Volume-2 Issue-10


Version
Download1
Stock
File Size4.00 KB
Create DateSeptember 6, 2017
Download

 Download Abstract Book

S. No

Volume-2 Issue-10, September 2014, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Anand Mohan Sinha, Kumar Mukesh, Uma Shankar

Paper Title:

Data Processing System for LP and their uses in Modern Days

Abstract: This paper deals the data processing system for LP and their uses in modern days.

Keywords:
Linear Programming in data flows & linear approximation. AMS Subject Classification 2010: 90C05.


References:

1.        E. D. Andersen and Y. Ye (1999), ‘On a homogeneous algorithm for monotone complementarity system’, Math. Program. 84, 375-399.
2.        H. H. Bauschke, O. Guler, A. S. Lewis and H. S. sendov (2001), ‘Hyperbolic polynomials and convex analysis’, Canad. J. Math. 53, 470-488.

3.        A. Ben-Tal and A. S. Nemirovski (2001), Leactures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, SIAM, Philadelphia.

4.        R. E. 13ixby (2002), ‘solving real-world linear programs: A decade and more of progress’, Oper. Res. 50, 3-15.

5.        S. Boyd and L. Vandenberghe (2004), Convex Optimization, Cambridge University Press.

6.        S. Boyd, L. El Ghaoui, E. Feron and V. Balakrishnan (1994), Linear Matrix Inequalities in System and Control Theory, Vol. 15 of studies in Applied Mathematics, SIAM.

7.        R. H. Byrd, J. Nocedal and R. A. Waltz (2006), Knitro: An integrated package for nonlinear optimization. In large-Scale Nonlinear Optimization (G. di Pillo and M. Romma, eds), Springer, New York, pp. 35-59.

8.        J. S. Chai and K. C. Toh (2007), ‘Preconditioning and iterative solution of symmetric indefinite linear systems arising from interior point methods for linear programming’, Comput. Optim. Appl. 36, 221-247.

9.        L. Chen and D. Goldfarb (2006), ‘Interior-point 12-penalty methods for nonlinear programming with strong convergence properties’, Math. Program. 108, 1 -36.

10.     J. Faraut and A. Koranyi (1994), Analysis on Symmetric Cones, Clarendon Press, Oxford.

11.     L. Faybusovich (1997), ‘Linear systems in Jordan algebras and primal-dual interior-point algorithms’, J. Comput. Appl. Math. 86, 149-175.

1-4

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

2.

Authors:

K. Prahlad Rao, Mohammed Ahmed  Hanash, Gaafar Ahmed AL-Aidaros

Paper Title:

Development of Mobile Phone Medical Application Software for Clinical Diagnosis

Abstract: Rapid advancements in communication technology have spread to medicine also. Particularly, smartphone technology has made medical provisioning through mobile systems a reality. Innovations in mobile software application are potential benefits to the public health since the mobile platforms became more user-friendly, computationally powerful and are affordable. The innovative mobile apps can contribute in clinical consultation complementing face-to-face interaction in the health care at lower risk to the public. We have developed and evaluated mobile app for smartphone on Android platform to facilitate interaction between the patient and doctor where the patient seeks advice, diagnosis and treatment from the doctor from remote places. The Graphic User Interface (GUI) display screens of the smartphones are incorpotated the medical data needed by the clinician to interpret and respond to information.

Keywords:
Smartphone; Android; Clinical diagnosis; Doctor app; Patient app.

References:

1.        Epocrates (2013). Epocrates 2013 mobile trends report: maximizing multi-screen engagement among clinicians. Epocrates: An Athenahealth Company. pp. 1-10.
http://www.epocrates.com/oldsite/statistics/2013%20Epocrates%20Mobile%20Trends%20Report_FINAL.pdf.

2.        Sasan A. (2013). Mobile Health (mHealth) Biomedical Imaging Paradigm. 35th Annual International Conference Proceedings: 35th Annual International Conference of the IEEE EMBS 2013, pp. 6453-7.

3.        Altini M., Penders J., and Roebbers H. (2010). An Android based body area network gateway for mobile health applications. WH’10, Wireless Health, pp. 188-189.

4.        Rosenthal MB, Newhouse JP, and Zaslavsky AM. (2005). The Geographic Distribution of Physicians Revisited, Health Services Research. Part I, 40, pp. 1932-52.

5.        Surana S., Patra R., Nedevschi S., Brewer E. (2008). Deploying a Rural Wireless Telemedicine System: Experiences in Sustainability. Computer, Vol. 41 (6), pp.48-56.

6.        Wikipedia contributors. “Mobile app”.Wikipedia, The Free Encyclopedia, 25 September 2013.  http://en.wikipedia.org/wiki/Mobile_app.

7.        Patient Apps for Improved Healthcare: From Novelty to Mainstream. IMS Institute for Healthcare Informatics Report. October 2013.

8.        Pasha M.F., Supramanyam S., et al., (2012). An Android-based Mobile Medical Image Viewer and Collaborative Annotation: Development Issues and Challenges. Int. J of Digital Content Technology and its Applications. Vol. 6, No.1, 2012, pp. 208-217.

9.        Sears A., Arora R. (2002). Data entry for mobile devices: An empirical comparison of novice performance with Jot and Graffiti. Interacting with computers, Vol. 14, No. 5, pp. 413-433.

