Empirical Mode Decomposition as a tool for data analysis

Author

Jimenez, J.R. ; Wu, H.R.

Author_Institution

Sch. of Electr. & Comput. Eng., R. Melbourne Inst. of Technol., Melbourne, VIC, Australia

fYear

2011

fDate

21-23 June 2011

Firstpage

2538

Lastpage

2543

Abstract

The recently introduced Empirical Mode Decomposition (EMD) is a powerful data analysis tool that can deal with the non-stationary and non-linear characteristics of natural phenomena data. A comparison with two popular data analysis methods, i.e., the discrete Fourier transform and the discrete wavelet transform, has been conducted through simulation experiments. It can be seen that EMD is able to capture distinct features of the non-stationary data that the other methods can not, making EMD a valuable tool that can be applied to signal analysis, modelling and denoising.

Keywords

data analysis; discrete Fourier transforms; discrete wavelet transforms; data analysis method; data analysis tool; discrete Fourier transform; discrete wavelet transform; empirical mode decomposition; nonlinear characteristics; nonstationary data; signal analysis; Data analysis; Discrete Fourier transforms; Discrete wavelet transforms; Low pass filters; Time frequency analysis; Wavelet analysis; Discrete Fourier Transform (DFT); Discrete Wavelet Transform (DWT); Empirical Mode Decomposition (EMD); Intrinsic Mode Function (IMF); Signal Processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on

Conference_Location

Beijing

ISSN

pending

Print_ISBN

978-1-4244-8754-7

Electronic_ISBN

pending

Type

conf

DOI

10.1109/ICIEA.2011.5976020

Filename

5976020