Junmin Liu
Shenzhen University
88 Papers
115 Citations
Junmin Liu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Polarization (waves) & Multiplexing. The author has an hindex of 9, co-authored 63 publications.
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Papers
Deep learning based atmospheric turbulence compensation for orbital angular momentum beam distortion and communication.
Junmin Liu,Peipei Wang,Xiaoke Zhang,Yanliang He,Xinxing Zhou,Huapeng Ye,Ying Li,Shixiang Xu,Shuqing Chen,Dianyuan Fan +9 more
TL;DR: This work proposes and investigates a deep learning based atmospheric turbulence compensation method for correcting the distorted VB and improving the performance of OAM multiplexing communication.
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All-Optical Signal Processing of Vortex Beams with Diffractive Deep Neural Networks
Zebin Huang,Peipei Wang,Junmin Liu,Wenjie Xiong,Yanliang He,Jiangnan Xiao,Huapeng Ye,Ying Li,Shuqing Chen,Dianyuan Fan +9 more
TL;DR: In this paper, diffractive deep neural networks (DNNs) have been used for all-optical signal processing of VBs by configuring the phase and amplitude distribution of diffractive screens.
100
Switchable dual-wavelength Q -switched fiber laser using multilayer black phosphorus as a saturable absorber
TL;DR: In this paper, a multilayer BP flakes coated on microfiber (BCM) was used as a saturable absorber with a modulation depth of 16% and a saturation intensity of 6.8MW/cm2.
All-Optical Signal Processing in Structured Light Multiplexing with Dielectric Meta-Optics
Yanliang He,Peipei Wang,Chaofeng Wang,Junmin Liu,Huapeng Ye,Xinxing Zhou,Ying Li,Shuqing Chen,Xiaomin Zhang,Dianyuan Fan +9 more
TL;DR: Structured light beams with spatially nonuniform phase or polarization distributions have attracted enormous interest in optical multiplexing communications due to the mutual orthogonality of the SLB.
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Diffractive Deep Neural Network for Optical Orbital Angular Momentum Multiplexing and Demultiplexing
Peipei Wang,Wenjie Xiong,Zebin Huang,Yanliang He,Junmin Liu,Huapeng Ye,Jiangnan Xiao,Ying Li,Dianyuan Fan,Shuqing Chen +9 more
TL;DR: In this article, a diffractive deep neural network (D2NN) was proposed for OAM mode multiplexing and demultiplexing by designing the D2NN model and simulating light propagation through multiple diffractive screens, which can be automatically adjusted to manipulate the wavefront of light beams.
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