جستجوی استاد راهنمای دانشگاهی

Marko T. Milojkovicمشاهده پروفایل

استاد

• Control Systems
• Signal Processing
• Neural Networks
• +۴ مورد دیگر

Marko T. Milojkovic is a full professor at the Faculty of Electronics, University of Nis, leading the Department of Automation since 2022. He holds a PhD in Systems Management (2012), Master's in Automation (2008), and a Bachelor's in Computer Engineering & Informatics (2003), all from the same institution. His research focuses on adaptive control systems, neural networks, and dynamical systems modeling, with 27 papers in impact-factor journals. He currently heads the Laboratory for Modeling, Simulation and Systems Management and participates in 2 national and 2 international projects. Education: PhD: Systems Management (2012) MSc: Automation (2008) BSc: Computer Engineering & Informatics (2003) Research interests include neuro-fuzzy systems, MIMO system optimization, and endocrine neural networks applied to adaptive control. His publications demonstrate expertise in quasi-orthogonal filters, sliding mode control, and time-series forecasting. No scientific awards are explicitly mentioned, but his extensive project participation highlights active collaboration in control systems and automation. Prof. Milojkovic's work bridges theoretical modeling and practical applications, with recent emphasis on intelligent control systems and nonlinear dynamics. His laboratory facilitates interdisciplinary projects addressing complex system management challenges.

Sigurd Skogestadمشاهده پروفایل

استاد

• Process Control
• Systems Engineering
• Optimization
• +۲ مورد دیگر

Sigurd Skogestad is Professor in the Department of Chemical Engineering at the Norwegian University of Science and Technology (NTNU). He serves as Program Leader for the 5-year Master program in Chemical and Biochemical Engineering. His research develops simple yet rigorous methods to solve engineering problems, focusing on self-optimizing control structures that enable near-optimal operation under varying conditions. His approach moves economic optimization from slow real-time optimization to faster control layers. Key research areas: Self-optimizing control structures Process systems engineering Multivariable feedback control Plantwide process control Distillation modeling and control He has received significant recognition including the AIChE Computing in Chemical Engineering Award and IFAC Fellow status. He leads research groups in Process Control and contributes to the SUBPRO center on subsea production.

Mohammad Hassan Khoobanمشاهده پروفایل

دانشیار

• Power Systems
• Renewable Energy
• Control Systems
• +۴ مورد دیگر

Mohammad Hassan Khooban is an Associate Professor at the Department of Electrical and Computer Engineering, specializing in Electrical Energy Technology at Aarhus University . His research emphasizes advanced control strategies for power systems, renewable energy integration, and smart grid technology. While specific educational background details are not explicitly stated, his work demonstrates expertise in power electronics, control systems, and machine learning applications. His projects include pioneering initiatives like QuantumEcoCircuits (2024–2027) and Smart Synergy Mechanism (2023–2025), focusing on sustainable energy systems, electric vehicle charging dynamics, and resilient grid operations. His research interests span adaptive control methodologies, grid resilience under cyber threats, and the optimization of energy storage systems. He has contributed to peer-reviewed journals such as IET Renewable Power Generation and IEEE Transactions on Smart Grid , exploring topics ranging from PID controllers to fractional-order sliding mode control for unmanned aerial vehicles. No scientific awards are listed, but his work is supported through grants and collaborative projects. He is actively involved in lab initiatives related to power systems and renewable energy technologies.

