Volume-5 Issue-5

S. No

Volume-5 Issue-5, November 2015, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Adeyemo I. A, Okediran, O. O, Oyeleye, C. A.

Paper Title:

Particle Swarm Optimization Approach to Harmonic Reduction in Voltage Source Multilevel Inverter

Abstract:    In Selective Harmonic Elimination-Pulse Width Modulation (SHE-PWM) technique, optimal switching angles at fundamental switching frequency are computed such that low order harmonics are eliminated, while the fundamental voltage is obtained as desired. The main challenge associated with SHE-PWM technique is that a specified number of transcendental nonlinear equations known as Selective Harmonic Elimination (SHE) equations have to be solved to obtain the appropriate switching angles. In this paper, Particle Swarm Optimization (PSO) algorithm with random initial values is proposed for solving SHE equations of an 11-level inverter. The proposed method is derivative-free, accurate and globally convergent. Both computational and MATLAB simulation results show that the proposed method is highly efficient for elimination of the selected low order harmonics as well as minimization of the total harmonic distortion (THD).

Keywords: Multilevel inverter, PSO, modulation index, and harmonics.

References:
1.           K. A. Corzine, "Multi-Level Converters," The Handbook on Power Electronics, Edited by T.L. Skvarenina,  CRC Press, 2002, pp. 6-1 - 6-23
2.           J. Rodríguez, J. Lai, F. Peng, “Multilevel inverters: a survey of topologies, controls and applications,” IEEE Transactions on Industry Applications, vol. 49, no. 4, Aug. 2002, pp. 724-738.
3.           S. Khomfoi, L. M Tolbert, Chapter31. Multilevel Power Converters. The University of Tennessee. pp.31-1 to 31-50.
4.           J. Kumar, B. Das, and P. Agarwal, “Selective Harmonic Elimination Technique for Multilevel Inverter,’’ 15th National Power System Conference (NPSC), IIT Bombay, 2008, pp. 608-613.
5.           J. Chiasson, L. M. Tolbert, K. McKenzie, and Z. Du, “Elimination of Harmonics in a Multilevel Converter using the Theory of Symmetric Polynomial and Resultant,’’ Proceedings of the 42nd IEEE Conference on Decision and Control, Dec. 2005, pp. 216-223.
6.           F. Swift and A. Kamberis, “A New Walsh Domain Technique of Harmonic Elimination and Voltage Control In Pulse-Width Modulated Inverters,” IEEE Transactions on Power Electronics, volume 8, no. 2, 1993, pp. 170–185.
7.           T. J. Liang and R. G. Hoft, “Walsh Function Method of Harmonic Elimination,” Proceedings of IEEE Appl. Power Electron. Conference, 1993, pp.847–853.
8.           T. J. Liang, R. M. O’Connell, R. M. and R. G. Hoft, “Inverter Harmonic Reduction Using Walsh Function Harmonic Elimination Method,” IEEE Transaction on Power Electron, volume 12, no. 6, 1997, pp. 971–982.
9.           Ozpineci, L. M. Tolbert, and J. N. Chiasson, “Harmonic Optimization of Multilevel Converters Using Genetic Algorithm,’’ 35 Annual IEEE Power Electronics Specialists Conference, Germany, 2004.
10.        N. Vinoth, and H. Umesh prabhu, ‘‘Simulation of Particle Swarm Optimization Based Selective Harmonic Elimination,” International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 7, 2013, pp. 215-218.
11.        K. Sundareswaran,K. Jayant, and T. N. Shanavas, “Inverter Harmonic Elimination through a Colony of Continuously Exploring Ants,” IEEE Transactions on Industrial Electronics, volume 54, no. 5, 2007, pp. 2558-2565.
12.        Kavousi, et. al., “Application of the Bee Algorithm for Selective Harmonic Elimination Strategy in Multilevel Inverters,” IEEE Transaction on Power Electronics, vol. 27, no. 4, pp.1689-1696, April 2012.
13.        R. H. Baker and L. H. Bannister, “Electric power converter,” U.S. Patent 3867643, Feb. 1975.
14.        Nabae, I. Takahashi and H. Akagi, “A new neutral-point clamped PWM inverter,”IEEE Trans. Ind. Applicat., vol. IA-17, Sept./Oct. 1981, pp. 518–523.
15.        T. A. Meynard and H. Foch, “Multi-level conversion: High voltage choppers and voltage- source inverters,” in Proc. IEEE-PESC, 1992, pp. 397–403.
16.        P. Hammond, “A new approach to enhance power quality for medium voltage ac drives,” IEEE Trans. Ind. Applicat., vol. 33, pp. 202–208, Jan./Feb. 1997.
17.        S. Sirisukprasert, J. S. Lai, and T. H, Liu, “Optimum Harmonic with a Wide Range of Modulation Indixes for Multilevel Converters,” IEEE Transaction on Industrial Electronics, Vol. 49, no; 4, August 2002, pp. 875-881.
18.        J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conferences on Neural Networks (ICNN'95), Vol. IV,  1995, pp.1942-1948

1-5

2.

Authors:

V. Madhu Sudana Reddy, B. Subramanyam, M. Surya Kalavathi

Paper Title:

Modeling of Price Elasticity in Optimal Bidding Strategies by using Artificial Bee Colony (ABC)

Abstract: The electrical power business has the difficulties to build benefits and minimize their related dangers in the business framework. In this paper proposed a versatile system of Artificial Bee Colony (ABC) for streamline the target capacity and to improve results, with variety of value flexibility (Price elasticity). The Demand expectation is resolved with utilization of Neural Network. The proposed calculation is taking into account conduct of honey bee province of honey bees. The outcomes will clarify about business sector conduct amid flexibility and in-versatility. The execution of the proposed strategy could be actualized in the MATLAB.

Keywords: ABC, Price Elasticity, ANN, optimal bidding, electricity power market

References:
1.          Ferro, R. W., Rivera, J. F. & Shahidehpour, S. M. (1998). Application of games with incomplete information for pricing electricity in deregulated power pools. IEEE Transactions on Power Systems, 13(1), 184-189.
2.          He, Y., Song, Y. H. & Wang, X. F. (2002). Bidding strategies based on bid sensitivities in generation auctionmarkets. Proc. Inst. Elect.Eng.,Gen.,Transm. Dist.,149(1), 21-26.
3.          Richter, C. W. & Sheble, G. B. “Genetic algorithm evaluation of utility bidding strategies for the competitive markeyplace”, IEEE Trans on Power System, 13, 256-261, (1998).
4.          Weber, J. & Overbye, T. “A two-level optimization problem for analysis of market bidding strategies”, IEEE PES Summer Meeting, 2, Edmonton, Alberta, Canada, 682-687, (1999).
5.          Wen, F. S. & David, A. K. “Oligopoly electricity market production under incomplete information”, IEEEPower Engineering Review, 21(4), 58-61, (2001).
6.          Zhang, Y. B., Luo, X. J. & Xue, J. Y.” Constrained competitive Cournot equilibrium with incomplete information in electricity market”, International Conference on Power System Technology, 4, Kunming, China,2385-2389, (2002).
7.          M.P.Moghaddam, A.Abdollahi, and M.Rashidinejad, “Flexible demand response programs modeling in competitive electricity markets”, Appl. Energy, Vol.88, no.9, pp.3257-3269,2011.
8.          Dervis Karaboga, Bahirey Akay, “A comparative study of Artificial Bee Colony algorithm”, 0096-3003/$ - see front matter _ 2009 Elsevier Inc. doi:10.1016/j.amc.2009.03.090, Applied Mathematics and Computation 214 ,108–132, (2009).

