Photonics Research, 2021, 9 (4): 0400B159, Published Online: Apr. 6, 2021   

Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network Download: 501次

Author Affiliations
1 Institute of Electromagnetic Space, Southeast University, Nanjing 210096, China
2 State Key Laboratory of Millimeter Wave, Southeast University, Nanjing 210096, China
3 School of Electronic Engineering and Computer Sciences, Peking University, Beijing 100871, China
Copy Citation Text

Che Liu, Wen Ming Yu, Qian Ma, Lianlin Li, Tie Jun Cui. Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network[J]. Photonics Research, 2021, 9(4): 0400B159.

References

[1] V. G. Veselago. Electrodynamics of substances with simultaneously negative values of sigma and μ. Sov. Phys. Usp., 1968, 10: 509-514.

[2] R. Zhao, L. Huang, Y. Wang. Recent advances in multi-dimensional metasurfaces holographic technologies. PhotoniX, 2020, 1: 20.

[3] X. Ding, Z. Wang, G. Hu, J. Liu, K. Zhang, H. Li, B. Ratni, S. N. Burokur, Q. Wu, J. Tan, C.-W. Qiu. Metasurface holographic image projection based on mathematical properties of Fourier transform. PhotoniX, 2020, 1: 16.

[4] Q. Ma, Q. R. Hong, X. X. Gao, H. B. Jing, C. Liu, G. D. Bai, Q. Cheng, T. J. Cui. Smart sensing metasurface with self-defined functions in dual polarizations. Nanophotonics, 2020, 9: 3271-3278.

[5] X. Ni, N. K. Emani, A. V. Kildishev, A. Boltasseva, V. M. Shalaev. Broadband light bending with plasmonic nanoantennas. Science, 2012, 335: 427.

[6] N. Yu, P. Genevet, M. A. Kats, F. Aieta, J.-P. Tetienne, F. Capasso, Z. Gaburro. Light propagation with phase discontinuities: generalized laws of reflection and refraction. Science, 2011, 334: 333-337.

[7] D. Schurig, J. J. Mock, B. J. Justice, S. A. Cummer, J. B. Pendry, A. F. Starr, D. R. Smith. Metamaterial electromagnetic cloak at microwave frequencies. Science, 2006, 314: 977-980.

[8] J. Li, J. B. Pendry. Hiding under the carpet: a new strategy for cloaking. Phys. Rev. Lett., 2008, 101: 203901.

[9] R. Liu, C. Ji, J. J. Mock, J. Y. Chin, T. J. Cui, D. R. Smith. Broadband ground-plane cloak. Science, 2009, 323: 366-369.

[10] Q. Ma, Z. L. Mei, S. K. Zhu, T. Y. Jin, T. J. Cui. Experiments on active cloaking and illusion for Laplace equation. Phys. Rev. Lett., 2013, 111: 173901.

[11] W. X. Jiang, T. J. Cui, Q. Cheng, J. Y. Chin, X. M. Yang, R. Liu, D. R. Smith. Design of arbitrarily shaped concentrators based on conformally optical transformation of nonuniform rational B-spline surfaces. Appl. Phys. Lett., 2008, 92: 264101.

[12] Y. Lai, J. Ng, H. Chen, D. Han, J. Xiao, Z.-Q. Zhang, C. T. Chan. Illusion optics: the optical transformation of an object into another object. Phys. Rev. Lett., 2009, 102: 253902.

[13] L. Chen, Q. Ma, Q. F. Nie, Q. R. Hong, H. Y. Cui, Y. Ruan, T. J. Cui. Dual-polarization programmable metasurface modulator for near-field information encoding and transmission. Photon. Res., 2021, 9: 116-124.

[14] N. Kundtz, D. R. Smith. Extreme-angle broadband metamaterial lens. Nat. Mater., 2010, 9: 129-132.

[15] W. X. Jiang, C.-W. Qiu, T. C. Han, Q. Cheng, H. F. Ma, S. Zhang, T. J. Cui. Broadband all-dielectric magnifying lens for far-field high-resolution imaging. Adv. Mater., 2013, 25: 6963-6968.

