American Journal of Computing Research Repository:

Home » Journal » AJCRR » Archive » Volume 2, Issue 2

Article

Determination of Characteristic Frequency for identification of Hot spots in Proteins using Computational Simulations: a Review

1Department of Electronics and Telecommunication Engineering, Synergy Institute of Engineering & Technology, Dhenkanal 759001, Odisha, India


American Journal of Computing Research Repository. 2014, 2(2), 38-43
DOI: 10.12691/ajcrr-2-2-3
Copyright © 2014 Science and Education Publishing

Cite this paper:
Sidhartha Sankar Sahoo, Malaya Kumar Hota. Determination of Characteristic Frequency for identification of Hot spots in Proteins using Computational Simulations: a Review. American Journal of Computing Research Repository. 2014; 2(2):38-43. doi: 10.12691/ajcrr-2-2-3.

Correspondence to: Sidhartha  Sankar Sahoo, Department of Electronics and Telecommunication Engineering, Synergy Institute of Engineering & Technology, Dhenkanal 759001, Odisha, India. Email: sidhartha.nmiet@gmail.com

Abstract

Proteins perform their functions by interaction with other molecules known as target. Protein-target interactions are very specific in nature and occur at predefined locations in proteins known as hotspots. For successful protein-target interaction both protein and target must share common spectral component known as characteristic frequency. Characteristic frequency is very importance since it forms basis for protein-target interactions, thus far various computational simulations have been used for determination of characteristics frequency. In this paper we have applied all computational simulations used till now and also use comparative study based on computational time and Signal to Noise Ratio parameter to stress the best suitable technique. All computational simulation works in this paper are done using MATLAB.

Keywords

References

[[[[[[[[[[
[[1]  Alberts, B., Bray, D., Johnson, A., Lewis, J., Raff, M., Roberts, K., and Walter P., “Essential Cell Biology”, Garland Publishing, New York, 1998.
 
[[2]  Bogan, A. A. and Thorn, K. S., “Anatomy of hot spots in protein interfaces”, Journal of Molecular Biology, 280 (1). 1-9. 1998.
 
[[3]  Cosic, I., “Macromolecular bioactivity: is it resonant interaction between macro-molecules? - theory and applications”, IEEE Trans. on Biomedical Engr., 41 (12). 1101-1114. Dec. 1994.
 
[[4]  Vaidyanathan, P. P. and Yoon, B.J., “The role of signal-processing concepts in genomics and proteomics”, Journal of the Franklin Institute, 341 (1-2). 111-135. 2004.
 
[[5]  Ramachandran, P., Antoniou, A. and Vaidyanathan, P. P., “Identification and location of hot spots in proteins using the short-time discrete Fourier transform”, in Proc. 38th Asilomar Conf. Signals, Systems, Computers, Pacific Grove, CA. 1656-1660. Nov. 2004.
 
Show More References
[6]  Ramachandran, P. and Antoniou, A., “Localization of hot spots in proteins using digital filters”, in Proc. IEEE Int. Symp. Signal Processing and Information Technology, Vancouver, BC, Canada. 926-931. Aug. 2006.
 
[7]  Ramachandran, P. and Antoniou, A., “Identification of Hot-Spot Locations in Proteins Using Digital Filters”, IEEE journal of selected topics in signal processing, 2 (3). June 2008
 
[8]  Sahu, S.S. and Panda, G., “Efficient Localization of Hot Spot in Proteins Using A Novel S-Transform Based Filtering Approach”, IEEE/ACM Transaction on Computational Biology and Bioinformatics, 8 (5). 1235-1246. 2011.
 
[9]  Kasparek, J., Maderankova, D. and Tkacz, E., “Protein Hotspot Prediction Using S-Transform. In Information Technologies in Biomedicine”, Springer International Publishing. 3. 327-336. 2014.
 
[10]  Sharma, A. and Singh, R., “Determination of Characteristic Frequency in Proteins using Chirp Z-transform”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2 (6). June 2013.
 
[11]  Proakis, J.G. and Manolakis, D.G., “Digital Signal Processing: principles, algorithms and applications ”, published by Pearson Education, Inc., © 2012
 
[12]  Sahoo, S.S. and Hota, M.K., “A Computational Simulation of Determination of Characteristic Frequency for Identification of Hot Spots in Proteins.” American Journal of Systems and Software, 2 (3). 81-84. 2014
 
[13]  Swiss-Prot Protein Knowledgebase. Swiss Inst. Bioinformatics (SIB). [Online]. Available: http://us.expasy.org/sprot/.
 
[14]  Protein Data Bank (PDB), Research Collaboratory for Structural Bioinformatics (RCSB). [Online]. Available: http://www.rcsb.org/pdb/.
 
[15]  Yadav, Y. and Wadhwani, S., “Identification of Characteristic frequency in Proteins using Power Spectral Density”, International Journal of Advances in Electronics Engineering, 1 (1). 342-346. 2011.
 
Show Less References

Article

An Efficient Key Distribution Protocol Based on BB84

1Department of Electrical Engineering, Texas A&M University-Texarkana, Texarkana, USA


American Journal of Computing Research Repository. 2014, 2(2), 33-37
DOI: 10.12691/ajcrr-2-2-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Parag K. Lala. An Efficient Key Distribution Protocol Based on BB84. American Journal of Computing Research Repository. 2014; 2(2):33-37. doi: 10.12691/ajcrr-2-2-2.

