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Zoltán Sütő is an Associate Professor at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics. His research focuses on advanced power electronics and control systems, with expertise in real-time implementation using FPGA technology. He maintains an active presence through institutional contacts at Budapest 1117, Magyar tudósok krt. 2., Q.B114, and can be reached via phone (+36 1 463-2337) or email (Suto.Zoltan@aut.bme.hu). Dr. Sütő's research encompasses: Design and optimization of power converters (dual active bridge, multilevel inverters) Real-time control algorithms for grid-connected systems and microgrids FPGA-based hardware-in-the-loop simulation methodologies Nonlinear dynamics in power electronic systems Artificial intelligence applications for fault diagnosis in drive systems His work bridges theoretical control models with practical implementations in renewable energy integration and power quality management. Recent publications demonstrate a strong focus on predictive control techniques, adaptive compensation methods, and optimization of power converter topologies. Research trends emphasize real-time validation, FPGA implementation, and AI-enhanced diagnostics across applications ranging from energy storage systems to industrial drives. Articles consistently address efficiency improvements, stability challenges, and novel modulation strategies in power conversion.

• Power Electronics
• Control Systems
• Power Converters
• +5 more

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +3 more

Dr. Gergely Vakulya is an Associate Professor and Research Fellow at Óbuda University. He specializes in interdisciplinary research spanning cybersecurity, agricultural technology, sensor networks, and image processing. His work integrates hardware design, algorithm development, and real-world applications. Dr. Vakulya’s recent focus includes developing rumen bolus sensors for dairy cattle health monitoring, gamification of cybersecurity training, and innovative methods in camera exposure time measurement. He is affiliated with Óbuda University’s Budai Road and Pirosalma Street campuses, with an office at building F room 316. His research frequently addresses challenges in data collection, sensor fusion, and embedded systems. Research Interests: Dr. Vakulya’s expertise includes cybersecurity frameworks (e.g., CTF challenges), agricultural IoT systems (e.g., rumen bolus sensors for livestock monitoring), and image processing techniques (e.g., genetic algorithms for shape approximation). His work on visible light communication (VLC) and wireless sensor networks highlights his contributions to communication protocols and energy-efficient systems. Recent trends in his publications emphasize cross-disciplinary approaches, such as applying AI methods to agricultural sensor data and optimizing sensor networks for real-time applications. Advising & Grants: No specific advising relationships or grants are listed in available materials. His research infrastructure is likely supported through institutional and collaborative projects. Labs/Teams: While not explicitly stated, his research likely involves collaborations within Óbuda University’s engineering and computer science departments. His work on rumen sensors and VLC systems suggests potential affiliations with robotics, biomedical engineering, or smart agriculture research groups.

• Cybersecurity
• Agricultural Technology
• Sensor Networks
• +3 more

Dr. Koloman Brenner (Associate Professor) is affiliated with the Institute of Germanic Studies at Eötvös Loránd University (ELTE), Budapest, Hungary, within the Department of German Linguistics. His work focuses on German dialectology, phonetics, minority languages, and sociolinguistics in Central and Eastern Europe. Research Interests: German dialects in Western Hungary and historical sound analysis Phonetic-algorithmic methods (e.g., Vocal Hunter software) Language use in minority communities and multimedia translation Comparative language policy in Hungary and South Tyrol Lexicography and bilingual education for German minorities Language atlases and sociocultural language variation Recent Publications Trends: His recent work includes bilingual dictionaries, dialect revitalization strategies, and algorithmic phonetic analysis. Earlier studies explore acoustic parameters of dialect consonants, media representation of dialects, and generational language shifts since 1989.

• German Linguistics
• Dialectology
• Phonetics
• +6 more

Péter Stumpf is an Associate Professor at the Budapest University of Technology and Economics (BME), affiliated with the Department of Automation and Applied Informatics. His research focuses on advanced control systems, power electronics, and machine learning applications in electrical drives. Contact Information: • Office: Building Q.B114, 1117 Budapest, Magyar tudósok krt. 2. Hungary • Phone: +36 (1) 463-2870 • Email: Stumpf.Peter@aut.bme.hu His recent work explores predictive control methods, including Model Predictive Control (MPC) and Reinforcement Learning (RL), applied to permanent magnet synchronous motors, grid-side converters, and high-speed drives. He has developed novel algorithms for optimal current computation, weighting factor assignment, and harmonics compensation. Key research trends include: Integration of machine learning in control systems Optimization of power electronics for renewable energy Advanced modulation techniques in motor drives Compensation of nonlinear effects in high-speed systems

