Haleem Farman | Vehicular Adhoc Networks and Artificial Intelligence | Best Researcher Award

Posted on by Academic achievements

Dr.Haleem Farman | Vehicular Adhoc Networks and Artificial Intelligence | Best Researcher Award

Researcher Prince Sultan University Saudi Arabia

Haleem Farman is currently engaged in progressive research and development activities at the Smart Systems Engineering Laboratory, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia. His expertise spans R&D, research supervision of undergraduate students, and counseling. Haleem has a strong background in teaching and has supervised numerous BS, MS, and Ph.D. projects. He holds administrative responsibilities including proposal writing, organizing lab activities, and fostering industry-academia collaborations. Previously, he served as an Assistant Professor at Islamia College Peshawar, Pakistan, where he taught courses in Computer Science and led various educational initiatives.

Profile

Scopus

👨‍🎓 Education

Haleem Farman completed his Ph.D. in Computer Science from the University of Peshawar, Pakistan in May 2018, focusing on “Energy Efficient Grid-based Hybrid Network Deployment Approach in Wireless Sensor Networks.” He also holds an MS from the International Islamic University, Islamabad, Pakistan, and a Bachelor’s in Information Technology (Honors) from IQRA University, Karachi.

👨‍💼 Experience

Haleem Farman has a diverse professional background, currently serving as a Researcher at the Smart Systems Engineering Lab, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia, since May 2023. Previously, he held positions such as Assistant Professor at Islamia College Peshawar and various administrative roles, including Network Administrator and Deputy Controller of Examinations.

🔍 Research Interests

His research interests encompass Internet of Things (IoT), Advanced Computer Networks, Wireless Communication, and applications of Artificial Intelligence in Smart Cities and Smart Systems. He has supervised numerous undergraduate and graduate research projects, focusing on practical applications in telecommunications and network optimization.

🏆 Awards

Haleem Farman has received recognition for his contributions, including establishing the Smart Technologies Lab at Islamia College Peshawar and initiating the Software Technology Park. He was also awarded travel grants for presenting research papers at international conferences.

📚 Publications:

  1. Khalil, A., Farman, H., Nasralla, M. M., Jan, B., & Ahmad, J. (2024). Artificial Intelligence-based intrusion detection system for V2V communication in vehicular adhoc networks. Ain Shams Engineering Journal. [Cited by 6] Link
  2. Farman, H., Nasralla, M. M., Khattak, S. B. A., Jan, B. (2023). Efficient Fire Detection with EEFNet: A Lightweight Deep Learning-Based Approach for Edge Devices. Applied Sciences. [Cited by 2] Link
  3. Badshah, A., Rehman, G. U., Farman, H., Ghani, A., Sultan, S., Zubair, M., & Nasralla, M. M. (2023). Transforming Educational Institutions: Harnessing the Power of Internet of Things, Cloud, and Fog Computing. Future Internet. [Cited by 3] Link
  4. Khattak SBA, Nasralla MM, Farman H, Choudhury N. (2023). Performance Evaluation of an IEEE 802.15.4-Based Thread Network for Efficient Internet of Things Communications in Smart Cities. Applied Sciences. [Cited by 2] Link
  5. Amaouche, S., Guezzaz, A., Benkirane, S., Azrour, M., Khattak, S. B. A., Farman, H., & Nasralla, M. M. (2023). FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETS. Applied Sciences. [Cited by 2]

Mate Fazekas | Autonomous Vehicles | Best Researcher Award

Posted on by Academic achievements

Mr. Mate Fazekas | Autonomous Vehicles | Best Researcher Award

Ph.D. student at HUN-REN Institute for Computer Science and Control (SZTAKI), Hungary

Máté Fazekas is a dedicated researcher and senior software developer based in Budapest, Hungary. Since 2017, he has contributed significantly as a research associate at the HUN-REN Institute for Computer Science and Control, focusing on state estimation and model identification for autonomous vehicles. From 2022 to 2024, he expanded his expertise as a senior software developer at HUMDA Lab Kft., specializing in developing control systems for autonomous racecars. Currently pursuing a PhD in Robotics at the Budapest University of Technology and Economics, Máté’s research centers on model calibration for autonomous vehicles integrating machine learning techniques. He holds an MSc in Electrical Engineering and a BSc in Mechatronics Engineering from the same institution, both awarded with highest honors. Proficient in languages including Hungarian and English, he excels in programming languages like Matlab, Python, C/C++, and LabVIEW, complemented by strong skills in CAD software and Microsoft Office applications.

