Qun Mo

1.1k citations
24 papers · 713 · h-index 12

Impact in

Papers in

Qun Mo

23 papers receiving 678 citations

Peers

Qun Mo
Comparison fields: 5 of 74
  • Acoustics and Ultrasonics 46
  • Applied Mathematics 207
  • Computational Mechanics 395
  • Signal Processing 209
  • Computer Vision and Pattern Recognition 320
Replace Song Li with:
Song Li China
Karin Schnass Switzerland
Guangwu Xu United States
Ke Wei China
Jeffrey D. Blanchard United States
Shuyang Ling United States
Mihailo Stojnic United States
Валентина Станева United States
Lie Wang United States
Qun Mo relative to Song Li China Song Li's profile →
Citations per field
00.5×1.5×1.9×
Song Li · 1×
Citations per year

Countries citing papers authored by Qun Mo

Since Specialization
Citations

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

Fields of papers citing papers by Qun Mo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016148
2 2012112
3 2011101
4 200451
5 200348
6 202144
7 200437
8 201628
9 202023
10 200323
11 201020
12 200911
13 200711
14 201911
15 201210
16 20198
17 20158
18 20116
19 20055
20 20073

About Qun Mo

Qun Mo is a scholar working on Computer Vision and Pattern Recognition, Applied Mathematics, Computational Mechanics, Geophysics and Biomedical Engineering, having authored 24 papers that have together received 713 indexed citations. Recurring topics across this work include Mathematical Analysis and Transform Methods (12 papers), Image and Signal Denoising Methods (11 papers), Seismic Imaging and Inversion Techniques (6 papers), Advanced Numerical Analysis Techniques (5 papers), Microwave Imaging and Scattering Analysis (4 papers), Sparse and Compressive Sensing Techniques (4 papers), Advanced Steganography and Watermarking Techniques (3 papers) and Chaos-based Image/Signal Encryption (3 papers). The work is most often cited by research in Acoustics and Ultrasonics (46 citations), Applied Mathematics (207 citations), Computational Mechanics (395 citations), Signal Processing (209 citations) and Computer Vision and Pattern Recognition (320 citations). Qun Mo has collaborated with scholars based in China, Canada and France. Frequent co-authors include Bin Han, Yi Shen, Song Li, Zhengchun Zhou, Jinming Wen, Xiaohu Tang, Jian Wang, Chuan Qin, Heng Yao and Chin‐Chen Chang. Their work appears in journals such as Applied and Computational Harmonic Analysis, Journal of Fourier Analysis and Applications, Linear Algebra and its Applications, Advances in Computational Mathematics and IEEE Transactions on Signal Processing.

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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