Volume-2 , Issue-11 , Nov - 2014 Go Back

A pentaband slotted microstrip patch antenna for wireless applications operating in the frequency range of 2-25GHz is designed and simulated. Four pentagonal slots in radiating patch and a square slot in ground plane are introduced to get improved antenna parameters. The proposed antenna resonated at five resonant frequencies 11.373GHz with return loss -42.63dB, 16.85GHz with return loss -13.52dB, 18.67GHz with return loss -11.75dB, 21.56GHz with return loss -16.72dB and 24.28GHz with return loss -45.26dB resulting in five frequency bands. Transmission line model is used to calculate initial dimensions and IE3D simulation software based on method of moments is used for optimization of proposed antenna. The simple structure of proposed antenna makes its fabrication easy and multiband operation makes it suitable for X-band, Ku-band and K-band applications.

Pentaband; Patch; Resonant Frequency; Slot

[1] R.Garg ., P. Bhartia, I. Bahl and A. Ittipiboon , “Microstrip Antenna Design Handbook”, Artech House, Canton, 2001.
[2] A. F. A. Ayoub, “Analysis of rectangular microstrip antennas with air substrates,” Journal of Electromagnetic Waves and Applications, Volume-17, Issue No.-12, Page No. (1755–1766), 2003.
[3] G. Vetharatnam, B. K. Chung, and H. T. Chuah, “Design of a microstrip patch antenna array for airborne SAR applications,” Journal of Electromagnetic Waves and Applications, Volume-19, Issue No.-12, Page No. (1687–1701), 2005.
[4] A. Das, B. Datta,S. Chatterjee ,B. Sinhamahapatra ,S. Jana , M.Mukherjee , S. K. Chowdhury, "Multi-Band Microstrip Slotted Patch Antenna for Application in Microwave Communication", International Journal of Science and Advanced Technology, ISSN 2221-8386, Volume-2, Issue No.- 9, Page No.-(91-95), 2013.
[5] B. Datta, A. Das, M. Mukherjee, A. Kundu and S. K. Chowdhury, “Triple Band Slotted Patch Antenna for Microwave Communication”, IEEE 3rd International Advance Computing Conference (IACC), Page No.- (202-206), 2013,.
[6] A. M. El-Teger and A. M. Abdin, “Design of Multi-Band Dual-Polarized Two-Port E-shaped Microstrip Antenna”, Progress in Electromagnetics Research Symposium, Beijing, China, Page No.- (519-522), 2009.
[7] G. F. Khodaei, J. Nourinia and C. Ghobadi, “A Practical Miniaturized U-slot Patch Antenna with Enhanced Bandwidth”, Progress In Electromagnetics Research, Volume-3, Page No.- (47–62), 2008.
[8] G. Rafi and L. Shafai, “Broadband microstrip patch antenna with V-slot,” IEE Proc. Microwave Antenna Propagation, Volume-151, Issue No. 5, Page No.- (435–440), 2004.
[9] S. K. Banerjee, S. Saha and R. Sanyal, “Dual Band and Tri Band Pentagonal Microstrip Antenna for Wireless Communication Systems”, International Journal of Emerging Science and Engineering (IJESE), ISSN: 2319–638, Volume-1, Issue No.-5, Page No.- (23-26), 2013.
[10] R. Kumar, J. P. Shinde and M. D. Uplane, “Effect of Slots in Ground Plane and Patch on Microstrip Antenna Performance”, International Journal of Recent Trends in Engineering, Volume-2, Issue No. 6, 2009.
[11] C. Picher and J. Anguera, “Multiband Handset Antenna Using Slots on the Ground Plane: Considerations to Facilitate the Integration of the Feeding Transmission Line”, Progress In Electromagnetics Research, Volume-7, Page No.-(95–109), 2009.
[12] A. Cabedo, J. Anguera, C. Picher, M. Ribo and C. Puente, “Multiband Handset Antenna Combining a PIFA, Slots and Ground Plane Modes”, IEEE Transactions on Antennas and Propagation, Volume-57, Page No.- (2526-2533), 2009.
[13] S.S.Chauhan, R.P.S. Gangwar and A. Sharma, “Internal Compact Printed Loop Antenna With Matching Element For Laptop Applications”, International Journal on Computer Science and Engineering, ISSN : 0975-3397, Volume-5, Issue No.-9, Page No.- (797-805), 2013.
[14] C. A. Balanis, “Antenna Theory and Design”, 3rd edition, John Wiley & Sons. Inc, 2005.
[15] A. Das, B. Datta, S. Chatterjee, M. Mukherjee, S. K. Chowdhury, "Multi-resonant Slotted Microstrip Antenna for C, X and Ku- Band Applications”, IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), Volume-2, Page No. (47-52), 2012.
[16] A. Das, B. Datta, S. Chatterjee, B. Sinhamahapatra, S. Jana, M. Mukherjee, S. K. Chowdhury, “ Multi-band Microstrip Slotted Patch Antenna for Application in Microwave Communication”, International Journal of Science and Advanced Technology,ISSN: 2221-8386, Volume-2, Issue No.-9, Page No.- (91-95), 2012.
[17] Zeland IE3D Software Manual, Version 12.2.

