Dan Lu
13 Papers
1 Citations
Dan Lu is an academic researcher. The author has contributed to research in topics: Photonics & Computer science. The author has an hindex of 1, co-authored 6 publications.
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Papers
Hybrid bound states in the continuum in terahertz metasurfaces
Junxing Fan,Zhanqiang Xue,Hongyan Xing,Dan Lu,Guizhen Xu,Jianqiang Gu,Jiaguang Han,Longqing Cong +7 more
TL;DR: In this paper , the authors proposed a scheme to further reduce scattering losses and improve the robustness of symmetry-protected BICs by decreasing the radiation density with a hybrid BIC lattice.
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Recent Advances and Perspective of Photonic Bound States in the Continuum
Guizhen Xu,Hongyan Xing,Zhanqiang Xue,Dan Lu,Jinying Fan,Junxing Fan,Perry Ping Shum,Longqing Cong +7 more
TL;DR: In this article , the authors provide an overview of recent progress in photonic bound states in the continuum (BICs) based on metamaterials and photonic crystals, focusing on both the underlying physics and their practical applications.
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Dispersion‐Compensated Terahertz Ultra‐Broadband Quarter and Half Wave Plates in a Dielectric‐Metal Hybrid Metadevice
Shi-Tong Xu,Hui‐Fang Zhang,Longqing Cong,Zhanqiang Xue,Dan Lu,Ying Hua Wang,Xiaofei Hu,Lanju Liang,Yun-Yun Ji,Fei Fan,Shengjiang Chang +10 more
TL;DR: Dispersion-compensated terahertz ultra-broadband quarter and half wave plates in a dielectric-metal hybrid metadevice achieve high-efficiency and ultra-broadband THz polarization manipulation.
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Terahertz topological photonic crystals with dual edge states for efficient routing.
TL;DR: In this paper , a topological photonic crystal with robust pseudo-spin and valley edge states was proposed to enable a multipath routing solution for terahertz information processing and broadcasting.
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A Two-stage Raman Imaging Denoising Algorithm Based on Deep Learning
Quan Tang,Jiaqi Hu,Jinna Chen,Chenlong Xue,Hong Dang,Dan Lu,Huanhuan Liu,Qizhen Sun,Qiaozhou Xiong,Longqing Cong,Perry Ping Shum +10 more
- 05 Nov 2022
TL;DR: In this article , a two-stage denoising algorithm based on deep learning was proposed for low signal-to-noise ratio (SNR) of the image reconstructed from Raman spectra.
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