Fucai Li | Structural Health Monitoring and Fault Diagnosis | Best Researcher Award

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Dr. Fucai Li | Structural Health Monitoring and Fault Diagnosis | Best Researcher Award

Prof. Fucai Li is a distinguished academic at Shanghai Jiao Tong University 🇨🇳, specializing in vibration and ultrasonic signal processing, structural health monitoring, and intelligent sensor systems 🔍. With extensive global research experience across China 🇨🇳, Japan 🇯🇵, and Australia 🇦🇺, he has significantly contributed to mechanical engineering through innovative diagnostics and smart material systems 🛠️. He has published over 150 peer-reviewed papers and led numerous national and industrial research projects 📚🔬. His work bridges academia and industry, with collaborations involving giants like Bao Steel and Shanghai Electric ⚙️.

Profile

Education 🎓

Prof. Li earned his Ph.D. in Engineering from Xi’an Jiaotong University in 2003 🎓, where he also completed his Bachelor’s degree in Mechanical Engineering in 1998 🏫. His strong academic foundation from one of China’s premier technical universities laid the groundwork for a career focused on high-impact research 🧠. Throughout his education, he developed core expertise in mechanics, signal processing, and automation, setting the stage for innovations in sensor technologies and structural diagnostics 📡🔧.

Experience 👨‍🏫

Prof. Li currently serves as Professor at Shanghai Jiao Tong University (2015–present) 🏫. He was an Associate Professor there from 2009–2015 and held earlier roles including Assistant Professor (2003–2005) 🎓. His international experience includes research fellowships in Japan (JSPS, University of Tokyo, 2007–2009) 🇯🇵 and Australia (University of Sydney, 2005–2007) 🇦🇺. This global exposure enriched his expertise in structural innovation and smart systems 🌐. Across academia, he has shaped future engineers and researchers with cutting-edge knowledge and practical application insights 📘🔍.

Awards & Recognitions 🏅

Prof. Li has secured over 30 research grants from top national bodies like NSFC, Ministry of Science and Technology, and major industries including Bao Steel and Shanghai Electric 💼💡. He is widely recognized for his pioneering work in structural health monitoring and has published more than 150 influential journal papers 📑. His contributions to smart sensor networks and machine diagnostics have earned him national acclaim and trust from key industrial stakeholders 🤝. Through sustained innovation and impact, he has built a legacy of excellence in engineering research and collaboration 🏅.

Research Interests 🔬

Prof. Li’s research targets intelligent diagnostics and health monitoring of mechanical systems 🤖. His focus areas include vibration and ultrasonic signal analysis, fiber optic and piezo-electric sensor networks, and AI-driven fault detection systems 📊📡. He develops smart sensing technologies for predictive maintenance and infrastructure resilience, with applications ranging from aerospace to heavy machinery 🏗️✈️. His work integrates signal processing, materials science, and systems engineering to enable next-gen monitoring solutions. With over 150 publications and extensive funding, his research continues to push the frontier of smart mechanical engineering 🚀.

Publications 

Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

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Prof. Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

Mingshun Jiang is a professor at the School of Control Science and Engineering, Shandong University 🎓. He is a doctoral supervisor and a young expert of Mount Taishan Scholars 🌟. He serves as the director of the Shandong Engineering Research Center for Intelligent Sensor and Detection Technology 🔬 and deputy director of the Institute of Intelligent Perception 🏛️. His research primarily focuses on intelligent sensors and detection technologies, with over 20 funded projects, including the National Natural Science Foundation and the National Key R&D Program 🏆. He has authored 60+ high-level academic papers in renowned journals 📑. His innovative contributions aim at monitoring complex structural states in high-end equipment 🚀. With extensive industry collaborations, his work has applications in aerospace, rail transit, and military technology 🛰️🚆.

Profile

Education 🎓

Mingshun Jiang earned his doctoral degree in Control Science and Engineering from Shandong University 🎓. His academic journey focused on developing intelligent sensor systems and detection methodologies 📡. His research expertise was cultivated through interdisciplinary learning, integrating control science, artificial intelligence, and structural health monitoring 🤖. His doctoral research emphasized advanced ultrasonic-guided wave detection and probabilistic diagnostic imaging techniques 🏗️. Jiang’s educational background provided him with expertise in designing smart sensor networks, optimizing detection mechanisms, and enhancing structural health monitoring systems ⚙️. With strong mathematical and engineering foundations, he developed novel algorithms for real-time damage localization and predictive maintenance 📊. His continuous learning and research efforts have been instrumental in bridging technological gaps in aerospace, rail transit, and high-end industrial applications 🚆✈️.

