PROJECT TITLE :

Dynamically sampled multivariate empirical mode decomposition

ABSTRACT:

A method for correct multivariate local mean estimation within the multivariate empirical mode decomposition algorithm by using a statistical data-driven approach based mostly on the Menger curvature measure and normal-to-anything variate-generation technique is proposed. This is achieved by aligning the projection vectors within the direction of the maximum 'activity' of the input signal by considering the native curvature of the signal in multidimensional spaces, ensuing in correct mean estimation even for a terribly little range of projection vectors.


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