• Automation
• Robotics
• Control Systems
• +۵ مورد دیگر

Prof. Igor Krčmar is a full professor at the Faculty of Electrical Engineering of the University of Banja Luka, specializing in Automation and Robotics . He holds a position in the Department of Automation and teaches courses at both undergraduate and graduate levels. His research focuses on control systems, neural networks, electrical drives, and adaptive control methodologies. He has authored several textbooks including Upravljanje u realnom vremenu (Real-Time Control) and Senzori i Aktuatori (Sensors and Actuators). His recent work emphasizes sensorless motor control, PI controller optimization for industrial systems, and machine learning applications in occupancy estimation. Over 25 years, he has published extensively in journals like IEEE Transactions on Industrial Electronics and conferences such as INFOTEH-JAHORINA and IcETRAN. His work bridges theoretical advancements with practical implementations in industrial automation and energy efficiency. Key contributions include: Development of discrete rotor flux estimators for high-speed drives Adaptive neural control algorithms for nonlinear systems Integration of fuzzy logic with hydrodynamic process control Optimization of PMSM drives for energy efficiency He collaborates internationally on EU-funded projects and serves on editorial boards of electrical engineering journals.

Nathir Rawashdehمشاهده پروفایل

استادیار

• Image Processing
• Autonomous Vehicles Perception
• Mobile Robot Applications
• +۷ مورد دیگر

Nathir Rawashdeh is an Assistant Professor in the Department of Applied Computing at Michigan Technological University , with an affiliated appointment in Electrical and Computer Engineering . He is a Senior Member of the IEEE and a member of the Institute of Computing and Cybersystems (ICC) and Great Lakes Research Center . Education: Ph.D., Electrical Engineering, University of Kentucky, 2007 MS, Electrical and Computer Engineering, University of Massachusetts, Amherst, 2003 BS, Electrical Engineering, University of Kentucky, 2000 Dr. Rawashdeh's research focuses on unmanned vehicle perception , image analysis , control systems , and mechatronics , with applications in autonomous driving, winter weather adaptation, and industrial automation. His work includes sensor fusion, deep learning, and AI-enhanced manufacturing solutions. Recent publications highlight advancements in winter weather autonomous driving , UV disinfection robotics , and AI-driven industrial inspection systems . His research spans mechatronics curriculum development, industry 4.0 integration, and cross-cultural educational initiatives. Scientific Awards: Senior Member of the IEEE Dr. Rawashdeh has secured over $2 million in funding from organizations including the NSF , Ford Motor Co. , NIST , and the European Commission . His grants support projects like GPU clusters for research, winter weather autonomous driving standards, and UV sterilization robotics. He leads the Mobile Robotics Lab at Michigan Tech, focusing on collaboration and innovation in autonomous systems and mechatronics research.

• Control Engineering
• Telecommunications
• Cloud Computing
• +۳ مورد دیگر

Johan Eker is a Professor at the Department of Automatic Control at Lund University and an Adjunct Professor at ELLIIT: the Linköping-Lund initiative on IT and mobile communication . He is also a member of the LTH Profile Area: AI and Digitalization and LU Profile Area: Natural and Artificial Cognition . His research focuses on control engineering, telecommunications, cloud computing, real-time systems, IoT, and anomaly detection. He actively contributes to UN Sustainable Development Goals through his work. He has received notable awards including the Best paper runner-up award at IEEE CloudNet 2023 , Best Paper Award at RTCSA 2004 , and Best Student Paper Award at RTCSA 1999 . Key projects include: AORTA: Advanced Offloading for Real-Time Applications (2023–2025) ICS: Industrial Cloud Sandbox (2019) AutoDC: Autonomous datacenter for long-term deployment (2018–2021) He has organized workshops such as the Real-Time Cloud Workshop and serves on the advisory board for Internet of Things and People (IoTaP) .

Rabah Aoufiمشاهده پروفایل

مدرس ارشد

• Digital Signal Processing
• Microcontroller Systems
• Control Systems
• +۳ مورد دیگر

Rabah Aoufi is a Senior Lecturer in the Department of Engineering Technology & Industrial Distribution at the College of Engineering, Texas A&M University. His primary affiliation includes the Electronic Systems Engineering Technology program. He can be reached at raoufi@tamu.edu and is located in FERM 304AA. His work focuses on digital signal processing, microcontroller systems, control systems, and engineering entrepreneurship. Research interests span embedded systems design, real-time data processing, and innovative educational projects. His publications reflect expertise in both foundational engineering principles (e.g., signal processing fundamentals) and applied technologies (e.g., microcontroller programming and big data frameworks). His articles demonstrate a career-long engagement with evolving technologies—from foundational 1980s multiprocessor architectures to modern big data systems. No scientific awards or grants are explicitly mentioned in the provided materials.

