Image Compression: Combination of Discrete Transformation and Matrix Reduction
Research Paper | Journal Paper
Vol.5 , Issue.1 , pp.1-6, Jan-2017
Abstract
Nowadays, compressing large data using different compression methods increase rapidly. This explains the recent importance and popularity of compressing data of multimedia applications as well as wavelet transforms in this field. Wavelet transforms tend to benefit of block-based transforms, including the Discrete Cosine Trans-form (DCT). DCT is responsible for displaying blocking artifacts while wavelets have compact support and can offer a DCT an adaptable substitute to DCT. The popularity of single-wavelets, formed through converting and expanding of single approximation functions as well as detail functions, offered high multi-resolution function-approximation bases. This paper discusses the idea of the image compression using two levels DWT with two-dimensional DCT on every 8x8 block. Hence, the low-frequency sub-band is reduced. The DC-Column stores the DC-coefficients. As a result of using Huffman coding the DC-Column will be coded. Meanwhile the other AC-Coefficient has to be quantized in order to gain additional zeros, allowing it to be converted easily to bits through the Huffman coding. HL2, HH2, as well as LH2 are other high-frequencies coefficients that are coded using the Minimize-Matrix-Size Algorithm. The mentioned proposed algorithm converts the three high-frequency coefficients into a single real number. Nevertheless, the use of the proposed algorithm; one-dimensional-array that has many real values will be reduced and will be converted it to many bits. The results of the compression algorithm are based on Mean Square Error ( MSE).
Key-Words / Index Term
Minimize Matrix Size; Huffman Coding; DWT; DCT
References
[1] Tsai, M. & Hung, H., � DCT and DWT based image watermarking using sub sampling�. In Proceeding of the 2005 IEEE Fourth International Conference on Machine Learning and Cybernetics, China (pp. 5308�5313). 2005.
[2] Grigorios, D., Zervas, N. D., Sklavos, N., & Goutis, C. E � Design techniques and implementation of low power high-throughput discrete wavelet transform tilters for jpeg 2000 standard�. WASET International Journal of Signal Processing, 4(1), 36�43,2008.
[3] Sayood, K. � Introduction to data compression (2nd ed.) �. Morgan Kaufman Publishers: Academic Press.Google Scholar, 2000.
[4] Esakkirajan, S., Veerakumar, T., Senthil Murugan, V., & Navaneethan, P. � Image compression using multiwavelet and multi-stage vector quantization�. WASET International Journal of Signal Processing, 4(4), 524�531. 2008.
[5] ShivlalMewada, Umesh Kumar Singh, "Measurement Based Performance of Reactive and Proactive Routing Protocols in WMN ", International journal of Advance Research in Computer science and software Engineering,Volume-1,Issue-1, December 2011.
[6] Navpreet Saroya , Prabhpreet Kaur �Analysis Of Image Compression Algorithm Using DCT And DWT Transforms� International Journal of Advanced Research in Computer Science and Software Engineering 4(2), pp. 897-900, February � 2014.
[7] Mohammed Mustafa Siddeq, � Using Two Levels DWT with Limited Sequential Search Algorithm for Image Compression�,Journal of Signal and Information Processing,3, 51-62 doi:10.4236/jsip.2012.
[8] M.M. Siddeq and M.A. Rodrigues, � A New 2D Image Compression Technique for 3D Surface Reconstruction �, 18th International Conference on Circuits, Systems, Communications and Computers, Santorin Island, Greece: 379-386, 2014a
[9] M.M. Siddeq and M.A. Rodrigues, � A Novel Image Compression Algorithm for high resolution 3D Reconstruction �, 3D Research-Springer Vol. 5 No.2.DOI 10.1007/s13319-014-0007-6, 2014b.
[10] M.M. Siddeqand RODRIGUES, Marcos, � Applied sequential-search algorithm for compression-encryption of high-resolution structured light 3D data �. In: BLASHKI, Katherine and XIAO, Yingcai, (eds.)MCCSIS : Multi-conference on Computer Science and Information Systems 2015. IADIS Press, 195-202, 2015a
[11] M.M. Siddeq and RODRIGUES, Marcos, � A novel 2D image compression algorithm based on two levels DWT and DCT transforms with enhanced minimize-matrix-size algorithm for high resolution structured light 3D surface reconstruction�. 3D Research-Springer, 6 (3), p. 26. DOI 10.1007/s13319-015-0055-6, 2015b
Citation
M.A. Anwer, D.A. Anwar and S.A. Anwer , "Image Compression: Combination of Discrete Transformation and Matrix Reduction," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.1-6, 2017.
Single-criteria Collaborative Filter Implementation using Apache Mahout in Big data
Research Paper | Journal Paper
Vol.5 , Issue.1 , pp.7-13, Jan-2017
Abstract
In everyday life recommendation system plays an important role and collaborative filtering (CF) is used widely in many e-commerce applicationsfor online product recommendation. A recommender system is mainly used for better predictions as better decision making by using preferences during searching, shopping etc. The preferences of other users, user`s past preferences and big data is the driving force behind Recommendation systems. In this paper, we present collaborative filter types and their main challenges. Using the open source library Apache Mahout, we implemented collaborative filter using single-criteria to recommend items to particular users. Also, we showed the flow of Apache Mahout command�s execution to process the huge data using LogLikelihood similarity algorithm in big data scenario.
Key-Words / Index Term
Recommendation system; Collaborative filtering; Apache Mahout; Big data; Hive
References
[1] Dr. Sarika Jain, Anjali Grover, Praveen Singh Thakur, Sourabh Kumar Choudhary, �Trends, problems and solutions of recommender system,� International Conference on Computing, Communication and Automation (ICCCA2015), IEEE 2015, ISBN:978-1-4799-8890-7/15.
[2] BakirKarahodza, DzenanaDonko, HarisSupic, �Temporal dynamics of changes in group user`s preferences in recommender systems,� MIPRO 2015, , Opatija, Croatia, 25-29 May 2015
[3] Burke R, �Hybrid web recommender systems,�in: The Adaptive Web, pp. 377�408. Springer Berlin / Heidelberg (2007)
[4] Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl, �The adaptive web: methods and strategies of web personalization,� page no. 325, Volume: 4321, 2007, ISBN: 978-3-540-72078-2.
[5] ParitoshNagarnaik, A.Thomas, �Survey on recommendation system methods,� IEEE Sponsored 2nd International Conference on Electronicsand Communication System (ICECS 2015), 2015
[6] D.Deepika, K.Pugazhmathi, "Efficient Indexing and Searching of Big Data in HDFs", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (237-243), Apr -2016
[7] Poornima Sharma, Varun Garg , Prof. Randeep Kaur , Prof. Satendra Sonare , "Big Data in Cloud Environment", International Journal of Computer Sciences and Engineering, Volume-01, Issue-03, Page No (15-17), Nov -2013.
[8] Bo Xie, Peng Han, Fan Yang, Shen, �An efficient neighbor searching scheme of distributed collaborative filtering on p2p overlay network,� database and expert systems applications pp. 141�150, Springer 2004.
[9] Mantripatjit Kaur and Gurleen Kaur Dhaliwal, "Performance Comparison of Map Reduce and Apache Spark on Hadoop for Big Data Analysis", International Journal of Computer Sciences and Engineering, Volume-03, Issue-11, Page No (66-69), Nov -2015
[10] YiBo Chen, �Solving the sparsity problem in recommender systems using association retrieval,� Academy Publisher, Journal of Computers, Vol. 6, No. 9, September 2011.
[11] Lili Wu, �Browsemap: Collaborative Filtering At LinkedIn,� October 23, 2014
[12] Greg Linden, Brent Smith, Jeremy York, �Amzon.com: recommendations- item-to-item collaborative filtering,� Industry report, Published by the IEEE Computer Society 1089-7801/03/$17.00�2003 IEEE Internet computing
[13] Jai Prakash Verma, Bankim Patel, Atul Patel, �Big Data Analysis: Recommendation System with Hadoop Framework,� Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on 13-14 Feb. 2015, pp.92�97, DOI: 10.1109/CICT.2015.86.
