Ming Tie
Impact in
-
- Chaos-based Image/Signal Encryption
- Advanced Steganography and Watermarking Techniques
- Advanced Neural Network Applications
Papers in
-
- Chaos-based Image/Signal Encryption 7
- Advanced Steganography and Watermarking Techniques 6
- Digital Media Forensic Detection 3
-
- Fault Detection and Control Systems 5
- Co-authors
- Chong Fu (14 shared papers)Wei Song (4 shared papers)Yu Zheng (3 shared papers)Chiu‐Wing Sham (9 shared papers)Junxin Chen (4 shared papers)Jun Liu (1 shared paper)Ye Qi (1 shared paper)Qijiao Song (1 shared paper)
- Journals
- Neural Computing and Applications (3 papers)Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering (2 papers)Journal of Computational Physics (2 papers)Resources Conservation and Recycling (2 papers)Journal of Ambient Intelligence and Humanized Computing (2 papers)
- Partner nations
- ChinaNew ZealandHong Kong
In The Last Decade
Ming Tie
34 papers receiving 429 citations
Peers
Comparison fields: 5 of 76
- Computer Vision and Pattern Recognition 220
- Mathematical Physics 30
- Automotive Engineering 30
- Artificial Intelligence 85
- Statistical and Nonlinear Physics 29
Countries citing papers authored by Ming Tie
This map shows the geographic impact of Ming Tie's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming Tie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Tie more than expected).
Fields of papers citing papers by Ming Tie
This network shows the impact of papers produced by Ming Tie. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming Tie. The network helps show where Ming Tie may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming Tie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 81 | |
| 2 | 2019 | 57 | |
| 3 | 2022 | 51 | |
| 4 | 2022 | 33 | |
| 5 | 2021 | 25 | |
| 6 | 2021 | 22 | |
| 7 | 2022 | 18 | |
| 8 | 2018 | 16 | |
| 9 | 2021 | 14 | |
| 10 | 2021 | 13 | |
| 11 | 2022 | 11 | |
| 12 | 2023 | 10 | |
| 13 | 2022 | 10 | |
| 14 | 2021 | 9 | |
| 15 | 2022 | 8 | |
| 16 | 2023 | 8 | |
| 17 | 2005 | 8 | |
| 18 | 2023 | 6 | |
| 19 | 2022 | 6 | |
| 20 | 2021 | 5 |
About Ming Tie
Ming Tie is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Mechanical Engineering, Artificial Intelligence and Automotive Engineering, having authored 35 papers that have together received 437 indexed citations. Recurring topics across this work include Chaos-based Image/Signal Encryption (7 papers), Advanced Steganography and Watermarking Techniques (6 papers), Fault Detection and Control Systems (5 papers), Vehicle Dynamics and Control Systems (4 papers), Hydraulic and Pneumatic Systems (3 papers), AI in cancer detection (3 papers), Digital Media Forensic Detection (3 papers) and Mathematical Dynamics and Fractals (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (220 citations), Mathematical Physics (30 citations), Automotive Engineering (30 citations), Artificial Intelligence (85 citations) and Statistical and Nonlinear Physics (29 citations). Ming Tie has collaborated with scholars based in China, New Zealand and Hong Kong. Frequent co-authors include Chong Fu, Wei Song, Yu Zheng, Chiu‐Wing Sham, Junxin Chen, Jun Liu, Ye Qi, Qijiao Song, Lin Cao and Yunlong Wang. Their work appears in journals such as Neural Computing and Applications, Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, Journal of Computational Physics, Resources Conservation and Recycling and Journal of Ambient Intelligence and Humanized Computing.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.