Recent works have shown that artifact removal in biomedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both...

As a new approach of blind source separation (BSS), independent component analysis (ICA) has attracted extensive attention of researchers in the field of information processing. In this paper, the basic theory and algorithm of ICA are briefly introduced, and then ICA is used for the preprocessing of engine acoustic signals to identify the engine noise sources. The ICA decomposes the signals int...

Independent Component Analysis (ICA) is a multivariate data analysis process largely sudied these last years in the signal processing community for blind source separation. This paper proposes to show the interest of ICA as a tool for unsupervised analysis of hyperspectral images. The commonly used Principal Component Analysis (PCA) is the mean square optimal projection for gaussian data leadin...

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of t...

Independent subspace anlaysis (ISA) is a linear modelbased method which generalizes independent component analysis (ICA) by incorporating the invariant feature subspace into multidimensional ICA. In this paper we apply ISA to the problem of gene expression data analysis and show the useful behavior of the independent subspaces of gene expression data in the task of gene clustering and gene-gene...

This paper addresses the relative usefulness of Independent Component Analysis (ICA) for Face Recognition. Comparative assessments are made regarding (i) ICA sensitivity to the dimension of the space where it is carried out, and (ii) ICA discriminant performance alone or when combined with other discriminant criteria such as Bayesian framework or Fisher’s Linear Discriminant (FLD). Sensitivity ...

Remote sensing image fusion can effectively improve the accuracy of image classification. This paper proposes an image fusion algorithm based on extensions of independent component analysis (ICA) and multi-classifier system. Firstly a novel method of fusing panchromatic and multi-spectral remote sensing images is developed by contourlet transform which can offer a much richer set of directions ...

The scheme proposed in this paper combines independent components analysis (ICA) with discrete wavelet transform (DWT) and discrete cosine transform (DCT). Firstly, the original image is decomposed by 2-D DWT and the detail sub-bands are reserved. Then, the approximate image is transformed by DCT and embedded with watermark. The watermark is detected through ICA. The simulation results demonstr...

This paper proposes an analysis of high-impedance fault detection algorithms for medium voltage distribution lines based on the discrete wavelet transform (DWT) technique and a more advanced named independent component (ICA) independently. Three-phase line model two diodes high impedance model, which represents unsymmetrical current electric arc, simulated using MATLAB/Simulink. High (HIF) algo...

Fetal electrocardiogram (fECG) analysis, i.e., the analysis of the heart rate (HR) and the fECG morphology, offers clinicians vital information about fetal health condition. The fECG can be acquired non-invasively by applying electrodes on maternal abdomen, but the recorded abdominal signal (ADS) contains not only the signal of interest, fECG, but also some physiological (e.g., maternal electro...