Experience 👨‍🏫

Mingshun Jiang has extensive research and academic experience, currently serving as a professor at Shandong University 🏛️. He has led over 20 major research projects, including the National Natural Science Foundation and National Key R&D Program 🌍. As the director of the Shandong Engineering Research Center, he focuses on intelligent sensor development and detection technologies 🔍. His research has been successfully applied in aerospace, rail transit, and high-end industrial monitoring 🚀🚆. He has supervised numerous doctoral students and collaborated with various enterprises on engineering solutions 🏗️. Jiang has also played a key role in technical verification and real-world applications of his research findings 📡. His leadership in academia and industry-driven research has established him as a leading expert in intelligent perception and structural health monitoring 🏆.

Awards & Recognitions 🏅

Mingshun Jiang has received multiple prestigious recognitions, including being a young expert of Mount Taishan Scholars in Shandong Province 🌟. His work has been supported by national and provincial funding agencies, highlighting his contributions to intelligent sensor technology 🏆. He has been awarded numerous grants under the National Natural Science Foundation and National Key R&D Program 🎖️. Jiang’s research achievements have been recognized through invited talks at leading academic conferences and industry collaborations 🤝. He has served as an executive director of the China Inspection and Testing Society, further solidifying his reputation in the field 🔬. His high-impact publications in top-tier journals have earned him accolades for innovation and research excellence 📑. Jiang continues to receive recognition for his contributions to the monitoring of complex structural states in high-end equipment 🚀.

Research Interests 🔬

Mingshun Jiang’s research focuses on intelligent sensors, structural health monitoring, and detection technology 📡. His work integrates artificial intelligence, probabilistic diagnostic imaging, and ultrasonic-guided wave techniques for real-time damage localization and predictive maintenance 🏗️. Jiang has developed innovative methodologies for monitoring key structural indicators such as boundary loads, damage detection, and component failures 🚆. His research aims to bridge the gap between technological innovation and application in aerospace, rail transit, and industrial monitoring 🛰️. His team has successfully engineered high-end monitoring systems that have undergone technical validation and real-world implementation 🔍. Jiang’s expertise extends to developing smart sensing layers for structural health monitoring, contributing to safer and more efficient industrial systems ⚙️. Through his interdisciplinary research, he continues to advance intelligent perception systems for next-generation monitoring applications 🚀.

Publications 
  • Ruijie Song, Lingyu Sun, Yumeng Gao, Juntao Wei, Chang Peng, Longqing Fan andMingshun Jiang*. Unsupervised temperature-compensated damage localization method based on damage to baseline autoencoder and delay-based probabilistic imaging. Mechanical Systems and Signal Processing, 230: 112649, 2025.
  • Hong Zhang ,Feiyu Teng , Juntao Wei , Shanshan Lv , Lei Zhang , Faye Zhang  and Mingshun Jiang*. Damage Location Method of Pipeline Structure by Ultrasonic Guided Wave Based on Probability Fusion.  IEEE Transactions on Instrumentation and Measurement, 73, 9504914, 2024.
  • . LingyuSun , Juntao Wei , Chang Peng , Wei Hao , Feiyu Teng , Longqing Fan , Lei Zhang , Qingmei Sui  and Mingshun Jiang. Ultrasonic guided wave-based probabilistic diagnostic imaging method with Single-Path-Scattering sparse reconstruction for Multi-Damage detection in composite structures.  Mechanical Systems and Signal Processing, 223, 111858, 2024.
  • XiaoshuQin , Shanshan Lv , Changhang Xu , Jing Xie , Lei Jia , Qingmei Sui  and Mingshun Jiang*. Implications of liquid impurities filled in breaking cracks on nonlinear acoustic modulation response: Mechanisms, phenomena and potential applications.  Mechanical Systems and Signal Processing, 200, 110550, 2023.
  • Shanshan Lv , Juntao Wei  and Mingshun Jiang*. Damage localization method for plate-like composite structure based on valid path optimization and search point matching.  Mechanical Systems and Signal Processing, 182, 109562, 2023.

Radhika Subramanian | Speech Processing | Women Researcher Award

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Dr. Radhika Subramanian | Speech Processing | Women Researcher Award

 

Profile

Education

She is currently pursuing a PhD at Anna University, Chennai, with an expected completion in 2025. She obtained her Master of Engineering in Communication Systems from B.S. Abdur Rahman Crescent Engineering College, Chennai, achieving 82.3% in the academic years 2007-2009. Prior to that, she completed her Bachelor of Engineering in Electronics and Communication Engineering from Kanchi Pallavan Engineering College, Kanchipuram, affiliated with Anna University, securing 84% from 2003 to 2007. She completed her Higher Secondary education at S.S.K.V Higher Secondary School, Kanchipuram, with 88% marks from 2001 to 2003, and her Secondary School Leaving Certificate from the same institution, scoring 84% in the year 2000-2001.

Work experience

As of January 31, 2025, she has a total academic experience of 14 years, 7 months, and 15 days. She has been serving as an Assistant Professor Grade-II at Sri Venkateswara College of Engineering, Sriperumpudur, since June 11, 2010. Prior to this, she worked as a Lecturer at Arulmigu Meenakshi Amman College of Engineering, Kanchipuram, from July 1, 2009, to May 7, 2010, gaining 10 months of experience. Her cumulative teaching experience amounts to 14 years, 17 months, and 15 days.