5-8

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

3.

Authors:

Devesh Narayan, Sipi Dubey

Paper Title:

A Survey Paper on Human Identification using Ear Biometrics

Abstract: Human identification is about verifying a people for accessing information or permitting to enter in a restricted zone. Using ear as biometric tool has benefits involved in it; subjects never participate actively in the identification or verification process. Ear biometric finds its applications in the crime investigation, stopping ATM fraudulent and prevention of small baby swapping and mixing them in hospitals. This paper gives a detailed overview of different technical approaches that have been implemented for identifying subjects. Our survey provides good future prospects for the upcoming researchers in the field of ear biometric.

Keywords:
Ear Biometric, identification, verification.


References:

1.        Bertillon A. 'La Photographie Judiciaire: Avec Un Appendice' Sur La Classification Et L'Identification Anthropometriques'. Gauthier-Villars, Paris; 1890
2.        Ibrahim MIS, Nixon MS, Mahmoodi S. 'The effect of time on ear biometrics'. In:International Joint Conference on Biometrics (IJCB); 2011. p. 1 6.

3.        Kumar A, Wu C. 'Automated human identifcation using ear imaging'. PatternRecogn. 2012 March;45(3):956{968. Available from: http://dx.doi.org/10.1016/j.patcog.2011.06.005.

4.        Prakash S, Gupta P. 'An Efficient Ear Recognition Technique Invariant to Illumination and Pose'. Telecommunication Systems Journal, special issue on Signal Processing Applications in Human Computer Interaction. 2011;30:38-50

5.        http://www1.ustb.edu.cn/resb/en/doc/Imagedb_4_intro_en.pdf

6.        http://cse.nd.edu/~cvrl/CVRL/Data_Sets.html..

7.        Yan P, Bowyer KW. 'Biometric Recognition Using 3D Ear Shape'. Pattern Analysis and Machine Intelligence. 2007 August;29:1297 - 1308.

8.        Raposo R, Hoyle E, Peixinho A, Proenca H. 'UBEAR: A dataset of ear images captured on-the-move in uncontrolled conditions'. In: Computational Intelligence in Biometrics and Identity Management (CIBIM), 2011 IEEE Workshop on; 2011.p. 84-90.

9.        Burge M, Burger W. 13. In: Jain AK, Bolle R, Pankanti S, editors. 'Ear Biometrics'.Springer US; 1998. p. 273-285..

10.     Chang K, Bowyer KW, Sarkar S, Victor B. 'Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics'. IEEE Transactions in Pattern Anallysis and Machine Intelligene. 2003 September;25:1160-1165.

11.     Yuan L, Mu Z. 'Ear Recognition Based on 2D Images'. In: First IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS); 2007. p. 1-5.
12.     Xie Z, Mu Z. 'Ear Recognition Using LLE and IDLLE Algorithm'. In: 19th International Conference on Pattern Recognition (ICPR); 2008. p. 1-4.

13.     Zhang Z. , Liu H. 2008. Multi-View ear recognition based on b-spline pose manifold construction. In Proceedings of the 7th IEEE World Congress on Intelligent Control and Automation.

14.     Hurley DJ, Nixon MS, Carter JN. 'Force Field Energy Functionals for Image Feature Extraction'. Image and Vision Computing. 2002;20(5-6):311 - 317.

15.     Abdel-Mottaleb M, Zhou J. 'Human Ear Recognition from Face Prole Images'.In: Zhang D, Jain A, editors. Advances in Biometrics. vol. 3832 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg; 2005. p. 786-792.

16.     Dong J, Mu Z. 'Multi-Pose Ear Recognition Based on Force Field Transformation'. In: Second International Symposium on Intelligent Information Technology Application (IITA). vol. 3; 2008. p. 771 -775.

17.     Choras M. 'Perspective Methods of Human Identication: Ear Biometrics'. Opto-Electronics Review. 2008;16:85-96.

18.     Kumar A., Zhang D. 2007. Ear authentication using log-gabor wavelets. In SPIE  Defense and Security Symposium. Vol. 6539

19.     Abate AF, Nappi M, Riccio D, Ricciardi S. 'Ear Recognition by means of a RotationInvariant Descriptor'. In: 18th International Conference on Pattern Recognition, ICPR 2006.. vol. 4; 2006. p. 437 -440.

20.     SANA, A. AND GUPTA, P. 2007. Ear biometrics: A new approach. In Proceedings of the 6th International Conference on Advances in Pattern Recognition.

21.     HAILONG, Z. AND MU, Z. 2009. Combining wavelet transform and orthogonal centroid algorithm for ear recognition. In Proceedings of the 2nd IEEE International Conference on Computer Science and Information Technology.

22.     Nosrati M, Faez K, Faradji F. 2007. Using 2D wavelet and principal component analysis for personal identification based on 2D ear structure. In Proceedings of the IEEE International Conference on Intelligent and Advanced Systems.

23.     Wang Y, Mu Z, Zeng H. 2008. Block-Based and multi-resolution methods for ear recognition using wavelet transform and uniform local binary patterns. In Proceedings of the 19th IEEE InternationalConference on Pattern Recognition (ICPR). 1–4.