[14] Seikyung Jung, Juntae Kim, Herlocker, �Applying collaborative filtering for efficient document search,� Web Intelligence, Proceedings 2004, IEEE/WIC/ACM International Conference,pp. 640-643, DOI: 10.1109/WI.2004.10126.
[15] ShivlalMewada, Umesh Kumar Singh, "Measurement Based Performance of Reactive and Proactive Routing Protocols in WMN ", International journal of Advance Research in Computer science and software Engineering,Volume-1,Issue-1, December 2011.
[16] Sarita Sharma, Priyanka Agiwal, Rakesh Gaherwal, Shivlal Mewada and Pradeep Sharma, �Analysis of Recovery Techniques in Data Base Management System�, Research Journal of Computer and Information Technology Sciences, Vol-4, Issue-3, pp.(4-8), March 2016 . -ISSN 2320 � 6527, DOI: dx.doi.org/10.13140/RG.2.2.23964.49289
[17] Albert Bifet, �Mining Big Data in Real Time,� Informatica 37 (2013) 15�20.
[18] Chong-Ben Huang, Song-Jie Gong, �Employing rough set theory to alleviate the sparsity issue in recommender system�, Proceeding of the Seventh International Conference on Machine Learning and Cybernetics (ICMLC2008), IEEE Press, 2008, pp.1610-1614.
[19] GediminasAdomavicius, Alexander Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions". IEEE Transaction on Knowledge and Data Engineering, 2005.17(6): pp. 734-749.
[20] YiBo Huang, �An item based collaborative filtering using item clustering prediction,� 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (Volume: 4), Aug 2009, pp.54 � 56, 2009 IEEE, ISBN: 978-1-4244-4247-8,
[21] Gui-RongXue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-Jun Zeng, Yong Yu, Zheng Chen, �Scalable collaborative filtering using cluster-based smoothing,� Proceedings of the ACM SIGIR Conference 2005, pp.114�121
[22] Yi-Chung Hu, �Non-additive similarity-based single-layer perceptron for multi-criteria collaborative filtering,� Journal Neurocomputing, Volume 129, April, 2014, pp. 306-314.
[23] Z. R. Deng, X. Zhang, X. Deng, L. Xu, W. M. Huang, �An improvement of video recommender similarity measurement model,� International Conference on Automation, Mechanical Control and Computational Engineering(AMCCE 2015), pp.675-680, 2015.
[24] J. Chandrika, K.R Ananda Kumar, �Data stream querying: challenges and issues,� Int.Conf. on Computer Applications, ISBN: 978-981-08-7300-4, 2011.
[25] Y. K. Park, S. C. Park, W. S. Jung, S. G. Lee, �Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph,� Expert Systems with Applications, vol. 42, no.8 pp.4022-4028, 2015.
[26] SongJie Gong, HongWu Ye, HengSong Tan, �Combining memory-based and model-based collaborative filtering in recommender system�, 2009 Pacific-Asia Conference on Circuits, Communications and System, 978-0-7695-3614-9/09 �2009 IEEE, DOI 10.1109/PACCS.2009.66
[27] Changbingchen, Xia Yang, Bong Zoebir, SivadonChaisiri, �A workflow framework for big data analytics: event recognition in a building,� 2013 IEEE Ninth World Congress on Services, pp.21�28, 2013 IEEE, DOI: 10.1109/SERVICES.2013.29.
[28] Skategui, �Recommendation algorithms with Apache Mahout,� [Online] Apr 11.
[29] R. Murugesh and I. Meenatchi, "A Study Using PI on: Sorting Structured Big Data In Distributed Environment Using Apache Hadoop MapReduce", International Journal of Computer Sciences and Engineering, Volume-02, Issue-08, Page No (35-38), Aug -2014,
[30] Dunning, �Accurate methods for the statistics of surprise and coincidence,� Computer Linguist, vol. 19, no. 1, pp. 61-74, Mar, 2003.
[31] N. Rastin and M. ZolghadriJahromi, �Using content features to enhance performance of user-based collaborative filtering performance of user-based collaborative filtering,� Int. journal of artificial intelligence and applications, vol. 5, no. 1, pp. 53-62, Jan 2014.
[32] F. Ricci, L. Rokach, B. Shapira, �Introduction to recommender systems handbook,� New York: Springer, 2011, pp. 1-35.
[33] Manu M N, Anandakumar K R, �A current trends in big data landscape,� 2015 IEEE International Conference on Computational Intelligence and Computing Research, IEEE 2015, 978-1-4799-7849-6/15.
[34] Qiao Cheng, Xiangke Wang, Dong Yin, YifengNiu, �The new similarity measure based on user preference models for collaborative filtering,� Information and Automation, 2015 IEEE International Conference, Aug 2015, pp.577-582, 2015 IEEE, DOI: 10.1109/ICInfA.2015.7279353.
[35] Mazin S. Al-Hakeem, "A Proposed Big Data as a Service (BDaaS) Model", International Journal of Computer Sciences and Engineering, Volume-04, Issue-11, Page No (1-6), Nov -2016, E-ISSN: 2347-2693
[36] V.Vijayadeepa, Archana.G "Semantic Based Service Recommendation using Collaborative Filtering", SSRG International Journal of Computer Science and Engineering (SSRG - IJCSE), V3 (10), 13-19 October 2016. ISSN:2348 � 8387.
Citation
M.N. Manu, B. Ramesh , "Single-criteria Collaborative Filter Implementation using Apache Mahout in Big data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.7-13, 2017.
A Novel Approach utilizing Permutation Polynomials over integer rings as a Cryptological Application for Effective Encryption of Digital Images
Research Paper | Journal Paper
Vol.5 , Issue.1 , pp.14-21, Jan-2017
Abstract
Internet Technology and its constant evolvement gives humans an opportunity to be an active explorer over social media and micro blogging sites. Due to which people have resorted to a route of Internet with images, a tempting platform which is hard avoid. This theme gained an overwhelming response as a communication medium for the obvious reason that people nowadays like to share or express their thoughts through images. Unlike text, images helps to annotate viewers` flow of emotions much conveniently with depth. But people relying more on power imagery to share information over unreliable channel, well-known as Internet invites exploitation and misapplication of confidential data which should be avoided. Considering the importance of the issue raised in the light of protecting images, encryption is a way to assure security. Image encryption techniques converts an original image to illegible form, so only authorized access is possible with a key. Over years, different image encryption schemes had been put forward with various issues being addressed according to the requirement of applications. In this paper, we proposed a novel image encryption algorithm based on permutation polynomials over integer rings which makes an attempt to overcome the limitations of existing methods. Here, the original image is scrambled by applying permutation polynomial to its rows and columns. Eventually, the experimental results and calculating the evaluation parameters using MATLAB shows that the proposed encryption scheme achieve satisfactory hiding aspect. Also, the comparison with respect to existing ones is made to analyze performance of the proposed technique.
Key-Words / Index Term
Cryptography; Image Encryption; Permutation Polynomials; Internet; integer rings; MATLAB
References
[1] William Stallings, �Cryptography and network security: principles and practiceˮ, Sixth Edition- 2014, ISBN:978-93-325-1877-3
[2] What is encryption, http://www.searchsecurity.techtarget.com/ definition/encryption, Nov 2014.
[3] Basic Properties of Digital Images, http://www.olympus-lifesciencce.com/en/primer/digitalimaging/digitalimagebasics/
[4] Cryptography: The Science of Secrecy, http://www.ankitjain. info/articles/Cryptography_ankit2.htm
[5] Sudipta Sahana and Abhipsa Kundu, "A Novel Approach on Adaptive Block Steganography Based Crypting Technique for Secure Message Passing", International Journal of Computer Sciences and Engineering, Volume-02, Issue-12, Page No (42-46), Dec -2014
[6] Mohammad Ali Bani Younes and Aman Jantan, �An image encryption approach using block based transformation algorithm,� IAENG International Journal of Computer Science, 35:1, IJCS_35_1_03, Feb 19, 2008.
