Intelligent metasurface imager and recognizer

Lianlin Li; Ya Shuang; Qian Ma; Haoyang Li; Hanting Zhao; Menglin Wei; Che Liu; Chenglong Hao; Cheng-Wei Qiu; Tie Jun Cui

doi:10.1038/s41377-019-0209-z

Go to Nature Website

Your Location：

Home >

Browse articles >

Intelligent metasurface imager and recognizer

Articles

- Intelligent metasurface imager and recognizer
- Intelligent metasurface imager and recognizer
- LSA 2019年8卷第5期页码：893-901
- Affiliations：
  
  1.State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University, Beijing 100871, China
  2.State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
  3.Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore, Singapore
- Author bio：
  
  Lianlin Li (lianlin.li@pku.edu.cn)
  Tie Jun Cui (tjcui@seu.edu.cn)
- Funds：
- DOI：10.1038/s41377-019-0209-z
  中图分类号：
- 纸质出版日期：2019，
  
  网络出版日期：2019-10-21，
  
  收稿日期：2019-09-06，
  
  修回日期：2019-10-12，
  
  录用日期：2019-10-09
- Accepted：
Scan QR Code
Intelligent metasurface imager and recognizer[J]. LSA, 2019,8(5):893-901.

Li, L. L. et al. Intelligent metasurface imager and recognizer. Light: Science & Applications, 8, 893-901 (2019).
Intelligent metasurface imager and recognizer[J]. LSA, 2019,8(5):893-901. DOI： 10.1038/s41377-019-0209-z.

Li, L. L. et al. Intelligent metasurface imager and recognizer. Light: Science & Applications, 8, 893-901 (2019). DOI： 10.1038/s41377-019-0209-z.

摘要

Abstract

There is an increasing need to remotely monitor people in daily life using radio-frequency probe signals. However

conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish complicated successive tasks using a single device in real time

we propose the simultaneous use of a smart metasurface imager and recognizer

empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here

three ANNs are employed in an integrated hierarchy

transforming measured microwave data into images of the whole human body

classifying specifically designated spots (hand and chest) within the whole image

and recognizing human hand signs instantly at a Wi-Fi frequency of 2.4 GHz. Instantaneous in situ full-scene imaging and adaptive recognition of hand signs and vital signs of multiple non-cooperative people were experimentally demonstrated. We also show that the proposed intelligent metasurface system works well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open up a new avenue for future smart cities

smart homes

human-device interaction interfaces

health monitoring

and safety screening free of visual privacy issues.

关键词

Keywords

references

Xiao, J. et al. A Survey on wireless indoor localization from the device perspective.ACM Comput. Surv.49, 25 (2016)..

del Hougne, P. et al. Precise localization of multiple noncooperative objects in a disordered cavity by wave front shaping.Phys. Rev. Lett.121, 063901 (2018)..

Joshi, K. et al. WiDeo: fine-grained device-free motion tracing using RF backscatter.Proceeding of the 12th USENIX Conference on Networked Systems Design and Implementation, 189-204 (ACM, Oakland, CA, 2015).

Dai, X. X. et al. Ultra-wideband radar-based accurate motion measuring: human body landmark detection and tracking with biomechanical constraints.IET Radar, Sonar Navigation9, 154-163 (2015)..

Pu, Q. F. et al. Whole-home gesture recognition using wireless signals.Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, 27-38 (ACM, Miami, Florida, USA, 2013).

Sadreazami, H. et al. CapsFall: fall detection using ultra-wideband radar and capsule network.IEEE Access7, 55336-55343 (2019)..

Zhao, M. M. et al. Through-wall human pose estimation using radio signals.Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7356-7365 (IEEE, Salt Lake City, UT, USA, 2018).

Zhao, M. M. et al. RF-based 3D skeletons.Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, 267-281 (ACM, Budapest, Hungary, 2018).

Mercuri, M. et al. Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor.Nat. Electron.2, 252-262 (2019)..

Hung, W. P. et al. Real-time and noncontact impulse radio radar system forμm movement accuracy and vital-sign monitoring applications.IEEE Sens. J.17, 2349-2358 (2017)..

Huang, D., Nandakumar, R.&Gollakota, S. Feasibility and limits of Wi-Fi imaging.Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, 266-279 (ACM, Memphis, Tennessee, 2014).

Holl, P. M.&Reinhard, F. Holography of Wi-Fi radiation.Phys. Rev. Lett.118, 18390 (2017)..

Wang, G. H. et al. We can hear you with Wi-Fi!IEEE Trans. Mob. Comput.15, 2907-2920 (2016)..

Shelby, R. A., Smith, D. R.&Schultz, S. Experimental verification of a negative index of refraction.Science292, 77-79 (2001)..

Yu, N. F. et al. Light propagation with phase discontinuities: generalized laws of reflection and refraction.Science334, 333-337 (2011)..

