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Volume-4 Issue-4: Published on September 05, 2014
17
Volume-4 Issue-4: Published on September 05, 2014

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S. No

Volume-4 Issue-4, September 2014, ISSN: 2231-2307 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Hassan Amerehie, Rouhollah Dianat, Farshid Keynia

Paper Title:

A New Method to Improve the Difference of Gaussian Feature Detector A New Method to Improve the Difference of Gaussian Feature Detector

Abstract:    One of the basic requirements in images representation was the feature extraction and its proper description and has many applications in the image processing and the machine vision. Many of the local feature descriptors of image use the difference of Gaussian feature detector. This detector is too much invariant against the scale changes. In this paper, a procedure is presented to select a proper threshold for the standard deviation in Gaussian filter to improve the performance of difference of Gaussian detector. In this paper's method, based on the properties of co-occurrence matrixes, the spatial dependences between available points in the image are divided into three general classes: sharp points, middle points and unsharp points, and then, on the basis of this division, the appropriate position is determined for stopping the development of standard deviation in Gaussian filter in some way that it is prevented to destroy the sharp points in the image and also to select the noise points as the key points of image.   

Keywords:
Difference of Gaussian (DOG), Feature Detector, Interest Point, Key Point


References:

1.             T. Tuytelaars, K. Mikolajczyk, “Local Invariant Feature Detectors: A Survey”, Computer Graphics and Vision, Vol.3, No.3, 2007, pp.177-280.
2.             K. Mikolajczyk, C. Schmid, “A Performance Evaluation of Local Descriptors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 2005, pp.1615–1630.

3.             D. G. Lowe, “Distinctive Image Features From Scale-Invariant keypoints”, International Journal of Computer Vision (IJCV), vol. 60, no.2, 2004, pp.91-110.

4.             D. G. Lowe, “Object Recognition from  local Scale-Invariant Features”, in Proceedings of the International Conference on Computer Vision(ICCV),vol.2, 1999, pp. 1150-1157.

5.             S. Wei1, L. Na, S. Lijuan, S. Shulin, L. Xiangpeng , “Two Improved Methods of SIFT Algorithm Combined with Harris”, 24th Chinese Control and Decision Conference (CCDC), 2012 .

6.             C. Schmid, R. Mohr, and C. Bauckhage, “Evaluation of interest point detectors,”International Journal of Computer Vision, vol. 37, no.2, 2000, pp.151–172.

7.             T. Lindeberg, “Scale-Space Theory in Computer Vision”. Kluwer Academic Publishers, 1994.

8.             P. Witkin, “Scale-space filtering,” in Proceedings of the International Joint Conference on Artificial Intelligence, 1983, pp.1019–1023.

9.             J. Sporring, M. Nielsen, L. Florack, P. Johansen, “Gaussian Scale-Space Theory”, Springer-Verlag,1997.

10.          C. Schmid, R. Mohr, and C. Bauckhage, “Comparing and evaluating interest points,” in Proceedings of the International Conference on computer Vision, 1998, pp. 230–235.

11.          R. M. Haralick, K. Shanmugam, I. Dinstein, “Textural Features for Image Classification”, IEEE Trans.Syst.Man.Cybern, vol. SMC-3, issue.6, 1973, pp. 610-621.

12.          J. L. Crowley, A. C. Parker, “A representation for shape based on peaks and ridges in the difference of low pass transform,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no.2, 1984, pp. 156–170.

13.          Tony Lindeberg, “Feature Detection with Automatic Scale Selection”, Int. J. of Computer Vision, vol.30, no.2, 1998.

14.          P. Gaussier, J. P. Cocquerez, “Neural networks for complex scene recognition: Simulation of a visual system with several cortical areas”, in Proceedings of the International Joint Conference on Neural Networks, vol.3, 1992, pp. 233–259.

15.          S. Grossberg, E. Mingolla, D. Todorovic, “A neural network architecture for preattentive vision”, IEEE Transactions on Biomedical Engineering, 1989, vol.36, pp. 65–84.


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2.

Authors:

Anil Kumar Yadav, Ajay Kumar Sachan

Paper Title:

DNN Tree Search for Bayesian Reinforcement Learning to Machine Intelligence

Abstract:     Bayesian model-based reinforcement learning can be formulated as a partially observable Markova decision process (POMDP) to provide a principled framework for optimally balancing exploitation and exploration. Then, a POMDP solver can be used to solve the problem. If the prior distribution over the environment’s dynamics is a product of dirichlet distributions, the POMDP’s optimal value function can be represented using a set of multivariate polynomials. Unfortunately, the size of the polynomials grows exponentially with the problem horizon [3]. During machine learning agent required lots of training inputs of execution cycle. Due to this situation look up table contain huge amount of data base. In this paper, we observe the use of dynamic neural network tree search (DNNTS) algorithm for large POMDPs, to solve the Bayesian reinforcement learning problem. The keen idea of DNN tree search is to train agent and act as a NN classifier to help agent for taking self decision without prior knowledge of the system during data learning .We will show that such an algorithm successfully searches for a near-optimal policy and achieve goal. Experiments show that the used DNN methods improve performance of Bayesian reinforcement learning in the context of training episodes, reward and discount rate.

Keywords:
 Bayesian reinforcement learning, machine learning, DNN tree search, POMDP


References:

1.              Alpayadin, Introduction to machine learning, MIT press, Cambridge (2005).
2.              S.N & S.N, principal of soft computing, , (2008).

3.              Ngo AnhVien , Dang, Monte-Carlo tree search for Bayesian reinforcement learning, Springer, New York,(2013) pp.345-353.

4.              Matthew E. Taylor, Transfer Learning for Reinforcement Learning Domains: A Survey,  Journal of Machine Learning Research, (2009),pp.1633-1685.

5.              Brafman RI, Tennenholtz, R-max—a general polynomial time algorithm for near-optimal reinforcement learning. J Mach Learn Res ,(2002),pp.213–231.

6.              ManjeevanSeera, CheePeng Lim, A hybrid intelligent system for medical data classification”, Elsevier, (2013), pp.2239-2249.

7.              Andrew James, Julian, Cartesian Genetic Programming encoded Artificial Neural Networks: A Comparison using Three Benchmarks,(2013), pp.1005-1012.

8.              Pankaj Deep,Inderveer ,Cloud based intelligent system for delivering health care as a service, (2013), pp.346-359.

9.              Ima, H., Karo, Y,Swarm Reinforcement Learning Algorithms Based on Sara Method, (2008),pp. 2045–2049.

10.           Karbasian, H., Maida, N,Improving Reinforcement Learning Using Temporal Deference Network EUROCON,( 2009), pp. 1716–1722.

11.           Quinn, L., Ming, C.Z., The Research on the Spider of the Domain-Specific Search Engines Based on the Reinforcement Learning,(2009),pp. 588–592.

12.           Trooper, J.W.C, Optimizing Time Warp Simulation with Reinforcement Learning Techniques, (2007),pp. 577–584.

13.           Dam Silva, R.R., Claudio, An Enhancement of Relational Reinforcement Learning, pp. 2055–2060. IEEE (2008).

14.           Halmahera, K., Tadahiro, Effective integration of imitation learning and reinforcement learning by generating internal reward,(2008) pp. 121–126.

15.           Fang, Z., Tan, Reinforcement Learning Based Dynamic Network Self-Optimization for Heterogeneous Networks, (2009) pp. 319–324.
16.           Anil kumarYadav,Evaluation of Reinforcement Learning Techniques, pp. 1–4. ACM (2010).
17.           Tsung-Hsien, Lee, C.-K., Desin of dynamic neural network to forecast short-term railway passenger demand. (2005), 1651–1666.

18.           Li, H., Kozma, R., A Dynamic neural network metheod for time series prediction using the KIII model,(2003), pp. 347–352.

19.           Kakade S, Kearns MJ, Exploration in metric state spaces. In: International conference on machine learning (ICML),(2003), pp 306–312.

20.           Kearns MJ, Singh SP ,Near-optimal reinforcement learning in polynomial time. Mach Learn 49(2–3):209–232.

21.           Kolter JZ, Ng AY (2009) Near-Bayesian exploration in polynomial time. In: International conference on machine learning (ICML).

22.           Strehl AL, Littman ML (2008) An analysis of model-based interval estimation for Markov decision processes. J ComputSystSci 74(8):1309–1331.

23.           Szita I, Szepesvári C (2010) Model-based reinforcement learning with nearly tight exploration complexity bounds. In: International conference on machine learning (ICML), pp 1031–1038.

24.           Asmuth J, Littman ML (2011) Learning is planning: near Bayesoptimal reinforcement learning via Monte-Carlo tree search. In: Proceedings of the twenty-seventh conference on uncertainty in artificial intelligence, pp19-26.

25.           Dearden R, Friedman N, Russell SJ (1998) Bayesian Q-learning. 1998, pp 761–768.

26.           Duff M (2002) ,Optimal learning: computational procedures for Bayes-adaptive Markov decision processes.

27.           Engel Y, Mannor S, Meir R (2003) Bayes meets bellman,the Gaussian process approach to temporal difference learning. (ICML), pp 154–161.