[7] Shivlal Mewada, Sharma Pradeep, Gautam S.S., �Classification of Efficient Symmetric Key Cryptography Algorithms�, International Journal of Computer Science and Information Security (IJCSIS) USA, Vol. 14, No. 2, pp.(105-110), Feb 2016 .ISSN: 1947-5500.
[8] Tiegang Gao and Zengqiang Chen, �Image encryption based on a new total shuffling algorithm,� Chaos, Solitons and Fractals, ISSN 0960-0779, Volume-38(1), Page No (213-220), 2008.
[9] Li Zhang, Xiaolin Tian and Shaowei Xia, �A scrambling algorithm of image encryption based on Rubik`s cube rotation and Logistic sequence,� IEEE International Conference: Multimedia and Signal Processing (CMSP), Volume-1, Page No (312-315), 2011.
[10] Khaled Loukhaouka, Jean-Yves Chouinard and Abdellah Berdai, �A secure image encryption algorithm based on Rubik`s cube principle,� Hinsawi Publishing Corporation, Journal of Electrical and Computer Engineering, Volume-2012, Laval University, 2012.
[11] Md Asif Mushtaque, "Comparative Analysis on Different parameters of Encryption Algorithms for Information Security", International Journal of Computer Sciences and Engineering, Volume-02, Issue-04, Page No (76-82), Apr -2014
[12] Joshi Rohit A, Joshi Sumit S and G.P. Bhosale, �Improved image encryption algorithm using chaotic map,� International Journal of Computer Applications (0975-8887), Volume-32, Issue-09, Page N (6-10), October 2011.
[13] Nilesh Y.Choudhary and Ravindra K.Gupta, �Partial image encryption based on block-wise shuffling using Arnold catmap,� International Journal of Computer Applications, Volume-97, Page No (33-37), Issue-10, July 2014.
[14] R.A. Collin and C. Small, �On Permutation Polynomials over finite fields,� Internat. J. Math. & Math. Sci., Volume-10, Issue-03, Page No (535-544), 1987.
[15] R.Lidl and GL Mullen, �When does a polynomial over a finite field permute the ekements of the field,ˮ The American Math, Monthly, Volume-95, Issue-03, Page No (243-246), 1988.
[16] Ronald L Rivest, �Permutation Polynomial Modulo 2w,ˮ Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, October 25, 1999.
[17] Ashok Sharma, RS Thakur and Shailesh Jaloree, "Investigation of Efficient Cryptic Algorithm for Storing Video Files in Cloud", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Volume-04, Issue-06, Page No (8-14), Dec 2016
[18] Ashok Sharma, R S Thakur and Shailesh Jaloree, "Investigation of Efficient Cryptic Algorithm for image files Encryption in Cloud", ISROSET-International Journal of Scientific Research in Computer Science and Engineering, Volume-04, Issue-05, Page No (5-11), Oct 2016
[19] Shivlal Mewada, Sharma Pradeep, Gautam S.S., �Exploration of Efficient Symmetric Algorithms�, IEEE 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)�, pp(663 � 666), March, 2016, ISBN 978-93-80544-20-5
[20] H.Zhao and P. Fan,�Simple method for generating mth order permutation polynomials over integer rings,� Electronics letters, Volume-43, Issue-08, April 12, 2007.
[21] Wagh Neha Balu, �Permutation based Digital Image Encryption and Decryption Methods,ˮ CiiT International Journal of Digital Image Processing, Volume-08, Issue-10, Page No (320-323), Dec 2016.
[22] Yue Wu, Joseph P. Noonan, and Sos Agaian, �NPCR and UACI Randomness Tests for image encryption,� Cyber Jounals: Multidisplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), April 2011.
[23] Shrija Somaraj and Mohammed Ali Hussain, �Performance and Security Analysis for image encryption using Key image,� Indian Journal of Science and Technology, Volume-08, Issue-35, Dec 2015.
[24] Chaitanya Vijaykumar Mahamuni and Neha Balasaheb Wagh, �Study of CBIR Methods for Retrieval of Digital Images based on Colour and Texture Extraction,ˮ International Conference on Computer Communication and Informatics (ICCCI-2017), Jan 05th-07th, 2017, Coimbatore, pp (305-311) IEEE Xplore Digital Library.
[25] Mr. Chaitanya V. Mahamuni, �A Comphrensive Study of Cryptography and Content Hiding Techniques for Security of Digital Videos,ˮ International Journal of Advance Foundation and Research in Computer (IJAFRC), Volume-02, Issue-12, Page No (46-52), Dec 2015.
[26] Mr. Chaitanya V. Mahamuni,�Digital Video Watermarking using DWT and PCA in encrypted domain,ˮ Research Chronicler International Multidisplinary Research Journal (RCIMRJ), Volume-02, Issue-03, March 2014.
[27] MATLAB-Wikipedia, http://en.wikipdeia.org/wiki/MATLAB, Initial release-1984.
Citation
N.B. Wagh, M. Kolhekar, "A Novel Approach utilizing Permutation Polynomials over integer rings as a Cryptological Application for Effective Encryption of Digital Images," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.14-21, 2017.
Review of Change Detection Techniques for Remotely Sensed Images
Review Paper | Journal Paper
Vol.5 , Issue.1 , pp.22-25, Jan-2017
Abstract
The use of remotely sensed image has been a wide area in research. Different images of the same scene captured by the satellites at different times can provide information about the significant changes that held on earth with time. Many techniques for change detection has been developed while new techniques are also emerging. This paper gives a review of pixel based and transformation based techniques used for change detection. The paper begins with the discussion on pixel based techniques like image differencing, Image Ratioing and Image regression. Then, transformation based techniques like principal component analysis and change vector analysis has been discussed in detail. The paper has been concluded with the comparison of the discussed techniques based on advantages, limitations and applications.
Key-Words / Index Term
Change Detection; Principal Component Analysis; Image Ratioing; Image Differencing
References
[1] A. Singh, �Digital change detection techniques using remotely-sensed data�, International Journal of Remote Sensing, Volume-10, Issue-06, pp (989�1003), 1989.
[2] Turgay Celik, �Unsupervised multiscale change detection in multitemporal synthetic aperture radar images�,Signal processing conference, 17th European, 2009, pp (1547-1551).
[3] Masroor Hussain A et. al, �Change detection from remotely sensed images: From pixel-based to object-based approaches�, ISPRS Journal of Photo- grammetry and Remote Sensing, pp ( 91�106), 2013.
[4] X. Dai and S. Khorram, �The effects of image misregistration on the accuracy of remotely sensed change detection�, IEEE Trans. Geosci.Remote Sensing, Volume-36, pp (1566�1577), Sept. 1998.
[5] Swarnajyoti Patra, �Histogram thresholding for unsupervised change detection of remote sensing images�, International Journal of Remote Sensing, Volume- 32, Issue- 21, pp (6071�6089), Aug 2011.
[6] Lorenzo Bruzzone, et. al, �Automatic Analysis of the Difference Image for Unsupervised Change Detection�, IEEE Transactions on Geoscience and Remote Sensing, Volume-38, Issue-3, pp (1171-1182), May 2000.
[7] Howarthi, P. et al, �Procedure for change detection using Landsat digital data�, International Journal of Remote Sensing, Volume- 2, pp (277-291), 1981,
[8] Schowengerdt, R.A., �Techniques for Image Processing and Classification in Remote Sensing�, Academic Press, New York, 1983.
[9] Howarthi, P, �Landsat digital enhancements for change detection in urban environment�, Remote Sensing of Environment, Volume-13, pp (149-160), 1983.
[10] Todd, W. J, �Urban and regional land use change detected by using Landsat data�, Journal of Research by the US Geological Survey, Volume-5, pp (527-534), 1977.