• Electrical Engineering
• Control Systems
• Power Electronics
• +3 more

Krisztián Pomázi is a Lecturer at the Budapest University of Technology and Economics , affiliated with the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . His work bridges educational technology and cognitive science , utilizing machine learning and bioinformatics to enhance learning experiences. Department: Automation and Applied Informatics Email: Pomazi.Krisztian@aut.bme.hu Research interests include: Adaptive learning systems integrating biofeedback Machine learning applications in cognitive workload assessment Psychological profiling using computational models Usability evaluation via biomedical signal processing Intelligent exercise generation for cognitive assessment Educational game mechanics with physiological feedback Recent publication trends show a focus on blending machine learning with human-computer interaction to create personalized educational tools. His work incorporates cognitive science principles to dynamically adjust learning environments using biofeedback data, while also exploring psychometric and usability dimensions in digital education. Contact: Pomazi.Krisztian@aut.bme.hu

• Educational Technology
• Bioinformatics
• Cognitive Science
• +3 more

Prof. Gyula Simon is a faculty member at Óbuda University's Alba Regia Faculty, where he holds the rank of Professor. His research focuses on sensor networks, localization systems, and signal processing. He has contributed extensively to advancements in technologies like TDOA-based positioning, VLC communication, and acoustic source tracking. With over two decades of academic work, his publications span applications ranging from agricultural sensors to fault-tolerant indoor positioning systems. Key research interests include developing robust localization algorithms for both outdoor and indoor environments, optimizing wireless sensor networks for energy efficiency and reliability, and integrating emerging technologies like visible light communication (VLC) for beaconing and tracking. His work bridges theoretical signal processing with practical implementations in fields like smart agriculture and security systems. Recent articles highlight innovation in hyperbolic localization methods, sparse sampling techniques for camera systems, and semi-automatic plant growth monitoring. Despite prolific publication output, no scientific awards are explicitly listed. Consultation hours are arranged via email at simon.gyula@amk.uni-obuda.hu, with his office located at Building F Room 316 in Székesfehérvár.

• Sensor Networks
• Localization Systems
• Signal Processing
• +3 more

Dr. Bertalan Beszédes is a Senior Lecturer at Óbuda University's Alba Regia Faculty. His work focuses on control systems, renewable energy, and embedded electronics. He holds a consultation room at Building F, Room 321 in Székesfehérvár. His research emphasizes sustainable energy solutions, fault-tolerant systems, and automation applications in agriculture and manufacturing. Research interests include solar tracking systems, industrial welding control, and energy-efficient HVAC systems. He has contributed to projects involving smart security systems, vintage electronic device modernization, and educational robotics using LEGO Mindstorms. His publications span over two decades with a focus on embedded system reliability and renewable energy integration. Key innovations include off-grid power supply architectures and hybrid battery systems. His recent work (2023-2025) addresses challenges in smart agriculture, building automation, and legacy system upgrades. Beszédes maintains an active presence in both theoretical and applied electronics research.

• Control Systems
• Renewable Energy Systems
• Embedded Systems
• +5 more

Bence Kővári, Associate Professor at the Department of Automation and Applied Informatics, Budapest University of Technology and Economics, specializes in online signature verification , deep learning , and scheduling algorithms . His work bridges theoretical and applied computer science with engineering applications. University: Budapest University of Technology and Economics Department: Department of Automation and Applied Informatics Email: kovari@aut.bme.hu Research Interests His research focuses on biometric authentication through signature verification, integrating dynamic time warping and neural networks for efficient pattern recognition. He also explores fair scheduling in resource-constrained systems and applies deep learning to structural monitoring, notably in bridge weigh-in-motion systems . Scientific Contributions Kővári’s publications highlight advancements in signature verification (e.g., SVC-onGoing ), feature selection , and bridge load estimation . His work spans interdisciplinary domains, including machine learning , signal processing , and optimization . Scientific Awards SVC-onGoing: Signature verification competition (2022) ICDAR 2021 competition on on-line signature verification (2021) Advising and Collaborations While specific student names are not listed, Kővári actively contributes to academic research through publications, algorithm design, and participation in international competitions.

• Online Signature Verification
• Biometrics
• Machine Learning
• +5 more

Kristóf Gábor Bándy serves as a Lecturer at the Budapest University of Technology and Economics (BME), specifically within the Faculty of Electrical Engineering and Informatics and the Department of Automation and Applied Informatics . His work focuses on advanced control strategies for electrical drives and power conversion systems. Research Interests : Dr. Bándy's research centers on Model Predictive Control (MPC) in electrical engineering contexts, including optimization of permanent magnet synchronous motors (PMSM), grid-side converters, and power electronic topologies. He explores dynamic weighting factor assignment, LC/LCL filter compensation, and real-time implementation challenges. Publication Trends : His recent work emphasizes predictive control algorithms for renewable energy systems, electric vehicles, and industrial drives. Key themes include sorting networks for computational efficiency , quadratic regression models , and overmodulation techniques in multiphase drives.

