Intelligent Electromagnetic Sensing with Learnable Data Acquisition and Processing

Abstract

Electromagnetic (EM) sensing is a wide-spread contactless examination technique inscience, engineering and military. However, conventional sensing systems are mostly lack of intelligence, which not only require expensive hardware and complicated computational algorithms, but also pose important challenges for advanced 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. This strategy allows to learn an optimal sensing chain in systematic sense of variational autoencoder, i.e., to jointly learn an optimal measurement strategy along with matching data post processing schemes. A three-port deep artificial neural network (ANN) is designed to characterize the measurement process, such that an optimal measurement strategy is adaptive to the subject of interest by controlling the programmable metasurface for manipulating the EM illuminations. We design and fabricate a proof-of-principle sensing system in microwave, and demonstrate experimentally its significance on the highquality imaging and high-accuracy object recognition from a remarkably reduced number of measurements. We faithfully expect that the presented methodology will provide us with a fundamentally new perspective on the design of intelligent sensing architectures at various frequencies, and beyond.

Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals

“…3c ). In future work, refining the propagation model, e.g., with a learned forward model 31 , may further improve the performance of MBWC. During operation, we establish the programmable-metasurface-encoding scheme by mapping the three-channel binary information stream to control coding patterns of the metasurface, as reported in Fig.…”

“…The power needed to program the metasurface is minimal and can be as low as a few μW per meta-atom 24 . By now, programmable metasurfaces have found various valuable applications, for instance in programmable electromagnetic imaging and sensing [25][26][27][28][29][30][31] , wireless communication 8,[32][33][34][35][36][37] , dynamic holograms 38 , wireless energy deposition 39,40 , and analog computation with indoor Wi-Fi infrastructure 41 . The proposed MBWC paradigm utilizes the programmable metasurface for three major purposes: (1) encoding the digital information to be conveyed on the physical level; (2) directly modulating the ambient stray electromagnetic waves with high signal-to-noise ratio (SNR); and (3) facilitating the retrieval of digital information encoded into the metasurface with a matching classifier or decoder.…”

Electromagnetic metamaterials—from effective media to field programmable systems

“…如图 15 所示, 该 感知方法将可编程数字超材料和卷积神经网络机器学习相结合, 采用机器学习方法对超材料进行在线 训练, 实现了数据获取和数据处理的一体化操作, 能同时进行机器学习驱动的波束扫描和自适应数据 获取, 完成了运动目标的实时成像. 最近, 我们又充分发挥人工智能技术强大的数据处理能力, 通过现 场可编程超材料对空间 Wi-Fi 非合作无线信号的灵活调控, 实现了 GHz 帧率的非合作人体的实时微 波成像, 且可同时探测人体的生命体征和自动手语识别 [143] , 研制了 Wi-Fi 频段信息超材料智能电磁 感知系统 [144] , 为解决智慧家庭的人机交互、有语言障碍人群进行交流等问题提供了新途径. 目前我 们正在将数字编码超材料的智能化设计 [145,146] 、多种传感器, 以及机器学习算法相结合, 研究具有自 主学习能力的可认知超材料.…”

Information Metamaterial Systems

“…Making the metamaterial be field programmable is a big progress in the developments of metamaterials because of the following features: A single programmable metamaterial can accomplish many significantly distinct functions (e.g. single-beam radiation, different multi-beam radiations, beam scanning, wave diffusion, and vortex beam generation) ( Cui et al., 2014 , 2017 ; Liu et al., 2016a , Liu et al, 2016 , 2016c , 2016d , 2016e ; Li et al., 2019a , 2019b ; Shannon, 2001 ); All these functions are switched in real time by changing the digital states and sending instructions by FPGA ( Cui et al., 2014 ; Li et al., 2019a , 2019b ); The digital coding metamaterial builds up a bridge between the physical world and the digital world ( Wan et al., 2016 , 2019 ; Shuang et al., 2020 ; Zhang et al., 2018a , 2018b ), which helps establish new information systems, pushing the metamaterials to system-level applications ( Zhang et al, 2020a , Zhang et al, 2020b , 2020c ; Lipworth et al., 2013 ; Hunt et al., 2013 ; Li et al., 2016 ; Wang et al., 2016 ; Li et al., 2018 ; Li et al., 2017 ; Cui et al., 2019 ; Li et al., 2019a , 2019b ; Li, 2019 ; Li et al., 2020 ; Zhao et al., 2019 ; Dai et al., 2018 ; Dai et al., 2019 ; Tang et al., 2019a , 2019b ; Dai et al., 2020 ; Zhang et al., 2018a , 2018b ; Zhang et al., 2019a , 2019b ; Hadad et al., 2015 ; Shaltout et al., 2015 ; …”