Hao Shen

43 papers receiving 972 citations

Peers

Hao Shen
Comparison fields: 5 of 116
  • Acoustics and Ultrasonics 36
  • Computational Mathematics 18
  • Industrial and Manufacturing Engineering 185
  • Signal Processing 142
  • Computer Vision and Pattern Recognition 259
Replace Miguel Figueroa with:
Miguel Figueroa Chile
Vishnu Naresh Boddeti United States
Yehui Tang China
Prabir Kumar Biswas India
Shuang Cong China
Shuxue Ding Japan
Morium Akter Bangladesh
Yasuhiro Mukaigawa Japan
Hendrik P. A. Lensch Germany
Hao Shen relative to Miguel Figueroa Chile Miguel Figueroa's profile →
Citations per field
00.5×6.9×
Miguel Figueroa · 1×
Citations per year

Countries citing papers authored by Hao Shen

Since Specialization
Citations

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

Fields of papers citing papers by Hao Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Hao Shen, 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 Hao Shen Line = papers co-authored together Hao Shen links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014217
2 2015135
3 2012114
4 201484
5 201384
6 201145
7 200944
8 201831
9 200931
10 200823
11 202121
12 201018
13 201715
14 201613
15 201913
16 202212
17 202012
18 201711
19 202210
20 20139

About Hao Shen

Hao Shen is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing, Electrical and Electronic Engineering and Artificial Intelligence, having authored 45 papers that have together received 1.0k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (12 papers), Blind Source Separation Techniques (9 papers), Spectroscopy and Chemometric Analyses (6 papers), Image and Signal Denoising Methods (6 papers), Face and Expression Recognition (3 papers), Welding Techniques and Residual Stresses (3 papers), Neural Networks and Applications (3 papers) and Terahertz technology and applications (3 papers). The work is most often cited by research in Acoustics and Ultrasonics (36 citations), Computational Mathematics (18 citations), Industrial and Manufacturing Engineering (185 citations), Signal Processing (142 citations) and Computer Vision and Pattern Recognition (259 citations). Hao Shen has collaborated with scholars based in Germany, China and United States. Frequent co-authors include Klaus Diepold, Johannes Günther, Patrick M. Pilarski, Martin Kleinsteuber, Hongxing Chang, Shuxiao Li, Xian Wei, Qinru Qiu, Ying Tan and Jun Lu. Their work appears in journals such as IEEE Transactions on Signal Processing, Measurement, IEEE Wireless Communications Letters, IEEE Transactions on Image Processing and IEEE Transactions on Neural Networks and Learning 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.

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