Multidimensional empirical mode decomposition: Difference between revisions

Revision as of 18:06, 24 November 2019

Revision as of 18:08, 24 November 2019

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: <math>\ell = n - \frac{w_{ex} - 1}{2}:n + \frac{w_{ex} - 1}{2},(\ell \ne n)</math><ref name=":0" />

;[[File:Flow chart for FABEMD algorithm.jpg|thumb|upright|Flow chart for FABEMD algorithm<ref>{{Cite journalbook |urldoi=https:10.1109//ieeexploreICASSP.ieee2008.org/document/4517859/|titlechapter=Sharif M. A. Bhuiyan, Reza R. Adhami, Jesmin F. Khan, "A Novelnovel Approachapproach of Fastfast and Adaptiveadaptive Bidimensionalbidimensional Empiricalempirical Modemode Decomposition",decomposition|title=2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008.|pages=1313–1316|lastyear=2008|firstlast1=Bhuiyan|datefirst1=MarchSharif 2008M. A.|websitelast2=Adhami|publisherfirst2=Reza R.|access-datelast3=Khan|doifirst3=10Jesmin F.1109/ICASSP.2008.4517859|isbn=978-1-4244-1483-3}}</ref>]]Step 2 – Obtain the size of window for order-statistic filter

At first, we define <math>d_{\mathrm{adj}-\max}</math> and <math>d_{\mathrm{adj}-\min}</math> to be the maximum and minimum distance in the array which is calculated from each local maximum or minimum point to the nearest nonzero element. Also, <math>d_{\mathrm{adj}-\max}</math> and <math>d_{\mathrm{adj}-\min}</math> will be sorted in descending order in the array according to the convenient selection. Otherwise, we will only consider square window. Thus, the gross window width will be as follows: