Tinggui Chen | Engineering | Research Excellence Award

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Dr. Tinggui Chen | Engineering | Research Excellence Award 

Hefei University of Technology | China

Dr. Tinggui Chen is a highly accomplished researcher and academic in the field of mechanical engineering, with a specialized focus on acoustic metamaterials, phononic crystals, and advanced signal detection techniques. He completed his doctoral studies in mechanical engineering under the supervision of Prof. Dejie Yu at Hunan University, after earning both his bachelor’s degree from Hainan University and master’s degree from Hunan University. During his doctoral tenure, he developed innovative methodologies for enhancing acoustic sensing and signal detection using engineered metamaterials, establishing a strong foundation for his research career. Dr. Chen’s work is characterized by its combination of theoretical insight and experimental rigor, particularly in the design and application of gradient metamaterials, coiling-up structures, and space-time-modulated systems. His research has led to significant advancements in weak signal detection, directional acoustic sensing, and energy amplification in phononic systems. Notably, his studies on multi-frequency signal enhancement via gradient defect phononic crystals and space-time-modulated airborne acoustic circulators demonstrate his ability to bridge fundamental physics with practical engineering applications. He has actively contributed to the international scientific community through his extensive publication record, which includes articles in high-impact journals such as Measurement, Physical Review Applied, IEEE Transactions on Industrial Informatics, Mechanical Systems and Signal Processing, Journal of Sound and Vibration, IEEE Sensors Journal, Journal of Physics D: Applied Physics, and Physical Review B. These publications reflect his sustained focus on acoustic metamaterials, phononic crystal resonators, and novel techniques for signal demodulation and amplification, marking him as a leading expert in his domain. Dr. Chen’s research trajectory has also been enriched by international exposure and collaborative experiences. As a visiting scholar at EPFL under Prof. Romain Fleury, he explored cutting-edge experimental demonstrations in acoustic systems, further strengthening his expertise in wave manipulation and signal processing. Currently, as a postdoctoral researcher at Shanghai Jiao Tong University and an assistant professor at Hefei University of Technology, he continues to advance both fundamental and applied research, integrating computational modeling, experimental acoustics, and material design. His contributions have significant implications for industrial monitoring, structural health assessment, and the development of high-precision acoustic devices. With a strong focus on innovation, interdisciplinary collaboration, and practical application, Dr. Chen exemplifies the integration of scientific research and engineering solutions, positioning him as a rising leader in the field of mechanical engineering and acoustic metamaterials.

Profile: Orcid

Featured Publications

Chen, T., Zhu, M., Li, L., Wei, H., & Xia, B. (2026). Multi-frequency weak signals enhancement detection via gradient defect phononic crystals. Measurement, 261, 119933. https://doi.org/10.1016/j.measurement.2025.119933

Chen, T., Malléjac, M., Bi, C., Xia, B., & Fleury, R. (2025). Experimental demonstration of a space-time-modulated airborne acoustic circulator. Physical Review Applied, 23, 054017. https://doi.org/10.1103/PhysRevApplied.23.054017

Chen, T., Xia, B., Yu, D., & Bi, C. (2024). Robust enhanced acoustic sensing via gradient phononic crystals. Physics Letters A, 440, 129242. https://doi.org/10.1016/j.physleta.2023.129242

Chen, T., Wang, C., & Yu, D. (2022). Pressure amplification and directional acoustic sensing based on a gradient metamaterial coupled with space-coiling structure. Mechanical Systems and Signal Processing, 181, 109499. https://doi.org/10.1016/j.ymssp.2022.109499

Chen, T., & Yu, D. (2022). A novel method for enhanced demodulation of bearing fault signals based on acoustic metamaterials. IEEE Transactions on Industrial Informatics, 18(10), 6857–6864. https://doi.org/10.1109/tii.2022.3143161

Chen, T., Jiao, J., & Yu, D. (2022). Strongly coupled phononic crystals resonator with high energy density for acoustic enhancement and directional sensing. Journal of Sound and Vibration, 529, 116911. https://doi.org/10.1016/j.jsv.2022.116911

Sedighe Mirbolouk | Engineering | Editorial Board Member

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Dr. Sedighe Mirbolouk | Engineering | Editorial Board Member 

