Abstract
Independent component analysis (ICA) is a technique that separates the independent source signals from their mixtures by minimizing the statistical dependence between components. This paper presents a floating point implementation of a novel fast confluence adaptive independent component analysis (FCAICA) technique with reduced number of iterations that provides the high convergence speed. Fixed point ICA algorithms cover only smaller range of numbers. To handle large as well as tiny numbers and hence to improve the dynamic range of the signal values, floating point operations are performed in ICA. The high convergence speed is achieved by a novel optimization scheme that adaptively changes the weight vector based on the kurtosis value. To validate the performance of the proposed FCAICA, simulation and synthesis are performed with super-gaussian mixtures and sub Gaussian mixtures and experimental results provided. The proposed FCAICA processor separates the super-Gaussian signals with a maximum operating frequency of 2.91MHz with improved convergence speed.
Highlights
Independent component analysis (ICA), a statistical signal processing technique, is one of the most commonly used algorithms in blind source separation
Though different ICA algorithms have been reported, the FastICA algorithm has been shown to have advantages in terms of convergence speed [1].It measures non-Gaussianity using kurtosis to find the independent sources from their mixtures [2]
Independent component analysis is a major task in signal processing to extract the source signals from the observed mixtures
Summary
ICA, a statistical signal processing technique, is one of the most commonly used algorithms in blind source separation. Algebraic ICA Algorithm performs ICA by solving simultaneous equations derived from the definition of the independence It works very fast for two sources separation but it becomes extremely complex when the number of sources goes more than two [3]. Nonlinear Decorrelation Algorithm has been proposed in order to reduce the computational overhead and to improve stability [5] Another approach to ICA that is related to PCA is the non-linear method. Fixed-point VLSI architecture was proposed for 2-Dimensional Kurtotic FastICA with reduced and optimized arithmetic units [13]. Two different ICA methods named as Shuffled Frog Leap optimization based ICA and Fast Confluence Adaptive ICA are proposed in floating point arithmetic in this paper.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
