Junmei Yang

33 papers receiving 589 citations

Peers

Junmei Yang
Comparison fields: 5 of 59
  • Computational Mathematics 26
  • Signal Processing 200
  • Media Technology 136
  • Computational Mechanics 127
  • Computer Vision and Pattern Recognition 110
Replace Praneeth Narayanamurthy with:
Praneeth Narayanamurthy United States
Xie Sheng-li China
Argyrios Zymnis United States
Shuangyan Yi China
Manqi Zhao China
Yao Hu China
Mostafa Rahmani United States
Guangliang Chen United States
Karl Werner Sweden
Yong-Qing Cheng China
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Citations per field
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Citations per year

Countries citing papers authored by Junmei Yang

Since Specialization
Citations

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

Fields of papers citing papers by Junmei Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010134
2 201287
3 201165
4 201149
5 201735
6 201533
7 201524
8 201023
9 200519
10 201718
11 201915
12 201612
13 201610
14 200810
15 201910
16 20238
17 20148
18 20176
19 20245
20 20075

About Junmei Yang

Junmei Yang is a scholar working on Signal Processing, Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 35 papers that have together received 601 indexed citations. Recurring topics across this work include Speech and Audio Processing (8 papers), Advanced Wireless Communication Techniques (8 papers), Domain Adaptation and Few-Shot Learning (6 papers), Blind Source Separation Techniques (6 papers), Image and Signal Denoising Methods (5 papers), Advanced MIMO Systems Optimization (5 papers), Error Correcting Code Techniques (4 papers) and Time Series Analysis and Forecasting (4 papers). The work is most often cited by research in Computational Mathematics (26 citations), Signal Processing (200 citations), Media Technology (136 citations), Computational Mechanics (127 citations) and Computer Vision and Pattern Recognition (110 citations). Junmei Yang has collaborated with scholars based in China, Japan and Australia. Frequent co-authors include Guoxu Zhou, Zuyuan Yang, Shengli Xie, Chuan Zhang, Xiaohu You, Xie Sheng-li, Jun Zhang, Shuxue Ding, Shugong Xu and Liu Yang. Their work appears in journals such as Expert Systems with Applications, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Instrumentation and Measurement, Automatica and IEEE Transactions on Circuits & Systems II Express Briefs.

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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