Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-25T05:03:12.616Z Has data issue: false hasContentIssue false
Series:   Elements in Emerging Theories and Technologies in Metamaterials

Intelligent Metasurface Sensors

Published online by Cambridge University Press:  20 December 2023

Lianlin Li ,
Hanting Zhao  and
Tie Jun Cui
Lianlin Li
Affiliation:
Peking University
Hanting Zhao
Affiliation:
Peking University
Tie Jun Cui
Affiliation:
Southeast University, China

Summary

Intelligent electromagnetic (EM) sensing is a powerful contactless examination tool in science, engineering and military, enabling us to 'see' and 'understand' visually invisible targets. Using intelligence, the sensor can organize by itself the task-oriented sensing pipeline (data acquisition plus processing) without human intervention. Intelligent metasurface sensors, synergizing ultrathin artificial materials (AMs) for flexible wave manipulation and artificial intelligences (AIs) for powerful data manipulation, emerge in response to the proper time and conditions, and have attracted growing interest over the past years. The authors expect that the results in this Element could be utilized to achieve the goal that conventional sensors cannot achieve, and that the developed strategies can be extended over the entire EM spectra and beyond, which will produce important impacts on the society of the robot-human alliance.
Get access

Keywords

electromagnetic sensingwireless sensingartificial intelligenceartificial materialsmetamaterials and metasurfaces
Type
Element
Information
Series: Elements in Emerging Theories and Technologies in Metamaterials
Online ISBN: 9781009277242
Publisher: Cambridge University Press
Print publication: 01 February 2024