Anukriti Chauhan, B K Singh, R P S Gangwar and Shakti Singh Chauhan, "Pentaband Slotted Microstrip Patch Antenna for Wireless Applications," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.1-5, 2014.

In modern day communication technology has been developed a lot. With the help of communications technology in modern vehicles we estimate the people injured in an accident. By using artificial Intelligent system communication takes place between the vehicle to the emergency service and is also notified to the relatives of that person who met the accident .This paper proposes a method which is able to automatically detect road accident, notify them through vehicular network and estimate their severity based on the concept of data mining and knowledge inference. In this project we estimate the severity based upon the vehicle speed, the type of vehicle, status of airbags in the vehicle. It will estimate the severity of accident occurred using Knowledge Discovery Database (KDD) process. We develop a prototype of the vehicle based upon the crash test and previous reports. It totally reduces the time to alert the emergency service.

Data Mining; Knowledge Discovery Database

[1]J.Miller, “Vehicle-to-vehicle-to-infrastructure (V2V2I) Intelligent Transportation System Architecture,” in Proc. IEEE Intel l Veh Symp. Eindhoven, Netherlands, Jun. 2008, pp. 715–720.
[2] M. Fogue et al., “Evaluating the impact of novel message dissemination scheme for vehicular networks using real maps,” Transp. Res. Part C:
Emerg. Technol., vol. 25, pp. 61–80, Dec. 2012.
Available: http://www.obdii.com.
[3] M. Fogue et al., “Prototyping an automatic notification scheme for traffic accidents in vehicular networks,” in Proc. 4th IFIP WD, Niagara Falls, ON, Canada, Oct. 2011.
[4] B&B Electronics. (2012). The OBD-II Home Page [Online].
[5] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “The KDD process For extracting useful knowledge from volumes of data,” Commun. ACM, vol. 39, pp. 27–34, Nov. 1996.
[6] G. F. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Mach. Learn., vol. 9, pp. 309–347, Oct. 1992.
[7] T. Beshah and S. Hill, “Mining road traffic accident data to improve safety: Role of road-related factors on accident severity in Ethiopia,” in Proc. AAAI AI-D, Stanford, CA, USA, Mar. 2010.

Fenil.E, Dhivya Bharathi.R, Joana Sherly.B, "Estimation of Accident Severity and Automatic Notification to Emergency Service," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.6-10, 2014.

In this modern world, finding the desired image from huge databases has been a vital problem. Content Based Image Retrieval is an efficient method to do this. Many texture based CBIR methods have been proposed so far for better and efficient image retrieval. We aim to give a better image retrieval method by extending the Local Tetra Patterns (LTrP) for CBIR using texture classification by using additional features like Moment Invariants and Color moments. These features give additional information about the color and rotational invariance. So an improvement in the efficiency of image retrieval using CBIR is expected.

Content Based Image Retrieval (CBIR), Local Tetra Patterns (LTrP), Gabor Filters, Histogram Equalization, Moment Invariants

[1] Baochang Zhang, Yongsheng Gao, Sanqiang Zhao, and Jianzhuang Liu, “Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High- Order Local Pattern Descriptor” in IEEE Transactions on Image Processing, Vol. 19, No. 2, pp 533 - 544 Feb. 2010.
[2] Subrahmanyam Murala, R. P. Maheshwari and R. Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval” in IEEE Transactions on Image Processing, Vol. 21, No. 5, pp 2874 - 2886 May. 2012.
[3] S. Liao, Max W. K. Law, and Albert C. S. Chung “Dominant Local Binary Patterns for Texture Classification” in IEEE Transactions on Image Processing, Vol. 18, No. 5, pp 1107 – 1118 May. 2009.
[4] P.V. N. Reddy and K. Satya Prasad, “Inter Color Local Ternary Patterns for Image Indexing and Retrieval”, DOI: DIP102011005, CiiT International Journal for Digital Image Processing, Oct.2011.
[5] T. A. Mitchell, R. Bowden & M. Sarhadi, “Efficient texture analysis for industrial inspection”, International Journal of Production Research, 38:4, 967-984, DOI: 10.1080/002075400189248, Nov.2010.
[6] Abdenour Hadid, “The Local Binary Pattern Approach and its Applications to Face Analysis”, Image Processing, Theory, Tools and Applications, Conference of Sousse, pp 1-9 Nov.2010

