Independent component analysis (ICA) is a computational method, based on neural learning algorithm, to separate source signals from the observed mixtures by assuming that the sources are non-Gaussian in nature. Convergence speed, Area and Power are important parameters to be improved in VLSI implementation of ICA techniques, since they involve large number of iterative calculations, area and po...

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of n...

Independent component analysis (ICA) was used for the processing of cardiological signals obtained by means of fetal magnetocardiography (fMCG), a technique allowing the non-invasive recording of the weak magnetic field variations associated to the electrical activity of the fetal heart. Purpose of the present work was to verify whether a computational-light ICA algorithm (FastICA), tailored to...

This paper proposes three approaches to content-based watermarking for image authentication based on Independent Component Analysis (ICA). In this scheme, ICA is applied to blocks of the cover image and the resulting mixing matrix is used as the content-based feature. This is embedded in the mid-frequency DCT coefficient of the block in the first method. The watermark is embedded in the 3 rd le...

Spatial independent components analysis (sICA) has become a widely applied data-driven method for fMRI data, especially for resting-state studies. These sICA approaches are often based on iterative estimation algorithms and there are concerns about accuracy due to noise. Repeatability measures such as ICASSO, RAICAR and ARABICA have been introduced as remedies but information on their effects o...

This paper deals with the validation of Blind Source Separation techniques for an automatic elimination of effects due to atmospheric fluctuations and microclimates or any other spurious artifact eventually present in SAR interferograms. Specifically, an Independent Component Analysis algorithm (the FastICA) is applied on arrays of ERS-1/ERS-2 interferograms and the results are shown for mounta...

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In recent years, many algorithms were proposed that perform very well in many situations. Nevertheless, when outlying values are present in the data, these methods often lead to wrong conclusio...

Independent component analysis (ICA) is a signal processing technique that can be used to extract meaningful components from a dataset. Biogeography based optimization (BBO) algorithm is a recently developed stochastic optimization algorithm. In this paper, we report the use of the BBO algorithm to optimize a contrast function that measures the statistical independence of the recovered componen...

A novel approach is proposed for Electroencephalogram signal classification using Artificial Neural Network based on Independent Component Analysis and Short Time Fourier Transform. The source EEG signals contain the electrical activity of the brain produced in the background by the cerebral cortex nerve cells. EEG is one of the most utilized methods for effective analysis of the brain function...

In this paper, we propose to use the Huber M -estimator cost function as a contrast function within the complex FastICA algorithm of Bingham and Hyvarinen for the blind separation of mixtures of independent, non-Gaussian, and proper complex-valued signals. Sufficient and necessary conditions for the local stability of the complex-circular FastICA algorithm for an arbitrary cost are provided. A ...