Serial-EMD : Fast Empirical Mode Decomposition Method for Multi-dimen‐sional Signals Based on Serialization

Jin Zhang ... [et al.].

Serie: Trabajos publicados del IAR ; no. 1624

Resumen: Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signalanalysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase inamount of data puts forward higher requirements for the capability of real-time signal analysis, it is difficultfor existing EMD and its variants to trade off the growth of data dimensionand the speed of signal analysis.In order to decompose multi-dimensional signals at a faster speed, we present a novel signal-serializationmethod (serial-EMD), which concatenates multi-variate or multi-dimensional signals into a one-dimensionalsignal and uses various one-dimensional EMD algorithms to decompose it. Toverify the effects of theproposed method, synthetic multi-variate time series, artificial 2Dimages with various textures and real-world facial images are tested. Compared with existing multi-EMD algorithms, the decomposition timebecomes significantly reduced. In addition, the results of facial recognition with Intrinsic Mode Functions(IMFs) extracted using our method can achieve a higher accuracy than those obtained by existing multi-EMD algorithms, which demonstrates the superior performance of our method in terms of the quality ofIMFs. Furthermore, this method can provide a new perspective tooptimize the existing EMD algorithms,that is, transforming the structure of the input signal rather than being constrained by developing envelopecomputation techniques or signal decomposition methods. In summary, the study suggests that the serial-EMD technique is a highly competitive and fast alternative for multi-dimensional signal analysis