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Proceedings Paper

FPGA-based real-time blind source separation with principal component analysis
Author(s): Matthew Wilson; Uwe Meyer-Baese
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Paper Abstract

Principal component analysis (PCA) is a popular technique in reducing the dimension of a large data set so that more informed conclusions can be made about the relationship between the values in the data set. Blind source separation (BSS) is one of the many applications of PCA, where it is used to separate linearly mixed signals into their source signals. This project attempts to implement a BSS system in hardware. Due to unique characteristics of hardware implementation, the Generalized Hebbian Algorithm (GHA), a learning network model, is used. The FPGA used to compile and test the system is the Altera Cyclone III EP3C120F780I7.

Paper Details

Date Published: 20 May 2015
PDF: 9 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960W (20 May 2015); doi: 10.1117/12.2178334
Show Author Affiliations
Matthew Wilson, Florida State Univ. (United States)
Uwe Meyer-Baese, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)

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