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International Journal of Soft Computing and EngineeringInternational Journal of Soft Computing and EngineeringInternational Journal of Soft Computing and Engineering
n E d n g i na e g e n i r t i n u g p m o C t f o S I n f t eo l r n a a n r t i u o o n J a l
IJSCEIJSCE
Exploring Innovation
www.ijsce.org
EXPLORING INNOVA
TION
ISSN : 2231 - 2307Website: www.ijsce.org
Volume-7 Issue-1, March 2017Volume-7 Issue-1, March 2017
Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
Published by: Blue Eyes Intelligence Engineering and Sciences Publication Pvt. Ltd.
Editor In Chief
Dr. Shiv K Sahu
Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT)
Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Dr. Shachi Sahu
Ph.D. (Chemistry), M.Sc. (Organic Chemistry)
Additional Director, Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd., Bhopal (M.P.), India
Vice Editor In Chief
Dr. Vahid Nourani
Professor, Faculty of Civil Engineering, University of Tabriz, Iran
Prof. (Dr.) Anuranjan Misra
Professor & Head, Computer Science & Engineering and Information Technology & Engineering, Noida International University,
Noida (U.P.), India
Advisory Chair
Dr. Deepak Garg
Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India, Senior Member of IEEE,
Secretary of IEEE Computer Society (Delhi Section), Life Member of Computer Society of India (CSI), Indian Society of Technical
Education (ISTE), Indian Science Congress Association Kolkata.
Dr. Vijay Anant Athavale
Director of SVS Group of Institutions, Mawana, Meerut (U.P.) India/ U.P. Technical University, India
Dr. T.C. Manjunath
Principal & Professor, HKBK College of Engg, Nagawara, Arabic College Road, Bengaluru-560045, Karnataka, India
Dr. Kosta Yogeshwar Prasad
Director, Technical Campus, Marwadi Education Foundation’s Group of Institutions, Rajkot-Morbi Highway, Gauridad, Rajkot,
Gujarat, India
Dr. Dinesh Varshney
Director of College Development Counceling, Devi Ahilya University, Indore (M.P.), Professor, School of Physics, Devi Ahilya
University, Indore (M.P.), and Regional Director, Madhya Pradesh Bhoj (Open) University, Indore (M.P.), India
Technical Chair
Dr. Haw Su Cheng
Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia, 63100 Cyberjaya
Dr. Hossein Rajabalipour Cheshmehgaz
Industrial Modeling and Computing Department, Faculty of Computer Science and Information Systems, Universiti Teknologi
Malaysia (UTM) 81310, Skudai, Malaysia
Dr. Sudhinder Singh Chowhan
Associate Professor, Institute of Management and Computer Science, NIMS University, Jaipur (Rajasthan), India
Dr. Neeta Sharma
Professor & Head, Department of Communication Skils, Technocrat Institute of Technology, Bhopal(M.P.), India
Dr. Ashish Rastogi
Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Santosh Kumar Nanda
Professor, Department of Computer Science and Engineering, Eastern Academy of Science and Technology (EAST), Khurda (Orisa),
India
Dr. Hai Shanker Hota
Associate Professor, Department of CSIT, Guru Ghansi Das University, Bilaspur (C.G.), India
Dr. Sunil Kumar Singla
Professor, Department of Electrical and Instrumentation Engineering, Thapar University, Patiala (Punjab), India
Dr. A. K. Verma
Professor, Department of Computer Science and Engineering, Thapar University, Patiala (Punjab), India
Dr. Durgesh Mishra
Chairman, IEEE Computer Society Chapter Bombay Section, Chairman IEEE MP Subsection, Professor & Dean (R&D), Acropolis
Institute of Technology, Indore (M.P.), India
Managing Chair
Mr. Jitendra Kumar Sen
International Journal of Soft Computing and Engineering (IJSCE)
Reviewer Chair
Dr. R. Devi Priya
Associate Professor, Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu-638052, India.
Dr. P. Rathnakumar
Professor & Head, Department of Mechanical Engineering, Navodaya Institute of Technology, Raichur, Karnataka 584103, India.
Dr. Abhinav Vidwans
Associate Professor, Department of Computer Science and Egineering, Vikrant Group of Institutions Campus, Morar, Gwalior
474001, India.
Dr. A. K. Priya
Associate Professor, Department of Civil Engineering, KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil
Nadu 641407, India.
Dr. K Ashok Reddy
Associate Professor, Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
Dr. T. V. Surya Narayana
Assistant Professor, Department of Information Technology, Manipal University, SMUDDE, Gangtok, Sikkim, India.
Dr. Srinivasa Raju Rallabandi
Assistant Professor, Department of Mathematics, Gandhi Institute of Technology and Management, Hyderabad (Telangana). India.
Dr. Deepika Garg
Assistant Professor, Department of Applied Science, GD Goenka University, Gurgaon, Haryana-122103. India.
Dr. Girish Madhukar Tere
Assistant Professor, Department of Computer Science, Thakur College of Science and Commerce, Affiliated to University of Mumbai,
Mumbai, Maharashtra-400098, India.
Dr. Sameh G.Salem
Associate Professor, Department of Electrical Engineering, Military Technical College, Cairo Governorate, Egypt.
Dr. Abhishek Singh
Associate Professor, Department of Mathematics, African Institute for Agrarian Studies, Amity University, Noida- 201304. (U.P).
India.
Dr. Kompella Venkata Ramana
Associate Professor, Department of Computer Science and Systems Engineering, Engineering College, Andhra University,
Visakhapatnam (A.P.)-530003. India.
Dr. Bala Siddulu Malga
Assistant Professor, Department of Mathematics, Gandhi Institute of Technology and Management, Visakhapatnam (Andhra
Pradesh)-530045. India.
Dr. Meeravali Shaik
Professor, Department of Master of Business Administration, Rise Krishna Sai Prakasam Group of Institutions, Valluru, Ongole,
(A.P.)-523272. India.
Dr. Mohammad Valipour
Assistant Professor, Department of Water Sciences and Engineering, Payame Noor University, Tehran, Iran.
Dr. Arvind Kumar Drave
Associate Professor, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur (Uttar Pradesh)-208016. India.
Dr. Krishna Banana
Assistant Professor, Department of Commerce and Business Administration, Acharya Nagajuna University Ongole Campus, Ongole.
Prakasam (Andra Pradesh). India.
Dr. Christo Ananth
Associate Professor, Department of Electrical & Communication Engineering, Francis Xavier Engineering College, Tirunelveli (Tamil
Nadu)-627003. India.
Dr. Dhananjaya Reddy
Assistant Professor, Department of Mathematics, Govt. Degree College, Puttur (Andhra Pradesh)-517583. India.
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said
Department of Computer and Information Technology, Arab Academy for Science and Technology and Maritime Transport
(AASTMT) Alexandria, Egypt.
Dr. Srijit Biswas
Professor, Department of Civil Engineering, Manav Rachna International University, Faridabad (Haryana)-121004, India.
Dr. K. Suresh Babu
Professor & HOD, Department of Computer Science & Engineering, RISE Krishna Sai Prakasam Group of Institutions, Ongole
(Andhra Pradesh)-523272, India.
Dr. K. Krisnaveni
Associate Professor, Department of Computer Science, Sri S. Ramaswamy Naidu Memorial College, Sattur, Virudhunagar Dist,
(Tamil Nadu) India.
Dr. R. Venkat Reddy
Professor, Department of Mechanical Engineering, Anurag Group of Institutions (CVSR), Venkatapur (Telangana)-501301, India.
Dr. Hamid Ali Abed AL-Asadi
Professor, Department of Computer Science, Faculty of Education for Pure Science, Basra University, Basra, Iraq.
S.
No
Volume-7 Issue-1, March 2017, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.
Page
No.
1.
Authors: Samir Khudhir Al-ani, Nada Abdulfatah Khattab
Paper Title: Computational Optimization Aberration Coefficients of an Einzel Lens Operated Under Zero
Magnification
Abstract: In this researcher has been studied to design an einzel lens and this present researcher, Which concerted
about the design of electrostatic potential lens for focused charge particle beam by using inverse method in designing to
electrostatic lens ,the paraxial ray equation was solved using Rung - Kutta method ,The spherical and chromatic
aberration coefficient Cs and Cc, respectively have been computed using Simpsons rule. The shape of the electrode of
the electrostatic lens were determined by solving Laplace's equation, in this research, the results showed low values of
spherical and chromatic aberrations which are considered as good criteria for good design Electron Optics, einzel Lens.
Keywords: Electrostatic Lens, Spherical Aberrations, Chromatic Aberrations
References: 1. Nagy A. and Szilagyi M. (1974) '' introduction to the theory of space charge optics (Macmillan Press: London).
2. Szilagyi, M. (1988), Electron and ion optics, (Plenum press: New York). 3. Kirestein, P. T., Gordon, S, K. and Willam, E. W. (1967) Space- Charge Flow
4. Hawkes P.W (2004). Recent advances in electron optics and electron microscopy, Annales de la Fondation Louis de Broglie, 29(1), 837-855.
5. Sise, O.; Ulu, M.and Dogan, M. (2007), Characterization and modeling of multi-element electrostatic lens systems, 6. P.W. Hawkes and A. Septier ed. Septier, Lens aberration Focusing of charged partical'' ,Academic press ,New York, 1967.
7. Polyanin, A. D. (2002). Handbook of Linear Partial Differential Equations for Engineers and Scientists. Boca Raton: Chapman & Hall/CRC
Press. 8. Hawkes P. W. and Kasper E., (1989), Principles of electron optics ,1 (Academic Press: London).
9. Al-Meshhdany, l. A. M. (2002) "Theoretical desigen of an electron gun lenses using numerical methods, M.Sc. Thesis College of education for
women, university of Baghdad, Iraq . 10. Munro. E. 1975. A Set of Computer Programs for Calculating the Properties of Electron Lenses, Cambridge University, Eng. Dept., Report
CUED/B-ELECT/TR45.
11. Al-Khashab, M.A. and M. T. Al-Shamma (2009) , ''Minimizing the aberration of the unipotential electrostatic lenses of multi-electrodes
1-2
2.
Authors: Poorva Khemaria, Shiv Kumar, Babita Pathik
Paper Title: Implementation of Fog Computing in Cloud Enterprise for Data Security and Privacy Management
Abstract: advancement of cloud technology named as fog computing. The process of fog computing faced a problem of
latency and internet connectivity. The access of data over the fog computing need some trust based authentication and
authorization process. In fog computing environment two major issue one is data leakage and other is location privacy.
The location privacy preserve the user access and authentication process. The location privacy in fog computing is major
issue. For the location privacy used various authentication and authorization process. To address these dangers,
auditable information stockpiling administration has been proposed with regards to distributed computing to secure the
information. Strategies, for example, holomorphic encryption and searchable encryption are consolidated to give
uprightness, confidentiality and variability for distributed storage framework to permit a customer to check its
information put away on untrusted servers. In this paper used Bloom filter data structure for the location privacy in fog
computing model. The fog computing model work very efficiently in terms of low latency and high speed.
Keywords: WSN, AOI, POI, SA, BM, CEP.
References: 1. Flavio Bonomi, Rodolfo Milito, Jiang Zhu and Sateesh“Fog Computing and Its Role in the Internet of Things”, ACM, 2012, Pp 13-16.
