Scientific Projects

1 Nov 2016– 31 Oct 2020

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.

1 Jan 2018– 31 Dec 2019

The goal of the project is to reduce the death rate of neonatal and prenatal infants, and increase their chances of living via developing vision based non-contact body devices for monitoring physiological signals like pulse rate, breath rate, blood oxygenation, activity, and body temperature using remote photoplethysmographic methods. 

 

1 Dec 2017– 30 Nov 2019

The project aims to process the data of novel 3D sensors (e.g. Microsoft Kinect, Lidar, MRI, CT) available in a wide range of application fields and to fuse them with 2D image modalities to build saliency models, which are able to automatically and efficiently emphasize visually dominant regions. Such models not only tighten the region of interest for further image processing steps, but facilitate and increase the efficiency of segmentation in different application fields with available 3D sensor data, e.g.

1 Dec 2017– 30 Nov 2019

Numerous automotive and small aircraft companies have announced promising new applications in the field of autonomous vehicles. Alongside self-driving cars, in the near future small-size micro aerial vehicles could be used for goods delivery (Amazon Prime Air, DHL, Alibaba, Matternet, Swiss Post), in healthcare (Matternet, Flirtey, Wingtra, RedLine), to carry out various inspection and surveillance tasks (SenseFly, Skycatch), or can be deployed at accidents as remote-controlled first aid/responder devices (Drone Aventures, Microdrones).

1 Nov 2017– 10 Mar 2018
Representative images of cameras with different modalities
Representative images of cameras with different modalities

The aim of the project is to develop an image fusion and processing method that uses images of cameras with different modalities to track various objects, taking into account the needs of border surveillance end-users.

2 Oct 2017– 30 Sep 2019

Various key aspect of machine-based environment interpretation are the automatic detection and recognition of objects, obstacle avoidance in navigation, and object tracking in certain applications. Integrating visual sensors, such as video cameras, with sensors providing direct 3D spatial measurements, such as Lidars may offer various benefits (high spatial and temporal resolution, distance, color or illumination invariance). However, fusing the different data modalities often implies sensor specific unique challenges.

1 Oct 2017– 31 Mar 2021

The mission of CloudiFacturing is to optimize production processes and producibility using Cloud/HPC-based modelling and simulation, leveraging online factory data and advanced data analytics, thus contributing to the competitiveness and resource efficiency of manufacturing SMEs, ultimately fostering the vision of Factories 4.0 and the circular economy. CloudiFacturing will empower over 60 European organizations (many of them being manufacturing SMEs) and will support about 20 cross-national application experiments that will primarily be selected via two Open Calls.

1 Sep 2017– 31 Aug 2019

New materials, novel material technology: The goal of the project is to develop a new environment friendly packaging and labeling material and technology. Nowadays, the labels are stuck with various adhesives, which generates extra chemical washing steps the recycling phase of these bottles. The new technology - developed in the framework of this project - targets to use thermal bonding of the labels without any added glue. Moreover the labels will be printed on the same material sheets as the bottle, therefore the label and the bottle can be recycled together. 

1 Sep 2017– 31 Aug 2019

We can efficiently use digital holographic microscopy for monitoring of sparse samples. From a recorded hologram the whole illuminated volume can be reconstructed using numerical simulation of wave propagation. From a single recorded hologram we can reconstruct several objects at different depths within the volume. Thus we can avoid the small depth of field constraint of conventional microscopes, and even 200 times larger volume can be observed from a single exposure.

1 Apr 2017– 31 Mar 2024

The main, overall objective of the project is to establish the Centre of Excellence in Production Informatics and Control (EPIC CoE) as a leading, internationally acknowledged focus point in the field of cyber-physical production systems. The main goals of the EPIC CoE are, one the one hand, to upgrade this scientific centre of excellence, and on the other hand, to strengthen the ability of SZTAKI and the two faculties of BME to transfer the research results to the industry with the support of the participating FhG institutions, with other words to enhance the applied resea