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Volume-1 Issue-9: Published on August 15, 2013
41
Volume-1 Issue-9: Published on August 15, 2013

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S. No

Volume-1 Issue-9, August 2013, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Abayomi O. Ibiyemi, Martins T. A. Adenipekun

Paper Title:

Self Study Approach to Self Discovery and Motivational Training for Real Estate Professionals in Nigeria

Abstract: Entrepreneurship is a key factor in the production of goods and services. This paper reviews the theoretical, empirical, and the conceptual approaches to determining the significance of entrepreneurship to national economic growth and empowerment, and also draws evidence from China, Thailand, Singapore, India and Korea to support these approaches. It provides a sample of a Self-Discovery Exercise to Real Estate Professionals (REPs) based on ranking of 12 motivators in order of their importance from 1st to 12th in comparison with standard rankings.  It concludes that entrepreneurship is significant to national economic empowerment and development and that REPs can add value in their various areas of professional practice and become players in the international real estate markets.  The paper recommends sincere and regular self-discovery based on the standardised scheme for motivational training exercise, and that Governments at all levels synergise efforts towards improving entrepreneurial framework conditions by  increasing access to finance, facilitate entry and exit, and create Government support programmes.  

Keywords:
Entrepreneurship, Real Estate Professionals, Self-Discovery Exercise, Economic Growth and Empowerment.


References:

1.        Audretsch, David B., and Roy Thurik (2001). Linking Entrepreneurship to Growth. Paris: OECD Directorate for Science, Technology and Industry Working Papers.
2.        Barreto, Humberto (1989). The Entrepreneur in Microeconomic Theory: Disappearance and Explanation. London: Routledge.

3.        Baumol, William J. (1990). Entrepreneurship: Productive, unproductive and destructive.  Journal of Business Venturing 11: 3–22.

4.        Carree, M., and A. Roy Thurik (1998). Small firms and economic growth in Europe.

5.        Atlantic Economic Journal 26 (2): 137 –146.

6.        Carree, M., and A. Roy Thurik (2002). The Impact of Entrepreneurship on Economic Growth. In Zoltan Acs and David B. Audretsch (2003), International Handbook of   Entrepreneurship Research, Boston/Dordrecht: Kluwer Academic Publishers.

7.        Cecora, James, Cultivating Grass-Roots for Regional Development in a Globalizing Economy: Innovation and Entrepreneurship in Organized Markets, Aldershot: Ashgate, 1999

8.        Dejardin, Marcus (2000). Entrepreneurship and Economic Growth: An Obvious Conjunction?

9.        Namur, Belgium: University of Namur.

10.     Friijs, Christian, Thomas Pa ulsson and Charlie Karlsson (2002). Entrepreneurship and Economic Growth: A Critical Review of Empirical and Theoretical Research.Östersund, Sweden: Institutet för tillväxtpolitiska studier.

11.     Ibiyemi, Abayomi (2012). Relating Entrepreneurship to Economic Development. Paper delivered at the Entrepreneurship Educators Training Workshop on the 18th of April, 2012.  Held at Engineering Lecture Theatre, LASPOTECH, Ikorodu, Lago, Nigeria

12.     Jääskeläinen, Miko (2000). Entrepreneurship and Economic Growth. Helsinki: Institute of Strategy and International Business.

13.     Nickell, Stephen J. (1996). Competition and corporate performance. Journal of Political Economy 104 (4): 724–746.

14.     Nickell, Stephen J., Daphne Nicolitsas and Neil Dryden (1997). What makes firms perform well? European Economic Review 41: 783–796.

15.     Organisation for Economic Co-operation and Development (OECD) (1998). Fostering Entrepreneurship. Paris: OECD.

16.     Organisation for Economic Co-operation and Development (OECD) (2002). Benchmarking :Fostering Firms Creation and Entrepreneurship. Paris: OECD

17.     Pirich, Amir (2001). An interface between entrepreneurship and innovation: New Zealand SMEs perspective. Paper prepared for the 2001 DRUID Conference, Aalborg, Denmark.

18.     Porter, Michael E. (1990). The Competitive Advantage of Nations. New York: Free Press.

19.     Reynolds, Paul D., William D. Bygrave, Erkko Autio, Larry W. Cox and Michael Hay (2000). Global Entrepreneurship Monitor 2000 Executive Report. Wellesley, MA/London: Babson College/London Business School.

20.     Reynolds, Paul D., William D. Bygrave, Erkko Autio, Larry W. Cox and Michael Hay (2002). Global Entrepreneurship Monitor 2002 Executive Report. Wellesley, MA/London: Babson College/London Business School.

21.     Schmitz, James A. (1989). Imitation, entrepreneurship and long-run growth. Journalof Political Economy 97 (3): 721–739.

22.     Schumpeter, Joseph A. (1911). The Theory of Economic Development: An Inquiryinto Profits, Capital, Credit, Interest and the Business Cycle. 1934 translation. Cambridge, MA: Harvard University Press.

23.     Schumpeter, Joseph A. (1942). Capitalism, Socialism, and Democracy. 3rd ed. New York: Harper and Bros., 1950.

24.     Thurik, Roy, and Sander Wennekers (2001). A Note on Entrepreneurship, Small Business and Economic Growth. Rotterdam: Erasmus Research Institute of Management Report Series.

25.     United Nations Millennium Project (2005).  Innovation: Applying knowledge in development.UK and USA: Earthscan

26.     Wennekers, Sander, and Roy Thurik (1999). Linking entrepreneurship and economic growth. Small Business Economics 13: 27–55.


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2.

Authors:

Amruta G. Whatte, S. S. Jamkar

Paper Title:

Comparative Study of Design of Steel Structural Elements by using IS 800:2007, AISC 13th Edition and BS: 5950, 1:2000

Abstract: The task of the structural engineer is to design a structure which satisfies the needs of the client and the user. Specifically the structure should be safe, economical to build and maintain, and aesthetically pleasing. By considering the above needs of user this study gives the comparative design of structural element by using three different International Design Codes.Structural Elements such as tension member, compression member, flexural member, beam column, gusseted base, and beam column connection are designed for this comparative study. Same data is considered for the design of particular element and that element is designed by using Indian Standard (IS 800:2007), American Standard (AISC 13th Edition) and British Standard (BS 5950, 1:2000). The design methodology used in this study is same for all the codes but there are some differences in the constants or parameters depending on the code used. Finally the results are evaluated and compared in the tabular format.

Keywords:
IS 800:2007, AISC 13th edition, BS 5950 1:2000, LRFD, and ASD.

References:

1.        Danny J. Yong, AitziberLópez and Miguel A. Serna , “A comparative Study of AISC-LRFD and EC3 approaches to beam-column buckling resistance”, Tecnun – University of Navarra, Spain ,║6 September 2006.
2.        Prof. Ravindra Bhimarao Kulkarni, Rohan Shrikant Jirage, “Comparative Study of Steel Angles as Tension Members Designed by Working Stress Method and Limit State Method”, International Journal of Scientific & Engineering Research Volume 2, Issue 11, November-2011.

3.        Prof. RavindraBhimaraokulkarni, VikasArjunPatil, “Design Aids of Flexural Members and Beam-Columns Based on Limit State Method”, International Journal of Scientific & Engineering Research Volume 2, Issue 10, ║Oct-2011.

4.        Abbas Aminmansour, “A New Approach for Design of Steel Beam-Columns”, Engineering Journal/Second Quarter ║2000.

5.        M E Brettle BEng (Hons), “Steel Building Design: Worked Examples - Open Sections In accordance with Eurocodes and the UK National Annexes”, The Steel Construction Institute Silwood Park, Ascot, Berkshire SL5 7QN, ║18 September 2012

6.        Richard M. Drake and Sharon J. Elkin, “Beam-Column Base Plate Design-LRFD Method”, Engineering Journal║ First Quarter ║1999.

7.        M Krishnamoorthy, “Design of Compression Member based on IS 800: 2007 and IS 800:1984 comparisons”, Journal of Information, Knowledge and Research in Civil Engineering, ISSN: 0975 – 6744║NOV 11 TO OCT 12 ║Volume 2, Issue 1

8.        Bureau Indian Standard, General Construction in steel – Code of Practice, IS 800:2007 ║3rd Revision║ New Delhi ║December 2007.

9.        American Institute of steel Construction, 13th edition ║United State of America ║ December 2005 .

10.     Part 1: Code of practice for design - Rolled and welded sections║ BS 5950-1:2000║ British Standard ║ BSI 05-2001.

