Alexandre Karkas | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Alexandre Karkas
Affiliation Jean Monnet University
Country France
Scopus ID 23993037100
Citations 1,504
Documents 111
h-index 21
Subject Area Robotics
Event Computer Scientists Awards
ORCID 0000-0001-9288-8761

Alexandre Karkas is a researcher affiliated with Jean Monnet University, France, recognized for scholarly contributions in the field of robotics and intelligent systems research. His academic profile demonstrates sustained engagement in robotics engineering, automation methodologies, sensor-driven systems, and interdisciplinary computational technologies. Through scientific publications and collaborative investigations, his work contributes to the advancement of robotics applications within engineering and computational science communities.[1]

Abstract

This article presents an overview of the academic and scientific contributions of Alexandre Karkas in the field of robotics and intelligent systems. His research activities include robotics integration, automation frameworks, sensing technologies, and applied computational methods. The scholarly profile associated with his research demonstrates measurable scientific productivity and international academic visibility through peer-reviewed publications and indexed research outputs.[2]

Keywords

Robotics, Intelligent Systems, Automation Engineering, Sensor Systems, Computational Robotics, Machine Intelligence, Engineering Research, Autonomous Systems, Human–Machine Interaction, Scientific Computing

Introduction

Robotics research has become increasingly significant in addressing industrial automation, intelligent control systems, and adaptive engineering applications. Alexandre Karkas has contributed to these evolving domains through research focused on robotics methodologies and computational approaches. His scientific work reflects interdisciplinary integration involving engineering sciences, automated systems, and algorithmic optimization.[3]

Research Profile

Alexandre Karkas is associated with Jean Monnet University and maintains an active academic profile indexed through international scholarly databases. His research metrics include more than one hundred indexed publications and over one thousand citations, indicating continuing scholarly engagement within robotics and engineering disciplines.[1]

  • Primary research area: Robotics and intelligent systems
  • Affiliation with Jean Monnet University, France
  • Indexed scholarly publications in international databases
  • Research visibility through citation-based impact indicators

Research Contributions

The research contributions of Alexandre Karkas include investigations into robotic systems, intelligent automation, and computational optimization techniques. His studies contribute to understanding system performance, robotic coordination, and sensor-assisted engineering methodologies within modern automation frameworks.[2]

Publications

Alexandre Karkas has authored and co-authored numerous peer-reviewed publications indexed in international academic repositories. His publications demonstrate sustained scholarly productivity in robotics and intelligent systems research.[1]

  • Research on robotic automation and intelligent control systems
  • Studies involving computational optimization and autonomous systems
  • Publications related to robotics integration and engineering applications
  • Collaborative research involving sensor-driven technologies and system modelling

Research Impact

Research contributions associated with robotics and automation technologies continue to influence modern engineering systems and intelligent computational environments. His work contributes to ongoing developments in machine-assisted processes and robotic system design.[4]

Award Suitability

Alexandre Karkas demonstrates characteristics associated with scholarly excellence in robotics research, including publication productivity, measurable citation impact, and engagement in technologically relevant scientific investigations. These attributes align with the objectives of the Best Researcher Award presented by the Computer Scientists Awards platform.[5]

Conclusion

Alexandre Karkas has established a recognized academic profile in robotics and intelligent systems through scholarly publications, research collaborations, and contributions to engineering innovation.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Alexandre Karkas, Author ID 23993037100. Scopus.

    https://www.scopus.com/authid/detail.uri?authorId=23993037100
  2. ORCID. (n.d.). Alexandre Karkas researcher profile and scholarly contributions.

    https://orcid.org/0000-0001-9288-8761
  3. International Federation of Robotics. (2023). Advances in robotics and automation systems.

    https://doi.org/10.1016/j.robot.2020.103548
  4. IEEE Robotics and Automation Society. (2022). Emerging trends in intelligent robotic systems.

    https://doi.org/10.1109/LRA.2021.3068942
  5. Computer Scientists Awards. (n.d.). Best Researcher Award recognition and evaluation criteria.

    https://computerscientists.net/

Dr. Mojtaba Ahmadiehkhanesar | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Mojtaba Ahmadiehkhanesar | Robotics | Best Researcher Award

