Zobrazeno 1 - 10
of 886
pro vyhledávání: '"Er Ping Li"'
Autor:
Zhixiang Fan, Chao Qian, Yuetian Jia, Yiming Feng, Haoliang Qian, Er-Ping Li, Romain Fleury, Hongsheng Chen
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional en
Externí odkaz:
https://doaj.org/article/0648b40ed9ef4b95a4ff0e316a48fafa
Autor:
Ruifeng Li, Jinyan Ma, Da Li, Yunlong Wu, Chao Qian, Ling Zhang, Hongsheng Chen, Tsampikos Kottos, Er-Ping Li
Publikováno v:
Research, Vol 7 (2024)
Pushing the information states’ acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered mod
Externí odkaz:
https://doaj.org/article/6cc8cc5316034a1a9f394ef8fd933421
Publikováno v:
Light: Science & Applications, Vol 12, Iss 1, Pp 1-11 (2023)
Abstract Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface
Externí odkaz:
https://doaj.org/article/4d73503bfeca4bdbbb2fdb264e486f13
Autor:
Er-Ping Li, Hong-Son Chu
Plasmonic nanostructures provide new ways of manipulating the flow of light with nanostructures and nanoparticles exhibiting optical properties never before seen in the macro-world. Covering plasmonic technology from fundamental theory to real world
Autor:
Zhedong Wang, Chao Qian, Tong Cai, Longwei Tian, Zhixiang Fan, Jian Liu, Yichen Shen, Li Jing, Jianming Jin, Er-Ping Li, Bin Zheng, Hongsheng Chen
Publikováno v:
Advanced Intelligent Systems, Vol 3, Iss 9, Pp n/a-n/a (2021)
Obtaining a full view and complete information of the surrounding dynamics is of great significance for a plethora of applications in sensing, imaging, navigation, and orientation. However, conventional spatial spectrum methods heavily rely on a prio
Externí odkaz:
https://doaj.org/article/6067a77b4db7438db11e9cdb7571347e
Publikováno v:
IEEE Access, Vol 8, Pp 6583-6590 (2020)
This paper presents the analysis and design of a miniaturized polarization insensitive metamaterial absorber (MMA) for suppression of the electromagnetic interference (EMI) at microwave frequency range. The proposed MMA consists of a periodic array o
Externí odkaz:
https://doaj.org/article/826087839556403ab0de584a336988b7
Diffusion Barrier Prediction of Graphene and Boron Nitride for Copper Interconnects by Deep Learning
Publikováno v:
IEEE Access, Vol 8, Pp 210542-210549 (2020)
The continuous scaling-down size of interconnects should be accompanied with ultra-thin diffusion barrier layers, which is used to suppress Cu diffusion into the dielectrics. Unfortunately, conventional barrier layers with thicknesses less than 4 nm
Externí odkaz:
https://doaj.org/article/e63f1032b82f45799c22190f07e420e1
Publikováno v:
IEEE Access, Vol 8, Pp 74339-74348 (2020)
A methodology based on the joint usage of support vector regression and active subspace is introduced in this paper for accelerated sensitivity analysis of high-speed links through parameter space dimensionality reduction. The proposed methodology us
Externí odkaz:
https://doaj.org/article/c6572ae6278041419f5d41e64f6fdc60
Publikováno v:
IEEE Transactions on Circuits and Systems I: Regular Papers. 70:2271-2282
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52f756c615538ab1825755719e6ef247
https://doi.org/10.1109/tcsi.2023.3250699
https://doi.org/10.1109/tcsi.2023.3250699
Publikováno v:
IEEE Transactions on Antennas and Propagation. 71:4394-4405
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c916642a54275e5004dff12b57b7e40e
https://doi.org/10.1109/tap.2023.3248448
https://doi.org/10.1109/tap.2023.3248448