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Abstract: Blind source extraction (BSE) is a special class of blind source separation (BSS) method. Due to its low computation load and fast processing speed, BSE has become one of the promising methods in signal processing and analysis. This paper addresses BSE problem when a desired source signal has temporal structures. Based on the generalized autocorrelations of the desired signal and the non-Gaussianity of its innovations, we develop an objective function. Maximizing this objective function, we present a BSE algorithm and further give its stability analysis in this paper. Simulations on image data and electrocardiogram (ECG) data indicate its better performance and the better property of tolerance to the estimation error of the time delay. [Copyright &y& Elsevier]

Abstract: A unifying model that combines three properties is proposed by Hyvärinen, and a gradient ascent algorithm for independent component analysis (ICA) is performed by maximum likelihood estimation. In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. Firstly, according to the useful property of Gaussian moments, we introduce Gaussian moments algorithm to estimation of the noisy unifying model when the noise covariance matrix is known. Next, when the noise covariance is unknown, a new Gaussian moments algorithm is developed. Finally, the validity and performance of our algorithms are demonstrated by computer simulations. [Copyright &y& Elsevier]

Abstract: Complexity pursuit is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. We derive a simple algorithm combining Gaussian moments and complexity pursuit for noisy ICA. Validity and performance of the described approaches are demonstrated by computer simulations. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of blind source separation and presents a fixed-point nonlinear principal component analysis (NPCA) algorithm. It is a block-wise batch algorithm and gives an alternative perspective on existing adaptive online NPCA algorithms. Utilizing new activation functions that automatically satisfy a stability condition, the proposed algorithm can separate mixed signals with sub- and super-Gaussian source distributions. The efficiency is confirmed by extensive computer simulations on man-made sources as well as practical speech signals. [Copyright &y& Elsevier]