Title :
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
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;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8754-7
Electronic_ISBN :
pending
DOI :
10.1109/ICIEA.2011.5976020
