Jie Tian | Experimental methods | Best Researcher Award

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Prof. Jie Tian | Experimental methods | Best Researcher Award

Dr. Jie Tian is a distinguished Professor at the Institute of Acoustics, Chinese Academy of Science, Beijing, China. He holds a Ph.D. in Automatic Control from Beijing Institute of Technology (2002) and a Bachelor’s degree in Automatic Control from Northwestern Polytechnic University (1995). His primary research focus lies in the fields of underwater information and signal processing and classification & image processing.

👨‍🎓Profile

Scopus

🎓 Early Academic Pursuits

Dr. Tian’s academic journey began at Northwestern Polytechnic University, where he earned his Bachelor’s degree in Automatic Control in 1995. Building on this foundation, he pursued his Ph.D. at Beijing Institute of Technology, specializing in Automatic Control. His studies laid the groundwork for his deep engagement with signal processing and image processing algorithms, disciplines that continue to define his career today.

💼 Professional Endeavors

Dr. Tian’s professional career spans over two decades, marked by significant contributions to both academia and research. He is currently a Professor at the Institute of Acoustics, Chinese Academy of Science, where he has worked since 2002. His career trajectory includes a Postdoctoral fellowship and Associate Professorship at the same institution, where he developed theoretical algorithms for image processing and worked extensively on information processing systems. His transition from postdoc to professor reflects his growing influence in his field, particularly in the domain of underwater acoustic communication networks and image classification.

🔬 Contributions and Research Focus

Dr. Tian’s research contributions are far-reaching and impactful. His expertise includes underwater information processing, with a particular focus on underwater object classification, and sonar image processing. Notable areas of his work include:

  • Cross-layer routing protocols for underwater acoustic communication networks.
  • Deformable residual networks and transfer learning for underwater object classification in SAS images.
  • Deep neural networks for classification in high-resolution sonar images.

His focus on advanced algorithms such as deep neural networks and SVM-based techniques has helped push forward the frontiers of image classification and signal processing in challenging underwater environments.

🧑‍🏫 Teaching Experience

Dr. Tian is not only a researcher but also a dedicated educator. As a Professor, he has mentored countless students and guided the next generation of researchers in the Institute of Acoustics. His expertise in image processing and signal processing provides students with valuable insights into cutting-edge technologies, preparing them for careers in academic research and industry applications.

🔮 Legacy and Future Contributions

Dr. Tian’s work has already left a lasting impact on underwater imaging and signal processing. Looking ahead, his future contributions are likely to expand into AI-driven underwater communication systems and real-time processing algorithms, further advancing the practical applications of his research. His continued focus on image processing algorithms and deep learning will undoubtedly lead to more innovative breakthroughs that enhance the capabilities of underwater technologies, benefiting both scientific exploration and practical communication systems.

Publications Top Notes

  • Cross-Layer Routing Protocol Based on Channel Quality for Underwater Acoustic Communication Networks
    Authors: He, J., Tian, J., Pu, Z., Wang, W., Huang, H.
    Journal: Applied Sciences (Switzerland)
    Year: 2024
  • Underwater Object Classification in SAS Images Based on a Deformable Residual Network and Transfer Learning
    Authors: Gong, W., Tian, J., Liu, J., Li, B.
    Journal: Applied Sciences (Switzerland)
    Year: 2023
  • Underwater Object Classification Method Based on Depthwise Separable Convolution Feature Fusion in Sonar Images
    Authors: Gong, W., Tian, J., Liu, J.
    Journal: Applied Sciences (Switzerland)
    Year: 2022
  • Underwater objects classification method in high-resolution sonar images using deep neural network
    Authors: Zhu, K., Tian, J., Huang, H.
    Journal: Shengxue Xuebao/Acta Acustica
    Year: 2019
  • Small Underwater Objects Classification in Multi-View Sonar Images Using the Deep Neural Network
    Authors: Zhu, K., Tian, J., Huang, H.
    Journal: Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument
    Year: 2020

 

 

Seyed Rasoul Nabavian | Experimental methods | Best Researcher Award

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Assist. Prof. Dr. Seyed Rasoul Nabavian | Experimental methods | Best Researcher Award

