Institute of Information Theory and Automation

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Department: ZOI Duration: 2021 - 2023 Grantor: GACR
The project aims at developing a conceptually novel approach to inverse problems by proposing a unified theory of invariants to integral transformations using projection operators, and applying it to image restoration and classification problems in biomedical imaging and robotics.
Department: Duration: 2021 - 2023 Grantor: GACR
Project will be devoted to the investigation of novel concepts for the analysis and design of nonlinear controllers of flexible and chained mechanical systems to either compensate or control efficiently their oscillatory modes and limit cycles.
Department: MTR Duration: 2021 - 2022 Grantor: MSMT
We will propose a mathematically lucid setting for the derivation of reduced models from nonlinear continuum thermomechanics. We will justify linearized models in thermoviscoelasticity and viscoplasticity as limits of the nonlinear deformation theory employing variational convergence.
Department: MTR Duration: 2021 - 2025 Grantor: GACR
The aim of the project is to develop analytical tools to support the investigation of the locomotion of soft-bodied animals and robots, with a focus on limbless locomotion. Topics such as stabilization to limit cycles, optimal control and multiscale models will be studied.
Department: SI Duration: 2021 - 2023 Grantor: GACR
We want to conduct some meaningful and fruitful research into multivariate nonparametric econometrics.
Department: ZOI Duration: 2020 - 2021 Grantor:
Řešení automatické analýzy spadu v úlech za účelem detekce Varroa pomocí metod digitálního zpracování obrazu
Department: E Duration: 2020 - 2021 Grantor: TACR
Cílem projektu je vytvoření softwarového nástroje pro simulaci sociálních vazeb a protiepidemických opatření, který umožní porovnat efektivitu opatření s jejich dopadem na život jednotlivce i společnosti. Jádrem tohoto nástroje je síťový model dosavadního šíření COVID-19 v ČR.
Department: ZS Duration: 2020 - 2022 Grantor: FG
The EIT Urban Mobility Doctoral Training Network is a collective of universities, academics, and PhD candidates that seeks to promote innovation and entrepreneurship in the field of urban mobility based on the knowledge triangle (education, research, and business).
Department: E Duration: 2020 - 2022 Grantor: GACR
The pre-2008 consensus on what is the most appropriate economic policy melted away. Since then, economists and policymakers have faced new challenges like the zero lower bound, the non-neutrality of financial regulation for the long-term economic performance, and increased uncertainty.
Department: SI Duration: 2020 - 2022 Grantor: GACR
In interacting stochastic models, simple rules on the local level can give rise to complex behaviour on large scales. A natural way to study this phenomenon is through scaling limits and examination of the corresponding asymptomatic behaviour. Sometimes, randomness is present even at the macroscopic level, motivating the study of random continuum models.
Department: E Duration: 2020 - 2022 Grantor: GACR
This project focuses on the construction of complex networks of financial markets’ linkages around the world. First, several measures of associations will be considered, describing different aspects of market relationships. Second, suitable subgraphs will be identified, in order to create a suitable network representation capturing main dependencies between markets.
Department: ZOI Duration: 2020 - 2022 Grantor: TACR
The main objective of the project is to develop a dairy cow health control system, the ultimate goal of which would be to significantly reduce the use of antibiotics in the treatment and prevention of infectious mammary gland inflammation. Sub-goals: - Design a system of continuous microbiological diagnostics on dairy farms - Monitor the health and economic benefits of consistently applyi
Department: AS Duration: 2020 - 2022 Grantor: GACR
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.