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Vaclav Smidl
Vaclav Smidl
Institute of Information Theory and Automation & University of West Bohemia & Czech Technical
Bestätigte E-Mail-Adresse bei utia.cas.cz
Titel
Zitiert von
Zitiert von
Jahr
The variational Bayes method in signal processing
A Quinn, V Smidl
Springer Science & Business Media, 2006
483*2006
Variational bayesian filtering
VÁ Smidl, A Quinn
IEEE Transactions on Signal Processing 56 (10), 5020-5030, 2008
1562008
Advantages of square-root extended Kalman filter for sensorless control of AC drives
V Smidl, Z Peroutka
IEEE Transactions on Industrial Electronics 59 (11), 4189-4196, 2011
1442011
Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
E Özkan, V Šmídl, S Saha, C Lundquist, F Gustafsson
Automatica 49 (6), 1566-1575, 2013
1262013
Adaptive speed control of induction motor drive with inaccurate model
J Talla, VQ Leu, V Šmídl, Z Peroutka
IEEE Transactions on Industrial Electronics 65 (11), 8532-8542, 2018
942018
Tracking of atmospheric release of pollution using unmanned aerial vehicles
V Šmídl, R Hofman
Atmospheric Environment 67, 425-436, 2013
772013
On Bayesian principal component analysis
V Šmídl, A Quinn
Computational statistics & data analysis 51 (9), 4101-4123, 2007
582007
Marginalized particle filters for Bayesian estimation of Gaussian noise parameters
S Saha, E Özkan, F Gustafsson, V Šmídl
2010 13th International Conference on Information Fusion, 1-8, 2010
512010
Are generative deep models for novelty detection truly better?
V Škvára, T Pevný, V Šmídl
arXiv preprint arXiv:1807.05027, 2018
502018
Direct speed control of a PMSM drive using SDRE and convex constrained optimization
V Šmídl, Š Janouš, L Adam, Z Peroutka
IEEE Transactions on Industrial Electronics 65 (1), 532-542, 2017
442017
Challenges and limits of extended Kalman Filter based sensorless control of permanent magnet synchronous machine drives
Z Peroutka, V Smidl, D Vosmik
2009 13th European Conference on Power Electronics and Applications, 1-11, 2009
432009
Improved stability of DC catenary fed traction drives using two-stage predictive control
V Šmídl, Š Janouš, Z Peroutka
IEEE Transactions on Industrial Electronics 62 (5), 3192-3201, 2015
422015
Bayesian blind separation and deconvolution of dynamic image sequences using sparsity priors
O Tichý, V Šmídl
IEEE transactions on medical imaging 34 (1), 258-266, 2014
402014
Fast AHRS filter for accelerometer, magnetometer, and gyroscope combination with separated sensor corrections
J Justa, V Šmídl, A Hamáček
Sensors 20 (14), 3824, 2020
392020
Bayesian inverse modeling and source location of an unintended 131I release in Europe in the fall of 2011
O Tichý, V Šmídl, R Hofman, K Šindelářová, M Hýža, A Stohl
Atmospheric Chemistry and Physics 17 (20), 12677-12696, 2017
392017
Rao-Blackwellized point mass filter for reliable state estimation
V Šmídl, M Gašperin
Proceedings of the 16th International Conference on Information Fusion, 312-318, 2013
352013
LS-APC v1. 0: a tuning-free method for the linear inverse problem and its application to source-term determination
O Tichý, V Šmídl, R Hofman, A Stohl
Geoscientific Model Development 9 (11), 4297-4311, 2016
302016
Mixture-based extension of the AR model and its recursive Bayesian identification
V Smídl, A Quinn
IEEE Transactions on Signal Processing 53 (9), 3530-3542, 2005
282005
Blind deconvolution with model discrepancies
J Kotera, V Šmídl, F Šroubek
IEEE transactions on image processing 26 (5), 2533-2544, 2017
272017
Non-stationary autoregressive model for on-line detection of inter-area oscillations in power systems
D Sidorov, D Panasetsky, V Šmídl
2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT …, 2010
252010
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