Relevant bibliographies by topics / Nonlinear blind sources separation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Nonlinear blind sources separation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Nonlinear blind sources separation"

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Harmeling, Stefan, Andreas Ziehe, Motoaki Kawanabe, and Klaus-Robert Müller. "Kernel-Based Nonlinear Blind Source Separation." Neural Computation 15, no. 5 (2003): 1089–124. http://dx.doi.org/10.1162/089976603765202677.

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We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibl
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Burel, Gilles. "Blind separation of sources: A nonlinear neural algorithm." Neural Networks 5, no. 6 (1992): 937–47. http://dx.doi.org/10.1016/s0893-6080(05)80090-5.

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Akhavan, S., and H. Soltanian-Zadeh. "Blind separation of sparse sources from nonlinear mixtures." Digital Signal Processing 118 (November 2021): 103220. http://dx.doi.org/10.1016/j.dsp.2021.103220.

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Isomura, Takuya, and Taro Toyoizumi. "On the Achievability of Blind Source Separation for High-Dimensional Nonlinear Source Mixtures." Neural Computation 33, no. 6 (2021): 1433–68. http://dx.doi.org/10.1162/neco_a_01378.

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For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately—when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality. Our proposed theorem, termed the asymptotic linearization theorem, theoretically guarante
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PENG, HANCHUAN, ZHERU CHI, and WANCHI SIU. "A SEMI-PARAMETRIC HYBRID NEURAL MODEL FOR NONLINEAR BLIND SIGNAL SEPARATION." International Journal of Neural Systems 10, no. 02 (2000): 79–93. http://dx.doi.org/10.1142/s0129065700000089.

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Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules
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Ye, Meng Di, Zhi Nong Li, Yao Xian Xiao, Xu Ping He, and Jing Wen Yan. "Kernel Independent Component Analysis and its Application in Blind Separation of Mechanical Faults." Applied Mechanics and Materials 703 (December 2014): 394–99. http://dx.doi.org/10.4028/www.scientific.net/amm.703.394.

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A nonlinear blind separation method of mechanical fault sources is proposed. In the proposed method, the signal is transformed from the low-dimensional nonlinear original space into a high-dimensional linear feature space by the kernel function, so that nonlinear mixture mechanical fault sources can be separated by the linear ICA method in a new feature space. The simulation result shows that the proposed method is superior to the traditional ICA method in processing nonlinear blind separation problem. Finally the proposed method is applied to the nonlinear blind separation of bearing faults.
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Shi, Zhenwei, and Changshui Zhang. "Nonlinear innovation to blind source separation." Neurocomputing 71, no. 1-3 (2007): 406–10. http://dx.doi.org/10.1016/j.neucom.2007.08.007.

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Martinez, D., and A. Bray. "Nonlinear blind source separation using kernels." IEEE Transactions on Neural Networks 14, no. 1 (2003): 228–35. http://dx.doi.org/10.1109/tnn.2002.806624.

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Zhang, Kun, and Lai-Wan Chan. "Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures." Neural Computation 17, no. 2 (2005): 425–52. http://dx.doi.org/10.1162/0899766053011500.

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The linear mixture model has been investigated in most articles tackling the problem of blind source separation. Recently, several articles have addressed a more complex model: blind source separation (BSS) of post-nonlinear (PNL) mixtures. These mixtures are assumed to be generated by applying an unknown invertible nonlinear distortion to linear instantaneous mixtures of some independent sources. The gaussianization technique for BSS of PNL mixtures emerged based on the assumption that the distribution of the linear mixture of independent sources is gaussian. In this letter, we review the gau
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Diamantaras, Konstantinos, Gabriela Vranou, and Theophilos Papadimitriou. "Multi-Input Single-Output Nonlinear Blind Separation of Binary Sources." IEEE Transactions on Signal Processing 61, no. 11 (2013): 2866–73. http://dx.doi.org/10.1109/tsp.2013.2255046.

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Dissertations / Theses on the topic "Nonlinear blind sources separation"

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Zhang, Jingyi. "Statistical blind source separation of post-nonlinear mixture." Thesis, University of Newcastle upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485858.

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Blind Source Separation (BSS) is a statistical signal processing technique and has recently been developed for many applications. The aim of this thesis is to investigate the blind signal separation problem under the environment where noise, reverberation and nonlinear distortion exist in the mixture and to develop novel solutions to solve the problem. The success and efficacy of the proposed algorithms is analysed in terms of robustness to noise, accuracy of recovered signal and speed of convergence. Linear BSS algorithms for instantaneous and convolutive mixtures are investigated and tested
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Babaiezadeh, Malmiri Massoud. "On blind source separation in convolutive and nonlinear mixtures." Grenoble INPG, 2002. http://www.theses.fr/2002INPG0065.

