Eklas Hossain is a researcher with extensive contributions to power systems, smart grids, and renewable energy. His work spans power electronics, machine learning applications, and biomedical imaging, reflecting interdisciplinary expertise. Research Interests include: Power quality and grid stability in distributed generation systems Advanced DC-DC and inverter designs for renewable energy Optimal PMU placement and network optimization IoT-based load classification and demand response Publication Trends highlight: Innovations in multilevel converters and GaN devices (2023) Hybrid hierarchical networks and PMU analytics (2022) Smart grid optimization, cardiac imaging, and ceiling fan drives (2021)
Carlo Alberto Boano serves as an Associate Professor at Graz University of Technology within the Institute of Technical Informatics, where he leads the Low-power Embedded Networked Systems (LENS) research group. His office is located at Inffeldgasse 16/I, Graz, Austria (Room IE01144), with contact via cboano@tugraz.at or +43 316 873 6413. His research centers on critical embedded systems domains: Dependable & sustainable systems engineering Networked embedded systems architecture Wireless Sensor Networks optimization Cyber-Physical Systems integration Internet of Things infrastructure Boano holds habilitation in Embedded Systems and actively supervises student theses while advancing low-power design methodologies for resource-constrained networked applications through the LENS group.
Prof. Nikolaos Voros is a Professor at the Department of Computer and Electrical Engineering, University of the Peloponnese. He holds a PhD (2001) and Diploma (1996) in Computer and Informatics Engineering from the University of Patras. He leads the Embedded Systems Laboratory , an official Digital Innovation Hub of the European Commission. His research focuses on embedded/cyber-physical systems, including specification techniques, hardware-software co-design, formal refinement, and IoT platforms. He has coordinated over 30 EU-funded projects, including Horizon 2020 initiatives like ARGO and RADIO. He serves as an associate editor for journals like IEEE Transactions on Nanotechnology and has authored multiple books on system-level design and reconfigurable systems. He directs the Master's program 'Smart ICT Services & Infrastructures' and organizes international conferences like ISVLSI and ARC. Education: Diploma in Computer and Informatics Engineering (1996), University of Patras PhD in Computer and Informatics Engineering (2001), University of Patras Research Interests: Embedded systems, cyber-physical systems, hardware-software co-design, formal methods, IoT, and AI-driven assistive technologies. His work emphasizes safety-critical systems, real-time applications, and EU-funded innovations. Grants & Projects: Scientific coordinator of Horizon 2020 projects ARGO and RADIO. Managed FP7 projects ALMA and ARMOR. Over 30 EU-funded initiatives in embedded systems and cyber-physical systems. Labs & Teams: Leads the Embedded Systems and Applications Design Laboratory , a European Commission-recognized hub for innovation in embedded systems and IoT platforms.
Dr. Min Xu is a Professor at the School of Electrical and Data Engineering (SEDE) within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She leads the Visual and Aural Intelligence Laboratory at UTS's Global Big Data Technologies Centre (GBDTC). Her research focuses on multimedia, computer vision, and machine learning, with recent emphasis on machine learning algorithms for data analytics, computer vision applications, and CV/RF integrated sensing for healthcare and smart homes. Education: Doctorate of Philosophy, University of Newcastle, Australia Master of Computing, National University of Singapore Bachelor of Engineering, University of Science and Technology of China Dr. Xu has authored over 250 publications in top-tier venues like IEEE T-PAMI and CVPR, secured over $5M in research funding since 2018, and holds editorial roles at IEEE Transactions on Multimedia and Neurocomputing. Her work bridges theory and application, addressing challenges in multi-modality data fusion, personalized services, and affective computing. Key Contributions: Pioneer in multimedia affective/semantic analysis and multi-modality fusion Developed frameworks for privacy-preserving cross-domain recommendation and WiFi-based human tracking Recipient of ARC Future Fellowship (2018–present) She supervises PhD and Master’s students and teaches courses including Image Processing and Cloud Computing. Current projects emphasize AI-driven healthcare solutions and multimodal AI search engines.
