TY - JOUR
T1 - Noise-modulated empirical mode decomposition
AU - Tsui, Po Hsiang
AU - Chang, Chien Cheng
AU - Huang, Norden E.
PY - 2010/1
Y1 - 2010/1
N2 - The empirical mode decomposition (EMD) is the core of the Hilbert-Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.
AB - The empirical mode decomposition (EMD) is the core of the Hilbert-Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.
KW - Empirical mode decomposition (EMD)
KW - ensemble empirical mode decomposition (EEMD)
KW - noise-modulated empirical mode decomposition (NEMD)
UR - http://www.scopus.com/inward/record.url?scp=80052090213&partnerID=8YFLogxK
U2 - 10.1142/S1793536910000410
DO - 10.1142/S1793536910000410
M3 - 期刊論文
AN - SCOPUS:80052090213
SN - 1793-5369
VL - 2
SP - 25
EP - 37
JO - Advances in Adaptive Data Analysis
JF - Advances in Adaptive Data Analysis
IS - 1
ER -