, reporting of fire incidents, and vehicular movement tracking, etc. [3, 8, 9, 17]. These tiny sensor nodes have abundant possible uses for numerous applications, likewise: showbiz and entertainment industry, drugs and human trafficking control, border surveillance, sports industry, smart spaces, crisis management, drones, under water surveillance and environment monitoring, and health and medical industries [12]. Since all these applications handle sensitive data, so the sensor nodes should communicate data securely and efficiently [7].

It is well known among wireless sensor networks researchers that sensor nodes run with limited resources.

The sensor nodes have limited processing power, have low bandwidth availability, have small transmitting range, and have inadequate memory [1, 15, 16]. Thus it becomes very significant to realize key arrangement in sensor networks because the nodes in sensor networks are resource constrained and communication security is mandatory. Usually key-distribution techniques hone on probabilistic key distribution. It could be seen in Eschenauer and Gligor (E-G) [5] random key management technique. The E-G technique works in three phases: The first phase is for key pre-distribution; in second phase it computes shared keys; in third and final step it sets up path keys. The technique given by E-G utilizes a considerable size of memory simply to store the keys.

However, probabilistic key distribution techniques suffer from various adverse effects that public key based techniques do not.

The probabilistic key distribution techniques cannot assure that a given sensor node could establish a shared secret key with its neighbour sensor nodes. The probabilistic key distribution techniques also cannot assure security for the genuine and uncompromised sensor nodes, when there are a number of compromised sensor nodes in the given network. In [19], Zhu et al proposed Localized Encryption and Authentication Protocol (LEAP). It is again a probabilistic key distribution scheme. Main idea behind this protocol is that all the different communications happening among the sensor nodes need various security measures. Moreover, to satisfy different security needs it is not appropriate to use same security techniques. In [14] a new security protocol named Self-Configuring Bootstrapping Keys (SBK) is proposed, where the protocol does not require keys to be stored over the nodes before the network is deployed. The wireless sensor network supports two dissimilar kinds of sensor nodes: the service sensor nodes and the worker sensor nodes. The service nodes are responsible to generate key spaces and pass on the keys to worker sensor nodes. The worker sensor nodes then use these keys to bootstrap and establish pairwise keys with their neighbour sensor nodes.

On the other hand, it is shown in the research that Elliptical Curve Cryptography is useful for tiny sensor nodes [10]. Various current researches have presented that Heterogeneous Sensor Networks (HSNs) are good structural lay out to improve the sensor networks performance significantly [8, 13]. In the given HSNs there are

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two dissimilar kinds of nodes: the high end sensor nodes and the low end sensor nodes. The low end sensor nodes are small, simple with little processing power and cheap, whereas the high end sensor nodes have good processing power, more memory, and are costly. Since the sensor nodes those are high end are better equipped with resources than low end sensor nodes, thus could be used to design significantly good security techniques.

The paper organization is as follows: Section II elaborates on the layout of the network and provides detail of key distribution, some assumptions, what different keys are stored on low end sensor nodes, and what different keys are stored on high end sensor nodes. Section III describes how one hop, two hops, and n hop clusters are generated by the high end sensor nodes, and how sensor nodes setup their keys. Section IV discusses the performance evaluation of the presented scheme in terms of memory usage, communication cost, and computation costs. Section V explains the weaknesses, comparison with probabilistic key distribution technique, and network connectivity. Section VI concludes the paper.

II.

The Layout of Network with Key Distribution

This section presents the layout of the network and the key distribution technique. In the given area of interest thousands of low end sensor nodes are laid out and there could be hundreds of high end sensor nodes. The complete network is supported by one base station. All the nodes sense the data and forward it to the base station.

The eavesdropper nodes usually have more sophisticated capabilities than the wireless sensor nodes in the laid network. The eavesdropper sensor nodes could be equipped with global overhearing capabilities, thus could compromise some low end wireless sensor nodes in the network. Unambiguously, some of the eavesdropper nodes capabilities are higher energies, more computation capability, and larger memories. In the following we present some assumptions those are followed before laying out the sensor nodes in the network.

1) In the wireless sensor network, every node is identified with its unique ID. 2) The high end sensor nodes are powerful nodes and are resistant to tempering, so high end sensor nodes cannot be compromised.

3) Every high end sensor node stores a common public key pair.

4) Since low end nodes are less capable so are not tamper resistant and invader or eavesdropper could capture them and these nodes could be compromised.

A. Key Management The base station assigns the unique ID’s to all the nodes and also pre-calculates the keys as well. There are two key sets one for low end sensor nodes and one for high end sensor nodes. Each low end sensor node is loaded with the key sets represented in table I. Notation IDa KRa KPa KiPBS [KPa || IDa]KRBS

Table I: Key sets used by low end nodes Explanation An unique ID for low end node Private Key Public Key A public key shared between given low end node i and the only one base station It is the certificate generated with the concatenation of public key and the unique ID, and it is encrypted with, KRBS , the private key of the base station

The keys could also be computed based on [7], where they have used an algorithm to compute the keys based on the node IDs and the level of that node in the given network. By doing that the nodes can self-certify the communication and the node does not have to communicate back to the high end node or cluster head for end to end communication.

Similarly, each high end sensor node is loaded with the keys represented in table II. Notation IDH KRH KPH KiPBS [KPH || IDH]KRBS

Table II: key sets used by high end nodes Explanation An unique ID for high end node Private Key Public Key A public key shared between given high end node i and the base station It is the certificate generated with the concatenation of public key and the unique ID, and it is encrypted with the, KRBS, private key of the base station III.

Clustering and Key Distribution

Once the network is deployed, the high end sensor nodes start reaching to other high end sensor nodes and low end sensor nodes around it to form clusters by broadcasting HELLO messages. The HELLO message is composed of: Node’s unique IDH, and its public key KPH that is encrypted with KRBS : HELLO[KPH || IDH]KRBS.

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The HELLO messages are broadcasted repeatedly after a random delay of time, so that high end sensor node could reach to maximum number of sensor nodes within its transmission range.

On the other hand, the low end sensor nodes keep receiving every HELLO message for some predetermined time frame. Once that time is over the low end node picks the HELLO message it received with maximum signal strength. The low end sensor node replies back to that high end node with its certificate to authenticate itself. The high end sensor node then sets up a shared secret key with that low end sensor node as follows: First, low end sensor node sends [KPa || IDa]KRBS and high end sensor node sends [KPH || IDH]KRBS to authenticate each other.

Second, they compute the shared secret key KeyH,a = KRH x H1 (KPa ⊕ IDa ) + KiPBS (1) Keya,H = KRa x H1 (KPH ⊕ IDH ) + KiPBS (2) Here H1 is a hash function and⊕ is an XOR operation. This computation should check if KeyH,a = Keya,H then low end and high end nodes both are sharing the same secret key and that is what is expected. Now both nodes save this secret key in their neighbor tables. Similarly, all other nodes in the network set up shared secret keys.

At this point, all the high end sensor nodes have setup one hop cluster around themselves. Now each high end node will generate one hop symmetric cluster key KeyH,Ci , and will use private key, KRH , to encrypt it: [KeyH,Ci]KRH then will distribute this cluster key to all its one hop neighbors.

After forming one hop clusters, the nodes start looking for multi hop low end and or high end sensor nodes. In the following fig. 1(a) and fig. 1(b) there is one high end sensor node surrounded by several one hop low end sensor nodes. In fig. 1(a) there is one low end node that is receiving HELLO messages from high end node but cannot transmit back since it does not have enough transmission power. So this low end sensor node must choose appropriate single hop neighbor to form multi hop communication connection to the high end sensor node being its cluster head.

Figure 1(b)

Figure 1(a)

Figure 1(a): The multi hopped low end sensor node trying to reach to the high end sensor node, 1(b): One low end sensor node outside the transmission range of any high end sensor node, therefore not part of any cluster In fig. 1(b) there is one low end sensor node that is not connected to any high end node. So this low end sensor node should pick another low end node within its transmission range so that it can establish pairwise communication with the high end sensor node. Using eq. (1) and (2) such nodes set up their pairwise keys for multi hop clusters. All the nodes in the network are given some time frame to set up pairwise keys with the neighbors within its radio range for secure communication of data.

The nodes are laid out in a manner that these form a connected graph. It means that each high end sensor node is within the transmission range of at least one other high end sensor node, so that these two nodes could send and receive signals from each other. The high end nodes also share secret keys as described in eq. (1) and (2) after authenticating each other. This will form high end sensor nodes cluster.

The base station also listens to the HELLO messages from the high end nodes and form cluster with those high end nodes with strongest transmission signals.

IV.

Performance Evaluation

For the given pairwise key distribution protocol (PKDP) the performance is evaluated in terms of memory utilization, communication cost, and computational cost. a) Memory Utilization When the nodes are deployed in the given network, the nodes have some pre-deployed keys by the base station, for example, for high end sensor node it has: IDH, KRH, KPH, KiPBS, one certificate, one group key. After the network deployment, the nodes set up pairwise keys with neighbors and cluster key with the other low end and high end sensor nodes to form the cluster. For high end sensor nodes it depends upon the degree, DH, that is, the

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number of low end and high end sensor nodes with which it can connect to establish the two way communication.

Therefore memory requirements to store keys for high end sensor node is: the keys stored on the node before deployment plus the degree of high end node, DH, and x bytes per key. So total memory used by high end sensor node is given by: MH = x · (6 + DH) bytes.

Similarly, the low end sensor nodes keep pre-deployed keys as: IDa, KRa, KPa, KiPBS, and one certificate. Say low end sensor node has a degree of, Da, and it stores group keys for the neighbor connected to it. So total memory used by low end sensor node is given by: Ma = x · (5 + 2Da) bytes.

Suppose we have one network, having 1000 low end nodes and 10 high end nodes in number. The average degree for high end nodes is DH =100, and for low end nodes is Da =20. Therefore, the total memory requirements by the complete network are given by M= MH + Ma bytes M = 10x · (6 + DH) + 1000x · (5 + 2Da) bytes M = 10x · (6 + 100) + 1000x · (5 + 2*20) bytes or 46060 keys. If the node use 10 bytes to store one key, means x = 10 then total network memory requirement to store keys is 460600 bytes or less than 500 KB. Therefore each high end sensor node is storing 106 keys using 1060bytes or approximately 1KB of memory. Similarly, each low end node is storing 45 keys using 450 bytes or 0.44KB of memory. Presently, one TelosB mote possesses 10 KB RAM and 48 KB flash memory [2], so the given pairwise key distribution protocol (PKDP) could fairly easily implemented on such sensor nodes. b) Communication Cost The sensor nodes generally consume most of its energy while transmitting [6]. In the given pairwise key distribution protocol (PKDP) the network topology formation is quite efficient and the nodes perform the transmissions only two times as explained in section III. One when high end sensor nodes send HELLO messages to all its neighboring nodes, another when low end sensor nodes supply their certificate for authentication. Then both nodes compute their shared secret keys and if these two keys are similar then nodes are assumed to be authentic. The nodes then generate the group keys and the network can start communicating the data. c) Computation Cost In the given pairwise key distribution protocol (PKDP) the most computation intensive step is when high end nodes form clusters with other low end and high end sensor nodes. In cluster formation step the high end and low end sensor nodes compute their shared secret keys. Initially high end sensor nodes send HELLO messages and low end nodes authenticate themselves to high end node. After authentication they compute the shared secret key: KeyH,a = KRH x H1 (KPa ⊕ IDa )

+ KiPBS

This operation is performed with one XOR operation, one hash function, one multiplication and one addition operation.

V.

Security Analysis

It is made sure that in pairwise key distribution protocol (PKDP) every essential information is kept confidential. This way an eavesdropper cannot manage to break into the system. Once the nodes are deployment in the given network the low end and high end sensor nodes do not know their neighbors. The nodes do not know about which sensor nodes is transmitting to them with strongest transmission range. Moreover, the HELLO messages those are sent by high end sensor nodes are all encrypted HELLO[KPH || IDH]KRBS with the private key, KRBS. Even all the communications after the hello message are all encrypted. Thus makes this communication highly secure that an eavesdropper can neither read nor modify the contents of the communication. a) Weaknesses There could be some situation like: the enemy could capture the sensor node physically. If the captured node is high end sensor node then it is not much of a concern in terms of security for the given network. Since the high end sensor nodes are tamper resistant. Though, the network will not be able to gather any information from that cluster. In contrast, if the captured node is a low end node then for sure the attacker could extract the stored keys and could use those to collect more information from the network. But to collect information the hostile node has to use the certificate [KPa || IDa]KRBS to get data from other nodes. The most important key of all is KiPBS. Other than base station it is mathematically impossible for other nodes to generate another certificate for authentication, thus base station will get to know about it. Once base station has the information about damaged or captured node, it will invalidate the node and will send new public shared key. The base station broadcasts the message to all other nodes in the network <FFFF, [INVALIDATET||IDi||IDn||IDp], KRBS >. Thus, other nodes do not send any data through invalidated node and start using new public shared key.

Sometime eavesdropper could intercept some message and could replay that message. Certainly, if this message replay happens during cluster formation, the nodes receiving this replay would not be able to understand this message. This replayed message could confuse them and could result into lost sensor node energy. This problem could be solved if we add sequence number to the messages. Hence, add a seqn to the HELLO message that a high end node broadcasts HELLO[KPH || IDH]KRBS || [seqn] KRH The receiving node will decrypt the message using KiPBS key and will verify the high end node. Then it will authenticate the sequence number. Only the

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authentic high end node keeps the key, KRH, thus mathematically it is impossible that eavesdropper could fake the given sequence number. a) Comparison with Probabilistic Key Distribution techniques The E-G technique [5] gives the probability of connectivity for two host as in eq. (3): p = 1 â&#x20AC;&#x201C; (((P-k)!)2/(P-2k)!P!) (3) The P refers to key pool size, and k represents the size of keychain [4]. Let key pool is 100 and the key chain is 5 then there is probability, p, of 23% that any two sensor nodes will have single shared key from the key pool, P. It signifies that that E-G just provides one fourth of connectivity chances for the 100 key pools. On the other hand with our technique having 106 keys, it shows that there is probability of 92% that any two nodes will share a key.

Moreover, pairwise key distribution protocol (PKDP) uses approximately one third of the memory only.

With probabilistic key distribution, if the key pool, P, is large, it increases the security, instead decreases the chances that any two probable sensor nodes will have shared keys. However, with pairwise key distribution protocol (PKDP), the keys are computed cryptographically, so all keys are dissimilar except the, KiPBS, key and group keys. b) Connectivity For a complete network while deploying the nodes there is one concern of how many sensor nodes must be distributed so that the network is connected. This is represented in fig. 2, where probability to make a connected network is computed with respect to key pool size and key chain size and the partial data of complete computation using eq. (3) is presented in table III. From table III we can infer that if the key pool size is 3000 and key chain size is 121 then it is 99% probable that two sensors in a cluster will share a key. However, if the key pool size is 3000 and key chain size is 60 (almost half of the previously chosen key chain size) then it is approximately 71% probable that two sensors in a cluster will share a key. This means the sensor nodes are far apart. If the sensor nodes are far apart then they will be using longer transmission signals and may die soon as could be seen with probability2. However, to increase the network life time it is good idea that sensor nodes use shorter transmission range. This could be gained by increasing the small fraction of sensor nodes in the given network this could be seen with probability1 as presented in fig 2.

Figure 2: Two different probabilities of forming connected network Key pool size

Table III Probabilities of making connected network Key chain1 Prob1 Key chain2

Prob2

100

5

23

2

4

1000

41

82.67

20

33.5

1500

61

92.47

30

45.78

2000

81

96.73

40

55.79

3000

121

99.38

60

70.61

4000

161

99.88

80

80.46

5000

201

99.97

100

87

6000

241

99.99

120

91.36

This problem could also be solved by adding extra powerful transmitters, but we will just add extra sensor nodes for the connectivity. In other words we can state that: Theorem 1: Finding the probability that a random undirected graph G(x,y) is connected.

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Proof: Say f(x) is the probability of G(x,y) to be connected and is given by

Now it is sufficient to show that

Since

Suppose v1, v2, v3,….vn be n vertices.

Trivially,

But If A is set of all i-1 subset of { v2, v3,….vn}, then

The last statement tells that edges in In A there are only

are not dependent on the edges from A to Ac

elements

The probability that

is connected is equal to the probability that G(1+|A|, p) is connected is f(x).

Figure 3(a)

Figure 3(b)

Figure 3(a): 1, 2, 3 hop+ clusters when low end sensor node range is one third that of high end nodes 3(b): 1, 2, 3 hop+ clusters when low end sensor node range is one fourth that of high end nodes The low end sensor nodes are making either one hop or two hops or n hop clusters with its high end sensor node. Simulations are used to find the number of low end sensor nodes in one hop or two hops or n hop clusters and the results are presented in fig. 3(a) and fig. 3(b). In the presented data we have varied the high end sensor node range, and set the low end sensor nodes range equal to one third (as presented in fig. 3(a)) and one fourth (as presented in fig. 3(b)) of the high end sensor node. From fig. 3(a) and fig. 3(b) it could be observed that one hop clusters form the best configuration in terms of energy efficiency otherwise both the plots are almost similar.

Another observed point is when high end sensor nodes have high transmission range then there is fairly good number of low end sensor nodes in one hop clusters than in two hop or n hop clusters.

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VI.

Conclusion

This paper presents pairwise key distribution protocol (PKDP) for heterogeneous sensor network. The performance evaluation of the given mechanism shows optimal usage of sensor resources, such as little memory usage, fewer computations, and smaller communication costs. The given mechanism assures the connectivity of the network and each communication is made secure using cryptography. The given technique makes sure that compromised node does not disturb the functioning of rest of sensor network. However, it does affect the immediate neighbors of the compromised nodes. Moreover, if the network detects that a node is compromised the security breach could be effortlessly clogged. The security analysis of pairwise key distribution protocol (PKDP) shows that it is performing better than probabilistic key distribution techniques such as E-G while storing lesser number of keys and the communication mechanism is encrypted in a manner that increases its efficiency.

VII. References [1]

Blundo, Carlo, Santis, Alfredo De, Herzberg, Amir, Kutten, Shay, Vaccaro, Ugo, Yung, Moti, “Perfectly-Secure Key Distribution for Dynamic Conferences”, 12th International Crytology Conference on Advances in Cryptology, Santa Barbara, CA, pages 471-482, 1992.

[2]

Crossbow Technology Inc. URL: http://www.xbow.com.

[3]

Du, Wenliang, Deng, Jing, Han, Yunghsiang S., Varshney, Pramod, Katz, Jonathan, and Khalili, Aram, “A Pairwise Key Predistribution Scheme for Wireless Sensor Networks,” Proc. 10thACM Conf. Computer and Comm. Security (CCS ’03), 2003.

[4]

Duarte-Melo E. J., and Liu M., “Data-gathering wireless sensor networks: Organization and capacity,” Computer Networks, vol. 43, no. 4, pp. 519–537, 2003.

[5]

Eschenauer, Laurent and Gligor, Virgil D. “A Key- Management Scheme for Distributed Sensor Networks,” Proc. Ninth ACM Conference on Computer and Communication Security (CCS ’02), pages 41-47, November 18-22, 2002.

[6]

Heinzelman, Wendi, Rabiner, Chandrakasan, Anantha, and Balakrishnan, Hari, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” HICSS, Jan 4-7, 2000.

[7]

Gagneja, Kanwal, "Pairwise Key Distribution Scheme for Two-Tier Sensor Networks", IEEE ICNC, Honolulu, Hawaii, USA, pages 1081-1086, Feb. 3-6, 2014.

[8]

Gagneja K.K. and Nygard K., "Energy Efficient Approach with Integrated Key Management Scheme for Wireless Sensor Networks", ACM MOBIHOC, Bangalore, India, pages 13-18, July 29, 2013.

[9]

Gagneja K.K. and Nygard K., "Heuristic Clustering with Secured Routing in Heterogeneous Sensor Networks", IEEE SECON, New Orleans, USA, pages 9-16, June 24-26, 2013.

[10] Gura N., Patel A., Wander A., Eberle H., and Shantz S.C., “Comparing elliptic curve cryptography and rsa on 8-bit cpus,” in Proc. of the 6th International Workshop on Cryptographic Hardware and Embedded Systems, Boston, Massachusetts, Aug. 2004.

[11] Intanagonwiwat, Chalermek, Govindan, Ramesh, and Estrin, Deborah, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” Proceedings of ACM Mobicom’00, pages 56-67, Aug 2000.

[12] Karlof, Chris, and Wagner, David, “Secure Routing in Sensor Networks: Attacks and Countermeasures,” Ad Hoc Networks, Vol. 1, Issue 2-3, pages 293-315, Sept 2003.

[13] Liu, Donggang, Ning, Peng, Li, Rongfang,” Establishing Pairwise Keys in Distributed Sensor Networks”, Proceedings of the 10th ACM Conference on Computer and Communications Security (CCS ’03), pages 52–61, October 2003.

[14] Liu, Fang, Cheng, Xiuzhen, Ma, Liran, and Xing, Kai, “SBK: A Self-Configuring Framework for Bootstrapping Keys in Sensor Networks,” IEEE Transactions on Mobile Computing, Vol. 7, Issue 7, pages 858-868, July 2008.

[15] Li Qun, Rus, Daniela ,“Global Clock Synchronization in Sensor Networks”, IEEE Transactions on Computers, pages 214—226, 2004.

[16] Madria, Sanjay, and Yin, Jian, “SeRWA: A Secure Routing Protocol Against Wormhole Attacks in Sensor Networks,” Ad Hoc Networks, Vol. 7, Issue 6, pages 1051-1063, Aug 2009.

[17] Perrig, Adrian, Szewczyk, Robert, Tygar J. D., Victorwen, and Culler, David E., “SPINS: Security Protocols for Sensor Networks,” Journal of Wireless Networks, Vol. 8, Issue 5, pages 521-534, Sept 2002.