Correspondence to: Parag  K. Lala, Department of Electrical Engineering, Texas A&M University-Texarkana, Texarkana, USA. Email: plala@tamut.edu

Abstract

Private key cryptography suffers from a major weakness - it requires sharing of a secret key between two parties. An intruder can copy the secret key as it is being exchanged, thereby severely compromising the security of the system. Thus a private key cryptographic system depends entirely on secrecy of the key. Public key cryptography does not have a key distribution problem but its security relies on the fact that determining the factors of a number that is the product of two very large prime numbers is not computationally feasible. It has been shown that a quantum computer can solve the prime factors of very large numbers in polynomial time which would otherwise take millions of years. Public key cryptography will therefore become insecure if quantum computing becomes a reality. Quantum cryptography, originally presented in BB84 protocol, avoids all these issues by encrypting the shared key using a series of photons. In this paper a key distribution protocol based on the concepts of BB84 is proposed It provides an additional layer of security by sending the key data bits twice; during the second transmission the original key bits or their complements are randomly chosen for transmission. The sender informs the receiver about the orientation of the key bits during the second round of transmission only after the data has been sent out.

Keywords

References

[[[[[[
[[1]  Vernam, G.S., “Cipher Printing Telegraph Systems for secret wire and radio telegraphic communications,” J. AIEE 45, pp. 109-115, 1926.
 
[[2]  Diffie, W. and Hellman, M., "New directions in cryptography", IEEE Transactions on Information Theory, vol. IT-22, No. 6, pp. 644-654, Nov. 1976.
 
[[3]  Rivest, R, Shamir, A and Adleman, L, “A method for obtaining digital signatures and, public key cryptosystems,” Communications of the ACM, pp. 120-126, 21, 1978.
 
[[4]  Riefel, E. and Polak, W., “An introduction to quantum computing for non-physicists”, arXiv: quant-ph/9809016, 1998.
 
[[5]  Wiesner, S. “Conjugate coding”, SIGACT News, 15 (1): 78-88, 1983. Original manuscript written circa 1970.
 
Show More References
[6]  Yanofsky, N.S. and M.A. Mannucci, Quantum Computing for Computer Scientists, Cambridge University Press, 2008.
 
[7]  Bennett, C.H., and Bassard, G. “Quantum cryptography: public key distribution and coin tossing”, International Conference on Computers, Systems & Signal Processing, pp. 175-179, 1984.
 
[8]  Wooters, W.K., and Zurek, W.H., “A Single Quantum Cannot Be Cloned”, Nature 299, pp. 802-803, 1982.
 
[9]  Bennett, C.H., “Quantum cryptography using any two non-orthogonal states”, Phys. Rev. Letts. 68, pp. 3121-3124, 1992.
 
[10]  H. Bechmann-Pasquinucc and N. Gisin, “Incoherent and coherent eavesdropping in the six state protocol of quantum computing”, Phys. Rev. A 59, pp. 4238-4248, 1999.
 
[11]  A. Scarani, A. Acin, G. Ribordy and N. Gisin, “Quantum cryptography protocols robust against photon number splitting attacks”, Physical Review Letters, vol. 92, No. 5, pp. 2004.
 
Show Less References

Article

Developing Kinect-like Motion Detection System using Canny Edge Detector

1Department of Computer Science and Engineering, University of Bridgeport, Bridgeport


American Journal of Computing Research Repository. 2014, 2(2), 28-32
DOI: 10.12691/ajcrr-2-2-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Skander Benayed, Mohammed Eltaher, Jeongkyu Lee. Developing Kinect-like Motion Detection System using Canny Edge Detector. American Journal of Computing Research Repository. 2014; 2(2):28-32. doi: 10.12691/ajcrr-2-2-1.

Correspondence to: Skander  Benayed, Department of Computer Science and Engineering, University of Bridgeport, Bridgeport. Email: sbenayed@my.bridgeport.edu

Abstract

With the advance in video technology, motion-based computing system has an effect on both hardware and software, such as video surveillance, security alarm systems and game entertainment. For example, Kinect is a webcam style add-on peripheral for Xbox 360 game console manufactured by Microsoft, which is featured by RGB camera and multi-array microphone. Kinect is a motion-based software technology that can provide 3-D motion detection, skeleton motion tracking, and voice and facial recognitions. It is currently considered the cutting edge in the gaming world. In this paper, we developed a Kinect-like motion detection system for video streams without using real Kinect device. There are many approaches of motion detection for video streams. However, most of them are based on frame-based comparisons, which could be resource intensive and make it challenging to keep up with the continuous need for speed. Therefore, we present a novel method for motion tracking and identification that are based on the Canny edge detector. The experimental results show that the method is fast and effective in simulating applications, such as Microsoft Kinect motion detection and video surveillance system.

Keywords

References

[[[[
[[1]  Bowyer, K., Kranenburg, C., and Dougherty, S. Edge detector evaluation using empirical roc curve. Comput. Vision Image Understand.2001, pg.10, 77-103.
 
[[2]  Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, pg.679-698.
 
[[3]  Elena Deza and Michel Marie Deza, “Encyclopedia of Distances”, Springer, 2009, pg.94.
 
[[4]  Gary Bradski & Adrian Kaehler. Learning OpenCV: Computer Vision with the OpenCV Library, O'REILLY, 2008.
 
[[5]  http://en.wikipedia.org/wiki/Feature_detection_(computer_vision), March 2012.
 
Show More References
[6]  http://www.codeproject.com/Articles/28465/Easy-to-use-Wrapper-DLL-for-Intel-s-OpenCV-Library, March 2012.
 
[7]  http://www.focuspeinc.com/UploadFile/Download/2009090716450143.pdf, March 2012.
 
[8]  http://en.wikipedia.org/wiki/Canny_edge_detector, March 2012.
 
[9]  http://www.matlabcodes.com/2011/01/canny-edge-detection.html, March 2012.
 
Show Less References
comments powered by Disqus