2. Kirak Hong, David Lillethun, BeateOttenwälder and Boris Koldehofe “Opportunistic Spatio-temporal Event Processing for Mobile Situation
Awareness”, ACM, 2013, Pp 1-12. 3. Kirak Hong, David Lillethun, Umak is hore Ramachandran, Beate Ottenwälder and Boris Koldehofe “Mobile Fog: A Programming Model for
Large–Scale Applications on the Internet of Things”, ACM, 2013, Pp 1-6. 4. Takayuki Nishio, Ryoichi Shinkuma, Tatsuro Takahashi and Narayan B. Mandayam “Service-Oriented Heterogeneous Resource Sharing for
Optimizing Service Latency in Mobile Cloud”, ACM, 2013, Pp 19-26.
5. Beate Ottenwälder, Boris Koldehofe, Kurt Rothermel and Umakishore Ramachandran “MigCEP: Operator Migration for Mobility Driven Distributed Complex Event Processing”, ACM, 2013, Pp 1-12.
6. Ivan Stojmenovic and Sheng Wen “The Fog Computing Paradigm: Scenarios and Security Issues”, ACSIS, 2014, Pp 1-8.
7. Stavros Salonikias, IoannisMavridis and Dimitris Gritzalis “Access Control Issues in Utilizing Fog Computing for Transport Infrastructure”, Springer, 2011, Pp 1-12.
8. Tom H. Luan, Longxiang Gao, Zhi Li, Yang Xiang, Guiyi Weand Limin Sun “Fog Computing: Focusing on Mobile Users at the Edge”, arXiv,
2016, Pp 1-11. 9. Salvatore J. Stolfo, Malek Ben Salem and Angelos D. Keromytis “Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud”, IEEE,
2012, Pp 125-128.
10. Mohammad Aazam andEui-Nam Huh “Fog Computing and Smart Gateway Based Communication for Cloud of Things”, IEEE, 2014, Pp 464-470.
11. Flavio Bonomi, Rodolfo Milito, Preethi Natarajan and Jiang Zhu “Fog Computing: A Platform for Internet of Things and Analytics”, Springer,
2014, 2014, Pp 169-186. 12. Luis M Vaquero and Luis. Rodero-Merino “Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing”, HPL, 2014,
Pp 1-6.
13. Flavio Bonomi, “Connected Vehicles, theInternet of Things, and Fog Computing”, VANET 2011, Pp 44-56. 14. Behrisch, M., Bieker, L., Erdmann, J., andKrajzewicz, D. Sumo “Simulation of urban mobility-an overview”, The Third International
Conference on Advances in System Simulation, 2011, Pp 55–60.
15. Bonomi, F., Milito, R., Zhu, J., and Addepalli,S. “Fog Computing and Its Role in the Internet of Things”, ACM, 2012,Pp. 13–16. 16. A., Lu, H., Zheng, X., Musolesi, M., Fodor, K., and Ahn, G.-S. “The rise of pe
17. Campbell, A. T., Eisenman, S. B., Lane, N. D.,Miluzzo, E., Peterson, R. ople-centric sensing”, IEEE, 2010, Pp 12–21.
18. Cugola, G., and Margara, A. “Tesla: a formallydefined event specification language” ACM, 2010, Pp 50–61.
3-6
19. Cugola, G., and Margara, A. “Low latencycomplex event processing on parallel hardware”, JPDC, 2012, Pp 205–218. 20. Hendawi, A. M., and Mokbel, M. F. “Panda: APredictive Spatio-Temporal Query Processor”. International Conference on Advances in
Geographic Information Systems, 2012, Pp 13–22.
3.
Authors: Ratnesh Kumar Jain, Shiv Kumar, Babita Pathik
Paper Title: An Enhancement on Block Cipher Key for Advanced Encryption Standard
Abstract: The United State Government has standardized algorithm for encrypting and decrypting data which is known
as AES (Advanced Encryption Standard). Information security is becoming very essential in data storage and
transmission with the rapid growth of digital data exchange in an electronic way Cryptography play a vital role in
information security system against different attacks which uses algorithms to scramble data into unreadable text which
is only decrypted by those who has the associated key. It is of two types one for Symmetric and Asymmetric. Symmetric
system has 288 bit block 128 bit commotional AES algorithm for 288 bit using 6x6 matrixes after implementation these
points system is throughput at both sites encryption and decryption.
Keywords: (Advanced Encryption Standard), United State, AES, Information security, Cryptography.
References: 1. Lee, NIST Special Publication 800-21, Guideline for Implementing Cryptography in the Federal Government, National Institute of Standards and
Technology, November.
2. Advanced Encryption Standard (AES), Federal Information Processing Standards Publication 197, November 26, 2001.
3. Amish Kumar , Mrs. Namita Tiwari,”Efficient implementation and avalanche effect of AES” International Journal of Security, Privacy and Trust
Management (IJSPTM), Vol. 1, No 3/4, August 2012. 4. Chih-Pin Su, Tsung-Fu Lin, Chih-Tsun Huang, and Cheng-Wen Wu, National Tsing Hua University,”A high throughput low cost AES
processor” IEEE Communications Magazine 63-804/03 © 2003 IEEE.
5. Chong Hee Kim,”Improved Differential Fault Analysis on AES Key Schedule” IEEE Transaction on Information Forensics and Security, Vol. 7, No. 1, Feb 2012.
6. Diaa Salama Abdul. Elminaam, Hatem M. Abdul Kader and Mohie M. Hadhoud,” Performance Evaluation of Symmetric Encryption Algorithms
on Power Consumption for Wireless Devices” International Journal of Computer Theory and Engineering, Vol. 1, No. 4, October, 2009. 7. Irbid, Jordan, “A new approach for complex encrypting and decrypting data” International Journal of Computer Networks & Communications
(IJCNC) Vol.5, No.2, March 2013.
8. J. Nechvatal, et. al., Report on the Development of the Advanced Encryption Standard (AES), National Institute of Standards and Technology, October 2, 2000.
9. Mohan H.S and A Raji Reddy,”Performance analysis of AES and MARS encryption algorithm” IJCSI International Journal of Computer Science
Issues, Vol. 8, Issue 4, No 1, July 2011. 10. Navraj Khatri, Rajeev Dhanda , Jagtar Singh ,”Comparison of power consumption and strict avalanche criteria at encryption/Decryption side of
Different AES standards‟‟International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 4, August 2012.
11. Xinmiao Zhang and Keshab K. Parhi,”Implementation approaches for the advanced encryption standard algorithm”, IEEE Transactions 1531-636X/12©2002IEEE.
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4.
Authors: Vikash Kumar, Sanjay Sharma
Paper Title: Lossless Image Compression through Huffman Coding Technique and Its Application in Image
Processing using MATLAB
Abstract: Images include information about human body which is used for different purpose such as medical
examination security and other plans Compression of images is used in some applications such as profiling information
and transmission systems. Regard to importance of images information, lossless or loss compression is preferred.
Lossless compressions are JPEG, JPEG-LS and JPEG2000 are few well-known methods for lossless compression. We
will use differential pulse code modulation for image compression with Huffman encoder, which is one of the latest and
provides good compression ratio, peak signal to noise ratio and minimum mean square error. . In this paper we try to
answer the following question. Which entropy coding, Huffman, is more suitable compared to other from the
compression ratio, performance, and implementation points of view? We have implemented and tested Huffman
algorithms. Also we compare it with other existing methods with respect to parameter compression ratio, peak signal
noise ratio.
Keywords: Lossless Compression, PSNR, Compression-Ratio, Encoding Technique, Huffman Coding, JPEG2000,
JPEG-LS, JPEG
References: 1. N. Parvatham and Seetharaman Gopalakrishnan, 2012 Third International Conference on Intelligent Systems Modelling and Simulation “A Novel
Architecture for an Efficient Implementation of Image compression using 2D-DWT”
2. Giridhar Mandyam, Nasir Ahmed, Neeraj Magotra, “Lossless Image compression using Discrete Cosine Transform”, Journal of Visual Communication and Image Representation ,Vol.8, No.1, March, pp.21-26, 1997, Article no. VC970323.
3. Donapati, S. Yagain “A Comparative Study of Effects of CSC on Image Compression Ratios While Using JPEG-XR”, Year of Publication (2013),
pp. 158-161. 4. J. Wang, “Shot Cut Detection Based On The Statistical Parameter Histogram With The Discrete Walsh Transform”, Second International
Conference on MultiMedia and Information Technology, (2010).
5. J. Ziv and A. Lempel, "A Universal Algorithm for Sequential Data Compression", IEEE Transactions on Information Theory, May 1977
6. Dr. T. Bhaskara Reddy, Miss.Hema suresh yaragunti, Dr.S.kiran, Mrs.T.Anuradha “ A novel approach of lossless image compression using
hashing and Huffman coding “,International Journal of Engineering research and technology ,vol.2 issue 3,march-2013.
7. G.C Chang Y.D Lin (2010) “An Efficient Lossless ECG Compression Method Using Delta Coding and Optimal Selective Huffman Coding” IFMBE proceedings 2010, Volume 31, Part 6, 1327-1330, DOI: 10.1007/978-3-642-14515-5_338.
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5.
Authors: Kanos Matyokurehwa, Nehemiah Mavetera, Osden Jokonya
Paper Title: Requirements Engineering Techniques: A Systematic Literature Review
Abstract: Requirements engineering is a torrid task to requirements engineers because requirements keep changing and
this affect the project’s delivery schedule and cost. Although various authors proposed numerous techniques to be used
in requirements engineering, software projects still fail. The issue now lies on which technique to use to minimize
14-20
project failures. The aim of the study was to identify gaps in requirements engineering techniques used. The paper used
a systematic literature review of requirements engineering techniques used from January 2000 to July 2016. The study
found out that a lot of techniques are used in requirements engineering and some of the techniques used are not
adequately addressing the problem space but the solution space. The study identified some gaps in requirements
engineering techniques that need further research in order to solve those gaps.
Keywords: Requirements Engineering, Project Failure, Techniques, Changing Requirements, Technique limitations.
References: 1. Aguilar Calderón, J.A., Garrigós Fernández, I. and Mazón López, J.N., 2016. Requirements Engineering in the Development Process of Web
Systems: A Systematic Literature Review. 2. Hull, E., Jackson, K. and Dick, J., 2010. Requirements engineering. Springer Science & Business Media.
3. Wang, X., Bettini, C., Brodsky, A., Jajoida, S.: Logical Design for Temporal Databases with Olaronke, G.E., Olaleke, J.O. and Olajide, M.S.,
2010. A Survey on Requirement Analysis in the Nigerian Context. 4. Batra, M and Bhatnagar, A, 2015, Descriptive Literature Review of Requirements Engineering Models. International Journal of advanced
Research in Computer Science and Software Engi-neering (Volume 5, Issue 2, pp. 289-293).
5. Clancy, T., 2014. The Standish Group CHAOS Report. Project Smart. 6. Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J. and Linkman, S., 2009. Systematic literature reviews in software
engineering–a systematic literature review. Information and software technology, 51(1), pp.7-15.
7. Jiang, L., Eberlein, A., Far, B.H. and Mousavi, M., 2008. A methodology for the selection of requirements engineering techniques. Software &
Systems Modeling, 7(3), pp.303-328.
8. Nuseibeh, B. and Easterbrook, S., 2000, May. Requirements engineering: a roadmap. In Pro-ceedings of the Conference on the Future of
Software Engineering (pp. 35-46). ACM. 9. Neill, C.J. and Laplante, P.A., 2003. Requirements engineering: the state of the practice. IEEE software, 20(6), p.40.
10. Paetsch, F., Eberlein, A. and Maurer, F., 2003, June. Requirements Engineering and Agile Software Development. In WETICE (Vol. 3, p. 308).