Dr. Conor Muldoon is a Senior Lecturer in Software Engineering at the Department of Computing and Mathematics, Manchester Metropolitan University. His research focuses on distributed artificial intelligence, sensor networks, and multi-agent systems with applications in environmental monitoring and smart home technologies. Previously, he held postdoctoral positions at University College Dublin (UCD) and the University of Oxford, supported by prestigious fellowships including the INSPIRE Marie Curie and Government of Ireland Embark awards. He earned a Ph.D. in Computer Science from UCD, along with a first-class B.Sc. in Computer and Software Engineering and a Postgraduate Certificate in Higher Education Teaching. His research portfolio includes developing water quality forecasting systems, citizen science platforms (e.g., COBWEB), and autonomic energy management solutions. He has published extensively in journals and conferences, addressing topics such as sensor network optimization, incentive mechanisms for crowdsourcing, and adaptive middleware for ambient intelligence. Dr. Muldoon is a Fellow of the Higher Education Academy and supervises PhD candidates in areas like multi-agent systems and environmental informatics. Education: Ph.D. in Computer Science, University College Dublin B.Sc. (Honours) in Computer and Software Engineering, University College Dublin Postgraduate Certificate in Teaching and Learning in Higher Education His work bridges theoretical computer science with real-world applications, emphasizing sustainability and participatory technologies. Key projects include the Dublin Bay Water Quality Modelling and the MERA Data Extraction Toolkit. Awards include recognition for his contributions to agent-based systems and environmental data science.
Karthikeyan Sundaresan is a Professor in the School of Electrical and Computer Engineering at Georgia Tech, holding an Adjunct Professor role in the School of Computer Science. He is a NAI Fellow (2023) , IEEE Fellow (2020) , and ACM Distinguished Scientist (2019) . His research focuses on wireless networking, mobile computing, and embedded systems, emphasizing both algorithmic innovation and practical system development. Education: M.S. (2003) and Ph.D. (2006) in Electrical and Computer Engineering from Georgia Tech. Research Interests: Dr. Sundaresan’s work spans wireless networking protocols, mobile sensing, low-power embedded systems, and infrastructure-free tracking technologies. He leads the MARGA research group, translating theoretical advancements into real-world applications such as first-responder tracking in GPS-denied environments and sustainable supply chain monitoring. Awards: ACM SIGMOBILE Rockstar Award (2016) Technology Commercialization Award (NEC, 2015) 60+ patents and best paper awards at ACM and IEEE conferences Advising & Collaborations: He advises Ph.D. students from both ECE and CS departments. His industry engagements include patent litigation expertise and leading spin-outs of technologies commercialized through Georgia Tech. Labs & Teams: Director of the MARGA (Mobile Advanced Research @ GAtech) group, focusing on cutting-edge wireless systems and mobile computing innovations.
Prof. W.P.M.H. (Maurice) Heemels is a Full Professor at the Eindhoven University of Technology in the Mechanical Engineering department, leading the Control Systems Technology group. His work focuses on hybrid systems , networked control , and model predictive control (MPC) with applications in cyber-physical systems , precision agriculture , and cancer treatment optimization . PhD in Mathematics (1999) and MSc in Mathematics (1995) from TU/e Assistant Professor at TU/e Electrical Engineering (2000-2004) Research Fellow at Embedded Systems Institute (2004-2006) His research addresses resource-aware control strategies to replace traditional time-triggered systems with event-triggered schemes , enabling efficient use of computational and communication resources. Applications span automated driving , precision agriculture , thermal control in lithography , and nuclear fusion reactors . He has developed innovative hybrid integrator-gain systems (HIGS) and reset controllers to overcome limitations of linear time-invariant controllers. Model predictive control is a core methodology, applied to hyperthermia treatments, wafer scanner thermal control, and drone-based farming systems. His 2025 publications emphasize networked control for nuclear fusion, safety-critical MPC in autonomous vehicles, and data-driven control barrier functions for nonlinear systems. Key trends include hybrid systems theory , resource-aware control , and scalable optimization for large-scale applications. 2025: DISC PhD Award for Stefan Heijmans 2019: EECI European PhD Award for Victor Dolk 2017: Best Paper Award @ EBCCSP Conference 2014: Automatica Outstanding Service Award 2000: ASML-award for best fundamental PhD thesis He has supervised 88+ students and secured over 7 MEuro in research funding from NWO, EU, and industry. His Control Systems Technology group operates labs including the Automotive Engineering Science Laboratory and Robotics Laboratory , contributing to the UN Sustainable Development Goals for sustainable cities and climate action through cyber-physical systems.
Dr. Majid Nabi is an Assistant Professor in the Electronic Systems group of the Electrical Engineering Department at Eindhoven University of Technology. His research focuses on efficient and reliable networked embedded systems, wireless sensor networks, and IoT technologies. He received his PhD in Electrical and Computer Engineering from TU/e in 2013. His work addresses challenges in dependable low-power embedded networks through protocol stack optimization, network modeling, and power-efficient baseband processor design. Applications span healthcare, automotive, and environmental monitoring systems. Recent publications include performance evaluations of Bluetooth mesh networks and energy efficiency mechanisms.
