Tayyab Ashfaq | Electrical Engineering | Best Researcher Award

Posted on by Academic achievements

Mr.Tayyab Ashfaq | Electrical Engineering | Best Researcher Award

Jr. Researcher Comsats University Islamabad Pakistan

Tayyab Ashfaq is a dedicated Electrical Design Engineer with extensive experience in designing and managing photovoltaic solar systems. With a solid background in electrical engineering and a passion for renewable energy solutions, he excels in creating efficient and innovative designs for residential, commercial, and industrial projects. Tayyab is committed to advancing sustainable energy practices through meticulous engineering and research.

Profile

ORCiD

Education

🎓 Tayyab holds a Master of Science in Electrical Engineering from Comsats University Islamabad, completed in 2023, and a Bachelor of Science in Electrical Engineering from the University of Wah, completed in 2019. His academic journey has equipped him with a deep understanding of electrical systems, renewable energy technologies, and advanced engineering principles.

Experience

🔧 Currently, Tayyab is an Electrical Design Engineer at OMECTA International, Pakistan, where he develops detailed designs for PV solar systems. His previous roles include Site Engineer at Renergetic Engineering Pvt Ltd, where he ensured compliance with standards and oversaw the installation and commissioning of solar panels. Additionally, Tayyab has interned at Ordinance Factories, gaining foundational knowledge in SCADA/HMI systems and plant operations.

Research Interests

🔍 Tayyab’s research interests lie in renewable energy integration, power system optimization, and advanced control systems. His projects include developing global maximum power point tracking controllers under partial shading conditions and energy management systems for grid-connected microgrids. He also focuses on automatic generation control in multi-area power systems.

Awards

🏆 Tayyab received the Best Project award in the Final Year Project Competition at the Department of Electrical Engineering, University of Wah, recognizing his innovative approach and technical excellence in engineering solutions.

Publications

đź“š Tayyab has contributed to the field of electrical engineering with his publication in the journal Sustainability:

  • Automatic Generation Control in Renewables-Integrated Multi-Area Power Systems: A Comparative Control Analysis (2024). Sustainability, 16(13), 5735. Cited by articles.
  •  Best Project in Final Year Project Competition at Department of Electrical Engineering, University of Wah

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

MátĂ© 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, MátĂ©’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

MátĂ© 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

MátĂ© 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, MátĂ© 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., NĂ©meth, 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: NĂ©meth, 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., NĂ©meth, B.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 0
  6. Calibration of Front Wheel Odometry Model
    • Authors: Fazekas, M., Gáspár, P., NĂ©meth, B.
    • Book: Lecture Notes in Mechanical Engineering, 2022
    • Citations: 0
  7. Implementation of a variable-geometry suspension-based steering control system
    • Authors: FĂ©nyes, D., Fazekas, M., NĂ©meth, 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., NĂ©meth, 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., NĂ©meth, 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