Projects

The Industry 4.0 National Technology Platform was established under the leadership of the Institute for Computer Science and Control (SZTAKI), Hungarian Academy of Sciences, with the participation of research institutions, companies, universities and professional organizations having premises in Hungary, and with the full support and commitment of the Government of Hungary, and specifically that of the Ministry of National Economy.

The aim of the present study is to develop and evaluate a computer-based methods for automated and improved detection and classification of different colorectal lesions, especially polyps. For this purpose first, pit pattern and vascularization features of up to 1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and subsequently removed for histological analysis. The polyp images are analyzed by a newly developed deep learning computer algorithm.

Recent disaster events, like the 2021 flood in Germany showed clearly, that even the best alert systems and top first responder organisations can not prevent fatalities and serious damage on property without having prepared the citizens how to act and react during disaster situations and crises, understand alerts and follow instructions. B-prepared offers a cost-effective solution for building a culture of disaster preparedness with a multi-actor approach in realistic historical scenarios.

With technology rapidly enhancing and becoming more affordable, commercial and consumer drones have become increasingly popular since the beginning of the 21st century. They pose serious threats to safety (e.g. disturbances for airports and airplanes) and privacy (recording without consent, etc) as well. It is evident that in order to mitigate these issues and increase security it is critical to develop solutions that incorporate the detection and tracking of UAVs with autonomous systems.

The project will further develop and optimise the road defect detection algorithm developed within ARNL. The algorithm uses a self-learning neural network, LiDAR and camera fusion to determine road surface deviations in front of vehicles that are important for vehicle speed. The resulting system warns the driver if a speed reduction is required, or generates an intervention signal to the vehicle control system in the case of a self-driving vehicle.

Our undertaking focuses on enhancing metadata schema capabilities, which includes offering a user-friendly metadata schema authoring and registry tool, leveraging the CEDAR system. Furthermore, we aim to enrich the description of datasets by employing the RO-Crate format.

Under Support Offer #2, our objective is to expand the Dataverse functionalities to encompass RO-Crate import and export. This will utilize the metadata schemas, also known as metadata blocks, available in a Dataverse installation.

The Multinational Capability Development Campaign (MCDC) Artificial Intelligence Supported by Sensor Fusion project was jointly organised by HUN-REN SZTAKI (Hungarian Research Network Computer and Automation Research Institute), the MH Military Modernisation and Transformation Command (MH HTP) and the HVK Capability Development Office.

The Sun is an enigmatic star that produces the most powerful explosive events in our solar system - solar flares and coronal mass ejections. Studying these phenomena can provide a unique opportunity to develop a deeper understanding of fundamental processes on the Sun, and critically, to better forecast space weather.

The activities of the National Laboratory are focused on fundamental and applied research on the autonomous functionality and the control of road vehicles, aircraft and drones, robots and cyber-physical manufacturing systems. The main fundamental research topics are modelling, model reduction and identification, adaptive, robust, distributed and networked systems control. These areas are closely related to the applied research areas, which include environment perception and situation assessment, vehicle dynamics modelling and control, as well as testing and validation.

The aim of the research project

When controlling automated vehicles, it is essential that the control system has information about the state of the ego-vehicle and its environment.