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Institute of Information Theory and Automation
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Projects
Department: AS
Duration: 2014
- 2016
Grantor: GACR
The project aims to develop a dynamic distributed estimation framework, intended for fully distributed low-cost parameter estimation of
stationary signals and reduced-complexity tracking of nonstationary processes.
Department: ZS
Duration: 2013
- 2016
Grantor: MSMT
Department: E
Duration: 2013
- 2015
Grantor:
The project deals with modelling of options implied volatility where the implied volatility is considered as a function of strike
price and time to maturity.
Department: E
Duration: 2013
- 2015
Grantor:
The project focuses on studying multivariate time-frequency dynamics of financial markets using spectral methods. First target is to formulate new spectral-based realized measure of variance and covariance using wavelets, which will be applied to measure the integrated volatililty and covolatility under the various types of dependent microstructure noise.
Department: E
Duration: 2013
- 2015
Grantor: GACR
The project will theoretically and empirically investigate the effects that systemic, mostly supranational financial shocks and
policy responses (local and global) to these shocks have on behavior, economic performance and welfare of the private sector in
open economies. We want to contribute to answering the following questions:
(a) What type of economic integration, i.e.
Department: E
Duration: 2013
- 2015
Grantor:
Economic and financial activities are often influenced simultaneously by a decision and random factors. Since the decision parameter must be constructed mostly without knowledge of a random element realization, an optimization problem depending on a probability measure corresponds to this situation. Usually in applications this measure must be replaced by an empirical one.
Department: MTR
Duration: 2013
- 2015
Grantor: GACR
The application of algebraic and geometric methods is one of the present trends in modern statistics. The aim of the project is to apply the methods of combinatorial optimization to problems with motivation in statistics and
artificial intelligence.
Department: AS
Duration: 2013
- 2016
Grantor: GACR
Decision making (DM) is a targeted choice of actions based on given knowledge and preferences. Normatively, Bayesian DM, maximising expected utility, should be used under uncertainty but this happens less than desirable. Often, imperfection of the DM participant can be blamed as it limits the deliberation effort spent.
Department: AS
Duration: 2013
- 2016
Grantor: GACR
Department: ZOI
Duration: 2013
- 2015
Grantor: GACR
In the past decade, we have seen a remarkable growth in ability to capture and distribute digital videos. But, taking into consideration the
availability of too many powerful and user-friendly video editors, the question is how to verify the authenticity of these digital videos. The
problem becomes crucial, when it comes to videos used in courts as evidences or journalism.
Department: ZOI
Duration: 2013
- 2016
Grantor: GACR
We addresses one of the key problems of image processing, which is restoration of a latent image from its degraded observations. As the main source of degradation we consider convolution with a unknown blur. The restoration task is a blind deconvolution problem, which is intrinsically ill-posed.
Department: MTR
Duration: 2013
- 2016
Grantor: GACR
Formal systems of (non-)classical logics are essential in many areas of computer science. Their appreciation is due to their deductive nature, universality and portability, and the power they gain from their rigorous mathematical background. Such a diverse landscape of logical systems has greatly benefited from a unified approach offered by Abstract Algebraic Logic.
Department: MTR
Duration: 2013
- 2015
Grantor: GACR
The aim of this project is to develop reliable, theoretically supported, simulation tools for damage processes in quasi-brittle materials at small strains driven by mechanical and transport phenomena.
Department: ZOI
Duration: 2013
- 2015
Grantor: AV_IP
Department: ZOI
Duration: 2013
- 2015
Grantor:
Automatic processing of microscopic images allows (compared to manual processing by human operator) a better and more efficient acquisition of a large amount of information stored in the captured images. When working with time-lapse images, we need to segment cells from the background, to determine their exact boundaries and monitor their movements.