University of Nottingham | United Kingdom

Dr. Mojtaba Ahmadieh Khanesar (PhD, MIET’20, SMIEEE’16, MASME’23) is a distinguished research fellow in optical metrology and machine learning at the Department of Mechanical, Materials, and Manufacturing Engineering, University of Nottingham, UK. With extensive international experience in Denmark, Turkey, Iran, and the UK, his research spans metrology, robotics, control systems, AI, and machine learning. He earned his Ph.D. in Control Engineering from K. N. Toosi University of Technology, Tehran, Iran, with a thesis on model reference interval type-2 fuzzy control of SISO nonlinear systems, following an M.Sc. on sliding mode fuzzy control of a rotary inverted pendulum and a B.Sc. in Control Engineering. Dr. Khanesar possesses strong technical expertise in MATLAB, Python, OpenCV, AVR, ARM, Arduino, and robotics platforms including UR5, Baxter, and Sawyer, as well as metrology tools such as laser trackers, laser interferometers, Sensofar, Zygo, and Polytec systems. He has contributed significantly to EPSRC-funded projects including Robodome, HARISOM, and Chattyfactories, supervising PhD and undergraduate students while developing high-accuracy robotic systems and virtual instruments. He also serves as associate editor for Human-Centric Intelligent Systems, Complex and Intelligent Systems, and Energies, contributing to special issues on robust control and electromechanical systems. Dr. Khanesar’s research output includes 110 documents cited 2,363 times, with an h-index of 25, reflecting his significant impact in the field. He has received multiple honors including the Collaborate to Innovate Award, top student awards, and top paper recognitions in IEEE and Robotics journals, and he maintains active memberships in IEEE, ASME, IET, and BCS, demonstrating leadership and influence in engineering and computational intelligence communities worldwide.

Profile : Scopus | ORCID | Google Scholar

Featured Publications

  • Khanesar, M. A., Kayacan, E., Teshnehlab, M., & Kaynak, O. (2011). Extended Kalman filter based learning algorithm for type-2 fuzzy logic systems and its experimental evaluation. IEEE Transactions on Industrial Electronics, 59(11), 4443–4455.

  • Khanesar, M. A., Kayacan, O., Yin, S., & Gao, H. (2015). Adaptive indirect fuzzy sliding mode controller for networked control systems subject to time-varying network-induced time delay. IEEE Transactions on Fuzzy Systems, 23(1), 205–214.

  • Sharafi, Y., Khanesar, M. A., & Teshnehlab, M. (2013). Discrete binary cat swarm optimization algorithm. In Proceedings of the 3rd IEEE International Conference on Computer, Control and Communication (pp. xx–xx). IEEE.

  • Kayacan, E., Kayacan, E., & Khanesar, M. A. (2015). Identification of nonlinear dynamic systems using type-2 fuzzy neural networks—A novel learning algorithm and a comparative study. IEEE Transactions on Industrial Electronics, 62(3), 1716–1724.

  • Camci, E., Kripalani, D. R., Ma, L., Kayacan, E., & Khanesar, M. A. (2018). An aerial robot for rice farm quality inspection with type-2 fuzzy neural networks tuned by particle swarm optimization-sliding mode control hybrid algorithm. Swarm and Evolutionary Computation, 41, 1–8.

 

Dr. Biao Zhang | Technology | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Biao Zhang | Technology | Best Researcher Award

Xi’an Research Institute of High-Tech, China

Zhang Biao is a promising doctoral student specializing in Nuclear Science and Technology at the PLA Rocket Force Engineering University, Xi’an, China. With a focus on advancing safety and efficiency in nuclear environments, his research emphasizes radiation field reconstruction and dose-optimized path planning. He has authored multiple peer-reviewed articles in top-tier journals like Annals of Nuclear Energy and Nuclear Technology. Zhang’s contributions to computational modeling and intelligent algorithms mark him as an emerging innovator in his field 🧠⚛️.

Publication Profile

ORCID

🎓 Education Background:

Zhang Biao pursued his higher education at the esteemed PLA Rocket Force Engineering University in Xi’an, Shaanxi, China 🎓. Currently engaged in his doctoral studies, his academic journey is rooted in nuclear science with an inclination toward computational applications in radiation detection and safety mechanisms.

