Multidimensional empirical mode decomposition: Difference between revisions

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==Motivation==

Multidimensional empirical mode decomposition is a popular method because of its applications in many fields, such as texture analysis, financial applications, [[Digital image processing|image processing]], [[Ocean Engineering|ocean engineering]], [[Seismology|seismic]] research, etc. Recently, several methods of Empirical Mode Decomposition have been used to analyze characterization of multidimensional signals. In this article, we will introduce the basics of Multidimensional Empirical Mode Decomposition, and then look into various approaches used for Multidimensional Empirical Mode Decomposition.

==Introduction to empirical mode decomposition (EMD)==

[[File:Flow chart of EMD algorithm.jpg|thumb|400x400px|Flow chart of basic EMD algorithm<ref>{{Cite journal|url=http://www.ripublication.com/irph/ijeee_spl/ijeeev7n8_14.pdf|author=Sonam Maheshwari |author2=Ankur Kumar |title=Empirical Mode Decomposition: Theory & Applications |journal=International Journal of Electronic and Electrical Engineering |issn=0974-2174 |volume=7 |issue=8 |year=2014 |pages=873–878}}</ref>{{Predatory open access publisher}}]]

The "empirical mode decomposition" (EMD) method can extract global structure and deal with fractal-like signals.

The EMD method was developed so that data can be examined in an adaptive time–frequency–amplitude space for nonlinear and non-stationary signals.

The EMD method decomposes the input signal into several Intrinsicintrinsic Modemode Functionsfunctions (IMF) and a residue. The given equation will be as follows:

: <math>I(n)=\sum_{m=1}^M \operatorname{IMF}_m(n)+\operatorname{Res}_M(n)</math>