28.           Engel Y, Mannor S, Meir R (2005) Reinforcement learning with Gaussian processes, pp. 201–208.   

29.           Ghavamzadeh M, Engel Y (2006) Bayesian policy gradient algorithms, pp 457–464.

30.           Ghavamzadeh M, Engel Y (2007) Bayesian actor-critic algorithms,pp 297–304.

31.           Poupart P, Vlassis NA, Hoey J, Regan K (2006) An analytic solution to discrete Bayesian reinforcement learning, pp 697–704.
32.           Strens MJA (2000) A Bayesian framework for reinforcement learning. 2000, pp 943–950.
33.           Vien NA, Yu H, Chung T (2011) Hessian matrix distribution for Bayesian policy gradient reinforcement learning, pp.1671–1685

34.           Wang T, Lizotte DJ, Bowling MH, Schuurmans D (2005) Bayesian sparse sampling for on-line reward optimization.pp 956–963.

35.           Anil Kumar Yadav, Ajay Kumar Sachan, Research and Application of Dynamic Neural Network Based on Reinforcement Learning,springer, AISC ,vol.132, pp. 931–942. 2012.

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3.

Authors:

Jipsa Antony, Jyotirmoy Pathak

Paper Title:

Design of Baugh Wooley Multiplier using HPM Reduction Tree Technique

Abstract:      Baugh Wooley Multiplier is one of the different techniques for signed multiplication. It is not widely used. Here design and implementation of 8 bit Baugh Wooley multiplier using conventional method as well as using High Performance Multiplier Reduction tree (HPM) technique and the comparative analysis of both the design for power, delay and the area foot print has done using Cadence RTL complier 180nm process technology.

Keywords:
 Multiplier, Baugh Wooley, HPM, Cadence RTL


References:

1.              H. Eriksson, P. Larsson-Edefors, M. Sheeran, M. Själander, D. Johansson, and M. Schölin, “Multiplier reduction tree with logarithmic logic depth and regular connectivity,” in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), May 2006, pp. 4–8.
2.              Pramodini Mohanty., “An Efficient Baugh-WooleyArchitecture forBothSigned & Unsigned Multiplication” International Journal of Computer Science & Engineering Technology (IJCSET) Vol. 3 No. 4 April 2012.

3.              Steve Hung-Lung Tu., Chih-Hung Yen., “A High-Speed Baugh-Wooley Multiplier Design Using Skew-Tolerant Domino Techniques” IEEE 2006.

4.              M. Hatamian, “A 70-MHz 8-bit x 8-bit Parallel Pipelined Multiplier in 2.5-μm CMOS,” IEEE Journal on Solid-State Circuits, vol. 21, no. 4, pp. 505–513, August 1986.

5.              M. Själander, H. Eriksson, and P. Larsson-Edefors, “An efficient twin-precision multiplier,” in Proc. 22nd IEEE Int. Conf. Comput. Des., Oct.2004, pp. 30–33.

6.              M. Själander and P. Larsson-Edefors, “The Case for HPM-Based Baugh-Wooley Multipliers,” Department of Computer Science and Engineering, Chalmers University of Technology, Tech. Rep. 08-8, March 2008.

7.              http://www.sjalander.com/research/multiplier

8.              Magnus Själander., Per Larsson-Edefors., “High-Speed and Low-Power Multipliers Using the Baugh-Wooley Algorithm and HPM Reduction Tree.” February 2008.

9.              Joshin Mathews Joseph., V.Sarada., “Reconfigurable High Performance Baugh-Wooley  Multiplier for DSP Applications” ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE), ISSN (PRINT)  : 2320 – 8945, Volume -1, Issue -4, 2013

10.           Jin-Hao Tu., Lan-Da Van., “Power-Efficient Pipelined Reconfigurable Fixed-Width Baugh-Wooley Multipliers” IEEE Transactions On Computers, Vol. 58, No. 10, October 2009.

11.           M. Sjalander and P. Larsson-Edefors, "High-speed and low-power multipliers using the Baugh-Wooley algorithm and HPM reduction tree," 15th IEEE International Conference on Electronics, Circuits and Systems, 2008.

12.           M.V.P. Kumar, S. Sivanantham, S. Balamurugan, and P.S. Mallick, "Low power reconfigurable multiplier with reordering of partial products," International Conference on Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), 2011.

13.           V.B. Dandu, B. Ramkumar, and H.M. Kittur, "Optimization of hybrid final adder for the high performance multiplier," Third International Conference on Computing Communication & Networking Technologies (ICCCNT), 2012. 


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4.

Authors:

Talaat S. EL-Danaf, K. R. Raslan, Khalid K. Ali

Paper Title:

New Numerical Treatment for the Generalized Regularized Long Wave Equation Based on Finite Difference Scheme

Abstract:       In this paper, the generalized regularized long wave (GRLW) equation is solved numerically using the finite difference method. Fourier stability analysis of the linearized scheme shows that it is unconditionally stable. Also, the local truncation error of the method is investigated. Three invariants of motion are evaluated to determine the conservation properties of the problem, and the numerical scheme leads to accurate and efficient results. Moreover, interaction of two and three solitary waves is shown. The development of the Maxwellian initial condition into solitary waves is also shown and we show that the number of solitons which are generated from the Maxwellian initial condition can be determined. Numerical results show also that a tail of small amplitude appears after the interactions.

Keywords:
      Finite difference; generalized Regularized long wave equation; Solitary waves; Solitons.


References:

1.              D. H. Peregrine (1966), Calculations of the development of an undular bore, J. Fluid Mech. vol. 25 (2) , pp. 321–330.
2.              J. C. Eilbeck, G.R. McGuire (1975), Numerical study of regularized long wave equation, I: numerical methods, J. Comput. Phys. vol. 19, pp. 43–57.

3.              J. L. Bona, P.J. Pyrant (1973), A mathematical model for long wave generated by wave makers in nonlinear dispersive systems, Proc. Comp. Philos. Soc. vol. 37 , pp. 391.

4.              J. L. Bona, W.G. Pritchard, L.R. Scott (1985), Numerical scheme for a model of nonlinear dispersive waves, J. Comput. Phys. vol. 60, pp. 167– 176.

5.              M.E. Alexander, J.H. Morris (1979), Galerkin method for some model equation for nonlinear dispersive waves, J. Comput. Phys. vol.30, pp. 428–451.

6.              D. J. Evans, K.R. Raslan (2005), The Tanh function method for solving some important nonlinear partial differential equations, Int. J. comput. Math. vol. 82 (7), pp. 897–905

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8.              D. Bhardwaj, R. Shankar (2000), A computational method for regularized long wave equation, Comput. Math. Appl. vol. 40, pp. 1397–1404.

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10.           L. R. T. Gardner, G.A. Gardner, A. Dogan (1996), A least squares finite element scheme for the RLW equation, Commun. Numer. Meth. Eng. vol. 12, pp. 795–804.

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13.           Dag, B. Saka, D. Irk (2004), Application of cubic B-splins for numerical solution of the RLW equation, Appl. Math Comput. vol. 195, pp. 373–389.

14.           K. R. Raslan (2005), A computational method for the regularized long wave (RLW) equation, Appl. Math. Comput. vol. 176, pp. 1101–1118.

15.           A.A. Soliman, M.H. Hussien (2005), Collocation solution for RLW equation with septic splines, Appl. Math. Comput. vol. 161, pp. 623–636.

16.           L. R .T. Gardner, G.A. Gardner, F.A. Ayoub, N.K. Amein (1997), Approximations of solitary waves of the MRLW equation by B-spline finite element, Arab. J. Sci. Eng. Vol.  22, pp. 183–193.

17.           K. Khalifa, K.R. Raslan, H.M. Alzubaidi, (2007), A finite difference scheme for the MRLW and solitary wave interactions Applied Mathematics and Computation, vol. 189, pp. 346–354.

18.           K. R. Raslan, S. M. Hassan (2009), Solitary waves for the MRLW equation, Applied Mathematics Letters, vol. 22 , pp. 984-989.

19.           K. Khalifa, K. R. Raslan, H. M. Alzubaidi(2007), A collocation method with cubic B-  splines for solving the MRLW equation, Comput. Appl. Math. vol. 212, pp. 406- 418.

20.           Saleh M .Hassan,D.G.Alamery (2009), B-splines Collocation Algorithms for Solving Numerically the MRLW Equation ,international Journal of Nonlinear Science. vol. 8(2), pp. 131-140.

21.           Mokhtari, R., Mohammadi, M. (2010), Numerical solution of GRLW equation using Sinc- collocation method. Comput. Phys. Commun. vol. 181, pp. 1266-1274.

22.           D. Kaya (2004), A numerical simulation of solitary wave solutions of the generalized regularized long wave equation, Appl. Math.Comput. vol. 149, pp. 833–841.

23.           Talaat S. El-Danaf, Mohamed A. Ramadan and Faysal E.I. Abd Alaal (2005), The use of adomian decomposition method for solving the regularized long-wave equation , Chaos, Solitons & Fractals. vol. 26(3), pp. 747-757.

24.           P.J. Olver (1979), Euler operators and conservation laws of the BBM equation, Math. Proc. Comb. Phil. Soc. vol. 85 , pp. 143-159.

25.           Thoudam Roshan, (2012), A petrov-Galerkin method for solving the generalized regularized long wave(GRLW) equation,Appl.Math.Comput.vol. 63, pp. 943-956.

26.           Mohammadi, M., Mokhtari, R. (2011), Solving the generalized regularized long wave equation on the basis of a reproducing kernel space. J. Comput. Appl. Math.vol.  235, pp. 4003–4014 .


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5.

