  Empirical mode decomposition (EMD) has developed into a prominent tool for
adaptive, scale-based signal analysis in various fields like robotics, security
and biomedical engineering. Since the dramatic increase in amount of data puts
forward higher requirements for the capability of real-time signal analysis, it
is difficult for existing EMD and its variants to trade off the growth of data
dimension and the speed of signal analysis. In order to decompose
multi-dimensional signals at a faster speed, we present a novel
signal-serialization method (serial-EMD), which concatenates multi-variate or
multi-dimensional signals into a one-dimensional signal and uses various
one-dimensional EMD algorithms to decompose it. To verify the effects of the
proposed method, synthetic multi-variate time series, artificial 2D images with
various textures and real-world facial images are tested. Compared with
existing multi-EMD algorithms, the decomposition time becomes 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 of IMFs.
Furthermore, this method can provide a new perspective to optimize the existing
EMD algorithms, that is, transforming the structure of the input signal rather
than being constrained by developing envelope computation 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.
