Jingxian Liu | Computer Vision | Best Researcher Award

Posted on by Academic achievements

Mr. Jingxian Liu | Computer Vision | Best Researcher Award

Associate Research Fellow‌ at Guangzhou Maritime University, China.

Mr. Jingxian Liu is an Associate Research Fellow at Guangzhou Maritime University. Born in November 1984 in Guangzhou, China, he specializes in remote sensing and communication systems. His research focuses on digital twins, intelligent state prediction, and maneuvering-target tracking using advanced computational methods. Liu has authored numerous high-impact publications and has led several national and regional research projects, contributing significantly to the field of geoscience and remote sensing.

Profile Verification

Scopus 

Education

Jingxian Liu pursued his doctoral studies in Communication and Information Systems at Beihang University (2013-2018) after completing a Master’s degree in Geodetection and Information Technology from China University of Geoscience (Beijing, 2007-2010). He holds a Bachelor’s degree in Electronic Information Engineering from China University of Geoscience (Beijing, 2003-2007). His educational background has equipped him with a solid foundation in engineering and remote sensing technologies.

Experience

Jingxian Liu currently serves as an Associate Research Fellow at Guangzhou Maritime University since March 2024. Prior to this, he was an Associate Research Fellow at Guangxi University of Science and Technology from December 2018 to December 2023. During this period, he contributed significantly to research projects focusing on remote sensing and digital twin technologies. Earlier in his career, Liu worked as an Engineer at the China Shipbuilding Industry Corporation’s 760th Research Institute from July 2010 to June 2013. His role there involved conducting research and development activities aimed at advancing engineering technologies in shipbuilding and marine industries.

Research Interests

Jingxian Liu’s research primarily revolves around remote sensing image processing, digital twins, intelligent state prediction, and maneuvering-target tracking. His innovations include fast arbitrary-oriented object detection for remote sensing images, differential correction based shadow removal methods, and deep learning algorithms for maneuvering-target tracking. His work significantly advances understanding in these areas, applying cutting-edge computational techniques to solve complex challenges.

Publications

Fast arbitrary-oriented object detection for remote sensing images

Authors: Liu, J.; Tang, J.; Yang, F.; Zhao, Y.

Citations: 0

Year: 2024

Task Demands-Oriented Collaborative Offloading and Deployment Strategy in Software-Defined UAV-Assisted Edge Networks

Authors: Yan, J.; Wang, W.; Liu, J.; Yuan, H.; Zhu, Y.

Citations: 0

Year: 2024

HDDet: A More Common Heading Direction Detector for Remote Sensing and Arbitrary Viewing Angle Images

Authors: Ding, S.; Liu, J.; Yang, F.; Xu, M.

Citations: 1

Year: 2024

Digital Twins Based Intelligent State Prediction Method for Maneuvering-Target Tracking

Authors: Liu, J.; Yan, J.; Wan, D.; Al-Dulaimi, A.; Quan, Z.

Citations: 5

Year: 2023

Locating the propagation source in complex networks with observers-based similarity measures and direction-induced search

Authors: Yang, F.; Li, C.; Peng, Y.; Wen, J.; Yang, S.

Citations: 7

Year: 2023

Diffusion characteristics classification framework for identification of diffusion source in complex networks

Authors: Yang, F.; Liu, J.; Zhang, R.; Yao, Y.

Citations: 1

Year: 2023

A differential correction based shadow removal method for real-time monitoring

Authors: Liu, S.; Chen, M.; Li, Z.; Liu, J.; He, M.

Citations: 0

Year: 2023

A cross-and-dot-product neural network based filtering for maneuvering-target tracking

Authors: Liu, J.; Yang, S.; Yang, F.

Citations: 6

Year: 2022

Micro-Knowledge Embedding for Zero-shot Classification

Authors: Li, H.; Wang, F.; Liu, J.; Zhang, T.; Yang, S.

Citations: 3

Year: 2022

An identification strategy for unknown attack through the joint learning of space–time features

Authors: Wang, H.; Mumtaz, S.; Li, H.; Liu, J.; Yang, F.

Citations: 6

Year: 2021

 

Conclusion

Jingxian Liu is a highly deserving candidate for the Research for Best Researcher Award due to his significant contributions to remote sensing, digital twins, and maneuvering-target tracking. His innovative research methodologies, high-impact publications, and leadership in large-scale research projects position him as a leader in his field. Continued efforts to enhance industry collaborations and community engagement will further solidify his status as a key figure in advancing technological solutions for environmental and geospatial challenges.

