1. bookVolume 16 (2019): Issue 1 (June 2019)
Journal Details
License
Format
Journal
eISSN
2668-4217
First Published
30 Jul 2019
Publication timeframe
2 times per year
Languages
English
access type Open Access

Empirical Mode Decomposition in Discrete Time Signals Denoising

Published Online: 06 Aug 2019
Volume & Issue: Volume 16 (2019) - Issue 1 (June 2019)
Page range: 10 - 13
Journal Details
License
Format
Journal
eISSN
2668-4217
First Published
30 Jul 2019
Publication timeframe
2 times per year
Languages
English
Abstract

This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Keywords

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