Aasish Sipani, Phani Krishna and Sarath Chandra, "Content Based Image Retrieval Using Extended Local Tetra Patterns," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.11-17, 2014.

Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts, jokes, restaurants, financial services, life insurance, persons (online dating), and Twitter followers. In this paper, we present review of collaboration filtering for accurate web recommendation service using characteristics of QoS and user location and we use recommendation visualization map.

Service Recommendation, Collaboration Filtering, Visualization, QoS

[1] Xi chen,Zibin zheng ,Xudong Liu,Zicheng Huang and Hailong Sun, “Personalized Qos-Aware Web Service Recommendation and visualization”.
[2] M.B. Blake and M.F. Nowlan, “A Web Service Recommender System Using Enhanced Syntactical Matching,” Proc. Int’l Conf. Web Services, pp. 575-582, 2007.
[3] J.S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proc. 14th Conf. Uncertainty in Artificial Intelligence (UAI ’98), pp. 43-52, 1998.
[4] Y.H. Chen and E.I. George, “A Bayesian Model for Collaborative Filtering,” Proc. Seventh Int’l
Workshop Artificial Intelligence and Statistics, http://www.stat.wharton.upenn.edu/~edgeorge/ Research_papers/Bcollab.pdf, 1999.
[5] S. Haykin, Neural Networks: A Comprehensive Foundation, seconded. Prentice-Hall, 1999.
[6] J.L. Herlocker, J.A. Konstan, and J. Riedl, “Explaining Collaborative Filtering Recommendations,” Proc. ACM Conf. Computer Supported Cooperative Work, pp. 241-250, 2000.
[7] J. Himberg, “A SOM Based Cluster Visualization and Its Application for False Coloring,” Proc. IEEE-INNS-ENNS Int’l Joint Conf. Neural Networks, pp. 587-592, 2000, vol. 3, doi:10.1109/ IJCNN.2000.861379.
[8] Hsu, and S.K. Halgamuge, “Class Structure Visualization with Semi-Supervised Growing Self-Organizing Maps,” Neurocomputing, vol. 71, pp. 3124-3130, 2008.
[9] T. Kohonen, “The Self-Organizing Map,” Proc. IEEE, vol. 78, no. 9, pp. 1464-1480, Sept. 1990.
[10] S. Kaski, J. Venna, and T. Kohonen, “Coloring that Reveals High- Dimensional Structures in Data,” Proc. Sixth Int’l Conf. Neural Information Processing, vol. 2, pp. 729-734, 1999.
[11] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordan, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Comm. ACM, vol. 40, no. 3, pp. 77-87, 1997.
[12] G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, Jan./Feb. 2003.
[13] Z. Maamar, S.K. Mostefaoui, and Q.H. Mahmoud, “Context for Personalized Web Services,” Proc. 38th Ann. Hawaii Int’l Conf., pp. 166b-166b, 2005.
[14] M.R. McLaughlin and J.L. Herlocker, “A Collaborative Filtering Algorithm and Evaluation Metric That Accurately
Model the User Experience,” Proc. Ann. Int’l ACM SIGIR Conf., pp. 329-336, 2004.
[15] B. Mehta, C. Niederee, A. Stewart, C. Muscogiuri, and E.J.Neuhold, “An Architecture for Recommendation Based Service Mediation,”
Semantics of a Networked World, vol. 3226, pp. 250-262,2004.
[16] J. Zhang, H. Shi, Y. Zhang, “Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping,” Proc. Digital Image Computing: Techniques and Applications, pp. 229-235, 2009, doi:10.1109/DICTA.2009.46.
[17] G.shoba, A.delphie, K.lakshmi, A.rajeswari “Services recommendation accuracy and interactive Visualization from personalized QoS”International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Volume-2,Issue-2,Feb.-2014

Ms. Kshatriya Komal D. and Durugkar Santosh , "Personalized QoS Web Service Recommendation and Visualization," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.18-21, 2014.