11. Gomes–andrigo, A., Pettersson, A. and Gorschek–tony, T., Market-Driven Requirements En-gineering Process Model, version 1.0. 12. Van Lamsweerde, A., 2001. Goal-oriented requirements engineering: A guided tour. In Re-quirements Engineering, 2001. Proceedings. Fifth
IEEE International Symposium on (pp. 249-262). IEEE.
13. Darimont, R. and Lemoine, M., 2006, June. Goal-oriented Analysis of Regulations. In ReMo2V. 14. Fowler, M., 2004. UML distilled: a brief guide to the standard object modeling language. Ad-dison-Wesley Professional.
15. Ghezzi, C., Jazayeri, M. and Mandrioli, D., 2002. Fundamentals of software engineering. Prentice Hall PTR.
16. Mauw, S., Reniers, M.A. and Willemse, T.A.C., 2000. Message Sequence Charts in the soft-ware engineering process. Handbook of Software Engineering and Knowledge Engineering, World Scientific Publishing Co, 1, pp.437-463.
17. Jones, C., 2009. Software engineering best practices. McGraw-Hill, Inc..
18. Pandey, D., Suman, U., Ramani, A.K. and AhilyaVishwavidyalaya, D., 2011. A Framework for modelling software requirements. International Journal of Computer Science, 8.
19. Brace, W. and Cheutet, V., 2012. A framework to support requirements analysis in engineering design. Journal of Engineering Design, 23(12),
pp.876-904. 20. Hoorn, J.F. and Van der Veer, G.C., 2003. Requirements analysis and task design in a dynamic environment. Human-centred computing:
Cognitive, social, and ergonomic aspects, 3, pp.472-476.
21. Brinkkemper, J. and Solvberg, A., 2000. Tropos: A framework for requirements-driven soft-ware development. Information systems engineering: state of the art and research themes, p.11.
22. Bleistein, S.J., Cox, K., Verner, J. and Phalp, K.T., 2006. B-SCP: A requirements analysis framework for validating strategic alignment of
organizational IT based on strategy, context, and process. Information and software technology, 48(9), pp.846-868. 23. Ali, R., Dalpiaz, F. and Giorgini, P., 2010. A goal-based framework for contextual requirements modeling and analysis. Requirements
Engineering, 15(4), pp.439-458.
24. Robinson, W.N., 2006. A requirements monitoring framework for enterprise systems. Re-quirements engineering, 11(1), pp.17-41. 25. Yu, E. and Liu, L., 2001. Modelling trust for system design using the i* strategic actors framework. In Trust in Cyber-societies (pp. 175-194).
Springer Berlin Heidelberg.
26. Tung, Y.W. and Chan, K.C., 2009. A Unified Human–Computer Interaction Requirements Analysis Framework for Complex Socio-technical Systems. International Journal of Hu-man-Computer Interaction, 26(1), pp.1-21.
27. Uszok, A., Bradshaw, J.M., Lott, J., Johnson, M., Breedy, M., Vignati, M., Whittaker, K., Jakubowski, K., Bowcock, J. and Apgard, D., 2011,
November. Toward a flexible ontolo-gy-based policy approach for network operations using the KAoS framework. In 2011-MILCOM 2011 Military Communications Conference (pp. 1108-1114). IEEE.
28. Thüm, T., Kästner, C., Benduhn, F., Meinicke, J., Saake, G. and Leich, T., 2014. FeatureIDE: An extensible framework for feature-oriented
software development. Science of Computer Programming, 79, pp.70-85. 29. Lee, S.W. and Gandhi, R.A., 2005, December. Ontology-based Active Requirements Engi-neering Framework. In APSEC (pp. 481-490).
30. Zong-yong, L., Zhi-xue, W., Ying-ying, Y., Yue, W.U. and Ying, L.I.U., 2007, July. Towards a multiple ontology framework for requirements
elicitation and reuse. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International (Vol. 1, pp. 189-195). IEEE.
31. Génova, G., Fuentes, J.M., Llorens, J., Hurtado, O. and Moreno, V., 2013. A framework to measure and improve the quality of textual requirements. Requirements engineering, 18(1), pp.25-41.
32. Saiedian, H., Kumarakulasingam, P. and Anan, M., 2005. Scenario-based requirements analysis techniques for real-time software systems: a
comparative evaluation. Requirements Engineering, 10(1), pp.22-33. 33. Chatzikonstantinou, G. and Kontogiannis, K., 2016. Run-time requirements verification for reconfigurable systems. Information and Software
Technology, 75, pp.105-121.
34. Martins, L.E.G. and Gorschek, T., 2016. Requirements engineering for safety-critical systems: A systematic literature review. Information and Software Technology, 75, pp.71-89.
35. MITRE, 2016 June 10, Systems Engineering Guide.[Online]. Available. https://www.mitre.org/publications/systems-engineering-guide/se-
lifecycle-building-blocks/requirements-engineering/eliciting-collecting-and-developing-requirements 36. Outsource2india, 2016 June 10, Software Development.[Online]. Available.
https://www.outsource2india.com/software/SoftwareProjectFailure.asp
37. Sofia, 2010, Software Development Process- activities and steps. [Online]. Available. http://www.uacg.bg/filebank/acadstaff/userfiles/publ_bg_397_SDP_activities_and_steps.pdf
38. Chua, B.B. and Verner, J., 2010. Examining requirements change rework effort: A study. arXiv preprint arXiv:1007.5126.
39. Ghosh, S.M., Sharma, H.R. and Mohabay, V., 2011. Study of Impact Analysis of Software Requirement Change in SAP ERP. International Journal of Advanced Science and Technology, 33, pp.95-100.
40. Korban,S, 2013, How to Prevent the Negative Impacts of Poor Requirements. [Online]. Available. https://www.batimes.com/articles/how-to-
prevent-the-negative-impacts-of-poor-requirements.html 41. Bachmann, F., Bass, L., Chastek, G., Donohoe, P. and Peruzzi, F., 2000. The architecture based design method (No. CMU/SEI-00-TR-001).
CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST.
42. Suryn, W., Abran, A. and April, A., 2003. ISO/IEC SQuaRE. the second generation of stand-ards for software product quality. 43. Mead, N.R. and Hough, E.D., 2006, April. Security requirements engineering for software systems: Case studies in support of software
engineering education. In 19th Conference on Software Engineering Education & Training (CSEET'06) (pp. 149-158). IEEE. 44. Aranda, J., Easterbrook, S. and Wilson, G., 2007, October. Requirements in the wild: How small companies do it. In 15th IEEE International
Requirements Engineering Conference (RE 2007) (pp. 39-48). IEEE.
45. Pacheco, C. and Garcia, I., 2012. A systematic literature review of stakeholder identification methods in requirements elicitation. Journal of Systems and Software, 85(9), pp.2171-2181.
46. Fitzgerald, B., 2012. Software crisis 2.0.
47. Zowghi, D., Firesmith, D.G. and Henderson-Sellers, B., 2005. Using the OPEN process framework to produce a situation-specific requirements engineering method. Proceedings of SREP, 5, pp.29-30.
48. Beecham, S., Hall, T. and Rainer, A., 2005. Defining a requirements process improvement model. Software Quality Journal, 13(3), pp.247-279.
49. Hull, E., Jackson, K. and Dick, J., 2002. DOORS: a tool to manage requirements. In Require-ments engineering (pp. 187-204). Springer London. 50. Damian, D.E. and Zowghi, D., 2003. RE challenges in multi-site software development or-ganisations. Requirements engineering, 8(3), pp.149-
160.
51. Cant, T., McCarthy, J. and Stanley, R., 2006. Tools for Requirements Management: a Com-parison of Telelogic DOORS and the HIVE (No. DSTO-GD-0466). DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION SALISBURY (AUSTRALIA) INFO SCIENCES LAB.
52. Lu, C.W., Chang, C.H., Chu, W.C., Cheng, Y.W. and Chang, H.C., 2008, July. A requirement tool to support model-based requirement
engineering. In 2008 32nd Annual IEEE International Computer Software and Applications Conference (pp. 712-717). IEEE. 53. Stal, M,. 2012, IRQA - A Requirements Definition and Management Solution for Systems Engineering Projects.
https://www.infoq.com/news/2012/01/irqa
54. Delor, E., Darimont, R. and Rifaut, A., 2003, December. Software quality starts with the mod-elling of goal-oriented requirements. In 16th International Conference Software & Systems Engineering and their Applications (pp. 1-6).
55. Lami, G., Gnesi, S., Fabbrini, F., Fusani, M. and Trentanni, G., 2004. An automatic tool for the analysis of natural language requirements.
Informe técnico, CNR Information Science and Technology Institute, Pisa, Italia, Setiembre.
56. Wieringa, R. and Ebert, C., 2004. Guest Editors' Introduction: RE'03--Practical Requirements Engineering Solutions. IEEE Software, 21(2), p.16.
57. Wang, M. and Zeng, Y., 2009. Asking the right questions to elicit product requirements. In-ternational Journal of Computer Integrated
Manufacturing, 22(4), pp.283-298. 58. Ang, J.K., Leong, S.B., Lee, C.F. and Yusof, U.K., 2011, March. Requirement engineering techniques in developing expert systems. In
Computers & Informatics (ISCI), 2011 IEEE Symposium on (pp. 640-645). IEEE.
59. Adam,S., Riegel, N., Doerr,J., 2014, TORE. A Framework for Systematic Requirements De-velopment in Information Systems. http://re-magazine.ireb.org/issues/2014-4-steady-flight/tore/
60. Jiang, L. and Eberlein, A., 2008, March. A framework for requirements engineering process development (FRERE). In 19th Australian
Conference on Software Engineering (aswec 2008) (pp. 507-516). IEEE. 61. Kheirkhah, E. and Deraman, A., 2008, August. Important factors in selecting requirements en-gineering techniques. In 2008 International
Symposium on Information Technology (Vol. 4, pp. 1-5). IEEE.6. Ribière, M., Charlton, P.: Ontology Overview. Motorola Labs, Paris
(2002). [Online]. Available: http://www.fipa.org/docs/input/f-in-00045/f-in-00045.pdf (current October 2003)
6.
Authors: N. Nachammai, R. Kayalvizhi
Paper Title: Dragonfly Algorithm Based Fuzzy Logic Controller for Power Electronic Converter
Abstract: Due to the time varying and switching nature of the Luo converters, their dynamic behavior becomes highly
non-linear. Conventional controllers require a good knowledge of the system and accurate tuning in order to obtain the
desired performances. A fuzzy logic controller neither requires a precise mathematical model of the system nor complex
computations. Swarm Intelligence [SI] is a branch of evolutionary computing that inspired by the behavior of swarms in
real life to search or optimizean objective function. The Dragonfly Algorithm [DA] is a global optimization technique
based on swarm intelligence. Two essential phases of optimization, exploration and exploitation, are designed by
modelling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming
dynamically or statistically. The drawback of fuzzy controller has the tendency to oscillate around the final operating
point. Proper selection of the normalizing gains for the inputs avoids oscillations. Hence Dragonfly Algorithm, an
optimization technique is required to tune the fuzzy parameters. An attempt has been made in this work to design,
simulate and implement, fuzzy logic and DA-fuzzy logic controllers for regulating the output voltage. The performances
of the Luo converter with Fuzzy and DA-Fuzzy controllers are evaluated under line and load disturbances using Matlab-
Simulink based simulation and compared. Comparison clearly shows the superiority of the proposed Dragonfly
Algorithm over fuzzy controller applied for the control of Luo converter.
