Institute of Information Theory and Automation

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Projects

Dept.: AS Duration: 2024 - 2026
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. We propose to analyze and...
Dept.: AS Duration: 2022 - 2026
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.
Dept.: AS 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. neural networks) have been proposed, however they rely on supervised learning on a training set...
Dept.: AS Duration: 2018 - 2021
The proposed project aims to contribute to theoretical and algorithmic development of cooperation and negotiation aspects while respecting agent imperfection and deliberation. The targeted solution should be applicable to decentralised dynamic DM under complexity and uncertainty. It will support a single agent acting within a network of strategically interacting agents. A flat cooperation...
Dept.: AS 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. This in sharp contrast to the area of supervised classification, where hierarchical models with large number of layers stacked on top of...
Dept.: AS Duration: 2018 - 2020
Optimal processing of distributed knowledge is key agenda in machine learning, signal processing and control, driven by sensor networks for smart environments, autonomous agents and distributed infrastruktures (clouds, Internet) serving the tnternet of things. Nodes may communicate via partially specied probability distributions (moments, etc.). If a remote node or central coordinator is to...
Dept.: AS Duration: 2017 - 2021
Objective of the project is to contribute to theoretical and algorithmic development of cooperation and negotiation under complexity and uncertainty. The desired theory should be applicable to decentralised dynamic decision making under a flat cooperation structure without pre-coordination. It will support single agent acting within a network of strategically interacting agents.
Dept.: AS Duration: 2017 - 2018
Linear and bilinear models arise in many research areas including statistics, signal processing, machine learning, approximation theory, or image analysis. In cases when the problem of interest is ill-conditioned or suffers from separation ambiguity, the classical solutions such as ordinary least squares or non-negative matrix factorization fail and additional information is needed for acceptable...
Dept.: AS Duration: 2016 - 2018
Rapid development of information and computer technology as well as availability of multiple, very frequently incompatible, informational sources have caused that decision makers (both humans and devices) are overloaded with information. Their imperfectness (i.e. limited cognitive, informational and evaluational capabilities) is confronted with decision making (DM) of growing complexity...