جست‌وجوی استاد و پژوهشگر

Hartmut Paschenمشاهده پروفایل

استاد

• Mechatronics
• Embedded Systems
• Motor Control
• +۴ مورد دیگر

Prof. Dr. Hartmut Paschen is a Professor of Mechatronics at Ruhr West University of Applied Sciences (HS Ruhrwest), where he has been serving since April 2013. He is the Head of the Bachelor's program in Mechatronics and is affiliated with the Institute of Measurement and Sensor Technology. His office contact information includes email at hartmut.paschen@hs-ruhrwest.de and phone number +49 208 88254-394. Prof. Paschen was born in 1966 and studied Electrical Engineering at TH Darmstadt with a focus on Electromechanical Constructions. He completed his Diplom-Ingenieur degree in 1994 and then worked as a scientific researcher at Ruhr-Universität Bochum (Faculty of Mechanical Engineering) in the field of automation technology. He completed his doctorate in 1999 with the dissertation "Vollständig verteiltes SPS-System auf der Basis einer horizontalen Kommunikation in der Sensor/Aktorebene" (Fully distributed PLC system based on horizontal communication in the sensor/actuator level). Prof. Paschen's research interests center around mechatronic systems, with particular focus on: Embedded Mechatronics and control systems Electrical drive technology and motor control Sensor technology and measurement systems Automotive electronics and control units Distributed control systems and horizontal communication in sensor/actuator levels Development of universal test stands for small electric motors His current research projects include the development of a universal test stand for small electric motors, control of brushless and brushed DC motors (including hardware, software, and sensorless methods), and embedded mechatronics applications. Well-equipped laboratories for mechatronics and drive technology are available for these research activities. Prof. Paschen's publication record shows a strong focus on motor control systems, circuit design for automotive applications, and distributed control systems. His work spans from fundamental research in control theory to practical applications in the automotive industry. The evolution of his publications demonstrates a progression from theoretical work on distributed PLC systems to applied research in automotive electronics and motor control. His recent work (2018) shows continued interest in reconfigurable distributed control systems using software agents, indicating an adaptation to modern software engineering approaches in industrial automation. Prof. Paschen's technical competencies include: Hardware and software development of automotive control units Automotive industry development processes and Automotive Spice Drive technology control Microcontroller technology / Embedded Systems Measurement technology (automotive and industrial) Before joining academia, Prof. Paschen spent 14 years in the automotive industry working on series development of control units for mechatronic systems, eventually becoming head of the "Electronics for Adjustment Systems" department. This industry experience has strongly influenced his practical approach to mechatronics education and research. At Ruhr West University of Applied Sciences, Prof. Paschen teaches courses including: Introduction to Mechatronics Embedded Systems Mechatronics Project Work Electrical Drive Technology Measurement Technology Electrical Engineering

• measurement systems and techniques
• vehicle and mechatronic system diagnostics
• microcontroller programming
• +۳ مورد دیگر

Mariusz Graba serves as a Lecturer at the Department of Mechatronics of Vehicles and Machines, Opole University of Technology, where he maintains active teaching responsibilities including student consultations and course scheduling. His research focuses on interdisciplinary engineering domains, particularly: Advanced measurement systems and diagnostic techniques for vehicles Embedded systems development through microcontroller programming Real-time data communication via automotive data buses Mathematical modeling of dynamic control objects Algorithmic synthesis for precision control systems His work bridges theoretical control theory with practical mechatronic applications in automotive engineering.

