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Dimitrios Prousalis on Muck Rack

Dimitrios Prousalis

As seen in: Physical Review B
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Articles

Forschungsportal der TU Dresden

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By Dimitrios Prousalis, Ioannis Messaris, Khaleelulla K. Nazeer
| tu-dresden.de
As 6G networks enable real-time data acquisition from millions of embedded sensors, the challenge of efficiently processing vast multi-modal datasets becomes paramount. This chapter explores how memcomputing, specifically through Memristor Cellular Neural Networks (M-CellNNs), can address these challenges by diverging from conventional compute-centric models.
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Extreme multi-stability in hyperjerk memristive system with hidden attractors and its adaptive synchronisation scheme

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By Dimitrios Prousalis, Christos Volos, Ioannis N. Stouboulos, Ioannis M. Kyprianidis
| tu-dresden.de
This paper presents a study of the phenomenon of extreme multi-stability in a novel 4D hyperjerk memristive system. The proposed system appertains to the category of dynamical systems with hidden attractors due to infinite equilibrium points. The behaviour of the system is investigated through numerical simulations, by using well-known tools of nonlinear theory, such as phase portrait, bifurcation diagram and Lyapunov exponents.
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Dynamical analysis of novel Memristor Cellular Nonlinear Network cell topologies

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By Chenyang Yu, Technische Universität Dresden, Vasileios Ntinas, Dimitrios Prousalis
| tu-dresden.de
As demand grows for efficient, localized processing in edge and in-sensor computing, novel architectural approaches are essential to meet low-power, high-density requirements. Memristor Cellular Nonlinear Networks (M-CNNs) offer a promising path forward, leveraging the unique properties of memristors for adaptable and scalable computation.
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