24.     Yaqubi M, Faez K, Motamed S. 'Ear Recognition Using Features Inspired by Visual Cortex and Support Vector Machine Technique'. In: International Conference on Computer and Communication Engineering (ICCCE); 2008. p. 533 -537.

25.     Nanni L, Lumini A. 'A Multi-Matcher For Ear Authentication'. Pattern Recognition Letters. 2007 December;28:2219-2226.

26.     Dewi K. Yahagi T. 2006. Ear photo recognition using scale invariant keypoints. In Proceedings of the International Computational Intelligence Conference. 253–258. Kisku D. R., Mehrotra H., Gupta P., Sing J. K. 2009a. SIFT-Based ear recognition by fusion of detected key-points from color similarity slice regions. In Proceedings of the IEEE International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). 380–385.

27.     Kisku D. R., Mehrotra H., Gupta P., Sing J. K. 2009a. SIFT-Based ear recognition by fusion of detected key-points from color similarity slice regions. In Proceedings of the IEEE International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). 380–385.

28.     Ratha N.K., Seniior A, Bolle R.M Automated Biometrics in Proceedings of International Conference on Advances in Pattern Recognition, Rio de Janerio, Brazil, March 2001.

9-13

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

4.

Authors:

Priyanka Shrivastava, Prashant Purohit, Pushpraj Singh Tanwar, Harishanker Shrivastava

Paper Title:

Concepts of Primitive Polynomial and Galois Field in Designing More Randomize PN Sequence Generators for Maximum Fault Coverage in Modern VLSI Testing

Abstract: This paper deals with the vital role of primitive polynomials for designing PN sequence generators. The standard LFSR (linear feedback shift register) used for pattern generation may give repetitive patterns. Which are in certain cases is not efficient for complete test coverage.  The LFSR based on primitive polynomial generates maximum-length PRPG.

Keywords:
1. LFSR (linear feedback shift register). 2. PRPG (Pseudo feedback shift register).3 Primitive polynomial 4. Galois field.


References:

1.        A. Miczo, Digital Logic Testing and Simulation, Second Edition, John Wiley, 2003
2.        Berlekamp, E. R. Algebraic Coding Theory. New York: McGraw-Hill, p. 84, 1968

3.        B. Koenemann. LFSR-coded test patterns for scan de-signs. Proc. Euro. Test Conf., pages 237{242, 1991

4.        B. Konemann, “LFSR-Coded Test Pattems for Scan Designs”, European Test Conference, Munich, 1991.Vol. 10, No.1, pp. 73-82. 1999, pp.358-367.20f

5.        Church, R. "Tables of Irreducible Polynomials for the First Four Prime Moduli." Ann. Math. 36, 198-209, 1935

6.        Derbyshire, J. Prime Obsession: Bernhard Riemann and the Greatest Unsolved Problem in Mathematics. New York: Penguin, pp. 266-268, 2004.

7.        Lidl, R. and Niederreiter, H. Introduction to Finite Fields and Their Applications, rev. ed. Cambridge, England: Cambridge University Press, 1994.

8.        Mohamed H. El-Mahlawy, Pseudo-Exhaustive Built-In Self-resf for Boundary Scan, Ph.D. thesis, Kent University, U.K., 2000.

9.        Michael L. Bushnell and Vishwani D. Agrawal, Essentials of Electronic Testing For Digital, Memory, & Mixed-Signal VLSI Circuits, Kluwer Academic Publishers, 2000.

10.     N. K. Jha and S. Gupta, Testing of Digital Systems, Cambridge University Press, UK, 2003.

11.     N.C. Lai, S.J. Wang, “A Reseedin Technique for LFSRBased BIST Applications”, Asian test Symposium 2002, pp.200-205.

12.     Peterson, W. W. and Weldon, E. J. Jr. Error-Correcting Codes, 2nd ed. Cambridge, MA: MIT Press, p. 476, 1972.

13.     P.H. Bardell, W.H. McAnney, Parallel Pseudo-random Sequences for Built-In Test, Proc. Int. Test Conf., IEEE, 1984, pp. 302-308.

14.     P. H. Bardell, W. H. McAnney, and J. Savir. Built-in test for VLSI: Pseudorandom techniques. 1987.

14-17

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

5.

Authors:

Lakshmanan K, Anand Mahendran

Paper Title:

Identifying Ambiguity Levels in Gene Sequences using Matrix Ins-Del Systems

Abstract: Ambiguity is one of the important issues not only in natural and programming languages, but also in gene sequences.   In programming languages, the ambiguity is defined as existence of (at least) two distinct derivations that yield a same word.  Considering in that line, ambiguity in gene sequences may be interpreted as a gene sequence can be obtained by more than one way such that its intermediate gene sequences are different.   Analyzing the ambiguity issues in gene sequences will help us to know the evolution of gene sequences.  Recently, in [9] a new variant called Matrix insertion-deletion systems has been introduced as a biologically inspired computing model to represent various bio-molecular structures such as pseudoknot, hairpin, stem and loop, attenuator, dumbbell and cloverleaf.  But the ambiguity issues of Matrix insertion-deletion systems has not been analyzed in detail yet.  In this paper, we formally define various levels (0,1,2,3) of ambiguity for Matrix insertion-deletion systems based on the components used in the derivation such as axiom, context, string (used for insertion/deletion). Next, we relate the newly defined ambiguity levels of Matrix insertion-deletion systems with bio-molecular structures and analyze their ambiguity issues. We notice that ideal language obeys the level 0-ambiguity, stem and loop structure obeys level 1-ambiguity, cloverleaf structure obeys level 2-ambiguity and orthodox language obeys level 3-ambiguity.