• Circuit Reliability
• System-on-Chip (SoC) Testing
• Embedded Systems
• +۳ مورد دیگر

Paolo Bernardi is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) , Politecnico di Torino. His academic career spans over 20 years with continuous contributions to circuit reliability and SoC testing. Research focuses on System-on-Chip Testing , Embedded Systems , and Automotive Electronics Key projects: ARBoard (Augmented Reality for PCB testing), TC4xx NVM Test Strategies, and multiple commercial collaborations with automotive industry leaders His work bridges Computer Architecture and Industrial Engineering , with significant contributions to Test Automation and Reliability Engineering . Recent publications analyze scan test cost models and logic diagnosis techniques for automotive SoCs. Scientific recognitions include: EDAA Outstanding Dissertation Award (2006) Best Paper Awards at DATE (2006) and DDECS (2016) IEEE Fellowship (2020-) As a PhD supervisor, he guides students in Automotive Reliability and Embedded Testing projects. His research involves collaborative efforts with institutions like CNRS and Université de Montpellier through the LIA LAFISI laboratory.

Subhasish Mitraمشاهده پروفایل

استاد

• Nano-electronic systems
• Electronic design automation
• Design and test of electronic systems
• +۶ مورد دیگر

Subhasish Mitra is Professor of Electrical Engineering and Computer Science at Stanford University. He directs the Stanford Robust Systems Group , leads the Computation focus area of the Stanford SystemX Alliance , and is a faculty member at the Wu Tsai Neurosciences Institute . He also holds the Carnot Chair of Excellence in NanoSystems at CEA-LETI in Grenoble, France. His research spans Nano-electronic systems Robust computing Electronic design automation Neurosciences 3D integrated circuits Key publications focus on revolutionary NanoSystems like the first carbon nanotube computer and 3D NanoSystems with computation immersed in data storage . These works combine Nano-electronics , Machine Learning Hardware , and Energy-Efficient Computing , achieving global recognition including Nature Cover Features and NSF Research Highlights to the US Congress. Scientific awards include: SIA-SRC University Researcher Award (2021) Humboldt Research Award (2019) ACM SIGDA/IEEE CEDA Technical Impact Award Presidential Early Career Award (PECASE, 2008) Intel Achievement Award (2004) His X-Compact and QED methodologies for test compaction and system validation are widely adopted in industry (Synopsys, Cadence) and have generated billions in cost savings . His group's work on soft error resilience and circuit failure prediction is standard practice in sub-10nm chip design.

Timo Kehrerمشاهده پروفایل

استاد

• Software Engineering
• Model-Based Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Prof. Timo Kehrer is a Professor and Head of the Software Engineering Group (SEG) at the Institute of Computer Science, University of Bern. He also serves as Deputy Director of Studies, overseeing academic programs in software engineering. His research focuses on variability modeling, model-based systems engineering, simulation-based testing of autonomous systems, and empirical studies in software development practices. Key areas include software product lines, cyber-physical systems, and the application of formal methods in industrial contexts. Notable contributions include work on Community-driven variability management in open-source software, Automated testing tools like ScoutSL and TEASER , Simulation-based frameworks for self-driving cars (e.g., Sensodat datasets), and Empirical analyses of GitHub Actions workflows and merge conflict resolution. His research bridges theory and practice, addressing challenges in software sustainability, AI-driven development, and the integration of formal verification tools like TLA+. He actively participates in conferences such as VaMoS (Variability Modelling) and contributes to open-source projects in model-driven engineering. Prof. Kehrer collaborates with industry partners to advance tools for model repositories, vulnerability detection (e.g., VUDENC ), and semantic analysis of software changes. His work emphasizes reproducibility, tool support for developers, and educational initiatives to modernize software engineering curricula.

• Real-Time Systems
• Embedded Systems
• Cyber-Physical Systems (CPS)
• +۴ مورد دیگر

Soheil Samii is an Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University. His research focuses on real-time systems, cyber-physical systems (CPS), and embedded systems, with particular emphasis on automotive applications, fault-tolerance, and networking. He leads the Adaptive Software for the Heterogeneous Edge-Cloud-Continuum (ASTECC) project, funded by the Swedish Foundation for Strategic Research (SSF). His work integrates software engineering, real-time scheduling, and safety-critical systems design. Samii’s research interests include resource optimization for 5G networks, time-sensitive networking (TSN), and automotive cybersecurity. He has contributed to frameworks for fault-tolerant embedded systems and cloud-assisted control systems. His publications often address challenges in distributed embedded control, vehicular networks, and reliability engineering. Recent articles highlight advancements in multi-traffic resource allocation for real-time applications and automotive CPS security. He collaborates with industry and academia on projects like zone-based architectures for trailering systems and perception-aware autonomous vehicle controllers. Samii supervises multiple PhD students and contributes to the Software and Systems (SAS) division at IDA, which emphasizes software engineering and computer systems research. His work bridges theoretical foundations and practical industrial applications in automotive and embedded computing domains.

