Wu Xin

676 citations
47 papers · 482 · h-index 11

Impact in

Papers in

Wu Xin

41 papers receiving 470 citations

Peers

Wu Xin
Comparison fields: 5 of 65
  • Cellular and Molecular Neuroscience 135
  • Cognitive Neuroscience 123
  • Electrical and Electronic Engineering 342
  • Artificial Intelligence 122
  • Health Information Management 10
Replace Tao Yin with:
Tao Yin China
Hongyu An United States
Emiliano Torre Germany
Yujie Liu China
Tariq Alshawi Saudi Arabia
Jorge Igual Spain
Stephen J. Weddell New Zealand
David A. Stanley United States
Sunghun Kim South Korea
Xudong Xie China
Wu Xin relative to Tao Yin China Tao Yin's profile →
Citations per field
00.5×5.6×
Tao Yin · 1×
Citations per year

Countries citing papers authored by Wu Xin

Since Specialization
Citations

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

Fields of papers citing papers by Wu Xin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015121
2 201566
3 200943
4 199938
5 201530
6 201524
7 201823
8 201819
9 201814
10 202111
11 202210
12
Multiple Criteria Rainfall-Runoff Model Calibration Using a Parallel Genetic Algorithm in a Cluster of Computers
20068
13 20148
14 20177
15 20165
16
Contrast test of the transient electromagnetic system (CASTEM) at the Dawangzhuang iron mine in Anhui province
20165
17 20024
18 20154
19 20154
20 20174

About Wu Xin

Wu Xin is a scholar working on Electrical and Electronic Engineering, Cellular and Molecular Neuroscience, Artificial Intelligence, Safety, Risk, Reliability and Quality and Biomedical Engineering, having authored 47 papers that have together received 482 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (10 papers), Ferroelectric and Negative Capacitance Devices (5 papers), Geoscience and Mining Technology (5 papers), Neuroscience and Neural Engineering (5 papers), Topic Modeling (5 papers), Photonic and Optical Devices (5 papers), Advancements in PLL and VCO Technologies (4 papers) and Optical Network Technologies (4 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (135 citations), Cognitive Neuroscience (123 citations), Electrical and Electronic Engineering (342 citations), Artificial Intelligence (122 citations) and Health Information Management (10 citations). Wu Xin has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Vishal Saxena, Kehan Zhu, Xiaojun Tang, Wan Kuang, Werner Dubitzky, N.D. Black, Francisco Azuaje, John Albert White, Philippe Lopes and Yongzhe Li. Their work appears in journals such as IEEE Transactions on Circuits & Systems II Express Briefs, Communications Biology, Smart Materials and Structures, Artificial Intelligence in Medicine and IEEE Transactions on Nanotechnology.

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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