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cui, T. J., Qi, M. Q., Wan, X., Zhao, J., and Cheng, Q., Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl., 3 (2014), e218.CrossRefGoogle Scholar
Cui, T. J., Microwave metamaterials: from passive to digital and programmable controls of electromagnetic waves. J. Optics, 19 (2017), 084004.Google Scholar
Li, L. and Cui, T. J., Information metamaterials: from effective media to real-time information processing systems. Nanophotonics, 8 (2019), 703–724.CrossRefGoogle Scholar
Zhang, L., Chen, X. Q., Liu, S. et al., Space-time-coding digital metasurfaces. Nat. Commun., 9 (2018), 4334.Google Scholar
Gholipour, B., Zhang, J., MacDonald, K. F., Hewak, D. W., and Zheludev, N. I., An all-optical, non-volatile, bidirectional, phase-change meta-switch. Adv. Mater., 25 (2013), 3050–3054.Google Scholar
Wang, Q., Rogers, E. T. F., Gholipour, B. et al., Optically reconfigurable metasurfaces and photonic devices based on phase change materials. Nat. Photonics, 10 (2015), 60–65.Google Scholar
Kaplan, G., Aydin, K., and Scheuer, J., Dynamically controlled plasmonic nano-antenna phased array utilizing vanadium dioxide. Opt. Mater. Express, 5 (2015), 2513.CrossRefGoogle Scholar
Dicken, M. J., Aydin, K., Pryce, I. M. et al., Frequency tunable near-infrared metamaterials based on VO2 phase transition. Opt. Express, 17 (2009), 18330.Google Scholar
Ju, L., Geng, B., Horng, J. et al., Graphene plasmonics for tunable terahertz metamaterials. Nat. Nanotechnol., 6 (2011), 630–634.Google Scholar
Ou, J.-Y., Plum, E., Zhang, J., and Zheludev, N. I., An electromechanically reconfigurable plasmonic metamaterial operating in the near-infrared. Nat. Nanotechnol., 8 (2013), 252–255.Google Scholar
Website of Teratech Components Ltd, www.teratechcomponents.com/.Google Scholar
Peatman, W. C. B., Wood, P. A. D., Porterfield, D., Crowe, T. W., and Rooks, M. J., Quarter-micrometer GaAs Schottky barrier diode with high video responsivity at 118μm. Appl. Phys. Lett., 63 (1992), 284–290.Google Scholar
Ghanekar, A., Ji, J., and Zheng, Y., High-rectification near-field thermal diode using phase change periodic nanostructure. Appl. Phys. Lett., 109 (2016), 123106.Google Scholar
Barker, A. S. Jr., Verleur, H. W., and Guggenheim, H. J., Infrared optical properties of vanadium dioxide above and below the transition temperature. Phys. Rev. Lett., 17 (1966), 1286–1289.Google Scholar
Li, Y. B., Li, L. L., Xu, B. B. et al., Transmission-type 2-bit programmable metasurface for single-sensor and single-frequency microwave imaging. Sci. Rep., 6 (2016), 23731.Google Scholar
Li, L. L., Ruan, H., Liu, C. et al., Machine-learning reprogrammable metasurface imager. Nat. Commun., 10 (2019), 1082.CrossRefGoogle ScholarPubMed
Yang, H., Cao, X., Yang, F. et al., A programmable metasurface with dynamic polarization, scattering and focusing control. Sci. Rep., 6 (2016), 35692.CrossRefGoogle ScholarPubMed
Shuang, Y., Zhao, H., Ji, W., Cui, T. J., and Li, L., Programmable high-order OAM-carrying beams for direct-modulation wireless communications. IEEE J. Emerg., 10 (2020), 29–37.Google Scholar
Han, J. Q., Li, L., Yi, H., and Shi, Y., 1-bit digital orbital angular momentum vortex beam generator based on a coding reflective metasurface. Opt. Mater. Express, 8 (2018), 3470–3478.CrossRefGoogle Scholar
Zhang, D., Cao, X. Y., and Yang, H. H., Radiation performance synthesis for OAM vortex wave generated by reflective metasurface. IEEE Access, 6 (2018), 28691–28701.Google Scholar
Zhang, X. G., Sun, Y. L., Yu, Q. et al., Smart Doppler cloak operating in broad band and full polarizations. Adv. Mater., 33 (2021), 2007966.Google Scholar
Li, L., Cui, T.-J., Ji, W. et al., Electromagnetic reprogrammable coding-metasurface holograms. Nat. Commun., 8 (2017), 197.Google Scholar
Li, L., Shuang, Y., Ma, Q. et al., Intelligent metasurface imager and recognizer. Light Sci. Appl., 8 (2019), 97.Google Scholar
Li, H.-Y., Zhao, H.-T., Wei, M.-L. et al., Intelligent electromagnetic sensing with learnable data acquisition and processing. Patterns, 1 (2020), 100006.CrossRefGoogle ScholarPubMed
