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

Published on by Research Awards

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.