Serial-EMD: Fast empirical mode decomposition method for multi-dimensional signals based on serialization

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 amount of data puts forward higher requirements capability real-time analysis, it is difficult existing EMD its variants to trade off growth dimension speed analysis. In order decompose multi-dimensional signals at faster speed, we present novel signal-serialization method (serial-EMD), which concatenates multi-variate or one-dimensional uses algorithms it. To verify effects proposed method, synthetic time series, artificial 2D images with textures real-world facial are tested. Compared multi-EMD algorithms, becomes significantly reduced. addition, results recognition Intrinsic Mode Functions (IMFs) extracted using our can achieve accuracy than those obtained by demonstrates superior performance terms quality IMFs. Furthermore, this provide new perspective optimize that is, transforming structure input rather being constrained developing envelope computation techniques methods. summary, study suggests serial-EMD technique highly competitive fast alternative