Wang, J., Li, Y., Jiang, Z. H. et al., Metantenna: when metasurface meets antenna again. IEEE Trans. Antennas Propag., 68 (2020), 1332–1347.Google Scholar
Shuang, Y., Li, L., Wang, Z. et al., Controllable manipulation of Wi-Fi signals using tunable metasurface.J. Radars, 10 (2021), 313–325.Google Scholar
Gerchberg, R. W. and Saxton, W. O., A practical algorithm for the determination of the phase from image and diffraction plane pictures. Optik, 35 (1972), 227–246.Google Scholar
Gabor, D., A new microscopic principle. Nature, 161 (1948), 777–778.CrossRefGoogle ScholarPubMed
Poon, T.-C. and Liu, J.-P., Introduction to Modern Digital Holography: with Matlab. (Cambridge: Cambridge University Press, 2014).Google Scholar
Zheng, G., Mühlenbernd, H., Kenney, M. et al., Metasurface holograms reaching 80% efficiency. Nat. Nanotechnol., 10 (2015), 308–312.Google Scholar
Genevet, P., Lin, J., Kats, M. A., and Capasso, F., Holographic detection of the orbital angular momentum of light with plasmonic photodiodes. Nat. Commun., 3 (2012), 1278.Google Scholar
Yu, N., Genevet, P., Kats, M. A. et al., Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science, 334 (2011), 333–337.Google Scholar
Larouche, S., Tsai, Y.-J., Tyler, T., Jokerst, N. M., and Smith, D. R., Infrared metamaterial phase holograms. Nat. Mater., 11 (2012), 450–454.Google Scholar
Huang, L., Mühlenbernd, H., Li, X. et al., Broadband hybrid holographic multiplexing with geometric metasurfaces. Adv. Mater., 27 (2015), 6444–6449.Google Scholar
Durnin, J., Miceli, J. J., and Eberly, J. H., Diffraction-free beams. Phy. Rev. Lett., 58 (1987), 1499–1501.CrossRefGoogle ScholarPubMed
Bouchal, Z., Wagner, J., and Chlup, M., Self-reconstruction of a distorted nondiffracting beam. Opt. Commun., 151 (1998), 207–211.Google Scholar
Chen, J., Ng, J., Lin, Z., and Chan, C. T., Optical pulling force. Nat. Photon., 5 (2011), 531–534.Google Scholar
Dogariu, A., Sukhov, S., and Sáenz, J. Jose, Optically induced “negative forces.” Nat. Photon., 7 (2013), 24–27.Google Scholar
Thidé, B., Then, H., Sjöholm, J. et al., Utilization of photon orbital angular momentum in the low-frequency radio domain. Phys. Rev. Lett., 8 (2007), 087701.Google Scholar
Tamburini, F., Mari, E., Sponselli, A. et al., Encoding many channels in the same frequency through radio vorticity: first experimental test. New J. Phys., 14 (2013), 3.Google Scholar
Ren, Y., Li, L., Xie, G. et al., Line-of-sight millimeter-wave communications using orbital angular momentum multiplexing combined with conventional spatial multiplexing. IEEE Trans. Wirel., 5 (2017), 3151–3161.Google Scholar
Yan, Y., Xie, G., Lavery, M. et al., High-capacity millimetre-wave communications with orbital angular momentum multiplexing. Nat. Comms., 5 (2014), 4876.Google Scholar
Li, L. and Fang, Li, Beating the Rayleigh limit: orbital-angular-momentum-based super-resolution diffraction tomography. Phys. Rev. E., 88 (2013), 033205.Google Scholar
Liu, S., Cui, T. J., Zhang, L. et al., Convolution operations on coding metasurface to reach flexible and continuous controls of terahertz beams. Adv. Sci., 10 (2016), 1600156.Google Scholar
Shuang, Y., Zhao, H., Wei, M. et al., One-bit quantization is good for programmable coding metasurfaces. Sci. China Inf. Sci., 65 (2022), 172301.Google Scholar
Derode, A., Tourin, A., and Fink, M., Ultrasonic pulse compression with one-bit time reversal through multiple scattering. J. Appl. Phys., 85 (2014), 6434.Google Scholar
Li, D., Ergodic capacity of intelligent reflecting surface-assisted communication systems with phase errors. IEEE Commun. Lett., 24 (2020), 1646.Google Scholar
Xu, P., Chen, G., Yang, Z., and Renzo, M. Di, Reconfigurable intelligent surfaces assisted communications with discrete phase shifts: how many quantization levels are required to achieve full diversity? IEEE Wirel. Commun. Lett., 1 (2020), 358–362.Google Scholar