11.     Design Examples , Version 13.1║AISC , USA, May 2010.

12.     T. Bat Quimby, “A Beginner’s guide to the Steel Construction Manual”,║University of Alaska Anchorage ║August 2008.

13.     Chan Chee Han, “ Comparison between BS 5950: Part 1:2000 and Eurocode 3 for the design of multi-story braced steel frame” ║UniversitiTeknologi Malaysia November 2006.

14.     Perry Green, Thomas Sputo, “Design of all bolted extended angle, single angle, and tee shear connection”,║ University of Florida,║AISC║ January 2005.

15.     “Steel Building Design: Design Data, ║The Steel Construction Institute, Tata Steel, The British Construction Steelwork Association Ltd.

16.     “ Eurocode 3: Design of Steel Structure” – Part 1-1 : General rules and rules for building,║ BS EN 1993-1-1:2005 ║February 2006 and April 2009.

17.     “Steelwork Design Guide to BS 5950-1:2000(Blue Book),Volume ,7th Edition,║The steel Construction Institute and The British Constructional Steelwork Association Limited.

18.     Prof. S.R. Satish Kumar and Prof. A.R. Santha Kumar, “Design of Steel Structure”.IIT Madras

19.     Prof. Dr. A. Verma, “Design of Steel Structure”, CE 405.

20.     S.K. Duggal, “ Design of Steel Structure as per IS 800: 2007”.

21.     Design Examples , Version 14.1║AISC , USA, October 2011.

22.     Prof. ChanakyaArya, “Design of Structural Elements”, 3rd Edition, designs to British Standards and Eurocodes.

23.     Redbook, Handbook of Structural of Steelwork, 4th Eidition,The Steel Construction Institute silwood Park, Ascot, berkshrie, SL5 7QN.-

24.     Steel Sectional Table.


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3.

Authors:

Pravin W. Raut, S. L. Badjate

Paper Title:

FPGA Based Design & Implementation of Alamouti MIMO Encoder for Wireless Transmitter

Abstract: This paper address the Design and implementation of Alamouti Transmit Diversity Scheme using FPGA for Multi-Input-Multi-Output (MIMO) wireless communication transmitter. The task of the FPGA based MIMO Encoder is to process two digital signals (S1 & S1) having real(q) and Imaginary (i) parts, are being transmitted using two transmitting antennas by employing Alamouti transmitting scheme in VHDL. The FPGA devices of the Xilinx family are used to report the results. The performance is checked for optimized device resource utilization, data link for two symbol period. The role of MIMO Encoder/transmitter to handle the traffic of multiuser though multiple channels, to ensure the quality signals at the receiver even in failure of any channel.

Keywords:
FPGA, MIMO Encoder, Transmitter, MIMO Decoder, Receiver, antenna, Signal to Noise Ratio (SNR), OFDM.


References:

1.        G. J. Foschini, and M. J. Gans, “On limits of wireless communications in a fading environment when using multiple antennas,” Wireless Pers. Commun., vol. 6, pp.311–335, Mar. 1998.
2.        MIMO: The next revolution in wireless data communications By Babak Daneshrad

3.        V. Jungnickel, V. Pohl, and C. von Helmolt, “Capacity of MIMO systems with closely spaced antennas,” IEEE Communications Letters, vol. 7, no. 8, pp. 361–363, Aug. 2003.

4.        Siavash M. Alamouti. A Simple Transmit Diversity Technique for Wireless Communications. IEEE Journal on Select Areas in Communcations, 16(8):1451–1458,October 1998.

5.        David Gesbert, Mansoor Shafi, Da-shan Shiu, Peter J. Smith, and Ayman Naguib.From theory to practice: An overview of mimo space-time coded wireless systems.IEEE Journal on Selected Areas in Communications, 21(3):281–302, 2003.

6.        MIMO: from Theory to Reality ,  Ruifeng Wang July 2009 Peter J. Smith, Member, IEEE, and Ayman Naguib, Senior Member, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 21, NO. 3, APRIL 2003

7.        Raleigh, G.G. and Cioffi, J.M. . Spatio-temporal coding for wireless communications. IEEE Transactions on Communications, 46(3):357–366, 1998.

8.        An FPGA-Based MIMO and Space-Time Processing Platform -J. Dowle,1 S. H. Kuo,2   K. Mehrotra,1 and I. V.McLoughlin1

9.        VLSI Design Volume 2008, Article ID 312614. This work has been supported by European FP6 IST 2002 507039 Project 4 MORE and by the Spanish Ministry of  Science  and Technology under Project TEC2006-13067-C03-03 from IEEE site

10.     From Theory to Practice: An Overview of MIMO Space–Time Coded Wireless Systems David Gesbert, Member, IEEE, Mansoor Shafi, Fellow, IEEE, Da-shan Shiu, Member, IEEE,

11.     MIMO-OFDM Decoding: From Theory to Practice, 1E. R. de Lima, 2K.Iguchi, 3F.Angarita,3M.J. Canet, 3J.Valls, 1V.Almenar and 1 S. J. Flores Technical University of Valencia, Spain

12.     From Theory to Practice: An Overview of MIMO Space–Time Coded Wireless Systems David Gesbert, Member, IEEE, Mansoor Shafi, Fellow, IEEE, Da-shan Shiu, Member, IEEE, Peter J. Smith, Member, IEEE, and Ayman Naguib, Senior Member, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 21, NO. 3, APRIL 2003

13.     Implementation of Synchronization for 2×2 MIMO WLAN System, Hsin-Lei Lin, Robert C. Chang, Member, IEEE, Kuang-Hao Lin, Chia-Chen Hsu, IEEE Transactions on Consumer Electronics, Vol. 52, No. 3, AUGUST 2006

14.     G. J. Foschini, “Layered space–time architecture for wireless communication in a fading environment when using multielement antennas,” Bell Labs Tech. J., pp. 41–59, Autumn 1996.

15.     V. Jungnickel, V. Pohl, and C. von Helmolt, “Capacity of MIMO systems with closely spaced antennas,” IEEE Communications Letters, vol. 7, no. 8, pp. 361–363, Aug. 2003.

16.     W. Y. Ge, J. S. Zhang and G. L. Xue, “MIMO-Pipe Modeling and Scheduling for Efficient Interference Management in Multihop MIMO Networks,” IEEE Transactions on Vehicular Technology, vol. 59, no. 8, pp.3966-3978, 2010.

17.     C. N. Chuah, D. Tse, J. M. Kahn, and R.Valenzuela, “Capacity scaling in MIMO wireless systems under correlated fading,” IEEE Trans. Inform. Theory, vol. 48, pp. 637–650, Mar. 2002.

18.     P. J. Smith and M. Shafi, “On a Gaussian approximation to the capacity of wireless MIMO systems,” in Proc. Int. Conf. Communications, ICC 2002, 2002, pp. 406–410.

19.     “A standardized set of MIMO radio propagation channels,” Lucent,Nokia, Siemens, Ericsson, Jeju, Korea, 3GPP TSG-RAN WG1 23, Nov.19–23, 2001.

20.     L. Hanlen and M. Fu, “Multiple antenna wireless communication systems: Capacity limits for sparse scattering,” in Proc. 3rd Australian Communication Theory Workshop, Aus CTW 2002, Canberra, Australia,2002.

21.     Jack Winters "Optimum Combining in Digital Mobile Radio with Cochannel Interference," Special Issue on Mobile Radio Communications IEEE Journal on Selected Areas in Communications, July 1984, IEEE Trans. on Vehicular Technology, August 1984.


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4.

Authors:

Asawari Dudwadkar, Y. S. Rao

Paper Title:

Contributions to Optimization of Functioning Of Induction Heating System Using Simulink

Abstract: This paper presents complete modeling of induction heating system comprising (Rectifier Filter, Inverter, Digital PLL control block and work coil) for high frequency around 100 KHz and high Power around 100Kwatt applications. The Modeling is done using Simulink & results at all the stages are presented.  

Keywords:
PLL.


References:
1.        N.S. Baytndtr, 0. Kukrer and M. Yakup, “DSP-based PLL-controlled 50-1 00 kHz 20 kW high frequency induction heating system for surface hardening and welding applications.” IEEE Proc-Electr. Power Appl., Vol. 150, No. 3, May 2003
2.   Xiaorong Zhu, Yonglong Peng, Xinchun Shi, Heming Li  “200kW/400kHz High Frequency Inverter For Induction Heating application”,School of electrical engineering,North China Electrical Power University ,BaoDing, China

3.    www.ijcee.org/papers/279-E748.pdf  www.mathworks.in -Basics of analog PLL and its implementation in Simulink, implementation of single phase full bridge inverter in Simulink, how to use Simulink, how to use ‘powerlib’ library, Simulink guide
4.        www.inductionatmospheres.com/industry.html


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5.

