GMR Institute of Technology

Rajam andhra Pradesh, India

GMR Institute of Technology

Rajam andhra Pradesh, India

Time filter

Source Type

Naidu R.L.,GMR Institute of Technology | Satyanarayana B.,GMR Institute of Technology | Reddy D.R.K.,Rashtreeya Vidyalaya College of Engineering
International Journal of Theoretical Physics | Year: 2012

A spatially homogeneous and anisotropic Bianchi type-V universe with variable equation of state (EoS) parameter and constant deceleration parameter is obtained in a scalar-tensor theory of gravitation proposed by Saez and Ballester (Phys. Lett. A 113:467, 1986). The physical and kinematical properties of the universe have been discussed. © 2012 Springer Science+Business Media, LLC.


Naidu R.L.,GMR Institute of Technology | Satyanarayana B.,GMR Institute of Technology | Reddy D.R.K.,Rashtreeya Vidyalaya College of Engineering
International Journal of Theoretical Physics | Year: 2012

In this paper, we investigate Bianchi type-III universe which has dynamical energy density. We introduce three different skewness parameters along spatial directions to quantify the deviation of pressure from isotropy. We also assume that the skewness parameters are time dependent. The Saez-Ballester (J. Phys. Lett. A 113:467, 1986) field equations have been solved by applying a variation law for generalized Hubble's parameter given by Bermann (Nuovo Cimento B 74:182, 1983). Some physical and kinematical properties of dark energy model are discussed. © 2012 Springer Science+Business Media, LLC.


Reddy D.R.K.,Rashtreeya Vidyalaya College of Engineering | Naidu R.L.,GMR Institute of Technology | Satyanarayana B.,GMR Institute of Technology
International Journal of Theoretical Physics | Year: 2012

A five dimensional Kaluza-Klein space-time is considered in the presence of perfect fluid source in f(R,T) gravity proposed by Harko et al. (arXiv:1104.2669 [gr-qc], 2011). A cosmological model with a negative constant deceleration parameter with an appropriate choice of a function f(T) is presented. To find a determinate solution of the field equations it is assumed that scalar of expansion is proportional to the shear scalar of the space time. The physical behavior of the model is also studied. © 2012 Springer Science+Business Media, LLC.


Biswal B.,GMR Institute of Technology | Biswal M.,Silicon Institute of Technology | Mishra S.,Indian Institute of Technology Delhi
IEEE Transactions on Industrial Electronics | Year: 2014

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances. © 1982-2012 IEEE.


Behera H.S.,Orissa Engineering College, Bhubaneswar | Dash P.K.,S O A University | Biswal B.,GMR Institute of Technology
Applied Soft Computing Journal | Year: 2010

This paper presents a new approach for power quality time series data mining using S-transform based fuzzy expert system (FES). Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the fuzzy expert system for power quality event detection. The proposed expert system uses a data mining approach for assigning a certainty factor for each classification rule, thereby providing robustness to the rule in the presence of noise. Further to provide a very high degree of accuracy in pattern classification, both the Gaussian and trapezoidal membership functions of the concerned fuzzy sets are optimized using a fuzzy logic based adaptive particle swarm optimization (PSO) technique. The proposed hybrid PSO-fuzzy expert system (PSOFES) provides accurate classification rates even under noisy conditions compared to the existing techniques, which show the efficacy and robustness of the proposed algorithm for power quality time series data mining. © 2009 Elsevier B.V. All rights reserved.


Swain G.,GMR Institute of Technology | Lenka S.K.,University of Rajasthan
Advanced Materials Research | Year: 2012

In this paper we propose a technique for secure communication between sender and receiver. We use both cryptography and steganography. We take image as the carrier to use steganography. We have extended the existing hill cipher to increase its robustness and used it as our cryptography algorithm. By using this extended hill cipher (a new block cipher) which uses a 128 bit key, we encrypt the secret message. Then the cipher text of the secret message is embedded into the carrier image in 6th, 7th and 8th bit locations of some of the selected pixels (bytes). The 8th bit in a pixel (byte) is called as the least significant bit (LSB). The pixel selection is done depending on the bit pattern of the cipher text. So for different messages the embedding pixels will be different. That means to know the pixels of the image where the cipher text is embedded we should know the cipher text bits. Thus it becomes a stronger steganography. As the pixels where we embed are chosen during the run time of the algorithm, so we say that it is dynamic steganography. After embedding the resultant image will be sent to the receiver, the receiver will apply the reverse process what the sender has done and get the secret message. © (2012) Trans Tech Publications, Switzerland.