Authors:

Maysam Behmanesh, Majid Mohammadi, Vahid Sattari Naeini

Paper Title:

Chaotic Time Series Prediction using Improved ANFIS with Imperialist Competitive Learning Algorithm

Abstract:   This paper presents an improved adaptive Neuro-fuzzy inference system (ANFIS) for predicting chaotic time series. The previous learning algorithms of ANFIS emphasized on gradient based methods or least squares (LS) based methods, but gradient computations are very computationally and difficult in each stage, also gradient based algorithms may be trapped into local optimum. This paper introduces a new hybrid learning algorithm based on imperialist competitive algorithm (ICA) for training the antecedent part and least square estimation (LSE) method for optimizing the conclusion part of ANFIS. This hybrid method is free of derivation and solves the trouble of falling in a local optimum in the gradient based algorithm for training the antecedent part. The proposed approach is used in order to modeling and prediction of three benchmark chaotic time series. Analysis of the prediction results and comparisons with recent and old studies demonstrates the promising performance of the proposed approach for modeling and prediction of nonlinear and chaotic time series.

Keywords:
    chaotic time series, Gradient based, imperialist competitive algorithm, Fuzzy systems, ANFIS, least square estimation.


References:

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2.             Kantz, Holger, and Thomas Schreiber. Nonlinear Time Series Analysis. Cambridge: Cambridge University Press, 1997. Print.

3.             Box, George E. P., Gwilym M. Jenkins, and Gregory C. Reinsel. Time Series Analysis: Forecasting and Control. Englewood Cliffs, N.J.: Prentice Hall, 1994.

4.             De Gooijer, J.G., and R.J. Hyndman. 2006. "25 Years of Time Series Forecasting". International Journal of Forecasting. 22, no. 3: 443-473.

5.             Bodyanskiy, Y., and O. Vynokurova. 2013. "Hybrid Adaptive Wavelet-Neuro-Fuzzy System for Chaotic Time Series Identification". Information Sciences. 220:
170-179.

6.             Hsu, C.F. 2011. "Adaptive Fuzzy Wavelet Neural Controller Design for Chaos Synchronization". Expert Systems With Applications. 38, no. 8: 10475-10483.

7.             Henry Leung, H.C., H.C. Titus Lo, and H.C. Sichun Wang. 2001. "Prediction of noisy chaotic time series using an optimal radial basis function neural network". IEEE Transactions on Neural Networks. 12, no. 5: 1163-1172.

8.             Han, M., J. Xi, S. Xu, and F.-L. Yin. 2004. "Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network". IEEE Transactions on Signal Processing. 52, no. 12: 3409-3416.

9.             Lee, C-H L, A Liu, and W-S Chen. 2006. "Data Mining - Pattern Discovery of Fuzzy Time Series for Financial Prediction". IEEE Transactions on Knowledge and Data Engineering. 18, no. 5: 613.

10.          Gu, H., and H. Wang. 2007. "Fuzzy Prediction of Chaotic Time Series Based on Singular Value Decomposition". Applied Mathematics and Computation. 185, no. 2: 1171-1185.

11.          Ang, K.K., and C. Quek. 2006. "Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach". IEEE Transactions on Neural Networks. 17, no. 5: 1301-1315.

12.          Zaheeruddin and Garima. 2006. "A Neuro-Fuzzy Approach for Prediction of Human Work Efficiency in Noisy Environment". Applied Soft Computing. 6, no. 3: 283-294.

13.          Aznarte M., J.L., J.M. Benitez Sanchez, D.N. Lugilde, C. de Linares Fernandez, C.D. de la Guardia, and F.A. Sanchez. 2007. "Forecasting Airborne Pollen Concentration Time Series with Neural and Neuro-Fuzzy Models". Expert Systems With Applications. 32, no. 4: 1218-1225.

14.          Zhang J., Chung H.S.-H., and Lo W.-L. 2008. "Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates". IEEE Transactions on Knowledge and Data Engineering. 20, no. 7: 956-964.

15.          Samanta, B. 2011. "Prediction of Chaotic Time Series Using Computational Intelligence". Expert Systems With Applications. 38, no. 9: 11406-11411.

16.          Gromov, Vasilii A., and Artem N. Shulga. 2012. "Chaotic Time Series Prediction with Employment of Ant Colony Optimization". Expert Systems With Applications. 39, no. 9: 8474-8478.

17.          Niu, D., Y. Wang, and D.D. Wu. 2010. "Power Load Forecasting Using Support Vector Machine and Ant Colony Optimization". Expert Systems With Applications. 37, no. 3: 2531-2539.

18.          Wang, Jianzhou, Dezhong Chi, Jie Wu, and Hai-yan Lu. 2011. "Chaotic Time Series Method Combined with Particle Swarm Optimization and Trend Adjustment for Electricity Demand Forecasting". Expert Systems with Applications. 38, no. 7: 8419-8429.

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32.          Aliyari Shoorehdeli, Mahdi, Mohammad Teshnehlab, and Ali Sedigh. 2009. "Identification Using ANFIS with Intelligent Hybrid Stable Learning Algorithm Approaches". Neural Computing and Applications. 18, no. 2: 157-174.

33.          Aliyari. Sh, M. Teshnehlab, A. K. Sedigh. 2007. "A Novel Hybrid Learning Algorithm for Tuning ANFIS Parameters using Adaptive Weighted PSO". IEEE Int Fuzzy Sys Conf.

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6.

Authors:

V. Rahmati, M. Husainy Yar, J. Khalilpour, A. R. Malekijavan

Paper Title:

Back Propagation Artificial Neural Network Structure Error Reduction by Defined Factor of Capacity and Algorithm Reinforcement Method

Abstract:    This paper investigates how to reduce error and increase speed of Back propagation ANN by certain defined Capacity factor. For the years from 1965 to 1980 the use of a variety of ANNs for problem solving was relented significantly because of limitations in one layer networks that weren’t good enough for enhancements of a specific issue, although there were low expectancies for even simple tasks and mathematical operations. Multi-layer networks have a serious covenant to improve this privation by more effective error reduction for example by least squares error method and a better learning factor like the one that is considered in MLP which is modified, enhanced version of Perception network that has provided a better chance of using these networks for intelligent signal processing. But the purpose of this paper is not showing capabilities of these networks alone but to consider error reduction while the weighting equations both satisfy ordinary task of algorithm and at the same time reduces presumptions of errors by a predetermined Capacitance factor that is not very anomalous to other bunch of clustering pedagogy styles anent the other types of ANNs. Unlike a single layer network with many limitations in learning, approximating and estimating a mapping function, multi-layer networks are well prepared for estimation of any uniformly continues subordination with tunable accuracy. Hidden layer in many applications does the job of enhancement, but sometimes poly-layer methods are used for this error reduction separately by some factor definitions (and new hidden parts that paper adds to gets error reduced) that paper tries to measure for exact improvements which were envisaged in design process. And as a result understand how to use Capacity factor for BPANN algorithm, and error reduction in general that holds convergence, speed improvement and error smoothing at the same time.

Keywords:
     BPANN enhancement, Error smoothing, MLP, Intelligent signal processing.


References:

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7.

Authors:

K. B. Vaishnavee, K. Amshakala

Paper Title:

Study of Techniques used for Medical Image Segmentation Based on SOM

Abstract:     In image processing, segmentation is an important technique which is based on the homogeneous features utilized to partition the image into various regions. In Medical field MR images are widely used, but due to its noise, intensity in homogeneity, Partial Volume Effect (PVE) through voluntary and involuntary movement of the patients and equipments the segmentation process is highly complex. White Matter (WM), Grey matter (GM) and Cerebrospinal Fluid (CSF) are the three main tissue segmentation of MR brain image segmentation. The accurate segmentation of brain tissues facilitates the estimation of tissue volume, tumor detection and estimation of volumes of tumor, which is done by making the image smoother and thus easier to measure. In addition this technique facilitates to estimate the Region of Interest (ROI) in an image. Segmentation is mainly classified as supervised and unsupervised and based on these two there have been various techniques developed for the image segmentation. In medical field, the supervised has less demand as it requires prior knowledge from the external entity. On the other hand, unsupervised segmentation provides more accurate result where it does not need any prior knowledge at any time. The well known Self Organizing Map (SOM) segmentation technique is a type of unsupervised clustering technique utilized to make image quite simple and yields significant accurate segmentation results for the MRI images. This survey paper addresses the various existing methodologies for segmentation of MRI images and presents the issues and advantages related to those approaches.

Keywords:
      MRI Brain image, Segmentation, SOM - Self-organizing maps, Image Segmentation, unsupervised segmentation.


References:

1.             Chang PL, Teng WG (2007) Exploiting the self-organizing map for medical image segmentation. In: Twentieth IEEE international symposium on computer-based medical systems, pp 281–288
2.             Zhang Y et al (2007) A novel medical image segmentation method using dynamic programming. In: International conference on medical information visualisation-bioMedical visualisation, pp 69–74

3.             Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek J (1992) A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 3:672–682

4.             Tian D, Fan L (2007) A brain MR images segmentation method based on SOM neural network. In: The 1st international conference on bioinformatics and biomedical engineering, pp 686–689

5.             T. Kohonen, Self-organizing maps, Springer, 2001

6.             Guler I., Demirhan A. and Karakis R., Interpretation of MR Images using Self Organizing Maps and Knowledge based Expert systems, Digital Signal Processing 19  66866, 2009.

7.             Ortiz, J. M. Górriz, J. Ramírezb, J. M. Llamas-Elvira, D. Salas González, Two fully-unsupervised methods  for  MR brain image segmentation using SOM-based strategies, Applied Soft Computing, 2668–2682. 2013a

8.             Ortiz , Gorriz J. M, and J. Ramorez, Segmentation of Brain MRI Using SOM-FCM based method and 3D statistical Descriptors Computational and Mathematical Methods in Medicine, 2013b.

