AIJRSTEM issue 9 vol 1 by iasir journals

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

Issue 9, Volume 1, 2 & 3 December-2014 to February-2015

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

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

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

PREFACE We are delighted to welcome you to the ninth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM). In recent years, advances in science, technology, engineering, and mathematics have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. AIJRSTEM is publishing high-quality, peer-reviewed papers covering topics such as Computer and computational sciences, Physics, Chemistry, Mathematics, Applied

mathematics,

Biochemistry,

Robotics,

Statistics,

Electrical

Electronics

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

Technology,

Engineering

Mathematics

order

develop

and

implement widespread expansion of highâ&#x20AC;?quality common standards and assessments. These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to science, technology, engineering & mathematics. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the AIJRSTEM for entrusting us with the important job. We are thankful to the members of the AIJRSTEM editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this ninth issue, we received 149 research papers and out of which only 57 research papers are published in three volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of science, technology, engineering & mathematics.

This issue of the AIJRSTEM has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in science, technology, engineering & mathematics and may open new area for research and development. We hope you will enjoy this ninth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM), ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 (December-2014 to February-2015, Issue 9, Volume 1, 2 & 3). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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

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

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) 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 â€“ 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:  Computer and computational sciences  Physics  Chemistry  Mathematics  Actuarial sciences  Applied mathematics  Biochemistry, Bioinformatics  Robotics  Computer engineering  Statistics  Electrical engineering & Electronics  Mechanical engineering  Industrial engineering  Information sciences  Civil Engineering  Aerospace engineering  Chemical engineering  Sports sciences  Military sciences  Astrophysics & Astronomy  Optics  Nanotechnology  Nuclear physics  Operations research  Neurobiology & Biomechanics  Acoustical engineering  Geographic information systems  Atmospheric sciences  Educational/Instructional technology  Biological sciences  Education and Human resource  Extreme engineering applications  Environmental research and education  Geosciences  Social, Behavioral and Economic sciences  Advanced manufacturing technology  Automotive & Construction  Geospatial technology  Cyber security  Transportation  Energy and Power  Healthcare & Hospitality  Medical and dental sciences  Pesticides  Marine and thermal sciences  Pollution  Renewable sources of energy  Industrial pollution control  Hazardous and e-waste management  Green technologies  Artificial/computational intelligence  Theory and models

TABLE OF CONTENTS American International Journal of Research in Science, Technology, Engineering & Mathematics ISSN(Print): 2328-3491, ISSN(Online): 2328-3580, ISSN(CD-ROM): 2328-3629 (December-2014 to February-2015, Issue 9, Volume 1, 2 & 3) Issue 9, Volume 1 Paper Code

Paper Title

Page No.

AIJRSTEM 15-106

TBSC Compensator: Application and Simulation Results for Starting and Voltage Sag Mitigation of Induction Motor Swapnil Dadaso Patil, Dr. Anwar Mubarak Mulla, Dr. Dadgonda Rajgonda Patil

01-11

AIJRSTEM 15-109

On Volterra Type Integral Inequalities in Metric Spaces Hristo Kiskinov, Stepan Kostadinov, Lozanka Trenkova, Andrey Zahariev

12-19

AIJRSTEM 15-113

Review of Public Safety in Some Non Governmental Medical Radiation Facilities in Bangladesh M.Haider, S. Shill and QMR Nizam

20-24

AIJRSTEM 15-115

Reactions of MoOCl4 & MoO2Cl2 with Imidazole, Pyrazole, Acetamide, Succinimide, Benzamide & 2-Thiazoline-2-thiol Gursharan Singh, Seema Sharma, Vikas Mangla, Mamta Goyal, Kavita Singla, Deepika Rani

25-33

AIJRSTEM 15-118

Experimental Investigation on Plain Circular and 2:1 Rectangular Jets at Low Speed Dr. A. Arokkiaswamy

34-38

AIJRSTEM 15-126

Fingerprint Based Gender Classification for Biometric Security: A State-Of-The-Art Technique S. S. Gornale

39-49

AIJRSTEM 15-133

Energy Consumption in Wireless Network Control System Data Transmission K.Sanakkiyan, R.Kannan

50-55

AIJRSTEM 15-134

IMAGE RECONSTRUCTION USING SPARSE BASED MODEL Reetumala Thakre, Prof. Rajendra Singh, Dr. Soni Changlani

56-61

AIJRSTEM 15-135

Automatic Bangladeshi Vehicle Number Plate Recognition System using Neural Network Mohammad Badrul Alam Miah, Sharmin Akter and Chitra Bonik

62-66

AIJRSTEM 15-136

Navigation System for Multi-Floor Indoor Positioning during Urban Combat Operations using Geo-Magnetic Module N.Kanagapriya, N.Solaiyammal

67-70

AIJRSTEM 15-137

Survey on cloud computing technology, application, usage and its stack terminology Santvana Singh, Sarla Singh, Sumit Dubey

71-73

AIJRSTEM 15-139

Future monthly, seasonal and annual rainfall trend prediction for Tarai region of Uttarakhand Arvind Singh Tomar, Praveen Vikram Singh and Om Prakash Kumar

74-76

AIJRSTEM 15-140

Synthesis and Structural Studies of some complexes of Ni(II) and Co(III) with Hexamethylenedibiguanide Bina Rani, R.K. Prasad, and Divya Singh

77-80

AIJRSTEM 15-142

Experimental/Computational Results for Shielding Neutron & Photon Radiations C .E. Okon, I. P. Etim

81-89

AIJRSTEM 15-149

Intense Geomagnetic Storms and their Association with Solar Wind Plasma and Interplanetary Parameters Mukesh Kanwal Tripathi, R.S. Gupta, M.P. Yadav

90-94

AIJRSTEM 15-150

Analysis of modulation technique in free space optics system Vasundhara Shukla, Durgesh Shukla, Jayant Shukla, Richi Nigam

95-98

AIJRSTEM 15-152

Comparison: Introduction to Mobile Ad-Hoc Network and Routing Implementation Meenakshi Mishra, Niketan Mishra, Soni Changlani

99-101

Issue 9, Volume 2 Paper Code

Paper Title

Page No.

AIJRSTEM 15-153

Building scalable geo-replicated storage back ends for web application Rajkumar M. Chauhan

102-106

AIJRSTEM 15-156

Short term Size Deviation of Multivoltine, Multi x Bi and Bivoltine Raw Silk Niharendu Bikash Kar, Mrinal Kanti Majumdar, S. Nirmalkumar

107-112

AIJRSTEM 15-157

Synthesis of Well-defined Dihydroxyl End-Functional Polystyrene using Trifunctional Initiator via Atom Transfer Radical Polymerization Samiul Islam Chowdhury, Tariqul Hasan

113-118

AIJRSTEM 15-158

Identification of Data Quality Issues in Homogeneous Environment Sapna Pujara, Dr. Kanwal Garg

119-123

AIJRSTEM 15-160

Variablity of Rainfall in Bangalore city - Mapping of hourly variation of heavy rainfall event Roopa Bhandiwad

124-139

AIJRSTEM 15-161

Intuitionistic Z-Numbers G Velammal, M Shahila Bhanu

140-142

AIJRSTEM 15-162

Interchangeability of Motor-Cycle Parts for Maintenance Purposes Joseph W. and Chindapi N.

143-149

AIJRSTEM 15-163

Signal Processing for Music Analysis Poonam Priyadarshini, Soubhik Chakraborty

150-153

AIJRSTEM 15-164

REVIEW ON MODELING OF RESISTANCE SPOT WELDING PROCESS Boriwal Lokesh, Sarviya R. M. & Mahapatra M. M

154-159

AIJRSTEM 15-165

Dynamic Query Search Over Graph Stream Ms.Vaishali G. Powar and Mr.Pradeep C. Bhaskar

160-164

AIJRSTEM 15-171

A review on Optimization of material removal rate in micro-drilling process Ranadhir R Landge, Dr. Atul B Borade

165-169

AIJRSTEM 15-178

Application of Various Multi Criteria Decision Making Methods for the Assessment of Different Waste Water Treatment Systems Case Study: JSPM Hadapsar Campus, Pune Poonam K. Wakchaure, Dr. Sameer S. Shastri

170-175

AIJRSTEM 15-179

An Experimental Study of Thermal Performance of Concentric Annular Heat Pipe Nishtha Vijra, Tejinder Paul Singh

176-182

AIJRSTEM 15-183

Stability: Abstract Roadmap of Software Security Kavita Sahu, Raj Shree

183-186

AIJRSTEM 15-185

Image Filtering and Registration by using Hybrid Area and Feature Based Method Kiran P. Dange, Neha P. Athavale

187-193

AIJRSTEM 15-186

An Investigation on the Potential of Biogas Production from Elephant Grass and Guinea Grass Sindra L Summoogum-Utchanah and Doolar Poorneema

194-197

AIJRSTEM 15-187

Discursive Institutionalism: Analysis of the Forest Policy Review Process of Ghana Yaw Amo Sarpong, Daniel K. B. Inkoom, Ibrahim Latif Apaassongo

198-205

Issue 9, Volume 3 Paper Code

Paper Title

Page No.

AIJRSTEM 15-188

Survey on Clustering Techniques of Data Mining Namrata S Gupta, Bijendra S.Agrawal, Rajkumar M. Chauhan

206-211

AIJRSTEM 15-189

Effect of Spacer Length from Vinyl Group of Vinyl-bromoester Initiator on Atom Transfer Radical Polymerization of Styrene Samiul Islam Chowdhury and Tariqul Hasan

212-215

AIJRSTEM 15-190

The Influences of Surface Area on the Efficiency of a New Design of Solar Collector Suitable for Basrah City 30.50 N

216-220

Kawther K. Mankhi; Noori H.N. Al-Hashimi; Jassim M. Al-Asadi AIJRSTEM 15-191

Inertial effects on hydrodynamic convection in a passive mushy layer Prof. Dr.P.K.Srimani, Mr. R.Parthasarathi

221-229

AIJRSTEM 15-192

Initial Thermal Hydraulic Design of a 1000MWe Water Reactor with an Improved Thermal Efficiency C .E. Okon, D. E. Oku

230-238

AIJRSTEM 15-195

A hybrid approach to measure design improvement factor of website Prafulla Bafna, Hema Gaikwad

239-241

AIJRSTEM 15-197

EXCITATION OF DOUBLY-CHARGED YTTRIUM IONS IN ELECTRON-ATOM COLLISIONS Yu. M. Smirnov

242-245

AIJRSTEM 15-199

Simulated Results for Neutron Radiations Shielding Using Monte Carlo C .E. Okon, I. O. Akpan

246-251

AIJRSTEM 15-200

A Review on Various Error Detection and Correction Methods Used in Communication Varinder Singh, Narinder Sharma

252-257

AIJRSTEM 15-205

Dual Layer Image Scrambling Method Using Improved Arnold Transform Gyan Vardhan Artist, Dr. Mahesh Kumar Porwal

258-264

AIJRSTEM 15-209

An Open type Mixed Quadrature Rule using Fejer and Gaussian Quadrature Rules Dwiti Krushna Behera, Ajit Kumar Sethi & Rajani Ballav Dash

265-268

AIJRSTEM 15-215

Land holding effect on energy inputs for soybean production in Malwa plateau of Madhya Pradesh Dilip Jat, R.K. Naik, N.K.Khandelwal, Bharat Patel and Prateek Shrivastava

269-274

AIJRSTEM 15-219

Comparative Study of Dynamic Performance of Multi-Area Interconnected Power Systems with EHVAC/HVDC Links Ram Naresh Mishra, Dr. Prabhat Kumar

275-283

AIJRSTEM 15-223

Behavior of a Discrete SIR Epidemic Model A. George Maria Selvam, R. Janagaraj and D. Jerald Praveen

284-287

AIJRSTEM 15-227

Effect of additives on the structural and magnetic properties of electrodeposited NiMn thin film M.Rajeswari, S.Ganesan

288-292

AIJRSTEM 15-232

Does the knowledge and skill acquired during simulator training gets applied on the job by the seafarers- An empirical study Surender Kumar, Dr. Neeraj Anand, Dr. DK Punia, Dr. BK Saxena

293-297

AIJRSTEM 15-235

An Investigation on Causes and Preventive Measures of Trespass Accidents and Suicides in Katpadi Railway Jurisdiction of Tamil Nadu, India Dr. N. Sundaram, Mr. M. Sriram

298-301

AIJRSTEM 15-239

ACTIVE LEARNING METHODS IN HIGHER EDUCATION Badri Meparishvili, Lela Turmanidze, Gulnara Janelidze

302-305

AIJRSTEM 15-240

Appari’s Design – Get Extra Energy From Existing Resources Sidramappa Shivashankar Dharane, Archita Vijaykumar Malge, Savita Gururaj Malage

306-308

AIJRSTEM 15-242

Heat Transfer and Turbulent Nanofluid Flow over a Double Forward-Facing Step Mohammed Saad Kamel

309-316

AIJRSTEM 15-243

Experimental Simulation Analysis of Current Density Distribution by Kirchhoff’s laws in a resistor network cell model S. R. Rajkumar, Dr.M. Alagar, Dr.S. Somasekaran, S.R. Ravisankar

317-322

AIJRSTEM 15-245

Measuring infrastructure sustainability with the use of eco efficient performance criteria Saroop s and Allopi d

323-327

AIJRSTEM 15-246

Glauber Modeling in Heavy Ion Collisions Abhilasha Saini, Dr. Sudhir Bhardwaj

328-331

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

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

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

TBSC Compensator: Application and Simulation Results for Starting and Voltage Sag Mitigation of Induction Motor Swapnil Dadaso Patil PG Scholer Student, Annasaheb Dange College of Engineering & Technology, Ashta, MS, India. Dr. Anwar Mubarak Mulla, Principal of Annasaheb Dange College of Engineering & Technology, Ashta, MS, India. Dr. Dadgonda Rajgonda Patil Prof. at Walchand College of Engineerin Sangli, MS, India.

Abstract: This dissertations work deals with the analysis, design and implementation of Thyristor Binary Switched Capacitor (TBSC) banks. The performance of various TBSC topologies for reactive power compensation suitable for fast dynamic loads in closed loop systems are investigated by simulation. The scheme consists of Thyristor Binary Switched Capacitor (TBSC) banks. TBSC is based on a chain of Thyristor Switched Capacitor (TSC) banks arranged in binary sequential manner. Frequent switching of capacitors reduces the life of switched capacitor bank. Hence, a control circuitry has been proposed in such a way that transient free switching of TBSCs will takes place. Proposed topology allows almost step-less reactive power compensation for fast varying dynamic loads in closed loop. This scheme has error in reactive power compensation equals to half of the lowest step size of the capacitor bank. The suitable chain of binary switched capacitor bank will be proposed. The proposed scheme can achieve reactive power compensation cycle to cycle basis and the harmonics contents of sours are maintained at insignificant levels due to filtering action of TBSC as well as transient free switching of capacitor bank. Proposed TBSC scheme compensates the fast varying dynamic reactive load. Also the proposed scheme can be used for direct online starting of I.M.s with voltage sag mitigation at starting, which helps improving stability of the system and Power Factor (P.F.) improvement in steady state. Keywords: Reactive power compensation, TBSC, transient free switching, voltage sag, power factor, dynamic load, starting of induction motor. I. Introduction It is well documented in literature and through public discussions at various levels that a substantial power loss is taking place in our low voltage distribution systems on account of poor power factor, due to inadequate reactive power compensation facilities and their improper control. Switched LT capacitors can directly supply the reactive power of loads and improve the operating condition. Government of India has been insisting on shunt capacitor installations in massive way and encouraging the state electricity boards through Rural Electrification Corporation and various other financing bodies. The expansion of rural power distribution systems with new connections and catering to agricultural sector in wide spread remote areas, giving rise to more inductive loads resulting in very low power factors [1]. The voltages at the remote ends are low and the farmerâ&#x20AC;&#x2DC;s use high HP motors operating at low load levels with low efficiencies. This is giving rise to large losses in the distribution network. Thus there exists a great necessity to closely match reactive power with the load so as to improve power factor, boost the voltage and reduce the losses. The conventional methods of reactive power supply are through switched LT capacitors, mostly in equal steps in various automatic power factor controllers developed by number of companies. In this paper, a more reliable, technically sound, fast acting and low cost scheme is presented by arranging the thyristor switched capacitor units in five binary sequential steps. This enables the reactive power variation with the least possible resolution. As there is reduction in loss with shunt compensation in the feeders, the efficiency increases and conservation of energy takes place. Besides the enhancement of transformer loading capability the shunt capacitor also improves the feeder performance, reduces voltage drop in the feeder and transformer, better voltage at load end, improves power factor and improves system security, increases over all efficiency, saves energy due to reduced system losses, avoids low power factor penalty, and reduces maximum demand charges [2&3]. Induction motors (I.M.) load constitute a large portion of power system. Three-phase induction motors represent the most significant load in the industrial plants, over the half of the delivered electrical energy. Starting of induction motor may cause a problem of voltage sag in the power system. The IEEE defines voltage sag as: A

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

decrease to between 0.1 and 0.9 p.u. in rms voltage or current at the power frequency for durations of 0.5 cycle to 1 min. An induction motor at rest can be modeled as a transformer with the secondary winding short circuited. Thus when full voltage is applied, a heavy inrush current (of 6 to 10 times the rated value) is drawn from the power system that causes voltage sag. As the motor accelerates and attains the rated speed, the inrush current decays and the system voltage recovers [4]. Voltage sag can cause mal-operation of voltage sensitive devices such as computers, relays, programmable logic controllers etc. Also because of the highly inductive nature of the motor circuit at rest, the power factor is very low, usually of the order of 10 to 20 percent. Thus reactive power demand at the starting of I.M. is very high and it reduces as motor picks up the speed. There are several solutions to minimize this problem; the most common are reactor start, auto transformer start, star-delta, capacitor start, soft starter, frequency variable driver (FVD) etc. All these methods except capacitor start are based on a motor terminal voltage reduction to decrease the rotor current, reducing the line voltage drop. Problem with this method of starting is that the motor torque is directly proportional to the square of the supply voltage hence decrease in the motor terminal voltage will cause the motor torque to decrease, which may be insufficient for driving the required load. Soft starter and frequency variable driver methods are the most expensive and complex, requiring more expert maintenance. In capacitor start system, reactive current required by the motor during acceleration is supplied by capacitors which reduce the source current. This in turn reduces the magnitude of voltage sag in the system. Capacitor start method has a lower cost in comparison with other methods however one has to consider the transitory effects of switching of capacitor banks [5&6]. The desirable features of the proposed scheme are as follows [7&8]: • It maintains the power factor at the PCC to any specified value. • It compensates for rapid variation in reactive power or voltages. • Maximum compensation time is 20 msec. • No transients or harmonics are allowed to be present due to fast selective instants of switching in well co-ordinate manner. • It is adaptive in the sense that the amount of the compensation is determined and provided on a cycle by cycle basis. • It can compensate each phase independently which makes ideal for unbalanced systems. • Capacitors are sized in binary sequential ratio for minimum size of switching steps. • The control strategy is error activated to match with the load reactive power for the chosen time interval. • It eliminates possible over compensation and resulting leading power factor. • It is flexible to choose required number of steps as per the resolution. • Resolution can be made small with more number of steps. • Simple in principle, elegant in usage and of low cost .. II PROPOSED TOPOLOGY Following figure shows the schematic block diagram of proposed work. Figure.1. TBSC Compensator

Distribution Transformer

Point Of Common Coupling (PCC)

P.T

Induction Motor 0

2 c

2 c 8 TBSC Banks V

CONTROLLER

C.T-Current Transformer P.T-Potential Transformer TBSC-Thyristor Binary Switch Capacitor C-Capacitor Value

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

This paper presents a topology, which is shown in Fig.1. The proposed scheme consists of Thyristor Switched Capacitor (TSC) banks in binary sequential steps known as Thyristor Binary Switched Capacitor (TBSC) [9&10]. This TBSC facilitates step-less control of reactive power closely matching with load requirements so as to maintain desired power factor. The proposed topology has following distinctive features [2]: • TSC (Thyristor Switched Capacitor) banks are arranged in Binary sequential steps to provide almost continuous reactive power compensation. • Transient free switching is obtained by switching the capacitors to the negative/positive peak of supply voltage and firing the thyristors at the negative/positive peak of supply voltage. • It compensates for rapid variation in reactive power. • Reactive power compensation is achieved in cycle by cycle basis. That is step-less compensation. • Inrush current problems during connection and Outrush current disconnection are avoided. At the distribution transformer requiring total reactive power Q for improving the power factor from some initial value P.f1 to the desired value P.f2 at the load. This Q can be arranged in binary sequential ‘n’ steps, satisfying the following equation [1]: Q = 2nC + 2n−1C + ......... + 22C + 21C + 20C The schematic diagram of the capacitor bank in five binary sequential steps through thyristor and with respective current limiting reactors is shown in Fig.1. TBSC compensator connected at the point of common coupling (PCC) for reactive power compensation is shown in Fig.1 and the operating principle of each equipment is analyzed in the following sections. A TBSC: TBSC consists of an anti-parallel connected thyristor as a bidirectional switch in series with a capacitor and a current limiting small reactor. Transient free switching of capacitors is obtained by satisfying following two conditions a. Firing the thyristors at the negative/positive peak of supply voltage. b. Pre-charging the Capacitors to the negative/positive peak of supply voltage. c. TBSC current is sinusoidal and free from harmonics, thus eliminating the need for any filters. Smallseries inductor is placed in series with capacitor. It serves following purposes: d. It limits current transients during overvoltage conditions and when switching at incorrect instants or at the inappropriate voltage polarity. e. The chosen inductor magnitude gives a natural resonant frequency of many times the system nominal frequency. This ensures that the inductance neither creates a harmonic-resonant circuit with the network nor dampers the TBSC control system. In the proposed scheme, capacitor bank step values are chosen in binary sequence weights to make the resolution small. If such ‘n’ capacitor steps are used then 2n different compensation levels can be provided [4]. In this scheme five TBSC banks are arranged as 2.5, 5, 10, 20, 40 KVAR in star connected with neutral grounded configuration. B TBSC Closed Loop Operation: A block diagram of reactive power compensator using TBSC banks is shown in Fig.2. Reference reactive power, QRef is calculated from the desired power factor. Actual reactive power at PCC, QActual is calculated by sensing voltage and current at PCC by P.T. and C.T. respectively. Error between QRef and Q Actual is given to PI Controller. A Discrete PI Controller is used. Output of PI Controller is given to ADC and its output is given to TBSC banks in such a way that no transients occur. In this way closed loop operation of TBSC banks for reactive power compensation is achieved. [12] Fig.2. TBSC Closed Loop Operation.. Reactive load Demand QL

Qref + _ Qactual

PID Controller

ADC

Transient Free Switching

TBSC Bank

Q(TBSC) _

Q Sensing

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

III MATLAB SIMULATION RESULTS A.

Binary Current and Voltage Generation:

The Fig.3 shows the binary operation of the TBSC compensator proposed in fig.1. The total compensating current from phase "R" (total ic), is being increased step by step. The capacitor currents from the branches Bl (ic1), B2 (ic2), B4 (ic4), and B8 (ic8) are shown in fig.3 respectively. In fig.3 the total compensating current for the phase "R" (total ic) is displayed (total ic=ic1+ic2+ic4+ic8). [11] Fig.3. Compensating current for phase “R”. (Ic1) Current through B1 (Ic2) Current through B2 (Ic3) Current through B4 (Ic4) Current through B8

It can be noted that harmonics or inrush problems are not generated, and that the current total ic seems to vary continuously. The transitions during connection and disconnection are quite clean. Fig.4. Voltage across capacitor (Vc1, Vc2, Vc3, Vc4).

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B. TBSC Compensator for fast varying dynamic load: Data used in Simulation:Source Voltage, V = 400 V, Rs = 0.0287Ω, Ls = 0.20471mH. TBSC banks:- Five TBSC banks are used in the simulation whose values are shown in Table I. Continuously changing reactive power, QL is obtained by simulating three phase dynamic load. The nature of load variation is as shown in Fig.4. Table I: Values of five TBSC banks Sr. No. 1. 2. 3. 4. 5.

Q in KVAr 2.5 5 10 20 40

C in µf 45 90 180 360 720

L in mH 0.32 0.16 0.08 0.04 0.02

Minimum reactive power Qmin, maximum reactive power Qmax, and base reactive power Qbase can be varied by changing the parameters of three phase dynamic. Fig.4. Simulation of three phase dynamic load.

C. TBSC Closed Loop Operation for dynamic load: Discrete PI controller with KP = 0.565 and KI = 25 is used. 5 bit ADC is used in simulation. Parameters of Three-phase dynamic load block are adjusted in such a way that QL varies continuously from QMin. = 0.25 KVAR to QMax. = 77.5 KVAR with base load QBase. = 40 KVAR. This variation takes place in five seconds. Waveforms of load reactive power QL, reactive power given by TBSC, Qcomp.(TBSC) and actual reactive power QActual at PCC are shown in Fig.5. Fig.4. Simulation Result of TBSC operation.

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From simulation results it is seen that Qcomp.(TBSC) closely follows QL shown in Fig.4, and actual reactive power QActual at PCC is approximately +500 to -500 VARs at all discrete switching instances. The small error is due to the binary switching arrangement of TBSCs. These errors can be minimized by adding more number of capacitor banks in TBSC. Fig.5. Current waveforms through all TBSC bank and source (of R phase only).

Fig.5 clearly shows the current waveforms which are free from both harmonics and transients. D. TBSC Compensator for voltage sag mitigation of induction motors: Data used in the simulation is shown below. a. SourceVoltage, V = 400 V, Rs = 0.0287Ω, Ls = 0.20471mH b. Induction motor (I.M.) – Three identical I.M.s are used in the simulation which are switched on at t = 0 sec, 0.8 sec and 1.6 sec respectively. For Simulation purpose at 1.6 sec, two 50 h.p. motors are switched on simultaneously to get 100 h.p. load Table III:

Sr. No.

Parameter

Value

Line Voltage

400v

Frequency

50H.z

Nominal Power

50HP

Speed

1440rpm

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

Table IIIII: Values of Eight TBSC banks are shown in Sr. No.

Q(in KVAR)

C (in ÂľF)

L (in mH)

2.5

0.10775

100

0.0538

200

0.0269

4. 5. 6. 7. 8.

20 40 80 160 320

400 800 1600 3200 6400

0.0134 0.0067 0.0033 0.0016 0.00084

Fig.6. shows the waveform of motor line voltage. When I.M.1 is switched on at t=0sec, the motor line voltage drops to 351V i.e. voltage sag of 11.14% takes place. Line voltage returns to steady value of 395V in 0.5sec. When I.M.2 is switched on at t=0.8sec, the motor line voltage drops to 349V i.e. voltage sag of 11.64% takes place. Line voltage returns to steady value of 392V in 0.5sec. When I.M.3 is switched on at t=1.6sec, the motor line voltage drops to 309V i.e. voltage sag of 21.77% takes place. Line voltage returns to steady value of 382V in 0.7sec Fig.6. Motor Line Voltage without TBSC compensator

Fig.7. shows the variation of reactive power with time. When I.M.1 & 2 is switched on at t= 0sec and 0.8sec respectively, reactive power demand is around 250 KVAR at starting period. Reactive power demand is around 380 KVAR when I.M.3 is switched on at t=1.6 sec. It is seen that reactive power demand is very high at the time of starting of motor and it reduces as the motor reaches the steady state condition. Because of high reactive power requirement at start voltage drops as shown in Fig. 6. Fig.7. Reactive Power variation of I.M. without TBSC compensator

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

Fig.8. Motor line current without TBSC compensator.

Fig.8 shows the variation of motor current with time. When I.M.1 & 2 is switched on at t= 0sec and 0.8 sec respectively, current is around 500 A at starting period while at the time of starting of I.M. 3 it is around 1000 A. It is seen that when motor is switched on, current is very large at the starting period & it reduces as motor attains steady speed. E. TBSC Closed Loop Operation for Induction Motor load: Discrete PI controller with KP = 0.54 & KI = 25 is used. 8 bit ADC is used in simulation. Waveforms of I.M. reactive power demand QMotor and reactive power given by TBSC QTBSC are shown in Fig. 9. From simulation results it is seen that QTBSC closely follows QMotor and actual reactive power QActual at PCC is approximately zero at all times. Thus power factor is maintained near unity at all time. The small error is due to the binary switching arrangement of TSCs Fig.9. Waveforms of QMotor and QTBSC

Fig. 10 shows the motor line voltage with TBSC compensator. When I.M.1 is switched on at t=0sec, motor line voltage drops to 389V i.e. small voltage sag of 2.01% takes place for a duration of 0.4sec. Line voltage returns to steady value of 400V in 0.4sec. When I.M.2 is switched on at t=0.8sec, the motor line voltage drops to 377V i.e. voltage sag of 5.3% takes place for a duration of 0.4 sec. steady value of 396V in 0.4sec. When I.M.3 is switched on at t=1.6sec, the motor line voltage drops to 360V i.e. voltage sag of 7.92% takes place for a duration of 0.65 sec. Line voltage returns to steady value of 391V in 0.7sec. These results show that with TBSC compensator there is considerable reduction in voltage sag and there is improvement in the voltage profile. Fig.11 shows the comparison of motor line voltage with and without TBSC compensator Current waveforms through all TSC banks & which are free from harmonics and have negligibly small transients only at few switching instants.

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

Fig. 10 Motor Line Voltage with TBSC compensator

Fig.3.12 Motor Line Voltage without TBSC compensator (Top) and with TBSC compensator (Bottom).

F. Comparisons of results with & without TBSC Compensator The simulation results are compared with and without TBSC compensator and are tabulated in the Table number IV. The comparison has been carried out based on wsitching instant, % Voltage sag,reactive power at starting and starting current. It shows clearly that around 12% voltage sag mitigation takes place.

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11

Table IV Without TBSC Compensator

Sr. No.

Parameter

1 2 3 4

With TBSC Compensator

I.M.1 (50 h.p.)

I.M.2 (50 h.p.)

I.M.3 (100 h.p.)

I.M.1 (50 h.p.)

I.M.2 (50 h.p.)

I.M.3 (100 h.p.)

Switching instant (in sec)

0.0

0.8

1.6

0.0

0.8

1.6

% Voltage sag

11.14

11.64

21.77

2.01

5.3

7.92

250

380

500

1000

Reactive power at starting(in KVAR) Starting current (in A)

Closely matches with the required value 300

300

700

IV. Conclusiona A.

Conclusion based on TBSC Compensator for Fast Varying Dynamic Load simulation:

A topology using a TBSC has been presented. The TSC bank step values are chosen in binary sequence weights to make the resolution small. Current flowing through TBSC as well as source is transient free. Harmonic content in source current is negligibly small. By coordinating the control of TBSC, it is possible to obtain fully stepless control of reactive power. Also one can operate the system at any desired power factor. Proposed topology can compensate for rapid variation in reactive power on cycle to cycle basis. An attempt is made through this work to develop a scheme with thyristors to reduce the cost by avoiding IGBT‘s and IGCT‘s, technically sound with reliable performance during both steady state and transient conditions, suitable for rapidly changing / fluctuating loads such as arc furnaces, tractions loads, welding equipment’s etc., and self-regulating operations are practically both transient and harmonics free. The scheme developed is most suitable for highly nonlinear, fluctuating and harmonic generating loads. It gives following benefits: • Maintaining the power factor at unity. • Minimum feeder current and loss reduction. • Improvement in distribution feeder efficiency. • Improvement in the voltage at load end. • Relief in maximum demand and effective utilization of transformer capacity. • Saving in monthly bill due to reduction in penalty on account of poor power factor, and reduction in maximum demand charges. • Conservation of energy takes place.. B. Conclusion based on TBSC Compensator for Induction Motor Load simulation: A topology for direct online starting of induction motors using TBSC compensator is presented. TSC bank step values are chosen in binary sequence weights to make the resolution small in order to achieve almost stepless reactive power compensation. Harmonic contents in source current are negligibly small. With the use of TBSC compensator; voltage sag magnitude gets reduced as well as voltage profile is improved. Controller operates in a closed loop to determine the number of capacitor units to be switched in the system. At the time of starting of I.M.s higher capacitor banks are switched in the system while once the motor reaches the rated speed only few lower capacitor banks will remain connected at the PCC. Thus at all times power factor is maintained near unity. The proposed scheme is effective during both steady state and transient conditions. Separate starting method for individual induction motors can be avoided and many motors can be started direct online using the proposed scheme as long as TBSC banks are capable of supplying the required reactive power demand. VI.References [1]

D. R. Patil, Member IAENG, U. Gudaru, Senior Member IEEE, “A Comprehensive Microcontroller for SVC wit Capacitor Bank in Binary Sequential Step Minimizing TCR Capacity”, 978-1-4244-1762-9/08/$25.00 c2008 IEEE.

[2]

D. R. Patil and U. Gudaru, “The Experimental Studies of Transient Free Digital SVC Controller with Thyristor Binary Compensator at 125 KVA Distribution Transformers”, Proceedings of the World Congress on Engineering 2012 Vol II WCE 2012, July 4 - 6, 2012, London, U.K.

[3]

D. R. Patil and U. Gudaru, “An Innovative Transient Free Adaptive SVC in Stepless Mode of Control”, International Science Index Vol:5, No:5, 2011 waset.org/Publication/6880.

[4]

Irfan I. Mujawar, Swapnil D. Patil, U. Gudaru,Senior Member IEEE, D. R. Patil Member,IAENG, “A Closed Loop TBSC Compensator for Direct Online Starting of Induction Motors With Voltage Sag Mitigation” Proceedings of the World Congress

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Swapnil Dadaso Patil et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 01-11 on Engineering and Computer Science 2013 Vol I WCECS 2013, 23-25 October, 2013, San Francisco, USA, ISBN: 978-98819252-3-7. [5]

IEEE Std 1159-1995, IEEE recommended practice for monitoring electric power quality. Stout, John H. “Capacitor Starting of Large Motors” – Industry Applications, IEEE Transactions on volume IA-14, Issue 3, May 1978.

[6]

Eben-ezer Prates De silveira, Robson Celso Pires, Antonio Tadeu Lyrio de Almeida, Angelo José , Junqueira Rezek, “Direct on line starting induction motor with thyristor switched capacitor based voltage regulation”, IEEE pp.1124-1129, 2009.

[7]

Swapnil Patil, Yogesh Shinde, Khushal Shende U. Gudaru, Senior Member IEEE, D. R. Patil Member,IAENG, “Transient Free TBSC Compensator for Dynamic Reactive Load with Closed Loop Control”, Proceedings of the WCECS 2013 Vol I WCECS 2013, 23-25 October, 2013, San Francisco, USA, ISBN: 978-988-19252-3-7.

[8]

Irfan Mujawar, Isak Mujawar, Swapnil. D. Patil, D. R. Patil, Member, IAENG, U. Gudaru, Senior Member IEEE, “TBSC-TCR Compensator Simulation: A New Approach in Closed Loop Reactive Power Compensation of Dynamic Loads”, Proceedings IMECS 2014, Vol II, March 12 - 14, 2014, Hong Kong, ISBN: 978-988-19253-3-6.

[9]

Maffrand, J. W. Dixon, and L. Morán, “Binary controlled, static VAR compensator, based on electronically switched capacitors,” in Proc. IEEE PESC’98, pp.1392–1396, 1998.

[10]

Juan Dixon, Yamilledel Valle, et al.: ‘A Full Compensating System for General Loads, Based on a Combination of Thyristor Binary Compensator’, and a PWM-IGBT Active Power Filter, IEEE Trans. Industrial Electronics, vol. 50, no. 5,pp. 982989.(2003).

[11]

Juan Dixon, Luis Morán, José Rodríguez, Ricardo Domke, “Reactive power compensation technologies, state of–the-art review”,Proc. IEEE, vol. 93, no. 12, pp.2144-2164, 2005.

[12]

Swapnil D. Patil, A. M. Mulla, U. Gudaru,Senior Member IEEE, D. R. Patil Member,IAENG, “An Innovative Transient Free TBSC Compensator with Closed Loop Control for Fast Varying Dynamic Load” Proceedings of the World Congress on Engineering and Computer Science 2014 Vol I WCECS 2014, 23-25 October, 2014, San Francisco, USA, ISBN: 978-98819252-0-6.

V. Acknowledgments This work was carried out with the help of Annasaheb Dange College of Engineering and Technology, Ashta, Sangli. Maharashtra. India

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

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

On Volterra Type Integral Inequalities in Metric Spaces Hristo Kiskinov1, Stepan Kostadinov2, Lozanka Trenkova3, Andrey Zahariev4 Department of Mathematical Analysis, Faculty of Mathematics and Informatics, Plovdiv University “Paisii Hilendarski” 236, Bulgaria Blvd., 4003 Plovdiv, BULGARIA Abstract: In the present work we consider a class of abstract Volterra type integral equations of second kind. Sufficient conditions are obtained for existence and uniqueness of the solutions of this class integral equations. Some applications of the obtained results to the integral inequalities involving maxima are given. Keywords: Volterra Type Integral Equations, Metric space, Integral Inequalities with maxima I. Introduction As an evergreen theme in the past few years the integral equations have proved to be of great use in several applied fields, such as population dynamics, automatic control theory, dynamics of nuclear reactors. The main application of the corresponding integral inequalities is that they provide explicit bounds of the unknown functions, which are a very useful and important device in the study of many qualitative as well as quantitative properties of solutions of nonlinear differential and integral equations. The techniques of the proofs of the integral inequalities in general are based on the classical mathematical analysis and are leading up to virtuosity. The major problem is that these proofs significantly depend of the number of independent variables and the geometry of the domains of integration. A lot of information about the integral equations and inequalities can be found in the monographs [1] - [5] and the comprehensive list of references therein. Many results about differential equations and integral inequalities with maxima can be found in the fundamental monograph [6]. Note that the authors of [6] made substantial contributions in this field of investigations. The aim of this paper is to extend the approach introduced for the linear case in the work [7] and partially developed for the nonlinear one in [8]. The main idea introduced in [7] is to establish conditions under which the unique solution of the equation (2.1) is an upper bound of all solutions of the inequality (2.2). In view of the applications it is important to study also the cases when the domains of integration have different geometric forms. The paper is organized as follows – in section 2 we introduce an abstract analogue of the Volterra equation and its corresponding inequality. Section 3 is devoted to the study of the Volterra type equation (2.1) introduced below. Section 4 includes the results concerning the corresponding integral inequality (2.2) and in section 5 are given some applications of the results obtained in the previous sections to the integral inequalities with maxima. II. Preliminary Notes and Denotations Let  be a complete metric space with metric  , B  2 denotes the   algebra of the Borel subsets of  and let  : B  [0, ] be a nontrivial, nonatomic   additive Borel measure. We will denote by U  x,     y   |   x, y     the open balls with a center point x  , and radius   0 . If G is an arbitrary

subset of  then G denotes the boundary of G and U (G,  ) denotes the   neighborhood of G . Let B be a real Banach space with norm || . ||B , B* be the Banach space of all linear bounded operators acting in B with the norm || . ||B* and V  B be a cone in B . Then we can introduce a partial order in B associated with

the cone V , i.e. u  v when u  v V . We shall write u  v to indicate that u  v V , but u  v . Denote by C (G, B) ,where G   is an arbitrary compact subset, the Banach space of all continuous maps

f : G  B with norm || f ||G  sup yG || f ( y) ||B . Definition 2.1. [8] The point x  will be called essential for the set G , if for each   0 we have   G U  x,     0 . Otherwise the point x  will be called nonessential. We will denote by G  the set of all points x  G , which are essential for G and by G the set G  G \ G  , i.e. G includes all nonessential points belonging to G . Definition 2.2. [9] The sets G, H  B , will be called  -equivalent G  H , if   GH   0 , where

GH  (G \ H )

 H \ G

is the symmetric difference.