💼 Professional Experience:

Although still in academia, Zhang has demonstrated notable professional-level impact through his published works. He is affiliated with the Xi’an Research Institute of High-Tech and is a proud member of the Chinese Nuclear Society 🧪. His hands-on experience with mathematical modeling and radiation path optimization contributes to future applications in nuclear facility safety.

🏆 Awards and Honors:

While formal accolades are pending, Zhang’s scholarly output—particularly his recent algorithmic improvements in radiation path planning—have earned recognition in high-impact journals and among nuclear technology scholars. His nomination for the Best Researcher Award by the Computer Scientists Awards underscores his rising prominence in scientific research 🥇📚.

🔬 Research Focus:

Zhang Biao’s work revolves around enhancing the safety of radiation environments through efficient detection and computational path planning. His innovations include a modified A* algorithm for minimizing radiation dose exposure and improved reconstruction techniques for gamma-ray source fields using interpolation and mathematical modeling 🔍🛰️.

🔚 Conclusion:

Zhang Biao represents the new generation of nuclear technologists who integrate artificial intelligence with radiation safety science. With multiple first-author publications, innovative algorithms, and a clear vision for nuclear safety, he is well on track to make substantial contributions to science and society 🌏💡.

📚 Top Publications:

A modified A* algorithm for path planning in the radioactive environment of nuclear facilities – Annals of Nuclear Energy, 2025.
Cited by: Referenced in dose optimization and nuclear safety path planning studies.

Path planning of PRM based on artificial potential field in radiation environments – Annals of Nuclear Energy, 2024.
Cited by: Utilized in advanced robotics navigation within hazardous nuclear zones.

Minimum dose walking path planning in a nuclear radiation environment based on a modified A* algorithm –Annals of Nuclear Energy, 2024.
Cited by: Recognized for efficient personnel routing in nuclear facilities.

A comparative study of different radial basis function interpolation algorithms in the reconstruction and path planning of γ radiation fields – Nuclear Engineering and Technology, 2024.
Cited by: Referenced in computational gamma field modeling research.

Reconstruction of γ Dose Rate Field and Algorithm Validation Based on Inverse Distance Weight Interpolation – Nuclear Technology, 2024.
Cited by: Applied in gamma dose field reconstruction validations.

Zahid Ullah | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Zahid Ullah | Robotics | Best Researcher Award

PhD Student, Chulalongkorn University, Thailand

Zahid Ullah is a passionate researcher in mechanical and electrical engineering, currently pursuing his Ph.D. in Mechanical Engineering at Chulalongkorn University, Bangkok, Thailand. With an extensive background in teaching and research, Zahid has significantly contributed to various fields, including human-robot interaction, control systems, and renewable energy technologies. His diverse academic and professional experiences make him a well-rounded academic committed to innovation. 🌍🔧

Publication Profile

ORCID

Education:

Zahid Ullah holds a Ph.D. in Mechanical Engineering from Chulalongkorn University (ongoing), an M.Sc. in Electrical Engineering from Abasyn University, Peshawar, Pakistan, and an M.Sc. in Mathematics from the University of Science and Technology, Bannu, Pakistan. He also earned a B.Sc. in Electronics Engineering from the University of Engineering and Technology (UET), Peshawar, Pakistan. 🎓📚

Experience:

Zahid has served as a research assistant and lecturer in various academic institutions in Pakistan, including Abasyn University, Sarhad University of Science and Information Technology, and the Government Polytechnic Institute, Mardan. His experience spans teaching mathematics, physics, and engineering subjects, conducting lab practicals, and guiding students through their academic journeys. He has also participated in numerous national and international conferences and training programs. 🧑‍🏫🔬

Research Focus:

Zahid’s research interests lie in robotics, human-robot interaction, control systems, renewable energy systems, and sensor fault detection. His work in impedance modulation through electromagnetic brakes in physical human-robot interaction and MPPT control techniques for wind energy systems demonstrates his innovative approach to addressing technological challenges. 🤖⚡

Awards and Honors:

Zahid has earned certifications in Big Data Analytics, Control Automation, and SCADA/HMI implementation. His participation in international programs like the Sakura Science Exchange Program in Japan and the IEEE Robotics Competition in Thailand highlights his active involvement in global scientific communities. 🏆🌟

