Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing

• Main Authors: Hao-Yang Li, Han-Ting Zhao, Meng-Lin Wei, Heng-Xin Ruan, Ya Shuang, Tie Jun Cui, Philipp del Hougne, Lianlin Li
• Format: Article
• Language: English
• Published: Elsevier 2020-04-01
• Series: Patterns
• Subjects: programmable metamaterials; artificial neural network; intelligent electromagnetic
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666389920300064

Summary: 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. The Bigger Picture: Many futuristic “intelligent” concepts that will affect our society, from ambient-assisted health care via autonomous vehicles to touchless human-computer interaction, necessitate sensors that can monitor a device's surroundings fast and without extensive computational effort. To date, sensors indiscriminately acquire all information and only select relevant details during data processing, thereby wasting time, energy, and computational resources. We demonstrate intelligent electromagnetic sensing that uses learned illumination patterns to already select relevant details during the measurement process. Our experiments use a home-made programmable metasurface to generate the learned microwave patterns that enable a remarkable reduction in the number of necessary measurements. Our demonstration addresses a widespread need for high-quality contactless electromagnetic sensing under strict time, energy, and computation constraints.