Authors:

S. V. D. Prasad, V. Krishnanaik, K. R. Babu

Paper Title:

Analysis of Organic Photovoltaic Cell

Abstract: In this paper analysis of photo voltaic (PV) electricity is one of the best options for most impartment and ecological future energy requirements of the world. Organic photovoltaic (OPV) cells are hopeful views for common renewable energy unpaid to light weight, low cost, and flexibility. But, presently the best total power conversion efficiency of OPV cell is round 8.3% that is very low. The research paper focuses, the basic design, the recent progresses in organic materials, processing technique and operation of organic photovoltaic cells as well as crucial feature of their performance are reviewed. To achieve significantly higher efficiency future development that need to be addressed are discussed to this article.

Keywords:
Efficiency, Cost effective, Molecular Materials, OPV-Organic Photovoltaic cell, Temperature Effect, Processing Technique.


References:

1.        Cheng, Y.J.; Yang, S.H.; Hsu, C.S. Synthesis of conjugated polymers for organic solar cell applications. Chem. Rev. 2009, 109, 5868-5923.
2.        Rivers N.P.Leading edge research in solar energy,(2007).                                                       

3.        Tom J. Savinije, DelftChemTech, Organic solar cells, Faculty of Applied Sciences Delft University of Technology.

4.        Pulfry L.D., Photovoltaic Power Generation,(New York : Van Nostrand Reinhold Co., 1978).

5.        Lungenschmied, C.; Dennler, G.; Neugebauer, H.; Sariciftci, S.N.; Glatthaar, M.; Meyer, T.; Meyer, A. Flexible, long-lived, large-area, organic solar cells. Sol. Energy Mater. Sol. Cells 2007,91,379-384.

6.        Green, M. A., Emery, K., Hishikawa, Y. and Warta, W. (2010), Solar cell efficiency tables (version 36). Progress in Photovoltaics: Research and Applications, 18: 346–352. doi: 10.1002/pip.1021

7.        Kim, M.-S.; Kang, M.-G.; Guo, L.J.; Kim, J. Choice of electrode geometry for accurate measurement of organic photovoltaic cell performance. Appl. Phys. Lett. 2008, 92, 133301.  

8.        Gregg, B.A., The photoconversion mechanism of excitonic solar cells. Mrs Bulletin,2005. 30(1): p. 20-22.

9. Kim, M.-S.; Kang, M.-G.; Guo, L.J.; Kim, J ,Understanding Organic Photovoltaic Cells: Electrode, Nanostructure, Reliability, and Performance..2009.                                        
10.     X. Yang et al., Nanoletters 5, 579 (2005).                                                                            
11.     Darren Quick, World record efficiency for organic based photovoltaic solar cells, 22:14 December 5, 2010.
12.     D. Chirvase, Z. Chiguvare,a) M. Knipper, J. Parisi, V. Dyakonov,b) and J. C. Hummelenc,”Temperature dependent characteristics of poly.3 hexylthiophene.-fullerene” .Journal of applied physics ,volume 93 number 6, 15 March 2003.                                                   
13.     Dramatically Extending Lifetime Of Organic Solar Cells, Science Daily (Oct. 15, 2008)              

14.     Chaitanya S, Low cost solar energy: thanks to organic solar cells, April 12, 2009.

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6.

Authors:

M. S. M. Aras, M. F. Basar, S. S. Abdullah, F. A. Azis, F. A. Ali

Paper Title:

Obstacle Avoidance System for Unmanned Underwater Vehicle Using Fin System

Abstract: An underwater glider is a type of an unmanned underwater vehicle (UUV). The movement of an underwater glider in the water is based on the buoyancy-propelled for float and fixed-winged for stabilizing the glider’s body. However, a fixed wing underwater glider has limitation to avoid hitting the obstacle in front of it. To overcome this problem, the application of fin system in underwater glider is needed. In this project, a methodology was introduced which is design a flexible fin system of an underwater glider for obstacle avoidance purpose. This final year project mainly focused on SolidWorks’s simulation and analysis of -30°, -45°, -60° for submerge and rise up at 30°, 45°, 60° to get the most suitable angle for the glider’s fin system to submerge and rise up. The UTeM underwater glider is modified from fixed to flexible wing. Hence, Peripheral Interface Controller (PIC) is used to program the movement of the glider’s wings for upward at 45°and downward at -45°in the water. Thus, a flexible fin system for obstacle avoidance is designed and applied in UTeM underwater glider. 

Keywords:
Underwater glider, fin system, obstacle avoidance.


References:

1.        Wood Hole, The Slocum Mission Henry Stomel, Woods Hole Oceangrafic institution, 1989.
2.        Eric O.Roger, Weston S.Smith, Gerald F. Denny and Paul J.Farley. An Underwater Acoustic Glider. IEEE HOES Autonomous Underwater Vehicles, pp 2241-2244, 2004.

3.        M.S.M Aras, H.A. Kasdirin, M.H. Jamaluddin, M F. Basar, Design and Development of an Autonomous Underwater Vehicle (AUV-FKEUTeM), Proceedings of MUCEET2009 Malaysian Technical Universities Conference on Engineering and Technology, MUCEET2009, MS Garden, Kuantan, Pahang, Malaysia, 2009.

4.        MSM Aras, FA Azis, MN Othman, SS Abdullah,A Low Cost 4 DOF Remotely Operated Underwater Vehicle Integrated With IMU and Pressure Sensor, 4th International Conference on Underwater System Technology: Theory and Applications (USYS'12). Malaysia,  2012.

5.        Teledyne Webb Resarch (TWR) designs and manufactures scienticfic instruments for oceanographic research and monitoring,     http://www.webbresearch.com.

6.        M. F. Basar, A. Ahmad, N. Hasim and K. Sopian, “Introduction to the Pico Hydropower and the status of implementation in Malaysia,” IEEE Student  Conference on Research and Development (SCOReD), pp. 283-288, ISBN: 978-1-4673-0099-5, Cyberjaya, Malaysia, 19-20 December 2011.

7.        M. F. Basar, A.A. Rahman, Z Mahmod, “Design and Development of Green Electricity Generation System Using Ocean Surface Wave,” PEA-AIT International Conference on Energy and Sustainable Development : Issues and Strategies (ESD 2010), pp. 1-11, ISBN: 978-1-4244-8563-5, Chiang Mai, Thailand, 02-04 June 2010.

8.        Daniel L. Rudnick, Russ E. Davis, Scripps Institution of Oceanography, Charles C. Eriksen, University of Washington, David M. Fratantoni Woods Hole, Oceanographic Institution, Mary Jane Perry, Darling Marine Center, Universityof Maine PA P E R Underwater Gliders for Ocean Research, pp48-59.

9.        Yen-Sheng Chen and Jih-Gau Juang. Intelligent Obstacle Avoidance Control Strategy for Wheeled Mobile Robot. ICROS-SICE International Joint Conference, 2009.

10.     Yi Jincong, Zhang Xiuping, Ning Zhengyuan, Huang Quanzhen. Intelligent Robot Obstacle Avoidance System Based on Fuzzy Control. The 1st International Conference on Information Science and Engineering ,2009

11.     Z.Huanyin, L.Jinsheng, L.Gangyong and W.Xiong. The Analysis of Fish Body for Automatic Underwater Vehicle Movement. IEEE Journal East China Institute of Technology, Fuzhou Jiangxi, China. pp 699-701, 2009

12.     Ikuo Yamamoto, Yuuzi Terada, Tetuo Nagamatu, and Yoshiteru Imaizumi, Propulsion System with Flexible Rigid Oscillating Fin, IEEE Journal Of Oceanic Engineering, Vol 20, pp 23-30, 1995.

13.     Mark W. Westneat, Dean H, Thorsen, Jeffrey A. Walker, and Melina E.Hale. Structure, Function, and Neural Control of Pectoral Fins in Fishes. IEEE Journal of Oceanic Engineering, Vol. 29, pp 674-683, 2004

14.     M.F. Basar and A. Rahman, “Investigation the Performance of Green Electricity Generation System Using Ocean Surface Wave for Small Scale Application”, Journal of Enginering Technology, 1(2), pp 34-46, 2011.

