<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/7538" mdate="2010-03-24 00:00:00"> <author>D Sudheer Reddy and N Gopal Reddy and P V Radhadevi and J Saibaba and Geeta Varadan</author> <title>Peakwise Smoothing of Data Models using Wavelets</title> <pages>638 - 643</pages> <year>2010</year> <volume>4</volume> <number>3</number> <journal>International Journal of Electronics and Communication Engineering</journal> <ee>https://publications.waset.org/pdf/7538</ee> <url>https://publications.waset.org/vol/39</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>Smoothing or filtering of data is first preprocessing step for noise suppression in many applications involving data analysis. Moving average is the most popular method of smoothing the data, generalization of this led to the development of SavitzkyGolay filter. Many window smoothing methods were developed by convolving the data with different window functions for different applications; most widely used window functions are Gaussian or Kaiser. Function approximation of the data by polynomial regression or Fourier expansion or wavelet expansion also gives a smoothed data. Wavelets also smooth the data to great extent by thresholding the wavelet coefficients. Almost all smoothing methods destroys the peaks and flatten them when the support of the window is increased. In certain applications it is desirable to retain peaks while smoothing the data as much as possible. In this paper we present a methodology called as peakwise smoothing that will smooth the data to any desired level without losing the major peak features.</abstract> <index>Open Science Index 39, 2010</index> </article>