Qualification: 
M.Tech
ok_sikha@cb.amrita.edu

Sikha O. K. joined Amrita School of Engineering, Coimbatore in June 2013. She received her B. Tech. degree in Information Technology from Calicut University Institute of Engineering and Technology, Calicut  and M. Tech. degree in Computational Engineering and Networking  from Amrita School of Engineering, Coimbatore. She currently serves as Assistant Professor in Department of Computer Science and Engineering. Her areas of interest include Image Processing ,Big Data Analytics and High Performance Computing .

Publications

Publication Type: Journal Article

Year of Conference Publication Type Title

2015

Journal Article

M. Suchithra and Sikha, O. K., “Accelerating the performance of secret sharing algorithm using GPU”, International Journal of Applied Engineering Research, vol. 10, pp. 2921-2925, 2015.[Abstract]


The speed of the computer system is getting down as the complexity of the technology grows. Nowadays the popularity of GPU is getting increased and played an important role to accelerate the performance of the computer system. It maintains the computational efficiency and flexibility effectively compared to the CPU systems. In this work, we exploit the fundamental visual cryptographic algorithm by utilizing the computational power of GPU. Here we focus the parallel implementation of basic (2,2) visual cryptographic and compare the efficiency of execution with traditional CPU system.. This paper mainly emphasizes the strength of GPU. © Research India Publications.

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2014

Journal Article

P. Prabha, Sikha, O. K., Suchithra, M., and Dr. Soman K. P., “Accelerating the performance of DES on GPU and a visualization tool in Microsoft Excel Spreadsheet”, Advances in Intelligent Systems and Computing, vol. 246, pp. 405-411, 2014.[Abstract]


Graphic processing units (GPU) have attained a greater dimension based on their computational efficiency and flexibility compared to that of classical CPU systems. By utilizing the parallel execution capability of GPU, traditional CPU systems can handle complex computations effectively. In this work, we exploit the parallel structure of GPU and provide an improved parallel implementation for data encryption standard (DES), one of the famous symmetric key cryptosystems. We also developed a visualization tool for DES in Microsoft Excel Spreadsheet which helps the students to understand the primitive operations that constitute the DES cryptosystem clearly. The main objective of this work is to investigate the strength of parallel implementation, on the basis of execution time on GPU as well as on CPU systems. © Springer India 2014. More »»

Publication Type: Conference Proceedings

Year of Conference Publication Type Title

2013

Conference Proceedings

M. Suchithra, Sukanya, P., Prabha, P., Sikha, O. K., Sowmya V., and Dr. Soman K. P., “An experimental study on application of orthogonal matching pursuit algorithm for image denoising”, 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013. IEEE, Kochi, Kerala, pp. 729-736, 2013.[Abstract]


<p>Signal or image reconstruction has now become a common task in many applications. According to linear algebra perspective, the number of measurements made or the number of samples taken for reconstruction must be greater than or equal to the dimension of signal or image. Also reconstruction follows the Shanon's sampling theorem which is based on the Nyquist sampling rate. The reconstruction of a signal or image using the principle of compressed sensing is an exception which makes use of only few number of samples which is below the sampling limit. Compressive sensing also known as sparse recovery aims to provide a better data acquisition and reduces computational complexities that occur while solving problems. The main goal of this paper is to provide clear and easy way to understand one of the compressed sensing greedy algorithm called Orthogonal Matching Pursuit (OMP). The OMP algorithm involves the concept of overcomplete dictionary that is formulated based on different thresholding methods. The proposed method gives the simplified approach for image denoising by using OMP only. The experiment is performed on few standard image data set simulated with different types of noises such as Gaussian noise, salt and pepper noise, exponential noise and Poisson noise. The performance of the proposed method is evaluated based on the image quality metric, Peak Signal-to-Noise Ratio (PSNR). © 2013 IEEE.</p>

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2012

Conference Proceedings

R. Anand, Prabha, P., Sikha, O. K., Suchithra, M., Dr. Soman K. P., and Sowmya V., “Visualization of OFDM using Microsoft Excel spreadsheet in Linear Algebra Perspective”, International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 58-64, 2012.[Abstract]


Orthogonal Frequency Division Multiplexing (OFDM) is one of the leading technology that is ruling the communication field. But unfortunately, it is shrouded in mystery. A good knowledge in Linear Algebra is required to appreciate the technology in a better way. So the work focuses on explaining OFDM system from linear algebra point of view. Also, OFDM model communication system is simulated using Excel which makes ease for anyone experiment with OFDM and understand the underlying principle. The paper aims to provide strong foundation on the concept behind OFDM without the need of having much knowledge in electronics field. © 2012 IEEE.

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2012

Conference Proceedings

P. Prabha, Sikha, O. K., Suchithra, M., Sukanya, P., Sowmya V., and Dr. Soman K. P., “Computation of continuous wavelet transform using microsoft excel spreadsheet”, Proceedings - 2012 International Conference on Advances in Computing and Communications, ICACC 2012. IEEE, Kochi, Kerala, pp. 73-77, 2012.[Abstract]


Wavelet theory has become an essential and significant tool for signal and image processing applied in the analysis of various real time signals. It is thus necessary to include wavelet transform and its application in multifractal analysis as a part of the engineering curriculum. In this paper, we present simple and effective way of computing Continuous Wavelet Transform (CWT) using Microsoft Excel Spreadsheet which serves as an user friendly mathematical tool for beginners. The motivation of this paper is to prove the computational power of excel, using which students can have better understanding of the basic concept behind the computation of Continuous Wavelet Transform. The plot of Continuous Wavelet Transform of Brownian signal computation in Excel is compared with that of the result in the Matlab Toolbox. The singularities present in the signal can be inferred from the wavelet modulus maxima plot. The visual interpretation proves that Excel tool provides computational power comparable to that of the Matlab software. The codes for the implementation of CWT in Excel are available on nlp.amrita.edu:8080/sisp/wavelet/cwt/cwt.xlsm, nlp.amrita.edu:8080/sisp/wavelet/cwt/modmax.xlsm, nlp.amrita.edu:8080/sisp/ wavelet/cwt/thermo.xlsm © 2012 IEEE.

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Publication Type: Conference Paper

Year of Conference Publication Type Title

2013

Conference Paper

P. Sukanya, Suchithra, M., Sikha, O. K., Prabha, P., and Dr. Soman K. P., “Understanding CDMA in linear algebra point of view and its simulation in Excel”, in Proceedings - 2013 IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing, iMac4s 2013, Kerala, 2013, pp. 78-83.[Abstract]


Code Division Multiple Access (CDMA) is one of the famous channel access method, mainly used in radio communication technologies. Unfortunately this concept is less understood by the student community due to the lack of understanding the mathematical rules behind it. This paper is intended to provide a linear algebra point of explanation of the concepts behind CDMA. The CDMA concept which was otherwise analyzed in spectral point of view is explained using the orthogonality of the bases. The Microsoft Excel Spread Sheet is used as an aid for the simulation since every one can go deep in to the basic concepts. © 2013 IEEE.

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