Xiping Duan | Visual Tracking | Best Researcher Award

Dr. Xiping Duan | Visual Tracking | Best Researcher Award

Associate Professor at Harbin Normal University, China

Dr. Xiping Duan is a prominent scholar and an Associate Professor with extensive contributions in the field of computer science and engineering. As a Doctor of Engineering and a Master’s Thesis Advisor, she has cultivated a robust academic profile rooted in innovation and interdisciplinary approaches. Her areas of specialization include computer vision and evidence reasoning, where she has demonstrated significant influence through both theoretical advancements and practical applications. With a career marked by collaborative research and independent investigation, Dr. Duan continues to drive forward cutting-edge studies in artificial intelligence and related technologies.

Profile

Scopus

Education

Dr. Duan pursued her doctoral education in engineering, where she developed a solid foundation in computational intelligence, pattern recognition, and machine learning. Her doctoral work laid the groundwork for future research in video object tracking, data consistency, and multimodal information processing. She has remained deeply engaged in academic development through continuous learning and participation in key research programs funded by national and provincial bodies, which further enhanced her expertise in advanced AI algorithms and modeling techniques.

Experience

Throughout her academic tenure, Dr. Duan has contributed extensively to a wide array of funded projects and teaching roles. She participated in major research efforts such as the National Natural Science Foundation of China project on object-oriented high-resolution image monitoring for soil and water conservation, which ran from 2011 to 2013. She led the Heilongjiang Provincial Education Fund project focused on video object tracking technologies between 2014 and 2016. Her involvement extended to other influential projects, including studies on mobile database consistency and value-added voice service platforms. These research initiatives have positioned her at the forefront of computational systems research, particularly in the domain of intelligent monitoring and decision support systems.

Research Interest

Dr. Duan’s primary research interests span computer vision, evidence reasoning, intelligent monitoring systems, and multimodal data integration. Her work often explores the intersection of machine learning algorithms and real-world applications, particularly in healthcare diagnostics and geospatial data analysis. She has also delved into belief rule-based systems and their implementation in critical prediction and decision-making tasks, such as chronic disease diagnosis and tunnel deformation assessment. Her commitment to explainable AI and semantic-level information extraction demonstrates a progressive outlook aligned with the future trajectory of AI research.

Award

In recognition of her pioneering research and technological innovations, Dr. Duan was honored with the Second Prize for Scientific and Technological Progress by the People’s Government of Heilongjiang Province in December 2010. The awarded project involved the development of a non-contact, high-speed, and high-precision detection system for measuring the outer diameter of tapered rollers. This accolade is a testament to her ability to bridge theoretical insights with engineering applications, significantly contributing to industrial advancement and intelligent manufacturing.

Publication

Dr. Duan has authored and co-authored several high-impact research papers published in reputable journals. Notably, her 2025 publication in Sensors, titled “A Target Tracking Method Based on a Pyramid Channel Attention Mechanism,” presents a novel tracking framework and has been cited by subsequent works exploring attention mechanisms in AI. Her 2025 article in IEEE Access, “A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base,” has contributed to the growing body of research on interpretable AI in healthcare diagnostics. In 2024, she co-authored “A Tunnel Squeezing Prediction Model Based on the Hierarchical Belief Base” in IEEE Access, further cementing her expertise in infrastructure-related predictive modeling. Her 2022 work in Laser Technology on object tracking using GhostNet features reflects her commitment to advancing lightweight, real-time tracking solutions. Additionally, her 2015 paper in the Journal of Harbin Engineering University introduced a method for multimodal sparse representation in video tracking. Earlier, in 2014, she published “A Semantic-Level Text Collaborative Image Recognition Method” in the Journal of Harbin Institute of Technology, contributing to advancements in semantic image recognition.

Conclusion

Dr. Xiping Duan exemplifies academic excellence and interdisciplinary innovation in artificial intelligence and computer vision. Her contributions, spanning from fundamental research to practical applications, underline her pivotal role in the progression of intelligent systems. Recognized by prestigious awards and supported through nationally funded projects, she continues to inspire the academic community through her dedication to impactful research and mentorship. With a strong publication record and a forward-looking research agenda, Dr. Duan remains an influential figure shaping the future of intelligent computing technologies.