Mario Marchese is a Professor at the Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN) at the University of Genoa, Italy. His research spans satellite communications, space networks, IoT, and cybersecurity, with extensive publications in top IEEE journals and conferences. He collaborates frequently with researchers including Fabio Patrone, Franco Davoli, and Igor Bisio on cutting-edge networking technologies. Professor Marchese's research focuses on the integration of satellite networks with terrestrial systems, particularly for 5G and beyond. His work addresses critical challenges in non-terrestrial networks, edge computing in space environments, and cybersecurity for critical infrastructure. Recent projects include developing frameworks for secure energy communities, anomaly detection in industrial control systems, and advanced satellite handover mechanisms using AI techniques. His research bridges theoretical networking concepts with practical implementations for real-world applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating AI with satellite and space networks, particularly focusing on cybersecurity applications for smart grids and industrial systems. His work increasingly incorporates deep learning for satellite resource management and employs software-defined approaches for network security. The research spans theoretical frameworks, practical implementations, and dataset creation for security testing in emerging network environments. Professor Marchese has made significant contributions through numerous publications in prestigious venues including IEEE Access, IEEE Transactions on Aerospace and Electronic Systems, and IEEE Globecom. His collaborative work extends across multiple European research projects focused on next-generation networking technologies. He actively mentors students and researchers, with his work forming the foundation for several PhD theses and research projects at the University of Genoa. His laboratory focuses on experimental validation of networking concepts using software-defined radio platforms and advanced simulation environments for space networking scenarios. Current research directions include federated learning applications for space networks and secure integration of satellite systems with terrestrial 5G infrastructure.
Gian Carlo Cardarilli is a researcher specializing in digital hardware design and machine learning acceleration. His work focuses on FPGA implementations, Residue Number System (RNS) architectures, and reconfigurable computing for applications in wireless communication, edge AI, and fault-tolerant systems. Key collaborations with institutions like IEEE and ACM through publications. Active in translating theoretical algorithms into practical hardware solutions for real-time systems. Research interests include: Optimizing deep learning models for heterogeneous platforms. Developing radiation-hardened memory systems. Creating energy-efficient signal processing architectures. Advancing reconfigurable functional units for embedded processors. His article analyses span fields like Quantum Cellular Automata , Variable Fractional Delay Filters , and RNS-Based Position Estimation , reflecting a trend toward adaptive, low-power, and domain-specific hardware.
Dr. Anna Li is a Lecturer (Assistant Professor) at the School of Computing and Communications, Lancaster University since September 2023. She holds a Ph.D. in Computer Science from Queen Mary University of London and an M.Sc. in Physics from the University of Science and Technology of China. Her research focuses on human activity recognition, signal processing, wireless communication systems, and integrated navigation-communication technologies. She leads the EU-funded Marie Skłodowska-Curie Individual Fellowship project (2024-2026) exploring satellite navigation-communication integration in LEO systems. Key achievements include pioneering work on UWB-based fall detection systems, radar health monitoring, and RIS-aided network optimization. She teaches modules like Computer Networks and Human-Computer Interaction, and has supervised projects funded at Lancaster University and through Chinese Scholarship Council (CSC). Recent publications emphasize trajectory-based gesture recognition, STAR-RIS network designs, and INAC systems. Dr. Li actively contributes to IEEE conferences (GLOBECOM, VTC) as TPC member and reviewer for journals like IEEE IoT Journal. She co-leads the 'Empowering Women in Employment' initiative at Lancaster University (2023-2024).
