包围块变换：一种新的信号分解工具
Ensemble Patch Transformation: A New Tool for Signal Decomposition
摘要：
本文考虑信号分解和数据可视化的问题。为此，我们引入了一种新的多尺度变换，称为“集成补丁变换”，增强了识别嵌入在信号中的局部特征，并提供多尺度可视化根据不同的水平，因此，它是有用的数据分析和信号分解。在文献中，存在数据自适应分解方法，例如Huang等人（1998）的经验模式分解（EMD）。沿着同一条线的EMD，我们提出了一个新的分解算法，提取有意义的成分，从一个信号，属于一个大类的信号，相比以前的方法。该算法的一些理论性质进行了研究。为了评估所提出的方法，我们分析了几个合成的例子和现实世界的信号。
英文摘要：
This paper considers the problem of signal decomposition and data visualization. For this purpose, we introduce a new multiscale transform, termed `ensemble patch transformation' that enhances identification of local characteristics embedded in a signal and provides multiscale visualization according to different levels; hence, it is useful for data analysis and signal decomposition. In literature, there are data-adaptive decomposition methods such as empirical mode decomposition (EMD) by Huang et al. (1998). Along the same line of EMD, we propose a new decomposition algorithm that extracts meaningful components from a signal that belongs to a large class of signals, compared to the previous methods. Some theoretical properties of the proposed algorithm are investigated. To evaluate the proposed method, we analyze several synthetic examples and a real-world signal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
