Transputer implementation of block regularized filtering
Jiří Kadlec (1996)
Kybernetika
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Jiří Kadlec (1996)
Kybernetika
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Mou'ath Hourani, Ibrahiem M. M. El Emary (2009)
Computer Science and Information Systems
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Rafał Zdunek (2014)
International Journal of Applied Mathematics and Computer Science
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Rafał Zdunek (2014)
International Journal of Applied Mathematics and Computer Science
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Miroslav Kárný, Martin Šámal, Josef Böhm (1998)
Kybernetika
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Reconstruction of underlying physiological structures from a sequence of images is a long-standing problem which has been solved by factor analysis with a success. This paper tries to return to roots of the problem, to exploit the available findings and to propose an improved paradigm.
Petr Tichavský, Zbyněk Koldovský (2011)
Kybernetika
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This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram...
Witold Andrzejewski, Artur Gramacki, Jarosław Gramacki (2013)
International Journal of Applied Mathematics and Computer Science
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The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density Estimator (KDE). However, a very serious drawback of using KDEs is the large number of calculations required to compute them, especially...
Ch. Aswani Kumar (2009)
Computer Science and Information Systems
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Antonio Ciampi, Ana González Marcos, Manuel Castejón Limas (2005)
SORT
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Correspondence analysis followed by clustering of both rows and columns of a data matrix is proposed as an approach to two-way clustering. The novelty of this contribution consists of: i) proposing a simple method for the selecting of the number of axes; ii) visualizing the data matrix as is done in micro-array analysis; iii) enhancing this representation by emphasizing those variables and those individuals which are 'well represented' in the subspace of the chosen axes. The approach...