Keywords: Dragonfly Algorithm, Fuzzy Logic Controller, Positive Output Elementary LUO Converter.
References: 1. F.L.Luo and Hong Ye, Advanced DC/DC Converters, CRC Press, LLC, 2004.
2. Tarun Kumar Bashishtha and Laxmi Srivastava, “Nature Inspired Meta-heuristic Dragonfly Algorithms For Solving Optimal Power Flow
Problem”, International Journal of Electronics, Electrical and Computational System, Vol.5, Issue 5, May 2016, pp. 111-120. 3. Gururaghav Raman, Guru praanesh Raman, Chakkarapani Manickam and SaravanaIlango Ganesan, “Dragonfly Algorithm Based Global
Maximum Power Point Tracker for Photovoltaic Systems”, Advances in Swarm Intelligence, Springer, 2016, pp. 211-219.
4. Seyedali Mirjalili, “Dragonfly Algorithm: A New Meta-heuristic Optimization Technique for Solving Single-Objective, Discrete and Multi-Objective Problems”, Neural computing & Applications, Springer,2016, pp. 1053-1073.
5. R.H. Bhesdadiya, Mahesh H. Pandya, Indrajit N. Trivedi, Narottam Jangir, Pradeep Jangir and Arvind Kumar, “Price Penalty Factors Based
Approach for Combined Economic Emission Dispatch Problem Solution Using Dragonfly Algorithm”, Proceedings of International conference on Energy Efficient Technologies for sustainability, Nagarcoil, 2016, pp. 436-441.
6. Mustafa Abdul Salam, Hossam M. Zawbaa, E. Emary, Kareem Kamal A. Ghany and B. Parv, “A Hybrid Dragonfly Algorithm With Extreme
Learning Machine For Prediction”, Proceedings of International Symposium on innovations in Intelligent systems and applications, Sinaia, 2016, pp. 1-6.
7. A. Hema Sekhar and Dr. A. Lakshmidevi, “Voltage Profile Improvement and Power System Losses Reduction with Multi TCSC Placement in
Transmission System by Using Firing Angle Control Model With Heuristic Algorithms”, IOSR Journal of Electrical & Electronics Engineering, Vol. 11, Issue 5, Oct 2016, pp. 10-21.
8. Philip T.Daely and Soo Y.Shin, “ Range Based Wireless Node Localization Using Dragonfly Algorithm”, Proceedings of Eighth International
Conference on Uniquitous and Future Networks, Vienna, 2016, pp.1012-1015. 9. S.Gomariz, F.Guinjoan, E.Vidal, L.Martinz and A.Poreda, ‘On the use of the describing function in fuzzy controller design for switching DC-DC
regulators’, in Proc. IEEE International Symposium on Circuits and Systems, Geneva, Switzerland, 2000, pp. 247-250.
21-25
7.
Authors: Silpa Rajan, Minu Lalitha Madhavu
Paper Title: Survey on Reversible Data Hiding in Encrypted Images by Reversible Image Transformation (RIT)
Abstract: To increase the security of the data, an image is in taken in an encrypted format. This process is followed in 26-29
earlier techniques like RRBE, VRAE etc. In RIT, instead of converting it into an encrypted format, it is converted into
another image. Hence this image appears simply an anotherimage which is difficult for other users to decrypt. Using
contrast – enhancement RDH method, data is then hidden in to the image. The advantage of using RDH is that there
occurs no loss of data and contrast of the image is highly enhanced. Hence visual quality of the image is increased. The
embedded data is extracted after which it is decrypted to recover the original data.
Keywords: prediction error expansion, reversible data hiding, RRBE (reserving room before encryption), RIT
(reversible image transformation), VRAE (vacating room after encryption).
References: 1. SilpaRajan, MinuLalithaMadhavu, “Reversible Data Hiding by His-togram Modification for Image Contrast Enhancement ” , Internation-al
Research Journal of Engineering and Technology, vol .3 Issue 11 pp.761-766 November 2016 2. W. Hong, T. Chen, and H. Wu, “An improved reversible data hiding in encrypted images using side match,” IEEE Signal Process. Lett., vol. 19,
no. 4, pp. 199–202, Apr. 2012
3. W. Zhang, X. Hu, X. Li, and N. Yu, “Recursive histogram modification: Establishing equivalency between reversible data hiding and lossless data compres-sion,”IEEETrans.ImageProcess.,vol.22,no.7,pp.2775–2785, Jul. 2013.
4. B.ou, X. Li, Y. Zhao, R. Ni, and Y. Shi, “Pairwise prediction-error expansionforefficientreversibledatahiding,”IEEETrans.ImageProcess.,vol. 22,
no. 12, pp. 5010–5021, Dec. 2013. 5. X. Zhang, “Reversible data hiding in encrypted images,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011.
6. Ioan – CatalinDragoi, DinuClotuc ,”Local – prediction – based differ-ence expansion reversible watermarking ”, “IEEE Trans. On Image
Processing, vol.23, no.4, pp 1779- 1790, April 2014 ” 7. X. Hu, W. Zhang, X. Li, and N. Yu, “Minimum rate prediction and optimized histograms modification for reversible data hiding,” IEEE Trans.
Inf. Forensics Security, vol. 10, no. 3, 653–664, Mar. 2015.
8. 2014 celebrity photo hack [Online].Available:http://en.wikipedia.org/wiki/2014_celebrity_photo_hack 9. K. Ma, W. Zhang, X. Zhao, N. Yu, and F. Li, “Reversible data hiding in encrypted images by reserving room before encryption,” IEEE Trans.
Inf. Forensics Security, vol. 8, no. 3, pp. 553–562, Mar. 2013.
10. Z. Qian and X. Zhang, “Reversible data hiding in encrypted image with distributed source encoding,” IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 4, pp. 636–646, Apr. 2016.
11. W. Zhang, K. Ma, and N. Yu, “Reversibility improved data hiding in encrypted images,” Signal Process., vol. 94, pp. 118–127, Jan. 2014.
12. W. Zhang, K. Ma, and N. Yu, “Reversibility improved data hiding in encrypted images,” Signal Process., vol. 94, pp. 118–127, Jan. 2014. 13. X. Zhang, “Reversible data hiding in encrypted images,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255–258, Apr. 2011
14. Y. Lee and W. Tsai, “A new secure image transmission technique via secret-fragmentvisible mosaic images by nearly reversible colour
transformation,” IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 4, pp. 695–703, Apr. 2014.
8.
Authors: Naveen Pathak, Anand Bisen
Paper Title: A Review on MANET using Soft Computing and Dempster-Shafer Theory
Abstract: Mobile ad hoc networks (MANETs) is an substructure-less, dynamic network include of a sets of wirelessly
mobility nodes which communicate with all different without the exploit of any centralized authority. Because of its
fundamental characteristics, like as wireless medium, dynamic topology, distributed cooperation. In this paper we study
MANET and its characteristics, application, security goals and different types security attacks, soft computing approach
and dempster-shafer theory of evidence.
Keywords: MANET; soft computing appproch; dempster-shafer theory of evidence;
References: 1. PriyankaGoyal, VintiParmarand Rahul Rishi, “MANET: Vulnerabilities, Challenges, Attacks, Applications”, IJCEM, Vol.11, January 2011
2. Aarti, Dr. S. S. Tyagi “ Study of MANET: Characteristics, Challenges, Application and Security Attacks” Volume 3, Issue 5, May 2013. 3. C. R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Networks,” IEEE JSAC, vol. 15, pp. 1265–75, Sept. 1997
4. Chlamtac, I., Conti, M., and Liu, J. J.-N. Mobile ad hoc networking: imperatives and challenges. Ad Hoc Networks, 1(1), 2003, pp. 13–6 5. HaoYang, Haiyun& Fan Ye ― Security in mobile ad-hoc networks : Challenges and solutions,‖, Pg. 38-47, Vol 11, issue 1, Feb 2004.
6. Bin Lu and Udo W. Pooch, “Cooperative Security-Enforcement Routing in Mobile Ad Hoc Networks,” in proceedings of the 4th IEEE
International Conference on Mobile and Wireless Communications Network (MWCN 2002), Stockholm, Sweden, September 2002, pp.157 – 161.
7. Siddesh.G.K,K.N.Muralidhara,Manjula.N.Harihar,2011. Routing in Ad Hoc Wireless Networks using SoftComputing techniques and
performanceevaluation using HypernetsimulatorInternational Journal of Soft Computing and Engineering (IJSCE)ISSN: 2231-2307, Volume-1, Issue-3, July 2011.
8. Skabar and I. Cloete,2001. Discovery of financial traading rules. In Proc. Artificial Intelligence and Applications (AIA2001), pages 121–125,
Marbella, Spain. 9. Cloete and A. Skabar,2001. Feature selection for financial trading rules. In Proceedings of 13th.EuropeanSimulation Symposium:Simulation in
Industry, pages 713–717, Marseille,France,
10. Parimal Kumar Giri, Member,IACSIT,2012.A Survey on Soft Computing Techniques forMulti-Constrained QoS Routing in MANETIJCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 03, ISSUE 02, MANUSCRIPT CODE: 130103.
11. T. Kohonen,1982. Self-organized formation of topologically correctfeature maps. Biological Cybernetics, 43:59–69.
12. Jaspal Jindal Vishal Gupta Associate Professor in ECE Deptt. M.Tech (ECE) Student P.I.E.T College Smalkha (Panipat) ,2013. International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 6, June2013 ISSN: 2277 128X,June 2013
13. Sharad Sharma, Shakti Kumar and Brahmjit Singh,1,3Deptt. of Electronics & Communication Engineering, National Institute
ofTechnology,Kurukshetra, India2Computational Intelligence (CI) Lab, IST Klawad, Yamunanagar, India2013. Routing in Wireless Mesh Networks: Two Soft Computing Based Approaches. International Journal of Mobile Network Communications & Telematics (IJMNCT) Vol. 3,
No.3, June 2013DOI: 10.5121/ijmnct.2013.3304 29.
14. Luis Bernardo, Rodolfo Oliveira, Sérgio Gaspar, David Paulino and Paulo Pinto A Telephony Application for Manets: Voice over a MANET-Extended JXTA Virtual Overlay Network
15. Indira N, “Establishing a secure routing in MANET using a Hybrid Intrusion Detection System”, 978-1-4799-8159-5/14/$31.00©2014 IEEE.
16. V. G. Muralishankar, Dr. E. George Dharma PrakashRaj”Routing Protocols for MANET: A Literature Survey” ©2014, IJCSMA All Rights Reserved, www.ijcsma.com.
17. R.RagulRavi , V.Jayanthi “A Survey of Routing Protocol in MANET” R.RagulRavi et al, / (IJCSIT) International Journal of Computer Science
and Information Technologies, Vol. 5 (2) , 2014, 1984-1988. 18. Alex Hinds, Michael Ngulube, Shaoying Zhu, and Hussain Al-Aqrabi “A Review of Routing Protocols for Mobile Ad-Hoc NETworks
(MANET)” International Journal of Information and Education Technology, Vol. 3, No. 1, February 2013.
19. Boaz Benmoshe, Eyal Berliner. AmitDvir “Performance Monitoring Framework for Wi-Fi MANET” 2013 IEEE Wireless Communications and
30-36
Networking Conference (WCNC): SERVICES & APPLICATIONS 20. Parimal Kumar Giri, Member,IACSIT,2012.A Survey on Soft Computing Techniques forMulti-Constrained QoS Routing in MANETIJCIT,
ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 03, ISSUE 02, MANUSCRIPT CODE: 130103/2012.