15.     Yong-Jai Park, Useok Jeong, Jeongsu Lee, Seok-Ryung Kwon, Ho-Young Kim, and Kyu-Jin Cho, Kinematic Condition for Maximizing the Thrust of a Robotic Fish Using a Compliant Caudal Fin, IEEE Transactions on Robotic, Vol.28, pp 1216-2227, 2012

16.     Yamamoto, I., Aoki, T., Tsukioka, S., Yoshida, H., Hyakudome, T., Sawa, T.,Ishibashi, S., Inada, T., Yokoyama, K., Maeda, T., et al., “Fuel Cell System  ofAUV „Urashima,” IEEE Oceans/Techno-ocean, Vol 1732-1377, 2004.

17.     W.Yijun, W.Yanhui and HeZhigang. Buoyancy Compensation Analysis of an Autonomous Underwater Glider. IEEE International Conference on Electronic & Mechanical Engineering and Information Technology, 2011.

18.     M.F.M. Basar, M.H. Jamaluddin, H. Zainuddin, A. Jidin and M.S.M. Aras, “Design and Development of a Small Scale System for Harvesting the Lightning Stroke Using the Impulse Voltage Generator at HV Lab, UTeM”, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), pp. 161-165, ISBN: 978-1-4244-5585-0, Singapore, 26-28 February 2010.

19.     Cytron Technologies Sdn. Bhd. http://www.cytron.com.my


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7.

Authors:

S. Roy Chatterjee, R. Mandal, M. Chakraborty

Paper Title:

A Comparative Analysis of Several Back Propagation Algorithms in Wireless Channel for ANN-Based Mobile Radio Signal Detector

Abstract: Application of Artificial Neural Network (ANN) in cognitive radio has received considerable attention to incorporate artificial intelligence in cognitive radio based communication system. This paper introduces multilayer feed-forward neural network (MFNN) for spectrum sensing to detect the primary users. This in turn would enable the detector to identify the vacant bands that are devoid of primary users. As the accuracy of detection depends on the structure of the network and on the learning algorithms, an MFNN is trained with different back propagation algorithms varying the number of hidden neurons to find out the best suitable structure of the MFNN for spectrum sensing in different conditions of the wireless channel. Distinct cyclostationary features of different primary users are extracted to generate the input feature vectors for the MFNN as these features are well accepted for signal detection in low signal-to-noise ratio (SNR). The False Alarm Rate (FAR) of the detector is also evaluated with SNR and multipath delay. Simulation results prove that MFNN is suitable for designing a highly robust vacant band detector in the time varying wireless channel as it provides low and almost constant FAR in high multipath delay and low SNR RF environment.

Keywords:
Back propagation algorithms, cognitive radio, false alarm rate, multilayer feed forward neural network, spectrum sensing.


References:

1.        Tevfik Y¨ucek and H¨useyin Arslan,”A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications”, IEEE Communications Surveys & Tutorials, vol. 11, no. 1, first quarter 2009.    
2.        Shahzad A. Malik, Madad Ali Shah, Amir H. Dar, Anam Haq, Asad Ullah  Khan, Tahir Javed, Shahid A. Khan, “Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing Techniques in Cognitive Radio Systems, Australian Journal of Basic and Applied Sciences, 4(9): 4522-4531, 2010.

3.        Ian F. Akyildiz, Won-Yeol Lee and Mehmet C. Vuran and Shantidev Mohanty,” NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey “,Computer Networks 50 (2006) 2127–2159

4.        Fehske, A., Gaeddert, J.; Reed, J., “A New Approach to Signal Classification Using Spectral Correlation and Neural Networks”, Proceedings of First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. (DySPAN 2005), pp 144-150.

5.        D .Sutton, K.E. Nolan,L.E. Doyle,” ,Cyclostationary Signatures in Practical Cognitive Radio Applications” IEEE Journal on Selected Areas in Communications,  Vol: 26  , Issue: 1  pp.13 - 24 ,January 2008

6.        Kyouwoong Kim ,Akbar, I.A.,Bae, K.K., Jung-Sun Um, Spooner, C.M.; Reed, J.H. “Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio”, Proceedings of 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007. (DySPAN 2007), pp 212-215.

7.        Randy S. Robert, William A Brown and Herschel H. Loomis, “Computationally Efficient Algorithms for Cyclic Spectral Analysis”, in IEEE signal Processing MagazineApril,1991

8.        Zhenyu Zhang, Xiaoyao Xie “Intelligent Cognitive Radio: Research on Learning and Evaluation of CR Based on Neural Network”  Proceedings of ITI 5th International conference on Information and Communications Technology, 2007(ICICT,2007),pp  33 – 37.

9.        C.Clancy, J. Hecker, E. Stuntebeck and  T. O'Shea, “Applications of Machine Learning to Cognitive Radio Networks”, Wireless Communications, IEEE , vol: 14, pp: 47 – 52, 2007

10.     Yu-JieTang,Qin-yu Zhang,Wei Lin,”Artificial Neural Network Based Spectrum Sensing Method for Cognitive Radio” Proceedings of  6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM),2010,pp.1-4

11.     Ani1 K. Jain, Jianchang Mao, K.M. Mohiuddin, “Artificial Neural Network: A Tutorial”, IEEE Computer, Vol. 29 (Mar 1996 )pp. 31 – 44.

12.     A. Bhavani Sankar, D. Kumar, K. Seethalakshmi, “Neural Network Based Respiratory Signal Classification Using Various Feed-Forward Back Propagation Training Algorithms” , European Journal of Scientific Research , Vol.49 No.3 (2011), pp.468-483.

13.     S. Jadhav, S. Nalbalwar, A. Ghatol, “Performance Evaluation of Generalized Feed forward Neural Network Based ECG Arrhythmia Classifier” , IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 3, July 2012

14.     Rivero. Daniel, Dorado. Julián, Rabuñal. Juan R.,Pazos. Alejandro, “Evolving simple feed-forward and recurrent ANNs for signal classification: A comparison”, Proceedings of  International Joint Conference onNeural Networks, 2009, IJCNN 2009, pp. 2685-2692.

15.     Chuan-Yu Chang, Yong-Cheng Hong,Pau-Choo Chung, Chin-Hsiao Tseng,”A Neural Network for Thyroid Segmentation and Volume Estimation in CT Images “IEEE Computational Intelligence Magazine, November2011

16.     Gardner, W.A. , “An Introduction to Cyclostationary Signals, Chapter 1, Cyclostationarity in Communications and Signal Processing”, IEEE Press,Piscataway,NJ,1993.

17.     Gardner, W.A. Brown and C.K.Chen , “Spectral Correlation of Modulated Signals” Part 2 –Digital Modulation, IEEE Transcations and Communications. Jun 1987, vol. 35, Issue 6, pp-595-601.

18.     Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth, 2010. “Neural Network Toolbox 7-User’s Guide, The Math Works Inc”.

19.     Mohammed A. Ayoub, Birol M. Demiral,”Application of Resilient Back-Propagation Neural Networks for Generating a Universal Pressure Drop Model in Pipelines” UofKEJ ,Vol. 1 ,Issue 2 pp. 9-21,October 2011

20.     T. Pradeep, P.Srinivasu, P.S.Avadhani andY.V.S. Murthy,”Comparison of variable learning rate and Levenberg-Marquardt back-propagation training algorithms for detecting attacks in Intrusion Detection Systems”,  International Journal on Computer Science and Engineering (IJCSE) , vol. 3, no. 11(2011)


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8.

Authors:

P. Nagasekhar Reddy

Paper Title:

Modeling and Simulation of Space Vector Pulse Width Modulation based Permanent Magnet Synchronous Motor Drive using MRAS

Abstract: The Permanent Magnet Synchronous Motors (PMSM) is extensively used in low and mid power applications such as robotics, adjustable speed drives, electric vehicles and also in industrial automation. The MRAS is based on the comparison of the outputs of two estimators. The first is independent of the observed variable named as model reference. The second is the adjustable one. The error between the two models feed an adaptive mechanism to turn out the observed variable. This paper presents a detailed modeling of PMSM and a novel space vector pulse width modulation (SVPWM) based control of permanent magnet synchronous motor (PMSM) drive by using a Model Reference Adaptive System (MRAS) for estimating rotor position angle and speed based on a stator current estimator. The three-phase, two-level voltage source inverter (VSI) has a quite simple design and generates a low-frequency output voltage with controlled amplitude and frequency by programming gating pulses at high-frequency. The whole drive system is simulated in Matlab/Simulink based on the mathematical modeling of the system and the results are presented.