Zhao, H., Shuang, Y., Wei, M., and Cui, T.-J., Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat. Commun., 11 (2020), 3926.CrossRefGoogle ScholarPubMed
Cui, T.-J., Liu, S., and Li, L.-L., Information entropy of coding metasurface. Light Sci. Appl., 5 (2016), e16172.Google Scholar
Wu, H., Bai, G. D., Liu, S. et al., Information theory of metasurfaces. Natl. Sci. Rev., 7 (2020), 561.Google Scholar
Shannon, C. E., A mathematical theory of communication. Bell Syst. Tech. J., 27 (1948), 379.Google Scholar
Duarte, M. F., Davenport, M. A., Takhar, D. et al., Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag., 25 (2008), 83–91.CrossRefGoogle Scholar
Watts, C. M., Shrekenhamer, D., Montoya, J. et al., Terahertz compressive imaging with metamaterial spatial light modulators. Nat. Photonics, 8 (2014), 605–609.Google Scholar
Chan, W. L., Charan, K., Takhar, D. et al., A single-pixel terahertz imaging system based on compressed sensing. Appl. Phys. Lett., 93 (2008), 121105.Google Scholar
Liutkus, A., Martina, D., Popoff, S. et al., Imaging with nature: compressive imaging using a multiply scattering medium. Sci. Rep., 4 (2014). doi: https://doi.org/10.1038/srep05552.Google Scholar
Hunt, J. and Smith, D. R., Metamaterial apertures for computational imaging. Science, 339 (2013), 310–313.Google Scholar
Candes, E. J., Romberg, J., and Tao, T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52 (2004), 489–509.Google Scholar
Donoho, D. L., For most large underdetermined systems of equations, the minimal l1-norm near-solution approximates the sparsest near-solution. Comm. Pure Appl. Math., 59 (2004), 907–934.Google Scholar
Donoho, D. L., Compressed sensing. IEEE Trans. Inform. Theory, 52, 1289–1306 (2006).Google Scholar
Donoho, D. and Tanner, J., Precise undersampling theorems. Proc. IEEE, 98(2010), 913–924.Google Scholar
Elad, Y. C. and Kutyniok, G., Compressed Sensing: Theory and Applications. Cambridge: Cambridge University Press (2012).Google Scholar
Elad, M., Sparse and Redundant Representations: from Theory to Applications in Signal and Image Processing. Berlin: Springer (2010).Google Scholar
Candes, E. and Recht, B., Exact matrix completion via convex optimization. Found. Comput. Math., 6 (2009), 717–772.Google Scholar
Candes, E. and Plan, Y.. Matrix completion with noise. Proc. IEEE, 6 (2010), 925–936.Google Scholar
Mairal, J., Sapiro, G., and Elad, M., Learning multiscale sparse representations for image and video restoration. Multiscale Model Simul., 7 (2007), 214–241.Google Scholar
Yu, G., Sapiro, G., and Mallat, S., Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans. Image Process., 21 (2012), 2481–2499.Google Scholar
Lipworth, G., Mrozack, A., Hunt, J. et al., Metamaterial apertures for coherent computational imaging on the physical layer. J. Opt. Soc. Am. A, 30 (2013), 1603.Google Scholar
Li, L., Li, F., and Cui, T. J., Feasibility of resonant metalens for the subwavelength imaging using a single sensor in the far field. Optics Express, 22 (2014), 18688–18697.Google Scholar
Li, L., Li, F., Cui, T. J., and Yao, K., Far-field imaging beyond diffraction limit using single sensor in combination with a resonant aperture. Optics Express, 23 (2015), 401–412.Google Scholar
Wang, L., Li, L., Li, Y., Zhang, H., and Cui, T. J., Single-shot and single-sensor high/super-resolution microwave imaging based on metasurface. Sci. Rep., 6 (2016), 26959.Google Scholar
Li, L. et al., A survey on the low-dimensional-model-based electromagnetic imaging. Found. Trends Signal Process., 12 (2018), 107–199.Google Scholar
Krahmer, F., Mendelson, S., and Rauhut, H., Suprema of chaos processes and the restricted isometry property. Commun. Pure Appl. Math., 67 (2014), 1877–1904.Google Scholar
Xie, Y., Tsai, T. H., Konneker, A. et al. Single-sensor multispeaker listening with acoustic metamaterials. PNAS, 112 (2015), 10595–10598.Google Scholar
Lemoult, F., Lerosey, G., de Rosny, J., and Fink, M., Resonant metalenses for breaking the diffraction barrier. Phys. Rev. Lett., 104 (2010), 203901.Google Scholar