[11] Maoguo Gong et al., �Change detection in synthetic aperture radar images based on Image fusion and fuzzy clustering�, IEEE transactions on Image processing, Volume-21, Issue-4, April 2012.
[12] M. L. Nordberg, Evertson J, �Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM _ and ETM+ _ imagery�, Land Degradation & Development, Volume-16, pp (139-149), March 2005.
[13] Singh, A., �Change detection in the tropical forest environment of northeastern India using Landsat, Remote sensing and tropical land management�, J. Wiley, New York, pp (237�254), 1986.
[14] Rouhollah Dianat, "Change Detection in Optical Remote Sensing Images Using Difference-Based Methods and Spatial Information�, IEEE Geoscience and Remote Sensing Letters, Volume-7, Issue- 1, pp (215 - 219), 2010.
[15] Singh A., �Standardized principal component�, International Journal of Remote Sensing, Volume-6, pp (883-896), 1985.
[16] Deng, J.S. et. al, �PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data�, International Journal of Remote Sensing, pp (4823�4838), 2008.
[17] Fung F. et. al, �Application of Principle Components Analysis to Change Detection�, Photogrammetric Engineering & Remote Sensing, Volume-53, Issue-12, pp (1649-1658), 1987.
[18] Byrne, G.F., et. al,�Monitoring land-cover change by principal component analysis of multitemporal landsat data�, Remote Sensing of Environment, Volume-10, pp (175�184), 1980.
[19] Turgay Celik, �Unsupervised change detection in satellite images using principal component analysis and k means clustering�, IEEE Geoscience and remote sensing letters, Volume-6, Issue-4, pp (772-776), 2009.
[20] Johnson, R. D., et. al, �Change vector analysis: a technique for the multispectral monitoring of land cover and condition�, International Journal of Remote Sensing, pp (411�426), 1998.
[21] Francesca Bovolo, et. al,�A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images�, IEEE Transactions on Geoscience and Remote Sensing, Volume-50, Issue-6, pp (2196 � 2212), 2012.
[22] Chang Huang et al., �Surface water change detection using change vector analysis�, IEEE international Geoscience and remote sensing symposium, pp (2834-2837), July 2016.
[23] Abhishek Sharma et al, �A review of Hyperspectral imaging and its applications�, proceedings of National Conference on Emerging Trends in Applications of Electronics and Communication Technology, pp (427-431), January 2014.
[24] Son Tong Si et al., �Land Cover Change Analysis using Change Vector Analysis method in Duy Tien District, Ha Nam Province in Vietnam�, 7th FIG Regional Conference, Hanoi, Vietnam, pp (19-22), 2009.
Citation
A. Sharma, T. Gulati, "Review of Change Detection Techniques for Remotely Sensed Images," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.22-25, 2017.
Wavelet approximated texture data watershed transform (WATDWT) segmentation of Bio-Medical Images
Research Paper | Journal Paper
Vol.5 , Issue.1 , pp.26-31, Jan-2017
Abstract
Extraction of features from the biomedical image using the texture and color space based image processing analysis algorithm is developed using hybrid of DWT, entropy filtering and watershed transform is discussed in this article. To extract the textures we have used entropy features using function on the MATLAB algorithm where it corresponds to the input image parameter with the use of spatial based parameters. The texture analysis based skin texture extraction algorithm consists of steps related to decomposing the input image into a set of binary images from which the color space dimensions of the resulting regions can be computed in order to describe segmented texture patterns.
Key-Words / Index Term
Image segmentation, Image texture analysis, Image watershed transform, Image dwt2
References
[1] Rafael C. Gonzalez, Richard E. Woods, �Digital Image Processing�, 2nd ed., Beijing: Publishing House of Electronics Industry, 2007.
[2] W. X. Kang, Q. Q. Yang, R. R. Liang ,�The Comparative Research on Image Segmentation Algorithms�, IEEE Conference on ETCS, pp. 703-707, 2009
[3] Ankur Mudgal and Rajdeep Singh, "A Hybrid Method of Medical Image De-nosing Using Subtraction Transform and Radial Biases Neural Network", International Journal of Computer Sciences and Engineering, Volume-03, Issue-09, Page No (54-59), Sep -2015
[4] Kamalakshi N, H Naganna and M N Shanmukhaswamy , "Modification of BM3D Algorithm for Representing Volumetric Data on Medical Images", International Journal of Computer Sciences and Engineering, Volume-01, Issue-04, Page No (11-17), Dec -2013
[5] W. X. Kang, Q. Q. Yang, R. R. Liang ,�The Comparative Research on Image Segmentation Algorithms�, IEEE Conference on ETCS, pp. 703-707, 2009.
[6] Rohlfing T, Brandt R, Menzel R, Russakoff DB,Maurer CR Jr (2005) Quo vadis, atlas-based segmentation? In: Suri JS, Wilson DL, Laxminarayan S (eds) Handbook of Biomedical Image Analysis, vol III: Registration Models. Kluwer Academic/Plenum Publishers, New York, pp 435�486, chapter 11
[7] Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, van Ginneken B (2009) Multiatlas-based segmentation with local decision fusion-application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging 28(7):1000�1010.
[8] Stancanello J, Romanelli P, Modugno N, Cerveri P, Ferrigno G, Uggeri F, Cantore G (2006) Atlas-based identification of targets for functional radiosurgery. Med Phys 33(6):1603�1611.
[9] Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM (2003) Establishing a normative atlas of the human lung: intersubject warping and registration of volumetric CT images. Acad Radiol 10(3):255�265
[10] Park H, Bland PH, Meyer CR (2003) Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging 22(4):483�492
[11] Leemput KV (2009) Encoding probabilistic brain atlases using Bayesian inference. IEEE Trans Med Imaging 28(6):822�837.
[12] Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226�239
[13] Artaechevarria X, Munoz-Barrutia A, Ortiz-de-Solorzano C (2009) Combination strategies in multi-atlas image segmentation: application to brain MR data. IEEE Trans Med Imaging 28(8):1266�1277
[14] Rohlfing T, Russakoff DB, Maurer CR (2004) Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans Med Imaging 23(8):983�994.
[15] Rohlfing T, Maurer CR Jr (2007) Shape-based averaging. IEEE Trans Image Process 16(1): 153�161
Citation
P. Ranjan, P.R. Khan, "Wavelet approximated texture data watershed transform (WATDWT) segmentation of Bio-Medical Images," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.26-31, 2017.
Automatic Clustering Based On Outward Statistical Testing Using Advanced Density Metrics
Survey Paper | Journal Paper
Vol.5 , Issue.1 , pp.32-35, Jan-2017
Abstract
Clustering is the process of organizing objects into several groups whose members are similar in some way and is very important technique in data mining as it has applications spread extensively example marketing, biology, pattern recognition etc. Various algorithms have been proposed, published, implemented for clustering like the one published by Rodriguez and Liao but this algorithm is dependent and sensitive to specified parameters and also faces difficulties in identification of ideal problems. Another one published by G. Wang and Q. Song this algorithm do not list all possible numbers of the nearest neighbors and the accuracy is not better in terms of Olivetti face data set then impact of this on performance. This paper overcome the problem faces by above algorithm so, here proposes a new clustering method that will identify cluster centers automatically via statistical testing. Here first define a new metric to evaluate the local density of an object which is named K-density and second metric is define to evaluate the distance of an object to its neighbors with higher density. Then, product of these two metrics is used to evaluate the centrality of each object. After analyzing the distribution of these metrics further transformed the clustering center identification into a problem of extreme-value detection from a long-tailed distribution Finally, apply outward statistical testing method to detect the clustering centers automatically and then completed the clustering process by assigning each of the rest objects to the cluster that contains its nearest neighbor with higher K-density.
Key-Words / Index Term
Clustering, Clustering Center Identification, Long-tailed Distribution, Outward Statistical Testing
References
[1] G. Wang and Q. Song, "Automatic Clustering via Outward Statistical Testing on Density Metrics," in IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 8, pp. 1971-1985, Aug. 1 2016.