[18] Pallapa G.V., “A privacy enhanced situation-aware middleware framework for ubiquitous computing environments”, dissertation PhD, University of Texas, Arlington, 2010. http://hdl.handle.net/10106/4867 [19] Roland M., Langer J., and Scharinger J., “Security vulnerabilities of the ndef signature record type,” 3rd International Workshop on Near Field Communication (NFC), pages 65–70, Feb. 2011.

[20] Yang, Yi, Wang, Xinran, Zhu, Sencun, and Cao, Guohong, “SDAP: A Secure Hop-by-Hop Data Aggregation Protocol for Sensor Networks,” ACM Transaction on Information and System Security, Vol. 11, Issue 4, pages 1-43, July 2008.

[21] Zhu, Sencun, Setia, Sanjeev, and Jajodia Sushil, “LEAP+: Efficient Security Mechanisms for Large-Scale Distributed Sensor Networks,” ACM Transactions on Sensor Networks,Vol. 2, pages 500-528, Nov. 2006.

VIII. Acknowledgments This research is partially funded by ETIC and by E-Tech. We thank all involved in the process of supporting this research.

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

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Influence and Role of Technology on Stress: A Mathematical Analysis Dr. M.S.Saleem Basha1, Dr. Esam Al Lawati2, Mrs. Gargi Bhattacharya3and Dr. Nasir Ahmed Khan4 1 Assistant Professor, Department of Computer Science, Mazoon University College, Muscat, Sultanate of Oman.

2 Assistant Professor, Department of Business, Mazoon University College, Muscat, Sultanate of Oman.

3 Assistant Professor, Department of English, Mazoon University College, Muscat, Sultanate of Oman.

4 Professor, Department of Quality Assurance, Mazoon University College, Muscat, Sultanate of Oman. ___________________________________________________________________________________ Abstract: This paper reviews the possible reason of stress which is encountered in all the spheres of our lives.

Human beings have to combat with stress in their daily situation. We must first analyze the cause of stress, then try to find effective solution for the same. This paper also analyzes the difference between the use of technology in the previous industrial fields and the modern updated computer softwares. The discussions made in the paper are supported with the minmax theorem and its mathematical derivation. ___________________________________________________________________________________ I. Introduction Stress has become a part of modern existence. Human begins encounter stress in their family, due to difference of opinion among the family members, in their work place because of the anxiety produced from excessive work load, and also in the social lives when we are confronted with uncertain circumstances. The effect of the stress is multi dimensional. Most of the times the signs and symptoms of the mental stress are evident in the body and physical activities. Stress should be controlled otherwise it can lead to harmful consequences like interrupted sleep, diabetes, high blood pressure and other health problems. When people over use technology, then they lose their capacity to motivate themselves in their surroundings. The balanced use of technology can increase our efficiency. However, techno mania or excessive addiction to technology may have adverse consequences.

The idea of the people that machines can replace human skills has become outdated. Modern technology, the multimedia, upgraded computer softwares demand the role of the human brains to complement them. The modern role of technology thus, depends more on the efficient functioning of the human minds to make it a success.

II. Background Work The demand of the body is expressed through the symptoms of stress. Excitement can be the impact of both positive and negative experiences. The result of stress can be the release of certain harmful chemicals in the blood. Stressed men or women are seen both in family and workplaces. Sometimes the chemicals are considered positive, especially when they give us strength and energy in dangerous situations. Stress can be negative when it is caused by emotional setbacks and psychological problems.

Thus, we can define stress as both a mental and external or physical response to certain changes in our lives. It is a signal to suggest adaptations to the changing and challenging circumstances of our lives. It is a type of stimulus that makes our bodies to react to certain social, psychological or physical events.

The factors that influence our reactions to stress are as follows: 1. The type of stress, whether it can be controlled or predicted.

2. The biological vitals such as age or gender.

3. Recurrence of events and the frequency of similar experience [1].

The effects of stress can be tangible such as deduction from salary. It can be intangible also such as the feeling of anxiety when one gets an invitation from the legal one gets an invitation from the legal court for the first time. We can enumerate the physical effects of stress in the following points: a) Cardiovascular disease (CVD): Research has revealed the impact of chronic stress on blood pressure, frequency of heart attacks etc. [2]. b) Weight gain: Stressed people are prone to gain weight faster than the general increased stress optimized the production of cortisol in the blood stream. This results in enhanced hunger and activates the fat storing enzymes [3]. c) Diabetes: Stress must be controlled to prevent the development of diabetes. Anxiety and stress may result into diabetic problems. Uneven sleep, interrupted eating habit etc. endanger a personâ&#x20AC;&#x2122;s life with diabetes [4]. d) Effect on the mind and intellectuality: When the mind is stressed then it loses its capacity to think and act sensibly. On the contrary, the productivity of the mind, the power to focus and concentrate increases when the mind remains stress free. So, indirectly stress effects our career.

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e) Memory and concentration: The short term memory of the human mind is adversely affected with the production of the key stress hormone known as “cortical”. Cortical leads to the shrinking of the hippocampus, the major memory centre of the brain [5]. f) Insomnia: The importance of a sound sleep is often ignored by the people. Sleep refreshens the mind to face new challenges of the life. Sleeping disorders may debilitate the ability of the mind. Without adequate sleep the vitality of the brain cannot be restored. Restricting the sleep may result in attention lapses, reduced recognition power, forgetting thing easily etc. The performance is enhanced with proper sleep [6].

The psychosocial causes of stress can be noted in the following way: This refers to the daily work pressures attributed to our social and physical environments. a) Hassles: The inevitable problems of our life which cause petty annoyances are collectively referred to as hassles. Some common examples are loud music, traffic hazards, paucity of parking areas, excess work load etc.

These all trigger our anger and frustrations. b) Techno Stress: This symptom is common with the people who excessively depend on technology. Techno mania is prevalent among the modern generation. People in modern age use the technology in all spheres of life from entertainment, casual activities to serious work. However, obsession with technology may lead to distracted driving. Interestingly, the researchers like Michel Weil and Larry Rosen have mentioned about; “Technosis” which is a syndrome where people are so addicted using technology that they can even risk their live [7].

It is mandatory for the users of technology to set limits and give adequate time to face to face interaction with people. Virtual relationships may have loop holes and cannot foster intimate relationships. It is common in our busy schedules that we don’t answer our phones immediately or postpone our replies to emails. Discussing the problems face to face sometimes help us more than trying to solve them virtually. c) Frustrations and Conflicts: Frustrations occurs when the people are unable to achieve their goals as they had decided. Conflicts may be defined as the compulsion to choose among competing desires, instincts and behavior. When the immediate demand of the situation is incompatible with our values or there are contradictions in values among the colleagues, then also conflicts may occur.

III. Proposed Mathematical Model for Stress Influence by Technology: The current relationship between humans and machines is quite different than what can be assumed to be used before in business and industries, the previous automated technology targeted mainly the replacement of manual skills. Engineers in the factories mechanized the production methods. Some humans were there to attend the operation of the machines. Thus, the role of the humans became subordinate to the machines. The new technology on the contrary, requires diverse human skills without attempting to replace them. Thus, in the modern concept machine and human beings complement each other. The use of modern technologies such as the mobile phones, ATM machines, computers etc. depend on the successful functioning of the human mind rather than the physical labour. So it is not appropriate to think the modern computer aided technology has reduced the mental work load. On the contrary, modern technology has increased the work of the mind while weakening the social support [8].

The major factors influencing the stress due to technology for human kind are: 1. Social interaction on Technology 2. Internal Factors 3. External Factors 4. Lack of Understanding of Technology 5. User Friendliness The above five factors are the major concerning with the stress on the human kind due to the influence of the stress. Considering the above factors a mathematical model has been proposed in the forth coming section. The above factors are purely a non functional attributes and cannot be quantify the values or cannot be judges but can be empirically derive base on the mathematical model.

The formulation of mathematical model The proposed mathematical model can be represented in terms of MinMax method. It is a mathematical method for the solution and assignment of decision rules in the non functional parameters and components. In theory, MinMax method can be used to minimize the possible loss in a worst case context. First, it was applied in a small scale while abiding by the rules and regulations of the decision. Later, MinMax method was applied to solve complex decision at the time of uncertainty.

This theorem was established by John von Neumann, who is quoted as saying "As far as I can see, there could be no theory of games … without that theorem … I thought there was nothing worth publishing until the Minimax Theorem was proved"[9].

The above stated factors can be formalized in a mathematical model as follows and these are the minmax function of ‘x’, this can be written as:

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where, f(x) is the direct factor influencing the stress. f(y) is the indirect factor influencing the stress. where, IF is the Influencing Factor. f(x) can be visualized in two folds namely summation and product properties 1. Summation Properties (3) where, n is the form factor for the each IF’s k is the total number of IF’s (4) (5) where, d is the min term constant 2. Product Properties (5) (6) (7) where, r is the max term constant 3. Relating r and d By using Cauchy’s mean theorem, the product of five IF’s of constant sum attains the maximum value at (8) Similarly, by Cauchy’s mean theorem the sum of 5 IF’s of constant product attains the minimum value at (9) Therefore, (10) is always at least five times of r and equals to five times of r only when .

Equating and relating ‘d’ and ‘r’ of equation (8) and (9), If C ϵ (d,r) also if f is continuous on (d,r) and differentiating (d,r) will yield (11) IV. Analysis All the implementations are done using MATLAB 7.13 (2011). The instances that have been chosen for experimentation are collected from individual experience and have been assigned the value empirically for the analysis purpose. A stress matrix is generated for every instance with maximum utilization of the technology which the individual has.

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Parameter

Value / Method

1.

Number of individuals

150

2.

Task Given to the individuals

40

3.

Termination condition

Based on the saturation of the individuals.

Saturation for the individuals: The individual is given with a task to complete within the specified time. During this task completion process, to predict the saturation point for the individual due to stress is obtained by the problem solving and repeated questionnaires to the individual. The Individual is asked by simple arithmetic calculations, the individual have to answer to the arithmetic within 5 seconds also answer to the simple and general questions. The individual can able to answer to those questions quickly if their mental stress in under control and takes more time to answer if the level of stress increases. Once the time taken to answer is increased, which means that the person is on stress. For the analysis purpose the time was extended upto 5 times. Beyond it is concluded that the individual is saturated. Further the number of task to be completed is increased gradually until 40 tasks, and correspondingly the time period also increased to complete the task. All the task is related to computing with technology. For example to format a word file, searching for a particular mail in the spam or inbox. If the individual was fluent in the technology and computing facility then he can complete the task soon than those who are not familiar with those facilities and may be searching for the options and buttons to complete the task. This opens the room for stress in their work.

Table 1: Analysis of Stress with the Effect of Technology Instance

Optimal Solutio n

Compu tation Time

2.24

Quality of the Solution Best

Worst

Average

Convergence Rate (%) Best

Worst 207.14 150.38

Average

Best

Worst

138.3 9 106.8 7

307.1 4 250.3 8 171.5 1 163.1 7 150.6 6 152.3 1 118.2 9 106.5 4

5.34

9.12

7.23

-38.39

5.42

9.18

7.3

-6.87

7.12

10.1

8.61

8.6

-71.51

-31.45

91.4

7.29

10.29

8.79

13.55

-63.17

-24.81

86.45

8.66

11.33

10

8.41

-50.66

-21.24

91.59

8.86

11.48

10.17

5.27

-52.31

-23.52

94.73

9.71

12.29

11

27.53

-18.29

4.62

72.47

9.82

12.31

11.07

35.23

-6.54

14.26

64.77

10.59

13.27

11.93

62.11

27.21

44.66

37.89

10.93

13.28

12.11

68.15

39.81

53.92

11.72

14.19

12.96

69.92

42.51

56.16

9.03

11.73

14.21

12.97

70.1

42.64

9.96

12.67

15.55

14.11

72.79

43.88

12.93

15.67

14.3

71.85

13.84

16.56

15.2

13.92

16.61

15.11

Convergence Diversity

Average 222.77

-168.75

178.63

-143.51

131.45

-80.11

124.81

-76.72

121.24

-59.07

123.52

-57.58

95.38

-45.82

85.74

-41.77

72.79

55.34

-34.9

31.85

60.19

46.08

-28.34

30.08

57.49

43.84

-27.41

56.37

29.9

57.36

43.63

-27.46

58.33

27.21

56.12

41.67

-28.91

44.7

58.28

28.15

55.3

41.72

-27.15

74.07

49.32

61.69

25.93

50.68

38.31

-24.75

15.27

75.27

51.16

63.17

24.73

48.84

36.83

-24.11

17.89

16.5

74.19

51.04

62.61

25.81

48.96

37.39

-23.15

15.12

17.93

16.53

74.94

51.7

63.28

25.06

48.3

36.72

-23.24

16.05

18.47

17.26

77.2

58.68

67.94

22.8

41.32

32.06

-18.52

13.15

16.05

18.83

17.44

77.95

56.81

67.38

22.05

43.19

32.62

-21.14

14.02

17.25

20.1

18.68

76.96

56.63

66.76

23.04

43.37

33.24

-20.33

17.28

20.7

18.99

76.84

52.46

64.65

23.16

47.54

35.35

-24.38

18.05

22.16

20.11

80.94

53.83

67.35

19.06

46.17

32.65

-27.11

18.06

22.38

20.22

81.88

53.63

67.76

18.12

46.37

32.24

-28.25

19.19

23.58

21.39

81.98

54.98

68.45

18.02

45.02

31.55

-27

19.49

23.65

21.57

81.74

56.49

69.11

18.26

43.51

30.89

-25.25

2

4.86 2.62 3.72

4

-122.77

Error Rate (%)

-78.63

7.63 3.91 4.52

6

9.07 4.55 5.63

8

11.59 5.96 7.68

10

15.97 8.29 9.01

12

18.04

14

20.05 10.09 10.99

16

22.15 11.16 12.01

18

24.1 12.09 13.07

20

26.22

22

28.05 14.03 15.16

24

30.45 15.29 16.26

26

32.74 16.48

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17.51 28

21.27

24.15

22.71

78.53

62.08

70.3

21.47

37.92

29.7

-16.45

21.58

24.25

22.92

78.97

63.99

71.45

21.03

36.01

28.55

-14.98

22.93

25.47

24.2

74.56

60.67

67.61

25.44

39.33

32.39

-13.89

23.19

26.06

24.63

73.62

57.98

65.78

26.38

42.02

34.22

-15.64

23.83

29.16

26.5

79.04

51.98

65.48

20.96

48.02

34.52

-27.06

23.96

30.38

27.17

79.84

47.64

63.74

20.16

52.36

36.26

-32.2

24.83

31.58

28.21

93.2

64.17

78.67

6.8

35.83

21.33

-29.03

25

34.65

29.83

92.93

51.61

72.25

7.07

48.39

27.75

-41.32

26.46

35.15

30.81

89.61

53.36

71.46

10.39

46.64

28.54

-36.25

27.34

39.25

33.3

86.41

36.93

61.65

13.59

63.07

38.35

-49.48

29.46

43.47

36.47

82.54

26.67

54.59

17.46

73.33

45.41

-55.87

32.34

47.06

39.7

71.41

12.88

42.15

28.59

87.12

57.85

-58.53

35.99

53.97

44.98

64.5

-3.2

30.65

35.5

103.2

69.35

-67.7

34.5

106.9 3

70.72

-72.43

35.34 17.83 18.28

30

36.63 18.35 19.7

32

39.64 19.94 23.25

34

46.6 23.35 23.97

36

48.04 24.07 25.08

38

50.23 25.15 26.56

40

53.4 26.84

36.1

55.54

45.82

65.5

-6.93

29.28

The figure 1 and figure 2, shows that, initially for two task the convergence diversity is high negative value. It means that the individual is mentally relaxed and stress is negative. Once if the given task increases the stress also increases. This increase in stress is clearly shown in the graph, the point moves from high negative towards the positive. If we increase the task the stress also increases, this increase can be noticed in that graph that the point keep on moving towards the positive number. At certain point of time stress, the individual will be saturated and stress will be at maximum. In the figure 1, stress attains the maximum at 30 task and the stress value is -13.89. After attaining maximum stress the individual get saturated and fails to concentrate to finish the task and looses the interest. Then, of course the stress keeps reducing.

Convergence Diversity

Convergence Diversity of Stress -180 -160 -140 -120 -100 -80 -60 -40 -20 0 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Number of Instance

Figure 1: Graph Between Number of Instance And Convergence Diversity Convergence Diversity of Stress

-150 Convergence Rate

-100 -50 0 50 100 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 Number of Instance

Figure 2: Graph between Number of Instance and Convergence Rate V. Conclusion The advent and sophistication of the technology is to brighten the human life and to minimize the man-hour effort. The goal of each and every technology is also the same as said earlier. Unless until the intended user have not trained or educated how to use the technology, the goal of such technology may not be achieved. The ease of

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use of technology without any stress will be got by the fluency of such technology to be used. Instead, proposed analysis shows that the cause for the stress is mainly depends on the illiteracy about the technology.

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

K. Glanz and M. Schwartz, “Health Behavior and Health Education: Theory, Research and Practice”, Jossey Bass, Wiley publication, 4th edition, 2008.

S. Cohen, D. Janicki-Deverts, and G. Miller, “Psychological Stress and Cardiovascular Disease”, Journal of the American Medical Association, Volume 298, issue 14, 1685–1687, 2007 C. Shively, T. Register, and T. Clarkson, “Social Stress, Visceral Obesity and Coronary Atherosclerosis in Female Primates”, Obesity 17, no. 8 (2009).

M. Scollan-Koliopoulos, “Managing Stress Response to Control Hypertension in Type 2 Diabetes”, Journal of Nurse Practitioner, Volume 30 issue 2, 46-49, 2005.

D. de Quervan et al., “Glucocorticoids and the Regulation of Memory in Health and Disease”, Frontiers in Neuroendocrinology, Volume 30, issue no. 3, 358 - 70, 2009.

J. M.Ellenbogen, J. C. Hulbert, Y. Jiang, and R. Stickgold, “The Sleeping Brain’s Influence on VerbalMemory: Boosting Resistance to Interference”, PLoS ONE, Volume 4, issue, e4117, 2009..

M. Weil and L. Rosen, “Techno stress: Are You a Victim?”, www.technostress.com.

Sharma, H. O. (1999). Effects of training, automation reliability, personality, and arousal on automation-induced complacency in flight simulation task. Unpublished doctoral dissertation, Department of Psychology,Banaras Hindu University, Varanasi.

Osborne, Martin J., and Ariel Rubinstein. A Course in Game Theory. Cambridge, MA: MIT, 1994. Print.

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

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Design of Unified Power Quality Conditioner (UPQC) To Improve the Power Quality Problems by Using Instantaneous Real & Reactive Power Theory 1

S.Natarajan, 2Dr.M.AntoBennet, 3M. Manimaraboopathy,4S.Sankararnarayan,5N.Srinivasan 2.3,4 Department of ECE, VELTECH, Chennai-600062, Tamil Nadu, INDIA. 1,5 Department of EEE, VELTECH, Chennai-600062, Tamil Nadu, INDIA. ___________________________________________________________________________________ Abstract: Paper presents a Design of a Unified Power Quality conditioner (UPQC) connected to three phase four wire system (3P4W). The main aim is to maintain the load bus voltage sinusoidal and at desired constant level in all operating conditions. The neutral of series transformer used in the fourth wire for the 3P4W system.

The neutral current that may flow toward transformer neutral point is compensated by using a four-leg voltage source inverter topology for shunt part. The series transformer neutral will be at virtual zero potential during all operating conditions. In this simulation we observe the power quality problems such as unbalanced voltage and current, harmonics by connecting nonlinear load to 3P4W system with Unified Power Quality conditioner.

A new control strategy such as unit vector template is used to design the series APF to balance the unbalanced current present in the load currents by expanding the concept of single phase P-Q theory.

Keywords: Unified Power Quality conditioner (UPQC), Active Power Filter (APF) and Shunt (SAPF), Vector Template & PQ Controller ___________________________________________________________________________________ I. INTRODUCTION To improve the power quality by connecting the series active power filter (APF) and shunt (APF).They are two types of filters one is passive filters and another one is active filters. In passive filters they are using L and C components are connected. By connecting passive Filters the system is simplicity and cost is very low. And so many disadvantages are there, that is resonance problems and filter for every frequency and Bucky. That’s we are choosing the active filters.By using active filters the power converter circuit using active components like IGBTs, MOSFETs, etc., and energy storage device (L or C). The advantages are filtering for a range of frequencies and no resonance problems and fast response. But only very few disadvantages are the cost is high.

By connecting series active filters the voltage harmonic compensation, high impedance path to harmonic currents these are the main functions. All these non-linear loads draw highly distorted currents from the utility system, with their third harmonics component almost as large as the fundamental. The increasing use of nonlinear loads, accompanied by an increase in associated problems concerns both electrical utilities and utility customer alike.

II. LITERATURE SURVEY The power electronic devices due to their inherent non linearity draw harmonic and reactive power from the supply. In three phase systems, they could also cause unbalance and draw excessive neutral currents. The injected harmonics, reactive power burden, unbalance, and excessive neutral currents cause low system efficiency and poor power factor. The design of shunt active filter is described in [1]. The use of the sophisticated equipment/loads at transmission and distribution level has increased considerably in recent years due to the development in the semiconductor device technology [2]. The equipment needs clean power in order to function properly. At the same time, the switching operation of these devices generates current harmonics resulting in a polluted distribution system. The power electronics-based devices have been used to overcome the major power quality problems. A 3P4W distribution system can be realized by providing the neutral conductor along with the 3 power lines from generation station. The unbalanced load currents are very common and an important Problem in 3P4Wdistribution system. To improve the power quality by connecting the series active power filter (APF) and shunt (SAPF).They are two types of filters one is passive filters and another one is active filters. In passive filters they are using Land C components are connected [3-10]. By connecting passive filters the system is simplicity and cost is very low and so many disadvantages are there, that is resonance problems and filter for every frequency and Bucky. That’s we are choosing the active filters. By using active filters the power using active components like IGBTs, MOSFETs, etc., and energy storage device (L or C).The advantages are filtering for a range of frequencies and no resonance problems and fast response. But only very few disadvantages are there that is cost is high. By connecting series active filters the voltage harmonic compensation, high impedance path to harmonic currents these are the main functions. All these non-linear loads draw highly distorted currents from the utility system, with their third harmonics component almost as large as the fundamental [11&12]. The equipment needs clean power in order to function properly. At the same

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time, the switching operation of these devices generates current harmonics resulting in a polluted distribution system. The power-electronics-based devices have been used to overcome the major power quality problems. A 3P4W distribution system can be realized by providing the neutral conductor along with the 3 power lines from generation station. The unbalanced load currents are very common and an important Problem in 3P4Wdistribution system [13].