9.             Demirhan A., and Guler I., Combining stationary wavelet transform and self organizing map for brain MR image segmentation, Engineering applications of Artificial Intelligence24 58-367, 2011.

10.          The Internet Brain Database Repository (IBSR), Massachusetts General Hospital, Center for Morphometric Analysis,http://www.cma.mgh.Harvard.edu/ibsr/data.html

11.          H. U Bauer, K. Pawelzik, Quantifying the neighborhood preservation of self-organizing feature map, IEEE Transaction, Neural Network3(4)(1992) 570-579.

12.          J.C. Rajapakse, J.N. Giedd, J.L. Rapoport, Statistical approach to segmentation of single-channel cerebral MR images, IEEE Transactions on Medical Imaging 16 (2) (1997) 176–186

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14.          Han X, Fischl B (2007) Atlas renormalization for improved brain MR image segmentation across scanner platforms. IEEE Trans Med Imaging 26(4):479–486

15.          Rickard, H. E.; Tourassi, G. D. &  Elmaghraby, A.S. (2004). Breast segmentation in screening mammograms using multiscale analysis and self-organizing maps,
Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA.

16.          Yuan Jiang and Zhi-Hua Zhou, “SOM Ensemble-Based Image Segmentation”, National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China

17.          Saeid Pashazadeh ,  Masume Kheyri, “Color Image Segmentation Using Self Organizing Map Artificial Neural Network,” International Journal of Computer & Information Technologies (IJOCIT), ISSN = 2345-3877, November, 2013

18.          Sourav Paul  1 , Mousumi Gupta, “Image Segmentation By Self Organizing Map With Mahalanobis Distance,” International Journal of Emerging Technology and Advanced Engineering, (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 2, February 2013)   

19.          Heng-Da Cheng, Manasi Datar, Wen Ju, “Natural Scene Segmentation Based on Information Fusion and Homogeneity Property”, Computer Science Department, Utah State University, Logan, UT 84322-4205

20.          Jesna M , Kumudha Raimond, “MR Brain Image Segmentation Based on Principle Component Analysis and Self-Organizing Map”, International Journal For Research In Applied Science And Engineering Technology (IJRASET) Vol. 2 Issue III, March 2014, ISSN: 2321-9653

21.          Y. Li and Z. chi, (2005), “MR Brain Image Segmentation Based on Self-Organizing Map Network”, International Journal of Information Technology Vol.11, No.8.

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23.          Andrés Ortiz, Javier Ramírez, Antonio A. Palacio, Juan M. Górriz, and Diego Salas-González, “Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors”, Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2013, Article ID 638563, 12 pages

24.          M. Arfan Jaffar, Muhammad Ishtiaq, Ayyaz Hussain and Anwar M. Mirza, “Wavelet-Based Color Image Segmentation using Self-Organizing Map Neural Network”, 2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011)

25.          M.Kanimozhi  , CH.Hima Bindu, Soodabeh Safa and Behrouz Bokharaeian, “New methods in Brain MR Segmentation with Fuzzy EM algorithm”, Brain MR Image Segmentation Using Self Organizing Map”, International Journal of Advanced Research in Computer and Communication Engineering  Vol. 2, Issue 10, October 2013

26.          N.C. Yeo, K.H. Lee, Y.V. Venkatesh, S.H. Ong, “Colour image segmentation using the self-organizing map and adaptive resonance theory”, www.elsevier.com/locate/imavis, Image and Vision Computing 23 (2005) 1060–1079

27.          Dr.Samir Kumar Bandhyopadhyay*, Tuhin Utsab Paul,“Segmentation of Brain MRI Image – A Review”, International Journal of Advanced Research in  Computer Science and Software Engineering, Volume 2, Issue 3, March 2012 , ISSN: 2277 128X 
  

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8.

Authors:

Devikarani Patil, Varalakshmi B.D

Paper Title:

Hand Gesture Recognition for MP3 Player using Image Processing Technique and PIC16F8779

Abstract:      The scope of the project is to control MP3 player using gesture. Here, gesture image is taken from Web camera and image will be processed in remote interface using MATLAB controller. But, the challenging problem is that capturing the image from external device does not depend on unique only and Identification of the exact action from an unclear image is not an easy task. Hence, capturing action from the images is always puzzling task of separating different sources of images when its different or noisy. Finally, the images are forwarded to MATLAB to compare the images with our knowledge database via three dimension (x, y, and z) readings of a particular object. So if we move any object in any direction then the corresponding values are noted by the accelerometer. Most of the music players are controlled through the remote controls which contain buttons. But through embedding the PIC16F8779 controller, we can make music player be controlled by gesture performance in the air. The application of this three axis controller together with suitable interfacing with the PIC16F8779 micro controller and the music player  development through coding in software platform such as MPLab IDE which could  recognize the terminal input instructions and perform functions like play, stop, play back  and play forward of music player controlled by  gesture. We need to move the accelerometer in a particular set of directions then it will recognize one of the directions like REWIND, FORWARD, PLAY and STOP and operate the songs present in the list of music system. Additionally, Karhunen-Loeve (K-L) Transform is used to capture the image without any noise and accurate in result and Canny Edge Detection for image segmentation and edge detection using Principal component analysis (PCA) which add more value in expected result.

Keywords:
   Hand Gesture Recognition, Karhunen-Loeve (K-L) Transform, Skin Filtering, Canny Edge Detection, Image Segmentation, Human Computer Interaction, matching algorithm; PIC16F8779


References:

1.             T. Kapuscinski and M. Wysocki, “Hand Gesture Recognition for Man-Machine interaction”, Second Workshop on Robot Motion and Control, October 18-20, 2001, pp. 91-96.
2.             C. Yu, X. Wang, H. Huang, J. Shen and K. Wu, “Vision-Based Hand Gesture Recognition Using Combinational Features”, IEEE Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010, pp. 543-546.

3.             P.S. Rajam and G. Balakrishnan, “Real Time Indian Sign Language Recognition System to aid Deaf-dumb People”, IEEE, 2011, pp. 737- 742.

4.             Malima, E. Ozgur and M. Cetin, “A Fast Algorithm for Vision- Based Hand Gesture Recognition for Robot Control”, IEEE, 2006.

5.             Manigandan M and I.M Jackin, “Wireless Vision based Mobile Robot control using Hand Gesture Recognition through Perceptual Color Space”, IEEE International Conference on Advances in Computer Engineering, 2010, pp. 95-99.

6.             D.Y. Huang, W.C. Hu and S.H. Chang, “Vision-based Hand Gesture Recognition Using PCA+Gabor Filters and SVM”, IEEE Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, pp. 1-4.

7.             E. Koh, J. Won and C. Bae, “On-premise Skin Color Modeing Method for Vision-based Hand Tracking”, The 13th IEEE International Symposium on Consumer Electronics (ISCE), 2009, pp. 908-909.

8.             J.L. Raheja, K. Das and A. Chaudhury, “An Efficient Real Time Method of Fingertip Detection”, International Conference on Trends in Industrial Measurements and automation (TIMA), 2011, pp. 447-450.

9.             J. Rekha, J. Bhattacharya and S. Majumder, “Shape, Texture and Local Movement Hand Gesture Features for Indian Sign Language Recognition”, IEEE, 2011, pp. 30-35.

10.          R. Gopalan and B. Dariush, “Towards a Vision Based Hand Gesture Interface for Robotic Grasping”, The IEEE/RSJ International Conference on Intelligent Robots and Systems, October 11-15, 2009, St. Louis, USA, pp. 1452-1459.

11.          Y. Fang, K. Wang, J. Cheng and H. Lu, “A Real-Time Hand Gesture Recognition Method”, IEEE ICME, 2007, pp. 995-998.

12.          I.-K. Park, l -H. Kim, and K.-S. Hong, "An Implementation of an FPGA-Based Embedded Gesture Recognizer Using a Data Glove,"  Conference on Ubiquitous Information Management and Communication Proceedings of the 2nd International conference on Ubiquitous information  management and communication 2008, Suwon, Korea,January31 – February  01, 2008, pp.496-500

13.          Jiayang Liu, Zhen Wang, and Lin Zhong, Jehan ickramasuriya  and Venu Vasudevan “Accelerometer-based Personalized Gesture”

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9.

Authors:

Faris E. Mohammed, Eman M. ALdaidamony, A. M. Raid

Paper Title:

Multi Model Biometric Identification System: Finger Vein and Iris

Abstract:       Personal identification process is a very important process that resides a large portion of daily usages. Identification process is applicable in work place, private zones, banks …etc. Human is a rich subject having many features that can be used for identification purpose such as finger vein, iris, face …etc. In this paper, a personal identification system with multi model architecture have been proposed. The proposed system fuse personal finger vein and iris which utilizes a vein feature matcher for finger vein and Hamming Distance Matcher for iris with matching score level to provide higher accuracy of 92.4%, with FAR and FRR of 0% and 7.5%, respectively.  It has been more secure than a framework used a single identification of personal feature.

General Terms:
Multi-model System, Biometric, finger vein, Iris, Identification, Recognition.

Keywords: Biometric Computing, finger vein Recognition, IRIS Recognition, Minutiae Extraction, FAR, and FRR.


References:

1.              N. Ratha, S. Chen, and A. K. Jain, "Adaptive flow orientation-based feature extraction in fingerprint images," Pattern Recognition, vol. 28, pp. 1657-1672, 1995
2.              RAVI. J, K. B. RAJA, VENUGOPAL. K. R,” FINGERPRINT RECOGNITION USING MINUTIA SCORE MATCHING”, Ravi.J. et al /International Journal of Engineering Science and Technology Vol.1(2), 2009, 35-42.