Palanichamy Naveenمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. Palanichamy Naveen is an Assistant Professor in the Department of Electronics and Communication Engineering at Dr. N.G.P. Institute of Technology, India. His research lies at the intersection of computer vision, machine learning, and signal processing, with applications in healthcare, control systems, and academic policy. He actively collaborates with researchers globally, including institutions in the Czech Republic, and has contributed over 60 publications to academic literature. His research interests span Computer Vision , Deep Learning , Image and Video Processing , Signal Processing , and Optimization Algorithms . He also explores interdisciplinary topics such as academic performance appraisal and sports analytics. His work reflects a strong commitment to both technical innovation and institutional improvement in engineering education. The recent trend in his publications demonstrates a dual focus: applying AI to solve real-world problems in medical imaging and industrial control, while also contributing to academic governance through models for faculty evaluation and research incentivization. This blend of technical and administrative research highlights his broad impact in engineering academia. Scientific Contributions: Developed lightweight transformer models for retinal disease detection from OCT scans Proposed novel bio-inspired optimization algorithms for PID controller tuning Designed frameworks for faculty performance appraisal and research contribution assessment Created a relative grading system for ODI cricket player rankings Dr. Naveen is actively involved in academic discourse and knowledge dissemination through preprint platforms and peer-reviewed journals. While no formal advising or grant information is available in the text, his extensive publication record and international collaborations suggest active mentorship and research leadership. He is also engaged with academic networks like SciProfiles and Multidisciplinary Digital Publishing Institute, contributing to the broader scholarly ecosystem.

Furat Al-Obaidyمشاهده پروفایل

مدرس

• Machine Learning
• Intelligent Systems
• VLSI Circuits
• +۷ مورد دیگر

Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)

• Electrical Machines and Energy Conversion
• Control Theory and Applications
• Artificial Intelligence
• +۳ مورد دیگر

Hilmi AYGÜN is an Assistant Professor in the Department of Mechatronics Engineering at Karabük University's Faculty of Engineering and Natural Sciences, where he has been serving since 2019. His academic career began as a Research Assistant in the Electrical and Electronics Engineering Department from 2009-2019 before transitioning to his current position in Mechatronics Engineering. He also serves as the Erasmus Coordinator for both the Mechatronics Engineering Department and the university since 2021. Education: PhD in Electrical and Electronics Engineering (2011-2019), Karabük University, Institute of Science MSc in Electrical and Electronics Engineering (2010-2011), Karabük University, Institute of Science BSc in Electrical and Electronics Engineering (2003-2007), Kırıkkale University, Faculty of Engineering Hilmi AYGÜN's research primarily focuses on electrical machines and energy conversion systems, with particular emphasis on motor control algorithms for electric vehicle applications. His work integrates control theory with artificial intelligence techniques, especially optimization algorithms like Particle Swarm Optimization (PSO) and the Yusufcuk Algorithm. He has made significant contributions to field-oriented control of induction motors, DC motor control systems, and wind energy applications. His publication record shows a clear trajectory toward increasingly sophisticated control systems for electromechanical applications, with recent work focusing on metaheuristic methods for motor control, optimization of wind energy systems, and advanced DC motor control techniques. His research bridges theoretical control concepts with practical engineering applications, particularly in transportation and renewable energy sectors. Hilmi AYGÜN has successfully advised at least one Master's student, Hersh Hasan Taha Al Dawoodı, whose thesis focused on field-oriented control of induction motors using metaheuristic methods. He has participated in two major research projects: one on optimal flux reference direct torque control for asynchronous motors used in electric vehicles (2015-2019), and another on controlling bed temperature in fluidized bed boilers using PSO-PID controllers (2011-2013). As an educator, he has taught numerous courses including Electric Machine Dynamics, Modeling and Control of Biomedical Systems, Control of Electric Machines, Electric and Hybrid Vehicles, Industrial Automation, and various electronics courses. His teaching spans both undergraduate and graduate levels, demonstrating his versatility across multiple engineering disciplines within the mechatronics field.