With the wireless media becoming the most accessible commodity of the time to the people, barcodes are used as the message carriers with significant information transferred through at both ends. This paper aims to provide details about the most commonly 2D barcode, QR code in respect to its structure, symbology. Also it gives overview of different schemes that the attacker can use to deceive the people by directing them to malicious websites almost similar in domain name to the source.

QR code, Error Correction, Reed Solomon, Malicious Code, Alteration, Phishing

[1]. Damon Gura, Kevin O’Shea, Arjuna Reddy ,Micheline (Mich) Sabatté, "QR Codes",Kellogg School of Management, Friday ,March 11, 2011
[2]. A. Sankara Narayanan, "QR Codes and Security Solutions", International Journal of Computer Science and Telecommunications,Volume 3, Issue 7, July 2012
[3]. Peter Kieseberg, Manuel Leithner, Martin Mulazzani, Lindsay Munroe, Sebastian Schrittwieser, Mayank Sinha, Edgar Weippl, "QR Code Security", SBA Research, Favoritenstrasse 16 AT-1040,Vienna
[4]. Ioannis Kapsalis, "Security of QR Codes", Norwegian University of Science andTechnology, June 2013
[5]. Sona Kaushik, "Strength of Quick Response Barcodes and Design of Secure Data Sharing System ", (IJACSA) International Journal of Advanced Computer Science and Applications, Volume 2, Issue 11, 2011
[6]. Denso Wave. To two-dimensional code from the barcode, http://www.qrcode.com/aboutqr.html, November 5,2014

Dhwanish Shah and Yash Shah , "QR Code and its Security Issues," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.22-26, 2014.

Record linkage is traditionally performed among the entities of same type. It can be done based on entities that may or may not share a common identifier. In this paper we propose a new linkage method that performs linkage between matching entities of different data types as well. The proposed technique is based on one-class clustering tree that characterizes the entities which are to be linked. The tree is built in such a way that it is easy to understand and can be transformed into association rules. The inner nodes of the tree consist of features of the first set of entities. The leaves of the tree represent features of the second set that are matching. The data is split using two splitting criteria. Also two pruning methods are used for creating one-class clustering tree. The proposed system results better in performance of precision and recall.

Linkage, Clustering, Splitting, Decision Tree

[1] M.Dror, A.Shabtai, L.Rokach, Y. Elovici, “OCCT: A One- Class Clustering Tree for Implementing One-to- Many Data Linkage,” IEEE Trans. on Knowledge and Data Engineering, TKDE-2011-09-0577, 2013.
[2] M.Yakout, A.K.Elmagarmid, H.Elmeleegy, M.Quzzani and A.Qi, “Behavior Based Record Linkage,” in Proc. of the VLDB Endowment, vol. 3, no 1-2, pp. 439-448, 2010.
[3] A.J.Storkey, C.K.I.Williams, E.Taylorand R.G.Mann, “An
Expectation Maximisation Algorithm for One-to-Many Record Linkage,” University of Edinburgh Informatics Research Report, 2005.
[4] S.Ivie, G.Henry, H.Gatrell and C.Giraud-Carrier, “AMetric Based Machine Learning Approach to Genea- Logical Record Linkage,” in Proc. of the 7th Annual Workshop on Technology for Family History and Genealogical Research, 2007.
[5] P.Christen and K.Goiser, “Towards Automated Data Linkage and Deduplication,” Australian National University, Technical Report, 2005.
[6] P.Langley, Elements of Machine Learning, San Franc-Isco, Morgan Kaufmann, 1996.
[7] S.Guha, R.Rastogi and K.Shim, “Rock: A Robust Clustering Algorithm for Categorical Attributes,” Information Systems, vol. 25, no. 5, pp. 345-366, July 2000.
[8] D.D.Dorfmann and E.Alf, “Maximum-Likelihood EstiMation of Parameters of Signal-Detection Theory and Determination of Confidence Intervals-Rating- Method Data,” Journal of Math Psychology, vol. 6, no. 3, pp. 487-496, 1969
[9] A.Gershman et al., “A Decision Tree Based ecommender System,” in Proc. the 10th Int. Conf. on Innovative Internet Community Services, pp. 170-179, 2010.
[10] J.R.Quinlan, “Induction of Decision Trees,” Machine
Learning, vol. 1, no. 1, pp. 81-106, March 1986.