[2] A. Rodriguez and A. Laio, �Clustering by fast search and find of density peaks,� Science, vol. 344, no. 6191, pp. 1492�1496, 2014.
[3] C.-P. Lai, P.-C. Chung, and V. S. Tseng, �A novel two-level clustering method for time Series data analysis,� Expert Systems with Applications, vol. 37, no. 9, pp. 6319�6326, 2010.
[4] I. Rui Xu, Donald C. Wunsch, �Clustering algorithms in biomedical research: a review,� IEEE Reviews in Biomedical Engineering, vol. 3, pp. 120�154, 2010.
[5] W. C. Xiankun Yang, �A novel spatial clustering algorithm based on delaunay triangulation,� J. Software Engineering & Applications, vol. 3, pp. 141�149, 2010.
[6] B. Nadler and M. Galun, �Fundamental limitations of spectral clustering,� in Advances in Neural Information Processing Systems, 2006, pp. 1017�1024.
[7] T. Warren Liao, �Clustering of time series data-a survey,� Pattern Recognition, vol. 38, no. 11, pp. 1857�1874, Nov. 2005.
[8] o. Dongquan Liu, Sourina, �Free-parameters clustering of spatial data with non-uniform density,� in IEEE conference on cybernetics and intelligent systems, 2004, pp. 387 � 392.
[9] T. Kanungo, D. M. Mount, N. S. Netanyahu, C.D. Piatko, R. Silverman, and A. Y.Wu, �An efficient k-means clustering algorithm: analysis and implementation,� IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881 � 892, 2002.
[10] V. Estivill-Castro and I. Lee, �Argument free clustering for large spatial point-data sets via boundary extraction from Delaunay diagram,� Computers, Environment and Urban Systems, vol. 26, no. 4, pp. 315�334, 2002.
[11] P. S. Bradley, O. L. Mangasarian, and W. N. Street, �Clustering via concave minimization,� Advances in neural information processing systems, pp. 368�374, 1997.
[12] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, �A density-based algorithm for discovering clusters in large spatial databases with noise.� in Proceedings of International Conference on Knowledge Discovery and Data Mining, vol. 96, no. 34, 1996, pp. 226�231.
[13] L. Hagen and A. B. Kahng, �New spectral methods for ratio cut partitioning and clustering,� IEEE Transactions on Computer-aided design of integrated circuits and systems, vol. 11, no. 9, pp. 1074�1085,1992.
[14] W. E. Donath and A. J. Hoffman, �Lower bounds for the partitioning of graphs,� IBM Journal of Research and Development, vol. 17, no. 5, pp. 420�425, 1973.
Citation
A.A. Jadhav, V.S. Gaikwad, "Automatic Clustering Based On Outward Statistical Testing Using Advanced Density Metrics," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.32-35, 2017.
Relaxed Constraints Formulation for Non-linear Distance Metric Learning in Hierarchical Clustering
Survey Paper | Journal Paper
Vol.5 , Issue.1 , pp.36-39, Jan-2017
Abstract
The process of Learning distance function over different objects is called as Metric learning. In various of data mining processes like clustering, nearest neighbours etc. is very important problem that relies on distance function. For many types of data, linear model is not very useful but most of metric learning methods assumes linear model of distance. In the recent nonlinear data demonstrated potentialpower of non-Mahalanobis distance function, particularly tree-based functions. This leads to a more robust learning algorithm. We compare our method to a number of state-of-the-art benchmarks on k-nearest neighbour classification, large-scale image retrieval and semi supervised clustering problems. Then we find that our algorithm yields results comparable to the state-of-the-art. A novel tree-based non-linear metric learning method can have information from both constrained and unconstrained points. And hierarchical nature of training can minimize the constraint satisfaction problem as it won�t have to go through the constraint satisfaction process per object but per hierarchy. Combining the output of many of the resulting semi-random weak hierarchy metrics and by introducing randomness during hierarchy training, we can obtain a powerful and robust nonlinear metric model.
Key-Words / Index Term
Similarity Measures, Clustering, , Image Retrieval, Classification, Data Mining,Constrained And Unconstrained Point
References
[1] D. M. Johnson, C. Xiong and J. J. Corso, "Semi-Supervised Nonlinear Distance Metric Learning via Forests of Max-Margin Cluster Hierarchies," vol. 28, no. 4, pp. 1035-1046, April 1 2016.
[2] A. Bellet, A. Habrard, and M. Sebban, �A survey on metric learning for feature vectors and structured data,� arXiv preprint arXiv:1306.6709, 2013.
[3] J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, �Information theoretic metric learning,� in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 209�216.
[4] C. Shen, J. Kim, L. Wang, and A. van den Hengel, �Positive semidefinite metric learning with boosting,� in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1651�1660.
[5] J. Blitzer, K. Q. Weinberger, and L. K. Saul, �Distance metric learning for large margin nearest neighbor classification,� in Proc. Adv. Neural Inf. Process. Syst., 2005, pp. 1473�1480.
[6] Y. Ying and P. Li, �Distance metric learning with eigen value optimization,� J. Mach. Learn. Res., vol. 13, pp. 1�26, 2012.
[7] R. Chatpatanasiri, T. Korsrilabutr, P. Tangchanachaianan, and B. Kijsirikul, �A new kernelization framework for Mahalanobis distance learning algorithms,� Neurocomputing, vol. 73, no. 10, pp. 1570�1579, 2010.
[8] S. Chopra, R. Hadsell, and Y. LeCun, �Learning a similarity metric discriminatively, with application to face verification,� in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog.,2005, pp. 539�546.
[9] A. Frome, Y. Singer, and J. Malik, �Image retrieval and classification using local distance functions,� in Proc. Adv. Neural Inf. Process. Syst., 2006, pp. 417�424.
[10] K. Q. Weinberger and L. K. Saul, �Fast solvers and efficient implementations for distance metric learning,� in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 1160�1167.
Citation
A. Mhetre, V.S. Gaikwad, "Relaxed Constraints Formulation for Non-linear Distance Metric Learning in Hierarchical Clustering," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.36-39, 2017.
Reputation Based Trust Evaluation in E-Commerce Applications by Using Feedback Comments
Review Paper | Journal Paper
Vol.5 , Issue.1 , pp.40-42, Jan-2017
Abstract
Reputation and trust are two important key factors in e-commerce applications where as sellers or product ratings. In ecommerce applications, reputation is used to select best sellers among different available sellers by users. The reputation system models are used to project the different sellers based on their offering services and quality they provide to users. The trustworthiness of sellers are computed based on different models and method they opt in each model. Feedback comments of trust computation will be good impression as the users are free to direct themselves in free text feedback reviews. In proposed work we have calculating good reputation scores from users feedbacks based on a multidimensional trust model. In order to work this model, we have used algorithm for weights and ratings computation by mining feedback comments in which NLP, Topic Modelling techniques are used.
Key-Words / Index Term
Electronic commerce, CommTrust, text mining, Repudiation based models, Sentiment Analysis
References
[1] Xiuzhen Zhang, Lishan Cui, and Yan Wang, "Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments " IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING VOL:26 NO:7 YEAR 2014 .
[2] P. Resnick, R. Zeckhauser, E. Friedman, and K. Kuwabara. Reputation systems. Communications of the ACM, 43(12), 2000.
[3] P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman, �Reputation systems: Facilitating trust in internet interactions,� Commun. ACM, vol. 43, no. 12, pp. 45�48, 2000.
[4] P. Resnick and R. Zeckhauser, �Trust among strangers in internet transactions: Empirical analysis of eBay�s reputation system,� Econ. Internet E-Commerce, vol.11, no. 11, pp. 127�157, Nov. 2002.
[5]J.O�Donovan,B.Smyth,V.Evrim,andD.McLeod,�Extracting and visualizing trust relationships from online auction feedback comments,� in Proc. IJCAI, San Francisco, CA, USA, 2007, pp. 2826�2831.