6-11

3.

Authors:

Persis Voola

Paper Title:

Questions Related to Uncertainty of Requirements Prioritization

Abstract:     Requirements prioritization activity of a software intensive system is significant in finding the priorities of requirements for implementation, thereby ensuring that the product developed meets the needs and expectations of the stakeholders. It is a challenge for software organizations because it demands a significant amount of decision making, which plays an important role in delivering high quality software to the customers. Decision makers who judge the priorities of requirements are human beings and human judgments may not be absolutely sure. The difficulties are aggravated with varying, incomplete, uncertain, undifferentiated and evolving requirements. Hence, techniques that aid in determining priorities of requirements must give space to the inclusion of uncertainty as a central aspect. The objective of this paper is to present a brief overview of requirements prioritization activity and some related questions to be expounded that interested researchers can dig into. The questions focus on proper acknowledgment of uncertainty during prioritization. 

Keywords: requirements prioritization uncertainty;human judgment;aggregation algorithm

References:
1.              Persis Voola, A Vinaya Babu, “Comparison of Requirements Prioritization Techniques Employing Different Scales of Measurement,” ACM SIGSOFT Software Engineering Notes Vol. 38, Issue. 4, pp: 1-10. DOI: 10.1145/2492248.2492278, July 2013.
2.              Sommerville I and Sawyer P. Requirements Engineering - A Good Practice Guide, John Wiley and Sons, Chichester, UK, 1997.
3.              Schulmeyer G G and McManus J I.  Handbook of Software Quality Assurance, 3rd Edition, Prentice Hall, Upper Saddle River, NJ, 1999.
4.              Karlsson J, Wohlin C and Regnell B, “ An evaluation of methods for prioritizing software requirements,” Elsevier Journal of Information and Software Technology, New York, pp. 939-947, Feb 1997.
5.              Roger S Pressman. Software Engineering: A Practitioner’s Approach. 5th Edition, McGraw Hill, ISBN 0073655783, 2001.
6.              Patrik Berander. Evolving Prioritization for Software Product Management. Blekinge Institute of Technology, Sweden. Doctoral Dissertation Series No 2007:07. ISSN 1653-2090, ISBN 978-91-7295-108-2, 2007.
7.              David Garlan, “Software Engineering in an Uncertain World,” In Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, pp. 125-128, 2010.
8.              S.D.Sandri, D.Dubois, and H.W.Kalfsbeek. Elicitation, Assessment and Pooling of Expert Judgments Using Possibility Theory. Technical Report IRIT/93-24-R, IRIT, Univ.P.Sabatier, Toulouse, France, 1993.
9.              Donald G Fire Smith, “Prioritising Requirements,” Journal of Object Technology. Vol. 3, No. 8, Sep-Oct 2004.
10.           Sommerville I and Sawyer P. Requirements Engineering - A Good Practice Guide, John Wiley and Sons, Chichester, UK, 1997.
11.           Lena Karlsson, Björn Regnell, Joachim Karlsson and Stefan Olsson, “ Post-Release   Analysis of Requirements Selection Quality: An Industrial Case Study,”  In Proceedings of 9th International Workshop on Requirements Engineering: Foundation for Software Quality (REFSQ’03), Velden, Austria, pp. 47-56, June 2003.
12.           G. Ruhe, “ Bees and the Art of Estimating ,”  IEEE Software, Vol. 17, No. 6, page 2, November 2000.
13.           Frank Moisiadis, “The Fundamentals of Prioritizing Requirements,” In Proceedings of Systems Engineering, Test and Evaluation Conference, Sydney, Australia, October 2002.
14.           Paolo Avesani, Anna Perini and Angelo Susi, “Supporting the Requirements Prioritization Process: A Machine Learning Approach,”. In Proceedings of 16th International Conference on Software Engineering and Knowledge engineering, Banff, Alberta, June 2004.
15.           Ruhe G and Saliu M O. The Science and Practice of Software Release Planning. University of Calgary, C. Robson, Real World Research. Blackwell:Oxford,2002.
16.           G. Ruhe and M. O. Saliu, “The art and science of software release planning,” IEEE Software, vol. 22, pp. 47–53, November 2005.
17.           Rudolf Vetschera, “Preference-Based Decision Support in Software Engineering,” Value-Based Software Engineering. Springer. pp.67-89. doi: 10.1007/3-540-29263-2_4, 2006.
18.           B.Regnell, M.Host. J.Nattoch Dag, P.Beremark and T.Hjelm, “An industrial case study on distributed prioritization in market driven requirements engineering for packaged software requirements,”  Requirements Engineering, Vol. 6, pp 51-62, doi: 10.2007/s007660170015, 2001.
19.           Jane Cleland-Huang and Bamshad Mobasher. Using Data Mining and Recommender Systems to Scale up the Requirements Process. ULSSIS, Leipzig, Germany, 2008.
20.           Hermann and A.Daneva, “Requirements Prioritization Based on Benefit and Cost Prediction: An Agenda for Future Research,” In Proceedings of International Conference on Requirements Engineering, pp. 125-134, 2008.
21.           Andrea Herrmann and Barbara Paech, “Practical Challenges of Requirements Prioritization Based on Risk Estimation: Result of Two Student Experiments,” Technical Report SWEHD-TR-2008-03. A publication of the Software Engineering Group University Heidelberg, Germany, 2009.
22.           Annabella Loconsole, Hannes Gruber, et al., “Construction and Evaluation of an Algorithmic and Distributed Prioritization Method,” Second Workshop on Requirements Prioritization for Customer-Oriented Software-Development, 2011.
23.           Tom Gilb and Mark W Maier, “Managing Priorities: Key to Systematic Decision Making,” In Proceedings of INCOSE Annual Conference, Rochester, NY, USA, 2005.
24.           L Lehtola and M Kauppinen, “ Suitability of Requirements Prioritization Methods for Market-driven Software Product Development. Software Process Improvement and Practice,” Vol. 11, pp. 7-19, 2006.
25.           Karlsson L, Berander P, Regnell B and Wohlin C, “Requirements Prioritisation :An Experiment on Exhaustive Pair-Wise Comparisons Versus Planning Game Partitioning,” In Proceedings of Empirical Assessment in Software Engineering (EASE 2004), Edinburgh, Scotland, 2004.
26.           Ahl V, “An Experimental Comparison of Five Prioritization Techniques- Investigating Ease of Use, Accuracy, and Scalability,” Master Thesis No. MSE-2005-11, School of Engineering, Blekinge Institute of Technology, 2005.
27.           Berander P. Prioritization of Stakeholder Needs in Software Engineering: Understanding and Evaluation. Blekinge Institute of Technology, Licentiate Series No.2004:12, Department of Systems and Software engineering, Sweden, 2004.
28.           Joseph Momoh and Gunter Ruhe, “Release Planning Process Improvement- An Industrial Case Study,” Software Process Improvement and Practice, Vol.11, pp. 295-307, 2006.
29.           Martin Weber, “Decision making with incomplete information,” European Journal of Operational Research. Vol.28, pp. 44-57, 1987.
30.           Kukreja N, Payyavula S S, Boehm B and Padmanabhuni S, “ Selecting an Appropriate Framework for Value-Based Requirements Prioritization,” In Proceedings of 12th IEEE International Requirements Engineering Conference. pp. 303-308, 2012.
31.           Hung T Nguyen, Vladik Kreinovich and Qiang Zuo, “ Interval Valued Degrees of Belief: Applications of Interval Computations to Expert Systems and Intelligent Control,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. Vol.5, Issue. 3, pp. 317-358, 1997.
32.           Karlsson. L, Host M and  Regnell.B, “ Evaluating the Practical Use of Different Measurement Scales in Requirements Prioritization,” In Proceedings of ACM/IEEE International Symposium on Empirical Software Engineering, pp. 326-335, 2006.
33.           Evangelos Triantaphyllou and Khalid Baig, “The Impact of Aggregating Benefit and Cost criteria in Four MCDA Methods,” IEEE Transactions of Engineering Management Vol. 52, Issue. 2, pp. 213-226, 2005.
34.           L Keeney and H Raiffa, “Decisions with Multiple Objectives-Preferences and Value Trade-Offs,” 2nd ed Cambridge UK: Cambridge University Press, 1993.
35.           Glenn Shafer A. Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ, 1976.
36.           Dong-Ling Xu, Jian-Bo Yang, Ying-Ming Wang, “The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty,” European Journal of Operational Research. Vol. 174 pp.1914-1943. Elsevier, 2006.

