Xin Ding

54 papers receiving 608 citations

Peers

Xin Ding
Comparison fields: 5 of 100
  • Signal Processing 133
  • Computational Mathematics 6
  • Computer Vision and Pattern Recognition 186
  • Artificial Intelligence 225
  • Computational Theory and Mathematics 82
Replace Fen Wang with:
Fen Wang China
Jialiang Lu China
Yuting Wang China
Ningbo Zhu China
Juan Chen China
Ruoyu Li China
Grigorios Tzortzis Greece
Xin Ding relative to Fen Wang China Fen Wang's profile →
Citations per field
00.5×4.7×
Fen Wang · 1×
Citations per year

Countries citing papers authored by Xin Ding

Since Specialization
Citations

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

Fields of papers citing papers by Xin Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019168
2 201874
3 202240
4 201738
5 201232
6 202225
7 202119
8 202018
9 201918
10 202213
11 202312
12 202210
13
CcGAN: Continuous Conditional Generative Adversarial Networks for Image Generation
202010
14 20209
15 20228
16 20228
17 20208
18 20246
19 20176
20 20186

About Xin Ding

Xin Ding is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Signal Processing and Computational Mechanics, having authored 58 papers that have together received 624 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (7 papers), Advanced Image Processing Techniques (7 papers), Microwave Imaging and Scattering Analysis (5 papers), Generative Adversarial Networks and Image Synthesis (5 papers), Data Management and Algorithms (4 papers), Process Optimization and Integration (4 papers), Image and Signal Denoising Methods (4 papers) and Ultrasound in Clinical Applications (4 papers). The work is most often cited by research in Signal Processing (133 citations), Computational Mathematics (6 citations), Computer Vision and Pattern Recognition (186 citations), Artificial Intelligence (225 citations) and Computational Theory and Mathematics (82 citations). Xin Ding has collaborated with scholars based in China, Canada and United Kingdom. Frequent co-authors include Shuyin Xia, Hong Yu, Yunsheng Liu, Guoyin Wang, Yunjun Gao, Lu Chen, Ian Wassell, Wei Chen, Christian S. Jensen and Z. Jane Wang. Their work appears in journals such as IEEE Internet of Things Journal, Neurocomputing, IEEE Transactions on Signal Processing, Chemical Engineering & Technology and IEEE Transactions on Medical Imaging.

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.

Explore authors with similar magnitude of impact