Institute of Information Theory and Automation

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Department: ZOI Duration: 2024 - 2027 Grantor: MSMT
The project aims to develop AI/ML models and a theoretical framework to identify and record culturally significant and climate-endangered scents, utilizing internet data for scent valuation.
Department: ZOI Duration: 2024 - 2026 Grantor: GACR
Current convolutional networks work with inefficient pixel-wise image representation, which does not provide almost any invariance. This leads to using very large training sets and massive augmentation.
Department: E Duration: 2024 - 2026 Grantor: GACR
V rámci projektu budou navrženy a realizovány strategie zajištění na komoditních trzích zaměřené na snížení finančních rizik. Náš přístup zohlední rozdíly ve zpracování informací a investičních horizontech účastníků trhu využitím informačního obsahu realizovaných semikovariančních matic a frekvenčně specifického přenosu rizik napříč aktivy.
Department: AS Duration: 2024 - 2026 Grantor: GACR
Quantification of sources of atmospheric pollutants is crucial for regulatory purposes as well as for atmospheric science in general. Due to many physical limitations in observation and modeling, the existing methodologies have many simplifying assumptions, e.g. linear observation model or uncorrelated emission values, which cause inevitable bias in pollutant estimates.
Department: E Duration: 2024 - 2026 Grantor: GACR
The project will develop a new family of models for identification of tail risks in financial markets from possibly large datasets using deep learning algorithms. Our newly developed methods will allow us to revisit several classical problems in empirical asset pricing. We believe that the results will be of fundamental character and will open number of questions.
Department: ZS Duration: 2023 - 2026 Grantor: FG
The main aim of the EECONE project is to reduce e-waste on a European scale. The environmental impact arising from e-waste can thus be reduced by working in three principal areas: 1) Increase service lifetime of electronic products by application of ecodesign guidelines for increasing their reliability and their repair rate, thereby reducing the volume of e-waste.
Department: ZOI Duration: 2023 - 2024 Grantor:
This interdisciplinary scientific project aims to examine the creative context of one of Leonardo's most reproduced subjects: the Salvator Mundi.
Department: ZOI Duration: 2023 - 2023 Grantor:
The project focuses on the protection of video and image content and looks at ways to prevent illegal sharing. It will explore how artificial intelligence methods can circumvent these protections and the challenges of applying protection to large volumes of data.
Department: ZS Duration: 2023 - 2026 Grantor: FG
The European research project Listen2Future started with 27 partners from 7 countries.
Department: E Duration: 2023 - 2025 Grantor: GACR
Decentralized finance has been often synonymized with cryptocurrencies, cryptoassets, or even simply with Bitcoin not only in the public perception but to a high degree also in financial research. This project aims to dive deeper into decentralized finance and in a comprehensive manner explore and describe its structural aspects.
Department: ZOI Duration: 2022 - 2025 Grantor: FG
At a time of cloud computing and cyber-physical systems like computer-assisted driving, software complexity is growing faster than the rate of improvement in related quality assurance techniques. The ERC-funded VAMOS project will develop monitoring software to identify potential vulnerabilities, errors, and unfair decisions at runtime.
Department: ZS Duration: 2022 - 2025 Grantor: FG
Project HiPE brings together 13 participants covering the whole value chain, to develop a new highly energy-efficient, cost-effective, modular, compact and integrated wide bandgap (WBG) power electronics solutions for the next generation of battery electric vehicles (BEV), and to facilitate a significant market penetration of WBG in the automotive sector.
Department: AS Duration: 2022 - 2026 Grantor: FG
The aim of the project is to promote understanding of complex interactions and the dynamics of decision making (DM) under complexity and uncertainty. The theory under consideration should be applicable to dynamic DM and interaction within a flat structure without any coordination. It will support modelling a living agent acting within a complex network of interacting heterogeneous agents.
Department: SI Duration: 2022 - 2024 Grantor: GACR
The project deals with the development of active fault diagnosis (AFD) algorithms for stochastic discrete-time large-scale systems. To achieve the feasibility of the algorithms, tensor decompositions (TDs) will be employed in several components of the AFD algorithm design.