• Robotics
• Autonomous System Design
• Control Theory and Applications
• +۳ مورد دیگر

Abdurrahman Yılmaz is an Assistant Professor at Istanbul Technical University's Department of Control and Automation Engineering within the College of Electrical-Electronics. His career spans academic and industrial roles, including a Research Assistant position at Istanbul Technical University (2018-2022) and Test Rig Design Engineer at ASELSAN INC. (2014). Education: PhD in Control and Automation Engineering (Istanbul Technical University, 2014-2022) MSc in Mechatronics Engineering (Yildiz Technical University, 2014-2017) BSc in Electronics Engineering (Istanbul Technical University, 2009-2014) Double Major in Control and Automation Engineering (Istanbul Technical University, 2011-2014) Research Interests focus on Robotics, Autonomous System Design, and Control Theory. His work includes localization frameworks for autonomous mobile robots, adaptive control algorithms for quadcopters, and soft haptic sensing technologies. Recent research explores digital twin simulators and quadruped robot dynamics. Scientific Output trends highlight advancements in mobile robot navigation, industrial automation, and autonomous systems. Publications span journals like Journal of Field Robotics and conferences including TAROS and RoboSoft. Awards: The Most Successful Doctoral Thesis Award of 2022 (University, 2023) 1st Water Management Awards (Ministry of Forestry and Water Affairs, 2019) Collaborations extend to international researchers in robotics and control systems. He serves as PI for the project "Development of a Design-Aiding Analysis Tool for Four-Legged Robots" (2023-2024).

Mike Wagnerمشاهده پروفایل

استاد

• Renewable Energy Systems
• Energy Storage Optimization
• Thermal Systems Modeling
• +۳ مورد دیگر

Mike Wagner is a Professor of Mechanical Engineering at the University of Wisconsin-Madison. He leads the Energy Systems Operations Lab and is affiliated with the Solar Energy Lab and Wisconsin Electric Machines and Power Electronics Consortium. His research focuses on thermal systems modeling, energy storage optimization, and high-temperature power generation technologies. Dr. Wagner holds a B.S. and M.S. from the University of Wisconsin-Madison (2007, 2008) and a Ph.D. from Colorado School of Mines (2017). Prior to UW-Madison, he spent 11 years as a senior researcher and PI at NREL’s Thermal Systems group. His research spans renewable energy systems, battery testing, and hybrid generation technologies. Recent work includes nuclear-solar integration, molten salt thermal storage, and AI-driven control systems for power plants. Awards: PECASE (2019), NREL President’s Award (2019), and multiple NREL recognition awards. He teaches courses in heat transfer (ME 364), graduate research (ME 790/890), and nuclear engineering (NE 790). His lab develops tools like SRLIFE, SolarPILOT, and SAM for energy system modeling and optimization.

Niko Nevarantaمشاهده پروفایل

دانشیار

• Electrical and Electronic Engineering
• Control and Systems Engineering
• Modelling and Simulation
• +۶ مورد دیگر

