Abdelsalam Ahmed | Electrical Properties of Materials | Best Researcher Award

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Prof. Abdelsalam Ahmed | Electrical Properties of Materials | Best Researcher Award

Prof. Abdelsalam Ahmed | Tanta University | Egypt

Prof. Abdelsalam A. Ahmed is a distinguished Egyptian academic currently serving as a Professor in the Department of Electrical Power and Machines Engineering at Tanta University’s Faculty of Engineering. Based in Kafr El-Sheikh, he received his bachelor’s and master’s training at Tanta University before earning his doctorate in China at Harbin Institute of Technology. He then advanced his scholarly portfolio with postdoctoral experiences at Harbin Institute of Technology followed by further refinement of his expertise through a fellowship in Seoul, South Korea. In recent years his career has been marked by a blend of high-level academic leadership, direction of an Electric and Hybrid Electric Vehicles Technology Laboratory, and industrial engagement in China’s electric vehicle sector. Complemented by strategic planning coordination at his faculty and a commitment to driving quality teaching, Prof. Ahmed exemplifies the modern global engineer-scholar who bridges theory, innovation and practical application.

Profiles

SCOPUS

ORCID

Education

Prof. Ahmed’s academic path reflects a deeply rooted foundation in electrical engineering and advanced vehicle systems. He embarked on his studies at Tanta University, where he completed rigorous training in electrical power and machines. He then pursued a Ph.D. in Electrical Engineering and Automation at Harbin Institute of Technology in China, focusing on intelligent power management and control strategies for hybrid electric vehicles based on permanent magnet machines. Following his doctoral studies, he continued to enrich his expertise through postdoctoral fellowships in both China and South Korea, where he deepened his understanding of instrumentation science, electric traction, and renewable energy systems. These experiences across continents cultivated a rich interdisciplinary and international perspective that underpins his teaching, research, and leadership in electric vehicle technologies.

Professional Experience

Prof. Ahmed has built a dynamic academic and professional career anchored in teaching, research leadership, and industrial collaboration. After attaining his doctorate, he served in postdoctoral roles in China and Korea, during which time he honed his expertise in electric vehicle control systems and renewable energy. He joined Tanta University and gradually advanced through academic ranks to become a full professor in the department of electrical machines and power systems, where he presently leads initiatives in electric and hybrid electric vehicles technologies. He directed a university laboratory dedicated to vehicle powertrain innovation, coordinated faculty-level strategic planning, and also ventured into industrial practice in China’s electric vehicle manufacturing sector. His broad roles encompass supervision of graduate theses, peer reviewing for international journals, participation in research grants, and contributing to curriculum enhancement and administration, reflecting a career that bridges academia and real-world applications.

Research Interests

Prof. Ahmed’s research is anchored in the design, control, and optimization of electric vehicle systems and renewable energy technologies. His interests span advanced control schemes for electric traction, such as field-oriented control, direct torque control, model predictive control, and fuzzy logic optimization. He has developed power electronics modules including inverters, converters, and battery management systems geared toward lithium-ion and lead-acid energy storage solutions. Additionally, he explores renewable energy integration via solar, wind, and fuel cell technologies, and applies heuristic techniques like genetic algorithms and grey wolf optimization for system performance improvement. His interdisciplinary and application-driven approach addresses both the hardware and intelligent algorithmic aspects of sustainable transport and energy platforms.

Awards

Prof. Ahmed’s dedication to research and scholarship has been consistently recognized through numerous publication awards conferred by his university over several years, acknowledging his sustained contributions to scientific literature. He also earned prestigious postdoctoral fellowship support in China through a science and technology partnership program, and in Korea via a national research foundation fellowship, both of which underscore the international esteem of his work. Furthermore, he secured competitive research funding through an Egypt-France cooperation program that enabled advanced vehicle systems research in a specialized fuel cell laboratory in France. These honors reflect his exceptional research performance, international collaboration, and ability to attract competitive funding for innovative projects.

Publication Top Notes

Energy Management and Power Distribution for Battery/Ultracapacitor Hybrid Energy Storage System in Electric Vehicles with Regenerative Braking Control

Journal: Mathematical and Computational Applications (2025)
Authors: Abdelsalam A. Ahmed, Young Il Lee, Saleh Al Dawsari, Ahmed A. Zaki Diab, Abdelsalam A. Ezzat

Impartial near-optimal control and sizing for battery hybrid energy system balance via grey wolf optimizers: Lead acid and lithium-ion technologies

Journal: IET Renewable Power Generation (2022)
Author: Abdelsalam A. Ahmed.

