Plenary speakers

Péter Baranyi

Short bio

Prof. Dr. Péter Baranyi
Doctor of the Hungarian Academy of Sciences
Eötvös Loránd Research Network

Internet of Digital & Cognitive Realities

The presentation introduced the concept of Internet of Digital & Cognitive Realities. The Digital Reality (DR) is a high-level integration of virtual reality (including augmented reality, virtual and digital simulations and twins), artificial intelligence and 2D digital environments which creates a highly contextual reality for humans in which previously disparate realms of human experience are brought together. DR encompasses not only industrial applications but also helps increase productivity in all corners of life (both physical and digital), thereby enabling the development of new social entities and structures, such as 3D digital universities, 3D businesses, 3D governance, 3D web-based digital entertainment, 3D collaborative sites and marketplaces. The Internet of Digital Reality (IoD) is a set of technologies that enables digital realities to be managed, transmitted and harmonized in networked environments (both public and private), focusing on a higher level of user accessibility, immersiveness and experience with the help of virtual reality and artificial intelligence.

Gábor Szederkényi

Short bio

Gábor Szederkényi is a professor at the Faculty of Information Technology and Bionics of the Pázmány Péter Catholic University, and a part-time scientific advisor at the Systems and Control Laboratory of the Institute for Computer Science and Control (SZTAKI), both in Budapest, Hungary. His background is computer engineering and nonlinear control. His main research interest is the computation-based analysis, identification and control of nonlinear dynamical systems with special emphasis on models with biological and biochemical motivation. He is the author or co-author of three books with international publishers, 80 journal papers and 110 conference papers. He has been the advisor of 7 PhD graduates.

Kinetic modeling for the analysis and control of nonlinear dynamical systems

The use of dynamic models is essential in science and also in everyday life when we want to define or influence the relationships between quantities that vary in space/time. Dynamic nonlinearities cannot be ignored in a large part of practical problems. Within nonlinear systems, so-called nonnegative (positive) models play an important role in such applications where the state variables can naturally be represented in a nonnegative coordinates system with quantities such as pressure, concentration, absolute temperature, or composition. According to [1], "One is tempted to assert that positive systems are the most often encountered systems in almost all areas of science and technology, except electro mechanics ... ". Kinetic systems (also called chemical reaction networks) developed as a mathematical abstraction of dynamical models describing chemical transformations belong to the family of nonnegative systems, and they can be considered as universal descriptors of nonlinear dynamics [2,3]. It is interesting and useful that kinetic models can also be interpreted and applied in a stochastic discrete event context, equivalently, e.g. to Petri nets. In this contribution, two groups of related results are summarized. Firstly, optimization based methods are shown for computing possible reaction graph structures realizing given dynamics which also allows the design of feedback controllers for nonlinear systems [4]. Secondly, the application of kinetic systems' theory will be illustrated for the analysis of compartmental models including (generalized) flow models originally obtained from the spatial discretization of partial differential equations [5], and epidemic models used for data reconstruction and prediction during the COVID-19 pandemics [6].

References:
[1] Farina, L., & Rinaldi, S. (2000). Positive linear systems: theory and applications (Vol. 50). John Wiley & Sons.
[2] Érdi, P., & Tóth, J. (1989). Mathematical models of chemical reactions: theory and applications of deterministic and stochastic models. Manchester University Press.
[3] Feinberg, M. (2019). Foundations of chemical reaction network theory. Springer.
[4] Lipták, G., Szederkényi, G., & Hangos, K. M. (2016). Kinetic feedback design for polynomial systems. Journal of Process Control, 41, 56-66.
[5] G. Szederkényi, B. Ács, Gy. Lipták, and M. A. Vághy. Persistence and stability of a class of kinetic compartmental models. Journal of Mathematical Chemistry, 60:1001-1020, 2022.
[6] P. Polcz, B. Csutak, and G. Szederkényi. Reconstruction of epidemiological data in Hungary using stochastic model predictive control. Applied Sciences, 12(3):1113, 2022.

Arban Uka

Short bio

Dr. Arban Uka received his PhD at the University of Texas at Austin, USA in 2009. Later he spent three years at Leiden University, the Netherlands, where he established a new experimental setup to study the gas dynamics on surfaces. His expertise include laser-enabled pump-probe experiments, vacuum systems and constructing experimental instruments. Since 2012 he has been working at the Department of Computer Engineering of Epoka University, in Tirana, Albania. His current research is focused on point-of-care microscopy, point-of-care diagnosis, computational imaging (image reconstruction algorithms) and image analysis.

Image acquisition for point of care microscopy and image analysis using deep learning

The developments in implanted biomaterials have brought the need for an analysis of large volume of images. The samples (saliva, blood, skin cells etc) from patients are put in contact with the biomaterial inside microfluidic chambers and monitoring over time is employed. The main invasive technique employed is imaging and recent developments allow the use of portable microscopy that would adapt to the system used and not the other way around. The use of portable microscopy on the same sample over time is facilitated by the use of algorithms for registration, focus measure evaluation and image reconstruction. Once the images are acquired there is a need to analyze the images. The analysis includes cell image classification, cell segmentation and cell counting. Results in evaluating biomaterial toxicity level from the cell images will be presented in details.

Pál Varga

Short bio

Pal Varga received his Ph.D. degree from the Budapest University of Technology and Economics, Hungary, where he is currently the Head of Department of Telecommunications and Media Informatics. His main research interests include communication systems, Cyber-Physical Systems and Industrial Internet of Things, network traffic analysis, end-to-end QoS and SLA issues — for which he is keen to apply hardware acceleration and artificial intelligence, machine learning techniques as well. Besides being a member of HTE, he is a senior member of IEEE, where he is active both in the IEEE ComSoc (Communication Society) and IEEE IES (Industrial Electronics Society) communities. He is Editorial Board member of both the Sensors and the Electronics (MDPI) journals, and the Editor-in-Chief of the Infocommunications Journal. Within the Hungarian 5G coalition, he is the leader of the testing environment workgroup. Besides, he is leading the AI workpackage of the EU project AIMS 5.0: "Artificial Intelligence in Manufacturing leading to Sustainability and Industry5.0".