Tak-Shing Chan
Impact in
- Signal Processing top 5%
- Biometric Identification and Security
- Speech and Audio Processing
- Music and Audio Processing
- Computational Mathematics top 10%
Papers in
-
- Biometric Identification and Security 4
- Speech and Audio Processing 4
- Blind Source Separation Techniques 3
-
- Face and Expression Recognition 2
- Image and Signal Denoising Methods 2
- Co-authors
- Ajay Kumar (4 shared papers)Yi‐Hsuan Yang (6 shared papers)Jyh‐Shing Roger Jang (3 shared papers)Hung-Wei Chen (1 shared paper)Li Su (1 shared paper)Chun-Wei Tan (1 shared paper)Jonathan Chambers (1 shared paper)Ben Mitchinson (1 shared paper)
- Journals
- Scientific Reports (1 paper)Advanced Engineering Informatics (1 paper)Computational Statistics & Data Analysis (1 paper)Pattern Recognition Letters (1 paper)Statistics and Computing (1 paper)
- Partner nations
- TaiwanUnited KingdomHong Kong
In The Last Decade
Tak-Shing Chan
14 papers receiving 307 citations
Peers
Comparison fields: 5 of 65
- Signal Processing 236
- Computational Mathematics 13
- Computer Vision and Pattern Recognition 186
- Information Systems 51
- Artificial Intelligence 47
Countries citing papers authored by Tak-Shing Chan
This map shows the geographic impact of Tak-Shing Chan'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 Tak-Shing Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tak-Shing Chan more than expected).
Fields of papers citing papers by Tak-Shing Chan
This network shows the impact of papers produced by Tak-Shing Chan. 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 Tak-Shing Chan. The network helps show where Tak-Shing Chan may publish in the future.
Co-authors
The 21 scholars most cited alongside Tak-Shing Chan, 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 | 2012 | 79 | |
| 2 | 2015 | 69 | |
| 3 | 2011 | 54 | |
| 4 | 2012 | 23 | |
| 5 | 2012 | 19 | |
| 6 | 2016 | 18 | |
| 7 | 2009 | 16 | |
| 8 | 2021 | 13 | |
| 9 | 2016 | 12 | |
| 10 | 2019 | 7 | |
| 11 | 2018 | 7 | |
| 12 | 2017 | 3 | |
| 13 | 2024 | 1 | |
| 14 | 2024 | 1 | |
| 15 | 2019 | 0 |
About Tak-Shing Chan
Tak-Shing Chan is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Computational Mechanics, Computational Mathematics and Artificial Intelligence, having authored 15 papers that have together received 322 indexed citations. Recurring topics across this work include Biometric Identification and Security (4 papers), Speech and Audio Processing (4 papers), Tensor decomposition and applications (3 papers), Blind Source Separation Techniques (3 papers), Sparse and Compressive Sensing Techniques (3 papers), Face and Expression Recognition (2 papers), Gait Recognition and Analysis (2 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Signal Processing (236 citations), Computational Mathematics (13 citations), Computer Vision and Pattern Recognition (186 citations), Information Systems (51 citations) and Artificial Intelligence (47 citations). Tak-Shing Chan has collaborated with scholars based in Taiwan, United Kingdom and Hong Kong. Frequent co-authors include Ajay Kumar, Yi‐Hsuan Yang, Jyh‐Shing Roger Jang, Hung-Wei Chen, Li Su, Chun-Wei Tan, Jonathan Chambers, Ben Mitchinson, Kevin Gurney and Tony J. Prescott. Their work appears in journals such as Scientific Reports, Advanced Engineering Informatics, Computational Statistics & Data Analysis, Pattern Recognition Letters and Statistics and Computing.
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.