Trajectory sensitivity analysis-based systematic Q-matrix of DFIG with LQR auxiliary voltage and power compensation for oscillation damping

Journal: International Journal of Electrical Power and Energy Systems (2022)
Authors: Abdelsalam, H.A, Ahmed, A.A., Diab, A.A.Z.

A comparative dynamic analysis between model predictive torque control and field-oriented torque control of IM drives for electric vehicles

Journal: International Transactions on Electrical Energy Systems (2021)
Authors: A. Ahmed, A, M. Akl, M, M. Rashad, E.E.

A new modulated finite control set-model predictive control of quasi-Z-source inverter for PMSM drives

Journal: Electronics (Switzerland) (2021)
Authors: Ahmed, A.A, Bakeer, A., Alhelou, H.H, Siano, P, Mossa, M.A.

Conclusion

Prof. Abdelsalam A. Ahmed represents a highly accomplished academic whose career integrates rigorous research, dynamic teaching, international collaboration, and impactful industrial engagement. From his foundational education at Tanta University to his advanced postdoctoral research in China and South Korea, and active participation in global research projects, his profile exemplifies the modern engineer-scientist. His leadership in electric vehicle technologies and renewable energy systems fosters innovation and practical solutions to energy challenges. As an educator, he guides future engineers through advanced courses and lab work; as a researcher, he advances control strategies and energy management; and as a leader, he contributes to strategic planning and academic institutional development. Prof. Ahmed’s trajectory is a compelling model of scholarly excellence, impactful research, and the bridging of academia and industry.

Marija Milijic | Electrical Properties of Materials | Best Researcher Award

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Dr. Marija Milijic | Electrical Properties of Materials | Best Researcher Award

Dr. Marija Milijic | University of Nis | Serbia

Dr. Marija Milijić is a dedicated researcher and academic in the field of telecommunications, specializing in antenna modeling and microwave systems. She has built her career at the Faculty of Electronic Engineering, University of Niš, Serbia, where she has contributed extensively through teaching, research, and conference leadership. Her expertise lies in bridging theoretical approaches with practical applications, particularly in developing printed antenna structures and advancing techniques in biosensing and wireless communication. Over the years, she has played an active role in international scientific communities through membership in IEEE societies, organizing major conferences, and contributing to collaborative projects with distinguished global researchers. Her career path reflects a strong balance of scientific rigor, innovative thinking, and dedication to academic mentorship, ensuring the growth of future engineers and scientists in the field of microwave theory, antennas, and telecommunications systems.

Profiles

SCOPUS

ORCID

Education

Dr. Marija Milijić completed her higher education at the Faculty of Electronic Engineering, University of Niš, Serbia, where she pursued undergraduate, postgraduate, and doctoral studies in telecommunications. Her undergraduate studies equipped her with a strong foundation in electrical engineering, fostering an early interest in applied electromagnetics and communication technologies. She continued with postgraduate research on the modeling of electromagnetic propagation and microstrip patch antennas in wireless communication systems using artificial neural networks, marking her early exploration into intelligent computational methods in engineering. Her doctoral research advanced these interests significantly, focusing on modeling integrated printed antenna structures and three-dimensional reflectors with optimized side lobe suppression, a topic of great significance for modern communication and radar systems. Her educational path demonstrates a consistent progression from fundamental engineering to advanced interdisciplinary integration of antennas, neural networks, and applied telecommunications, establishing her as a well-rounded expert with solid academic and research credentials.

Experience

Dr. Milijić has steadily advanced through academic and research positions at the Faculty of Electronic Engineering, University of Niš, Serbia, where she began her career as a research assistant supported by a scholarship from the Ministry of Science and Technological Development. She then contributed as a research associate, expanding her technical knowledge and building collaborative ties in the field of antennas and microwaves. Her academic contributions were further enriched when she took on teaching roles, first as a teaching assistant and later as a teaching assistant with a doctoral degree, guiding students in both theoretical learning and practical applications of telecommunications. Beyond her teaching responsibilities, she has served in organizing committees of major international conferences, such as TELSIKS and ICEST, actively supporting knowledge exchange in the global scientific community. Her professional trajectory reflects a seamless blend of teaching, research, and organizational leadership that has significantly strengthened the academic and research ecosystem at her institution.