Iran National Science Foundation | Iran

Dr. Sedighe Mirbolouk is a dedicated postdoctoral researcher and advanced machine learning specialist with strong expertise in communication systems, data science, and artificial intelligence. She is affiliated with the Iran National Science Foundation and has built a diverse research portfolio spanning deep learning, wireless communication optimization, image processing, and intelligent sensing systems. Her technical proficiency covers a wide spectrum of tools and programming environments, including Python, MATLAB, LATEX, and advanced libraries such as TensorFlow, PyTorch, Scikit-learn, NumPy, SciPy, Pandas, and Matplotlib. With a strong theoretical foundation in data telecommunication networks, convex optimization, communication theory, and signal and image processing, she integrates computational intelligence with modern communication challenges. In her role as a Postdoctoral Researcher (2024–2025) at the Iran National Science Foundation, Dr. Mirbolouk focuses on cutting-edge topics in graph learning and federated learning, particularly designing machine learning approaches for predictive beamforming in Reconfigurable Intelligent Surface (RIS)-aided Integrated Sensing and Communication (ISAC) systems. Her work aims to improve efficiency, adaptability, and intelligence in next-generation wireless communication networks. Previously, she served as a Visiting Researcher (2022) at the University of Oulu in Finland, where she explored advanced deep reinforcement learning methods to enhance ISAC designs. These research experiences have positioned her at the frontier of combining AI with communication technologies. During her doctoral studies at the University of Urmia (2018–2021), Dr. Mirbolouk contributed significantly to satellite–UAV cooperative network optimization. She developed innovative solutions involving UAV selection and power allocation for CoMP-NOMA transmissions, introducing both Lagrangian and heuristic algorithms that advanced energy-efficient communication frameworks. Alongside communications research, she proposed image processing solutions such as fuzzy histogram weighting methods and contrast enhancement techniques. Her academic involvement includes teaching core engineering subjects—Digital Communication, Probability and Statistics, and Signals and Systems—and assisting courses on Stochastic Processes and Digital Signal Processing. Her work at the National Elite Foundation (2020–2022) expanded her portfolio into biomedical machine learning applications, where she designed systems for automatic breast cancer detection using histopathology images and cardiac arrhythmia recognition using ECG signals through deep learning approaches. Dr. Mirbolouk holds a Ph.D. in Electrical Engineering, with earlier B.Sc. and M.Sc. degrees from the University of Guilan, where she studied SAR radar Doppler ambiguity for moving targets. Her scholarly contributions include high-impact publications in journals such as IEEE Transactions on Vehicular Technology, Physical Communication, and Multimedia Tools and Applications. Collectively, her research reflects an outstanding integration of machine learning, optimization, sensing, and communication technologies.

Profile: Google Scholar

Featured Publications

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Ali, S. (2022). Relay selection and power allocation for energy efficiency maximization in hybrid satellite-UAV networks with CoMP-NOMA transmission. IEEE Transactions on Vehicular Technology, 71(5), 5087–5100.

Mirbolouk, S., Valizadeh, M., Amirani, M. C., & Choukali, M. A. (2021). A fuzzy histogram weighting method for efficient image contrast enhancement. Multimedia Tools and Applications, 80(2), 2221–2241.

Mirbolouk, S., Choukali, M. A., Valizadeh, M., & Amirani, M. C. (2020). Relay selection for CoMP-NOMA transmission in satellite and UAV cooperative networks. 2020 28th Iranian Conference on Electrical Engineering (ICEE), 1–5.

Choukali, M. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2023). A desired histogram estimation accompanied with an exact histogram matching method for image contrast enhancement. Multimedia Tools and Applications, 82(18), 28345–28365.

Hussein, A. A., Valizadeh, M., Amirani, M. C., & Mirbolouk, S. (2025). Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework. Scientific Reports, 15(1), 25071.

Choukali, M. A., Mirbolouk, S., Valizadeh, M., & Amirani, M. C. (2024). Deep contextual bandits-based energy-efficient beamforming for integrated sensing and communication. Physical Communication, 68, 102576.

Yong Li | Engineering | Best Researcher Award | 13407

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Assoc Prof Dr Yong Li | Engineering | Best Researcher Award 

Assoc Prof Dr Yong Li, Fujian Police College, China

Dr. Yong Li is an Associate Professor in the Department of Public Security at Fujian Police College, China, with a strong academic background in Traffic Information Engineering and Control, holding both a Ph.D. and Master’s degree from Beijing Jiaotong University. His research focuses on intelligent transportation systems, electromagnetic tomography, and traffic accident imaging. He has led and participated in several nationally funded projects and published extensively in top-tier journals such as IEEE Transactions and Ultrasonics. Dr. Li also holds multiple patents related to traffic monitoring and sensing technologies, and serves as a thesis supervisor at Fuzhou University, contributing actively to academic mentorship and innovation in traffic safety and public security.