Keywords:
Bio-molecular structures, pseudoknot, stem and loop, Matrix insertion-deletion systems, ambiguity, gene sequence.


References:

1.        Cristian S. Calude and Gheorghe Paun, Computing with cells and atoms, An introduction to Quantum, DNA and Membrane Computing, London: Taylor and Francis, 2001.
2.        David B. Searls, “Representing genetic information with formal grammars”, in  Proceedings of the National Conference on Artificial Intelligence, 1988, pp. 386–391.

3.        David B. Searls, “The linguistics of DNA”, in American Scientist, 1992, pp. 579–591.

4.        David B. Searls, “The computational linguistics of biological sequences (Hunter, L.ed.)”, in Artificial Intelligence and Molecular Biology, AAAI Press, 1993, pp.47–120.

5.        Elena Rivas and Sean R. Reddy, “The language of RNA: A formal grammar that includes pseudoknots”, in Bioinformatics, vol. 16., 2000, pp. 334–340.

6.        Gheorghe Paun, Grzegorz Rozenberg and Arto Salomaa, DNA Computing, New Computing Paradigms. Springer, 1998.

7.        Gheorghe Paun, Membrane Computing-An introduction. Springer, 2002.

8.        John E. Hopcroft, Rajeev Motwani and Jeffrey D. Ullman, Introduction to Automata Theory, Languages and Computation. Addison-Wesley, 2006.

9.        Lakshmanan Kuppusamy, Anand Mahendran and Krishna S,`` Matrix Insertion-Deletion Systems for Bio- molecular Structures”, in Proceedings of ICDCIT-2011, LNCS proceedings #6536, 2011, pp. 301-312.

10.     Rozenberg and Arto Salomaa, Handbook of formal languages, Vol 1, Vol 2, Vol 3, Springer, 1997.

11.     Setubal., Meidanis.: Introduction to Computational Molecular Biology. PWS Publishing Company, 1997.

12.     Yasuo Uemura, Aki Hasegawa, Satoshi Kobayashi and Takashi Yokomori,” Tree adjoining  Grammars for RNA structure prediction”, in Theoretical Computer Science, vol. 210, 1999,  pp. 277–303.

18-22

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

6.

Authors:

N. Senthilkumaran, J. Thimmiaraja

Paper Title:

A Note on Magnetic Resonance Imaging

Abstract: Medical image processing goes beyond the limitations. Imaging information considers anatomical, functional and quantitative it produce images of the internal aspect of the body. Recent advances in imaging techniques have made it possible to acquire images in real time during an interventional procedure. In such procedure, usually the real-time images themselves may be sufficient to provide the necessary guidance information needed for the procedure. There are many types of imaging like Magnetic resonance imaging (MRI), Computer Tomography (CT), positron emission tomography (PET) and X-ray. In the above images, MRI is a wide variety of applications in medical diagnosis. MRI can be used to find exact method to find and analysis throughout the body compared to the other imaging Techniques. MRI is used to locate problems such as bleeding, tumours, blood vessel diseases, injury and also it shows the abnormal tissues more clearly.

Keywords:
Medical Image, MRI.


References:

1.        N. Senthilkumaran, R. Rajesh, “Brain Image Segmentation”, International journal of wisdom based computing, vol. 1(3), december 2011
2.        Isaac N. Bankman, “Handbook of medical image processing and analysis” Second Edition, Academic Press, 2000.

3.        Geoffrey S. Young, MD, “Advanced MRI of Adult Brain Tumors “, Elsevier, Neurol Clin 25 (2007) 947–973.

4.        Marta Tanasiewicz, “Magnetic resonance imaging in human teeth internal space visualization for requirements of dental prosthetics”, Journal section: Oral Medicine and Pathology, 2010; 2(1):e6-11.

5.        E. Ben George, M.Karnan, “MRI Brain Image Enhancement Using Filtering Techniques”, International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: 2229-3345 Vol. 3 No. 9 Sep 2012.

6.        Hajime Sakuma, “Magnetic Resonance Imaging for Ischemic Heart Disease”, Journal of magnetic resonance imaging 26:3–13 2007.

7.        N. A. Losseff, S. L. Webb, J. I. O'Riordan, R. Page, L. Wang, G. J. Barker, P. S. Tofts, W. I. McDonald, D. H. Miller, A. J. Thompson, “Spinal cord atrophy and disability in multiple sclerosis A new reproducible and sensitive MRI method with potential to monitor disease progression”, Brain, Volume 119, Issue 3, Pp. 701-708.

8.        M. De Maeseneer, M. Shahabpour, P. Van Roy, C. Pouders,  “MRI of cartilage and subchondral bone injury. a pictorial review”, BR–BTR, 2008, 91: 6-13.

9.        Marta Tanasiewicz, “Magnetic resonance imaging in human teeth internal space visualization for requirements of dental prosthetics”, ournal section: Oral Medicine and Pathology, 2(1):e6-11, 2010.

