Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals

Mruthun R. Thirumalaisamy, Phillip J. Ansell

Research output: Contribution to journalArticlepeer-review

Abstract

Over the last decade, empirical mode decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational demands of multidimensional signal processing. The current work introduces an alternative approach to multivariate EMD, and by combining it with existing fast and adaptive algorithms, paves the way for performing multivariate EMD on multidimensional signals.

Original languageEnglish (US)
Article number8447300
Pages (from-to)1550-1554
Number of pages5
JournalIEEE Signal Processing Letters
Volume25
Issue number10
DOIs
StatePublished - Oct 2018

Keywords

  • Empirical mode decomposition (EMD)
  • FA-EMD
  • modal analysis
  • multidimensional EMD
  • multivariate signal

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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