Synergy Institute of Engineering and Technology

Dhenkānāl, India

Synergy Institute of Engineering and Technology

Dhenkānāl, India
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Dash S.,Synergy Institute of Engineering and Technology
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering | Year: 2012

The proliferation of mobile computing and cloud services is driving a revolutionary change in our information society. We are moving into the Ubiquitous Computing age in which a user utilizes, at the same time, several electronic platforms through which he can access all the required information whenever and wherever needed. The mobile devices provides the easiest solution for ubiquitous access through wireless network. Mobile users can use their cellular phone to check e-mail, browse internet; travelers with portable computers can surf the internet from airports, railway stations etc. The mobile capabilities can be integrated with cloud computing services to give more secure and advanced services to the subscribers. The emerging domain of Mobile-Cloudextends the Mobile Computing paradigm to the sharing of cloud resources in distributed computing environment. A Mobile-cloud is the result of the integration of mobile application with the cloud. In this paper we propose a Mobile Cloud Computing architecture to integrate mobile application with various cloud services. Our Paper aims at using cloud computing techniques for storage and processing of data on mobile devices, thereby reducing their limitations. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.


Singh J.,Synergy Institute of Engineering and Technology | Mohanty G.C.,Khallikote Autonomous College | Mohanty G.C.,Ravenshaw University | Acharya S.,Khallikote Autonomous College | Acharya S.,Ravenshaw University
Indian Journal of Pure and Applied Physics | Year: 2013

In view of utility of ethyl methyl ketone (EMK) as a nuclear extractant used in atomic energy industry, several ultrasonic parameters namely ultrasonic velocity, isentropic compressibility, acoustic impedance and intermolecular free length have been determined in binary mixture of EMK and alcohols at temperature 303.16 K and frequency 2 MHz by using ultrasonic interferometer. The excess parameters βE, ZE are also computed for different mole fraction of EMK, the results indicate that there is formation of micro heterogeneous clusters of unlike molecules in both the systems.


Singh J.,Synergy Institute of Engineering and Technology | Mohanty G.C.,Khallikote Autonomous College | Acharya S.,Ravenshaw University
Canadian Journal of Physics | Year: 2014

Dielectric constant of binary mixtures of ethyl methyl ketone (EMK) with some carboxylic acid, namely, acetic acid, propionic acid, and butyric acid, is measured at 303.16 K and 455 kHz. The data are used to compute the mutual correlation factor, gab, excess molar polarization, ΔP, and excess Gibbs free energy, ΔG, of mixing to study the molecular interaction. The study reveals that interaction is maximum in EMK + butyric acid system and microheterogeneous β-clusters with antiparallel orientation of dissimilar molecules predominate in it. © 2013 Published by NRC Research Press.


Sahoo B.B.,Synergy Institute of Engineering and Technology | Saha U.K.,Indian Institute of Technology Guwahati | Sahoo N.,Indian Institute of Technology Guwahati
International Journal of Exergy | Year: 2012

This work presents the exergy balances of biogas dual fuel combustion modes using two different pilot fuels in a single-cylinder diesel engine. The main focus is on the use of biodiesel (BD) as a pilot in place of fossil diesel fuel. The results reveal that the important exergy loss recovery sources are the exhaust gas and cooling water exergy. By accessing these losses, there is an increase of 28-30% in work exergy for dual fuel modes as compared to diesel mode. Use of BD, in place of diesel as a pilot, reduces the maximum exergy efficiency roughly by 2%. Copyright © 2012 Inderscience Enterprises Ltd.


Mohanty J.R.,Synergy Institute of Engineering and Technology | Verma B.B.,National Institute of Technology Rourkela | Ray P.K.,National Institute of Technology Rourkela | Parhi D.R.K.,National Institute of Technology Rourkela
Expert Systems with Applications | Year: 2011

In service components and structures frequently come across complicated fatigue loading situations such as interspersed mixed-mode (I and II) spike load on subsequent mode-I fatigue crack growth. The designers rely on different fatigue life prediction methodology in order to avoid costly and time consuming fatigue tests. Earlier authors' have proposed exponential and ANN models to predict the fatigue life of 7020 T7 and 2024 T3 Al alloys under the above loading conditions. In the present work, an attempt has been made to predict the fatigue life by adopting adoptive neuro-fuzzy inference (ANFIS) technique. It is observed that the predicted results for both the alloys are within the maximum range of 0.05% in comparison to experimental findings. © 2010 Elsevier Ltd. All rights reserved.


