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Sökning: WFRF:(Huang Yunfei)

  • Resultat 1-3 av 3
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  • Wang, Cheng-Xiang, et al. (författare)
  • On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds
  • 2023
  • Ingår i: IEEE Communications Surveys and Tutorials. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1553-877X. ; 25:2, s. 905-974
  • Tidskriftsartikel (refereegranskat)abstract
    • Fifth generation (5G) mobile communication systems have entered the stage of commercial deployment, providing users with new services, improved user experiences as well as a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified to stimulate the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed.
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3.
  • Zhou, Yunfei, et al. (författare)
  • DCTN: Dual-Branch Convolutional Transformer Network With Efficient Interactive Self-Attention for Hyperspectral Image Classification
  • 2024
  • Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 62, s. 1-16
  • Tidskriftsartikel (refereegranskat)abstract
    • Hyperspectral image (HSI) classification is an essential task in remote sensing with substantial practical significance. However, most existing convolutional neural network (CNN)-based classification methods focus only on local spatial features while neglecting global spectral dependencies. Meanwhile, Transformer-based methods exhibit robust capabilities for global spectral feature modeling but struggle to extract local spatial features effectively. To fully exploit the local spatial feature extraction capabilities of CNN-based networks and the global spectral feature extraction capabilities of Transformer-based networks, this article proposes a dual-branch convolutional Transformer method with efficient interactive self-attention (EISA) for HSI classification, namely the dual-branch convolutional transformer network (DCTN), which can aggregate local and global spatial-spectral features fully. Specifically, DCTN includes two core modules: the spatial-spectral fusion projection module (SFPM) and the EISA module. The former utilizes 3-D convolution with adaptive pooling and 2-D group convolution with residual connection to parallel extract fused and grouped spatial-spectral features, respectively. The latter performs EISA across height, width, and spectral dimensions, enabling deep fusion of spatial-spectral features. Extensive experiments on three real HSI datasets demonstrate that the proposed DCTN method outperforms existing classification methods, yielding state-of-the-art classification performance. The code is available at https://github.com/AllFever/DeepHyperX-DCTN for reproducibility.
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  • Resultat 1-3 av 3

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