Fengbo Ren
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering top 10%
- Biomedical Engineering top 10%
- Artificial Intelligence top 5%
- Polymers and Plastics top 10%
- Topics
- Sparse and Compressive Sensing Techniques (11 papers)Advanced Neural Network Applications (11 papers)Advanced Memory and Neural Computing (7 papers)
- Partner nations
- United StatesChinaSingapore
In The Last Decade
Fengbo Ren
48 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Computer Vision and Pattern Recognition 443
- Electrical and Electronic Engineering 405
- Biomedical Engineering 294
- Artificial Intelligence 252
- Polymers and Plastics 112
Countries citing papers authored by Fengbo Ren
This map shows the geographic impact of Fengbo Ren'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 Fengbo Ren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fengbo Ren more than expected).
Fields of papers citing papers by Fengbo Ren
This network shows the impact of papers produced by Fengbo Ren. 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 Fengbo Ren. The network helps show where Fengbo Ren may publish in the future.
Co-authorship network of co-authors of Fengbo Ren
This figure shows the co-authorship network connecting the top 25 collaborators of Fengbo Ren. A scholar is included among the top collaborators of Fengbo Ren 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 Fengbo Ren. Fengbo Ren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 20 | |
| 4 | 28 | |
| 5 | Learning in the Frequency Domainbreakdown → | 309 |
| 6 | 7 | |
| 7 | 90 | |
| 8 | 55 | |
| 9 | A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks | 13 |
| 10 | 21 | |
| 11 | CSVideoNet: A Recurrent Convolutional Neural Network for Compressive Sensing Video Reconstruction | 4 |
| 12 | 18 | |
| 13 | 191 | |
| 14 | 3 | |
| 15 | 61 | |
| 16 | 8 | |
| 17 | 2 | |
| 18 | 67 | |
| 19 | 2 | |
| 20 | 56 |
About Fengbo Ren
Fengbo Ren is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Space and Planetary Science, having authored 48 papers that have together received 1.3k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (11 papers), Advanced Neural Network Applications (11 papers) and Advanced Memory and Neural Computing (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (443 citations), Hardware and Architecture (103 citations) and Computational Mathematics (7 citations). Fengbo Ren has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include Dejan Marković, Yixing Li, Yuhao Wang, Fei Sun, Kai Xu, Minghai Qin, Richard Dorrance, Zichuan Liu, Hao Yu and Juan Li. Their work appears in journals such as ACS Nano, IEEE Transactions on Industrial Electronics and IEEE Journal of Solid-State Circuits.
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.