Institute of Information Theory and Automation

You are here

Projects

Department: MTR Duration: 2015 - 2017 Grantor: FG
The main aim of the project is to deepen and extend the mathematical foundations for adequate modeling of vague quantifiers as fuzzy quantifiers in the framework of MFL.
Department: ZOI Duration: 2015 - 2017 Grantor: GACR
The project falls into the area of image analysis and object recognition. We mainly focus on two important connected areas: object description by invariant features and image fusion. The theoretical concept is based on the formulation of all tasks as optimization problems and looking for proper cost functions and algorithms for their maximization.
Department: AS Duration: 2014 - 2017 Grantor: MSMT
This proposal brings together experts in information theory with experts in atmospheric dispersion modelling, to tackle a particularly difficult and highly relevant scientific problem.
Department: ZS Duration: 2014 - 2017 Grantor: TACR
The aim of this project is to define parameters representing skid resistance and longitudinal road unevenness on the base of evaluation vehicle data, available on CAN bus and that are shared between different vehicle assistant systems like ABS or ASR, e.g.
Department: ZOI Duration: 2014 - 2017 Grantor: TACR
The goal of the project is to develop a sophisticated software for videokymography (VKG) which will enable automatic evaluation of medical videokymographic recordings of vibrating vocal folds and arrive at correct medical diagnosis. Further goal is to develop a certified method of VKG evaluation to be used in clinical practice.
Department: ZOI Duration: 2014 - 2017 Grantor: TACR
The aim of the proposed project is the development of a new ultrasonic equipment (US), allowing early detection of breast cancer and reduce the need to use the ionizing radiation during examinations. The aim is to eliminate the disadvantages of current ultrasonic systems in the diagnosis of breast cancer, which hinders its use.
Department: ZS Duration: 2014 - 2017 Grantor: FG
EMC2 project addresses the ARTEMIS Innovation Pilot Programme AIPP5: Computing platforms for embedded systems. The objective of the EMC2 project is to foster changes through an innovative and sustainable service-oriented architecture approach for mixed criticality applications in dynamic and changeable real-time environments. EMC2 project focuses on the industrialization of European research
Department: ZOI Duration: 2014 - 2017 Grantor: FG
ALMARVI project targets low-power adaptive platform solution for healthcare, smart phone and security industries which are as part of big societal challenges affordable healthcare and wellbeing and green and safe transportation. ALMARVI aims at providing cross-domain many-core platform solution, system software stack, tool chain, and adaptive algorithms that will enable massive data-rate image/
Department: ZS Duration: 2014 - 2017 Grantor: FG
ALMARVI project targets low-power adaptive platform solution for healthcare, smart phone and security industries which are as part of big societal challenges affordable healthcare and wellbeing and green and safe transportation. ALMARVI aims at providing cross-domain many-core platform solution, system software stack, tool chain, and adaptive algorithms that will enable massive data-rate image/
Department: E Duration: 2014 - 2016 Grantor: GACR
The aim of the research project is to analyze financial risk and market co-movements using novel econometric methods and their theoretically grounded modifications. The main focus will be on emerging European markets with respect to global developed markets, as well as important assets from commodities markets.
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: SI Duration: 2014 - 2016 Grantor: GACR
We want to conduct some meaningful and fruitful econometric research into multivariate regression quantiles.

Pages