Keywords:
SVPWM, PI controller, PMSM, model reference adaptive system (MRAS), mathematical modeling, MATLAB, About four key words or phrases in alphabetical order, separated by commas.


References:

1.        S. Ozçira, N. Bekiroglu, E. Ayçiçek “Speed control of permanent magnet synchronous motor based on direct torque control method”, IEEE/ISPE, pp.268-272,2008.
2.        A. H. Wijenayake and P. B. Schmidt, "Modeling and analysis of permanent magnet synchronous motor by taking saturation and core loss into account," International Conference on Power Electronics and Drive Systems, (Volume:2 ) 1997.

3.        Sen, P. C. Electric motor drives and control-past, present, and future[J], IEEE Transactions on  Industrial Electronics, 1990,37(1),562–575.

4.        B. Cui, J. Zhou, and Z. Ren, "Modeling and simulation of permanent magnet synchronous motor drives," 2001.

5.        Tzann-Shin Lee, Chih-Hong Lin, Faa-Jeng Lin. An adaptive H controller design for permanent magnet synchronous motor drives[J], Control Engineering Practice,2005,13 (4): 425-439

6.        Z.Ibrahim, E.Levi; An experimental investigation of fuzzy logic speed control in permanent magnet synchronous  motor drives, European Power Electronics and Drives Journal, vol. 12, no. 2, 2002, pp. 37-42.

7.        PETROVIC, V.—ORTEGA, R.—STANKOVIC, A. M.—TADMOR, G. : Design and Implementation of an Adaptive Controller for Torque Ripple Minimization in PM Synchronous Motors, IEEE Trans. on Power Electronics 15 No. 5 (Sep 2000), 871–880.

8.        KO, J.—YOUN, S.—KIM, Y.: : A Robust Adaptive Precision Position Control of PMSM, Industry Applications Conference, 37th IAS Annual Meeting, vol. 1, Pittsburgh, PA , USA, 2002, pp. 120–125.

9.        LIU, T.-H.—PU, H.-T.—LIN, C.-K.: : Implementation of an Adaptive Position Control System of a Permanent-Magnet Synchronous Motor and its Application, IET Electr. Power Appl. 4 No. 2 (2010), 121-130.

10.     Tian Ming-xiu, Wang Li-mei, Zhen Jian-feng. ”Sensorless position control scheme for permanent magnet synchronous motor drive", School of Electrical Engineering, Shengyang University of Technology, 10, 27(5), 2005.

11.     J. Luukko, “Direct Torque Control of Permanent Magnet Synchronous Machines-Analysis and Implementation”, Diss. Lappeenranta University  of Technology, Stockholm, Sweden, 2000.

12.     S. Dan, F. Weizhong, H. Yikang, “Study on the direct torque control of  permanent magnet synchronous motor drives”. IEEE/ICEMS, Vol.1, pp.571 – 574, 2001.


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9.

Authors:

N. Zeelan Basha, G.Mahesh, N. Muthuprakash

Paper Title:

Optimization of CNC Turning Process Parameters on ALUMINIUM 6061 Using Genetic Algorithm

Abstract: This paper presents the effect of process parameter in turning operation to predict surface roughness.  Application of alumunium 6061 can be found in many manufacturing industries such as aircraft and aerospace components, marine fittings, transport, bicycle frames, camera lenses, drive, shafts, electrical fittings and connectors, brake components, valves, couplings. But some of the limitations during machining of aluminum 6061 are lower strength at elevated temperatures and limited formability affects quality of desired output. A lot of parameters that affect the turning operation are vibration, tool wear, surface roughness etc. Among this surface roughness plays a major role which affects the quality in the manufacturing process. This paper presents the effect of process parameter by considering the Spindle speed, Feed rate and Depth of cut.  The main objective of this paper is to predict the surface roughness. Aluminium 6061 is taken into a consideration, machining is done by using coated carbide tool. A second order mathematical model is developed using regression technique of Box-Behnken of Response Surface Methodology (RSM) in design expert software 8.0 and optimization carried out by using genetic algorithm in matlab8.0. This study attempts the application of genetic algorithm to find the optimal solution of the cutting conditions.

Keywords:
Surface Roughness, Genetic Algorithm, Optimization, CNC Turning Centre.


References:

1.        Mahesh,G.;Muthu,S.;Devadasan, S. R. “Experimentation and Prediction of Surface Roughness of the Machining Parameter with Reference to the Rake Angle in End Mill”,International Review of Mechanical Engineering;Nov2012, Vol. 6 Issue 7, pp.1418-1426
2.        K.Saravanakumar,M.R.PratheeshKumar,Dr.A.K.Shaik -Dawood, “Optimization of CNC Turning Process  Parameters on INCONEL 718 Using  Genetic Algorithm”, IRACST – Engineering Science and Technology: An International Journal (ESTIJ)Vol.2, No.4, August 2012,pp.532-537

3.        Mr.Ch.Madhu.V.N,Prof.,A.V.N.L.Sharma,Dr.K.Venkata-Subbiah, “Optimization of cutting parameters for surface roughness  prediction  using artificial neural network  in CNC Turning”, IRACST – Engineering Science and Technology: An International Journal (ESTIJ), Vol.2, No. 2, April 2012,pp.207-214

4.        J.S.Senthilkumaar1;P.Selvarani and RM.Arunachalam3, “Selection of Machining Parameters Based On the Analysis Of Surface Roughness And Flank Wear In Finish Turning And Facing Of Inconel 718 Using Taguchi Technique”, Emirates Journal for Engineering Research, November 2010,pp.1-19.

5.        Oktem a, T. Erzurumlu b, H. Kurtaran b, “Application of response surface methodology in the optimization of cutting conditions for surface roughness”, Journal of Materials Processing Technology, 12 April 2005, pp.11-16

6.        Mathew.A.Kuttolamadom,SinaHamzehlouia,M.LaineMears, “Effect of Machining Feed on Surface Roughness in Cutting6061Aluminum”, Clemson University –International Center for Automotive Research,2010,pp.1-19

7.        H.M.Somashekara, “Optimizing Surface Roughness in Turning Operation Using Taguchi Technique And Anova” ,International Journal Of Engineering Science And Technology (IJEST)ISSN, Vol. 4 No.05 May 2012,pp.1962-1973.

8.        S.S.K. Deepak, “Applications of Different Optimization Methods for Metal Cutting Operation”–A Review, Research Journal of Engineering Sciences,Vol. 1(3), Sept.2012,pp. 52-58

9.        HamdiAouici,Mohamed AthmaneYallese, KamelChaoui, TarekMabroukic, Jean-François Rigal, “Analysis of surface roughness and cutting force components in hard turning with CBN tool”, Prediction model and cutting conditions Optimization, Measurement 45 (2012),pp.344–353.

10.     MaciejGrzendaa, Andres Bustillo,    ”The     evolutionary development of roughness prediction models”, Applied Soft Computing (2012), pp.1-10

11.     Arokiadass,K. Palaniradja,N.Alagumoorthi, “Prediction and Optimization of End Milling Process Parameters Of cast Aluminium Based MMC.”, Trans. Nonferrous Met. Soc. China, 21 February 2012, pp. 1568-1574.

12.     M.Brezocnik,M.Kovacic,M.Ficko,“Prediction of surface roughness with Genetic programming”,Journal of Materials Processing Technology, 2004,pp.28-36

13.     SuleymanNeseli,SuleymanYaldız,ErolTurkes,”Optimiz-ation of tool geometry parameters for turning operations based on the response surface methodology”, Measurement 44 2011, pp. 580–587.

14.     Chen Lu, “Study on prediction of surface quality in machining process”, journal of materials processing technology, 24 November 2007, pp.439-450


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10.

Authors:

K. Sagar, S. Vathsal

Paper Title:

Design of Combinational Circuits Using Evolutionary Techniques

Abstract: With the increasing demand for high quality, more efficient design of logic circuits, the problem of circuit design has become a multi-objective optimization problem. Therefore, there should evolve new methodologies for designing logic circuits. Usually, logic circuits are designed by human beings who have a specific repertoire of conventional design techniques. These techniques limit the solutions that may be considered during the design process in both form and quality. The application of evolutionary algorithms has allowed the creation of circuits which are substantially superior to the best known human designs. Several evolutionary algorithms are applied in design of combinational circuits, namely Genetic Algorithms, Particle Swarm Optimization and Ant colony Optimization techniques. We compared these approaches and produces better performance both in terms of quality of solution and in terms of speed of convergence.

Keywords:
Circuit Design, Optimization, Evolutionary Algorithms, Convergence.