III. PROPOSED SYSTEM In proposed system easy expansion of 3P3W system to 3P4W system is possible. The neutral current, present if any, would flow through this fourth wire toward transformer neutral point. This neutral point current can be compensated by using a split capacitor. This neutral current achievement is used the method P-Q theory in UPQC. The extensive use of power electronic based equipment /loads almost in all areas, the point of common coupling (PCC) could be highly distorted. Also, the switching ON/OFF of high rated load connected to PCC may result into voltage sags or swells on the PCC. There are several sensitive loads, such as computer or microprocessor based AC/DC drive controller, with good voltage profile requirement; can function improperly or sometime can lose valuable data or in certain cases get damaged due to these voltage sag and swell conditions. One of the effective approaches is to use a unified power quality conditioner (UPQC) at PCC to protect the sensitive loads. A UPQC is a combination of shunt and series APFs, sharing a common dc link. It is a versatile device that can compensate almost all power quality problems such as voltage harmonics, voltage unbalance, voltage flickers, voltage sags & swells, current harmonics, current unbalance, reactive current. This method is based on the steady state analysis of UPQC during voltage sag and swells on the system. Aim is to maintain the load bus voltage sinusoidal and at desired constant level in all operating conditions. The major concern is the flow of active and reactive power during these conditions, as it plays an important role in selecting the KVA ratings of both shunt and series APF. IV.

SYSTEM CONFIGURATION

Fig.1: Block Diagram of UPQC The UPQC is installed in order to protect a sensitive load from all disturbances. It consists of two voltage source inverters connected back to back, sharing a common dc link. One inverter is connected parallel with the load. It acts as shunt APF, helps in compensating load harmonic current, reactive current and maintain the dc link voltage at constant level. The second inverter is connected in series with the line using series transformers, acts as a controlled voltage source maintaining the load voltage sinusoidal and at desired constant voltage level is shown in fig 1.

V. PROPOSED CIRCUIT OPERATION In series APF the Inverter injects a voltage in series with the line which feeds the polluting load through a transformer. The injected voltage will be mostly harmonic with a small amount of sinusoidal component which is in-phase with the current flowing in the line. The small sinusoidal in-phase (with line current ) component in the injected voltage results in the right amount of active power flow into the Inverter to compensate for the losses within the Series APF and to maintain the DC side capacitor voltage constant. Obviously the D.C voltage control loop will decide the amount of this in-phase component. Series active power filter compensate current system distortion caused by non-linear load by imposing a high impedance path to the harmonic current. The line diagram of series active power filter is shown in fig 2.

Fig. 2: Line diagram of series active power filter

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Implementation of Shunt APF The active filter concept uses power electronics to produce harmonic current components that cancel the Harmonic current components that cancel the harmonic current components from the non- linear loads. The active filter uses Power electronic switching to generate harmonic currents that cancel the harmonic currents from a non-linear load. In this configuration, the filter is connected in parallel with the load being compensated .Therefore the configuration is often referred to as an active parallel or shunt filter is shown in fig 3.

VI.

SHUNT ACTIVE POWER FILTER

Fig. 3: Line Diagram of shunt active power filter Illustrates the concept of the harmonic current cancellation so that the current being supplied from the source is sinusoidal. The voltage source inverter used in the active filter makes the harmonic control possible. This inverter uses dc capacitors as the supply and can switch at a high frequency to generate a signal that will cancel the harmonics from the non-linear load.

VII. STEADY-STATE POWER FLOW ANALYSIS The powers due to harmonics quantities are negligible as compared to the power at fundamental component, therefore, the harmonic power is neglected and the steady state operating analysis is done on the basis of fundamental frequency component only. The UPQC is controlled in such a way that the voltage at load bus is always sinusoidal and at desired magnitude shown in fig 4.

Fig. 4: Equivalent Circuit of a UPQC The voltage injected by series APF can vary from 0 0 to 3600. Depending on the voltage injected by series APF, there can be a phase angle difference between the load voltage and the source voltage. However, in changing the voltage phase angle of series APF, the amplitude of voltage injected can increase, thus increasing the required kVA rating of series APF. The single phase equivalent circuit for a UPQC is shown in the Fig.2. The source voltage, terminal voltage at PCC and load voltage are denoted by v s, vt and VL respectively. The source and load currents are denoted by is and iL respectively. The voltage injected by series APF is denoted by v Sr, whereas the current injected by shunt APF is denoted by i Sh. The load voltage, VL, as a reference phase and suppose the lagging power factor of the load is COSL then,

(1) (2) (3) Where factor k represents the fluctuation of source voltage, defined as,

(4) The voltage injected by series APF must be equal to, (5) The UPQC is assumed to be lossless and therefore, the active power demanded by the load is equal to the active power input at PCC. The UPQC provides a nearly unity power factor source current, therefore, for a given load condition the input active power at PCC can be expressed by the following equations,

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(6) The current provided by the shunt APF, is the difference between the input source current and the load current, which includes the load harmonics current and the reactive current. Therefore, we can write as,

(7) (8) CASE I: In this condition the reactive power required by the load is completely supplied by the source only. When the UPQC is connected in the network and the shunt APF is put into the operation, the reactive power required by the load is now provided by the shunt APF alone; such that no reactive power burden is put on the mains. So as long as the shunt APF is ON, it is handling all the reactive power even during voltage sag, voltage swell and voltage harmonic compensation shown in fig 5.

a) No UPQC

b) With Shunt APF Fig. 5: a) - b) Reactive Power Flow

CASE II: If k < 0, or vt<vL, Ps will be positive, means series APF supplies the active power to the load. This condition is possible during the utility voltage sag condition. is will be more than the normal rated current. Thus the required active power is taken from the utility itself by taking more current so as to maintain the power balance in the network and to keep the dc link voltage at desired level.

Fig. 6: Active Power Flow during Voltage Sag Condition This active power flows from the source to shunt APF, from shunt APF to series APF via dc link and finally from series APF to the load. Thus the load would get the desired power even during voltage sag condition.

Therefore in such cases the active power absorbed by shunt APF from the source is equal to the active power supplied by the series APF to the load shown in fig 6.

CASE III: If k > 0, or vt>vL, Ps will be negative, this means series APF is absorbing the extra real power from the source.

This is possible during the voltage Swell condition. is will be less than the normal rated current. Since vs is increased, the dc link voltage can increase. To maintain the dc link voltage at constant level the shunt APF controller reduces the current drawn from the supply shown in fig 7.

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Fig.7: Active Power Flow during Voltage Swell Condition CASE IV: If k = 0, or Vt = VL, then there will not be any real power exchange though UPQC. This is the normal operating condition shown in fig 8 &9.

Fig. 8: Active Power Flow during Normal Working Condition

Fig. 9: a)-d) Phasor Representation of all Possible Conditions VIII. CONTROL TECHNIQUE The control strategy is based on the extraction of unit vector templates from the distorted input supply. These templates will be then equivalent to pure sinusoidal signal with unity amplitude. The three phase distorted input source voltage at PCC contains fundamental component and distorted component. To get unit input voltage vectors US the input voltage is sensed and multiplied by equal to (1/V m) where Vm is equal to peak amplitude of fundamental input voltage. These unit input voltage vectors are taken to phase locked loop (PLL).With proper delay; the unit vector templates are generated.

Ua=sinωt (9) Ub=sin(ωt-1200) (10) Uc=sin(ωt+ 1200) (11) Multiplying the peak amplitude of fundamental input voltage with unit vector templates gives the reference load voltage signals.

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V∗=Vm∙Uabc (12) The measured load voltages are compared with reference load voltage signals. The error generated is then taken to a hysteresis controller to generate the required gate signals for series APF. The unit vector templates can be applied for shunt APF to compensate the harmonic current generated by non-linear load. The shunt APF is used to compensate for current harmonics as well as to maintain the dc link voltage at constant level.To achieve this dc link voltage is sensed and compared with reference dc link voltage. The error is then processed by PI controller. The output signal from PI controller is multiplied with unit vector templates giving reference source current signal. The three phase source currents are sensed and compared with reference current signals. The error generated is then processed by a hysteresis current controller with suitable band, generating gating signals for shunt APF. IX. EXPERIMENTAL RESULTS The simulation block diagram of 3P4W system realized from a3P3W system utilizing UPQC is shown in Figure 10-13.Non-linear loads means by connecting power electronics devices to system. The plant load is assumed to be the combination of a balanced three-phase diode bridge rectifier followed by an R-L load, which acts as a harmonic generating load and three different single phase loads on each phase, with different load active and reactive power demands. By using equations (1), (3) and (5) to design the unit vector template of series APF is shown in figure.12 and figure.11is Series active power filter controller shown in below figures and also shunt APF is Design in fig 10.Finally we compared with the existing load side V-I Output with the proposed output shown in fig 14 & 15.

Fig. 10: Simulation Design Block Of Shunt Active Power Filter

Fig. 11: Simulation Block of Series Active Power Filter

Fig. 12: Simulation Design Block of Vector Template

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Fig. 13: Simulation Design Block of PQ Controller

Fig. 14: Existing output of load side Voltage & Current Waveforms

Fig. 15: Proposed output of load side Voltage & Current Waveforms IX. CONCLUSION The design of a unified power quality conditioner (UPQC) connected to 3 phase 4 wire distribution system has been presented in this paper. Where upqc is installed to compensate the different power quality problems, which may play an important role in future upqc- based distribution system. The simulation results shows that the distorted and unbalanced load currents seen from the utility side act as perfectly balanced source currents and are free from distortion. Here we can absorb the power quality problems like voltage and current unbalanced and reduced the total harmonic distortion (THD) of 3P4W system utilizing 3p3w system to connect the UPQC. REFERENCES [1] [2] [3]

Vinodkhadkikar, Ambrish Chandra, “A novel structure for three phase four-wire distribution system utilizing unified power quality conditioner (UPQC),” IEEE Transaction on industry application,Vol.45,no.5,pp.1897-1902,Sep/oct.2009.

B. Singh, K. Al-Haddad, and A. Chandra, “A review of active power filters for power quality improvement,” IEEE Trans.

Ind.Electron., vol. 45, no. 5, pp. 960–971, Oct. 1999.

Y.Komatsu and T.Kawabata, “A Control method of active power filter in unsymmetrical and distorted voltage system,” inproc.Conf.IEEE Power Convers. 1997, vol.1, pp.161-168.

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[9] [10] [11] [12] [13]

C. A. Quinn and N Mohan, “Active filtering of harmonic currents in three-phase, four- wire systems with three-phase and single phase nonlinear loads,” in Proc. 7th IEEE APEC, 1992, pp. 829–836.

M.Aredes, K.Heumann, and E.h.Watanabe, “An universal active power line conditioner,” IEEE Trans. power Del., vol.13, no.2,pp.542-551,April1998.

R. Faranda and I. Valade, “UPQC compensation strategy and design aimed at reducing losses,” in Proc. IEEE ISIE, 2002, vol. 4, pp. 1264–1270.

G. Chen, Y. Chen, and K. M. Smedley, “Three-phase four-leg active power quality conditioner without references calculation,” inProc. 19th IEEE APEC, 2004, vol. 1, pp. 829–836.

V.Khadkikar, A. Chandra, A.O.Barry, and T.D.Nguyen, “Application of UPQC to protect a sensitive load on a polluted 2012 International Conference on Computer Communication and Informatics (ICCCI -2012), Jan. 10 – 12, 2012, Coimbatore, INDI distribution network,” in proc. IEEE PES General Meeting.Montreal, QC, Canada, 2006, 6 pp.

M. T. Haque, “Single-phase PQ theory,” in Proc. 33rd IEEEPESC, 2002, vol. 4, pp. 1815–1820.

J.M.Correa , S.Chakraborty, M.G.Simoes, and F.A.Farret, “A single phase high frequency AC micro grid with an unified power quality conditioner,” in Conf.Rec.38th IEEE IASAnnu.Meeting,2003,vol.2,pp.956-962.

H.Akagi, Y.kanazawa, and A. Nabae, “Instantaneous reactive power compensators comparing switching devices without energy storage components,” IEEE Trans. Ind.Appl., vol IA-20,no.3,pp.625-630,may/Jun.1984.

L.H.Tey,P.L.So and Y.C.Chu,“Unified power quality conditioner for improving power quality using ANN with Hysteresis control” IEEE Tran. Power Electronics, vol.9,no.3,may 1994,pp.1441-1446.

Moleykutty George,karthik Prasad Basu, “ Three-phase Shunt active power filter” American journal of applied sciences,5(8):909-916,2008.

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Enhanced More Secure Trust Based Routing Scheme for DSR Protocol in MANETs Ankita Sahu, Vikas Sejwar Department of CSE / IT, Madhav Institute of Technology and Science, Morar, Gwalior, Madhya Pradesh, INDIA. _________________________________________________________________________________________ Abstract: A Mobile Ad hoc Network (MANET) is a group of movable hosts with wireless network interfaces that structure a temporary network without the support of any permanent infrastructure or central administration. A MANET is also referred as an infrastructure less network because the mobile nodes in the network dynamically locate paths among themselves to transfer packets provisionally. In this paper we evaluate the routing paths of nodes to some features before to transmit data to these routes. Experimental results show that our gateway based DSR improves network performance to much extent.

Keywords: Mobile ad hoc network security; Trust feature; DSR Protocol; RREP; RREQ; ____________________________________________________________________________________________________

I. Introduction Mobile Ad hoc Networks (MANET) are placed to set up wireless communication in improvised scenario without a predefined centralized administration or infrastructure [1]. MANET has been normally deployed in adverse and hostile environments where central authority point is not necessary. Another unique characteristic of MANET is the dynamic nature of its network topology which would be frequently changed due to the unpredictable mobility of nodes [2]. MANET is a self organizing network of mobile nodes forming a temporary network. MANET has many salient features which make it different from other networks and enforce many constraints on it. These features include lack of infrastructure, dynamic topology, bandwidth constraint, power constraint etc. The most challenging task in such a dynamic network is routing. Many routing protocols have been proposed and optimized till date and constantly undergoing further evolution. DSR (Dynamic Source Routing) [3, 4] is the most famous and basic routing protocol which works on on-demand paradigm. In this paper the basic functionality of DSR is appended with some intelligent decisions which ultimately lead to energy conservation. Thus this modified approach is called IDSR (Intelligent Dynamic Source Routing).

II. Dsr Overview Wireless routing can be broadly classified into two types: Proactive and Reactive. DSR [3, 4] is a reactive protocol i.e. it works only when a node wishes to send data or whenever there is a link failure. This is called “on - demand” approach of routing protocol. This method is effective in contrast to proactive protocols which use table based approach producing large routing overhead. DSR is based on source routing [5, 6] in contrast to hopby hop routing. In source routing the source collects the complete knowledge of the path to be travelled by the packet. The source appends this path information in the packet header. Thus every packet generated by source has complete path information specifying the identity of all the intermediate nodes to be visited before reaching its destination. The traditional DSR consists of two phases: Route discovery and Route maintenance.

A. Route Discovery This phase allows a source to dynamically discover a path to the destination node. The process begins with generation of RREQ (Route Request) packet. It contains Request ID, sequence number, source ID, destination ID. This packet is then broadcasted to all the neighbor nodes. A node which receives this packet appends its own ID in packet header and forward RREQ to its neighbors. Finally RREQ packet reaches its destination. The destination node generates RREP (Route Reply) and sends it to the reverse path travelled by RREQ packet.

Each node maintains a route cache and caches the path when RREP travels through them.

B. Route Maintenance This phase works whenever there is a link breakage/failure in the network. An intermediate node upon discovering a link failure generates RERR (Route Error) packet and sends it to the source using the path stored in its cache. All the intermediate nodes delete their cache entry for the failed link upon receiving RERR. Traditional DSR works quite efficiently but there is always a scope of improvement. If any one of the internal nodes dies out of energy while the communication is established then the whole process will be interrupted and

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again the path has to be chosen for the specified source to destination. Moreover traditional DSR is based on minimum hop count; it does not bother about the nodes’ residual energy and treat all nodes equivalent.

III. Related work Mittal et al[7] developed the routing protocol which can deal with single and cooperative black hole attack and also without degrading the performance of such networks. The approach provides a feasible solution for the Black hole node detection within the network by making use of “reputation tables” and assigning reputation values to the participating nodes. It improved the routing security of the existing association based DSR protocol using the concept of reputation of nodes value, by identifying and isolating black hole nodes working in a group.

Xu Xiang et al. [8] performed a work," Providing Efficient Secure DHTs Routing". Authors propose a secure protocol which consists of two phases: detecting malicious nodes and bypassing them. Author present a novel efficient routing strategy called tracer routing to control routing progress, accompanied by a node-ID based signature scheme. Combine these two; the source of every query can verify each step and malicious nodes can be identified. Authors also present a scheme to create a secure path to bypass malicious nodes.

Yanzhi Ren et al. [9] performed a work," Detecting Black hole Attacks in Disruption-Tolerant Networks through Packet Exchange Recording". In this paper, Authors present a technique that provide the security from history records of packet delivery information at each contact so that other nodes can detect insider attacks by analyzing these packet delivery records. Author evaluated Presented approach through extensive simulations using both Random Way Point and Zebranet mobility models. Presented results show that Presented method can detect insider attacks efficiently with high detection rate and low false positive rate.

Lanjun Dang et al. [10] performed a work," DASR: Distributed Anonymous Secure Routing with Good Scalability for Mobile Ad Hoc Networks". In this paper, Authors present a new distributed anonymous secure routing protocol with good scalability for mobile ad hoc networks. The proposed protocol addresses the problems of anonymous routing and later anonymous data transmission via a dynamic identity pseudonymity approach based on Incomparable Public Keys. Good scalability is achieved by using Diffie-Hellman key exchange scheme and symmetric key cryptography, instead of public key cryptography.

Saurabh Gupta et al. [11] performed a work," BAAP: Black hole Attack Avoidance Protocol for Wireless Network". In this paper, Authors propose a protocol for avoiding black hole attack without the constraint of special hardware and dependency on physical medium of wireless network. BAAP forms link disjoint multipath during path discovery to provide greater path selection in order to avoid malicious nodes in the path using legitimacy table maintained by each node in the network.

Rajesh Yerneni et al. [12] performed a work, “Enhancing performance of AODV against Black hole Attack". In this paper, a method is proposed called Secure-Ad hoc on demand Distance Vector (SAODV) algorithm that mitigates black hole attack by analyzing destination sequence number and validating the destination by random value. Simulation results show that the proposed protocol provides better security by increasing the packet delivery ratio when compared to AODV in presence of black hole attacks.

IV. Proposed Methodology In this section, we present our friendship-based framework proposed to secure DSR .

Proposed Technique 1) In proposed technique for DSR, Start with source node sends RREQ to their neighbor’s.

2) All nodes update their Routing table.

3) Here we calculate the trust from [f-(d+m)]/ f 4) If value for trust is greater than trust worthy path.

5) Malicious node, do not accept RREQ. 6) In that condition do not listen the node.

Two algorithms are used here forward and reverse routes respectively in DSR protocol.

STEPS OF OUR ALGORITHM: STEP 1: S is the source node. D is the intended destination STEP 2: S sends the data packets to neighbors and update its routing table as per the forwarding packets number STEP 3: The neighbors also forward the routing packets further and the trust value is calculated as per the following formula of step 4 STEP 4: Trust parameters f= number of packets forwarded d=number of packets dropped

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m=number of packets misrouted 4.1: Collect data for f,d,m.

4.2: Calculate total number of packets which were dropped or misrouted i.e (d+m) 4.3: Calculate total number of packets which were successfully reached to the destination i.e (f-{d+m}) 4.4: Calculate direct trust by using formula Trust (t)= [f-(d+m)]/ f ………….(1) STEP 5: If the value of the calculated trust exceeds the obtained value then it does not forward it to the next nodes and considers it as malicious. So the path becomes malicious gateway path. All neighbors are informed not to forward data packets via that route.

STEP 6: If the obtained value is less than the threshold then the packets are forwarded and that gateway or path is considered as path of maximum trust.

STEP 7: In case when maximum values are trust values then obtained maximum valued data packets are forwarded first and considered as path or gateways of that route.

V. Simulation Result Nasty nodes create the following types of attacks against data and control packets: 1. Modification Attack. These attacks are carried out by adding, altering or deleting IP addresses from the ROUTE REQUEST, ROUTE REPLY, ROUTE ERROR and Data packets that pass through the malicious nodes.

2. Black Hole Attack. In this attack the malicious node drops all packets, which it is supposed to forward.

3. Grey Hole Attack. Here the malicious node selectively dumps data and control packets at random intervals.

All malicious nodes work in a non-colluding manner. Each malicious node sporadically alters its attack profile by randomly switching between the three types of attacks. metrices used include: 1) Packet Drop: It is a kind of denial of service attack in the packets i.e. the packets that were dumped by malicious nodes without any notification.

2) Packet delivery ratio (PDR): The PDR in this simulation is defined as the ratio between the number of packets sent by constant bit rate sources and the number of received packets by the CBR sink at destination. It describes percentage of the packets which reach the destination.

PDR= ΣCRB PKtRecv / Σ CRB PktSent 3) Packet forwarding: It is the defined as the forwarding or direction of the packet traffic to the further nodes or neighbors to reach the destination 4) Packet Sent: This is the correct forwarding of the packets to the group of nodes (multicast and broadcast) between the control packets and the data packets received during the simulation time.