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Kiskinov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 12-19

Definition 2.3. [8], [9] The set G  B , is called  -dense, if each x  G is an essential point for the set G . Definition 2.4. [9] We say that the set G   is M  star if for every x  G the inclusion M x  G holds. Remark 2.5. From Definition 2.1. it follows, that for arbitrary G  B with  (G)  0 every external or isolated point of G is nonessential and every internal point is essential for G . Then it follows if  (G)  0 and G   then G not include essential points. Moreover if the point x  is nonessential for the set G , then there exists  0  0 that   G U  x,  0    0 . Remark 2.6. It is simply to see that condition A3 implies that for each x  the set M x is M  star set. Moreover the union and the intersection of an arbitrary family of M  star sets are M  star set. The sets KerM  {x  |  (M x )  0} and M   M x are M  star sets too. x

Following [7] we introduce the map M :   2 , which associates every point x  with a subset M x  2 . We will say that the conditions (A) hold if for the map M :   2 the following conditions are fulfilled: A1. For every point x  the set M x is compact. A2. For each   0 and every x  , there exists   0 such that for each y   with  ( y, x)   we have that

  M x M y    . A3. For every x  , the inclusion M y  M x holds for each y  M x . A4. There exists x0   such that   M x   0 . 0

Definition 2.7. Every map M :   2 for which the conditions (A) hold will be called Volterra type (VT map). We say that a VT map M is  -dense if the sets M x are  -dense for each x  . Denote by M the set of al VT maps and by M 0 the set of all  -dense VT maps. For each M  M denote with M  {M x | x } the set of all sets which are values of the map M . We introduce partial order in the set

M as follow: M *

M if we have that KerM *  KerM .

Let M , M *  M be arbitrary maps and consider the equation

f ( x)  p( x)   W ( x, y ) f ( y )d  y   Q ( x, y , f ( y ))d  y M *x

(2.1)

and the corresponding inequality

g ( x)  p( x)   W ( x, y ) g ( y )d  y  M *x

 Q(x, y, g ( y ))d 

(2.2)

where the operators Q :  B  B , W :   B* and f , g , p  C(M x , B), x  . If the operator Q is continuous in the set  M   B and the operator-valued function

Remark 2.8.

W :   B is continuous in  M  , then the Bochner integrals in (2.1) and (2.2) exist on each *

M x*  M * and M x  M . ([10], Chapter 3). For each map M  M we define its associated map M  for each x  by the relation M x   M x  . 

Remark 2.9. From Lemma 3.11 and Lemma 3.14 of [8] it follows, that for every map M  M its associated map M  is  -dense, i.e. M   M 0 and for each x  we have that  (M x M x )  0 . Thus without loss of generality we can use for every x  as a domain of integration instead M x its  -equivalent domain M x which is  -dense. III. Main Results In our discussion below we will assume that for the operator Q :  B  B and the operator-valued function W :   B* are fulfilled some of the following conditions: (S1) The operator Q :  B  B is continuous in the set  M   B and the operator-valued function W :   B* is continuous in  M  .

(S2) For each x  and every f  C (M x , B) , there exist numbers  (M x , f )  0 and L(M x , f ,  )  0 such that for

every

sup

( s , y )M x M x

function

g  C (M x , B) for

which || f  g ||M x  

the

following

inequality

holds:

|| Q(s, y, f ( y))  Q(s, y, g ( y)) ||B  L(M x , f ,  ) || f  g ||M .

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(S3) For every x  and arbitrary r  0 , there exists a constant L(M x , r )  0 , such that for every two functions

f , g   f  C (M x , B) | || f ||M x  r the following inequality holds:

sup

( s , y )M x M x

|| Q(s, y, f ( y))  Q(s, y, g ( y)) ||B  L(M x , r ) || f  g ||M . x

(S4) For each M -star continuum *   there exists a number L(* )  0 , such that for every two functions f1 , f 2  C (* , B) and for each x  * the following inequality holds

|| Q(s, y, f1 ( y))  Q(s, y, f 2 ( y)) ||B  L(* ) || f1 ( y)  f 2 ( y) ||B . Consider the operators K and W* defined by the equations Kf ( x)  p( x) 

 Q( x, y, f ( y))d 

(3.1)

W* f ( x) 

 W ( x, y) f ( y)d 

(3.2)

M *x

and the operator T defined by Tf ( x)  Kf ( x)  W* f ( x)

(3.3)

where f , p  C ( , B) . As we mentioned above in Remark 2.8, if the condition S1 holds, then the Bochner integrals in (3.1) and (3.2) exist on each M x*  M * and M x  M . Remark 3.1. Obviously if for each M -star continuum *   the operator-valued function W is continuous in *  * then for it the conditions (S3), (S3) and (S4) hold. Lemma 3.2. Let the following conditions be fulfilled: 1. The conditions (S1) and (S2) hold. 2.  is a connected metric space and 0        . The maps M , M *  M 0 and the relation M * M holds. Then the operator T defined by the equality (3.3) maps C (* , B) into C (* , B) continuously for each p  C (* , B) . 3.

Proof: The proof that the operator K defined by the equality (3.1) maps C (* , B) into C (* , B) continuously for each p  C (* , B) is completely analogical of the proof of Lemma 4.1 in [8] and will be omitted. Condition (S1) and condition 3 of Lemma 3.2 implies that there exists a constant W0 (* )  0 such that for each

x  * and y  M x* the following estimation holds

|| W* f ( x) ||B 

 || W ( x, y) ||

|| f ( y) ||B d  y  W0 (* )  (M x* ) || f ||M  W0 (* )  (* ) || f ||M , * x

M *x

* x

(3.4)

where f  C (M x* , B) is arbitrary. Then (3.4) implies that the operator W* defined by the equality (3.2) maps

C (* , B) into C (* , B) continuously and therefore from (3.3) it follows that the operator T maps C (* , B) into C (* , B) continuously for each p  C (* , B) .□ Let x  be an arbitrary point and *   is arbitrary M -star continuum. Then for every f  C (* , B)

|| f (s) ||B and || f (s*M ) ||B  max || f ( s) ||B . there exist two points s*m , s*M  * such that || f (s*m ) ||B  min s *

s*

Definition 3.3. A continuous function F :   B is called an extension of the function f  C (* , B) if F (s)  f (s) for each s  * . If in additional for some extension F ( s) and each s   the inequalities

|| f (s*m ) ||B || F (s) ||B || f (s*M ) ||B hold, then such an extension will be called middle extension. Definition 3.4. We say that the equation (2.1) has a local solution in some M -star set *   for some p  C (* , B) if there exist a point x p  * with  (M x p )  0 and a function f  C (M x p , B) which satisfies the equation (2.1) for each s  M x p . If p, f  C (* , B) and f satisfies the equation (2.1) for each x  * then we say that f is a solution of (2.1) in *   . Theorem 3.5. Let the following conditions be fulfilled: 1. The conditions (S1) and (S3) hold. 2. Conditions 2 and 3 of Lemma 3.2 hold. Then for every x  such that M x \ KerM   and for every p  C (M x* least one local solution in M

* x

M x , B) the equation (2.1) has at

Mx .

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Proof: Let KerM   (the case when KerM   is trivial). For each x  , from Lemma 3.16 [8], it follows that KerM M x   , KerM * M x*   and then condition 2 implies that KerM M x*   also. Theorem 3.18 [14] implies that there exist a point y0 KerM

M x* and a sequence { yn }n  M x* \ KerM * , such that

lim  ( yn , y0 )  0 . Then for the sequence { (M y* )} we have that  (M y* )  0 for each n  n n

and if the set

{ (M y ) |  (M y )  0}n is finite then the statement of Lemma 3.2 follows from Lemma 4.1 [8]. n

Let us consider the case when the set { (M yn ) |  (M yn )  0}n is infinite. Then there exists a subsequence

{ yn }k  { yn }n  M x* \ KerM * , such that  (M ynk )  0 for each k 

and condition 2 implies that

lim  ( M ynk )  0 . k 

Let us denote Ax  M x* each k 

M x , Ak  M y*nk

My , k  nk

, P  max sAx || p(s) ||B , Q0  sup || Q( s, y,0B ) ||B . For s, yAx

let us define the operators Tk by the following equality

Tk f ( s)  p( s)   W ( s, y) f ( y)d  y  M s*

 Q(s, y, f ( y))d  ,

(3.5)

where p, f  C ( Ax , B) and s  Ak . For every function f  C ( Ak , B) and each k 

from Lemma 4.3 [8] it

follows that there exists a middle extension, Fk  C ( Ax , B) such that || Fk ||Ax || f ||Ak . Let r  P be an arbitrary number and for every r  P and each k  introduce the sets U k (r )   f | f  C ( Ak , B), || f || A  r , U k (r )  {Fk | f U k (r )} and U x (r )  F | F  C ( Ax , B), || F || A  r . k

Then for each k  we have that U k (r )  U x (r ) . From Lemma 3.2 it follows that for each k  the operator Tk maps C ( Ak , B) into C ( Ak , B) continuously and from (3.4) and (3.5) for each f U k (r ) it follows that the inequality || Tk f || A  P  sup  || W (s, y ) f ( y ) ||B d  y  sup  || Q(s, y, f ( y ))  Q( s, y, 0)  Q( s, y, 0) ||B d  y  k

sAk

(3.6)

 P   ( Ak )((W0 ( Ax )  L( Ax , r )) || Fk || A Q0 )   ( Ak )((W0 ( Ax )  L( Ax , r ))r  Q0 ) x

 ( ynk , y0 )  0 then holds, where W0 ( Ax )  W0 (M x )  W0 (M x* ) and L( Ax , r )  L(M x , r )  L(M x* , r ) . Since lim k  there exists a number k0  k0 ( y0 , r ) , such that for every k  k0 from (3.6) it follows that the inequalities

 ( Ak )((W0 ( Ax )  L( Ax , r ))r  Q0 )  r ,  ( Ak )(W0 ( Ax )  L( Ax , r ))  q  1

(3.7)

hold. Obviously the first of them implies that T|k (U k (r ))  U k (r ) . Let k  k0 be an arbitrary fixed number and let f , g U k (r ) be arbitrary functions. If we denote by Fx and Gk their middle extensions on Ax , then from (3.6) it follows the estimation

|| Tk f  Tk g || A   ( Ak )(sup || W (s, y )( f ( y )  g ( y )) ||B  sup || Q( s, y, f ( y ))  Q( s, y, g ( y )) ||B )  k

s , yAk

  ( Ak )(sup || W ( s, y )( Fk ( y )  Gk ( y )) ||B  sup || Q( s, y, Fk ( y))  Q( s, y, Gk ( y)) ||B )  s , yAx

s , yAx

  ( Ak )(W0 ( Ax )  L( Ax , r )) || Fk  Gk || A   ( Ak )(W0 ( Ax )  L( Ax , r )) || f  g || A  q || f  g || A . x

Therefore the operator Tk maps U k (r ) into U k (r ) and is a contraction. Therefore the equation (2.1) has at least one solution f U k (r ) .□ Theorem 3.6. Let the set *   be an arbitrary M  star and M *  star continuum with  (* )   and the following conditions be fulfilled: 1. The conditions (S1) and (S3) hold. 2. Conditions 2 and 3 of Lemma 3.2 hold and for each x  * the sets M x and M x* are connected. Then for each p  C (* , B) equation (2.1) has exactly one solution f  C (* , B) . Proof: Suppose f1 , f 2  C (* , B) are two solutions of the equation (2.1). Let x  * be an arbitrary point such that f1 ( x)  f 2 ( x) . Denote by r  sup || f1 (s) ||B  sup || f 2 (s) ||B  0 and by L(M x , r )  0 the constant, existing sM x

sM x

according to condition S3. From equation (2.1) and condition S3 it follows that for each s  M x the inequality

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|| f1 (s)  f 2 ( s) ||B  (W0 (* )  L( M x , r ))



|| f1 ( y)  f 2 ( y) ||B d  y

M s M s*

holds. Using Theorem 2 and Theorem 3 from [7] we get that || f1 (s)  f 2 (s) ||B  0 for each s  M x which contradicts our supposition.  Everywhere below in our consideration we will assume that B 

and denote by V  the following cone

V   { f  C (, ) | f ( x)  0, x } . Theorem 3.7. Let the following conditions be fulfilled: 1. The conditions (S1) and (S4). 2. Conditions 2 and 3 of Lemma 3.2 hold and for each x  the sets M x and M x* are connected. 3. For each x  there exists a M  star and M *  star continuum  x   with  (x )   such that

M x , M x*  x and x  int x . 4. For each ( x, y)  M  the operator Q is positive and monotone in the cone V  with regard to the order induced by V  . 5. For each ( x, y)  M  the operator W* have the form W* f ( x) 

 k ( x, y) f ( y)d 

, where the function

M *x

k :  



 [0, ) is continuous in  M  and f  C (, ) .

Then for each p  V  the equation (4.1) has a unique solution f  V  in  . Proof: Let p  V  is an arbitrary function, x   be an arbitrary fixed point and  x   is the M  star continuum existing according to condition 3. Denote by px (s)  p(s) , s   x the restriction of p on  x and by Vx  {  C(x , ) |  ( x)  0, x } the cone in C (x , ) . Obviously condition 4 implies that for each x   the operator Q is positive and monotone in the cone Vx with regard to the order induced by Vx . Since

C (x , ) is a Banach space and px  C (x , ) then according to Lemma 3.2 the operator T defined by the equality (3.3) maps C (x , )  C(x , ) continuously. Then, from (2.1), condition 5 and (3.3) for each s   x and   C (x , R) we have T (s)  px (s)   Q(s, y,0))d  y   k (s, y) ( y)d  y   (Q( s, y,  ( y))  Q( s, y,0))d  y M s*

Let us denote hx ( s)  px ( s) 

(3.8)

 Q(s, y, 0))d 

and define for every   C (x , ) the linear and bounded in

M s*

C (x , ) operator L (s)  (W0 ( x )  L( x ))   ( y)d  y , s   x , W0 ( x )  sup k (s, y ) . Since the operator L M s*

s , yx

is positive in Vx with regard to the order induced by Vx , then from Theorem 2 of [7] it follows that the spectral radius of the operator L is equal to zero. Therefore the equation h(s)  L (s)   (s) has a unique solution

 x0  Vx . From condition S4 and equality (3.8) it follows that for every   Vx the inequality T (s)  h(s)  L (s) holds for each s   x . Then we can conclude that the operator T maps the order interval [0, x0 ]  { Vx | 0   (s)  x0 (s), s x } into itself and therefore the operator T has at least one fixed point

 x belonging to the same order interval ([11], Chapter 8). Theorem 3.6 implies that solution  x is unique in C (x , ) . Define the function f  for every x   as follow: f (s)  x (s) , s   x . From Theorem 3.6 it follows that for every x, y   for which M x M y   for the corresponding unique solutions   C (x , ) and  y  C ( y , ) we have that x (s)   y (s) for each s  x

 y . Therefore f  C (, ) and is unique

solution of (2.1) in  .  IV. Inequalities In this section we apply the results obtained in section 3 to study the inequality (2.2). Definition 4.1. We say that the inequality (2.2) has a local solution in some M -star set *   for some

p  C (* , B) if there exists a point x p  * with  (M x p )  0 and a function g  C (M xp , B) such that g satisfies the inequality (2.2) for each s  M x p . If p, g  C (* , B) and g satisfies the inequality (2.2) for each x  * then we say that g is a solution of (2.2) in *   . Theorem 4.2. Let the following conditions be fulfilled:

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1. Condition 2 of Theorem 3.6 and condition (S1) hold. 2. The function Q :  M    for every two fixed elements x   and y  M  is a monotonously increasing function of v . 3. The condition 5 of Theorem 3.7 holds. Then, if for the functions f , g  C (, ) the inequality

g ( x)  Tg ( x)  f ( x)  Tf ( x)

(4.1)

holds for each x   , then the inequality g ( x)  f ( x) holds for each x   also. Denote by  the following subset of  ,   {x  | g ( y)  f ( y), y  M x } . Since the maps

Proof.

M holds, then condition A4 implies that there exists x0   such that

M , M  M 0 and the relation M * *

 M

* x0

 M

* x

  0 and therefore   M   0 . Then from condition A2 it follows that for each x0

0

x  M x we have that 0

  M x   0 too. Since W* f ( x)  W* g ( x) for every x  M x* and

and for each x  M x ,

Kf ( x)  Kg ( x) for each x  M x , then (4.1) implies that x0   and therefore    . 0

Let  xn n1   be an arbitrary convergent sequence and denote by x0  lim xn . If we assume that x0    , then 

n

there exists a point y  M x0 such that g ( y)  f ( y) and therefore from (4.1) it follows that Tg ( y)  Tf ( y)  0 . Then there exists a set G  M y , such that  (G)  0 and g (s)  f (s) for each s  G . Then for each n 

have that  (G)   (M y \ M x )   (M x M x ) and from condition A2 it follows that  (G)  0 which n

contradicts our assumption. Thus we proved that  is a closed set. It is simply to see that if x  then obviously M x   , i.e.  is a M - star set. Since f , g  C (, ) then for every x  and each y  M x there exists an open ball U ( y,  y ) such that U ( y,  y )   . Then the set U ( y,  y ) is an open cover of M x such that U x   . Because M x is compact there exist finite

Ux  yM *x

number of balls, such that

U ( yk ,  y )   . Then according to Theorem 3.19 [8], there exists  ( y ,...,  y )  0

k 1

such that for each s ,  (s, x)   we have that M s   and therefore we proved that  is an open set. Since  is a connected metric space then we can conclude that    . Then we have that for every x  the inequality Q( x, y, g ( y))  Q( x, y, f ( y)) holds for each y  M x . Then from condition 3 and (4.1) it follows that

g ( x)  f ( x)  Tf ( x)  Tg ( x)  f ( x) and therefore g ( x)  f ( x) for each x  . □ Remark 4.3. It is simply to see that if we replace the strictly inequality (4.1) by a non-strictly one, the assertion of the theorem still holds for every x  \ KerM . Corollary 4.4. Let the conditions of Theorem 3.7 hold. Then for each x  the inequality g ( x)  f ( x) holds, where f  C (, ) is the unique solution of equation (2.1) and g  C (, ) is an arbitrary solution of the inequality (2.2). Proof: Let for each p  V  is arbitrary. Then according to Theorem 3.7 the equation (2.1) has a unique solution

f  C (, ) . For every solution g  C (, ) of the inequality (2.2) we have that the inequality g ( x)  Tg ( x)  p( x)  f ( x)  Tf ( x) (4.2) holds for every x  . Then if for some x  we have that g ( x)  Kg ( x)  p( x)  f ( x)  Kf ( x) and therefore g ( x)  f ( x) . For the points x  for which the inequality (4.2) is strictly, the assertion of the Corollary 4.4 follows from Theorem 4.2. □ V. Applications to the Integral Inequalities with Maxima As an illustration of the results obtained in the previous sections we will consider integral equations and corresponding inequalities with maxima. For simplicity and visibility we will consider the two dimensional case ( n  2 ), but all conclusions hold for n  3 too. Let   2  [0, )  [0, ) ,  be the Euclidean metric, B  ,  be the Lebesgue measure,

a, b, c  0, ab  0 be arbitrary constants and x0  ( x10 , x20 )  order in x y.

2 

as follow: for x, y 

2 

be an arbitrary fixed point. Introduce partial

we write that x  y if x1  y1 and x2  y2 . If x  y and x  y we write

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Consider

the

following

( x0 )  {x 

sets:

2 

| x  x0 }

J ( x0 )  {[ x10  a, )  [ x20  b, )}

and

J  ( x0 )  J ( x0 ) \ ( x0 ) . Let M , M  M 0 for which we have that KerM  KerM  cl J  ( x0 ) , the sets H x  J ( x0 ) are compact sets such that for each x  ( x0 ) we have that x  H x and ( x0 ) H x  M x . Consider the equation *

 w( x, y) f ( y)dy   k ( x, y) max f (s)dy

f ( x)  c 

M *x

(5.1)

sH y

and the inequality

g ( x)  c 

 w( x, y) g ( y)dy   k ( x, y) max g (s)dy ,

M *x

(5.2)

sH y

where the functions w, k  C(( x0 ) ( x0 ),

) and g ( x)  c  f ( x) for each J ( x0 ) . It is not hard to verify that all condition of Corollary 4.4 are fulfilled and therefore we have that g ( x)  f ( x) for x  J ( x0 ) , where f  C ( J ( x0 ),  ) is the unique solution of (5.1) and g  C ( J ( x0 ),  ) is an arbitrary solution of (5.2). The next theorem is a generalization of Theorem 2.3.1 in [6]. Theorem 5.1. Let the following conditions be fulfilled: 1. The functions w, k  C(( x0 ),  ) . 2. The functions   C ([ x10 , ),



) and   C ([ x20 , ),



) are nondecreasing,  ( x1 )  x1 for x1  [ x10 , ) ,

 ( x2 )  x2 for each x2  [ x20 , ) and  ( x10 )  x10 ,  ( x20 )  x20 . 3. The maps M * and M are defined as follow: M x*  M x   for x  J ( x0 ) and M x*  [ x10 , x1 ]  [ x20 , x2 ] ,

M x  [ ( x10 ),  ( x1 )] [  ( x20 ),  ( x2 )] for every x  ( x0 ) . 4.

H x  J ( x0 )

are

compact

sets

such

that

for

each

x  ( x0 ) we

have

that

x  Hx

and

( x0 ) H x  [ ( x ),  ( x1 )] [ ( x ),  ( x2 )] . 0 1

0 2

5. The function g  C ( J ( x0 ),

g ( x)  c 



) for every x  ( x0 ) satisfies the inequality

 w( y) g ( y)dy   k ( y) max g (s)dy

M *x

(5.3)

sH y

and the inequality g ( x)  c for x  J  ( x0 ) . Then for x  J ( x0 ) the inequality

g ( x)  c exp(  w( y)dy  M *x

 k ( y)dy)

(5.4)

holds. Proof: Conditions 2 and 3 implies that KerM *  KerM . Then it is not difficult to see that all condition of Corollary 4.4 are fulfilled and therefore the inequality

f  C ( J ( x0 ), f ( x)  c 



g ( x)  f ( x) holds, for x  J ( x0 ) where

) is the unique solution of the equation

 w( y ) f ( y )dy   k ( y ) max f (s )dy

M *x

(5.5)

sH y

From (5.5) and condition 4 for x  ( x0 ) it follows that the inequality

f ( x)  c 

 w( y) max f (s)dy   k ( y) max f (s)dy sH y

M *x

(5.6)

sH y

holds. Let denote  ( x)  max f ( y) . Condition 4 implies that for each x  ( x0 ) we have that f ( x)   ( x) . Let yH x

x  ( x0 ) be an arbitrary fixed point. Then if max max f (s)  f ( x) then from (5.6) it follows that the sH yM *x

inequality

 ( x)  c 

 w( y) ( y)dy   k ( y) ( y)dy

M *x

(5.7)

holds. If max* max f (s)  f ( y0 ) , y0  M x* , y0  x then yM x

sH y

Page 18

Kiskinov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 12-19

 ( x)  max f ( y)  f ( y0 )  c  yH x

 w( y) ( y)dy   k ( y) ( y)dy  c   w( y) ( y)dy   k ( y)( y)dy

M *y0

M *x

M y0

and therefore the inequality (5.7) holds for each x  ( x0 ) . Let x  ( x0 ) be an arbitrary fixed point and let z  ( x0 ) be an arbitrary point for which we have x  z . Then we have that M s* , M s  Bz  [0, z1 ]  [0, z2 ] for each s  Bz . Denote by K M  max k ( s) and by Cz  max k ( y) sM x

yBz

and let   0 be an arbitrary number. Condition A2 implies that for the point x  Bz there exists sx  Bz with

sx  x such that  (Ms \ M x ) Cz   . Let h  C ( Bz ,[0,1]) is an Urisohn function for the compact sets M x and x

Bz \ int M s . Then for the continuous function kx (s)  h(s)k (s) we have that x

k x ( s)  k ( s), s  M x k x ( s)   k x ( s)  0, Bz \ int M sx Then from (5.7) it follows that for each s  Bz the inequality

 (s)  c   qx ( y) ( y)dy

(5.8)

M s*

holds, where qx (s)  w(s)  kx (s) . Consider the equation  ( s)  1   qx ( y) ( y)dy

(5.9)

M s*

Then from the results in [7] it follows that for each solution   C ( Bz ,

) of the inequality (5.8) the inequality  (s)  c (s), s  Bz holds, where  ( s) is the unique continuous solution in Bz of the equation (5.9) when c  1 . Moreover for each s  Bz the following estimation 

 (s)  exp(  qx ( y)dy)

(5.10)

M s*

holds. Therefore from (5.10) it follows that for each solution   C ( Bz , the estimation  (s)  c (s)  c exp(  qx ( y)dy)



) of the inequality (5.8) we have that (5.11)

M s*

is fulfilled for every s  Bz . Particularly for s  x from (5.11) it follows that

 ( x)  c exp(  w( y)dy  M *x

 k ( y)dy   Mx

(5.12) Since in the inequality (5.12)

g ( x)  c exp(  w( y)dy  M *x

k x ( y)dy))  c exp(  w( y )dy  M *x

M sx \ M x

  0 is arbitrary then passing   0 we receive that the inequality

 k ( y)dy) holds. □ Mx

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

 k ( y)dy   ) Mx

References

R. P Agarwal, Difference Equations and Inequalities: Theory, Methods and Applications (Pure and Applied Mathematics 228), Second Edition, Revised and Expanded, Marcel Dekker, New York, 2000. R. P. Agarwal, D. O’Regan, P.J.Y. Wong, Constant–Sign Solutions of Systems of Integral Equations, Springer-Verlag, 2013. B.G. Pachpatte, Multidimensional Integral Equations and Inequalities. Atlantis Studies in Mathematics for Engineering and Science, Vol. 9, Atlantis Press, Amsterdam – Paris, 2011. S. S. Dragomir, Some Gronwall Inequalities and Application, Nova, 2003. J. M. Appell, A. S. Kalitvin, P. P. Zabrejko. Partial Integral Operators and Integro-Differential Equations. Monographs and Textbooks in Pure and Applied Mathematics, Marcel Dekker, Inc., NY, 2000. D.D. Bainov, S.G. Hristova, Differential Equations with Maxima. Pure and Applied Mathematics, A Series of Monographs and Textbooks, Chapman & Hall/CRC Press, Taylor & Francis Group, Boca Raton, London, New York. 2011. D.D. Bainov, A.D. Myshkis, A.I. Zahariev, “On an Abstract Analog of the Bellman-Gronwall Inequality”, Publ. RIMS, Kyoto University, Vol. 20, No.5, 903-911, 1984. A. Zahariev, A. Georgieva, L. Trenkova, “On Volterra Type Integral Equations in Noncompact Metric Space”, Journal of Inequalities and Applications, 2014:260, 2014. I. E. Guryanova, A.D. Myshkis, “Non extendable solutions of abstract Volterra type integral equations”, Differential Equations (in Russian). Vol. 22, No. 10, 1786-1789 (1986) K. Yosida, Functional analysis. Springer-Verlag, Berlin, Heidelberg, New York, 1980. V. C. L. Hutson, J. S. Pym, Application of Functional Analysis and Operator Theory. Academic Press, London, 1980

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

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Review of Public Safety in Some Non Governmental Medical Radiation Facilities in Bangladesh M.Haider1*, S. Shill2 and QMR Nizam3 Bangladesh Atomic Energy Regulatory Authority, E-12/A, Agargaon, Dhaka-1207, BANGLADESH. 3 Department of Physics, University of Chittagong, Chittagong-4331, BANGLADESH. *Corresponding Author: Dr. Md. Mofazzal Haider, Radiation Control Unit, Bangladesh Atomic Energy Regulatory Authority, E-12/A, Agargaon, Dhaka-1207, BANGLADESH.

1,2

Abstract: The use of ionizing radiations is being greatly extended especially in medical diagnostic radiological facilities in Bangladesh. Diagnostic Radiology is the use of x-rays to investigate the structure and function of the human body. The underlying aim in radiology is to obtain the best possible diagnostic image with the least radiation exposure to concerned people. In order to ensure radiation safety of the public adequate shielding is required during operation of the x-ray machine. Therefore, to find out the adequacy of shielding of an x-ray room shielding calculation is performed by the application of National Radiological Council and Measurements (NCRP 49 & 147) concepts incorporating real data at applicable points. To perform the work thirty facilities were randomly chosen in Narayanganj district of Bangladesh. A data collection checklist was utilized during investigation of the facilities in order to accumulate the shielding calculation information. In the chosen facilities, mostly lead and brick have been found as the shielding materials for the entrance door and walls of the x-ray room respectively. The average thickness of lead used in the entrance door of x-ray facilities have been calculated 1.04 and 0.51 mm respectively based on NCRP 49 and 147 approaches. In most of the facilities, the existing thicknesses of the shielding material are found higher than the calculated values. Besides, radiation dose was measured across the existing shielding thicknesses to examine the safety of the public. Keywords: NCRP Approach, Shielding Adequacy, Permissible Level, Shielding Material, Safety, Radiation Dose. I. INTRODUCTION The traditional x-ray machines have become an important tool over the years, in medical diagnosis even though many powerful imaging techniques are available these days. The use of x-ray machine is expanding as the people are becoming more and more health conscious. The trend is likely to be continued at future dates too, as the facilities are still inadequate to meet the needs of over 150 million population of the country. However, the x-ray facilities for diagnostic purposes in several third world countries are very few compared to the demand. Because of high patient workload and less preventive maintenance of the facilities, breakdowns are common and sometime even two x-ray machines are found in operation at the same room. In addition to that most of the rooms used to host x-ray facilities are not originally intended for the purposes and are often smaller than the recommended size (6m x 3m) for general purpose x-ray machines [1]. In most of the cases, the location of the xray machines are not optimized relative to the layout of the rooms and the real composition of building materials used to construct most walls are precisely unknown. Therefore, the issue of the radiation safety of occupational and the public are needed to be ensured by the introduction of sufficient amount of shielding in the x-ray room. In this connection, the NCRP 49 report which provides widely accepted traditional methodology for radiation shielding design [2]. These traditional techniques for designing radiation barriers may be unrealistic because the assumptions taken in shielding designing do not reflect the existing situation. It has, for example, underestimated or overestimated the scattered and leakage radiation respectively from modern x-ray units. However, the new NCRP 147 report was published to overcome the deficiencies outlined in NCRP 49 [2, 3]. The calculated shielding thickness based on NCRP 49 approach which didnâ&#x20AC;&#x2122;t reflect the real value [4]. The current study has addressed the deficiencies found in NCRP 49 report by including real data for the workload, KVp and design dose mentioned in NCRP 147 report for controlled and uncontrolled areas. The introduction of NCRP 147 report for shielding calculation which reduces the shielding cost significantly [5]. However, in some other studies existing thickness was found even larger than the calculated one [6]. In the present study in the 73 % of the facilities, the existing thicknesses are found larger than the average thickness calculated by NCRP 147 approach.

Page 20

M.Haider et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 20-24

II. MATERIALS AND METHODS A cross sectional investigation among the 30 private facilities of Narayanganj district was carried out to collect the information which are required to perform shielding calculation for the facilities in order to evaluate the safety of the public. A data collection checklist was prepared before. Thirty facilities of the concerned areas were randomly selected. The checklist mainly includes machine specification, machine location, number of patients studied per day, thickness of aluminum filter, room size, distance from tube to patient bed, wall, door etc, status of control panel barrier, entrance door, wall thickness, attenuated dose, unattenuated dose etc. Radiation doses were recorded at different locations by using radiation dose rate meter. Shielding calculations were done by using NCRP approaches 49 and 147. For radiation dose rate measurements a gas filled radiation monitor was utilized during investigation of the facilities. Before use, this equipment was calibrated in Secondary Standard Dosimetry Laboratory of Atomic Energy Research Establishment, Savar, Dhaka. Radiation dose rate were measured at different points of the 24 facilities to assess the safety of the public. Radiation facilities have been identified in the table by putting code number NFC. III. RESULTS AND DISCUSSION Generally lead was found as a shielding material in the entrance doors of x-ray facilities and its average thickness was recorded 0.87 mm. However, the non uniform thicknesses of lead were also found in some of the facilities of the Narayanganj district. Based on the approaches NCRP 49 and NCRP 147, the average lead shielding for entrance door were estimated in the work 1.04 and 0.51 mm respectively as shown in Table 1. The thickness calculated by NCRP 147 is significantly lesser than the NCRP 49 approach. The barrier thicknesses calculated by different approaches are shown in (Table1). Table 1: Existing and calculated thickness of shielding material at entrance door (ED)

2.82

Existing shielding material thickness in mm(lead) 1mm

NFC-02

1.7

0.5mm

0.89

0.69

NFC-03

2.38

1mm

1.02

0.35

NFC-04

1.6

1mm

1.38

0.45

NFC-05

2.08

1mm

0.72

0.85

NFC-06

1.57

1mm

0.71

0.46

NFC-07

2.26

1mm

1.3

1.1

NFC-08

3.12

1mm

0.9

0.22

NFC-09

1.72

0.5mm

0.92

0.5

NFC-10

1.4

1mm

0.81

0.7

NFC-11

2.41

1mm

1.22

0.5

NFC-12

3.02

1mm

1.23

0.34

NFC-13

2.08

1mm

1.08

0.58

NFC-14

1.55

0.5mm

1.42

0.75

NFC-15

2.1

1mm

1.21

NFC-16

2.92

1mm

NFC-17

2.13

1mm

1.65

0.57

NFC-18

1.93

1mm

1.08

0.37

NFC-19

3.05

1 mm

0.73

0.21

NFC-20

2.43

0.5mm

0.83

0.43

NFC-21

2.13

0.5mm

0.68

0.27

NFC-22

1.67

0.5mm

1.01

0.77

NFC-23

1.22

1 mm

1.27

0.68

NFC-24

3.45

0.5mm

0.53

0.15

NFC-25

1.95

1 mm

0.85

0.39

NFC-26

1.37

1 mm

1.47

0.82

NFC-27

1.54

1 mm

1.35

0.55

NFC-28

3.3

1mm

0.77

0.3

NFC-29

2.49

1mm

1.1

0.42

NFC-30

2.44

0.5mm

0.44

0.3

Facilities Code

Distance from scatterer to ED (dsec) in m

NFC-01

Average of existing material thickness in mm

0.87

Calculated thickness in mm (lead) As per NCRP 49 0.76

1.77

Average of calculated thickness in mm(lead)

1.04

Calculated thickness in mm (lead) as per NCRP 147 0.43

0.41 0.64

Average of calculated thickness in mm(lead)

0.51

Page 21

M.Haider et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 20-24

The shielding thickness required for scattered radiation according to national standard is 2 mm of lead which is found significantly higher value in this study that has been verified by measuring radiation dose rate across the facilities [1]. The measured dose rates are shown in Table 2. In some facilities dose rate couldn’t be measured due to power failure. The door shielding in the 30 % facilities were not sufficient according to Nuclear Safety and Radiation Control (NSRC) Rules’97 as a result public receive more radiation doses than the permissible level (0.5μSv/h) [7]. On the other hand, the walls of x-ray room in the 17 % of the facilities were not in proper thickness to ensure the safety of the public. Fig. 1 shows a comparison of the calculated shielding with the existing shielding of the facilities. The shielding for primary and secondary barriers calculated by NCRP 49 is higher than the existing shielding of the facilities. NCRP 147 approach leads to smaller value of shielding thickness for the primary and the secondary barriers (control panel and entrance door) compared to existing one. This means that the occupational and public are safe in most of the facilities of Narayanganj district since NCRP 147 approach is a more realistic approach for shielding calculation. However, for the confirmation of 100 % safety of the public in the x-ray facilities still significant improvement is required from the present status. During calculation of barrier thickness by using NCRP 49, the entire workload is considered at a single kVp, 1000 mA min wk-1 at 100 KVp. This may cause over estimation in the barrier thickness calculation. The penetration of radiation through barrier varies exponentially with KVp [8]. Therefore, for shielding design KVp is more vital than the workload. In the present study real workload is considered for NCRP approach for example 70 KVp is utilized. Still it results in overestimation in thickness calculation. This is because in the diagnostic radiology a wide range of x-ray potentials is utilized. Apart from this, the limitations of NCRP 49 approach are quite a lot such as the design dose P is considered to 0.02 mSv/week for public exposure, use factor 1 and occupancy factor 1. In the NCRP 147 approach more realistic value of design dose P, use factor U and Occupancy factor T are assumed to 0.02 mSv/week, 1 and 0.5 respectively and the reasonable workloads are utilized. The recommended workloads for NCRP 147 in the earlier study were 240 mA min wk -1 for an average and 320 busy radiography room respectively [4]. These values are still higher in practice. In the present study average workload is estimated less than 150. However, according to Table 1, the average calculated thickness by NCRP 147 is 0.51mm which is almost half of the value obtained by NCRP 49 (1.04 mm lead). According to NCRP 147 approach the calculated value was found reasonably smaller by considering radiation attenuation caused by patient, image receptor for the primary beam [6]. The door thicknesses in the 30 % of the facilities for the secondary radiation are almost same by the both approaches. According to Table 2, for about70% of the facility’s entrance door with average shielding thickness of 0.87 mm are adequate for controlling of public exposure. On the other hand, for the wall it is 83 %. In Bangladesh, the permissible limit for public exposure is 1 mSv per year as per national regulations [7]. Therefore, the acceptable radiation dose limit for the public is 0.5 Sv/h. Table 2: Measurement of external dose rate in the x-ray facilities Facilities Code

Dose rate at ED in μSv/h

Percentage of facilities emit exposure through ED more than permissible level

NFC-01 NFC-02

0.8 0.25

Dose rate outside the wall in μSv/h 0.6 0.25

NFC-03

120

0.25

NFC-04

0.25

NFC-05

0.5

0.25

NFC-06

N/A

0.25

NFC-07

0.5

N/A

NFC-08

N/A

NFC-09

0.25

NFC-10

NFC-11

N/A

NFC-12

0.5

0.25

NFC-13

0.6

0.25

NFC-14

0.25

NFC-15

0.2

NFC-16

N/A

NFC-17

0.25

N/A

NFC-18

0.25

NFC-19

0.25

NFC-20

0.2

0.8

NFC-21

0.25

Percentage of facilities emit exposure through wall more than permissible level

Permissible limit of exposure for public as per national requirements in μSv/h

17%

0.5

N/A 30%

N/A

Page 22

M.Haider et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 20-24

NFC-22

180

0.25

NFC-23

0.25

NFC-24

0.25

Figure 1: Comparison of existing and calculated thickness of x-ray facilities in Naryanganj District According to the Table 1, 30 % of the entrance doors of the facilities are not adequately shielded to protect the radiation hence to ensure safety of the public. From these facilities, the public are receiving more radiation dose than their acceptable limit (0.5 Sv/h). Outside the wall of the x-ray room the average dose received by the public is 0.70 Sv/h. According to the national requirements the recommended thickness for door shielding is 2 mm of lead and the thickness for brick wall is 10 inch. In this work, this recommendation has been found overestimated which is found even higher than the value calculated NCRP 49 approach.