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Dans cette thèse, la séparation aveugle de sources dans des mélanges convolutif Post Non-linéaire (CPNL) est étudiée. Pour séparer ce type de mélanges, nous avons d'abord développé des nouvelles méthodes pour séparer les mélanges convultifs et les mélanges Post Non-Linéaires (PNL). Ces méthodes sont toutes basées sur la minimisation de l'information mutuelle des sorties. Pour minimiser l'information mutuelle, nous calculons d'abord sa "différentielle", c'est-à-dire, sa variation en fonction d'une petite variation de son argument. Cette différentielle est alors utilisée pour concevoir des appro
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Vayá, Salort Carlos. "Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8416.

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Las arritmias supraventriculares, en particular la fibrilación auricular (FA), son las enfermedades cardíacas más comúnmente encontradas en la práctica clínica rutinaria. La prevalencia de la FA es inferior al 1\% en la población menor de 60 años, pero aumenta de manera significativa a partir de los 70 años, acercándose al 10\% en los mayores de 80. El padecimiento de un episodio de FA sostenida, además de estar ligado a una mayor tasa de mortalidad, aumenta la probabilidad de sufrir tromboembolismo, infarto de miocardio y accidentes cerebrovasculares. Por otro lado, los episodios de FA paro
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Dias, Tiago Macedo. "Algoritmos heuristicos em separação cega de fontes." [s.n.], 2008. http://repositorio.unicamp.br/jspui/handle/REPOSIP/259288.

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Orientadores: João Marcos Travassos Romano, Romis Ribeiro de Faissol Attux<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-12T15:14:54Z (GMT). No. of bitstreams: 1 Dias_TiagoMacedo_M.pdf: 3219855 bytes, checksum: 5572e53d65cb457f420e78b3150dd6ee (MD5) Previous issue date: 2008<br>Resumo: Esta dissertação se propõe a estudar um novo método para separação cega de fontes baseado no modelo Post-Nonlinear, que une uma ferramenta de busca global baseada em computação bioinspirada a uma etapa de b
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Ando, Rafael Assato 1986. "Blind source separation in the context of polynomial mixtures = Separação cega de fontes no contexto de misturas polinomiais." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260202.

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Orientadores: Romis Ribeiro de Faissol Attux, Leonardo Tomazeli Duarte<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-23T17:30:21Z (GMT). No. of bitstreams: 1 Ando_RafaelAssato_M.pdf: 1700685 bytes, checksum: 9befef5632e55e27a24af3e73c648c47 (MD5) Previous issue date: 2013<br>Resumo: Neste trabalho, estudamos o problema de BSS no contexto de misturas polinomiais sob três perspectivas: uma teórica - voltada ao estudo de separabilidade estrutural -, uma ligada à proposta de novas abordagens
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Vaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.

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En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no li
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Ehsandoust, Bahram. "Séparation de Sources Dans des Mélanges non-Lineaires." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAT033/document.

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La séparation aveugle de sources aveugle (BSS) est une technique d’estimation des différents signaux observés au travers de leurs mélanges à l’aide de plusieurs capteurs, lorsque le mélange et les signaux sont inconnus. Bien qu’il ait été démontré mathématiquement que pour des mélanges linéaires, sous des conditions faibles, des sources mutuellement indépendantes peuvent être estimées, il n’existe dans de résultats théoriques généraux dans le cas de mélanges non-linéaires. La littérature sur ce sujet est limitée à des résultats concernant des mélanges non linéaires spécifiques.Dans la présente
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Ito, Masanori, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo, and Noboru Ohnishi. "Blind Signal Separation of Moving Sound Sources." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10347.

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Blaschke, Tobias. "Independent component analysis and slow feature analysis." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2005. http://dx.doi.org/10.18452/15270.

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Der Fokus dieser Dissertation liegt auf den Verbindungen zwischen ICA (Independent Component Analysis - Unabhängige Komponenten Analyse) und SFA (Slow Feature Analysis - Langsame Eigenschaften Analyse). Um einen Vergleich zwischen beiden Methoden zu ermöglichen wird CuBICA2, ein ICA Algorithmus basierend nur auf Statistik zweiter Ordnung, d.h. Kreuzkorrelationen, vorgestellt. Dieses Verfahren minimiert zeitverzögerte Korrelationen zwischen Signalkomponenten, um die statistische Abhängigkeit zwischen denselben zu reduzieren. Zusätzlich wird eine alternative SFA-Formulierung vor
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Suyama, Ricardo. "Proposta de metodos de separação cega de fontes para misturas convolutivas e não-lineares." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260846.