12-15

4.

Authors:

Emmanuel Thyaka Mbusi, Titus Kivaa Mbiti, Githae Wanyona

Paper Title:

Monetary and Fiscal Policy Factors That Affect Construction Output in Kenya

Abstract:     The main role of construction industry is provision of physical constructed facilities to give other activities space for taking place Hillebrandt, (2000). She further observes that, these constructed facilities are referred to as construction output and are quantified on monetary terms. This quantification is done by Kenya National Bureau of Statistics in this country. Construction industry in Kenya mostly maintains an upward trend in its growth. Recently; 2013 and 2014, an economic survey report released by KNBS showed Kenya’s building and construction as having contributed 4.8% to the Gross Domestic Product (GDP). The GDP rose from Kshs.4.73 trillion to Kshs.5.36 trillion in 2014 Macharia, (2015). This gives an indication that the sector is growing, though at a slow pace. Description of monetary and fiscal policy factors in Kenya was thought of, as a way of sensitization to the construction sector stakeholders and players about them. These factors play a key role in decision making concerning construction projects, but are usually not accounted for carefully at this crucial stage of decision making. Time series data was collected on quarterly bases for the period between 2000 and 2013, for the five factors. This data showed normal distribution for all the variables with the mean and median being very close. It was concluded that in Kenya, there are five monetary and fiscal policy factors that affect construction output and hence policy makers, stakeholders and players in the sector should give these factors a fair consideration during decision making stage.  This will foster growth in the sector and push the country’s GDP towards the ardently desired two digit growth.

Keywords: construction output, time series, fiscal policy, monetary policy.

References:
1.          Bon, R. (1992). The Future of International. Pergamon Press Ltd.
2.          Businessdictionary.com. (2014.). Interest rate. Retrieved 10 11, 2015, from Businessdictionary.com: http://www.businessdictionary.com/definition/interest
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3.          CBK.(2013). CBK Rates. Retrieved from Interest Rates: www.centralbank.go.ke
4.          Girardi, D., & Mura, A. (2013, September).Construction and economic development: empirical evidencefor the period 2000-2011. Retrieved from Universita` Di Siena 1240: http://www.econ-pol.unisi.it/quaderni/684.pdf
5.          Gransberg, D. D., Popescu, C. M., & Ryan, R. C. (2006).Construction Equipment Management ForEngineers, Estimators and Owners. Boca Raton: CRC Press.
6.          Gruneberg, M. B. (2013). London Interbank Offered Rate.Jounal of Financial Management of Propertyand Construction, 268-281.
7.          Gruneberg, S. L. (1997). Construction Economics, An Introduction. New York: Palgrave.
8.          HandBook, C. (1998, March).Commercial Real Estate and Construction Lending. Retrieved 2014
9.          Hillebrandt, P. M. (2000). Economic Theory and the Construction Industry. London: Palgrave Macmillan.
10.       Investopedia. (2015, May 3). Exchange rate. Retrieved from Investopedia:
11.       http://www.investopedia.com/terms/e/exchangerate.asp
12.       Kalinga, Maehle, Powell, S., McIntyre, & Jacobs. (2003). Kenya: Selected Issues and Statistical Appendix. Washington. D.C: International Monetary Fund Publication Service.
13.       Macdonald, R. (2007). Exchange Rate Economics Theories and Evidence. New York: Routledge.
14.       Macharia, N. (2015, May 5). Overview: Kenya Construction Industry. Retrieved from The Nation Business Review: http://www.kenyanbusinessreview.com/562/construction-industry-in-kenya/
15.       Masinde, J. (2015, May 7). Centrak Bank holds basic lending rate as shilling takes more beating. Dailynation, p. 35.
16.       Mawdesley, M. J., & Qambar, S. (2000). System Thinking and Construction Productivity.17.       1st.InternationalConference on Systems thinking in Management, (pp. 414 - 419).
18.       Wahome, M. (2015, May 1). Treasury Proposes KSh. 215 billion in Budget to boost Security. Daily Nation, p. 4.
19.       Wokabi, C. (2015, May 2). treasury plans to raise Kshs. 5 billion from National Bank, Mumias. Daily Nation, p. 42.

16-20

5.

Authors:

Ashwani Kumar Aggarwal

Paper Title:

On the Use of Artificial Intelligence Techniques in Transportation Systems

Abstract:      Transportation systems are becoming more and more advanced due to progress made in computational techniques used in transportation. Autonomous navigation of vehicles is not only useful in robotics but also in town planning and management. In this paper, use of artificial navigation techniques used in navigation of autonomous vehicles is discussed. The methods are based on machine vision techniques which extract feature points in images captured by cameras mounted on vehicles. These images are fed to artificial intelligence algorithms to estimate self-position of vehicles. Knowing the self-position of vehicles, autonomous navigation of vehicles is made feasible. The methods work effectively and vehicles are navigated in cluttered environments.