IV.

CONCLUSION

According to dose rate measurement the door shielding of about 70 % facilities and the shielding of brick wall in the 83 % facilities are safe for the public. The rest of the facilities required considerable improvement for their shielding status in order to ensure public and environmental safety. Although x-ray is a very powerful tool in modern medical diagnosis, but its useful uses may cause a great biological radiation hazard to human body. Therefore, the radiation exposure to the concerned people should not exceed the limit according to the national and international standard [7, 9]. Each x-ray installation should be provided with adequate shielding arrangements. Even though the dose rates recorded in the most of the facilities are within the regulatory limit, it doesn’t still make sense until 100% of facilities would become safe for the public and other concerned parties. Additionally, the result of radiation dose measurement might have been more accurate if it could have been accomplished by introducing TLD badges including more facilities for longer period of time. Therefore, the barrier thicknesses have been evaluated in this work by applying NCRP approach is still influencing the calculation to be performed involving more realistic values considering national aspects from which more reasonable shielding thickness can be estimated in order to ascertain the radiation safety of the public.

ACKNOWLEDGEMENTS The authors would like to thank the owner of the radiation facilities for their excellent support during measurement of radiation doses in and around the facilities. The authors are also grateful to Bangladesh Atomic Energy Regulatory Authority (BAERA) which provided access to radiation measuring equipments.

REFERENCES [1] [2] [3] [4] [5] [6]

[7]

Nuclear Safety and Radiation Control Division. “Regulatory Guide on Radiation Protection in Medical Diagnostic x-ray”. ISBN 984-32-0671-1, 2002, NSRC-XR-G-01. National Council on Radiation Protection and Measurements. “Structural Shielding Design and Evaluation of Medical Use of Xrays and Gamma-rays of Energies up to 10 MeV”. NCRP Rep.49, 1976, Bethesda, MD, 20814. National Council on Radiation Protection and Measurements. “Structural Shielding Design for Medical Use of X-rays Imaging Facilities”. NCRP Report No.147. 2004, Bethesda, MD, 20814. Pesianian I., Mesbahi A..& Shafaee A. “Shielding Evaluation of a Typical Radiography Department: A Comparison Between NCRP Reports No. 49 and 147”. Iran.J.Radiat.Res. 6. 2009, 183-188. Costa PR, Coldas LV. “Evaluation of protective shielding thickness for diagnostic radiology rooms, theory and computer simulation”. Medical Physics 29. 2002, 785-793. Mohammad Javad Keikhai Farzaneh, Sabihe Farsi, Fatemeh Ramroodi, Mahdi Shirin Shandiz and Mojtaba Vardian. “The assessment of shielding status of conventional radiographic rooms according to the National Council on Radiation Protection Reports No.49 and No.147 and recommendation to national and international authorities of radiation protection to prevent wasting shielding costs of conventional radiographic rooms”. Indian Journal of Science and Technology 11. 2011, 1434-1437. Bangladesh Government. “Nuclear Safety and Radiation Control Rules-1997 (SRO No. 205-Law/97)”. Bangladesh Gazette, 1997.

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M.Haider et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 20-24 [8] [9]

Archer.B.R. “Shielding of Diagnostic X-ray Facilities for Cost- Effective and Beneficial Use and Protection”. IRPA-10 CourseEO-6(unpublished work) Department of Radiology, Baylor College of Medicine, Houston, 1990, Texas 77030. International Atomic Energy Agency. “Basic Safety Standards for Protection against Ionizing Radiation and for the Safety of Radiation Sources”. Safety Series No.115, 1996, IAEA, Vienna.

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

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

Reactions of MoOCl4 & MoO2Cl2 with Imidazole, Pyrazole, Acetamide, Succinimide, Benzamide & 2-Thiazoline-2-thiol Gursharan Singh1, Seema Sharma2, Vikas Mangla3, Mamta Goyal4, Kavita Singla5, Deepika Rani6 1 Professor, 2Associate Professor, 3,4,5,6 Ph.D. Research Scholars Department of Applied Chemistry,Giani Zail Singh, PTU Campus, Dabwali Road, Bathinda-151001, Punjab, INDIA Abstract: MoO2Cl2 reacts with imidazole, pyrazole, acetamide & succinimide in CH3CN medium to yield multinuclear Mo complexes: Mo4O7CI6(CH3CN)4(C3H4N2)Cl2 [1], Mo4O7CI6(CH3CN)4(C3H4N2)4 [2], Mo4O7CI6(CH3CN)4(CH3CONH2)2Cl2 [3] & Mo4O7CI6(CH3CN)4(C4H5NO2)2Cl2 [4], respectively. MoOCl4 reacts with imidazole, pyrazole, benzamide & 2-thiazoline-2-thiol to form Mo2O3CI2(CH3CN)2(C3H4N2)Cl3 [5], Mo2O3CI2(CH3CN)2(C3H4N2)Cl4 [6], Mo2O3CI2(CH3CN)2(C6H5CONH2)2Cl4 [7] & Mo2O3CI2(CH3CN)2(C3H5NS2)2Cl4 [8], respectively. Compounds have been characterized by elemental analysis, 1H NMR, FTIR, Mass (LC-MS), electronic spectra and molar conductance studies. Keywords: MoOCl4; MoO2CI2; imidazole; pyrazole; acetamide; succinimide; benzamide, 2-thiazoline-2thiol. I. Introduction Molybdenum is known to form multinuclear complexes.1, 2, 3. These complexes may be cyanide bridged1, 2. CH3CN medium is increasingly used in inorganic reactions, which increases solubility of precursors, otherwise these are not much soluble in less polar solvents, such as, CH2CH2, CHCl3, etc. Poor solubility of precursors may lead not only to slowing down of reaction, but also to inhomogeneity of reaction mixture. CH3CN being a polar solvent (3.92 D), is able to dissolve a wide variety of compounds – both ionic and non- polar. CH3CN can coordinate to metal via L→M, where terminal –C N group is involved in end-on coordination. There may not be M→L π-back bonding4, thus there is increase in C N stretching frequency to 2268 – 2361 cm-1 from 2253 cm-1 in free5 CH3CN (thin film in KBr). However, if there is M→L π-back bonding into ligand π* orbitals, then there will be a decrease in C N stretching frequency. Sometimes, CH3CN may not be involved in L→M end-on coordination, rather it exhibits L→M coordination through cross-bridging1. IR band at 1638-1658 cm-1 is assigned to this cross bridged 1 CH3CN ligand in contrast to terminal CH3CN stretchings at 2317 cm-1 and 2290 cm-1. Effect of cross-bridging of CH3CN ligand is also seen on 1H NMR chemical shifts. Terminal1 CH3CN ligand shows peak at 1.95 ppm [3H] in CD3CN medium. However, cross-bridged1 CH3CN ligand shows chemical shift at 3.37 – 3.63 ppm [3H] in CD3CN medium. The author reported6, 7, 8, 9, 10 reactions of MoOCl4, MoO2Cl2, MoCl5 in CH2Cl2 medium. Owing to feeble solubility of MoO2CI2, MoCl5 and ligands in CH2Cl2 medium, above said difficulties were encountered. This stimulated the author to study behaviour of MoOCl4, MoO2CI2 & MoCl5 towards ligands in CH3CN medium. In reactions reported in this paper, enhanced polarity of CH3CN medium has plays an important role in reaction process. Products obtained are soluble in CH3CN. In reactions carried out in CH2Cl2 medium, products were obtained as residues. Enhanced solubility of MoOCl4, MoO2CI2 & MoCl5 is due to formation of solvent stabilized complexes, like MoO2Cl2(CH3CN)2. The compounds reported in this paper are multinuclear as assessed by the fragments detected in mass (LC-MS) spectra. There is some similarity in the fragments formed from the compounds prepared from MoO 2CI2. Similarly, there is some similarity in the fragments formed from the compounds prepared from MoOCI 4. Pyrazoles and their derivatives exhibit various biological activities including antimicrobial 11, anticyclooxygenase12, anticonvulsant13, antitubercular14, antitumor15, antiinflammatory16, analgesic17, antidiabetic18, antipshycotic19, 20, 21. Many drugs, such as, antifungal drugs and nitroimidazole contain an imidazole22, 23 ring. II. Experimental Materials: Molybdenumoxytetrachloride MoOCl4 has been synthesized in laboratory by reaction of MoO3 (CDH, AR Grade) with thionyl chloride at reflux temperature for about 6 h. After refluxing, excess thionyl chloride was evacuated in liquid nitrogen traps. Dark green crystals obtained were dissolved in dry CH2Cl2 to get a dark red solution. Solution was filtered through filtration unit having G-4 sintered glass crucible to remove any unreacted MoO3. On evacuation, filtrate yielded MoOCl4 shining dark green crystals, having m.p. 100°-102° C.

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Gursharan Singh et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 25-33

Molybdenumdioxodichloride MoO2Cl2 (Sigma-Aldrich, USA) is a light yellow hygroscopic solid. It was purchased from market and used as such. Imidazole is a colourless solid (SD Fine, LR Grade, m.p. 88°-91° C). It is soluble in water and many polar organic solvents including CH3CN. Pyrazole is a colourless solid (SD Fine, LR Grade, m.p. 66°-70° C). Acetamide (NICE, LR grade, m.p. 79°-81° C) is a colourless solid and was procured from market. It is soluble in CH3CN, but insoluble in CH2Cl2. Succinimide (CDH, LR Grade, m.p. 125°-127° C) is a white solid, soluble in polar solvents. Benzamide (Loba Chemie, m.p. 127°-130° C) is a white solid, soluble in polar solvents. 2-Thiazoline-2-thiol also called 2-mercapto-2-thiazoline (Sigma-Aldrich, USA, m.p. 100°-105° C) is a white solid, soluble in acetonitrile. All ligands were dried under vacuum. SOCl2 (CDH, LR grade, b.p. 76°-78° C) was kept over quinoline for 48 h (50 g SOCl2 to 10 g quinoline) to remove acid impurities and then fractionally distilled to get a colourless liquid. CH3CN was dried by standard methods. Physical Measurements: Molybdenum and chlorine were estimated by standard methods24. The elemental analyses (C, H, N and O) were carried out using Organic Elemental Analyzer Series-Flash 2000 Thermo Fisher Scientific (USA), FTIR spectra were recorded in the range 4000 – 400 cm-1 using Perkin-Elmer 400 FTIR Spectrometer (Germany), in KBr disks, 1 H-NMR spectra were recorded using Brucker Avance-II 400 (Fallanden) NMR, at SAIF/CIL Panjab University, Chandigarh (India), in DMSO-d6 and UV-VIS spectra in DMSO solvent were recorded using Thermoscientific Genesys 10S Series UV-VIS spectrophotometer (USA), at GZS PTU Campus, Bathinda (India). LC-MS spectra were recorded in the range 0 – 1100 m/z using WATERS, Q-TOF Micromass LC-MS (UK), at SAIF/CIL Panjab University, Chandigarh (India). Molar conductance measurements of millimolar solutions were carried out on Electronic India Digital Conductivity Meter Model 621 (India) at 25° C. Synthesis of Compounds [1] to [8]: A 100 ml round bottomed flask containing a magnetic bead was attached to a pressure stabilized dropping funnel fitted with a teflon rotaflow stop-cock. Assembly was connected to vacuum line and flame dried under vacuum (10-3 torr). Dry N2 gas purged with O2 was allowed into assembly at room temperature, through liquid N2 traps. A known weight of MoOCl4 or MoO2Cl2 dissolved in dry CH3CN was taken in round bottomed flask. An equimolar amount of C3H4N2 (imidazole or pyrazole), CH3CONH2, C4H5NO2 (succinimide), C6H5CONH2 or C3H5NS2 (2-thiazoline-2-thiol) dissolved in CH3CN in dropping funnel, was added drop wise to round bottom flask with constant stirring, at room temperature. Products were recovered from filtrate by filtration through G-4 bed of a filtration unit, at reduced pressure and under inert atmosphere. All compounds synthesized are very much air and moisture sensitive. They have tendency to turn blue in colour. So all reactions and work ups were handled under dry N2 gas purged with O2 using vacuum line and liquid N2 traps. CH CN

3  Mo O Cl (CH CN) (C H N )Cl , [1]  4 7 6 3 4 3 4 2 2

4 MoO 2 Cl 2 + 4 C3 H 4 N 2 Imidazole

CH CN

4 MoO 2 Cl 2 + 4 C3 H 4 N 2

3  Mo O Cl (CH CN) (C H N ) , [2]  4 7 6 3 4 3 4 2 4

Pyrazole CH CN

4 MoO 2 Cl 2 + 4 CH3CONH 2

3  Mo O Cl (CH CN) (CH CONH ) Cl , [3]  4 7 6 3 4 3 2 2 2

CH CN

4 MoO 2 Cl 2 + 4 C 4 H 5 NO

3  Mo O Cl (CH CN) (C H NO ) Cl , [4]  4 7 6 3 4 4 5 2 2 2

Succinimide CH3CN 2 MoOCl 4 + 2 C3 H 4 N 2   Mo 2 O3Cl 2 (CH3CN) 2 (C3H 4 N 2 )Cl3 , [5] Imidazole CH CN

2 MoOCl 4 + 2 C3 H 4 N 2

3  Mo O Cl (CH CN) (C H N )Cl , [6]  2 3 2 2 3 4 2 3 4

Pyrazole CH CN

2 MoOCl 4 + 2 C6 H 5CONH 2

3  Mo O Cl (CH CN) (C H CONH ) Cl , [7]  2 3 2 3 2 6 5 2 2 4

CH CN

2 MoOCl 4 + 2 C3 H 5 NS2

3  Mo O Cl (CH CN) (C H NS ) Cl , [8]  2 3 2 3 2 3 5 2 2 4

2 - Thiazoline - 2 - thiol

III. Results & Discussions Analytical Measurements: All compounds are moisture and air sensitive. They are insoluble in common organic solvents like CH 2Cl2, CHCl3, n-hexane, but soluble in polar solvents like CH3CN, DMSO and DMF. Formulations of these compounds have been done on basis of their elemental analytical data, mass (LC-MS) and molar conductance

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Gursharan Singh et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 25-33

ɅM measurements. Analytical data and mass (LC-MS) spectra reveal that these compounds are multinuclear. ɅM of compounds [1] to [8] in DMSO are 2 – 4 cm2ohm-1mol-1, suggesting their non-electrolytic nature (Table-1).

Table-1 (Analytical & Molar Conductance Data) ɅM*

Compounds (Colour/F.W.) Mo 4 O7 Cl 6 (CH3CN) 4 (C3H 4 N 2 )Cl 2 , [1]

% Composition Observed (Calculated) Mo

37.12 (37.94)

21.37 (21.04)

12.48 (13.04)

1.64 (1.58)

07.39 (08.30)

11.9 (11.0)

----

33.73 (33.53)

18.93 (18.60)

20.12 (20.96)

2.57 (2.44)

13.78 (14.67)

10.50 (09.7)

----

35.37 (35.10)

26.17 (25.95)

12.27 (13.16)

2.16 (2.01)

06.77 (07.67)

13.9 (13.1)

----

32.74 (33.62)

23.95 (24.86)

15.99 (16.81)

2.03 (1.92)

06.82 (07.35)

15.0 (15.4)

----

33.04 (33.83)

31.22 (31.37)

15.80 (14.80)

2.06 (1.76)

10.71 (09.86)

09.3 (08.4)

----

32.67 (31.84)

36.27 (35.32)

13.04 (13.93)

2.04 (1.65)

09.51 (09.28)

08.6 (07.9)

----

24.50 (24.71)

28.32 (27.41)

28.29 (27.89)

2.74 (2.57)

06.59 (07.20)

09.8 (10.2)

----

24.17 (24.83)

28.20 (27.55)

16.47 (15.52)

2.32 (2.06)

7.87 (7.24)

06.5 (06.2)

17.2 (16.5)

(Light blue/1012.0) Mo 4 O7 Cl 6 (CH3CN) 4 (C3H 4 N 2 ) , [2]

(Light blue/1145.0) Mo 4 O7 Cl 6 (CH3CN) 4 (CH3CONH 2 ) Cl , [3]

(Light blue/1094.0) Mo 4 O7 Cl 6 (CH3CN) 4 (C 4 H 5 NO ) Cl , [4]

2 2

(White/1142.0) Mo 2 O3Cl 2 (CH3CN) 2 (C3H 4 N 2 )Cl3 , [5]

(Green/567.5) Mo 2 O3Cl 2 (CH3CN) 2 (C3H 4 N 2 )Cl , [6]

(Green/603.0) Mo 2 O3Cl 2 (CH3CN) 2 (C6 H5CONH 2 ) Cl 4 , [7] 2

(Green/777.0) Mo 2 O3Cl 2 (CH 3CN) 2 (C3H 5 NS2 ) Cl 4 , [8] 2 (Light green/773.0)

* Molar conductance of millimolar solutions in DMSO at 25° C. FTIR Spectra: Imidazole25, 26 shows N-H stretchings at 3724-3237 cm-1. Band at 3254.15 cm-1 shows that N-H group is present in compound [1] (Table-2). This peak is broad in the solid state (KBr disk) due to hydrogen bonding. Two medium bands at 943.15 cm-1 & 927.14 cm-1 are attributable to the presence of cis-MoO22+ core39. Pyrazole27, 28 shows N-H stretching at 3450 cm-1. Band at 3259.2 cm-1 shows that N-H group is present in compound [2] (Table-3). Mo-N coordination bond lowers frequency of N-H stretching. Strong bands at 941.3 cm-1 & 905.3 cm-1 show the presence of cis-MoO22+ core39. Acetamide29, 30 shows N-H stretchings at 3374 cm-1 & 3302 cm-1. Band at 3348.1 cm-1 shows that N-H group is present in compound [3] (Table-4). Strong bands at 956.6 cm-1 & 916.5 cm-1 show the presence of cis-MoO22+ core39. Shift in C=O stretching frequency from 1672 cm-1 to 1650.2 cm-1 of acetamide indicates a Mo-O coordination bond. Succinimide31, 32 shows N-H stretchings at 3410 cm-1 & 3223 cm-1. Band at 3332.1 cm-1 shows that N-H group is present in compound [4] (Table-5). Strong bands at 960.4 cm-1 & 921.2 cm-1 show the presence of cis-MoO22+ core39. There is no shift in C=O stretching frequency from 1771 cm-1, which indicates that there is no Mo–O coordination bond. Imidazole25, 26 shows N-H stretchings at 3724-3237 cm-1. Band at 3291 cm-1 shows that N-H group is present in compound [5] (Table-2). This peak is broad in the solid state (KBr disk) due to hydrogen bonding. A strong band at 977.7 cm-1 shows the presence of terminal Mo=O group40, 41. Pyrazole27, 28 shows N-H stretching at 3450 cm-1. Band at 3366.7 cm-1 shows that N-H group is present in compound [6] (Table-3). Mo-N coordination bond lowers frequency of N-H stretching. A strong band at 977.15 cm-1 shows the presence of terminal Mo=O group40, 41. Benzamide33, 34, 35 shows N-H stretching at 3366 cm-1. Bands at 3426.24 cm-1 & 3329.23 cm-1 show that N-H group is present in compound [7] (Table-6). A strong band at 973.21 cm-1 shows the presence of terminal Mo=O group40, 41. 2-Thiazoline-2-thiol shows N-H stretching at 3145 cm-1. Strong band at 3336.8 cm-1 shows that N-H group is present in compound [8] (Table-7). A strong band at 982.10 cm-1 shows the presence of terminal Mo=O group40, 41 . Absorptions N-H str.

Table 2 (FTIR absorptions in cm−1) C3H4N2 [1] (Imidazole)25, 26 3724 vb, 3656 vb, 3270, 3241, 3237

3254.15 s, br

[5] 3291 s, br

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Gursharan Singh et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 25-33 C-H str.

3196, 3165

cross bridged C N str. of CH3CN C=C ring str. N-C ring str. C-H in plane bending C-H bending Ring bending C-H out of plane bending (wagging), Ring twisting Ring twisting Ring twisting, N-H wagging Mo=O40, 41 str. Mo=O str. of cis-MoO22+ core39

Absorptions

3151.2 s, 2993.5 sh, 2861.7 sh

---1558, 1500 1434 1092, 1074 964 909 816, 730

3148.11 s, 2984.20 sh, 2851.24 sh, 2636.36 sh 1621.36 m 1581.22 s 1423.42 m 1093.39 w, 1448.35 m ------843.3 s, 703.9 s, br

646 528 -------

621.11 m 557.19 m ---943.15 m, 927.14 m

623.11 m 499.19 w 977.7 s ----

1618.7 s 1584.7 s 1427.15 w 1093.16 w, 1048.14 m ------757.10 s, br

Table 3 (FTIR absorptions in cm−1) C3H4N2 [2] (Pyrazole)27,

[6]

N-H, str. C-H str. Cross bridged C N str. of CH3CN C=C ring str. N-C ring str.

3450 3155, 3144 ---1558 1468, 1138

C-H in plane bending C-H bending Ring bending C-H out of plane bending (wagging), Ring twisting Ring twisting Ring twisting, N-H wagging Mo=O40, 41 str. Mo=O Str. of cis-MoO22+ core39

1046, 1035 938 918 893, 840, 760

3259.2 vs, b 3122 vs, b 1656.3 vs 1526.7 m 1451.7 sh, 1399.2 s, 1329.1 w, sh, 1240.9 s, 1077.13 sh, 1061.10 s ------826.5 sh, 771.2 s, b

656, 619 498 -------

570.5 m 475.18 w ---941.3 m, 905.3 m

3366.7 vs, b 3138.7 vs, b 1620.17 vs ---1475.27 sh, 1408.26 w, 1351.2 w, sh, 1119.2 m 1053.25 m ---913.29 sh 786.19 m 735.30 m 609.24 w 497.29 w 977.15 vs ----

Table 4 (FTIR absorptions in cm−1) Absorptions

CH3CONH229, 30

[3]

N-H Str. CH2 Str. C=O Str., Cross bridged C N str. of CH3CN

3302, 3374 2820 1672, 1631

3348.1 s, br ---1650.2

CH3 Deformation C-N asym str.

1450 1396

1492.9 m, sh 1403.10 w, sh

NH2 Deformation

1150

1120.14 m

C-N sym str.

1048

1050.16 w

NH2 rocking, twisting and wagging N=C=O bending

900 600

752.3 s, br 633.8 w, sh, 564.5 s

Mo-N (Strong)

----

481.16 w, sh

Mo=O Str. of cis-MoO22+ core39

----

956.6 s, 916.5 s

Table 5 (FTIR absorptions in cm−1) Absorptions NH str. CH2 sym. str. CO sym., HNC in plane bending

C4H5NO2 (Succinimide)31, 32 3410 s, b, 3223 s, b 2964, 2945 w 1771 m

3332.1 vb ---1771.6 m

CO asym., HNC in plane bending CH2 sym. scissoring CH2 asym. scissoring CNC asym. str., HNC in plane bending CH2 bending, ring in plane bending CH2 bending CNC asym. str., HNC in plane bending C4-C5 str, CH2 bending Ring in plane bending C-C str., CNC sym. str. CH2 bending, ring out of plane bending

1713 vs, b 1431 m 1400 m 1349 s, 1334 1294 s 1241 1186 s, 998 m 901 849 817 s

1697.0 vs 1421.1 vw 1383.7 s ---1298.6 m 1249.11 vw 1187.3 vs ------859.9 vw 819.5 sh

[4]

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Gursharan Singh et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 25-33 OCN asym. out of plane bending OCN sym. out of plane bending, CH2 bending CH2 bending, ring out of plane bending Mo=O Str. of cis-MoO22+ core39

Absorptions N-H str. asym.

632 m 543 w

645.5 w 562.7 m

528 ----

---960.4 s, 921.2 s

Table 6 (FTIR absorptions in cm−1) C6H5CONH233, 34, 35 3366

[7] 3426.24 s, 3329.23 s

N-H str. sym.

3170

CH2 str.

2820

3154.26 s ----

C=O Str., Cross bridged C N str. of CH3CN NH2 Deformation

1656 s

1634.12 s

1624, 1606

1553.22 sh, 1525.17 s

CH3 Deformation

1450

1452.24 s

C-N asym. str.

1398 s

1305.38 w

NH2 rocking

1142 s, 1122 s

1260.41 w, 1182.41 w, 1162.41 w

C-N sym. str.

1048

1102.99 w, sh, 1082.38 w

C-H out of plane bending

797.7 m

797.33 sh

NH2 rocking, twisting and wagging

770.4 m, 700 s, 684 s

710.25 s, 684.25 s

N=C=O bending

600

624.30 sh, 561.26 s

Mo-N (Strong)

----

422.45 w

----

973.21 s

40, 41

Mo=O

str.

Absorptions N-H asym. str. C-H str. S-H str. Cross bridged C N str. of CH3CN C=N str. C-N ring str. C-N sym. str. + C-C str. C-S str. + C-SH str. Mo=O40, 41 str.

Table 7 (FTIR absorptions in cm−1) C3H5NS2 (2-Thiazoline-2-thiol)36, 37, 38 3145 2968 s, 2926 s, 2852 s 2709 ---1518 s 1260 sh 1217 sh 1160 sh ----

[8] 3336.8 vs, very br ------1665.13 1602.12 1256.52 w, sh 1205.13 s 1155.15 m 982.10 s

H NMR Spectra: Spectrum of imidazole42, 43 in CDCl3 shows adsorptions due to CH proton (between two nitrogen atoms) at 7.73 ppm, CH protons on other two carbons at 7.15 ppm and due to N-H proton at 11.62 ppm. Spectrum of imidazole44 in DMSO-d6 shows relatively downfield adsorptions for CH proton (between two nitrogen atoms) at 9.37 ppm, CH protons on other two carbons at 7.80 ppm. Spectrum of compound [1] (Table-8) in DMSO-d6 shows that peaks due to CH proton (between two nitrogen atoms) and CH protons on other two carbons of imidazole have shifted up-field. Two equivalent C–H protons of imidazole appear as singlets, because of the tautomerization equilibrium. Spectrum of pyrazole45 in CCl4 shows adsorptions due to middle CH proton at 6.31 ppm, CH protons on side two carbons at 7.61 ppm and due to N-H proton at 12.64 ppm. Spectrum of compound [2] (Table-9) in DMSO-d6 shows that peaks due to middle CH proton & CH protons on side two carbons have retained their positions in pyrazole. Peaks of C–H protons of pyrazole appears as singlet, because of the tautomerization equilibrium. Spectrum of compound [3] (Table-10) in DMSO-d6 shows that peak due to CH3 in acetamide46 has retained its position in the compound. Spectrum of compound [4] (Table-11) in DMSO-d6 shows that peak due to CH2 in succinimide47, 48 has moved downfield due to Mo-N coordination. Spectrum of compound [5] (Table-8) in DMSO-d6 shows that peaks due to CH proton (between two nitrogen atoms) and CH protons on other two carbons of imidazole have shifted up-field. Two equivalent C–H protons of imidazole appear as singlets, because of the tautomerization equilibrium. Spectrum of compound [6] (Table-9) in DMSO-d6 shows that peaks due to middle CH proton & CH protons on side two carbons have moved down field, because of Mo-N coordination. Peaks of C–H protons of pyrazole appear as singlets, because of the tautomerization equilibrium. Spectrum of compound [7] (Table-12) in DMSO-d6 shows that peak due to CH3 in benzamide49 has retained its position in the compound.

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Gursharan Singh et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 25-33

Spectrum of compound [8] (Table-13) in DMSO-d6 shows that peak due to CH2 near N & peak due to CH2 near sulphur have moved down field due to coordination of 2-thiazoline-2-thiol50, 51 with Mo. Table 8 (1H NMR absorptions in ppm) C3H4N2 (Imidazole)44 [1]

Absorptions

[5]

12.4 1H

13.35 (s) 1H

14.59 (s) 1H

CH (between two nitrogens)

9.37 1H

9.07 (s) 1H

CH (other carbons)

7.80 2H

7.60 (s) 2H

7.61 (s) 2H

Residual DMSO-d6

----

2.49 (s)

2.57 (s)

Table 9 (1H NMR absorptions in ppm) Absorptions

C3H4N2 in CCl4 (Pyrazole)45

[2]

[6]

12.64 1H

----

Middle CH2

6.31 (s) 1H

6.30 (s) 1H

6.67 (s) 1H

Side CH2

7.61(s) 2H

7.67 (s) 2H

8.21 (s) 2H

Residual DMSO-d6

----

2.52 (s)

2.56 (s)

Table-10 (1H NMR absorptions in ppm) Absorptions

CH3CONH246

NH2

7.04 broad 2H

----

CH3

2.00 3H

2.06 (s) 3H

Absorptions

Table-11 (1H NMR absorptions in ppm) C4H5NO2 (Succinimide)47, 48

[4]

NH CH2

---2.73 4H

11.06 1H 2.58 4H

[3]

Table-12 (1H NMR absorptions in ppm)

Comp.

Absorptions

C6H5CONH249 in CDCl3

[7]

CH (m & p)

7.34-7.51(s) broad 3H

7.40 (s) broad 2H

CH (o)

7.73-7.80 (s) broad 2H

7.86 (s) broad 2H

6.04 (s) broad 2H

----

Absorptions

Table-13 (1H NMR absorptions in ppm) C3H5NS2 in CDCl3 (2-Thiazoline-2-thiol)50, 51

[8]

CH2 near N CH2 near S NH

3.32 2H 3.55 2H 7.43 1H

3.40 2H 3.89 2H ----

[Mo2O3Cl2(CH3CN)2]2+ (m/z = Calc. 197.893)

Table-14 (m/z values of some fragments) [Mo2O3Cl2(CH3CN)2]+ [Mo4O7Cl6(CH3CN)4]2+ (m/z = Calc. 395.786) (m/z = Calc. 438.752)

[Mo4O7Cl6(CH3CN)4]+ (m/z = Calc. 877.505)

[1] [2]

197.80 (100.0 %) 197.80 (88.68 %)

-------

439.60 (98.12 %) 439.14 (100.0 %)

879.20 (11.97 %) 880.30 (74.04 %)

[3] [4]

197.79 (90.72 %) 197.79 (49.86 %)

-------

439.13 (100.0 %) 439.63 (100.0 %)

880.27 (87.15 %) 880.27 (76.76 %)

[5] [6] [7]

197.80 (100.0 %) 197.79 (100.0 %) ----

------395.00 (35.25 %)

----------

[8]

----

Mass Spectra (LC-MS): Compounds [1] to [4] have been prepared from MoO 2Cl2 and compounds [5] to [8] have been prepared from MoOCl4 as the precursors. Mass spectra (LC-MS) of these compounds have some correlations. Compounds [1] to [4] decompose to form some common fragments (Table-14). Similarly, Compounds [5] to [7] decompose to

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form some common fragments (Table-14). Based on the fragments formed, formulae of the compounds have been derived. Fragmentation behaviour of these compounds are as under. Mo 4 O 7 Cl 6 (CH 3 CN) 4 (C 3 H 4 N 2 )Cl 2  [Mo 4 O 7 Cl 6 (CH CN) 4 ] 3 [1] (F.W. = 1012.0)

 [Mo 4 O 7 Cl 6 (CH CN) 4 ] 3

m / z = 879.2

m / z = 439.6

 [Mo O Cl (CH CN) ] 2 3 2 3 2

 [Mo O Cl (CH CN) ] 2 3 2 3 2

m / z = 395.786

m / z = 197.8

(Unstable) Mo 4 O 7 Cl 6 (CH 3 CN) 4 (C 3 H 4 N 2 ) [2](F.W. = 1145.0)

 [Mo 4 O 7 Cl 6 (CH CN) 4 ] 3

 [Mo 4 O 7 Cl 6 (CH CN) 4 ]

m / z = 880.30

m / z = 439.14

 [Mo O Cl (CH CN) ] 2 3 2 3 2

 [Mo O Cl (CH CN) ] 2 3 2 3 2

m / z = 395.786

m / z = 197.80

(Unstable) Mo 4 O 7 Cl 6 (CH 3 CN) 4 (CH 3 CONH 2 ) Cl  [Mo 4 O 7 Cl 6 (CH CN) 4 ] 2 2 3 [3](F.W. = 1094.0)

 [Mo 4 O 7 Cl 6 (CH CN) 4 ]

m / z = 880.27

m / z = 439.13

 [Mo O Cl (CH CN) ] 2 3 2 3 2

 [Mo O Cl (CH CN) ] 2 3 2 3 2

m / z = 395.786

m / z = 197.79

(Unstable) Mo 4 O 7 Cl 6 (CH 3 CN) 4 (C H 5 NO 2 ) Cl  [Mo 4 O 7 Cl 6 (CH CN) 4 ] 4 2 2 3 [4](F.W. = 1142.0)

 [Mo 4 O 7 Cl 6 (CH CN) 4 ] 3

m / z = 880.27

m / z = 439.63

 [Mo O Cl (CH CN) ] 2 3 2 3 2

 [Mo O Cl (CH CN) ] 2 3 2 3 2

m / z = 395.786

m / z = 197.79

(Unstable) Mo O Cl (CH CN) (C H 4 N )Cl 3  [Mo O Cl (CH CN) ] 2 3 2 3 2 3 2 2 3 2 3 2 [5](F.W. = 567.5)

m / z = 197.80

Mo O Cl (CH CN) (C H 4 N )Cl 4  [Mo O Cl (CH CN) ] 2 3 2 3 2 3 2 2 3 2 3 2 [6](F.W. = 603.0)

m / z = 197.79

Mo O Cl (CH CN) (C H 5 CONH ) Cl 4  [Mo O Cl (CH CN) ] 2 3 2 3 2 6 2 2 2 3 2 3 2 [7](F.W. = 777.0)

 [C H 5CONH ] 6 2

m / z = 395.0

m / z = 122.0

Mo O Cl (CH CN) (C H 5 NS ) Cl 4  [Mo O Cl (C H 5 NS )(CH CN)] 2 3 2 3 2 3 2 2 2 3 2 3 2 3 [8](F.W. = 773.0)



 [Mo O Cl (C H 5 NS )(CH CN)] 2 3 2 3 2 3

m / z = 471.0

m / z = 235.0

 [MoOCl(C H 5 NS )] 3 2 m / z = 267.0

+ [MoO Cl(CH CN)] 2

m / z = 205.0

Electronic Spectra: Imidazole shows a very broad absorption in DMSO at ʎmax 284 nm. Compound [1] shows two sharp absorptions at ʎmax 291 nm & ʎmax 361 nm. Compound [5] shows sharp absorptions at ʎmax 294 nm, ʎmax 300 nm & 447 nm. Absorption at 447 nm is due to ligand → metal charge transfer transitions O(π) → d(Mo), that is ligand → metal charge transfer transitions due to movement of π electron from Mo = O to empty d-orbital of molybdenum. Pyrazole shows a very broad absorption in DMSO at ʎmax 264 nm. Compound [2] shows a broad absorptions at ʎmax 300 nm (ɛ =13228). Compound [6] shows two sharp absorptions at ʎmax 294 nm (ɛ =10477) & ʎmax 300 nm (ɛ =11348) and a very broad absorption at ʎmax 457 nm (ɛ =1404). Absorption at 457 nm is due to ligand →

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metal charge transfer transitions O(π) → d(Mo), that is ligand → metal charge transfer transitions due to movement of π electron from Mo = O to empty d-orbital of molybdenum. Acetamide shows absorption in DMSO at ʎmax 267 nm O(n) → C(π*) transitions due to due to presence of C = O group. Compound [3] shows sharp absorption at ʎmax 292 nm, showing bathochromic shift. Succinimide shows absorption in DMSO at ʎmax 259 nm. Compound [4] shows sharp absorption at ʎmax 299 nm. Benzamide shows a very broad absorption in DMSO at ʎmax 273 nm O(n) → C(π*) transitions due to due to presence of C = O group. Compound [7] shows sharp absorptions at ʎmax 293 nm, 300 nm showing bathochromic shift and at 447 nm. Absorption at 447 nm is due to ligand → metal charge transfer transitions O(π) → d(Mo), that is ligand → metal charge transfer transitions due to movement of π electron from Mo = O to empty d-orbital of molybdenum. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44]

[45] [46] [47] [48] [49] [50] [51]

F. Albert Cotton, Lee M. Daniels, Carlos A. Murillo & Xiaoping Wang, Polyhedron, 17 (17) (pp. 2781-2793) 1998. http://worldwidescience.org/topicpages/c/cyanide+bridges+link.html#. Comrie, A. G., McVitie, A. and Peacock, R. D., Polyhedron, 13 (pp. 193) 1994. http://thesis.library.caltech.edu/2349/7/Chapter7.pdf. Pouchert, C. J.; The Aldrich Library of FT-IR Spectra, Edition I, Aldrich Chemical Company, Inc.: Milwaukee, Wisconsin, 1985. Gursharan Singh, Vikas Mangla, Mamta Goyal, Kavita Singla & Deepika Rani, American International Journal of Research in Science, Technology, Engineering & Mathematics, 8(2) (pp. 131-136) 2014. S K Vasisht, Gursharan Singh, in: VII International Symposium on Organosilicon Chemistry, Kyoto, Japan, (pp. 40) 1984. Sham K Vasisht. Gursharan Singh & (Ms) Sarita Chaudhary, Indian Journal of Chemistry, vol. 24A (pp. 574-577) 1985. Sham K. Vasisht, Gursharan Singh & Pawan K. Verma, Monatshefte fur Chemie, vol. 117 (pp. 177-183) 1986. S K Vasisht & Gursharan Singh, Z, Anorg. Allg. Chemie, vol. 526 (pp. 161-167) 1985. Bekhit AA, Ashour HMA, Ghang YSA, Bekhit AEA, Baraka A., Eur J Med Chem, 43 (pp. 456-463) 2008. Frigola J, Colombo A, Pares J, Martinez L, Sagarra R, Rosert R., Eur J Med Chem, 24 (pp. 435-445) 1989. Aziz MA, Abuorahma GEA, Hassan AA., Eur J Med Chem, 44 (pp. 3480-3487) 2009. Castagnolo D, Mantti F, Radi M, Bechi B, Pagano M, Logu AD., Bioorg Med Chem, 17 (pp. 5716-5721) 2009. Ahmed OM, Muhamed MA, Ahmed RR, Ahmed SA., Eur J Med Chem, 44 (pp. 3519-3523) 2009. Bekhit AA, Aziem TA., Bioorg Med Chem, 12 (pp. 1935-1945) 2004. Bondock S, Rabie R, Etman HA, Fadda AA., Eur J Med Chem, 43 (pp. 2122-2229) 2008. Gopalakrishnan S, Ravi TK, Manojkumar P., Eur J Med Chem, 44 (pp. 4690-4694) 2009. Barcelo M, Ravina E, Masaguer CF, Dominguez E, Areias FM, Brea J., Bioorg Med Chem Lett, 17 (pp. 4873-4877) 2007. Pospisil P, Folkers G., FABAD J Pharm Sci, 29 (pp. 81-92) 2004. Cho AE, Guallar V, Berne BJ, Friesner R., J computchem, 26 (pp. 915-931) 2005. A.F. Pozharskii; Heterocycles in Life and Society, Chichester, Newyork, (pp. 301) 1997. TL Gilchrist, Heterocyclic Chemistry, John Wiley & Sons The Bath press, New york, 1985. A I Vogel, A text book of Quantitative Inorganic Analysis; John Wiley and Sons: New York, 1963. Naji A. Abod, M.AL-Askari and Bahjat A. Saed, Basrah Journal of Science (C), 30 (119-131) 2012. Jag Mohan, Organic Spectroscopy: Principles and Applications, CRC Press, 2004. Spectrochim Acta A Mol Biomol Spectrosc. 2011 Sep;79(5):1959-68. doi: 10.1016/j.saa.2011.05.100. Epub 2011 Jun 12. Chemistry of Heterocyclic Compounds, 10, Issue 4, pp (pp. 471-476) 1974. Moamen S Refat and Khaled M Elsabawy, Bull. Mater. Sci., 34 (pp. 873–881) 2011. Nakamoto K, Infrared and Raman spectra of inorganic and coordination compounds (New York: Wiley Interscience, 6th edn.), 2008. B.A. Stamboliyska et al. /Journal of Molecular Structure, 516 (pp. 237–245) 2000. Toyozo Uno, Katsunosuke Machida, Bulletin of the Chemical Society of Japan, 35 (2) (pp. 276-283) 1962. Brian C. Smith, Infrared Spectral Interpretation: A Systematic Approach (CRC Press LLC, Florida USA), (pp. 128) 1998. Kniseley, R. N., Fassel, V. A., Farquhar, E. L. and Gray, L. S., Spectrochimica Acta, 18, Issue 9 (pp. 1217-1230) 1962. Shigeru Yoshida, Pharmaceutical Society of Japan, 11 (pp. 628-638) 1963. MOAMEN S. REFAT and ROBSON F. DE FARIAS, J. Serb. Chem. Soc. 71 (12) (pp. 1289–1300) 2006. Henryk T. Flakus, Artur Miros, Peter G. Jones, Spectrochimica Acta Part A, 58 (pp. 225–237) 2002. http://www.sigmaaldrich.com/catalog/product/aldrich/m6204?lang=en&region=IN. V L Abramenko, V S Sergienko & A V Churakov, Russian J Coord Chem., 26(12) (pp. 866-871) 2000. B.G. Ward and F.E. Stafford, Inorg. Chem., 7 (pp. 2569) 1968. Bodo Heyn, Hoffmann and Regina, Z. Chem., 16 (pp. 407) 1976. Nuran Özçiçek Pekmez, Muzaffer Can, Attila Yildiza, Acta Chim. Slov., 54 (pp. 131–139) 2007. Katritzky, A. R. and Lagowski, J. M. H. 1984, Comprehensive Heterocyclic Chemistry, Katritzky, A. R. and Rees, C. W. Eds., Pergamon Press, Oxford, Volume 5, Chapter 4.01, 1. Xinjiao Wanga, Frank W. Heinemann, Mei Yangb, Berthold U. Melcherc, Melinda Feketec, Anja-Verena Mudringb, Peter Wasserscheidc, Karsten Meyer, Supplementary Material (ESI) for Chemical Communications. This journal is (c) The Royal Society of Chemistry, 2009. Editor: Teresa M. V. D. Pinho e Melo, Recent Research Developments in Heterocyclic Chemistry,: 397-475 ISBN: 81-308-01698, 2007. http://www.chemicalbook.com/SpectrumEN_109-76-2_1HNMR.htm. http://www.molbase.com/en/hnmr_moldata-2973.html?search_keyword=123-56-8. http://www.nmrdb.org/new_predictor/index.shtml?v=v2.9.10. Rupesh Narayana Prabhu, Rengan Ramesh, Electronic Supplementary Material (ESI) for RSC Advances, The Royal Society of Chemistry, 2012. Anwarul Hoque, Md. Arzu Miah, Md. Nurul Abser, Abul Khair Azad, Kamrun, Journal of Bangladesh Chemical Society, 25(1) (pp. 62-70) 2012. http://www.molbase.com/en/hnmr_96-53-7-moldata-630.html.