[6] M. De Marneffe, B. MacCartney, and C. Manning, �Generating typed dependency parses from phrase structure parses,� in Proc. LREC, vol. 6, 2006, pp. 449�454.
[7]S. Ramchurn, D. Huynh, and N. Jennings, �Trust in multi-agent systems,�Knowl. Eng. Rev., Vol. 19, no. 1, pp. 1�25, 2004.
[8]B. Yu and M. P. Singh, �Distributed reputation management for electronic commerce,�Comput. Intell., vol. 18, no. 4, pp. 535�549, Nov. 2002.
[9] M. Schillo, P. Funk, and M. Rovatsos, �Using trust for detecting deceptive agents in artificial societies,�Appl.Artif. Intell., vol. 14, no. 8, pp. 825�848, 2000.
[10] J. Sabater and C. Sierra, �Regret: Reputation in gregarious societies,� inProc. 5th Int. Conf. AGENTS, New York, NY, USA, 2001, pp. 194�195.
[11]S. Ramchurn, D. Huynh, and N. Jennings, �Trust in multi-agent systems,�Knowl. Eng. Rev., Vol. 19, no. 1, pp. 1�25, 2004.
[12] G. Qiu, B. Liu, J. Bu, and C. Chen, �Opinion word expansion and arget extraction through double propagation," Comput. Linguist, vol. 37, no. 1, pp. 927, 2011.
[13] M. De Marneffe, B. MacCartney, and C. Manning, �Generating typed dependency parses from phrase structure parses,� in Proc. LREC, vol. 6, 2006, pp. 449�454.
[14] M. De Marneffe and C. Manning, �The stanford typed dependencies representation,� in Proc. the workshop on Cross- Framework and Cross-Domain Parser Evaluation, 2008.
Citation
P. Balaji, O. Nagaraju, D. Haritha, "Reputation Based Trust Evaluation in E-Commerce Applications by Using Feedback Comments," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.40-42, 2017.
Observing of Mobility Model against Reactive Routing Protocols in MANETs of throughput, Average end-to-end delay, packet delivery ratio
Research Paper | Journal Paper
Vol.5 , Issue.1 , pp.43-52, Jan-2017
Abstract
The Ad-hoc network is a collection of wireless mobile nodes which dynamically form a temporary network without the use of any existing network infrastructure or centralized administration. It may connect hundreds to thousands of mobile nodes. The mobile nodes communicate directly with each other without the aid of access points. They form an arbitrary topology, where the routers are free to move randomly and arrange themselves as required. In this paper, an attempt has been made to investigate the impact of mobility models on the performance of three MANET on-demand reactive routing protocols: AODV, DSR and DYMO. The mobility models that are used in this work are: Random Waypoint mobility model and Group mobility model. The performance differentials are analyzed using varying network size, varying pause time, and varying velocity. We used Qual-Net [18] from scalable networks for the simulation purpose. The performance analysis is based on different network metrics such as packet delivery ratio, throughput, and average end.
Key-Words / Index Term
Mobile Ad-hoc Network (MANET) Routing protocol; AODV; DSR; DYMO; Random Waypoint mobility; Group mobility model
References
[1] Fan Bai, Narayanan Sadagopan, and Ahmed Helmy �A framework to systematically analyze the Impact of Mobility on Performance of Routing protocols for Ad-hoc Networks � ,0-7803-7753-2/03/$17.00 (C) 2003 IEEE INFOCOM 2003
[2] Mrs. Geeta V, Dr. Sridhar Aithal, and Dr. K. Chandra Sekaran �Effect of Mobility over Performance of the Ad hoc Networks �, IEEE INFOCOM 2006
[3] Leena Pal, Pradeep Sharma, Netram Kaurav and Shivlal Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc Networks", International Journal of Scientific Research in Network Security and Communication, Vol-1, Issue-05, pp.(1-4), Dec 2013.ISSN 2321-3256, USA
[4] Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, �Simulation Based Performance Evaluation of Routing Protocols for Mobile Ad-hoc Networks (MANET)", International Journal of Computer Science, Information Technology and Security, Vol. 2, No. 4, Aug 2012. ISSN: 2249-9555
[5] Ashish Shrestha and Firat Tekiner,� International Conference on Parallel and Distributed Computing�, Applications and Technologies, International Conference on Signal Processing Systems 978-0-7695-3654-5/09 $25.00 IEEE 2009.
[6] Sanjay Singh Kushwah and G .S. Tomar,� Investigation of the effects of mobility on routing protocols in MANETs�IEEE International Conference on Ubiquitous Computing and Multimedia Applications, 2011 IEEE, DOI 11109/UCMA.2011.26 pp1-3 .
[7] S. Mohapatraa, P.Kanungo ,� Performance analysis of AODV, DSR, OLSR and DSDV Routing Protocols using NS2 Simulator � International Conference on Communication Technology and System Design Procedia Engineering 30 (2012) pp 69 � 76.
[8] S. Sagar, J. Saqib, A. Bibi, N. Javaid ,� Evaluating and Comparing the Performance of DYMO and OLSR in MANETs and in VANETs �IEEE INFOCOM, 2011.
[9] Banoj kumar Panda1 , Manoranjan Das2, Benudhar Sahu3 ,Rupanita Das,� Impact of Mobility and Terrain Size on Performance of AODV and DSR in Mobile Ad-hoc Network� 978-1-4673-1989-8/12/$31.00 IEEE INFOCOM, 2012.
[10] Performance of AODV, DSR and DSDV Protocols under varying node Movement by Veena Anand and Suresh Chandra Gupta IEEE INFOCOM, 2015.
[11] C. E. Perkins and P. Bhagwat, � Highly Dynamic Destination- Sequenced Distance-Vector Routing (DSDV) for Mobile Computers,� in Proceedings of ACM SIGCOMM, pp. 234-244, 1994.
[12] A. Boukerche,� A performance comparison of routing protocols for ad hoc networks,� In proceedings of the 15th International Parallel and Distributed Processing Symposium, SanFrancisco, CA, 2001.
[13] A. Divecha, A.C Grosan and S. Sanya , � Impact of node mobility on MANET routing protocols models� Journal of Digital Information Management, Vol.5, pp.19-23, 2007.
[14] M.S. Islam, A.Riaz and M.Tarique, �Performance Analysis of the Routing Protocols for Video Streaming Over Mobile Ad Hoc Networks� International Journal of Computer Networks &Communication (IJCNC), Volume 4, Issue 3, pp. 133-150, 2012.
[15] Umesh Kumar Singh, Shivlal Mewada, Lokesh Laddhani and Kamal Bunkar, �An Overview and Study of Security Issues & Challenges in Mobile Ad-hoc Networks (MANET)�, International Journal of Computer Science and Information Security, Vol-9, No.4, pp.(106-111), April 2011.ISSN: 1947-5500,
[16] A.H.A Rahman and Z.A. Zukarnain, �Performance comparison of AODV, DSDV and I-DSDV routing protocols in mobile ad hoc networks,� European Journal of Scientific Research, Vol.31, No.4, pp.566�576, 2009.
[17] M. K. Gulati and K. Kumar, �QoS routing protocols for mobile ad hoc networks: a survey,� International Journal of Wireless and Mobile Computing (IJWMC), Vol.5, No.2, pp.107-118, May 2012.
[18] Er. Hanisha Goyal, Er. Parveen Kakkar, � Performance Investigation of DYMO, DSR, AODV and LAR Routing Protocols using Different Mobility in MANETs�, International Journal of Engineering Research and Technology, Vol. 2, Issue 12, December 2013.
[19] Rashmi Rohankar, et. al�s, �Performance Analysis of Various Routing Protocols (Proactive and Reactive) for Random Mobility Models of Adhoc Networks�, IEEE conf., 2012.
