T. Sunil Kumar
Impact in
- Signal Processing top 5%
- Blind Source Separation Techniques
- Cognitive Neuroscience top 10%
- EEG and Brain-Computer Interfaces
Papers in
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- Context-Aware Activity Recognition Systems 3
- Digital Imaging for Blood Diseases 3
- Image and Signal Denoising Methods 3
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- EEG and Brain-Computer Interfaces 8
- Co-authors
- Vivek Kanhangad (8 shared papers)Sweta Jain (2 shared papers)Mahesh Gour (2 shared papers)Ram Bilas Pachori (1 shared paper)Kandala N. V. P. S. Rajesh (3 shared papers)Ravindra Dhuli (1 shared paper)U. Rajendra Acharya (2 shared papers)Bart Vanrumste (8 shared papers)
In The Last Decade
T. Sunil Kumar
30 papers receiving 514 citations
Peers
Comparison fields: 5 of 81
- Signal Processing 127
- Cognitive Neuroscience 190
- Neurology 56
- Artificial Intelligence 209
- Computer Vision and Pattern Recognition 124
Countries citing papers authored by T. Sunil Kumar
This map shows the geographic impact of T. Sunil Kumar'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 T. Sunil Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Sunil Kumar more than expected).
Fields of papers citing papers by T. Sunil Kumar
This network shows the impact of papers produced by T. Sunil Kumar. 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 T. Sunil Kumar. The network helps show where T. Sunil Kumar may publish in the future.
Co-authors
The 25 scholars most cited alongside T. Sunil Kumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 194 | |
| 2 | 2014 | 122 | |
| 3 | 2020 | 31 | |
| 4 | 2022 | 30 | |
| 5 | 2018 | 20 | |
| 6 | 2024 | 17 | |
| 7 | 2016 | 15 | |
| 8 | 2023 | 12 | |
| 9 | 2022 | 12 | |
| 10 | 2024 | 10 | |
| 11 | 2019 | 10 | |
| 12 | 2017 | 8 | |
| 13 | 2015 | 6 | |
| 14 | 2022 | 6 | |
| 15 | 2024 | 5 | |
| 16 | 2023 | 4 | |
| 17 | 2013 | 4 | |
| 18 | 2024 | 3 | |
| 19 | 2024 | 3 | |
| 20 | 2024 | 2 |
About T. Sunil Kumar
T. Sunil Kumar is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience, Signal Processing, Biomedical Engineering and Artificial Intelligence, having authored 35 papers that have together received 528 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (8 papers), Blind Source Separation Techniques (5 papers), ECG Monitoring and Analysis (4 papers), Nutritional Studies and Diet (4 papers), Advanced Chemical Sensor Technologies (3 papers), Context-Aware Activity Recognition Systems (3 papers), Digital Imaging for Blood Diseases (3 papers) and Image and Signal Denoising Methods (3 papers). The work is most often cited by research in Signal Processing (127 citations), Cognitive Neuroscience (190 citations), Neurology (56 citations), Artificial Intelligence (209 citations) and Computer Vision and Pattern Recognition (124 citations). T. Sunil Kumar has collaborated with scholars based in India, Sweden and Belgium. Frequent co-authors include Vivek Kanhangad, Sweta Jain, Mahesh Gour, Ram Bilas Pachori, Kandala N. V. P. S. Rajesh, Ravindra Dhuli, U. Rajendra Acharya, Bart Vanrumste, Hans Hallez and W. De Raedt. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, International Journal of Imaging Systems and Technology, Engineering Applications of Artificial Intelligence, Frontiers in Plant Science and Computers in Biology and Medicine.
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.