3.              Online url: http://www.griaulebiometrics.com/en-us/book/understanding-biometrics/types/strengthen, Last visit: on Feb 2014.

4.              Sudha Gupta, A.Prof,LMIETE, LMISTE, “Iris Recognition System using Biometric Template Matching Technology”, International Journal of Computer Applications, Volume: 1, NO: 2, pp:1-4, 2010.

5.              MohamadAbdolahi, Majid Mohamadi, Mehdi Jafari, “Multimodal Biometric system Fusion Using Fingerprint and Iris with Fuzzy Logic”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013

6.              M. A. Medina-Pérez, M. García-Borroto, A. E. Gutierrez-Rodríguez, and L. Altamirano-Robles, “Improving Fingerprint Verification Using Minutiae Triplets,” Sensors, vol. 12, pp. 3418–3437,  2012.

7.              Ghazvini, M.; Sufikarimi, H.; Mohammadi, K. Fingerprint Matching Using Genetic Algorithm andTriangle Descriptors. In Proceedings of the 19th Iranian Conference on Electrical Engineering,Tehran, Iran, 17–19 May 2011; pp. 1–6.

8.              Hoyle, K. Minutiae Triplet-Based Features with Extended Ridge Information for Determining Sufficiency in Fingerprints. Master Thesis, Virginia Polytechnic Institute and State University, Burruss Hall Blacksburg, VA, USA, 2011.

9.              Prabhakar, S.; Ivanisov, A.; Jain, A.K. Biometric recognition: Sensor characteristics and image quality. IEEE Instrum. Meas. Mag. 2011, 14, 10–16.

10.           Jain, A.K.; Feng, J. Latent fingerprint matching. IEEE Trans. Pattern. Anal. Mach. Intell. 2011, 33, 88–100.

11.           HimanshiBudhiraja, HimaniTomar and HarshiGoel“FUSION OF IRIS AND FINGERPRINT BIOMETRIC FOR RECOGNITION”, International Journal of advances in computing & communications, vol 1, 2013

12.           Naoto Miura, Akio Nagasaka, Takafumi Miyatake, “Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification”, Machine Vision and Applications, 2004, 15, 194-203.

13.           Hyeon Chang LEE1, Byung Jun KANG2, Eui Chul LEE3, Kang Ryoung PARK, “Finger vein recognition using weighted local binary pattern code based on a support vector machine”, Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 2010, 11, 514-524.

14.           NagaoM(1983) Methods of image pattern recognition. Corona, San Antonio, TX .

15.           Iris database, online url: http://biometrics.idealtest.org/findTotalDbByMode.do?mode=Iris, Last visit: on August 11, 2014.

16.           Gaganpreet Kaur, Akshay Girdhar and Manvjeet Kaur, " Enhanced Iris Recognition System an IntegratedApproach to Person Identification", International Journal of Computer Applications, Vol.8, No.1, October 2010.

17.           JainAK, DuinRPW,MaoJ (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37 . 

18.           Ujwalla Gawande, Anushree Sapre, Apurva Jain, and Sanchita Bhriegu, " Fingerprint-Iris Fusion Based Multimodal Biometric System Using Single Hamming Distance Matcher", International Journal of Engineering Inventions, Vol. 2, Issue 4, PP:  54-61, February 2013.

19.           Zhang W, Wang Y (2002) Core-based structure matching algorithm of fingerprint verification. In: Proceedings of the IEEE international conference on pattern recognition, 1:70–74.

20.           Finger vein database, online url: http://mla.sdu.edu.cn/sdumla-hmt.html , Last visit: on August 11, 2014.


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10.

Authors:

E. Gomathi, K. Baskaran

Paper Title:

An Efficient Method for Face Recognition based on Fusion of Global and Local Feature Extraction

Abstract: Face recognition is a process of identifying people from their face images.  Face recognition technology has many applications such as ATM access, verification of credit card, video surveillance etc. In this paper, we propose a novel face recognition algorithm which exploits both local and global features for feature extraction.  Local features are extracted by Gabor wavelets and for global feature extraction, contourlet transform is applied. Then statistical parameters for local and global features are calculated and both the features are combined. Finally face recognition is performed using distance classifier. This proposed algorithm is implemented using MATLAB.  The experimental results on ORL face database demonstrate the efficiency of proposed method as 98.5% as against non-fusion face recognition schemes.

Keywords:
  Face recognition, contourlet transform, feature extraction, local features, global features.


References:

1.              M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no.1, pp. 71-86, 1991.
2.              P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigen faces vs. fisher faces: recognition using class specific linear projection,” IEEE Trans. on Pattern Analysis and Machine Intellignece, vol. 19, no. 7, pp. 711-720, 1997.

3.              W. Zhao, R. Chellappa, and A, Krishnaswamy, “Discriminant analysis of principal components for face recognition,” in Proc. of the 3rd IEEE International Conf. on Automatic Face and Gesture Recoginition, Nara, Japan, 1998, pp. 336-341.

4.              Martinez, A.M.& Kak, A.C. PCA versus LDA. In IEEE Transactions (PAMI),Vol.23, No.2, 228-233, 2001

5.              X.-Y. Jing and D. Zhang, “A face and palm print recognition approach based on discriminant DCT feature extraction,” IEEE Trans. on System, Man, and Cybernetics-Part B: Cybernetics, vol. 34, no. 6, pp. 2405-2415, 2004.

6.              H. Othman and T. Aboulnasr, “A separable low complexity 2D HMM with application to face recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no.10, pp. 1229-1238, 2003.

7.              M. J. Er, W. Chen, and S. Wu, “High speed face recognition based on discrete cosine transform and RBF neural network,” IEEE Trans. on Neural Networks, vol. 16, pp. 679-691, May 2005.

8.              M. Er, S. Wu, J. Lu, and L. H. Toh, “Face recognition with radial basis function (RBF) neural networks,” IEEE Trans. on Neural Networks, vol. 13, pp. 697-710, May 2002.

9.              K. Lee, Y. Chung, and H. Byun, “SVM based face verification with feature set of small size,” Electronic Letters, vol. 38, no.15, pp. 787-789, 2002.

10.           Li, S., Wang, Y., 2000. “ Multisensor image fusion using discrete multiWavelet Transform”. In: Proc. 3rd International Conference on Visual Computing, pp. 93–103.

11.           Do M.N., Vetterli M., “The Contourlet : An efficient directional multiresolution image representation”, IEEE Transactions on Image Processing 14(12), pp. 2091 –2106, 2005.

12.           Wei-wei Yu .,Xiao -long ,Teng., Chong-qing Liu ,“Face recognition fusing global and local features” ,J. Electron. Imaging. 15(1), 2006

13.           Aman R. Chadha, Pallavi P. Vaidya, M. Mani    Roja, “Face Recognition Using Discrete     Cosine Transform for Global and Local     Features”, Proceedings of the
2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering (IConRAEeCE) ,

14.           Zhao Z. and Hao X., “Contourlet based Manifold learning for face recognition,” International Conference on Uncertainty Reasoning and Knowledge Engineering, Indonesia, pp.196-199, 2012

15.           N.G. Chitaliya, A.I. Trivedi, “An Efficient Method for Face Feature Extraction and Recognition based on Contourlet Transforms and Principal Component Analysis” , Procedia Computer Science 2 (2010) 52–61

16.           Chunghoon K. and Oh J.Y., “ Combined subspace method using global and local features for face recognition,” Proceedings of IEEE International Joint Conference on  Neural Networks, Montreal, vol. 4, pp. 2030 - 2035, 2005.

17.           P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE  Trans. Commun., vol. COM-31, no. 4, pp. 532– 540, 1983

18.           Yu Su, Shiguang shan, Xilin Chen and Wen Gao. “Hierarchical Ensemble of Global and Local Classifiers for Face Recognition”, IEEE Trans. on image processing, vol.18, no.8, 2009.

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11.

Authors:

Ankita Nag, Vinay Kumar Jain

Paper Title:

The Hardware Implementation of Improved RSA Algorithm

Abstract:   RSA algorithm is an asymmetric key cryptography. It is a block cipher. RSA has stronger security than single key cryptography. RSA has a pair of key- a private key a and public key. Sender sends the message encrypting it with the public key of receiver. Receiver receives the message by decrypting it with its private key. RSA provides authentication and integrity. So it is used in SSL for key exchange. At present 512 bit is considered insecure after the implementation of General Field Sieve Number [1].So in reference paper the idea of bit stuffing is introduced. RSA is bit stuffed after encryption that means a random number is appended to the cipher text and sent. At receiver, stuffed bit that is that random number is removed and then the cipher text is decrypted. Bit stuffing is suggested as a logic or measure to be used instead of increasing the number of bits in RSA. Since larger bit numbers will require more time and effort for calculation, bit stuffing will save time and effort. In this paper, this idea is implemented in hardware. Same security as with larger bit number say 1024 can be get in almost same time with lesser bit numbers say 512 bits with lesser band width requirement. In this paper, improving the SSL using modified RSA algorithm, coding is done in MATLAB.