D. Angelin Ponrani, "Decision Models for Record Linkage Using OCCT-One Class Clustering Tree," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.27-30, 2014.

Thresholding is an important technique for remote sensing image classification that tries to identify and extract a target from its background on the basis of the distribution of gray levels. Most thresholding techniques are based on the statistics of the one-dimensional histogram of gray levels and the two-dimensional co-occurrence matrix of an image. The remote sensing image classification refers to the process of separating an image into the multi gray level classes or features. The main goal of classification is to simply change the representation of an image into something that is more meaningful and easier to analyze. We present a new algorithm based on robust and realistic classification of massive gray level thresholding representation of remote sensing image. This algorithm allows the distribute the number of gray levels for an image in a fine to grainy fashion, starting with the original gray levels present in the image and all the way down to two gray levels that simply create a binary based version of the original image. This algorithm can also be used to find different gray level threshold image in a natural way without forcing a specific number ahead of time. The accuracy of this algorithm can be most demanding part of a computer vision application. In this work, gray level histogram thresholding is proposed in order to help the classification step to found to be robust way regardless of the classification approach. A proposed method over a remote sensing images have shown that offers very good classification results with a low computation time. Our experimental results show that the thresholding method based on gray level optimization is more efficient than the other classical thresholding methods.

RSI, LCS, EBC, RBC, TBT, LTT, GTT and SMGT

[1] Balaji T., amd Sumathi M., “Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method”, International Conference Proceedings, ICICA, Coimbatore, pp. 149-153, March 2014.
[2] Balaji T., amd Sumathi M., “A Simple and Efficient Feature Based Image Classification of Remote Sensing Images using Local Threshold Method”, International Conference Proceedings, ENIAC, Kovilpatti, pp. 49-55, January 2014.
[3] Balaji T., amd Sumathi M., “Relational Color Features of Remote Sensing Image Classification Using Dynamic Global Threshold Method”, International Conference Proceedings, ICRSI, Gujarat, pp. 29-34, December 2013.
[4] Balaji T., and Sumathi M., “Remote Sensing Image Classification – A Perspective Analysis”, International Journal of Third Concept, pp. 37-41, September 2013.
[5] D. M. Tsai and Y. H. Chen, “A Fast Histogram Clustering Approach for Multilevel Thresholding”, Pattern Recognition, Vol. 53, No. 24, 2012, pp. 245-252.
[6] N. Otsu, “A Threshold Selection Method from Gray Level Histogram”, IEEE Transactions on System Man Cybernetics, Vol. 19, No. 3, 2012, pp. 62-66.
[7] J. Gong, L. Li, and W. Chen, “Fast Recursive Algorithms for Two-dimensional Thresholding”, Pattern Recognition, Vol. 61, No. 14, 2011, pp. 295-300.
[8] N. Ahuja and A. Rosenfeld, “A Note on the Use of Second-order Gray Level Statistics for Threshold Selection”, IEEE Trans. Vol. 98, 2010, pp.383–388.
[9] B. Chanda and D. D. Majumder, “A Note on the Use of Gray Level Co-occurrence Matrix in Threshold Selection”, Signal Processing, Vol. 55, 2010, pp. 149–167.
[10] A. S. Abutaleb, “Automatic Thresholding of Gray Level Pictures Using Two-dimensional Entropy”, Computer Vision Graphics, Vol. 77, 2010, pp. 122–132.
[11] A. D. Brink, “Gray Level Thresholding of Images Using a Correlation Criterion”, Pattern Recogn. Vol. 99, 2010, pp. 335–341.
[12] Y. J. Zhang, ‘‘A Survey on Evaluation Methods for Image Classification’’, Pattern Recogn. Vol. 129, 2009, pp. 1335–1346.
[13] Otsu. N, “A Thresholding Selection Method from Gray Level Histogram”, IEEE Trans.,Vol. 48, 2009, pp. 62–66.
[14] Kapur, J.N., Sahoo, P.K., and Wong, “A New Method for Gray Level Picture Thresholding Using the Entropy of the Histogram”, Computer Vision Graphics, Vol. 109, 2009, pp. 273–285.

T. Balaji , "Robust and Realistic Classification of Massive Gray Level Thresholding in Remote Sensing Images," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.31-38, 2014.

