Hao Zhu
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Control and Systems Engineering top 2%
- Computer Networks and Communications top 5%
- Automotive Engineering top 2%
- Co-authors
- Henry LeungYongfu LiXiaojun PengJiangli FanBenhua WangKa‐Veng YuenPiotr KoniuszLyudmila Mihaylova
- Topics
- Target Tracking and Data Fusion in Sensor Networks (15 papers)Autonomous Vehicle Technology and Safety (14 papers)Traffic control and management (12 papers)
- Journals
- Chemical Society ReviewsSHILAP Revista de lepidopterologíaACS Nano
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Hao Zhu
121 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Artificial Intelligence 727
- Computer Vision and Pattern Recognition 475
- Control and Systems Engineering 450
- Computer Networks and Communications 335
- Automotive Engineering 312
Countries citing papers authored by Hao Zhu
This map shows the geographic impact of Hao Zhu'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 Hao Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hao Zhu more than expected).
Fields of papers citing papers by Hao Zhu
This network shows the impact of papers produced by Hao Zhu. 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 Hao Zhu. The network helps show where Hao Zhu may publish in the future.
Co-authorship network of co-authors of Hao Zhu
This figure shows the co-authorship network connecting the top 25 collaborators of Hao Zhu. A scholar is included among the top collaborators of Hao Zhu 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 Hao Zhu. Hao Zhu 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 | 20 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 18 | |
| 7 | 0 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 48 | |
| 11 | 5 | |
| 12 | 0 | |
| 13 | 74 | |
| 14 | 15 | |
| 15 | 2 | |
| 16 | 6 | |
| 17 | 6 | |
| 18 | 16 | |
| 19 | 19 | |
| 20 | Embeded hardware and software co-design for SoPC-based condition monitoring device | 0 |
About Hao Zhu
Hao Zhu is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Media Technology, having authored 138 papers that have together received 2.9k indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (15 papers), Autonomous Vehicle Technology and Safety (14 papers) and Traffic control and management (12 papers). The work is most often cited by research in Automotive Engineering (312 citations), Computer Vision and Pattern Recognition (475 citations) and Artificial Intelligence (727 citations). Hao Zhu has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Henry Leung, Yongfu Li, Xiaojun Peng, Jiangli Fan, Benhua Wang, Ka‐Veng Yuen, Piotr Koniusz, Lyudmila Mihaylova, Shangbo Zhou and Zhongshi He. Their work appears in journals such as Chemical Society Reviews, SHILAP Revista de lepidopterología and ACS Nano.
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.