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Articles by S. Radhakrishnan
Total Records ( 3 ) for S. Radhakrishnan
  R. Ganesan and S. Radhakrishnan
  Unlike research on brain segmentation of Magnetic Resonance Imaging (MRI) data, research on Computed Tomography (CT) brain segmentation is relatively scarce. We have begun to explore methods for soft tissue segmentation of CT brain data with a goal of enhancing the utility of CT for brain imaging. In this study, a novel method for automatic segmentation of Computed Tomography (CT) brain images has been presented. The method consists of two major phases. In the first phase, the original images are enhanced by using Selective Median Filter (SMF) and in the second phase the Genetic Algorithm (GA) is used to segment the image. The proposed method has been applied to real patient CT images and the performance is evaluated using Receiver Operating Characteristic (ROC) curve analysis. The result shows the superior performance of the proposed algorithm.
  R. Ravi and S. Radhakrishnan
  Active and programmable networks change the functionality of routers and switches by using VPN endpoints and active packets. The authors present a new packet scheduling scheme called active scheduling to control and maintain QoS parameters in virtual private networks (VPNs) within the confines of adaptive and programmable networks. In active scheduling a VPN endpoint on the router monitors the accumulated queuing delay for each service. To control and to keep the end-to-end delay within the bounds, the delays of the packet transmitted are adjusted dynamically by VPN endpoints on the routers spanning the VPN. If there is an increase or decrease in delay of packets, VPN endpoints on a source node informs the destination node to adjust the delay of the packets. This keeps the end-to-end delay of services within the specified bounds and offers better QoS compared with VPNs using static priority scheduling. An algorithm for active scheduling is described and simulation results are presented and compared with priority scheduling.
  K. Somasundaram and S. Radhakrishnan
  In Bio-Informatics application, the analysis of protein sequence is a kind of computation driven science which has rapidly and quickly growing biological data. Also databases used in these applications are heterogeneous in nature and alignment of protein sequence using physical techniques is expensive, slow and results are not always guaranteed/accurate. So this application requires cross-platform, cost-effective and more computing power algorithm for sequence matching and searching a sequence in database. Grid is one of the most emerging technologies of cost effective computing paradigm for large class of data and compute intensive application which enables large-scale aggregation and sharing of computational data and other resources across institutional boundaries. We proposed the Grid architecture for searching of distributed, heterogeneous genomic databases which contained protein sequences to speed up the analysis of large scale sequence data and performed sequence alignment for residues match.
 
 
 
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