References:

1.        Uthman Salem al-saiari, “Digital Circuit Design Through Simulated evolution” , King Fahd University of petroleum and minerals , Dhahran, Saudi Arabia, November 2003
2.        Russell, S. and Norvig, “Artificial Intelligence: A Modern Approach. Prentice Hall”, New Jersey. (2003).

3.        Carlos A. CoelloCoello, Alan D. Christiansen, Arturo Hernandez Aguirre: “Design of Combination Logic Circuits through an Evolutionary Multi-objective Optimization Approach” Department of Electrical Engineering and Computer Science, Tulane University, New Orleans, LA, USA, 2000

4.        McCluskey E. J.: “Minimization of Boolean functions” Bell System technical Journal, (1996).

5.        Karnaugh M.: “A map method for synthesis of combinational logic circuit” Transactions of the AIEE, Communications and Electronics,( 1993).

6.        Koza,J.R ,“Genetic Programming on the programming of computers by means of natural selection” ,the MIT press, Cambridge, Massachusetts. (1992)

7.        Louis, and Rawlins, G.Designer Genetic algorithms: “Genetic algorithms in structure design” , In Belew, R.K. and booker,L.B.(eds) proceedings of the fourth international conference on genetic algorithms, San mateo, California, Morgan Kaufmann Publishers. (1991)

8.        Tutorials for Genetic Algorithm ;

9.        Miller, J.F., Thompson, P. and Fogarty, “Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study.” Genetic Algorithm and Evolution Strategy in Eng. and Comp. Sci., 105-131T. (1997).

10.     Carlos  A, Coello Coello, Erika Hern’andez Luna and Arturo Hern’andez Aguirre : “Use of Particle Swarm Optimazation to design Combinational Logic circuits” (2003)

11.     Carlos A. Coello Coello, Rosa Laura Zavala G., benito Mendoza Garcia and Arturo Hernandez Aguirre ” Ant Colony System for the design of Combinational Logic Circuits”  (2001)


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11.

Authors:

Abhinav Aggarwal, Rupika Srivastava, Sumit Malik, Kirti Meena, Poonam

Paper Title:

Virtual Differential Storage Based K-Rollback Concurrency Control Algorithm in Distributed Shared Memory Systems

Abstract: Most of the algorithms that exist today for concurrency control over distributed shared memory, either fail to provide a scalable model or involve a large communication overhead for establishing consensus over the state of the shared variables. After a thorough study of some of the efficient algorithms this field, this paper introduces a functional view of a holistic approach, which exploits the best features of all others. It provides a virtual differential storage, which allows fast replication and compact storage, along with a strong subversion control over rollbacks in time, which provides better fault tolerance. It also talks of an intelligent logging mechanism, where the read/write records are used actively by the central controller to provide exclusion over Above all, the algorithm is best implemented in LISP or Scheme due to its functional nature. This make  the implementation computationally very fast. A trade off, however, exists between the implementation complexity and the quality of the final product.

Keywords:
Log, Page, Concurrency, Shared Memory.


References:

1.        Tanenbaum A. S., Robert Van Renesse, “Distributed Operating Systems”, ACM Computing Surverys, vol. 17, no. 4, pp. 419-471, Dec. 1985
2.        G. LeLann, "Algorithms for distributed data-sharing systems which use tickets," in Proc.

3.        R. H. Thomas, "A majority consensus approach on concurrency control for multiple copy databases," ACM Trans. Database Syst., vol. 4, no. 2, pp. 180-209, June 1979.

4.        D. J. Rosenkrantz, R. E. Stearns, and P.M. Lewis, "System level concurency control for distributed database systems," ACM Trans. Database Syst., vol. 3, no. 2, June 1978.

5.        Pei-Jyun Leu & B Bhargava, “Multidimension Timestamp Protocols for Concurrency Control”, IEEE Transations on Software Engineering, Vol. SE-13, No. 12, December 1987

6.        H. T. Kung and J. T. Robinson, "On optimistic methods for concurrency control," ACM Trans. Database Syst., vol. 6, no. 2, June 1981.

7.        P. Bernstein and N. Goodman, "Concurrency control in distributed database systems," ACM Comput. Surveys, vol. 13, no. 2, June 1981.

8.        Jiwu Tao, J. G. Williams, Concurrency Control and Data Replication Strategies for Large-scale and Wide-distributed Databases, ISBN: 0- 7695-0996-7/01, ©IEEE 2001

9.        Yoav Raz, “The Principle of Commitment Ordering, or Guuaranteeing Serializability in a Heterogeneous Environment of Multiple Autonomous Resource Managers Using Commitment”, Proceedings of the 18th VLDB Conference, Vancouver, Canada 1992.

10.     William E. Weihl, “Commutavity-Based Concurrency Control for Abstract Data Types”, IEEE Transactions on Computers, Vol 37, No. 12, December 1992.

11.     P. A. Bernstein, D. W. Shipman, and J. B. Rothnie, Jr., "Concurrency control in a system for distributed databases (SDD-1)", ACM Trans. Database Syst., vol. 5, no. 1, pp. 18-25, Mar. 1980.

12.     Managing Virtual Hard Disks Using Differencing Disks, Microsoft

13.     Corporation, available at: http://technet.microsoft.com/en- us/library/cc720381(v=ws.10).aspx

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12.

Authors:

Revanasiddappa B, K. V. Ramana Reddy

Paper Title:

CPLD Implementation of Low Power Multi Serial to Ethernet Gateway for UAV Data Acquisition Systems by Using PIC

Abstract: An Unmanned Aerial Vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot on board. It’s a flight controlled under the remote of a pilot on the ground. Historically, UAVs were simple remotely controlled aircraft, but day-to-day autonomous control is rapidly being employed. The development of autonomy technology makes UAV to combining information from different sensors like temperature sensor and humidity sensor. The collected information communicated to pc. With the help of camera motion planning determines an optimal path for vehicle to go while meeting certain objectives and constraints. CPLD’s flexible programming features also allow further upgrade for system. Low power, as the multi card solution can come in single CPLD card with smaller modules around it communicating local area number of systems transferring the data from one system to another system. In this project all the components which are using they required maximum 3.3V power supply instead of 5V.

Keywords:
Ethernet, PIC, CPLD, UAV, UART, Gateway, Multi serial.


References:

1.        Mr. M.Venkatavinod, Mr. K.Sreenivasarao “FPGA Implementation of low power multiserial to Ethernet gateway for Unmanned Aerial Vehicle (UAV) data acquisition systems,” IJERA, ISSN: 2248-9622, Vol. 2, Issue 3, May-Jun 2012, pp.1692-1695.
2.        YAO, Yonghong HU, Lu DING, “FPGA-based for Implementation of Multi-Serials to Ethernet Gateway,” IEEE, May 2010, pp.1-4.

3.        Ding Wang, JiadongXu, Rugui Yao, Ruifeng Miao, “Simulation system of telemetering and telecontrol for unmanned aerial vehicle,” IEEE, Sept. 2006, pp. 3-5.

4.        Shouqian Yu, Lili Yi, Weihai Chen, Zhaojin Wen, “Implementation of a Multi-channel UART Controller Based on FIFO Technique and FPGA,” Harbin, China, May 2007, pp. 2633-2638.

5.        William Stalling, “Wireless Communication and Networking”, Prentice Hall 2002, ISBN 0-13-040864-6.

6.        A.Willig, M.Kubish, C Hoene, A.Wohsz, "Measurement of a wireless link in industrial Environment using an IEEE802.11 compliant Physical layer", IEEE trans. On Ind Electronics, vol.49, no.6, 2002, pp. 1265-1282.

7.        Halit Eren, "Wireless Sensors and Instruments Networks, Design and Applications, "China Machine Press. Beijing, Jan. 2008, pp.102-143, 144-170.

8.        L. K. Hu and Q.CH. Wang, "UART-based Reliable Communication and performance Analysis", Computer Engineering, Vol. 32 No. 10, May 2006, pp15-21.

9.        S-Q. Yu, L-L. Yi, W-H. Chen, Z-J. "Wen, Implementation of a Multi-channel UART Controller Based on FIFO Technique and FPGA", Industrial Electronics and Applications, 2007. ICIEA 2007. IEEE Press, 2007, pp. 2633-2638.

10.     X. D. Wu and B. Dai, "Design of Interface Between High Speed A/D and DSP Based on FIFO", Journal of Beijing Institute of Petrochemical Technology Vol. 14 No.12, Jun. 2006, pp26-29.


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13.

