En‐hui Yang
- Signal Processing top 1%
- Video Coding and Compression Technologies 36
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- Advanced Data Compression Techniques 48
- Advanced Vision and Imaging 20
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- Cellular Automata and Applications 32
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- Error Correcting Code Techniques 35
- Artificial Intelligence top 2%
- Algorithms and Data Compression 53
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- Wireless Communication Security Techniques 34
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- DNA and Biological Computing 34
- Cited by
- Signal ProcessingComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- IEEE Transactions on Information Theory (35 papers)IEEE Transactions on Image Processing (6 papers)IEEE Transactions on Circuits and Systems for Video Technology (5 papers)
- Partner nations
- CanadaUnited StatesChina
In The Last Decade
En‐hui Yang
149 papers receiving 1.9k citations
Peers
Comparison fields: 5 of 88
- Signal Processing 523
- Computer Vision and Pattern Recognition 782
- Computational Theory and Mathematics 468
- Computer Networks and Communications 599
- Artificial Intelligence 825
Countries citing papers authored by En‐hui Yang
This map shows the geographic impact of En‐hui Yang'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 En‐hui Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites En‐hui Yang more than expected).
Fields of papers citing papers by En‐hui Yang
This network shows the impact of papers produced by En‐hui Yang. 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 En‐hui Yang. The network helps show where En‐hui Yang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside En‐hui Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 4 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 4 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 1 | |
| 7 | 2023 | 1 | |
| 8 | 2022 | 1 | |
| 9 | 2021 | 16 | |
| 10 | 2021 | 5 | |
| 11 | 2017 | 12 | |
| 12 | Bipartite grammar-based representations of large sparse binary matrices: Framework and transforms | 2016 | 1 |
| 13 | 2016 | 9 | |
| 14 | 2010 | 1 | |
| 15 | 2009 | 17 | |
| 16 | 2008 | 0 | |
| 17 | 2008 | 13 | |
| 18 | 2007 | 1 | |
| 19 | 2006 | 6 | |
| 20 | 2000 | 55 |
About En‐hui Yang
En‐hui Yang is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 163 papers that have together received 1.9k indexed citations. Recurring topics across this work include Algorithms and Data Compression (53 papers), Advanced Data Compression Techniques (48 papers), Video Coding and Compression Technologies (36 papers), Error Correcting Code Techniques (35 papers), Wireless Communication Security Techniques (34 papers), DNA and Biological Computing (34 papers), Cellular Automata and Applications (32 papers) and Advanced Vision and Imaging (20 papers). The work is most often cited by research in Signal Processing (523 citations), Computer Vision and Pattern Recognition (782 citations) and Computational Theory and Mathematics (468 citations). En‐hui Yang has collaborated with scholars based in Canada, United States and China. Frequent co-authors include John C. Kieffer, Dake He, Nan Hu, Jin Meng, Xiang Yu, Haiquan Wang, Zhijin Zhao, Wei Zhang, Chang Sun and Victor K. Wei. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Communications and IEEE Transactions on Wireless Communications.
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.