Ho-Cheung Ng
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Artificial Intelligence
- Hardware and Architecture top 10%
- Computer Networks and Communications
- Co-authors
- Wayne LukShuanglong LiuXinyu NiuHayden Kwok‐Hay SoZhiqiang QueHongxiang FanCheng LiuGeorge A. Constantinides
- Topics
- Advanced Neural Network Applications (6 papers)Embedded Systems Design Techniques (4 papers)Parallel Computing and Optimization Techniques (4 papers)
- Journals
- Science AdvancesACM Computing SurveysIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- United KingdomHong KongChina
In The Last Decade
Ho-Cheung Ng
22 papers receiving 281 citations
Peers
Comparison fields: 5 of 48
- Computer Vision and Pattern Recognition 138
- Electrical and Electronic Engineering 115
- Artificial Intelligence 83
- Hardware and Architecture 62
- Computer Networks and Communications 53
Countries citing papers authored by Ho-Cheung Ng
This map shows the geographic impact of Ho-Cheung Ng'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 Ho-Cheung Ng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ho-Cheung Ng more than expected).
Fields of papers citing papers by Ho-Cheung Ng
This network shows the impact of papers produced by Ho-Cheung Ng. 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 Ho-Cheung Ng. The network helps show where Ho-Cheung Ng may publish in the future.
Co-authorship network of co-authors of Ho-Cheung Ng
This figure shows the co-authorship network connecting the top 25 collaborators of Ho-Cheung Ng. A scholar is included among the top collaborators of Ho-Cheung Ng 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 Ho-Cheung Ng. Ho-Cheung Ng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 4 | |
| 3 | 1 | |
| 4 | 17 | |
| 5 | 2 | |
| 6 | 8 | |
| 7 | 5 | |
| 8 | 15 | |
| 9 | 50 | |
| 10 | 8 | |
| 11 | 9 | |
| 12 | 19 | |
| 13 | 17 | |
| 14 | 20 | |
| 15 | 2 | |
| 16 | 2 | |
| 17 | 2 | |
| 18 | 3 | |
| 19 | 2 | |
| 20 | 26 |
About Ho-Cheung Ng
Ho-Cheung Ng is a scholar working on Hardware and Architecture, Biophysics and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 284 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (6 papers), Embedded Systems Design Techniques (4 papers) and Parallel Computing and Optimization Techniques (4 papers). The work is most often cited by research in Hardware and Architecture (62 citations), Computer Vision and Pattern Recognition (138 citations) and Artificial Intelligence (83 citations). Ho-Cheung Ng has collaborated with scholars based in United Kingdom, Hong Kong and China. Frequent co-authors include Wayne Luk, Shuanglong Liu, Xinyu Niu, Hayden Kwok‐Hay So, Zhiqiang Que, Hongxiang Fan, Cheng Liu, George A. Constantinides, Rongxuan Zhao and James J. Davis. Their work appears in journals such as Science Advances, ACM Computing Surveys and IEEE Transactions on Neural Networks and Learning Systems.
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.