Keywords: feature detectors, artificial intelligence, navigation, localization, 3D transformation

References:
1.          Jean-Philippe Tardif Yanis Pavlidis Kostas Daniilidis, “Monocular Visual Odometry in Urban Environments Using an Omnidirectional Camera”, in Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Sept. 2008 pp 2531-2538 DOI:10.1109/IROS.2008.4651205.
2.          Emanuele Frontoni, Andrea Ascani, Adriano Mancini, Primo Zingaretti, “Omnidirectional vision for robot localization in urban environments”, Proc. of int. conf. on Simulation, Modeling and Programming for Autonomous Robots, SIMPAR 2008. Venice (Italy) November 3-4, 2008. pp 343-353.
3.          Jonathan Dixon, Oliver Henlich, Mobile Robot Navigation, Final Report, Imperial College, London information systems engineering year 2, 10 June 1997.
4.          Henrik Andreasson, Andre Treptow, Tom Duckett, “Self-localization in non-stationary environments using omni-directional vision”,in Proc. of Robotics and Autonomous Systems, March 2007, pp541-551.
5.          David G. Lowe, “ Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision archive Volume 60 , Issue 2, November 2004, pp 91 - 110.
6.          Andea Ascani, Emanuele Frontoni, Adriano Mancini, Primo Zingaretti, “Robot localization using omnidirectional vision in large and dynamic outdoor environments”,
DOI: 10.1109/MESA.2008.4735695 pp 576-580, IEEE 2008.
7.          E. Menegatti, G. Gatto, E. Pagello, Takashi Minato, Hiroshi Ishiguro, (2008) “Robot localisation enhanced by a distributed vision system in dynamic environments”, Sensor Review, Vol. 28 Issue: 3, pp.222 – 228.
8.          David C. K. Yuen and Bruce A Mac Donald,“Robot Localization Using Omnidirectional Color Images”,  Robot Vision, Lecture Notes in Computer Science, DOI:10.1007/3-540-44690-7_21, 2001, pp167-175.
9.          Zoltan Vamossy, “Map Building and Localization of a Robot Using Omnidirectional Image Sequences”, Acta Polytechnica Hungarica, vol.4, No.3, 2007, pp 103-112.
10.       Cyril, Charron, Ouiddad Labbani-Igbida, El Mustapha Mouaddib, “Qualitative Localization using Omnidirectional images and invariant features”,IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005.
11.       Gehua Yang, “Estimating the location of a camera with respect to a 3D model”,in Proc. of IEEE Sixth International Conference on 3-D Digital Imaging and Modeling(3DIM 2007) 0-7695-2939-4/07,2007.
12.       Nobuhiro Aihara, Hidehiko Iwasa,Naokazu Yokoya, Haaruo Takemura,“Memory Based Self-Localization Using Omnidirectional Images”, in Proc. of Fourteenth International Conference on Pattern Recognition, Aug 1998 Volume: 2, pp 1799-1803 Brisbane, Qld., Australia ISBN: 0-8186-8512-3.
13.       Nikos Vlassis, Yoichi Motomura, Isao Hara, Hideki Asoh, “ Edge-Based features from omnidirectional images for robot localization”,in Proc. ICRA’01, IEEE Int. Conf. on Robotics and Automation, Seoul, Korea, May 2001, pp 1579-1584.
14.       Guiherme N. Desouza, Avinash C Ka, “ Vision for mobile Robot Navigation: A Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, No.2, February 2002, pp 237-267.
15.       Hough Durrant- Whyte, Tim Bailey, “Simultaneous Localization and Mapping: Part I”, IEEE Robotics and Automation Magazine June, 2006, pp 99-108.
16.       Hough Durrant- Whyte, Tim Bailey, “Simultaneous Localization and Mapping: Part II”, IEEE Robotics and Automation Magazine September, 2006, pp 108-117.
17.       Parra, M. A. Sotelo, D. F. Llorca and M.Ocana,“Robust visual odometry for vehicle localization in urban environments”, Robotica Cambridge University Press,
Cambridge University Press 2009 doi:10.1017/S026357470900575X.
18.       Jesse Levinson, Michael Montemerlo, Sebastian Thrun,“Map-Based Precision Vehicle Localization in Urban Environments”, Stanford Artificial Intelligence Laboratory in Proc. of Robotics: Science and Systems, Atlanta, GA,USA, June 2007.
19.       Aihara, N.; Iwasa, H.; Yokoya, N.; Takemura, H.”, Memory-based self-localization using omnidirectional images,” in Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on, vol.2, no., pp.1799-1803 vol.2, 16-20 Aug 1998.
20.       C. Murillo, J. J. Guerrero and C. Sagues, “SURF features for efficient robot localization with omnidirectional images”,in Proc. of IEEE International Conference on Robotics and Automation, Roma , 2007, pp 3901 – 3907.
21.       Christoffer Valgren, Achim Lilienthal, “SIFT, SURF and Seasons: Long-term Outdoor Localization Using Local Features”, in Proc. of European Conference on Mobile Robots (EMCR), At Freiburg, Germany, 2007.
22.       Jurgen Wolf, Wolfram Burgard, Hans Burkhardt, “Robust Vision-based Localization by Combining an Image Retrieval System with Monte Carlo Localization” IEEE Transactions on Robotics, 21(2), 2005.
23.       Guillaume Caron, Eric Marchand and El Mustapha Mouaddib, “3D Model Based Pose Estimation for Omnidirectional Stereovision”, in Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009 at St. Louis, MO  on 10-15 Oct. 2009, pp 228 - 5233, DOI: 10.1109/IROS.2009.5353955.
24.       Robert Baczyk, Andrzej Kasinski, Piotr Skrzypczynski,“Vision-Based Mobile Robot localization with Simple Artificial Landmarks”, in Proc. of 7th IFAC Symp. on Robot Control, Wroclaw,2003 pp 217-222.
25.       Herbert Bay and TinneTuytelaars and Luc Van Gool,"Surf: Speeded up robust features|", in proc. of ECCV 2006, pp 404—417.

21-24

6.

Authors:

Karthikeyan Shanmugasundaram, Ahmad Sufril Azlan Mohamed, Ibrahim Venkat

Paper Title:

An Overview of Multimodal Biometrics Using Meta-Heuristic Optimization Techniques for F2R System

Abstract:     Multimodal biometrics is the combination of more than one unimodal biometrics which involves more accuracy by implementing F2R(Face and Fingerprint Recognition) .This survey paper deals with the review of multimodal biometrics using F2R  and analyzing various meta heuristic optimization algorithms used at the feature selection level  of F2R.

Keywords:     F2R - Face and Fingerprint Recognition, multimodal biometrics, Meta heuristics, Optimization

References:
1.             M. Deriche, “Trends and Challenges in Mono and Multi Biometrics,” in Proc. of Image Processing Theory, Tools and Application (IPTA), Sousse, pp.1-9, 23-26 Nov 2008.
2.             Ross, K. Nandakumar and A. K. Jain. Handbook of Multibiometrics. Springer, New York, USA, 1st edition, 2006.
3.             A.K.Jain,LinHong and YatinKulkarni, A Multimodal Biometric system Using Fingerprint ,Face and Speech. 2nd International conference on Image Processing, 1999.
4.             Zhao et al, “Face Recognition :A Literature survey”,ACM computing surveys,pp399-458,Vol.35,No.4,December 2003.
5.             DavideMaltoni et al, “Handbook of Fingerprint Recognition,” springer 2009.
6.             IlhemBoussaïd et al, “A survey on optimization meta heuristics”Information Sciences 237 (2013) 82–117.
7.             B. Alsalibi et al, “The impact of bio inspired approached towards the advancement of Face recognition,” ACM Computing Surveys, Vol. 48, No. 1, Article 5, Publication date: August 2015
8.             M.T. Das, L.C. Dulger,”signature verification (SV)tool box: Application of PSO-NN,” Engineering Applications of Artificial Intelligence 22 (2009) 688–694.
9.             L. Hong and A. Jain, “Integrating faces and fingerprints for personal identification”, IEEE Trans. Pattern Anal. Mach. Intell. , Vol. 20, No. 12, pp. 1295– 1307, December 1998.
10.          A. Pour, K. Faez, and R. Amirfattahi “Multimodal biometric system using face, ear and gait biometrics”, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010)..

