Jun Qin

894 citations
75 papers · 593 · h-index 13

Impact in

Papers in

Jun Qin

68 papers receiving 561 citations

Peers

Jun Qin
Comparison fields: 5 of 116
  • Sensory Systems 52
  • Computer Vision and Pattern Recognition 189
  • Fluid Flow and Transfer Processes 39
  • Speech and Hearing 31
  • Spectroscopy 65
Replace Masahiro Suzuki with:
Masahiro Suzuki Japan
Lei Feng China
Xinxing Chen China
Xin Shan China
Dongxu Liu China
Ji Soo Ha South Korea
Ning Gao China
Xuefeng Li China
Jun Qin relative to Masahiro Suzuki Japan Masahiro Suzuki's profile →
Citations per field
00.5×10×15×21.7×
Masahiro Suzuki · 1×
Citations per year

Countries citing papers authored by Jun Qin

Since Specialization
Citations

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

Fields of papers citing papers by Jun Qin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202255
2 200851
3 199948
4 199842
5 202138
6 200336
7 201927
8 201023
9 201722
10 200822
11 200812
12 201312
13 201912
14 201510
15 20218
16 20168
17 20188
18 20218
19 20237
20 20217

About Jun Qin

Jun Qin is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience, Speech and Hearing, Sensory Systems and Biomedical Engineering, having authored 75 papers that have together received 593 indexed citations. Recurring topics across this work include Noise Effects and Management (11 papers), Hearing Loss and Rehabilitation (11 papers), Visual Attention and Saliency Detection (10 papers), Hearing, Cochlea, Tinnitus, Genetics (8 papers), Advanced Neural Network Applications (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Hydrocarbon exploration and reservoir analysis (3 papers) and Visual perception and processing mechanisms (3 papers). The work is most often cited by research in Sensory Systems (52 citations), Computer Vision and Pattern Recognition (189 citations), Fluid Flow and Transfer Processes (39 citations), Speech and Hearing (31 citations) and Spectroscopy (65 citations). Jun Qin has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Zheng Qin, Xiaolong Zhang, Pengfei Sun, Jie Shen, Minghui Sun, Jie Yan, Guihe Qin, Mingfa Yao, Yanhua Liang and Garth V. Crosby. Their work appears in journals such as IEEE Transactions on Cognitive and Developmental Systems, Rapid Communications in Mass Spectrometry, The Journal of the Acoustical Society of America, Neurocomputing and International Journal of Audiology.

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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