• Embedded Systems
• Real-Time Systems
• Cyber-Physical Systems
• +۳ مورد دیگر

Marco Caccamo is an Adjunct Professor at the University of Illinois at Urbana-Champaign (UIUC) and holds a courtesy appointment in the Coordinated Science Lab and Department of Aerospace Engineering. He earned a Ph.D. in Computer Engineering from Scuola Superiore Sant'Anna (Italy) in 2002. His research focuses on embedded systems, real-time computing, and cyber-physical systems, with applications in avionics, automotive systems, and UAVs. He is a recipient of the IEEE Fellow (2018) and Alexander von Humboldt Professorship (2018). He has chaired major conferences such as RTSS and RTAS and served as General Chair of CPSWeek 2011. His work includes contributions to real-time scheduling, multicore resource management, and reinforcement learning for autonomous systems. Current research highlights include sandboxing AI-based controllers and 6D pose estimation for robotic systems. He leads the Real-Time and Embedded System Laboratory and collaborates with industries to develop innovative software architectures for embedded systems.

• Model Predictive Control
• Autonomous Systems
• Robotics
• +۵ مورد دیگر

Petr Listov is a former researcher at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Engineering. His work focuses on advanced control systems, particularly Model Predictive Control (MPC) for autonomous systems, robotics, and mechatronics. Listov contributed to developing the PolyMPC software framework, enhancing real-time embedded optimization for applications like rocket control and autonomous driving. His research emphasizes nonlinear control, stochastic systems, and optimization under uncertainty. Education: Completed a doctoral thesis titled Real-Time Nonlinear Model Predictive Control for Fast Mechatronic Systems under supervision at EPFL. Research Interests: Development of MPC algorithms for high-speed systems Applications in autonomous vehicles and robotics Stochastic control and uncertainty quantification Optimization-based control strategies Key Contributions: His publications address real-time MPC implementations, thrust vector control for rockets, and stochastic trajectory planning. Experimental validations include indoor/outdoor rocket testing and automotive hardware-in-the-loop systems. Labs/Teams: Collaborated with the Laboratoire d’Automatique (LA) and contributed to the PolyMPC open-source library.

Todd R. Andelمشاهده پروفایل

استاد

• Cyber Security
• Network Security
• Embedded Systems Protection
• +۵ مورد دیگر

Dr. Todd R. Andel serves as Dean of the School of Computing and Professor of Computer Science at the University of South Alabama. He previously held roles including Department Chair (2019–2023) and Associate Professor (2012–2015). His expertise spans cyber security, embedded systems protection, and network security protocols. Education: Ph.D. in Computer Science from Florida State University (2007), M.S.C.E. in Computer Engineering from Air Force Institute of Technology (2002), and B.S.C.E. in Computer Engineering from University of Central Florida (1998). Research focuses on side-channel analysis/defense, embedded systems security, and active cyber defenses. He leads the System Protection and Exploitation Research (SPERG) group, emphasizing malware analysis, program obfuscation, and formal methods in reverse engineering. The CFITS Center explores forensic technology and information security. Publications span 20+ years with notable work on MTD strategies, SCADA security, and IoT threats. No awards listed but maintains active research labs and educational toolkits for undergraduate research. Teaching includes courses on network security, computer architecture, and distributed systems. Advising details and grants are not explicitly listed.