Authors:

Kalyan Chatterjee, Prasenjit Maji, Arka Banerjee, Debarati Das, Manisha Gupta

Paper Title:

A Comparative Analysis to Determine the Optimum Approach for Image Denoising

Abstract: Image denoising demands serious attention and is usually the first and foremost step in any image processing application. Erroneous denoised results lead to improper and inaccurate final results. So it is of prime importance to eliminate the noise from the image to the utmost extent..In this paper an analysis is performed for image denoising by imposing different types of noise on the original image,using a choice of wavelet decomposition techniques and also different feasible thresholding techniques to find the optimum denoised result image and also the best combination involved in the process .

Keywords:
Image Denoising, Discrete Wavelet Transformation,Wavelet Decomposition,Wavelet Thresholding.


References:

1.        Survey of image denoisingtechniques,MCMotwani, MC Gadiya,RCMotwan - Proceedings of GSPX, 2004 – Citeseer
2.        Comparative Performance Analysis of Haar,Symlets and Bior Wavelets on Image Compression using Discrete Wavelet Transformation,Jashanbir Singh Kaleka,ReechaSharma

3.        Wavelet Browser by PYWAVELETS, wavelet.pybytes.com/wavelet/sym2/,Symlets 2 wavelet (sym2) properties, filters and functions – Wavelet Properties Browser.html

4.        Wavelet Browser by PYWAVELETS, wavelet.pybytes.com/wavelet/db2/,Daubechies 2 wavelet (db2) properties, filters and functions - Wavelet Properties Browser.html

5.        Wavelet Browser by PYWAVELETS,  wavelet.pybytes.com/wavelet/db4/,Daubechies 4 wavelet (db4) properties, filters and functions - Wavelet Properties Browser.html

6.        Image Denoising using Wavelet Thresholding LakhwinderKaur, Savita Gupta, R.C.

7.        A Novel Approach of Harris Corner Detection of Noisy Images using Adaptive Wavelet Thresholding Technique By NilanjanDey, Pradipti Nandi, Nilanjana Barman

8.        Image Denoising using Wavelet Thresholding By LakhwinderKaur, Savita Gupta, R.C.  Chauhan

9.        An Improved Image Denoising Method Based on Wavelet    ThresholdingMethod”,H Om, M Biswas - Journal of Signal and Information  Processing, 2012 - scirp.org

10.     De-noising Mems vibratinggyro using wavelet transform, RasmitaKumari Das

11.     Demodulation of FM Data in Free-Space Optical Communication Systems Using Discrete Wavelet Transformation By Nader Namazi, Ray Burris, G. Charmaine Gilbreath, Michele Suite   and Kenneth Grant

12.     Image de-noising using discrete wavelet transform,SK Mohideen,  SAPerumal - International Journal of …, 2008 -paper.ijcsns.org

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14.

Authors:

T. Lakshmi Priyanka, G. Deepthi, B. Sunil Kumar

Paper Title:

Reusable Test Bench for Network on Chip Router using Advanced Verification Methodologies

Abstract: The focus of this Paper is the actual implementation of Reusable Network On Chip Router IP(Intellectual Property) and verifies the functionality of the five port  IP router for System on chip applications  using the latest verification methodologies (OVM,UVM,VMM) Hardware Verification Languages (Verilog, System Verilog),EDA tools. The Design of Network on Chip Router Implementing by using Verilog LRM as for Synthesis Environment. This Router design contains Four output ports and one input port, it is packet based Protocol. This Design consists of Registers, FSM and FIFO’s. The Verification goes on it finds functional coverage of the Network on Chip Router by using Verilog ,System Verilog using Questa-Sim 6.5e ,Synthesis is Xilinx ISE 9.2i EDA Tools.

Keywords:
System Verilog, Fictional Coverage, assertions, Randomization, FIFO, FSM, Network-On-Chip, Verification Methodologies, Register blocks.


References:

1.    “Nortel Secure Router 4134”, Nortel Networks Pvt. Ltd.
2.     “LRM”, IEEE Standard Hardware Description Language Based n the Verilog Hardware Description Language – IEEE STD 1364-1995

3.    D.Chiou,“MEMOCODE2011Hardware/SoftwareCoDesignContest”,https://ramp.ece.utexas.edu/redmine/Attachments/ DesignContest.pdf

4.     Xilinx,“ML605HardwareUserGuide”,http://www.xilinx.com/support/documentation/boardsand its/ug534.pdf

5.     C. F. Lin and J. B. Anderson, “-algorithm decoding of channel convolution codes,” presented at the Princeton Conf. Info. Sci. Syst., Princeton, NJ, Mar. 1986.

6. Verification Methodology Manual for SystemVerilog, Bergeron, J., Cerny, E., Hunter, A., Nightingale, A. 2005, ISBN: 0-387-25538-http://www.synopsys.com/news/announce/press2005/snps_sourcode_licsvpr.html Reference Verification Methodology Tutorial, Synopsys documentation 2005

7.    SystemVerilog Assertions Handbook, Ben Cohen, Srinivasan Venkataramanan, Ajeetha Kumari , 2005 ISBN 0-9705394-7-9 http://www.abvsva.org/vmm/snug06_cohen_sri_aji.tar

8.        Application Note: Using the Router Interface to Communicate Motorola, ANN91/D Rev. 1, 01/2001. Cisco Router OSPF: Design& Implementation Guide, Publisher: McGraw-Hill

9.   Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series), Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides


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15.

Authors:

Rupinderpal Singh, Pankaj Sapra, Varsha Verma

Paper Title:

An Advanced Technique of De-Noising Medical Images using ANFIS

Abstract: Noise reduction has been a traditional problem in image processing. Medical images like X-RAY, CT, MRI, PET and SPECT have minute information about heart, brain, nerves etc. These images are corrupted during transmission. When these are corrupted by noise, it is impossible to rescue a human being from harmful effects. Recent wavelet thresholding based denoising methods proved promising, since they are capable of suppressing noise while maintaining the high frequency signal details. However, the local space-scale information of the image is not adaptively considered by standard wavelet thresholding methods. In this thesis, a new type of technique neural network and fuzzy has been proposed. The proposed technique confiscates the Additive white Gaussian Noise from the CT images and improves the quality of the CT images. The proposed work is comprised of three phases; they are preprocessing, training and testing. In the preprocessing phase, the CT image which is affected by the AWGN noise is transformed using multi wavelet transformation. In the training phase the obtained multi-wavelet coefficients are given as input to the Neural Network and Fuzzy System. In the testing phase, the input CT image is examined using this trained Neural Network and Fuzzy System and then to enhance the quality of the CT image thresholding is applied and then the image is reconstructed. Hence, the denoised and the quality enhanced CT images are obtained in an effective manner.

Keywords:
Image processing, filters, de-noising, Discrete Wavelet Transform (DCT), neural network, fuzzy logic.


References:

1.        Motwani, M.C., M.C. Gadiya and R.C. Motwani, 2004. Survey of Image Denoising Techniques. In the Proceedings of the 2004 GSPx Conference, Santa Clara, CA.
2.        Saba, T., A. Rehman and G. Sulong, 2010. An Intelligent Approach to Image Denoising. Journal of Theoretical and Applied Information Technology.

3.        Buades, A., B. Coll and M. Morel, 2005. Image Denoising by Non-local Averaging. In the Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing, pp: 25-28.

4.        Mahmoudi, M. and G. Sapiro, 2005. Fast Image and Video Denoising via Non-local Means of Similar Neighborhoods. IEEE Signal Processing Letters, 12(12).

5.        Lindenbaum, M., M. Fischer and A. Bruckstein, 1994.. On Gabor Contribution to Image-Enhancement. Pattern Recognition, 27: 1-8.

6.        Perona, P. and J. Malik, 1990. Scale Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. Patt. Anal. Mach. Intell., 12: 629-639.

7.        Alvarez, L., P.L. Lions and J.M. Morel, 1992. Image Selective Smoothing And Edge Detection By Nonlinear Diffusion (II). SIAM Journal of Numerical Analysis, 29: 845-866.

8.        Yaroslavsky, L.P. and M. Eden, 1996. Fundamentals of Digital Optics. Birkhauser.

9.        Rudin, L. and S. Osher, 1992. Nonlinear Total Variation Based Noise Removal Algorithms. Physica D, 60: 259-268.

