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pro vyhledávání: '"Goncharov, Fedor P."'
We give new formulas for reconstructions from band-limited Hankel transform of integer or half-integer order. Our formulas rely on the PSWF-Radon approach to super-resolution in multidimensional Fourier analysis. This approach consists of combining t
Externí odkaz:
http://arxiv.org/abs/2409.17409
We continue studies of the uncertainty quantification problem in emission tomographies such as PET or SPECT when additional multimodal data (e.g., anatomical MRI images) are available. To solve the aforementioned problem we adapt the recently propose
Externí odkaz:
http://arxiv.org/abs/2108.00866
Autor:
Goncharov, Fedor
In this work we investigate numerically the reconstruction approach proposed in Goncharov, Novikov, 2016, for weighted ray transforms (weighted Radon transforms along oriented straight lines) in 3D. In particular, the approach is based on a geometric
Externí odkaz:
http://arxiv.org/abs/1911.05470
Autor:
Goncharov, Fedor, Novikov, Roman
We consider weighted ray-transforms $P\_W$ (weighted Radon transforms along straight lines) in $\mathbb{R}^d, \, d\geq 2,$ with strictly positive weights $W$. We construct an example of such a transform with non-trivial kernel in the space of infinit
Externí odkaz:
http://arxiv.org/abs/1711.06163
Autor:
Goncharov, Fedor, Novikov, Roman
We consider weighted Radon transforms $R_W$ along hyperplanes in $R^3$ with strictly positive weights $W$. We construct an example of such a transform with non-trivial kernel $\mathrm{Ker}R_W$ in the space of infinitely smooth compactly supported fun
Externí odkaz:
http://arxiv.org/abs/1709.09954
Autor:
Goncharov, Fedor, Novikov, Roman
In this work we study weighted Radon transforms in multidimensions. We introduce an analog of Chang approximate inversion formula for such transforms and describe all weights for which this formula is exact. In addition, we indicate possible tomograp
Externí odkaz:
http://arxiv.org/abs/1604.06349
Autor:
Buzun, Nazar, Gasnikov, Alexander, Goncharov, Fedor, Gorbachev, Oleg, Guz, Sergey, Krymova, Ekaterina, Natan, Andrey, Chernousova, Elena
This book contains a large number of exercises related to different stochastic disciplines. Difficulty of the problems varies from the basic level in the first chapter up to the analysis of articles in Probability, Statistics and Computer Science. Ra
Externí odkaz:
http://arxiv.org/abs/1508.03461
Akademický článek
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Akademický článek
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Autor:
Kolesnikova, Tatyana D.1,2 kolesnikova@mcb.nsc.ru, Goncharov, Fedor P.1, Zhimulev, Igor F.1,2
Publikováno v:
PLoS ONE. 4/16/2018, Vol. 13 Issue 4, p1-33. 33p.
