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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. Source


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. Source


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. Source


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. Source


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 Source

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