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.

经验模式分解 (EMD) 已发展成为机器人、安全和生物医学工程等各个领域的自适应、基于尺度的信号分析的重要工具。由于数据量的急剧增加对实时信号分析的能力提出了更高的要求，现有的EMD及其变体很难在数据维度的增长和信号分析的速度之间进行权衡。为了更快地分解多维信号，我们提出了一种新的信号序列化方法（serial-EMD），它将多变量或多维信号连接成一维信号，并使用各种一维EMD算法来分解它。为了验证所提出的方法的效果，合成多变量时间序列，测试了具有各种纹理的人工 2D 图像和真实世界的面部图像。与现有的多 EMD 算法相比，分解时间显着减少。此外，使用我们的方法提取的具有内在模式函数（IMF）的面部识别结果可以达到比现有多EMD算法获得的结果更高的准确度，这表明我们的方法在IMF质量方面的优越性能。此外，该方法可以为优化现有的 EMD 算法提供一个新的视角，即转换输入信号的结构，而不受发展包络计算技术或信号分解方法的约束。总之，