Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Li, Shuai"'
Autor:
Zhang, Yue, Li, Shuai, Xu, Yingchao, Tian, Rui, Zhang, Miao, Li, Hongrong, Gao, Hong, Zubairy, M. Suhail, Li, Fuli, Liu, Bo
The non-Bloch topology leads to the emergence of various counter-intuitive phenomena in non-Hermitian systems under the open boundary condition (OBC), which can not find a counterpart in Hermitian systems. However, in the non-Hermitian system without
Externí odkaz:
http://arxiv.org/abs/2407.18485
We investigate the localization-delocalization transition (LDT) in an electromagnetically induced photonic lattice. A four-level tripod-type scheme in atomic ensembles is proposed to generate an effective photonic moir\'{e} lattice through the electr
Externí odkaz:
http://arxiv.org/abs/2308.09287
Autor:
Goy, Alexandre, Rughoobur, Girish, Li, Shuai, Arthur, Kwabena, Akinwande, Akintunde I., Barbastathis, George
We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to $\pm $10$^\circ$. Whereas previous approaches to phase tomography generally require two steps, first to ret
Externí odkaz:
http://arxiv.org/abs/1812.07380
Autor:
Li, Shuai, Barbastathis, George
The Phase Extraction Neural Network (PhENN) is a computational architecture, based on deep machine learning, for lens-less quantitative phase retrieval from raw intensity data. PhENN is a deep convolutional neural network trained through examples con
Externí odkaz:
http://arxiv.org/abs/1808.10753
Publikováno v:
Phys. Rev. Lett. 121, 243902 (2018)
Imaging systems' performance at low light intensity is affected by shot noise, which becomes increasingly strong as the power of the light source decreases. In this paper we experimentally demonstrate the use of deep neural networks to recover object
Externí odkaz:
http://arxiv.org/abs/1806.10029
Autor:
Wang, Pengfei, Li, Yanyan, Li, Wenkai, Su, Hongpeng, Shao, Beijie, Li, Shuai, Cheng, Wang, Wang, Ding, Zhao, Ruirui, Peng, Yujie, Leng, Yuxin, Li, Ruxin, Xu, Zhizhan
A carrier envelope phase stable near-single cycle mid-infrared laser based on optical parametric chirped pulse amplification and hollow-core-fiber compression is demonstrated. 4 {\mu}m laser pulses with 11.8 mJ energy are delivered from a KTA based O
Externí odkaz:
http://arxiv.org/abs/1712.07327
Computational imaging through scatter generally is accomplished by first characterizing the scattering medium so that its forward operator is obtained; and then imposing additional priors in the form of regularizers on the reconstruction functional s
Externí odkaz:
http://arxiv.org/abs/1711.06810
Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks. Here, we demonstrate for the first time, to our knowledge, that deep neural networks (DNNs) can be trained to solve inverse problems
Externí odkaz:
http://arxiv.org/abs/1702.08516
The Simplified Modal Method (SMM) provides a quick and intuitive way to analyze the performance of gratings of rectangular shapes. For non-rectangular shapes, a version of SMM has been developed, but it applies only to the Littrow-mounting incidence
Externí odkaz:
http://arxiv.org/abs/1606.03061