Fengjun Yanمشاهده پروفایل

دانشیار

• Control Systems
• Automotive Applications
• Hybrid Vehicles
• +۵ مورد دیگر

Dr. Fengjun Yan is an Associate Professor in the Department of Mechanical Engineering at McMaster University. His research focuses on modeling, estimation, and control of dynamic systems, particularly in automotive applications such as internal combustion engines, hybrid electric vehicles, and powertrains. He also explores robotics, mechatronics, and thermal-fluid sciences. Dr. Yan teaches courses like Robotics (MECH ENG 4K03/6K03), Internal Combustion Engines (MECH ENG 4Y03), and Advanced Control on Internal Combustion Engines (MECH ENG 755). He holds a B.Sc. from Harbin Institute of Technology (2004), M.Sc. from Tsinghua University (2006), and Ph.D. from Ohio State University (2012). His research interests span advanced control strategies for automotive systems, battery management, data-driven modeling, and autonomous vehicle technologies. Notable contributions include work on diesel engine after-treatment systems, lithium-ion battery health estimation, and path-following control for autonomous vehicles. Dr. Yan actively mentors graduate students and has published extensively in top-tier journals and conferences. His articles highlight advancements in control systems for hybrid vehicles, thermal management in data centers, and machine learning applications for traffic prediction. While no specific awards are listed, his prolific research output underscores his expertise in mechanical and energy systems. He maintains a lab focused on practical applications of robotics and mechatronics, integrating theory with real-world engineering challenges.

• Machine Learning
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

DUPUIS Yohan is a Research Director at CESI, leading the Engineering and Numerical Tools research team. His work focuses on perception and mapping for cyber-physical systems, intelligent robotics, and transportation systems. He holds an HDR from the University of Rouen Normandy (2019) and a PhD in Electrical Engineering (2012). He coordinates major national projects including the France 2030 CAIRE project (AI sector), ASTRID Robotics SCOPES, and Battery School initiatives. Education includes an Engineering Diploma from ESIGELEC (2009), MSc in Electrical Engineering from Union Graduate College (USA), and a PhD focused on omnidirectional vision for biometrics. His research spans semantic mapping, autonomous systems, and sensor fusion with applications in smart cities, industrial automation, and maritime networks. Key research interests include: Multi-agent cooperative perception SLAM algorithms for low-texture environments Autonomous vehicle localization Human-robot interaction in industrial contexts LiDAR and radar sensor systems He advises 8 active PhD students working on topics like human-robot affordance modeling, object pose estimation, and mobility prediction in smart cities. Notable contributions include the SynWoodScape autonomous driving dataset and innovative digital twin methodologies for industrial workstations. Led projects include: France 2030 CAIRE project (AI sector coordination) ASTRID Robotics SCOPES (2022-2025) Battery School initiative (2022-2027) ConfluenceS Excellence program (2024-2032) His publications span over 50 peer-reviewed articles, with recent focus on multimodal perception systems and AI-driven urban mobility solutions.

Philippe Xuمشاهده پروفایل

پژوهشگر

• Autonomous Systems
• Computer Vision
• Robotics
• +۳ مورد دیگر

Philippe Xu is a Researcher at the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS) at ENSTA Paris. His work focuses on autonomous systems, sensor fusion, and robotics, with a strong emphasis on localization, perception, and decision-making for autonomous vehicles. He has contributed to projects involving LiDAR integration, HD map-based navigation, and machine learning for object detection and semantic segmentation. Key research areas include cooperative localization in vehicle platoons, map-aided annotation systems, and fusion of evidential classifiers for improved perception. His work often addresses challenges in safety-critical autonomous systems, such as integrity management and real-time data processing. Recent publications (2020–2024) highlight advancements in LiDAR-based obstacle detection, lane-level context analysis, and the application of Dempster-Shafer theory in neural networks.

