Projects

The main goal of the project is to propose a complete methodological approach for 2D/3D image fusion, based on 2D/3D registration. We assume two types of 2D data, fluoroscopy and USG data. The USG data represents a more challenging scenario due to their nature. As 3D data, the CT scans are to be used. Based on this we focus mainly on the hip anatomical area and thoracic area.

The goal of this project is to explore the ongoing impact of the Metaverse, the digital twin of autonomous transport, edge computing, and 6G networks on both the safety and energy efficiency of autonomous vehicles. In particular, we will consider the role of vehicle batteries and task processing, distributed across the edge and the digital twin.

The main objective of this project is to develop new methods for processing medical images while using the class-imbalanced nature of these data. We also aim to propose a new segmentation method for anatomical structures in the abdominal area, such as organs, tumors, and cysts. This task represents a significant challenge due to the complexity of the abdominal area and the diversity of anatomical structures that need to be precisely identified.

The EAM-ECDG project epitomizes an innovative AI approach to significantly advance autonomous mobility by addressing the critical challenge of unpredictable edge cases.

The ABIGAIL project epitomizes a meticulous AI approach to enhance brain glioma diagnosis using advanced integration of MRI techniques and patient health records.

This project endeavors to develop Ainnotator, an innovative medical annotation tool set to improve medical imaging analysis.

The project aims to propose a model for modeling and reconstruction of organs and other anatomical structures. Ultimately, the project aims to enhance the effectiveness and objectivity of cancer diagnosis and treatment through cutting-edge technology and interdisciplinary collaboration.

The main objective of this project is to design and experimentally verify effective solutions for the joint allocation of computing and mobile communication resources to support the distributed processing of complex applications, including those based on neural networks.

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.

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.

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.

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

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.

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.