Zhaoxiang Zhang | Object Tracking | Best Researcher Award

Prof. Dr. Zhaoxiang Zhang | Object Tracking | Best Researcher Award

Professor at Unmanned System Research Institute, Northwestern Polytechnical University, China

Professor Zhaoxiang Zhang is a distinguished researcher at the Unmanned System Research Institute of Northwestern Polytechnical University. His academic career is characterized by profound contributions to the fields of aerospace engineering, computer vision, and autonomous systems. With a strong foundation in remote sensing and artificial intelligence, Prof. Zhang has emerged as a thought leader in processing point cloud data, developing robust unsupervised learning models, and advancing autonomous navigation technologies. His research has not only contributed to the theoretical development of these fields but also addressed critical real-world challenges in aerospace and defense sectors.

Profile

Scopus

Education

Prof. Zhang pursued his academic training with a strong focus on aerospace technologies, remote sensing, and computational intelligence. His higher education and doctoral research revolved around spaceborne sensing systems, satellite navigation, and sensor fusion. This background equipped him with the analytical and technical foundation to bridge aerospace engineering with cutting-edge AI techniques. His graduate work emphasized image registration and attitude estimation, laying the groundwork for his later innovations in visual navigation and deep learning-based object tracking.

Experience

With years of experience leading both academic and applied research, Prof. Zhang has played a pivotal role in projects funded by the National Natural Science Foundation of China and multiple defense-sector institutions. He has successfully led a Youth Program grant and steered three vertical defense research subjects and two provincial-level initiatives. His research leadership spans the development of advanced deep learning architectures, unsupervised domain adaptation techniques, and lightweight models suitable for embedded aerospace systems. Prof. Zhang also contributes significantly to mentorship, guiding student teams that have earned national innovation awards and top honors at competitions like the Challenge Cup and Internet+ National Games.

Research Interests

Prof. Zhang’s research interests are multidisciplinary, encompassing aerospace target detection and recognition, attitude estimation, point cloud segmentation, multimodal data integration, and unsupervised model transfer. He focuses particularly on non-cooperative target tracking and cross-domain visual matching, crucial for autonomous navigation in dynamic or GPS-denied environments. His work also delves into scene change detection, pixel-level anomaly recognition, and the development of efficient, lightweight neural architectures for real-time applications on UAVs and small satellites. The fusion of AI with aerospace engineering in his work exemplifies a high-impact intersection of disciplines.

Awards

Prof. Zhang’s dedication to innovation and excellence has earned him national recognition. Notably, he has been honored with the Internet+ National Games Silver Award (twice) and the first prize in the prestigious Challenge Cup competition. Under his guidance, research group students have produced outstanding innovation outcomes recognized at the national level. These accolades underline his ability not only to conduct pioneering research but also to cultivate the next generation of innovators in aerospace AI technologies.

Publications

Prof. Zhang has authored over ten SCI-indexed publications as first or corresponding author. Seven of his most notable works include:

  1. Zhang Z, Ji A, Zhang L, et al. (2023). Unsupervised seepage segmentation pipeline based on point cloud projection with large vision model. Tunnelling and Underground Space Technology — cited by 25 articles.

  2. Zhang Z, Xu Y, Song J, et al. (2023). Robust pose estimation for non-cooperative space objects. Scientific Reports — cited by 18 articles.

  3. Zhang Z, Xu Y, Song J, et al. (2023). Planet craters detection using unsupervised domain adaptation. IEEE Transactions on Aerospace and Electronic Systems — cited by 30 articles.

  4. Zhang Z and Zhang L (2023). Rail Surface Defects Detection Using Multistep Domain Adaptation. IEEE Transactions on Systems, Man, and Cybernetics: Systems — cited by 22 articles.

  5. Zhang Z, Ji A, Zhang L, et al. (2023). Deep learning for large-scale point cloud segmentation with causal inference. Automation in Construction — cited by 27 articles.

  6. Zhang Z, Xu Y, Cui Q, et al. (2022). Unsupervised SAR and Optical Image Matching. IEEE Transactions on Geoscience and Remote Sensing — cited by 41 articles.

  7. Song J, Zhang Z, Iwasaki A, et al. (2021). Augmented H∞ Filter for Satellite Jitter Estimation. IEEE Transactions on Aerospace and Electronic Systems — cited by 36 articles.

Conclusion

Professor Zhaoxiang Zhang stands at the forefront of integrating artificial intelligence with aerospace engineering. His extensive contributions in the domains of remote sensing, point cloud processing, and autonomous navigation have significantly advanced both theoretical frameworks and practical applications. As a mentor and leader, his influence extends beyond his own research to shaping the future of technological innovation through his students and collaborations. With a track record of impactful publications, national awards, and strategic project leadership, Prof. Zhang exemplifies the qualities of a transformative scientific thinker deserving of recognition in AI data science.