Results 211 to 220 of about 32,702 (254)

A novel measure for independent component analysis (ICA)

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
Measures of independence (and dependence) are fundamental in many areas of engineering and signal processing. Shannon introduced the idea of information entropy which has a sound theoretical foundation but sometimes is not easy to implement in engineering applications. In this paper, Renyi's entropy is used and a novel independence measure is proposed.
Dongxin Xu, José C. Príncipe, John W. Fisher III, Hsiao-Chun Wu   +3 more
openaire   +1 more source

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
openaire   +1 more source

Independent Component Analysis (ICA) for texture classification

2008 5th International Multi-Conference on Systems, Signals and Devices, 2008
This paper presents a texture classification algorithm using independent component analysis (ICA). ICA is used for creating basis functions or basis images bank. These basis functions are used in texture classification because they are able to capture the inherent properties of textured images. These properties enable us to use the ICA bank to generate
Dia Abu Al Nadi, Ayman M. Mansour
openaire   +1 more source

Fault detection in induction motors with independent component analysis (ICA)

2009 IEEE Bucharest PowerTech, 2009
In this study, the aim is to detect an induction motor's winding faults by using independent component analysis (ICA). Many laboratory experiments have been done on an induction motor to check the performance of the proposed method. The phase currents of the induction motor are used in the fault detection algorithm.
Guney, Irfan, Kilic, Erdal, Ozgonenel, Okan, Ulutas, Mustafa, Karadeniz, Erol   +4 more
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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.
Chiu Shun Wong, Dragan Obradovic, Nilesh Madhu   +2 more
openaire   +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
openaire   +1 more source

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
openaire   +1 more source

Visualisasi Kerusakan Bearing Menggunakan Metode Independent Component Analysis (ICA)

Bandung Conference Series: Statistics, 2022
Abstract. Vibration is a response of a mechanical system either caused by a given excitation force or changes in operating conditions as a function of time. The force that causes this vibration can be caused by several sources such as contact/impact between moving/rotating components, rotation of an unbalanced mass, misalignment and also Bearing faults
Silvya Rahmatiara Putri, Sutawanir Darwis   +1 more
openaire   +1 more source