基于FPGA的多变量EMD（MEMD）在线计算设计
FPGA based design for online computation of Multivariate EMD (MEMD)
摘要：
多变量或多通道数据在许多现代科学和工程应用中已经变得无处不在，例如，生物医学工程，由于传感器和计算技术的最新进展。处理这些数据集是具有挑战性的，因为：i）它们的大尺寸和多维性质，因此需要专门的算法和高效的硬件设计用于在线和实时处理; ii）在许多现实生活应用中出现的数据的非平稳性质，需要标准多尺度非平稳信号处理工具的新扩展。在本文中，我们解决了前一个问题，提出了一个完全基于FPGA的硬件架构的流行的多尺度和多变量信号处理算法，称为多元经验模式分解（MEMD）。MEMD是一种数据驱动的方法，它将标准经验模式分解（EMD）算法的功能扩展到多通道或多变量数据集。自2010年成立以来，该算法已经在不同的工程相关领域得到了广泛的应用。然而，没有基于并行FPGA的硬件设计的算法是可用于其在线和实时处理。我们提出的MEMD架构使用固定点操作，并采用三次样条插值筛选过程中。最后，多元合成和真实世界的生物信号的分解的例子。
英文摘要：
Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to: i) their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for on-line and real-time processing; ii) the nonstationary nature of data arising in many real life applications demanding new extensions of standard multiscale non-stationary signal processing tools. In this paper, we address the former issue by proposing a fully FPGA based hardware architecture of a popular multi-scale and multivariate signal processing algorithm, termed as multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets. Since its inception in 2010, the algorithm has found wide spread applications spanning different engineering related fields. Yet, no parallel FPGA based hardware design of the algorithm is available for its on-line and real-time processing. Our proposed architecture for MEMD uses fixed point operations and employs cubic spline interpolation within the sifting process. Finally, examples of decomposition of multivariate synthetic and real world biological signals are provided.
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