iosr-JCE   National Conference in Emerging Technologies' 14 (NCET)

Abstract: Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, thereis a study on denoising scheme and its architecture for the removal of random valued impulse noise. To achieve the goal of low cost, a low-complexity architecture is proposed. Decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Extensive experimental results demonstrate that the proposed technique can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods. Moreover, the performance can be comparable to the higher complexity methods.

[1]. P.-Y. Chen, C.-Y. Lien, and H.-M. Chuang, "A Low-Cost VLSI Implementation for Efficient Removal of Impulse Noise," IEEE Trans. Very Large Scale Integration Systems, vol. 18, no. 3, pp.473-481, Mar. 2010.
[2]. I. Aizenberg and C. Butakoff, "Effective Impulse Detector Based on Rank-Order Criteria," IEEE Signal Processing Letters,vol. 11, no. 3, pp. 363-366, Mar. 2004.
[3]. Z. Wang and D. Zhang, "Restoration of Impulse Noise Corrupted Images Using Long-Range Correlation," IEEE Signal Processing Letters, vol. 5, no. 1, pp. 4-7, Jan. 1998.
[4]. T. Chen and H.R. Wu, "Space Variant Median Filters for the Restoration of Impulse Noise Corrupted Images," IEEE Trans. Circuits Systems II, Analog Digital Signal Processing, vol. 48, no. 8, pp. 784-789, Aug. 2001

Abstract: Rail inspection is an important task in railway maintenance. The speed and loads of trains have been increasing greatly in recent years, and these factors inevitably raise the risk of producing rail defects. Mainly the discrete surface defects impact the riding quality and safety of a railway system. However, it is a challenge to inspect such defects in a vision system because of illumination inequality and the variation of reflection property of rail surfaces. This project presents a real-time VIS for discrete surface defects of rail heads.

[1]. Qingyong Li, Member, IEEE, and Shengwei Ren,"A Real Time Visual Inspection System For Discrete Surface Defects of Rail Heads",ieee transactions on instrumentation and measurement, vol. 61, no. 8, august 2012.
[2]. R. Clark, "Rail flaw detection: Overview and needs for future developments," NDT & E Int., vol. 37, no. 2, pp. 111–118, 2004.
[3]. R. Clark, S. Singh, and C. Haist, "Ultrasonic characterisation of defects in rails," Insight, vol. 44, no. 6, pp. 341–347, 2002.
[4]. R. Edwards, S. Dixon, and X. Jian, "Characterisation of defects in the railhead using ultrasonic surface waves," NDT & E Int., vol. 39, no. 6, pp. 468–475, 2006.
[5]. M. Bentoumi, P. Aknin, and G. Bloch, "On-line rail defect diagnostics with differential eddy current probes and specific detection processing," Eur. Phys. J. Appl. Phys., vol. 23, no. 3, pp. 227–233, 2003.

Abstract: This paper describes several different algorithms for Reversible Data Hiding(RDH). Data hiding is a technique for embedding information into covers such as image, audio and video files. Reversible Data Hiding (RDH) or lossless data hiding, is a method by which the original cover can be lossless restored after the embedded message is extracted. Many RDH techniques have been developed. This paper summarizes and reviews these techniques.

[1]. J. Tian, "Reversible data embedding using a difference expansion,"IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp. 890–896, Aug. 2003
[2]. Z. Ni, Y. Shi, N. Ansari, and S. Wei, "Reversible data hiding," IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 3, pp. 354–362, Mar.2006.
[3]. A.S. Al-Fahoum, "Reversible data hiding using contrast enhancement approach", International Journal of Image Processing(IJIP),Vol (7):Issue (3):2013
[4]. V. Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y.-Q. Shi, "Reversible watermarking algorithm using sorting and prediction," IEEE Trans.Circuits Syst. Video Technol., vol. 19, no. 7, pp. 989–999, Jul. 2009.
[5]. L. Luo et al., "Reversible image watermarking using interpolation technique," IEEE Trans. Inf. Forensics Security, vol. 5, no. 1, pp. 187–193,Mar. 2010.