Niko Nevaranta serves as an Associate Professor (Tenure Track) in the Department of Electrical Engineering at Lappeenranta-Lahti University of Technology (LUT), specifically within the School of Energy Systems. His academic journey began at LUT where he earned his B.Sc. (2010), M.Sc. (2011), and D.Sc. (2016) degrees in electrical engineering. His career path includes a visiting researcher position at KTH Royal Institute of Technology in Stockholm (2018) and a post-doctoral researcher role funded by the Academy of Finland (2019-2022). Nevaranta's research focuses on data-driven modeling and control systems for electromechanical and energy applications. His primary expertise lies in Industrial Informatics for Energy System Disruption, with specific interests in high-speed rotating machinery, system identification, parameter estimation, and intelligent control systems. His work bridges theoretical control engineering with practical industrial applications, particularly in the energy transition sector. Analysis of his recent publications reveals a strong emphasis on active magnetic bearing technology, rotor dynamics, and high-speed machinery applications. His research increasingly incorporates data-driven approaches and machine learning techniques to address complex control challenges in energy systems. The publications demonstrate a clear progression from fundamental control theory toward practical implementation in real-world energy conversion systems. As an educator, Nevaranta has developed innovative teaching materials for control engineering, including MATLAB-based tools and virtual learning environments. His educational contributions focus on making complex control concepts accessible to undergraduate students through interactive and visual learning approaches. Nevaranta works within the laboratory of Control Engineering and Digital Systems at LUT, where he leads research on advanced control methodologies for energy systems. His current projects involve high-speed machine technology, particularly in applications related to biomass and waste heat recovery, as well as emerging technologies in direct air capture systems.

Pouria Sarhadiمشاهده پروفایل

مدرس ارشد

• Control Theory
• Autonomous Vehicles
• Adaptive Control
• +۳ مورد دیگر

Pouria Sarhadi is a Senior Lecturer at the University of Hertfordshire's School of Physics, Engineering & Computer Science. He has held roles as a Research Fellow at Queen's University Belfast (2021-2022) and the University of Surrey (2019-2021), and served as an Adjunct Professor at Babol Noshirvani University of Technology (2016-2018). He is a Chartered Engineer (CEng), Member of the IET, and recognized as UK 'Global Talent' by UKRI. His research focuses on control theory applications, adaptive control, autonomous vehicles, and systems engineering. He has contributed to over 50 peer-reviewed publications and led projects such as HertsLynx CAM On-Demand (UKRI-funded). He is actively involved in editorial roles for Applied Ocean Research and advises on IFAC Technical Committees. Research interests include: Autonomous systems (ground, marine, aerial) Machine learning in control systems Adaptive control and system identification Autonomous vehicle safety and navigation Key awards include CEng, MIET, and UK Global Talent recognition. He has advised on UKRI and Horizon Europe projects and consults for industries. Current projects include hybrid navigation safety simulators and cooperative control of multi-AUV systems.

Satyam Paulمشاهده پروفایل

مدرس ارشد

• Mechanical Engineering
• Control Theory
• Vibration Control
• +۵ مورد دیگر

Satyam Paul is a Senior Lecturer in Mechanical Engineering at Örebro University, Sweden. His academic journey includes roles as Senior Research Fellow at University of Nottingham (2022-2023), Assistant Professor at University of the West of England (2020-2021), and postdoctoral researcher positions at Örebro University (2018-2020) and Tecnológico de Monterrey (2018). He holds a PhD from CINVESTAV-IPN (Mexico), MSc in Mechatronics from VIT University (India), and BEng in Mechanical Engineering from NIT (India). Research Focus: Vibration control, fault detection in mechanical systems, control theory, and mechatronics. Key areas include active vibration control of structures, chatter suppression in milling processes, and fuzzy logic-based control systems. Research Projects: "A general digital twin driving mining innovation" (ongoing) "Production Centred Maintenance (PCM)" "Digital twin for sustainable production as a service" Awards: Full Fellowship Status (FHEA, UK) for teaching excellence in higher education. Grants & Labs: Active in the "Digitalized product and production development" research group. Leads projects combining statistical/logical modeling with industrial applications. Publications: Over 15 peer-reviewed articles in journals like Applied Sciences , IEEE Access , and Journal of Vibration and Control , plus book chapters and conference proceedings on structural control and mechatronics.