World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
11929
World Ranking
6092
National Ranking
804

Xun Chen publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Xun Chen sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 168 publications — 34th percentile

34% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Xun Chen D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Xun Chen sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Xun Chen is affiliated with the University of Science and Technology of China. Their research spans numerous fields and subfields, particularly focused on Engineering and Computer Science.

The main fields of study for Xun Chen include:

  • Engineering
  • Computer Science

Their work also covers several subfields, notably:

  • Cognitive Neuroscience
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology

Xun Chen's research topics encompass a range of specialized areas such as:

  • EEG and Brain-Computer Interfaces
  • Muscle activation and electromyography studies
  • Advanced Image Fusion Techniques
  • Blind Source Separation Techniques
  • ECG Monitoring and Analysis
  • Advanced Sensor and Energy Harvesting Materials
  • Non-Invasive Vital Sign Monitoring

Frequent collaborators in Xun Chen's work include:

  • Aiping Liu
  • Chang Li
  • Yü Liu
  • Xu Zhang
  • Xiang Chen

Xun Chen has published extensively, with frequent contributions appearing in the following venues:

  • IEEE Sensors Journal
  • arXiv (Cornell University)
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • IEEE Transactions on Instrumentation and Measurement
  • Computers in Biology and Medicine

Among recent papers authored or co-authored by Xun Chen are:

  • EEG-Based Emotion Recognition via Channel-Wise Attention and Self Attention, 2020, IEEE Transactions on Affective Computing
  • Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity, 2020, Nature Biomedical Engineering
  • Multi-focus image fusion: A Survey of the state of the art, 2020, Information Fusion
  • EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network, 2020, Knowledge-Based Systems
  • Emotion Recognition From Multi-Channel EEG via Deep Forest, 2020, IEEE Journal of Biomedical and Health Informatics

Xun Chen has also contributed to academic books, including a publication with Frontiers Media titled Multimodal Brain Image Fusion: Methods, Evaluations, and Applications in 2023.

Best Publications

  • Multi-focus image fusion with a deep convolutional neural network

    Yu Liu;Xun Chen;Hu Peng;Zengfu Wang

  • Image Fusion With Convolutional Sparse Representation

    Yu Liu;Xun Chen;Rabab K. Ward;Z. Jane Wang

  • Deep learning for pixel-level image fusion: Recent advances and future prospects

    Yu Liu;Xun Chen;Xun Chen;Zengfu Wang;Z. Jane Wang

  • Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain

    Ming Yin;Xiaoning Liu;Yu Liu;Xun Chen

  • EEG-based Emotion Recognition via Channel-wise Attention and Self Attention

    Wei Tao;Chang Li;Rencheng Song;Juan Cheng

  • Infrared and visible image fusion with convolutional neural networks

    Yu Liu;Xun Chen;Juan Cheng;Hu Peng

  • A medical image fusion method based on convolutional neural networks

    Yu Liu;Xun Chen;Juan Cheng;Hu Peng

  • Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis

    Yu Liu;Xun Chen;Rabab K. Ward;Z. Jane Wang

  • Multi-focus image fusion: A Survey of the state of the art

    Yu Liu;Lei Wang;Juan Cheng;Chang Li

  • EEG-based emotion recognition using an end-to-end regional-asymmetric convolutional neural network

    Heng Cui;Aiping Liu;Xu Zhang;Xiang Chen

  • Emotion Recognition From Multi-Channel EEG via Deep Forest

    Juan Cheng;Meiyao Chen;Chang Li;Yu Liu

  • PulseGAN: Learning to Generate Realistic Pulse Waveforms in Remote Photoplethysmography

    Rencheng Song;Huan Chen;Juan Cheng;Chang Li

  • Video-Based Heart Rate Measurement: Recent Advances and Future Prospects

    Xun Chen;Juan Cheng;Rencheng Song;Yu Liu

  • Sparse Group Representation Model for Motor Imagery EEG Classification

    Yong Jiao;Yu Zhang;Xun Chen;Erwei Yin

  • Pattern recognition of number gestures based on a wireless surface EMG system

    Xun Chen;Z. Jane Wang

  • Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network.

    Yu Liu;Yufeng Ding;Chang Li;Juan Cheng

  • Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method

    Xiang Chen;Yu Li;Ruochen Hu;Xu Zhang

  • ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network.

    Jing Zhang;Aiping Liu;Min Gao;Xiang Chen

  • The Use of Multivariate EMD and CCA for Denoising Muscle Artifacts From Few-Channel EEG Recordings

    Xun Chen;Xueyuan Xu;Aiping Liu;Martin J. McKeown

  • Different Input Resolutions and Arbitrary Output Resolution: A Meta Learning-Based Deep Framework for Infrared and Visible Image Fusion

    Huafeng Li;Yueliang Cen;Yu Liu;Xun Chen

  • Classification of EEG signals using a multiple kernel learning support vector machine.

    Xiaoou Li;Xun Chen;Yuning Yan;Wenshi Wei

Frequent Co-Authors

Z. Jane Wang
Z. Jane Wang University of British Columbia
Yu Liu
Yu Liu Clarkson University
Martin J. McKeown
Martin J. McKeown University of British Columbia
Rabab K. Ward
Rabab K. Ward University of British Columbia
Zhengxia Zou
Zhengxia Zou Beihang University
Septimiu E. Salcudean
Septimiu E. Salcudean University of British Columbia
Peyman Servati
Peyman Servati University of British Columbia
Alfonso Farina
Alfonso Farina Finmeccanica (Italy)
Weifeng Su
Weifeng Su University at Buffalo, State University of New York
Xueyang Fu
Xueyang Fu University of Science and Technology of China

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