DocumentCode :
939667
Title :
Bivariate Empirical Mode Decomposition
Author :
Rilling, Gabriel ; Flandrin, Patrick ; Gonçalves, Paulo ; Lilly, Jonathan M.
Author_Institution :
Ecole Normale Superieure de Lyon, Lyon
Volume :
14
Issue :
12
fYear :
2007
Firstpage :
936
Lastpage :
939
Abstract :
The empirical mode decomposition (EMD) has been introduced quite recently to adaptively decompose nonstationary and/or nonlinear time series. The method being initially limited to real-valued time series, we propose here an extension to bivariate (or complex-valued) time series that generalizes the rationale underlying the EMD to the bivariate framework. Where the EMD extracts zero-mean oscillating components, the proposed bivariate extension is designed to extract zero-mean rotating components. The method is illustrated on a real-world signal, and properties of the output components are discussed. Free Matlab/C codes are available at http://perso.ens-lyon.fr/patrick.flandrin.
Keywords :
signal processing; time series; bivariate empirical mode decomposition; complex-valued time series; nonlinear time series; real-valued time series; zero-mean oscillating components; zero-mean rotating components; Bivariate time series; complex-valued signals; empirical mode decomposition;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2007.904710
Filename :
4358014
Link To Document :
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