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Mallasamudram, India

Nagarajan C.,Bharath University | Madheswaran M.,Center for Advanced Research
Electric Power Components and Systems | Year: 2011

A closed-loop inductance capacitance inductance-T (LCL-T) series parallel resonant converter has been simulated and presented in this article. The fuzzy logic controller has been used for closed-loop operation, and the performance of the proposed converter has been estimated with a closed-loop condition. The steady-state stability analysis of the LCL-T series parallel resonant converter has been analyzed using the state-space model and simulated using MATLAB (The MathWorks, Natick, Massachusetts, USA). The proposed approach is expected to provide better voltage regulation for dynamic load conditions. A prototype 300-W, 100-kHz converter is designed and built for experimental demonstrations, and transient and steady-state performances for the LCL-T series parallel resonant converter are compared from the simulation studies. Copyright © Taylor & Francis Group, LLC. Source


Madheswaran M.,Center for Advanced Research | Kannan K.,SASTRA University
Progress In Electromagnetics Research B | Year: 2010

The effect of optical radiation on a uniformly doped nanoscale FinFET considering quantum mechanical effects has been theoretically examined and analyzed. The device characteristics are obtained from the self-consistent solution of 3D Poisson-Schrödinger equation using interpolating wavelet method. To our best knowledge this is the first approach for the self-consistent solution to surface potential computations of nanoscale FinFET photodetector using interpolating wavelets. This method provides more accurate results by dynamically adjusting the computational mesh and scales the CPU time linearly with the number of mesh points using polynomial interpolation, hence reducing the numerical cost. A fine mesh can be used in domains where the unknown quantities are varying rapidly and a coarse mesh can be used where the unknowns are varying slowly. The results obtained for dark and illuminated conditions are used to examine the performance of the device for its suitable use as a photodetector. Source


Kumar S.J.J.,Rajaas Engineering College | Madheswaran M.,Center for Advanced Research
Journal of Medical Systems | Year: 2012

An improved Computer Aided Clinical Decision Support System has been developed to classify the retinal images using Neural Network and presented in this paper. The Optic Disc Parameters, thickness of the blood vessels, main vessel, and branch vessel and vein diameter have been extracted. Various types of Neural Network have been used for classification. The percentage of False Acceptance Rate and False Rejection Rate of the SVM classifier is found less than other classifiers. The accuracy of the proposed system has been verified and found to be 97.47%. © 2012 Springer Science+Business Media, LLC. Source


Santhiyakumari N.,KS Rangasamy College of Technology | Rajendran P.,KS Rangasamy College of Technology | Madheswaran M.,Center for Advanced Research | Suresh S.,Diagnostic Ultrasound Research and Training Center
Medical and Biological Engineering and Computing | Year: 2011

An active contour segmentation technique for extracting the intima-media layer of the common carotid artery (CCA) ultrasound images employing semiautomatic region of interest identification and speckle reduction techniques is presented in this paper. An attempt has been made to test the ultrasound images of the carotid artery of different subjects with this contour segmentation based on improved dynamic programming method. It is found that the preprocessing of ultrasound images of the CCA with region identification and despeckleing followed by active contour segmentation algorithm can be successfully used in evaluating the intima-media thickness (IMT) of the normal and abnormal subjects. It is also estimated that the segmentation used in this paper results an intermethod error of 0.09 mm and a coefficient of variation of 18.9%, for the despeckled images. The magnitudes of the IMT values have been used to explore the rate of prediction of blockage existing in the cerebrovascular and cardiovascular pathologies and also hypertension and atherosclerosis. © 2011 International Federation for Medical and Biological Engineering. Source


Raja K.B.,PSNA College of Engineering and Technology | Madheswaran M.,Center for Advanced Research | Thyagarajah K.,PSNA College of Engineering and Technology
Machine Vision and Applications | Year: 2010

A study on ultrasound kidney images using proposed dominant Gabor wavelet is made for classifying a few important kidney categories. Three kidney categories, namely, normal (NR), medical renal diseases (MRD) and cortical cyst (CC) are considered for the analysis. Of the 30 Gabor wavelets, a unique dominant Gabor wavelet is determined by maximizing the similarity between original pre-processed image and reconstructed Gabor image. The dominant Gabor features "μ D mn" and "AAD D mn" are then evaluated to characterize the tissues of kidney region and compared with the Gabor features derived by considering all Gabor wavelets individually and as a whole using the resultant classification efficiency. The results obtained show that the proposed dominant Gabor wavelet features provide the classification efficiency of 86.66% for NR, 76.66% for MRD and 83.33% for CC, while individual wavelet features offer less than 70%, 63.33% and 66% for NR, MRD and CC. The overall classification efficiency improves by 18.89% with dominant Gabor features when compared to the classification efficiency obtained by considering all the Gaborwavelets features. The outputs of the proposed technique are validated with medical experts to assess the actual efficiency. The overall discriminating ability of the systems is also evaluated with performance evaluation measures, F-score and ROC. It has been observed that the dominant Gabor wavelet improves the classification efficiency appreciably and explores the possibility of implementing a computer-aided diagnosis system exclusively for ultrasound kidney images. © Springer-Verlag 2008. Source

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