Package: xemd 0.1.5

xemd: eXtended Empirical Mode Decomposition

Signals are decomposed into oscillatory modes by empirical mode decomposition and its ensemble, complementary ensemble, complete ensemble, masked, bivariate and multivariate variants, by variational mode decomposition and its multivariate and successive variants, and by local mean decomposition, iterative filtering, time varying filter empirical mode decomposition and the empirical wavelet transform; the synchrosqueezed wavelet transform is also provided. Empirical mode decomposition is due to Huang et al. (1998) <doi:10.1098/rspa.1998.0193>, its ensemble variant to Wu and Huang (2009) <doi:10.1142/S1793536909000047> and variational mode decomposition to Dragomiretskiy and Zosso (2014) <doi:10.1109/TSP.2013.2288675>. The modes can be described by their instantaneous amplitude and frequency and their Hilbert spectrum, tested as intrinsic mode functions or against white noise, selected by energy and compared through orthogonality, mode splitting, reconstruction quality, ensemble dispersion and similarity measures. Acceleration, velocity and displacement records stored as a long table of time, value, quantity and component are decomposed for every record and quantity in one call, with a warning and a regularization of the time grid when it is not uniform.

Authors:Alejandro Verri Kozlowski [aut, cre, cph, trl], G. Rilling [ctb], Marcelo A. Colominas [cph], Pierre Masselot [ctb], Vinícius Rezende Carvalho [ctb, cph], Eduardo Mazoni [cph], Jerome Gilles [ctb, cph], Kathryn Heal [ctb], Emanuele Papini [ctb, cph], Antonio Cicone [ctb], Shubhra Prakash [ctb], Stefano Bianchi [ctb, cph], Alessandro Longo [ctb, cph], Eugene Brevdo [ctb], Hau-Tieng Wu [ctb], Jianfeng Lu [ctb], Dongik Jang [ctb], Hee-Seok Oh [ctb], Donghoh Kim [ctb]