Ming Yan

2.4k citations
175 papers · 1.5k · h-index 23

Impact in

Papers in

Ming Yan

147 papers receiving 1.5k citations

Peers

Ming Yan
Comparison fields: 5 of 132
  • Computer Networks and Communications 329
  • Computer Vision and Pattern Recognition 275
  • Human-Computer Interaction 42
  • Mechanics of Materials 179
  • Signal Processing 77
Replace Jon Rigelsford with:
Jon Rigelsford United Kingdom
Abhishek Kumar India
Bing Zhou China
Daniel F. García Spain
Hitoshi Matsubara Japan
Weiyang Lin China
H. P. Lee Singapore
Rong Liu China
Jianhua Liu China
Ming Yan relative to Jon Rigelsford United Kingdom Jon Rigelsford's profile →
Citations per field
00.5×10×12.8×
Jon Rigelsford · 1×
Citations per year

Countries citing papers authored by Ming Yan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 175 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201969
2 202153
3 202148
4 200346
5 198945
6 201945
7 201944
8 202343
9 198940
10 201340
11 202339
12 201838
13 202137
14 202236
15 200034
16 201634
17 202233
18 199732
19 202328
20 201327

About Ming Yan

Ming Yan is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering, Mechanical Engineering, Aerospace Engineering and Computer Vision and Pattern Recognition, having authored 175 papers that have together received 1.5k indexed citations. Recurring topics across this work include Advanced Wireless Communication Techniques (13 papers), Opportunistic and Delay-Tolerant Networks (9 papers), Caching and Content Delivery (9 papers), High Temperature Alloys and Creep (8 papers), Wireless Communication Networks Research (8 papers), Embedded Systems Design Techniques (8 papers), Fatigue and fracture mechanics (7 papers) and Telecommunications and Broadcasting Technologies (7 papers). The work is most often cited by research in Computer Networks and Communications (329 citations), Computer Vision and Pattern Recognition (275 citations), Human-Computer Interaction (42 citations), Mechanics of Materials (179 citations) and Signal Processing (77 citations). Ming Yan has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Chien Aun Chan, André F. Gygax, Bhaskar D. Rao, I Chih‐Lin, Ampalavanapillai Nirmalathas, Cong Jin, Shuhua Zhang, Yinghua Shen, Chunguo Li and Christopher Leckie. Their work appears in journals such as Sensors, Heritage Science, Engineering Fracture Mechanics, Journal of Materials Science and IEEE Access.

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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