Abstract: Leukemia is a disease that affects blood forming cells in the body. Early detection of the disease is necessary for proper treatment management. Abnormal white blood cells or blasts play important role for hematologists in their diagnostic process. The diagnosis and treatment of diseases have been significantly simplified by the Differential Counting of white blood cells as it furnishes critical information required by pathologists. The practice of manual counting of the white blood cells suffer from the obvious disadvantages associated with human errors.

[1]. Rubhala, "Image segmentation of leukemia using kernel based fuzzy c means clustering", International Journal of Communications and Engineering Volume 03 No.3, Issue: 03 March2012
[2]. Juma Al-Muhairy, Yousef Al-Assaf, "Automatic white blood cell segmentation based on image processing", 2005 IFAC
[3]. Nipon Theera-Umpon, "Patch-Based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering", ECTI transactions on electrical eng., electronics, and communications vol.3, no.1 February 2005
[4]. Fabio Scotti, "Automatic Morphological Analysis for Acute Leukemia Identification in Peripheral Blood Microscope Images", CIMSA 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications Giardini Naxos, Italy, 20-22 July 2005
[5]. T. Markiewicz and S. Osowski, "Data mining techniques for feature selection in blood cell recognition", ESANN'2006 proceedings of Symposium on Artificial Neural Networks Bruges (Belgium), 26-28 April 2006, d-side publi., ISBN 2-930307-06-4.

Abstract: Images we seen through a camera and direct observation of it is slightly different due to the calibration and less dynamic range of camera. In this paper we are proposing a method called Retinex method which models the human vision system. Through this we can interpret the actual image without any discrepancy between the two. So here the enhancement is done through Retinex Algorithm.

[1]. Jharna Majumdar, Mili Nandi, and P Nagabhushan" Retinex Algorith with Reduced Halo Artifacts" Defence Science Journal, Vol. 66, November 2011, pp. 559-566
[2]. D. J. Jobson, Z. Rahman, and G. A. Woodell, "A Multi-Scale Retinex For Bridging the Gap Between Color Images and the Human Observation of Scenes," IEEE Transactions on Image Processing: Special Issue on Color Processing, July 1997
[3]. Rahman, Z.; Jobson, D.J. & Woodell, G.A. MultiScale Retinex for color image enhancement. In Proceeding of the IEEE International Conference on Image Processing(ICIP), 1996.
[4]. Jobson, D.J.; Rahman, Z. & Woodell, G.A. Retinex image processing: Improved fidelity for direct visual observation. In Proceedings of the IS&T Fourth Color Imaging Conference: Color Science, Systems, and Applications, 1996, pp. 124-126.
[5]. Jobson, D.J; Rahman, Z. & Woodell, G.A. Properties and performance of a center/surround retinex. IEEE Trans Image Proces., 1997, 6, 451-62

Abstract: This paper explores de-noising based on Adaptive nonlocal means filter for Dynamic Contrast Enhanced MR Images. This algorithm utilizes singular value decomposition and K-means clustering for the efficient block classification of noisy image. Then a local window is adjusted adaptively for matching with the local property of a block and a rotated matching algorithm that aligns with the dominant orientation is used for similarity matching. Empirical evaluation on the performance of the proposed filter relative to other de-noising methods-simple Gaussian filtering, bilateral filtering, anisotropic filtering, non-local means filter and dynamic non-local means filter are also presented. The results of quantitative evaluation based on signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) is made with the proposed algorithm and the other conventional filtering methods.