[20] S. Mohapatra, P.Kanungo, �Performance analysis of AODV, DSR, OLSR and DSDV Routing Protocols using NS2 Simulator�, Procedia Engineering, Vol. 30, 2012, pp. 69-76.
Citation
P. Pal, R. Pal , "Observing of Mobility Model against Reactive Routing Protocols in MANETs of throughput, Average end-to-end delay, packet delivery ratio," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.43-52, 2017.
A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling
Survey Paper | Journal Paper
Vol.5 , Issue.1 , pp.53-59, Jan-2017
Abstract
In cognitive radio networks (CRN) an intelligent spectrum allocation is an immense subjects because of its deficiency of spectrum demand. Resources in CRN would be allocated based on dynamic access methods with respected to sensed radio environment. A principal research trial is that how would be allocated or assigned presented unused spectrum to unlicensed users. In this paper we provide a comprehensive survey of dynamic resource allocation and priority scheduling in MIMO-CRN. The appropriate portion of unmoving recurrence range existing together intellectual radios while amplifying all out transmission capacity utilization what`s more, reducing impedance is required for the dynamic range use in MIMO-CRN. The system for established extent segment came to achievement to less range utilization over the entire range. In this paper we presented the different approaches used for dynamic resource allocation and scheduling in heterogeneous MIMO-CRN.
Key-Words / Index Term
Cognitive Radio; Dynamic Scheduling; Priority Queue; Heterogenetive Services, MIMO
References
[1] S.Tamilarasasan, Dr.P.Kumar,�A Servey on Dynamic Resource Allocation in Cognitive Radio Networks�, International Journals of Computer Science and Engineering (IJCSE), volume: 4, Issue:7, E-ISSN: 2347-2693, Jul-2016, PP: 86-93.
[2] S.Tamilarasan, Dr.P.Kumar, �Dynamic Resource Allocation in Cognitive Radio Networks-Priority Secheduling approach: Literature Survey�, International Journals of Computer Science and Engineering (IJCSE), volume: 4, Issue:8, E-ISSN: 2347-2693, Aug-2016, PP:01-11.
[3] C.Gao, S.Chu, X.Wang, �Distributed Scheduling in MIMO Empowered Cognitive Radio Ad Hoc Networks�, IEEE Transactions On Mobile Computing, VOL. 13, NO. 7, July 2014, PP: 1456-1468.
[4] Santhamurthy Tamilarasan, Kumar Parasuraman�, Dynamic Resource Allocation and Priority Based Scheduling for Heterogeneous Services in Cognitive Radio Networks�, International Journal of Intelligent Engineering & Systems (IJIES), Vol.9, No.3, 2016, PP: 127-136.
[5] Senthilmurugan S, J. Ansari, P. Mahonen, �Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times�, arXiv:1607.04450v1 [cs.NI] 15 Jul 2016, PP: 1-14.
[6] Z.Khan, J. J. Lehtomaki, L. A. DaSilva, E.Hossain, M. L.Aho, �Opportunistic Channel Selection by Cognitive Wireless Nodes Under Imperfect Observations and Limited Memory: A Repeated Game Model�, IEEE Transactions On Mobile Computing, VOL. 15, NO. 1, January 2016, PP: 173-187.
[7] H.B. Salameh, �Opportunistic Spectrum Sharing in Dynamic Access Networks: Deployment Challenges, Optimizations, Solutions, and Open Issues�, arXiv:1605.02230v1 [cs.NI] 7 May 2016,
[8] J.Raiyn, �A Cognitive Radio Scheme for Dynamic Resource Allocation Based On QoE�, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 1, February 2016, PP: 1-10.
[9] S. Taori, �Energy Efficient Resource Allocation in Cognitive Radio using LabVIEW�, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 5, Issue 2, February 2016, PP: 193-197.
[10] R. Xie, F. R.Yu, H.Ji, �Dynamic Resource Allocation for Heterogeneous Services in Cognitive Radio Networks With Imperfect Channel Sensing�, IEEE Transactions On Vehicular Technology, VOL. 61, NO. 2, February 2012, PP: 770-780.
[11] B.S.Awoyemi, B.T.J.Maharaj, A.S.Alfa, �Solving resource allocation problems in cognitive radio networks: a survey�, EURASIP Journal on Wireless Communications and Networking (2016) 2016:176 DOI10.1186/s13638-016-0673-6, PP: 1-14.
[12] Dr.S.Tapaswi, Dr.R.S.Jadon, P.Shrma, �Dynamic Spectrum Allocation Technique with Reduced Noise in Cognitive Radio Networks�, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.1, January 2010, PP: 274-280
[13] W.Wang,, K.G. Shin, W. Wang,, �Distributed Resource Allocation Based on Queue Balancing in Multihop Cognitive Radio Networks�, IEEE/ACM Transactions On Networking,VOL.20,NO.3,june 2012, PP: 837-850.
[14] Z. Shu, Y. Qian, Yaoqing, Yang, H.Sharif, �A Game Theoretic Approach for Energy Efï¬cient Communications in Multi-hop Cognitive Radio Networks�
[15] A.C.Sudhir, B.Prabhakar Rao, �Priority Based Resource Allocation for MIMO-Cooperative Cognitive Radio Networks�, Journal of Scientific & Industrial Reaserch , Vol. 75, November 2016, PP: 667-670.
[16] D.Ruby, M.Vijayalakshmi, � Fuzzy Based Resource Allocation for Cognitive Radio Relay Networks�, Asian Journals of Technology 15 (7), 2016, PP: 1213-1222.
[17] A.Mohamedou, A.Sali, B.Ali, M.Othman, �Dynamical Spectrum Sharing and Medium Access Control for Heterogeneous Cognitive Radio Networks�, International Journal of Distributed Sensor Networks Volume 2016, PP: 1-15.
[18] Dr. R.K. Chauhan, Dr. Ramesh Kait, �Dynamic Fair Priority Optimization Task Scheduling Algorithm in Cloud Computing: Concepts and Implementations�, I. J. Computer Network and Information Security, 2016, 2, PP: 41-48.
[19] G.Aldabbagh, S.Bakhsh, N.Akkar, S.Tahirc, H. Tabrizi, J.Ciofï¬, �QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces�, Computer Networks-81(2015), PP: 136-146.
[20] S.Sun, N.Chen, T.Ran, J.Xiao, T.Tian, �A Stackelberg game spectrum sharing scheme in cognitive radio-based heterogeneous wireless sensor networks�, Signal Processing, 126 (2016), PP: 18-26.
[21] N.Tadayon, S.Aıssa, �A Multi-Channel Spectrum Sensing Fusion Mechanism for Cognitive Radio Networks: Design and Application to IEEE 802.22 WRANs�, IEEE Transactions On Cognitive Communications And Networking, January 2016, PP: 1-13.
[22] X.Yang, X.Tan, L.Ye, L.Ma, �Spectrum Handoffs Based on Preemptive Repeat Priority Queue in Cognitive Radio Networks�, Communication Research Center, Harbin Institute of Technology, Harbin 150080, China; 20 July 2016 , PP: 1-19.
[23] J.H.Park, J.Chung, �Prioritized channel allocation-based dynamic spectrum access in cognitive radio sensor networks without spectrum handoff�, EURASIPJournalonWirelessCommunicationsand Networking (2016) 2016:266, PP: 1-8.
[24] A.Devi.B.E, K.Jayarajan.M.E, A.Sabari.M.Tech, �Improve the Energy Efficiency in Cognitive Radio Sensor Network using Spectrum Allocation�, International Journal of Computer Engineering and Information Technology, VOL. 8, NO. 11, November 2016, PP: 208�212.
[25] S.Riahi, A.E.Hore, J.E. Kafi, �Optimization of Resource Allocation in Wireless Systems Based on Game Theory�, International Journal of Computer Sciences and Engineering (IJCSE), 30 Jan 2016, PP: 1-3.
