Controllable gripping mechanics: Modelling, control and experiments
The project strives to expand knowledge of gripping mechanics, based on 3D-printed electroactive materials and pneumatically stretchable structures, origami/kirigami type, for applications in robotics, biomedicine and space. Both concepts are compared with each other. The project focuses on solving open scientific issues in the field of soft robotics such as: whether electroactive materials from 3D […]
Bayesian methods for non-linear blind inverse problems
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 […]
Distributed rational decision making: cooperation aspects
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 structure without […]
Optimal Distributional Design for External Stochastic Knowledge Processing
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 process this […]
Hierarchical models for detection and description of anomalies
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 […]
The systems described by partial differential equations with different types of delays
Systems of partial differential equations (PDE) and ordinary differential equations (ODE) are studied from the point of view of Dynamical systems methods. Many problems from biology, chemistry, mechanics, control, information transmission, economics and other fields are changing in time and so they can be described by different types of Dynamical systems. It is well understood […]
Rationality and Deliberation
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 with incomplete DM preferences and imprecise […]
Bayesian regularization and inference for linear and bilinear models
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 solution. […]
Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI)
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 with incomplete DM preferences and imprecise […]
Distributed dynamic estimation in diffusion networks
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. Being designed for diffusion networks, where each network node can use information provided by neighbor nodes, it will not rely on the existence of a dedicated fusion center, nor […]