Xiaochen Wang

464 citations
50 papers · 325 · h-index 9

Impact in

Papers in

Xiaochen Wang

45 papers receiving 317 citations

Peers

Xiaochen Wang
Comparison fields: 5 of 75
  • Signal Processing 77
  • Energy Engineering and Power Technology 14
  • Computer Vision and Pattern Recognition 85
  • Renewable Energy, Sustainability and the Environment 40
  • Computer Science Applications 14
Replace Farman Ali Khan with:
Farman Ali Khan Pakistan
Muhammad Usama Islam United States
K. Latha India
Zhibing Wang China
Lingfeng Xu China
Najia Es-Sbai Morocco
Yi Ji China
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Citations per field
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Citations per year

Countries citing papers authored by Xiaochen Wang

Since Specialization
Citations

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

Fields of papers citing papers by Xiaochen Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Xiaochen Wang, 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 Xiaochen Wang Line = papers co-authored together Xiaochen Wang 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 202349
2 202246
3 201933
4 201723
5 202218
6 201711
7 201811
8 202210
9 20239
10 20207
11 20187
12 20226
13 20216
14 20185
15 20205
16 20205
17 20234
18 20224
19 20164
20 20214

About Xiaochen Wang

Xiaochen Wang is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Artificial Intelligence and Computational Mechanics, having authored 50 papers that have together received 325 indexed citations. Recurring topics across this work include Speech and Audio Processing (24 papers), Music and Audio Processing (13 papers), Hearing Loss and Rehabilitation (13 papers), Advanced Data Compression Techniques (7 papers), Advanced Adaptive Filtering Techniques (6 papers), Video Surveillance and Tracking Methods (6 papers), Image and Signal Denoising Methods (4 papers) and Speech Recognition and Synthesis (4 papers). The work is most often cited by research in Signal Processing (77 citations), Energy Engineering and Power Technology (14 citations), Computer Vision and Pattern Recognition (85 citations), Renewable Energy, Sustainability and the Environment (40 citations) and Computer Science Applications (14 citations). Xiaochen Wang has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Ruimin Hu, Lin Zhuang, Li Xiao, Gongwei Wang, Ruimin Hu, Zhongyuan Wang, Cheng Liu, Xiaoming Sun, Wenzheng Li and Weiguo Tian. Their work appears in journals such as Multimedia Tools and Applications, IEEE Transactions on Multimedia, Pattern Recognition, Information Sciences and China Communications.

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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