Daniel Sundmarkمشاهده پروفایل

استاد

• Software Testing
• Embedded Systems
• Fault Management
• +۷ مورد دیگر

Professor Daniel Sundmark is affiliated with Mälardalen University's School of Innovation, Design and Engineering, specifically within the Division of Computer Science and Software Engineering. His research focuses on software testing methodologies, embedded systems, fault management, and resilience engineering. He has published extensively on topics such as API testing, concurrency bugs, and safety-critical systems. Key research areas include: Model-based testing frameworks Automated test generation for embedded systems Resilience and fault recovery in networking systems Requirements engineering and similarity analysis His work spans both academic contributions and industrial collaborations, addressing challenges in automotive, railway, and telecommunications domains. Recent publications emphasize resilient system design, real-time systems, and automated testing strategies. Notable projects include the Alcea architecture analysis method and studies on fault management frameworks. He also explores integration testing challenges and the role of test automation in agile development. His research outputs demonstrate a strong focus on bridging theory and practice, with over 50 peer-reviewed articles since 2001 covering topics from concurrency bug detection to TDD optimization.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۶ مورد دیگر

E. Alonso is a Professor in the Department of Computer Science at City, University of London, with extensive contributions to artificial intelligence, machine learning, and cognitive science. His work spans theoretical foundations of AI, practical applications in power systems, automotive diagnostics, and ethical considerations in generative AI. Faculty member at City, University of London since at least 2010 Active researcher with publications extending through 2025 Collaborates extensively with researchers in AI, power systems, and cognitive science Regular presenter at major AI conferences including IJCAI, AAAI, and IEEE conferences Professor Alonso's research focuses on the intersection of theoretical AI and practical applications. His work in neural networks spans reinforcement learning, time series forecasting, and multi-agent systems. He has made significant contributions to understanding the mathematical foundations of AI systems, particularly in agent representations and learning mechanisms. His recent work increasingly addresses the ethical and legal implications of AI, particularly concerning copyright issues in generative AI training and moral value alignment in democratic AI systems. Analysis of Professor Alonso's recent publications (2023-2025) reveals a strong trend toward practical AI applications with theoretical grounding. His work spans multiple domains including power systems optimization using graph neural networks, automotive diagnostics with specialized neural architectures, and creative AI approaches to generalization. A notable pattern is his ability to bridge theoretical AI concepts with real-world engineering problems, particularly in energy systems and automotive applications. His research increasingly incorporates ethical considerations, reflecting the growing importance of responsible AI development. Professor Alonso has supervised numerous students who have gone on to publish with him in top venues. His collaborative approach is evident in the diverse range of co-authors across different disciplines. While specific grant information isn't detailed in the provided materials, his sustained publication record across multiple domains suggests successful funding acquisition for his research programs. His work appears connected to the Centre for Computational and Animal Learning Research, suggesting involvement in interdisciplinary teams exploring the connections between biological learning systems and artificial intelligence. Recent collaborations with researchers in medical imaging and radiotherapy implementation further demonstrate the breadth of his research impact across different application domains.

Yee Ling Booمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Image Processing
• Information Systems
• +۵ مورد دیگر

Dr. Yee Ling Boo is a Senior Lecturer in the Department of Accounting, Info Sys & Supply Chain at RMIT University in Australia. Her research focuses on Artificial Intelligence, Image Processing, Information Systems, Finance, and Business Analytics . She actively supervises Masters and PhD students in projects related to Large Language Models for Policy Analysis, Machine Learning in Social Services, and AI Readiness . Her teaching interests include Data Mining, Big Data, Business Analytics, and Cognitive Computing with applications across Business, Health, and Education . Recent publications highlight her work in Automated vehicle damage detection using AI Configurational approaches to cloud service provider selection Multimodal frameworks for car damage localization Decision-making agility through embedded analytics Community detection in multi-layer dark networks She has developed innovative methods for unsupervised expertise estimation in crowdsourcing , feature selection in imbalanced data , and Shari'ah-compliant stock analysis , with applications spanning fraud detection, spammer analysis, and educational technology . Her work bridges theoretical advancements with practical implementations for industry partners like Carsales and Baptcare .