Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

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Ms. Asima Sarwar | AI in Engineering | Best Researcher Award

Ms. Asima Sarwar | Ghulam Ishaq Khan Institute of Engineering Sciences and Technology | Pakistan

Ms. Asima Sarwar is a computer engineer and researcher with expertise in Artificial Intelligence, Data Engineering, and Machine Learning. She is pursuing a PhD in Computer Engineering with research focused on AI, data analytics, and distributed computing systems. Her academic background includes a master’s degree in Computer Systems Engineering, specializing in Smart Grids and the Internet of Things, and a bachelor’s degree in Electrical Engineering (Communication). She has professional experience as a computer engineer, research assistant, and lecturer, contributing to projects in secure IoT device development, cyber-secure systems, and AI-based data processing. Ms. Asima has taught various undergraduate and postgraduate courses including Big Data Analytics, Machine Learning, Generative AI, Operating Systems, and Ethical AI. Her work emphasizes technical innovation, algorithmic optimization, and the integration of intelligent systems for real-world applications. With strong analytical and problem-solving skills, she is actively involved in advancing research in AI-driven technologies, data engineering, and computer vision. Her contributions reflect a balance between academic rigor, applied research, and technological development aimed at improving system efficiency and advancing modern computing solutions.

Profile: Scopus

Featured Publications

  • Sarwar, A., Usman, M., Hussain, M., Jadoon, K. K., Manzoor, T., & Ali, S. (2025). AI-powered deep ultraviolet laser diode design for resource-efficient optimization. Journal of Computational Electronics, 24(4), 1–19.

  • Mahmood, M. A., Maab, I., Sibtain, M., Sarwar, A., Arsalan, M., & Hussain, M. (2025, March). Advancements in sentiment analysis: A methodological examination of news using multiple LLMs. In Proceedings of the 31st Annual Meeting of the Association for Natural Language Processing.

  • Sarwar, A., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Enhanced anomaly detection system for IoT based on improved dynamic SBPSO. Sensors MDPI, 22(4926).

  • Sarwar, A., Hassan, S., Khan, W. U., Marwat, S. N. K., & Ahmed, S. (2022). Design of an advance intrusion detection system for IoT networks. In Proceedings of the 2nd International Conference on Artificial Intelligence (ICAI) (pp. 46–51).

  • Ijaz, A. Z., Ali, R. H., Sarwar, A., Khan, T. A., & Baig, M. M. (2022). Importance of synteny in homology inference. In Proceedings of the IEEE International Conference on Emerging Technologies (ICET).

  • Azam, T., Tahir, F. A., Sarwar, A., & Qayyum, M. A. (2023). A high gain wide band compact size dual band patch antenna for 5G application. In Proceedings of the IEEE International Conference on Emerging and Sustainable Technologies (ICEST) (pp. 1–3).

 

 

Dr. S. Thirunavukkarasu | AI in Engineering | Best Researcher Award

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Dr. S. Thirunavukkarasu | AI in Engineering | Best Researcher Award

Dr. S. Thirunavukkarasu | Indira Gandhi Centre for Atomic Research | India

Dr. S. Thirunavukkarasu research focuses on quantitative nondestructive evaluation (NDE), finite element (FE) modeling, digital signal and image processing, and the development of innovative sensors and instrumentation for advanced inspection applications. His work emphasizes multi-parametric linear and nonlinear regression, radial basis function (RBF), and multidimensional RBF neural networks for accurate flaw sizing in eddy current testing. He has contributed to FE modeling of electromagnetic NDE phenomena, including the optimization of remote field eddy current probe parameters for ferromagnetic steam generator tube inspections and modeling of magnetic flux leakage considering nonlinear magnetic permeability. His studies extend to the simulation of pulsed and sweep frequency eddy current methods to improve detection efficiency. Additionally, his research in wavelet transform–based digital signal processing enhances the interpretation of eddy current signals from complex regions such as bends and support plate intersections. He has also advanced in-house development of remote field eddy current techniques for the inspection of modified 9Cr-1Mo steel steam generator tubes. His computational expertise includes MATLAB, Python, and LabVIEW, alongside specialized software such as COMSOL, FEMM, and CIVA for modeling and simulation in electromagnetic and NDE applications.

Profile: Orcid

Featured Publications

Arun, A. D., Rajiniganth, M. P., Chandra, S., & Thirunavukkarasu, S. (2025). A numerical model of parallel disc capacitor probe used in nondestructive dielectric permittivity evaluation by algebraic topological method. International Journal of Applied Electromagnetics and Mechanics, 2025-09.

Sharatchandra Singh, W., Haneef, T. K., Thirunavukkarasu, S., & Kumar, A. (2025). In-situ measurement of tensile deformation-induced magnetic fields in high strength low alloy steels using GMR based metal magnetic memory technique. International Journal of Applied Electromagnetics and Mechanics, 2025-09-10.