[16] X. M. Yang, X. Y. Zhou, Q. Cheng, H. F. Ma, T. J. Cui. Diffuse reflections by randomly gradient index metamaterials. Opt. Lett., 2010, 35: 808-810.

[17] T. J. Cui, M. Q. Qi, X. Wan, J. Zhao, Q. Cheng. Coding metamaterials, digital metamaterials and programmable metamaterials. Light Sci. Appl., 2014, 3: e218.

[18] J. Li, Y. Zhang, J. Li, X. Yan, L. Liang, Z. Zhang, J. Huang, J. Li, Y. Yang, J. Yao. Amplitude modulation of anomalously reflected terahertz beams using all-optical active Pancharatnam–Berry coding metasurfaces. Nanoscale, 2019, 11: 5746-5753.

[19] R. Y. Wu, L. Zhang, L. Bao, L. W. Wu, Q. Ma, G. D. Bai, H. T. Wu, T. J. Cui. Digital metasurface with phase code and reflection-transmission amplitude code for flexible full-space electromagnetic manipulations. Adv. Opt. Mater., 2019, 7: 1801429.

[20] Q. Ma, Q. R. Hong, G. D. Bai, H. B. Jing, R. Y. Wu, L. Bao, Q. Cheng, T. J. Cui. Editing arbitrarily linear polarizations using programmable metasurface. Phys. Rev. Appl., 2020, 13: 021003.

[21] Q. Ma, C. B. Shi, G. D. Bai, T. Y. Chen, A. Noor, T. J. Cui. Beam-editing coding metasurfaces based on polarization bit and orbital-angular-momentum-mode bit. Adv. Opt. Mater., 2017, 5: 1700548.

[22] Q. Ma, L. Chen, H. B. Jing, Q. R. Hong, H. Y. Cui, Y. Liu, L. Li, T. J. Cui. Controllable and programmable nonreciprocity based on detachable digital coding metasurface. Adv. Opt. Mater., 2019, 7: 1901285.

[23] G. Ding, K. Chen, X. Luo, J. Zhao, T. Jiang, Y. Feng. Dual-helicity decoupled coding metasurface for independent spin-to-orbital angular momentum conversion. Phys. Rev. Appl., 2019, 11: 044043.

[24] J. Han, L. Li, H. Yi, Y. Shi. 1-bit digital orbital angular momentum vortex beam generator based on a coding reflective metasurface. Opt. Mater. Express, 2018, 8: 3470-3478.

[25] Q. Zheng, Y. Li, Y. Han, M. Feng, Y. Pang, J. Wang, H. Ma, S. Qu, J. Zhang. Efficient orbital angular momentum vortex beam generation by generalized coding metasurface. Appl. Phys. A, 2019, 125: 136.

[26] T. J. Cui, L. Li, S. Liu, Q. Ma, Q. Cheng. Information metamaterial systems. iScience, 2020, 23: 101403.

[27] L. Li, T. J. Cui. Information metamaterials - from effective media to real-time information processing systems. Nanophotonics, 2019, 8: 703-724.

[28] T. J. Cui, S. Liu, L. Zhang. Information metamaterials and metasurfaces. J. Mater. Chem. C, 2017, 5: 3644-3668.

[29] Q. Ma, T. J. Cui. Information metamaterials: bridging the physical world and digital world. PhotoniX, 2020, 1: 1.

[30] L. Zhang, X. Q. Chen, S. Liu, Q. Zhang, J. Zhao, J. Y. Dai, G. D. Bai, X. Wan, Q. Cheng, G. Castaldi, V. Galdi, T. J. Cui. Space-time-coding digital metasurfaces. Nat. Commun., 2018, 9: 4334.

[31] T. J. Cui, S. Liu, G. D. Bai, Q. Ma. Direct transmission of digital message via programmable coding metasurface. Research, 2019, 2019: 2584509.

[32] H. Zhao, Y. Shuang, M. Wei, T. J. Cui, P. D. Hougne, L. Li. Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat. Commun., 2020, 11: 3926.

