An Efficient Algorithm by Kurtosis Maximization in Reference-Based Framework

This paper deals with the optimization of kurtosis for complex-valued signals in the independent component analysis (ICA) framework, where source signals are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast schemes, a similar contrast function is put forward, based on which a new fast fixed-point (FastICA) algorithm is proposed. The new optimization method is similar in spirit to the former classical kurtosis-based FastICA algorithm but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. The performance of this new algorithm is confirmed through computer simulations

A gradient-based optimum block adaptation ICA technique for interference suppression in highly dynamic communication channels

The fast fixed-point independent component analysis (ICA) algorithm has been widely used in various applications because of its fast convergence and superior performance. However, in a highly dynamic environment, real-time adaptation is necessary to track the variations of the mixing matrix. In this scenario, the gradient-based online learning algorithm performs better, but its convergence is slow, and depends on a proper choice of convergence factor. This paper develops a gradient-based optimum block adaptive ICA algorithm (OBA/ICA) that combines the advantages of the two algorithms. Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions, which is particularly useful in mobile communications. Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved

FPGA Implementation of Blind Source Separation using FastICA

Fast Independent Component Analysis (FastICA) is a statistical method used to separate signals from an unknown mixture without any prior knowledge about the signals. This method has been used in many applications like the separation of fetal and maternal Electrocardiogram (ECG) for pregnant women. This thesis presents an implementation of a fixed-point FastICA in field programmable gate array (FPGA). The proposed design can separate up to four signals using four sensors. QR decomposition is used to improve the speed of evaluation of the eigenvalues and eigenvectors of the covariance matrix. Moreover, a symmetric orthogonalization of the unit estimation algorithm is implemented using an iterative technique to speed up the search algorithm for higher order data input. The hardware is implemented using Xilinx virtex5-XC5VLX50t chip. The proposed design can process 128 samples for the four sensors in less than 63 ns when the design is simulated using 10 MHz clock

Automatic classification of lithofacies using fast independent component analysis

The problem of automatic classification of facies was addressed using the Fast Independent Component Analysis (FastICA) of a data set of geophysical well logs of the Namorado Field, Campos Basin, Brazil, followed by a k-nearest neighbor (k-NN) classification. The goal of an automatic classification of facies is to produce spatial models of facies that assist the geological characterization of petroleum reservoirs. The FastICA technique provides a new data set that has the most stable and less Gaussian distribution possible. The k-NN classifies this new data set according to its characteristics. The previous application of FastICA improves the accuracy of the k-NN automatic classification and it also provides better results in comparison with the automatic classification by means of the Principal Component Analysis (PCA). © 2015 Sociedade Brasileira de Geofísica.The problem of automatic classification of facies was addressed using the Fast Independent Component Analysis (FastICA) of a data set of geophysical well logs of the Namorado Field, Campos Basin, Brazil, followed by a k-nearest neighbor (k-NN) classification. The goal of an automatic classification of facies is to produce spatial models of facies that assist the geological characterization of petroleum reservoirs. The FastICA technique provides a new data set that has the most stable and less Gaussian distribution possible. The k-NN classifies this new data set according to its characteristics. 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Differential fast fixed-point algorithms for underdetermined instantaneous and convolutive partial blind source separation

This paper concerns underdetermined linear instantaneous and convolutive blind source separation (BSS), i.e., the case when the number of observed mixed signals is lower than the number of sources.We propose partial BSS methods, which separate supposedly nonstationary sources of interest (while keeping residual components for the other, supposedly stationary, "noise" sources). These methods are based on the general differential BSS concept that we introduced before. In the instantaneous case, the approach proposed in this paper consists of a differential extension of the FastICA method (which does not apply to underdetermined mixtures). In the convolutive case, we extend our recent time-domain fast fixed-point C-FICA algorithm to underdetermined mixtures. Both proposed approaches thus keep the attractive features of the FastICA and C-FICA methods. Our approaches are based on differential sphering processes, followed by the optimization of the differential nonnormalized kurtosis that we introduce in this paper. Experimental tests show that these differential algorithms are much more robust to noise sources than the standard FastICA and C-FICA algorithms.Comment: this paper describes our differential FastICA-like algorithms for linear instantaneous and convolutive underdetermined mixture

BMICA-independent component analysis based on B-spline mutual information estimator

The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis) exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA

New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications