World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
18762
World Ranking
1695
National Ranking
230

Electronics and Electrical Engineering

D-Index
71
Citations
18618
World Ranking
871
National Ranking
146

Research.com Recognitions

  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering
  • The Canadian Academy of Engineering

Overview

Max Q.-H. Meng is affiliated with the Chinese University of Hong Kong in China. Their research spans multiple fields including Engineering and Computer Science, with notable focus on several subfields such as Computer Vision and Pattern Recognition, Biomedical Engineering, Aerospace Engineering, Control and Systems Engineering, and Artificial Intelligence.

The scientist's work encompasses a broad range of topics primarily related to robotics and automation. Key areas of study include:

  • Robotics and Sensor-Based Localization
  • Robotic Path Planning Algorithms
  • Soft Robotics and Applications
  • Robot Manipulation and Learning
  • Medical Image Segmentation Techniques
  • Gastrointestinal Bleeding Diagnosis and Treatment
  • Advanced Vision and Imaging

Max Q.-H. Meng has contributed to numerous research papers published in prestigious venues. Recent notable publications include:

  • Neural RRT*: Learning-Based Optimal Path Planning (2020), IEEE Transactions on Automation Science and Engineering
  • EB-RRT: Optimal Motion Planning for Mobile Robots (2020), IEEE Transactions on Automation Science and Engineering
  • An Overview of Systems and Techniques for Autonomous Robotic Ultrasound Acquisitions (2021), IEEE Transactions on Medical Robotics and Bionics
  • Kinematic Constrained Bi-directional RRT with Efficient Branch Pruning for robot path planning (2020), Expert Systems with Applications
  • Deep Koopman Operator With Control for Nonlinear Systems (2022), IEEE Robotics and Automation Letters

The scientist has collaborated frequently with peers such as Jiankun Wang, Jiaole Wang, Zhe Min, Shuang Song, and Hongliang Ren. These collaborations have yielded a significant number of publications reinforcing the focus on robotics and automation.

The most common publication venues for Max Q.-H. Meng include:

  • arXiv (Cornell University)
  • IEEE Transactions on Automation Science and Engineering
  • IEEE Robotics and Automation Letters
  • IEEE Transactions on Instrumentation and Measurement
  • 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)

Among the recognitions received is an award from The Canadian Academy of Engineering. This acknowledgment reflects the scientist's engagement within the broader engineering community.

Best Publications

  • Neural RRT*: Learning-Based Optimal Path Planning

    Jiankun Wang;Wenzheng Chi;Chenming Li;Chaoqun Wang

  • Efficient magnetic localization and orientation technique for capsule endoscopy

    Chao Hu;Max Qinghu Meng;M. Mandal

  • Improving RGB-D SLAM in dynamic environments: A motion removal approach

    Yuxiang Sun;Ming Liu;Max Qing Hu Meng

  • An efficient neural network approach to dynamic robot motion planning

    Simon X. Yang;Max Meng

  • Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images

    Baopu Li;M.Q.-H. Meng

  • Neural network approaches to dynamic collision-free trajectory generation

    S.X. Yang;M. Meng

  • A Cubic 3-Axis Magnetic Sensor Array for Wirelessly Tracking Magnet Position and Orientation

    Chao Hu;Mao Li;Shuang Song;Wan'an Yang

  • Texture analysis for ulcer detection in capsule endoscopy images

    Baopu Li;Max Q. H. Meng

  • A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images

    Xiao Jia;Max Q.-H. Meng

  • Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection

    Baopu Li;M. Q.-H Meng

  • Deep learning for polyp recognition in wireless capsule endoscopy images.

    Yixuan Yuan;Max Q.-H. Meng

  • Computer-Aided Bleeding Detection in WCE Video

    Yanan Fu;Wei Zhang;Mrinal Mandal;Max Q.-H Meng

  • Motion removal for reliable RGB-D SLAM in dynamic environments

    Yuxiang Sun;Ming Liu;Max Q.-H. Meng

  • Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach

    S.X. Yang;M.Q.-H. Meng

  • A Linear Algorithm for Tracing Magnet Position and Orientation by Using Three-Axis Magnetic Sensors

    Chao Hu;M.Q.-H. Meng;M. Mandal

  • Augmented Bladder Tumor Detection Using Deep Learning

    Eugene Shkolyar;Xiao Jia;Timothy C. Chang;Timothy C. Chang;Dharati Trivedi;Dharati Trivedi

  • Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments

    Baopu Li;Max Q. H. Meng

  • Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video

    Yixuan Yuan;Baopu Li;Max Q.-H. Meng

  • A Bioinspired Neurodynamics-Based Approach to Tracking Control of Mobile Robots

    S. X. Yang;Anmin Zhu;Guangfeng Yuan;M. Q. Meng

  • Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis

    Yixuan Yuan;Jiaole Wang;Baopu Li;Max Q.-H. Meng

Frequent Co-Authors

Shuang Song
Shuang Song Harbin Institute of Technology
Hongliang Ren
Hongliang Ren Chinese University of Hong Kong
Peter X. Liu
Peter X. Liu Carleton University
Simon X. Yang
Simon X. Yang University of Guelph
Clarence W. de Silva
Clarence W. de Silva University of British Columbia
Huaicheng Yan
Huaicheng Yan East China University of Science and Technology
Mrinal Mandal
Mrinal Mandal University of Alberta
Kuanquan Wang
Kuanquan Wang Harbin Institute of Technology
Qing He
Qing He University of Chinese Academy of Sciences
Lei Xing
Lei Xing Stanford University

External Links

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For those interested in Electronics and Electrical Engineering, exploring flexible and accessible education options is crucial. Many military families benefit from attending online schools for military spouses, which offer tailored support and adaptable schedules to balance service commitments and studies.

Starting your degree program quickly is possible with online colleges that start soon. These institutions provide multiple enrollment opportunities throughout the year, allowing faster entry into coursework and accelerated progress towards your career goals.

For those seeking faster entry into the workforce, short term certificate programs offer efficient routes to gain valuable skills. These certificates can enhance your resume without the lengthy commitment of a full degree and often lead to well-paying roles in technical fields.

Additionally, if you identify as an introvert, it's encouraging to know there are careers for introverts within engineering disciplines that align with independent work and thoughtful problem-solving, making Electronics and Electrical Engineering a suitable choice for a wide range of personality types.

Best Scientists Citing Max Q.-H. Meng

Trending Scientists

Recently Published Articles