10.     Paul D. Friedman, Srirama V. Swaminathan, Kevin Herman, LesterKalisher, “Breast MRI: The Importance of Bilateral Imaging”, American Journal of Roentgenology, Volume 187, Number 2, 2006.

11.     Makoto Kato, Satoru Miyauchi, “Functional mri of brain activation evoked by intentional eye blinking”, NeuroImage, Volume 18, Issue 3, Pages 749–759, 2003. 

23-26

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

7.

Authors:

V. R. Vinothini, P. Thangaraj

Paper Title:

Modified Decision Based Algorithm Unsymmetric Hybrid Trimmed Median Filter Approach   for Removing Salt and Pepper Noise in Ultrasound Images

Abstract: Removing impulse noise from digital image is a very challenging research area in digital image processing. In recent years, technological development has significantly improved in analyzing digital images. This paper proposes a modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale and color images that are highly corrupted by salt-and-pepper noise from digital images, by topological approach. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0’s and 255’s are present in the selected window and when all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. The quality of the noise reduction in images is measured by the statistical quantity measures: Root Mean Square Error (RMSE) and Peak-Signal-to-Noise Ratio (PSNR).The proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA) and Modified Decision Based Algorithm (MDBA).  

Keywords:
Hybrid Filters, Median Filter, Noise reduction, Salt-and-Pepper noise, Ultrasound image, Unsymmetrical trimmed median Filter.


References:

1.        A. Rosenfeld, “Digital Topology, “American Math. Monthly 86, 1979,          pp. 621–630.
2.        R. Gonzalez and R. Woods, “Digital Image Processing”, Addison-Wesley, New York, 1992.

3.        T.Acharya and A.K. Ray, “, Image processing: principles and applications, John Wiley and Sons”, 2005, 116-127.

4.        H. Hu  and G.  De Haan, “ Classification-based hybrid filters for image processing”, Proc. SPIE, Visual Communications and Image Processing, Vol. 6077, (2006), 607711.1-607711.10

5.        Mamta Juneja and Rajni Mohana, “An Improved Adaptive Median Filtering Method for Impulse Noise Detection”, International Journal of Recent Trends in Engineering, Vol 1, 2009, 274-278.

6.        K. S. Srinivasan and D. Ebenezer, “A new fast and efficient decision based algorithm for removal of high density impulse noise,” IEEE Signal Process. Letters, vol.14,no.3,189–192, 2007.

7.        Abdullah Toprak, Inan Güler, “Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter”, Science Direct, Digital Signal Processing 17 ,711–723 , 2007.

8.        H. Ibrahim, N. S. P. Kong, and T. F. Ng . “Simple adaptive median filter for the removal of impulse noise from highly corrupted images”, IEEE Transactions on Consumer Electronics, Vol. 54, No. 4,  1920-1927 , 2008.

9.        Gnanambal Ilango and R.Marudhachalam, “New hybrid filtering techniques for removal of Gaussian noise from medical images”, ARPN Journal of Engineering and Applied Sciences, Vol 6, No. 2, 8-12 , 2011.

10.     CH.Sravana Lakshmi, V.Ambika, K.Suri Babu, “Impulse Noise Removal In images Using Modified Trimmed Median Filter: Mat lab Implementation and Comparative Study”, International Journal of Engineering Research and Applications Vol. 2,  Issue 5, 2163-2166 , 2012.

11.     Gnanambal Ilango and R. Marudhachalam, New hybrid filtering technique for removal of Impluse noise from digital images, International Journal of Mathematical Archive-3(6), 2354-2359, 2012.

12.     Rutuja N. Kulkarni, P.C. Bhaskar, “Decision Based Median Filter algorithm using Resource Optimized FPGA to Extract Impulse Noise”, International Journal of Reconfigurable and Embedded Systems, Vol. 3, No. 1, 1-10, 2014.

13.     J.W.Tukey , “Nonlinear (nonsuperposable) methods for smoothing data”, in Proc. Congress. Rec. EASCOM’74, 673-681, 1974.

27-31

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

8.

Authors:

M. Siva, M. Madhan, D. Ramkumar, S. Mylsamy, Nambi Shyam S. Sri Aswin

Paper Title:

A Probability Review of Missing MH 370 (A Hypothetical Approach)

Abstract: Hypothetical theoretical approaches exist worldwide. There are infinite number of solutions for a single problem.  Yet the degree of probability could yield us something in this missing mystery. Based on Probability approach the missing MH 370 is investigated here. In this review both probable and technical assumptions are stated and reasoned. Till missing MH 370 is a mystery rather this theoretical approach could reveal that.

Keywords:
Probability, Technical Data, MH 370, Theoretical approach, Hijack.


