Paper Info
Title
Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT
Abstract
In recent years, Hilbert-Huang Transform (HHT) is widely used to analyze nonlinear and non-stationary signals in various applications, such as seismic and biomedical signal processing. In HHT, the Empirical Mode Decomposition (EMD) is the key component for decomposing natural signals into intrinsic mode functions (IMFs). Since the EMD suffers from mode-mixing problem, in which some fast intermittent signals riding on a slow-oscillating wave, the Ensemble-EMD (EEMD) is proposed to solve this problem with the aids of noise. However, the EEMD requires high computational complexity in ensemble and is unsuitable for some real-time applications, such as ultrasound systems. In this paper, intermittent signals are modeled in mathematical forms for IMF decomposition. We then propose sinusoidal-assisted EMD (SAEMD) for efficient and effective HHT computation to solve mode-mixing problems. The type I of SAEMD (SAEMD-I) is initially proposed to solve the mode-mixing problem with very low computational complexity. However, if the maximum frequency of data is unknown in some real-world applications, the SAEMD-I may encounter estimation error caused by imprecise locations of extrema. For practical data, the type II of SAEMD (SAEMD-II) is proposed to solve the sampling rate issue. Compared with the ensemble-100 EEMD, the SAEMD-II can have 11-13 times improvement in terms of computation speed in El Nino application and comparable correlation coefficient (-0.95 at IMF 8). Hence, the proposed SAEMD-II scheme is a good candidate of implementing cost-effective HHT when computational complexity and real-time data processing are of major concern.
Year
DOI
Venue
2014
10.1016/j.dsp.2013.08.004
Digital Signal Processing
Keywords
Field
DocType
practical data,ensemble-100 eemd,low-complexity sinusoidal-assisted emd,proposed saemd-ii scheme,mode-mixing problem,low computational complexity,effective hht computation,high computational complexity,cost-effective hht,computational complexity,real-time data processing
Signal processing,Data processing,Nonlinear system,Sampling (signal processing),Artificial intelligence,Computation,Mathematical optimization,Pattern recognition,Algorithm,Maxima and minima,Mathematics,Hilbert–Huang transform,Computational complexity theory
Journal
Volume
ISSN
Citations 
24,
1051-2004
6
PageRank 
References 
Authors
0.56
4
3
Name
Order
Citations
PageRank
Wen-Chung Shen1353.23
Yu-Hao Chen2163.32
An-Yeu (Andy) Wu3977.92