[1]. Yaniv Gal, Andrew J. H. Mehnert, Andrew P. Bradley, Kerry McMahon ,Dominic Kennedy and Stuart Crozier, De-noising of Dynamic Contrast Enhanced MR Images Using Dynamic Nonlocal means ,IEEE Trans .Med Imag.,vol.29,no.2,pp.302-310,Feb 2010.
[2]. D. J. Collins and A. R. Padhani, "Dynamic magnetic resonance imaging of tumor perfusion," IEEE Eng. Med. Biol. Mag., vol. 23, no. 5, pp. 65–83, Sep./Oct. 2004.
[3]. A. Buades, B. Coll, and J. M. Morel, "A review of image denoising algorithms, with a new one," Multiscale Modeling Simulation, vol. 4, p. 490, 2005.
[4]. A. Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), 2005, vol. 2, pp. 60–65.
[5]. R. D. Nowak, "Wavelet-based Rician noise removal for magnetic resonance imaging," IEEE Trans. Image Process., vol. 8, no. 10, pp. 1408–1419, Oct. 1999.

Abstract: Following a natural disaster the authorities need to know information such as location and extend of affected areas. Visual interpretation of the affected area can provide sufficient information but it is a difficult and time consuming task. Here comes the importance of remote sensing. Remotely sensed images is increasingly becoming an important source of information for detecting building damages . Both medium resolution and high resolution satellites can be used for obtaining the images. This paper presents a new method for the detection of damaged buildings by integrating image difference, ratio, texture segmentation and change detection based on segmentation for producing change map. Hence the name Integrated Difference Ratio Texture Segmentation (IDRTS) method.

[1]. Richard J. Radke, , Omar Al-Kofahi,BadrinathRoysam,"Image Change Detection Algorithms: A Systematic Survey", IEEE Transactions On Image Processing, VOL. 14, NO. 3, MARCH 2005
[2]. ManjushaDeshmukh,UdhavBhosle,"A Survey Of Image Registration",International Journal of Image Processing (IJIP), Volume (5) :Issue (3) , 2011
[3]. P. Coppin*, I. Jonckheere, K. Nackaerts, B. Muys,"Digital Change Detection Methods In Ecosystem Monitoring: A Review",Int. J. Remote Detection By Object-Based Change Indications ",IPI Institute of Photogrammetry and geoinformation
[4]. P. Hofmann, P. Lohmann, S. Mller,"Change , Leibniz Universitt Hannover, Nienburger Str. 1, 30167 Hannover, Germany
[5]. P. Mausel , E. Brondzio c and E. Moran,"Change detection techniques", International Journal of Remote Sensing

Abstract: The purpose of image compression and the image steganography are antagonist.. Even if by definition they are contradictory, they can be combined to improve the security as well as the image compression rate can be increased. The main focus is on the improvement in security and image compression rate of JPEG images using unitary transform. Here an encryption technique by applying unitary transforms according to the encryption key generated from random permutation method at the transformation stage can be used to provide better security.

[1]. Reza Jafari, Djemel Ziou, Mohammad Mehdi Rashidib. "Increasing image compression rate using steganography", Elsevier Expert Systems with Applications 40, pp 6918–6927, 2013
[2]. Jafari, R., Ziou, D., & Mammeri, A. "Increasing compression of JPEG images using steganography", In IEEE International Symposium on Robotic and Sensors Environment, pp 226–230, 2011.
[3]. Swanson, M., Zhu, B., & Tewfik, A. H "Image coding by folding", IEEE International Conference on Image Processing , pp 665–668, 1997
[4]. Wallace, G. K. "The JPEG still picture compression standard", IEEE Transaction on Consumer Electronics, 38(1), pp 18–35, April 1992
[5]. Christopoulos, C., Skodras, A., & Ebrahimi, T. "The JPEG2000 still image coding system: an overview", IEEE Transaction on Consumer Electronics, 46(4), pp 1103-1127, November 2000.