 

Sunusi Bala Abdullahi | Computer vision | Best Researcher Award

Posted on by Academic achievements

Dr. Sunusi Bala Abdullahi | Computer vision | Best Researcher Award 

Researcher at King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

Dr. Sunusi Bala Abdullahi is a dedicated researcher and academic in the field of computer vision and artificial intelligence. With a strong foundation in electrical and computer engineering, he has contributed extensively to machine learning-based systems, computer vision technologies, and statistical modeling. His research delves into human-computer interaction (HCI), social signal processing, and digital image processing, impacting how technology interfaces with human behaviors and applications like sign language recognition. Dr. Abdullahi’s commitment to innovation is evident in his scholarly works, collaborations with international research teams, and mentorship of future researchers. His journey is marked by a blend of technical expertise, leadership, and cross-disciplinary collaboration.

Profile

Google Scholar

Education

Dr. Abdullahi’s academic foundation is robust, beginning with his Bachelor of Science degree in Electrical Engineering from Bayero University, Kano, Nigeria, followed by a Master of Science from the same institution in 2018. His most significant academic milestone came with the completion of his Doctor of Philosophy in Electrical and Computer Engineering from King Mongkut’s University of Technology Thonburi, Bangkok, in 2023. His Ph.D. focused on the intersection of machine learning and computer vision, developing cutting-edge solutions for real-world applications like dynamic gesture recognition and multimodal biometric systems.

Experience

Dr. Abdullahi’s academic career spans multiple roles, showcasing his ability to bridge research and teaching. He started as a research assistant and visiting researcher at the University of A Coruna in Spain in 2022. His experience as a teaching assistant at King Mongkut’s University of Technology Thonburi (2023) and subsequent roles as a postdoctoral fellow have allowed him to mentor students, co-supervise Ph.D. candidates, and conduct groundbreaking research in AI, computer vision, and image processing. His international collaborations with institutions in countries like Thailand, Nigeria, Spain, Saudi Arabia, and South Africa further illustrate his global academic network and diverse expertise.

Research Interests

Dr. Abdullahi’s primary research focus lies in the areas of computer vision, digital image processing, artificial intelligence, and their applications in human-computer interaction. His work explores the integration of machine learning models to improve multimodal data interaction, enhance human motion analysis, and advance social signal processing. A significant portion of his research is dedicated to improving sign language recognition technologies, contributing novel algorithms and methods to enhance accuracy in human motion detection and gesture recognition systems.

Awards

Throughout his career, Dr. Abdullahi has been recognized for his contributions to research. In 2023, he received the Young Researcher Encouragement Award at the 5th ASEAN-UEC Workshop on Informatics and Engineering in Bangkok. He also earned the same award in 2020 at the 2nd ASEAN-UEC Workshop on Energy and AI in Indonesia. His academic journey began with the Dean’s Honor Roll Award in 2006 for Best Outstanding Student Performance from the Federal College of Education in Nigeria. In addition to these accolades, Dr. Abdullahi secured multiple research grants, including the prestigious Petchra Pra Jom Klao Ph.D. Scholarship Award.

Publications

Dr. Abdullahi has authored numerous research articles in high-impact journals. His most recent publications include:

Abdullahi, S.B., Chamnongthai, K., Cancela, B., & Bolon-Canedo, V. (2024). Spatial-temporal feature-based end-to-end fourier network for 3D sign language recognition. Expert Systems with Applications, 248, 123258. Cited: 10

Abdullahi, S.B., Chamnongthai, K., Lubna, G., & Haruna, C. (2024). Fsign-net: Depth sensor aggregated frame-based fourier network for sign word recognition. IEEE Sensors Journal, 248, 123258. Cited: 14

Alamri, F. S., Abdullahi, S.B., Khan, R. A., & Saba, T. (2024). Enhanced weak spatial modeling through cnn-based deep sign language skeletal feature transformation. IEEE Access, 12, 43675–43689. Cited: 12

Bature, Z. A., Abdullahi, S.B., Chiracharit, W., & Chamnongthai, K. (2024). Translated pattern-based eye-writing recognition using dilated causal convolution network. IEEE Access, 12, 59079–59092. Cited: 4

Conclusion

Dr. Sunusi Bala Abdullahi is a strong contender for the Best Researcher Award. His comprehensive contributions to computer vision, AI, and machine learning, combined with his leadership in research supervision, international collaborations, and a prolific publication record, make him a standout candidate. His ability to innovate, collaborate globally, and mentor rising researchers establishes him as a deserving recipient of this award. With continued growth in interdisciplinary research and global academic leadership, Dr. Abdullahi’s influence will likely expand even further in the coming years.

