Bin Yang

181 papers receiving 3.0k citations

Bin Yang's Hit Papers

CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement 2024 · 41 citations
410+1Years since publication10203040

Peers

Bin Yang
Comparison fields: 5 of 140
  • Signal Processing 450
  • Aerospace Engineering 771
  • Computer Vision and Pattern Recognition 470
  • Radiology, Nuclear Medicine and Imaging 508
  • Artificial Intelligence 603
Replace John J. Soraghan with:
John J. Soraghan United Kingdom
Chengdong Wu China
Feng Jiang China
Sreeraman Rajan Canada
Tatsuya Harada Japan
Linga Reddy Cenkeramaddi Norway
Shinji Umeyama Japan
Jiquan Ngiam United States
Dong Seog Han South Korea
Bin Yang relative to John J. Soraghan United Kingdom John J. Soraghan's profile →
Citations per field
00.5×4.9×
John J. Soraghan · 1×
Citations per year

Countries citing papers authored by Bin Yang

Since Specialization
Citations

This map shows the geographic impact of Bin 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 Bin Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bin Yang more than expected).

Fields of papers citing papers by Bin Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bin 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 Bin Yang. The network helps show where Bin Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Bin Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Bin Yang Line = papers co-authored together Bin Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 196 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019204
2 2015150
3 2017133
4 2006114
5 200794
6 201086
7 201984
8 201980
9 201079
10 201776
11 201770
12 201866
13 199565
14 201964
15 201753
16 200753
17 200651
18 202047
19 201646
20 200645

About Bin Yang

Bin Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Aerospace Engineering and Electrical and Electronic Engineering, having authored 196 papers that have together received 3.1k indexed citations. Recurring topics across this work include Radar Systems and Signal Processing (30 papers), Advanced SAR Imaging Techniques (27 papers), Radiomics and Machine Learning in Medical Imaging (17 papers), Advanced Neural Network Applications (15 papers), Domain Adaptation and Few-Shot Learning (15 papers), Direction-of-Arrival Estimation Techniques (15 papers), Medical Imaging Techniques and Applications (15 papers) and Advanced MRI Techniques and Applications (14 papers). The work is most often cited by research in Signal Processing (450 citations), Aerospace Engineering (771 citations), Computer Vision and Pattern Recognition (470 citations), Radiology, Nuclear Medicine and Imaging (508 citations) and Artificial Intelligence (603 citations). Bin Yang has collaborated with scholars based in Germany, China and United States. Frequent co-authors include Gor Hakobyan, Lukas Mauch, Sergios Gatidis, Thomas Küstner, Fritz Schick, Karim Armanious, Marko Lugger, Sherif Abdulatif, Konstantin Nikolaou and Yi-Wen Liao. Their work appears in journals such as Magnetic Resonance in Medicine, IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Signal Processing, Radiology Artificial Intelligence and Medical Image Analysis.

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.

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