Keywords:
   RSA, Bit Stuffing, public key cryptography,  public key, private key, prime number


References:

1.                Yogesh Joshi, Debabrata Das, Subir Saha, International Institute of Information Technology Bangalore (IIIT-B), Electronics City, Bangalore, India. “Mitigating Man in the Middle Attack over Secure Sockets Layer, 2009
2.                What is SSL and how the SSL works http://docs.oracle.com/cd/E17904_01/core.1111/e10105/sslconfig.htm

3.                J. Kenneth, P. C. Van Orshot and S. A. Vanstone, Handbook of applied Cryptography, CRC press, 1977.

4.                IT security web site, The Secure Sockets Layer Protocol Enabling Secure Web Transactions, http://www.verisign.com/ssl/ssl-information-center/how-ssl-security-works/index.html

5.                RSA website, 5.1 Security on the Internet, http://www.emc.com/security/rsa-securid/rsaauthentication-manager.htm

6.                IT security web site, the risks of short RSA keys for secure communications using SSL, http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4259828&url=http%3A%2F%2Fieeexplore.ie ee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D4259828

7.                H. Otrok, Security testing and evaluation of Cryptographic Algorithms, M.S. Thesis, Lebanese American University, June 2003.

8.                Bit-Stuffing http://en.wikipedia.org/wiki/Bit_stuffing

9.                Cisco Systems, Introduction to Secure Sockets Layer, http://www.ehacking.net/2011/05/securesockets- layer-ssl-introduction.html

10.             0. Freier, P. Karlton and P. C. Kocher, The SSL Protocol, version 3.0, http://www.cryptoheaven.com/Security/Presentation/SSL-protocol.htm

11.             W. Stallings, Cryptography and Network Security, 2nd ed., Prentice Hall, Upper Saddle River, NJ, 1999.

12.             H. Otrok, PhD student, ECE Department, Concordia University, Montreal, QC, Canada and R. Haraty, Assistant Dean, School of Arts and Sciences, Lebanese American University, Beirut, Lebanon and A. N. El-Kassar, Full Professor, Mathematics Department, Beirut Arab University, Beirut, Lebanon “Improving the Secure Socket Layer Protocol by modifying its Authentication functions” 2006

13.             Krishna Kant and Ravishankar Iyer Server Architecture Lab Intel Corporation, Beaverton, OR Prasant Mohapatra Dept. of Computer Science and Engineering Michigan state University, East Lansing, MI,” Architectural Impact of Secure Socket Layer on Internet Servers” 2000

14.             Purshotam, Dept. of Computer Engineering, Lovely Professional University, Punjab and Rupinder Cheema, PEC University of Technology, Chandigarh and Ayush Gulati, Lovely Professional University, Punjab ,” Improving the Secure Socket Layer using modifying RSA algorithm” 2012

15.             Atul Kahate, Cryptography and Network Security, Tata McGraw-Hill Education, 2003


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12.

Authors:

Hamideh Hajiabadi, Saeideh Kabiri Rad

Paper Title:

Retrieving Frequent Item Sets from Distributed Data Base

Abstract:    With fast growing of the network and the data storage, large scale data are rapidly expanded and collected on the physically distributed storage, consequently traditional data mining approaches are not appropriate for information retrieval purpose. Distributed data mining techniques are developed in order to examine distributed data by parallel algorithms. Distributed data mining algorithms based on finding frequent itemsets are widely used for this purpose. The itemsets retrieved are numerous. In this paper proposed a tree based mining approach contributing to user such that reducing number of retrieved itremsets. The algorithm is implemented and the results are demonstrated. 

Keywords:
    Frequent itemsets, Non-Derivable itemsets, Distributed database


References:

1.              R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases, in: Proceedings of the ACM SIGMOD 1993, pp. 207–216.
2.              D.W. Cheung, et al.,“A Fast Distributed Algorithm for Mining Association Rules”, Proc. Parallel and Distributed Information Systems, IEEE CS Press,1996, pp. 31-42.

3.              D.W. Cheung, et al.,“Efficient Mining of Association Rules in Distributed Databases”, IEEE Trans. Knowledge and Data Eng., Vol. 8, No. 6, 1996, pp. 911-922.

4.              Roger S. Pressman,“Software Engineering”, USA, 2005, Lee,“Introduction to System Analysis and Design” , Galgotia Book Source Publications.

5.              R.T. Ng, L.V.S. Lakshmanan, J. Han, A. Pang, Exploratory mining and pruning optimizations of constrained associations rules, in: Proceedings of the ACM SIGMOD 1998, pp. 13–24.

6.              L.V.S. Lakshmanan, C.K.-S. Leung, R.T. Ng, Efficient dynamic mining of constrained frequent sets, ACM Trans. Database Syst. 28 (4) (2003) 337–389.

7.              Calders, T. (2004). Deducing bounds on the support of itemsets. Database Technologies for Data Mining- Discovering Knowledge with Inductive Queries, Vol. 2682 of LNCS, pages 214-233. Springer-Verlag.

8.              Calders, T. & Goethals, B. (2002). Mining all non derivable frequent itemsets. Proc. Principles and Practice of Knowledge Discovery in Databases PKDD’02, Vol. 2431 of LNAI, pp. 74-85, Helsinki, FIN, Springer-Verlag.

9.              J.J. Cameron, A. Cuzzocrea, F. Jiang, C.K.-S. Leung, Mining frequent itemsets from sparse data streams in limited memory environments, in: Proceedings of the WAIM 2013, Springer, pp. 51–57.

10.           E. Kem  O.Ozkasap, ProFID: practical frequent items discovery in peer-to-peer networks, Future Gener. Comput. Syst. 29 (6) (2013) 1544–1560. 

11.           C.K.-S. Leung, C.L. Carmichael, Exploring social networks: a frequent pattern visualization approach, in: Proceedings of the SocialCom 2010, IEEE Computer Society, pp. 419–424.

12.           R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases, in: Proceedings of the ACM SIGMOD 1993, pp. 207–216.


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13.

Authors:

Mohammed Ahmed, Ejike Chibuzo Anene, Hassan Sabo Miya, Saidu Kumo Mohammed

Paper Title:

Control of a Continuously Variable Transmission System

Abstract:     This paper proposed control strategies for the modern mechatronic Continuously Variable Transmission (CVT) System using three different techniques; The Ziegler-Nichols, Linear Quadratic and Pole-Placement methods. The results of the system responses of the designed control schemes were successfully simulated using MATLAB. Comparing the results showed that controller implemented using the Pole-Placement method was the best for the system which gave a rise time of 335milliseconds, paek time of 400milliseconds, settling time of 290 milliseconds and an overshoot of 0.72 percent.

Keywords:
     Controller, Continuously Variable Transmission (CVT) System, Ziegler-Nichols Method, Pole-Placement method, Linear Quadratic Regulator method.


References:
1.             M. R. Vallés, The use of Multiobjective Genetic Algorithms to Tune a Continuously Variable Transmission, Dissertation, University of Sheffield, Sheffield, UK, 2000.

2.             O. Katsuhiko,  Modern Control Engineering.  New Jersey, USA:  Prentice Hall Publishers, 2010.

3.             B. Stephen and B Craig,  Linear Controller Design: Limits of Performance. New Jersey, USA: Prentice Hall Publishers, 1991.

4.             Krista, An Introduction to Matlab. Denmark: Ventus Publishing ApS, 2012.

5.             Matlab. (2012). Matlab Primer (R2012a). Natick, MA 01760 -2098, USA: The Mathworks Company [Online]. Available: www.mathworks.com

6.             V. D. Rao, Analysis and Design of Control Systems using Matlab. New Delhi,  India:  New Age Publishers, 2006.

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14.

Authors:

Satish Saini, Ritu Vijay

Paper Title:

Performance Analysis of Artificial Neural Network Based Breast Cancer Detection System

Abstract:   Breast cancer is one of the leading cancers among women in developed countries including India. Early diagnosis of the cancer allows treatment which could lead to high survival rate or avoids further clinical evaluation or breast biopsy reducing the unnecessary expenditure. This paper aims to build Artificial Neural Network (ANN) model for detection of breast cancer based on Image Registration techniques. Gray Level Co-occurrence Matrix (GLCM) features are extracted and are used to train the ANN.  The performance is analysed on the basis of Mean Square Error (MSE) for different number of neurons of ANN.

Keywords:
 Artificial Neural Network, Breast Cancer Detection, Mammogram.


References:

1.             [Online] Available:       http://www.cancer.org. American Cancer Society, 2012.
2.             Fred S. Azar, "Imaging Techniques for Detecting Breast Cancer: Survey and Perspectives", Technical Reports (CIS), University of Pennsylvania, 2000, pp.1-6.

3.             Wang T., Karayiannis N., "Detection of microcalcifications in digital mammograms using wavelets", IEEE Transactions on Medical Imaging, 1998, vol. 17.4, pp. 498–509.

4.             Manjusha P. Deshmukh & Udhav Bhosle, "A survey of image registration", International Journal of Image Processing (IJIP), 2011, vol.5.3, 1-6.

5.             Keivanfard, F., Teshnehlab, M., Shoorehdeli, M. A., Ke Nie, Min-Ying Su.  "Feature selection and classification of breast cancer on dynamic Magnetic Resonance Imaging by using artificial neural networks", 17th Iranian Conference         of Biomedical Engineering, 3-4 Nov. 2010. 1-4.

6.             Ritthipravat P., "Artificial Neural Networks in Cancer Recurrence Prediction", International Conference on Computer Engineering and Technology, 22-24 Jan 2009, vol. 2, 103-107.

7.             Dheeba J., Tamil Selv S., "A CAD System for Breast Cancer Diagnosis Using Modified Genetic Algorithm Optimized Artificial Neural Network", SEMCCO (Springer). Part I.  LNCS 7076, 2011, 349–357.