25-27

7.

Authors:

M.H.Jali, M.K.Alias, R.Ghazali, T.A.Izzuddin, H.I.Jaafar

Paper Title:

Development of Prosthethic Hand Hardware and Its Control System

Abstract: This paper described the development of prosthetic hand and its control system. Initially the research start by reviewing the existing prosthetic design. After investigating of those designs, a better design approach has been presented in this work. The objective of this research is to design and develop the prosthetic hand control system in order to provide solutions for the amputee who lose their hand. This project describes an under actuated artificial hand for functional replacement of the natural hand. The hand comprised of an actuator embedded in an under actuated mechanical system. After the prosthetic hand hardware completed the fabrication process, it is modelled using system identification method. An input data (voltage) and output data (position) is collected using MyRIO. Then, the PID controller is designed based on Zigler Nichols method. Finally, the prosthetic control system is verified via simulation. It is expected that the PID controller could perform well for the prosthetic system.

Keywords: Prosthetic hand, Ziegler Nichols method, system identification, PID controller

References:
1.        E. P. Puig, N. Eduardo, N. Rodriguez, and M. Ceccarelli, “A Methodology for the Design of Robotic Hands with Multiple Fingers,” pp. 177–184, 2006.2.        D. S. Childress and D. Ph, “Historical Aspects of Powered Limb Prostheses,” 1945.
3.        M.S.M Aras, M.F. Basar, N. Hasim, M.N. Kamaruddin, H.I. Jaafar, “ Development and Modeling of Water Tank System Using System Identification Method,”International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-6, 2013.
4.        M.H.Jali, N.E.S.Mustafa, T.A.Izzuddina, R.Ghazali, H.I.Jaafar, “ANFIS-PID Controller for Arm Rehabilitation Device”, International Journal of Engineering and Technology (IJET), vol. 7 (5) , pp. 1589-1597, 2015.
5.        M.H. Jali, M.F. Sulaima, T.A. Izzuddin, W.M. Bukhari, M.F. Baharom, “Comparative Study of EMG based Joint Torque Estimation ANN Models for Arm Rehabilitation Device”, International Journal of Applied Engineering Research, Vol 9 (10), pp. 1289 - 1301, 2014.
6.        H. I. Jaafar, S. Y. S. Hussien, N. A. Selamat, M. N. M. Nasir and M. H. Jali, “Analysis of Transient Response for Coupled Tank System via Conventional and Particle Swarm Optimization (PSO) Techniques”, International Journal of Engineering and Technology, vol. 6 (5), pp. 2002-2007,2014.

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8.

Authors:

Raj Kushal Ananth Kumar, Pooja Singh, Naman Pradhan, Afroz Pasha

Paper Title:

A Client Centric Hadoop Ecosystem

Abstract:       Various public cloud service offerings such as Google App Eng, Amazon Web Services, Microsoft Azure, etc. are increasingly gaining popularity as a means to perform network, storage and compute operations. Also the Hadoop framework is rapidly becoming the industry standard for most organizations due to its scalability, fault tolerant and cost effective design. Hence deploying Hadoop on the cloud will allow leveraging extremely elastic and flexible operations all while being cost and time effective. However various cloud attacks can bypass the current Hadoop security mechanisms, and threaten the confidentiality of the client’s data and the overall system. In this paper we propose a Hadoop System that maintains the privacy and security of the information stored on the cloud through Client side validation and encryption along with a more resilient public cloud-based Hadoop model..

Keywords:  client encryption; Hadoop; Public cloud; Resiliency; Security and privacy;

References:
1.          HongBo  Zhou. Cloud computing: technology, application,standar, Electronic Industry Press.2011
2.          Al-Fares M et al (2008) A scalable, commodity data center network architecture. In: Proc SIGCOMM
3.          Cloud Computing: benefits, risks and recommendations for information security. D Catteddu - Web Application Security, 2010 - Springer
4.          Apache Hadoop. http://hadoop.apache.org/, 2012
5.          Inforchimps,“Cloud::hadoop,”http://www.infochimps.com/infochimpscloud/cloud- services/cloud-hadoop/ Accessed in Oct. 2015.
6.          D. Zissis and D. Lekkas, Addressing cloud computing security issues, Future Generation Computer Systems, vol. 28, no. 3, pp. 583 – 592, 2012.
7.          S. Advisory, Xen pv kernel decompression multiple vulnerabilities, http://secunia.com/advisories/44502/. Accessed in November 2014.
8.          F. Rocha and M. Correia, Lucy in the sky without diamonds: Stealing confidential data in the cloud, in Dependable Systems and Networks Workshops (DSN-W), 2011 IEEE/IFIP 41st International Conference on. IEEE, 2011, pp. 129–134.
9.          S. Bugiel, S. Nurnberger, T. P¨ oppelmann, A.-R. Sadeghi, and T. Schnei-¨ der, Amazonia: when elasticity snaps back, in Proceedings of the 18th ACM conference on Computer and communications security. ACM, 2011, pp. 389–400.
10.       T. Ristenpart, E. Tromer, H. Shacham, and S. Savage, Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds, in Proceedings of the 16th ACM conference on Computer and communications security. ACM, 2009, pp. 199212.

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9.

Authors:

Shubham Borikar, Mohan Bhagchandani, Raunak Kochar, Ketansing Pardeshi, Manisha Gahirwal

Paper Title:

A Survey on Applications of Big Data Analytics in Healthcare

Abstract:   The data in healthcare is increasing rapidly and is expected to increase significantly in coming years. Healthcare services although armed with modern technologies for curing the diseases grapples when it comes to preventing the diseases beforehand. Adoption of Big Data solutions will play an important role in transforming the outcomes of the healthcare industry by promoting evidence based reasoning and providing patient centric treatment. In this age of Big Data we can provide solutions to identify individuals who are prone to certain lifestyle diseases. Think of identifying an individual having an increased risk of diabetes after 10 years, now. With the advent of new big data analysis tools and technologies, such predictive systems can be designed which can identify individuals with increased risk. This paper provides an overview of big data analytics, different technologies that can be used in big data and its impact on healthcare domain to make some useful predictions based upon analyzing a variety of datasets. Finally we provide a model which can be used for predictive analytics using data mining and machine learning algorithms to predict the chances for a person to be prone to a disease.

