Czech
English
Institute of Information Theory and Automation
You are here
Prof. Ing. Václav Šmídl, Ph.D.
Position:
Research fellow
Department:
Research interests:
multivariate analysis, Bayesian filtering, recursive estimation, adaptive control
Publications ÚTIA:
Education:
2001-2004 PhD student in Trinity College Dublin, department of Electronics and electrical engineering. Defended thesis: The Variational Bayes approach in Signal Processing.
Teaching:
Czech Technical University: Hierarchical Bayesian Models
Employment:
2004-now ÚTIA, department of adaptive systems, research fellow
2010-now UWB, Regional innovation center for electrical engineering, senior researcher
2018-now CTU, Faculty of Electrical Engineering, senior researcher
2001-2004 Trinity College Dublin, PhD by research, EU projects MOUMIR and ProDacTool.
1999-2000 Sidat Praha, implementation of industrial control systems
Other sources:
Person detail
Duration: 2020
- 2022
Blind inverse problems (i.e. inverse problems with unknown parameters of the forward model)
are well studied for models with uniform grids, such as blind image deconvolution or blind signal
separation. Recently, new methods of learning of non-linear problems with differentiable
nonlinearities (i.e.
Duration: 2018
- 2020
Anomaly detection, which aims to identity samples very different from majority, is an important tool of unsupervised data analysis. Currently, most methods for anomaly detection use relatively simple shallow models without any complex layers and hierarchies.
Duration: 2014
- 2017
This proposal brings together experts in information theory with experts in atmospheric dispersion modelling, to tackle a particularly difficult and highly relevant scientific problem.
Duration: 2011
- 2014
The aim of this project is to explore new directions in diagnostics, control and parameter identification strategies of ac electric drives under critical operating conditions. Main attention will be paid to sensorless drive control and estimation in standstill and low speeds. We propose to explore suitability of methods from Bayesian identification and stochastic control in this area.
Duration: 2008
- 2010
Stochastic decentralized control of distributed systems is studied from theoretical and algorithmic point of view. Decentralization is formalized by imposing conditional independence assumptions in the centralized control problem. However, local models and aims are in general incompatible with this structure and a suitable projections must be found.