Wei Dai

102 papers receiving 3.1k citations

Wei Dai's Hit Papers

Subspace Pursuit for Compressive Sensing Signal Reconstruction 2009 · 1.7k citations
1.7k0+5+11Years since publication50010001.5k

Peers

Wei Dai
Comparison fields: 5 of 86
  • Acoustics and Ultrasonics 115
  • Computational Mechanics 2.1k
  • Signal Processing 935
  • Computational Mathematics 23
  • Computer Vision and Pattern Recognition 637
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Arian Maleki United States
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Citations per year

Countries citing papers authored by Wei Dai

Since Specialization
Citations

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

Fields of papers citing papers by Wei Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 112 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Subspace Pursuit for Compressive Sensing Signal Reconstruction
Hit paper breakdown →
20091684
2 2015172
3
Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity
2008137
4 201694
5 201280
6 201675
7 201462
8 202054
9 201152
10 200946
11 201143
12 201039
13 201334
14 201434
15 201233
16 201530
17 201626
18 200923
19 201622
20 201321

About Wei Dai

Wei Dai is a scholar working on Computational Mechanics, Signal Processing, Computer Networks and Communications, Electrical and Electronic Engineering and Biomedical Engineering, having authored 112 papers that have together received 3.2k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (68 papers), Blind Source Separation Techniques (35 papers), Microwave Imaging and Scattering Analysis (16 papers), Advanced MIMO Systems Optimization (14 papers), Image and Signal Denoising Methods (14 papers), Distributed Sensor Networks and Detection Algorithms (13 papers), Photoacoustic and Ultrasonic Imaging (10 papers) and Cooperative Communication and Network Coding (10 papers). The work is most often cited by research in Acoustics and Ultrasonics (115 citations), Computational Mechanics (2.1k citations), Signal Processing (935 citations), Computational Mathematics (23 citations) and Computer Vision and Pattern Recognition (637 citations). Wei Dai has collaborated with scholars based in United Kingdom, United States and China. Frequent co-authors include Olgica Milenković, Wenwu Wang, Linglong Dai, Zhaocheng Wang, Zhen Gao, Vincent K. N. Lau, Byonghyo Shim, Tao Xu, Hoa V. Pham and Youjian Liu. Their work appears in journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, IEEE Transactions on Communications and IEEE Journal on Selected Areas in 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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