References:

1.        http://en.wikipedia.org/wiki/Malaysia_Airlines_Flight_370 [Accessed on 7 June 2014]
2.    http://timesofindia.indiatimes.com/world/rest-of-world/MH370-Malaysian-jet-was-in-controlled-flight-after-contact-was-lost-officials-suspect/articleshow/37115566.cms [Accessed on 7 June 2014]

3.        http://www.mirror.co.uk/all-about/missing%20malaysian%20airlines%20flight  [Accessed on 7 June 2014]

4.        http://www.mirror.co.uk/news/uk-news/missing-flight-mh370-live-updates-3363920 [Accessed on 7 June 2014]

5.        http://www.mirror.co.uk/news/uk-news/missing-flight-mh370-live-updates-3363920 [Accessed on 7 June 2014]

6.        http://www.nbcnews.com/storyline/missing-jet/flight-mh370-what-do-we-know-about-missing-malaysian-jet-n139941 [Accessed on 7 June 2014]

7.        http://www.telegraph.co.uk/news/worldnews/asia/malaysia/10921715/Hunt-for-MH370-could-take-decades-admits-airline-chief.html [Accessed on 7 June 2014]

8.     http://www.telegraph.co.uk/news/picturegalleries/worldnews/10687257/In-pictures-The-hunt-for-missing-Malaysia-Airlines-plane-flight-MH370.html [Accessed on 7 June 2014]

9.        http://www.theguardian.com/world/2014/mar/21/what-happened-to-flight-mh370-missing-plane [Accessed on 7 June 2014]

10.     http://www.dnaindia.com/world/report-investigators-say-mh370-search-plan-based-on-altitude-data-not-reliable-1997528 [Accessed on 7 June 2014]

11.     http://www.nytimes.com/2014/03/15/world/asia/malaysia-military-radar.html?_r=0 [Accessed on 7 June 2014]

12.     http://edition.cnn.com/2014/06/24/world/asia/malaysia-mh-370-search/ [Accessed on 7 June 2014]

13. http://www.malaysia-chronicle.com/index.php?option=com_k2&view=item&id=306912:why-is-msian-govt-intent-on-blaming-mh370-pilot?-cops-name-capt-zaharie-as-%E2%80%98prime-suspect%E2%80%99&Itemid=2#axzz35dhu87Fs [Accessed on 7 June 2014]

14. http://www.independent.co.uk/news/world/asia/missing-malaysia-airlines-flight-mh370-the-13-theories-that-could-explain-where-the-plane-is--and-what-happened-to-it-9455120.html [Accessed on 7 June 2014]

15.     http://www.emirates247.com/news/missing-mh370-latest-pilot-s-wife-breaks-silence-says-he-spoke-from-cockpit-2014-06-25-1.546548 [Accessed on 7 June 2014]

32-33

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

9.

Authors:

Nivedita S. Sarode, A. M. Patil

Paper Title:

Review of Iris Recognition: An Evolving Biometrics Identification Technology

Abstract: A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Unlike other biometric such as fingerprints and face recognition, the distinct aspect of iris comes from randomly distributed features. Iris recognition is regarded as the most reliable and accurate biometric identification system available. This paper provides the review of related work in the iris recognition. A general framework of the iris recognition system is proposed and finally the advantages and disadvantages of the iris recognition technology are analyzed. It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. The estimation results reveal, as expected, that a wide variety of factors affect security transit times including the number of enplaning seats (reflecting flight schedules), weather conditions, day of week, as well as obvious variables such as traveler volume and the number of open security lanes. The recognition accuracy of a single biometric authentication system is often much reduced due to the environment, user mode and physiological defect. Iris and Retina biometric recognition offers a highly reliable solution to person authentication. Instead of using the entire iris code, only the bits that are consistent in the iris code called the best bits are considered in the feature matching process. This reduces the computational time and storage requirements of iris code. To enhance the performance of recognition, the iris recognition process is applied to left and right irises separately and the corresponding distance scores are generated for each iris of a person. These scores are combined using the weighted sum fusion rule which further increases the recognition rate. Iris recognition system is composed of segmentation, normalization, feature encoding and matching.

Keywords:
Biometric system, Iris recognition, segmentation, normalization.


References:

1.        Yung-Hui Li, Marios Savvides, “An Automatic Iris Occlusion Estimation Method based on High-Dimensional Density Estimation” IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 4, pp785-786, 2013.
2.        Adams Wai-Kin Kong, “Modeling IrisCode and Its Variants as Convex Polyhedral Cones and Its Security Implications”, IEEE Transactions on Image Processing, vol. 22, no. 3 , pp 1149-1150,  2013.

3.        Yao-Tung Chuang, Yu-Lun Hong, Kuo-Cheng Huang, Sheng-Wen Shih, “Autofocus of Iris Patterns Using a Triangle Aperture”, IEEE Transactions on Cybernetics, vol. 43, no. 4, pp 1304-1306,2013.

4.        Seung-Jin Baek, Kang-A Choi, Chunfei Ma, Young-Hyun Kim, “Eyeball Model-based Iris Center Localization for Visible Image-based Eye-Gaze Tracking Systems”, IEEE Transactions on Consumer Electronics, Vol. 59, No. 2, pp 415-420, 2013.

5.        Cemre Candemir, Cihat Çetinkaya, Onur Kılınççeker, Muhammed Cinsdikici, “Vascular Landmark Classification in Retinal Images Using Fuzzy RBF, IEEE Transaction, 2013.

6.        Emrullah Acar, Mehmet Siraç ÖzerdemElektrik, “An Iris Recognition System by Laws Texture Energy Measure Based k-NN Classifier”, IEEE Transaction, 2013.

