Junxiang Wang
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Molecular Biology
- Control and Systems Engineering
- Information Systems top 10%
- Co-authors
- Jiangqun NiYun-Qing ShiXing ZhangLiang ZhaoZhongdai WuChuntao WangDezhi HanXin Chen
- Topics
- Advanced Steganography and Watermarking Techniques (12 papers)Digital Media Forensic Detection (10 papers)Chaos-based Image/Signal Encryption (8 papers)
- Partner nations
- ChinaUnited StatesJapan
In The Last Decade
Junxiang Wang
70 papers receiving 849 citations
Peers
Comparison fields: 5 of 121
- Computer Vision and Pattern Recognition 355
- Artificial Intelligence 139
- Molecular Biology 108
- Control and Systems Engineering 61
- Information Systems 56
Countries citing papers authored by Junxiang Wang
This map shows the geographic impact of Junxiang Wang'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 Junxiang Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Junxiang Wang more than expected).
Fields of papers citing papers by Junxiang Wang
This network shows the impact of papers produced by Junxiang Wang. 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 Junxiang Wang. The network helps show where Junxiang Wang may publish in the future.
Co-authorship network of co-authors of Junxiang Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Junxiang Wang. A scholar is included among the top collaborators of Junxiang Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Junxiang Wang. Junxiang Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 0 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 10 | |
| 11 | 3 | |
| 12 | 2 | |
| 13 | 16 | |
| 14 | 2 | |
| 15 | 8 | |
| 16 | 14 | |
| 17 | 6 | |
| 18 | 15 | |
| 19 | Nonconvex Generalization of ADMM for Nonlinear Equality Constrained Problems | 6 |
| 20 | Research on the Principle and Application of Dynamic Programming Algorithm | 0 |
About Junxiang Wang
Junxiang Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 81 papers that have together received 872 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (12 papers), Digital Media Forensic Detection (10 papers) and Chaos-based Image/Signal Encryption (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (355 citations), Artificial Intelligence (139 citations) and Insect Science (51 citations). Junxiang Wang has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Jiangqun Ni, Yun-Qing Shi, Xing Zhang, Liang Zhao, Zhongdai Wu, Chuntao Wang, Dezhi Han, Xin Chen, Kuan‐Ching Li and Arcangelo Castiglione. Their work appears in journals such as Analytical Chemistry, Proceedings of the IEEE and Journal of Agricultural and Food Chemistry.
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.