Professional Profiles

Education

Máté Fazekas pursued his academic journey at the Budapest University of Technology and Economics, achieving academic excellence throughout. He completed his Bachelor of Science in Mechatronics Engineering from the Faculty of Mechanical Engineering, graduating summa cum laude in 2017. His undergraduate thesis focused on the development of an automated parking system. Subsequently, he earned his Master of Science in Electrical Engineering and Informatics from the Faculty of Electrical Engineering and Informatics, also graduating summa cum laude in 2019. His master’s thesis centered on state and parameter estimation for car-like robots. Currently, he is continuing his academic pursuits as a PhD student in Robotics at the Faculty of Vehicle Engineering, where his research involves model calibration for autonomous vehicles integrating machine learning techniques.

Professional Experience

Máté Fazekas has gained valuable experience in both research and software development roles. Since December 2017, he has served as a Research Associate at the HUN-REN Institute for Computer Science and Control in Budapest, focusing on state estimation and model identification for autonomous vehicles. Concurrently, from June 2022 to June 2024, he worked as a Senior Software Developer at HUMDA Lab Kft., specializing in the development of control systems for autonomous racecars. His expertise includes integrating GNSS, IMU, and odometry for vehicle localization, along with developing advanced software solutions for autonomous systems.

Research Interest

Máté Fazekas, a Budapest-based researcher and Senior Software Developer at HUMDA Lab Kft., specializes in robotics and autonomous systems. Since 2017, he has contributed significantly as a research associate at the HUN-REN Institute for Computer Science and Control, focusing on state estimation and model identification for autonomous vehicles using GNSS, IMU, and odometry technologies. His current role involves developing control systems for autonomous racecars, enhancing their performance and automation capabilities. Educationally, Máté is pursuing a PhD in Robotics at Budapest University of Technology and Economics, exploring model calibration for autonomous vehicles with machine learning integration. He holds a master’s degree in Electrical Engineering and Informatics, where his research focused on state and parameter estimation for robotic systems, and a bachelor’s degree in Mechatronics Engineering, emphasizing automated parking systems. His technical skills include proficiency in Matlab, Python, C/C++, and LabVIEW, as well as CAD and FEM tools like SolidWorks, Inventor, and Ansys.

Research skills

Mate Fazekas is proficient in multiple languages, with Hungarian as his mother tongue and English at an intermediate B2 level. His computer skills include intermediate proficiency in Microsoft Office tools such as Word, Excel, PowerPoint, Access, and Project. He also possesses intermediate skills in Computer-Aided Design (CAD) and Finite Element Method (FEM) software like SolidWorks, Inventor, and Ansys. In programming, Mate demonstrates intermediate proficiency in languages such as C/C++ and LabVIEW, along with advanced skills in MATLAB and intermediate proficiency in Python. These skills equip him well for his research and development roles, particularly in robotics and autonomous vehicle technologies.

Publications

  1. Wheel odometry model calibration with neural network-based weighting
    • Authors: Fazekas, M., Gáspár, P.
    • Journal/Conference: Engineering Applications of Artificial Intelligence, 2024
    • Citations: 0
  2. LPV-Based Control Design with Guarantees: a Case Study for Automated Steering of Road Vehicles
    • Authors: Nemeth, B., Fazekas, M., Bagoly, Z., Gaspar, P., Sename, O.
    • Conference: European Control Conference, ECC 2023, 2023
    • Citations: 0
  3. Calibration of the Nonlinear Wheel Odometry Model with an Improved Genetic Algorithm Architecture
    • Authors: Fazekas, M., Németh, B., Gáspár, P.
    • Conference: Proceedings of the International Conference on Informatics in Control, Automation and Robotics, 2022
    • Citations: 0
  4. Vehicle Control with Cloud-aided Learning Feature: an Implementation on Indoor Platform
    • Authors: Németh, B., Antal, Z., Marosi, A.C., Fazekas, M., Gáspár, P.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 1
  5. Wheel Odometry Model Calibration with Input Compensation by Optimal Control
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 0
  6. Calibration of Front Wheel Odometry Model
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Book: Lecture Notes in Mechanical Engineering, 2022
    • Citations: 0
  7. Implementation of a variable-geometry suspension-based steering control system
    • Authors: Fényes, D., Fazekas, M., Németh, B., Gáspár, P.
    • Journal: Vehicle System Dynamics, 2022
    • Citations: 6
  8. Parameter Identification of the Nonlinear Wheel Odometry Model with Batch Least Squares Method
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Conference: Conference on Control and Fault-Tolerant Systems, SysTol, 2021
    • Citations: 1
  9. Velocity estimation via wheel circumference identification
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Journal: Periodica Polytechnica Transportation Engineering, 2021
    • Citations: 1
  10. Improving the wheel odometry calibration of self-driving vehicles via detection of faulty segments
    • Authors: Fazekas, M., Gaspar, P., Nemeth, B.
    • Conference: IEEE International Conference on Automation Science and Engineering, 2021
    • Citations: 0