Qingxiang Wu
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Artificial Intelligence top 10%
- Cognitive Neuroscience top 10%
- Media Technology top 5%
- Co-authors
- T.M. McGinnityLiam MaguireLida QiuBrendan GlackinAmmar BelatrecheDavid BellYanfeng ChenRongtai Cai
- Topics
- Neural dynamics and brain function (18 papers)Advanced Memory and Neural Computing (17 papers)CCD and CMOS Imaging Sensors (10 papers)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Qingxiang Wu
73 papers receiving 588 citations
Peers
Comparison fields: 5 of 102
- Computer Vision and Pattern Recognition 243
- Electrical and Electronic Engineering 169
- Artificial Intelligence 167
- Cognitive Neuroscience 127
- Media Technology 81
Countries citing papers authored by Qingxiang Wu
This map shows the geographic impact of Qingxiang Wu'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 Qingxiang Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qingxiang Wu more than expected).
Fields of papers citing papers by Qingxiang Wu
This network shows the impact of papers produced by Qingxiang Wu. 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 Qingxiang Wu. The network helps show where Qingxiang Wu may publish in the future.
Co-authorship network of co-authors of Qingxiang Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Qingxiang Wu. A scholar is included among the top collaborators of Qingxiang Wu 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 Qingxiang Wu. Qingxiang Wu 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 | 9 | |
| 4 | 12 | |
| 5 | 18 | |
| 6 | 3 | |
| 7 | 2 | |
| 8 | 0 | |
| 9 | 2 | |
| 10 | 3 | |
| 11 | 9 | |
| 12 | 1 | |
| 13 | 3 | |
| 14 | 12 | |
| 15 | 11 | |
| 16 | 24 | |
| 17 | 7 | |
| 18 | A Design Flow for the Hardware Implementation of Spiking Neural Networks onto FPGAs | 1 |
| 19 | Noise Reduction In Synthetic Aperture Radar Imagery Using A Morphology-Based Nonlinear Filter | 9 |
| 20 | 6 |
About Qingxiang Wu
Qingxiang Wu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Cognitive Neuroscience, having authored 79 papers that have together received 608 indexed citations. Recurring topics across this work include Neural dynamics and brain function (18 papers), Advanced Memory and Neural Computing (17 papers) and CCD and CMOS Imaging Sensors (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (243 citations), Media Technology (81 citations) and Cognitive Neuroscience (127 citations). Qingxiang Wu has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include T.M. McGinnity, Liam Maguire, Lida Qiu, Brendan Glackin, Ammar Belatreche, David Bell, Yanfeng Chen, Rongtai Cai, Tong Yu and Lei Hou. Their work appears in journals such as Nature Communications, International Journal of Remote Sensing and Current Opinion in Immunology.
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.