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Rakesh N.,Kakatiya Institute of Technology and Science KITS | Nitya A.,National Institute of Technology Tiruchirappalli | Ram G.,National Institute of Technology Tiruchirappalli
2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives, AICERA/iCMMD 2014 - Proceedings | Year: 2014

This paper describes the development of a simple maximum power extracting (MPE) scheme for Wind Electric Energy Conversion System (WECS) comprising of an induction generator, capacitor banks, a diode bridge rectifier, dc link inductor and a line commutated inverter feeding the grid. Steady state analysis of Self Excited Induction Generator (SEIG) is done with the MATLAB programming. The operational speed range of SEIG is decided by proper choice of excitation capacitor banks. MPE is achieved over this speed range by the adjustment of the inverter firing angle that loads the system appropriately to extract maximum power for any given value of input rotor speed. A unique one-to-one relation between per unit rotor speed input and the predetermined inverter firing angle corresponding to MPE has been developed in the form of look-up table. The entire system has been simulated and modelled using the power system block set in MATLAB to obtain the results. The experimental and simulated waveforms validate the system performance. © 2014 IEEE. Source


Raghotham Reddy G.,Kakatiya Institute of Technology and Science KITS | Raghotham Reddy G.,Osmania University | Ramudu K.,Acharya Nagarjuna University | Srinivas A.,Kakatiya Institute of Technology and Science KITS | Rao R.,Osmania University
International Journal of Applied Engineering Research | Year: 2016

A novel method for Medical image segmentation is proposed in this paper. Segmentation is one of the important key tools in medical image analysis. The main application of segmentation is in delineating an organ reliably, quickly, and effectively. In this paper, we have proposed efficient region based segmentation with wavelet transform based Active Contour (WAC) model. The proposed algorithm is segmentation of the brain and bone tissue sarcoma (BTS) present in 2D medical images. The 2D medical images large amounts of in homogeneities are present in the foreground and background. WAC model can easily distinguish the image regions in the interior, exterior, background, edges of tissues by enhancing the wavelet coefficients. The proposed WAC model utilizes the energy minimization function for solving energy functional inside and outside the contours to ensure stability. After that, it eliminates the costly re-initialization and complexity from Level Set Equation. The proposed model is stable, accurate, and immune from boundary anti-leakage and easy to implement. We get promising results obtained on real world medical images over the conventional methods. © Research India Publications. Source


Reddy M.S.,Kakatiya Institute of Technology and Science KITS | Kumar M. R.,DCE Inc | Rao K.S.,Osmania University
Proceedings - 2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011 | Year: 2011

Human activities are recognized from the Electrooculogram (EOG) signal generated from the movement of eye. Hence early, accurate preprocessing of EOG signals is important. In recent years, this became an active area of research. The EOG signal captured using acquisition device is corrupted with the noise and device intrinsic, thus pre processing (noise reduction) is first and foremost step in any further analysis & activity recognition. In this paper a novel method of De-noising EOG signals using Dual Tree complex wavelet transform (DT-CWT) is proposed. The Denoising results obtained are compared with conventional wavelet (DWT) de-noising method. To demonstrate the efficacy of the proposed method, SNR calculations and the statistical analysis are evaluated. The proposed method is best suitable for real time EOG based applications like human-machine communication devices for disabled persons, eye movement analysis and gaming applications. © 2011 IEEE. Source


Mahesh Chandra M.,Kakatiya Institute of Technology and Science KITS | Raman Kumar M.,Kakatiya Institute of Technology and Science KITS | Swarnalatha B.,Kakatiya Institute of Technology and Science KITS
2011 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2011 | Year: 2011

Uncontrolled lighting Conditions poses obstacle to face recognition. To deal with this problem, this paper proposes a preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition. The proposed method first generates synthetic image using Histogram equalization. Original and synthetic images are singular value decomposed; from the estimates of singular values enhanced image is reconstructed. Enhanced image is discrete wavelet decomposed (Haar & Db4) in to different frequency sub bands (LL, LH, HL, HH). The LL sub band is the best approximation of original image with lower-dimensional space and is used as biometric template. Pose Invariant Feature vectors are extracted from this template using Kernel Principal Component Analysis (KPCA). To show the performance, the proposed method is tested on YaleB, ORL benchmarking Databases. The results obtained show the impact of the method and is compared with PCA, KPCA without any preprocessing. © 2011 IEEE. Source


Mahesh Chandra M.,Kakatiya Institute of Technology and Science KITS | Raman Kumar M.,Kakatiya Institute of Technology and Science KITS | Swarna Latha B.,Kakatiya Institute of Technology and Science KITS
2011 - International Conference on Signal Processing, Communication, Computing and Networking Technologies, ICSCCN-2011 | Year: 2011

Biometric devices provide secure mechanism towards gaining access. One of the Biometric features is Face and the system implemented is Face Recognition system. The Classical Face Recognition System is implemented with Principal Component Analysis and is successful. PCA is a linear method of extracting the features in a lower dimension space and is severely affected by the Pose and surrounding illumination variation. To implement effective face recognition system, pose variation is to be considered and the problem is well addressed with Kernel PCA (nonlinear PCA). KPCA extracts features in a higher dimension space, there by the system is rugged to pose variation. The illumination variation is accounted for capture range of the front end device and its surrounding and is not dealt in KPCA. In this work Singular Value Decomposition is used to deal with surrounding illumination and wavelets are employed to aid the KPCA in capturing the Multi Scale Features there by making the System robust to pose and illumination variation. To show the performance, the proposed method is tested on YaleB, ORL Databases. The results obtained show the impact of the method and is compared with PCA, KPCA. © 2011 IEEE. Source

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