Ling Xiao

496 citations
19 papers · 327 indexed · h-index 7
Topics
Indoor and Outdoor Localization Technologies (5 papers)Context-Aware Activity Recognition Systems (4 papers)Wireless Body Area Networks (3 papers)

In The Last Decade

Ling Xiao

19 papers receiving 301 citations

Peers

Ling Xiao
Comparison fields: 5 of 58
  • Computer Vision and Pattern Recognition 151
  • Artificial Intelligence 107
  • Signal Processing 83
  • Transportation 68
  • Computer Networks and Communications 48
Replace Arnold P. Boedihardjo with:
Arnold P. Boedihardjo United States
Martin Mladenov Germany
Kexin Xie Australia
Kefeng Xuan Australia
Kyosuke Nishida Japan
Gerardo Maximiliano Méndez Mexico
Yuntao Du China
Elias Frentzos Greece
Long Vu United States
Jilong Liao United States
Ling Xiao relative to Arnold P. Boedihardjo United States Arnold P. Boedihardjo's profile →
Citations per field
00.5×1.5×2.3×
Arnold P. Boedihardjo · 1×
Citations per year

Countries citing papers authored by Ling Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Ling Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Xiao

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Xiao. A scholar is included among the top collaborators of Ling Xiao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ling Xiao. Ling Xiao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
#WorkIndexed citations
1 1
2 6
3 5
4 2
5 10
6 3
7 6
8 1
9 8
10 2
11 13
12 1
13 2
14 7
15 2
16 4
17 82
18 5
19 167

About Ling Xiao

Ling Xiao is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 19 papers that have together received 327 indexed citations. Recurring topics across this work include Indoor and Outdoor Localization Technologies (5 papers), Context-Aware Activity Recognition Systems (4 papers) and Wireless Body Area Networks (3 papers). The work is most often cited by research in Transportation (68 citations), Signal Processing (83 citations) and Computer Vision and Pattern Recognition (151 citations). Ling Xiao has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Daniel S. Weld, Doantam Phan, Terry Winograd, Pat Hanrahan, Juan Luo, Kai Wu, Yu Song Meng, Renfa Li, David P. Anderson and Zhu Xiao. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Journal of Biomedical and Health Informatics and The Scientific World JOURNAL.

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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