Lemoult, F., Fink, M., and Lerosey, G., A polychromatic approach to far-field superlensing at visible wavelengths. Nat. Commun., 3 (2012), 177–180.Google Scholar
Schurig, D., Mock, J. J., and Smith, D. R., Electric-field-coupled resonators for negative permittivity metamaterials. Appl. Phys. Lett., 88 (2006), 041109–041109.Google Scholar
Chaumet, P. C., Sentenac, A., and Rahmani, A., Coupled dipole method for scatters with large permittivity. Phys. Rev., E, 70 (2004), 193–204.Google Scholar
Chaumet, P. C. and Belkebir, K., Three-dimensional reconstruction from real data using a conjugate gradient-coupled dipole method. Inverse Probl., 25 (2009), 24003–17.Google Scholar
Berry, M. V. and Popescu, S., Evolution of quantum superoscillations, and optical superresolution without evanescent waves. J. Phys. A: Math. Gen., 39 (2006), 6965–77.Google Scholar
Rogers, E. T. F. and Zheludev, N. I., Optical super-oscillations: sub-wavelength light focusing and super-resolution imaging. J. Opt., 15 (2013), 094008.Google Scholar
Rogers, E. T. F., Lindberg, J., Roy, T. et al., A super-oscillatory lens optical microscope for subwavelength imaging. Nat. Mater., 11 (2012), 432–435.Google Scholar
Wong, A. M. H. and Eleftheriades, G. V., An optical super-microscope for far-field, real-time imaging beyond the diffraction limit. Sci. Rep., 3 (2013), 6330–6337.Google Scholar
Fang, N., Lee, H., Sun, C., and Zhang, X., Sub-diffraction-limited optical imaging with a silver superlens. Science, 308 (2005), 534.Google Scholar
Zhang, X. and Liu, Z., Superlenses to overcome the diffraction limit. Nat. Mater., 7 (2008), 435.Google Scholar
Devaney, A. J., Mathematical Foundations of Imaging, Tomography and Wavefield Inversion. Cambridge: Cambridge University Press (2012).Google Scholar
Born, M. and Wolf, E., Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light. 7th edition. Cambridge: Cambridge University Press (2006).Google Scholar
Johnson, W. and Lindenstrauss, J., Extensions of Lipschitz mappings into a Hilbert space. Isr. J. Math., 54 (1986), 129–138.Google Scholar
Jolliffe, I. T., Principal Component Analysis. New York: Springer (2002).Google Scholar
Tipping, M. E. and Bishop, C. M., Probabilistic principal component analysis. J. R. Stat. Soc., B: Stat. Methodol., 61 (1999), 611–622.Google Scholar
Halko, N., Martinsson, P. G., and Tropp, J. A., Finding structure randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Rev., 53 (2011), 217–288.Google Scholar
Kulkarni, K. and Turaga, P.. Reconstruction-free action inference from compressive imagers. IEEE Trans. Pattern Anal. Mach. Intell., 38 (2016), 4, 772–784.Google Scholar
Nayar, S. K. and Branzoi, V.. Programmable imaging: toward a flexible camera. Int. J. Comput. Vis., 20 (2006), 1, 7–22.Google Scholar
Tao, Hu, Striwerda, A. C., Fan, K. et al., Reconfigurable terahertz metamaterials. Phys. Rev. Lett., 103 (2009), 147401.Google Scholar
Huang, Y.-W., Lee, H. W. H., Sokhoyan, R. et al., Gate-tunable conducting oxide metasurfaces. Nano Lett., 16 (2016), 5319–5325.Google Scholar
del Hougne, P., Imani, M. F., Fink, M., Smith, D. R., and Lerosey, G., Precise localization of multiple noncooperative objects in a disordered cavity by wave front shaping. Phys. Rev. Lett., 121 (2018), 063901.Google Scholar
Joshi, K. R., Bharadia, D., Kotaru, M., and Katti, S., WiDeo: fine-grained device-free motion tracing using RF backscatter. Proceeding of the USENIX Conference on Networked Systems Design and Implementation, (2015), 189–204.Google Scholar
Dai, X., Zhou, Z., Zhang, J., and Davidson, B., Ultra-wideband radar-based accurate motion measuring: human body landmark detection and tracing with biomechanical constraints. IET Radar Sonar Navig., 9 (2015), 154–163.Google Scholar
Pu, Q., Gupta, S., Gollakota, S., and Patel, S., Whole-home gesture recognition using wireless signals. Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, (2013), 27–38.Google Scholar
Sadreazami, H., Bolic, M., and Rajan, S., CapsFall: fall detection using ultra-wideband radar and capsule network. IEEE Access, 7 (2019), 55336–55343.Google Scholar
