FPGA-based real-time blind source separation with principal component analysis

Matthew Wilson; Uwe Meyer-Baese

doi:10.1117/12.2178334

20 May 2015 FPGA-based real-time blind source separation with principal component analysis

Matthew Wilson, Uwe Meyer-Baese

Author Affiliations +

Proceedings Volume 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII; 94960W (2015) https://doi.org/10.1117/12.2178334
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Matthew Wilson and Uwe Meyer-Baese "FPGA-based real-time blind source separation with principal component analysis", Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960W (20 May 2015); https://doi.org/10.1117/12.2178334

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