Research Interest

Her research interests focus on advancing antenna design, microwave modeling, and the application of artificial intelligence in telecommunications. She has devoted considerable effort to the modeling of printed antenna structures, integrated with three-dimensional reflectors for applications requiring high side lobe suppression and shaped radiation patterns. Another key area of her work involves the application of artificial neural networks to complex problems in microwaves, with particular contributions to the modeling and optimization of printed antennas and RF MEMS devices. Recently, her interests have extended to biosensing applications, where novel antenna designs can significantly enhance non-invasive biomedical monitoring. This interdisciplinary integration of telecommunications, artificial intelligence, and biomedical engineering highlights the innovative nature of her contributions. Through her research, she addresses both fundamental scientific questions and practical engineering challenges, advancing knowledge in antenna theory while enabling technologies with broad applications in wireless communication, healthcare, and energy-efficient systems for modern society.

Publication Top Notes

Polarimetric Assessment Methodology for Doppler Radar Respiratory Measurements

Authors: Jon H. Itokazu, Marija Milijić, Branka Jokanović, Olga Boric-Lubecke, Victor M. Lubecke
Journal: IEEE Transactions on Microwave Theory and Techniques

Dual-Port Butterfly Slot Antenna for Biosensing Applications

Authors: Marija Milijic, Branka Jokanovic, Miodrag Tasic, Sinisa Jovanovic, Olga Boric-Lubecke, Victor Lubecke
Journal: Sensors

Analysis of Feeding Methods for High-Gain Crossed Slot Antenna Arrays

Authors: Marija Milijic, Branka Jokanovic
Journal: 9th IcETRAN Conference

Advanced High-Gain Slot Antenna Arrays for 5G and Radar Applications

Authors: Marija Milijić, Branka Jokanović
Journal: Telfor Journal

Printed Antenna Array with Flat-Top Radiation Pattern

Authors: Marija R. Milijić, Aleksandar D. Nešić, Bratislav D. Milovanović, Dušan A. Nešić
Journal: Frequenz

Conclusion

Dr. Marija Milijić represents the profile of a researcher whose career blends academic excellence, scientific innovation, and professional leadership. Her body of work highlights critical advances in antenna design, microwave modeling, and neural network applications, all of which contribute directly to the evolution of telecommunications and related fields. Beyond her personal research achievements, she has also demonstrated consistent service to her community through her teaching role, mentorship, and active participation in professional organizations and conferences. Her commitment to promoting women in engineering and supporting young researchers underlines the broader social and academic value of her contributions. With her interdisciplinary research, impactful publications, and leadership in professional communities, she stands out as a scientist of high merit. Recognizing her through this award would not only honor her individual achievements but also encourage further innovation and inclusivity in the fields of engineering and telecommunications.

Dr Dongfang Zhao | Electrical Properties of Materials | Best Innovator Award

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Dr Dongfang Zhao | Electrical Properties of Materials | Best Innovator Award

Dr. Dongfang Zhao is a dedicated researcher and lecturer at Shanghai Polytechnic University, specializing in artificial intelligence and mechanical fault diagnosis. Since 2014, he has advanced the frontiers of intelligent systems in industrial diagnostics. After completing his Ph.D. at Shanghai University in 2021, he pursued postdoctoral research at the Control Science and Engineering Research Station. Recognized nationally for his outstanding doctoral dissertation, Dr. Zhao continues to drive innovation in AI-enhanced fault detection. He has published 20+ SCI/EI-indexed papers, earning ~500 citations, and contributes significantly to academic and practical advancements. 📚🤖🔧

Dr Dongfang Zhao, Shanghai Polytechnic University, China

Profile

SCOPUS

🎓 Education

Dr. Zhao earned his Ph.D. in Measurement, Control, and Instrumentation from Shanghai University in 2021, where his dissertation was nominated for a national excellence award. He built his foundation in engineering and research through a series of rigorous academic training steps. Following graduation, he immediately joined a prestigious postdoctoral program in Control Science and Engineering. His academic path reflects a commitment to merging theoretical depth with applied innovation in intelligent diagnostics and control systems. 📖🎓🔍