• Electrical Engineering
• Control Systems
• Power Electronics
• +3 more

Mohammad Saleem is a Senior Lecturer at the Budapest University of Technology and Economics , working within the Department of Automation and Applied Informatics. His research focuses on biometric security systems and machine learning applications in online signature verification. His work emphasizes techniques like Dynamic Time Warping, LightGBM, and Logistic Regression, with numerous publications in these areas. He actively contributes to the field through participation in international signature verification competitions and systematic algorithm evaluations. Contact: Mohammad.Saleem@aut.bme.hu

• Online Signature Verification
• Biometric Security
• Machine Learning
• +6 more

Gyenes Károly is an Associate Professor at the Budapest University of Technology and Economics within the Department of Control for Transportation and Vehicle Systems. He also serves as Vice Dean and has a career spanning over five decades in transportation engineering, railway systems, and vehicle mechatronics. Languages: English, German Education: Electrical Engineering M.Sc. (1968), Ph.D. (2000), and multiple specialized diplomas Research Focus: Fail-safe railway interlocking systems, intelligent vehicle tracking (GPS/WiFi), microcontroller applications in traffic control, and secure data transmission protocols. His work bridges computer science with transportation engineering to enhance safety and automation. Key Publications Trends: Focus on railway automation, data transmission security, and microcontroller-based solutions. Articles span from 1975 to 2000, highlighting innovations in metro modernization, CRC error analysis, and remote control protocols. Scientific Awards: Minister of Education Award Educational Contributions: Taught courses in Computing, Computer Hardware, and Electronics at both undergraduate and postgraduate levels. Authored textbooks on programming and railway safety systems. Collaborations: Long-term involvement with Siemens, HungaroControl, and Robert Bosch in industrial projects. Key role in EU-funded research initiatives like the Tempus Project.

• Railway Systems
• Vehicle Mechatronics
• Traffic Control
• +3 more

Dr. József SÜTŐ is an Associate Professor at the University of Debrecen's Faculty of Informatics, Department of IT Systems and Networks. His research focuses on machine learning, digital signal processing, and microcontroller applications for agricultural and biomedical systems. University of Debrecen, Faculty of Informatics Department of IT Systems and Networks Research areas combine computer science with environmental and health monitoring, including: Deep learning for insect pest management Edge computing in embedded systems Human activity recognition algorithms Computer vision for traffic and plant analysis Recent publications emphasize real-time AI deployment on low-power hardware, such as Raspberry Pi and microcontrollers, with applications in agricultural monitoring and wearable devices. He also investigates comparative model efficiency between shallow and deep learning architectures.

• Machine Learning
• Digital Signal Processing
• Microcontroller Applications
• +3 more

Dr. Anikó Vágner is an Assistant Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Information Technology . Her research focuses on Educational issues of database systems , Medical signal processing , and Clustering of datasets , with a strong emphasis on NoSQL databases, smart city applications, and health informatics. Academic Rank: Assistant Professor Email: vagner.aniko@inf.unideb.hu Research Trends: Analysis of her 15 most recent publications reveals sustained work in NoSQL database performance optimization (MongoDB, Redis), GTFS-based transportation algorithms, medical signal processing using microcontrollers, and clustering techniques like OPTICS. Applications span smart cities, health monitoring, and educational technologies. Labs/Teams: She is part of the Department of Information Technology, which focuses on databases, data mining, and smart city solutions. The department collaborates on projects like participatory sensing frameworks and time series analysis.

• Database Systems Education
• Medical Signal Processing
• Clustering of Datasets
• +3 more

Mats Köster is an Assistant Professor in the Department of Economics and Business at the Central European University (CEU) in Vienna. He holds a PhD in Economics from Heinrich-Heine-University Düsseldorf (2021). His research focuses on behavioral economics and microeconomic theory, particularly examining how salience-driven attention influences consumer decisions and market dynamics. Köster combines theoretical models with experimental methods in lab and field settings. Education: PhD (Dr. rer. pol.) in Economics, Heinrich-Heine-University Düsseldorf, 2021 Research Interests: Köster explores salience effects in decision-making, attention economics, and their implications for markets. His work bridges behavioral insights with traditional economic theory, addressing topics like consumer behavior, experimental economics, and the design of information strategies. He has published extensively on salience-driven choices, skewness preferences, and digital ecosystems. Labs/Teams: He leads the Center for Belief Update and Debiasing (DEBIAS) as Principal Investigator, focusing on cognitive biases and decision-making processes. He is also an Affiliate Member of the CESifo Research Network’s Behavioral Economics area. Key Trends in Work: His recent articles analyze how salience shapes consumer attention, the dynamics of chosen beliefs, and the structural features of digital platforms. Experimental methodologies underpin much of his research, validating theoretical models through real-world applications.

• Behavioral Economics
• Microeconomic Theory
• Experimental Economics
• +3 more