Sarat Kumar Patra

Bio: Sarat Kumar Patra is an academic researcher from Indian Institutes of Information Technology. The author has contributed to research in topics: Orthogonal frequency-division multiplexing & Additive white Gaussian noise. The author has an hindex of 14, co-authored 167 publications receiving 1080 citations. Previous affiliations of Sarat Kumar Patra include University of Edinburgh & National Institute of Technology, Rourkela.

Image Encryption Using Advanced Hill Cipher Algorithm

Abstract: The Hill cipher algorithm is one of the symmetric key algorithms that have several advantages in data encryption. But, the inverse of the key matrix used for encrypting the plaintext does not always exist. Then if the key matrix is not invertible, then encrypted text cannot be decrypted. In the Involutory matrix generation method the key matrix used for the encryption is itself invertible. So, at the time of decryption we need not to find the inverse of the key matrix. The objective of this paper is to encrypt an image using a technique different from the conventional Hill Cipher. In this paper a novel advanced Hill (AdvHill) encryption technique has been proposed which uses an involutory key matrix. The scheme is a fast encryption scheme which overcomes problems of encrypting the images with homogeneous background. A comparative study of the proposed encryption scheme and the existing scheme is made. The output encrypted images reveal that the proposed technique is quite reliable and robust.

Novel Methods of Generating Self-Invertible Matrix for Hill Cipher Algorithm

Abstract: In this paper, methods of generating self-invertible matrix for Hill Cipher algorithm have been proposed. The inverse of the matrix used for encrypting the plaintext does not always exist. So, if the matrix is not invertible, the encrypted text cannot be decrypted. In the self-invertible matrix generation method, the matrix used for the encryption is itself self-invertible. So, at the time of decryption, we need not to find inverse of the matrix. Moreover, this method eliminates the computational complexity involved in finding inverse of the matrix while decryption.

Efficient architecture for Bayesian equalization using fuzzy filters

Abstract: A normalized Bayesian solution is derived for digital communication channel equalization which uses estimates of scalar channel states. This equalizer is termed as a normalized Bayesian equalizer with scalar channel states (NBEST). The relationship between the NBEST and fuzzy equalizers is derived and computational aspects of fuzzy equalizers are investigated using different types of fuzzy basis functions. It is shown that the fuzzy equalizer in general demands much lower computational complexity than the optimum equalizer. Ways to further reduce the computation complexity of fuzzy equalizers is proposed and their performance evaluated. A novel scheme to select a subset of channel states close to the received vector, resulting in considerable reduction in the computational complexity, is also proposed. A fuzzy equalizer with this modified membership function is shown to perform close to the Bayesian equalizer.

Short Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimization

Abstract: Short term load forecasting is very essential to the operation of electricity companies It enhances the energy-efficient and reliable operation of power system Artificial neural networks have long been proven as a very accurate non-linear mapper ANN based STLF models generally use back propagation algorithm which does not converge optimally & requires much longer time for training, which makes it difficult for real-time application In this paper we propose a smaller MLPNN trained by genetic algorithm & particle swarm optimization The GA training gives better accuracy than BP training, where as it takes much longer time But the PSO training approach converges much faster than both the BP and GA, with a slight compromise in accuracy This looks to be very suitable for real-time implementation

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

Interval type-2 fuzzy logic systems: theory and design

Abstract: We present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate an improved performance over type-1 FLSs.

Type-2 fuzzy logic systems

Abstract: We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.

Design Of Analog Cmos Integrated Circuits

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