—We present a new high-performance Convex Cauchy– Schwarz Divergence (CCS-DIV) measure for Independent Component Analysis (ICA) and Blind Source Separation (BSS). The CCS-DIV measure is developed by integrating convex functions into the Cauchy–Schwarz inequality. By including a convexity quality parameter, the measure has a broad control range of its convexity curvature. With this measure, a ne...

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data to evaluate the functional connectivity, which assumes that the sources of functional networks are statistically independent. Recently, many researchers have demonstrated that sparsity is an effective assumption for fMRI signal separation. In this research, we present a sparse approxim...

ICA is applied to multi-channel vibration measurements of a 35 kW cage induction motor to fuse the information of several channels, and provide a robust and reliable fault detection routine. Independent components are found from the measurement data set with FastICA algorithm, and their PSD estimates are calculated with Welch’s method. A SVM based classification routine is applied to the PSD es...

Fetal electrocardiograms (FECG) contain important indications about the health and condition of the fetus. In this respect, it is crucial to apply a robust algorithm to ECG data for extraction of the FECG signal. Most of the independent component analysis (ICA) algorithms used for this purpose rely on simple statistical models. Such algorithms can fail to separate desired signals when the assum...

A kind of image digital watermarking scheme is proposed in this paper. The scheme is based on Fast Independent Component Analysis (Fast ICA) and Discrete Wavelet Transform (DWT). In this scheme, a binary image is embedded into a wavelet approach sub-image. When extracting the watermarking, Fast ICA method is used. The experiment results show that the scheme is robust to many attacks. Keyword— B...

Blind Source Separation (BSS) approaches for multi-channel EEG processing are popular, and in particular Independant Component Analysis (ICA) algorithms have proven their ability for artifacts removal and source extraction for this very specific class of signals. However, the blind aspect of these techniques implies wellknown drawbacks. As these methods are based on estimated statistics from th...

We propose a simulated annealing algorithm (stochastic non-negative independent component analysis, SNICA) for blind decomposition of linear mixtures of non-negative sources with non-negative coefficients. The demixing is based on a Metropolis-type Monte Carlo search for least dependent components, with the mutual information between recovered components as a cost function and their non-negativ...

We approach the estimation of material percentages per pixel (endmember fractional abundances) in hyperspectral remote-sensed images as a blind source separation problem. This task is commonly known as spectral unmixing. Classical techniques require the knowledge of the existing materials and their spectra, which is an unrealistic situation in most cases. In contrast to recently presented blind...

Independent component analysis (ICA) The ICA algorithm has been applied successfully to modeling V1 simple cell receptive fields [1, 2]. It is closely related to sparse coding methods, and can be cast in terms of a simple generative model [3]: We suppose that our data x ∈ R is an unknown linear mixture of independent, non-Gaussian sources, i.e. x = As where A ∈ Rn×n is unknown. During learning,...