7.        Ömer Faruk Söylemez, Burhan Ergen, “ Circular Hough Transform based Eye State Detection In Human Face Images”, IEEE Transaction, 2013.

8.        Ibrahim Mesecan, Alaa Eleyan, Bekir Karlik, “Sift-based Iris Recognition Using Sub-Segments”, IEEE Transaction, pp 350-353, 2013.

9.        Daniela Sánchez, Patricia Melin, Oscar Castillo, Fevrier Valdez, “Modular Granular Neural Networks Optimization with Multi-Objective Hierarchical Genetic Algorithm for human recognition based on iris biometric” IEEE Congress on Evolutionary Computation, pp 772-774, 2013.

10.     M.Fathima Nadheen, S.Poornima, “Fusion in Multimodal Biometric using Iris and Ear”, Proceedings of IEEE Conference on Information and Communication Technologies (ICT), pp 83-86, 2013.

11.     V.Saravanan1, R.Sindhuja, “Iris Authentication through Gabor Filter Using DSP Processor”, Proceedings of IEEE Conference on Information and Communication Technologies (ICT), pp 568-571, 2013.

12.     Milos Stojmenovic, Aleksandar Jevremovic, Amiya Nayak, “Fast Iris Detection via Shape based Circularity”, IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), pp 747, 2013.

13.     Sheikh Ziauddin, Sajida Kalsoom, “Effects of Enrollment Templates Count on Iris Recognition Performance using Reliable Bits”, 10th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp 750-751, 2013.

14.     Adams W. K. Kong, David Zhang, Mohamed S. Kamel, “An Analysis of IrisCode”, IEEE transactions on image processing, vol. 19, no. 2, pp 552,2010.

15.     Jinyu Zuo, Natalia A. Schmid, “On a Methodology for Robust Segmentation of Nonideal Iris Images”, IEEE transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 40, no. 3,pp 703, 2010.

16.     Judith Liu-Jimenez, Raul Sanchez-Reillo, Belen Fernandez-Saavedra, “Iris Biometrics for Embedded Systems”,  IEEE transactions on very large scale integration (vlsi) systems, vol. 19, no. 2, pp 274, 2011.

17.     Mahdi S. Hosseini,  Babak N. Araabi, Hamid Soltanian-Zadeh, “Pigment Melanin: Pattern for Iris Recognition”, IEEE transactions on instrumentation and measurement, vol. 59, no. 4, pp 792, 2010

18.     Vishnu Naresh Boddeti, B. V. K. Vijaya Kumar, “Extended-Depth-of-Field Iris Recognition using Unrestored Wavefront-Coded Imagery”, IEEE transactions on systems, man, and cybernetics—part a: systems and humans, vol. 40, no. 3, pp 495, 2010

19.     Yingzi Du,  Emrah Arslanturk, Zhi Zhou, Craig Belcher, “Video-Based Noncooperative Iris Image Segmentation”, IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 41, no. 1, pp 64, 2011

20.     Wenbo Dong, Zhenan Sun, Tieniu Tan, “Iris Matching Based on Personalized Weight Map”, IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 9, pp 1744,  2011.

21.     Adams Wai-Kin Kong, “Iris Code Decompression Based on the Dependence between Its Bit Pairs”, IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 3, pp  506,2012.

22.     Chung-ChihTsai, Heng-Yi Lin, Jin shiuh Taur, Chin-Wang Tao, “Iris Recognition Using Possibilistic Fuzzy Matching on Local Features”, IEEE transactions on systems ,man, and cybernetics part b:cybernetics, vol.42, no.1, pp 150, 2012.

23.     Ryan Connaughton, Amanda Sgroi, Kevin Bowyer, Patrick J. Flynn, “A Multialgorithm analysis of three iris biometric sensors”, IEEE transactions on information forensics and security, vol.7,no. 3, pp 919, 2012.

24.     Chun-Wei Tan, Ajay Kumar, “Unified Framework for Automated Iris Segmentation Using Distantly Acquired Face Images”, IEEE transactions on image processing, vol. 21, no. 9, pp 4068, 2012.

25.     Yung-Hui Li, Marios Savvides, An Automatic Iris Occlusion Estimation Method Based on High Dimensional Density Estimation”, IEEE transactions on pattern analysis and machine intelligence, pp 1, 2012.

26.     Lin Ma, David Zhang, Naimin Li, Yan Cai, Wangmeng Zuo, Kuanquan Wang, “Iris-Based Medical Analysis by Geometric Deformation Features”, IEEE journal of biomedical and health informatics, vol. 17, no. 1, pp 223,2013.

27.     M.Suganthy, P. Rama moorthy, R. Krishna moorthy, “Effective Iris Recognition For Security Enhancement”, International Journal of Engineering Research and Applications (IJERA), Vol. 2, Issue 2,  pp.1016-1019, 2012.

28.     Przemysław Strzelczyk,  “Robust and Accurate Iris Segmentation Algorithm for Color and Noisy Eye Images”, Journal of telecommunication & information technology, pp 5,  2010.

29.     Yulin Si, Jiangyuan Mei, Huijun Gao, “Novel Approaches to Improve Robustness, Accuracy and Rapidity of Iris Recognition System”, IEEE transactions on industrial informatics, vol. 8, no. 1,  pp 110,2012.