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Acknowledgements We are thankful to Department of Sophisticated Analytical Instruments Facility/Central Instrumentation Laboratory, Panjab University, Chandigarh (India) for providing us the facility for C, H, N, O analysis, UV-VIS spectra, FTIR spectra, Mass (LC-MS) spectra and 1H-NMR spectra to characterize samples synthesized by us. We are also thankful to Campus Director, Giani Zail Singh Punjab Technical University Campus Bathinda, Punjab, for providing us all infrastructural facilities and financial assistance out of TEQIP-II grant to execute this project.

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Experimental Investigation on Plain Circular and 2:1 Rectangular Jets at Low Speed Dr. A. Arokkiaswamy Professor, Department of Aeronautical Engineering, Dayananda Sagar College of Engineering Bangalore – 560 078, Karnataka, INDIA Abstract: The plane jet and circular jet are two fundamental classes of symmetrical jet. The symmetrical nature of the plane and circular jet has broad significance, for example, to reduce computational time in numerical modeling. These jets have received significant research attention both in experimental and numerical investigations. Exhaustive amount of research have been carried out in the past on these types of jets [1-3]. An experimental investigation was carried out to study the flow characteristics of jet issuing from a circular and an equivalent diameter 2:1 rectangular nozzle using two-component hotwire anemometry. Tests were conducted for a nominal jet exit velocity of 20ms-1 corresponding to a Reynolds number based on nozzle equivalent diameter of 5.02x104. Relative to the jet issuing from circular nozzle, the jet issuing from rectangular nozzle shows a significant reduction in potential core-length from 3.2De (for circular jet) to 2.13De (33% reduction). Plain circular jet does not switch it axis, while the 2:1 rectangular jet switches it axis at approximately X/De=4.0. Hence, the application of rectangular nozzle enhances the mixing with the ambient jet. Key Words: circular jet, rectangular jet, axis-switch, potential core length De ReDe u’/Ue u’v’/Ue 2 Ue U V X Y Z Y0.5 Z0.5 δmi δmj

= equivalent diameter of the rectangular nozzle, mm = Reynolds number based on equivalent diameter of the rectangular nozzle = non-dimensional streamwise velocity fluctuation = Reynolds shear stress = mean streamwise jet exit velocity, ms-1 = local mean streamwise jet velocity, ms-1 = local mean jet velocity along Y-direction, ms-1 = streamwise distance along the jet centerline, mm = cross-stream distance along minor-axis plane, mm = cross-stream distance along major-axis plane, mm = jet half-width growth along minor-axis plane, mm = jet half-width growth along major-axis plane, mm = jet exit shear-layer thickness along minor-axis plane, mm = jet exit shear-layer thickness along major-axis plane, mm

I. Introduction Mixing enhancement in jet flows is of paramount importance in many engineering applications and therefore, has been the subject of continuing research. Frequently the jet geometry is dictated by the nature of application since jet characteristics are known to be closely related to the dynamics of shear flow originating at the nozzle exit and hence, are strongly affected by the shape of the nozzle from which they issue. As a result one of the most commonly used methods of shear flow control in jets is the use of nozzles with non-circular exit cross-sections which significantly changes the jet flow development as compared to a jet issuing from a circular nozzle. Jets from non-circular nozzle geometries spread and mix faster thereby providing a unique capability to control the jet development (both fineand large-scale). In the case of a plain circular jet, the laminar flow from exit of the nozzle becomes unstable and breaks up into turbulence and forms a thin shear-layer. The instability of the thin shear-layer leads to roll-up of vortex and turbulence subsequently becomes a three-dimensional disorganized complex large scale coherent structure [4]. Plain

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rectangular jets, on the other hand, exhibit superior mixing characteristics relative to the circular jet. The vertices of the rectangular jet reduce the coherence of the large-scale vortical structures, thus weakening the self-induction process which promotes large spreading rates [5, 6]. The small-scale mixing is also more intense in the rectangular jet due to the effect of streamwise vorticity generated at the vertices [7, 8]. Rectangular nozzles are of particular interest because they offer passively improved mixing due to axis-switching and enhanced small-scale mixing near corner regions and farther downstream due to faster breakdown of vortex ring coherence and hence faster transition to turbulence. II. Experimental Set Up and Procedure A. Test Facility and Models Experiments were carried out to investigate the jet flow development from a 2:1 rectangular nozzle, with and without tabs, Figure 1 (a). The nozzle has a circular section of 203±0.1mm diameter, smoothly contoured to a rectangular section (2a=47.0±0.1mm and 2b=23.5±0.1mm) over a length of 300±0.1mm, where 2a and 2b are major-and minor-axis lengths, respectively. The equivalent diameter (De) of the rectangular nozzle is 37.5±0.1mm. The measurements are carried out at a nominal jet exit velocity (Ue) of 20±0.5 ms-1 and the Reynolds number based on the equivalent diameter of the jet (RDe) is 5.02±0.13x104. Figure 1: Schematic of (a) 2:1 rectangular nozzle, (b) Grid measurement plane (Y-Z) for overall jet, all dimensions are in mm

Further, the jet exit shear-layer thickness (U =0.99 Ue) along the minor-axis side, δmi, was about 1.25mm, along the major-axis side of the nozzle, δmj, was about 1.75mm, respectively. The corresponding displacement thickness along each nozzle axis is 0.381mm and 0.534mm, respectively. The jet flow longitudinal turbulence (u’/Ue) at the nozzle exit was about 0.3% at 20m/s. B. Instrumentation Hot-wire measurements were carried out at several axial locations in the Y-Z plane by means of a 99N10 DANTEC anemometry system using a Dantec 55P11 2-component probe. The probe has platinum plated tungsten wires (1.25mm long and 5μm diameter) and can be used for air applications with turbulent intensities up to 5-10. The Xwire was position in the flow in such a way that it allowed to measure the fluctuations of streamwise velocity (u’) and transverse velocity (v’). The positioning of the sensor was performed by using a PC controlled DANTEC 3dimensional precision traverse (Model # 41T33). The probe was calibrated using Dantec 9054H01 calibrator with 120mm2 nozzle in the velocity range between 0-25m/s. The signals from the probe were acquired at a sampling rate of 3kHz with 10000 samples. The anemometer analog output was acquired by using a differential mode National Instruments PCI-6036E having 16-Bit resolution, operating range of ±10V and maximum scan rate of 200Ks/samples. The linearization and processing of the hot wire signal was then carried out digitally. The actual streamwise velocity U and perpendicular velocity V were calculated from the hotwire anemometer output according to King’s law equation and equation procedure by Jorgenson(1971). The uncertainty in the jet exit velocity Ue and in the positioning of the hotwire X-probe is ±0.5ms-1 (2.5%) and ±0.5mm (2%), respectively. The projected dimension of the sensor elements to the oncoming flow is 0.8mm and the uncertainty in measurements with regards to the probe dimensions is approximately 2%. III. Results and Discussions A. Boundary Layer thickness Figure 2 shows the boundary-layer profile (U/Ue) along the Y-and Z-planes of the 2:1 rectangular plain nozzle. The thickness of the boundary layer is seen to be 0.048De and 0.075De which corresponds to the absolute value of 1.8mm on the major-axis side wall and about 2.8mm on the minor-axis side wall, respectively. The tab heights tested were well outside the boundary layer thickness. Therefore, the experimental data measured to investigate the

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effects of various cases considered in the present study were the true representation of variation of different parameters in this experimental work. Figure 2: Boundary layer velocity profile on the nozzle exit plane: major and minor axis side walls U/Ue 0.1

b_layer_major axis_side_wall b_layer_minor axis_side wall

Z/De, Y/De

0.08

0.06

0.04

0.02

0.2

0.4

0.6

0.8

1.2

U/Ue

B. Comparison of Centerline Velocity and Turbulence Intensity Figure 3 (a) shows a comparison of the centerline velocity decay (U/Ue) for a jet issuing from rectangular and circular nozzles. It may be noted that, relative to the jet issuing from circular nozzle, the jet issuing from rectangular nozzle shows a significant reduction in potential core length from 3.2De (for circular jet) to 2.13De (33% reduction). The above trends show enhancement of small-scale activity for jets with rectangular nozzle geometry. Figure 3: (a) Comparison of jet centerline velocity decay (U /Ue) , and (b) turbulence intensity (uâ&#x20AC;&#x2122;/Ue ) distribution

C. Comparison of Jet â&#x20AC;&#x201C; Half Width Growth Figure 4 (a) and (b) shows a comparison of the jet half-width growth for the cases of plain circular and rectangular jet, respectively. Relative to plain circular jet, the jet issuing from rectangular nozzle is seen to grow along its minoraxis plane while it shrinks along the major-axis plane. At approximately X/De=4.0, the jet half-width plots along the two planes cross each other indicating the axis-switching location of the jet, Fig. 4 (a)-(b). The axis-switching phenomenon observed in non-circular jets is responsible for the improved the mixing process. Figure 4: Half-width plots showing the variation in jet growth along Z- and Y-planes for the (a) plain circular and (b) 2:1 plain rectangular jet 3

2.5

Y0.5/De , Z0.5/De

3 Y0.5/De, Z0.5/De

2 1.5 1 0.5 0

(a) circular jet 0

Y0.5/De Z0.5/De

2.5 2 1.5 1

(b) 2:1_plain jet

0.5 0

X/De Page 36

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D. Overall Jet Development - Mean Velocity Distribution Contours Figure 5 (a) – (d) and Fig. 6 (a)-(d) show the contours of normalized streamwise mean velocity (U/Ue) for the circular and rectangular plain jets at four axial locations of X/De =0.5, 1.0, 3.0 and 8.0, respectively. Extensive grid measurements were made in one-half Y-Z plane of the jet with a step size of ΔZ= ΔY=1.0±0.1mm or 0.026De for X/De = 0.5, 1.0, 3.0 and 8.0 (2926, 3913, 6786, and 3740 data points, respectively). It may be observed that the jet issuing from a plain circular nozzle develops symmetrically along both Y- and Z- planes and the shear-layer thickness increases as mixing is initiated between the jet and ambient fluid. However for the jet issuing from a rectangular nozzle, the jet does not develop symmetrically along Y- and Z - planes. On the other hand, for the jet issuing from a rectangular nozzle, jet is seen to retain its original rectangular shape and shows a thin mixing-layer initially [9], as is evident from the closely spaced contours, Fig. 5 (a). As the mixing between the jet and the ambient mass is initiated and the shear-layer grows, the jet begins to gradually deform due to non- uniform induction of velocity [10], along the nozzle azimuth, while a outward bump in contours in the vicinity of the jet corners suggests the presence of corner vortices, Fig. 6 (b). Further downstream, the mixing layer begins to thicken and the jet cross section begins to change its shape as is indicated by a higher growth along minor-axis side (Fig. 6(a) - 6 (e)). The spacing between the contour levels is seen to increase with increase in downstream distance. Figure 5: Contours of mean velocity at different axial locations in Y-Z plane for a plain circular jet (a) X/De =0.5, (b) X/De =1.0, (c) X/De =3.0 and (d) X/De = 8.0

As a result, the jet is seen to undergo a three-dimensional deformation process associated with the azimuthal distortion and bending of the rectangular vortex ring wherein ambient mass is brought in towards the jet centerline along the major-axis side, and jet mass is thrown out along the minor-axis side. Figure 6: Contours of mean velocity at different axial locations in Y-Z plane for a plain2:1 rectangular jet (a) X/De =0.5, (b) X/De =2.0, (c) X/De =4.0 and (d) X/De = 6.0

IV. Conclusions In this section the experimental results of 2:1 aspect-ratio plain rectangular nozzle and plain circular nozzle of identical equivalent diameter of 37.5mm is presented and the following observations may be made from the investigation: Relative to the jet issuing from circular nozzle, the jet issuing from rectangular nozzle shows a significant reduction in potential core-length from 3.2De (for circular jet) to 2.13De (33% reduction). Comparison of the jet half-width

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growth of plain circular and rectangular jet, shows that relative to plain circular jet, the jet issuing from rectangular nozzle is seen to grow along its minor-axis plane while it shrinks along the major-axis plane. At approximately X/De=4.0, the jet half-width plots indicates that the two planes cross each other indicating the axis-switching location of the jet, Fig. 4.5 (a)-(b). The axis-switching phenomenon observed in non-circular jets is responsible for the improved the mixing process compared to circular jets. It may be observed that the jet issuing from a plain circular nozzle develops symmetrically along both the planes and the shear-layer thickness increases as mixing is initiated between the jet and ambient fluid. While the jet issuing from a rectangular nozzle, does not develop symmetrically along Y- and Z-planes and is seen to retain its original rectangular shape and shows a thin mixing-layer initially. The study reveals that rectangular nozzle significantly increases the mixing of jets. Acknowledgements The technical work reported here is carried out in the experimental Aerodynamics Division (EAD) at National Aerospace laboratories (CSIR), Bangalore. The author is thankful to Former Director, NAL , Dr. A.R. Upadhya, for granting permission to carry out the experiments as a part of his Ph.D work. The author also express his sincere thanks and gratitude to Dr. S.B. Verma, Principal Scientist, NAL for his technical guidance and to Mr. Sudhakar and Mr. Manisankar (scientists, EAD) for their assistance in conducting the experiments. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Bradshaw, P., “ Effect of external disturbances on the spreading rate of a plane turbulence jet”, J. Fluid. Mech. Vol. 80, (4), pp. 795 – 797, 1997. Browne, L.W.B., Antonia, R.A. and Chambers, A.J.,” The interaction region of a turbulence plane jet”, J. Fluid Mech. Vol.149, pp.355-373, 1984. Otugen, M.V., and Namer, I., “Velocity measurements in a Plane Turbulence Air Jet at Moderate Reynolds Number”, Expts in Fluids, Vol.6: pp. 387 – 399, 1988. Bradshaw, P., Ferriss, D.H., and Johnson, R.F.,“Turbulence in the noise-producing region of a circular jet.”, J. Fluid Mech., vol.19, pp.591, 1964. Sforza, P.M, Steiger, M.H, and Trentacoste, N. ,“ Studies on three dimensional viscous jets”, AIAA J. , Vol.4 , No.5, pp. 800 – 806, 1966. James H. Bell and Rabindra D. Mehta, “Measurement of Streamwise Vortical Structures in a Plane Mixing Layer”, J. Fluid Mech. Vol. 230, pp. 213 – 248, 1992. Sfeir, A.A. , “Investigation of three dimensional turbulence rectangular jets”, AIAA Journal , Vol.17(No.10):pp. 1055 – 1060, 1979. Hsia, Y., Krothapalli, A., Baganoff, D.,and Karamcheti, K.,“Effects of Mach Number on the Development of a Subsonic Rectangular Jet” , AIAA Journal, Vol. 21, No. 2, pp. 176-177, 1982. Krothapalli A., Baganoff D. and Karamcheti, “On the mixing of a rectangular jets”, J. Fluid. Mech. Vol.107, pp. 201 – 220, 1981. Pope, S.B., “Turbulence Flows”, Cambridge University Press, UK, 2002.

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

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Fingerprint Based Gender Classification for Biometric Security: A StateOf-The-Art Technique Shivanand. S. Gornale Department of Computer Science, School of Mathematics and Computing Sciences, , Rani Channamma University, Belagavi, Karnataka, INDIA. Abstract: Fingerprint Identification and Verification are certainly the most reliable and adequate evidence till date in the court of law. There are many biometric techniques like Fingerprints, Hand Geometry, Retina Scanning, Iris Scanning, Face Recognition, DNA fingerprint, Signature, Voice, Key Stroke Pattern, Gait (Body Dynamics),etc., that play a crucial role in identification and verification. The fingerprint biometric is one among most researched and used techniques for identification and verification. Comparatively a small number of machine vision techniques have been suggested for gender recognition and classification. So identifying the gender from fingerprints is an important step in forensic anthropology to shorten the list of suspects search. Very few researchers have worked on gender classification using fingerprints and have gained competitive results. This paper presents a comprehensive evaluation of lstate-of-the-art research techniques associated with gender classification using fingerprints and it is also proposed to combines the elaborate study of various methods and strategies with their comparative measures and to forecast results. This will help the researcher to undertake a comprehensive review and to carry out further research in fingerprint-based gender classification for biometric security. Key words: Gender classification, Minutia extraction, Image-based methods, Minutia based methods, feature classification. I. Introduction The science of fingerprint has generally been used for the identification or verification of a person for official documentation. There are many biometric techniques like Fingerprints, Hand Geometry, Retina Scanning, Iris Scanning, Face Recognition analysis, DNA fingerprint, Signature, Voice, Key Stroke Pattern, Gait (Body Dynamics),etc., A few of them are in the stage of research only (e.g., the odour analysis), but a significant number of techniques are already mature and commercially available. Fingerprint Identification and Verification is certainly the most reliable and adequate evidence till date in the court of law. Basically, the skin of human fingertips consists of ridges (this does not change over the time and is unique to a person) and valleys which constitute a distinctive pattern which is composed of many ridges and furrows. The fingerprint pattern, when analyzed at different scales, exhibits different types of features called Macro-Characteristic of Fingerprints and Micro-Characteristics of Fingerprints. The macro-characteristics are global features constituted by the ridge pattern and the singularity points. The ridge pattern characterizes the shape described by the ridge flow. The singularity points are localized in small regions where the ridge flow becomes irregular. Human fingerprint is comprised of a varied variety of ridge pattern, historically these are classified into (a) Arch (b) Tented Arch (c) Left Loop (d) Right Loop (e) Whorl. These are shown in figure-1. Core and delta points are shown for each class in this figure by circle and triangles, respectively.

Figure-1.1 (a) Arch (b) Tented Arch (c) Left Loop (d) Right Loop (e) Whorl.

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The micro-characteristics of fingerprints, or local features, are constituted by the discontinuities of the ridge lines, usually called minutiae points. Usually minutiae points are grouped into two types: the bifurcation and the termination of the ridge lines are shown in figure-2. The position and orientation of minutiae are claimed to be unique to each person. Therefore, they are the main features used in identification matching) process.

Figure-1.2: Minutiae points-Termination and Bifurcation The performance of a fingerprint feature extraction and matching algorithm depends upon the quality of the input fingerprint image. While the â&#x20AC;&#x2122;qualityâ&#x20AC;&#x2122; of a fingerprint image cannot be objectively measured, it roughly corresponds to the clarity of the ridge structure in the fingerprint image. Therefore, it is necessary to improve the clarity of the ridge structures of fingerprint images, maintain their integrity, avoid introduction of spurious structures or artefacts, and retain the connectivity of the ridges while maintaining separation between ridges. As the distance between minutiae is normalized by ridge frequency at each minutia, the distance variation by nonlinear deformation is minimized. The positions and ridge orientations of minutiae that are located in near region also are less affected by nonlinear deformations since they appear in some local areas and change gradually[24],[31]. Classification performance largely depends on the pre-processing steps where various ways to extract and represent distinguishable features among classes can be applied [44]. The main purpose of fingerprint classification is to facilitate the management of large fingerprint databases and to speed up the process of fingerprint matching. As the database of fingerprints increased, manual identification became tedious and automated methods became more widespread [49]. Gender classification from fingerprints is an important step in forensic anthropology in order to identify the gender of a criminal and shorten the list of suspects search. All the methods proposed in the literature are primarily based on the fingerprint ridges and have given insight about the ridge parameter mentioned about but fail to give accurate method of measuring the parameters. This may be due to the measurement made on the inked fingerprint impressions and manual measurements of the parameters where human error and recklessness is inevitable [19],[21],[36]. A. Classification of Algorithms Fingerprint based gender classification algorithms/methods can be classified into two categories: image-based and minutiae-based and follow the general steps involved in it. These are shown in figure-1.3

Figure-1.3: Shows the steps involved in the image-based and minutiae-based methods. A.1 Fingerprint acquisition: Image acquisition could be as simple as being specified in image that is already in digital form. Generally, the image acquisition steps include pre-processing, such as scaling. In digital image processing image augmentation is one of the easiest and most pleasing zones [17].

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A.2. Pre-processing After collecting fingerprint samples in different formats like .jpg, .png, bitmap, they are pre-processed for background elimination, resizing, cropping, converting colour image into binary image etc. For computer efficiency, the colour image is converted into binary image [17]. A.3. Feature Extraction Several representations have been used to assess fingerprint similarity. Fingerprint representations can be broadly categorized into two types: global and local [11],[42],[45].[50]. The Global approaches of automatic fingerprint matching have been proposed in the literature. The most popular ones are based on the minutiae pattern of the fingerprint and are collectively called minutiae-based approaches. Although rather different from one other, most of these methods require extensive pre-processing operations (such as orientation flow estimation, ridge segmentation, ridge thinning, and minutiae detection) in order to reliably extract the minutia features .Another class of fingerprint matching approaches does not use the minutiae features of the fingerprint. They either match directly the fingerprint images or match features extracted from the image by means of certain filtering or transform operations. The local representation consists of several components; each component typically derived from a spatially restricted region of the fingerprint. Typically, generic representations are used for fingerprint indexing and local representations are used for fingerprint matching. One of the significant global features used for fingerprints is its class or type. The overall fingerprint pattern is typically categorized into a small number of classes. Several fingerprint classification schema exist and as mentioned earlier, we will avoid delving into the details of the classification schema adopted by different automatic identification schemes. A simple fingerprint classification scheme categorizes the prints into following six major classes: whorl, right loop, left loop, arch, twin loop, and tented arch. Fingers can also be distinguished based on features such as ridge thickness, ridge separation, or ridge depths. Some examples of global representation include information about locations of critical points (e.g., core and delta) in a fingerprint. Core-delta ridge count feature, sometimes simply referred to as the ridge count, measures the number of ridges between core and delta points. All these features measure an overall property of a finger and we will refer to these similarities as global or generic features. Major representations of the local information in fingerprints are based on finger ridges, pores on the ridges, or salient features derived from the ridges [46]. The most widely used local features are based on minute details (minutiae) of the ridges which are shown in figure 1.4.

Figure-1.4: Local features of Fingerprint image A.4. Feature Matching Several approaches have been developed for automatic fingerprint classification. These approaches can be broadly put into four main categories:  Knowledge-based: This classification technique that uses the locations of singular points (core and delta) to classify fingerprints.  Structure-based: This classification technique uses the estimated orientation field in a fingerprint image to classify the fingerprints.  Frequency-based: This classification technique uses the frequency spectrum of the fingerprints for classification.  Syntactic: This classification technique uses a formal grammar to represent and classify fingerprints. II. Image-Based gender classification Methods Image-based methods include methods involving optical correlation and transform-based features [7],[15],[19],[21-23],[25],[30-32],[34],[37-39]. Very few researchers have worked on fingerprints for gender classification using these feature extractions and have obtained the competitive results. A. Related Work of Transform-Based Image Feature Extraction for Gender Classification Park et. al., (2004) have proposed a novel approach for fingerprint classification based on Discrete Fourier Transform and nonlinear discriminate analysis. The directional images are constructed from fingerprint images utilizing DFT. Applying directional filters in the frequency domain after the transformation by the DFT achieves effective low frequency filtering, reducing the noise effects in fingerprint images. Fast algorithm FFT for DFT speeds up the pre-processing to construct directional images. Once the transformation matrix by KDA/GSVD is computed, the classification in the reduced dimensional space saves computational complexities further. And they achieved satisfactory and competitive results [7].

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Uday Rajanna et. al., (2009) have proposed a comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion. The features were extracted like Gabor features, OM (Orientation Maps), MM (minutia Maps) and OC (orientation co-linearity). In MMs, the total rejection rate was 1.77% with 36 training samples and 35 testing samples rejected. Using OM the total rejection rate was 1.75% with 37 training images and 33 testing images rejected. Using OC the top-class accuracy was close to 77%, while the top-two classes accuracy was 93.7%. Using Gabor features the top-class accuracy was 83.86% while the top-two-classes accuracy was 96.1%. With processing time in sec for Gabor features 5.6, OM 0.03, MM 0.30 and OC 2.29 respectively [15]. Gnanaswami P, et. al., (2011) have proposed a method for gender identification using fingerprint through frequency domain analysis to estimate gender by analyzing fingerprints. The features were extracted from Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Power Spectral Density (PSD). A dataset of 400 persons of different age and gender is collected as internal database. Frequency domain calculations are compared with predetermined threshold and gender is determined. They obtained the results of 92.88 % and 94.85 % for male and female respectively.[19] Naveen Kumar Jain, et. al., (2012) have proposed a real time approach to determine the gender using fingerprint. The real time processing is done using the CODE composer Studio IDE. A dataset of 300 persons of different age and gender is collected as internal database. Initially the fingerprints of the subject were tested and after the manual analysis threshold is specified. Frequency domain calculations are compared with predetermined threshold and gender is determined. Of the samples tested, 138 samples were identified exactly out of 150 female samples, and 131 samples were identified exactly out of 150 male samples [25]. Shrikant Tiwari et. al.,(2012) have proposed a method for the recognition of the newborn using Fusion of Ear and Soft-Biometrics. A dataset of the newborn includes 2100 images from 210 subjects with 10 images per person. Features extracted Principal Component Analysis (PCA), Kernel Principal Component Analysis(KPCA),Fisher Linear Discriminate Analysis (FLDA) Independent Component Analysis(ICA), Geometrical Feature Extraction(GF) and HAAR. Identification accuracy of 90.72% was obtained [40]. Gnanaswami P, et al., (2012) have proposed a method for gender classification from fingerprint based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The feature set is obtained using DWT and SVD. This method is experimented with the internal database of 3570 fingerprints in which 1980 were male fingerprints and 1590 were female fingerprints. They obtained Finger-wise gender classification which is 94.32% for the left hand little fingers of female persons and 95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested is obtained as 91.67% and 84.69% for female persons [21]. Ritu Kaur et.al, (2012) have worked on fingerprint-based gender identification using frequency domain analysis. The feature set is extracted from Fast Fourier Transform, Discrete Cosine Transform and Power Spectral Density (PSD). A dataset of 220 persons of different age and gender is collected as internal database. Frequency domain calculations are compared with predetermined threshold and gender is determined. They obtained results of 90%, and 79.07% for female and male samples respectively [22]. Pallavi Chand et.al., (2013) have proposed a method for gender classification using DWT and SVD Techniques. The 2D-Discrete Wavelet Transformation (DWT) was used to find the frequency domain vector and Singular Value Decomposition (SVD) was implemented in order to find the spatial feature of the non-zero singular values. The K-nearest neighbour classifier is used to classify the fingerprint. The method is experimented with the internal database of 100 fingerprints of left hand index finger, 50 males and 50 females belonging to the same age group and achieved the success rate of classification that is more than 80% [32]. T Arulkumaran, et al., (2013) have proposed a method for fingerprint based age estimation which extracts the features through 2D Discrete Wavelet Transforms and Principal Component Analysis. A dataset of 400 fingerprints of the age of 12-60 was collected and the overall success rate of classification in age estimation was around 68% [27]. Rijo Jackson Tom, et al., (2013) have proposed a method for fingerprint based gender classification through frequency domain analysis to estimate gender by analyzing fingerprints using 2D Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA).A dataset of 400 persons of different age and gender is collected as internal database. Their overall success rate in gender classification is around 70% [30]. S. S Gornale et. al.,(2013) have proposed gender identification that is carried out using frequency and spatial domain by combined features using FFT, Eccentricity and Major Axis Length. A good quality internal database of 450 male and 550 female samples of left thumb impression of each sample were considered. An optimal threshold is chosen to achieve better results. The algorithm produces accurate classification of 80% of male and 78% of female [31]. Ravi Wadhwa et.al.,(2013) have proposed a gender classification based on age and gender determination from fingerprints using RVA and DCT coefficients. The age and gender finger prints are classified on the basis of ridge to valley area, entropy and RMS value of Discrete Cosine Transform (DCT) coefficients The novelty of

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the method lies in the fact that the identification of age and sex is independent of the pressure i.e. finger prints thickness or ridge/valley thickness. And they have achieved competitive results [34]. Pragya Bharti et.al.,(2014) have proposed a DWT-Neural Network based Gender Classification. The features were extracted using Discrete Wavelet Transform (DWT). Using 5-level Haar Wavelet Transform, the directional images of fingerprints are obtained. The Neural Networks are used to classify it and they achieved classification rate of 91.30% of an internal database of 300 images [37]. Samta Gupta et.al.,(2014) have proposed a method of fingerprint based gender classification using discrete wavelet transform & artificial neural network. The two methods are combined for gender classification. The first method is the wavelet transformation employed to extract fingerprint characteristics/features by doing decomposition up to 5 levels. The second method is the back propagation artificial neural network algorithm used for the process of gender identification. This method is experimented with the internal database of 550 fingerprints in which 275 were male fingerprints and 275 were female fingerprints. The overall classification rate of 91.45% has been achieved [38]. M Vadivel et.al.,(2014), have proposed a method of gender identification through fingerprint biometric using Discrete Curvelet Transform and back propagation with feed forward neural network classifier. These features were extracted from the fingerprint database utilized for learning stage of neural network. The trained network acts as automatic classifier to identify the gender from input image features. The Fast Discrete Curvelet Transform decomposes an image into different orientation wedges, details of which provide texture pattern and frequent occurrences of intensities. The orientation image represents an intrinsic property of the fingerprint images and defines invariant coordinates for ridges and valleys in a local neighbourhood. These patterns will be estimated using haralick or glcm features and can achieve better classification accuracy and low complexity in performance [39]. Image-based methods include methods involving optical correlation and transform-based features have been and still are significant in research field. Many methods have been proposed for each and every category and there are several approaches so for suggested in the literature for transform-based features. It was observed that some researchers have worked on gender classification by different approaches and predicted some promising results which are shown in Table-2.1. But still there is a scope for developing a robust algorithm using different parameters like age group, demographic characterization based on rural and urban people, and different robust features are required to be extracted for gender classification which will be more accurate and suitable for all types of applications to increase the classification rate. SL. NO 01

AUTHOR & YEAR Park et.al. (2004)

Uday Rajanna et.al (2009)

Gnanaswami P, et al., (2011)

Naveen Kumar Jain, et.al., (2012)

Shrikant Tiwari et al.,(2012)

Gnanaswami P, et al., (2012)

Ritu Kaur et.al, (2012)

Pallavi Chand et.al.,(2013)

T Arulkumaran, et. al., ( 2013)

METHOD Fingerprint classification based on Discrete Fourier Transform and nonlinear discriminate analysis. Comparative study of feature extraction for fingerprint classification and performance improvements using rank-level fusion Gender Identification Using Fingerprint through Frequency Domain Analysis A real time approach is proposed to determine the gender using finger-print. Recognition of the newborn using Fusion of Ear and SoftBiometrics. Gender Classification from Fingerprint based on discrete wavelet transform (DWT) and singular value decomposition (SVD). Fingerprint-based gender identification using frequency domain analysis. Gender Classification from Fingerprint based on discrete wavelet transform (DWT) and singular value decomposition (SVD). Fingerprint-Based Age Estimation extracted the features through 2D, DWT and PCA

FEATURES

RESULTS

FFT for DFT

Satisfactory and Competitive Results are obtained

Gabor Features OM (Orientation Maps), MM (minutia Maps) and OC (orientation co-linearity)

MMs-Rejection rate 1.77%. OM-Rejection rate 1.75%, OC -77%, and 93.7%. Using Gabor features 83.86% and 96.1%. 92.88 % and 94.85 % for male and female respectively. 92.00% and 87.33% for male and Female respectively, Identification accuracy rate of 90.72%

Frequency domain transform (FFT), (DCT) and (PSD) The real time processing is done using the CODE composer Studio IDE. PCA, KPCA, FLDA ICA, GF and HAAR. Discrete Wavelet Transform and Singular Value Decomposition

FFT, DCT and Power Spectral Density (PSD). Discrete Wavelet Transform and Singular Value Decomposition

Discrete Wavelet Transform and Principal Component Analysis

A 94.32% for the left hand for female and 95.46% male. For any finger of male 91.67% and 84.69% for female 90%, and 79.07% for female and male samples respectively K-nearest neighbour classifier is used to classify it and achieved the success rate of 80%. The overall success rate 68.00%

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Rijo Jackson Tom, et al., (2013)

S. S Gornale et. al.,(2013)

Ravi Wadhwa et.al.,(2013)

Pragya Bharti et.al.,(2014)

Samta Gupta et.al.,(2014)

M.Vadivel et.al.,(2014),

Fingerprint Based Gender Classification through frequency domain analysis to estimate gender Gender identification is carried out by combined features using frequency and Spatial domain techniques Gender classification based on age and gender of a person from fingerprint impression. DWT-Neural Network based Gender Classification

Wavelet Transforms (DWT) and Principal Component Analysis (PCA)

The overall success rate in gender classification is around 70.00%

FFT, Eccentricity and Major Axis Length

The overall success rate in gender classification is around 80% of male and 78% of female Satisfactory and Competitive Results are obtained

Fingerprint Based Gender Classification Using Discrete Wavelet Transform & Artificial Neural Network gender classification through fingerprint biometric using discrete curvelet transform and back propagation

wavelet for to 5 levels and artificial neural network

Ridge to valley area, entropy and RMS value DCT coefficients DWT using 5-level Haar Wavelet Transform

The curvelet transform wedges and frequent occurrences of intensities.

Neural Networks are used to classify it. A Success rate of 91.3%. Overall classification rate of 91.45% has been achieved.

Satisfactory and Competitive Results are obtained

Table-2.1: Comparative Results of transform-based image feature extraction for gender classification III. Minutia-Based gender classification Methods The minutia- based Other aspects of fingerprint identification are orientation, segmentation and core point detection [5],[9],[24],[36].The most common ridge characteristics of fingerprint image are as show in figure-3.1.

