Development 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.

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.

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.

Currently used methods rely exclusively on objective diagnostic tools for monitoring the condition of infants. However, professionals increasingly recognize the importance of individual quality of life for patients, although suitable tools for measuring this do not yet exist. For example, beyond measuring primary vital signs (heart rate, respiratory rate, blood pressure), monitoring the circadian and ultradian rhythms of premature infants in both clinical and home environments remains an unresolved challenge.

The goal of the project is to develop a series of instruments that  autonomously measures the physiological data of phototropic industrial microalgae and environmental parameters essential for automating algae cultivation. These instruments will integrate with algae reactors, processing measurements with timing aligned to the scheduling of production processes. The immediate objective of the project is to develop a prototype demonstrated at Technology Readiness Level 6 in a relevant industrial environment.

PREdictor for HUman-RObot COllaboration (PREHUROCO) utilizes interactive technologies in a new innovative way to create a cobot independent pre-collision approach for reducing these cumulative delays. PREHUROCO technology creates a shared virtual reality workspace over the Internet/Intranet for humans and robots. The human operator will be digitally immersed into the real-time digital twin of the manufacturing cell by XR visualization and/or haptic feedback.

The aim of the R&D project led by Medicor Ltd. is to increase the survival chances and ensure the quality survival of neonates born with very low birth weight (500 grams).

Knowledge graphs have become the most important tools of sharing and connecting research or industry information and contacts.  These graphs enable, in a flexible way, the access, use and publication of data as a distributed system.