Faculty Member at Ayatollah Boroujerdi University, Boroujerd, Iran

👨‍🎓 Profile

Summary🌟

Dr. Seyed Rasoul Nabavian is a highly accomplished civil engineer and academic leader with expertise in structural engineering, dynamic structural identification, and space structures. He holds a PhD in Civil Engineering from Noshirvani University of Technology and is currently a faculty member and head of the Civil Engineering Department at Ayatollah Boroujerdi University. With numerous awards and a strong research background, he has contributed extensively to the fields of concrete technology, modal testing, and structural health monitoring. 🏆

🎓 Education & Academic Excellence

Dr. Seyed Rasoul Nabavian holds a PhD in Civil Engineering from Noshirvani University of Technology, specializing in dynamic properties of double-layer grids. He ranked 19th nationally in the PhD entrance exam, and consistently topped his class during his Bachelor’s and Master’s studies in Structural Engineering, earning top honors and GPAs above 18.

💼Professional Experience

Dr. Nabavian has established himself as a leader in both academia and industry. As a Faculty Member and Head of the Civil Engineering Department at Ayatollah Boroujerdi University, he has mentored countless students and contributed to the growth of the department. His expertise extends beyond the classroom, as he has actively participated in various research initiatives with organizations such as the Defense Industries Organization and the Mazandaran Building Engineering System Organization. Dr. Nabavian’s professional experience also includes roles in concrete laboratory tests, geotechnical studies, and the management of residential building projects.

🌍 Contributions and Research Focus

Dr. Nabavian’s research interests focus on a wide range of cutting-edge topics in civil engineering, particularly in space structures, double-layer grids, cable domes, modal testing, and structural health monitoring. His work in Operational Modal Analysis (OMA) and output-only modal identification has contributed to advancements in damage detection and system identification of structures under dynamic conditions. Additionally, his research on recycled aggregate concrete, fiber-reinforced concrete, and impact-resistant materials aligns with the growing emphasis on sustainable construction.

👨‍🏫Teaching Experience

Dr. Nabavian has consistently demonstrated a passion for education throughout his career. He has taught at Noshirvani University of Technology, Ayatollah Boroujerdi University, and Tabari Higher Education Institute, where he has inspired students with his in-depth knowledge of civil engineering principles. His role as a thesis supervisor and advisor has allowed him to guide emerging researchers in structural health monitoring, seismic evaluation, and material science. He has supervised numerous graduate and postgraduate theses, including groundbreaking research on seismic isolation and fiber-reinforced concrete. Dr. Nabavian’s dedication to teaching is reflected in his students’ academic success and his recognition as an exemplary educator.

🛠️ Technical Skills and Software Expertise

Dr. Nabavian possesses an extensive skill set in structural analysis and engineering software, including proficiency in ARTeMIS, AutoCAD, ETABS, and MATLAB. His technical acumen is complemented by advanced knowledge of signal processing, noise reduction techniques, and data analysis, which have been applied to improve the accuracy and efficiency of output-only structural identification methods.

Top Noted Publications

Output-only modal analysis of a beam via frequency domain decomposition method using noisy data
  • Authors: S Mostafavian, SR Nabavian, MR Davoodi, B Navayi Neya
    Journal: International Journal of Engineering
    Year: 2019
Influence of nano-silica particles on fracture features of recycled aggregate concrete using boundary effect method: Experiments and prediction models
  • Authors: SR Nabavian, H Fallahnejad, A Gholampour
    Journal: Structural Concrete
    Year: 2024
Damping estimation of a double-layer grid by output-only modal identification
  • Authors: SR Nabavian, MR Davoodi, B Navayi Neya, SA Mostafavian
    Journal: Scientia Iranica
    Year: 2021
Effect of noise on output-only structural identification of beams
  • Authors: SR Nabavian, MR Davoodi, B Navayi Neya, SA Mostafavian
    Journal: Journal of Structural and Construction Engineering
    Year: 2020
Fracture characteristics of recycled aggregate concrete using work-of-fracture and size effect methods: the effect of water to cement ratio
  • Authors: H Fallahnejad, SR Nabavian, A Gholampour
    Journal: Archives of Civil and Mechanical Engineering
    Year: 2024