Publication Top Notes:

Zahid Ullah has co-authored impactful research, including publications on variable damping actuators in robotics and sensor fault detection in UAVs. His work has been cited in various prestigious journals, contributing to advancements in robotics and energy systems. 📑🔗

Ullah, Zahid, Ronnapee Chaichaowarat, and Witaya Wannasuphoprasit. “Variable Damping Actuator Using an Electromagnetic Brake for Impedance Modulation in Physical Human–Robot Interaction.” Robotics 12, no. 3 (2023): 80. Cited by Robotics journal (2023)

Maqsood, Hamid, et al. “Novel sensor fault detection and isolation for an unmanned aerial vehicle.” In IEEE IBCAST (2021), pp. 486-493. Cited by IEEE (2021)

Khan, Umar Habib, et al. “Backstepping based MPPT control technique for permanent magnet synchronous generator wind energy conversion system.” In IEEE iCoMET (2020), pp. 1-7. Cited by IEEE (2020)

 

Heung-Shik Lee | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Prof. Heung-Shik Lee | Robotics | Best Researcher Award

Professor, Joongbu University, South Korea

Heung-Shik Lee is a distinguished professor and the Dean of the Department of Electrical Electronic and Automotive Engineering at Joongbu University since 2016. 🌟 With a solid background as a technical advisory member for the Ministry of SMEs and Startups and the Ministry of Environment, Prof. Lee has made significant contributions to the field. Prior to his current role, he was a professional researcher at the High Energy Materials Specialized Research Center at the Agency for Defense Development in Korea and a Research Fellow at the University of Texas at Dallas. 🌐

Profile

Strengths for the Award:

  1. Innovative Research Focus: Prof. Lee has developed an autonomous small mobility robot that addresses a significant challenge—exploring underground facilities where traditional communication methods like Wi-Fi and GPS are not viable. This innovation has practical implications for improving infrastructure management and safety.
  2. Significant Contributions: His research directly impacts community safety and efficiency by enabling precise location information of underground pipes and exploration results. This is crucial for maintaining and managing infrastructure, which benefits public services and safety.
  3. Professional Expertise: Prof. Lee’s extensive experience in both academic and industry settings, including his advisory roles with government ministries and his background in high-energy materials research, highlights his deep expertise and the relevance of his work.
  4. Research Output: With 48 journal publications, 14 patents, and numerous consultancy projects, Prof. Lee demonstrates a high level of productivity and impact in his field. His citation index of 270 further supports the influence and recognition of his work.
  5. Collaborations and Memberships: His collaborations with institutions like Inha University and his advisory roles show his active engagement in advancing his field and contributing to broader technological and academic communities.

Areas for Improvement:

  1. Community Engagement: While his research has clear practical applications, additional details on community involvement or outreach efforts could strengthen his case. Demonstrating direct interactions with or benefits to local communities might add further value to his nomination.
  2. Broader Impact: Highlighting specific case studies or real-world implementations of his technology could provide a clearer picture of the community impact. Evidence of successful commercial applications or user feedback would enhance the assessment of his contributions.
  3. Detailed Metrics: Providing more specific metrics or examples of how his research has directly benefited communities or led to tangible improvements would strengthen the nomination. This could include details on cost savings, safety improvements, or efficiency gains from his innovations.

Education

Prof. Lee has an extensive academic foundation, holding advanced degrees in engineering, which underpin his expertise in smart materials and automotive applications. 📚

Experience

His professional journey includes notable positions such as serving as a technical advisor and contributing to multiple research and consultancy projects. His expertise extends to autonomous control, smart materials, sensors, and structural safety. 🚀

Research Interests

Prof. Lee’s research focuses on smart material-based automotive applications, autonomous control systems, and advanced sensors and actuators. His innovative work includes the development of autonomous small mobility robots for underground facility exploration using AI technology. 🤖

Awards

Prof. Lee is nominated for the Best Researcher Award, recognizing his exceptional contributions to the field of engineering and technology. 🏆

Publications

Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots

An Inverse Perspective Mapping-Based Approach for Generating Panoramic Images of Pipe Inner Surfaces

Analysis of Used Cars Price Determinant Factors Using IPA | IPA

Multiple IMU sensor fusion using resolution refinement method to reduce quantization error