Keywords: Big Data;Healthcare;Prediction system ;

References:
1.           Nambiar, R.; Bhardwaj, R.; Sethi, A.; Vargheese, R., "A look at challenges and opportunities of Big Data analytics in healthcare," in Big Data, 2013 IEEE International Conference on , vol., no., pp.17-22, 6-9 Oct. 2013
2.           Wullianallur Raghupathi, Viju Raghupathi “Big Data Analytics in Healthcare: Promise and Potential” http://www.hissjournal. Com/content/2/1/3 Health Information Science and Systems 2014, 2:3
3.           Viceconti, M.; Hunter, P.; Hose, R., "Big Data, Big Knowledge: Big Data for Personalized Healthcare," in Biomedical and Health Informatics, IEEE Journal of , vol.19, no.4, pp.1209-1215, July 2015
4.           Mathew, P.S.; Pillai, A.S., "Big Data solutions in Healthcare: Problems and perspectives," in Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on , vol., no., pp.1-6, 19-20 March 2015
5.           Keith Feldman, Nitesh V. Chawla, "SCALING PERSONALIZED HEALTHCARE WITH BIG DATA", 2nd International Conference on Big Data and Analytics in Healthcare, Singapore 2014.
6.           J.Senthil Kumar, N.Ramprasath, "A Scrutiny on Current and Parallel Big Data Analytics in Health Care", International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume 12 Issue 4 –FEBRUARY 2015.
7.           Kaul, C.; Kaul, A.; Verma, S., "Comparitive study on healthcare prediction systems using big data," in Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on , vol., no., pp.1-7, 19-20 March 2015
8.           Durham, E.-E.A.; Rosen, A.; Harrison, R.W., "Optimization of relational database usage involving Big Data a model architecture for Big Data applications," in Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on , vol., no., pp.454-462, 9-12 Dec. 2014
9.           AbuKhousa, E.; Campbell, P., "Predictive data mining to support clinical decisions: An overview of heart disease prediction systems," in Innovations in Information Technology (IIT), 2012 International Conference on , vol., no., pp.267-272, 18-20 March 2012

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10.

Authors:

Waddah Abdelbagi Talha, Mohammed A. A. Emaleeh, John Ojur Dennis

Paper Title:

Actuated Micro-Sensor for Magnetic Field Detection Based on Piezoresistor Transduction

Abstract:    one of the techniques used to measure the external magnetic field is by the transduction of the deflection produced by the Lorentz force into electrical signal using Piezoresistor technology. It is stated that the piezoresistive effect in silicon depends strongly on the crystal orientation, doping type, and concentration. In this paper the piezoresistive property of the Polysilicon is used as Piezoresistor transducer to study the transduction procedures. The results obtained observe the response of the different samples of piezoresistor transducer to transfer the deflection of the cantilever to electrical signal (voltage output).  The sensitivity of the system is calculated for different samples at a fixed value of the applied force the percentage rate of change of the resistivity (ΔRp %) for different values of the cantilever deflections were obtained. It is observed that the change in resistance of the piezoresistor increases nonlinearly with the increase of the cantilever displacement. The Polysilicon Piezoresistor in Whetstone’s bridge configuration is used to transducer the response of the cantilever to electrical measurements at various voltages. Various dimensions of the cantilever were considered in the measurements. The highest sensitivity of the measurements (64mV/mT) is obtained for a thin beam of 0.6 µm polysilicon embedded in 2 µm thick silicon cantilever beam.

Keywords:  Lorentz force; Polysilicon Piezoresistor; Bulk Micromachining; Wheatstone bridge.

References:
1.           Ronald Lane Reese “University Physics” Brooks/Cole, 2000.
2.           G. Villanueva, et al. “Crystalline Silicon Cantilevers for Piezoresistive Detection of Bimolecular Forces” Microelectronic Engineering, VOL 85, pp1120-1123, 2008.
3.           G. Villanueva, et al. “Submicron piezoresistive cantilevers in a CMOS-compatible technology for intermolecular force detection” Microelectronic Engineering, VOL 73-74, pp 480-486, 2004.
4.           D. Lange, O. Brand, H. Baltes “CMOS Cantilever Sensor Systems- Atomic Force Microscopy and Gas Sensing Application” springer publisher, 2002.
5.           Shyam Aravamudhan, Shekhar Bhansali  “Reinforced piezoresistive pressure sensor for ocean depth measurements” Sensors and Actuators A 142, pp 111-117, 2008.
6.           Vincent B, Yves B, Laurent L, Pascal N, “Monolithic piezoresistive CMOS magnetic field sensors” Sensors and Actuators A, vol.103, pp32-23, 2003.
7.           www.coventorware.com, valid at 12/7/2008
8.           Leonard Meirovitch “Fundamentals of Vibration” Mc Graw Hill, 2001.
9.           M.Elwensspoek, R. wiegerink, “Mechanical microsensors” Springer, Singapore, 2001.
10.        Vincent Beroulle, et al. “Micromachined CMOS Magnetic Field Sensor with Low Noise Signal Conditioning” IEEE international conference on micro electro mechanical system, pp. 256-259, 2002.
11.        N. Dumas, L. Latorre, P. Nouet “Low noise CMOS amplifier for a piezoresistive   magnetic field sensor”, 18th Conference on Design of Circuits and Integrated Systems (DCIS’03), Ciudad Real, Spain, pp 639-644, 2003.
12.        El Mehdi, Yannick S, Frederick M, Laurent L and Pascal, “Rejection of Power Supply Noise in Wheatstone Bridges Application to Piezoresistive MEMS”, Proc. of DTIP of MEMS & MOEMS, 2008.

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11.

Authors:

N. D. Liyanagedera, A. Ratnaweera, D. I. B. Randeniya

Paper Title:

Test for Efficient Increase/ Decrease Factors for Resilient Backpropagation using Combustion Engine Vibration Signals 

Abstract:     Resilient backpropagation is a recently emerging neural network with a high potential. This neural network is capable of handling a network structure with a large number of input nodes which many other networks fail. This is achieved based on the way the weights are updated in the algorithm. Resilient backpropagation uses two constant values, decrease factor [η^-] and increase factor [η^+] to update the weights and to get the optimal solution. This experiment checks on to find how the neural network performs when different values were used for these constants. The training, testing and validation of the neural networks were done using vibration signal data collected from a combustion engine corresponding to 16 different fault combinations available in the combustion engine. The performances of the networks were compared using mean square error, time and epoch. The final results indicate that, when the decrease factor is in the range of 0.5 to 0.6 and when the increase factor is in the range of 1.2 to 1.3 the resilient backpropagation algorithm has the best performance.

Keywords:  Resilient Backpropagation Algorithm, Increase/Decrease Factor, Combustion Engine, Vibration Signals.