Figure-3.1: Common ridge characteristics The gender classification using minutia extraction is still significant in research field. Very few researchers have worked on fingerprints for gender classification using minutia extractions and have achieved competitive results [1-2],[8-10],[12-14],[18],[26][33]. The minutia like intersecting points, number of blobs, ridge counts and terminating points etc., were extracted and classified accordingly. A. Related Work of Minutia-Based Image Feature Extraction for Gender Classification Angela Bell et.al., have proposed a method in contrast to Acreeâ&#x20AC;&#x2122;s M(1991) method of comparing ridge densities. This work compares fingerprint loop ridge counts from data set of 40 male and 40 female fingerprints. The analysis revealed no significant mean difference in the loop ridge counts across gender represented by these eighty fingerprints, F (1, 78) =.308, p>.05, MSE= 7.946. There is no difference in the number of loop ridge counts that males have (13.18, SD = 2.735) and females have (13.53, SD = 2.900). The work concluded that there were no significant differences in loop ridge counts between male and female [1],[2]. Ahmed Badawi, et. al., (2006) have proposed a method for Gender classification from fingerprints. A dataset of 10-fingerprint images for 2200 persons of different ages and gender (1100 males and 1100 females) was analyzed. Features like ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, and ridge count asymmetry, and pattern type concordance were extracted. Fuzzy- C Means (FCM), Linear Discriminate Analysis (LDA) and Neural Network with the most dominant features were used for the classification along with the most dominant features. They obtained results of 80.39%, 86.5%, and 88.5% using FCM, LDA, and NN, respectively [8]. Shimon K. Modi et. al., (2007) worked on Impact of Age groups on Fingerprint Recognition Performance. Features were extracted from fingerprints of different quality levels, for minutiae count, to test the performance of a minutiae-based matcher. A dataset of 18-25years, 26-39 years, 40-62 years and 62 years and above, in all 1620 samples was collected. The results confirm a difference in fingerprint image quality across age groups. The statistical result produced through the work indicates that the fingerprint image quality is not similar between age groups because the quality score was not within a reasonable tolerance to be similar [9].

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Sudesh Gungadin et. al., (2007) carried out a research work whose goal is to set up a correlation among sex and fingerprint ridge density. The fingerprints were collected from 500 persons (250 males and 250 females) within the age group of 18-60 years. The parameter used by them is ridge density. By counting the ridges in the upper segment of all ten fingers and calculating the mean value, they illustrated that a fingerprint ridge of < 13 ridges/25 mm2 is probably of male origin and that of > 14 ridges/25 mm2 is perhaps of female origin. It has been a great achievement to sustain the hypothesis that women are likely to comprise a statistically imperative greater ridge density than men [10]. Manish Verma, et. al., (2008) proposed a method for gender classification from fingerprints. Features like ridge width, ridge thickness to valley thickness ratio (RTVTR), and ridge density were extracted. This method is experimented with the internal database of 400 fingerprints in which 200 were male fingerprints and 200 were female fingerprints Support Vector Machine (SVM) was used for the classification and 91% correct classification for male and female classes was achieved [12]. Jen feng wang, et al., (2008) worked on gender determination using fingertip features. The fingerprints from 115 normal healthy adults of which 57 were male and 58 were female have been considered for research work. They used ridge count, ridge density, and finger size features for classification. However, the ridge count and finger size features of left little fingers were also used to achieve the classification. The best classification result of 86% accuracy is obtained by using ridge count and finger size feature together [13]. Nithin et. al., (2011) have proposed a study using Bayeâ&#x20AC;&#x2122;s theorem on the rolled fingerprint images belonging to south Indian population. The objective is to identify the gender depending upon the finger ridge count contained by a definite region. A set of database of 550 persons (275 men and 275 women) belonged to the age range of 18-65 years. The experimental results illustrated that women have an appreciably greater number of ridge count than men. Furthermore, it is established that fingerprint having ridge density < 13 ridges/25 mm2 is almost certainly of male origin and ridge count > 14 ridges / 25 mm2 are probably of female [14]. Ramanjit and Rakesh (2011) have carried out a research work and revealed that there is a considerable variation in epidermal ridge density among males and females. The research was conducted to scrutinize ridge density variations in two North Indian populations (Sikh Jat and Bania). The experimental results revealed that 92% of Sikh Jat females comprise a mean ridge density above 13, while 76% of Sikh Jat males comprise a mean ridge density below 13, while in Bania, 100% of females possess mean ridge density above 14 and 80% of males â&#x20AC;&#x201C; below 14. Hence it was concluded that there are considerable variations in epidermal ridge density amongst males and females within each of the two populations, and also considerable variations amongst the two populations [18]. S. Sudha Ponnarasi et. al., (2012) have proposed gender classification system derived from fingerprint minutiae extraction. The study was carried out over 500 people (250 male & 250 female) belonging to various age groups between 1 to 90. Features extracted were; ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, and ridge count asymmetry, and pattern type concordance. SVM was used for the classification using the most dominant features and competitive results were achieved [24]. E.O. Omidiora et.al.,(2012) have analyzed human fingerprint texture in order to determine their Age and Gender, correlation of RTVTR, and Ridge Count on gender detection. The Features extracted were like ridge count, standard deviation, mean etc. The results were obtained for age and gender, ridge thickness to valley thickness ratio and ridge count for trained fingerprints samples and they observed that females have a higher ridge thickness to valley thickness ratio compared with males. Males have a slightly higher ridge count compared with females. There is no particular relationship between the age of the subjects and their fingerprint pattern, as it does not change (only as a result of accident or mutation)[26] Ravi Wadhwa et.al.,(2013) have proposed a gender classification method based on age and gender of a person from fingerprint impression. The novelty of the method lies in the fact that the identification of age and sex is independent of the pressure i.e. fingerprints thickness or ridge/valley thickness. The age and gender based finger prints are classified on the basis of ridge to valley area, entropy and RMS value of Discrete Cosine Transform coefficients. Whereby and achieved the competitive results can be achieved [34]. Lidong Wang et al. (2014) have proposed a testing and frequency distribution analysis of African American fingerprint patterns (loop, whorl, and arch). It was shown that loops are the most common, whorls are the second most common, and arches are the least common with a very small percentage (4.33%). Most loops are ulnar loops while only 4.47% loops are radial loops. Of the total arches, 61.54% are plain arches and 38.46% are tented arches. A comparative study of gender difference in African American fingerprint patterns was conducted using a non-parametric method based on the U test. The U test results show that there is no significant gender difference in fingerprint patterns between African American males and females at the 0.05 level of significance [35]. From the above literature it is evident that the dilemma regarding minutia extraction and gender classification has been and still is a significant research field. Many methods have been proposed for each and every category and there are several approaches so far suggested in the literature for minutia extraction. Conventional methods have some shortcomings as discussed earlier. It was observed that a few researchers have worked on gender

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classification by different approaches and predicted some promising results but still there is a scope for developing a robust algorithm using different parameters like age group demographic characterization based on rural, urban people. At the same time different robust features are required to be extracted for gender classification which will achieve more accurate and suitable results for all types of applications to increase the classification rate [5][21][31][33]. SL.NO 01

AUTHOR & YEAR Angela Bell et.al., & Acree’s M (1991) Ahmed Badawi, et. al., (2006)

METHOD

FEATURES

Method of comparing ridge densities.

Loop ridge count, density

Concluded that no differences in loop ridge counts between male and female

Gender classification from fingerprints.

Obtained results of 80.39%, 86.5%, and 88.5% using FCM, LDA, and NN, classifier respectively Fingerprint image quality is not similar between age groups because the quality scores were not within a reasonable tolerance to be similar women are likely to comprise a statistically imperative greater ridge density than men 91% of correct classification for male and female using SVM classifier 86.00% accuracy is obtained by using ridge count and finger size feature together. Women have an appreciably greater number of ridge counts than men.

Shimon Modi et. (2007)

K. al.,

Impact of Age groups on Fingerprint Recognition Performance.

Ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, and ridge count asymmetry, and pattern type Different quality levels, minutiae count, and performance of a minutiae-based matcher.

Sudesh Gungadin et. al., (2007) Manish Verma, et. al., (2008)

Finding correlation among sex and fingerprint ridge density. Method for gender classification from fingerprints. Gender determination using fingertip features.

Ridge density. By counting the ridges in the upper segment of all ten fingers and mean value, Ridge width, ridge thickness to valley thickness ratio (RTVTR), and ridge density Ridge count, ridge density, and finger size

Study using Baye’s theorem on the rolled fingerprint images belonging to south Indian population. Proposed a research variation in epidermal ridge density among males and females

Finger ridge count contained by a definite region

Ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, and ridge count asymmetry, and pattern type concordance. Ridge count, standard deviation, mean etc.

Jen feng wang, et al., (2008)

Nithin et. al., (2011)

Ramanjit Rakesh al.,(2011)

S. Sudha Ponnarasi et. al., (2012)

Gender Classification System Derived from Fingerprint Minutiae Extraction.

E.O. Omidiora et.al.,(2012)

Determination of the Age and Gender, and correlation of RTVTR and Ridge Count on gender detection.

Ravi Wadhwa et.al.,(2013)

Gender classification based on age and gender of a person from fingerprint impression.

Lidong Wang et al. (2014)

Testing and frequency distribution analysis of African American fingerprint patterns

and et.

RESULTS

Mean value and Ridge density

Fingerprints thickness or ridge /valley thickness, ridge to valley area, entropy and RMS value of DCT coefficients loop, whorl, and arch

Considerable variations in epidermal ridge density amongst males and females within and amongst the two population Support Vector Machines (SVM) wer used for the classification using the most dominant features and achieved competitive results Males have a slightly higher ridge count compared with females, there is no particular relationship between the age of subjects and their fingerprint pattern, Achieved a satisfactory and the competitive results were predicted 4.47% loops are radial loops. 61.54% arches are plain arches and 38.46% arches are tented arches.

Table-3.1: Comparative Results of minutia-based image feature extraction for gender classification. IV. Fusion of fingerprint with soft biometrics. Basically, biometric techniques are classified into soft biometrics and hard biometrics.[16] Soft Biometric traits are physical, behavioural or adhered human characteristics providing categorical information about people such as age, beard, gender, glasses, ethnicity, eye/hair /skin colour, length of arms and legs, height, weight, gait and gestures, accent, ear shape, etc. soft biometric traits instances are created in a natural way and are used by humans to distinguish their peers. Soft Biometrics inherits a main part of the advantages of Biometrics and furthermore endorses its own assets. Some of the advantages include non obtrusiveness, the computational and time efficiency and human compliance. Furthermore they require neither enrolment, nor the consent nor the cooperation of the observed subject. Hard biometrics, which include face, fingerprint, retina, iris, voice etc., are generally unique and permanent personal characteristics, soft biometrics provide some vague physical or behavioural information which is not necessarily permanent or distinctive. Such soft biometric traits are usually

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easier to capture from a distance and often do not require cooperation from the subjects. Whilst they cannot provide robust authentication, they can be used for improving the verification performance of biometric recognition systems [16],[29],[41]. There are very few researchers who have fused the fingerprint with soft biometrics for identification and classification for gender. Again, these methods are also based on image-based and minutiae-based feature extraction. As per the study of earlier work no algorithm finds the perfect gender classification. [4],[6],[20],[28]. V. Current Issues and Challenges Many methods are used for identification and classification of gender of fingerprints based on image-based and minutiae-based feature extraction. As per the study of earlier work no algorithm finds the perfect gender classification. There are numerous challenging problems in fingerprint recognition and classification system; they are:  The tip of the finger is a small area from which taking measurements and ridge pattern can be affected by cuts, dirt, or even wear and tear. Acquiring high-quality images of distinctive fingerprint ridges and minutiae is a complicated task.  People with no or few minutia points (surgeons, as they often wash their hands with strong detergents, builders, and people with special skin conditions) cannot enrol or use the system. The number of minutia points can be a limiting factor for security of the algorithm. Results can also be confused by false minutia points (areas of obfuscation that appear due to low-quality enrolment, imaging, or fingerprint ridge detail).  Another open issue is the lack of robustness against image quality degradation. The performance of a fingerprint recognition system is heavily affected by fingerprint image quality. Several factors determine the quality of a fingerprint image: skin conditions (e.g., dryness, wetness, dirtiness, temporary or permanent cuts and bruises) sensor conditions (e.g., dirtiness, noise, size), user cooperation, etc. Poor quality images result in spurious and missed features, thus degrading the performance of the overall system. Therefore, it is very important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images [47],[48]  Fingerprint technology has been under development from nearly half a century, new challenging issues are yet to be deployed in it. These include Extraction of level 3 features (all dimensional attributes of the ridge such as ridge path deviation, width, shape, pores, edge contour, and other details, including incipient ridges, creases, and scars), aliveness detection, and automated latent fingerprint identification. Therefore, it is very important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images [3],[6][16][29],[43]. VI. Conclusion and Future Challenges: This paper presents a comprehensive evaluation of state-of-the-art research techniques associated with gender classification using fingerprints. In this paper it is proposed to combine the elaborate study of various methods and strategies with their comparatives measures to forecast results. Following are the conclusions which were made from the literature.  The Gender classification methods broadly classified into Image-based methods include methods involving optical correlation and transform-based features.  Image-based methods include methods involving optical correlation and transform-based features has been still is significant research filed. Many methods have been proposed for each and every category and there are several approaches so for suggested in the literature for transform-based features.  The dilemma regarding minutia extraction, gender classification has been and still is a significant research filed. Many methods have been proposed for each and every category and there are several approaches so for suggested in the literature for minutia extraction.  Conventional methods have some shortcomings as discussed earlier. It was observed that some researchers have worked on gender classification by different approaches and predicted some promising results but still there is a scope for developing a robust algorithm using different parameters like age group demographic characterization based on rural, urban people but different robust features are required to be extracted for gender classification which will bring greater accuracy and will be suitable for all types of applications to increase the classification rate. In future, many problems and open challenges will be posed for forthcoming young technocrats, they are;  Robust features will be investigated for gender estimation from large scale fingerprint image data sets and compared with existing estimation techniques.  Minutiae extraction finds diplomat features; therefore a strong as well as computationally efficient minutiae extraction algorithm should be developed.  Building a hybrid biometric system for gender classification that uses the face and fingerprint as the primary characteristics and ethnicity, height, skin colour, hair colour etc., and other parameters as the soft biometric parameters.

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

The Classification rate may be increased by extracting the features using wavelets, wavelet packets and multi-wavelets and also by using some standard classifiers like SVM, K-NN, Neural Networks, Fuzzy Sets, etc.,  As there is no standard database for male and female fingerprints, a separate database may be created using some standard technical and non technical specifications.  The efforts along with the capability to automatically extract behavioural traits may be necessary for developing surveillance and many large scale identification applications. Hence, an attempt has been made to put just another small brick into the wall of research on gender classification using fingerprints. So it is an important step in forensic anthropology to shorten the list of suspects search. References [1]. [2]. [3]. [4].

[5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15].

[16]. [17]. [18]. [19]. [20].

[21]. [22]. [23].

[24].

[25]. [26].

[27].

[28].

Angela Bell, “Loop ridge count differences between genders”. Nebraska Wesleyan University ( http://www.neiai.org/) Acree.M, “Is there a gender difference in fingerprint ridge density?” Forensic Science International, Vol. 102, No.1, 1999, PP: 35-44. Jain A. K. Maltoni D. Maio D. and Prabhakar S, “Handbook of Fingerprint Recognition. Springer, New York-2003. Ross, A. Jain, “Information fusion in biometrics”, Pattern Recognition Letters 24 (2003) 2115–21252003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0167-8655(03)00079-5, Pattern Recognition Letters 24 (2003) 2115–2125 www.elsevier.com/locate/pattern recognition, vol. 24, no. 13, pp. 2115-2125, 2003. Anil K. Jain, Karthik Nandakumar, Xiaoguang Lu,and Unsang park, “Integrating Faces, Fingerprints, and Soft Biometric Traits for user Recognition.” Proceedings of Biometric Authentication Workshop, LNCS 3087, PP.259-269, PRAGUE,-MAY 2004. Anil K Jain et.al., “Biometric: A Grand Challenge”, Proceedings of International Conference on Pattern Recognition, Cambridge, U K, August-2004. Cheong Hee Park and Haesun Park, "Fingerprint Classification Using Fast Fourier Transform and Nonlinear Discriminate Analysis", National Science Foundation Grants University of Minnesota Minneapolis, MN 55455, USA.-2004. Ahmed Badawi, Mohamed Mahfouz, Rimon Tadross, Richard Jantz, “Fingerprint-Based Gender Classification.” Proceedings of the International conference on Image Processing Computer Vision and Pattern Recognition (IPCV’06), June-2006, PP: 41-46. Shimon K Modi, Stephen J Elliott, Jeff, “Impact of Age Groups on Finger printing Recognition Performance”,1-4244 -13002/o7/ 2007 IEEE. Sudesh Gundadin et.al., “Sex Determination from Fingerprint Ridge Density”, Internet Journal of Medical Update 2007 JulDec;2(2):4-7. Vikas Humbe, S S Gornale , K V Kale, R R Manza’, “Mathematical Morphology Approach for Genuine Fingerprint Feature Extraction”, International Journal of Computer Science and Security, ISSN: 1985-1533 Volume No. 1 issue 2 PP: 53-59-2007. Manish Verma and Suneeta Agarwal, “Fingerprint Based Male-Female Classification’’ in Proceedings of the international workshop on computational intelligence in security for information systems ,Genoa, Italy, 2008, PP:251-257 Jen Feng Wang, et al, “Gender Determination using Fingertip Features”, Internet Journal of Medical Update 2008 JulDec;3(2):22-8. Nithin M. D. et.al., “Study of fingerprint classification and their gender distribution among South Indian population”, Journal Forensic Leg Medicine. 2009 Nov;16(8):460-3. doi: 10.1016/j.jflm.2009.07.001. Epub 2009 Aug 6. Uday Rajanna Ali Erol Æ George Bebis, “A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion”, Pattern Analysis Applications, DOI 10.1007/s10044-009-0160-3, _ Springer-Verlag London Limited 2009. Anil K Jain et.al, “Biometrics of Next Generation: an overview” Springer, 2010 (http://biometrics.cse.msu.edu/Publications/GeneralBiometrics/JainKumarNextGenBiometrics_BookChap10.pdf) R. C. Gonzlanz and R C Woods, “Digital Image Processing”, Pearson Education, 2010 Ramanjit Kaur, Rakesh K. Garg, “Determination Of Gender Differences From Fingerprint Ridge Density In Two Northern Indian Populations” Problems of Forensic Sciences, 2011, Vol. LXXXV, 5 – 10. Gnanasivam .P, and Dr. Muttan S, “Gender Identification Using Fingerprint through Frequency Domain analysis”, European Journal of Scientific Research ISSN 1450-216X Vol.59 No.2-2011. Min-Gu Kim, Hae-MinMoon, Yongwha Chung, and Sung BumPan, “A Survey and Proposed Framework on the Soft Biometrics Technique for Human Identification in Intelligent Video Surveillance System”, Journal of Biomedicine and Biotechnology, Volume 2012, Article ID 614146, 7 pages, doi:10.1155/2012/614146. Gnanasivam .P, and Dr. Muttan S, “Fingerprint Gender Classification Using Wavelet Transform and Singular Value Decomposition”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012 Ritu Kaur and Susmita Ghosh Mazumdar, “Fingerprint Based Gender Identification using Frequency Domain Analysis”, International Journal of Advances in Engineering & Technology, March 2012.©IJAET ISSN: 2231-1963. Ritu Kaur and Susmita Ghosh Mazumdar, Mr. Devanand Bhonsle, “A Study On Various Methods of Gender Identification Based on Fingerprints”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2,Issue 4, April 2012 S.Sudha Ponnarasi, M.Rajaram, “ Gender Classification System Derived from Fingerprint Minutiae Extraction”, International Conference on Recent Trends in Computational Methods, Communication and Controls (ICON3C 2012) Proceedings published in International Journal of Computer Applications® (IJCA). Naveen Kumar Jain, Sunil Sharma, Anurag Paliwal., A Real Time Approach To Determine The Gender Using Fingerprints”, IJAIR ISSN: 2278-7844, PP:229-233, 2012. E.O. Omidiora, O. Ojo, N.A. Yekini, T.O. Tubi, “Analysis, Design and Implementation of Human Fingerprint Patterns System “Towards Age & Gender Determination, Ridge Thickness To Valley Thickness Ratio (RTVTR) & Ridge Count On Gender Detection”, International Journal of Advanced Research in Artificial Intelligence, PP:57-63,Vol. 1, No. 2, 2012( www.ijarai.thesai.org) T. Arulkumaran, Dr.P.E.Sankaranarayanan, Dr.G.Sundari, “Fingerprint Based Age Estimation Using 2D Discrete Wavelet Transforms and Principal Component Analysis”, International Journal of advanced research in Electrical and Instrumentation Engineering, Vol.2. Issue 3, March 2013. Seema Verma, Sonu Agrawal, “A Study on “A Soft Biometric Approach: Face Recognition””International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 3, March 2013 ISSN: 2277 128X

Page 48

Shivanand. S. Gornale , American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 39-49 [29]. [30]. [31].

[32].

[33]. [34].

[35]. [36]. [37]. [38].

[39]. [40]. [41].

[42]. [43]. [44].

[45]. [46]. [47]. [48]. [49]. [50].

Ujwala T Tayade et.al., “A Survey on Soft biometrics” International Journal of Innovative and Applied Research (IJIAR) , Sept, 2013,Vol.2. Issue 8: ISSN 2278-7844, PP: 460-465, 2013. Rijo Jackson Tom, T. Arulkumaran , “Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis”, International Journal of Engineering Trends and Technology, Volume 4 Issue 2,2013 S. S. Gornale, Geetha D, Kruthi R “Analysis of fingerprint image for gender classification using spatial and frequency domain analysis”, American International Journal of Research in Science, Technology, Engineering and Mathematics”, ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629, PP: 46-50, 2013 Pallavi Chand, Shubhendu Kumar Sarangi, “A Novel Method for Gender Classification Using DWT and SVD Techniques”, International Journal of Computer Technology & Applications, Vol 4 (3),445-449, May-June 2013 Available online@www.ijcta.com. Sajid Alikhan, Maqsood Ahmad, Muhamamud Nazir and Naveed Riaz, “A comparative Analysis of Gender classification Techniques”, International Journal of Bio-science and Bio-technology,Vol.5, No.4, August, 2013. Ravi Wadhwa, Maninder Kaur, Dr. K.V.P. Singh, “Age and Gender Determination from Finger Prints using RVA and DCT Coefficients”, IOSR Journal of Engineering (IOSRJEN) e-ISSN: 2250-3021, p-ISSN: 2278-8719 Vol. 3, Issue 8 (August. 2013), PP: 05-09 (www.iosrjen.org) Lidong Wang Cheryl Ann Alexander, “Fingerprint Patterns and the Analysis of Gender Differences in the Patterns Based on the U Test”, International Transaction of Electrical and Computer Engineers System, 2014, Vol. 2, No. 3, 88-92. Heena Agrawal1 Siddhartha Choubey, “A Short Assessment on Male-Female Categorization Derived From Fingerprints”, IJSRD - International Journal for Scientific Research & Development, Vol. 2, Issue 03, 2014 ISSN (online): 2321-0613 Pragya Bharti and Dr. C. S. Lamba “DWT-Neural Network based Gender Classification”, International Journal of Digital Application & Contemporary Research, ISSN: 2319-4863 Volume 2, Issue 8, March 2014. Samta Gupta, A. Prabhakar Rao, “Fingerprint Based Gender Classification Using Discrete Wavelet Transform & Artificial Neural Network”, International Journal of Computer Science and Mobile Computing, ISSN 2320–088X Vol. 3, Issue. 4, April 2014, pg.1289 – 1296 M.Vadivel, T.Arulkumaran, “Gender Identification from Finger Print Images Based on a Supervised Learning Approach”, IPASJ International Journal of Computer Science, ISSN 2321-5992 ,Volume 2, Issue 7, PP:24-28,July 2014. Shrikant Tiwari, Aruni Singh, Sanjay Kumar Singh. “Fusion of Ear and Soft-Biometrics for Recognition fo Newborn”. Signal & image processing: an International Journal (SIPIJ) vol.3 No.3, June 2012. S. S. Gornale, Kruthi R, “Fusion of Fingerprint and Age biometric for gender classification using Frequency and Texture Analysis”, Signal & Image processing: an International Journal (SIPIJ), ISSN 0976-710X(Online);2229-3922(Print) Vol.5,No.6, PP:75-85,DOI:10:512/sipij.2014.5606 Mihran Tuceryan and Anil k Jain, “Texture Analysis”, The Handbook of Pattern Recognition and Computer Vision”,(2nd Edition) PP:207-248, World Scientific Publishing co.1998. Anil K Jain et.al., “Pores and Ridges: Fingerprint Matching using Level 3 features”, Michigan State University Jin Fei Lim,Reena Ka Yin Chin, “Enhancing Fingerprint Recognition using Minutiae-based and Image-based Matching Techniques”, First International Conference on Artificial Intelligence, Modelling and Simulation, 978-1-799-3231, PP:261-266 @ IEEE 2013. Anil K Jain, Arun Ross, Salil Prabhakar, “Fingerprint Matching using Minutiae and Texture Features”, International Conference on Image Processing(ICIP), PP:282-285, Thessaloniki, Greece, October 7-10, 2001. Nalini K Ratha et.al., “Adaptive Flow Orientation- based feature extraction in Fingerprint Images”, Pattern Recognition, Vol.28,No.11, PP:1657-1672,1995 Elsevier Science Ltd. Kevin O Connor, Stephen J Elliott, “The Impact of gender on image quality, Henry classification and Performance on Fingerprint Recognition System”, 7th International conference on Information Technology and Application”, PP:304-307,2011. Marasco, E Lugini L, Cucki B, “Exploiting quality and texture features to estimate age and gender through Fingerprint Images”, International Proceedings of SPIE 9075-2014. Ajita Rattan, Cunjain Chen, Arun Ross, “Evolution of Texture Descriptors for automated gender Estimation from Fingerprints”, Michigan University, USA. Jin Fei Lim, Renee Ka Yin Chin, “Enhancing Fingerprint Recognition Using Minutiae-Based and Image-Based Matching Techniques”, First International Conference on Artificial Intelligence, Modelling & Simulation, 978-1-4799-3251-1/13 $31.00 © 2013 IEEE DOI 10.1109/AIMS.2013.48, PP: No,261-266

About Author Dr. Shivanand S Gornale has completed M. Sc. in Computer Science. M.Phil. in Computer Science., Ph.D. in Computer Science from University of Pune, in 2009 under the guidance of Dr. K V Kale, Professor and BCUD Director, Dr. B. A. M. University, Aurangabad and has been recognized as research guide for Ph.D. in Computer Science and Engineering from Rani Channamma University, Belagavi and Jain University Bangalore. He has published more than 60 research papers in various National and International Journals and conferences. He is a Fellow of IETE New Delhi, Life Member of CSI, Life Member of Indian Unit of Pattern Recognition and Artificial Intelligence (IPRA), Member of Indian Association for Research in Computer Science (IARCS), Tata Institute of Fundamental Research (TIFR), Mumbai, Member of International Association of Computer Science and Information Technology (IACS&IT) Singapore, Member of International Association for Engineers’, Hong Kong, Member of Computer Science Teachers’ Association, USA and Graduate Member of IEEE, Life Member of Indian Science Congress Association, Kolkata-India. Presently he is working as Associate Professor, Department of Computer Science, Rani Channamma University, Belagavi - Karnataka. His research area of interest is Digital Image Processing, Pattern Recognition and Biometric analysis.

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

Energy Consumption in Wireless Network Control System Data Transmission K.Sanakkiyan1, R.Kannan2 Department of Electrical and Electronics Engineering 2 Head of the department Electrical and Electronics Engineering 1,2 Nehru Institute of Engineering and Technology, Tamilnadu, INDIA. 1

Abstract: The wireless sensor network control system communication protocol brings the additional challenge of providing stability of the closed loop control system compare to traditional wireless communication system; in this paper we give a optimization algorithm for increase the life time of battery using energy consumption in WNCS, the objective of the optimization is consume the energy in the battery connected wireless node data transmission, the constraints of problem is reduce the maximum transmit power, The formulation comprises communication system parameters including transmission power, rate and scheduling, and control system parameters including sampling period. the result of the optimization WNCS give the energy less data transmission, the simulation demonstrate the proposed method perform the very close to closed loop net work and very advantage then traditional method. Key words: WSN,Zigbee,Controll system,ARM. I. Introduction The wireless data transmission is the most advanced version over traditional point to point communication, the wireless data transmission in the Sensor Network control system is the much better then point to point communication. In this paper, the wireless data transmission in sensor network control system is proposed with energy consumption method, the energy consummation in wireless network control system means the data from the sensor unit was not send continuously, instant of continues data transmission the data will send when the sensor detect error, this will obtained by Energy consumption data transmission with error detection algorithm. In the existing model of WNCS will send the system data to the controller with particular time delay, due to this operation the battery connected sensor network will lost the energy, overcome this problem we go for Energy consumption with error detection algorithm. The Data Transmission in WNCS is optimized with scheduling algorithm; Scheduling algorithm is designed with Earliest Dead Line first algorithm (EDF) with error detection. After detecting the error the transmission unit will send the data to the controller unit with scheduling algorithm. II. Existing System Models and Problems A. Introduction about Existing models Wireless network control system with battery connected sensor network communication was implemented with energy consumption is achieved by continues data transmission only, the existing models is design with above principle overcome this we go for proposed model. B. Life Cycle Analysis Wireless Networked Control Systems (WNCSs) are spatially distributed systems in which sensors, actuators, and controllers connect through a wireless network instead of traditional point-to-point links in 2009. [9]. In this they said the wireless transmission was very efficient compare to the point to point communication, the implementation of the wireless network was low cost, less physical parameters. The point to point communication need physical contact between senders and receiver, but in the wireless network no need physical contact. WNCSs have a tremendous potential to improve the efficiency of many large-scale distributed systems in industrial automation, WNCS will change the automation industrial to wireless communication between the controller and sensor unit, this wireless communication is used to improve the industrial security and control in 2008 [4]. NCS (network control system) is connected with wireless communication, example zig bee,WiFi, etc., and research trends in NCS in 2010[12]. Co-design of wireless communication and NCS, in this they said the communication system will be designed parallel time of network control system design in 2011[11]. Wireless network control system the communication between the transmission unit and receiver unit has some interval, that interval will be will have some delay schedule [14]. The protocol about the industrial control application, they farm the set of code or rules for communication between the industrial application and

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the controller unit [12]. The system for minimum energy data transmission between the industrial machine and the control unit, they developed the algorithm for minimum energy data transmission that called Energy minimizing schedule algorithm (EMSA) in 2014[1]. In the algorithm the give the delay time for the every sensor node, the every sensor will send the data of the machine with every delay time, the delay time will increased in the model. So the battery life time will increase because the time of sending the data only the transmission unit will turn on, C. Disadvantage of existing models: The WNCS was having battery connected sensor network, the life time of battery is low, and the detection and rectifying speed of error in the system is very poor. Therefore, to overcome these problems we go for proposed system. III. Proposed Model A. Theoretical Background The theoretical back round of this model is sensor network will connect through the wireless communication network, the wireless communication in the WNCS model is ZigBee transceiver. The communication between the sensor unit and the controller unit will be monitor or control by scheduling algorithm. The scheduling algorithm has the principle of Earliest Dead Line first algorithm (EDF) with error detection. In this algorithm the transmission unit will transmit the sensor system dataâ&#x20AC;&#x2122;s to the controller when the system cross the threshold point, this called error in the system. The communication unit is used inside the industrial area only so we use the ZigBee communication; if the industrial area is wide means we use the WiFi communication. The joint optimization of controller and communication systems taking into account all the wireless network induced imperfections including packet error and delay; the parameters of the wireless communication system including the transmission power, rate and scheduling of the network nodes; and the parameters of the controller including the sampling period. The objective of the optimization problem is to minimize the power consumption of the communication system whereas the constraints guarantee the stability of the control system and the schedulability in the communication system. The original contributions of this paper are listed as follows: We provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems, which may lead to broader adoption and real-world deployment. We formulate the joint optimization of controller and communication systems for MQAM modulation in a network containing multiple sensors communicating with their corresponding controllers as a Mixed Integer Programming problem. We propose an efficient solution method for the formulated optimization problem based on the analysis of the relations between the optimal values of the decision variables. We prove the energy saving of the proposed joint optimization problem over the traditional separate design of controller and communication systems and the closeness to the optimality of the proposed solution method via extensive simulations. B. Design Methodology The WNCS was developed with the Network control system model, in industrial they each and every system was connected with the sensor unit; the sensor unit will send data to controller if the system sensor detects the error. The each and every system will scheduled by the algorithm, the which system will have the earliest dead line that system will send the data first if the two sensor detect the error at the same time. The scheduling algorithm was used in the Energy Consumption is Error Detection with Earliest Deadline First Algorithm (EDEDFA). The communication unit was ZigBee transceiver, the zigbee unit was turn on when the sensor detect the error, the communication unit have TDMA MAC technique method, the TDMA is used for scheduling communication between sensor node and the controller unit, C. EDEDFA Algorithm This algorithm is written for simulation purpose only. For future work we will design the algorithm with practical sensors. Algorithm: while(1) { b=0; lcdcmd(0x89); lcdstring("NO"); lcdcmd(0xcb); lcdstring("NO"); if(vib==1) { b=1; lcdcmd(0x84); lcdstring("Detected"); text(); txs('Sensor detected'); b=1; while(act==0);

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} if(act==0) { b=1; lcdcmd(0xC9); lcdstring("DETECTED"); text(); txs('Acuators detected'); txs(13); txs(10); delay(60000); b=1; while(vib==0);}}} void text() { txs('Sensor detected'); txs(13); txs(10); delay(60000); } D. Flowchart for EDEDFA START

Initialize the sensor and actuators

Set the threshold value for the system and

actuators

Sense the system

If Error detect

YES Send the data to the controller

STOP

Fig. 3.1 E. Overview of Existing Architecture The existing architecture of a WNCS where multiple plants are controlled over a wireless network, we assume that a sensor node is attached to each plant. The disadvantages of the existing block diagram is ever sensor node is connected the controller, and the each controller will connected through wireless medium to management controller, so the implementing the architecture need more controller for implement, then the cost of the implementation also will high, overcome these problem we go for the proposed model. F. Proposed Architecture PLANT/ PLANT/ SENSO SNSOR ACTUATO ACTUATOR R RS S CONTROLLER Fig.: 3.2

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G. Overview of Architecture Depicts the architecture of a WNCS where multiple plants are controlled over a wireless network. We assume that a sensor node is attached to each plant. Outputs of the plants are sampled at periodic intervals by the sensors and forwarded to the controller over a wireless network, which induces delays and packet errors. When the controller receives the measurements, a new control command is computed. The control command is forwarded to the actuator attached to the plant. We assume that the controller commands are successfully received by the actuator. Many practical NCSs have several sensing channels, whereas the controllers are collocated with the actuators, as in heat, ventilation and air conditioning control systems, because the control command is very critical. The time is partitioned into frames. Each frame is further divided into a beacon and time slots. The beacon is used by the network manager to provide time synchronization within the WNCS and broadcast updates on the scheduling decisions. The scheduling decisions include the time slot allocation and the values of the optimal node parameters including the transmission power, rate and sampling period corresponding to each sensor node. We assume that no concurrent transmissions are scheduled. The network manager continually monitors the received power and the packet error rate over each link. If the channel conditions do not change, the beacon only provides synchronization information. Otherwise, the beacon also includes the updates on the scheduling decisions. H. Energy Consumption Mode The nodes are assumed to operate their radio in sleep mode when they are not scheduled to transmit or receive a packet, and transient mode when they switch from sleep mode to active mode to transmit or receive a packet and vice versa. We consider only the energy consumption in the transmission of the packets in the optimization problem because it is much larger than that in the sleep and transient modes. IV. Communication Unit A. ZIG BEE Introduction With the development of network and communication technology, the WSN has solved the inconvenience into peopleâ&#x20AC;&#x2122;s life.WSN has good functions of data collection, transmission, and processing. It has many advantages compared to traditional wired network, for example, convenient organizing network, small influence to environment, low power dissipation, low cost, etc. At present, near field wireless communication technology has been used widely, especially Bluetooth, wireless local area network (WLAN), infrared, etc. But, they have a number of disadvantages, for example, complexity, large power dissipation, short distance, networking in small scale. In order to satisfy the demand of low power dissipation and low speed among Wireless communication devices, a new type of wireless net technology-Zigbee emerge as the times require. Outline, of the paper is, starts with description of ZigBee and IEEE802.11.4 specifications. Next describes ZigBee device types and protocol architecture and applications based on ZigBee in conclusion. ZigBee is a specification for a suite of high-level communication protocols used to create personal area networks built from small, low-power digital radios. ZigBee is based on an IEEE 802.15.4 standard. Though its low power consumption limits transmission distances to 10â&#x20AC;&#x201C;100 meters line-of-sight. ZigBee operates in the industrial, scientific and medical (ISM) radio bands: 2.4 GHz in most jurisdictions worldwide; 784 MHz in China, 868 MHz in Europe and 915 MHz in the USA and Australia. Data rates vary from 20 kbit/s (868 MHz band) to 250 kbit/s (2.4 GHz band). B. ZIGBEE Logical Device Types There are three categories of nodes in a ZigBee system. They are Coordinator, Router and End devices. 1) Coordinator: Forms the root of the network tree and might bridge to other networks. There is exactly one coordinator in each network. It is responsible for initiating the network and selecting the network parameters such as radio frequency channel, unique network identifier and setting other operational parameters. It can also store the information about network, security keys. 2) Router: Router acts as intermediate nodes, relaying data from other devices. Router can connect to an already existent network, also able to accept connections from other devices and be some kind of re-transmitters to the network. Network may be extended through the use of ZigBee routers. 3) End Devices: End Device can be low-power /battery-powered devices. They can collect various information from sensors and switches. They have sufficient functionality to talk to their parents (either the coordinator or a router) and cannot relay data from other devices. This reduced functionality allows for the potential to reduce their cost. They support better low power models. These devices do not have to stay awake the whole time, while the devices belonging to the other two categories have to. Each end device can have up to 240 end nodes which are separate applications sharing the same radio. C. Access Modes Two ways of multi-access in ZigBee protocol, are Beacon and Non-beacon. In non beacon enabled network, every node in the network can send the data when the channel is free. In beacon enabled network, nodes can only transmit in predetermined time slots. Here PAN coordinator allocates guaranteed time slots (GTS) for each

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device; therefore devices will transmit their data during their own slot. All devices should be synchronized for this process. This will be sending beacon signal. The coordinator is responsible to transmit beacon signals to synchronize the devices attached to it, Network in which the coordinator does not transmit beacon signal is known as non-beacon network. It cannot have GTS and contention free periods, because the devices are not synchronized. Battery life is better than beacon enabled network, because the devices are wake up less often. Advantages of ZIGBEE Power saving, as a result of the short working period, low power consumption of communication, and standby mode.  Reliability: Collision avoidance is adopted, with a special time slot allocated for those communications that need fixed bandwidth so that competition and conflict are avoided when transmitting data. The MAC layer adopts completely confirmed data transmission, that is, every data packet sent must wait for the confirmation from the receiver.  Low cost of the modules, and the ZigBee protocol is patent fee free.  Short time delay, typically 30 ms for device searching, 15 ms for standby to activation, and 15 ms for channel access of active devices.  Large network capacity: One ZigBee network contains one master device and maximum 254 slave devices. There can be as many as 100 ZigBee networks within one area.  Safety: ZigBee provides a data integrity check and authentication function. AES-128 is adopted and at the same time each application can flexibly determine its safety property. D. Implementation ECWNCS (Energy Consumption Wireless Network control System) was implemented through following methods,  Serial communication between controller and Sensor unit  Communication between controller and management controller  Scheduling Algorithm with program The interface with controller and the sensor with scheduling algorithm were implemented through the program. E. Schematic diagram for simulation

Fig.: 4.1 F. Simulation for Schematic Diagram The simulation of the research paper was design through the proteus hardware design software; in the proteus we design the overview schematic diagram of the research. The results are obtained by simulating the research work through insert and run the program. The above schematic figure 4.1 shows the how the connection are made from controller to other peripherals, in this diagram the peripherals are: (i) Transmission Unit, (ii) Input Sensor, and (iii) LCD G. Discussion about Simulation In the output discussion the sensor will detect the error the transmission unit will turn on and the data of the system will sent to the management controller. The management controller will analysis the error and sends the correct data to the system Actuators through communication model. In the simulation we use the controller with sensor switch and Actuators switch, if the system fail to response in the desired value the sensor will detect the

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error then immediately transmission unit will send the data to the management controller. The virtual screen will denote sensor detection and transmission,

V. Conclusion and Future Work A. Conclusion The phase I of the research work conclude with the schematic diagram design, and program implementation. In this we design the single sensor and actuator device are interface with the controller. In this we could not include the threshold value for the sensor, because the simulation will not show the threshold value for sensor and we connect manual switch. Moreover, in hardware design we include the threshold value for particular sensor. B. Future Work  To design the sensor unit with transceiver without controller.  Design the hardware of the above schematic diagram.  Implement the automatic error detection and rectifying unit in the management controller, without human error rectifier. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15].