[26] B.Awoyemi, B.Maharaja, A.Alfab, �Optimal resource allocation solutions for heterogeneous cognitive radio networks�, Digital Communications and Networks, 2016, PP: 1-15.
[27] X.Gelabert, O.Sallent, J.P.Romero, R. Agust�, �Spectrum sharing in cognitive radio networks with imperfect sensing: A discrete-time Markov model�, Computer Networks 54 (2010), PP: 2519�2536.
[28] M.S Moona, V.Gulhane, �Appropriate channel selection for data transmission in Cognitive Radio Networks�, International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015, Nagpur, INDIA , Procedia Computer Science 78 ( 2016 ) PP: 838 � 844.
[29] A.Asheralieva, Y.Miyanaga, �Joint Bandwidth and Power Allocation for LTE-Based Cognitive Radio Network Based on Buffer Occupancy�, Mobile Information Systems Volume 2016, PP: 1-24.
[30] M.L.Tigang Jiang Liang Tong, �Spectrum handoff scheme for prioritized multimedia services in cognitive radio network with ï¬nite buffer�, 2013 IEEE 11th International Conference on Dependable, Autonomic and Secure Computing, 978-1-4799-3381-5/13 $31.00 � 2013 IEEE, PP: 410-415.
[31] V.Kumar, S.Minz, V.Kumar, �Performance analysis of cognitive radio networks under spectrum sharing using queueing approach�, Computers And Electrical Engineering, 000(2016), PP: 1-9.
[32] B.Choi, H.Lim, H.Kang, B.J.Jeong, �Dynamic Priority Scheduling for Heterogeneous Cognitive Radio Networks�, 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 978-1-4673-1905-8/12/$31.00 �2012 IEEE , PP: 62-64.
[33] I.A.M. Balapuwaduge, L.Jiao, Frank Y. Li, �Channel Assembling with Priority-Based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation�, IEEE Transactions On Wireless Communications, VOL. 13, NO. 2, February 2014, PP: 630-645.
[34] Y.Qin, Y.Li, W.Wu, F.Yang, X.Wang, J. Xu, �Near-Optimal Scheme for Cognitive Radio Networks With Heterogeneous Mobile Secondary Us�, IEEE Transactions On Communications, Vol. 63, NO. 4, April 2015, PP: 1106-1120.
[35] Pranil Sunil Kale, Prof. Mrs. Padma Lohiya, �Performance Enhancement of Cognitive Radio Spectrum Sharing (CRSS) with MIMO System�, IEEE International Conference on Computer, Communication and Control (IC4-2015).
[36] C.Yi, J.Cai, �Two-Stage Spectrum Sharing With Combinatorial Auction and Stackelberg Game in Recall-Based Cognitive Radio Networks�, IEEE Transactions On Communications, Vol. 62, NO. 11, November 2014, PP: 3740 � 3752.
[37] Paulo M. R. dos Santos, Mohamed A. Kalil, Oleksandr Artemenko, Anastasia Lavrenko, Andreas Mitschele-Thiel, �Self-Organized Common Control Channel Design for Cognitive Radio Ad Hoc Networks�, 2013 IEEE 24th International Symposium on Personal, Indoor and Mobile Radio Communications: Mobile and Wireless Networks, PP: 2419 � 2423.
[38] Guangchi Zhang, Xueyi Li, Miao Cui, Guangping Li, Yizhi Tan, �Transceiver design for cognitive multi-user MIMO multi-relay networks using imperfect CSI�, nternational Journal of Electronics and Communications (AE�) , 2016.
[39] Abbas Ali Shariï¬, Morteza Shariï¬, Mir Javad Musevi Niya, �Secure cooperative spectrum sensing under primary user emulation attack in cognitive radio networks: Attack-aware threshold selection approach�, International Journal of Electronics and Communications (AE�), 2015.
[40] Vahid Esmaeelzadeha, Elahe S. Hosseinia, Reza Berangia, Ozgur B. Akan, �Modeling of rate-based congestion control schemes in cognitive radio sensor networks�, Ad Hoc Networks, 2015.
[41] Mahmoud Khasawneh, Saed Alrabaee, Anjali Agarwal, Nishith Goel, Marzia Zaman, �Power trading in cognitive radio networks�, Journal of Network and Computer Applications, 2016.
[42] Chhagan Charan, Rajoo Paney, �Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio�, Optik 127 (2016), PP: 5968-5975.
[43] Hongjuan Li, Xiuzhen Cheng, Keqiu Li, Xiaoshuang Xing, Tao Jing, �Utility-Based Cooperative Spectrum Sensing Scheduling in Cognitive Radio Networks�, 2013 Proceedings IEEE INFOCOM, PP: 165-169.
[44] Yang Li , Aria Nosratinia, �Hybrid Opportunistic Scheduling in Cognitive Radio Networks�, IEEE Transactions On Wireless Communications, VOL. 11, NO. 1, January 2012, PP: 328-337.
[45] Didem Gozupek, Fatih Alagoz, �A Fair Scheduling Model for Centralized Cognitive Radio Networks�, September 2013.
[46] Jeison Marın Alfonso, Leonardo Betancur Agudelo, �Centralized Spectrum Broker and Spectrum Sensing with Compressive Sensing Techniques for Resource Allocation in Cognitive Radio Networks�, 978�1�4799�1148-6/13/$31.00�2013 IEEE.
[47] Mehdi Ghamari Adian, Hassan Aghaeinia, �Optimal resource allocation for opportunistic spectrum access in multiple-input multiple-output�orthogonal frequency division multiplexing based cooperative cognitive radio networks�, IET Signal Processing, 2013, IET Signal Process., 2013, Vol. 7, Iss. 7, PP: 549�557.
[48] Yinglei Teng, Yuanyuan Liu, Yong Zhang, �An Energy Efficient Resource Allocation in Cognitive Radio Networks with Pairwise NBS Optimization for Multi-Secondary Users�, 2013 IEEE Wireless Communications and Networking Conference (WCNC): MAC, PP: 744-749.
[49] Renchao Xie, F. Richard Yu, Hong Ji, �Spectrum Sharing and Resource Allocation for Energy-Efï¬cient Heterogeneous Cognitive Radio Networks with Femtocells�, IEEE ICC 2012 - Cognitive Radio and Networks Symposium, PP: 1661-1665.
[50] Arun Francis.G, Priyadrachini.S, �Fast Optimal Resource Allocation Possible for Multiuser OFDM-Based Cognitive Radio Networks �,International Journal of Innovative Research in Computer and Communication Engineering Vol. 1, Issue 3, May 2013, PP: 708-711.
[51] R. Soujanya, V. Harini, �Adaptive Power Allocation in OFDM Based Cognitive Radio Network using Algorithm for the MIMO Relay Path Channels�, International Journal of Innovative Technologies, Vol.04,Issue.01, January-2016, PP: 0022-0027.
[52] Mujeeb Abdullah, �Priority Queuing Based Spectrum Sensing Methodology in Cognitive Radio Network�, School of Engineering Blekinge Institute of Technology.
[53] Mst. Najnin Sultana, Kyung Sup Kwak, �Non-Preemptive Queueing-Based Performance Analysis Of Dynamic Spectrum Access For Vehicular Communication System Over TV White Space�, ICUFN 2011, PP: 43-48.
[54] Yasir Saleem, Farrukh Salim, Mubashir Husain Rehmani, �Routing and channel selection from cognitive radio network�s perspective: A survey�, Computers and Electrical Engineering 42 (2015) , PP: 117�134,
[55] Rahman Doost-Mohammady, M. Yousof Naderi, Kaushik Roy Chowdhury, �Spectrum Allocation and QoS Provisioning Framework for Cognitive Radio With Heterogeneous Service Classes�, IEEE Transactions On Wireless Communications, Vol. 13, NO. 7, July 2014.
Citation
S. Tamilarasan, P. Kumar, "A Survey on Dynamic Resource Allocation in MIMO Heterogeneous Cognitive Radio Networks based on Priority Scheduling," International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.53-59, 2017.