21. Adnan Nadeem “A Survey of MANET Intrusion Detection & Prevention Approaches for Network Layer Attacks”2012. 22. Adnan Nadeem “A Survey of MANET Intrusion Detection & Prevention Approaches for Network Layer Attacks”2012.
23. S. A. Ade & P. A. Tijare, “Performance Comparison of AODV, DSDV, OLSR and DSRRouting Protocols in Mobile Ad Hoc Networks”,
International Journal of Information Technology and Knowledge Management, July-Dec 2010, Volume 2, No. 2, pp. 545-548 24. B.Praveen Kumar P.ChandraSekharN.PapannaB.BharathBhushan “A SURVEY ON MANET SECURITY CHALLENGES AND ROUTING
PROTOCOLS” P Chandra Sekhar et al, Int.J.Computer Technology & Applications,Vol 4 (2),248-256.
9.
Authors: Nor Azlina Abd Rahman, Vinothini Kasinathan, Rajasvaran Logeswaran, Nurwahida Faradila Taharim
Paper Title: Edutainment for Effective Teaching and Learning of Digital Natives
Abstract: This paper studies an effort to enhance the teaching and learning of Digital Natives (ages below 36 years old
or born after the year 1980). It explores the concept and current meaning of Edutainment with a focus on a game called
QR IT Seek, developed with consideration of the specific characteristics of Digital Native learners who are the future
workforce of a nation. The paper endeavors to respond to the demands of the Digital Natives who are distinctly different
from the previous generations. The pressure exists for teaching and delivering concepts to the younger generation due to
these characteristics. Hence, it is vital for educators of higher learning to develop innovative methods of teaching tertiary
education materials and rediscover the concept and application of Edutainment. The need for this study and its findings
is enhanced because without attention given to the specific needs of these students at institutions of higher education
today, there would be significant impact on the achievement of learning outcomes and result in long term global
consequences in this borderless world.
Keywords: Edutainment, QR-Code, QR IT Seek competition, Digital Natives, pedagogy.
References: 1. Metin Argan, Necip Serdar Sever “Constructs and Relationships of Edutainment Applications in Marketing Classes: How Edutainment Can be
Utilized to Act as a Magnet for Choosing a Course?,” Contemporary Educational Technology, 2010, 1(2). Available at:<
http://www.acarindex.com/dosyalar/makale/acarindex-1423874753.pdf> [Accessed 1 April 2015] 2. Wessels, P.L & Steenkamp, L.P. (2009). Generation Y students: Appropriate learning styles and teaching approaches in the economic and
management sciences faculty. South African Journal of Higher Education, 23(5), 1039-1058
3. Heather Fry, Steve Ketteridge and Stephanie Marshall, 2009, A Handbook for Teaching and Learning in Higher Education Enhancing Academic Practice, 3rd Edition, Routledge, ISBN 0-203-89141-4
4. Walia, 2015, “Entertainment vs. Edutainment: Bollywood Movies as Pedagogical Tools,” International Research Journal of Engineering and
Technology (IRJET), Vol2, Issue 1, pg 139 – 140. Available at: < https://www.irjet.net/archives/V2/i1/Irjet-v2i130.pdf> [Accessed 12 May
2015]
5. Anderson, D., Kisiel, J., & Storksdieck, M. (2006). Understanding teachers' perspectives on field trips: Discovering common ground in three
countries. Curator, 4(3), 365–386. 6. Saomya Saxena, 2013, “Best Educational Websites and Games for High-School Students”, EdTechReview. Available at:<
http://edtechreview.in/news/834-best-educational-websites-and-games-for-high-school-students> [Accessed 20 July 2015]
7. Mark Griffiths, 2002, “The educational benefits of videogames,” Education and Health, Vol 20, No 3, pg 47-51. Available at:< http://sheu.org.uk/sites/sheu.org.uk/files/imagepicker/1/eh203mg.pdf> [Accessed 21 July 2015]
8. Vinothini Kasinathan, Nor Azlina Abd Rahman and Mohamad Firdaus Che Abdul Rani “Approaching Digital Natives with QR Code
Technology in Edutainment. A case study: QR Technology in APU Campus Area,” International journal of Education and Research, Vol2, Issue 4, pg 169-178. Available at: <http://www.ijern.com/journal/April-2014/16.pdf> [Accessed 1 April 2015]
9. MM Mubaslat, “The Effect of Using Educational Games on the Students’ Achievement in English Language for the Primary Stage”, 2012,
Institute of Education Sciences. Available at: < http://files.eric.ed.gov/fulltext/ED529467.pdf> [Accessed 5 January 2016] 10. Buckingham, D. and Scanlon, M. (2005) ‘Selling learning: towards a political economy of edutainment media,’ in Media, Culture and Society,
vol. 27, no. 1. pp 41-58
37-43
10.
Authors: Shadrack Mutungi Simon, Abednego Gwaya, Stephen Diang’a
Paper Title: Exploring the Practice of Resource Planning and Leveling (RP&L) Among Contractors in the Kenyan
Construction Industry
Abstract: The performance of construction projects depends to a great extent on how best resources are managed.
Resource planning and leveling are critical aspects of resource management which need to be fully incorporated and
practised in any site. Failure to manage the resources available through planning and leveling is likely to result in
increased project costs, time overruns and poor quality. This assertion is supported by Tarek, (2010) who argues that
proper resource planning and leveling helps resolve resource conflicts, which cause numerous challenges to the
organization, such as: delay in completion of certain tasks, challenges in assigning a different resource to a certain task,
inability to alter task dependencies, addition or removal of certain tasks and overall time and cost overruns of projects.
He further argues that the aim of resource leveling is to increase efficiency when undertaking projects by maximizing on
the resources available at hand. While it would be true to say that quite a number of authors have addressed the issue of
resource management, the author feels that the subject of resource planning and leveling in the Kenyan construction
industry is not well covered. This is due to a number of reasons which create a gap to be researched on. Authors such as
Abeyasinghe et al., (2001); Ballard, (2000); Bandelloni et al., (1994) among others have covered different aspects of
resource planning and leveling. It is however important to note that all these authors address the topic in developed
countries. Some of the literature found on the topic is based on the manufacturing industry. This therefore creates the
need to study the Kenyan construction industry and establish the underlying factors behind the practice of resource
planning and leveling among construction industry players. The purpose of this research was to explore the practice of
resource planning and leveling (RP&L) adopted by contractors within the Kenyan construction industry and the factors
influencing the adoption of such techniques. This research mainly adopted a case study design where questionnaires
were used to collect data from respondents. The research site was Nairobi and the target population was NCA 1-3
contractors. Random sampling was used to identify the 106 respondents. A response rate of 76% was achieved. Data
obtained was analyzed using descriptive statistics, relative importance index analysis and spearman’s correlation
analysis. The study concluded that: though there is a high level of usage of RP&L in the Kenyan construction industry
44-52
much of which is non-structured, construction projects’ progress continue to be affected by delayed materials, lack of
labour and lack of equipment at the points of need; RP&L is practised more in older contracting firms and where there is
support from top management; and finally a high degree of RP is associated with reduced negative impact of
construction project progress
Keywords: Resource Planning, Resource Leveling, Construction Project Performance.
References: 1. Abeyasinghe, Greenwood, & Johansen. (2001). An efficient method for scheduling construction projects with resource constraints. International
Journal of Project Management, 19(15), 29–45.
2. Ala-Risku, T., & Kärkkäinen, M. (2006). Material delivery problems in construction projects: A possible solution. International Journal of Production Economics, 104(1), 19–29. http://doi.org/10.1016/j.ijpe.2004.12.027
3. Ankrah, A. (2007). An investigation into the impact of culture on construction project performance. University of Wolverhampton.
4. Aslani, P., Christodoulou, S., Griffis, F. H., Ellinas, G., & Chiarelli, L. (2009). Activity prioritisation under resource constraints using a utility index method. The Open Construction & Building Technology Journal, 3, 33–41.
5. Badawiyeh, B. H. (2010). The Effect of Planning and Resource Leveling.
6. Ballard. (2000). The last planner system of production control. University of Birmingham, UK. 7. Bandelloni, M., Tucci, M., & Rinaldi, R. (1994). Optimal resource leveling using non-serial dynamic programming. European Journal of
Operational Research, European Journal of Operational Research, 78(2), 162–177.
8. Broadhurst, K., Holt, K., & Doherty, P. (2012). Accomplishing parental engagement in child protection practice?: A qualitative analysis of
parent-professional interaction in pre-proceedings work under the Public Law Outline. Qualitative Social Work, 11(5), 517–534.
http://doi.org/10.1177/1473325011401471
9. Bryman, A. (2004). Social Research Methods (Fourth). London: Oxford university press. 10. Bryman, A. (2008). Social Research Methods (3rd ed.). New York: Oxford university press.
11. Bryman, A., & Bell, E. (2007). Business Research Methods. London: Oxford university press.
12. Charoenngam, C. (2003). Planning and scheduling consideration and constraints in automated construction environment. 13th ISARC, 475–482. 13. Chitkara. (1998). Essentialsof construction projct managemet. Newsouth publishing.
14. Clough, R., & Sears, G. (1991). Construction Project Management. New York: John Wiley & Sons, Inc.
15. Creswell, J. (2009). Research Design; Qualitative, Quantitative and Mixed Methods Approaches. Journal of Chemical Information and Modeling (Second, Vol. 53). London: Sage Publications. http://doi.org/10.1017/CBO9781107415324.004
16. Cunningham, T. (2013). Factors Affecting The Cost of Building Work - An Overview. Dublin Institute of Technology, 0–21.
17. Czaja, R., & Blair, J. (1996). Designing surveys: a guide to decisions and procedures. London: Pine Forge Press. 18. Dubey, A. (2015). Resource Levelling for a Construction Project, 12(4), 5–11. http://doi.org/10.9790/1684-12440511
19. Hegazy, T. (2010). Resource Leveling Vs Resource Allocation, 59–65. Retrieved from
http://www.tutorialspoint.com/management_concepts/resource_leveling.htm 20. K’Akumu, O. a. (2007). Construction statistics review for Kenya. Construction Management and Economics, 25(3), 315–326.
http://doi.org/10.1080/01446190601139883
21. Kass, M. M. A. E.-A. (2012). A construction resources management system for gaza strip building contractors, 131.
22. Kumari, K. S., & Vikranth, J. (2012). A Study On Resource Planning In Highway Construction Projects, 2(August), 1960–1967.
23. Lau, E., & Kong, J. J. (2006). Identification of Constraints in Construction. Projects To Improve Performance. Sustainable Development through Culture and Innovation, 655–663. Retrieved from http://www.irbnet.de/daten/iconda/CIB4451.pdf
24. Mendoza, C. (1995). Resource Planning and Resource Allocation in the Construction Industry. University of Florida.
25. Mugenda, O. ., & Mugenda, A. . (1999). Research methods: Quantitative and Qualitative Approaches. Nairobi: Acts Press. 26. Naief, H. (2002). A comparative evaluation of construction and manufacturing materials management. International Journal of Project
Management, 20(4), 263–270.
27. Reddy, B. S. K., & Nagaraju, S. K. (2015). A Study on Optimization of Resources for Multiple projects by using Primavera. Journal of Engineering Science and Technology, 10(2), 235–248.
28. Schultz, R. L., Slevin, D., & Pinto, S. K. (1987). Strategy and tactics in a process model of project management interfaces. Management Journal,
17(3), 34–46. 29. Siboe, W. (2016). Investigating the Adequacy of Construction Planning in Kenya. Jomo Kenyatta University of Agriculture and Technology
(JKUAT).