5) Packet Receive: It is the fraction of packets that were obtained by nodes after the multicast or broadcast.

6) End to End Delay: It is defined as the difference between two time instances: one when packets generated at the sender and the other, when packet received by the receiving application.

The results are shown as:

Fig. 1: Packet drop

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Fig. 2: Packet forward

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Fig. 3: Packet send

Fig. 5: Pdr (Packet delivery ratio)

Fig. 4: Packet receive

Fig. 6: End to End Delay VI. Conclusion In last few years, MANETs has gained much more attention in research community. There are many active research projects concerned with MANETs. MANETs are wireless networks that use multi-hop routing in place of static networks infrastructure to provide network connectivity. MANETs applications is mostly used in dynamic military and civilian systems. In MANETs the network topology usually varies with time. MANET is facing new challenges in routing protocols because traditional routing protocols may not be suitable for MANETs. In this paper, an overview of DSR in MANETs is present. Using DSR, the overhead is reduced and also eliminate the malicious node. By this trusted route between source and destination is created, that provide secure communication network. In simulation result we have found that the throughput of our proposed protocol remains notably higher than that of the standard DSR protocol in the presence of malicious nodes.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Zhao, Z., Hu, H., Ahn, G. J., and Wu, R. Risk-aware response for mitigating MANET routing attacks. IEEE Conference In Global Telecommunications (GLOBECOM 2010), December 2010, pp. 1-6.

Mohapatra, P., Li, J., and Gui, C. QOS in Mobile Ad hoc Networks. IEEE Wireless Communications, June 2003, 10(3), pp. 4452.

D. B. Johnson and D. A Maltz, "Dynamic Source Routing in ad hoc wireless networks." in Mobile computing (Imielinski and Korth, ), eds vol. 353. Kluwer Academic Publishers, 1996.

D. Johnson, Y. Hu, and D. Maltz, “The Dynamic Source Routing Protocol (DSR) for Mobile Ad Hoc Networks for IPv4”,Experimental RFC 4728, Internet Engineering Task Force (IETF), February 2007.

Roy C. Dixon and Daniel A. Pitt., “Addressing, bridging and source routing”. IEEE Network, 2(1):25–32, January, 1988.

Mittal, S., and Taluja, H. Analysis of Cooperative Black Hole Attack Using Dynamic Source Protocol. International Journal of Advanced Research in Computer Science and Software Engineering(IJARCSSE), August 2012, 2(8), pp. 139-142.

Xu Xiang," Providing Efficient Secure DHTs Routing", 2009 International Conference on Computational Intelligence and Security 978-0-7695-3931-7/09 © 2009 IEEE (pp 510-514) Yanzhi Ren," Detecting Blackhole Attacks in Disruption-Tolerant Networks through Packet Exchange Recording", 978-1-42447265-9/10@2010 IEEE .

Lanjun Dang," DASR: Distributed Anonymous Secure Routing with Good Scalability for Mobile Ad Hoc Networks", 2010 IEEE Asia-Pacific Services Computing Conference 978-0-7695-4305-5/10© 2010 IEEE (pp 454-461) Saurabh Gupta," BAAP: Blackhole Attack Avoidance Protocol for Wireless Network", International Conference on Computer & Communication Technology (ICCCT)-2011 978-1-4577-1386-611©2011 IEEE (pp 468-473) Rajesh Yerneni," Enhancing performance of AODV against Black hole Attack", CUBE 2012, September 3–5, 2012, Pune, Maharashtra, India. Copyright 2012 ACM 978-1-4503-1185-4/12/09 (pp 857-862) .

Yang H, Meng X, Lu S (2002) Scane: Self organized network-layer security in mobile ad hoc networks. In Proceedings of the 1 st ACM workshop on Wireless security, WiSE ’02, pp 11–20, New York, NY, USA. ACM

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Recognising Natural Emotions Using Artificial Neural Network In A Classroom Setting Rhea Mahajan1, Remia Langer2 Department of Computer Engineering, Yogananda College of Engineering and Technology, Jammu (J&K), India 2 PG Department of Psychology, University of Jammu, (J&K), India _________________________________________________________________________________________ Abstract: The advent of computer and its related technologies had brought much advancement. Still the interactive ability of computer is limited in many ways. One such area is the ability to understand and interpret ongoing emotions in human-computer interface. Such ability could be referred to as ‘emotional intelligence’.

Computers today are quite emotionally challenged, as they neither recognize nor possess emotions of their own.

In this paper we propose a novel approach for emotion recognition using artificial neural network in which we will try to examine the emotions of the students. The subjects for the study will be taken from the psychology department studying in a classroom, and their natural occurring emotions during the lectures would be captured with the help of video cameras installed in the classrooms. The still photographs of the students exhibiting different emotions(happy,sad,anger,disgust,neural) would then be subjected to backpropogation algorithm, which will filter the students who showed negative emotions (anger, sadness, disgust, etc.) consistently over few days. Lastly the pedagogical approaches would be employed in order to explore the causes behind such consistent pattern of negative emotions and improve upon that using the data from self-reported questionnaires and psychological counselling of students by the experts in the field.

1

Keywords: emotion recognition, pedagogical, emotional intelligence _____________________________________________________________________________________ I. Introduction The human face plays an important role in many aspects of verbal and non-verbal communication. Regarding verbal communication, the face is involved in speech and as far as non-verbal communication is concerned, the face communicates through expression of emotions and gestures such as nods, winks. A.Mehrabian (1968) mentioned in his paper that the verbal part of a message contributes only 7% of its meaning as a whole; the vocal part contributes 38% while facial movement and the expression gives 55% of the effect of that message and so one can say that the facial part does the major contribution in human communication [1].Computer scientists and engineers have worked on the face from the viewpoints of graphics, animation, speech recognition and pattern recognition but the work on human-computer interaction is still to achieve its desired efficiency.

Reason for this may be attributed to the technical limitations of the development of practical applications rather than any intrinsic lack of relevance to HCI. The attention of HCI researchers is shared by Eye gaze tracking technology, pattern recognition and facial emotion recognition. Gaze tracking technology is so advanced that several companies provide with commercial working systems which are usable even without special technical expertise. The research on face classification, through pattern recognition is also exploited but facial emotion recognition is still in its infant stage as the desired efficiency is still lacking [2].

The aim of this research is to make some advancement in human and computer interaction by electronically perceiving and understanding an image. With recent advances in image analysis, the possibility of automatic detection and classification of emotional and conversational facial signals has slightly increased but is yet to be developed so that it could be used in a reliable manner. Real-time automatic facial expression analysis would incorporate facial expressions into man-machine interaction and make interaction tighter, rational and more efficient. Such a system could also make classification of facial expressions widely accessible as a tool for research in behavioural science and medicine.

A. Emotions What are emotions? This question has intrigued mankind ever since the ancient philosophers. Aristotle was one of these early philosophers with clear ideas about emotion, and wrote in Rhetoric (1378b, English translation by W. Rhys Roberts) that: "The Emotions are all those feelings that so change men as to affect their judgements, and that are also attended by pain or pleasure. Such are anger, pity, fear and the like, with their opposites."

In words of Jonghwa Kim, an emotion is a concept involving three components:  Subjective experience  Expressions (audiovisual: face, gesture, posture, voice intonation, breathing noise)

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

Biological arousal (ANS: heart rate, respiration frequency/intensity, perspiration, temperature, muscle tension, brain wave)

A.1 Significance of emotions in human computer interaction When humans interact, a lot of communication is done using facial expressions or emotional states of the face. This idea can be incorporated in human computer interaction as well because it would lead to reduction in the gap between humans and computers and will make computers to act according to humans by judging their expressions. Thus we can summarise that:  Knowing the user emotion, the system can better adapt to the user needs.  Sensing (and responding appropriately!) to the user’s emotional state will be perceived as more natural, persuasive, and trusting and will shorten the gap between humans and machine.

II. Classification approaches There are several approaches taken in the literature for classification of emotion. Lien, cohn and kanade [8] have used Hidden-Markov Models (HMM) for classification. Other proposed systems use linear neural net classifiers [9] [10] [11] or support vector machines [17]. Support vector machines finds a hyperplane based on support vectors calculated from data to analyse data and recognize patterns. The complexity of the resulting recognizer is characterized by the number of support vectors rather than the dimensionality of the transformed space. The goal in training SVM is to find the separating hyperplane with the largest margin. We expect that the larger the margin, the better is the generalization of the recognizer.

Backpropogation [10] is a type of artificial neural networks in the supervised learning category which is commonly used as a classifier. A sigmoid or radial activation function along with a bias is used to find the output neuron which better represents the inputs. The neural net is used when dimensionality of the input data is high. It gives higher recognition rates.

Hyeob Kim, Chul Huh, Myoung-Ae Chung [19] have used Self organising maps and Naïve-Bayesian classifiers for emotion recognition. The Naïve Bayes algorithm is a classification algorithm based on Bayes rule and particularly suited when the dimensionality of the inputs is high. When the dependency relationships among the features used by a classifier are unknown, then it is assumed that the features are conditionally independent given the category.This so-called naïve Bayes rule often works quite well in practice, and it can be expressed by a very simple belief net.

III. Methodology Our aim is to study and design an efficient high-speed facial emotion recognition system in MATLAB. Emotion recognition, although a trivial task for the human brain has proved to be extremely difficult to imitate artificially, because although commonalities exist between faces, they can vary considerably in terms of age, skin colour, orientation, facial expression and presence of facial furniture such as glasses or facial hair. The problem is further complicated by differing light conditions, image qualities and geometries, as well as the possibility of partial occlusion and disguise. Hence, a robust emotion recognition system should be capable of correctly identify the facial features of an image and use this information to identify the emotion depicted by the image. Research objectives are enlisted as  To design a fully automatic real-time system model for an ideal facial emotion recognition system, this is invariant of background conditions, illumination variance and noise.  To develop a program in MATLAB based on the designed model.  To create a database set of web-cam clicked face images.  To validate and test the facial emotion recognition system.  To be able to recognize spontaneous expressions.  To be robust against different lighting conditions.  To develop a person independent face recognition system.  To be able to work on people from different cultures and different skin colors.  To filter out people exhibiting negative emotions and try to improve them.

IV. Proposed System After studying various a techniques and algorithms for facial emotion identification .We proposed our system in which images are acquired from image databases. Commonly used databases are Cohn-Kanade and JAFFE. Images in the database can be coloured or gray-coloured. For the project we have created our own database of hundred web-cam clicked images. The images of database are coloured. Pre-processing is normally performed before feature extraction, in order to increase system performance. The aim of this step which includes cropping, scaling and smoothing is to have images, which contain a face, expressing a certain emotion. Sometimes, histogram equalization is also used to adjust image brightness and contrast. Pre-processing is done in three

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steps: Cropping the image to remove unwanted areas from the image (‘imcrop’ function in MATLAB). Scaling of the image (‘imscale’ function in MATLAB).Filtering or smoothing the cropped image to remove noise (‘imfilter’ function in MATLAB). Facial animation parameters are used to extract features. Bounding box method is used to find the area of interest. These features are first normalised and then used for training and testing the classifier for the purpose of facial emotion recognition. The classifier used is artificial neural network which is trained using backpropogation algorithm. Classification technique is using the log sigmoid function as the activation function of the neural network.

A. Facial animation parameters (FAPs) Moving Pictures Experts Group (MPEG) introduced the Facial Animation (FA) specifications in the MPEG-4 standard. The MPEG-4 standard supports facial animation by providing Facial Animation Parameters (FAPs). In this approach, it is assumed that an image sequence represents only one expression. The neutral face is used as a baseline face, and for every input image, difference between the baseline and the input face image is calculated to determine image features. The MPEG-4 model defines a face model in its neutral state to have a specific set of properties like a) all face muscles are relaxed; b) eyelids are tangent to the iris; c) pupil is 1/3rd the diameter of the iris and so on [24]. The standard defines 84 key feature points for neutral face as shown in the figure 1.2

Fig. 1.2 : 84 FAPs of a neutral face [22] FAPs are a set of parameters that represent a complete set of facial actions along with head-motion, tongue, eye and mouth control. In other words, each FAP is a facial action that deforms a face model from its neutral state to one of the six expression states. The FAP value indicates the magnitude of the FAP which in turn indicates the magnitude of the deformation that is caused on the neutral model. The Facial Animation Parameters (FAPs) that are utilized in the framework of MPEG-4 are strongly related to the Action Units (AUs) which form the core of the Facial Action Coding System (FACS) [25]. Facial Action Cording System (FACS) is an expression descriptive method which was proposed by Ekman and Friesen, and is widely applied to the researches about facial expressions There are more than 40 AUs[9][10] and it is said that every sort of facial expression can be

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described by the AUs. For example, the combination of nation of AU6 (rise cheeks) and AU12 (pull up edges of mouth) can express "smile".

Fig. 1.3: Facial Action Coding System and Bounding Box Method to find area of interest B. Backpropogation algorithm The back propagation technique actually depends on the three main factors: No. of Iterations, No. of local maxima and local minima and Error difference rate. . First phase is the forward phase. This is the phase where the activations propagate from the input layer to the output layer.The second phase is the backward phase. This is the phase where the error between the observed actual value and the requested nominal value in the output layer are propagated backwards so it can modify the weights and bias values Our designed application would be able to judge the efficiency on all the parameters but the most efficient one is the error difference rate. This parameter would just help to provide a stopping condition or a stopping parameter to our algorithm According to this parameter as the in one reading of a face the value is suppose 76.37 while matching with a neutral face and in next reading for the same expression reading is 76.3705 the algorithm would stop and say that the result is obtained for the neutral expression i.e. it will match the expression as neutral. The technique of the back propagation used forms a six layered network with one input layer, two output layers and three hidden layers. It actually contains 16 inputs for the 16 input feature values in input layer, then it contains 5 nodes in the first hidden layer 40 units in the second and third hidden layer and finally 5 in the first output layer for the 5 expressions and 2 in the second output layer for detecting the male and female facial organs. This classification technique is using the log sigmoid function as the activation function of the neural network

Fig. 1.4: Artificial neural network trained with backpropogation algorithm V. Discussion and Conclusion After emotion classification we will filter out the students showing negative emotions (sad, anger, disgust) consistently over few days. Lastly the pedagogical approaches would be employed in order to explore the causes behind such consistent pattern of negative emotions and improve upon that using the data from self-reported questionnaires and psychological counselling of students by the experts in the field We chose backpropogation algorithm over other because of its ability to cope with noise data, prediction (reduction in mean square error) and effectiveness with large number of input fields is excellent. Apart from emotions our algorithm could also recognize gender which is one of the improvements over previous approaches. The facial emotion recognition program developed can be used as an education tool to study and learn how to develop an efficient facial emotion recognition technique. It can be used by intelligent systems to anticipate user behaviour and needs and act accordingly. System can be used to add rationality in human computer interaction.

Another application of Automatic face expression recognition systems is in robotics, especially humanoid robots. As robots begin to interact more and more with humans and start becoming a part of our living spaces and work spaces, they need to become more intelligent in terms of understanding the humanâ&#x20AC;&#x2122;s moods and emotions. Expression recognition systems will help in creating this intelligent visual interface between the man and the machine. Apart from the two main applications, namely robotics and HCI, expression recognition

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systems find uses in a host of other domains like Telecommunications, Behavioural Science, Video Games, Animations, Psychiatry, boxes and televisions, Educational Software, etc.

Many interesting and practical real-time applications have also been demonstrated. Bartlett et al. have successfully used their face expression recognition system to develop an animated character that mirrors the expressions of the user (called the CU Animate) [20]. They have also been successful in deployed the recognition system on Sony’s AiboRobot and ATR’s RoboVie [20].

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A. Mehrabian. “Communication Without Words.” Psychology Today, 2, (1968), 53-56.

Lyons, M., &Bartneck, C. “HCI and the Face”. Proceedings of the Conference on Human Factors in Computing Systems (CHI 2006), Montreal, pp 1671 - 1674.

HuaGuGuangda and Su Cheng Du, “FEATURE POINTS EXTRACTION FROM FACES”, image and vision computing: Palmerton North, November 2003 NicuSebe , Michael S. Lew , Ira Cohen , AshutoshGarg , Thomas S.HuangEmotion Recognition Using a Cauchy Naive Bayes Classifier ICPR, 2002 Ira Cohen NicuSebe , Larry Chen, AshutoshGarg , Thomas Huang , Facial Expression Recognition from Video Sequences: Temporal and Static ModelingComputer Vision and Image Understanding (CVIU) special issue on Face recognition (http://www.ifp.uiuc.edu/~iracohen/publications.html) P. Ekman and W.V. Friesen.Facial Action Coding System: Investigator’s Guide. Consulting Psychologists Press, Palo Alto, CA, 1978.

K. Scherer and Ekman.Handbook of methods in nonverbal behaviour research.Cambridge University Press, Cambridge, UK, 1982.

James J. Lien, Jeffrey F. Cohn, Takeo Kanade and Ching-Chung Li, “automated facial expression recognition based on facs action units”,IEEE (1998).

Ying-li-Tian , Takeo Kanade and Jeffrey F. Cohn. “recognizing lower face action units for facial expression analysis”, IEEE (2000) Ying-li-Tian , Takeo Kanade and Jeffrey F. Cohn. “recognizing upper face action units for facial expression analysis”, IEEE (2000) Ying-li-Tian , Takeo Kanade and Jeffrey F. Cohn. “recognizing action units for facial expression analysis”, IEEE (2001) H.ShimadaT.Kunihiro, D.Yang, H.Yoshikawa, “design of affective interface for realizing human-machine empathy”, IEEE (2000).

Hong-Bo Deng ,Lian – Wen Jin ,Li-Xin Zhen, Jian –Cheng Huang, “a new facial expression recognition method based on local gabor filter bank and pca plus lda” . International Journal of Information Technology Vol. 11 No. 11 (2005) MariofannaMilanoval, Nikolay Sirakov2 , “recognition of emotional states in natural human-computer interaction”, IEEE (2008).

MohammadShahidul Islam and SurapongAuwatanamongko, “a novel feature extraction technique for facial expression recognition”, IJCSI 2013 M. A. Turk and A. P. Pentland, “ face recognition using eigen faces”. In Proc. of Computer Vision and Pattern Recognition, pages 586–591. IEEE,(2002).

P. Li, S. L. Phung, A. Bouzerdom, and F. H. C. Tivive, “feature selection for facial expression recognition”, IEEE (2010) Devi Arumugam and Dr. S. Purushothaman, “emotion classification using facial expression”, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 7, 2011 Byoung-Jun Park, Eun-Hye Jang, Sang-Hyeob Kim, Chul Huh, Myoung-Ae Chung and Jin-Hun Sohn ,“emotion recognition using autonomic nervous system responses”,The Sixth International Conference on Advances in Computer-Human Interactions (2013) M.S. Bartlett, G. Littlewort, I. Fasel, and R. Movellan, “real time face detection and facial expression recognition: development and application to human computer interaction,” Proc. CVPR Workshop on Computer Vision and Pattern Recognition for Human-Computer Interaction, 2003.

P. Ekman and W.V. Friesen, “Manual for the Facial Action Coding System”, Consulting Psychologists Press,1977.

I.S. Pandzic and R. Forchheimer (editors), “MPEG-4 Facial Animation: The Standard, Implementation and Applications,” John Wileyand sons, 2002.

P. Ekman, W.V. Friesen, J.C. Hager, “Facial Action Coding System Investigator’s Guide”, A Human Face, Salt Lake City, UT, 2002.

VinayBettadapura, “Face Expression Recognition and Analysis: The State of the Art” Georgia Institute of Technology.

A. Raouzaiou, N. Tsapatsoulis, K. Karpouzis and S. Kollias, “Parameterized facial expression synthesis based on MPEG-4” Chuan-Yu Chang,, Jeng-Shiun Tsai, Chi-Jane Wang, and Pau-Choo Chung, “emotion recognition with consideration of facial expression and physiological signals”, IEEE, 2009.

Introduction to Neural Networks (John A. Bullinaria, 2004) Barbara Hammer, Marc Strickert and Thomas Villmann, “learning vector quantization for multimodal data”,University of Osnabruck, Germany, 2003.

Laurenefausett, Fundamentals of neural networks, architectures, algorithms and applications, 2005.

John A. Bullinaria, “Introduction to Neural Computation”, 2007 Yu Weiwei and Yan Nannan, “facial feature extraction on fiducial points and used in face recognition”, IEEE, 2009 RobertoValenti and Theo Gevers, “Accurate Eye Centre Location and Tracking Using Isophote Curvature”

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

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

International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net An Approach to Solve Single Machine Job Scheduling Problem using Heuristic Algorithm Satyasundara Mahapatra1, Dr. Rati Ranjan Dash2, Dr. Sateesh Kumar Pradhan3 1 Department of Computer Science, IISIT, Bhubaneswar, INDIA 2 Department of Mechanical Engineering, CET, Bhubaneswar, INDIA 3 Department of Computer Science, Utkal University, Bhubaneswar, INDIA __________________________________________________________________________________________ Abstract: Single Machine Job Scheduling (SMJS) problem is a scheduling problem where a batch of n jobs scheduled to be processed through a single machine. The significant of these types of problems are their own inherent value, as well as their role as building schedules by the scheduler for more generalized and complex problems. To enhance the scheduler efficiency of a router in computer networks an efficient and optimized scheduling algorithm is required. This seems to have improved the Quality of Service (QoS) of routers in computer networks. Minimizing the Waiting Time Variance (WTV) is one of the vital criteria for developing a heuristic algorithm. As minimizing the WTV is a NP-Hard problem, a heuristic based novel solution approach is intended in this paper. This approach illustrated with small size problems along with randomly new generated problems. The experimental results are compared with existing approaches. The experimental result shows the proposed approach gives best performance for the compared approaches and problems tested. The findings of intended algorithm are shown in the form of table for consonant problems.

