IEEE Transactions on Signal Processing · 2011 · 171 citations · 23 references
EngineeringElectrical EngineeringBiomedical Signal ProcessingComplex SystemsTime Series AnalysisDigital Signal ProcessingMixed-signal ModelingSignal ProcessingTime-frequency AnalysisEmpirical Mode DecompositionExtreme PointsMode MixingSignals
Empirical mode decomposition (EMD) is an adaptive and data-driven approach for analyzing multicomponent nonlinear and nonstationary signals. The stop criterion, envelope technique, and mode-mixing problem are the most important topics that need to be addressed in order to improve the EMD algorithm. In this paper, we study the envelope technique and the mode-mixing problem caused by separating multicomponent AM-FM signals with the EMD algorithm. We present a new necessary condition on the envelope that questions the current assumption that the envelope passes through the extreme points of an intrinsic mode function (IMF). Then, we present a solution to the mode-mixing problem that occurs when multicomponent AM-FM signals are separated. We experiment on several signals, including simulated signals and real-life signals, to demonstrate the efficacy of the proposed method in resolving the mode-mixing problem.
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Norden E. Huang, Steven Long, Man‐Li C. Wu et al. · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 1998 · 23.3K citations · Full text
Computing, Computational Engineering, Biomedical Signal Processing +16
Image analysis by bidimensional empirical mode decomposition
Jean‐Claude Nunes, Y. Bouaoune, Éric Deléchelle et al. · Image and Vision Computing · 2003 · 692 citations