Dr. Muhammad Asif Khan, Research Scientist, Qatar Mobility Innovations Center, Qatar University, Qatar
Title: Edge Intelligence for 6G and Massive IoT
Abstract: The next-generation Internet of Things (IoT) is an emerging field that promises to connect billions of devices and sensors, enabling various applications in various domains such as healthcare, agriculture, transportation, and smart cities. With a projection of over 75 billion connected devices by 2025, the number of devices and data generated far exceeds the capacity of traditional cloud computing architectures. Thus, IoT systems face several challenges, including scalability, reliability, and security. Mobile Edge Computing (MEC) is a new computing paradigm that brings data storage and computing resources closer to the end-users (i.e., at the network edge). The proximity between end users and the edge servers enables efficient access to data storage and faster processing, reduces network latency, and improves the Quality of Service (QoS). The integration of MEC with IoT has the potential not only to solve these challenges but also to enable unprecedented novel use cases. This talk aims to provide a comprehensive understanding of the topic by discussing various inter-related concepts, including fundamental concepts of edge computing and edge intelligence, massive IoT and its challenges, the principle of network slicing, and a rigorous understanding of research efforts and the most significant contributions to the state-of-the-art in this area.
Biography: Muhammad Asif Khan is a Research Scientist at Qatar Mobility Innovations Center (QMIC), Doha, Qatar. He was a postdoctoral research fellow at Qatar University. He received a Ph.D. degree in electrical engineering from Qatar University (2020), M.Sc. degree in telecommunication engineering from the University of Engineering and Technology Taxila, Pakistan (2013), and B.Sc. degree in telecommunication engineering from the University of Engineering and Technology Peshawar, Pakistan (2009). He is the recipient of the Postdoctoral Research Award (PDRA) from the Qatar National Research Fund (QNRF) in 2022. He has published over 50 peer-reviewed articles and book chapters. He is a senior member of IEEE, a member of IET, and a Chartered Engineer (CEng) with the Engineering Council (UK). Dr. Khan has delivered several keynote speeches and invited talks at international conferences and workshops. He served at program committees of several international conferences (ICC, GLOBECOM, CCNC, ICCE, IJCNN, GEM, etc.). He is also an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Consumer Electronics (TCE), IEEE Transactions on Technology and Society (TTS), and IEEE Future Directions Technology Policy and Ethics Newsletter.
Dr. Ihor Lubashevsky, Professor, HSE Tikhonov Moscow Institute of Electronics and Mathematics, Moscow, Russia
Title: The First-Person Perspective in Human Cognition as a Novel Paradigm for Cyber-Physical Systems
Abstract This talk explores parallels between human cognition and cyber-physical systems, emphasizing shared processes. Both systems involve three stages: (i) receiving inputs (physical objects sensed by humans or devices), (ii) processing signals (in the brain or artificial neural networks), and (iii) generating outputs (mental or artificial representations with evolving dynamics). Human cognition maps physical reality to mental space, but this mapping is imperfect, as mental images possess distinct properties and uncertainties. Despite differences, shared concepts like space, shape, and movement link physical and mental entities, enabling coherent descriptions of reality.
Predictive Coding and Active Inference provide a framework for cognition, highlighting two components: (i) sensory inputs and (ii) mental representations. Two key processes are bottom-up (sensory inputs integrated into the brain’s Global Neural Workspace) and top-down (mental representations influencing sensory processing). Discrepancies between inputs and predictions adjust mental models, ensuring alignment.
The talk introduces physico-mental and psycho-neural isomorphisms, linking physical object properties to mental images and neural patterns. A mathematical framework, based on space-time clouds, describes sensory signal processing and the dynamic interaction between mental images and physical origins. This approach underpins a novel concept of cyber-physical systems, unifying cyberspace and physical space for efficient system design and control.
Biography Ihor Lubashevsky received his M.S. degree from the Moscow Institute (University) of Physics and Technology in 1978, his Ph.D. in semiconductor physics from the same university in 1980, and his Doctor of Science degree (Habilitation in Physics & Mathematics) in synergetics from Lomonosov Moscow State University in 1993. After graduation, his research focused on self-organization phenomena, including human behavior and cognition. From 1981 to 2010, he worked as a Lead Research Fellow at the Prokhorov General Physics Institute of the Russian Academy of Sciences and as a Professor at the Moscow Technical University of Radio Engineering, Electronics, and Automation. From 2010 to 2021, he was a Professor at the University of Aizu (Aizu-Wakamatsu, Japan). Since 2021, he has been a Professor at the Tikhonov Moscow Institute of Electronics and Mathematics, National Research University Higher School of Economics. He has authored over hundred academic papers and six monographs.
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