Recent methods based on interest points and local fingerprints have been proposed to perform robust CBVCD (content-built visual copy discovery) of images and video. They include two steps: the search for similar local fingerprints in the database (DB) and a voting strategy that merges all the local results in order to perform a global decision. In most image or video retrieval systems, the search for similar features in the DB is performed by a geometrical query in a multidimensional index structure. Recently, the paradigm of approximate k-nearest neighbors query has shown that trading quality for time can be widely profitable in that context. In this paper, we evaluate a new approximate search paradigm, called Statistical Similarity Search (S3) in a complete CBVCD scheme based on video local fingerprints. Experimental results show that these statistical queries allow high performance gains compared to classical -range queries and that trading quality for time during the search does not degrade seriously the global robustness of the system, even with very large DBs including more than 20,000 hours of video.

Component; Formatting; Style; Styling; Insert

[1] Linfang Dong ; Dept. of Comput. Sci. & Technol., Tianjin Univ. of Finance & Econ., Tianjin, China ; Shang Liu ,”Adaptive Video Transmission Control Scheme in Wireless Networks”, Published in: Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on Date of Conference: 10-12 Aug. 2010, Page(s): 557 – 560.
[2] Kyeong-Jin Oh ; Dept. of Inf. Eng., Inha Univ., Incheon, South Korea ; Jin-Guk Jung ; Geun-Sik Jo,” A Context-Awareness for Mechanical Maintenance” Published in: Information Science and Applications (ICISA), 2011 International Conference on Page(s): 1 - 5
[3] Feng Xue ; VCC Div., Hefei Univ. of Technol., Hefei, China ; Zengwei Jiang, “An improved mean shift algorithm for object tracking” Published in: Multimedia Technology (ICMT), 2011 International Conference on, Page(s): 4833 – 4836
[4] Tankiz, S. ; Bilgisayar Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey ; Ates, T.K. ; Can, A.B. “Content Based Video Copy Detection by shot segmentation” Published in: Signal Processing and Communications Applications (SIU), 2011 IEEE 19th Conference on Date of Conference:20-22 April 2011 Page(s):582 – 585
[5] Xiao Bing Kang ; Key Lab. of Contemporary Design & Integrated Manuf. Technol., Northwestern Polytech. Univ., Xi''an, China ; Sheng Min Wei, “An efficient approach to still image copy detection based on SVD and block partition for digital forensics” Published in:Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on (Volume:3 ) Date of Conference:20-22 Nov. 2009 Page(s):457 – 461
[6] Xiaorong Yang ; Coll. of Stat. & Math., Zhejiang Gongshang Univ., Hangzhou, China ; Ke-Ang Fu “Copy number detection using self-weighted least square regression” Published in:Systems Biology (ISB), 2011 IEEE International Conference on Date of Conference:2-4 Sept. 2011 Page(s):47 – 51
[7] Ali Qureshi, M. ; Dept. of Electr. Eng., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia ; Deriche, M.” A review on copy move image forgery detection techniques” Published in:Multi-Conference on Systems, Signals & Devices (SSD), 2014 11th International, Date of Conference:11-14 Feb. 2014Page(s):1 – 5
[8] Muhammad, G. ; Coll. of Comput. & Inf. Sci., King Saud Univ., Riyadh, Saudi Arabia ; Hussain, M. ; Khawaji, K. ; Bebis, G.,” Blind copy move image forgery detection using dyadic undecimated wavelet transform” Published in:Digital Signal Processing (DSP), 2011 17th International Conference on Date of Conference:6-8 July 2011Page(s):1 – 6
[9] Esmaeili, M.M. ; Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada ; Fatourechi, M. ; Ward, R.K.,” A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting” Published in:Information Forensics and Security, IEEE Transactions on (Volume:6 , Issue: 1 ) Date of Publication :20 December 2010, Page(s):213 – 226
[10] Gao Jin-hua ; Shenzhen Inst. of Inf. Technol., Shenzhen, China ; Du Yi-hua ; Zhu Ying-ying ; Wen Zhen-kun,” Structure Information and Temporal Ordinal Measure Fused Video Copy Detection” Published in: Multimedia Information Networking and Security (MINES), 2011 Third International Conference on, Date of Conference:4-6 Nov. 2011 Page(s):51 – 54
[11] El Adel, A. ; REGIM: Res. Group on Intell. Machines, Univ. of Sfax, Sfax, Tunisia ; Zaied, M. ; Ben Amar, C. “Learning wavelet networks based on Multiresolution analysis: Application to images copy detection” Published in: Communications, Computing and Control Applications (CCCA), 2011 International Conference on Date of Conference: 3-5 March 2011 Page(s):1 – 6
[12] Barrios, J.M. ; Dept. of Comput. Sci., Univ. of Chile, Santiago, Chile ; Bustos, B.” P-VCD: A pivot-based approach for Content-Based Video Copy Detection” Published in: Multimedia and Expo (ICME), 2011 IEEE International Conference on Date of Conference: 11-15 July 2011 Page(s): 1 – 6
[13] Hung-Yi Lin ; Dept. of Logistics Eng. & Manage., Nat. Taichung Inst. of Technol., Taichung, Taiwan ; Shih-Ying Chen,” Indexing and Querying in Multimedia Databases” Published in: Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP '09. Fifth International Conference on Date of Conference: 12-14 Sept. 2009 Page(s): 475 – 478
[14] Xiaorui Zhang ; Sch. of Manage., Changchun Inst. of Technol., Changchun,” A Linear Cost Function Model and its Application” Published in: Informatics in Control, Automation and Robotics, 2009. CAR '09. International Asia Conference on Date of Conference: 1-2 Feb. 2009 Page(s): 32 – 36
[15] Bo Fu ; Coll. of Comput. & Inf. Technol., Liaoning Normal Univ., Dalian, China ; Chuan-Ming Song,” Object Enhancement and Recognition Based on Hough Forest for Underground Video” Published in: Parallel Architectures, Algorithms and Programming (PAAP), 2014 Sixth International Symposium on Date of Conference: 13-15 July 2014 Page(s): 75 - 80