Keywords: Job Scheduling, Quality of Services, Single Machine, Waiting Time Variance, Computer Network. __________________________________________________________________________________________ I. Introduction Scheduling is a decision-making problem used on a regular basis in many manufacturing and service provider industries. This mechanism deals with the allocation of resources to tasks over given time periods and its goal is to optimize one or more objectives. The form of resources (machines) and tasks (jobs) are varying from organization to organization. In computer and network organization routers are treated as resources and data packets are treated as jobs. Each job may have a certain priority level, an earliest possible starting time, a due date etc. Hence ‘Timeliness’ is one of the major attribute of QoS to measure the performance of a process.

Provide a schedule by minimizing WTV in a single machine is the objective of this paper.

SMJS problems are important for various reasons. The environment of single machine is very simple and designed for special cases. The most important properties of single machine are neither machines in parallel nor machines in series. The results of single machine not only provide insights into the single machine environment but also provide a basis for heuristics that are applicable to more complicated machine environments. In general, scheduling problems in more complicated machine environments are often decomposed into sub-problems that deal with single machines. In the single machine job scheduling problem ‘n’ number of jobs has to be processed by a single machine with one or more type of processing objectives, order and constraints. In consideration of a job has to wait for processing due to non availability of the machine in real time, as it is processing another job and may also due to the precedence process constraint. Many exact and heuristic algorithms have been proposed over the years by the researchers for solving the different SMJS problems. Research on design and implementation of scheduling algorithms has been carried out by many scholars to provide an improved QoS for Internet applications in network.

There are number of studies has been done by considering the variance minimization of job completion time or completion time variance (CTV) in due course of time by the scientists. A tabu search-based procedure, a dynamic programming algorithm and a branch and bound algorithm for the CTV problem have been developed and discussed in[1][19][3]. Several heuristic methods have been discussed for the CTV problem in [9][18][5].

The appropriateness of considering non-regular measures of performance with respect to scheduling problems is discussed in [12]. They considered the problem of scheduling n jobs on a single machine so as to minimize the variance of job completion times, called the CTV problem. The V-shaped property of the optimal sequence for CTV and WTV is explain in [2][5] . The problems of scheduling with non-regular penalty functions are discussed in [9][18].

There are only a few studies are done on job scheduling to minimize WTV, although a job sequence for WTV and a job sequence for CTV are discussed in[12]. Two heuristic methods are discussed in [5] for better performance in reducing WTV for a number of small data sets. Another two heuristic methods named as Verified Spiral (VS) and Balanced Spiral (BS) are also discussed in [15] and compared with the methods

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discussed in [5]. In a computer and network system, it is desirable to generate the sequence by minimize WTV rather than CTV, as CTV relies on the variable sizes of jobs.

This paper proposes a heuristic based novel approach as a solution to the problem with an effect of minimization of WTV where the jobs are distributed in the scheduling sequence with an extremely less computational cost.

The effectiveness with respect to WTV is done by comparing the result with existing heuristic methods. The findings are shown in the form of table for corresponding problems.

The rest of the paper is structured as follows. The problem of WTV is briefly described in Section 2, In Section 3, different existing heuristic methods are discussed along with the proposed heuristic method with pseudo code.

In Section 4 problems are generated for the testing as examples, In Section 5 the heuristic algorithms are implemented with the generated problems and the effectiveness with respect to WTV is tested and Section 6 contain the conclusion of the work and suggestion for future studies.

II. Problem Formulation for Job Scheduling Problem The Single Machine Job Scheduling problem by minimization of WTV can typically describe as an allocation of jobs to a machine by considering the concept that once a job get in to the machine for processing, it was not leaved from the machine until the processing time of that job was over. The decision whether the job “j” (i.e the job number) is scheduled to the machine successfully, then “i” the allocation variable is 1 (One) or 0(Zero) otherwise, which can be represented by an integer. These decision variable depends upon the position of job in the job sequence, which is represented by “di j” for i=1,2,…….,n and j=1,2,……….,n . The job to be scheduled first is placed at first position thus processed first; the job to be scheduled second is placed at second position thus processed second, and so on. Then the waiting time for job j at position i is represented as ‘wti’ and the processing time of job j is represented by ‘ptj’. The WTV of jobs in a complete sequence is obtained as follows in equation (1).

(1) The objective is to minimize the variance of waiting time of n number of jobs can be found by the following equation (2).

(2) Subject to: (3) (4) (5) (6) (7) The constraint that each position of the sequence is used exactly once by a job is described in equation (3). Each job is assigned to a position in the sequence is exactly once is described in equation (4). The integer constraint for decision variable is described in equation (5). The waiting time for first job is described in equation (6) and the waiting time wti of the job at position i (i >= 2) is described in recursive equation (7).

III. Job Scheduling Methods In the literature, various job sequencing methods are developed in course of time and also improved further. Out of these some are exact method and some others are heuristic or meta-heuristic method. To search the best all possible sequence has to be tested with desired objective one by one. For small problems this is possible, but it becomes complex and difficult as the problem size increase. Hence some heuristic methods are developed and adapted to obtain an optimum solution without calculating the total possible combinations of sequences. As one by one calculation expenses a lot of computer resources. Such as Computer memory, Computer network, CPU, time and so on. On the other side heuristic methods are good for providing a near optimum solution without searching for the most optimum solution with fewer resources (i.e time). These heuristics are used for calculation of CTV and WTV.

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Out of number of heuristic methods Verified Spiral (VS) and Balanced Spiral (BS) are described below with the help of Pseudo code.

A. Verified Spiral (VS) The Verified Spiral (VS) method developed by incorporating Schrage’s conjecture and Hall and Kubiak’s proof about the placement of the first, second and third longest jobs are described in[7]. And then, modify the spiral placement of remaining jobs by adding a selection between two positions to place the next job, either before the tail (end) or after the head (beginning) of the job sequence, based on which position produces a smaller variance of waiting times for jobs already in the sequence.

Pseudo code of the Verified Spiral method procedure VS Arrange all the jobs of job pool in ascending order on the basis of their processing time.

Pick the nth job from the job pool and placed in the last position of the job sequence.

Pick the (n-1)th job and placed in the last-but-one position of the job sequence.

Pick (n-2)th job and placed in the first position of the job sequence.

Pick the 1st job and placed in the second position of the job sequence. repeat Remove the longest job from the job pool and place the longest job either before the first job or after the first job in the job sequence, depending on which position of the job sequence produces a smaller WTV so far. until (the job pool is empty) return (arranged sequence) end procedure

B. Balanced Spiral (BS) To reduce the computational cost associated with the VS method, the Balanced Spiral (BS) method replaces the verification computational cost of WTV during the placement of individual job selection process for job sequencing. The developed BS method is based on an observation of balanced left (L) and right (R) optimal sequence of the processing time of individual jobs to get near optimum job sequence. To explain the balancing behavior of a job sequence problem is considered. The basic concept is to balance a job sequence arrived in ascending of their job processing time. By locating the processing time of different jobs on the right side (R) and left side (L) such that approximate sum of the processing time of jobs on both the side are almost same is known as Balance. For optimal sequence the first longest job is placed last, the second longest job is placed is placed first, and the third longest job is placed in last-but-one. The rest of the jobs in the job pool are placed on right side or left side by balancing the processing time of individual.

Pseudo code of the Balanced Spiral method procedure BS Arrange all the jobs of job pool in ascending order on the basis of their processing time.

Pick the nth job from the job pool and placed in the last position of the job sequence.

Pick the (n-1)th job and placed in the last-but-one position of the job sequence.

Pick (n-2)th job and placed in the first position of the job sequence. //(Note: Let the sequence L store the processing time of (n-2)th job and sequence R store the processing time of (n-1)th job. Remember that processing time of nth job is not including in R.)// Set SUM_L ← sum of the processing times of all jobs in L sequence.

Set SUM_R ← sum of the processing times of all jobs in R sequence.

Set k ← 4 //k represent for counter Repeat If (SUM_L < SUM_R) Append the kth job to the last position of the L sequence.

SUM_L ← SUM_L + Processing time of kth job.

End if If (SUM_L SUM_R) Add the kth job to the fast position of the R sequence.

SUM_R ← SUM_R + Processing time of kth job.

End if k ← k+1 until (the job pool is empty) return (arranged sequence) end procedure

C. Proposed Method (RSS) In our locality, the fishmonger are sales a complete unit of fish. Sometimes a large fish has to be distributed equally to two or more customers. These fishmongers are skilled enough to distributes along with the time of cutting to reduce the post measuring for equality and it is generally are most equal. This distribution style to serve two customers is used in this proposed method, which named as RSS. This is a method of allocation of jobs in the scheduling sequence with an extremely less computational cost with sorting of job on the basis of their processing time.

Pseudo code of the proposed RSS method procedure RSS Arrange all the jobs of job pool in ascending order on the basis of their processing time.

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Satyasundara Mahapatra et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 11(2), December 2014-February 2015, pp. 157-163 n ← total number of elements inside the job pool tnp ← n //tnp: Total number of elements left inside the job pool for place in job sequence // ctr ← 1 repeat if (ctr is an odd number) if (tnp<2) Add only one job from kth position backwards to the fast position of the RIGHT sequence and Decrement tnp value by 1. else Add two jobs from kth position backwards to the fast position of the RIGHT sequence and Decrement tnp value by 2. end If end if if (ctr is an even number) if (tnp<2) Append only one job from kth position backwards to the last position of the LEFT and sequence Decrement tnp value by 1. else Append two jobs from kth position backwards to the last position of the LEFT sequence and Decrement tnp value by 2. end if end if ctr ← ctr+1 until(ctr<=n/2) return (arranged sequence) end procedure

The effectiveness of the proposed RSS method is shown in the next section by compared with VS and BS methods.

IV. Problem for testing In the above section three heuristic algorithms are depicted. In this section for finding the effectiveness of these algorithms some problems are selected. Twenty four small test cases have been selected to find the effectiveness of the algorithms. The number of jobs varies from 5 to 10 in these cases. There are twelve integer time cases and twelve are of non-integer cases are considered. The data has generated with the help of Normal, Poisson, Exponential and Uniform distribution for all the cases. The same is represented in Table – 1. The case number and the number of jobs along with the table, shows the processing times of the jobs in order.

TEST CASE NO. 1 2 3 4 5 6 7 8 9 10 11 12

NO.OF JOBS 5 5 6 6 7 7 8 8 9 9 10 10

PROCESS TIME 19.13 22 27.2 20 18.31 21 14.69 13 8.35 23 20.69 13

23.08 21 30.16 21 22.64 17 22.25 28 12.75 18 10.46 23

24.32 20 15.11 23 25.28 21 18.64 27 21.67 14 18.18 15

12.92 31 16.79 18 20.07 22 19.49 12 21.96 25 15.76 12

21 12 22.6 9 28.07 22 12.85 22 22.26 22 16.18 18

18.1 20 15.92 17 16.18 18 19.35 17 14.36 16

10.01 19 22.05 21 20.92 19 20.39 20

16.05 9 17.62 23 30.53 27

24.31 24 16.42 20

18.6 26

Table – 1: Twelve small size test cases with their processing time.

V. Performance Analysis of the Algorithms To measure the performance of the proposed RSS algorithm, all possible sequences are generated by placing the jobs randomly for each problem of Table - 1. Each generated possible sequence is considered as one sub example of all possible Optimal Sequences. There are 120 numbers job sequences (e.g.5!) as there are five jobs in the test case – 1 & 2.There are 720 numbers of job sequences (e.g.6!) as there are six jobs in the test case – 3 & 4. There are 5,040 numbers of job sequences (e.g.7!) as there are seven jobs in the test case – 5 & 6. There are 40,320 numbers of job sequences (e.g.8!) as there are eight jobs in the test case – 7 & 8.There are 3,62,880 numbers of job sequences (e.g. 9!) as there are seven jobs in the test case – 9 & 10. There are 36,28,800 numbers of job sequences (e.g. 10!) as there are ten jobs in the test case – 11 & 12. But the above discussed three job scheduling heuristic methods generate only one job sequence for each test case of Table - 1. The basic aim is to calculate WTV for the cases and optimize it and the optimum sequence is always V-shaped. The WTV and Mean Waiting Time (MWT) for test case – 1 to test case – 12 of Table – 1 are shown in Table – 2 and Table – 3 and are rounded to four significant digits using double precision arithmetic.

Table – 2 shows the Comparative results of WTV by three heuristic methods along with optimal method for twelve small size test cases. It was found VS method gives ten numbers of optimum results and two numbers of

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near optimum results, and BS method gives eight optimum results and four near optimum results. The proposed RSS method gives nine optimum results and three near optimum results. All the sequence obtained by proposed RSS method in each test case is also satisfying the V-shape property.

TEST CASE NO. NO.OF JOBS OPTIMUM VS BS RSS 1 5 854.4793 854.4793 854.4793 854.4793 2 5 834 834 834 834 3 6 1269.9586 1269.9586 1269.9586 1269.9586 4 6 1003.6 1003.6 1003.6 1003.6 5 7 1451.6582 1451.7374 1451.985 1451.7374 6 7 1693.6191 1693.6191 1693.6191 1693.6191 7 8 1614.6995 1614.6995 1614.9288 1614.6995 8 8 1533.0715 1533.0715 1533.1429 1533.0715 9 9 2184.1714 2184.1714 2184.1714 2184.1714 10 9 2747.3612 2747.3612 2747.3612 2747.3612 11 10 2334.2729 2334.295 2334.295 2334.295 12 10 2599.6112 2599.6112 2599.8778 2599.8778 Table – 2: Comparative results of WTV by three heuristic methods along with Optimal method for twelve small size test cases.

The table – 3 given below shows the comparative results of MWT by five heuristic methods for twelve small size test cases. Overall The MWT value of proposed RSS method is considerable better than all the heuristic methods discussed above along with the optimum method.

TEST CASE NO. NO.OF JOBS OPTIMUM VS BS RSS 1 5 38.062 38.062 38.062 38.062 2 5 37 38 38 38 3 6 48.585 48.585 48.585 48.585 4 6 44 44 44 44 5 7 55.2172 55.1272 55.9715 55.1272 6 7 58.4286 58.5715 58.5715 58.5715 7 8 59.46 59.46 59.8 59.46 8 8 59.75 59.75 60 59.75 9 9 72.3923 72.4878 72.4878 72.4878 10 9 79.8889 79.8889 79.8889 79.8889 11 10 75.606 75.618 75.618 75.618 12 10 80.5 80.5 80.9 80.1 Table -3: Comparative results of MWT by three heuristic methods along with Optimal method for twelve small size test cases.

Twelve small sized test cases are solved by five heuristic methods discussed in section 3 including proposed RSS method. Each method generates a sequence of jobs with respect to WTV and MWT based on a particular arrangement. The deviation performance of each method’s sequence is measured by the WTV and MWT. The percentage of deviation of WTV is represented by WTVPD and defined as follows: (8) Where WTVOptimal :The minimum WTV produced by Optimum method.

WTVMethod :The WTV generated by the individual heuristic method as discussed above

And the percentage of deviation of MWT is represented by MWT PD and defined as follows: (9) Where MWTOptimal :The minimum MWT produced by the Shortest Path Method.

MWTMethod :The MWT generated by the individual heuristic method as discussed above

Table-4 given below shows the comparative performance deviation results of WTV for twelve small size test cases by three heuristic methods. Smallest value of WTVPD indicates the best performance of the heuristic method among the three heuristic methods. VS gives optimal sequence in ten test cases and near optimum in two test cases. BS gives optimal sequence in seven test cases and near optimum in five test cases. The proposed RSS gives optimum in nine test cases and near optimum in three test cases. It was found that in some test cases

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the WTVPD values of VS, BS and proposed RSS are same. And in some cases it was also found that the proposed RSS gives better near optimum result than BS method.

TEST CASE NO. NO.OF JOBS VS BS RSS 1 5 0 0 0 2 5 0 0 0 3 6 0 0 0 4 6 0 0 0 5 7 0.005456 0.022516 0.005456 6 7 0 0 0 7 8 0 0.014198 0 8 8 0 0.004659 0 9 9 0 0 0 10 9 0 0 0 11 10 0.000946 0.000946 0.000946 12 10 0 0.010258 0.010258 Table – 4: Comparative performance deviation results of WTV for Twelve small size test cases

The comparative performance deviation results of MWT for twelve small size test cases are given below in Table – 5. Minimum value of MWTPD indicates the best average performance on mean waiting time of a heuristic method out of the three heuristic methods. As shown on the table given below, the overall average performance of proposed RSS method is better than other heuristic methods.

TEST CASE NO. NO.OF JOBS VS BS RSS 1 5 9.279357 9.279357 9.279357 2 5 10.46512 10.46512 10.46512 3 6 8.203111 8.203111 8.203111 4 6 10.92437 10.92437 10.92437 5 7 12.28504 14.00471 12.28504 6 7 5.128205 5.128205 5.128205 7 8 8.106634 8.724802 8.106634 8 8 17.73399 18.2266 17.73399 9 9 13.77771 13.77771 13.77771 10 9 8.774584 8.774584 8.774584 11 10 9.82847 9.82847 9.82847 12 10 12.58741 13.14685 12.02797 Table – 5: Comparative performance deviation results of MWT for Twelve small size new test cases

To know the effectiveness of the WTVPD and MWTPD of individual heuristic methods a performance deviation analysis was done by comparing the methods among themselves. Each method was tested with 1000 small sized test cases where the jobs are between 5 to 10 numbers. These 1000 test cases are generated with the help of four distribution methods named as Normal, Poisson, Exponential and Uniform distributions. Table – 6 shows that in case of Normal distribution the proposed RSS method is 74.5834% better than VS and 75% better than BS. Table – 7 shows that in case of Poisson distribution the proposed RSS method is 77.9167% better than VS and 80.4167% better than BS method. Table – 8 shows that in case of Exponential distribution the proposed RSS method is 58.3334% is better than VS and 78.0834% better than BS method. And Table - 9 shows that in case of Uniform distribution the proposed RSS method is 85% better than VS and 85.8334% better than BS method.

BS RSS VS 82.5 74.5834 BS 75 Table – 6: Percentage result of WTVPD of small size test cases generated by Normal Distribution BS RSS VS 92.5 77.9167 BS 80.4167 Table – 7: Percentage result of WTVPD of small size test cases generated by Poisson Distribution BS RSS VS 61.6667 58.3334 BS 72.0834 Table – 8: Percentage result of WTVPD of small size test cases generated by Exponential Distribution BS RSS VS 97.9167 85 BS 85.8334 Table – 9: Percentage result of WTVPD of small size test cases generated by Uniform Distribution

In the processes of generating an efficient job sequence schedule by the different heuristic methods the computational average time is treated as computational cost. From the above discussed heuristic methods it was

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found that except optimal method all the heuristic methods are needed a sorting mechanism at the beginning of the process. In other word it can be says that only sorted data are required for processing through any heuristic methods. Among the entire available sorting techniques Quick sort is a sorting technique which takes O (n log n) times for computation. The computational cost of VS method is much larger then BS and proposed RSS method, as the calculation of WTV is made multiple times as may be required in a test case on the basis of the job numbers, and the computational cost of BS method is also larger than the proposed RSS method, as the calculation of total processing time is made multiple times as may be required in a test case on the basis of job numbers. As a result by minimizing WTV an optimum or near optimum sequence in comparatively very less and more acceptable computational cost has produced by using the concept of cutting a large fish into small pieces and distributed among the customers uniformly by a fishmonger’s named as ‘RSS’.

VI. Conclusion Application of proposed RSS heuristic method for minimizing WTV in job scheduling is described in this article with pseudo code. Two existing heuristic methods namely VS and BS methods are also described with the help pseudo code. Twelve numbers of small size test cases where the job varies from 5 to 10 numbers are generated randomly with the help of four distribution methods namely Normal, Poison, Exponential and Uniform distribution. These twelve small size test cases are again tested with RSS method and two existing heuristic methods. The experimental results are compared and found that the generated job sequence for individual test case is optimum or near optimum with minimum computational cost. To know the performance of WTV PD, the proposed RSS method is again tested and compared by two existing methods with 1000 numbers small size test cases randomly generated with four distribution methods. It was also found that the proposed RSS method gives optimum or near optimum sequences with minimal computational cost. To strengthen the service quality of the resources like router for scheduling data packets, scheduling services of web request in a web server in the computers and network the proposed RSS method may be used. This proposed method can also be applied for scheduling jobs in batch on a manufacturing unit of an organization. In future this work will be extended with large size of test cases generated by different distribution methods.

VII. [1] [2] [3] [4] [5] [6]

[7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]

References

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Cai X V-shape property for job sequences that minimize the expected completion time variance. European Journal of Operational Research. 91(1): 118–23, 1996.

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Kubiak W New results on the completion time variance minimization. Discrete Applied Mathematics. 58(2): 157–68, 1995.

Manna DK and Prasad VR Pseudo polynomial algorithms for CTV minimization in single machine scheduling. Computers and Operations Research. 24(12): 1119–28, 1997.

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Mittenthal J, Raghavachari M and Rana AI V- and -shaped properties for optimal single machine schedules for a class of nonseparable penalty functions. European Journal of Operational Research. 86(2): 262–9, 1995.

Mosheiov G Minimizing mean absolute deviation of job completion times from the mean completion time. Naval Research Logistics. 47(8): 657–68, 2000.

Nong Ye, Xueping Li, Toni Farley and Xiaoyun Xu Job scheduling methods for reducing waiting time variance. Science Direct.

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Schrage L Minimizing the time-in-system variance for a finite jobset. Management Science. 21(5): 540–3, 1975.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Performance Comparison of Unicast and Multicast Routing Protocol in MANETs using NS2 Mandeep Singh1 , Prakash Rao Ragiri2 Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, Geeta Colony, New Delhi, India 1,2

Abstract: MANET is a self organized network and is composed of various independent nodes connected in an arbitrary fashion. Communication over a MANET network basically occurs through some different types of communication, such as unicast and multicast. Our work is generally been focusing on two of the protocols i.e. unicast and multicast routing protocols AODV and PUMA respectively. We will be comparing the performance of both AODV and PUMA by varying the number of nodes and thereby calculating the throughput and packet delivery ratio.

