Yannis Kopsinis - short bio

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Y. Kopsinis, S. McLaughlin, “Development of EMD-based Denoising Methods Inspired by Wavelet Thresholding,” IEEE Trans. on Signal Processing, pp. 1351-1362, Apr. 2009.

Abstract
One of the tasks for which empirical mode decomposition (EMD) is potentially useful is nonparametric signal denoising, an area for which wavelet thresholding has been the dominant technique for many years. In this paper, the wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show that although a direct application of this principle is not feasible in the EMD case, it can be appropriately adapted by exploiting the special characteristics of the EMD decomposition modes. In the same manner, inspired by the translation invariant wavelet thresholding, a similar technique adapted to EMD is developed, leading to enhanced denoising performance.

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related software: EMD denoising Matlab code.
Bibtex 
@article{YK_SML_2009,
 author = {Kopsinis, Yannis and McLaughlin, Stephen},
 title = {Development of EMD-based denoising methods inspired by wavelet thresholding},
 journal = {IEEE Trans. Sig. Proc.},
 volume = {57},
 number = {4},
 year = {2009},
 pages = {1351--1362},
 doi = {http://dx.doi.org/10.1109/TSP.2009.2013885},
 }

* Pre-print manuscripts might have significant modifications from the finally published paper.

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