A.Samayadevi and A.Aarthi, "Content -Built Visual Copy Discovery in Huge Databanks: A Native Characteristics Statistical Resemblance Quest Method," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.39-43, 2014.

This paper aims to provide reliable indications of driver drowsiness describe of detecting early signs of fatigue in drivers and provide method for more security and attention for driver safety problem and to investigate driver mental state related to driver safety.As soon as the driver is falling in symptons of fatigue immediate message will be given to driver.In addition of the advance technology of Surff feature extraction algorithm is also added in the system for correct detection of status of driver.The Fatigue is detected in the system by the image processing method of comparing the images(frames) in the video and by using the human features we are eable to estimate the indirect way of detecting fatigue.The technique also focuses on modes of person when driving vehicle i.e awake, drowsy state or sleepy and sleep state.The system is very efficient to detect the fatigue and control the vehicle.

Eye Tracking, Driving Safety, Mad Functions, Face Detection

[1]. R.Grace,V.E.Byrne,D.M.Bierman, “A Drowsy Driver Detection System ForHeavy Vehicles”,Proc of the 17th DASC/IEEE/SAE.DigitalAvionicsSystem Conference, vol.2,issue 36,ppp1-8,Nov 1998,Bellevue,WA.
[2]. Q, Ji, and X. Yang, “Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring DriverVigilance”, Real-Time Imaging 8, pp. 357-377, 2002.
[3]. P. Smith, M. Shah, N. da Vitoria Lobo, “Determining Driver Visual Attention with On Camera”, IEEE Transactions on Intelligent Transportation Systems, vol. 4, No. 4, pp. 205-21,December 2003.
[4]. W. Rong-ben, G. Ke-you, S. Shu-ming, C. Jiang-wei, “A Monitoring Method of Driver Fatigue Behavior Based on Machine Vision”,Intelligent Vehicles Symposium, 2003.Proceedings. IEEE, pp. 110-113, Jun. 2003.
[5]. T. D’Orazio, M. Leo, P. Spagnolo, C. Guaragnella, “A Neural System for Eye Detection in a Driver Vigilance Application”, Proc. of the 7th International IEEE Conference on Intelligent Transportation Systems, pp. 320-325, Oct. 2004, Washington, DC.
[6]. [6] R. Valenti, and T. Gevers, “Accurate Eye Center Location Through Invariant Isocentric Patterns”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, pp 1785-1798, Sept. 2012.
[7]. F. Timm, and E. Barth, “Accurate eye centre localization by means of gradients”, In: Int. Conference on Computer Vision Theory and Applications (VISAPP), vol. 1, pp. 125-130, March 2011, Algarve, Portugal.
[8]. S. Ribaric, J. Lovrencic, N. Pavesic, “A Neural-Network-Based System for Monitoring Driver Fatigue”, MELECON 2010 – 2010 15th IEEE Mediterranean Electrotechnical Conference, pp. 1356-1361, Apr. 2010,Valletta.
[9]. Wei Sun,Xiaorui Zhang,Wei Zhuang,Huiqiang Tang.Driver Fatigue Driving Detection Based on Eye State.JDCTA;International Journal of Digital Content Technology and its Applications.2011;5:307-314.
Abhishek Bharadwaj, Pareesha Aggarawal, “Image Filtering Techniques Used For Monitoring Driver Fatigue”, International Journal of Scientific and Research Publications, Volume 3,Issue 2,Feb 2013.