Keywords: AODV; MANET; Multicast; PUMA; Unicast.

I. Introduction A mobile ad hoc network is composed of several independent nodes connected arbitrarily [1]. These intermediate nodes act as routers and communicate with other nodes within its range through radio waves. The nodes which are not in its neighbor communicate via multi- hop protocols [9][11]. In general, MANET supports different types of communication unicast, multicast and multipath. In Unicast communication, stream of packets are sent from single source to a single destination [3] [12], whereas in multicast communication data packets are sent from single source to a number of destinations [3] [7].

In this paper, we will be focusing on mesh based routing protocols. A Mesh based Routing Protocol is based on enabling multiple paths among sending and receiving nodes. With the presence of several alternative paths, the performance of multicast routing protocol becomes efficient [7]. This paper will be a considering a unicast protocol namely AODV (Ad hoc on demand Distance Vector) Routing Algorithm and a multicast protocol PUMA (Protocol for Unified Multicasting through Announcements) [3].

Ad hoc on demand Distance Vector (AODV) Routing Protocol is a unicast routing protocol [4]. In order to deliver a packet using this algorithm, a path will be created only when its need arrives. This packet forwarding is based on the usage of two messages: Route Request (RREQ) and Route Reply (RREP) [2] [4] [5]. When source node is willing to send packet to its destination, it first starts route discovery process by generating a route request (RREQ) message [8]. If the destination node receives this packet then it records a reverse path to the respective sender. It then rebroadcasts this established route over the network. After knowing the reverse path towards sender, the destination node will send an RREP message back tracking the sending node. Thus, by using unicast approach AODV establishes path to the destined node [8] [9] [10] [13].

PUMA is a multicast protocol that is based on Multicast Announcements (MA) where any multicast sender can send its data packets to multicast receivers without involving the group. Any non member node is capable of sending packets to multicast receiver without joining the group [3] [6]. PUMA is based on using a single control message; an MA is exchanged among other network nodes within the group [10]. Each multicast announcement specifies a sequence number, core ID, a group ID, distance to core, a mesh member with sending node belonging to the mesh, notifies other nodes about joining or leaving the group and parent node from which the node received latest announcement packet with minimum core distance [7]. Core node acts as a special receiver and is selected by core election algorithm. On the basis of MA packet received from the neighboring nodes, a node’s connectivity list is maintained [3] [7]. PUMA being a multicast routing algorithm is not dependent on any unicast approach and doesn’t support any pre- assigned nodes for its function [3] [10] [12] [14].

This paper is based on the comparisons made among the routing protocols. The main focus of this paper is on unicast and multicast routing protocol approaches. The organization of this paper is as follows: In Section II, this paper will be highlighting the related work in this stream. Section III defines these routing protocols, Section IV gives a brief introduction to the simulation tool used, Section V defines Experimental work, the simulation parameters, and the representation of the simulation result and Section V concludes the entire work and thereby describes the Future Work.

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II. Related Work A wide variety of MANET based application are based upon unicast communication [1] [11]. This type of communication allows MANET to successfully transmit data from one source to one single destination [12].

With advancement in application running over MANET multicast communication came into existence.

Multicast communication provides a high packet delivery ratio even with high mobility and high traffic load [7].

The Ad hoc on demand Distance Vector Routing protocol is primarily based on unicast communication. AODV is a famous on- demand routing protocol for ad hoc network, it basically consists of route discovery and route maintenance processes. It uses three kind of control messages, Route Request (RREQ), Route Reply (RREP) and Route Error (RERR) [9]. AODV is based on the principle of discovering routes when needed. AODV algorithm has some capabilities; low network utilization, low processing, memory overhead and efficiently working under high mobility condition. AODV can be used to establish routes among two different network nodes. The route request will be made as per demand, which accounts for continuous change in network structure, thereby updating routing table. The information regarding next hop to the destination and sequence number will be stored in the routing table. Whenever a link between two nodes is broken, the routing table gets updated [8] [9] [10] [13]. In a similar way another approach used in MANET is based on multicast communication, PUMA. Protocol for Unified Multicasting through Announcement allows any source to send multicast packet to a multicast group [10] [12] [14]. It is in contrast that mesh based protocol, which has several paths between source and destination pair. Existing studies show that tree based protocols are not best suited for multicasting in MANET. Due to availability of multiple paths a mesh based protocol performs efficiently as compared to tree based protocol [3] [12]. This helps datagram packets to be sent to the destination even if the link fails [3]. An experimental approach is been followed in order to define the variation in performance of both unicast (AODV) and multicast (PUMA) protocols by determining the throughput and packet delivery ratio upon varying the number of nodes.

III. Routing Protocol A. AODV Ad hoc on demand Distance Vector routing algorithm is a unicast routing protocol and sends data packet to a single host. This protocol first aims at establishing a route where server sends a Route Request (RREQ) message. Destination node on receiving the packet will then generate a reverse path to the sender and will forward a Route Reply (RREP) message. After establishing this path the destination traces back to send RREP message to the sender [10] [11].

AODV is a dynamic distance vector routing protocol. In reactive routing the path establishes as per request by the node. In AODV the reoute discovery process involves two messages, i.e. RREQ (Route Request) and RREP (Route Reply). Destination sequence number is an important attribute for both RREQ and RREP. This attribute keeps on incrementing and determines freshness of this path [9] [13]. a) Route Discovery Process The sending node S broadcasts message, RREQ to the neighboring nodes A, B and C and finds the optimal path to destined node D. After receiving RREQ message, the receiving nodes will [9]: 1) Sends RREP response, if the received node is the destination node and also determines “fresh enough route” in the path it establishes.

2) Updates routing table with reverse path from sender to destination. Rebroadcasting RREQ message until destined node achieves the “fresh enough route” value.

If for a given intermediate node, the destination sequence number of the RREQ message is less than or equal to the destination sequence number present in the routing table, then that node will have “fresh enough route”.

Figure 1 Route discovery process AODV A

E

RREQ RREP

B S D

S- Source node D- Dest node A B C E- intermediate node

C

In the above figure, when destination D receives a RREQ message from node C, then it will reply with RREP message to node S and then forwards the reply to the node C. Node C will also send message to node S. Once S node receives RREP, it will call AODV by recvReply() function. This function updates entry for D, if either of the condition suffices [9] [10] [13].

1) The destination sequence number in routing table is greater than the destination sequence number of RREP message.

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2) The destination sequence number in routing table and that of RREQ message is equal but one less than that of routing table.

If sending node S receives many RREP messages, the highest destination sequence number will be selected among these several RREPâ&#x20AC;&#x2122;s.

B. PUMA Protocol for Unified Multicasting through Announcements is a multicast routing algorithm that is generally working for a mesh based network. PUMA is based on multicast announcements (MA) and forwards packet from multicast sender to its destination within a multicast group [3] [14]. A Multicast Announcement is composed of Group ID, Core ID, Distance to Core, parent node sending latest announcement and notifies other nodes while an announcement is been sent. PUMA elects one of the nodes as core node and thereby informs other nodes of the elected core. PUMA being a mesh based multicast protocol is capable of collecting many receivers together and is liable to have many paths to reach the destination node. The multicast announcements in PUMA are capable of performing [3] [10] [12] [14]: 1) Dynamic election of core node: PUMA elects one of the receiving nodes of the group as core of the group, and informs every router about the relative next hop to the elected core in each group. Each router will have one or more paths to reach the core node. Receiver searches for shortest path between itself and core and then connects the core node. A mesh member is flooded with data packets, and each packet is numbered such that as soon as a duplicate packet is encountered in will get dropped [3] [11].

2) Multicast Announcement Propagation: A node that is elected as core keeps on sending multicast signals to the corresponding receivers. A connectivity list will be generated at each node as the multicast announcement reaches the receiving node. A connectivity list consists of details about parent node and distance to core. A node stores data from various MA that arrives from neighboring nodes. A new MA overwrites values that have lesser sequence number within that group [3]. Only latest information about sequence number will be kept for a given group A connectivity list stores the time of receiving the multicast announcement from the node, ID of the neighbor from which it was received and the multicast announcement. Best multicast announcement has to be selected.

3) Mesh Creation and Maintenance In this step, only the receiving nodes are considered as member of the mesh, if the node is the receiving node it sets the flag as TRUE. Non receiving nodes are depicted as mesh members if they have at least one child. A node is predicted to be child node if (i) It is the member of the mesh and sets the mesh member flag to be TRUE. (ii) The nodes distance to core is lesser than distance of the neighbor to core.

A node will be a mesh member if it has a child node and depicts that it lies on the shortest path from core to receiver.

IV. Simulation Tool Ns2 developed at the University of California, is an event driven network simulator program. It is an open source simulator used in the research field of networks by the research community. It supports both wired and wireless networks, is able to simulate various protocols such as UDP, TCP, unicast and multicast routing, etc. It is composed of several network objects protocols, applications and source traffic.

V. Experimental Work In order to perform the simulation, we have taken the following parameters as defined in the given Table 1. By keeping one parameter as constant and varying the other two parameters, results are taken. These simulations play a very important role in further evaluation of results.

Table 1. Simulation Parameters S.No,

Simulation Parameters and their values Values 10, 20, 30, 40, 50, 60

1.

Parameter No. of nodes

2.

Simulation Area

1000*1000 m

3.

Simulation Time

100 sec

4.

Speed

2 m/sec

5.

Mobility Model

Random Way Point

6.

Traffic Connection

CBR over UDP

7.

MAC

802.11

8.

Transmission Range

250m

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Packet Size

512 bytes

10.

Queue length

50

11.

Protocol

AODV, PUMA

Throughput The term throughput defined as the number of the data packet transmitted from source node to destination in specified amount of time.

Figure 2 shows the variation of throughput when the number of nodes in the network is varied from 10 with the increase in their number by 10 each time and with a constant node mobility of 2m/s and simulation area 1000*1000.

Figure 2 Throughput (kbps) versus Number of nodes

Figure 3 shows the variation of throughput when the mobility of the node is varied from 2m/s to 12 m/s, with a constant parametric value of simulation area as 1000*1000 m2 and 30 number of nodes.

Figure 3 Throughput (kbps) Versus Mobility

Packet Delivery Ratio (PDR) PDR is the ratio of the number of data packet successfully delivered to the destination node and the number of data packets produced by source node.

Figure 4 shows the variation of packet delivery ratio when the number of nodes in the network is varied from 10 with the increase in their number by 10 each time and with a constant node mobility of 2m/s and simulation area 1000*1000.

Figure 4 Packet Delivery Ratio (%) Versus No. of Nodes

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VI.

Conclusion and Future Work

The Protocol for the Unified Multicast through Announcements (PUMA) is based on the novel idea of using simple multicast announcements to elect a core for the group, inform all route of their distance and next hop to the core, join and leave the multicast group. In addition it provides high throughput compared to the unicast routing protocol (AODV). We also intend to compare characteristics of PUMA with other multicast protocols in the near future.

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

Jeroen Hoebeke, Ingrid Moerman, Bart Dhoedt, Piet Demeester, “An Overview of Mobile Ad Hoc Networks: Applications and Challenges” Journal of the communication networks, July 2004.

Fan-Hsun Tseng, Li-Der Chou and Han- Chi Chao, “A Survey of Black Hole Attacks in Wireless Mobile Ad hoc Networks” Human- Centric Computing and Information Sciences 2011 E.Suresh Babu, C.Nagaraju, MHM Krishna Prasad, “A Comparative Study of Tree based Vs. Mesh based Multicast Routing Protocols in Mobile Ad hoc Networks” IJETICS November 2013.

Elizabeth M.Royer, Santa Barbara ,Chai- Keong Toh, “A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks”, IEEE Personal Communication ,April 1999.

S.Kannan, T.Maragatham, S.Karthik and V.P.Arunachalam, “A Study of Attacks, Attack Detection and Prevention Methods in Proactive and Reactive Routing Protocols ” International Business Management 5(3):178-183,2011.

Rolando Menchaca-Mendez1, Ricardo Menchaca-Mendez1, and J. J. Garcia, “ADMP: An Adaptive Multicast Routing Protocol for Mobile Ad Hoc Networks”.

J. Rangarajan and K. Baskaran, “Performance analysis of multicast protocols: ODMRP, PUMA and OBAMP” Published in IJCSE journal, December 2011, pp. 577- 581.

Ashok M.Kanthe, Dina Simunic, Marijan Djurek, “Denial of Service (DoS) Attacks in Green Mobile Ad-hoc Networks”, MIPRO 20121,IEEE Conference, Proceedings of the 35th International Convention,978-1-4673-2511- 6,Opatija,Croatia.

C.Perkins,E.B. Royer, S.Das, “Ad hoc On-Demand Distance Vector Routing” Proceeding of the 2nd IEEE Workshops on Mobile Computing System and Applications (WMCSA),pp.90- 100, 1999.

G. S. Grewal, R. K. Nagra, J. S. Gurm, “Simulation based Analysis of AODV, BABEL and PUMA Protocols for Adhoc Network”, iRAFIT2012 Published in proceeding of IJCA. 2012.

E.Suresh Babu ,C. Nagaraju, Krishna Prasad “ An Implementation and Performance Evaluation of Passive DoS Attack on AODV in Mobile Adhoc Networks in IJETTCS-2013 Rajesh Deshmukh and Asha Ambhaikar. International Journal of Computer Applications December 2010, Performance Evaluation of AODV and DSR with Reference to Network Size.

Sunil Taneja, Ashwani Kush and Amandeep Makkar. International Journal of Innovation, Management and Technology, Vol. 1, No. 5, December 2010 , Experimental Analysis of DSR, AODV using Speed and Pause time.

R. Perlman, “An algorithm for distributed computation of a spanning tree in an extended lan,” in ACM Special Interest Group on Data Com. (SIGCOMM), 1985, pp. 44–53.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net A Study on Mining Approach under Cyber Crime Analysis Priyanka Maan1, Meghna Sharma2 Department of CSE, ITM University, Gurgaon, Haryana, India 2 Department of CSE and IT, ITM University, Gurgaon, Haryana, India 1

Abstract: Today, the use of social network is been increased in daily life of a user. But this increased use of social media has also increased the associated threats. There are number of criminal activities possible over the web or social network conversation. This kind of conversation includes spamming, blackmailing, cyber threatening etc. The identification of these kind of messages or text segments is always a challenge because of vast dictionary and data for social message. In this work, a basic fuzzy effective framework is presented to identify the criminal activities and to perform the message classification. Here a text mining model is presented under fuzzy logic to provide effective solution.

Keywords: Cyber Crime, Message Classification, Opinion Mining, Sentiment Analysis, Neural network

I. Introduction Opining Mining or Sentiment Analysis is having its significance to analyze the textual information and extract the valuable decisions. This text mining includes the document analysis or message review analysis so that the effective features will be obtained from the text message. This kind of message classification or categorization comes under prediction algorithm. The prediction algorithms are having the importance to provide the some conclusion driven results by applying a series of operations on input set. Here the input will be taken in the form of message from the specific domain or the application on which mining will be applied. One of such problem domain is social network. Social networks such as facebook, twitter etc provides the way to perform message conversion in the form of chats or the emails. These networks also enable the unknown person to interact or communicate some message to user. These kind of messages are having the changes of some interact with unknown person. This kind of message from unknown sources has the maximum changes of inclusion of some criminal activity. These criminal activities can be spam messages, threats, blackmailing etc. To avoid the misuse of the social network, it is required to identify such kind of messages at the earlier stage. To perform this some classification algorithm is required. Some of these classification algorithms are given here under.

The detection of the criminal activity depends on the activity prediction or the message content analysis. A dictionary based adjective specification is done to identify such kind of messages. More explored the word dictionary will be defined, more accurate results are expected from the system. This kind of analysis requires to collect the statistical information as well as the intelligent system. The work can be applied in real time system as an automated system that can be embedded on message communication to social network. It means, as the server will retrieve some message, the message will be check under the possible threats and the earlier decision can be taken for safe delivery of message. This kind of message processing is performed under different tests and constraints specific to the application and the domain. Once the rules are specified, the statistical features are obtained from the messages. Now this feature set work as the input method for classification algorithm. Here some of the most effective classification algorithms are described.

A) DECISION TREE Decision Tree is one of the most accurate classification approach that is based on the statistical decision. The algorithmic construction of this method is more simple. But it is based on multiple conditions and constraint.

This statistical information based approach is not effective when the size of dataset is large. While working with social message classification, it generates a topic set respective to the criminal activities. Now the distance based approach is defined to generate the associated features. Now the attribute prioritization is done based on which classification will be done. With each layer of decision tree, an attribute value is checked and based on this, the classification of message is done. The attribute value is defined in the form of range specification based on which the acceptance or resentence of a message is done. The method comes under supervised learning method that uses the traditional tree based approach. Each nod here represents the conditional constraints or activity to perform the message classification. When a message passes through all the levels of tree and comes to leaf, it itself identify the associated class. In this method, at each level M number of decisions is taken and each

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decision generates the respective dataset partition. As the number of attributes or the classes increases, the method is not effective.

B) BAYESIAN CLASSIFICATION Bayesian Network is considered as the intelligent approach that itself includes the statistical constraint to improve relevancy of network. It uses the conditional probability analysis approach to establish the relationship between different features and then identify the associated dependency. This approach is considered as a graph based approach represented in the form of acyclic graph. The dependency analysis is here based on the weighted probabilistic selection approach. In case of text mining, this kind of weightage can be obtained by analyzing the word frequency, relevancy etc. Once the statistical measures will be obtained, the intelligent classification will be performed under conditional probability approach. The work also includes the decision generation and class identification so that the attribute specification to the network is effective. This network based approach is defined as an acyclic graph based approach that is effective for class identification and to process the discrete predictive analysis. This classification approach also uses the ranking based on the work significance analysis respective to the topic or the criminal activity. The messages with highest ranked can be kept in the category of safe messages. In same way, the structural analysis can be performed to generate the feature vector and to take the decisions regarding the feature exploration.

C) Neural Network Neural network comes under soft computing approach used an intelligent neuron based system. This is the approach comes under expert system that divides the complete classification in a layered model. This layered model includes the input layer, hidden layer and output layer. The input layer will accept the feature vectors obtained from the input message set. The statistical values will be taken in this layer as input. Once the input set is defined, the hidden layer will work as the weightage layer. There can be more than one hidden layer to improve the effectiveness of work. Here the transformation rules are defined in this layer to transform the input feature vector to final results. The transformation is applied on the mapping between the data and the neurons.

This classification process is defined along with decision so that the intelligent results will be derived from the system, This layered model is shown in figure 1.

Figure 1: Neural Network System Here figure is showing the three stage model, with the specification of input, weightaged modeling and the output layer. The input layer can be described by one of more feature sets. These feature sets can be defined in the form of word feqruency and relevancy to the crime activities. The hidden layer provides the tranformation of this input set to the ouput. Finally the output set is generated as the classfied dataset.

D) SUPPORT VECTOR MACHINE Support Vector Machine is more evolutionary form of neural network. In this form of classification, the analytical system is composed in the form of data points and these data points are analyzed at different dimensional of the dataset. The dimensions are based on the analysis features such as margin analysis, distance analysis or interaction analysis. Once the data points are defined and explored, the conditional analysis is applied over it to filter the dataset and to improve the significant data feature. The vector formation relative to the vector space is defined. The intelligent decisions are taken regarding the neighbor point specification so that more accurate decision will be taken. The vector specific decisions are more accurate as well as intelligent.

In this, a study to the social network conversion message is defined under different criminal activities. As this kind of conversion includes the textual message communication so that the text mining is explored in this paper.

In this section, a review to the opinion mining, text mining and review mining is defined. In section II, the work defined by earlier researchers is defined. Here the research in the area of opinion mining and social conversion

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mining are defined. In section III, a fuzzy based model is presented for identification of criminal activities over the text. In section IV, the conclusion obtained from the work is presented.

II

LITERATURE SURVEY

Cyber threat detection during the web communication or online conversion is one of the major challenge for the researchers. For this, there is the requirement to analyze different kind of communications performed by different users. These communications includes emails, chat, SMS, reviews etc. In this section, the work defined by earlier researchers on cyber threat analysis are given. Chris Clifton[1] defined a work to study the textual passage and to identify the topic of the document. This kind of analysis is helpful to identify the relevant text respective to the specified title or header. While working with web application, different users can provide irrelevant inputs or reviews or feedbacks. This approach is helpful to perform the filtration under topic identification. The work is based on the cluster generation and classification so that the separation of different reviews will be done. Jian Hu[2] has presented a predictive model analyze the browsing behaviour of a web use.

Author defined the work to utilize the demographic information so that the usage identification over the web will be done. Author has defined the intelligent learning approach based on the events performed by user. This approach is helpful to track the invalid activity in terms of site visit by the user. Author has presented a Bayesian network based approach for the classification of user visit to track the abnormal behaviour. Faliang Huang[3] has defined a mining approach to analyze the chat messages to identify cyber threat. Author presented the message content analysis under threat structure identification so that message categorization will be done.

Author presented the rule based algorithm for message categorization that can improve the social networks under observations. Masafumi Hamamoto[4] has presented a study based work for topic identification schemes.

Author used the comparision between the SVD, Clustering, ICA methods so that the effective message classification will be done. Author performed the message analysis under different approaches. Reihaneh Rabbany[5] has presented an assessment behaviour to analyze the message over social network and to perform the actual partitioning. Author defined the study on the student message and provided the significant analysis over the message text. The information structure is identified to exploit the message so that more relevant decision will be taken. Author also explored the summarization under different aspects. Koosha Golmohammadi[6] has defined a data mining approach to identify the fraud from security market data. Author presented the work to analyze and study the associated message and generate the relevant patterns so that the identification of feature from the message will be done. Author presented a detection mechanism so that the more relevant decision regarding the recommendation to the research will be done.