[33] Y. Shuang, H. Zhao, W. Ji, T. J. Cui, L. Li. Programmable high-order OAM-carrying beams for direct-modulation wireless communications. IEEE J. Emerg. Sel. Top. Circuits Syst., 2020, 10: 29-37.

[34] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, B. Kingsbury. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag., 2012, 29: 82-97.

[35] G. Pironkov, S. U. N. Wood, S. Dupont. Hybrid-task learning for robust automatic speech recognition. Comput. Speech Lang., 2020, 64: 101103.

[36] GravesA.MohamedA. R.HintonG., “Speech recognition with deep recurrent neural networks,” in IEEE International Conference on Acoustics, Speech and Signal Processing (2013), pp. 66456649.

[37] LuK., “Intelligent recognition system for high precision image significant features in large data background,” in Cyber Security Intelligence and Analytics (2020), pp. 10561062.

[38] T. Tong, X. Mu, L. Zhang, Z. Yi, P. Hu. MBVCNN: joint convolutional neural networks method for image recognition. AIP Conf. Proc., 2017, 1839: 020091.

[39] SunH.ZhangQ.WangH.LiA., “Research of images recognition method based on RBF neural network,” in 7th International Conference on System of Systems Engineering (2012), pp. 2426.

[40] ChoK.Van MerrienboerB.GulcehreC.BahdanauD.BougaresF.SchwenkH.BengioY. J. C., “Learning phrase representations using RNN encoder-decoder for statistical machine translation,” arXiv:1406.1078v3 (2014).

[41] EscolanoC.Costa-JussaM. R.FonollosaJ. A. R., “From bilingual to multilingual neural-based machine translation by incremental training,” in 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (2019), pp. 236242.

[42] S. Kwon, B. H. Go, J. H. Lee. A text-based visual context modulation neural model for multimodal machine translation. Pattern Recogn. Lett., 2020, 136: 212-218.

[43] C. He, Y. Wan, Y. Gu, F. L. Lewis. Integral reinforcement learning-based multi-robot minimum time-energy path planning subject to collision avoidance and unknown environmental disturbances. IEEE Control Syst. Lett., 2021, 5: 983-988.

[44] B. Sangiovanni, G. P. Incremona, M. Piastra, A. Ferrara. Self-configuring robot path planning with obstacle avoidance via deep reinforcement learning. IEEE Control Syst. Lett., 2021, 5: 397-402.

[45] J. Yoo, D. Jang, H. J. Kim, K. H. Johansson. Hybrid reinforcement learning control for a micro quadrotor flight. IEEE Control Syst. Lett., 2021, 5: 505-510.

[46] Q. Zhang, X. Wan, S. Liu, J. Y. Yin, L. Zhang, T. J. Cui. Shaping electromagnetic waves using software-automatically-designed metasurfaces. Sci. Rep., 2017, 7: 3588.

[47] L. L. Li, H. X. Ruan, C. Liu, Y. Li, Y. Shuang, A. Alu, C. W. Qiu, T. J. Cui. Machine-learning reprogrammable metasurface imager. Nat. Commun., 2019, 10: 1082.

[48] T. Qiu, X. Shi, J. Wang, Y. Li, S. Qu, Q. Cheng, T. Cui, S. Sui. Deep learning: a rapid and efficient route to automatic metasurface design. Adv. Sci., 2019, 6: 1900128.

[49] Q. Ma, G. D. Bai, H. B. Jing, C. Yang, L. Li, T. J. Cui. Smart metasurface with self-adaptively reprogrammable functions. Light Sci. Appl., 2019, 8: 98.

[50] H. Li, H. Zhao, M. Wei, H. Ruan, Y. Shuang, T. J. Cui, P. del Hougne, L. Li. Intelligent electromagnetic sensing with learnable data acquisition and processing. Patterns, 2020, 1: 100006.

[51] L. Li, Y. Shuang, Q. Ma, H. Li, H. Zhao, M. Wei, C. Liu, C. Hao, C.-W. Qiu, T. J. Cui. Intelligent metasurface imager and recognizer. Light Sci. Appl., 2019, 8: 97.

