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About 10,000 papers for “Deconvolution”
Jean‐Luc Starck, Fionn Murtagh · Cambridge University Press eBooks · 1998
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Jan Kamenický, Filip Sroubek, Valery Kritchallo · OSF Preprints (OSF Preprints) · 2016
Optimization of various blind and non-blind deconvolution algorithms
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Filip Sroubek, Jan Kamenický, Valery Kritchallo · OSF Preprints (OSF Preprints) · 2016
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Joost Van Der Neut, Kees Wapenaar, Mrinal Sen · 2009
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios Ope n Pe e r Re v ie
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SPARACINO, GIOVANNI, G. DE NICOLAO, C. COBELLI · Research Padua Archive (University of Padua) · 2000
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Nicola Simola, Micaela Morelli, Tooru M. Mizuno et al. · 2010
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P.B. Moore, D.J. DeRosier · Journal of Molecular Biology · 1970
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Warren A. Keller · Patrick Moore's practical astronomy series/The Patrick Moore practical astronomy series · 2016
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Ziolkowski, Anton · 1984
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Paul Chapel · 2026
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Miroslav Dibák · Digital Repository (National Repository of Grey Literature) · 2009
The convolution has a big signification in mathematical statistics. In the opening chapter, we define basic terms used in the thesis and we introduce the convolution and basic relations related to this term. In the second chapter, we attend to kernel estimators, mainly the kernel density estimator and the kernel charakteristic function estimator. In the third chapter, we attend to the deconvolution and we summarize the basic theoretical properties of the deconvolution estimator. In the last chapter of this thesis we present a possible application in medicine. The properties of the proposed estimator are investigated in a small simulation study.
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Jerry M. Mendel · Elsevier eBooks · 1983
Cited by 6
Valery Kritchallo, Filip Sroubek, Jan Kamenický · OSF Preprints (OSF Preprints) · 2016
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Miroslav Dibák · Digital Repository (National Repository of Grey Literature) · 2009
The convolution has a big signification in mathematical statistics. In the opening chapter, we define basic terms used in the thesis and we introduce the convolution and basic relations related to this term. In the second chapter, we attend to kernel estimators, mainly the kernel density estimator and the kernel charakteristic function estimator. In the third chapter, we attend to the deconvolution and we summarize the basic theoretical properties of the deconvolution estimator. In the last chapter of this thesis we present a possible application in medicine. The properties of the proposed estimator are investigated in a small simulation study.
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Raimund J. Ober, E. Sally Ward, Jerry Chao · 2020
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Michael H. Neumann, Friedrich-schiller-universität Jena · 2006
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Mohammadreza Paraan (16436961), Mohamed Nasef (14173441), Lucy Chou-Zheng (16436964) et al. · Figshare · 2023
<p>EM models for the 276 kDa complex, the 318 kDa complex, and the 318 kDa complex without Csm2 were used to generate theoretical scattering curves, which were then used in OLIGOMER to deconvolute our experimental SAXS scattering curve. The resulting deconvolution strongly indicated that the 276 kDa complex alone was the best model for the experimental data. The composite fit is shown above as a line with the experimental data as open circles. The Chi<sup>s</sup> for this fit was 1.75.</p> <p>(TIF)</p>
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Filip Sroubek, Valery Kritchallo, Jan Kamenický · OSF Preprints (OSF Preprints) · 2016
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2021
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