💼 Experience

Currently serving as a lecturer and master’s supervisor at Shanghai Polytechnic University, Dr. Zhao is actively engaged in research and teaching. He completed a postdoctoral fellowship at the Control Science and Engineering Research Station of Shanghai University. Professionally, he has collaborated on several government and industrial research projects, including two National Natural Science Foundation of China initiatives and AI-enabled fault analysis programs. His diverse academic and industry-facing experiences reflect a holistic career in intelligent systems and real-world fault mitigation. 🏫🧪⚙️

🛠️ Contributions

Dr. Dongfang Zhao has developed three advanced AI models tailored to tackle diverse industrial diagnostic challenges. DRANet enhances fault detection accuracy under strong noise 💥, while DBANet ensures precise diagnosis under variable rotational speeds 🔄 using extreme multi-scale entropy. Furthermore, SCANet excels in unlabeled diagnostic scenarios 🚫🏷️, reducing domain confusion and boosting generalization 🌐. These innovations significantly advance smart maintenance, ensuring reliability, adaptability, and intelligence in complex mechanical systems—paving the way for next-generation predictive diagnostics in Industry 4.0 🔧🌍.

📡 Research Projects 

Dr. Dongfang Zhao has led five significant research projects, including a prestigious Super Postdoctoral Incentive Program project in Shanghai, an AI-enabled initiative from the Shanghai Education Commission, and two provincial/ministerial key lab open funds. He also directed a school-level youth innovation project at Shanghai Polytechnic University. Additionally, as a technical backbone, he has contributed to two National Natural Science Foundation of China projects, a Shanghai Science & Agriculture program, and a classified aerospace project. These engagements demonstrate his leadership in AI-driven diagnostics and interdisciplinary collaboration across academia and high-impact industries. 🚀🧠📡🔍💼

🔬 Research Focus

Dr. Zhao’s research revolves around AI-based fault diagnosis for mechanical systems, particularly in complex and noisy environments. His work spans domain adaptation networks (DRANet, DBANet, SCANet) and advanced entropy-based methods for speed-invariant diagnostics. He develops frameworks that increase system robustness, generalization, and diagnostic precision under non-ideal conditions. This includes intelligent modeling for unlabeled or variable-speed environments—challenges typical in modern industrial systems. His research directly supports smart manufacturing, predictive maintenance, and Industry 4.0 frameworks. 🧠📡🔩

📘 Publications

Pearson Coefficient Enhanced Multi-Branch Joint Attention Network and Adaptive Decomposition Based Dual Adaptive Fault Diagnosis Scheme for Rolling Bearing

👨‍🔬 Authors: Dongfang Zhao, et al.
📘 Journal: Signal, Image and Video Processing
📅 Year: 2025
📌 Overview: This paper presents a novel dual adaptive fault diagnosis framework leveraging Pearson correlation coefficients and a joint attention mechanism, significantly improving the accuracy of bearing fault detection under complex conditions.
📊🧠🛠️

Multiscale Attention Feature Fusion Network for Rolling Bearing Fault Diagnosis Under Variable Speed Conditions

👨‍🔬 Authors: Dongfang Zhao, et al.
📘 Journal: Signal, Image and Video Processing

📅 Year: 2024
📌 Overview: This study introduces a multiscale attention-based deep learning model that enhances feature fusion and generalization in rolling bearing fault diagnosis, particularly effective under varying speed scenarios.
⚙️🔍📉

Prof Can Li | Electrical Properties of Materials | Best Researcher Award

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Prof Can Li | Electrical Properties of Materials | Best Researcher Award

Prof. Dr. Can Li is an innovative researcher and Assistant Professor at the University of Hong Kong, specializing in neuromorphic computing, AI hardware, and memristor-based systems 🧬💾. He earned his Ph.D. in Electrical and Computer Engineering from UMass Amherst, after completing his M.S. and B.S. in Microelectronics at Peking University 📘🔌. With over 90 publications, HK$45M+ in grants, and multiple patents, his work bridges advanced electronics, machine learning, and brain-inspired systems 🌍📊. He actively mentors students, collaborates internationally, and serves on major editorial boards, shaping the future of intelligent computing technology 🚀🔍.

Prof Can Li, The University of Hong Kong, Hong Kong

Profile

SCOPUS

GOOGLESCHOLAR

Education 🎓

Prof. Dr. Can Li earned his Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst in 2018, under the mentorship of Prof. Qiangfei Xia 🧠📘. His dissertation, “CMOS Compatible Memristor Networks for Brain-Inspired Computing”, laid the foundation for his cutting-edge work in neuromorphic hardware ⚙️🧬. He holds both M.S. (2012) and B.S. (2009) degrees in Microelectronics from Peking University, China, mentored by Prof. Wengang Wu 📡🔌. His academic path combines solid-state electronics, AI hardware, and advanced semiconductor design, empowering his innovations in brain-inspired and in-memory computing systems 🌐🧑‍🔧.

