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<p>Real-time wireless transmission of large-scale images is essential for remote-sensing applications, yet conventional architectures treat imaging, compression and wireless transmission as separate processes, resulting in substantial latency under constrained channel capacity. Here we present an in-sensor wireless computing architecture that integrates imaging, compression, signal modulation and wireless transmission into a single step. The system exploits the unique alternating-current photoresponse of two-dimensional-material device arrays to perform in-sensor computation, enabling images to be wirelessly transmitted as compressed spatial-frequency representations. The received signals can be directly recognized with accuracy comparable to that obtained using the original images, while reducing transmission latency by up to 96.8% relative to conventional approaches. This work establishes in-sensor wireless computing as a promising route towards low-latency remote imaging, with potential applications in next-generation satellite–terrestrial networks and edge–cloud collaborative computing.</p>

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In-sensor wireless computing for intelligent remote sensing

  • 期刊论文

  • 发表日期：

    2026年3月13日

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  • Yong Wang,
  • Yuekun Yang,
  • Ni Yang,
  • Tianjiao Jiao,
  • Wentao Yu,
  • Zhangnan Li,
  • Wenbin Li,
  • Quan Liu,
  • Chenyang Li,
  • Zhoujie Zeng,
  • Xing-Jian Yangdong,
  • Gong-Jie Ruan,
  • Pengfei Wang,
  • Chen Pan,
  • Yi Wan,
  • Lain-Jong Li,
  • Shi-Jun Liang,
  • Feng Miao

摘要

Real-time wireless transmission of large-scale images is essential for remote-sensing applications, yet conventional architectures treat imaging, compression and wireless transmission as separate processes, resulting in substantial latency under constrained channel capacity. Here we present an in-sensor wireless computing architecture that integrates imaging, compression, signal modulation and wireless transmission into a single step. The system exploits the unique alternating-current photoresponse of two-dimensional-material device arrays to perform in-sensor computation, enabling images to be wirelessly transmitted as compressed spatial-frequency representations. The received signals can be directly recognized with accuracy comparable to that obtained using the original images, while reducing transmission latency by up to 96.8% relative to conventional approaches. This work establishes in-sensor wireless computing as a promising route towards low-latency remote imaging, with potential applications in next-generation satellite–terrestrial networks and edge–cloud collaborative computing.