[16].

Yalcin sadi, sinem coleri ergen, pangun park “Minimum energy data transmission for wireless networked control system” IEEE Trans. Wireless communication ., vol. 13, no. 4, pp. 2163–2175, Apr 2014 A. Aswani, N. Master, J. Taneja, D. Culler, and C. Tomlin, “Reducing transient and steady state electricity consumption in HVAC using learning-based model predictive control,” Proc. IEEE, vol. 100, no. 1, pp. 240–253, Jan. 2012. A. Fu, E. Modiano, and J. N. Tsitsiklis, “Optimal transmission schedulingover a fading channel with energy and deadline constraints,” IEEE Trans. Wireless Commun., vol. 5, no. 3, pp. 630–641, Mar. 2006. A. Willig, “Recent and emerging topics in wireless industrial communication,”IEEE Trans. Industrial Inf., vol. 4, no. 2, pp. 102– 124, May 2008. B. Demirel, Z. Zou, P. Soldati, and M. Johansson, “Modular co-design of controllers and transmission schedules in wirelesshart,” in Proc. 2011 IEEE Conf. Decision Control European Control, pp. 5951–5958. D. Carnevale, A. R. Teel, and D. Nesic, “A Lyapunov proof of an improved maximum allowable transfer interval for networked control systems,” IEEE Trans. Automatic Control, vol. 52, no. 5, pp. 892–897, May 2007. E. Uysal-Biyikoglu, B. Prabhakar, and A. E. Gamal, “Energy-efficient packet transmission over a wireless link,” IEEE/ACM Trans. Netw., vol. 10, no. 4, pp. 487–499, Aug. 2002. G. Walsh, H. Ye, and L. Bushnell, “Stability analysis of networked control systems,” IEEE Trans. Control Syst. Technol., vol. 10, no. 3, pp. 438–446, May 2002. J. P. Hespanha, P. Naghshtabrizi, and Y. Xu, “A survey of recent results in networked control systems,” Proc. IEEE, vol. 95, no. 1, pp. 138–162, Jan. 2007. O. Ozel, J. Yang, and S. Ulukus, “Optimal broadcast scheduling for an energy harvesting rechargeable transmitter with a finite capacity battery,” IEEE Trans. Wireless Commun., vol. 11, no. 6, pp. 2193–2203, June 2012. P. Park, J. Araujo, and K. H. Johansson, “Wireless networked control system co design,” in Proc. 2011 IEEE International Conf. Netw., Sensing and Control, pp. 486 491. P. Park, C. Fischione, A. Bonivento, K. Johansson, and A. Sangiovanni- Vincent, “Breath: an adaptive protocol for industrial control applications R. A. Gupta and M. Chow, “Networked control system: overview and research trends,” IEEE Trans. Industrial Electron., vol. 57, no. 7, pp. 2527–2535, July 2010. T. Arampatzis, J. Lygeros, and S. Manesis, “A survey of applications of wireless sensors and wireless sensor networks,” in Proc. 2005 IEEE International Symp. Mediterrean Conf. Control Automation, pp. 719–724. W. P. M. H. Heemels, A. R. Teel, N. van de Wouw, and D. Nesic, “Networked control systems with communication constraints: tradeoffs between transmission intervals, delays and performance,” IEEE Trans. Automatic Control, vol. 55, no. 8, pp. 1781– 1796, Aug. 2010. Y. Wu, G. Buttazzo, E. Bini, and A. Cervin, “Parameter selection for real-time controllers in resource-constrained systems,” IEEE Trans. Industrial Inf., vol. 6, no. 4, pp. 610–620, Nov. 2010.

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IMAGE RECONSTRUCTION USING SPARSE BASED MODEL Reetumala Thakre, Prof. Rajendra Singh, Dr. Soni Changlani Department of Electronics & Communication Engineering, Lakshmi Narain College of Technology & Science, Bhopal, Madhya Pradesh, INDIA. Abstract: Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. Sparse representation models code an image patch as a linear combination of a few atoms chosen out from an over-complete dictionary, and they have shown promising results in various image reconstruction applications. However, due to the degradation of the observed image, the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation-based image restoration, in this the concept of sparse noise is introduced, and the goal of image reconstruction turns to how to suppress the sparse noise. I. INTRODUCTION Reconstructing a high quality image from one or several of its degraded (e.g., noisy, blurred, and/or down sampled) versions has many applications, such as medical imaging, remote sensing, surveillance, and entertainment, etc. For an observed image y, the problem of image reconstruction (IR) can be generally formulated by y = Hx+ υ (1) Where H a degradation matrix, x is the original image vector and υ is the additive noise vector. With different settings of matrix H, Eq. (1) can represent different IR problems; for example, image denoising when H is an identity matrix, image deblurring when H is a blurring operator, image super resolution when H is a composite operator of blurring and down-sampling, and compressive sensing when H is a random projection matrix.In the past decades, extensive studies have been conducted on developing various IR approaches. Due to the nature of IR, the regularization-based techniques have been widely used by regularizing the solution spaces In order for an effective regularize, it is of great importance to find and model the appropriate prior knowledge of natural images, and various image prior models have been developed. The classic regularization models, such as the TV regularization are effective in removing the noise artifacts but tend to over smooth the images due to the piecewise constant assumption. Mathematically, the sparse representation model assumes that a signal canbe represented as x ≈ Φα, where Φ (N < M) is an over-complete dictionary, and most entries of the coding vector α are zero or close to zero. The sparse decomposition of x can be obtained by solving an minimization problem, formulated as: It is a pseudo norm that counts the number of non-zero entries in α, and ε is a small constant controlling the approximation error. Since l0-minimization is an upward combinatorial optimization problem, it is often relaxed to the convex l1-minimization. The l1-norm based sparse coding problem can be generally formulated in the following Lagrangian form: (2) where constant λ denotes the regularization parameter. With an appropriate selection of the regularization parameter λ, we can get a good balance between the sparse approximation error of x and the sparsity of α, and the term “sparse coding” refer to this sparse approximation process of x. Many efficient l1- minimization techniques have been proposed to solve Eq. (2),such as iterative thresholding algorithm. In addition, compared with the analytically designed dictionaries (e.g., wavelet/curve let dictionary),the dictionaries learned from

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example image patches can improve much the sparse representation performance since they can better characterize the image structures In the scenario of IR, what we observed is the degraded image signal y via y = Hx+ υ. To recover x from y, first y is sparsely coded with respect to Φby solving the following minimization problem: (3) and then x is reconstructed by = . Clearly, it is expected that could be close enough to Due to the degradation of the observed image (e.g., the image is blurry and noisy), however, it is very challenging to recover the true sparse code from y. Using only the local sparsity constraint ||α||1 in Eq. (3) may not lead to an accurate enough image reconstruction. On the other hand, it is known that image sparse coding coefficients α are not randomly distributed due to the local and nonlocal correlations existing in natural images.. One can say that the sparse coding coefficients are correlated, while the strong correlations allow us to develop a much more accurate sparse model by exploiting the local and nonlocal redundancies. Indeed, some recent works, such are based on such considerations. For example, a group sparse coding scheme was proposed to code similar patches simultaneously, and it achieves impressive denoising results. In this we improve the sparse representation performance by proposing a non-locally centralized sparse representation model. To faithfully reconstruct the original image, the sparse code [refer to Eq. (3)] should be as close as possible to the sparse codes [refer to Eq. (2)] of the original image. In other words, the difference = − , called as sparse coding noise, (SCN) should be reduced and hence the quality of reconstructed image = can be improved because − x ≈ − = . To reduce the SCN, we centralize the sparse codes to some good estimation of . In practice, a good estimation of can be obtained by exploiting the rich amount of nonlocal redundancies in the observed image. The proposed SR model can be solved effectively by conventional iterative shrinkage algorithm, which allows us to adaptively adjust the regularization parameters from a Bayesian viewpoint. The extensive experiments conducted on typical IR problems, including image denoising, deblurring and super-resolution, demonstrate that the proposed SRbased IR method can achieve highly competitive performance to state-of-theart denoising methods , and outperforms state-of-the-art image deblurring and super-resolution methods. II.

NONLOCALLY CENTRALIZED SPARSE REPRESENTATION (SR)

Using the notation for an image , let = x denote an image patch of size √n ×√n extracted at location i, where Riis the matrix extracting patch from x at location i Given an dictionary Φ (N < M) each patch can be sparsely represented as xi≈ Φ by solving an l1-minimization problem ,= argmin {|| − ||2+λ|| ||1}. Then the entire image x can be represented by the set of sparse codes { }. The patches can be overlapped to suppress the boundary artifacts, and we obtain a redundant patch-based representation. Reconstructing x from { } isan over-determined system, and a straightforward least-square solution is: For the convenience of expression let

(4) where denotes the concatenation of all . The above equation is nothing but the overall image is reconstructed by averaging each reconstructed patch of . In the scenario of image reconstruction (IR), the observed image is modeled as y = Hx+ υ. The sparsity-based IR method recovers x from y by solving the following minimization problem: (5) The image x is then reconstructed as xˆ = . A. SPARSE CODING NOISE In order for an effective IR, the sparse codes obtained by solving the objective function in Eq. (5) are expected to be as close as possible to the true sparse codes of the original image x. However, due to the degradation of the observed image y (e.g., noisy and blurred), the sparse code will deviate from and the IR quality depends on the level of the sparse coding noise (SCN), which is defined as the difference between and =

−

(6)

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To investigate the statistical property of SCN , we perform some experiments on typical IR problems .From that we can see that the empirical distributions of SCN can be well characterized by Laplacian distributions, while the Gaussian distributions have much larger fitting errors. B. MODELING OF NCSR The definition of SCN indicates that by suppressing the SCN we could improve the IR output ˆx. However, the difficulty lies in that the sparse coding vector is unknown so that cannot be directly measured. Nonetheless, if we could have some reasonably good estimation of denoted by β, available, then − β can be a good estimation of the SCN To suppress and improve the accuracy of and thus further improve the objective function of Eq. (5), we can propose the following centralized sparse representation (CSR) model:

(7) whereβiis some good estimation of , γ is the regularization parameter and p can be 1 or 2. In the above CSR model, while enforcing the sparsity of coding coefficients , the sparse codes are also centralized to some estimate of (i.e., β) so that SCN can be suppressed. One important issue of sparsity-based IR is the selection of dictionary Conventional analytically designed dictionaries, such as DCT, wavelet and curvelet dictionaries, are insufficient to characterize the so many complex structures of natural images.. In general the learned dictionaries are required to be very redundant such that they can represent various image local structures. However, it has been shown that sparse coding with an over complete dictionary is unstable , especially in the scenario of image restoration. we cluster the training patches extracted from a set of example images into Kclusters, and learn a PCA sub-dictionary for each cluster. Then for a given patch, one compact PCA sub-dictionary is adaptively selected to code it, leading to a more stable and sparser representation, and consequently better image restoration results.. We extract image patches from image x and cluster the patches into K clusters (typically K = 70) by using the Kmeans clustering method. Since the patches in a cluster are similar to each other, there is no need to learn an over-complete dictionary for each cluster. Therefore, for each cluster we learn a dictionary of PCA bases and use this compact PCA dictionary to code the patches in this cluster. In tthis, for each patch to be coded, we adaptively select one sub-dictionary from the trained K PCA sub-dictionaries to code it. This actually enforces the coding coefficients of this patch over the other sub-dictionaries to be 0, leading to a very sparse representation of the given patch. In other words, our algorithm will naturally ensure the sparsity of the coding coefficients, and thus the local sparsity regularization term || ||1 can be removed. Hence we propose the following sparse coding model:

(8) There is only one regularization term || − ||Pin the above model. In the case that p = 1, and the estimate βiis obtained by using the nonlocal redundancy of natural images, this regularization term will become a non-locally centralized sparsity term, and we call this model non-locally centralized sparse representation let’s discuss how to obtain a good estimation of the unknown sparse coding vectors C. NONLOCAL ESTIMATE OF UNKNOWN SPARSE CODE Generally, there can be various ways to make an estimate of depending on how much the prior Knowledge of we have. If we have many training images that are similar to the original image x, we could learn the estimate β of from the training set. However, in many practical situations the trainingimages are simply not available. On the other hand, the strong nonlocal correlation between the sparse coding coefficients, allows us to learn the estimate β from the input data. Based on the fact that natural images often contain repetitive structures, i.e., the rich amount of nonlocal redundancies, we search the nonlocal similar patches to the given patch i in a large window centered at pixel i. For higher performance, the search of similar patches can also be carried out across different scales at the expense of higher computational complexity, as shown in. Then a good estimation of , i.e., βi , can be computed as the weighted average of those sparse codes associated with the nonlocal similar patches (including patch i ) to patch i . For each patch , we have a set of its similar patches, denoted by i . Finally βi can be computed from the sparse codes of the patches within i . Denote by the sparse codes of patch within set i . Then βi can be computed as the weighted average of (9) where is the weight. Similar to the nonlocal means approach we set the weights to be inversely proportional to the distance between patches and

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(10) Such a procedure is iterated until convergence. In the lth iteration, the sparse vector is obtained by solving the following

(11) The restored image is then updated as In the above iterative process, the accuracy of sparse coding coefficient is gradually improved, which in turn improves the accuracy of βi . The improved βi are then used to improve the accuracy of , and so on. Finally, the desired sparse code vector is obtained when the alternative optimization processfalls into a local minimum. III. ALGORITHM In Eq. (8) or Eq. (11) the parameter λ that balances the fidelity term and the centralized sparsity term should be adaptively determined for better IR performance. In this subsection we provide a Bayesian interpretation of the NCSR model, which also provides us an explicit way to set the regularization parameter λ. In the literature of wavelet denoising, the connection between Maximum a Posterior (MAP) estimator and sparse representation has been established and here we extend the connection from the local sparsity to nonlocally centralized sparsity. For the convenience of expression, let’s define θ = α − β. For a given β, the MAP estimation of θ can be formulated as (12) The likelihood term is characterized by the Gaussian distribution (13) Where θ and β are assumed to be independent. In the prior probability P(θ ), θ reflects the variation of α from its estimation β. If we take β as a very good estimation of the sparse coding coefficient of unknown true signal, then = − βis basically the SCN associated with αy, and we have seen in Fig. 2 that the SCN signal can be well characterized by the Laplacian distribution. Thus, we can assume that θ followsi.i.d. Laplaciandistribution, and the joint prior distribution P(θ ) can be modeled as

(14) where ( j ) are the j th elements of and is the standarddeviations of Substituting Eqs. (13) and (14) into Eq. (12), we obtain

(j)

(15) Hence, for a given β the sparse codes α can then be obtained by minimizing the following objective function

(16) Compared with Eq. (8), we can see that the l1-norm (i.e., p= 1) should be chosen to characterize the SCN term − . Comparing Eq. (16) with Eq. (8), we have

. (17) In order to have robust estimations of , the image nonlocal redundancies can be exploited. In practice, we estimate using the set of computed from the nonlocal similar patches is then updated with the updated

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θ in each iteration or in several iterations to save computational cost. Next we present the detailed algorithm of the proposed NCSR scheme. Algorithm: NCSR-Based Image Restoration 1. Initialization: (a) Set the initial estimate as = y for imagedenoising and deblurring, or initialize by bicubic interpolator for image super-resolution; (b) Set initial regularization parameter λ and δ; 2. Outer loop (dictionary learning and clustering): iterateon l = 1, 2, . . . , L (a) Update the dictionaries {Φk} via k-means andPCA; (b) Inner loop (clustering): iterate on j = 1, 2, . . . , J (I) (II) Compute

where δ isthe pre-determined constant;

whereΦ is the dictionaryassigned to patch ; (III) Compute using the shrinkage operator (IV) If mod(j , J0) = 0 update the parameters and { } using Eqs. (17) and (9), respectively; (V) Image estimate update: using Eq. (4). In Algorithm, for fixed parameters λi,j and { βi } the objective function is convex and can be efficiently solved by the iterative shrinkage algorithm in the inner loop, and its convergence has been well established in IV. EXPERIMENTAL RESULTS The basic parameter setting of SR is as follows: the patch size is 7 × 7andK = 70. For image denoising, δ = 0 . 02, L = 3, and J = 3; for image deblurring and super-resolution, δ = 2 . 4, L = 5, and J = 160. To evaluate the quality of the restored images, the PSNR and the recently proposed powerful perceptual qualitymetric FSIM are calculated.

Fig. 1.Deblurring performance comparison on the Cameraman image. From left to right and top to bottom: noisy and blurred image (9 × 9 uniform blur,σ n =1.414), the deblurred images by IDD-BM3D (PSNR = 28.56 dBFSIM= 0.9007), ASDS-Reg (PSNR= 28.08 dB; FSIM = 0.8950), and the proposed NCSR (PSNR = 28.62dB; FSIM= 0.9026 ).

Fig. 2.Deblurring performance comparison on the Starfish image. From left to right and top to bottom: noisy and blurred image (9 × 9 uniform blur, σ n = √ 2), the deblurred images by IDD-BM3D [41] (PSNR = 29.48 dB; FSIM= 0.9167), ASDS-Reg [21] (PSNR= 29.72 dB; FSIM = 0.9208), and the proposed NCSR (PSNR = 30.28dB; FSIM= 0.9293). V. CONCLUSION In this paper we presented a novel non-locally centralized sparse representation (SR) model for image restoration. The sparse coding noise (SCN), which is defined as the difference between the sparse code of the degraded image and the sparse code of the unknown original image, should be minimized to improve the performance of sparsity-based image restoration. To this end, we proposed a centralized sparse constraint, which exploits the image nonlocal redundancy, to reduce the SCN. The Bayesian interpretation of the NCSR model was provided and this endows the NCSR model an iteratively reweighted implementation. An efficient iterative shrinkage function was presented for solving the l1 –regularized NCSR minimization problem. Experimental results on image denoising, deblurring and super-resolution demonstrated thatthe NCSR approach can achieve

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highly competitive performance to other leading denoising methods, and outperform much other leading image deblurring and super-resolution methods. REFERENCES [1] [2] [3] [4] [5]

E. Candès and T. Tao, “Near optimal signal recovery from random projections: Universal encoding strategies?” IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406–5425, Dec. 2006. D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006. E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory , vol. 52, no. 2, pp. 489–509, Feb. 2006. M. Bertero and P. Boccacci, Introduction to Inverse Problems Imaging. Bristol, U.K.: IOP Publishing, 1998. L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D, Nonlinear Phenomena , vol. 60, nos. 1–4, pp. 259–268, Nov. 1992.

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Automatic Bangladeshi Vehicle Number Plate Recognition System using Neural Network Mohammad Badrul Alam Miah, Sharmin Akter and Chitra Bonik Department of Information and Communication Technology (ICT) Mawlana Bhashani Science and Technology University (MBSTU), Santosh, Tangail-1902, Bangladesh Abstract: Now-a-days the necessity of traffic control is increasing day by day, because the number of vehicles in traffic system is increasing. To overcome this problem, computer based automatic traffic control systems are being developed. One of these systems is automatic vehicle number plate recognition system. This paper describes the Smart Vehicle Number Plate Recognition System from a photograph of Bangladeshi vehicle. This system defines image analytics as computer-vision-based surveillance algorithms and systems to extract contextual information from image. Here full-featured automatic system for Bangladeshi vehicle detection, tracking and license plate recognition is presented. This system has many applications in pattern recognition and machine vision and they ranges from complex security systems to common areas. This system has complex characteristics due to diverse effects as fog, rain, shadows, uneven illumination conditions, occlusion, number of vehicles in the scene and others. The main objective of this work is to show a system that solves the practical problem of car identification for real scenes. All steps of the process, from image acquisition to optical character recognition are considered to achieve an automatic identification of plates. Keywords: Number plate recognition; Artificial intelligence; Neural network; Image Processing; Automatic recognition System I. Introduction An automatic vehicle number plate recognition system plays an important role in a modern traffic system. It can be used in Bangladesh for many applications for example, traffic security, road traffic control, parking control, airport or harbor cargo control, speed control and so on. License Plate Recognition consists of three main phases: 1. License Plate Detection/Extraction 2. Character Segmentation and 3. Character Recognition. In this paper, still photo of vehicles are used as input. In the first step image enhancement is performed using Contrast stretching. Then the next step Sobel Operator is used for edge detection [2]. After edge detection series of morphological operations are performed in order to detect the license plate. The character segmentation is done using line scanning technique, scanning is done from left to right of the plate [3]. After Character Segmentation, feature extraction is performed to obtain the unique features of every character. Neural network techniques used for character recognition. II. Related Work In this system we recognize the number plate of a car using neural network. [1] Proposed “Vehicle License Plate Character Segmentation – A Study” here projection based method and binarization is used for character segmentation. [2] Proposed “Towards License Plate Recognition: Comparing Moving Objects Detection Approaches” that used Background Subtraction for objects [3] proposed “A hybrid method for robust car plate character recognition”. [4] Proposed “Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos” that used Sobel edge detection. [5] Proposed “Real-Time License Plate Recognition and Speed Estimation from Video Sequences” that used for recognize license plate. [6] Proposed “Design and Implementation of Car Plate Recognition System for Ethiopian Car Plates,” cut image of the pre- treatment. III. Design and Implementation A Number Plate Recognition process consists of two main stages: 1) locating number plates and 2) identifying license numbers. In the first stage, license plate candidates are determined based on the features of license plates. Features commonly employed have been derived from the license plate format and the alphanumeric characters constituting license numbers. A. System Architecture In this paper, we proposed an automatic vehicle license plate recognition system which based on artificial neural networks. This system consists of three main topics. These are localizing the plate region from the car

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image, segmenting the characters from the license plate image and recognizing the segmented characters. The block scheme of the proposed automatic vehicle license plate recognition system is shown in Fig. 1. B. Plate Detection The most difficult task is to identify the license plate, which could be at anywhere in the input image. This task becomes more challenging if the illumination of the image varies from one plate to another. The plate region consists of white background and black characters normally. In this region the black and whites color is very intensive. The captured image is converted into digital form using an image collection card. This image is then converted into a gray scale image the next step is image pre-processing. Finding the region that includes most transition points would be adequate for localizing the plate region. For identification plate region Sobel edge detection operator is applied to the vehicle images to get the transition points [1]. The Sobel edge detector uses a filter based on the first derivative of a Gaussian smoothing. After smoothing the image and eliminating the noise, the next step is to find the edge strength by taking the gradient of the image. Let us assume the image of a vehicle is represented by a function f (x, y), where x0≤x≤x1 and y0≤y≤y1. The upper left and the lower left corner of the image is represented by [x0, y0 ] and [ x1, y1] respectively. If w and h are dimensions of the image, then x0=0, y0=0, x1=w-1and y1=h-1. Band: The band b in the snapshot f is an arbitrary rectangle b= (xb0, yb0, xb1, yb1) such as (xb0=xmin) ^ (xb1=xmax) ^ (ymin≤ yb0≤yb1^ymax) Plate: The plate p in the band b is an arbitrary rectangle such as p= (xp0, yp0, xp1, yp1), (xb0≤ xp0≤ xp1≤ xb1) ^ (yp0= yb0) ^ (xp1= yb1). C. Character Segmentation The characters of the detected number plate can be segmented by detecting spaces in its horizontal projection. We often apply the adaptive thresholding filter to enhance an area of the plate before segmentation. The adaptive thresholding is used to separate dark foreground from light background with non-uniform illumination. After the thresholding, we compute a horizontal projection px(x) of the plate f(x, y) [4]. We use this projection to determine horizontal boundaries between segmented characters. The principle of the algorithm can be illustrated by the following steps: Determine the index of the maximum value of horizontal projection: Detect the left and right foot of the peak as:

Zeroize the horizontal projection px(x) on interval(xl, xr) If px (xm)<cw.vw, go to step 7. Divide the plate horizontally in the point xm Go to step 1. End D. Feature Extraction The description of an image region is based on its internal and external representation. The internal representation of an image is based on its regional properties, such as color or texture. The external representation is chosen when the primary focus is on shape characteristics. The description of normalized characters is based on its external characteristics because we deal only with properties such as character shape. The simplest way to extract descriptors from a bitmap image is to assign a brightness of each pixel with a corresponding value in the vector of descriptors. Then, the length of such vector is equal to a square (w. h) of the transformed bitmap: Where, i 0… w.h-1 Figure 1: The block diagram of automatic vehicle license plate recognition system.

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Figure 2: The “pixel matrix” feature extraction method

E. Character Recognition The neural network is defined as an oriented graph G = (N, E), where N is a non-empty set of neurons and E is a set of oriented connections between neurons. The connection e (n, n′) E is a binary relation between two neurons n and n′. The set of all neurons N is composed as: N = N0 N1 N2. The number of neurons in the input layer (m) is equal to a length of an input pattern x in order that each value of the pattern is dedicated to one neuron. Neurons in the input layer do not perform any computation function, but they only distribute values of an input pattern to neurons in the hidden layer. The number of neurons in the hidden layer (n) is scalable, but it affects the recognition abilities of a neural network at a whole. Too few neurons in the hidden layer causes that the neural network would not be able to learn new patterns. Too many neurons cause network to be over learned, so it will not be able to generalize unknown patterns as well. Figure 3: Architecture of the three layer feed-forward neural network.

IV. Experimental Results Two groups of images have been collected for our experiments. The first contains 238 images (640 by 480 pixels) taken from 71 cars of different classes. For each car, nine images were acquired from fixed viewpoints. The experimental results with the first group of images are summarized in Table I. In this table columns correspond to viewpoints, rows to the classes of vehicle (or the types of license plate), and the entries are the number of correctly located license plates. The percent of correctly located license plates (the success rate) is given in the bottom row of the table. The success rates for viewpoint (i.e. straight on) are 100%, independent of the type of license plate and viewing distance. In the worst case, viewpoints the success rates are 94.2%, 92.6%, and 91.8%, respectively. A. Learning and Traning Procedure This system has been learned by several sets of learning images of digits from “0” to “9” and alphabets from “A” to “Z” of various fonts and formats to make the system more reliable. The following images show such a set of learning images: Figure 4 Learning images.

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While learning, these images are converted into normalized images. These normalized images are used for testing purpose. B. Testing Procedure In this system, the learning images generate an .xml file which is used to recognize the characters of a number plate from a car image. How the system recognizes characters from an image that is shown in Fig. 5 followed by the original foreign-language citation [8]. Sometimes a problem occurs in the identification module because of the fact that the module recognizes characters based on their contours and that contours preserve only partial information about object shapes. C. Performance Analysis The automatic Bangladeshi vehicle number plate recognition system results in an accuracy rate of 91.47% on an average for images captured from different viewpoints. The performance decreases for unclear, blur and very distant vehicle images. Due to weather, the performance also fluctuates. In rainy and foggy weather the system cannot detect and recognize the characters correctly. It works best with images from sunny weather. Due to identification module problem sometimes the system makes misidentification between characters “I” and “1”, “O” and “0” etc. Figure 5 (a) 2-layer number plate clipping (b) Recognized characters from the plates.

Table I Experimental results. viewpoint A1

Vehicle class Private automobile

Bus

Truck

Vans

87.4

82.7

97.2

96.9

95.8

Success rate (%)

85.2

93.1

97.8

83.5

Average success rate (%): 91.47%

V. Limiations and Future Work In this study, there are some limitations. At first those should be removed. Adding the normalization step can improve the performance of license number identification. The aim of this study is to focus on night surveillance and to improve the existing algorithms reported in literature. However, the other segments of this system should be improved, focusing on the occlusion handling, vehicle matching procedure and also focus on improving the accuracy measure for character recognition by using the concept of neural network for recognizing all font type of a character by using back propagation algorithm. The ultimate target of this study is to work for Bengali characters.

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VI. Conclusions In this paper, Automatic Bangladeshi vehicle number plate recognition system is developed. Here used problematic of machine vision, pattern recognition, OCR and neural networks for plate recognition system. We can improve quality of the vehicle image using Java, then extract the license plate and isolate characters contained on the plate, and finally identify the characters on the license plate using artificial neural network. This work also contains demonstration AVNPR software, which comparatively demonstrates all described algorithms. Even though there is a strong succession of algorithms applied during the recognition process. VI. [1] [2] [3] [4] [5] [6] [7] [8] [9]

References

V. Karthikeyan, R. Sindhu, K. Anusha and D. S. Vijith, “Vehicle License Plate Character Segmentation – A Study”, International Journal of Computer and Electronics Research, vol. 2, Issue 1, February 2013. V. J. Oliveira-Netoand, G. Cámara-Chávez and D. Menotti, “Towards License Plate Recognition: Comparing Moving Objects Detection Approaches”, Computing Department, Federal University of Ouro Preto, Minas Gerais, Brazil, 2013. X. Pan, X. Ye, and S. Zhang, “A hybrid method for robust car plate character recognition”, Engineering Applications of Artificial Intelligence, vol. 18, no.8, 2005, pp. 963–972. Lucky Kodwani, “Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos”, Communication and Signal Processing, 2012-2013. Mayank Garg and Sahil Goel, “Real-Time License Plate Recognition and Speed Estimation from Video Sequences”, ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE), vol. 1, Issue 5, 2013. Huda Zuber Ahmed, “Design and Implementation of Car Plate Recognition System for Ethiopian Car Plates”, Addis Ababa Ethiopia, November, 2011. H. Erdinc Kocer, “Artificial neural networks based vehicle license plate recognition”, Procedia Computer Science , vol. 3, 2011, pp. 1033–1037. Anuja P. Nagare, “License Plate Character Recognition System using Neural Network”, International Journal of Computer Applications, vol. 25, No.10, July 2011. Ondrej Martinsky, “Algorithmic and mathematical principles of automatic number plate recognition systems”, Brno university of technology, 2007.

VII. Acknowledgments The authors are grateful who participated in this research.

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Navigation System for Multi-Floor Indoor Positioning during Urban Combat Operations using Geo-Magnetic Module 1

N.Kanagapriya, 2N.Solaiyammal Department of Electrical and Electronics Engineering, Nehru Institute of Engineering and Technology, Coimbatore, Tamil Nadu 641105, INDIA 1,2

Abstract: Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Numerous applications require a self contained personal navigation system that works in indoor and outdoor environments, does not require any infrastructure support, and is not susceptible to jamming. The soureless nature of this technique makes possible full body posture tracking in an area of unlimited size with no supporting infrastructure. Such sensor modules contain three orthogonally mounted angular rate sensors, three orthogonal magnetometers. This paper describes the inertial navigation system. This technology that is much needed during urban combat situation like military seeking operation. The location and movement of all soldiers would be known to the military troop outside the building. Keywords: Inertial navigation system (INS), Global positioning system (GPS),Micro electromechanical system (MEMS) I. INTRODUCTION Navigation system is a commercial application of military, scientific and commercial aircrafts application. This is usually attached on the body of the person and provides walking velocity and position. It can be applied to the location service of user in virtual reality, sports, games, military, ubiquitous health monitoring, etc. nowadays its demand is increasing. INS (Inertial navigation system) provides a navigation solution by using inertial sensors such as accelerometer, pressure sensor and magnetometer irrespective of the location. INS is an appropriate method for personal navigation systems, because the blockage of the GPS signal occurs frequently in urban and forest areas. A small size and low-cost INS is required in consideration of it being attached to the userâ&#x20AC;&#x2122;s body. This problem is solved by the development of MEMS type inertial sensors. However, INS exhibits errors that tend to increase with time in an unbounded manner because of integral processes. Therefore, an error compensation method is necessary Most types of human movement including walking, side stepping, and running include repeated recognizable periods during which the velocity and acceleration of the foot are zero. These brief periods occur before entering the swing phase of the gait cycle each time the foot contacts the ground during the stance phase. Recognition of these periods allows determination of the drift error that occurred in between them. This allows precise corrections to be made to accelerometer data in either a forward or backward manner. The corrected accelerometer data combined with magnetic and angular rate data can then be used to calculate the direction and magnitude of displacement that occurs during each step. This allows accurate measurement of position relative to an initial starting point. II. BACKGROUND

Many attempts were made in the inertial navigation system. In most cases, distance estimation errors when using more complex inertial sensor combinations have been only slightly better than those obtained using commercial pedometers. Simple pedometer focus on counting steps. The accuracy of the pedometer produced step count vary greatly and pedometer do not have the ability to differentiate between different types of gait such as running, shuffling and side stepping. The electronic pedometer in estimating step counts. Estimating of step is less accurate. A single triaxial accelerometer, measured leg length, and an algorithm based on an inverted pendulum model to predict the body center of mass trajectory during walking. III. SYSTEM DESCRIPTION The proposed system consists of an accelerometer, magnetometer, digital MEMS pressure sensor, transceiver and GSM module. The combination of the three sensors is digital MEMS geo-magnetic sensors. The message that is transferred from soldier mote to monitoring mote by GSM module and monitored by the help of QVGA TFT COLOR LCD. This lcd have the option of touch screen interface and have the diagonal size of 3.2 . The display technology if TFT. The pressure sensor is an ultra compact piezoresistive pressure sensor. It includes a IC interfaces able to take the

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information from the sensing element and to provide a digital signal to the external world. The complete measurement chain is composed by a low-noise amplifier which converts the resistance unbalancing of the MEMS sensors (pressure and temperature) into an analog voltage that is finally available to the user by an analog-to-digital converter.

Fig. 1. Block diagram for INS The pressure and temperature data may be accessed through an IÂ˛C/SPI interface thus making the device particularly suitable for direct interfacing with a microcontroller. These features are Data-Ready signal which indicates when a new set of measured pressure and temperature data are available thus simplifying data synchronization in the digital system that uses the device.

Fig. 2. Urban combat situation IV. METHOD FOR TRACKING THE SOLDIER POSITION IN MULTIFLOOR BUILDING The accelerometer sensors attached to the body are influenced not only by the acceleration of the body but also noise and other factors such as bias of the accelerometer, gravity, etc. The output of the accelerometer can be integrated twice to obtain displacement information. The position estimates based on double integration can diverge in a short time period lasting only a few seconds. Drift correction is thus essential for tracking position using low-cost accelerometers. In this section, a drift correction method is first described. An application of this method to position tracking of a walking person is then detailed. A. Correcting Accelerometer Drift An accelerometer is first placed on a level table top, and then is slid along a straight line for a distance of one meter. The initial and final velocities are zero. The velocity is obtained by integrating accelerometer measurements once, and the position is obtained by integrating the velocity. While the sensor actually moved a distance of one meter, the estimated distance obtained by double integration is 0.80m as seen in the lower-left plot. A close examination of the velocity in the middle-left plot indicates that the final estimated velocity is 0.23m/s at the end of the motion period, although the sensor stopped moving and the actual velocity was zero at this point. The error in the estimated velocity is due to drift in accelerometer measurements. Because the final velocity is known to be zero in this case, a drift correction can be applied to the accelerometer measurements so that the final estimated velocity is zero. Clearly, this drift correction method makes it possible to obtain accurate position information through double integration. B. Position Tracking of a Person Human gait motion is cyclic in nature. During walking, each gait cycle consists of two phases: a stance phase and a swing phase. The stance phase is the portion of the cycle during which a foot is in contact with the ground. The swing phase is the portion of the cycle during which the same foot is not in contact with the ground. The

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stance phase takes approximately 60% of the gait cycle, and the swing phase takes the remaining 40%. During walking (rather than running or jumping), there are two periods of time in a single gait cycle when both feet are in contact with the ground. This period of double support occupies about 20% of the gait cycle. During the stance phase, the foot is in contact with the ground, and foot velocity is zero. If an inertial/magnetic sensor module is attached to a foot, drift in accelerometer measurements can be corrected each time the foot is in the stance phase of the gait cycle . If the estimated foot velocity is not zero, a drift correction can be applied to the accelerometer measurements. The inertial/magnetic sensor modules considered for this study contains triads of orthogonally mounted accelerometers angular rate sensors, and magnetometers. These inertial/magnetic sensor modules are primarily designed for tracking 3-dimensional orientation. Algorithms used by these sensor modules for processing accelerometer, angular rate, and magnetometer measurements to produce orientation output typically use a Kalman filter. In addition to providing orientation output in Euler angles and/or quaternions, some sensor modules also optionally provide scaled measurements of acceleration, angular rate, and magnetic field.

