Results 261 to 270 of about 265,367 (299)

An alternative switching criterion for independent component analysis (ICA)

Neurocomputing, 2005
In solving the problem of noiseless independent component analysis (ICA) in which sources of super- and sub-Gaussian coexist in an unknown manner, one can be lead to a feasible solution using the natural gradient learning algorithm with a kind of switching criterion for the model probability distribution densities to be selected as super- or sub ...
Dengpan Gao, Jinwen Ma, Qiansheng Cheng
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Homeopathic ICA: A simple approach to expand the use of independent component analysis (ICA)

Chemometrics and Intelligent Laboratory Systems, 2015
Abstract Independent component analysis (ICA) is an increasingly popular method to resolve complex data sets, such as chemical image data, into images and their associated spectra. Unfortunately, the pre-requisite of statistical independence severely limits the application of ICA.
W. Windig, M.R. Keenan
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Independent Component Analysis (ICA) Using Pearsonian Density Function

2009
Independent component analysis (ICA) is an important topic of signal processing and neural network which transforms an observed multidimensional random vector into components that are mutually as independent as possible. In this paper, we have introduced a new method called SwiPe-ICA ( S tep wi se Pe arsonian ICA) that combines the methodology of ...
Abhijit Mandal, Arnab Chakraborty
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Face Recognition using independent component analysis of GaborJet (GaborJet-ICA)

2010 6th International Colloquium on Signal Processing & its Applications, 2010
In this paper a new face recognition technique based on Independent Component Analysis of GaborJet (GaborJet-ICA) is proposed. Existing face recognition systems using Gabor wavelets convolve a whole face image with a set of 40 Gabor wavelets. We have derived Gabor feature vector from facial landmarks (fiducial points) known as GaborJets.
K S Kinage, S G Bhirud
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A Robust Adaptive Filtering Method based on Independent Component Analysis (ICA)

2020 13th International Conference on Communications (COMM), 2020
The purpose of this paper is to present a method that uses Independent Component Analysis (ICA) in order to filter real-valued signals. The method is highly robust to noise, as it can estimate with high precision the noise that affects the input signal. To underline this, comparisons to high order, multiband FIR filters are performed.
Leontin Tuta, Mircea Nicolaescu, Georgiana Rosu, Alexandru Grivei, Bogdan Barbulescu   +4 more
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Authentication of DICOM medical images using independent component analysis (ICA)

International Journal of Medical Engineering and Informatics, 2012
This paper proposes a blind content-based watermarking scheme for image authentication using ICA and DCT. The watermark to be embedded is obtained from the host image itself in terms of the Frobenius norm of the mixing matrix obtained during ICA. These are embedded in the mid-frequency DCT coefficients.
A. Kannammal, S. Subha Rani
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Independent component analysis (ICA) for blind equalization of frequency selective channels

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 2004
In this paper we address the problem of blind source separation (BSS) in frequency selective multiple-input multiple-output (MIMO) channels, when the only available prior knowledge about the transmitted signals is their mutual statistical independence. The novelty of the paper is two-fold.
null Chiu Shun Wong, D. Obradovic, N. Madhu   +2 more
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An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks

IEEE Transactions on Instrumentation and Measurement, 2019
This paper presents a novel approach on motor current signature analysis (MCSA) for broken bar fault detection of induction motors (IMs), using as input the current signal measured from one of the three motor phases.
Juan Enrique Garcia-Bracamonte, J. Ramírez-Cortés, Jose de Jesus Rangel-Magdaleno, P. Gómez‐Gil, H. Peregrina-Barreto, V. Alarcón-Aquino   +5 more
semanticscholar   +1 more source

Independent Vector Analysis: An Extension of ICA to Multivariate Components

2006
In this paper, we solve an ICA problem where both source and observation signals are multivariate, thus, vectorized signals. To derive the algorithm, we define dependence between vectors as Kullback-Leibler divergence between joint probability and the product of marginal probabilities, and propose a vector density model that has a variance dependency ...
Taesu Kim, Torbjørn Eltoft, Te-Won Lee
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Classifying hemodynamics of MR brain perfusion images using independent component analysis (ICA)

Proceedings of the International Joint Conference on Neural Networks, 2003., 2004
Dynamic-susceptibility-contrast MR imaging is a widely used perfusion imaging technique that records signal changes on images caused by the passage of contrast-agent particles in the human brain after a bolus injection of contrast agent. The signal changes over time on different brain tissues represent distinct blood supply patterns and are crucial for
null Yu-Te Wu, null Yi-Hsuan Kao, null Wan-Yuo Guo, null Tzu-Chen Yeh, null Jen-Chuen Hsieh, M.M.H. Teng   +5 more
openaire   +1 more source