Longxi Chen
Impact in
- Computational Mathematics top 0.5%
- Tensor decomposition and applications
- Computational Mechanics top 5%
- Sparse and Compressive Sensing Techniques
Papers in
-
- Tensor decomposition and applications 6
-
- Sparse and Compressive Sensing Techniques 5
- Co-authors
- Ce Zhu (6 shared papers)Yipeng Liu (6 shared papers)Zhen Long (2 shared papers)Mingyi Zhou (1 shared paper)Xiang Zhang (1 shared paper)Bin Wu (1 shared paper)Yuqing Chen (1 shared paper)Yufu Ning (1 shared paper)
- Journals
- Signal Processing (2 papers)Signal Processing Image Communication (1 paper)Fusion Engineering and Design (1 paper)IEEE Journal of Selected Topics in Signal Processing (1 paper)Multimedia Systems (1 paper)
- Partner nations
- China
In The Last Decade
Longxi Chen
8 papers receiving 338 citations
Peers
Comparison fields: 5 of 51
- Computational Mathematics 208
- Computational Mechanics 188
- Computer Vision and Pattern Recognition 122
- Signal Processing 58
- Radiology, Nuclear Medicine and Imaging 60
Countries citing papers authored by Longxi Chen
This map shows the geographic impact of Longxi Chen'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 Longxi Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Longxi Chen more than expected).
Fields of papers citing papers by Longxi Chen
This network shows the impact of papers produced by Longxi Chen. 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 Longxi Chen. The network helps show where Longxi Chen may publish in the future.
Co-authors
The 12 scholars most cited alongside Longxi Chen, 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 | 2018 | 118 | |
| 2 | 2018 | 110 | |
| 3 | 2018 | 47 | |
| 4 | 2019 | 34 | |
| 5 | 2017 | 25 | |
| 6 | 2018 | 5 | |
| 7 | 2024 | 2 | |
| 8 | 2023 | 2 | |
| 9 | 2025 | 0 |
About Longxi Chen
Longxi Chen is a scholar working on Computational Mathematics, Computational Mechanics, Signal Processing, Nuclear and High Energy Physics and Astronomy and Astrophysics, having authored 9 papers that have together received 343 indexed citations. Recurring topics across this work include Tensor decomposition and applications (6 papers), Sparse and Compressive Sensing Techniques (5 papers), Blind Source Separation Techniques (3 papers), Particle accelerators and beam dynamics (1 paper), Ionosphere and magnetosphere dynamics (1 paper), Image and Signal Denoising Methods (1 paper), Photonic and Optical Devices (1 paper) and Photorefractive and Nonlinear Optics (1 paper). The work is most often cited by research in Computational Mathematics (208 citations), Computational Mechanics (188 citations), Computer Vision and Pattern Recognition (122 citations), Signal Processing (58 citations) and Radiology, Nuclear Medicine and Imaging (60 citations). Longxi Chen has collaborated with scholars based in China. Frequent co-authors include Ce Zhu, Yipeng Liu, Zhen Long, Mingyi Zhou, Xiang Zhang, Bin Wu, Yuqing Chen, Yufu Ning, Jinfang Wang and Xiaojuan Liu. Their work appears in journals such as Signal Processing, Signal Processing Image Communication, Fusion Engineering and Design, IEEE Journal of Selected Topics in Signal Processing and Multimedia 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.