The ICALAB toolboxes. ICALAB for Signal Processing and ICALAB for Image Processing are two independent demo packages for MATLAB that implement a number of efficient algorithms for ICA (independent component analysis) employing HOS (higher order statistics), BSS (blind source separation) employing SOS (second order statistics) and LP (linear prediction), and BSE (blind signal extraction) employing various SOS and HOS methods.

References in zbMATH (referenced in 93 articles )

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  1. Ashino, Ryuichi; Mandai, Takeshi; Morimoto, Akira: Continuous multiwavelet transform for blind signal separation (2017)
  2. Jari Miettinen and Klaus Nordhausen and Sara Taskinen: Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp (2017)
  3. Amezquita-Sanchez, Juan Pablo; Adeli, Hojjat: Signal processing techniques for vibration-based health monitoring of smart structures (2016)
  4. Piotrowski, Tomasz; Yamada, Isao: Reduced-rank estimation for ill-conditioned stochastic linear model with high signal-to-noise ratio (2016)
  5. Wang, Fasong; Li, Rui; Wang, Zhongyong; Zhang, Jiankang: Compressed blind signal reconstruction model and algorithm (2016)
  6. Wang, Rongjie; Zhan, Yiju; Zhou, Haifeng: A class of sequential blind source separation method in order using swarm optimization algorithm (2016)
  7. Zhang, Xinzhen; Huang, Zheng-Hai; Qi, Liqun: Comon’s conjecture, rank decomposition, and symmetric rank decomposition of symmetric tensors (2016)
  8. Albataineh, Zaid; Salem, Fathi: Robust blind multiuser detection algorithm using fourth-order cumulant matrices (2015)
  9. Gou, Xiaoming; Liu, Zhiwen; Ma, Jingyan; Xu, Yougen: Blind separation of noncircular sources via approximate joint diagonalization of augmented charrelation matrices (2015)
  10. Tadić, Vladislav B.: Convergence and convergence rate of stochastic gradient search in the case of multiple and non-isolated extrema (2015)
  11. Tomé, Ana M.; Schachtner, R.; Vigneron, V.; Puntonet, C.G.; Lang, E.W.: A logistic non-negative matrix factorization approach to binary data sets (2015) ioport
  12. Xu, Pengcheng; Shen, Yuehong; Jian, Wei; Zhao, Wei; Peng, Cheng: Maximization of nonlinear autocorrelation for blind source separation of non-stationary complex signals (2015)
  13. Li, Wei; Yang, Huizhong: A non-linear blind source separation method based on perceptron structure and conjugate gradient algorithm (2014) ioport
  14. Miettinen, Jari; Nordhausen, Klaus; Oja, Hannu; Taskinen, Sara: Deflation-based separation of uncorrelated stationary time series (2014)
  15. Nordhausen, Klaus: On robustifying some second order blind source separation methods for nonstationary time series (2014)
  16. Piotrowski, Tomasz; Yamada, Isao: Performance of the stochastic MV-PURE estimator in highly noisy settings (2014)
  17. Rouigueb, A.; Chitroub, S.; Bouridane, A.: Bayesian inference over ICA models: application to multibiometric score fusion with quality estimates (2014)
  18. Xia, Youshen; Leung, Henry: Performance analysis of statistical optimal data fusion algorithms (2014)
  19. Wang, Yang; Yılmaz, Özgür; Zhou, Zhengfang: Phase aliasing correction for robust blind source separation using DUET (2013)
  20. Deville, Yannick; Deville, Alain: Classical-processing and quantum-processing signal separation methods for qubit uncoupling (2012)

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