Faculty Publications
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Item An Algorithm Steps to Solve Coupled Case for Dual Input Dual Output SCC(Institute of Electrical and Electronics Engineers Inc., 2018) Zhaikhan, A.; Subburaj, V.; Mustafa, Y.; Jena, D.; Parthiban, P.; Ruderman, A.The proposed converter is designed for low power applications. In this paper, the algorithm is proposed to solve the coupled case of dual input and the dual output converter. The major contribution is R-parameters calculation for the coupled case is deliberated in detail where it includes all conduction and ohmic losses accounting for coupling effects. To validate the performance of designed SCC, modeling and mathematical analysis has been carried out. The results are verified using PSIM simulations and validated mathematically. The analytical and simulation results give excellent proof for the newly designed coupled converter. © 2018 IEEE.Item The success of automatic speaker recognition in laboratory environments suggests applications in forensic science for establishing the identity of individuals on the basis of features extracted from speech. A theoretical model for such a verification scheme for continuous normally distributed features is developed. The three cases of using a) single feature, b) multiple independent measurements of a single feature, and c) multiple independent features are explored. The number of independent features needed for a reliable personal identification is computed based on the theoretical model and an exploratory study of some speech features.(A pattern recognition model of voice-based personal verification systems for forensic applications) Dante, H.M.; Sarma, V.V.S.1980Item Background. Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads. Methods. The patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example. Results. It can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain. Conclusion. The Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient. © 2004 Nayak et al, licensee BioMed Central Ltd.(Simultaneous storage of medical images in the spatial and frequency domain: A comparative study) Nayak, J.; Subbanna Bhat, P.; Acharya, A.U.; Niranjan, U.C.2004Item Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor(2008) Shetty K, K.V.; Nandennavar, S.; Srinikethan, G.Background: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time-consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. Results: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude x frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. Conclusions: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data-driven tool to predict the performance of a PPBR for phenol biodegradation. © 2008 Society of Chemical Industry.Item Efficient storage and transmission of digital fundus images with patient information using reversible watermarking technique and error control codes(2009) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.; Kumar, M.Handling of patient records is increasing overhead costs for most of the hospitals in this digital age. In most hospitals and health care centers, the patient text information and corresponding medical images are stored separately as different files. There is a possibility of mishandling the text file containing patient history. We are proposing a novel method for the compact storage and transmission of patient information with the medical images. In this technique, we are using a reversible watermarking technique to hide the patient information within the retinal fundus image. There is a possibility that these medical images, which carry patient information, can get corrupted by the noise during the storage or transmission. The safe recovery of patient information is important in this situation. So, to recover the maximum amount of text information in the noisy environment, the encrypted patient information is coded with error control coding (ECC) techniques. The performance of three types of ECC for various levels of salt & pepper (S & P) noise is tabulated for a specific example. The proposed system is more reliable even in a noisy environment and saves memory. © 2008 Springer Science+Business Media, LLC.Item Hierarchical clustering algorithm for land cover mapping using satellite images(2012) Senthilnath, J.; Omkar, S.N.; Mani, V.; Tejovanth, N.; Diwakar, P.G.; Archana Shenoy, B.This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust. © 2012 IEEE.Item Crop stage classification of hyperspectral data using unsupervised techniques(2013) Senthilnath, J.; Omkar, S.N.; Mani, V.; Karnwal, N.; Shreyas, P.B.The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient. © 2008-2012 IEEE.Item Prediction of daily pan evaporation using support vector machines(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2014) Pammar, L.; Deka, P.C.Water scarcity globally has lead to severe problems in water management. Understanding the rate of evaporation, from surface water resources is essential for precise management of the water balance. However, evaporation is difficult to measure experimentally due to its nature. Preparing reliable forecasts of evaporation has become an essential element towards efficient water management. The objective of this paper is to predict daily pan evaporation using different kernel functions of Support Vector Machines (SVM's) based regression approach for the meteorological data obtained for the region 'Lake Abaya' which is located in the Great Rift Valley, southern part of Ethiopia. The meteorological parameters considered for study includes daily details of mean-temperature (T), wind speed (W), sunshine hours (Sh), relative humidity (Rh), rainfall (P). Among the kernel functions used for study, the polynomial kernel function proved its credibility by showing improved performance in training and testing periods. The evidence for performance of polynomial kernel function was seen in terms of correlation coefficient (CC) obtained for training and testing is respectively 0.940, 0.956 which is acceptable. © 2014 CAFET-INNOVA TECHNICAL SOCIETY.Item Fault tolerant mobile sensor node traversal schemes based on hexagonal coverage(2014) Santoshi, G.; D'Souza, R.J.Mobile sensor nodes (MSNs) are equipped with locomotive can move around after having been deployed. They are equipped with limited energy. A large portion of energy is drained during the traversal. In order to extend the life time of a MSN, the traveling distance must be minimized. Region of interest (ROI) is covered with multiple MSNs using coverage based pattern movement. When a group of MSNs are deployed to cover a given ROI, all the deployed MSNs should travel an approximately equal distance. Otherwise, the MSN which travels longer distance depletes more energy compared to the MSN which travels a shorter distance. In this work we show that, ROI partition plays great role in hole free coverage and makes the MSNs have optimized movement cost with fault tolerant support. © 2014 Ganala Santoshi and R. J. D'Souza.Item Nonlocal linear minimum mean square error methods for denoising MRI(Elsevier Ltd, 2015) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Rajan, J.The presence of noise results in quality deterioration of magnetic resonance (MR) images and thus limits the visual inspection and influence the quantitative measurements from the data. In this work, an efficient two stage linear minimum mean square error (LMMSE) method is proposed for the enhancement of magnitude MR images in which data in the presence of noise follows a Rician distribution. The conventional Rician LMMSE estimator determines a closed-form analytical solution to the aforementioned inverse problem. Even-though computationally efficient, this approach fails to take advantage of data redundancy in the 3D MR data and hence leads to a suboptimal filtering performance. Motivated by this observation, we put forward the concept of nonlocal implementation with LMMSE estimation method. To select appropriate samples for the nonlocal version of the LMMSE estimation, the similarity weights are computed using Euclidean distance between either the gray level values in the spatial domain or the coefficients in the transformed domain. Assuming that the signal dependent component of the noise is optimally suppressed by this filtering and the rest is a white and uncorrelated noise with the image, we adopt a second stage LMMSE filtering in the principal component analysis (PCA) domain to further enhance the image and the noise variance is adaptively adjusted. Experiments on both simulated and real data show that the proposed filters have excellent filtering performance over other state-of-the-art methods. © 2015 Elsevier Ltd. All rights reserved.