Xiaojun Zhai
- Computer Networks and Communications top 5%
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering
- Biomedical Engineering
- Media Technology top 2%
- Co-authors
- Fayçal BensaaliKlaus D. McDonald-MaierAbbes AmiraLu LiuHaider AliUmair Ullah TariqJohn PanneerselvamAmine Ait Si Ali
- Topics
- Vehicle License Plate Recognition (15 papers)IoT and Edge/Fog Computing (14 papers)Parallel Computing and Optimization Techniques (14 papers)
- Journals
- SHILAP Revista de lepidopterologíaBioinformaticsApplied Energy
- Partner nations
- United KingdomChinaQatar
In The Last Decade
Xiaojun Zhai
122 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 137
- Computer Networks and Communications 388
- Computer Vision and Pattern Recognition 387
- Electrical and Electronic Engineering 286
- Biomedical Engineering 253
- Media Technology 216
Countries citing papers authored by Xiaojun Zhai
This map shows the geographic impact of Xiaojun Zhai'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 Xiaojun Zhai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaojun Zhai more than expected).
Fields of papers citing papers by Xiaojun Zhai
This network shows the impact of papers produced by Xiaojun Zhai. 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 Xiaojun Zhai. The network helps show where Xiaojun Zhai may publish in the future.
Co-authorship network of co-authors of Xiaojun Zhai
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaojun Zhai. A scholar is included among the top collaborators of Xiaojun Zhai 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 Xiaojun Zhai. Xiaojun Zhai 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 | 2 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 6 | |
| 8 | 24 | |
| 9 | 1 | |
| 10 | 7 | |
| 11 | 3 | |
| 12 | 9 | |
| 13 | 20 | |
| 14 | 30 | |
| 15 | 29 | |
| 16 | 8 | |
| 17 | 25 | |
| 18 | 4 | |
| 19 | 10 | |
| 20 | 48 |
About Xiaojun Zhai
Xiaojun Zhai is a scholar working on Hardware and Architecture, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 134 papers that have together received 1.6k indexed citations. Recurring topics across this work include Vehicle License Plate Recognition (15 papers), IoT and Edge/Fog Computing (14 papers) and Parallel Computing and Optimization Techniques (14 papers). The work is most often cited by research in Hardware and Architecture (174 citations), Media Technology (216 citations) and Computer Vision and Pattern Recognition (387 citations). Xiaojun Zhai has collaborated with scholars based in United Kingdom, China and Qatar. Frequent co-authors include Fayçal Bensaali, Klaus D. McDonald-Maier, Abbes Amira, Lu Liu, Haider Ali, Umair Ullah Tariq, John Panneerselvam, Amine Ait Si Ali, Reza Sotudeh and Jo Jackson. Their work appears in journals such as SHILAP Revista de lepidopterología, Bioinformatics and Applied Energy.
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.