10.     Coifman, R.R. and D. Donoho, 1995. Translationinvariant De-noising. Wavelets and Statistics, pp: 125-150.

11.     Kazubek,M., 2003. Wavelet Domain Image Denoising 19. Kulkarni, A.D., 2001. Computer Vision and Fuzzyby Thresholding and Wiener Filtering. IEEE Signal Neural Systems. Prentice Hall. Processing Letters, 10: 324-326. 20. Cheema, T.A., I. Qureshi and M. Naveed A. 2007.

12.     Grace Chang, S., B. Yu and M. Vattereli, 2000. Blur and Image Restoration of Nonlinearly Degraded Adaptive Wavelet Thresholding For Image Images Using Neural Networks Based on Modified Denoising And Compression. IEEE Transactions of Nonlinear ARMA Model. The Arabian Journal for Image Processing, 9: 1532-1546. Science and Engineering, 32: 67-83.

13.     Jansen, M., 2001. Noise Reduction by Wavelet Thresholding. Springer Verlag, New York Inc. An image Denoising Technique Using Feedforward.

14.     Kaur, L., S. Gupta and R.C. Chauhan, 2002. Image Neural Network. In the Proceedings of the 2010 Denoising Using Wavelet Thresholding. In the International Symposium on Intelligent Systems Proceedings of the 2002 Indian Conference on (iFAN 2010), pp: 25-26.

15.     Kumar, S., P.M. Gupta and A.K. Nagawat, 2010. Image Restoration Using Variational PDE-Based Performance Comparison of Median and Wiener Filter Neural Network. Journal of Neurocomputing, in Image De-Noising. International Journal of 69: 2364-2368.

16.     Jin, F., P.W. Fieguth, L. Winger and E. Jernigan, 2003. Chaotic Neural Network Approach To Image Adaptive Wiener Filtering of Noisy Images and Denoising. In the Proceedings of the 2004.

17.     Dalong, L., S. Simske and M. Mersereau, 2007. Image Second Edition, Porto Alegre, Bookman. Denoising Through Support Vector Regression.

18.     Yu, C.C. and B.D. Liu, 2002. A Backpropagation In the Proceedings of the 2007 IEEE International Algorithm With Adaptive Learning Rate And Conference on Image Processing, 4: 425-428. Momentum Coefficient. In the Proceedings of the

19.     Buades, A., B. Coll and J. Morel, 2008. Nonlocal 2002 IEEE World Congress on Computational Image and Movie Denoising. International Journal of Intelligence, pp: 1218-1223.

20.     S. Haykin, Neural Network: A Comprehensive Foundation, Prentice-Hall, NJ, 2nd ed., 1999.


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16.

Authors:

Jibendu Kumar Mantri

Paper Title:

Comparison between SVM and MLP in Predicting Stock Index Trends

Abstract: Recently, data mining and time series prediction in financial forecasting has received much research attention. Many techniques are used in prediction on stock and fund trend, volatility, etc. In this paper, two technique of neural network is compared, namely, Support Vector Machine (Support Vector Machine, SVM) and MLP for considering four years of data of Sensex.(Bombay Stock Exchange).

Keywords:
SVM, MLP, Volatility.


References:

1.        F.Cruz, Julio A Afonso-Rodr´ıguez and Javier Giner (2003), Quantitave Finance volume ,pp 1-10.
2.        Gavrishchaka, Supriya B. Ganguli (2003)Neurocomputing 55 , 285 – 305

3.        M.Sap,etl (2005),Stock market prediction using SVM, Journal Teknologi Maklumant,pp 27-35

4.        Lijuan Cao (2003), Support vector machines experts for time series forecasting, Neurocomputing 51 (2003) 321 – 339.

5.        Wun-Hua Chen and etl.(2006),Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets, International Journal of Electronic Finance,pp49-67


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17.

Authors:

Pankaj Sapra, Rupinderpal Singh, Shivani Khurana

Paper Title:

Brain Tumor Detection Using Neural Network

Abstract: The segmentation of brain tumors in magnetic resonance images (MRI) is a challenging and difficult task because of the variety of their possible shapes, locations, image intensities. In this paper, it is intended to summarize and compare the methods of automatic detection of brain tumor through Magnetic Resonance Image (MRI) used in different stages of Computer Aided Detection System (CAD). Brain Image classification techniques are studied. Existing methods are classically divided into region based and contour based methods. These are usually dedicated to full enhanced tumors or specific types of tumors. The amount of resources required to describe large set of data is simplified and selected in for tissue segmentation. In this paper, modified image segmentation techniques were applied on MRI scan images in order to detect brain tumors. Also in this paper, a modified Probabilistic Neural Network (PNN) model that is based on learning vector quantization (LVQ) with image and data analysis and manipulation techniques is proposed to carry out an automated brain tumor classification using MRI-scans. The assessment of the modified PNN classifier performance is measured in terms of the training performance, classification accuracies and computational time. The simulation results showed that the modified PNN gives rapid and accurate classification compared with the image processing and published conventional PNN techniques. Simulation results also showed that the proposed system out performs the corresponding PNN system presented and successfully handle the process of brain tumor classification in MRI image with 100% accuracy.

Keywords:
Magnetic Resonance Image (MRI),Computer Aided Detection System (CAD), Probabilistic Neural Network (PNN), Edge detection.


References:

1.        American Brain Tumor Association. (2010). Facts and statistics, 2010. Retrieved from http://www.abta.org/sitefiles/pdflibrary/ABTAFactsandStatistics2010.pdf
2.        Central Brain Tumor Registry of the United States. (2010). CBTRUS statistical report:Primary brain and central nervous system tumors diagnosed in the United States in 2004–2006. Retrieved from http://www.cbtrus.org/2010-NPCR-SEER/CBTRUS -WEBREPORT-Final-3-2-10.pdf

3.        Sidney Croul, Jessica Otte and Kamel Khalili, “Brain tumors and polyomaviruses”, Journal of NeuroVirology, 9: 173–182, 2003

4.        S. M. Bhandarkar and P. Nammalwar, “Segmentation of Multispectral MR images Using Hierarchical Self-Organizing Map,” Proceedings of Computer-Based medical system CBMS 2001.

5.        C. A. Parra, K. Iftekharuddin and R. Kozma, “Automated Brain Tumor segmentation and pattern Recoginition using ANN,” Computional Intelligence Robotics and Autonomous Systems, 2003.

6.        Andreas Rimner, Andrei I. Holodny and Fred H. Hochberg, “Perfusion Magnetic Resonance Imaging to Assess Brain Tumor Responses to New Therapies,” US neurological disease, 2006.

7.        V. J. Nagalkar and S. S. Asole, “Brain tumor detection using digital image processing based on soft computing,” Journal of Signal and Image Processing , Vol. 3,No. 3, pp.-102-105, 2012

8.        J. Alirezaie, M. E. Jernigan and C. Nahmias, “ Neural Network based segmentation of Magnetic Resonance Images of the Brain,” IEEE Trans, Nuclear Science, Vol.18, No.2, pp:7-30, 2002

9.        S. Datta and M Chakraborty, “Brain Tumor Detection from Pre- Processed MR Images using Segmentation Techniques,” CCSN, 2011.

10.     Sudipta Roy and Samir K. Bandyopadhyay, “Detection and Quantification of Brain Tumor from MRI of Brain and it’s Symmetric Analysis,” International Journal of Information and Communication Technology Research, Vol. 2, No. 6, June 2012.

11.     D. Shevad, “Multiple object segmentation and tracking in image sequence,” Lamar University -Beaumont, April 2005.

12.     T. Logeswari and M. Karnan, “An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map”, International Journal of Computer Theory and Engineering, Vol. 2, No. 4, August, 2010.

13.     R. Gonzalez and R. Woods, Digital Image Processing, 3rd Edition. Prentice Hall, 2008.

14.     Alexander Statnikov, “Automatic cancer diagnostic decision support system for gene expression domain”, Thesis, August, 2005.

15.     M. M. Ibrahiem, E Emary and S. Ramakrishnan, “On the Application of Various Probabilistic Neural Networks in Solving Different Pattern Classification Problems,” World Applied Sciences Journal, Vol. 4 No. 6, pp. 772-780, 2008.

16.     S. Asif Hussain and M. Praveen Raju, “Neuro-Fuzzy System for Medical Image Processing’” Proceedings of the Inter. Conf. on Comm. and Computational Intellig., pp.382-385, India. Dec., 2010.

17.     John Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis And Machine Intelligence, Vol. Pami-8, No. 6, November 1986.

18.     F. Mai, Y. Hung, H. Zhong, and W. Sze. “A hierarchical approach for fast and robust ellipse extraction,” Pattern Recognition, Vol. 41, No. 8, pp.2512–2524, August 2008.


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