Guang Chen
- Computer Vision and Pattern Recognition top 10%
- Hardware and Architecture top 10%
- Artificial Intelligence
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Co-authors
- Can ZhangYuexian ZouThambipillai SrikanthanJigang WuLei GanXiaohui DuanThomas K. CresonHusseini K. Manji
- Topics
- Human Pose and Action Recognition (5 papers)GaN-based semiconductor devices and materials (3 papers)Gait Recognition and Analysis (2 papers)
- Journals
- SHILAP Revista de lepidopterologíaApplied Physics LettersAdvanced Functional Materials
In The Last Decade
Guang Chen
18 papers receiving 165 citations
Peers
Comparison fields: 5 of 49
- Computer Vision and Pattern Recognition 60
- Hardware and Architecture 53
- Artificial Intelligence 44
- Computer Networks and Communications 42
- Electrical and Electronic Engineering 41
Countries citing papers authored by Guang Chen
This map shows the geographic impact of Guang Chen'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 Guang Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guang Chen more than expected).
Fields of papers citing papers by Guang Chen
This network shows the impact of papers produced by Guang Chen. 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 Guang Chen. The network helps show where Guang Chen may publish in the future.
Co-authorship network of co-authors of Guang Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Guang Chen. A scholar is included among the top collaborators of Guang Chen 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 Guang Chen. Guang Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 0 | |
| 9 | 9 | |
| 10 | 1 | |
| 11 | 3 | |
| 12 | 7 | |
| 13 | 16 | |
| 14 | 37 | |
| 15 | End to End Learning of Spiking Neural Network based on R-STDP for a Lane Keeping Vehicle, to be appear | 1 |
| 16 | 4 | |
| 17 | 1 | |
| 18 | 4 | |
| 19 | 17 | |
| 20 | 46 |
About Guang Chen
Guang Chen is a scholar working on Software, Human-Computer Interaction and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 169 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (5 papers), GaN-based semiconductor devices and materials (3 papers) and Gait Recognition and Analysis (2 papers). The work is most often cited by research in Hardware and Architecture (53 citations), Computer Vision and Pattern Recognition (60 citations) and Human-Computer Interaction (11 citations). Guang Chen has collaborated with scholars based in China, Germany and Chile. Frequent co-authors include Can Zhang, Yuexian Zou, Thambipillai Srikanthan, Jigang Wu, Lei Gan, Xiaohui Duan, Thomas K. Creson, Husseini K. Manji, Bo Shen and Lei Guo. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Advanced Functional Materials.
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.