Abstract: Prediction of image sequences is widely used inimage processing and transmission schemes such as Differential Pulse Code Modulation (DPCM).Traditional linear prediction employs linear predictors for simplicity. The nonlinear Volterra predictor can be used as an alternative to linear predictor to account for the nonlinear components in the image signals. The proposed quadratic predictor is more robust to noise. Experimental results illustrate that the nonlinear predictor yields improved mean square error performance. The proposed method can be incorporated in new predictive coding schemes for high throughput.

[1]. Proakis, J. G. (2001). Digital Communications. (McGraw Hill, New York.)
[2]. V.J. Mathews, G.L. Sicuranza, Polynomial Signal Processing (John Wiley & Sons, Inc, New York, 2000. )
[3]. Sanjit K. Mitra and Giovanni L .Sicuranza, Nonlinear ImageProcessing (NJ: Academic Press, 2001)
[4]. Oppenheim, A. V. and Schafer, R. W. (1998). Discrete Time SignalProcessing (PHI, New Delhi, 5th edition)
[5]. Gonzalez, R. and Woods, R. (1992). Digital Image Processing (Addison-Wesley, Reading,MA, 3rd edition.)
[6]. N. Wiener, Response of a nonlinear device to noise, Report No. 129, Radiation Laboratory, MIT, Cambridge,

Abstract: This paper presents a novel method for implicit active contours, which is completely free of the costly re-initialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in a specialized-LSE equation, to which a piecewise constant solution can be derived. In order to have a stable numerical solution of the RD based LSE. I propose a two-step splitting method (TSSM) to iteratively solve the LSE equation: first iterating the LSE equation, and then solving this equation.

[1]. M. Kass, A. Witkin, and D.Terzopoulos, "Snakes: Active contour models," Int. J. Comput. Vis., vol.1, pp. 321–331, 1987.
[2]. S. Osher and J. Sethian, "Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations," J. Comp. Phys., vol. 79, pp. 12-49, 1988.
[3]. V. Caselles, F. Catte, T. Coll, and F. Dibos, "A geometric model for active contours in image processing," Numer. Math., vol. 66, pp. 1-31, 1993
[4]. R. Malladi, J. Sethian, and B. Vemuri, "Shape Modeling with Front Propagation: A Level Set Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 793–800, 1995.
[5]. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic Active Contours," Int. J. Comput. Vis., vol. 22, no. 1 pp. 61–79,1997.

Abstract: Cloud computing is a well emerging technology with wide use. Its IaaS, PaaS, SaaS etc properties will attract users to use Cloud facility. Several security factors are made in to consider in this area. There may be possibility of integrity loss of outsourced data in cloud. So the need for the security of confidential data is increasing day by day. Here we introduce a new technique which will check the integrity of data with the help of a trusted third party auditor. It also provides the mechanism for verifying against non-repudiation attack.

[1]. P. Mell and T. Grance, ―Draft NIST working definition of cloud computing,‖ Referenced on June. 3rd, 2009 online at http://csrc.nist.gov/groups/SNS/cloud-computing/index.html, 2009.
[2]. M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz, A. Konwinski,G. Lee, D. A. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, ―Above the clouds: A berkeley view of cloud computing,‖ University of California, Berkeley, Tech. Rep. UCB-EECS-2009-28, Feb 2009
[3]. J. Hendricks, G. R. Ganger, and M. K. Reiter. Verifying distributed erasure-coded data. In 26th ACM Symposium on Principles of Distributed Computing(PODC), 2007.
[4]. Ateniese G, Burns R, Curtmola R, Herring J, Khan O, Kissner L, et al. Remote data checking using provable data possession. ACM Transaction in Information System Security 2011;14:1–34
[5]. Cong Wang, Kui Ren, Jin Li, ―Toward publicly Auditable Secure cloud data storage Service‖, IEEE Network august 2010

Abstract: Barcodes are well known and useful in area product identification. Arrival of 2D barcodes improved the condition. QR codes, the most popular 2D barcodes are widely used for the purposes other than product identifications. The higher data capacity provided by the QR codes can still be enhanced by the use of colors. Even then insecure QR codes make its applications narrow. QR codes made available in public Wi-Fi and LAN networks can be an issue in user privacy

