Empirical Mode Decomposition for Signal Preprocessing and Classification of Intrinsic Mode Functions
- 1 Downloads
Empirical mode decomposition (EMD) is an adaptive, data-driven technique for processing and analyzing various types of non-stationary signals. EMD is a powerful and effective tool for signal preprocessing (denoising, detrending, regularity estimation) and time-frequency analysis. This paper discusses pattern discovery in signals via EMD. New approaches to this problem are introduced, which involve well-known information criteria along with some other proposed ones, which have been investigated and developed for our particular tasks. In addition, the methods expounded in the paper may be considered as a way of denoising and coping with the redundancy problem of EMD. A general classification of intrinsic mode functions (IMFs, empirical modes) in accordance with their physical interpretation is offered and an attempt is made to perform classification on the basis of the regression theory, special classification statistics and some cluster- analysis algorithm. The main advantage of the innovations is their capability of working automatically. Simulation studies have been undertaken on multiharmonic signals. We also cover some aspects of hardware implementation of EMD.
Keywordsempirical mode decomposition intrinsic mode function preprocessing denoising classification information criterion regression hardware implementation
Unable to display preview. Download preview PDF.
- 5.G. Rilling, P. Flandrin, and P. Goncalves, “On empirical mode decomposition and its algorithms,” in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP-03 (Grabo, 2003), pp. 1–5.Google Scholar
- 7.Nii Attoh-Okine, K. Barner, D. Bentil, and R. Zhang, “The empirical mode decomposition and the Hilbert- Huang transform,” EURASIP J. Adv. Signal Processing, 1–2 (2008).Google Scholar
- 10.D. Kaplun, D. Klionskiy, A. Voznesenskiy, and V. Gulvanskiy, “Digital filter bank implementation in hydroacoustic monitoring tasks,” Przeglad Electrotechniczny (Electr. Rev.) 91 (2), 47–50 (2015).Google Scholar
- 11.P. Stoica and R. Moses, Spectral Analysis of Signals (Upper Saddle River, NJ, 2005).Google Scholar
- 17.I. H. Witte, et al., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementation (Academic Press, 2000).Google Scholar
- 19.J. Sanders and E. Kandrot, CUDA by Example: an Introduction to General-Purpose GPU Programming (Addison- Wesley Professional, 2010).Google Scholar
- 21.D. I. Kaplun, V. V. Gulvanskiy, D. M. Klionskiy, M. S. Kupriyanov, and A. V. Veligosha, “Implementation of digital filters in the residue number system,” in Proc. 2016 IEEE NorthWest Russia Section Young Researchers in Electrical and Electronic Engineering Conf. (ElConRusNW) (Saint Petersburg, 2016), pp. 230–234.Google Scholar