Profile

Orcid

🎓 Early Academic Pursuits

Dr. Yong Li’s academic journey showcases a strong foundation in engineering and traffic systems. He began his higher education at Beijing Union University, earning a Bachelor’s degree in Electrical Engineering and Automation between 2011 and 2015. This foundational training set the stage for his specialized focus in traffic and control systems.

Continuing on an ambitious academic path, he pursued both his Master’s (2015–2017) and Ph.D. (2017–2021) degrees in Traffic Information Engineering and Control at Beijing Jiaotong University, one of China’s top institutions in transportation sciences. His graduate studies equipped him with in-depth knowledge in traffic monitoring, signal processing, and system optimization—areas that would become central to his later research.

👨‍🏫 Professional Endeavors

After completing his doctorate, Dr. Li immediately immersed himself in both academic and applied research environments. He initially served as a Researcher at the Human-like Perception Research Center, Zhejiang Laboratory from June to September 2021, contributing to cutting-edge studies in perception technologies.

In November 2021, Dr. Li joined the Department of Public Security at Fujian Police College as a Lecturer, and was later promoted to Associate Professor. His role encompasses teaching, guiding student research, and leading scientific inquiries into intelligent transportation and traffic safety systems. He is also an External Master’s Thesis Supervisor at the College of Big Data and Computer Science, Fuzhou University since December 2021, further reflecting his academic mentorship roles.

🔬 Contributions and Research Focus

Dr. Li has an impressive record of participation in high-level scientific research. His primary focus lies in electromagnetic tomography, traffic accident imaging, and intelligent transportation systems. He has been a Principal Investigator for multiple projects, including:

  • “Large-scale Electromagnetic Tomography for Road Traffic Monitoring” (NSFC Project No. 62301159)

  • “Dual-plane Linear Array Electromagnetic Tomography for Traffic Accident Imaging”

  • “Urban Road Traffic Situation Assessment System” funded by Fujian’s Department of Finance

He is also a core participant in a significant ongoing NSFC project titled “Optimization of Delay Operation in Integrated Subway-Bus Networks in Metropolises”.

His work spans both theoretical modeling and applied systems, focusing on real-time data acquisition, traffic state estimation, and sensor technology for safety enhancement.

🏅 Accolades and Recognition

While explicit awards or honors are not detailed in the provided profile, Dr. Li’s consistent leadership in nationally funded research projects—particularly as a Principal Investigator on NSFC and provincial-level grants—is a strong indicator of peer recognition and institutional trust. His appointment as an Associate Professor within just a few years of completing his Ph.D. further underscores his rising prominence in the field.

🌍 Impact and Influence

Dr. Li’s work has direct societal implications, especially in improving urban traffic safety, accident response efficiency, and transportation infrastructure monitoring. His research contributes to China’s broader smart city initiatives and public security advancements, particularly in densely populated urban areas.

By bridging electrical engineering, traffic systems, and intelligent sensing, Dr. Li plays a pivotal role in making city transportation safer, more responsive, and more technologically advanced.

🚀 Legacy and Future Contributions

Looking forward, Dr. Li is poised to continue expanding the intersection of AI, electromagnetic sensing, and traffic control systems. His current NSFC and provincial projects are likely to yield further innovations in how we understand and manage traffic flow, detect anomalies, and respond to emergencies in real time.

With his expanding role as an educator and mentor, his influence will also be felt through the next generation of public safety and traffic engineering professionals in China. Dr. Li’s combination of academic rigor, inventive spirit, and societal relevance makes him a key figure in the evolution of smart transportation technologies.

📚Publications Top Notes

A Kalman Filtering Method on Time–Frequency Discrimination Analysis

Contributors: Li, Y.; Xiao, F.

Journal: ircuits, Systems, and Signal Processing

Year:  2025

Simulation and analysis on dual-plane linear array electromagnetic tomography

A Fault Diagnosis Method for Turnout Switch Machines Based on Sound Signals

Contributors: Li, Y.; Tao, X.; Sun, Y.
Journal: Electronics (Switzerland)
Year: 2024