Dr. T. Palanisamy joined Department of Mathematics of Amrita Institute of Technology, Coimbatore in 2002 and presently Vice Chairperson and Associate Professor in Department of Mathematics of Amrita School of Engineering, Amritanagar Coimbatore. He obtained his Doctoral Degree from Amrita Vishwa Vidyapeetham, Coimbatore. His area of research is the estimation of variance function in heteroscedastic regression models. His research interest also includes regression analysis, signal processing and image processing using wavelet techniques and sparse representation theory. He has delivered number of lectures in FDP/Workshops/Seminars. He has organized an International Conference on Applications of Fractals and Wavelets in January 2015.
Conference Organized
International Conference on Applications of Fractals and Wavelets - ICAFW-2015 during January 10-11, 2015.
Guest Lectures Delivered
- Presented a seminar on “ MATLAB” to the Faculty and Post Graduate students at Arungarai Amman College of Arts and Science, Chinnadharapuram, Karur on January 2, 2008.
- Delivered a talk on “Getting Started With MATLAB” to the Faculty and Post Graduate students at Tiruppur Kumaran College of Arts and Science, Tiruppur on January 10, 2008.
- Delivered a talk on “Random Variables and Discrete Distributuions” as a resource person in a Faculty Development Program at Tamilnadu College of Engineerig, Coimbatore on January 23, 2008.
- Gave training on “Getting Started With MATLAB” to the Faculty in one day Workshop at Karpagam University, Coimbatore on December 22, 2009.
- Delivered a talk on “Transformations of Random Variable” as a resource person in a Faculty Development Program at Kathir College of Engineering, Coimbatore on January 25, 2012
- Delivered a talk on “LINEAR ALGEBRA AN INSIGHT INTO FOURIER ANALYSIS” as a resource person in a Faculty Development Program at KSR College of Engineering, Tiruchengode on November 22, 2014
- Delivered a talk on “LINEAR ALGEBRA AN INSIGHT INTO FOURIER ANALYSIS” as a resource person in a Faculty Development Program at Karpagam University, Coimbatore on June 4, 2015.
- Delivered a talk on “Partial Differentiation and Differential Equations” as a resource person at CIT, Coimbatore on July 6, 2015.
- Delivered a talk on “Covariance, Correlation, Regression and Transformation of Random variables” as a resource person in a UGC sponsored Faculty Development Program at CIT, Coimbatore on September 2, 2015.
- Delivered a talk on “Multiple Integrals” as a resource person at Kumarasamy Engineering College, Karur on September 2, 2015.
- Delivered a talk on “Linear Algebra – An Insight into Wavelets” as a resource person in a UGC sponsored QIP at CIT, Coimbatore on December 5, 2015.
- Delivered a talk on “Statistics and Linear Algebra – A walk together” as a resource person at Nirmala College, Coimbatore, Coimbatore on February 5, 2016.
- Delivered a talk on “Fourier’s Influencing Mathematics by Applications”as a resource person in a Faculty Development Program under Technical Education Quality Improvement Programme at PSG College of Technology, Coimbatore on July 8, 2016.
- Delivered a talk on “Wavelet Galerkin Methods for Differential Equations” as a resource person in a Workshop on Recent Trends in Differential Equations and Its Applications at CIT, Coimbatore on August 6, 2016.
- Delivered a talk on “Regression- A Possibilty Beyond Inconsistency” to the Faculty and PG students of Nirmala College for Women, Coimbatore on August 29, 2016.
- Delivered a talk on “ODE – A Bag of Tricks !” to the Faculty and students of Bannari Amman Institute of Technology, Sathyamangalam on September 14, 2016.
- Delivered a talk on “Covaraiance, Correlation and Linear Regression”as a resource person in a Faculty Development and Training Program for Quality Improvement at Sri Ramakrishna Engineering College, Coimbatore on November 24, 2016.
- Delivered a talk on “Google Matrix” to the Faculty and students of PSGR Krishnammal College for Women, Coimbatore on December 12, 2016.
- Delivered an Invited Lecture on “Wavelet Galerkin Methods for Differential Equations”in a State Level Conference on Applications of Mathematics at VHNSN College, Virudhunagar on December 17, 2016.
- Delivered an Invited Lecture on “Variance Function Estimation In Heteroscedastic Regression Models” in a National Level Conference on Current Scenario in Pure And Applied Mathematics at Kongunadu Arts and Science College, Coimbatore on December 23, 2016.
