| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 410939 | Neurocomputing | 2011 | 9 Pages |
Abstract
The local mean decomposition (LMD) has been recently developed for the analysis of time series which have nonlinearity and nonstationarity. The smoothed local mean of the LMD surpasses the cubic spline method used by the empirical mode decomposition (EMD) to extract amplitude and frequency modulated components. To process complex-valued data, we propose complex LMD, a natural and generic extension to the complex domain of the original LMD algorithm. It is shown that complex LMD extracts the frequency modulated rotation and envelope components. Simulations on both artificial and real-world complex-valued signals support the analysis.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Cheolsoo Park, David Looney, Marc M. Van Hulle, Danilo P. Mandic,