Snehal B. Meshram, and Sonali Bodkhe, "Image based Eye Tracking and Detection for avoiding accidents on Roads:A Review," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.44-46, 2014.

In this paper, we have implemented singular value decomposition to effectively update the value of decomposition, including the basis images. In this paper two dimensional incremental higher order singular value decomposition (HOSVD) is used for image fusion. Incremental higher order SVD will help us to store the images with less storage requirements and will keep the level of the error that must be acceptable in an application. The prime methods used here are HOSVD and its repetitive application. It is already known that singular value matrix obtained by SVD contains the illumination information. Therefore, we will combine this matrix for two different images. Large number of the variations made to this matrix will not affect the other attributes of the image. The incremental approach will be used to divide the image into sub-bands. When the images are separated on LH, HL and HH sub-bands, the effect of fusion will be smoothened by this method.

Singular Value Decomposition, Tensors, Image Fusion, Incremental HOSVD, Reduced HOSVD

[1] Andras Rovid, Laszlo Szeidl and Peter Varlaki, “The SVD and DWT Based Domain and the Related Image Processing Techniques.” INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND INFORMATICS, Volume 5, Issue 3, 2011
[2] Gagandeep Kour and Sharad P. Singh, “Low Quality Image Information Enhancement Using SVD Fusion Technique.” International Journal Of Engineering And Computer Science ISSN:2319-7242, Volume 2, Issue 11, November 2013, Page No. 3227-3231
[3] P.Ambika Priyadharsini, M.R.Mahalakshmi, “Multimodal Medical Image Fusion Based On SVD”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727, Volume 16, Issue 1, Ver. III (Jan. 2014), PP 27-31
[4] Asha P Kurian, Bijitha S R, Lekshmi Mohan, Megha M Kartha, K P Soman, “Performance Evaluation of Modified SVD based Image Fusion,” International Journal of Computer Applications (0975 – 8887) Volume 58– No.12, November 2012.
[5] Michael Thomason and Jens Gregor, “Fusion of Multiple Images by Higher-Order SVD of Third-Order Image Tensors,” 2007.
[6] Hatte S.C., Shingate V.S, “INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY,” International Journal of Engineering Sciences & Research Technology.
[7] Berkant Savas, Lars Eldén, “Handwritten digit classification using higher order singular value decomposition.”
[8] Piyu Tsai, yu-chen Hu, Hsiu-Lien Yeh. Reversible image hiding scheme using predictive coding and histogram shifting. Signal processing. 2009; 89(6): 1129-1143.
[9] Nadia Kreimer and Mauricio D. Sacchi, “A tensor higher-order singular value decomposition (SVD AND DWT) for pre-stack seismic data noise-reduction and interpolation,” 2012.
[10] Badrul Sarwar, George Karypis, Joseph Konstan and John Riedl, “Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems.”
[11] M. Gonzalez-Audicana, J.L. Saleta, R.G. Catalan, and R. Garcia, “Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition,” IEEE Trans. Geoscience and Rem. Sen., vol. 42, pp. 1291-1299, 2004.
[12] A. A. Goshtasby, “Fusion of Multi-Exposure Images,” Image and Vis. Comp., vol. 23, pp. 611-618, 2005.
[13] A. A. Goshtasby and S. Nikolov, “Image Fusion: Advances in the State of the Art,” Info. Fusion, vol. 8, pp. 114-118, 2007.
[14] M.T. Heath, Scientific Computing: An Introductory Survey, Second Ed., McGraw-Hill, NY, 2002.
[15] H. A. L. Kiers, “Towards a Standardized Notation and Terminology in Multiway Analysis,” J. Chemometrics, vol. 14, pp. 105-122, 2000

Indeevar Thakur and Hardeep Saini, "Image Fusion Using Incremental Higher Order Singular Value Decomposition Method," International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.47-49, 2014.