FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition

Sikender Gul, Muhammad Faisal Siddiqui, Naveed Ur Rehman*

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Abstract

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 their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for online and real time processing. In this paper, we address this 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 online and real time processing. Our proposed architecture for MEMD uses fixed-point operations and employs cubic spline interpolation (CSI) within the sifting process. Finally, examples of decomposition of multivariate synthetic and real-world biological signals are provided.
OriginalsprogEngelsk
Artikelnummer9159690
TidsskriftIEEE Transactions on Circuits and Systems Part 1: Regular Papers
Vol/bind67
Nummer12
Sider (fra-til)5040-5050
Antal sider11
ISSN1549-8328
DOI
StatusUdgivet - 2020
Udgivet eksterntJa

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