We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Article:Blind source separation using modified contrast function in fastICA algorithm

by Dr. Alka Mahajan, Gajanan Birajdar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 4
Year of Publication: 2010
Authors: Dr. Alka Mahajan, Gajanan Birajdar
10.5120/1069-1397

Dr. Alka Mahajan, Gajanan Birajdar . Article:Blind source separation using modified contrast function in fastICA algorithm. International Journal of Computer Applications. 6, 4 ( September 2010), 14-17. DOI=10.5120/1069-1397

@article{ 10.5120/1069-1397,
author = { Dr. Alka Mahajan, Gajanan Birajdar },
title = { Article:Blind source separation using modified contrast function in fastICA algorithm },
journal = { International Journal of Computer Applications },
issue_date = { September 2010 },
volume = { 6 },
number = { 4 },
month = { September },
year = { 2010 },
issn = { 0975-8887 },
pages = { 14-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume6/number4/1069-1397/ },
doi = { 10.5120/1069-1397 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:54:32.520087+05:30
%A Dr. Alka Mahajan
%A Gajanan Birajdar
%T Article:Blind source separation using modified contrast function in fastICA algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 6
%N 4
%P 14-17
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A novel contrast function is proposed to be used in fastICA algorithm for Blind Source Separation (BSS). Simulation results show that the proposed nonlinear function used to separate image mixtures, results in faster execution and good quality image separation. Peak Signal to Noise Ratio (PSNR), Improved Signal to Noise Ratio (ISNR), Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) are used to evaluate quality of separated images and Amari error is calculated to prove the performance of separation quality.

References
  1. A. Hyvärinen and Erkki Oja, 1997, "A fast fixed-point algorithm for independent component analysis", Neural Computation, 9(7):1483-1492.
  2. Bell, A. J. and Sejnowski, T. J., , 1995, "An information-maximization approach to blind separation and blind deconvolution", Neural Computation, 7(6):1129-59.
  3. J. F. Cardoso, and A. Souloumiac, , 1993, "Blind beamforming for non-Gaussian signals", IEE Proceeding Part F, Vol.140, No. 6: 362-370.
  4. A. Hyvärinen, J. Karhunen, E. Oja, , 2001, "Independent Component Analysis", John Wiley & Sons .
  5. Aapo Hyvärinen and Erkki Oja, 2000."Independent Component Analysis: Algorithms and Applications", Neural Networks, 13(4-5):411-430.
  6. A. Hyvärinen, 1999, "Survey on Independent Component Analysis", Neural Computing Surveys 2, 94-128.
  7. A. Cichocki, S. Amari, and K. Siwek, 2002, “ICALAB toolbox for image processing – benchmarks",
  8. Cichocki, A., Amari, S.-I., 1996, "A new learning algorithm for blind signal separation", Advances in neural information processing 8, 757-763.
  9. P. Tichavsky, Z. Koldovsky and E. Oja, Sept. 2007, "Speed and Accuracy Enhancement of Linear ICA Techniques Using Rational Nonlinear Functions", Proceedings of 7th International Conference on Independent Component Analysis (ICA2007), pp. 285-292.
  10. Cardoso J.F., Laheld B., ,1996, "Equivariant adaptive source separation" IEEE transaction Signal Processing, 45, pp. 434-444.
  11. Koldovsky Z., Tichavsky P., Oja E., 2006, "Efficient variant of algorithm fastICA for independent component analysis attaining the Cramer-Rao lower bound" IEEE transactions Neural Networks, 17, pp. 1265-1277.
  12. Karvanen, J., Eriksson, J., Koivunen, V., 2000, "Maximum likelihood estimation of ICA model for wide class of source distributions", Neural Networks for Signal Processing 1, pp. 445–454.
  13. Pham, D.T., Garat, P., 1997, "Blind separation of mixture of independent sources through a quasi-maximum likelihood approach", IEEE Transaction Signal Processing, 45, pp. 1712– 1725.
  14. Aapo Hyvärinen, 1997, "One unit contrast functions for Independent Component Analysis: A statistical analysis", IEEE Signal Processing, pp.388- 397.
  15. Aapo Hyvärinen, August 1997, "Independent Component Analysis by Minimization of Mutual Information", Helsinki University of Technology, Laboratory of Computer and Information Science, Finland, Report A46.
Index Terms

Computer Science
Information Sciences

Keywords

Blind source separation Independent component analysis FastICA Algorithm Nonlinearity function

Powered by PhDFocusTM