Flounders and cuttlefish have an impressive ability to change colouration, for camouflage and, in the case of cuttlefish, for communication. We pursue the hypothesis that these diverse patterns are created by combining a small number of distinct pattern modules. Independent component analysis (ICA) is a powerful tool for identifying independent sources of variation in linear mixtures of signals...

This study proposes a parallel six-axis force/torque sensor decoupling method based on an independent component analysis (ICA), and the experimental platform is built for exploration. First of all, structural model mathematical are introduced, which composed single-dimensional force sensors with central symmetry. Secondly, test prototype was developed to perform online static loading sensor, re...

This paper presents a feature parameter transformation method using ICA (independent component analysis) for text independent speaker identification of telephone speech. ICA is a signal processing technique which can separate linearly mixed signals into statistically independent signals. The proposed method transforms them into new vectors using ICA assuming that the cepstrum vectors of the tel...

This thesis is about convolutive ICA with application to EEG. Two methods for convolutive ICA are proposed. One method, the CICAP algorithm, uses a linear predictor in order to formulate the convolutive ICA problem in two steps: linear deconvolution followed by instantaneous ICA. The other method, the CICAAR algorithm, generalizes Infomax ICA to include the case of convolutive mixing. One advan...

Diabetes deteriorates atherosclerotic changes in the arteries. The aim of the study was to assess the prevalence and localization of stenotic atherosclerotic lesions of the internal carotid artery (ICA) in patients with diabetes. A prospective analysis of angiography findings was carried out in 150 diabetic and 150 non-diabetic patients with symptoms of cerebral ischemia using double-blind angi...

are only uncorrelated). More importantly , ICA does this in a fashion that renders the expression of the components non-Gaussian. In the implementation proposed by McKeown et al. these distributions are super-Gaussian or 'sparse'. This simply means that things happen infrequently. Why is a 'sparse', or more generally a non-Gaussian, distribution interesting? The answer to this question is simpl...

Independent component analysis (ICA) of an image sequence extracts a set of statistically independent images, and deenes a corresponding set of unconstrained dual time courses. However, the extra degrees of freedom implicit in these time courses can lead to physically improbable solutions. Accordingly, we introduce two methods for regularising ICA: smoothed independent component analysis (smICA...

EEG signals may be affected by physiological and non-physiological artifacts hindering the analysis of brain activity. Blind source separation methods such as independent component (ICA) are effective ways improving signal quality removing components representing non-brain However, most ICA-based artifact removal strategies have limitations, individual differences in visual assessment component...

The current theory for Independent Component Analysis (ICA) tries to model the observations as unknown linear combination or mixture of N independent components or sources S1(t), . . . , SN(t) whose distribution is also usually unknown. In the ICA problem one tries to recover all the N independent and non-Gaussian components from the only knowledge of the observations. In this paper, we address...

Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are the techniques that deal with extracting the independent components from linear mixtures of Gaussian and non-Gaussian data at the input respectively. PCA is a classical method that deals with the second order statistics of data. It is also known as Karhunen-Loeve Transform or the Hotelling Transform in some applicat...