ICALAB

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 102 articles )

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  1. Zhang, Fode; Shi, Yimin: Geometry on the statistical manifold induced by the degradation model with soft failure data (2020)
  2. Althahab, Awwab Qasim Jumaah: A new hybrid adaptive combination technique for ECG signal enhancement (2019)
  3. Lahat, Dana; Jutten, Christian; Shapiro, Helene: Schur’s lemma for coupled reducibility and coupled normality (2019)
  4. Litvinenko, Alexander; Keyes, David; Khoromskaia, Venera; Khoromskij, Boris N.; Matthies, Hermann G.: Tucker tensor analysis of Matérn functions in spatial statistics (2019)
  5. Zhang, Fode; Ng, Hon Keung Tony; Shi, Yimin; Wang, Ruibing: Amari-Chentsov structure on the statistical manifold of models for accelerated life tests (2019)
  6. Shitov, Yaroslav: A counterexample to Comon’s conjecture (2018)
  7. Ashino, Ryuichi; Mandai, Takeshi; Morimoto, Akira: Continuous multiwavelet transform for blind signal separation (2017)
  8. Jari Miettinen and Klaus Nordhausen and Sara Taskinen: Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp (2017) not zbMATH
  9. Maulik, R.; San, O.: A neural network approach for the blind deconvolution of turbulent flows (2017)
  10. Amezquita-Sanchez, Juan Pablo; Adeli, Hojjat: Signal processing techniques for vibration-based health monitoring of smart structures (2016)
  11. Piotrowski, Tomasz; Yamada, Isao: Reduced-rank estimation for ill-conditioned stochastic linear model with high signal-to-noise ratio (2016)
  12. Wang, Fasong; Li, Rui; Wang, Zhongyong; Zhang, Jiankang: Compressed blind signal reconstruction model and algorithm (2016)
  13. Wang, Rongjie; Zhan, Yiju; Zhou, Haifeng: A class of sequential blind source separation method in order using swarm optimization algorithm (2016)
  14. Zhang, Xinzhen; Huang, Zheng-Hai; Qi, Liqun: Comon’s conjecture, rank decomposition, and symmetric rank decomposition of symmetric tensors (2016)
  15. Albataineh, Zaid; Salem, Fathi: Robust blind multiuser detection algorithm using fourth-order cumulant matrices (2015)
  16. Gou, Xiaoming; Liu, Zhiwen; Ma, Jingyan; Xu, Yougen: Blind separation of noncircular sources via approximate joint diagonalization of augmented charrelation matrices (2015)
  17. Lei, J.; Qiu, J.H.; Liu, S.: Dynamic reconstruction algorithm for electrical capacitance tomography based on the proper orthogonal decomposition (2015)
  18. Tadić, Vladislav B.: Convergence and convergence rate of stochastic gradient search in the case of multiple and non-isolated extrema (2015)
  19. 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
  20. Xu, Pengcheng; Shen, Yuehong; Jian, Wei; Zhao, Wei; Peng, Cheng: Maximization of nonlinear autocorrelation for blind source separation of non-stationary complex signals (2015)

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