Arun, A. D., Chandra, S., Thirunavukkarasu, S., Rajiniganth, M. P., Malathi, N., & Sivaramakrishna, M. (2025). A novel algebraic topological method-based approach for evaluating stored electrostatic energy and 3D Maxwellian capacitance. Journal of Electrostatics, 2025-06.

Thirunavukkarasu, S., Kumar, A., Martin, J. P., Harini, T., Reddy, S., Emil, S., & Balu, C. (2025). Automated detection of defects in eddy current inspection data using machine learning methods. International Journal of Applied Electromagnetics and Mechanics, 2025-06-03.

Balakrishnan, S., Das, C. R., Thirunavukkarasu, S., & Kumar, A. (2025). In-situ hardness evaluation of hard-faced coatings through eddy current NDE. International Journal of Applied Electromagnetics and Mechanics, 2025-05-23.

Vijayachandrika, T., Arjun, V., Thirunavukkarasu, S., & Kumar, A. (2025). Design, fabrication, and characterization of staggered array radial coil RFEC probe for small diameter ferritic steel tube. IEEE Sensors Journal, 2025-05-01.

Dr. Mingwei Zhao | Electrical Engineering | Excellence in Research Award

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Dr. Mingwei Zhao | Electrical Engineering | Excellence in Research Award

Dr. Mingwei Zhao , Jiangsu Normal University & Shanghai University , China.

Dr. Mingwei Zhao 🎓, born in Shandong, China 🇨🇳, is a dedicated researcher and educator in the field of electrical engineering ⚡. He currently serves as a Lecturer at Jiangsu Normal University 🏫, specializing in power electronics and robotic systems 🤖. With a strong foundation in mechanical and electrical integration 🔧, he pursued advanced studies in power drive systems and control science 🎛️. His ongoing research is focused on innovative power electronic drive technologies and intelligent robotics 💡. Dr. Zhao’s work contributes significantly to automation and energy efficiency solutions in modern engineering 🌐.

Professional Profile

Scopus

Education & Experience

  • 🎓 B.S. in Mechanical and Electrical Integration, Nanjing University of Science and Technology, 2004

  • 🎓 M.S. in Power Electronics and Power Drive, Nanjing University of Aeronautics and Astronautics, 2012

  • 🎓 Ph.D. (in progress) in Control Science and Engineering, Shanghai University, since 2016

  • 👨‍🏫 Lecturer, School of Electrical Engineering and Automation, Jiangsu Normal University

Summary Suitability

Dr. Mingwei Zhao is a highly dedicated and innovative researcher, making him a strong candidate for the Excellence in Researcher Award. With a solid academic foundation and over a decade of hands-on experience, Dr. Zhao has significantly advanced the fields of power electronics drive technologies and robotic systems through both applied research and academic leadership.

Professional Development

Dr. Zhao has continually expanded his expertise through rigorous academic pursuits and hands-on teaching experience 👨‍🔬. Currently undertaking his Ph.D. at Shanghai University 🎓, he stays at the forefront of technological innovation by integrating control systems and robotics into his curriculum 🤖📘. His involvement in both academic and applied research ensures that his contributions are both theoretically sound and practically relevant 🛠️. As a Lecturer, he mentors students and fosters a dynamic learning environment 🎤💡. His professional development reflects a balanced focus on research, teaching, and practical implementation in modern automation systems 🚀.

Research Focus

Dr. Zhao’s research is centered in the Electrical and Automation Engineering category ⚙️, with a specialized emphasis on power electronic drive systems ⚡ and robotic control integration 🤖. He explores the design and control of energy-efficient power systems, automation technology, and intelligent robotic operations 🔋🔍. His current studies contribute to advancing smart drive technologies that are vital for automation and Industry 4.0 initiatives 🏭💡. By leveraging multidisciplinary principles, he aims to develop sustainable and intelligent engineering systems that enhance the performance and adaptability of electrical devices 🌱🧠.

Publication Top Notes

  • Title:
    An Open-Circuit Fault Diagnosis for Three-Phase PWM Rectifier Without Grid Voltage Sensor Based on Phase Angle Partition

  • Authors:
    Mingwei Zhao, et al.

  • Journal:
    IEEE Transactions on Circuits and Systems I: Regular Papers

  • Publisher:
    IEEE (Institute of Electrical and Electronics Engineers)

  • Year:
    2024

  • Citations:
    4 (as of the latest count)

  • Summary:
    This article presents a fault diagnosis method for three-phase PWM rectifiers that eliminates the need for a grid voltage sensor. It uses phase angle partitioning to detect open-circuit faults efficiently, enhancing system reliability.

Conclusion

Dr. Zhao’s pioneering efforts and sustained contributions to power electronics and robotics make him a worthy nominee for the Excellence in Researcher Award. His blend of theoretical expertise, practical engineering solutions, and educational leadership continues to push the boundaries of automation and intelligent systems, benefiting both academia and industry.