Independent Component Analysis (ICA) can be described in several ways, one of which is as a technique that seeks to find a set directions (components) underlying multivariate data that are most independent of one another. While there are several ICA models and many ICA methods, in this report we focus on the most basic model and one of the most popular and simple algorithms; the One-Unit FastIC...

Audio source separation is the task of isolating sound sources that are active simultaneously in a room captured by a set of microphones. Convolutive audio source separation of equal number of sources and microphones has a number of shortcomings including the complexity of frequency-domain ICA, the permutation ambiguity and the problem’s scalabity with increasing number of sensors. In this pape...

Independent component analysis (ICA) has many practical applications in the fields of signal and image processing and several ICA learning algorithms have been constructed via the selection of model probability density functions. However, there is still a lack of deep mathematical theory to validate these ICA algorithms, especially for the general case that superand sub-Gaussian sources coexist...

Many traffic accidents are caused by drivers with fatigue states. It is important to detect fatigue states of drivers from their eye opening degrees, namely normal, dozing and fatigue. The paper proposes a fatigue state recognition algorithm that combines independent component analysis (ICA) and one-dimensional hidden Markov model (HMM) together. The algorithm firstly does binarisation processi...

Linear instantaneous independent component analysis (ICA) is a well-known problem, for which efficient algorithms like FastICA and JADE have been developed. Nevertheless, the development of new contrasts and optimization procedures is still needed, e.g. to improve the separation performances in specific cases. For example, algorithms may exploit prior information, such as the sparseness or the ...

Fast algorithms for linear blind source separation are developed. The fast convergence is rst derived from low-noise approximation of the EM-algorithm given in 2], to which a modiication is made that leads as a special case to the FastICA algorithm 5]. The modii-cation is given a general interpretation and is applied to Bayesian blind source separation of noisy signals.

The thesis deals with several problems in blind separation of linear mixture of unknown sources using independent component analysis. Among other things, it focuses on a key question: how accurate the separation can be done, and how to achieve the best possible separation in practice. First, the problem with indeterminacy of order and signs of original sources is addressed. The indeterminacies ...

The FastICA or fixed-point algorithm is one of the most successful algorithms for linear independent component analysis (ICA) in terms of accuracy and computational complexity. Two versions of the algorithm are available in literature and software: a one-unit (deflation) algorithm and a symmetric algorithm. The main result of this paper are analytic closed-form expressions that characterize the...

Abstract A wide range of signs are acquired from the human body called biomedical or biosignals, and they can be at cell level, organ sub-atomic level. Electroencephalogram is electrical activity cerebrum, electrocardiogram heart, action muscle sound signals referred to as electromyogram, electroretinogram eye, so on. Studying these helpful for doctors, it help them examine predict cure many di...

The economic and environmental losses due to serious leakage in the urban water supply network have increased the effort to control the water leakage. However, current methods for leakage estimation are inaccurate leading to the development of ineffective leakage controls. Therefore, this study proposes a method based on the blind source separation theory (BSS) to calculate the leakage of water...