Advances in Signal Processing 1(3): 37-43, 2013 http://www.hrpub.org DOI: 10.13189/asp.2013.010301 A Novel Floating Point Fast Confluence Adaptive Independent Component Analysis for Signal Processing Applications Jayasanthi Ranjith. M.E. 1 , NJR.Muniraj 2 1 Anna University, Tamil nadu,India 2 Principal,Tejaa Sakthi Institute of Technology for Women,Tamil Nadu, India *Corresponding Author: mjayasanthi@yahoo.co.in Copyright © 2013 Horizon Research Publishing All rights reserved. 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. Keywords Adaptive Independent Component Analysis, Blind Source Separation, Contrast Function Optimization, Field Programmable Gate Array, Floating Point Independent Component Analysis, Very Large Scale Integration 1. Introduction ICA, a statistical signal processing technique, is one of the most commonly used algorithms in blind source separation. The term “blind” means that both the original independent sources and the way the sources were mixed are all unknown. Estimates of the source signals are found only from the observed signal mixtures. ICA recovers source signals from their mixtures by finding a linear transformation that maximizes the mutual independence or non-gaussianity of the mixtures regardless of the probability distribution. It plays an important role in a variety of signal processing, image processing techniques and communication networks. 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]. 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]. Infomax Estimation is a desirable choice due to its asymptotic optimality properties when the number of samples is large. The simplest algorithm for maximizing the likelihood uses stochastic gradient methods [4]. Maximum likelihood (ML) estimation is based on the assumption that the unknown parameters to be estimated are constants or no prior information is available. 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. Since learning rule uses higher order information in the learning when nonlinearities are introduced, this method indeed performs ICA, if the data is whitened. Algorithms for exactly maximizing the nonlinear PCA criteria are introduced in [6]. Simple algorithms are derived from the one-unit contrast functions using the principle of stochastic gradient descent. Hebbian like learning rule is obtained by taking the instantaneous gradient of the contrast function with respect to w[7] .Joint approximate diagonalization of eigenmatrices (JADE) is based on the principle of computing several cumulant Tensors. With low dimensional data, JADE is a competitive alternative to more popular FastICA algorithms. Other approaches include maximization of squared cumulants [8] and fourth-order cumulant based methods [9].Fourth-order blind identification (FOBI) method deals with the Eigen value decomposition (EVD) of the weighted