Alexandra Balahur[7] has presented a feature based opinion mining approach for document feature exploration and classification. Author has performed the work to extract these features from the document review and performed a stage level work. At first the sentence level and then feature level approach is defined to perform the document summarization and sentiment analysis. Author has defined a machine learning approach for the categorization of these reviews and to perform the message classification. Author defined a name entity based syntactic parsing approach classification. Chun-hung Li[8] has presented the knowledge extraction approach using machine learning approach. Author has defined a grained analysis approach with specification of threshold for specific features. These features are then evaluated for message classification. Peter Koncz[9] has presented feature selection approach to identify the embedded sentiments in the messages. Author defined a subjective approach for feature classification. Author used the review dataset as an application of work. Amir A. Sheibani[10] has provided the spam detection over the message obtained from social networks. Author applied work for blogs to estimate the brand reputation. Author has presented a complete framework for spam recognition so that the review filteration will be done. Ms.K.Mouthami[11] has defined a work to analyze the review so that the sentiment extraction from the reviews will be done. Author has presented the valuable information analysis with effective of pattern identification. Author has analyze the social media to monitor the customer mood and negative message will be identified. Author used the SVM approach applied on feature set so that the review categorization will be obtained.

Mostafa Karamibekr[12] has performed a work on structure analysis applied in social domain. Author applied the text mining approach to analyze the opinion in the form of a topic or the issue and obtained the statistical measures. These measures are able to obtain the structure from subjective documents and obtained the more accurate results for classification. Rabeah Al-Zaidy[13] has defined a mining work un unstructured documents.

Author collected the valuable information to perform forensic analysis so that the suspect identification and criminal activity identification will be done. Author has presented an investigation for criminal network analysis so that the suspect identification will be done. Author has presented a cyber crime dataset for the analysis.

Farkhund Iqbal[14] presented a work on classification of chat messages obtained from social networks. The classification is here done under spamming, cyber predation, blackmailing etc. Author has defined an online message system so that the crime investigation will be done. Author has defined chat log effective framework so that the message enforcement will be done. Ching-Yung Lin[15] has defined intelligent work to analyze the

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social network to perform the expertise search and to identify the message spams. Author has defined work under community analysis and knowledge specification so that the spam messages will be identified.

III Fuzzy based Analytical Model In this section, a fuzzy effective predictive model is presented to identify the criminal activities over the social network. Social network is the most common and popular way for communication between web users. This kind of criminal activity detection can be done online or offline. In case of online message filtration and classification, the model can be combined with mail server or chat server to classify the message before delivery. This kind of analysis can generate alert messages if some criminal activity is identified. In case of offline message classification, the particular message can be defined in the form of dataset. These messages will be defined along with the specification of sender and receiver. Now the messages will be extracted from dataset for analysis.

While working with such kind of message classification, there is the requirement of some preprocessing approach to explore the message features. At the earlier stage of work, there is the requirement of identification of the terms involved in different kind of criminal activities and to generate the dataset respective to the activity class. These activities can be in the form of spam messages, hacking, blackmailing, threatening, advertisements.

Once the classes will be identified, the criticality of each class as well as the relevant word set will be defined.

The work also requires a stop list dataset to filter the input message at earlier stage so that the only relevant message words will be processed. After this all, the processing environment will be defined.

While working with the model, the first stage is to extract the messages under the specification of the user or the message itself. To extract this message, some sql query can be defined. Once the message is extracted, the next work is to filter the message by removing the stop list words. Now it will return the main message words as the input set. The next stage of work is to explore the message features. To explore the message features and represent this features as the main dataset. These features can be in terms of words frequency or the relevancy to the message words. This model is the proposed model and shows all the stages clearly. The model presented here is shown in figure 2.

Accept the Social Network Message

Obtain the main message work by applying the filteration

Identify the message features under statistical measures

Apply fuzzy logic to define the message class

Identify the criminal activity present in the message Figure 2: Proposed Model Once the features will be explored, the next work is to define some rule so that the relevancy between the criminal activity and the input message can be identified. Here some distance analysis based approach can be applied along with fuzzy logic. Fuzzy logic is considered as the soft computing approach used to taken the decision based on current statistics and the integration of the message attributes. The inter relation analysis is here performed under fuzzy restriction. These restrictions or constraints are defined along with fuzzy operators.

These fuzzy operators will be applied on all features individually as well as collectively. Based on these all rules, the message class will be identified. Once the message class will be identified, the criminal activity can be recognized from the message along with message criticality. The use of fuzzy logic improves the effectiveness of work in decision making.

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IV. CONCLUSION In this paper, a study based work is presented to perform the social network mining and to identify the criminal activities of web users. The paper has explored different classification algorithms respective to the social conversation. The paper also presented a fuzzy based model for message classification.

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

[5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Chris Clifton," TopCat: Data Mining for Topic Identification in a Text Corpus",IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING @2003 Jian Hu," Demographic Prediction Based on User’s Browsing Behavior", Predictive Modeling of Web Users , 2007, ACM 9781-59593-654-7/07/0005 Faliang HUANG," An Approach to Mining Social Networks in Chat Room", Journal of Computational Information Systems 2011 Masafumi Hamamoto," A Comparative Study of Feature Vector-Based Topic Detection Schemes for Text Streams", Proceedings of the 2005 International Workshop on Challenges in Web Information Retrieval and Integration (WIRI’05) 0-7695-2414-1/05 © 2005 IEEE Reihaneh Rabbany k.," Social Network Analysis and Mining to Support the Assessment of Online Student Participation".

Koosha Golmohammadi," Data Mining Applications for Fraud Detection in Securities Market".

Alexandra BALAHUR," A Feature Dependent Method for Opinion Mining and Classification", 978-1-4244-2780-2/08 ©2008 IEEE Chun-hung Li," Sentence Factorization for Opinion Feature Mining", 2009 International Conference on Computational Aspects of Social Networks 978-0-7695-3740-5/09 © 2009 IEEE Peter Koncz," An approach to feature selection for sentiment analysis", INES 2011 , 978-1-4244-8956-5/11 ©2011 IEEE Amir A. Sheibani," Opinion Mining and Opinion Spam-A Literature Review Focusing on Product Reviews", 6'th International Symposium on Telecommunications (IST'2012) , 978-1-4673-2073-3/12©2012 IEEE Ms.K.Mouthami," Sentiment Analysis and Classification Based On Textual Reviews".

Mostafa Karamibekr," A Structure for Opinion in Social Domains", SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013, 978-0-7695-5137-1/13 © 2013 IEEE Rabeah Al-Zaidy," Mining Criminal Networks from Unstructured Text Documents", 2012 Farkhund Iqbal," Mining Criminal Networks from Chat Log", 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology , 978-0-7695-4880-7/12© 2012 IEEE Ching-Yung Lin," SmallBlue: Social Network Analysis for Expertise Search and Collective Intelligence", IEEE International Conference on Data Engineering, 1084-4627/09 © 2009 IEEE

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Design of Universal Gates Based on Reversible Logic Deepali Samtani, Naman Kumar Patel, Aditya Gupta, Shruti Jain Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh 173234, INDIA. ________________________________________________________________________________________ Abstract: The Reversible Logic has received great attention in the past recent years due to its ability in reducing the power dissipation. Owing to its unique technique of one-to-one mapping between the inputs and the corresponding outputs, the reversible logic gates are now finding profound as well as promising applications in emerging growing fields such as digital signal processing, nanotechnology etc. The main purposes of designing reversible logic are to decrease quantum cost, depth of the circuits and the number of garbage outputs. We have successfully designed the universal gates (NAND and NOR) using reversible gates and irreversible logic and calculated the electrical parameters. Parameters with reversible logic are the best.

Keywords: Irrevresible logic, Reversible logic, PSPICE, Electrical parameters, Universal gates __________________________________________________________________________________________ I. Introduction The design that does not result in information loss is called reversible. It naturally takes care of heating generated due to the information loss. This will become an issue as the circuits become smaller. There are two main types of gates: (a) Irreversible Gates, and (b) Reversible Gates.

A. Irreversible Gates The typical computer is logically irreversible - its transition function i.e., the partial function that maps each whole machine state onto its successor, if the state has a successor, lacks a single-valued inverse which means models of computation which are logically irreversible lose information in the process of execution, that lost information is actually translated in the form of heat. So loss of information results in power dissipation.

B. Reversible Gates Reversible logic is one of the most vital issue at present time and it has different areas for its application, those are low power CMOS, quantum computing, nanotechnology, cryptography, digital signal processing (DSP), communication, computer graphics etc. It is not possible to realize these applications without implementation of reversible logic [1].

In reversible logic feedbacks and fan-outs are not permitted. This makes the synthesis substantially different.

From the point of view of reversible logic, we have one more factor, which may be more important than the number of gates used, namely the number of garbage outputs. Since reversible design methods use reversible gates, where number of inputs is equal to the number of outputs, the total number of outputs of such a network will be equal to the number of inputs [2, 3].

Each reversible gate has a cost associated with it called the quantum cost. The quantum cost of a reversible gate is the number of 1×1 and 2×2 reversible gates or quantum logic gates required in its design. The quantum costs of all reversible 1×1 and 2×2 gates are taken as unity [1].

There are various types of reversible gates i.e. Not Gate, Feynman Gate [4], Toffoli Gate[5], Fredkin Gate, Peres Gate, TR Gate and New gate [6, 7]. Out of which we have implemented Feynman Gate, Toffoli Gate, and Peres Gate because these are the best when we have implemented the circuits using Verilog HDL. Later on we have designed universal gates using reversible logic [8, 9] and compare their results with irreversible gates. The results with reversible logic are the best. The electrical parameters which we have calculated are as follows: differential input resistance, output resistance, large signal voltage gain ( 20 log 10 Vo/Vid in dB), common mode rejection ratio(CMRR) [20 log(Ad / Acm) in dB], slew rate (SR)[max (dVo / dt ) in V/µsec] and power dissipation (mW).

II. Implementation of the Reversible Logic Using Pspice Simulation Program with Integrated Circuit Emphasis (PSPICE) is a general-purpose, open source analog electronic circuit simulator. It is a powerful program that is used in integrated circuit and board-level design to check the integrity of circuit designs and to predict circuit behavior. The gates implemented in PSPICE are Irreversible and Reversible Feynman Gate, Irreversible and Reversible Toffoli Gate, Irreversible and Reversible Peres Gate. These are compared and the parameters are noted.

The following gates are implemented in Pspice as follows: A. Feynman Gate The Feynman gate (FG) or the Controlled-NOT gate (CNOT) is a 2-input 2-output reversible gate having the mapping (A, B) to (P = A, Q = A⊕B) where A, B are the inputs and P, Q are the outputs, respectively. The

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Feynman Gate is implemented in two ways that is, Irreversible Feynman Gate (shown in Fig. 1a) and Reversible Feynman Gate (shown in Fig. 1b).

(a) (b) Figure 1: (a) Irreversible Feynman Gate, (b) Reversible Feynman Gate Table 1: Comparison of Parameters of Feynman Gate.

Parameters

Reversible

Irreversible

Slew Rate (V/µsec)

1.2x103

13x106

Power Dissipation (mW)

1.60

12.7

Voltage Gain (dB)

26.5

50.55

Input Resistance (KΩ)

7.529

3.5

Output Resistance (KΩ)

0.9189

0.805

CMRR (dB)

3.975

140.747

The table 1 shows the comparison of Irreversible and Reversible Feynman Gate on various parameters such as Slew Rate, Power Dissipation, Voltage Gain, Input Resistance, Output Resistance and CMRR (Common Mode Rejection Ratio).

B. Toffoli Gate A Toffoli Gate (TG) is a 3×3 two-through reversible gate. Two-through means two of its outputs are the same as inputs with the mapping (A, B, C) to (P = A, Q = B, R = A · B ⊕ C), where A, B, C are inputs and P, Q, R are outputs, respectively. The Toffoli Gate is implemented in two ways that is, Irreversible Toffoli Gate (shown in Fig. 2 a) and Reversible Toffoli Gate (shown in Fig. 2b).

(a) (b) Figure 2: (a) Irreversible Toffoli Gate, (b) Reversible Toffoli Gate

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The Table 2 shows the comparison of Irreversible and Reversible Toffoli Gate on various parameters such as Slew Rate, Power Dissipation, Voltage Gain, Input Resistance, Output Resistance and CMRR (Common Mode Rejection Ratio).

Table 2: Comparison of Parameters of Toffoli Gate Parameters

Reversible

Irreversible

Slew Rate (V/µsec)

6x104

9.8x104

Power Dissipation (mW)

1.40

13.1

Voltage Gain (dB)

44.222

67.79

Input Resistance (KΩ)

162.6

95.7

Output Resistance (KΩ)

0.9643

0.9285

CMRR (dB)

347.82

163.365

C. Peres Gate A Peres gate is a 3 inputs 3 outputs (3×3) reversible gate having the mapping (A, B, C) to (P = A, Q = A ⊕ B, R = (A · B) ⊕ C), where A, B, C are the inputs and P, Q, R are the outputs, respectively. The Peres Gate is implemented in two ways that is, Irreversible Peres Gate (shown in Fig. 3) and Reversible Peres Gate (shown in Fig. 4).

Figure 3: Irreversible Peres Gate

Figure 4: Reversible Peres Gate

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The table 3 shows the comparison of Irreversible and Reversible Peres Gate on various parameters such as Slew Rate, Power Dissipation, Voltage Gain, Input Resistance, Output Resistance and CMRR (Common Mode Rejection Ratio).

Table 3: Comparison of Parameters of Peres Gate Parameters

Reversible

Irreversible

Slew Rate (V/µsec)

2x103

3x10-7

Power Dissipation (mW)

5.68

22.6

Voltage Gain (dB)

73.77

51.78

Input Resistance (KΩ)

3.33x108

1.92 x105

Output Resistance (KΩ)

7.380

0.3725

CMRR (dB)

98.86

0.478

The best gate among these three gates is the Toffoli Gate, since it has a very high Slew Rate, less Power Dissipation, high Input Impedance, low Output Impedance and high value of CMRR, which are the required characteristics of a gate.

III. Indigenous Development Of Reversible Gates We have implemented Irreversible and Reversible Universal Gates (NAND and NOR Gate) and compared those on the same parameters as above such as Slew Rate (SR), Power Dissipation (PD), Input Resistance, Output Resistance, Voltage Gain and Common Mode Rejection Range (CMRR). We have implemented As a result of which any circuit can be implemented with the help of Universal Gates.

A. NAND Gate The NAND gate represents the complement of the AND operation. Its name is an abbreviation of NOT AND. The graphic symbol for the NAND gate consists of an AND symbol with a bubble on the output, denoting that a complement operation is performed on the output of the AND gate. The NAND gate is implemented in two ways, that is, Irreversible NAND Gate (shown in Fig. 5a) and Reversible NAND Gate (shown in Fig. 5b). The table 4 shows the comparison of Irreversible and Reversible NAND Gate on various parameters such as Slew Rate, Power Dissipation, Voltage Gain, Input Resistance, Output Resistance and CMRR (Common Mode Rejection Ratio).

(a) (b) Figure 5: (a) Irreversible NAND Gate, (b) Reversible NAND Gate Table 4: Comparison of Parameters of NAND Gate Parameters

Reversible

Irreversible

Slew Rate (V/µsec)

2.9

2.5

Power Dissipation (mW) Voltage Gain (dB)

6.58 72.37

9 1.33

Input Resistance (KΩ)

141.6

88.5

Output Resistance (KΩ) CMRR (dB)

0.464 272.54

0.957 12.2

B. NOR Gate The NOR gate represents the complement of the OR operation. Its name is an abbreviation of NOT OR. The graphic symbol for the NOR gate consists of an OR symbol with a bubble on the output, denoting that a

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complement operation is performed on the output of the OR gate. The NOR gate is implemented in two ways, that is, Irreversible NOR Gate (shown in Fig. 6a) and Reversible NOR Gate (shown in Fig. 6b).

(a) (b) Figure 6: (a) Irreversible NOR Gate, (b) Reversible NOR Gate The table 5 shows the comparison of Irreversible and Reversible NOR Gate on various parameters such as Slew Rate, Power Dissipation, Voltage Gain, Input Resistance, Output Resistance and CMRR (Common Mode Rejection Ratio).

Table 5: Comparison of Parameters of NOR Gate Parameters

Reversible

Irreversible

Slew Rate (V/µsec)

2.3

0.6

Power Dissipation (mW)

9.11

0.303

Voltage Gain (dB)

246.22

48.56

Input Resistance (KΩ)

318.2

100.5

Output Resistance (KΩ)

0.554

0.317

CMRR (dB)

325.54

5.477

We have indigenously designed NAND gate and NOR gate with both the methods i.e. reversible gates and irreversible gates. The parameters with reversible gates are the best, since it has a very high Slew Rate, less Power Dissipation, high Input Impedance, low Output Impedance and high value of CMRR, which are the required characteristics of a gate. Later on, we will implement digital circuits with the help of reversible gate and compare it with irreversible.

IV. Conclusion and Future Work We have discussed irreversible and reversible gates and how reversible gates are better than traditional irreversible gates. The three gates are implemented using pspice and compared them to find out the best reversible gate. Out of which Toffoli gate is the best. We have also designed and implemented Universal gates (NAND, NOR) using reversible and irreversible logic in PSPICE and compared them to show reversible logic are better to implement digital circuits.

References [1]

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

http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&sqi=2&ved=0CDwQFjAE&url=htt p%3A%2F%2Fweb.cecs.pdx.edu%2F~mperkows%2FCLASS_VHDL_99%2Ftran888%2Flecture003-reversiblelogic.pdf&ei=TVWLVPDMLpKiugTR_IKICQ&usg=AFQjCNHkmENNodMQLgQnWuoWmfBnnvKYow&sig2=UoUThnM16 60qJKc-4Z7AlA&bvm=bv.8765400,rf.c34 Yibin Ye, K. Roy, "Energy recovery circuits using reversible and partially reversible logic", in Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on., Vol. 43, Issue 9, 1996, pp. 769-778.

Dai Hongyu, Zhou Runde, "Improved energy recovery logic for low power computation", in Communications, Circuits and Systems and West Sino Expositions, IEEE 2002 International Conference on., Vol.2, 2002, pp. 1740-1743.

R. Feynman, “Quantum mechanical computers”, Optic News, 11:11– 20, 1985.

T. Toffoli, “Reversible computing,” Tech memo MIT/LCS/TM-151, MIT Lab for Comp. Sci, 1980.

A. Peres, “Reversible logic and quantum computers”, Physical Review A, 1985, 32:3266–3276.

M. Frank, “Introduction to Reversible Computing: Motivation, Progress, and Challenges,” ACM Inc., New York, NY, pp. 385– 390, 2005.

S.M. Kang, Y. Leblebici, CMOS Digital Integrated Circuit, 3rd Edition, Tata McGraw Hill, 2006.

A. P. Chandrakasan, and R. W. broderson, “Low Power Digital CMOS Design”, Kluwer Academic Publishers, Boston, MA, 1995.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Turbulent Heat Transfer and Pressure Drop in an Internally Ribbed Rectangular Duct Sohil Akhter, Ishwar Singh Mechanical Engineering Department Scope College of Engineering, Bhopal, MP, India.

Abstract: Computational study was carried out to determine average heat transfer coefficients and friction factors for turbulent flow through rectangular ducts with ribs. A commercial finite volume package ANSYS FLUENT 12.1 is used to analyze and visualize the nature of the flow across the ribbed duct The fluid in the duct was air, and the average heat transfer coefficients were determined by measuring the overall heat transfer coefficients of the duct. To attain fully developed conditions at the entrance and exit, the duct was built with additional lengths before and after the test section. The walls of the air side were ribbed with an array of short ribs. The second wall of the duct was well insulated and can be considered adiabatic. The results are presented in dimensionless form, in terms of Nusselt numbers and friction factors as functions of the Reynolds number. It was found that the ribs increase both the heat transfer and the pressure drop compared with a smooth duct.

Keywords: Solar Energy, Solar Air Heater, Heat transfer, Pressure Drop, CFD I.

Introduction

The rectangular duct geometry is commonly employed in engineering applications, such as compact heat exchangers, solar collectors, and others. A survey of the available published literature on the heat transfer and pressure drop characteristics of rectangular ducts reveals a great lack of information about turbulent conditions.

With regard to laminar heat transfer and pressure drop, the situation is better. Turbulent heat transfer and fluid flow characteristics of air in rib-roughened tubes, annuli, ducts and between parallel plates have been studied extensively because of their important applications. Such a heat transfer enhancement method is widely used in cooling passages of gas turbine blade, compact heat exchangers, fuel elements in advanced gas-cooled reactor electronic cooling devices, etc. Taking the cooling passages is usually approximated by a rectangular duct with a pair of opposite rib-roughened walls.

Sparrow and Haji-Sheikh [1] report analytical results for laminar fully developed heat transfer in isosceles triangular ducts for a large range of apex angles. The thermal boundary condition was constant heat flux at the triangle walls. In addition, pressure drop coefficients are presented. Schmidt and Newell [2] also give results that can be applied to laminar flows. Several thermal boundary conditions were considered. All the situations were concerned with fully developed flows. One of the most extensive sets of results in noncircular ducts is that of Shah and London [3]. Only laminar flows were studied. With regard to turbulent flows, Eckert and Irvine [4] and Altemani and Sparrow [5] appear to the only authors with information about triangular ducts. In [4], measurements of heat transfer and friction characteristics were made in a narrow isosceles triangular duct with an apex angle of 11.46 °. In [5], experiments were performed in an equilateral triangular duct. Entrance-region and fully developed heat transfer characteristics were determined; friction factors were also measured. The heating arrangement was designed to yield the following thermal boundary conditions: uniform heat input per unit axial length and circumferentially uniform temperature on two heated walls, the third wall being insulated. Braga and Saboya reported average heat transfer results and friction characteristics for isosceles triangular ducts with an apex angle of 120° [6] and for equilateral triangular ducts [7]. The thermal boundary conditions in both cases consisted of uniform temperature on two heated walls while the third wall was insulated Chaube et al. [8] conducted a CFD based analysis of heat transfer and fluid flow characteristics of an artificially roughened solar air heater. Kumar and Saini [9] performed a CFD analysis of fluid flow and heat transfer characteristics of a solar air heaters having arc shaped artificial roughness on the absorber plate. Karmare and Tikekar [10] carried out CFD investigation of fluid flow and heat transfer in a solar air heater duct with metal grit ribs as roughness elements on the absorber plate. Yadav and Bhagoria [11] presented the numerical prediction of fluid flow and heat transfer in a conventional solar air heater by CFD. A commercial finite volume package ANSYS FLUENT 12.1 was used to analyze the nature of the flow across the duct of a conventional solar air heater. Yadav and Bhagoria [12] presented a detailed literature survey about different CFD investigations on artificially roughened duct.