References:
1.       T. Denton, Advanced Automotive Fault Diagnosis, 2nd Edition, Elsevier Ltd, 2006.
2.       S. Klinchaeam, P. Nivesrangsan, M. Lokitsangthong, Condition Monitoring of a Small Fourstroke Petrol Engine using Vibration Signals. KMITL Sci. Tech. J. Vol. 9 No. 1 Jan. - Jun. 2009.
3.       S. Ramezani , A. Memariani , A Fuzzy rule base system forfault diagnosis, using oil analysis results, International journal of industrial engineering and production research, June 2011, volume 22, number 2, pp 91-98.
4.       K. Wang , Neural network approach to vibration feature selection and multiple fault detection for mechanical systems, International Conference on Innovative Computing, Information and Control, 2006 IEEE.
5.       K. Suzuki, Artificial Neural Networks - Industrial and Control Engineering Applications,InTech, 2011, ISBN 978-953-307-220-3.
6.       H. Su, K. Chong, R. R. Kumar Vibration signal analysis for electrical fault detection of induction machine using neural networks, Springer-Verlag London Limited 2011.
7.       R. Ahmed, M.E.l. Sayed, S.A Gadsden, J. Tjong, S.Habibi, Automotive Internal-Combustion-Engine Fault Detection and Classification Using Artificial Neural Network Techniques. IEEE Transactions on vehicular Technology, Vol. 64, No. 1, January 2015.
8.       N. D. Liyanagedera, A. Ratnaweera, D. I. B. Randeniya, Vibration Signal Analysis for Fault Detection of Combustion Engine Using Neural Network, Industrial and Information Systems (ICIIS), 2013 8th IEEE International Conference.
9.       X. Pan, B. Lee, C. Zhang, A Comparison of Neural Network Back propagation Algorithms for Electricity Load Forecasting. Intelligent Energy Systems (IWIES), 2013 IEEE International Workshop.
10.     Vilovic, N. Burum, A Comparison of MLP and RBF Neural Network Architectures for Location Determination in Indoor Environments. Antennas and Propagation (EuCAP), 2013 7th European Conference, IEEE.
11.    C. Juan, G. Higuera, G. Alba, A. Bernal, M. Julian, F. Angel, A Comparison of Neural Networks to Detect Failures in Micro-electro-mechanical Systems , Electronics, Robotics and Automotive Mechanics Conference, 2010, ISBN: 978-0-7695-4204-1,pp: 191-196.
12.    N. D. Liyanagedera, A. Ratnaweera, D. I. B. Randeniya, Performance Comparison of Four Neural Networks to Detect Faults in a Combustion Engine, NNGT Int. J. on Artificial Intelligence, Vol. 2, Feb 2015.
13.    M. Riedmiller , H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, IEEE International Conference on Neural Networks, 1993.
14.    X. Wang, H. Wang, G. Dai, Z. Tang, A Reliable Resilient Backpropagation Method with Gradient Ascent, Computational Intelligence, International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006.

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12.

Authors:

Umashankar Thakur, Vaibhav Nagare, Mita Shimpi, Mubarak Pathan, Nuzhat F. Shaikh

Paper Title:

Tour Guide System using Augmented Reality: A Review

Abstract:      One of the largest, most important and constantly growing industries in the world is, without doubt, tourism. Such rapid growth calls for enticing ways to engage tourists and keep them interested, which is a big challenge for the professionals, having to maintain standards and increasing expectations of tourists. Tools such as augmented reality (AR) hold a vast potential in attracting and retaining visitors. The rise in smart mobile devices only boosts this further as it becomes possible to have information and tour generation at one’s fingertips. In this paper, we first try to enlist all the limitations and challenges encountered while utilizing concepts of AR to develop a tour guide system. We describe various state-of-the-art AR applications that provide such service, having their own set of drawbacks, and we give a brief introduction to our proposed system. All the facts and literature we have surveyed and studies  related to various aspects of developing an AR-based tourism system are presented in the paper, which include several methods and algorithms that can be used for image comparison required to recognize objects of interest.

Keywords: Augmented reality (AR), image comparison, tourism, mobile AR

References:
1.        Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,”   International Journal of Computer Vision,vol. 60, no. 2, pp. 91–110, 2004.
2.        K. Greene, “Hyperlinking Reality via Phones,” MIT Technology Review, Nov/Dec  2006.
3.        R. Datta, J. Li, and J. Z. Wang, “Content-based image retrieval: approaches and trends of the new age,” in MIR ’05: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval. New York, NY, USA: ACM, 2005, pp. 253–262.
4.        Stephanidis and M. Antona (Eds.): “Using Augmented Reality and Social Media in Mobile Applications to Engage People on Cultural Sites.” UAHCI/HCII 2014, Part II, LNCS 8514, pp. 662–672, 2014.Springer International Publishing Switzerland 2014.
5.        Giaccardi, E. (ed.): Heritage and Social Media. Understanding Heritage in a Participatory Culture. Routledge, London (2012).
6.        Heeseung Choi, Gyu Chull Han, and Ig-Jae Kim “Smart Booklet: Tour Guide System with Mobile Augmented Reality” 978-1-4799-1291-9/14 2014 IEEE International Conference on Consumer Electronics (ICCE).
7.        V. Vlahakis, N. Loannidis, J. Karigiannis, M.Tsotros, and M. Gounaris, "Archeoguide: An Augmented Reality Guide for Archaeological Sites," IEEE Computer Graphics and Applications, vol. 22, pp.52-60, 2002.
8.        Zhanpeng Huang, Pan Hui, Christoph Peylo, Dimitris Chatzopoulos, “Mobile Augmented Reality Survey: A Bottom-up Approach”

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13.

Authors:

Jay Nanavati, Yogesh Ghodasara

Paper Title:

A Comparative Study of Stanford NLP and Apache Open NLP in the view of POS Tagging

Abstract:    To perform a comparative study of two popular Natural Language Processing tools – Stanford NLPand Apache Open NLP, is the main objective of this paper. This paper also provides an insight into use of these two tools for analysis of requirements specification expressed in Natural Language English.

Keywords: NLP, Requirements Specifications, Part Of Speech (POS) tagging, Tokens, Tag set

References:
1.        http://www.oed.com (Dt. 3/6/15)
2.        N. Boyd, “Using Natural Language in Software Development”, Journal of Object Oriented Programming, Feb. 1999.
3.        M. Osborne, C.K. MacNish, "Processing Natural Language Software Requirement Specifications", Proceedings of the 2th International Conference on Requirements Engineering, IEEE, 15-18 April 1996, pp. 229-236
4.        http://www.cs.umd.edu/~nau/cmsc421/part-of-speech-tagging.pdf (Dt. 3/6/15)
5.        https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html (Dt. 6/6/15)
6.        Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL 2003, pp. 252-259.
7.        https://opennlp.apache.org/documentation/1.5.3/manual/opennlp.html#tools.postagger (Dt. 12/6/15)
8.        https://en.wikipedia.org/wiki/Part-of speech_tagging#Unsupervised_taggers (Dt. 12/6/15)

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14.

Authors:

Abdulnaser M. Alshoaibi

Paper Title:

Finite Element Modelling of Mixed Mode Crack Propagation

Abstract:     This paper illustrates an algorithm for automatic simulations of crack propagations in 2D linear elastic finite element representation. The crack tip singularity and stress intensity factors around the crack tip are obtained by using the displacement extrapolation method. The crack propagation direction can be predicted by using the maximum circumferential stress theory criterion. The developed program has been examined with two different types of geometries namely: single edge cracked plate with one hole and double edge cracked plate with two holes with different types of loading. The results obtained by the current program have been assessed by comparing with the relevant works.

Keywords: Finite element method, crack trajectories, holes, stress intensity factors, adaptive mesh.