30. Spector, P. E. (1981). Research designs, 84. http://doi.org/10.4135/9781412985673 31. Stukhart, G. (1995). Construction materials management. New York: Marcel Dekker Inc.
32. Tarek, H., & Wail, M. (2010). Critical path segments scheduling technique. Journal of Construction Engineering and Management, American
Society for Civil Engineers (ASCE), 138(6), 786–787. 33. Thomas, S., Skitmore, R., Lam, & Poon, A. (2004). Demotivating factors influencing the productivity of civil engineering projects. International
Journal of Project Management, 136–146.
34. United Nations Centre for Human Settlements. (1984). The Construction Industry in Developing Countries. UNCHS Habitat, 2.
11.
Authors: Jerry Chong Chean Fuh, Khalida Shajaratuddur Harun, Nor Azlina Abd Rahman, Sandra A. P Gerald
Paper Title: MENTOR as a Learning Method for Slow Learners
Abstract: This paper proposed a prototype of an electronic learning system for slow learning children to enable the
kindergarten education to create a better learning environment for children between the ages of four to six years old. The
purpose is to enable the slow learning children to learn in more effectively and independently at anytime. In general, the
term 'slow learning children' is referring to children who tend to take longer time to understand certain information when
compared to other children with similar age. To elaborate further, kids who require multiple explanations before they are
able to grasp a concept. The system should help children improve their ability to be flexible and creative as well as
encourage slow learning children to gain confidence in their daily life. The prototype developed after considering
several elements that is suitable for slow learner that focusing more on multimedia elements which are images, sounds
and interactive activities. The prototype is not just focusing on learning but also enable the teachers to share the children
progress with the parents. This paper presented a workable E-learning software prototype which is MENTOR system for
young age users for self-improvement and learning. The prototype has 3 users; slow learner children, tutors and parents.
In other words the parents able to monitor their child progress using this MENTOR system. The technologies used to
develop the prototype and advantages of MENTOR system are also highlighted.
Keywords: component; MENTOR; slow learning children
References:
53-61
1. Abdollah. N., Ahmah. W, Akhir. E ‘Multimedia courseware for slow learners: A preliminary analysis’ (2010) Available at: http://ieeexplore.ieee.org/xpl/article/slow+learner+teachers
2. Ahmed Al Hamad, Norlaily Yaacob and A. Y. Al-Zoubi(2008) Integrating ‘Learning Style’ Information into Personalized e-Learning System .
ENGINEERING EDUCATION, Available at: http://www.ewh.ieee.org/soc/e/sac/itee/index.php/meem/article/viewFile/9/12 3. Rahmah Lob Yussof and Halimah Badioze Zaman, “Usability Methodology of Multimedia Courseware (Mel-Sindd) for Down Syndrome
Learner,” Proceedings of the 3rd International Malaysian Educational Technology Convention, Penang, Malaysia,2009.
4. Lee Lay Wah, “Development of Multimedia Learning Resources for Children with Learning Disabilities in an Undergraduate Special Education Technology Course,” MEDC Vol.1.,2007.
5. Mohamad Firdaus Che Abdul Rani, Rizawati Rohizan, Nor Azlina Abd Rahman, 'Web-based learning tool for primary school student with
Dyscalculia', The International Conference on Information Technology and Multimedia (ICIMu 2014), 19 – 20 November 6. Earnshaw, T. & Seargeant, A. (2005). Dealing with dyslexia and other reading difficulties. Prentice Hall
7. J. P. Khas, Huraian Sukatan Pelajaran Pendidikan Khas Bermasalah Pembelajaran Sekolah Rendah & Menengah. Kuala Lumpur: Kementerian
Pendidikan Malaysia, 2003. 8. Norfarhana Abdollah, Wan Fatimah Wan Ahmad, Emelia Akashah Patah Akhir, Development and Usability Study of Multimedia Courseware
for Slow Learners: 'Komputer Saya', 2012 International Conference on Computer & Information Science (ICCIS), 2012
9. Booch, G. et al (1999) Object oriented analysis and design [online] Available from http://openpdf.com/ebook/object-oriented-analysis-and-design-in-uml-bygrady-booch-pdf.html [Accessed 21 January 2015]
10. Msdn.microsoft.com (2005) Introduction to WPF. [online] Available from http://msdn.microsoft.com/en-us/library/aa970268.aspx [Accessed 21
January 2015] 11. Viescas, J.L., Hernandez, M.J, Addison Wesley Professional, SQL Queries for Mere Mortals: A Hands-On Guide to Data Manipulation in SQL.
2nd Edition
12. Paul Deitel, Harvey M. Deitel ,Pearson Educaton, Inc., C# 2012 for Programmers (5th Edition) (Deitel Developer Series), 2013
13. Jared, M. (2001) ‘Microsoft C# vs. Java: A Syntactical and Functional comparison’ [online] Available from
http://www.devhood.com/tutorials/tutorial_details.aspx?tutorial_id=76 [Accessed 17 January 2015]
12.
Authors: Monicah Wairimu Chonge
Paper Title: An Investigation of the Performance of Local Contractors in Kenya
Abstract: The performance of contractors is a great determinant of their success or failure. Poor performance is linked
to failure whereas good performance is linked to success. Despite of this, contractors in most industries of the world, and
most especially the developing countries, have been accused of poor performance. In Kenya, the situation is not
different as the performance of the contractors has been termed as poor as far as time, cost and quality is concerned. This
study therefore sought to validate this accusation by finding out the level of the performance of contractors in Kenya.
Thirteen performance measures as identified in the literature review were used as the scale of measure. These were:
time, cost, quality, client satisfaction, health and safety, environment protection, participants’ satisfaction, community
satisfaction, sustainability of the development, functionality of the development, communication, profitability and
productivity. The study employed the quantitative strategy as well as the cross-sectional research design. Quantitative
data was collected through the use of structured questionnaires which were administered to local contractors of category
NCA 1, 2 and 3. The contractors were sampled using the stratified random sampling and the systematic random
sampling techniques. The data was analyzed using the Statistical Package for Social Sciences (SPSS for windows
version 20). The method used for data analysis was descriptive statistics. The analysis revealed that the local contractors
are average performers when all the performance measures are used to gauge their performance. But when these
performance measures are considered separately, they performed poorly on time, cost, profitability, productivity and
client satisfaction. They have an average performance on health and safety, participants’ satisfaction, community
satisfaction, environmental protection, sustainability, communication, quality and functionality. This study therefore
concludes that local contractors in Kenya of category NCA 1, 2 and 3 can be termed as average performers rather than
poor performers.
Keywords: Contractors performance, Performance measures, Construction industry
References: 1. Akintoye, A., & Takim, R. (2002). Performance indicators for successful construction project performance. University of Northumbria.
Association of Researchers in Construction Management, 2, 545–555.
2. Atkinson, A. R. (1999). The role of human error in construction defects. Structural Survey, 17(4), 231–236.
3. Auma, E. (2014). Factors affecting the performance of construction projects in Kenya: A survey of low rise buildings in Nairobi Central Business District. The International Journal of Business Management.
4. Business dictionary. (2015). The meaning and definition of performance.
5. Chan, A. ., & Tam, C. M. (2000). Factors affecting quality of building projects in Hong Kong. International Journal of Quality and Reliability Management, 17(4/5), 423–441.
6. Chan, D. W. M., Chan, M. M., & Kumaraswamy, M. M. (2002). Compressing construction duration: lesson learned from Hong Kong building
projects. International J0urnal of Project Management, 20, 23–25. 7. Cheung, S. O., Suen, H. C., & Cheung, K. K. W. (2004). PPMS: a Web-based construction Project Performance Monitoring System. Automation
in Construction, 13(3), 361–366.
8. Flyvbjerg, B., S., M. K., & B., S. L. (2003). Howcommon and how large are cost overruns in transportinfrastructure projects. TransportReviews, 23(1), 71–88.
9. Hussaini, M., Syuhaida, I., & Lee, M. R. (2014). Key performance indicators (KPI) of contractor on project performance for housing
construction in Malasyia. Razak School of Engineering and Advanced Technology, Kualalumpur Malasyia. 10. Jha, K. N., & Lyer, K. C. (2006). Critical factors affecting quality performance in constructionprojects. Total Quality Management, 17(9), 1155–
1170.
11. Kibuchi, N., & Muchungu, P. (2012). The contribution of human factors in the performance of construction projects in Kenya: a case study of construction project team participants in Nairobi.
12. KNBS. (2015). Economic survey 2015.
13. Kometa, S. T., & Olomolaiye, P. O. (1995). An evaluation of clients’ needs and responsibilities in the construction process. Engineering, Construction and Architectural Management, 2(1), 57–76.
14. Kumaraswamy, M. M., & Chan, D. W. M. (1995). Determinants of construction duration. Construction Management and Economics, 13, 209–
217. 15. Kuta, J., & Nyaanga, D. M. (2014). The effect of competence of contractors on construction of substandard buildings in Kenya. Prime Journal of
Social Sciences, 3(3), 637–641.
16. Macharia, S. M. (2015). Determinants of successful completion of power projects in Kenya Power and Lightning. International Journal of Social
62-68
Sciences Entrepreneurship, 1(12), 570–580. 17. Megha, D., & Rajiv, B. (2013). A methodology for ranking of causes of delay for residential construction projects in Indian context. International
Journal of Emerging Technology and Advanced Engineering, 3(3), 396–404.
18. Muguiyi, M. W. (2012). Factors influencing performance of contractors of government funded projects in Kirinyaga county- Kenya. University of Nairobi.
19. Nassar, K., & Hosny, O. (2013). Fuzzy clustering validity for contractor performance evaluation: Application to UAE contractors. Automation in
Construction, 158–168. 20. Navarre, & Schaan, C. (1998). Design of project management systems from top management perspective. Retrieved from
http://hdl.handle.net/10393/18602
21. Ndaiga, H. (2014). Construction industry posed for growth. Construction Business Review. 22. Nyangilo, A. O. (2012). An assessment of the organization structure and leadership effects on construction projects’ performance in Kenya: a
case study of public building projects within Nairobi region. University of Nairobi.
23. Ogoma, G. (2014). Introduction to the national costruction authority. 24. Pinto and Pinto, J. K. (1991). Determinants of cross functional cooperation in the project implementation process. Project Management Journal,
22(2), 13–20.
25. Pocock, J., Hyun, C., Liu, L., & Kim, M. (1996). Relationship between Project Interaction and Performance Indicators. Journal of Construction Engineering and Management, 2(165), 165–176.
26. Songer, A. D., & Molenaar, K. R. (1997). Project characteristics for successful public-sector design-build. Journal of Construction Engineering
and Project Management, 123(1), 34 – 40. 27. Walker, D. H. T. (1996). The contribution of the construction management team to good construction time performance-An Australian
experience. Journal of Construction Procurement, 2(20), 4–18.
13.
Authors: Maha Abdul Ameer Kadhum
Paper Title: Design A Program to Simulate the Active Antennas
Abstract: In this research has been studying and analyzing some types of properties antennas normal, then been
improved characteristics after conversion to efficient antennas with compared to the old characteristics of antennas and
new characteristics which distinguish solving Maxwell's equations have . Allantij showed an antenna model improved
the overall value of the proportion of the voltage wave, increase bandwidth In addition to giving him a more stable long
distance. Study antenna adaptive, which is the best levels used in smart antennas and signal systems with different levels
of intelligence and work simulation using (demand) to one of the levels in the system and analyze its results were used
algorithm less square error rate high Astaqraratha and simplicity mathematically was a simulation of the system
operation.
