Muhammet Emre Sanci | Robotics | Innovative Research Award

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Dr. Muhammet Emre Sanci | Robotics | Innovative Research Award 

Dr. Muhammet Emre Sanci | Robotics | Istanbul Technical University at Turkey

Robotics expertise forms the foundation of the academic and research journey of Dr. Muhammet Emre Sanci, whose professional path seamlessly blends advanced theoretical knowledge with applied innovation in intelligent systems, nonlinear control, autonomous technologies, and UAV swarm intelligence. Dr. Muhammet Emre Sanci completed his PhD in Mechatronics Engineering at Istanbul Technical University, funded by the Ministry of Education Turkiye, where his thesis focused on adaptive inverse optimal controller design for nonlinear non-affine systems using machine learning methods. He also earned an MSc (Hons) in Electrical and Electronics Engineering at Pamukkale University, focusing on model-based control and autonomy in magnetic levitation systems, and previously completed a BE (Hons, 1st) in Electrical and Electronics Engineering at Anadolu University, where he compared fuzzy PID and PI controllers for DC microgrid energy systems. Adding multidisciplinary strength to his robotics vision, he studied Physics at Vilnius University through Erasmus+ mobility and completed BSc (Hons, 1st) in Physics at Abant Izzet Baysal University. Professionally, Dr. Muhammet Emre Sanci served as a Postdoctoral Fellow at the University of Idaho, advancing research in autonomous robotics, adaptive optimal control, multi-drone swarming systems, and agricultural automation. His work included adaptive disturbance rejection-based fuzzy PID control for UAV swarms, deep learning-based greenhouse automation, irrigation distribution modeling under heterogeneous soil conditions, and drone-based pest management systems, while providing data-driven modeling, algorithm development, and comprehensive documentation for large-scale research initiatives. Earlier, he served at Istanbul Technical University as Teaching & Research Assistant in Control and Automation Engineering, contributing to intelligent control, UAV-based autonomous mine sweeping, multi-agent local path planning, velocity obstacle avoidance, and neural network plus SVR-based nonlinear non-affine system identification strategies. His earlier experience at Pamukkale University integrated Robotics with materials and machining optimization, focusing on magnetic levitation system modeling and composite machining surface roughness prediction. He has extensive teaching expertise across multiple modules including Control Systems, Mechatronics, Real-Time Embedded Systems, Linear Algebra, Probability Theory, Microcontroller Systems, State-Space Methods, Intelligent Systems, Deep Learning, Hardware-in-the-Loop, Electronic Instrumentation, MATLAB Programming, and more, supporting robotics education at undergraduate and postgraduate levels. His research interests remain deeply rooted in Robotics, nonlinear intelligent control, adaptive UAV swarming, multi-agent autonomy, artificial neural networks for system identification, optimal control theory, and real-time embedded computation. His core research skills include nonlinear model design, optimal robotics controller development, multi-UAV coordination, intelligent system modeling, path-planning algorithms, machine learning implementation, and multi-environment dynamic optimization. His innovation has been supported by major competitive grants from national and international science foundations, including drone-based sensing system development for infrastructure inspection and UAV optimization technology for autonomous missions. His excellence is reflected through distinguished graduation achievements, academic honors, international research scholarships, and best presentation recognitions. In conclusion, Dr. Muhammet Emre Sanci stands as a robotics-focused scholar whose interdisciplinary expertise, highly adaptive control systems research, and forward-thinking approach to autonomous UAV swarming technologies significantly advance the global state-of-the-art in intelligent engineering systems, making him an invaluable contributor to emerging frontiers in Robotics.

Profile: Google Scholar

Featured Publications 

Sanci, M. E., Halis, S., & Kaplan, Y. (2017). Optimization of machining parameters to minimize surface roughness in the turning of carbon-filled and glass fiber-filled polytetrafluoroethylene. 2017, 6 citations.
Sanci, M. E., & Günel, G. Ö. (2024). Neural network based adaptive inverse optimal control for non-affine nonlinear systems. 2024, 5 citations.
Sanci, M. E., Uçak, K., & Günel, G. Ö. (2023). A novel adaptive LSSVR based inverse optimal controller with integrator for nonlinear non-affine systems. 2023, 5 citations.
Candan, F., Sanci, M. E., & Li, L. (2024). Vision-based relative navigation and drone swarming control for inspection in GPS-denied environment. 2024, 1 citation.
Sanci, M. E., Halis, S., & Kaplan, Y. (2016). Study on surface roughness of carbon and glass fiber filled polytetrafluoroethylene in turning process. 2016, 1 citation.

