Projects

Advanced feature selection methods for high dimensional data

Feature selection (FS) has become a significant part of the data processing pipeline.FS techniques reduce the original feature space without transformation, so that the original features are preserved and cogent interpretation is possible

Intelligent Management of 5G Mobile Network based on the Comprehensive Learning with deep learning

The basic goals within the project are the design of new heuristic algorithms based on the application of machine and deep learning to optimize the performance of the 5G network, verification of these algorithms in realistic models and studies as well as experimentally validation of the proposed methods on functional USRP devices.

Green heterogeneous network topologies with support of UAV mobile stations for 5G+ wireless communication systems

The project goal is the design of the novel original methods for the energy efficient heterogeneous (HetNet) topologies to be considered in 5G+ wireless communication standard.

Computer-Aided Decision Support System for Hepatic Encephalopathy

The goal of this project is to provide deep speech and handwriting analysis and investigate whether speech and handwriting can be used for diagnosis and monitoring of hepatic encephalopathy and whether there exists relationship between speech, handwriting and other biomarkers in hepatic encephalopathy

Intelligent Dynamic Spectrum Access Management for the Future Cognitive Communication Networks

The spectrum utilization efficiency is one of the most challenging issues related to the 5th generation mobile networks. The main goal of the project is to jointly optimize the functionality of the spectrum sensing and sharing parameters and investigate the impact of the optimization on the spectrum trading activities in cognitive radio network.

Agent based modeling of the spectrum distribution in the cognitive radio networks

The goal of the project is the design of the novel and efficient models of the spectrum sharing and trading mechanisms in the cognitive radio networks. The special emphasis is put on the exploiting of the agent-based models incorporating strong interdisciplinary character.