Ming Yu is a Professor in the Department of Electrical and Computer Engineering at Florida A&M University (FAMU) and Florida State University (FSU) College of Engineering. He holds a Ph.D. from Rutgers University (2002) and a Doctor of Engineering from Tsinghua University (1994). His research focuses on cyber-physical systems (CPS) security, smart grid communications, wireless sensor networks (WSN), and intelligent transportation systems (ITS). He has secured funding from agencies like NSA, ONR, NSF, and Florida DOT for projects such as the FREEDM smart grid initiative and WSN jamming attack detection. Dr. Yu has served on the DOE Smart Grid R&D Roadmap and NSF review panels. He has published extensively in top journals/conferences including IEEE Transactions and GLOBECOM, with a focus on secure communication protocols, network modeling, and fault management. His work bridges theory and practice, addressing critical challenges in CPS resilience and grid reliability. Education: Ph.D., Electrical & Computer Engineering, Rutgers University, 2002 Doctor of Engineering, Tsinghua University, 1994 Key Awards: IEEE Millennium Medal (2000) Senior Member of IEEE Grants & Projects: NSA/ONR-funded CPS security research NSF ERC FREEDM smart grid project Army R&D Center-funded WSN research
Tolga Soyata is an Associate Professor in the Department of Electrical and Computer Engineering at George Mason University's College of Engineering, specializing in cyber-physical systems, digital health, and high-performance computing. Previously, he served as an Associate Professor at SUNY Albany (2016-2019), Senior Lecturer at Johns Hopkins University (2020), and Assistant Research Professor at the University of Rochester (2008-2016). PhD, Electrical and Computer Engineering, University of Rochester (2000) MS, Electrical and Computer Engineering, Johns Hopkins University (1992) BS, Electrical and Communications Engineering, Istanbul Technical University (1988) His research focuses on cyber-physical systems , digital health applications , and FPGA/GPU-based high-performance computing , with significant contributions in medical cyber physical systems, brain-computer interfaces, and energy harvesting for embedded systems. His work bridges hardware design with healthcare applications and smart city infrastructure. Analysis of his recent publications reveals a strong emphasis on medical applications of cyber-physical systems (30%), smart city infrastructure (25%), energy-efficient computing (20%), and security/privacy frameworks (15%). His work consistently integrates hardware innovation with real-world applications, particularly in healthcare monitoring and urban systems. BEST PAPER AWARD (UEMCON18-BCI) PRESIDENTIAL AWARD FOR UNDERGRADUATE RESEARCH BEST PAPER AWARD (HONET-ICT'15) IEEE Senior Member (2016-present) ACM Senior Member (2016-present) As an advisor, he has mentored 12 PhD students (including 2 who completed degrees under his supervision) and over 25 master's/undergraduate researchers, many of whom received awards for their work. His TTIP directorship at GMU focuses on expanding graduate education in computer engineering through innovative recruitment and bridge programs. His research has been supported by industry partnerships with NVIDIA, Xilinx, and MOSIS. He leads research in medical cyber physical systems and energy-harvesting architectures, with lab facilities supporting FPGA development, GPU programming, and medical sensor integration for digital health applications.
Alexandre Oleon serves as Senior Lecturer in Automotive Engineering within the Faculty of Computing, Engineering and Science at the University of South Wales, actively contributing to the Engineering Research and Innovation Group. His office is located in room J433 with direct contact via alexandre.oleon@southwales.ac.uk and +44 1443 483447. His research spans Embedded Systems, Automotive Engineering, and Biomedical Monitoring, with emphasis on real-time wireless applications. Technical expertise includes Embedded C programming, PCB design, EMC compliance, and low-power hardware development for electrocardiogram monitoring and vibration analysis systems. Recent work bridges automotive testing and sports engineering through sensor network innovation. Publications reveal a clear trajectory toward integrating Bluetooth Low Energy with embedded processors for health diagnostics and athlete performance tracking, demonstrating cross-disciplinary applications in biomedical and mechanical contexts. The fingerprint analysis confirms dominant activity in electrocardiography (95%), vibration systems (93%), and Bluetooth wireless protocols (70%). As a PRINCE2-certified Project Management professional from the University of Glamorgan, he applies industry-grade engineering methodologies to academic research. His affiliation with the Engineering Research and Innovation Group facilitates collaborative development of monitoring technologies for automotive and biomedical use cases.
Dr. Muhammad Amjad is a Lecturer in Computer Science at the University of Wolverhampton's School of Engineering, Computing and Mathematical Sciences. He holds a PhD from the University of Essex and is a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE. His expertise spans 6G, uRLLC, wireless communications, and cognitive radio networks. Prior roles include KTP Associate with SEA-KIT International and Tutor at Swansea University. Dr. Amjad's research focuses on advancing wireless communication technologies, including NOMA, 5G/B5G systems, and energy-efficient networks. Notable achievements include a Best Paper Award (2016) and Top Peer Reviewer recognition (2019). His work has been featured in prestigious venues like IEEE Transactions on Vehicular Technology and IEEE Communications Surveys & Tutorials. Key research themes: Non-Orthogonal Multiple Access (NOMA) in finite blocklength regimes Ultra-reliable low-latency communications (uRLLC) Cognitive radio networks and spectrum management Wireless sensor network algorithms Electric vehicle charging optimization Professional contributions include over 20 peer-reviewed publications, numerous conference presentations, and active engagement with industry through KTP collaborations. He currently supervises PhD candidates in wireless communication domains.
