Jun Shi

1.7k citations
56 papers · 1.3k · h-index 23

Impact in

Papers in

Jun Shi

54 papers receiving 1.3k citations

Peers

Jun Shi
Comparison fields: 5 of 100
  • Applied Mathematics 630
  • Signal Processing 515
  • Computer Vision and Pattern Recognition 745
  • Control and Systems Engineering 233
  • Media Technology 56
Replace Kulbir Singh with:
Kulbir Singh India
Lütfiye Durak-Ata Türkiye
Dongpo Xu China
Greg Knowles United Kingdom
Yacine Chitour France
Tianqi Zhang China
Hidemitsu Ogawa Japan
Shengheng Liu China
Jun Shi relative to Kulbir Singh India Kulbir Singh's profile →
Citations per field
00.5×4.1×
Kulbir Singh · 1×
Citations per year

Countries citing papers authored by Jun Shi

Since Specialization
Citations

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

Fields of papers citing papers by Jun Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011135
2 202077
3 201268
4 201067
5 200763
6 201252
7 201348
8 201647
9 201644
10 201143
11 202342
12 201842
13 202132
14 201832
15 202031
16 201831
17 201731
18 200829
19 201229
20 201527

About Jun Shi

Jun Shi is a scholar working on Computer Vision and Pattern Recognition, Applied Mathematics, Signal Processing, Control and Systems Engineering and Electrical and Electronic Engineering, having authored 56 papers that have together received 1.3k indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (26 papers), Mathematical Analysis and Transform Methods (24 papers), Digital Filter Design and Implementation (21 papers), Antenna Design and Optimization (4 papers), Radar Systems and Signal Processing (4 papers), Advanced Control Systems Optimization (3 papers), Process Optimization and Integration (3 papers) and IoT Networks and Protocols (2 papers). The work is most often cited by research in Applied Mathematics (630 citations), Signal Processing (515 citations), Computer Vision and Pattern Recognition (745 citations), Control and Systems Engineering (233 citations) and Media Technology (56 citations). Jun Shi has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Naitong Zhang, Xiaoping Liu, Xiaoping Liu, Qinyu Zhang, Xuejun Sha, Wei Xiang, Yonggang Chi, Yanan Zhao, Lorenz T. Biegler and Feng‐Gang Yan. Their work appears in journals such as IEEE Transactions on Signal Processing, Signal Processing, IEEE Communications Letters, Digital Signal Processing and Wireless Communications and Mobile Computing.

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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