Environment‐Adaptive Meta‐Channel

The fundamental transmission channels of smart wearable devices and systems should support function reconfiguration and environment cognition or self‐adaption. Here, a environment‐adaptive meta‐channel (EAMC) is proposed that is adaptive to the near‐field environment. Benefiting from the capability...

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Bibliographic Details
Published in:Advanced functional materials 2023-11, Vol.33 (47), p.n/a
Main Authors: He, Pei Hang, Zhang, Hao Chi, Fan, Yi, Niu, Ling Yun, Liu, Che, Bao, Jianghan, Zhang, Le Peng, Li, Baiyu, Lu, Zukun, Liu, Shuo, Tang, Wenxuan, Cui, Tie Jun
Format: Article
Language:English
Subjects:
Channels
Cognition
environment self‐adaption
environment‐wave interactions
Impurities
Machine learning
Materials science
meta‐channels
Reconfiguration
Signal quality
Wave interaction
Wearable technology
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Summary:The fundamental transmission channels of smart wearable devices and systems should support function reconfiguration and environment cognition or self‐adaption. Here, a environment‐adaptive meta‐channel (EAMC) is proposed that is adaptive to the near‐field environment. Benefiting from the capability to manipulate environment‐wave interactions, EAMC can cognize the near‐field environment impurities with subwavelength resolution, in which different impurities can be distinguished and located accurately with the aid of machine learning method. Thus, EAMC can adapt itself to varying environment so that its customized function is stabilized. For instance, EAMC is used as a communication modulator to adapt to different dielectric impurities and human body contacts dynamically. The quality of transmitted picture signals with impurities will deteriorate seriously but can be repaired efficiently as the self‐adaptive process of EAMC is used. Hence, the proposed EAMC could be a candidate for fundamental transmission channels for future smart wearable devices and systems. This article proposes the concept of an environment‐adaptive meta‐channel (EAMC). The EAMC can cognize the near‐field environment impurities with subwavelength resolution with the aid of machine learning method and adapt itself to the varying environments. Hence, the EAMC can be a candidate for fundamental transmission channels for future smart wearable devices and systems.
ISSN:1616-301X
1616-3028
DOI:10.1002/adfm.202302080