AREA OF INTEREST

  • Data Communication and Networking
  • Satellite communication
  • Signal Processing
  • Machine Learning

Publication

  • Radhika, S & Prasanth, A 2024, „An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm‟, Circuits, Systems, and Signal Processing, vol. 43, pp. 2477–2506, ISSN: 1531-5878,DOI: 10.1007/s00034-023-02571-4, Impact Factor: 2.3.
  • Radhika, S & Prasanth, A 2024, „An Effective Speech Emotion Recognition Model for Multi-Regional Languages Using Threshold-based Feature Selection Algorithm‟, Circuits, Systems, and Signal Processing, vol. 43, pp. 2477–2506, ISSN: 1531-5878,DOI: 10.1007/s00034-023-02571-4, Impact Factor: 2.3.
  • A Survey of Human Emotion Recognition Using Speech Signals: Current Trends and Future Perspectives
    R Subramanian, P Aruchamy
    Micro-Electronics and Telecommunication Engineering: Proceedings of 6th

 

 

Kaveri Hatti | Engineering| Women Researcher Award

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Mrs. Kaveri Hatti | Engineering| Women Researcher Award

 

 

Profile

Education

averi Hatti is a dedicated researcher and educator in the field of VLSI Design, Embedded Systems, and Hardware Security. She is currently pursuing a Ph.D. at Amrita School of Engineering, Bangalore, focusing on FPGA-based security architectures. With a strong academic background, she holds an M.Tech in VLSI Design and Embedded Systems from VTU Regional Office, Gulbarga, and a B.Tech in Electronics and Communication Engineering from SLN College of Engineering, Raichur.

Kaveri has extensive teaching experience, having served as a Lecturer at Tagore Memorial Polytechnic College and Government Polytechnic College in Raichur before joining Amrita School of Engineering, Bangalore, as a Teaching Assistant in 2022. Her expertise lies in FPGA design, Verilog, RTL design, and hardware security implementations, utilizing tools like Xilinx ISE, VIVADO, ModelSim, and Cadence.

 

Work experience

Kaveri Hatti has a strong background in academia, with extensive teaching experience spanning several years. She began her career as a Lecturer at Tagore Memorial Polytechnic College, Raichur, from August 2009 to December 2012, where she played a key role in instructing and mentoring students in electronics and communication engineering. Simultaneously, she also served as a Lecturer at Government Polytechnic College, Raichur, from June 2009 to June 2011. Currently, she is working as a Teaching Assistant at Amrita School of Engineering, Bangalore, since February 2022, contributing to research and assisting in the academic development of students in the field of VLSI Design and Embedded Systems.

Publication

Raveendra Pilli | Image Processing | Best Researcher Award

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Mr. Raveendra Pilli | Image Processing | Best Researcher Award

He mentored B.Tech. projects focused on the early detection of Alzheimer’s Disease. One project involved utilizing multi-modality neuroimaging techniques, where MRI and PET images were collected from the OASIS database, preprocessed, and robust features were extracted for classification. MATLAB and the SPM-12 toolbox were used for this task. Another project focused on the early detection of Alzheimer’s Disease using deep learning networks, where an MRI dataset from the ADNI database was collected, preprocessed, and the performance was compared with baseline algorithms. For this project, he used MATLAB and Python.

NIT-Silchar, India

Profile

Education

A dedicated research scholar with a Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Silchar (Thesis Submitted, CGPA 9.0), specializing in brain age prediction and early detection of neurological disorders using neuroimaging modalities. With extensive teaching experience, a strong passion for research, and a proven ability to develop engaging curricula, deliver effective lectures, and guide students toward academic success, I am committed to contributing to the field through research, publications, and presentations. My academic journey includes an M.Tech. from JNTU Kakinada (76.00%, 2011) and a B.Tech. from JNTU Hyderabad (65.00%, 2007), along with a strong foundational background in science, having completed 10+2 (MPC) with 89.00% in 2003 and SSC with 78.00% in 2001.

Work experience

He worked as a Junior Research Fellow at the National Institute of Technology, Silchar, Assam, from July 2021 to June 2023, where he assisted professors with course delivery for Basic Electronics, conducted laboratory sessions, graded assignments, and provided office hours for student support. From July 2023 to December 2024, he served as a Senior Research Fellow at the same institute, taking on additional responsibilities, including mentoring B.Tech. projects and assisting with Digital Signal Processing laboratory duties. Prior to his research roles, he was an Assistant Professor at SRK College of Engineering and Technology, Vijayawada, Andhra Pradesh, where he taught courses such as Networks Theory, Digital Signal Processing, RVSP, SS, and LICA. He utilized innovative teaching methods, including active learning techniques, to enhance student engagement and learning outcomes. He also mentored undergraduate research projects in image processing and received positive student evaluations for his teaching effectiveness.

Publication