Iterative filtering decomposition based on local spectral evolution kernel.

Journal of Scientific Computing
Yang WangSiyang Yang

Abstract

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dyn...Continue Reading

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Citations

Feb 9, 2012·International Journal of Biomedical Imaging·Yang WangSiyang Yang
Mar 13, 2012·Journal of Scientific Computing·Yang WangSiyang Yang
Dec 31, 2011·International Journal for Numerical Methods in Biomedical Engineering·Yang WangSiyang Yang

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