Keywords: antenna, wavelengths, antennas adaptive simulation.
References: 1. A.Andrews, TEEE "standard for local metropolis Area network, part 16, air interface for fixed broadband Wires access system".IEEE, std 802,
16-2004.
2. Kenjon, "An examination of the processing comp laxity of an adaptive antenna system (AAS), for W IMAX", IEEE, 2005, Southampton.
3. M.Nicoli, L.Samiretro et al, "Deployment Journal of wireless for OFDM, HAP-Based Communication international network, vo.13, no.1, Journal, 2006.
4. L.Gadara,"Application of antenna array comunication, IEEE .Vo.85, no.8,august 1997.
5. D.Gore,"smart antenna for broadband wireless access network",paper application in IEEE communication magazine, nov,1999. 6. R.sampirtro etal, "Guide lines for evaluation of radio transmission technology for IMT-2000" ,Jan,2005.
7. Chand, L."Smart antenna", CRC press, jan, 2004.Technology and engineering.
8. K.Sarah,"adaptive antenna system design and applications for next generation mobile device ", international journal of engineering and innovative technology (IJET) vo, 1,issue 2,February ,2012.
9. Rameshaver,"advances in smart antenna ", journal of scientific and industry research, vo.64, September, 2005.
10. Santhi,"smart antenna algorithm for WCDMA mobile communication system, journal of science and network, vo.8, no.7, July, 2008. 11. Nayoo,"application for radiation pattern control in WCDMA network submitted for the degree of doctor of philology, department of electronic
engineering queen Mary university of London ,2007.
12. Seangwon etal,"an adaptive beam forming algorithm smart antenna engineering in protocol CDMA environment ,IEEE trans., commutation , vo.E86, no.3, march, 2003.
69-72
14.
Authors: Rucha Dilip Patlil, C. M. Jadhav
Paper Title: Autonomously-Reconfigurable Wireless Mesh Networks
Abstract: Multi-hop wireless mesh network experience link-fail due to channel interference (i/f), dynamic obstacles etc.
which causes performance degradation of the network in Wireless Mesh Networks. The paper proposes “The base of
Autonomously Reconfigurable Wireless Mesh Networks system is IEEE 802.11” for mr-WMN to recover autonomously
when the network failure occurs & to improve the performance of network. The paper uses an autonomously network
reconfiguration system (ARS) algorithm to maintain network performance that allows a multi radio WMN to own
recover from local link failure. ARS generates needful changes in local radio and channel assignments in order to
recover from failures by using channels and radio variability in WMN's. Next, the system cooperatively reconfigures
network setting among local mesh routers based on the generated configuration changes.
Keywords: IEEE 802.11, multi-radio wireless mesh networks (mr-WMNs), Autonomous-Reconfigurable Network,
Wireless Link Failures.
References: 1. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: Survey,” Comput. etw., vol. 47, no. 4, pp. 445–487, Mar. 2005.
2. Brzezinski, G. Zussman, and E. Modiano, “Enabling distributed throughput maximization in wireless mesh networks: A partitioning approach,” in Proc. ACM MobiCom,Los Angeles, CA, Sep. 2006, pp.
3. F. AKYILDIZ, GEORGIA INSTITUTE OF TECHNOLOGYXUDONG WANG, KIYON, INC.A “Survey on Wireless Mesh Networks”
4. P. S. Khanagoudar “A New Autonomous System (AS) for Wireless Mesh Network”, JEITVol 2, Issue 1, july 2012. 5. kyu-Han kim, Member, IEEE and Kang G. Shin “ Self-Reconfigurable Wireless MeshNetwork”, IEEE ACM TRANSACTION ON
NETWORKING, VOL 19.NO.2, April 2011.
6. Jensilin Mary A, “Autonomously Reconfiguring Failure in Wireless Mesh Network”,Journal of Computer Application ISSN, Vol-5, EICA 2012
73-74
Feb 10 7. R. Draves, J. Padhye, and B. Zill, “Routing in multi-radio, multi-hop wireless meshnetworks,” in Proc. ACM MobiCom, Philadelphia, PA, Sep.
2004, pp. 114–128.
8. Raniwala and T. Chiueh, “Architecture and algorithms for an IEEE 802.11-basedmulti- channel wireless mesh network,” in Proc. IEEE IN-FOCOM, Miami, FL, Mar. 2005,vol. 3
9. Xiao Shu, Xining Li, “Link Failure Rate and Speed of Nodes in Wireless Network”, Computingand Info. SCi. University Canada, 2008 IEEE.
10. L.Qiu, P.Bahl,A. Rao, and L. Zhou, “Troubleshooting multi-hop wire- less networks,”in Proc. ACM SIGMETRICS, Jun. 2005, pp. 380–381. 11. P. Kyasanur and N. Vaidya, “Capacity of multi-channel wireless net-works: Impact ofnumber of channels and interfaces,” in Proc. ACM Mobi
Com, Cologne, Germany, Aug.2005, pp. 43–57.
15.
Authors: Issa Y. S. Ali, Sedat Nazlibilek
Paper Title: Design and Performance Analysis of a Robust Power System Stabilizer for Single Machine Infinite Bus
using ADRC Approach
Abstract: Due to the rapid growing demand for electricity, power systems nowadays have become operating under
continually changing in loads and operating conditions which is a major cause of instabilities and could potentially result
in serious consequences. This paper presents a novel design approach by employing a robust damping control of power
systems based on ‘Active Disturbance Rejection Control’ (ADRC) algorithm in order to improve system stability. The
advantage of this algorithm is that it requires little information from the plant model since the relative order of open loop
transfer function information is quite sufficient to design a robust controller. This makes the power system more robust
against a wide range of disturbances that are commonly encountered in such systems. Here, the proposed ADRC control
algorithm is developed for a synchronous machine connected to infinite bus (SMIB) through external reactance under
small-disturbance condition. The effectiveness of the proposed algorithm has been verified by comparing it with an
optimally tuned Conventional Power System Stabilizer (CPSS) under various loading conditions. The comparison shows
that the proposed approach guarantees system stability and exhibits higher performance than CPSS which lacks
robustness at some severe operating points despite being optimally tuned.
Keywords: Active Disturbance Rejection Control (ADRC); Dynamic Analysis; Small Signal Stability; Power system
stabilizer (PSS); Single Machine Infinite Bus (SMIB).
References: 1. K. Padiyar, Power system dynamics: BS publications, 2008.
2. D. Mondal, A. Chakrabarti, and A. Sengupta, Power System Small Signal Stability Analysis and Control: Academic Press, 2014. 3. Y. Peng, H. Nouri, Q. M. Zhu, and L. Cheng, "Robust controller design survey for damping low frequency oscillations in power systems," in
Power and Energy Engineering Conference (APPEEC), 2011 Asia-Pacific, 2011, pp. 1-4.
4. G. Kasilingam and J. Pasupuleti, "Coordination of PSS and PID controller for power system stability enhancement–overview," Indian Journal of
Science and Technology, vol. 8, pp. 142–151, 2015.
5. H. P. Patel and A. T. Patel, "Performance evaluation of PSS under different loading condition," in Communication Technologies (GCCT), 2015 Global Conference on, 2015, pp. 281-284.
6. Jalilvand, M. D. Keshavarzi, and M. Khatibi, "Optimal tuning of PSS parameters for damping improvement using PSO algorithm," in Power
Engineering and Optimization Conference (PEOCO), 2010 4th International, 2010, pp. 1-6. 7. S. Paul and P. Roy, "Optimal design of power system stabilizer using oppositional gravitational search algorithm," in Non-Conventional Energy
(ICONCE), 2014 1st International Conference on, 2014, pp. 282-287.
8. H. T. Canales, F. C. Torres, and J. S. Chávez, "Tuning of power system stabilizer PSS using genetic algorithms," in Power, Electronics and Computing (ROPEC), 2014 IEEE International Autumn Meeting on, 2014, pp. 1-6.
9. D. Sambariya and R. Prasad, "Robust tuning of power system stabilizer for small signal stability enhancement using metaheuristic bat
algorithm," International Journal of Electrical Power & Energy Systems, vol. 61, pp. 229-238, 2014. 10. Y. Huang and W. Xue, "Active disturbance rejection control: methodology and theoretical analysis," ISA transactions, vol. 53, pp. 963-976,
2014.
11. P. Kundur, N. J. Balu, and M. G. Lauby, Power system stability and control vol. 7: McGraw-hill New York, 1994. 12. B. Sun and Z. Gao, "A DSP-based active disturbance rejection control design for a 1-kW H-bridge DC-DC power converter," IEEE Transactions
on Industrial Electronics, vol. 52, pp. 1271-1277, 2005.
13. S. Li, J. Yang, W.-H. Chen, and X. Chen, Disturbance observer-based control: methods and applications: CRC press, 2014.
14. Z. Gao, "Scaling and bandwidth-parameterization based controller tuning," in Proceedings of the American control conference, 2006, pp. 4989-
4996.
15. R. Krishan and A. Verma, "Robust tuning of power system stabilizers using hybrid intelligent algorithm," in Power and Energy Society General Meeting (PESGM), 2016, 2016, pp. 1-5.
75-80
16.
Authors: Hamed Ghasemian, Qasim Zeeshan
Paper Title: Failure Mode and Effect Analysis (FMEA) of Aeronautical Gas Turbine using the Fuzzy Risk Priority
Ranking (FRPR) Approach
Abstract: Failure Mode and Effect Analysis (FMEA) is a mitigative risk management tool which prevents probable
failures in the system and provides the foundation for policies and remedial measures to tackle them. In this article, a
method called Fuzzy Risk Priority Ranking (FRPR) is proposed based on fuzzy if-then rules and determination of fuzzy
rule-based Risk Priority Number (RPN). The different combination modes of risk factors (i.e. severity (S), occurrence
(O), and detection (D)) are prioritized between 1 and 1000. Comparing between FRPR and RPN approaches, and an
illustrative example of an aeronautical gas turbine system the merits of the proposed method are explained.
Keywords: Failure Mode and Effect Analysis, Fuzzy rule-based RPN, Fuzzy Risk Priority Ranking
References: 1. Abdelgawad, M., & Fayek, A. R. (2010). Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. Journal of
Construction Engineering and Management, 136, 1028–1036.
2. Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50, 203–213.
3. Bozdag E., Asan U., Soyer A., Serdarasan S., (2015). Risk prioritization in Failure Mode and Effect Analysis using interval type-2 fuzzy sets.
Expert Systems with Applications, 42, 4000-4015. 4. Carter T. J., Common failures in gas turbine blades, Engineering Failure Analysis 12 (2005) 237–247.
5. Chang D.S. & Sun K.L.P. (2009) Applying DEA to enhance assessment capability of FMEA. International Journal of Quality & Reliability
81-92
Management. 26. 629-643. 6. Chin, K. S., Wang, Y. M., Ka Kwai Poon, G., & Yang, J. B. (2009). Failure mode and effects analysis using a group-based evidential reasoning
approach. Computers & Operations Research, 36, 1768–1779.
7. Ford Motor Company. (1988) Potemtial failure mode and effect analysis (FMEA) reference manual. 8. Garcia, P. A. A., Schirru, R., & Frutuoso Emelo, P. F. (2005). A fuzzy data envelopment analysis approach for FMEA. Progress in Nuclear
Energy, 46, 359–373.
9. Gargama, H., & Chaturvedi, S. K. (2011). Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic. IEEE Transactions on Reliability, 60, 102–110.