8.             Salim MI, Ahmad AH, Arffin I, "Developmemnt of Breast cancer detection tool using Hybrid Magnetoacoustic method and Artificial Neural Network", International Journal of Biomedical Engineering, 2012, vol.6, 61-68.

9.             Ahmad Taher Azhar, Ahmed Shaimaa, El-Said, "Probabilistic Neural Networks for Breast cancer Classification", Neural Computing and Applications, Springer, 2013, vol. 23, 1737-1751.

10.          Guzman-Cabrera R., Guzman J. R., "Digital Image Processing Technique for breast cancer detection", International Journal of Thermo physics, Springer, 2013, vol.34, 1519.1531.

11.          Dheeba J., Albert Singh N., Tamil Selvi S., "Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach", Journal of Biomedical Informatics, Elsevier, 2014, vol.49, 45–52.

12.          Senapati M. R., Panda G., Dash Hybrid P. K., "Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection", Neural Computing & Applications, Springer, 2014, vol.24,745–753.

13.          Gonzalez R. C.,  Woods R. E.,  Richard E, Steven L. E.s, “Digital  Image  Processing”  ,Second Edition, Pearson Education, New Delhi,India.

14.          Devendram V., Thiagarajan H., " Texture  based  scene categorization  using  artificial  neural  networks  and  support vector  machines: a  comparative  study",  ICGST-GVIP , 2008, vol.8,45-52.


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15.

Authors:

A. K. M. Al-Shaikhli, Amanoeel Thomas Meka

Paper Title:

Design and Implementation of Practical Induction Heating Cooker

Abstract:    Induction heating is a famous technology and very usually used for cooking appliances because of its high-energy efficiency. This paper presents a practical design procedure for small induction heating devices. Also, we model the magneto thermal phenomena of the system by a finite element method (FEM) to determine the temperature evolution in the bottom of the pan, taking into account the nonlinearity of the system.

Keywords:
 Finite element method, magneto thermal devices, a.c. resistance, metallic object.


References:

1.                Z. A. Kansab, M. Feliachi,, "Modeling and optimization of induction cooking by the use of magneto-thermal finite element analysis and genetic algorithms,," FEE Engineering Springer, vol. 7 (3) (2012) 312-317., 2012.
2.                P. J. H. J. Acero, J.M. Burdio, R. Alonso, L.A. Barragan,, "Simple resistance calculation in Litz-Wire planar windings for induction cooking appliances," IEEE Trans. on Magnetics 41 (4) (2005) 1280-1288., 2005.

3.                K. Z. Oudni, H. Mohellebi, M. Feliachi,, "Finite elements modelling of Ferromagnetic plate induction heating control using curie temperature," IEEE Conference on Electromagnetic Field Computation CEFC, Athens, Greece, 2008,, 2008.

4.                J. C. s. Y. Duterrail, Ph.masse and J.L. coulomb, "Non- Linear Complex Finite Element Analysis of Electromagnetic Field in Steady-State AC Devices," IEEE Trans. On Magnetics, Vol.Mag-20, no.4, 1984.

5.                K. C. J.K. Byun, H.S. Roh, S.Y. Hahn, "Optimal design procedure for a practical induction heating cooker," IEEE Trans. Mag. 36 (4) (2000) 1390-1393., 2000.

6.                H.-C. C. a. K.-H. Huang, "Finite element analysis of coupled electromagnetic and thermal fields within a practical induction heater," International Journal of Applied Electromagnetics and Mechanics, vol. 28, pp. 413-427., 2008.

7.                U. Has, "Temperature control for food in pots on cooking hobs," IEEE Trans. on Industrial Electronics, vol. vol. 46, issue 5, pp. 1030-1034, 1999.

8.                J. Acero et al, "Domestic induction appliances," IEEE Industry Applications Magazine, pp. 39-47., 2010.

9.                S. K. G. Cerri, and V.M. Pimiani, "Modelling of Litz-wire planar winding geometry for an accurate reactance evaluation," Sci. Meas. Technol., vol. 4, issue 4, pp. 214-219., 2010.

10.             S. K. V.M. Primiani, and G. Cerri, "Rigorous electromagnetic model of an induction cooking system," IET Sci. Meas. Technol., vol. 6, issue 4, pp. 238-246, 2012.

11.             F. Forest et al, "Frequency-Synchronized Resonant Converters for the Supply of Multiwinding Coils in Induction Cooking Appliances," IEEE Trans. on Industrial Electronics, vol. 54, issue 1, pp. 441-452., 2007.

12.             H. Sarnago et al, "Modulation Scheme for Improved Operation of a RB-IGBT Based Resonant Inverter Applied to Domestic Induction Heating," IEEE Trans. on Industrial Electronics, vol. 60, issue 5, pp. 2066-2073., 2013.

13.             O. Lucia et al, "Load-Adaptive Control Algorithm of Half-Bridge Series Resonant Inverter for Domestic Induction Heating," IEEE Trans. on Industrial Electronics, vol. 56, issue 8, pp. 3106-3116., 2009.

14.             Carretero et al, "Temperature influence on equivalent impedance and efficiency of inductor systems for domestic induction heating appliances," Proc. of IEEE Applied Power Electronic Conference, pp. 153-158., 2007.

15.             J. G. Collier and J. R. Thome, "Convective Boiling and Condensation, 3rd ed. Oxford: Clarendon, 1994.," 1994.

16.             I.-g. K. I.-h. Park, H.-B. Lee, K.-s. Lee, and S.-y. Hahn,, "Optimal design of transient eddy current systems driven by voltage source,”" IEEE Trans. on Magn., vol. 33, pp. 1624–1629, Mar. 1997., 1997.

17.             Microchip, "PIC16f628A Data Sheet 28/40/44-Pin Enhanced Flash Microcontrollers. ," 2003.

18.             IR2110, "Data Sheet No. PD60147 rev.U," 2005.

19.             http://www.irf.com/product/_/N~1njchu#tab-tab1.


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16.

Authors:

Prianka R. R, Bibin M. R, Saravanan, Sivnthiram

Paper Title:

ADHOC Networks Improving Power Efficiency using Multicast Multi-Path Routing Technology

Abstract:     The proposal of this paper presents a measurement-based routing algorithm to load balance intra domain traffic along multiple paths for multiple multicast sources. Multiple paths are established using application-layer overlaying. The proposed algorithm is able to converge under different network models, where each model reflects a different set of assumptions about the multicasting capabilities of the network. The algorithm is derived from simultaneous perturbation stochastic approximation and relies only on noisy estimates from measurements. Simulation results are presented to demonstrate the additional benefits obtained by incrementally increasing the multicasting capabilities. The main application of mobile ad hoc network is in emergency rescue operations and battlefields. This paper addresses the problem of power awareness routing to increase lifetime of overall network. Since nodes in mobile ad hoc network can move randomly, the topology may change arbitrarily and frequently at unpredictable times. Transmission and reception parameters may also impact the topology. Therefore it is very difficult to find and maintain an optimal power aware route. In this work a scheme has been proposed to maximize the network lifetime and minimizes the power consumption during the source to destination route establishment. The proposed work is aimed to provide efficient power aware routing considering real and non real time data transfer.

Keywords:
 Consumption, Lifetime, Perturbation, Stochastic


References:

1.             S.-Y. R. Li and R. W. Yeung, “Linear network coding,” IEEE Transactions on Information Theory, vol. 49, pp. 371–381, 2003.
2.             T. Noguchi, T. Matsuda, and M. Yamamoto, “Performance evaluation of new multicast architecture with network coding,” IEICE Trans. Commun, vol. E86-B, pp. 1788– 1795, 2003.

3.             Y. Zhu, B. Li, and J. Guo, “Multicast with network coding in application-layer overlay networks,” IEEE Journal on Selected Areas in Communications, vol. 22, pp. 107–120, 2004.

4.             Li Q, Aslam J, Rus D, “Online Power-aware Routing in Wireless Ad-hoc Networks,” Proceedings of Int’l Conf. on Mobile Computing and Networking (MobiCom’2001), 2001.

5.             Stojmenovic I, Lin X. “Power-Aware Localized Routing in Wireless Net108 works,” IEEE Trans. Parallel and Distributed Systems 2001; 12(11):1122-1133.

6.             Doshi S, Brown TX, “Minimum Energy Routing Schemes for a Wireless Ad Hoc Network,” Proceedings of the Conference on Computer Communications (IEEE Infocom 2002), 2002.

7.             Woo K, Yu C et al., “Localized Routing Algorithm for Balanced Energy Consumption in Mobile Ad Hoc Networks,” Proc. of Int'l Symp. on Modeling, Analysis and Simulation of Computer and Telecommunication Systems 2001,117-124.

8.             Toh C-K,“Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad hoc Networks,” IEEE Communications Magazine, vol. 39, no. 6, pp. 138-147, June 2001. 

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17.

Authors:

Cini Kurian

Paper Title:

A Review on Technological Development of Automatic Speech Recognition

Abstract:      Speech recognition has been a challenging and multidisciplinary research area since decades. Speech Recognition technology has tremendous potential as it is an integral part of future intelligent devices, in which speech recognition and speech synthesis are used as the basic means for communicating with humans. In this paper, a  survey of major landmarks in the research and development of automatic speech recognition is presented to provide a review of technological perspective and an appreciation of the fundamental progress that has been made in this area.