Fig. 3. Simulation result C. Detecting Gait Events While all three acceleration components exhibit a cyclical pattern, it can be observed that z-axis acceleration data provide the strongest indication of gait events. During the stance phase, acceleration is near zero. Since there are a number of zero-crossings during the swing phase, a zero threshold and a time heuristic must be applied to the acceleration data to detect stance phases. The time heuristic is required to avoid classifying any zero crossing in the swing phase as a stance phase. If the acceleration is within the threshold for a specified period of time, the foot is determined to be in the stance phase Angular rate measurements also provide an indication of gait events. The angular rate in the sensor coordinates measuring ankle axis rotation is more prominent in differentiating the stance phase from the swing phase. The angular rate is near zero during the stance phase. A heuristic similar to the method discussed above can be applied to the angular rate data to detect the V. CONCLUSION Self-contained position tracking using data from inertial/magnetic modules has applicability to a wide number of applications. Preliminary experimental results presented in this paper document that this technique can be used to track three dimensional position during a variety of motion types. Thus the accurate position is determined by the inertial navigation system work is currently underway to further refinement. VI. REFERENCES [1] [2] [3]

[4]

F. Eric, “Pedestrian tracking with shoe-mounted inertial sensors,” in Proc. IEEE Comput. Graph. Appl. 2005, vol. 25, pp. 38– 46 S. H. Shin, C. G. Park, J. W. Kim, H. S. Hong, and J. M. Lee, “Adaptive step length estimation algorithm using low-cost MEMS inertial sensors,”in Proc. IEEE Sensors Appl. Symp., Feb. 6–8, 2007, pp. 1–5 C.-M. Su, J.-W. Chou, C.-W. Yi, Y.-C. Tseng, and C.-H. Tsai, “Sensoraided personal navigation systems for handheld devices,” in Proc. 39th Int. Conf. Parallel Process. Workshops., Washington, DC, USA: IEEE Computer Society, 2010, pp. 533– 541.Ion P. Wiebren Zijlstra and At L. Hof, “Displacement of the pelvis during human walking: experimental data and model predictions,” Gait and Posture, Volume 6, Number 3, December 1997.

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[9]

[10] [11] [12] [13] [14]

Wiebren Zijlstra and At L. Hof, “Assessment of spatio-temporal gait parameters from trunk accelerations during human walking,” Gait & Posture, Volume 18, Issue 2, October 2003. Koichi Sagawa, Yutaka Satoh, and Hikaru Inooka, “Non-restricted Measurement of Walking Distance,” Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Volume 3, Nashville, TN., October 2003. Angelo M. Sabatini, Chiara Martelloni, Sergio Scapellato, and Filippo Cavallo, “Assessment of walking features from foot inertial sensing,” IEEE Transactions on Aerospace and Electronics Systems, vol. 52, no. 3, pp. 486–494, Mar. 2005. Filippo Cavallo, Angelo M. Sabatini, and Vincenzo Genovese, “A step toward GPS/INS personal navigation systems: real-time assessment of gait by foot inertial sensing,” Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005), Edmonton, Alberta, Canada, August 2005. Rommanee Jirawimut, Piotr Ptansinski, Vanja Garaj, Franjo Cecelja, and Wamadeva Balachandran, “A Method for Dead Reckoning Parameter Correction in Pedestrian Navigation System,” IEEE Transactions on Instrumentation & Measurement, Vol. 52 Issue 1, February 2003. C. Tom Judd, “A Personal Dead Reckoning Module,” Proceeding of the Institute of Navigation (ION FPS-97) Kansas City, MO., September, 1997. Quentin Ladetto, Betrand Merminod, “In Step With INS,” GPS World, October 2002. Vectronix (2005), “Position Yourself Anytime!”, Vectronix AG, Max-Schmidheiny-Strasse 202, CH-9435 Heerbrugg, Switzerland, Available: www.wectronix.ch. Michael May, “Seamless Outdoor/Indoor Navigation for Blind and Visually Impaired Individuals,” Technology and Persons with Disabilities Conference, Los Angeles, CA., March 2006. Pamela K. Kevangie and Cynthia C. Norkin, Joint Structure and Function, F.A. Davis Company, Philadelphia, PA, Third Edition, 2001.

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Survey on cloud computing technology, application, usage and its stack terminology Santvana Singh1, Sarla Singh2, Sumit Dubey3 Student (M.Tech. EC), 2Head of Department, 3Assistant Professor 1,2,3 Department of Electronics and Communication Engineering, Jawahar Lal Nehru College of Technology Rewa, Madhya Pradesh, INDIA. 1

Abstract: In this paper we will discuss about cloud computing technology, cloud applications, its usage and about its stack terminology. This paper is basically a brief introduction to cloud computing technology and what are the cloud stack and their facilities, why cloud computing is more secure than a normal network why should we use cloud, and how to use cloud computing technology. This paper is a literature survey of cloud computing technology. I. Introduction Cloud refers to a distinct IT environment that is designed for the purpose of remotely provisioning scalable and measured IT resources. The term originated as a metaphor for the Internet which is, in essence, a network of networks providing remote access to a set of decentralized IT resources. Prior to cloud computing becoming its own formalized IT industry segment, the symbol of a cloud was commonly used to represent the Internet in a variety of specifications and mainstream documentation of Web-based architectures. This same symbol is now used to specifically represent the boundary of a cloud environment, as shown in Figure 1.0.

Fig 1.0 Cloud computing architecture It is important to distinguish the term "cloud" and the cloud symbol from the Internet. As a specific environment used to remotely provision IT resources, a cloud has a finite boundary. There are many individual clouds that are accessible via the Internet. Whereas the Internet provides open access to many Web-based IT resources, a cloud is typically privately owned and offers access to IT resources that is metered. Much of the Internet is dedicated to the access of content-based IT resources published via the World Wide Web. IT resources provided by cloud environments, on the other hand, are dedicated to supplying back-end processing capabilities and user-based access to these capabilities. Another key distinction is that it is not necessary for clouds to be Web-based even if they are commonly based on Internet protocols and technologies. Protocols refer to standards and methods that allow computers to communicate with each other in a pre-defined and structured manner. A cloud can be based on the use of any protocols that allow for the remote access to its IT resources. Cloud Computing Stack: The main services (Stack) of cloud computing technologies are following: ď&#x201A;ˇ SaaS applications are designed for end-users, delivered over the web.

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

PaaS is the set of tools and services designed to make coding and deploying those applications quick and efficient.  IaaS is the hardware and software that powers it all – servers, storage, networks, operating systems. Stack of cloud computing is shown in Fig.1.1. Cloud-based applications—or software as a service (SaaS)—run on distant computers “in the cloud” that are owned and operated by others and that connect to users’ computers via the Internet and, usually, a web browser. Platform as a service provides a cloud-based environment with everything required to support the complete life cycle of building and delivering web-based (cloud) applications—without the cost and complexity of buying and managing the underlying hardware, software, provisioning and hosting. Infrastructure as a service provides companies with computing resources including servers, networking, storage, and data centre space on a pay-peruse basis.

Fig 1.1 Cloud stack II. Cloud Applications On-demand self-service: A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider. Broad network access: Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations). Resource pooling: The provider’s computing resources are pooled to serve multiple consumers using a multitenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth. Rapid elasticity: Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time. Measured service: Cloud systems automatically control and optimize resource use by leveraging a metering capability1 at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. III. Disadvantages of Cloud Computing In spite of its many benefits, as mentioned above, cloud computing also has its disadvantages. Businesses, especially smaller ones, need to be aware of these cons before going in for this technology. Technical Issues: Though it is true that information and data on the cloud can be accessed anytime and from anywhere at all, there are times when this system can have some serious dysfunction. You should be aware of the fact that this technology is always prone to outages and other technical issues. Even the best cloud service providers run into this kind of trouble, in spite of keeping up high standards of maintenance. Besides, you will need a very good Internet connection to be logged onto the server at all times. You will invariably be stuck in case of network and connectivity problems. Security in the Cloud: The other major issue while in the cloud is that of security issues. Before adopting this technology, you should know that you will be surrendering all your company’s sensitive information to a thirdparty cloud service provider. This could potentially put your company to great risk. Hence, you need to make absolutely sure that you choose the most reliable service provider, who will keep your information totally secure.

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IV. Conclusion cloud computing is very new, advanced and flexible technology which has a stack concept of three common services , Saas, Paas, Iaas and all these three services provides software, platform, environment and virtualization services. References 1) 2) 3)

http://mobiledevices.about.com/od/additionalresources/a/Cloud-Computing-Is-It-Really-All-That-Beneficial.htm. http://www.rackspace.com/knowledge_center/whitepaper/understanding-the-cloud-computing-stack-saas-paas-iaas http://whatiscloud.com/basic_concepts_and_terminology/cloud.

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Future monthly, seasonal and annual rainfall trend prediction for Tarai region of Uttarakhand 1,3

Arvind Singh Tomar1, Praveen Vikram Singh2 and Om Prakash Kumar3 Department of Irrigation & Drainage Engineering, 2Department of Soil & Water Conservation Engineering, College of Technology, Govind Ballabh Pant University of Agriculture & Technology, Pantnagar-263145, Uttarakhand, INDIA

Abstract: In this study, trend analysis of rainfall on monthly, seasonal (monsoon, winter and summer)and annual basis for Tarai region of Uttarakhand was conducted by considering long-term daily rainfall dataset of 53 years (1961-2013) recorded at G.B. Pant University of Agriculture & Technology, Pantnagar. The positive trend of departure from mean value of rainfall on time basis shows groundwater reservoir recharge, whereas, negative trend implies extraction from aquifers. During last few years, recorded values of rainfall on monthly, seasonal and annual basis showed positive trend at study area which was further strengthened by different statistical indices. The expected future trend of rainfall occurrence on monthly, seasonal and annual basis for next 12 years indicated a positive trend. Keywords: Rainfall, trend, prediction, statistical analysis. I. Introduction Rainfall, a continuous random variable, is one of the most important natural input resources for crop production and its non-availability of certain amount at critical growth stages can influence failure of crops and various agricultural related issues. The marginal and small farmers constituting 80% of agricultural income group still depend on rainfed farming. In our country, rainfall distribution is very erratic in nature and varies from region to region and year to year though adequate rainfall is received through four different types of weather phenomenon namely, south-west monsoon (about 74%), north-east monsoon (about 3%), pre-monsoon (about 13%) and post-monsoon (about 10%) with an average annual rainfall as 119 cm. Since crop yields are strongly related to growing season rainfall, successful trend prediction for periods of one to three years in advance would be helpful to plan crops and estimate future yields and, thereby, emphasis was placed to predict future rainfall trend in this study with widely acclaimed straight line method of least square fitting. II. Materials and Methods The long-term daily rainfall data of Tarai region of Uttarakhand for a period of 53 years (1961-2013) collected from the Meteorological observatory situated at Crop Research Centre of the Govind Ballabh Pant University of Agriculture & Technology, Pantnagar was analyzed by using mathematical and statistical techniques. The mathematical procedure involved calculation of mean value of rainfall on monthly, seasonal and annual basis to evaluate its departure in order to understand rainfall pattern over a longer period of time. The most commonly used mathematical method to understand rainfall variability is to calculate its arithmetic mean. Being the mean of a particular distribution is mostly affected by extreme values, therefore, in addition to its mean value, median and mode of rainfall values were also calculated. Similarly, the statistical method employed for analyzing rainfall data of study area includes determination of mean, median, mode, standard deviation, coefficients of Dispersion, Variation, Skewness and Kurtosis [2]. The time series analysis generates valuable information regarding trend of a series of observations which can be defined as systematic increase or decrease over a period of time. It also helps to measure deviation from trend and gives an idea regarding nature of trend and is used as a tool to predict future behavior of trend. Based on time series analysis proposed by [1], prediction of future rainfall trend on monthly, seasonal and annual basis for next 12 year period (2014-2025) is being presented. The standard method of least square fit of straight line has been used to conduct trend analysis for different months, seasons (monsoon, winter and summer) and annual rainfall. The straight-line equation can be represented as, Y = aX + b, where Y is trend value of dependable variable, X is independent variable and a, b are unknowns. To establish a best fit straight line, values of “a” and “b”, the equation parameters must be determined from the observed data.

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III. Results and Discussion The variation in monthly rainfall for the study period on the basis of mathematical analysis is presented in Table 1. The values of different statistical parameters namely, mean, median, mode, standard deviation, coefficients of Dispersion, Variation, Skewness and Kurtosis obtained through different mathematical formulae [2] is presented in Table 2 from which it is clear that mean annual rainfall of study area was 1454.72 mm, whereas, computed value of median (1442.86 mm) and mode (927.27 mm) indicates ideal rainfall at the study area. The calculated value of Standard Deviation reveals that deviation of rainfall is 426.60 mm over a period of 53 years. The coefficient of Variation indicates that amount of rainfall varies up to 29.325 with coefficient of Skewness (1.2364) showing positive trend, whereas, coefficient of Kurtosis (1.0236) confirms that annual distribution is relatively a peaked one. For time series analysis, straight line equations obtained for different months, seasons and years is shown in Table 3. With the help of these developed equations, future forecast of rainfall amount for twelve year period (2014-2025) on monthly (Table 4a), seasonal and yearly basis (Table 4b) was being done. IV. Conclusions The rainfall data analysis of Tarai region for a period of 53 years (1961-2013) reveals strong variation in amount and frequency of rainfall and points out a positive trend of rainfall in future for all the months (except January), seasons (monsoon, winter and summer) and years. The impact of rainfall variation on monthly, seasonal and annual basis in recharge phenomena of groundwater system in Tarai region of Uttarakhand for next 12 years is also being presented in this paper. References [1] [2]

F.E. Croxton and D.J. Cowden, Applied General Statistics, Prentice-Hall of India (Private) Limited, New Delhi, 1966, pp. 302-10. J.N. Kapur and H.C. Saxena, Mathematical Statistics, S. Chand & Company Ltd., New Delhi, 2005, 792p.

Table 1: Average values of monthly and seasonal rainfall (1961-2013) Season(s)

Months July August September October November December January February March April May June

Monsoon

Winter

Summer

Mean monthly rainfall (mm) 430.33 423.79 259.12 41.92 3.89 13.06 27.43 35.61 17.18 15.17 44.88 187.22

Percent of mean annual rainfall (%) 28.70 28.26 17.28 2.80 0.26 0.87 1.83 2.37 1.15 1.01 2.99 12.48

Mean seasonal rainfall (mm) 288.79

20.00

66.11

Table 2: Statistical parameters of rainfall on seasonal and annual basis (1961-2013) Statistical parameters

Mathematical formulae

Mean ( , mm) Median (mm) Mode (mm) Standard Deviation (mm) Coefficient of Dispersion Coefficient of Variation Coefficient of Skewness Coefficient of Kurtosis

Monsoon

Computed rainfall values for Winter Summer

Annual

1160.38

82.55

266.04

1454.72

1108.33

72.37

239.13

1442.86

730.00

54.17

210.00

927.27

433.65

56.13

185.25

426.60

0.3737

0.6799

0.6963

0.2933

37.3715

67.9976

69.6337

29.3252

0.9925

0.5056

0.3025

1.2364

-0.0233

0.0424

3.7667

1.0236

= mean; N = total frequency; C = cumulative frequency of group preceding the median group; l = lower limit of modal class; fm = maximum frequency; f1, f2 = frequencies of classes preceding and following modal class; h = width of uniform class.

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Table 3: Developed monthly, seasonal and annual rainfall trend equations Period (months / seasons / Annual) January February March April May June July August September October November December Monsoon Winter Summer Annual

Developed trend equation Y = 30.753 - 0.123X Y = 22.550 + 0.484X Y = 15.975 + 0.045X Y = 11.984 + 0.118X Y = 24.856 + 0.741X Y = 149.253 + 1.406X Y = 416.113 + 0.527X Y = 310.473 + 4.197X Y = 214.350 + 1.658X Y = 36.508 + 0.200X Y= 3.613 + 0.010X Y = 12.898 + 0.006X Y = 977.444 + 6.582X Y = 69.814 + 0.377X Y = 202.067 + 2.310X Y = 1249.324 + 9.270X

Table 4a: Expected future trend of monthly rainfall (mm) S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

27.43 27.31 27.19 27.06 26.94 26.82 26.69 26.57 26.45 26.33 26.20 26.08

35.62 36.10 36.59 37.07 37.55 38.04 38.52 39.01 39.49 39.97 40.46 40.94

17.19 17.24 17.28 17.33 17.37 17.42 17.46 17.51 17.55 17.60 17.64 17.69

15.17 15.29 15.41 15.52 15.64 15.76 15.88 16.00 16.11 16.23 16.35 16.47

44.86 45.60 46.35 47.09 47.83 48.57 49.31 50.05 50.79 51.53 52.27 53.01

187.22 188.62 190.03 191.43 192.84 194.25 195.65 197.06 198.46 199.87 201.28 202.68

430.34 430.87 431.40 431.92 432.45 432.98 433.50 434.03 434.56 435.09 435.61 436.14

423.79 427.99 432.19 436.38 440.58 444.78 448.97 453.17 457.37 461.57 465.76 469.96

259.12 260.77 262.43 264.09 265.75 267.41 269.06 270.72 272.38 274.04 275.70 277.35

41.91 42.11 42.31 42.51 42.71 42.91 43.11 43.31 43.51 43.71 43.91 44.11

3.88 3.89 3.90 3.91 3.92 3.93 3.94 3.95 3.96 3.97 3.98 3.99

13.06 13.07 13.07 13.08 13.08 13.09 13.10 13.10 13.11 13.11 13.12 13.13

Table 4b: Expected future trend of seasonal and annual rainfall (mm) S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Year 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Monsoon 1155.17 1161.75 1168.33 1174.91 1181.50 1188.08 1194.66 1201.24 1207.82 1214.42 1221.00 1227.58

Seasons Winter 80.00 80.38 80.76 81.13 81.51 81.89 82.27 82.64 83.02 83.40 83.77 84.15

Summer 264.45 266.76 269.07 271.38 273.69 276.00 278.31 280.62 282.93 285.24 287.55 289.86

Annual 1499.62 1508.89 1518.16 1527.43 1536.70 1545.97 1555.24 1564.51 1573.78 1583.05 1592.32 1601.59

Page 76

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

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Synthesis and Structural Studies of some complexes of Ni(II) and Co(III) with Hexamethylenedibiguanide a

Bina Rania, R.K. Prasadb, and Divya Singha Department of Chemistry, Magadh Mahila College, Patna University, Patna-800 001, Bihar, INDIA b Department of Chemistry, Science College, Patna University, Patna-800 005, Bihar, INDIA

Abstract: Complexes of Ni  II  and Co  III  with Hexamethylenedibiguanide  Hm  BigH +   of the 2

molecular composition Ni Hm  BigH +    OH 2 .H2O , Ni  Hm  BigH +   SO4 .4H2 O , Ni  Hm  BigH +   Cl 2 .4H 2 O , 2 2 2

Co2  Hm  BigH + 2   OH 6 .H 2O and Co 2  Hm  BigH + 2   SO4 3 .6H 2 O have been prepared and 3 3

characterized by the studies of IR, UV, electrical conductance and magnetic susceptibility measurements. The complexes of both the Ni  II  and Co  III  are found to be diamagnetic which indicates the low spin square planar structure of Ni  II  complexes and Octahedral geometry of Co  III  complexes. Keywords: Cobalt, Complexes, Hexamethylenedibiguanide, Nickel

I. Introduction  SO .H  

 Hm  BigH + 2 

O Hexamethylenedibiguanide sulfate (Fig. 1) is a quadridentate chelating ligand and its complexes are known with only some metals. Its donor behavior has not been studied extensively.

CH2

NH3

(3)

(5)

(1)

(4)NH

(2)NH

SO4 . H2O

(CH2)4 (5)

CH2

(2)NH

(4)NH

(3)

(1)

NH3

Fig. 1

This ligand coordinates with the N(2) and N(4) of both the biguanide substituent groups to form a six membered chelate ring with the metal ion. To determine the chelating behavior of hexamethylenedibiguanide ligand, we have prepared and characterized the complexes of Ni(II) and Co(II) with this ligand. II. Materials and Methods The ligand Hexamethylene dibiguanide was prepared by the reported method. Preparation of the complexes:

Ni   Hm  BigH     OH 2 .H 2 O : A. Nickel hexamethylenedibiguanidium hydroxide monohydrate, It was obtained by mixing the strong alkaline solution of hexamethylenedibiguanide sulphate (2.8 g) with a solution of nickel sulfate (1.2 g) and digesting it in a water bath for 3-4 hours. A yellow colored precipitate was obtained, it was filtered, washed with water, then with alcohol and was finally dried over KOH. This substance liberated ammonia from ammonium salts. +

Analysis:

Found

(Calculated.)

Ni, 14.90% 14.94 ; N, 35.22%  35.44  ; C, 30.05%  35.44 ,

H, 07.10%  07.09 ; H2O, 04.80%  04.55

Nickel hexamethylenedibiguanidium sulfate tetrahydrate,

+ Ni   Hm  BigH 2   SO 4 .4H 2 O

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Bina Rani et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 77-80

The complex Sulphate was obtained by digesting the complex Nickel hexamethylenedibiguanidium hydroxide monohydrate, with ammonium sulfate.

Ni, 11.30% 11.54 ; SO4 , 18.32% 18.78 ; N, 27.12%  27.39 ;

Analysis: Found (Calculated.) C, 23.20%  23.48 , H, 06.30%  06.26 ; H2O, 14.20% 14.09 

Ni  Hm  BigH + 2  Cl .4H O

2   2 C. Nickel hexamethylenedibiguanidium sulfate tetrahydrate, : The complex chloride was obtained by the action of ammonium chloride on the complex Nickel hexamethylenedibiguanidium hydroxide monohydrate. Ni, 12.09% 12.14  ; Cl, 14.58% 14.61 ; C, 24.65%  24.69  ; Analysis: Found (Calculated.)

H, 06.60%  06.58 ; H2O, 14.65% 14.81

. D. Dicobaltic (III) tris-hexamethylenedibiguanidium hydroxide monohydrate, Co 2  Hm  BigH + 2   OH 6 .H 2O 3 . A solution of hexamethylenedibiguanide sulfate (0.5 g) in water (200 ml) was made strongly alkaline with

NaOH solution (20ml, 4N NaOH) and treated with 1.6 g cobalt sulfate solution (30 ml). The mixture was heated on the water bath. As ammonia was given off, the complex base separated as a soft mass. This was cooled and filtered after 1.5 hours. The product was washed as usual and dried over KOH . It forms dark red crystals, insoluble in water and alcohol. Co, 10.80% 10.82  ; N, 38.48%  38.53 ; C, 32.80%  33.03 ; Analysis: (Calculated.)

H, 07.36%  07.34 ; H2O, 01.68%  01.65

. Dicobaltic (III) tris-hexamethylenedibiguanidium sulphate hexahydrate, Co 2  Hm  BigH + 2   SO4 3 .6H 2 O 3 . The sulphate was obtained by digesting the Dicobaltic(III) tris-hexamethylenedibiguanidium hydroxide monohydrate with ammonium sulphate. E.

Co, 08.38%  08.64 ; N, 30.50%  30.74 ; SO4 , 20.89%  21.08 ; Analysis: (Calculated.) C, 26.32%  26.35 ; H, 06.18%  06.15 ; H2O, 07.99%  08.10  . III.

Result and discussion

It has been found that hexamethylenebiguanide,

Hm  BigH 2 ,  C10 H24 N10 

forms complexes with the first

row transition metal ions in alkaline medium. The ease with which parent biguanide,

BigH,  C2 H7 N5 

complexes with general transition metal ions is not retained in case of hexamethylenedibiguanide,

forms Hm  BigH 2

Hm  BigH 

2 forms complexes slowly in ammoniacal medium on gentle with cobalt (II) and nickel (II). refluxation. The cobalt (II) complex could not be isolated as it gets oxidized to cobalt (III) and sparingly soluble

Co2  Hm  BigH+    OH  .H2O

6  2 3 product of the composition is formed. In case of simple biguanide, phenylbiguanide and alkylbiguanide, both cobalt(II) and cobalt (III) complexes have been isolated. The cobalt (II) complexes of parent biguanide are susceptible to aerial oxidation and cobalt (III) complexes are formed Co2  Hm BigH+   OH 6 .H2O  2 3 immediately. The complex base of composition was formed by this ligand in sodium hydroxide solution. The complex base after refluxing with appropriate ammonium salt gave complex chloride and sulphate. These complex salts have low solubility in cold water but get dissolved appreciably on heating. The aqueous solution of complex salts are conducting, indicating their ionic



character





 400 ohm-1 mol-1 cm2 



. The complex salts are diamagnetic indicating their low spin

octahedral geometry in oxidation state 1 . The electronic absorption spectrum of complex chloride in aqueous

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Bina Rani et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 77-80 1

1

solution displays one broad band at 490 cm and a shoulder near 360 cm . The electronic absorption bands 1

are attributed to

A1g  1T1g

and

A1g  1T2g

transitions in octahedral field.

Nickel (II) has been found to form a cream yellow complex

Ni Hm  BigH +    OH 2 .H 2O  2 3 in strongly

alkaline solution of hexamethylenedibiguanide sulphate in NaOH and nickel (II) sulphate solution in water at

Ni  Hm  BigH + 2  SO4 .4H 2 O

reflux temperature. The complex sulphate and chloride of composition

Ni  Hm  BigH

and

  Cl .4H O respectively were obtained by refluxing complex base with excess of ammonium 2

sulphate/chloride in aqueous suspension. These complex salts of nickel (II) are quite stable and retain water 



molecules at room temperature but gradually lose water molecule on heating. At 110 -120 C the complex base as well as other salts completely eliminate all water molecules and become anhydrous. The complex base  Ni Hm BigH +  2 becomes an anhydro base such as  . The complex base is sparingly soluble in water whereas the complex chloride and complex sulphate are slightly soluble in hot water. The aqueous solutions of complex







salts are conducting



180-190 ohm-1 mol-1 cm2 

indicating their ionic character. At room

temperature the complexes are diamagnetic supporting their square planar structure with bonding. In the planar field, the d orbital of nickel (II) complexes splits into four energy level

a1g

metal s orbital also spans as

b1g a1g ,

d x 2 -y2  b1g 

and

while p orbitals span

b1g , b2g , a1g

and

. The

e u and a1g representation. The ligand orbitals transform

e u state. Thus, in square planar field the splitting of the orbitals raises the energy of a1g  d z2  b2g  d xy 

orbital at relatively higher energy from the other three d levels viz;

eg  d xz ,d yz 

dsp 2 covalent hybrid

. The ground state for low spin d configuration is thus, 3

b



 e , b  and

2g 1g 2g 2g g excited states are , , , , g expect three spin allowed and three spin forbidden bond. The spin allowed bands are:

a 3

2 1g

4 g

4 1g

e b ,



A1g 

B1g  a1g , b1g 

and

. The ligand field

. Therefore, one should

A1g   B2g 1 1 A1g   B1g 1 1 A1g   Eg 1

and three spin forbidden bonds: 3

A1g   A 2g 3 1 A1g   Eg 3 1 A1g   B1g 1

Due to relatively low ɛmax value of spin forbidden transition the singlet triplet transitions are not observed in planar nickel (II) complexes. The planar Ni (II) complexes can also display spin forbidden transitions of very low ɛmax value in case of σbonding

ligands.

Ni  Hm  BigH

present

investigation

  SO .4H O display abroad 4

(II)

complex

Ni  Hm  BigH + 2   OH 2 .H 2 O 1

A1g   B1g

and shoulder near 460 nm attributable to transition in planar field. Hence, the probable structure of Ni (II) complexes is square planar. Unlike Co (II), the majority of Co (III) complexes are diamagnetic and low spin type. The term for free Co (III) ion (d6) is

D . The free

ion ground state energy level term

5 5 D splits into T2g and E g states in spin

free

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Bina Rani et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 77-80 1

complex ion. Due to high cationic charge on Co III (compared to FeII, d6 system),

A1g

of the high energy singlet

state originating from one

 I  state of the free ion drops very rapidly and crosses the 1

T2g

state at a very low

3 6

CoF ) are low spin type. The visible absorption value of  . Thus, all Co (III) complexes known (expect 1

spectra of cobalt (III) complexes display absorption from the 1

and 1

T2g

A1g

ground state to next upper energy level 1

state. In complexes of trivalent cobalt the spin forbidden transition

T1g

A1g   Eg

A1g   T2g

are

A1g   T1g

seldom observed.

and 1

A1g   T2g

The

complexes

show absorption

. In

bands

attributed

from

 T1g D 4h symmetry the transition A1g  splits or broadened 1

due to splitting of 1g state. The electronic absorption spectra of complex sulphate (very qualitatively due to incomplete dissociation) displayed a broad band at490 nm and second shoulder at 380 nm attributed from 1

A1g   T1g

A1g   T2g

and transitions respectively in octahedral field. Thus, from above experimental finding the probable structure of cobalt (III) complexes is octahedral. Acknowledgements We are thankful to the staffs of the Department of chemistry, Patna University, Patna for their cooperation and support throughout this experiment. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25.

P. Ray, Chem. Rev., 1961, 61, 313. D.Sen, J.Chem.Soc., (A), 1969, 1304, J.Indian Chem.Soc.,1974.,51, 183, J.Chem.Soc. (D)., 1975, 1701. N.R. Kunchur, Nature., 1968, 217, 589. T.C. Creitz, R. Gsell and D.L. Wampler, Chem. Comm., 1969, 1371. Smith, Aust.J.Chem, 1969, 22, 659. N.E. Swartz and P.D. Alphoso, J. Electr. Spectra., Rel. Phenomena, 1974, 4, 351. L. Coghi, M. Nardeli and G. Pelizzi, Acta. Crystallogr., 1976, 32B, 842. R.L Dutta, J. Indian Chem. Soc., 1960, 32, 37. P.Ray and A.K. Chaudhary, J. Indian Chem. Soc., 1950, 27, 651. C.J. Ballahausen, “Introduction to Ligand Field Theory”, Mcgraw Hill, Ed. 1961. R.H. Holm, G.W. Everett and A. Chakravorty, Progr. Inorg. Chem., 1966, 7, 83. S.E. Livingstone, Quart. Rev., 1965, 19, 386. L.J. Bellamy, “Infrared Spectra of Complex Molecules”, Methen and Co. Ltd., John Wiley & Sons, INC, Ed. 1958. K. Nakanishi, “Infrared Spectra of Organic Molecules”, John Wiley, 1958. W.C.E. Higginson, S.C. Nyburg and J.S. Wood, Inorg. Chem., 1964, 3, 463. S.C. Nyburg and J.S. Wood, Inorg. Chem., 1964, 3, 468. C.M. Nelson and T.M. Shephard, J. Chem. Soc., 1965, 3284. D.R. Eaton and W.D. Phillips, J. Chem. Phys., 1965, 43, 392. B.T. Kilborn, H.M. Powell and J.A.C. Darbyshire, Proc. Chem. Soc., 1963, 207. R.H. Holm, G.W. Everett and A. Chakravorty, Prog. Inorg. Chem., 1967, 7, 81. W.A. Baker (Jr.) and H.M. Bobonich, Inorg. Chem., 1964, 3, 1164. K.V. Krishnamurthy, G.M. Harris and V.S. Shastri, Chem. Rev., 1970, 70, 171. A.L. Underwood and L.H. Howe, Anal. Chem., 1962, 34, 692. G. Lapidus and G.M. Harris, J. Amer. Chem. Soc., 1963, 85, 1223. M. Mori, M. Shibata, E. Kyuno and T. Adachi, Bull. Chem. Soc., 1956, 29, 883.

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

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

Experimental/Computational Results for Shielding Neutron & Photon Radiations C. E. Okon*1, I. P. Etim*2 School of Physics & Astronomy, University of Manchester, Manchester, United Kingdom. *2 Department of Physics, University of Calabar, NIGERIA.

Abstract: This report demonstrates the results of the experiment that was carried out in the Radiation Laboratory in University of Liverpool. For the Photon experiment, two sources were used Cesium137(661.5eV) and Sodium-22(1275eV) and absorber materials were Aluminum, Lead and Copper. The detector used for the photon experiment was Sodium Iodide Scintillator. For the attenuation of gamma radiation, the origin of these radiations is not significant; it is only their energy which determines the attenuation in a given medium. The degree to which gamma radiation is attenuated is dependent upon the energy of the incident gamma radiation, the atomic number and density of the elements in the shielding material, and the thickness of the shielding[1]. All substances will attenuate gamma rays to some extent, although the linear attenuation coefficient generally increases with the density, for photons of given energy. This coefficient is very roughly proportional to the density and so, the thickness of different materials required to attenuate gamma radiation, of specified energy, to the same extent are inversely proportional to their respective densities. Consequently, where thickness of the shield is an important consideration, a material of high densities would be used to attenuate the gamma rays[5]. I. Introduction The most significant radiations for which shielding is required are the primary neutrons and photons. However, shield designed involves more than choosing a suitable shielding material (or materials) and determining the thickness required to decrease the total radiation effectiveness, i.e, the sum of the neutrons and gamma-ray does equivalents to an acceptable value. Absorption of the radiations in the shield is accompanied by the liberation of energy which appears as heat. In order to ensure the physical integrity of the shield material, the temperature distribution must be known. Thus a study of heat generation is an essential aspect of shield design, and this requires a knowledge of the distribution and absorption of neutrons and gamma rays throughout the thickness of the shield[4]. Neutrons interact in matter via nuclear reactions, which depends strongly on the energy and the particular nuclei with which the neutrons collide. Neutrons may be scattered in which case energy is transferred to recoiling nuclei. Alternatively, they may be absorbed in the variety of different processes. At energies of a few MeV, fusion is the main contributor to the absorption cross section. In fusion, a neutron is first captured to form a compound nucleus excited to energy approximately equal to the initial kinetic energy plus the neutronâ&#x20AC;&#x2122;s biding energy which is, typically, 7 to 8 MeV. This energy is subsequently released in the form of reaction products, which may be gamma rays, charge particles or neutrons. Reactions that result in the emission of the energetic charged particles are important because they enable the presence of neutrons to be detected [2]. Photons transfer their energy to electrons via Compton scattering, photoelectric effect and pair production. The attenuation coefficient of photons depends strongly on energy and the atomic number of the material. Compton scattering is the most important interaction process for photon energies above 40KeV to several tens of MeV. The scattered photons may interact further by a second Compton scattering or by photo electric absorption, depending on the energy[4]. II. Aim & Objective The aim of this experiment was to establish the relationship between the source strength and shield thickness. Monte Carlo simulations using MCNP was also employed to compare the experimental results with the computational. III. Methodology & Results A. Comparison between Laboratory & Computational Simulations for Neutron Comment: the comparison of the experimental result and the MCNP simulations as shown in Fig.11, are both in agreement, although the number of neutrons detected in the experimental result when there is no shield and at

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 81-89

small moderator thickness of 2cm & 4cm is greater than that detected using MCNP simulations, whereas the reverse is the case for moderator thickness greater than 4cm. From the graph below, I can conclude that the MCNP simulations results was a bit more accurate than the experimental results because it shows that more neutrons are being captured and some scattered of at smaller thickness than that observed in the experiment. But when the moderator is placed far from the detector as shown in Fig.12, the number of neutrons detected from the experiment point of view is greater than that of MCNP simulations from when there is no shield to the last shield thickness. This result still confirms the fact that MCNP simulations is a bit more reliable than the experimental results I got in the laboratory. See Data: Appendix “A” Table 11 to Table 14

B. Comparison between Laboratory & MCNP Simulation for the Photon Shielding Comment: the graphs below shows the correlations between the experimental results and the MCNP simulations for the photon experiment using cesium-137 source and sodium-22(1275eV) source. The comparison was carried out for Aluminium, Lead and Copper. According to the result I got (Fig. 15-17), it shows that for Cesium source of the three different materials used, the number of photons detected decreases with increasing thickness for both the experiment and the MCNP simulations. Therefore it can be concluded that the results gotten in both cases where in agreements. For sodium-22 source, we can observed that for the case of aluminium (Fig.18) the detected flux decreases with increasing thickness as expected without any significant change in the number of detected flux for both cases (experiment & MCNP). But for the case of Lead (Fig.19) we observed that the number of photons detected by the scintillator for the MCNP simulations was higher than that recorded in the experiment. This shows that the experimental results obtained was more accurate than the MCNP simulation. Also, a large observable change is seen in the case of copper (Fig.20), the detected flux for experiment reduces drastically more than the simulations for different absorber thickness. This result implies that the experimental result for using a SodiumIodide scintillator is more reliable than MCNP simulations. Fig 22. Shows the efficiencies for different materials used for the photon experiments. According the graph in Fig22., we can see that the Lead material is the best absorbing material compare to Aluminium and Copper. Fig. 23 also confirm by showing that the number of neutrons detected using a lead material is less than that detected using aluminium and copper, and as such we can conclude that a Lead material is better for shielding of gamma radiation than Aluminium or Copper.

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 81-89

See Data: Appendix “B” Table 15 – Table 20

See Data: Appendix “B” Table 26 & Table 27

See Data: Appendix “B” Table 28 & Table 29

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 81-89

C. Photon Shield Calculations Na-22 decay scheme spectrum show a gamma ray at 1274.5keV, an annihilation peak at 511keV (from the betaplus), and probably x-ray from the electron capture. For the shielding scenario, let us assume that all gammarays are 1274.5keV and the rest are ignored for the shielding calculations. Count rate from Na-22 source (A1274.5keV)= 1 mCi = 3.7 x 107Bq=37MBq = 37 x 106 cps Count rate required after shielding =1.625MBq (A1274keV=0.325MBq and A511keV=1.30MBq)= 1.625 x 106 cps The dose received from exposure to an external source emitting radiation in all direction depends on its distance and any shielding which may be in place. Approximate formula for estimating the rate at which a dose is accumulated in tissue at a distance r meters from a radioactive source emitting A photons per second of energy (Ey) in MeV is given as (Lamarsh & Baratta 2001)[3]:

Hence, the maximum allowed activity,

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 81-89

Let us consider the case where all the gamma-ray photons travel towards the radiation worker. The Effective dose,

For radiation protection purposes, an average absorbed dose for a tissue or organ

Assuming mass of a whole body to be 70kg

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C. E. Okon et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014February 2015, pp. 81-89

Neutron Shield Calculations

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

M. El-Wakil, Nuclear Power Engineering, McGraw-Hill, 1962. Glassione and Sesonske, Nuclear Reactor Engineering, Van Nostrand Reinhold Company, 1981. J. Lamarsh and A. Baratta, Introduction to Nuclear Engineering, Prentice Hall Inc., 2001. J. Lilley, Nuclear Physics Principles and Applications, John Wiley and Sons, Ltd, 2001. N. Todreas and M. Kazimi, Nuclear Systems 1 Thermal Hydraulic Fundamentals, Thermisphere Publishing Corporation, 1990.

