Abstract This paper proposes a cost-effective and variable-channel floating-point fast independent component analysis (FastICA) hardware architecture and implementation for EEG signal processing. The Gram-Schmidt orthonormalization based whitening process is utilized to eliminate the use of the dedicated hardware for eigenvalue decomposition (EVD) in the FastICA algorithm. The proposed two proc...

In this paper, we propose a blind signal separation procedure for digital modulation signal using FastICA algorithm. FastICA is one of ICA (Independent Component Analysis) algorithms and it enables very fast optimal weight calculation for signal separation without any the prior information of reference signal. ICA is based on the independence of signal to each other. ICA simultaneously separate...

Fast independent component analysis (FastICA) is an efficient feature extraction tool widely used for process fault detection. However, the conventional FastICA-based fault detection method does not consider the ubiquitous measurement noise and may exhibit unsatisfactory performance under the adverse effects of the measurement noise. To solve this problem, we propose a new process fault detecti...

Independent Component Analysis (ICA) is a computational method to solve Blind Source Separation (BSS) problem. In this study, an improved Fast ICA based on eighth-order Newton’s method is proposed to solve BSS problems. Eight-order Newton’s method for finding the solution of nonlinear equations is much faster than ordinary Newton’s iterative method. The improved FastICA algorithm is applied to ...

FastICA is arguably one of the most widespread methods for independent component analysis. We focus on its deflation-based implementation, where the independent components are extracted one after another. The present contribution evaluates the method’s speed in terms of the overall computational complexity required to reach a given source extraction performance. FastICA is compared with a simpl...

Neural learning algorithms developed for blind separation of mixed source signals give rise to a Global Separating-Mixing (GSM) matrix that can be used to measure the performance of the unmixing system. In the case of the instantaneous linear noiseless mixing model, we consider the GSM as a transformation operator and show that it is equivalent to a combined stretching and rotation in the signa...

In this work, a new approach to background subtraction based on independent component analysis is presented. This approach assumes that background and foreground information are mixed in a given sequence of images. Then, foreground and background components are identified, if their probability density functions are separable from a mixed space. Afterwards, the components estimation process cons...

A novel approach for the problem of estimating the data model of independent component analysis (or blind source separation) in the presence of gaussian noise is introduced. We de ne the gaussian moments of a random variable as the expectations of the gaussian function (and some related functions) with di erent scale parameters, and show how the gaussian moments of a random variable can be esti...

Marine controlled source electromagnetic (CSEM) sensing method used for the detection of hydrocarbons based reservoirs in seabed logging application does not perform well due to the presence of the airwaves (or sea-surface). These airwaves interfere with the signal that comes from the subsurface seafloor and also tend to dominate in the receiver response at larger offsets. The task is to identi...

The non-negative ICA problem is here defined by the constraint that the sources are non-negative with probability one. This case occurs in many practical applications like spectral or image analysis. It has then been shown by [10] that there is a straightforward way to find the sources: if one whitens the non-zero-mean observations and makes a rotation to positive factors, then these must be th...