Keywords:
 Automatic Speech Recognition


References:

1.                B. H. Juang and L. R. Rabiner (2005), ‘Automatic speech recognition–a brief history of the technology’, in Elsevier Encyclopaedia of Language and Linguistics, Second Edition, Elsevier.
2.                Wiqas Ghai , Navdeep Singh  "Literature Review on Automatic Speech recognition" International Journal of Computer Applications  Volume 41– March 2012.

3.                H. Dudley and T. H. Tarnoczy, The Speaking Machine of Wolfgang von Kempelen, J. Acoust.Soc. Am., Vol. 22, pp. 151-166, 1950.

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18.

Authors:

G. Suganya, L. Jenifer Amla, S. P. Dwarakesh

Paper Title:

Model Reference Adaptive Controller using MOPSO for a Non-Linear Boiler-Turbine

Abstract:       Biologically inspired algorithms are attracting lot of researchers in recent decades. Particle swarm optimization is a recent algorithm based on the movement and intelligence of swarms. This paper proposes an application of Multi Objective Particle Swarm Optimization (MOPSO) for tuning the Model Reference Adaptive Controller (MRAC) for a non-linear Boiler-Turbine system. The Boiler-turbine system is a Multi Input Multi Output (MIMO) system which is non-linear in nature and hence MOPSO is used to obtain the solution. This paper proposes a new approach combining a priority based lexicography method and the overall error for tuning the parameters of swarm in order to get the best optimal solution. The model was developed using MATLAB simulink tool and the results were compared.

Keywords:
     Boiler-Turbine system, MOPSO, MRAC ,Overall error.


References:

1.                Bell R. D. and Astrom K. J. (1987), ‘Dynamic Models for Boiler-Turbine-Alternator Units: Data Logs and Parameter Estimation for a 160MW Unit’, Lund Institute of Technology, Sweden, Rep. TRFT-3192.
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3.                Behesti, M.T.H., Rezaee ,M.M. and Tarbiat Modarres (2002), ‘A New Hybrid Boiler Master Controller’, IEEE proceedings on American control conference, Vol.3, pp. 2070 – 2075.

4.                Un-Chul Moon and Y.Lee (2003), ‘Boiler-Turbine System Control Using a Fuzzy Auto-Regressive Moving Average (FARMA) Model’, IEEE transactions on Energy Conversion, Vol.18, Issue-1, pp.142-148.

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8.                Un-Chul Moon, Woo-Goon Kim,Seung-Chul Lee and Kwang Y.Lee (2005), ‘Application of Dynamic Matrix Control to a Boiler-Turbine System’, IEEE transactions on Power Engineering Society General Meeting, Vol.2, pp. 1595 - 1600.

9.                Un-Chul Moon,  seung-Chul Lee and Kwang  Y.Lee (2007), ‘An Adaptive   Dynamic Matrix Control of a Boiler-Turbine System Using Fuzzy Inference’, 14th International conference on Intelligent system applications to Power   systems(ISAP), pp.1-6.

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12.             Nor Azlina Ab. Aziz, Ammar W. Mohemmed, Mohamad Yusoff Alias and Kamarulzaman Ab. Aziz (2011), ‘Particle Swarm Optimization for Constrained and Multiobjective Problems:A Brief Review’, International Conference on Management and Artificial Intelligence, IPEDR vol.6.

13.             Carlos A. Coello Coello, Gregorio Toscano Pulido, and Maximino Salazar Lechuga (2004), ‘Handling Multiple Objectives with Particle Swarm Optimization’, IEEE transactions on Evolutionary Computations, Vol. 8, No. 3.

14.             Pavan K. Vempaty, Ka C.Cheok, Robert N.K.Loh and Safa      Hasan (2007), ‘Model Reference Adaptive control of Biped Robot  Actuators for Mimicking Human Gait’, Engineering Letters transaction, Vol.18, Issue-2.

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19.

Authors:

Tahereh Ghafghazi, Rouhollah Dianat, Bagher Babaali

Paper Title:

A New Method to Improve Feature Detection Methods Based on Scale Space

Abstract:  Scale-space theory provides a well-founded framework for modelling image structures at multiple scales, and the output from the scale-space representation can be used as input to a large variety of visual modules. Visual operations such as feature detection, feature classification, stereo matching, motion estimation, shape cues and image-based recognition can be expressed directly in terms of (possibly non-linear) combinations of Gaussian derivatives at multiple scales. In this sense, scale-space representation can serve as a basis for early vision. The Gaussian scale-space is widely used to model the human visual system. The main reason why Gaussian scale-space solely being used is that the Gaussian function is the unique kernels which satisfies the causality property i.e., it states that no new feature points are created as the scale increasing. The Gaussian filter are highly suitable for smoothing image. The amount of smoothing depends on the value of the standard deviation parameter of the Gaussian function. The problem of creating Gaussian scale- space is that if image smoothing does not stop in a proper point; it may lead to extreme destruction of local features of the image. In this paper, an approach has been presented to enhance a scale-space based on Gaussian function in order that a threshold is chosen for standard deviation in Gaussian filter with aim of preventing extreme destruction of image local features. Results of the proposed method indicate that this method affects considerably prevention of extreme destruction of image features and it can be very effective on creation of scale-space with high accuracy.                                                                                       

Keywords:
      Destruction Rate, Feature Detection, Gaussian Scale Space, Scale-space representation


References:

1.                 S.Nilufar, N.Ray, H.Zhang, “Object Detection With DoG Scale-Space:A Multiple Kernel Learning Approach”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL.21, NO.8, 2012, pp.3744-3756.
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3.                 T.Tuytelaars, K.Mikolajczyk, “Local Invariant Feature Detectors: A Survey”, Computer Graphics and Vision, Vol.3, No.3, 2007, pp.177-280.

4.                 T.Lindeberg, “Generalized  Gaussian scale-space axiomatics comprising linear scale-space,affine scale-space and spatio-temporal scale-space”, School of Computer Science and Communication, 2010.

5.                 S.A.Karim, K.V. Pang, “Data Smoothing Using Gaussian Scale-space and Discrete Wavelet Transform”, International Conference on Electrical, Control and Computer Engineering (INECCE), 2011, pp. 334-338.

6.                 J.A.Kalomiros, “Optimization of a Scale-Invariant Feature detector using scale-space scans”, IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), Vol.1, 2013, pp.388-393.

7.                 A.Kuijper, “On Manifolds in Gaussian Scale Space”, Scale Space Methods in Computer Vision Lecture Notes in Computer Science, Vol. 2695, 2003, pp.1-16.

8.                 L.Florack, A.Kuijper, “The Topological Structure of Scale-Space Images”, Journal of Mathematical  Imaging and Vision, Vol.12, Issue.1, 2000, pp.65-79.

9.                 Witkin, “Scale-space filtering: A new approach to multi-scale description”, Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP, Vol.9, 1984, pp.150-153.

10.              D.G.Lowe, “Object Recognition from local Scale-Invariant Features”, in Proceedings of the International Conference on Computer Vision(ICCV), Vol.2, 1999, pp.1150-1157.

11.              D. G. Lowe, “Distinctive image features from scale-invariant keypoints,”International Journal of Computer Vision, Vol. 60, No.2, 2004,  pp.91-110.

12.              T.Lindeberg, “Scale-space theory: A basic tool for analysing structures at different scales”. Journal of Applied Statistics, Vol.21, No.2, 1994, pp.224-270.

13.              T.M.Sezgin, R.Davis, “Scale-space Based Feature Point Detection for Digital Ink”, American Association for Artificial Intelligence (AAAI), 2004,  pp.145-151.

14.              H.Amerehie, R.Dianat, F.Keynia, “A New Method to Improve the Difference of Gaussian Feature Detector”, International Journal of Soft Computing and
Engineering (IJSCE),Vol.4, Issue.4, 2014,  to be published.

15.              T.Lindeberg, “Scale-Space Theory in Computer Vision”, The Kluwer International Series in Engineering and Computer Science, Kluwer Academic Publishers, 1994.

16.              J. Sporring, M. Nielsen, L. Florack, P. Johansen, “Gaussian Scale-Space Theory”, Springer- Computational Imaging and Vision, Vol. 8, 1997.

17.              A.Zhihui, C.Wenjuan, Z.Mengmeng, “Deep Structure of Gaussian Scale Space”, International Conference on Computer Science and Software Engineering, Vol.6, 2008, pp.381-384.

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19.              T.Lindeberg, “Feature detection with automatic scale selection”, International  Journal of Computer Vision, Vol.30, No.2, 1998, pp.77-116.

20.              T.Lindeberg, “Scale-space”. Encyclopedia of Computer Science and Engineering, Vol.4, 2009, pp.2495-2504.


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20.

Authors:

K. Seena Naik, G. A. Ramachandra, M. V. Brahmananda Reddy

Paper Title:

Self-Adaptive Routing in Multi-Hop Sensor Networks

Abstract:   Sensor networks are used for applications in moni- toring harsh environments including reconnaissance and surveil- lance of areas that may be inaccessible to humans. Such applica- tions depend on reliable collection, distribution and delivery of information to processing centres which may involve multi-hop wireless networks which experience disruptions in communica- tion and exhibit packet drops, connectivity loss and congestion. Some of these faults are periodic, attributed to external, recurring factors. In this paper, we study an effective way to forecast such repetitive conditions using time-series analysis. We, further, present an application-level, autonomic routing service that adapts sensor readings routes to avoid areas in which failures or congestion are expected. A prototype system of the approach is developed based on an existing middleware solution for sensor network management. Simulation results on the performance of this approach are also presented.

Keywords:
  Monitoring, Surveillance, Communication, Middleware, Reconnaissance.


References:

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