Nayak P.K.,Synergy Institute of Engineering and Technology | Pujari S.S.,Sambalpur University | Sahu B.N.,Siksha ‘O’ Anusandhan University
2015 IEEE Power, Communication and Information Technology Conference, PCITC 2015 - Proceedings | Year: 2015

In this paper a recursive adaptive filter based on a Gauss-Newton method has been proposed for the estimation of amplitude, phase, and frequency for time- signals in power networks. The presented algorithm is based on the minimization of a weighted forgetting factor based error cost function by the use of recursive Gauss-Newton method. Further a Hessian matrix approximation is used to produce a fast recursive algorithm, that is immune to random noise, waveform distortion and increases the speed of convergence and accuracy. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time parameter estimation of nonstationary signals in noise. Extensive digital simulations have been carried out to prove the validity of the proposed filter. © 2015 IEEE.


Nayak P.K.,Synergy Institute of Engineering and Technology | Mishra S.,Centurion University | Dash P.K.,Soa University | Bisoi R.,Soa University
Neural Computing and Applications | Year: 2015

Abstract: This paper presents a modified TLBO (teaching–learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances. Cumulative sum average filter has been designed for localization and feature extraction of multiple power signal disturbances. The extracted features are fed as inputs to the modified TLBO-based LLRBFNN for classification. The performance of the proposed modified TLBO-based LLRBFNN model is compared with the conventional model in terms of convergence speed and classification accuracy. Also, an extreme learning machine (ELM) approach is used to optimize the performance of the proposed LLRBFNN and is compared with the TLBO method in classifying the multiple power signal disturbances. The classification results reveal that although the TLBO approach produces slightly better accuracy in comparison with the ELM approach, the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data. © 2015 The Natural Computing Applications Forum


Bisoi R.,Siksha ‘O’ Anusandhan University | Dash P.K.,Siksha ‘O’ Anusandhan University | Nayak P.K.,Synergy Institute of Engineering and Technology
International Journal of Knowledge-Based and Intelligent Engineering Systems | Year: 2014

This paper presents a new approach for detection and classification of various power signal disturbances, which constitute an important aspect of power quality assessment. A frequency filtering fast S-transform algorithm is developed with different types of frequency scaling, bandpass filtering and interpolation techniques to reduce the computational cost. The new time-frequency transform based on dyadic scaling has been used for the extraction of relevant features from the power quality disturbance signals. The extracted features are then passed through a decision tree based classifier for the identification of the disturbance patterns. Various simultaneous power signal disturbances have been simulated to prove the efficiency of the technique. The simulation results show superior performance of the new frequency filtering S-transform while classifying overlapping disturbance patterns. Because of the frequency filtering dyadic S-transform algorithm and a relatively simpler classifier methodology, this technique can be used for real time localization, detection, and classification of various power quality events. © 2014 - IOS Press and the authors. All rights reserved.


Hota M.K.,Synergy Institute of Engineering and Technology | Srivastava V.K.,Motilal Nehru National Institute of Technology
Digital Signal Processing: A Review Journal | Year: 2012

A major area of research in genomic sequence analysis is the identification of protein coding regions using the period-3 property. Previously antinotch filter has been used for this purpose. In this paper, three antinotch filters, namely conjugate suppression antinotch filter, antinotch filter followed by moving average filter and harmonic suppression antinotch filter are proposed to improve the identification accuracy. Conjugate suppression antinotch filter suppresses the conjugate frequency component, antinotch filter followed by moving average filter reduces the background noise and harmonic suppression antinotch filter suppresses the harmonic frequency component. Several existing DNA to numerical mapping techniques are compared for GENSCAN test set and based on the result one mapping technique is recommended so that detailed analysis can be performed using various datasets. The computational complexity of the antinotch filters is evaluated in comparison with the ST-DFT method and it is found that the computational load is reduced to a greater extent in antinotch filter. The identification accuracy of the proposed antinotch filter methods is compared with the existing antinotch filter method at the nucleotide level for benchmark datasets. The results show that proposed methods outperform the existing method, giving improved identification of the protein coding regions. © 2012 Elsevier Inc. All rights reserved.


Sahu B.N.,Siksha ‘O’ Anusandhan University | Dash P.K.,Siksha ‘O’ Anusandhan University | Nayak P.K.,Synergy Institute of Engineering and Technology
Proceedings - 2011 International Conference on Energy, Automation and Signal, ICEAS - 2011 | Year: 2011

This paper presents uncented Kalman filter technique for identification of non linear dynamic systems. A novel unscented Kalman filter (UKF) has been proposed that has the advantage over EKF since it does not use linearization for state prediction and covariance's and costly calculations of derivatives. This leads to an accurate computation of Kalman gain and error covariance matrices which ultimately leads to an accurate identification of the system. The approach is shown to exhibit robustness characteristics and fast convergence property. A simulation example dealing with applications of the proposed algorithm is given. © 2011 IEEE.

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