Faculty Publications
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Item Wavelet based Noise Reduction Techniques for Real Time Speech Enhancement(Institute of Electrical and Electronics Engineers Inc., 2018) Ravi, B.R.; Deepu, S.P.; Ramesh Kini, M.; Sumam David, S.Fixed noise suppression techniques are generally used for speech enhancement in different low power real time systems. In this paper, we propose a modified adaptive system for classification of speech signals and noise reduction based on multi-band techniques. It involves initial identification of incoming speech segments as clean speech, speech in noise or pure noise. For the noisy speech segments, background noise classification is carried out using different wavelet-based feature sets. Noise Reduction system consists of removal of adaptive stationary noise and non-stationary noise based on classified noise type. Simulation results show that the proposed system provides optimal noise reduction and better speech quality with reduced computational complexity in adverse noisy environments. © 2018 IEEE.Item Bearing health condition monitoring: Wavelet decomposition(Indian Society for Education and Environment indjst@gmail.com, 2015) Shanmukha Priya, V.; Mahalakshmi, P.; Naidu, V.P.S.Background/Objectives: Condition monitoring is one of the important functions to be carried out in the maintenance of any machine. In condition monitoring, there are several techniques among which the most commonly used technique for rotating machines is the vibration analysis. Methods/Statistical analysis: Discrete Wavelet Transform is used to decompose the vibration signal into 9 levels. For each level, mean ±std (standard deviation) are computed for both approximated and detailed coefficients. Findings: Bearing data obtained from the bearing test rig of Case Western Reserve University are used to test the algorithm. The standard of coefficients in level to 3 shows distant classification of faults. The levels which show clear classification among the bearings are those frequency bands in which the characteristic defect frequencies of faults occur. It is inferred that, the wavelet decomposition classifies the ball defect clearly than the frequency domain methods. Application/Improvements: Wavelet based bearing health condition monitoring technique can be used for bearing fault diagnosis and it can be extended for prognosis.Item A Noise Reduction Technique Based on Nonlinear Kernel Function for Heart Sound Analysis(Institute of Electrical and Electronics Engineers Inc., 2018) Mondal, A.; Saxena, I.; Tang, H.; Banerjee, P.The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods. © 2013 IEEE.Item Short-term wind speed forecasting using S-transform with compactly supported kernel(John Wiley and Sons Ltd, 2021) Kamath, P.R.; Senapati, K.This paper presents a modified S-transform (ST) based on a compactly supported kernel. A version of Cheriet-Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S-transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency-based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural-network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short-term forecasting results. © 2020 The Authors. Wind Energy published by John Wiley & Sons LtdItem Fault diagnosis of antifriction bearing in internal combustion engine gearbox using data mining techniques(Springer, 2022) Ravikumar, K.N.; Aralikatti, S.S.; Kumar, H.; Kumar, G.N.; Gangadharan, K.V.Ball bearing failure are most common failure in rotating machinery, which can be catastrophic. Hence obtaining early failure warning along with precise fault detection technique is at most important. Early detection and timely intervention are the key in condition monitoring for long term endurance of machine components. The early research has used signal processing and spectral analysis extensively for fault detection however data mining with machine learning is most effective in fault diagnosis, the same is presented in this paper. The vibration signals are acquired for an output shaft antifriction bearing in a two-wheeler gearbox operated at various loading conditions with healthy and fault conditions. Data mining is employed for these acquired signals. Statistical, discrete wavelet and empirical mode decomposition are employed for feature extraction process and J48 decision tree for feature selection. Classification is carried out using K*, Random forest and support vector machine algorithm. The classifiers are trained and tested using tenfold cross validation method to diagnose the bearing fault. A comparative study of feature extraction and classifiers are done to evaluate the classification accuracy. The results obtained from K* classifier with wavelet feature yielded better accuracy than rest other classifiers with classification accuracy 92.5% for bearing fault diagnosis. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Item Hydrodynamic performance of an oscillating water column WEC integrated with a pile-restrained H-type breakwater(Taylor and Francis Ltd., 2025) Vishwakarma, R.D.; Muduli, R.; Karmakar, D.The present study examines the hydrodynamic performance of an oscillating water column (OWC) wave energy converter (WEC) integrated into a pile restrained H-type breakwater. A three-dimensional model study is performed using ANSYS-AQWA based on potential flow theory. The results for the incident wave excitation force, shear force, and bending moment on the pile restrained breakwater and the transmission coefficient are obtained for the regular waves. The effect of incident wave angle on the forces is assessed along with the impact of changes in relative draft and width of the device. The power capture efficiency as well as wave transformation characteristics of the device are evaluated using Boundary Element Method (BEM). The study performed will be helpful to scientists and researchers to design and develop an integrated hybrid breakwater system that can protect the coast and provide useful energy by minimising the impact on the marine ecological system and environment. © 2025 Informa UK Limited, trading as Taylor & Francis Group.Item Nonlinear analysis of groundwater levels: investigating trends and the impact of El Niño on groundwater drought in a southern region of India(Springer Science and Business Media Deutschland GmbH, 2025) Poojitha, K.; Dodamani, B.M.The expansion of groundwater irrigation and the cultivation of water-intensive sugarcane, combined with low rainfall, have exacerbated groundwater depletion and intensified droughts in the semi-arid Upper Krishna basin, India. This study employs an iterative singular spectrum analysis (iterative SSA) approach to impute missing groundwater level data from 25 monitoring wells. Cross-validation results show that iterative SSA effectively preserves the overall data structure when missing data was random, achieving good performance metrics with NSE > 0.79, R2 > 0.8 and RMSE < 0.88 under optimal parameters (L = 12 and k = 5). The reconstructed groundwater levels were then used to identify nonlinear trends with a 180-month smoothing SSA window and to investigate the impact of strong El Niño events on groundwater drought through cross-wavelet transform (XWT) and wavelet coherence (WTC) analyses between 1983 and 2017. The nonlinear trends revealed short-term deviations in groundwater levels during 1991–2000, 2002–2003, and 2015–2017. These deviations were corroborated by significant cross-wavelet power and high wavelet coherence between the Niño 3.4 SST Index and groundwater drought, particularly under low rainfall conditions, indicating stress on the region’s groundwater system. Although the study effectively captures the nonlinear nature of groundwater levels and the influence of climate variability on drought, the complexity of the groundwater system in the region persists due to physical water scarcity and high groundwater extraction for irrigation. This study highlights the importance of imputing missing data and applying nonlinear trend and wavelet analyses to detect short-term deviations caused by severe droughts, driven by strong El Niño events and high irrigation demands. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.