When exploiting independent component analysis (ICA) to perform blind source separation (BSS), it is assumed that sources are mutually independent. However, in practice, the latent sources are usually dependent to some extent. Subband decomposition ICA (SDICA) is an extension of ICA to admit source dependence. It assumes that each source is represented as the sum of some independent sub-compone...

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important this field as they require few and weak assumptions allow for blindness regarding the original sou...

BACKGROUND AND OBJECTIVE The relationship between EEG source signals and action-related visual and auditory stimulation is still not well-understood. The objective of this study was to identify EEG source signals and their associated action-related visual and auditory responses, especially independent components of EEG. METHODS A hand-moving-Hanoi video paradigm was used to study neural corre...

Independent component analysis (ICA) attempts to nd a linear decomposition of observed data vectors into components that are statistically independent. It is well known, however, that such a decomposition cannot be exactly found, and in many practical applications, independence is not achieved even approximately. This raises the question on the utility and interpretation of the components given...

Title of Dissertation LEARNING ALGORITHMS FOR AUDIO AND VIDEO PROCESSING INDEPENDENT COMPONENT ANALYSIS AND SUPPORT VECTOR MACHINE BASED APPROACHES Yuan Qi Master of Science Dissertation directed by Professor Rama Chellappa Department of Electrical and Computer Engineering In this thesis we propose two new machine learning schemes a Subband based Independent Component Analysis scheme and a hybr...

Independent component analysis (ICA) is a new effective technique for separation of statistically independent sources existing simultaneously in observations. Generally, ICA requires that the number of sensors should be no less than the number of independent sources to ensure enough information for separation of all sources. In some practical applications, this requirement of ICA is not met and...

Independent Component Analysis (ICA) is a method for blind source separation of a multivariate dataset that transforms observations to new statistically independent linear forms. Infinite variance of non-Gaussian α-stable distributions makes algorithms like ICA non-appropriate for these distributions. In this note, we propose an algorithm which computes mixing matrix of ICA, in a parametric sub...

Separation of independent sources using independent component analysis (ICA) requires prior knowledge of the number of independent sources. Performing ICA when the number of recordings is greater than the number of sources can give erroneous results. To improve the quality of separation, the most suitable recordings have to be identified before performing ICA. Techniques employed to estimate su...

conclusions data reported here indicated that there is no specific genetic combination beyond the quantity of biofilm biomass in s. epidermidis. biofilm biomass seemed to be controlled by rnaiii expression level. further interest should be directed to biofilm dispersal since it seems that the key difference in biofilm biomass ability of s. epidermidis strains relates to factors regulating this ...

Independent component analysis (ICA) aims to recover a set of unknown mutually independent source signals from their observed mixtures without knowledge of the mixing coefficients. In some applications, it is preferable to extract only one desired source signal instead of all source signals, and this can be achieved by a one-unit ICA technique. ICA with reference (ICA-R) is a one-unit ICA algor...