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Asian Journal of Scientific Research
  Year: 2014 | Volume: 7 | Issue: 4 | Page No.: 460-471
DOI: 10.3923/ajsr.2014.460.471
High Performance Independent Component Analysis
Jayasanthi Ranjith and N.J.R. Muniraj

Independent Component Analysis (ICA), a statistical signal processing technique, separates the independent source signals from their observed mixtures by maximizing the statistical independence of the components. Since the ICA algorithm is so complex to implement on the FPGA, implementation of this algorithm leads to excessive area and power consumption. This study presents FPGA implementation of a novel area and power efficient Fast Confluence Adaptive Independent Component Analysis (FCAICA) technique with reduced number of recursive iterations. This method occupies less area, less power and provides the high convergence speed. The reduction in area is achieved by hardware optimization and high convergence speed is achieved by a novel optimization scheme that adaptively changes the weight vector based on the kurtosis value. To increase the number precision and dynamic range of the signal, the Floating-point (FP) arithmetic units are used. To validate the performance of the proposed FCAICA, simulation and synthesis are performed with super-gaussian mixtures and experimental results are compared with FastICA and SFLO-ICA (Shuffled Frog Leap Optimization ICA). The proposed FCAICA processor separates the super-Gaussian signals with maximum operating frequency of 2.91 MHz.
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How to cite this article:

Jayasanthi Ranjith and N.J.R. Muniraj, 2014. High Performance Independent Component Analysis. Asian Journal of Scientific Research, 7: 460-471.

DOI: 10.3923/ajsr.2014.460.471






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