Cao Changqing | Intelligent Perception | Best Researcher Award

Published on by China Scientist

Prof. Cao Changqing | Intelligent Perception | Best Researcher Award 

Prof. Cao Changqing is a researcher at Xidian University specializing in optoelectronic technology, remote sensing, and artificial intelligence image processing. He earned his bachelor’s, master’s, and doctoral degrees in optical engineering from Xidian University and has since dedicated his career to advancing research and teaching in optoelectronics engineering. His work has gained international recognition, including being listed among the top global scientists. He has published widely in leading journals and serves as both reviewer and editor for renowned publishers in optics and photonics. His research focuses on optical imaging algorithms, interferometric techniques, laser dynamics, and phase compensation methods, contributing to the progress of modern optoelectronic science and technology.

Prof. Cao Changqing | Xidian University | China

Profiles

SCOPUS

ORCID

Education

  • Cao Changqing pursued his academic journey entirely at Xidian University, where he built a strong foundation in optical engineering. He advanced from undergraduate to postgraduate studies, ultimately completing his doctoral degree in the same discipline. His long-term academic engagement at the same institution reflects both dedication and a deepening of expertise in the field of optoelectronics.

Experience

  • After completing his education, Prof. Cao continued his career at Xidian University, where he has been engaged in teaching and research in optoelectronics engineering. His role has centered on advancing innovations in optical technologies, remote sensing, and artificial intelligence in image processing. Over the years, he has guided scientific inquiry and contributed significantly to the academic and research environment of the institution.

Awards and Recognition

  • Prof. Cao has earned global recognition for his research contributions, being listed among the top global scientists in recent editions of a prestigious ranking. His achievements demonstrate the international impact of his work and highlight his role as a leading scholar in his areas of expertise.

Skills and Expertise

  • Prof. Cao possesses extensive skills in optoelectronic technology, remote sensing systems, and artificial intelligence image processing. His proficiency extends to advanced imaging algorithms, optical communication techniques, and laser dynamics. He is also highly skilled in scientific publishing and reviewing, serving as a reviewer for well-known international journals and as an editor for respected publications in optics and photonics.

Research Focus 

  • The core of Prof. Cao’s research is centered on advancing optoelectronics, remote sensing, and artificial intelligence image processing. His studies explore optical imaging algorithms, interferometric techniques, and the dynamic characteristics of lasers. He also works on innovations in phase compensation methods, photon-integrated imaging, and polarimetric scattering phenomena, ensuring his contributions remain at the forefront of technological development.

Publication

  • Investigation polarimetric scattering of light from the randomly rough surface based on the calculation of the Mueller matrix
    Author: Song B., Cao C., Feng Z., Wu Z., Yu C., Wei R
    Journal: Optics Express

  • Calibration of SOI Optical Phased Arrays via Improved SPGD Algorithm
    Author: Wang Z., Wu B., Liao J., Li X., Wang C., Sun Y., Jin L., Feng J., Cao C.
    Journal: Optics and Laser Technology, 2023

  • Factors Influencing the Performance of Optical Heterodyne Detection System
    Author: Wu Z., Cao C., Feng Z., Ye S., Li M., Song B., Wei R.
    Journal: Optics and Lasers in Engineering, 2023

  • Improving Distance Imaging Accuracy through Temporal Position Correction with Phase Difference Compensation
    Author: Wu Z., Cao C., Feng Z., Wu X., Duan C., Liu H.
    Journal: Applied Optics, 2023

  • Innovative OPA-based Optical Chip for Enhanced Digital Holography
    Author: Wang Z., Linke Liu, Jiang P., Liao J., Jiamu Xu, Yanling Sun, Ji’n Li, Zhenzhong Lu, Feng J., Cao C.
    Journal: Optics Express, 2023

Conclusion

  • Prof. Cao Changqing is an accomplished scholar whose career combines education, research, and service in the field of optoelectronics. His strong academic background, professional contributions, and international recognition underline his influence in advancing both theoretical and applied aspects of optical engineering and remote sensing. Through his innovative research and editorial service, he continues to shape the future of optical science and technology.