[52] L. Li, T. J. Cui, W. Ji, S. Liu, J. Ding, X. Wan, Y. B. Li, M. Jiang, C.-W. Qiu, S. Zhang. Electromagnetic reprogrammable coding-metasurface holograms. Nat. Commun., 2017, 8: 197.

[53] J. Wu, Z. Wang, L. Zhang, Q. Cheng, S. Liu, S. Zhang, J. Song, C. T. Jun. Anisotropic metasurface holography in 3D space with high resolution and efficiency. IEEE Trans. Antennas Propag., 2020, 69: 302-316.

[54] LiuC.MaQ.LiL.CuiT. J., “Work in progress: intelligent metasurface holograms,” in 1st ACM International Workshop on Nanoscale Computing, Communication, and Applications (2020), pp. 4548.

[55] Z. Y. Zhou, J. Xia, J. Wu, C. L. Chang, X. Ye, S. G. Li, B. T. Du, H. Zhang, G. D. Tong. Learning-based phase imaging using a low-bit-depth pattern. Photon. Res., 2020, 8: 1624-1633.

[56] LopezR.RegierJ.JordanM. I.YosefN., “Information constraints on auto-encoding variational Bayes,” in 32nd Conference on Neural Information Processing Systems (NeurIPS) (2018), pp. 61146125.

[57] DeshpandeI.ZhangZ.SchwingA., “Generative modeling using the sliced Wasserstein distance,” in IEEE Conference on Computer Vision and Pattern Recognition (2018), pp. 34833491.

[58] ArjovskyM.BottouL., “Towards principled methods for training generative adversarial networks,” in 5th International Conference on Learning Representations (ICLR) (2017), pp. 117.

[59] PanaretosV. M.ZemelyY., “Statistical aspects of Wasserstein distances,” arXiv:1806.05500v3 (2019).

[60] ArjovskyM.ChintalaS.BottouL., “Wasserstein generative adversarial networks,” in 34th International Conference on Machine Learning (ICML) (2017), pp. 214223.

[61] GulrajaniI.AhmedF.ArjovskyM.DumoulinV.CourvilleA., “Improved training of wasserstein GANs,” in 31st Annual Conference on Neural Information Processing Systems (NIPS) (2017), pp. 57685778.

[62] MirzaM., “Conditional generative adversarial nets,” arXiv:1411.1784v1 (2014).

[63] IsolaP.ZhuJ.-Y.ZhouT.EfrosA. A., “Image-to-image translation with conditional adversarial networks,” in 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017), pp. 59675976.

[64] WangT.-C.LiuM.-Y.ZhuJ.-Y.TaoA.KautzJ.CatanzaroB., “High-resolution image synthesis and semantic manipulation with conditional GANs,” in 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 87988807.

[65] HeK.ZhangX.RenS.SunJ., “Identity mappings in deep residual networks,” in 21st ACM Conference on Computer and Communications Security (CCS) (2016), pp. 630645.

[66] IoffeS.SzegedyC., “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in 32nd International Conference on Machine Learning (ICML) (2015), pp. 448456.

[67] UlyanovD.VedaldiA., “Instance normalization: the missing ingredient for fast stylization,” arXiv:1607.08022v3 (2017).

[68] LeCunY.CortesC.BurgesC. J. C., “The MNIST database of handwritten digits,” (2012).

[69] X. Zou, G. Zheng, Q. Yuan, W. Zang, R. Chen, T. Li, L. Li, S. Wang, Z. Wang, S. Zhu. Imaging based on metalenses. PhotoniX, 2020, 1: 2.

[70] A. Ahad, M. Tahir, K. A. Yau. 5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions. IEEE Access, 2019, 7: 100747.

Che Liu, Wen Ming Yu, Qian Ma, Lianlin Li, Tie Jun Cui. Intelligent coding metasurface holograms by physics-assisted unsupervised generative adversarial network[J]. Photonics Research, 2021, 9(4): 0400B159.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!