 

Zhengquan Piao | Robotics | Best Researcher Award

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Dr. Zhengquan Piao | Robotics | Best Researcher Award

Dr. Zhengquan Piao | Robotics | – Engineer at China North Artificial Intelligence & Innovation Research Institute, China

Zhengquan Piao is an emerging researcher in computer vision, autonomous systems, and intelligent detection technologies. His research reflects a growing focus on advanced methodologies such as deep learning, pattern recognition, and sensor fusion. With several peer-reviewed publications and a rising citation profile, Piao is positioning himself as a significant contributor to the fields of intelligent transportation, object detection, and AI-driven robotics. His research emphasizes practical, scalable solutions that address real-world challenges, particularly in vehicle detection, underground mapping, and smart navigation systems.

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Education:

Zhengquan Piao received his academic training in computer science and artificial intelligence, where he developed a strong foundation in machine learning, algorithm design, and control theory. His education likely includes postgraduate study from a research-focused institution, possibly Beijing Institute of Technology (BIT), where he deepened his understanding of computer vision, neural networks, and autonomous systems. This academic background has provided him with the analytical and technical tools essential for his cutting-edge research in object recognition and navigation.

Experience:

Professionally, Piao has gained hands-on experience through a range of academic and technical projects that integrate AI with robotics and automation. He has played key roles in designing object detection architectures, enhancing vehicle perception systems, and developing algorithms for real-time localization in complex environments. His participation in national conferences and collaborations with multidisciplinary teams reflects a well-rounded profile of academic research and practical engineering. Piao’s project involvement also demonstrates his ability to work across domains, including transportation safety, aerial imaging, and intelligent mapping.

Research Interest:

Piao’s research interests center around few-shot learning, domain adaptation, autonomous navigation, and sensor-based object detection. He is especially interested in how to enable machines to learn from limited data in resource-constrained environments. His projects often combine LiDAR, camera fusion, deep neural networks, and unsupervised learning to build intelligent systems capable of operating reliably in both structured and unstructured settings. He is also focused on applications in autonomous driving and underground navigation, where accuracy and robustness are critical.

Awards:

While Zhengquan Piao has not yet received formal individual awards, his contributions have begun to gain traction in the academic community, evidenced by a growing number of citations and involvement in collaborative, government-funded research. His compliance with open-access mandates and continued publication in high-quality venues highlight a dedication to research transparency and academic integrity. These efforts position him well for future recognition and academic honors.

Publications:

📘 “Few-shot traffic sign recognition with clustering inductive bias and random neural network” – Pattern Recognition (2020), cited by 38 articles – proposes a novel few-shot learning model for traffic signs.
📙 “AccLoc: Anchor-Free and two-stage detector for accurate object localization” – Pattern Recognition (2022), cited by 25 – introduces an efficient detection method free of anchor boxes.
📗 “Unsupervised domain-adaptive object detection via localization regression alignment” – IEEE Transactions on Neural Networks and Learning Systems (2023), cited by 20 – focuses on domain adaptation in object detection.
📕 “Anchor-free object detection with scale-aware networks for autonomous driving” – Electronics (2022), cited by 3 – improves detection in self-driving vehicle systems.
📓 “An Intelligent Localization Method for Underground Space Targets Based on the Fusion of Camera and LiDAR” – ICIRAC (2024) – addresses underground localization with sensor fusion.
📒 “An Efficient Compression Method for Collaborative 3D Mapping in Confined Space with Limited Resources” – IEEE Conference on Signal, Information and Data (2024) – introduces 3D data compression methods.
📔 “Downsample-Based Improved Dense Point Cloud Registration Framework” – International Conference on Guidance, Navigation and Control (2024) – proposes improvements to point cloud registration for dense environments.

Conclusion:

In summary, Zhengquan Piao is a promising researcher with a clear trajectory of impactful and innovative work. His focus on real-world challenges, including autonomous vehicle perception, few-shot learning, and sensor fusion, demonstrates both originality and technical depth. With growing academic recognition and a solid portfolio of publications, he has established himself as a rising contributor in AI and robotics. Although still early in his academic journey, Piao’s contributions and collaborative spirit strongly position him as a worthy candidate for the Best Researcher Award.

 

 

 

 

Osama Elshazly | Mechatronics and Robotics | Best Researcher Award

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Dr. Osama Elshazly | Mechatronics and Robotics | Best Researcher Award

Assistant Professor at Faculty of Electronic Engineering Menouf – Menoufia University, Egypt

 

Dr. Osama Elshazly is an accomplished Assistant Professor with a robust background in Mechatronics and Robotics Engineering. Based in Cairo, Egypt, Dr. Elshazly has dedicated his career to advancing control systems, artificial intelligence in engineering, and automation technology. His innovative approach integrates research, teaching, and practical applications, contributing to various significant projects in the field. He has also demonstrated leadership in mentoring students for international robotics competitions, emphasizing the real-world application of theoretical knowledge. Dr. Elshazly’s commitment to academic excellence and research innovation positions him as a leader in his field.

