Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing

doi:10.1016/j.patter.2020.100006

2020-04-01

Tie Jun Cui (Tie Jun Cui) *

Philipp del Hougne *

Lianlin Li *

DOI：10.1016/j.patter.2020.100006

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摘要

En 中文

Electromagnetic (EM) sensing is a widespread contactless examination technique with applications in areas such as health care and the internet of things. Most conventional sensing systems lack intelligence, which not only results in expensive hardware and complicated computational algorithms but also poses important challenges for real-time in situ sensing. To address this shortcoming, we propose the concept of intelligent sensing by designing a programmable metasurface for data-driven learnable data acquisition and integrating it into a data-driven learnable data-processing pipeline. Thereby, a measurement strategy can be learned jointly with a matching data post-processing scheme, optimally tailored to the specific sensing hardware, task, and scene, allowing us to perform high-quality imaging and high-accuracy recognition with a remarkably reduced number of measurements. We report the first experimental demonstration of learned sensing'' applied to microwave imaging and gesture recognition. Our results pave the way for learned EM sensing with low latency and computational burden.

Keyword:

NEURAL-NETWORK

METASURFACE

METAMATERIALS

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7.4

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943

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3.6K

机构

southeast university - china

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5.3W

论文数: 4.9W

被引数: 57

peking university

学者数:

11.9W

论文数: 8.7W

被引数: 146

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