[1]. Information Technology-Automatic Identification and Data Capture Techniques-QR Code 2005 Bar Code Symbology Specification. (2011, Jan.) [Online].
[2]. A. Grillo, A. Lentini, M. Querini, and G. F. Italiano, "High capacity colored two dimensional codes", in Proc. Int. Multiconf. Comput. Sci. Inf. Tech., Oct. 2010, pp. 709a€.716.
[3]. M.Querini,A.Grillo,A.Lentini and G.F. Italiano,"2D Color Barcodes For Mobile Phones",International Journal of Computer Science and Applications Techno mathematics ResearchFoundation Vol. 8 No. 1, pp. 136 - 155, 2011.
[4]. Tan Jin Son Section-.Three QR Codes;

Abstract: The performance of opportunistic routing in wireless ad-hoc network depends on selection of next relay node. While selecting next relay node should consider the selfish nodes and congestion present in the network. In this paper, propose a technique to improve performance of the distributed adaptive opportunistic routing protocol for multi-hop wireless ad-hoc network. This algorithm utilizes 3-way handshake to find next forwarder. If any of the packets; DATA, ACK, and FO is lost, the performance will significantly reduce. Loss of packets may be due to selfish node or congestion present in the network, the proposed work overcome this and produces a better and efficient routing.

[1]. A.A. Bhorkar, M. Naghshvar, and T. Javidi "An Adaptive Opportunistic Routing Scheme for Wireless
Ad-hoc Networks" IEEE/ACM TRANSACTIONS ON ETWORKING, VOL. 20, February 2012.
[2]. S. Biswas and, R. Morris "EXOR: Opportunistic Multi Hop Routing for Wireless Networks", ACM
SIGCOMM Computer Communication review-proceedings, October 2005.
[3]. C. Lott and D. Teneketzis, "Stochastic routing in ad hoc wireless networks," in Proc. 39th IEEE Conf.
Decision Control, 2000, vol. 3, pp. 2302–2307, vol. 3.
[4]. C. E. Perkins, E. M. Royer "Ad-hoc Distance vector routing" IEEE WMCA‟99 FEB-1999 PP-90-100.
[5]. R. Bruno, M. Conti, and M. Nurchis "Maxopp Routing for Wireless Mesh Networks" IEEE Symposium
on Computer and Communication (ISCC), PP-250-260 June 2010.

Abstract:In Natural Language Processing (NLP), the morphological analyzer plays an important role in processing different forms of human language for formal writing and reading. Generally morphological analyzer's of natural languages provide information concerning grammatical properties of the word it analyses. Malayalam is morphologically rich and agglutinative language, natural language processing like, web searching, machine translation, speech recognition, speech synthesis etc. for Malayalam is complex in nature. The proposed system identifies grammatical information of Malayalam, depending upon its word category using string matching algorithm. Experimental results show that the proposed system provide an efficient tense categorization, while comparing with the existing algorithms available in NLP.

[1]. Rajeev R R, Dr Elizabeth Sherly, "Morphological Analyzer for Malayalam Language: A Suffix Stripping
approach", Proceedings of the 20th Kerala Science Congress, Thiruvananthapuram, 2008.
[2]. Dr. K. Sukumara Pillai, "Kairali Sabdhanusanam", Published by Kerala Bhasha Institute,
Thiruvananthapuram, July 1980.
[3]. Vinod P M, Jayan V, Bhadran V K, CDAC Thiruvananathapuram, "Implementation of Malayalam
Morphological Analyzer Based on Hybrid Approach", Proceedings of the 24thConference on
Computational Linguistics & Speech Processing , 2012.
[4]. Dinesh Kumar, Prince Rana, "Stemming of Punjabi Words By Using Brute Force Technique",
International Journal of Engineering Science and Technology, Vol. 3 No. 2 Feb 2011