Experience 💼

Prof. Can Li is currently an Assistant Professor at The University of Hong Kong (2020–Present) 🇭🇰, serving in the Department of Electrical and Electronic Engineering ⚡. His academic role focuses on advanced system design, hardware acceleration, and energy-efficient computing 💻🔋. Before this, he worked as a Research Associate at Hewlett Packard Labs in Palo Alto, California (2018–2020) 🇺🇸, contributing to architectural innovation within the System Architecture Lab 🧠🛠️. His industry and academic experience reflect a deep commitment to cutting-edge research in computer architecture and system performance optimization across real-world and theoretical applications 🚀📊.

Achievements & Innovations 🏅

Prof. Can Li has received numerous prestigious honors, including being ranked in the Top 1% worldwide by citations (Clarivate, 2022–2024) 🌍📊, the Croucher Tak Wah Mak Innovation Award (2023) 🧠🏆, the RGC Early Career Award (2021) 🧑‍🔬, and the NSFC Excellent Young Scientists Fund (2021) 🌟. He holds 17 granted patents across the US and China, focused on analog content-addressable memory (CAM), fuzzy search, optical TCAMs, and neuromorphic systems 🔧💡. These contributions demonstrate his pioneering work at the interface of hardware acceleration, AI computing, and next-gen memory systems 🚀🖥️.

Book Contributions 📘

Prof. Can Li has significantly contributed to the field of neuromorphic and in-memory computing through key book chapters in high-impact scientific texts. He co-authored “In-Memory Computing with Non-volatile Memristor CAM Circuits” and “Ta/HfO₂ Arrays for In-Memory Memristor Computing” in Memristor Computing Systems (Springer, 2022) 📘⚡. Additionally, he authored “Silicon Based Memristor Devices and Arrays” in the Handbook of Memristor Networks (Springer, 2019) 🔍🧠. These works showcase his pioneering role in advancing memristor technology and its integration into next-generation computational architectures 🖥️🧩.

Research Focus 🔬

Prof. Can Li’s research centers on cutting-edge innovations in artificial intelligence hardware and emerging memory technologies. His work focuses on developing AI inference and training accelerators using advanced memristor-based architectures 🧠💾. He explores novel applications of memristor crossbar arrays, including image processing, hardware security, and pattern matching 🖼️🔐🔍. A core part of his research also involves the CMOS-compatible integration of memristor devices at the array level, enhancing scalability and manufacturability ⚙️🔧. By bridging nanotechnology with AI computing, Prof. Li is advancing the future of energy-efficient, high-performance computing systems for next-generation intelligent electronics 🚀🖥️.

Publications 📚

Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks

Authors: Junyi Yang, Ruibin Mao, Mingrui Jiang, Can Li, Arindam Basu
Journal: Nature Communications, 2025

Current Opinions on Memristor-Accelerated Machine Learning Hardware

Authors: Mingrui Jiang, Yichun Xu, Zefan Li, Can Li
Journal: Current Opinion in Solid State and Materials Science, 2025
Emojis: 💾🧠🧩📈

Efficient Coherent Polarization Beam Combining of 16-Channel Femtosecond Fiber Lasers

Authors: Jiayi Zhang, Bo Ren, Can Li, Wenxue Li, Pu Zhou
Journal: Guangxue Xuebao/Acta Optica Sinica, 2025

Research Progress of Ultrafast Fiber Laser Amplifier Based on Gain Managed Nonlinearity (Invited)

Authors: Can Li, Bo Ren, Kun Guo, Jingyong Leng, Pu Zhou
Journal: Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2025

Event-Based Multi-Object Tracking With Sparse Motion Features

Authors: Song Wang, Zhu Wang, Can Li, Xiaojuan Qi, Hayden Kwok Hay So
Journal: IEEE Access, 2025

An InGaZnO Synaptic Transistor Using Titanium-Oxide Traps at Back Channel for Neuromorphic Computing

Authors: B. F. Yang, Chen Zhang, Z. H. Zhang, Can Li, Xiaodong Huang
Journal: IEEE Transactions on Electron Devices, 2025