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.

 

Shivaraj S – Control systems – Best Researcher Award

Posted on by Academic achievements

Shivaraj S - Control systems - Best Researcher Award

Université de Lorraine - Germany

Professional Profiles

Early Academic Pursuits:

Shivaraj S. Mohite embarked on his academic journey with a strong foundation in Electrical Engineering at Veermata Jijabai Technological Institute (VJTI), Mumbai, India. He earned his Bachelor of Technology (B. Tech) degree with a commendable CGPA of 7.82/10. Building on this academic success, Shivaraj pursued a Master of Technology (M. Tech) in Control System - Electrical Engineering at VJTI, achieving an impressive CGPA of 9.01/10. His academic prowess reached new heights as he pursued a Ph.D. in Automation and Control Systems at the University of Lorraine, CRAN, France. The title of his doctoral thesis, "Observer Design for Nonlinear Systems using LMI Relaxation Techniques," reflects his deep engagement with cutting-edge control system research.

Professional Endeavors:

Shivaraj's professional journey encompasses diverse roles that have contributed to his growth and expertise. As a Site Engineer at Lodha Developers Limited in Mumbai, India, from July 2016 to August 2018, he gained hands-on experience in the field, honing his practical skills in engineering applications. During his M. Tech studies, Shivaraj served as a Teaching Assistant at VJTI, Mumbai, from July 2019 to August 2020. He played a crucial role in imparting knowledge in courses such as Linear Control Design and Nonlinear System Analysis, showcasing his commitment to both academic and practical aspects of control systems. His dedication to education extended beyond traditional classroom settings, as he also served as a Private Tutor, covering a spectrum of subjects including Mathematics, Algebra, Geometry, Physics, Chemistry, and Science from April 2014 to March 2020.

Contributions and Research Focus On Control systems:

Shivaraj's research journey is marked by significant contributions to the field of control systems, particularly in the design of observers for nonlinear systems. His Ph.D. research, titled "Nonlinear Observer Design using LMI-Relaxation Techniques," exemplifies his commitment to advancing the understanding and application of control methodologies. During his M. Tech studies, Shivaraj engaged in impactful projects such as the design of an observer for Autonomous Vehicles and the estimation of State of Charge (SOC) for battery models using Kalman filters and nonlinear observers. These projects showcase his versatility in addressing real-world challenges through innovative control system solutions. In his current role as a Research Assistant at the Mechanical and Process Engineering Department, RPTU, Kaiserslautern, Germany, Shivaraj is actively involved in a project focused on Linear Parameter Varying (LPV) systems. His postdoctoral work involves designing an LMI-based observer for a nonlinear Lipschitz LPV system, showcasing a continuation of his research excellence.

Accolades and Recognition:

Shivaraj's academic and research achievements have not gone unnoticed. His consistently high CGPA during his master's and doctoral studies attests to his academic excellence. His contributions to the field of control systems have likely earned him recognition within the academic and research communities.

Impact and Influence:

Shivaraj's work has the potential to significantly impact the field of control systems, particularly in the context of nonlinear observer design. The application of LMI relaxation techniques in his research could pave the way for more robust and efficient control methodologies, with implications for various autonomous systems and vehicles.

Legacy and Future Contributions:

Shivaraj S. Mohite's legacy is characterized by a dedication to academic excellence and a commitment to pushing the boundaries of control system research. His future contributions are anticipated to further advance the understanding and application of control methodologies, leaving a lasting impact on the field and inspiring future generations of researchers and engineers.

Notable Publications:

Application of Regression based Speed Estimation for Sensorless Vector Controlled im Drive 2020

Enhancing the Performance Index of Battery Management System Using Nonlinear Approach 2020

MPC based State Observer and its application for system with input-output disturbances 2019