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UCE
Hyderabad, India

Srikala K.,JNTUH College of Engineering | Ramachandram S.,UCE
Global Conference on Communication Technologies, GCCT 2015 | Year: 2015

Computational grids have the potential for solving large-scale and scientific problems using geographically distributed and heterogeneous resources. In addition to the challenges of managing and scheduling resources reliable challenges arise because the grid infrastructure is unreliable. There are two major problems in Scheduling the Grid 1) Efficient Scheduling of jobs 2) Providing fault tolerance in a reliable manner. Most of the existing strategies do not provide fault tolerance for scheduling the workflows. There are some algorithms which provide fault tolerance but, they do a significant measure of redundant computation to provide fault tolerance. This paper addresses this issue and reduces the redundant work by using a group level table of data. This technique is suitable for workflow of jobs. © 2015 IEEE. Source


Kumar M.,UCE
Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015 | Year: 2015

Speaker recognition has made great progress under the laboratory environment, but in real life the performance of speaker recognition system is affected by various factors including environmental noise. This paper studies the performance of speaker recognition system in noisy environment and presents Speaker recognition system using Mel-Frequency Cepstral Coefficients (MFCC) technique based on different classifiers likes Euclidean distance, Back-Propagation Neural Network (BPNN), Self Organizing Map (SOM). This paper presents comparative plots of different classifier. Speaker recognition system based on SOM Neural Network classifier is provide better recognition rate compare to BPNN and Euclidean Distance based systems. © 2015 IEEE. Source


Kumar M.,UCE
Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015 | Year: 2015

Speaker recognition has made great progress under the laboratory environment, but in real life the performance of speaker recognition system is affected by various factors including environmental noise. This paper studies the performance of speaker recognition system in noisy environment and presents Speaker recognition system using modified Mel-Frequency Cepstral Coefficients (MFCC) technique based on different classifiers likes Euclidean distance, Back-Propagation Neural Network (BPNN), Self Organizing Map (SOM). Modified Mel-Frequency Cepstral Coefficients (MFCC) technique includes Blackman windowing instead of hamming window. This paper presents comparative plots of different classifiers based on modified Mel-Frequency Cepstral Coefficients (MFCC) technique. Speaker recognition system based on SOM Neural Network classifier provides better recognition rate compare to BPNN and Euclidean Distance based systems. © 2015 IEEE. Source


Radhika K.,CBIT | Venugopal Reddy A.,UCE
IET Conference Publications | Year: 2012

Next generation wireless networks are evolving towards the integration of various wireless networks to provide high bandwidth and QoS support for real-time multimedia applications to mobile users in a seamless manner. Vertical Handoff Decision (VHD) problem is one of the crucial design issues that need to be resolved in order to provide seamless mobility in heterogeneous wireless environment. In this paper, we present two models to solve VHD problem based on Fuzzy MCDM and Game Theory approaches. Further we analyze the sensitivity of these models and propose an AHP based analytical model to evaluate their performance in terms of a set of decision criteria including number of parameters, user preference, scalability and elimination of unnecessary handoffs. Source


Suvarchala P.V.L.,Research Scholar | Kumar S.S.,UCE | Mohan B.C.,P.A. College
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Iris recognition is the most reliable and dependable biometric system as the features of human eye are invariant and distinctive for every individual. Present iris recognition algorithms are tested using the bench mark databases which are assumed to be almost ideal except for eyelid and eyelash occlusions and rotational inconsistencies. It has been discussed elaborately by Daugman in [3] that, non-ideal imaging conditions affect the "authentics" distribution in the decision environment graph. Getting motivation from this observation, all possible non-ideal imaging conditions and Charge Coupled Device (CCD) noise are simulated and applied on the available databases. Legendre moments, introduced by Teague can achieve translation and scale invariance and also, close to zero value of redundancy measure, so that the moments correspond to distinct and autonomous features of the image. In the proposed method it is proved that, they can also work very well on noise affected features when trained and tested using SVMs. The performance of the Exact Legendre Moments (ELM) on UBIRIS and CASIA datasets proves to be very good with Correct Recognition Rate (CRR) = 99.6% under non-ideal imaging conditions and CCD noise. © Springer-Verlag 2013. Source

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