10. Gulnar et al, 2015. Model based reasoning approach for automated failure analysis: An industrial gas turbine application. Annual conference of
the prognostics and health management society. 11. Kazempour-Liacy H. et al. Failure analysis of a repaired gas turbine nozzle. Engineering Failure Analysis 18 (2011) 510–516.
12. Keskin, G. A., & Ozkan, C. (2009). An alternative evaluation of FMEA: Fuzzy ART algorithm. Quality and Reliability Engineering
International, 25, 647–661. 13. Linton JD. Facing the challenges of service automation: an enabler for e-commerce and productivity gain in traditional services. IEEE Trans Eng.
Manage 2003;50(4):478–84.
14. Liu H.C., Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory, Expert Systems with Applications 38 (2011) 4403–4415.
15. Liu H.C. et al. (2012) Risk evaluation in failure mode and effect analysis with extended VIKOR method under fuzzy environment. Expert
systems with Applications. 38. 4403-4415. 16. Liu H.C., Liu L., Liu N., (2013). Risk evaluation approaches in failure mode and effect analysis: A literature review. Expert Systems with
Application, 40, 828-838.
17. Maktouf W., Saï K., An investigation of premature fatigue failures of gas turbine Blade. Engineering Failure Analysis 47 (2015) 89–101.
18. Mamdani, E.H., "Applications of fuzzy logic to approximate reasoning using linguistic synthesis," IEEE Transactions on Computers, Vol. 26,
No. 12, pp. 1182-1191, 1977.
19. Maria jayaprakash A, Senthilvelan T. Failure detection and optimization of sugar mill boiler using FMEA and Taguchi method. Engineering Failure Analysis 2013;30:17–26.
20. Mazur Z., A. Luna-Ramı´rez, J.A. Jua´rez-Islas, A. Campos-Amezcua, Failure analysis of a gas turbine blade made of Inconel 738LC alloy,
Engineering Failure Analysis 12(2005) 474–486. 21. Meher Homji C. B. & Gabriles G., 1995. Gas turbine blade failures- Causes, avoidance, and troubleshooting. Proceeding of the 27th
turbomachinery symposium.
22. Pela´ez, C. E., & Bowles, J. B. (1996). Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Information Sciences, 88, 177–199.
23. Pillay, A., & Wang, J. (2003). Modified failure mode and effects analysis using approximate reasoning. Reliability Engineering and System
Safety, 79, 69–85. 24. Power systems reliability subcommittee of the power systems engineering committee, IEEE industry applications society, IEEE Std. 493-2007
recom- mended practice for the design of reliable industrial and commercial power systems, IEEE-SA standards board, New York, NY, IEEE
493-2007. 25. Puente, J., Pino, R., Priore, P., & de la Fuente, D. (2002). A decision support system for applying failure mode and effects analysis. International
Journal of Quality and Reliability Management, 19(2), 137–150.
26. Sankar, N. R., & Prabhu, B. S. (2001). Modified approach for prioritization of failures in a system failure mode and effects analysis. International
Journal of Quality and Reliability Management, 18(3), 324–335.
27. Shaout A. & Trivedi J., Performance Appraisal System– Using a Multistage Fuzzy Architecture, International Journal of Computer and Information Technology (ISSN: 2279 – 0764) Volume 02– Issue 03, May 2013.
28. Sharma, R. K., Kumar, D., & Kumar, P. (2005). Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modeling. International
Journal of Quality & Reliability Management, 22(9), 986–1004. 29. Šolc M. Applying of Method FMEA (Failure Mode and Effects Analysis) in the logistics process. Advanced Research in Scientific Areas,
Section12, Industrial and Civil Engineering 2012: 1906–11.
30. Stamatis, D. H. (1995). Failure mode and effect analysis—FMEA from theory to execution. New York: ASQC Quality Press. 31. Su C.T., Lin H.C., Teng P.W., Yang T. Improving the reliability of electronic paper display using FMEA and Taguchi methods: a case study.
Microelectron Reliab 2014;54:1369–77.
32. Tay, K. M., & Lim, C. P. (2006). Fuzzy FMEA with a guided rules reduction system for prioritization of failures. International Journal of Quality and Reliability Management, 23(8), 1047–1066.
33. Wang Y.M. et al, Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean, Expert Systems with Applications
36 (2009) 1195–1207. 34. Xu K, Tang LC, Xie M, Ho SL, Zhu ML. Fuzzy assessment of FMEA for engine systems. Reliab Eng Syst Saf 2002;75:17–29.
35. Yang, Z. L., Bonsall, S., & Wang, J. (2008). Fuzzy rule-based bayesian reasoning approach for prioritization of failures in FMEA. IEEE
Transactions on Reliability, 57(3), 517–528.
17.
Authors: Pankaj Agarwal, Shreeya Sharma, Lavanya Gupta, B. Manideep
Paper Title: Smart Electronic Garbage Management System-Based IOT
Abstract: This paper aims to provide an overview of the voluntary approaches towards enhancing the design of a smart
dustbin for the implementation of advanced waste management systems. In most of the places, the Municipal garbage
bins are overflowing and they are not cleaned at proper time. As a result of which the consequences are severe. It
includes overflow of garbage which results in land pollution, spread of diseases, also it creates unhygienic conditions for
people, and ugliness to that place. There needs to be system that gives prior information of the filling of the bin that
alerts the municipality so that they can clean the bin on time and safeguard the environment. To avoid all such situations
we intend to propose a solution for this problem “Smart Garbage Bin”, which will alarm and inform the authorized
person by buzzer and alert system when the garbage bin is about to fill. To avoid all such unhygienic circumstances we
are going to implement a project based on iot called smart trash management by interfacing an trash bin with infrared
sensors, lcd, buzzer, wifi modules via an arduino atmega .The current status of trash bin is depicted by sensors and
automatically updates garbage level on html page with the help of a wifi module. The main objective of this paper is to
propose a plan to reduce human effort and resources along with the enhancement of smart city vision and to maintain a
pollution free environment around our homes and specially in public places
Keywords: Smart Garbage Bin, Level of Garbage Detection, Wifi Module, Update Garbage level, Buzzer and Alert
System, Smart City Vision.
References: 1. L.A. Guerrero, G Ger, H William, "Solid waste management challenges for cities in developing countries", Garbage Management, vol. 33, no. 1,
pp. 220-232, January 2013.
93-96
2. Akyildiz, X. Wang, "A survey on wireless mesh networks", IEEE Communications Magazine, vol. 43, no. 9, pp. S23-S30, September 2005. 3. D.M. Scott, "A two-color near infrared sensor for sorting recycled plastic waste", Measurement Science and technology, vol. 6, pp. 156-159, 1995.
4. Narayan Sharma, Nirman Singha, Tanmoy Dutta, "Smart Bin Implementation for Smart Cities", International Journal of Scientific & Engineering
Research, Volume 6, Issue 9, September-2015, pp. 787--791 5. Vikrant Bhor, Pankaj Morajkar, Maheshwar Gurav, Dishant Pandya4 “Smart Garbage Management System” International Journal of Engineering
Research & Technology (IJERT) ISSN: 2278-0181 IJERTV4IS031175 Vol. 4 Issue ,03 March-2015
6. Arkady Zaslavsky, Dimitrios Georgakopoulos ”Internet of Things: Challenges and State-of-the-art solutions in Internet-scale Sensor Information Management and Mobile Analytics” 2015 16th IEEE International Conference on Mobile Data Management
18.
Authors: V.Kanchana, M. Prabu
Paper Title: An Implementation of Sensors based to Mitigate over Train-Elephant Conflicts
Abstract: Animal accidents caused due to train are one of the major issues these days. “Train-Elephant Conflict”
Causes difficulties for both the human and the elephants. It is very dangerous issues and this causes a vast reduction in
the animal species. More over elephants are the species that are rare to see and this accident still reduces the population
of elephants. Mostly at night times the forest officials and the train operator cannot so attentive due which accidents
occur. In the proposed system, there is an acoustic sensor fixed at the path of elephant which would be sensed and an
automatic message will be sent to the train operator, thereby minimizing the accidents occurring.
Keywords: Acoustics, Train-Elephant Conflict, Sensor, accidens
References: 1. Ce´ dric Vermeulen, Philippe Lejeune, Jonathan Lisein, Prosper Sawadogo, and Philippe Bouche, “Unmanned Aerial Survey of Elephants”,
PLOS ONE, Volume 8, Issue2, e54700,2013.
2. Chinthaka Dissanayake, Ramamohanarao Kotagiri, Malka Halgamuge, Bill Moran, Peter Farrell, “Propagation Constraints in Elephant
Localization Using an Acoustic Sensor Network”, ICIAfS, Page 101-105, 2012, DOI: 10.1109/ICIAFS.2012.6419889. 3. ttp://hosuronline.com/index.php/12-elephants-diedhosur-forest-range-24-months/. Retrieved 07-11-2015
4. www.newindianexpress.com/states/tamil_nadu/article1450085.ece. Retrieved 14-07-2015 5. http://www.thehindu.com/news/cities/chennai/wildelephant-tramples-man-to-death-nearhosur/article5693880.ece. Retrieved 1-09-2015
6. Lalith Seneviratne, Rossel, Gunasekera, Madanayake, Yapa and Doluweera, “Elephant Infrasound Calls as Method for Electronic Elephant
Detection”, Proc. Of the Symp. On Human-Elephant Relationships and Conflicts, 2004. 7. Mathur, Nielsen, and Prasad, “Wildlife conservation and rail track monitoring using wireless sensor networks”, VITAE, DOI:
10.1109/VITAE.2014.6934504, 2004.
8. Mayilvaganan and Devaki, “Elephant Vocalization Direction of Arrival Estimation for real Time Data in Forest under Acoustic Sensor Network Using Hyperbolic circular Array”, IJRCAR, ISSN 2320-7345, 2014.
9. Mayilvaganan and Devaki, “Elephant Localization Estimation within Acoustic Sensor Network Based On Real Time Data”, IJCTT, Vol 17,
Number 4, 2014.
10. Nimain Palei, Bhakta Rath and Kar, “Death of Elephants Due to Railway Accidents in Odisa, India”, Gajah, Volume 38, 2103, PP 39-4.
11. Nirmal Prince and Sugumar, “Surveillance and Tracking of Elephants Using vocal Spectral Information”, IJRET, eISSN: 2319-1163, 2014.
12. Patrick Clemins and Michael Johnson, “Automatic Type Classification and speaker Identification of African Elephant Vocalization”, J Acoust Sco Am, Volume 117, Number 2, 2005, PP 956-63.
13. Ritesh Joshi, “Train Accident Deaths of Leopards Panthera Pardus in Rajaji National Park: A Population in threat”, World Journal of Zoology,
Volume 5, Number 3, 2010, PP. 156-161. 14. Singh and Chalisagaonkar, “Restoration of Corridors to Facilitate the Movement of Wild Asian Elephant of Wild Asian Elephant in Rajaji-
Corbett Elephant Range”, India, 2006.
15. Vartika Anand, Shalini Shah and Sunil Kumar, “Intelligent Adaptive Filtering For Noise Cancellation”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Volume. 2, Issue 5, 2013.
16. Vibha Tiwari, “MFCC and its applications in speaker recognition” International Journal on Emerging Technologies, Volume 1,Number 1, 2010,
PP 19-22, ISSN : 0975-8364. 17. Jashvir Chhikara and Jagbir Singh, “Noise cancellation using adaptive filter”, International Journal of Modern Engineering Research (IJMER),
Vol.2, Issue.3, 2012, PP 792-795.
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