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Orientador: João Marcos Travassos Romano<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-09T16:56:34Z (GMT). No. of bitstreams: 1 Suyama_Ricardo_D.pdf: 28793623 bytes, checksum: cf06bdad425402b4624bbd169bfad249 (MD5) Previous issue date: 2007<br>Resumo: O problema de separação cega de fontes (BSS - Blind Source Separation) vem despertando o interesse de um número crescente de pesquisadores. Esse destaque é devido, em grande parte, à formulação abrangente do problema, que torna possível o uso das
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Books on the topic "Nonlinear blind sources separation"

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Deville, Yannick, Leonardo Tomazeli Duarte, and Shahram Hosseini. Nonlinear Blind Source Separation and Blind Mixture Identification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64977-7.

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Almeida, Luis B. Nonlinear Source Separation (Synthesis Lectures on Signal Processing). Morgan & Claypool Publishers, 2006.

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Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical mod
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Book chapters on the topic "Nonlinear blind sources separation"

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Deville, Yannick. "Sparse Component Analysis: A General Framework for Linear and Nonlinear Blind Source Separation and Mixture Identification." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_6.

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Ehsandoust, Bahram, Masoud Babaie-Zadeh, and Christian Jutten. "Blind Source Separation in Nonlinear Mixture for Colored Sources Using Signal Derivatives." In Latent Variable Analysis and Signal Separation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_22.

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Almeida, Mariana S. C., Harri Valpola, and Jaakko Särelä. "Separation of Nonlinear Image Mixtures by Denoising Source Separation." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_2.

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Almeida, Luís B., and Gonçalo C. Marques. "Nonlinear blind source separation by pattern repulsion." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100535.

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Levin, David N. "Model-Independent Analytic Nonlinear Blind Source Separation." In Contributions to Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55789-2_21.

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Larue, Anthony, Christian Jutten, and Shahram Hosseini. "Markovian Source Separation in Post-nonlinear Mixtures." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_89.

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Levin, David N. "Model-Independent Method of Nonlinear Blind Source Separation." In Latent Variable Analysis and Signal Separation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_30.

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Blaschke, Tobias, and Laurenz Wiskott. "Independent Slow Feature Analysis and Nonlinear Blind Source Separation." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_94.

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Zheng, Chun-Hou, Zhi-Kai Huang, Michael R. Lyu, and Tat-Ming Lok. "Nonlinear Blind Source Separation Using Hybrid Neural Networks." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_172.

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Duarte, Leonardo Tomazeli, and Christian Jutten. "Blind Source Separation of a Class of Nonlinear Mixtures." In Independent Component Analysis and Signal Separation. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74494-8_6.

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Conference papers on the topic "Nonlinear blind sources separation"

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Ehsandoust, Bahram, Bertrand Rivet, Christian Jutten, and Massoud Babaie-Zadeh. "Nonlinear blind source separation for sparse sources." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760515.

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Diamantaras, Konstantinos I., and Theophilos Papadimitriou. "Blind separation of three binary sources from one nonlinear mixture." In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2010. http://dx.doi.org/10.1109/mlsp.2010.5589211.

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Diamantaras, Konstantinos, Theophilos Papadimitriou, and Gabriela Vranou. "Blind separation of multiple binary sources from one nonlinear mixture." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946742.

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Shi, Zhenwei, Xueyan Tan, Zhiguo Jiang, Hongjuan Zhang, and Chonghui Guo. "Noisy blind source separation by nonlinear autocorrelation." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5647807.

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Zhang, Jingyi, W. L. Woo, and S. S. Dlay. "A Nonlinear State Space Approach to Nonlinear Blind Source Separation." In 2007 15th International Conference on Digital Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icdsp.2007.4288640.

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Yu, Wei, Liu Zhenxing, and Li ChangHai. "Improved Particle Swarm to Nonlinear Blind Source Separation." In 2007 International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. IEEE, 2007. http://dx.doi.org/10.1109/mape.2007.4393468.

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Zhinong Li, Yudong Zeng, Tao Fan, Yaping Lv, and Jinge Ren. "Source Separation method of Machine Faults Based on Post-Nonlinear Blind Source Separation." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594464.

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Zhang, Jingyi, W. L. Woo, and S. S. Dlay. "Hidden Markov blind source separation of post-nonlinear mixture." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518013.

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Shi, Zhenwei, Xinya Zhai, Zhenyu An, and Zhiguo Jiang. "Fast fixed-point algorithm for blind separation of nonlinear autocorrelation and non-Gaussian sources." In 2011 3rd International Conference on Awareness Science and Technology (iCAST). IEEE, 2011. http://dx.doi.org/10.1109/icawst.2011.6163093.

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Milani, Fabio, Mirko Solazzi, and Aurelio Uncini. "Blind source separation of convolutive nonlinear mixtures by flexible spline nonlinear functions." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5744933.

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