Naifeng He | Robotics | Best Researcher Award

Published on by China Scientist

Dr. Naifeng He | Robotics | Best Researcher Award

Innovative Projects:
  • He is working on dynamic obstacle avoidance control systems for wheel-legged robots and path planning using reinforcement learning. His projects demonstrate practical applications and innovations in robotics.
 Dr . Naifeng He, Nanjing University of Aeronautics and Astronautics, China

Profile

Scopus

🎓Early Academic Pursuits

  • He Naifeng began his academic journey with a deep interest in robotics and automation, eventually leading him to pursue a PhD at the School of Automation, Nanjing University of Aeronautics and Astronautics. His early focus revolved around motion control systems for autonomous robots, particularly exploring how robots could navigate complex, dynamic environments. His foundational studies set the stage for his later research in integrating advanced control techniques with artificial intelligence.

💼Professional Endeavors

  • As a PhD candidate, He Naifeng specializes in the field of motion control and navigation of wheel-legged mobile robots. His work is recognized for its innovative approach to solving challenges in robot autonomy and mobility. Combining traditional control techniques with reinforcement learning, he has made notable advancements in enhancing the agility and adaptability of mobile robots. His primary professional focus includes optimizing navigation systems, path planning, and obstacle avoidance for these robots.

🔬 CONTRIBUTIONS AND RESEARCH FOCUS

  • He Naifeng’s key contributions lie in developing motion control systems that enable wheel-legged robots to operate autonomously in unpredictable environments. His research incorporates deep reinforcement learning to improve the robots’ decision-making capabilities, especially in complex and unmapped areas. Through his approach, the efficiency of path planning and dynamic obstacle avoidance has been significantly improved, paving the way for practical applications such as industrial inspection.

🌍Impact and Influence

  • He Naifeng’s research has impacted various sectors, including industrial automation and robotics. By focusing on the development of control algorithms for mobile robots, he has enabled more efficient autonomous navigation in unmapped and unknown environments. His innovations have led to improvements in both the performance and safety of these robots, allowing them to be used in more complex, real-world scenarios.

🏅ACADEMIC CITES 

  • He Naifeng has made significant contributions to the field of autonomous robotics through his published works in reputable scientific journals. His research has gained recognition, particularly in the areas of autonomous navigation and control algorithms for mobile robots.

 📚LEGACY AND FUTURE CONTRIBUTIONS

  • He Naifeng’s ongoing work on wheel-legged robots and autonomous navigation systems positions him as an innovator in the field of mobile robotics. His efforts in improving dynamic obstacle avoidance and path optimization have the potential to revolutionize industries where autonomous inspection and mobility are critical. As his research continues to evolve, He Naifeng is poised to make even greater contributions to the advancement of intelligent mobile systems, particularly through the application of reinforcement learning in control systems.

📰PUBLICATIONS

  •  A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering
    Authors: Wu, J., Yang, Z., Zhuo, H., Liao, L., Wang, Z.
    Journal: Drones, 2024, 8(3), 69
  • A Self-Adaptive Double Q-Backstepping Trajectory Tracking Control Approach Based on Reinforcement Learning for Mobile Robots
    Authors: He, N., Yang, Z., Fan, X., Sui, Y., Zhang, Q.
    Journal: Actuators, 2023, 12(8), 326
  •  A State-Compensated Deep Deterministic Policy Gradient Algorithm for UAV Trajectory Tracking
    Authors: Wu, J., Yang, Z., Liao, L., Wang, Z., Wang, C.
    Journal: Machines, 2022, 10(7), 496
  • Adaptive PID Trajectory Tracking Algorithm Using Q-Learning for Mobile Robots
    Authors: Fan, X., Sui, J., He, N., Yang, J., Cui, L.
    Journal: 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022, pp. 1112–1117