Zhao, M., Li, T., Alsheikh, M. et al., Through-wall human pose estimation using radio signals. 2018 IEEE/CVF Computer Vision and Pattern Recognition, (2018), 7356–7365.Google Scholar
Zhao, M., Tian, Y., Zhao, H. et al., RF-based 3D skeletons. Proceedings of the 2018 Conference of the ACM Special Group on Data Communication, (2018), 267–281.Google Scholar
Mercuri, M., Lorato, I. R., Liu, Y. H. et al., Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor. Nat. Electron., 2 (2019), 252–262.CrossRefGoogle Scholar
Huang, W. P., Chang, C. H., and Lee, T. H., Real-time and noncontact impulse radio system for µm movement accuracy and vital-sign monitoring applications. IEEE Sens. J., 17 (2018), 2349–2358.Google Scholar
Ren, S., He, K., Girshick, R., and Sun, J., Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39 (2016), 1137–1149.Google Scholar
Huang, D., Nandakumar, R., and Gollakota, S., Feasibility and limits of Wi-Fi imaging. Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, (2014), 266–279.Google Scholar
Holl, P. M. and Reinhard, F., Holography of Wi-Fi radiation. Phys. Rev. Lett., 118 (2017), 18390.Google Scholar
Wang, G., Zou, Y., Zhou, Z., Wu, K., and Lionel, M. Ni, We can hear you with Wi-Fi!, IEEE Trans. Mob., 15 (2016), 2907–2920.Google Scholar
Wang, Z., Zhang, H., Zhao, H. et al., Intelligent electromagnetic metasurface camera: system design and experimental results, Nanophotonics, 11 (2022), 0665.Google Scholar
Chakrabarti, A., Learning sensor multiplexing design through back-propagation. Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, (2016), 3081–3089.Google Scholar
Kellman, M. R., Bostan, E., Repina, N. A., and Waller, L., Physics-based learned design: optimized coded-illumination for quantitative phase imaging. IEEE Trans. Comput. Imaging, 5 (2019), 344–353.Google Scholar
Sitzmann, V., Diamond, S., Peng, Y. et al., End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph., 37 (2018), 1–13.Google Scholar
Chang, J., Sitzmann, V., Dun, X., Heidrich, W., and Wetzstein, G., Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep., 8 (2018), 12324.Google Scholar
Muthumbi, A., Chaware, A., Kim, K. et al., Learned sensing: jointly optimized microscope hardware for accurate image classification. Biomed. Opt. Express, 10 (2019), 6351.Google Scholar
Vedula, S., Senouf, O., Zurakhov, G. et al., Learning beamforming in ultrasound imaging. Proc. Mach. Learn., 102 (2019), 493–511.Google Scholar
Kingma, D. P. and Ba, J. L., Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
He, K., Zhang, X., Ren, S., and Sun, J., Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770–778.Google Scholar
del Hougne, P., Imani, M. F., Diebold, A. V., Horstmeyer, R., and Smith, D. R., Learned integrated sensing pipeline: reconfigurable metasurface transceivers as trainable physical layer in an artificial neural network. Adv. Sci., 7 (2019), 1901913.Google Scholar
Doersch, C., Tutorial on variational autoencoders. arXiv:1606.05908 (2016).Google Scholar
Li, L., Jafarpour, B., and Mohammad-Khaninezhad, M. R., A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty. Comput. Geosci., 17 (2013), 167–188.Google Scholar
Friston, K., The free-energy principle: a unified brain theory? Nat. Rev. Neuro., 11, 127–138 (2010).Google Scholar
Ronneberger, O., Fischer, P., and Brox, T., U-net: convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. (2015), 234–241. www.stereolabs.com/zed-2/.Google Scholar
Guler, R. Alp, Neverova, N., and Kokkinos, I., DensePose: dense human pose estimation in the wild. EEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 7297–7306. (Code is available at http://densepose.org/.)Google Scholar

Save element to Kindle

To save this element to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Intelligent Metasurface Sensors
Available formats
×

Save element to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Intelligent Metasurface Sensors
Available formats
×

Save element to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Intelligent Metasurface Sensors
Available formats
×