Biswal B.,GMR Institute of Technology | Dash P.K.,Siksha ‘O’ Anusandhan University | Mishra S.,Centurian Institute of Technology
Expert Systems with Applications | Year: 2011

This paper presents a novel clustering and pattern classification of power signal disturbances using a variant of S-transform, which is termed as a phase corrected wavelet transform. This variant is obtained by taking the inverse Fourier transform of S-transform and is known as time-time transform (TT-transform). The output from the TT-transform based power signal processing is a set of relevant features that is used for visual localization, detection, and disturbance pattern classification. The TT-transform is a method of dividing a primary time series into a set of secondary, time-localized time series, through use of a translatable, scalable Gaussian window. These secondary time series resemble ordinary windowed time series, except that higher frequencies are more strongly concentrated around the midpoint of the Gaussian, as compared with lower frequencies. In this paper the TT-transform is generalized to accommodate arbitrary scalable windows. The generalized TT-transform can be useful in resolving the times of event initiations when used jointly with a related time-frequency distribution, the generalized S-transform. The extracted features are the input to a fuzzy C-means clustering algorithm (FCA) to generate a decision tree for power signal disturbance pattern classification. To improve the pattern classification of the fuzzy C-means decision tree, the cluster centers are updated using a hybrid ant colony optimization technique (HACO). Further a comparative assessment of power signal disturbance pattern classification accuracy for different population based optimization approach like the genetic algorithm (GA) and particle swarm optimization technique are presented in this paper. The various computational simulations presented in this paper reveal significant improvement in the pattern classification accuracy, the average number of function evaluations and processing time, etc. © 2010 Elsevier Ltd. All rights reserved.


Biswal B.,GMR Institute of Technology | Biswal M.,Silicon Institute of Technology | Hasan S.,Siksha ‘O’ Anusandhan University | Dash P.K.,Siksha ‘O’ Anusandhan University
Applied Soft Computing Journal | Year: 2014

A new approach to time-frequency analysis and pattern recognition of non-stationary power signals is proposed in this paper. In this manuscript, visual localization, detection and classification of non-stationary power signals are achieved using wavelet packet decomposition and automatic pattern recognition is carried out through learning vector quantization neural network. The wavelet packet decomposition (WPD) of the non-stationary power signals is carried out to extract the coefficients at multiple level of decomposition. The relevant features for pattern classification are derived from the time-scale information obtained by WPD. The extracted features are used to classify different power quality disturbances by using learning vector quantization neural net. Various non-stationary power signal waveforms are considered to verify the applicability of the proposed technique. © 2014 Elsevier B.V.


Biswal B.,GMR Institute of Technology | Mishra S.,Indian Institute of Technology Delhi
IET Generation, Transmission and Distribution | Year: 2014

This study presents a novel approach to localise, detect and classify non-stationary power signal disturbances using a modified frequency slice wavelet transform (MFSWT). MFSWT is an extension of frequency slice wavelet transform (FSWT), which provides frequency-dependant resolution with additional window parameters for better localisation of the spectral characteristics. An advantage of the MFSWT is attributed to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas a localising scalable modified Gaussian window dilates and translates. Several practical power signals are considered for visual analysis using MFSWT, and the disturbance patterns are appropriately localised with unique signature corresponding to each type. This work also evaluates the detection capability of the proposed methodology and a comparison with earlier FSWT and Hilbert transform to show the superiority of proposed technique in detecting the power quality disturbances. A probabilistic neural network (PNN) based classifier is used for identifying the various disturbance classes. The spread parameter of the Gaussian activation function in PNN is tuned and its effect on the classification at different strengths of noise is studied. © The Institution of Engineering and Technology 2014.


Swain G.,GMR Institute of Technology
International Journal of Security and its Applications | Year: 2013

In this paper a pixel value differencing steganography based on the maximum difference of the neighboring pixel values have been proposed. There are four variants such as five, six, seven and eight neighbor differencing. In five neighbor differencing method the maximum difference amongst the five neighboring pixels such as upper, left, right, bottom and upper-right corner are used to take embedding decision. In six neighbor differencing method the maximum difference amongst the six neighboring pixels such as upper, left, right, bottom, upper-right corner, and upper-left corner are used to take embedding decision. In seven neighbor differencing method the maximum difference amongst the seven neighboring pixels such as upper, left, right, bottom, upper-right corner, upper-left corner and bottom-left corner are used to take embedding decision. In eight neighbor differencing method the maximum difference amongst all the eight neighboring pixels are used to take embedding decision. The message extraction process is very simple and does not require any knowledge of the original image. The experimental results show that the distortion is the minimum in five neighbor differencing. But the theoretical studies reveal that the hiding capacity is the highest in eight neighbor differencing. © 2013 SERSC.

Loading GMR Institute of Technology collaborators
Loading GMR Institute of Technology collaborators