Profile 

Google Scholar

Education

Dr. Elshazly completed his Ph.D. in Mechatronics and Robotics Engineering at the Egypt-Japan University of Science and Technology (E-JUST) in 2015. His research during this time focused on cutting-edge control techniques and artificial intelligence applications in dynamic systems. Before pursuing his doctoral studies, he earned an MSc in Automatic Control Engineering from Menoufia University in 2012, where he developed a strong foundation in control theory and electronic systems. He also holds a BSc in Electronic Engineering from Menoufia University, awarded in 2004, where he laid the groundwork for his future endeavors in Mechatronics and Robotics.

Experience

Dr. Elshazly has extensive teaching and research experience, having served as an Assistant Professor in several prestigious institutions. His career began at Menoufia University, where he worked as an Assistant Lecturer, later advancing to Assistant Professor from 2015 to 2020. During this time, he was actively involved in teaching courses related to Mechatronics, Robotics, and Control Systems, as well as managing quality assurance processes. He continued his academic career at the High Institute of Engineering and Technology, taking on a full-time Assistant Professor role while also engaging with other institutions such as Modern University for Technology and Information, and Université Française d’Egypte, where he taught part-time. His experience extends internationally, having been a Research Fellow at Catania University in Italy, where he contributed to collaborative projects in dynamic systems control.

Research Interests

Dr. Elshazly’s research focuses on the intersection of Mechatronics, Robotics, and Automatic Control Engineering, with specific interests in dynamical systems, robotic manipulators, and AI-based control techniques. He is particularly passionate about advancing control strategies for complex systems, such as iterative learning control and adaptive control methodologies. His work in artificial intelligence control, fractional order control, and new AI model architectures has yielded significant contributions to the development of autonomous systems. His ongoing research explores the integration of AI algorithms in real-time control and optimization, aiming to solve complex engineering problems with innovative solutions.

Awards

Dr. Elshazly has received recognition for his contributions to the field of engineering, including grants and funds that support his research initiatives. Notably, he was involved in the Mobility Grant Project between Catania University and Egypt-Japan University, funded by the Science and Technological Development Fund (STDF) of Egypt, which provided substantial resources for collaborative research. He has also been acknowledged for his role as a supervisor for students in international competitions, such as the International Autonomous Robot Racing Challenge and the IDC Robocon, which further showcases his ability to guide young talent towards technical excellence.

Publications

Omar Shaheen, Osama Elshazly, Hossam Khalil. “Quantum Neural Networks Based Lyapunov Stability and Adaptive Learning Rates for Identification of Nonlinear Systems.” Ain Shams Engineering Journal, 2024. Link.

Omar Shaheen, Osama Elshazly, Abdullah Baihan, Walid El-Shafai, Hossam Khalil. “Advancing Nonlinear Dynamics Identification with Recurrent Quantum Neural Networks: Emphasizing Lyapunov Stability and Adaptive Learning in System Analysis.” Alexandria Engineering Journal, 2024. Link.

Hossam Khalil, Osama Elshazly, Omar Shaheen. “Optimal Fuzzy Pre-compensated TID Controller for Nonlinear Dynamical Systems.” Menoufia Journal of Electronic Engineering Research (MJEER), 2024.

Mostafa E., Osama Elshazly, El-Bardini M. et al. “Embedded Adaptive Fractional-Order Sliding Mode Control Based on TSK Fuzzy System for Nonlinear Fractional-Order Systems.” Soft Computing, 2023. Link.

E. Mostafa, A. M. El-Nagar, Osama Elshazly, M. El-Bardini. “Fractional Order Sliding Mode Controller for Coupled Tank System.” Menoufia Journal of Electronic Engineering Research (MJEER), 2021.

E. Mostafa, A. M. El-Nagar, Osama Elshazly, M. El-Bardini. “Adaptive Fractional Order Sliding Mode Control for a Nonlinear System.” 2021 International Conference on Electronic Engineering (ICEEM), 2021. Link.

Conclusion

Dr. Osama Elshazly has demonstrated a commitment to advancing engineering research, particularly in Mechatronics and Control Engineering. His innovative research contributions, combined with a passion for teaching and mentoring, make him a strong candidate for the Best Researcher Award. His career reflects a dedication to solving complex engineering challenges through interdisciplinary approaches, showcasing his potential to continue making significant impacts in his field.