 

Yong-Guk Kim | Computer Vision | Best Researcher Award

Posted on by Academic achievements

Prof. Dr.Yong-Guk Kim | Computer Vision | Best Researcher Award

Professor at Sejong University, South Korea

Dr. Yong-Guk Kim is a Full Professor in the Department of Computer Engineering at Sejong University, Seoul, Korea, and a renowned expert in artificial intelligence and computer vision. His academic journey has taken him from Korea to prestigious institutions in the UK, Netherlands, and the US, contributing significantly to fields like Generative AI, facial expression recognition, and autonomous drone technology. With a career spanning over three decades, Dr. Kim has excelled in both academic research and industry collaborations, leading innovative AI projects and earning multiple accolades in international AI challenges.

Profile

ORCID

Education:

Dr. Kim completed his Ph.D. in Experimental Psychology, specializing in computational vision, from Cambridge University, where he explored visual surface representation for transparency, occlusion, and brightness. He also holds an M.S. in Electrical Engineering, majoring in Automatic Control, and a B.S. in Electrical Engineering, both from Korea University. His education set the foundation for a career at the intersection of engineering and cognitive science, particularly in AI and computer vision applications.

Experience:

Dr. Kim’s diverse career includes research roles at major organizations such as LG and KT in Korea, followed by advanced research opportunities abroad. He worked as a postdoctoral fellow at the Smith-Kettlewell Vision Institute in San Francisco and as an EU fellow at the Robotics Institute of Utrecht University in the Netherlands. He has served as a faculty member at Sejong University for over two decades, holding leadership positions such as Dean of International Affairs and Head of the Start-up Incubator. He has successfully founded the startup Affectronics, specializing in mobile 3D avatars, and played a pivotal role in several AI challenges, showcasing his expertise in applied AI.

Research Interest:

Dr. Kim’s primary research areas lie in Generative AI and Computer Vision. His work encompasses multi-modal large language models, video anomaly detection, and facial expression recognition, with a particular focus on real-world applications such as autonomous drones and personalized advertising platforms. His lab has made significant strides in AI-driven tasks, such as autonomous drone racing and emotion detection, winning multiple international competitions. His research is well-funded by both governmental bodies and private industries, highlighting the practical and impactful nature of his work.

Awards:

Dr. Kim has received numerous awards for his contributions to AI, particularly in global competitions. Notably, his lab won the prestigious Game of Drones Challenge in 2019, organized by Microsoft and Stanford University at the NeurIPS conference. He also placed second in the Inpainting and Denoising Challenge at the European Conference on Computer Vision (ECCV) in 2018 and won the Fake Emotion Detection Challenge at the International Conference on Computer Vision (ICCV) in 2017. These achievements underscore his prominence in the AI research community and his ability to lead teams in high-stakes, competitive environments.

Publications:

Dr. Kim has published extensively in top-tier journals, contributing to the advancement of AI and computer vision. His recent works include:

“Reinforcement Learning Based Drone Simulators: Survey, Practice, and Challenge” (2024) – Artificial Intelligence Review (Cited by: 281) Link.

“UET4Rec: U-net Encapsulated Transformer for Sequential Recommender” (2024) – Expert Systems with Applications (Cited by: 781) Link.

“Meme Analysis using LLM-based Contextual Information“ (2024) – IEEE Access (Cited by: 5) Link.

“Attention-based Residual Autoencoder for Video Anomaly Detection” (2023) – Applied Intelligence (Cited by: 240) Link.

“A Promising AI-based Tool to Simulate Hydrogen Sulfide Elimination” (2023) – Separation and Purification Technology (Cited by: 472) Link.

Conclusion:

Dr. Yong-Guk Kim’s extensive contributions to AI and computer vision, coupled with his successful track record in international AI challenges, academic excellence, and industry collaboration, make him a strong candidate for the Best Researcher Award. His teaching and entrepreneurial achievements further add to his case, demonstrating both academic prowess and real-world impact. By expanding his research into newer domains and engaging more with public discourse on AI, he could further solidify his standing as a world-class researcher.