Jun Yao

46 papers receiving 590 citations

Peers

Jun Yao
Comparison fields: 5 of 100
  • Media Technology 107
  • Computer Vision and Pattern Recognition 110
  • Aerospace Engineering 112
  • Electrical and Electronic Engineering 224
  • Neuropsychology and Physiological Psychology 5
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Yu Hou United States
Asunción Santamaría Spain
James S. Goddard United States
Peter J. Shull United States
Jeroen Peeters Belgium
Yibin Tian China
Hugo G. Espinosa Australia
Raju Shrestha Norway
Douglas Burleigh Russia
Yu‐Ta Chen Taiwan
Jun Yao relative to Yu Hou United States Yu Hou's profile →
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Citations per year

Countries citing papers authored by Jun Yao

Since Specialization
Citations

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

Fields of papers citing papers by Jun Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201591
2 201662
3 201656
4 201551
5 201730
6 201928
7 202224
8 201423
9 201621
10 202219
11 201516
12 201914
13 201712
14 201412
15 202210
16 20169
17 20189
18 20099
19 20159
20 20169

About Jun Yao

Jun Yao is a scholar working on Computer Vision and Pattern Recognition, Mechanical Engineering, Media Technology, Electrical and Electronic Engineering and Health, having authored 50 papers that have together received 607 indexed citations. Recurring topics across this work include Optical measurement and interference techniques (10 papers), Health disparities and outcomes (6 papers), Advanced Measurement and Metrology Techniques (6 papers), Structural Health Monitoring Techniques (6 papers), Image Processing Techniques and Applications (5 papers), RFID technology advancements (5 papers), Advanced Sensor and Energy Harvesting Materials (4 papers) and Antenna Design and Analysis (4 papers). The work is most often cited by research in Media Technology (107 citations), Computer Vision and Pattern Recognition (110 citations), Aerospace Engineering (112 citations), Electrical and Electronic Engineering (224 citations) and Neuropsychology and Physiological Psychology (5 citations). Jun Yao has collaborated with scholars based in China, United States and New Zealand. Frequent co-authors include Haiying Huang, Jeremiah Sanders, Saibun Tjuatja, Jubing Chen, Hong Miao, Ankur Jain, Man Yang, Hao Wang, Yihao Zhou and Xiong Chen. Their work appears in journals such as IEEE Sensors Journal, Optical Engineering, International Journal of Environmental Research and Public Health, Optics and Lasers in Engineering and Journal of Community Psychology.

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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