Empirical Mode Decomposition in Discrete Time Signals Denoising

Journal Title: Acta Marisiensis. Seria Technologica - Year 2019, Vol 16, Issue 1

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.

Authors and Affiliations

Zoltan German-Sallo

Keywords

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  • EP ID EP590849
  • DOI -
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How To Cite

Zoltan German-Sallo (2019). Empirical Mode Decomposition in Discrete Time Signals Denoising. Acta Marisiensis. Seria Technologica, 16(1), 10-13. https://europub.co.uk/articles/-A-590849