References:
1.       Zhu, W.X. and Smith, D.J, On The Use of Displacement Extrapolation to Obtain Crack Tip Singular Stresses and Stress Intensity Factors, Engineering Fracture Mechanics, vol. 5, pp. 391-400, 1995.
2.       Isida, M, Effect Of Width And Length On Stress Intensity Factors Of Internally Cracked Plates Under Various Boundary Conditions, International Journal of Fracture, vol. 7, pp. 301-316, 1971.
3.       Chan, S.K,  Tuba, I.S and Wilson, W.K, On the Finite Element Method in Linear Fracture Mechanics, Engineering Fracture Mechanics, Vol. 2, pp.1–17, 1970.
4.       Parks, D.M, A Stiffness Derivative Finite Element Technique for Determination of Crack Tip Stress Intensity Factors, International Journal of Fracture, vol. 10, pp. 487–502, 1974.
5.       Moran, B and Shih, C.F, A General Treatment of Crack Tip Contour Integrals, Engineering Fracture Mechanics, vol. 35, pp. 295–310, 1987.
6.       Guinea, G.V, Planan, J and  Elices, M, KI Evaluation By The Displacement Extrapolation Technique, Engineering Fracture Mechanics, vol. 66, pp. 243-255, 2000.
7.       El-Hamalawi, A 2D Combined Advancing Front Delaunay Mesh Generation Scheme, Finite Element in Analysis and Design, vol. 40, pp. 967-989, 2004.
8.       Phongthanapanich, S and Dechaumphai, P, Adaptive Delaunay Triangulation With Oject-Oriented Programming For Crack Propagation Analysis, Finite Element in Analysis and Design, vol. 40, pp. 1753-1771, 2004.
9.       Rashid M.M, The arbitrary Local Mesh Replacement   Method: An Alternative to Remeshing for Crack Propagation Analysis, Comput. Methods Appl. Mech.  Engrg.
Vol. 154 , pp. 133-150, 1998.
10.    Bittencourt, T.N, Wawrzynek, P.A, A.R. Ingraffea and J.L. Sousa, Quasi-Automatic Simulation Of Crack Propagation For 2D LEFM Problems, Eng. Fract. Mech. Vol. 55, pp. 321–334, 1996.
11.    Bouchard, P.O, Bay F and Chastel Y, Numerical Modeling Of Crack Propagation: Automatic Remeshing And Comparison Of Diffeent Criteria, Comput.Methods Appl. Mech. Engrg. Vol 192, pp. 3887–3908, 2003.
12.    Alshoaibi M. Abdulnaser, Finite Element Procedures for the Numerical Simulation of Fatigue Crack Propagation under Mixed Mode Loading. Structural Engineering and Mechanics, vol. 35, No.3, pp.283-299, 2010.
13.    Zienkiewicz, O.C., Taylor, R.L. and Zhu, J.Z. 2005. The finite element method: its basis and fundamentals. 6th Edition, Elsevier Butterworth-Heinemann.
14.    Alshoaibi M. Abdulnaser, A. K. Ariffin and M.N. Almaghribi, 2009. Development of efficient Finite Element software of Crack Propagation simulation Using  Adaptive Mesh Strategy. American Journal of Applied Science. Vol. 6, No. 4, pp: 661-666.
15.    Erdogan F and Sih GC, On The Crack Extension In Plates Under Plane Loading And Transverse Shear, J Basic  Eng. Vol. 85, pp. 519–27, 1963.
16.    Bouchard, P.O,  Bay, F, Chastel, Y and Tovena I. Crack Propagation Modeling Using an Advanced Remeshing Technique, Computer Methods Applied Mechanics Engineering, pp. 723-742, 2000.

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15.

Authors:

Mohammed Jassim Mohammed Jassim

Paper Title:

Shape-Based Retrieval of Arbitrarily Shaped Video Objects

Abstract:      The increasing availability of object-based video content requires new technologies for automatically extracting and matching of the low level features of arbitrarily shaped video. In this paper, methods for shape retrieval of arbitrarily shaped video objects are proposed. Along with still shape features the shape deformations that may occur in an object’s life span is also taken. In this paper a novel method is used for measuring shape similarities between arbitrarily shaped video objects by comparing the low-level still shape features of the representative frames of the video objects.

Keywords: object-based, video, extracting, shape similarities, video objects.

References:
1.          H.T.Nguyen, M. Worring, and A.Dev, “Detection of moving objects in video using a robust motion similarity measure,” IEEE Trans. Image Process., vol. 9, no.1, pp. 88–101,Jan. 2000.
2.          MPEG-4 Video Group,” Coding of Audio-Visual Objects: Video,” SO/IEC 14 496-2, 2000.3.          S. F. Chang, W. Chen, H. J. Meng, H. Sundaram, and D. Zhong, “A Fully automated content-based video search engine supporting spatiotemporalqueries,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp.602–615, Sep. 1998.
4.          Y. Deng and B. S.Manjunath, “NeTra-V: Toward an object-based video representation,” IEEE Trans. Circuits Syst. Video Technol., vol. 8, pp.616–627, Sep. 1998.
5.          B. Erol and F.Kossentini, “Automatic key video object plane selection using the shape information in the MPEG-4 compressed domain,” IEEE Trans. Multimedia, vol. 2, no. 2, pp. 129–138, Jun. 2000

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16.

Authors:

Chiranjit Dutta, Ranjeet Singh

Paper Title:

Automatic Face Detection Using RGB Color Model for Authentication

Abstract:       The paper entitled Automatic face detection using RGB color model is the application developed to recognise the face and use it as a biometric security in the email protection. The face is captured through the webcam and stored in the database with the email id, password and then if we  try to login then it again ask for face input and if the face matches then we can login  . The face that we capture during login is matched with the image present in the database by matching the R,G,B component . This paper provides more secure and robust email security using biometric as a password. Biometric security comes with its own unique set of challenges. While face recognition have been used as a biometric security, which make the email more secure; its accuracy is still a problem. As it provide only 80 to 90 % accuracy. Therefore, we must find a way to make it more accurate and secure.This application may also be used to prevent various crimes going on in the country. Even it can be used in capturing the images of suspected person and matching it with the databases of criminals to catch them.Further, the algorithm can detect both dark skin-tone and bright skin-tone using YUC Color Space model

Keywords:  Euclidean space, face Detection, Principal Component Analysi

References:
1.          EE368 Digital Image Processing Project - Automatic Face Detection Using Color Based Segmentation , Michael Padilla and Zihong Fan , Department of Electrical Engineering Stanford University
2.          M. H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting faces in images: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34–58, 2002.
3.          J. Brand and J. S. Mason, “A comparative assessment of three approaches to pixel-level human skin-detection,” in Proceedings of IEEE International Conference on Pattern Recognition, 2000, vol. 1, pp. 1056–1059 vol.1.
4.          M. M. Aznaveh, H. Mirzaei, E. Roshan, and M. Saraee, “A new and improved skin detection method using RGB vector space,” in Proceedings of IEEE International Multi-Conference on Systems, Signals and Devices, July 2008, pp.1–5.
5.          M. J. Jones and J. M. Rehg, “Statistical color models with application to skin detection,” International Journal of Computer Vision, vol. 46, no. 1, pp. 81–96, 2002.6.          J. Y. Lee and S. I. Yoo, “An elliptical boundary model for skin color detection,” in Proceedings of the International Conference on Imaging Science, Systems, and Technology, 2002
7.          R. Kjeldsen and J. Kender, “Finding skin in color images,” Automatic Face and Gesture Recognition, IEEE International Conference on, vol. 0, pp. 312, 1996.
8.          B. Jedynak, H. Zheng, M. Daoudi, and D. Barret, “Maximum entropy models for skin detection,” in Proceedings of Indian Conference on Computer Vision, Graphics
and Image Processing, 2002, pp. 276–281.
9.          Albiol, L. Torres, and E. J. Delp, “Optimum color spaces for skin detection,” in Proceedings of International Conference on Image Processing, 2001.
10.       R. Gonzalez and R. Woods, Digital Image Processing - Second Edition
11.       R. Gonzalez and R. Woods, Digital Image Processing using MATLAB

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