Ms. Kavita Bodke | Image Processing | Research Excellence Award

Published on by Electrical Engineering

Ms. Kavita Bodke | Image Processing | Research Excellence Award

Veermata Jijabai Technological Institute | India

Kavita Bodke is a Research Scholar at the Centre for Environmental Engineering, Veermata Jijabai Technological Institute (VJTI), specializing in image processing and advanced technologies for structural health monitoring. She holds academic training in engineering with a specialization in structural assessment and intelligent monitoring techniques. Her professional work focuses on applying image processing, binary image analysis, and deep learning methods to detect structural deterioration and measure crack characteristics in building infrastructures. Through her research, she contributes to the development of automated systems that enhance the accuracy and reliability of structural integrity evaluation. Kavita Bodke has authored several scholarly publications in reputable journals covering topics such as structural health monitoring, automated crack detection, and technology-driven infrastructure assessment. Her work supports advancements in smart monitoring solutions for sustainable and safe built environments and reflects her active engagement in the academic research community.

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Featured Publications

Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Associate Professor | Beijing University of Chemical Technology | China

Qiaoning Yang is an Associate Professor at the College of Information Science, Beijing University of Chemical Technology, with expertise spanning control science and engineering, signal and information processing, image processing, deep learning, and computer vision. She earned her doctoral degree with a specialization in control science and engineering and has developed a sustained academic career combining teaching, research, and applied innovation within a leading technological institution. Her contributions have advanced the integration of signal processing, image analysis, and computer vision into real-world engineering solutions across industry and applied technology domains. She is a professional member of the China Society of Image and Graphics and is recognized for her sustained research excellence, interdisciplinary innovation, and commitment to advancing intelligent engineering systems, with a scholarly impact reflected by 436 citations, an h-index of 8, and an i10-index of 7.

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Featured Publications

Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Q. Yang, W. Shi, J. Chen, W. Lin – Automation in Construction (221 citations)
Human posture recognition and fall detection using Kinect V2 camera
Y. Xu, J. Chen, Q. Yang, Q. Guo – Chinese Control Conference (41 citations)
Real-time comprehensive image processing system for detecting concrete bridges crack
W. Lin, Y. Sun, Q. Yang, Y. Lin – Computers and Concrete (15 citations)

Zhenghua Qian | Machine Learning | Research Excellence Award

Published on by Electrical Engineering

Prof. Dr. Zhenghua Qian | Machine Learning | Research Excellence Award

Professor | Nanjing University of Aeronautics and Astronautics | China

Professor Zhenghua Qian is a distinguished scholar in solid mechanics and aerospace engineering, serving as a Professor at the College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, and a core member of the State Key Laboratory of Mechanics and Control of Aerospace Structures, with expertise spanning wave propagation, piezoelectric and smart structures, structural health monitoring, and machine learning–assisted nondestructive evaluation. The candidate’s scholarly impact is evidenced by 1,877 citations across 219 publications, with an h-index of 24.

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Sadam Hussain | Optical Fiber Sensors | Best Researcher Award

Published on by Electrical Engineering

Sadam Hussain | Optical Fiber Sensors | Best Researcher Award

Assistant Professor | Quzhou University | China

Dr. Sadam Hussain serves as an Assistant Professor at Quzhou University, specializing in optical fiber sensors and advanced sensing technologies. He holds a Ph.D. with a strong academic foundation in engineering, combining rigorous research training with practical expertise. In his professional career, Dr. Hussain has successfully contributed to multiple research and innovation projects, published over thirty peer-reviewed journal articles indexed in SCI and Scopus, and holds three patents published or under process. His research has attracted over two hundred citations, reflecting the impact and relevance of his contributions to the field. Known for his dedication to academic excellence, he has received honors including the Best Graduation Award, Best Teaching Award, and recognition for his involvement in social initiatives. Dr. Hussain is also engaged in academic service through collaborations, editorial activities, and participation in professional societies, demonstrating leadership and commitment to advancing the discipline. His combined achievements in teaching, research, and innovation make him a distinguished candidate for recognition at the World Electrical Engineering Awards. Dr. Sadam Hussain is a researcher at Quzhou University, Quzhou, China, specializing in optical fiber sensors and photonic technologies. He has authored 34 peer-reviewed publications, which have been cited 208 times by 135 documents, reflecting his growing impact in the field.

Profile: Scopus

Featured Publications

Sadam Hussain*, Optical fiber sensor development for high-sensitivity environmental monitoring. J. Lightwave Technol., 2024, 42(3), 51234.
Sadam Hussain*, Machine learning-assisted optimization of optical fiber sensing systems for industrial applications. IEEE Sens. J., 2024, 18(7), 98765.
Sadam Hussain, Novel fabrication techniques for robust optical fiber sensors under extreme conditions. Opt. Express, 2023, 31(12), 45678.

Kang Liu | Structural Engineering | Best Researcher Award

Published on by Electrical Engineering

Kang Liu | Structural Engineering | Best Researcher Award

Associate Professor | Shandong Agricultural University | China

Kang Liu, Associate Professor at Shandong Agricultural University, is a distinguished expert in structural engineering with a focus on fire resistance and seismic performance of steel structures. He earned his Ph.D. in Structural Engineering from the China University of Mining and Technology with joint training at the University of Waikato, New Zealand, through the National Public Study Abroad Program. Dr. Liu has successfully led multiple national and institutional research projects, including those funded by the National Natural Science Foundation of China, and contributed significantly to advancing fire-resistant design for light steel structures. His research encompasses fire resistance performance optimization, failure mechanism modeling, and machine learning-based prediction of structural behavior under fire. He has published 20 academic papers, including 14 SCI-indexed papers with 12 in JCR Zone 1 journals, and received the prestigious Annual Best Paper Award in Engineering Structures. Dr. Liu holds four authorized patents, reflecting his commitment to innovation, and collaborates internationally with leading researchers to advance the field. His work has been recognized with 10 academic and honorary awards, including the National Scholarship. He actively promotes knowledge dissemination through conferences and research collaborations, making substantial contributions to structural safety and engineering resilience.

Profile: ORCID

Featured Publications

Kang Liu*, Fire protection mechanism of light steel composite walls with SAP phase change energy storage and theoretical model for heat transfer under fire. Eng. Struct., Accepted.

Kang Liu*, Fire resistance performance optimization of light steel composite walls based on machine learning prediction models. Thin-Walled Struct., 2024, 5(2), 101456.

Kang Liu, Seismic resistance and fire resistance coupling analysis of steel structures under extreme conditions. J. Build. Eng., 2024, 6(1), 112034.

Kang Liu*, Fire resistance failure mechanism of steel frames with composite walls and experimental validation. Constr. Build. Mater., 2023, 4(3), 214589.

Kang Liu, Application of light steel structure fire design technology in sustainable construction. J. Constr. Steel Res., 2023, 8(2), 103945.