Publications
| Year of Publication | Title |
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2016 |
A. S, Sriram, A., and Dr. Palanisamy T., “A Comparative Study on Decomposition of Test Signals Using Variational Mode Decomposition and Wavelets”, International Journal on Electrical Engineering and Informatics, vol. 8, no. 4, pp. 885-895, 2016.[Abstract] The decomposition of signals into their primitive or fundamental constituents play a vital role in removing noise or unwanted signals, thereby improving the quality and utility of the signals. There are various decomposition techniques, among which the linear wavelet technique and the Variational Mode Decomposition (VMD) are the most recent and widely used ones. This paper presents a comparative study of the decomposition of spatially inhomogeneous test functions namely Doppler and Bumps used by statisticians. An effort is made in this article to compare the efficiency of the noise removal in the resulting decompositions at various approximation levels using wavelets and by varying the number of reconstruction modes in VMD. Surprisingly it is found that the VMD technique yields better results with more accuracy for a specific set of parameters irrespective of the spatial character of the function. More »» |
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2016 |
Aa Vishnupriyadharshini, Vanitha, Va, and Dr. Palanisamy T., “Wind speed forecasting based on statistical Auto Regressive Integrated Moving Average (ARIMA) method”, International Journal of Control Theory and Applications, vol. 9, pp. 7681-7690, 2016.[Abstract] Wind power forecasting is of greater importance to increase the wind power penetration to the grid as well as to maintain the grid stability. Wind power varies with cubic times the wind speed. Thus, accurate forecasting of wind speed is a preliminary process. This paper projects the analysis of wind speed forecasting using various statistical approaches and describes Auto Regressive Integrated Moving Average (ARIMA) method based wind speed forecasting in detail. Historical time series wind speed data, with a time interval of 3 hours average, collected from Amrita Wind Energy Centre is considered for the analysis. Results show that ARIMA model forecast the wind speed with better accuracy. Consideration of multivariate data and seasonal factors are also suggested to improve the wind speed forecasting accuracy. © International Science Press. More »» |
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2016 |
Dr. Palanisamy T., “Smoothing the Difference-based Estimates of Variance Using Variational Mode Decomposition”, Communications in Statistics - Simulation and Computation , 2016.[Abstract] We propose a variational mode decomposition approach to estimate the variance function in a nonparametric heteroscedastic fixed design regression model. A data-driven estimator is constructed by applying variational mode decomposition technique to the difference-based initial estimates. The numerical results show that the proposed estimator performs better than the existing variance estimation procedures in the mean square sense. DOI 10.1080/03610918.2016.1140777 More »» |
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2015 |
G. Menon, Dr. Palanisamy T., and Dr. Lavanya R., “Hardware Architecture for Variational Mode Decomposition for Breast Cancer Feature Extraction on Ultrasound Images”, International Journal of Applied Engineering Research, vol. 10, no. 7, pp. 16343-16354, 2015.[Abstract] Ultrasound (US) imaging proved to be less harmful than the traditional mammography is used for diagnosing breast cancer and this has helped reduce the number of unnecessary biopsies.The most important feature of malignant breast lesion is its infiltrative nature in US images.This infiltrative nature having composed of the frequency components that are adjacent to the lower frequency band contains the local variances that are characterized by Variational Mode Decomposition (VMD).On comparison with the existing decomposition models such as Empirical Mode Decomposition (EMD) and Wavelet Transform (WT) which are known for their limitations like sensitivity to noise and sampling which could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition.To overcome these limitations, a non-recursive VMD was selected.In this paper, we have presented an algorithmbased on VMD and a suitable architectureto obtain the infiltrative nature of the malignant breast lesion from the US image. More »» |
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2015 |
Dr. Palanisamy T. and Dr. Ravichandran J., “Variance Estimation in Heteroscedastic Models by Undecimated Haar Transform”, Communications in Statistics-Simulation and Computation, vol. 44, pp. 1532–1544, 2015.[Abstract] We propose a method in order to maximize the accuracy in the estimation of piecewise constant and piecewise smooth variance functions in a nonparametric heteroscedastic fixed design regression model. The difference-based initial estimates are obtained from the given observations. Then an estimator is constructed by using iterative regularization method with the analysis-prior undecimated three-level Haar transform as regularizer term. We notice that this method shows better results in the mean square sense over an existing adaptive estimation procedure considering all the standard test functions used in addition to the functions that we target. Some simulations and comparisons with other methods are conducted to assess the performance of the proposed method. More »» |
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2015 |
Dr. Palanisamy T. and Dr. Ravichandran J., “A wavelet-based hybrid approach to estimate variance function in heteroscedastic regression models”, Statistical Papers, vol. 56, pp. 911-932, 2015.[Abstract] We propose a wavelet-based hybrid approach to estimate the variance function in a nonparametric heteroscedastic fixed design regression model. A data-driven estimator is constructed by applying wavelet thresholding along with the technique of sparse representation to the difference-based initial estimates. We prove the convergence of the proposed estimator. The numerical results show that the proposed estimator performs better than the existing variance estimation procedures in the mean square sense over a range of smoothness classes. © 2014, Springer-Verlag Berlin Heidelberg. More »» |
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2015 |
Dr. Palanisamy T., “Smoothing noisy data using variational mode decomposition”, International Journal of Applied Engineering Research, vol. 10, pp. 26579-26586, 2015.[Abstract] This article is about the estimator of an unknown regression function which is smooth but observed with noise on a bounded interval. The method is based on applying results of the recently developed theory of variational mode decomposition. It is illustrated with simulation. © Research India Publications More »» |
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2014 |
Dr. Palanisamy T. and Dr. Ravichandran J., “Estimation of variance function in heteroscedastic regression models by generalized coiflets”, Communications in Statistics: Simulation and Computation, vol. 43, pp. 2213-2224, 2014.[Abstract] A wavelet approach is presented to estimate the variance function in heteroscedastic nonparametric regression model. The initial variance estimates are obtained as squared weighted sums of neighboring observations. The initial estimator of a smooth variance function is improved by means of wavelet smoothers under the situation that the samples at the dyadic points are not available. Since the traditional wavelet system for the variance function estimation is not appropriate in this situation, we demonstrate that the choice of the wavelet system is significant to have better performance. This is accomplished by choosing a suitable wavelet system known as the generalized coiflets. We conduct extensive simulations to evaluate finite sample performance of our method. We also illustrate our method using a real dataset. Copyright © 2014 Taylor & Francis Group, LLC. More »» |
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2012 |
Dr. Ravichandran J. and Dr. Palanisamy T., “Nonparamteric Regression Curve Estimation Using Generalized Coiflets”, Bulletin of Calcutta Mathematical Society, vol. 104, no. 2, pp. 139-146, 2012. |
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