30.     Ronaldo Martins da Costa and Adilson Gonzaga, “Dynamic Features for Iris Recognition”, IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 42, no. 4, pp 1072,  2012.

31.     H. B. Kekre, Tanuja K. Sarode, Vinayak Ashok Bharadi, Abhishek A. Agrawal, Rohan J. Arora, and Mahesh C. Nair, “Performance Comparison of DCT and VQ Based Techniques for Iris Recognition”, Journal of electronic science and technology, vol. 8, no. 3, pp 223,2010.

32.     Neil Yager and Ted Dunstone,“The Biometric Menagerie” , IEEE transactions on image  processing, 2011.

33.     Alexander M. Hainen*, Stephen M. Remias, Darcy M. Bullock, Fred L. Mannering.“ A hazard-based analysis of airport security transit times”, Journal of Air Transport Management, pp (32-38),2013.

34.     L.Latha,, S.Thangasamy,“ A Robust Person Authentication System based on Score Level Fusion of Left and Right Irises and RetinalFeatures”, Procedia Computer Science, pp 111–120, 2010.

35.     Jaishanker K. Pillai, Maria Puertas, Student Member, Rama Chellappa, “Cross-Sensor Iris Recognition through Kernel Learning”, IEEE transactions on pattern analysis and machine intelligence, pp 73-85, vol. 36, No. 1, 2014.

36.     Thomas Bücher, Cristobal Curio, Johann Edelbrunner, Christian Igel, David Kastrup, Iris Leefken, Gesa Lorenz, Axel Steinhage, and Werner von Seelen, “Image Processing and Behavior Planning for Intelligent Vehicles”, IEEE transactions on industrial electronics, vol. 50, no. 1, pp 62-75, 2003.

37.     Natalia A. Schmid, Joseph A. O’Sullivan, “Performance Prediction Methodology for Biometric Systems Using a Large Deviations Approach”, IEEE transactions on signal processing, vol. 52, no. 10, pp 3036-3045, 2004.

38.     Zhenan Sun, Yunhong Wang, Tieniu Tan, and Jiali Cui, “Improving Iris Recognition Accuracy via Cascaded Classifiers”, IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 35, no. 3, pp 435-440, 2005.

39.     Kang Ryoung Park, Jaihie Kim, “A Real-Time Focusing Algorithm for Iris Recognition Camera”, IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 35, no. 3, pp 441, 2005.

40.     Amjad Zaim, “Automatic  segmentation of iris images for the purpose of identification”, IEEE Transactions on Image Processing, 2005.

41.     Ching-Han CHEN, Hsueh-Cheng Rd.Ta-Hsu Hsiang Kaohsiung County, “Low Complexity Iris Recognition Based on Wavelet Probabilistic Neural Networks”, Proceedings of International Joint Conference on Neural networks, pp 1930-1935, 2005.

42.     Weiqi Yuan, Wei He, “A Novel Eyelash Detection Method for Iris Recognition”, Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference, pp 6536-6539, 2005

43.     Daniel Schonberg.Darko Kirovski, “EyeCerts”, IEEE transactions on information forensics and security, vol. 1, no. 2, pp 144-153, 2006.

44.     Judith Liu-Jimenez, Raul Sanchez-Reillo, “Almudena Lindoso, John G. Daugman, Architecture of a Search Engine for Massive Comparison in an Iris Biometric System” IEEE Transactions, pp 103-108, 2005.

45.     Jen-Chun Lee, Ping S. Huang, Chung-Shi Chiang, T-M Tu, and Chien-Ping Chang, “An empirical mode decomposition approach for iris recognition”, IEEE Transactions, pp 289-292, 2006.

46.     Hugo Proenc¸ Luı´s A. Alexandre, “Toward Noncooperative Iris Recognition: A classification approach using multiple signatures”, IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 4, pp 607-612, 2007.

34-40

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html

10.

Authors:

Parvez Hussain S. D, C. N. Veeramani, B. Amala Priya Shalini, R. Karthika

Paper Title:

An Innovative Energy Efficient Automobile Design

Abstract: The present paper deals with the innovative energy efficient automobile design is mainly focused on safety, reliability and cost effectiveness. The smart innovative design is done on safety basis. Main features of the smart vehicle design are long battery back-up and energy efficient use of drives. A back-up supply from the source is available when the vehicle is out of charge. The back-up source is combination or coupling of solar power, wind energy, and shaft coupled dynamo. The design of the motor vehicle(kart) is in accordance with the specifications laid down by the rule book given in this paper. The motor runs with a power output of 750W and 36V. The sources employed are a combination of three 12V 40Ah batteries in series. There is one more back-up battery on board, which is charged by the 2 dynamos and 1 solar panel dynamically. Efforts have been put to validate our design by theoretical calculations, simulations and known facts.

Keywords:
Microcontroller, GSM module, Wind dynamo , solar panel, Finite Element Analysis( FEA) module, analysis software.


References:

1.        Study of various kart designs.
2.        Study of various electrical equipment specifications.

3.        Gain practical knowledge by designing of different kart.

4.        Investigate various reports.

5.        Installation of innovative ideas

41-45

www.blueeyesintelligence.org/attachments/File/fee/2checkout_download.html