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• W2897667961 abstract "The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on time-frequency analysis of nonlinear and nonstationary signals, high computational load and over-decomposition have restricted wider applications of MEMD. To address these challenges, a fast MEMD (FMEMD) algorithm is proposed and featured by the following contributions: 1) A novel concept, pseudo direction-independent multivariate intrinsic mode function (IMIMF) which allows the interchange of sifting and projection operations, is defined for the purpose of developing FMEMD; 2) FMEMD is computationally efficient. Compared with MEMD, the number of time-consuming sifting operations reduces from $K cdot p$ to $K$ for each iteration, where $K$ and $p$ denote the number of projection directions and signal dimension, respectively; 3) FMEMD is consistent with EMD in terms of the dyadic filter bank property; and 4) FMEMD is more effective in working at low sampling rate. Validity of the raised approach is demonstrated on a wide variety of real world applications." @default.
• W2897667961 created "2018-10-26" @default.
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• W2897667961 date "2018-01-01" @default.
• W2897667961 modified "2023-10-17" @default.
• W2897667961 title "Fast Multivariate Empirical Mode Decomposition" @default.
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• W2897667961 doi "https://doi.org/10.1109/access.2018.2877150" @default.
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