function [IMFs_s,IMFs_t,stats] = MdMvFIF_v5(f,options,M)

It generates the decomposition of the signal f :

f = IMFs_s(:, :, :) + IMFs_s(:, :, :) + ... + IMFs_s(:, :, :) + IMFs_t(:, :, :) + IMFs_t(:, :, :) + ... + IMFs_t(:, :, :)

where the last row in the matrix IMF is the trend and the other rows are actual IMFs

                           Inputs

f Tensor containing in the first two variables the space variability and in the third one the time

options Structure, generated using function Settings_MdMvFIF_v1, containing all the parameters needed in the various algorithms

options_FIF2 Structure, generated using function Settings_FIF2_v2, containing all the parameters needed in the various algorithms

options_MvFIF Structure, generated using function Settings_FIF_v3, containing all the parameters needed in the various algorithms

                          Output

IMF_s and IMFs_t Cells containing the IMFs decomposition in space and time

stats Statistics regarding the IMFs logM Mask length values used for each IMF posF position of the first minimum in the filter DFT which is forced to become zero valF filter DFT first minimum value before the downward shift

See also Settings_MdMvFIF_v1, Settings_FIF2_v2, SETTINGS_FIF_V3, mask_v1, Maxmins_v3_8, FIF2_v3, MvFIF_v10.

Please cite:

A. Cicone, H. Zhou. 'Numerical Analysis for Iterative Filtering with New Efficient Implementations Based on FFT'. Numerische Mathematik, 147 (1), pages 1-28, 2021. doi: 10.1007/s00211-020-01165-5 ArXiv http://arxiv.org/abs/1802.01359

R. Cavassi, A. Cicone, E. Pellegrino, H. Zhou. 'A novel algorithm for the decomposition of non-stationary multidimensional and multivariate signals'. Submitted