The present research was undertaken to determine average heat transfer coefficients and friction factors for turbulent flows in ribbed duct. Computational study was performed by using a ribbed duct. The thermal boundary conditions were constant temperature over the surfaces of walls the second wall being insulated. The FLUENT

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software solves the mathematical equations which governs fluid flow, heat transfer and related phenomena for a given physical problem.

II.

Computational Analysis

The following assumptions are imposed for the computational analysis.

(1) The flow is steady, fully developed, turbulent and two dimensional.

(2) The thermal conductivity of the duct wall, absorber plate and roughness material are independent of temperature.

(3) The duct wall, absorber plate and roughness material are homogeneous and isotropic.

(4) The working fluid, air is assumed to be incompressible for the operating range of solar air heaters since variation in density is very small.

(5) No-slip boundary condition is assigned to the walls in contact with the fluid in the model.

(6) Negligible radiation heat transfer and other heat losses.

The computational domain used for CFD analysis is shown in Fig. 1.

Figure 1 computational domain

The geometrical and operating parameters for roughened duct are listed in Table 1.

Table 1 Range of parameters for CFD analysis.

Geometrical and operating parameters

Range

Entrance length of duct, ‘L1’

245 mm

Test length of duct, ‘L2’

280 mm

Exit length of duct, ‘L3’

115 mm

Width of duct, ‘W’

50 mm

Depth of duct, ‘H’

20 mm

Hydraulic diameter of duct, ‘D’

28.6 mm

Rib height, ‘e’

0.5-2 mm

Rib Pitch, ‘P’

15 mm

Reynolds number, ‘Re’

3800-18000 (6 values)

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

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bottom edges are inlet length, outlet length and solar plate. All internal edges of rectangle 2D duct are defined as turbulator wall. Meshing of the domain is done using ANSYS ICEM CFD V12.1 software. Since low-Reynoldsnumber turbulence models are employed, the grids are generated so as to be very fine. To select the turbulence model, the previous experimental study is simulated using different low Reynolds number models such as Standard k-ω model, Renormalization-group kε model, Realizable kε model and Shear stress transport k-ω model.

The results of different models are compared with experimental results. The RNG k-ε model is selected on the basis of its closer results to the experimental results. The working fluid, air is assumed to be incompressible for the operating range of duct since variation is very less. The mean inlet velocity of the flow was calculated using Reynolds number. Velocity boundary condition has been considered as inlet boundary condition and outflow at outlet. Second order upwind and SIMPLE algorithm were used to discretize the governing equations. The FLUENT software solves the following mathematical equations which governs fluid flow, heat transfer and related phenomena for a given physical problem.

III.

Results And Discussion

The computational model studied in the present study includes a segment of the inlet plenum and the test section. Inlet for the computational domain is the inlet to the domain, so that more realistic flow with entry effect can be simulated for the test section. The flow is assumed to leave the test section to ambient with a zero static pressure boundary condition at the outlet. Fig. 2 shows the plot between Reynolds number and average Nusselt number for different values of rib height. The average Nusselt number increases with increase of Reynolds number due to the increase in height of rib. It can be seen that the enhancement in heat transfer of the roughened duct with respect to the smooth duct also increases with an increase in Reynolds number. It can also be seen that Nusselt number values increases with the increase in relative roughness height (e/d) for fixed value of roughness pitch (P). This is due to the fact that heat transfer coefficient is low at the leading edge of the square rib and high at the trailing edge. Higher relative roughness height produced more reattachment of free shear layer which creates the strong secondary flow. The roughened duct with relative roughness height of 0.07 provides the highest Nusselt number (Nu= 154.4) at a Reynolds number of 18000. The roughened duct with relative roughness height (e/d) of 0.0175 provides the lowest Nusselt number at a Reynolds number of 3800.

The maximum enhancement of average Nusselt number is found to be 3.05 times that of smooth duct for relative roughness height of 0.07 at a Reynolds number of 18000.

Figure 2: Nusselt number vs Reynolds number

The heat transfer phenomenon can be observed and described by the contour plot of temperature. The intensities of temperature are reduced at the flow field near the rib and wall and a high temperature intensity region is found between the adjacent ribs close to the main flow which yields the strong influence of turbulence intensity on heat transfer enhancement.

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Figure 3: Contour plot of turbulence intensity

IV.

Conclusion

The following conclusions are drawn from present analysis: 1.

2.

3.

The use of artificial roughness on a surface is an effective technique to enhance heat transfer to fluid flowing in the duct. Artificially ribbed ducts have enhanced rate of heat transfer as compared to the smooth duct.

The RNG k-ε turbulence model gives very close results to the experimental results. RNG k-ε turbulence model has been validated for smooth duct and grid independence test has also been conducted to check the variation with increasing number of cells.

Nusselt number increases with an increase of Reynolds number. The maximum value of average Nusselt number is found to be 154.4 for relative roughness height of 0.07 at a higher Reynolds number, 18,000.

The maximum enhancement of average Nusselt number is found to be 3.05 times that of smooth duct for relative roughness height of 0.07.

V.

[1] [2] [3] [4] [5] [6] [7] [8] [9 [10] [11] [12]

References

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Schmidt, F. W., and Newell, M. E., Heat Transfer in Fully Developed Laminar Flow Through Rectangular and Isosceles Triangular Ducts, Int. J. Heat Mass Transfer, 10, 1121-1123, 1967.

Shah, R. K., and London, A. L., Laminar Flow Forced Convection in Ducts, Academic, New York, 1978.

Eckert, E. R. G., and Irvine, T. F., Pressure Drop and Heat Transfer in Duct with Triangular Cross Section, J. Heat Transfer, 82, 125-138, 1960.

Altemani, C. A. C., and Sparrow, E. M., Turbulent Heat Transfer and Fluid Flow in an Unsymmetrically Heated Triangular Duct, J. Heat Transfer, 102, 590-597, 1980.

Braga, S. L., and Saboya, F. E. M., Heat Transfer and Pressure Drop for Triangular Ducts with Laminar and Turbulent Flows (in Portuguese), pp. 207-210, December 1986.

Braga, S. L., and Saboya, F. E. M., Transport Coefficients for Triangular Ducts (in Portuguese), IX Congresso Brasileiro de Engenharia Mecfinica, FIorian6polis, Brazil, pp. 33-36, December 1987.

A. Chaube, P.K. Sahoo, S.C. Solanki, Analysis of heat transfer augmentation and flow characteristics due to rib roughness over absorber plate of a solar air heater, Renewable Energy 31 (2006) 317-31.

S. Kumar, R.P. Saini, CFD based performance analysis of a solar air heater duct provided with artificial roughness, Renewable Energy 34 (2009) 1285-91.

S.V. Karmare, A.N. Tikekar, Analysis of fluid flow and heat transfer in a rib grit roughened surface solar air heater using CFD, Solar Energy 84 (2010) 409–17.

A.S. Yadav, J.L. Bhagoria, Heat transfer and fluid flow analysis of solar air heater: a review of CFD approach, Renewable and Sustainable Energy Reviews 23 (2013) 60-79.

A.S. Yadav, J.L. Bhagoria. A CFD based heat transfer and fluid flow analysis of a solar air heater provided with circular transverse wire rib roughness on the absorber plate, Energy, Elsevier, 55 (2013) 1127-1142.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Brain Image Segmentation Using Chan-Vese algorithm using Active Contours and Level Set Functions Payal1, Harpal Singh2 Student, Information Technology, Guru Kashi University, Talwandi Sabo, Punjab, INDIA 2 Assistant Professor, UCCA , Guru Kashi University, Talwandi Sabo, Punjab, INDIA __________________________________________________________________________________________ 1

Abstract: Region-based level set segmentation is a paradigm for the automatic segmentation of brain tumor image. Unfortunately, region-based segmentation, which is relied on the intensity difference of different regions, has been of limited used in presence of complex background. Algorithm based on calculating the variational energy of the Chan-Vese model without the length. The multiphase level set formulation is of interest on its own, by construction, it automatically avoids the problems of vacuum and overlap, it needs only log n level set functions form phases in the piecewise constant case. In this work, we specially solve the Chan-Vese active contour model by multiphase level set methods.

Keywords: Image Segmentation, Level Set Methods, Chan-Vese model, Energy minimization, multi-phase motion, image segmentation, active contours __________________________________________________________________________________________ 1.

Introduction Image segmentation is arguably the most ubiquitous and is difficult in technical problem such as medical image processing. The problem of partitioning an image into meaningful pieces interest has proven to be as difficult as a host of other computational problems that attempt to mimic the capabilities of human intelligence or perception. The main difficultly which arrives in image segmentation is not from a lack of attention; thousands of papers and theses on image segmentation describe a wide variety of approaches ranging from statistics, differential geometry, and partial differential equations to discrete geometry, game theory, and computational mechanics. As a result, engineers designing clinical systems that require an image segmentation capability are left with wide range of possible approaches virtually all of which claim to be effective to some degree. To help with this, several researchers have proposed mechanisms for evaluating or validating the effectiveness of various segmentation algorithms [2]. However, quantifying the validity of segmentation has proven to be almost as difficult as the segmentation itself. The challenge stems from the fact that quantifying differences in shapes is also an important, open problem in computer vision and image processing. In the midst of these difficult challenges and proposed solutions one might ask, “How well are we doing?” This paper is a case study that looks at the effectiveness of a relatively simple, well-known segmentation paradigm, hierarchical watersheds with user interaction. In our experience, this method is moderately effective on a wide range of segmentation problems. We systematically study its effectiveness on two different types of data using several commonly cited validation metrics. This study is designed to address the question of whether or not one should use user-assisted watershed segmentation in lieu of hand contouring. The results, however, also provide some insight into the watershed algorithm itself as well as the methodology of validating segmentation algorithms against an expert defined ground truth. Image segmentation is useful in many applications [7]. It can identify the regions of interest in a scene or annotate the data. We categorize the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. All of them expand each region pixel by pixel based on their pixel value or quantized value so that each cluster has high positional relation. For data clustering, the concept of them is based on the whole image and considers the distance between each data.

The characteristic of data clustering is that each pixel of a cluster does not certainly connective. The basis method of data clustering can be divided into hierarchical and partitioned clustering [4]. Furthermore, we show the extension of data clustering called mean density estimation. The last classification of segmentation is edgebased segmentation. This type of the segmentations generally applies edge detection or the concept of edge. The typical one is the watershed algorithm, but it always has the over-segmentation problem, so that the use of markers was proposed to improve the watershed algorithm by smoothing and selecting markers. Finally, we show some applications applying segmentation technique in the preprocessing. Medical image segmentation is an essential step for most subsequent image analysis tasks. The segmentation of an atomic structure in the brain plays crucial role in Nero imaging analysis. Successful numerical algorithms can help researchers, physicians and neurosurgeons to investigate and diagnose the structure and function of the brain in both health and disease [10]. This has motivated the need for segmentation techniques that are robust in application involving abroad

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range of anatomic structure, disease and image type .The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Actually, partitions are different objects in image which have the same texture or color. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image. All of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristics.

II. RELATED WORK Sudipta Roy, Samir K. Bandyopadhyay, et al. (2012), says that segmentation of tumor from magnetic resonance imaging (MRI) data is an important but performing it manually by medical experts is time consuming task. It’s a challenging task to automate it because there is high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. There exist different methods to detect brain tumor and to segment a brain tumor from MRI images. They provide a brief review of different segmentation methods for detection of brain tumor from MRI of brain.

M. Usha, A. Sanyam, et al. (2013), performed denoising and segmentation of medical image is using morphological filters and watershed algorithm. A complete division of image is provided by Watershed Algorithm. This algorithm has low computational complexity but it suffers from over-segmentation. This technology has enhanced but still, noise may come into image during the acquisition of image. To denoise image authors used various morphological filters with the improved watershed segmentation. They applied proposed algorithm on different medical images like Ultrasound, X-Ray and MRI and evaluate the on the basis of MSE, MAE, PSNR and number of segments.

K. Gurpreet, K. Sumit, et al. (2013), examined the effect of image gradients as initial step for “watershed segmentation”. They studied effects of gradients in the presence of different operators and then these operators are used by watershed transformation to segment the image. Convolution is performed using different mask operations. Reason behind doing this is to identify the operator which provides better results as initial step for watershed approach. The effect is studied in terms of time and peak signal to noise ratio.

Y. Hongzhe, Z. Lihui and T. Songyuan, (2014), proposed a hybrid technique for brain tumor segmentation, which is based on the approach of global intensity difference, curve evolution, local edge properties and level set method. By combining the edge information into the region-based framework, the images with intensity in homogeneity and complex background can be efficiently segmented. In addition, a penalizing energy is used for avoiding the time-consuming re-initialization step of the level set method.

Then they took some synthetic and real images to perform experiments and demonstrate that proposed method take few iterations to segment tumor.

III. METHODOLOGY A. Flow chart for Chan-Vese Algorithm Upload Image of the human brain

Segmenting the probable areas of the brain

Creating customized Chan-Vese Mask

Selecting a probable area of the brain

Start the initial Counter on the brain image

Process the number of iterations on the infected brain image

Figure 1: Flow chart for Chan-Vese Algorithm B. Steps for Algorithm: Step 1: Upload the image and dicom images.

Step 2: Create the active counter for segmentation with the help of mask.

Step 3: Start the initial counter manual and automatically on the image and dicom.

Step 4: Process the number of iterations in square and elliptic form.

Step5: Chan-Vese approach is used for segmentation is discussed above.

Step6. Finally we get the image part which is affected from a selected portion if extra part is encounter as affected that is also selected by region based approach i.e Chan- Vese.

IV. RESULT & DISCUSSION In proposed work, an approach is used which is derived from initializing small curves inside the region part which we are selecting; it grows until it encounters the affected part.

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Figure 2(b): Original image Figure 2(a): Original image Browsing window Above said image represents the original image, from which we select the some region or boundary of a region.

After this Chan-Vese approach is used with initialize point with iteration for mask to be performed.

Figure 3: Selected Region with 60 iterations

Figure 4: Selected Region with 400 iterations

Figure 3(b) Selected Region with 300 iterations In Above figure 2, 3 and 4 iteration process works to have complete boundary region which is affected.

Figure 6: Browsing window input image Figure 5: Affected Region part as output Above window, we used to select the input image for boundary selection with circle or elliptical region finding as an affected part.

Figure 8: Elliptical region selected part with Figure 7: Elliptical region based counter affected part as output iteration Above said image shows us the results with elliptical iteration using Chan-Vese approach.

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Figure 10: Circle region selected part with affected part as output Above said image shows us the results with elliptical iteration using Chan-Vese approach.

The result in above all said figures is example to test our work, where we segment and superimpose accurate edges for other different images with good results of image segmentation and edge detection. Also it is easy to calculate region area and boundary length. Our results show the accuracy for edge position and region segmentation compared with other segmentation techniques.

Figure 9: Circle region based counter iteration

V. CONCLUSION & FUTURE SCOPE An active contours based on curves, Chan-Vese approach is good and best in edge and segmentation for detecting object and region boundaries, it isolate and extract individual parts from an image with accuracy. In this paper we have can detected objects whose boundaries are not necessarily defined by gradient or for which the classical active contour models are not applicable. Finally, we can automatically detect interior contours starting with only one initial curve. Initial curve can lie anywhere in the image, and it does not necessarily surround the objects to be detected. We validated our model by various numerical results. The energy functional for the proposed model consists of global term of the region intensity difference, local ter m of the geodesic length and regularization term.

Further improvements of the method could be incorporating more information into the scalar image, such as space correlation of adjacent slices, symmetry of the brain, and the multi- spectral data. From segmentation to image in painting and denoising problems and beyond, such methods will no doubt play an important role in future image analysis research.

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K. Ramanpreet, “Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer”, International Journal of Computers & Technology, Volume 3 No. 2,2012 S. Geman, D. Geman, Stochastic relaxation, gibbs distributions and the bayesian restoration of images, IEEE Trans. on Image Processing 6 (6); pp.721–741; 1984.

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International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net Thermoluminescence of nanocrystalline Eu doped BAM Phosphor Vinit Kumar1, M. K. Dhasmana1, R.B.S. Rawat2 Department of physics, K.G K. College Moradabad, Uttar Pradesh, INDIA. 2 Department of Physics, M. S. College Saharanpur, Uttar Pradesh, INDIA. _________________________________________________________________________________________ Abstract: BaMgAl10O17: Eu phosphors have been synthesized by Solid State Reaction method. Results are shown in the well defined glow curves. The achieved TL spectra emission peaks do show changes because of electronic charges and dopant ion size. The experimental results of XRD, SEM, and thermo luminescence showed that the phosphor had hexagonal single phase, the increase of average particle size and the decrease of thermoluminescent intensity when annealed temperature increases. The emission spectra was a broad band with maximum intensity at around temp 450K due to transitions from the 4f65d1 to the 4f7 electronic configuration of Eu2+ ion that was according to emission of Eu2+ ions located at different positions in the lattice.

1

Keywords: BaMgAl10O17: Eu, nanoparticle, Thermoluminescence. __________________________________________________________________________________________ I. Introduction: Thermoluminescence as mentioned by McKever and et al. is one of the processes in Thermally Stimulated Phenomena. In a general view, thermoluminescence is a temperature stimulated light emission from a crystal after removal of excitation. Nevertheless, microscopically, it is much more complicated. Thermoluminescence Radiataion Dosimetry(TLD) is one of the popular technology of the luminescence research. Divalent europium (Eu2+) activated barium magnesium aluminate (BaMgAl10O17: Eu - BAM) phosphor, is currently used in plasma display panels (PDP’s) as a blue emitting phosphor. A limited number of papers have been published explaining various reasons, but no one is able to fully understand Nanocrystalline BaMgAl10O17: Eu2+ blue emitting phosphor. It has been used extensively in manufacturing tricolor fluorescent lamps (FL), field emission displays (FED), plasma display panels (PDPs) and liquid crystal displays (LCD). This phosphor has high luminous efficiency and brightness. Emission spectra of BaMgAl 10O17: Eu2+ (BAM) phosphor have a broad band with peak at 450 nm due to transition from the 4f65d1 excited state to the 4f7 ground state of ion Eu 2+.

However, the stability of BAM is not good. Its luminous efficiency decreases during the panel manufacture and also during the operation of the panel (1-4).

II. Methodology BaMgAl10O17:Eu(.5%) phosphors have been synthesized by Solid State Reaction method(5). The chemical used are BaCO3, MgO MgF, Al2O3, MnCO3 and Eu2O3as starting material. The appropriate oxides were thoroughly ground and the annealed up to 12000C for 6-8h. The formation of the powder was confirmed by the Powder Xray Diffraction (PXRD) analysis. Shape and size of the synthesized powder were examined using Transmission Electron Microscope (TEM). Dosimetric characteristics have been carried out of the γ-ray irradiated BaMgAl10O17:Eu phosphors using Thermoluminescence (TL) technique. The TL glow curve of the samples shows the dosimetric peak at around 450 K which is sufficient above the room temperature. The glow curves were recorded with linear heating rate of 6.67 0CS-1.

Fig 1. X – ray diffraction pattern of nanocrystalline BaMgAl10O17:Eu.

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1.6

TL Intensity (a.u.)

1.4 1.2 E-1000Gy

1

D-100Gy

0.8

C-10 Gy

0.6

D-.1 Gy

0.4 0.2

350 357 364 371 378 385 392 399 406 413 420 427 434 441 448 455 462 469 476 483 490 497 504 511 518 525 532 539 546

0

Figure 3: TL glow curves with Temp (K) of nanocrystallineBaMgAl10O17:Eu powder exposed to various doses of γ-rays from a 137Cs source.

III. Result and Discussion TL glow curve for BaMgAl10O17: Eu exposed with .1-1000Gy of 137Cs gamma rays are shown in Fig 2. The comparison of these is shown in figure. Tl glow curve of the nanocrystalline BaMgAl 10O17: Eu shows prominent peak around 445K to 450K. This suggest that the trap responsible for TL at this temperature prominent for nanophosphor. The position of peak along the growth of higher temperature depends on the impurity concentration. At lower temperature range below 383 K and corresponding higher temperature range above 480 these peaks vanishes. The graph brings out the fact that the sample with Eu impurity shows good TL response to various gamma irradiations. Trapping parameters are calculated with the help of Chen’s formula (6). The trap depth and frequency factor values are in the range of 0.31 to 0.39ev and 10 8 to 109 sec-1 for low temperature peak and 0.51 to 0.57ev and 107 to 108sec-1 for high temperature peaks. The broad nature of observed peaks indicates the allowed nature for emission transition of Eu +2 ion.

IV. Conclusion Nanoparticles of BaMgAl10O17:Eu phosphors have been synthesized by Solid State Reaction method.. TL properties has been investigated. The average particles size 80 nm were controlled by taking the appropriate concentration of starting materials. The intense dosimetric well defined isolated peak was found at around 445 K, which shows linear dose response. It may be useful in accidental dosimeter in display devices.

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R. S. Yadav, S. K. Pandey, A. C. Pandey, “Blue –shift and enhanced photoluminescence in BaMgAl10O17: Eu2+ nanophosphor under VUV excitation for PDPs application”, Materials sciences and applications, vol 1, pp. 25-31, February 2010.

Zhe Chen, Youwei Yan, “Nano –sized PDP phosphors prepared by combustion method”, J Mater Sci, vol 41, pp. 5793-5796, September 2006.

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