References

Table 11:

POLYTHENE BEFORE DETECTOR Moderator Thickness (cm)

Detected Flux Experiment

0 2 4 6 8 10 12

14163 21887 22094 20752 18333 16166 14040

Detected Flux MCNP 3.60E-10 2.97E-08 1.29E-07 1.98E-07 2.10E-07 1.91E-07 1.60E-07

Normalized Flux Experiment 0.64 0.99 1.00 0.94 0.83 0.73 0.64

Normalized Flux MCNP 0.00 0.14 0.61 0.94 1.00 0.91 0.76

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Table 12:

POLYTHENE AFTER DETECTOR Detected Flux Detected Flux Normalized Flux Experiment MCNP Experiment 14163 3.60E-10 0.81 15855 2.38E-09 0.90 16384 1.00E-08 0.93 16762 1.73E-08 0.95 17079 2.19E-08 0.97 17245 2.49E-08 0.98 17360 2.72E-08 0.99

Moderator Thickness (cm) 0 2 4 6 8 10 12 Table 13:

Normalized Flux MCNP 0.01 0.08 0.34 0.59 0.75 0.85 0.93

BORATED POLYTHENE BEFORE DETECTOR Moderator Thickness (cm)

Detected Flux Experiment

Detected Flux MCNP

14196 14163 13027 11965 11537 10711

0 1 2 3 4 5 Table 14:

Normalized Flux Experiment

3.60E-10 6.05E-10 6.53E-10 6.58E-10 6.36E-10 6.03E-10

Normalized Flux MCNP

1.00 1.00 0.92 0.84 0.81 0.75

0.55 0.92 0.99 1.00 0.97 0.92

BORATED POLYTHENE AFTER DETECTOR Moderator Thickness (cm)

Detected Flux Experiment

0 1 2 3 4 5

14163 12692 12606 12183 12066 12269

Detected Flux MCNP

Normalized Flux Experiment

3.60E-10 3.78E-10 3.86E-10 3.93E-10 3.98E-10 4.01E-10

Normalized Flux MCNP

1.00 0.90 0.89 0.86 0.85 0.87

0.90 0.94 0.96 0.98 0.99 1.00

APPENDIX B Cs-137 Gamma Source (Aluminium)

Table 15: Absorber Thickness (mm) 0 6 12 18 24 30

Detected Flux Experiment

Detected Flux MCNP

Normalized Experiment

Normalized MCNP

25593.20 21911.80 19627.80 17442.80 15389.60 13431.00

3.59E-03 3.18E-03 2.82E-03 2.49E-03 2.21E-03 1.95E-03

1.0 0.9 0.8 0.7 0.6 0.5

Cs-137 Gamma Source (Lead)

Table 16: Absorber Thickness (mm) 0 3 6 9 12 15 Table 17: Absorber Thickness (mm) 0 6 12 18 24 30

Detected Flux Experiment 25593.20 18605.80 13610.00 10074.00 7469.60 5844.00

Detected Flux MCNP

Normalized Experiment

Normalized MCNP

3.59E-03 3.30E-03 3.04E-03 2.79E-03 2.57E-03 2.36E-03

1.00 0.73 0.53 0.39 0.29 0.23

1.00 0.92 0.85 0.78 0.72 0.66

Cs-137 Gamma Source (Copper) Detected Flux Detected Flux Normalized Experiment MCNP Experiment 25593.20 3.59E-03 1.00 16265.80 3.19E-03 0.64 10337.00 2.84E-03 0.40 6741.00 2.53E-03 0.26 4496.20 2.25E-03 0.18 3328.20 2.00E-03 0.13

Normalized MCNP 1.00 0.89 0.79 0.70 0.63 0.56

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Na-1275 Gamma Source (Aluminium)

Table 18: Absorber Thickness (mm)

Detected Flux Experiment

Detected Flux MCNP

Normalized Experiment

Normalized MCNP

0 6 12 18 24

5862.97 5381.03 4951.48 4415.73 4182.61 3662.15

1.80E-03 1.65E-03 1.51E-03 1.38E-03 1.26E-03 1.16E-03

1.00 0.92 0.84 0.75 0.71

1.00 0.92 0.84 0.77 0.70

0.62

0.64

Na-1275 Gamma Source (Lead)

Table 19: Absorber Thickness (mm)

Detected Flux Experiment

Detected Flux MCNP

Normalized Experiment

Normalized MCNP

0 3 6 9 12 15

25593.20 21911.80 19627.80 17442.80 15389.60 13431.00

1.80E-03 1.72E-03 1.63E-03 1.57E-03 1.50E-03 1.44E-03

1.00 0.86 0.77 0.68 0.60 0.52

1.00 0.96 0.91 0.87 0.83 0.80

Na-1275 Gamma Source (Copper)

Table 20:

Table 21:

Absorber Thickness (mm)

Detected Flux Experiment

Detected Flux MCNP

Normalized Experiment

Normalized MCNP

0 6 12 18 24 30

5862.97 4378.09 3191.58 2494.06 1656.3 1210.42

1.80E-03 1.62E-03 1.52E-03 1.40E-03 1.29E-03 1.18E-03

1.00 0.75 0.54 0.43 0.28 0.21

1.00 0.92 0.84 0.78 0.72 0.66

MCNP RESULTS-Aluminium Absorber Thickness (mm) 0 6 12 18 24 30 36

Table 22:

Table 23:

Cs-137 Gamma Source

Incident Flux

Detected Flux

Normalized Incident

Normalized Detected

2.09E-05 1.88E-05 1.70E-05 1.51E-05 1.37E-05 1.21E-05 1.11E-05

3.59E-03 3.18E-03 2.82E-03 2.49E-03 2.21E-03 1.95E-03 1.73E-03

1.000 0.900 0.813 0.722 0.656 0.579 0.531

1.000 0.886 0.786 0.694 0.616 0.543 0.482

MCNP RESULTS-Lead Absorber Thickness Incident Flux (mm) 2.09E-05 0 1.94E-05 3 1.80E-05 6 1.66E-05 9 1.53E-05 12 1.42E-05 15 1.31E-05 18 MCNP RESULTS-Copper Absorber Thickness Incident Flux (mm) 2.09E-05 0 1.89E-05 6 1.71E-05 12 1.53E-05 18 1.40E-05 24 1.24E-05 30 1.13E-05 36

Detected Flux 3.59E-03 3.30E-03 3.04E-03 2.79E-03 2.57E-03 2.36E-03 2.17E-03 Detected Flux 3.59E-03 3.19E-03 2.84E-03 2.53E-03 2.25E-03 2.00E-03 1.78E-03

Normalized Incident 1.000 0.928 0.861 0.794 0.732 0.679 0.627 Normalized Incident 1.000 0.904 0.818 0.732 0.670 0.593 0.541

Efficiency % 100.0 98.5 96.6 96.0 93.9 93.8 90.7

Cs-137 Gamma Source Normalized Efficiency % Detected 1.000 100.0 0.919 99.0 0.847 98.3 0.777 97.8 0.716 97.8 0.657 96.8 0.604 96.4 Cs-137 Gamma Source Normalized Efficiency % Detected 1.000 100.0 0.889 98.3 0.791 96.7 0.705 96.3 0.627 93.6 0.557 93.9 0.496 91.7

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Table 24:

Table 25:

Table 26:

Table 27:

MCNP RESULTS-Aluminium Na-1275 Gamma Source Absorber Thickness Normalized Normalized Incident Flux Detected Flux Efficiency % (mm) Incident Detected 7.34E-06 1.80E-03 1.000 1.000 100.0 0 6.36E-06 1.65E-03 0.866 0.917 105.8 6 5.83E-06 1.51E-03 0.794 0.839 105.6 12 5.48E-06 1.38E-03 0.747 0.767 102.7 18 5.06E-06 1.26E-03 0.689 0.700 101.5 24 4.57E-06 1.16E-03 0.623 0.644 103.5 30 4.18E-06 1.06E-03 0.569 0.589 103.4 36 MCNP RESULTS-Lead Na-1275 Gamma Source Absorber Thickness Normalized Normalized Incident Flux Detected Flux Efficiency % (mm) Incident Detected 7.45E-06 1.80E-03 1.000 1.000 100.0 0 6.55E-06 1.72E-03 0.879 0.956 108.7 3 6.27E-06 1.63E-03 0.842 0.906 107.6 6 5.98E-06 1.57E-03 0.803 0.872 108.7 9 5.79E-06 1.50E-03 0.777 0.833 107.2 12 5.57E-06 1.44E-03 0.748 0.800 107.0 15 5.39E-06 1.37E-03 0.723 0.761 105.2 18 MCNP Simulation for Cs-137 Gamma Source (Normalized Efficiency Values) Absorber Absorber Efficiency(%) Efficiency(%) Thickness Thickness Efficiency(%) Copper Aluminium Lead (mm) (mm) 100.00 100.00 100.00 0 0 98.50 99.00 98.30 3 6 96.60 98.30 96.70 6 12 96.00 97.80 96.30 9 18 93.90 97.80 93.60 12 24 93.80 96.80 93.90 15 30 90.7 96.40 91.70 18 36 MCNP Simulation for Na-1275 Gamma Source (Normalized Efficiency Values) Absorber Absorber Efficiency(%) Efficiency(%) Thickness Thickness Efficiency(%) Copper Aluminium Lead (mm) (mm) 100.0 100.0 100.0 0 0 105.8 108.7 107.1 3 6 105.6 107.6 106.4 6 12 102.7 108.7 104.5 9 18 101.5 107.2 104.7 12 24 103.5 107.0 104.4 15 30 103.4 105.2 107.2 18 36

Table 28:

MCNP Simulation for Cs-137 Gamma Source Absorber Thickness (mm) 0 3 6 9 12 15 18

Absorber Thickness (mm) 0 6 12 18 24 30 36

Absorber Thickness (mm) 0 3 6 9 12 15 18

Absorber Thickness (mm) 0 6 12 18 24 30 36

Table 29:

Detected Aluminium

Detected Lead

Detected Copper

Normalized Aluminium

3.59E-03 3.59E-03 3.59E-03 1.00 3.18E-03 3.30E-03 3.19E-03 0.89 2.82E-03 3.04E-03 2.84E-03 0.79 2.49E-03 2.79E-03 2.53E-03 0.69 2.21E-03 2.57E-03 2.25E-03 0.62 1.95E-03 2.36E-03 2.00E-03 0.54 1.73E-03 2.17E-03 1.78E-03 0.48 MCNP Simulation for Na-1275 Gamma Source

Normalized Lead 1.00 0.92 0.85 0.78 0.72 0.66 0.60

Detected Aluminium

Detected Lead

Detected Copper

Normalized Aluminium

Normalized Lead

1.80E-03 1.65E-03 1.51E-03 1.38E-03 1.26E-03 1.16E-03 1.06E-03

1.80E-03 1.72E-03 1.63E-03 1.57E-03 1.50E-03 1.44E-03 1.37E-03

1.80E-03 1.62E-03 1.52E-03 1.40E-03 1.29E-03 1.18E-03 1.07E-03

1.00 0.92 0.84 0.77 0.70 0.64 0.59

1.00 0.96 0.91 0.87 0.83 0.80 0.76

Normalized Copper 1.00 0.89 0.79 0.70 0.63 0.56 0.50 Normalized Copper 1.00 0.90 0.84 0.78 0.72 0.66 0.59

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Intense Geomagnetic Storms and their Association with Solar Wind Plasma and Interplanetary Parameters Mukesh Kanwal Tripathi1, R.S. Gupta2 and M.P. Yadav3 Govt. P.G. College, Satna, Madhya Pradesh, 485001, India. 3 Govt. Tilak P.G. College Katni, Madhya Pradesh, 483501, India. 1,2

Abstract: In the present study we investigate intense, super intense geomagnetic storms (GMSs) by using solar wind plasma (SWP), interplanetary magnetic field (IMF), planetary disturbance index (Ap ≥ 50 nT), disturbance storm time (Dst ≤ -100 nT) index and coronal mass ejections (CMEs) data during the period 1996-2010. We have also find out different correlation between SWP, IMF parameter with Dst, Ap indices covering the time interval 1996-2010. We have observed forty eight intense (-200nT ≤ Dst < - 100nT) and sixteen super intense (Dst ≤ -200nt) GMSs. Maximum number of GMSs (92%) have occurred during maximum activity years. More than 60% intense and super intense GMSs are associated with -20nT < Bz < -10nT and almost 28% intense and super intense GMSs are associated with Bz < -20nT during study period. Dst is negatively correlated with V, T and B while Dst is positively correlated with Bz. It has been observed that Ap is positively correlated with V, T and B with negatively correlated with Bz. It has also observed that maximum number of halo, phalo and total CMEs have occurred during maximum solar activity years and peak is observed around solar maxima. Key words: Geomagnetic Storms (GMSs), coronal mass ejection (CMEs), SWP parameters, IMF parameters and correlation. I. Introduction The solar terrestrial relationship includes the effect of solar output and its variations. The origin of geomagnetic storms (GMSs) in the interplanetary medium has been investigated for over three decades (Akasofu & Chapman 1963, Akasofu 1981). A GMS occurs when an interplanetary (IP) structure containing southward magnetic field (Bz) merges with the northward field in the magnetopause, resulting in the flow of solar wind energy into magnetosphere and causing the enhancement of the ring current. The storm mechanism is elucidated by Dungey (1961). The suggestion that the southward component of interplanetary magnetic field (IMF) Bz is the dominant parameter responsible for the development of the storms. Intense southward interplanetary magnetic field are well documented as causing GMSs (Rostoker & Falthmmar 1967, Gonzalez & Tsurutani 1987). Gonzalez & Tsurutani (1987) have shown that interplanetary events have a one to one causal relationship with intense GMSs. Numerous intense storms occur during the maximum phase of the solar cycle and they are mostly associated with coronal mass ejections (Zhang et al 2003, Gopalswamy et al 2007). Zhang et al (2006) studied interplanetary causes of intense geomagnetic storms at different stages of solar cycle. Lysatsky and Tan (2003) have studied geomagnetic storms with disturbances in solar wind plasma parameters. Coronal mass ejections are most energetic solar events that eject huge amount of mass and magnetic fields into the heliosphere. When coronal mass ejections (CMEs) erupt from the sun, high speed particles and strong magnetic field can hurl earthward which causing a significant impact on the near earth space environment causing (disturbances) an adverse effects on satellites and communications, electric power, pipeline etc. The disturbance of the near earth environment are measured by various parameter such as Ap (Bartels et al 1939) and disturbance storms time, Dst (Sugiura 1964) indices. Variations in solar activity are traced by measuring sunspot numbers (Hoyt & Schatten 1998). Gopalswamy (2006) introduced CME daily rate as a new solar activity indicated closely correlated to the geomagnetic activity. In the present investigation, major/moderate, intense and super intense GMSs have been studied. The CMEs with an apparent width (W) of 3600 are taken as ‘halo’ where as the CMEs with 120 ≤ W < 3600 are taken as ‘partial halo’ CMEs. Almost similar criterion has been taken by Zhang et al (2003) and Gopalswamy et al (2007). The halo, partial halo CMEs are also investigated for the period.

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However, we are still far from the final stages of the quantitative understanding toward the goal of predicting the cause of solar and geomagnetic activities (Geomagnetic storms) from the knowledge of solar and interplanetary parameters. In this paper, an attempt has been made to identify the solar and interplanetary parameters (IPs) that contribute to the occurrence of major/moderate, intense, super intense GMSs. Furthermore, to investigate the correlation coefficients of SWP, IMF and solar features with geomagnetic indices during the period 1996-2010. II. Data Analysis Sixty four geomagnetic storms having intense and super intense nature have been observed during the period 19962010. The values of Dst indices are taken from World Data Center, Japan (http://swdc.www.kugi.kyoto-u.ac.Jp). Solar geophysical data are used to study storms sudden commencement (SSC). The GMSs have been cross verified by J.H. Allen list. The OMNIWEB data is used to obtain the values of solar wind parameters (SWP) while Advanced charge Explorer (ACE) data helped in providing the interplanetary magnetic field (IMF) data. The data sets used in this study included all the 64 GMSs with Dst ≤ - 100 nT along with Ap ≥ 50. We have used peak values of SWP and IMF parameters during the individual event (GMS) during the period 1996-2010. In this study, we defined the geomagnetic storms with Ap ≥ 50, as : (1) Intense storms, minimum Dst falls between -100 nT and -200 nT and (2) super intense storm when its minimum Dst is -200 nT or less. Almost similar criterion has been taken by Gonzalez et al (1999). III. Results and discussion Yearly occurrence of intense and super intense GMSs have been plotted in fig.1 for the period 1996-2010. The numbers of intense and super intense geomagnetic stroms have occurred 48 and 16 respectively during 1996-2010. It is apparent from fig. 1 that the large number of high strength GMSs are observed during maximum phase of 23 rd solar cycle as compared to the rising and declining phase. Further, 92% intense and super intense GMSs have occurred during the years from 1998-2005. We have investigated halo, partial halo and total CMEs for the study period which are shown in fig. 2. It is observable from fig. 2 that the maximum number of halo, partial halo and total CMEs have occurred during the maximum activity years. We have observed total 15629 CMEs for the period 19962010. Out of 15629 CMEs, the 409 and 911 are halo and partial halo CMEs respectively. The results between Solar Wind Plasma and Interplanetary parameters with Geomagnetic index have discussed in different sections. A. Relation between SWP and IMF parameters with Dst Index One obvious parameters from the solar wind data is the speed of shock wave, which can be used to calculate the maximum travel time of solar features from the sun to the near earth space at the onset of GMS. The minimum and maximum value of solar wind velocity is observed for intense and super intense 490, 1059 km/sec and 625, 1189 km/sec respectively. The hourly Dst index is one another important obvious parameter that can measure the intensity of GMSs. Dst index (Suglura 1964) is obtained from several magneto meter station near the equator. The Dst index is a direct measure of the hourly averaged perturbation of the horizontal component of earth magnetic field caused by the varying magnetosphere ring current. Large negative Dst values indicate an increase in the intensity of ring current which is known as GMSs. Fig. 3(a) shows the scatter plots between solar wind velocity versus Dst index. It is evident from fig. 3(a) that the Vsw and Dst are anti correlated and thus leading clearly to the dependence of Dst on Vsw. So, we concluded that solar wind plasma of high speed cause the GMSs of high intensity. Thus, solar wind velocity seems to be an important parameter in determining the nature of the GMSs. The scatter plot between Dst and proton temperature have been plotted in fig. 3(b). Fig. 3(b) shows that the proton temperature is anti correlated to Dst and correlation coefficient and slope of regression line found to be -0.27, -1728 respectively. This result shows that proton temp is not a good parameter for the determination of intensity of GMSs. The main cause of intense/super intense GMSs is believed to be large IMF structure, which have an intense, long duration and southward magnetic filed compound B z (Gonzalez et al 1999). They interact with the earth magnetic field and facilitate the transport the energy into the earth’s atmosphere through the reconnection process. In order to understand the response of the magnetosphere to inter planetary conditions. Interplanetary magnetic field (IMF) strength (B) and southward component of (B z) are investigated. We conclude that the more than 60% GMSs are associated with -20nT < Bz < -10nT and almost 28% GMSs are associated with B z < -20nT have occurred during the period 1996-2010. The scatter diagram of B(nT) versus Dst, and Bz(nT) versus Dst have been plotted in fig. 4(a),(b) respectively. The correlation coefficient between B(nT), Dst and Bz(nT), Dst are -0.78 and 0.79 respectively. B(nT) is strongly anti correlated to Dst whereas B z(nT) is highly positive correlated to Dst. Thus, we conclude that the B(nT), Bz(nT) are very good significant and key cause reliable parameters for initiation of GMSs. B. Relation between SWP and IMF parameters with Ap Index The planetary disturbance index (Ap) measures the solar particle effect on earth’s magnetic field and characterizes the general level of geomagnetic activity over the earth. It is derived from a and Kp indices (Bartets et al 1939),

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measured at a number of mid latitude stations worldwide. The Ap characterizing the variations of the geomagnetic field due to current flowing in the earth ionosphere and to a lesser extent in earthâ&#x20AC;&#x2122;s magnetosphere. The scatter diagram of solar wind velocity (V) versus Ap and proton temp (T) versus Ap index have been plotted in fig. 5(a),(b)

Fig.1 Shows yearly occurrence of intense and super intense GMSs during the period 1996-2010.

Fig. 2 shows annual occurrence rate of Halo, Phalo and total CMEs during 1996-2010.

Fig. 3 (a),(b) Shows scotter plot between Dst(nt) Vs V(km/s) and Dst(nt) Vs T( 0K) during GMS 1996-2010.

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Fig. 4 (a),(b) Shows scotter plot between Dst(nt) Vs B av(nt) and Dst(nt) Vs Bz(nt) during GMSs 1996-2010.

Fig. 5 (a),(b) Shows scotter plot between Ap(nt) Vs V(km/s) and Ap(nt) Vs T( 0K during GMSs 1996-2010.

Fig. 6(a),(b) Shows scotter plot between Ap(nt) Vs Bav(nt) and Ap(nt) Vs Bz(nt) during GMSs 1996-2010. respectively. The correlation co-efficient of V versus Ap and T versus Ap are 0.70, 0.55 respectively. V, T are positive good correlated to Ap index. The fig 6(a),(b) shows, the scatter plots of B(nT), Ap and Bz(nT) , Ap respectively for the period 1996-2010. The correlation coefficient of B(nT), Ap and Bz(nT), Ap have been observed to be 0.71 and -0.68 respectively. B(nT) is strongly positive correlated to Ap index where as Bz(nT) is strongly anti correlated to Ap index. The B(nT), Bz(nT) are best correlated to Dst rather than that of B(nT), Bz(nT) with Ap. Hence, B(nT), Bz(nT) may be considered as causal contributors in determining the strength of GMSs.

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IV. Conclusions On the basis of observational results and discussions, we have drawn following conclusions as: (a) Maximum number of intense and super intense GMSs(92%) have occurred during maximum solar activity years. (b) More than 60% intense and super intense GMSs are associated with -20nT < Bz < -10nT and almost 28% intense and super intense GMSs are associated with Bz < -20nT during 1996-2010. (c) Maximum number of halo, phalo and total CMEs have occurred during maximum solar activity years and peak is observed around solar maxima. (d) Solar wind velocity (V), proton temp (T) are better positive correlated to Ap index rather than that of Dst (Sign reversed) index. (e) B (nT), Bz (nT) are better correlated to Dst than that of Ap index. The sign of correlation coefficient of Dst index is reversed than that of Ap index. (f) The high speed solar wind plasma may be in the form of CMEs or else is more likely to cause the intense and super intense GMSs. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14].

S.I. Akasofu, and S. Chapman, 1963, The development of main phase of magnetic storms,J., Geophys. Res., 68, 125 S.I. Akasofu, 1981,Energy coupling between the solar wind and magnetosphere, Space Sci. Rev., 28, 121 J. Bartels, N.H. Heck, and H.F. Johnstone, 1939, The three hour range index measuring geomagnetic activity, J. Geophys. Res., 44,411 J. W. Dungey, 1961, Interplanetary magnetic field and auroral zones, Phys. Rev. Lett., 6, 47 N. Gopalswamy, S. Yashiro, and S.J. Akiyama, 2007, Geoeffectiveness of halo coronal mass ejections, J. Geophys. Res., 112, A06112 N. Gopalswamy, S. Yashiro, and S.J. Akiyama, 2006, Coronal mass ejections and their heliospheric consequence, In Proc. ILWS Workshop, 79 W.D. Gonzalez, and B.T. Tsurutani, 1987, Criteria of Interplanetary parameters causing intense magnetic storms (Dst < -100 nT), Planet. Space Sci., 35,1101 W. D. Gonzalez, B.T. Tsurutani, and A.L.C. Gonzalez, 1999, Interplanetary origin of geomagnetic storms ,Space Sci. Rev., 88, 529 D.V. Hoyt, and K.H. Schatten, 1998, Group sunspot numbers: A new solar activity Reconstruction, Sol. Phys., 181, 491 T. Lyatsky, and A. Tan, 2003, Solar wind disturbances responsible for geomagnetic storms, J. Geophys. Res.,108, 1134 G. Rostoker, and C.G. Falthammar, 1967, Relationship between changes in the interplanetary magnetic field and variations in the magnetic field at Earth surface, J. Geophys. Res., 72, 5853 M. Sugiura, 1964, Hourly values of equatorial Dst for the IGY, Ann. Int. Geophys., 35, 49 J. Zhang, K.P. Dere, R.A. Howard, and V. Bothmer, 2003, Identification of major sources of geomagnetic storms between 1996 & 2000, Astrophys. J., 582,520 J. Zhang, M.W. Liemohn, J.U. Kozyra, M.F. Thomsen, H.A. Elliott, and J.M. Weygand, 2006, A statistical comparison of solar wind sources of moderate and intense geomagnetic storms at solar minimum and maximum, J. Geyphys. Res., 111, A01104

Acknowledgements The authors are highly indebted to various experimental groups for providing data on the Web.

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Analysis of modulation technique in free space optics system Vasundhara Shukla1, Durgesh Shukla2, Jayant Shukla 3, Richi Nigam4 1,3,4 Corporate Institute of Science & Technology, Bhopal, Madhya Pradesh, INDIA. 2 Technocrat’s Institute of Technology, Bhopal, Madhya Pradesh, INDIA. Abstract: Free Space Optics (FSO) systems are generally employed for ‘last mile' communications and can function over distances of several kilometers as long as there is a clear line of sight between the source and the destination, and the optical receiver can reliably decode the transmitted information. Our paper is based on relative performance comparison of different modulation techniques for coherent transmission system is presented to improve the receiver sensitivity for wireless optical communications. Index Terms: wireless optical communication, differential phase-shift keying, balanced receiver, scintillation. I. Introduction The basic sub system of an optical wireless system is transmitter based on either (LEDs or LDs), the channel (the medium between the transmitter-receiver) the receiver based on (PIN or APD based) .the link length can vary from a few meters to a few km.The electrical information signal produced by source modulates an optical carrier. The one commonly modulate is intensity modulation (IM).The modulated optical carrier is propagated through the channel. At the receiver, the received optical field is optically collected and converted back to an electrical signal by detector, which is further processed by electronic stages to recover the original information with an acceptable level of error. II. Free Space Optical Communication In telecommunications, is an optical communication technology that uses light propagating in free space to transmit data between two points. Free Space Optical (FSO) communication systems, also known as wireless optical communications, provide tremendous potential for low-cost time-constrained high-bandwidth connectivity in a variety of network scenarios. III. Element of wireless optical communication A. Laser diode A laser diode is a laser where the active medium is a semiconductor similar to that found in a light-emitting diode.Laser diode is formed from a p-n junction and powered by injected electric current. B. LED A light-emitting diode (LED) It is a semiconductor light source. LEDs are used as indicator lamps in many devices, and are increasingly used for lighting. C. MZI Mach–Zehnder interferometer It is a device used to determine the relative phase shift between two collimated beams from a coherent light source. The interferometer has been used, amongst other things, to measure small phase shifts in one of the two beams caused by a small sample or the change in length of one of the paths. D. Optical amplifier The optical amplifier is a device that amplifies an optical signal directly, without the need to first convert it to an electrical signal. E. Optical filter The optical filters, generally, selectively transmits light having certain properties, while blocking the remainder. IV. Overview of Modulation Formats A. RZ-OOK RZ means ‘return-to-zero’, so the width of optical signal is smaller than its bit period. First, NRZ optical signal is generated by an external intensity modulator then, it is modulated by a synchronized pulse train with the same data rate as the electrical signal using another intensity modulator

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Vasundhara Shukla et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(1), December 2014-February 2015, pp. 95-98

B. NRZ-DPSK A very important characteristic of NRZ-DPSK is that its signal optical power is always constant. a one-bit-delay Mach-Zehnder Interferometer (MZI) is usually used as a DPSK optical receiver.

C. RZ-DPSK In order to improve system tolerance to nonlinear distortion and to achieve a longer transmission distance, return-to-zero DPSK (RZ-DPSK) has been proposed. Similar to NRZ-DPSK modulation format, the binary data encoded as either a “0” or a “π” phase shift between adjacent bits. V. Experimental result Comparison of ASK, PSK, DPSK S/N v/s BER & SR v/s BER A. Input data bit

Figure 1.1: Input data bit B.

Modulated data bit

Figure 1.2: Modulated data bit Comparison of ASK, PSK, DPSK S/N v/s BER

Figure 1.3: SNR v/s BER

Figure 1.4: SR v/s BER

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Figure 1.5: SNR vs BER at SI=0.3 & SR = 0.1

(a)

Figure 1.6: SNR vs BER at SI = 0.7 & SR = 0.1

(b)

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(a)

(b)

(c) Figure 1.9: SR v/s BER at Scintillation index=0.1 bit rate (a) 10Mbps, (b) 10Gbps, (c) 40 Gbps VI. Conclusion Wireless optical system, the DPSK format has a better performance than OOK in atmosphere turbulence for its longer symbol distance and being signal intensity insensitive. An appropriate coding technique improves the performance for the Wireless optical transmission system. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8].

Song Gao,Anhong Dang and hong Guo, “Performance of wireless optical communication system using DPSK Modulation” in ICACT, pp.1793-1796, Feb. 15-18 2009. K. Kikuchi, “Coherent optical communication systems,” in Optical Fiber Telecommunications V, vol. B, Systems and Networks, Eds. New York Academic, pp. 95–129, 2008. Jia Li,John Q.Liu. and desmond P. Taylor “optical communication using subcarrier PSK intensity modulation through Atmospheric Turbulence channels,” in Proc. IEEE ,vol.55,No.8, pp. 1598-1606,August 2007. P. J. Winzer and R.-J. Essiambre, “Advanced optical modulation formats,” Proc. IEEE, vol. 94, no. 5, pp. 952–985, 2006. A.H. Gnauck and P. J. Winzer Meng, “Optical Phase shifted key transmission atmospheric turbulence channel,” IEEE J. Light wave Technology, vol.23 no.1, pp.115-130, Jan 2005. M. Razavi and J. H. Shapiro, “wireless Optical communications via diversity reception and optical preamplification,” IEEE Transaction on communications vol.4 ,pp.975-983,May 2005. K. Kiasaleh, "Performance of APD-based, PPM free-space optical communication systems in atmospheric turbulence", IEEE Trans. Commun., vol.53 ,pp. 1455-1461,2005. Ron Hui, Sen. Zhang,“Advanced Optical Modulation Formats and Their Comparison in Fiber-Optic Systems”, A Technical Report to Sprint, by Light wave Communication Systems Laboratory, The University of Kansas, 2004.

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

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

Comparison: Introduction to Mobile Ad-Hoc Network and Routing Implementation Meenakshi Mishra Student (M.Tech. ECE)

Niketan Mishra Asst. Prof.of ECE

Soni Changlani HOD Dept. ECE

Abstract: in this paper we will discuss about basic of mobile ad hoc network (MANET) mobile Ad Hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any centralized administration, in which individual nodes cooperate by forwarding packets to each other to allow nodes to communicate beyond direct wireless transmission range. Routing is a process of exchanging information from one station to other stations of the network. Routing protocols of mobile ad-hoc network tend to need different approaches from existing Internet protocols because of dynamic topology, mobile host, distributed environment, less bandwidth, less battery power. Keywords: routing, topology, dynamic, bandwidth, exchange, transmission

I. Introduction Wireless communication between mobile users is becoming more popular than ever before. This is due to recent technological advances in laptop computers and wireless data communication devices, such as wireless modems and wireless LANs. This has lead to lower prices and higher data rates, which are the two main reasons why mobile computing continues to enjoy rapid growth. There are two distinct approaches for enabling wireless communication between two hosts. The first approach is to let the existing cellular network infrastructure carry data as well as voice. The major problems include the problem of handoff, which tries to handle the situation when a connection should be smoothly handed over from one base station to another base station without noticeable delay or packet loss. Another problem is that networks based on the cellular infrastructure are limited to places where there exists such a cellular network infrastructure. The second approach is to form an Ad hoc network among all users wanting to communicate with each other. This means that all users participating in the Ad hoc network must be willing to forward data packets to make sure that the packets are delivered from source to destination. This form of networking is limited in range by the individual nodes transmission ranges and is typically smaller compared to traditional cellular system. Ad hoc networks have several advantages compared to traditional cellular systems. These advantages include:  On demand setup  Fault tolerance  Unconstrained connectivity  II. Application of Mobile Ad hoc Networks Because Ad hoc network are flexible network that can be set up anywhere at any time, without infrastructure including reconfiguration and administration, Mobile Ad hoc Networks is widely used. The main application areas of Mobile Ad hoc Networks are:  Tactical network related application to improve the battle field communication and survivability.  Sensor network services e.g. remote sensors for weather, earth activities, sensor for manufacturing equipment.  Emergency services, search and rescue operation as well as disaster recovery e.g. early retrieval and transmission of patient data.  Application in the field of e- commerce (electronic payment for any where). In vehicular services for transmission of news, road condition and weather.  Educational application, in a classroom, students and instructors can set up an Ad hoc wireless network to share data using laptops.  Set up virtual classrooms or conference rooms.  Location aware services, automatic call forwarding, transmission of actual workspace to the current location.

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III. General Issues in Mobile Ad hoc Networks In mobile ad hoc networks, all the nodes co-operate amongst each other to forward the packets in the network and hence, each node is effectively a router. Thus one of the most important issues is routing. This Dissertation focuses mainly on routing issues in ad hoc networks. In this section, some of the other issues in ad hoc networks are described. (a) Distributed network: A MANETs can be considered as a distributed wireless network without any fixed infrastructure. By distributed, it is meant that there is no centralized server to maintain the state of the clients, similar to peer-to-peer (P2P) networks. (b) Dynamic topology: The nodes are mobile and hence the network is self-organizing. Due to this, the topology of the network keeps changing with time. Hence the routing protocols designed for such networks must also be adaptive to the changes in the topology. (c) Power awareness: Since the nodes in ad hoc networks typically run on batteries and deployed in hostile terrains, they have stringent power requirements. This implies that the underlying protocols must be designed to conserve battery life, or in other words, they must be power aware. (d) Addressing scheme: The network topology keeps changing dynamically and hence the addressing scheme used is quite significant. A dynamic network topology entails a ubiquitous addressing scheme, which avoids any duplicate addresses. Mobile IP is currently being used in cellular networks where a base station handles all the node addressing. However, such a scheme doesnâ&#x20AC;&#x2122;t apply to ad hoc networks due to their decentralized nature. (e) Network size: Commercial applications of ad hoc networks such as data sharing in conference halls, meetings, etc. are an attractive feature of ad hoc networks. However, the delay involved in the underlying protocols places a strict upper bound on the size of the network. (f) Security: Security in ad hoc networks is of prime importance in scenarios of deployment such as battlefield. The three goals of security confidentiality, integrity and authenticity are very difficult to achieve since every node in the network participates equally in the network. IV. Advantages of Mobile Ad hoc Networks Low cost of deployment: As the name suggests, ad hoc networks can be deployed on the fly, thus requiring no expensive infrastructure such as copper wires, data cables, etc. (a) Fast deployment: When compared to WLANs, ad hoc networks are very convenient and easy to deploy requiring less manual intervention since there are no cables involved. (b) Dynamic Configuration: Ad hoc networks configuration can change dynamically with time. For the many scenarios such as data sharing in classrooms, etc., this is a useful feature. When compared to configurability of LANs, it is very easy to change the network topology. V. Ad-hoc On Demand Distance Vector Routing Algorithm (AODV) The Ad Hoc On-Demand Distance-Vector Protocol (AODV) is a distance vector routing for mobile ad-hoc networks. AODV is an on-demand routing approach, i.e. there are no periodical exchanges of routing information. (c) The protocol consists of two phases: (d) i) Route Discovery (e) ii) Route Maintenance. Algorithm for the Calculation of TTL in Normal AODV I. Initialization Set TTL_START = 5 Set TTL_THRESHOLD = 7 Set TTL_INCREMENT = 2 Set NETWORK_DIAMETER = 30 II. Calculation of TTL Time rt->rt_req_last_ttl = max(rt->rt_req_last_ttl,rt->rt_last_hop_count); III. Check Whether TTL is zero or not if (0 == rt->rt_req_last_ttl) { // first time query broadcast ih->ttl_ = TTL_START; } else { IV. Expanding ring search if (rt->rt_req_last_ttl < TTL_THRESHOLD) ih->ttl_ = rt->rt_req_last_ttl + TTL_INCREMENT;

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else { V. Network-wide broadcast ih->ttl_ = NETWORK_DIAMETER; rt->rt_req_cnt += 1; } } VI. Remember the TTL used for the next time rt->rt_req_last_ttl = ih->ttl_; VI.

Result

Packet Analysis for Normal AODV Packet Analysis for Normal AODV Packet Analysis for Updated AODV Sender Node

Total Packet Sent

Receiver Node

Total Packet Recvd.

2 6

249

330

911

247

548

909

352

462

1222

1144

463

489

960

1210

1155

884

Total Packet Sent

5948

Total Pkt Recvd.

5751

VII. Conclusion AODV is one of the most popular ad-hoc on-demand routing protocols. In the AODV routing protocol, local repair operation done by broadcasting RREQ packet with TTL equal to Eq. (1). This process produces high routing message overhead which consumes high portions from the bandwidth of the connected nodes. Whereas the new adaptive MAODV routing protocol, local repair done on one or more trials with TTL in the first trial initialized to a small value equal to LR_TTL_START. This will reduce the routing message overhead resulted from local repair operation in the AODV routing protocol. From the obtained results it could be concluded that in small ad-hoc networks, MAODV is suitable for the applications that need low routing message overhead. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

S. R. Das, C. E. Perkins, E. M. Royer, and M. K. Marina, “Performance Comparison of Two On-Demand Routing Protocols for Ad Hoc Networks,” IEEE personal Comm,Vol 8,pp.16-28,feb, 2001. Srdjan krco and marina dupcinov, Improved neighbour detection algorithm for aodv routing protocol, ieee communications letters, December 2003. Pradeep kumar Mani, David W Petr, Development and Performance Characterization of Enhanced AODV Routing for CBR and TCP Traffic, 864-7762 0-7803-8246-3 2004 IEEE. Zhao Qiang Zhu Hongbo, “An optimized AODV protocol in mobile ad hoc Network”, In Wireless comm. networking & mobile computing 2008(WiCOM’08), 4th international conference on Oct 12-14, 2008, pp.1-4. Ammar Zahary and Aladdin Ayesh, “On-demand Multiple Route Maintenance in AODV”, in Computer Engineering & System, 2008, International Conference on Nov 25-27, 2008, pp.225-230. Xinsheng Wang, Qing Liu, Nan Xu, The Energy-Saving Routing Protocol Based on AODV, Fourth International Conference on Natural Computation, 978-0-7695-3304-9/08,2008 IEEE. Mehdi Zarei, Karim Faez, Javad Moosavi Nya, Modified Reverse AODV Routing Algorithm using Route Stability in Mobile Ad Hoc Networks, 978-1-4244-2824-3,2008 IEEE. YU Bin, SUN Bin, “Modify AODV For MANET/INTERNET Connection Through Multiple Mobile Gateways”, ISBN 978-895519-139-4 -1519- Feb. 15-18, 2009 ICACT 2009. Nastooh Taheri Javan, Reza Kiaeifar, Bahram Hakhamaneshi, Mehdi Dehghan“ZD-AOMDV: A New Routing Algorithm for Mobile Ad-Hoc Networks”2009 Eigth IEEE/ACIS International Conference on Computer and Information Science. Hothefa Sh.Jassim, Salman Yussof, Tiong Sieh Kiong, S. P. Koh1, Roslan Ismail “A Routing Protocol based on Trusted and shortest Path Selection for Mobile Ad hoc Network” Proceedings of the 2009 IEEE 9th Malaysia International Conference on Communications 15 -17 December 2009.

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