Estakhri, N. M., Edwards, B.&Engheta, N. Inverse-designed metastructures that solve equations.Science363, 1333-1338 (2019)..

Pendry, J. B. Negative refraction makes a perfect lens.Phys. Rev. Lett.85, 3966-3969 (2000)..

Fang, N. et al. Sub-diffraction-limited optical imaging with a silver superlens.Science308, 534-537 (2005)..

Schurig, D. et al. Metamaterial electromagnetic cloak at microwave frequencies.Science314, 977-980 (2006)..

Ni, X. J. et al. Metasurface holograms for visible light.Nat. Commun.4, 2807 (2013)..

Zheng, G. X. et al. Metasurface holograms reaching 80% efficiency.Nat. Nanotechnol.10, 308-312 (2015)..

High, A. A. et al. Visible-frequency hyperbolic metasurface.Nature522, 192-196 (2015)..

Khorasaninejad, M. Metalenses at visible wavelengths: diffraction-limited focusing and subwavelength resolution imaging.Science352, 1190-1194 (2016)..

Ou, J. Y. et al. An electromechanically reconfigurable plasmonic metamaterial operating in the near-infrared.Nat. Nanotechnol.8, 252-255 (2013)..

Cui, T. J. et al. Coding metamaterials, digital metamaterials and programmable metamaterials.Light.: Sci. Appl.3, e218 (2014)..

Li, L. L.&Cui, T. J. Information metamaterials-from effective media to real-time information processing systems.Nanophotonics8, 703-724 (2019)..

Tao, H. et al. Reconfigurable terahertz metamaterials.Phys. Rev. Lett.103, 147401 (2009)..

Li, L. L. et al. Electromagnetic reprogrammable coding-metasurface holograms.Nat. Commun.8, 197 (2017)..

Li, L. L. et al. Machine-learning reprogrammable metasurface imager.Nat. Commun.10, 1082 (2019)..

Li, Y. B. et al. Transmission-type 2-bit programmable metasurface for single-sensor and single-frequency microwave imaging.Sci. Rep.6, 23731 (2016)..

Watts, C. M. et al. Terahertz compressive imaging with metamaterial spatial light modulators.Nat. Photonics8, 605-609 (2014)..

Sleasman, T. et al. Dynamic metamaterial aperture for microwave imaging.Appl. Phys. Lett.107, 204104 (2015)..

Zhao, J. et al. Programmable time-domain digital-coding metasurface for non-linear harmonic manipulation and new wireless communication systems.Natl. Sci. Rev.6, 231-238 (2019)..

Zhang, L. et al. Space-time-coding digital metasurfaces.Nat. Commun.9, 4334 (2018)..

Yoo, I. et al. Enhancing capacity of spatial multiplexing systems using reconfigurable cavity-backed metasurface antennas in clustered MIMO channels.IEEE Trans. Commun.67, 1070-1084 (2018)..

Yang, H. H. et al. A programmable metasurface with dynamic polarization, scattering and focusing control.Sci. Rep.6, 35692 (2016)..

LeCun, Y., Bengio, Y.&Hinton, G. Deep learning.Nature521, 436-444 (2015)..

Sinha, A. et al. Lensless computational imaging through deep learning.Optica4, 1117-1125 (2017)..

Kamilov, U. S. et al. Learning approach to optical tomography.Optica2, 517-522 (2015)..

Waller, L.&Tian, L. Computational imaging: machine learning for 3D microscopy.Nature523, 416-417 (2015)..

Li, L. L. et al. DeepNIS: deep neural network for nonlinear electromagnetic inverse scattering.IEEE Trans. Antennas Propag.67, 1819-1825 (2019)..

Kalinin, S. V., Sumpter, B. G.&Archibald, R. K. Big-deep-smart data in imaging for guiding materials design.Nat. Mater.14, 973-980 (2015)..

Malkiel, I. et al. Plasmonic nanostructure design and characterization via deep learning.Light.: Sci. Appl.7, 60 (2018)..

Liu, D. J. et al. Training deep neural networks for the inverse design of nanophotonic structures.ACS Photonics5, 1365-1369 (2018)..

Jin, K. H. et al. Deep convolutional neural network for inverse problems in imaging.IEEE Trans. Image Process.26, 4509-4522 (2017)..

Han, Y. S., Yoo, J.&Ye, J. C. Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis. Preprint athttps://arxiv.org/abs/1611.06391https://arxiv.org/abs/1611.06391(2016).

Ren, S. Q. et al. Faster R-CNN: towards real-time object detection with region proposal networks.IEEE Trans. Pattern Anal. Mach. Intell.39, 1137-1149 (2017)..

Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. Preprint athttps://arxiv.org/abs/1603.04467v1https://arxiv.org/abs/1603.04467v1(2016).

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据