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optical Fiber {1. (OFC) Conference

作者： Haiyun Xin Xiaoling Zhang Deming Kong Shi Jia Weisheng Hu Hao Hu DTU Fotonik Technical University of Denmark Kgs. Lyngby Denmark State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China Key Lab. of Optical Fiber Sensing and Communications University of Electronic Science and Technology of China Chengdu China

We propose and demonstrate, for the first time, carrier-recovery-free KK detection for a PDM-bipolar-PAM signal in simplified coherent TDM-PON. 32 dB power budget is achieved for the 1.0-Gbps signal after 25km downstr... 详细信息

ISBN: (纸本)9781943580866

关键词： Receivers optical distortion Coherence Distortion optical fiber {1.

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Distortion-aware 2D soft decision for VCSEL-MMF optical PAM interconnection

Distortion-aware 2D soft decision for VCSEL-MMF optical PAM ...

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optical Fiber {1. Conference, OFC 2020

作者： Sun, Lin Du, Jiangbing Zhang, Wenjia Chi, Nan Lu, Chao He, Zuyuan State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China Department of Electronic and Information Engineering Photonics Research Centre Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Department of Communication Science and Engineering State Key Lab of ASIC and System Fudan University Shanghai200433 China

A distortion-aware 2D soft decision method of PAM signals have been proposed for VCSEL-MMF interconnection system. Improvements and application potential have been experimentally investigated on a 1.2-Gbps optical PAM... 详细信息

ISBN: (数字)9781943580712

ISBN: (纸本)9781943580712

关键词： optical fiber {1.

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Deterministic bulk-boundary correspondences for skin and edge modes in a general two-band non-Hermitian system

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Physical Review Research 2022年第4期4卷 043222-043222页

作者： Chong Hou Lingfang Li Shihua Chen Yi Liu Luqi Yuan Yiqi Zhang Zhenhua Ni School of Physics and Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing 211189 China Purple Mountain Laboratories Nanjing 211111 China Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai 200093 China CAS Center for Excellence in Ultra-Intense Laser Science Shanghai 201800 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory for Physical Electronics and Devices of the Ministry of Education & Shaanxi Key Lab of Information Photonic Technique School of Electronic Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 China

In isolated Hermitian systems, the bulk-boundary correspondence is generally thought of as a compelling principle guiding the occurrence of robust edge {1.s through the topological invariants of the bulk. However, in open non-Hermitian systems that support non-Hermitian skin effects, the universality of the bulk-boundary correspondence has so far remained disputable in spite of being subjected to intensive studies. Here we provide analytical and numerical evidence of bulk-boundary correspondences for both skin and edge modes in 1. two-band non-Hermitian systems within the framework of a prototypical non-Hermitian Su-Schrieffer-Heeger model. Two different topological winding numbers, one on a Brillouin zone and the other on a generalized Brillouin zone, are defined to fully characterize the topological phases of matter and to understand such deterministic correspondences. In addition to the exact solutions for bulk {1.s, we also obtain explicit solution forms for edge modes along with analytical conditions for their existence and localization, all exhibiting remarkable agreement with numerical results. Our results solve the elusive non-Hermitian topology and may facilitate experimental investigations of bulk-boundary correspondence in a wide range of non-Hermitian systems.

关键词： Edge states Skin effect Topological phases of matter Non-Hermitean systems Topological materials

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Bidirectional dark soliton mode-locked fiber laser based on thulium-doped fiber saturable absorber

Bidirectional dark soliton mode-locked fiber laser based on ...

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Laser Science and Technology, LST_201.

作者： Zhang, Wenyan Zhan, Li Xian, Tianhao Gao, Lirun State Key Lab of Advanced Optical Communication Systems and Networks Department of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China

A bidirectional dark-soliton fiber laser is demonstrated for the first time. The experimental results of the output counter-propagating pulses and its potential applications for rotation sensing are discussed. &#1.9; ... 详细信息

ISBN: (纸本)9781943580729

关键词： Fiber lasers

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Graphene based all-optical switch and tunable optical filter by using the thermo-optic effect

Graphene based all-optical switch and tunable optical filter...

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7th advanced Electromagnetics Symposium, AES 201.

作者： Qiu, Ciyuan Guo, Tao Wu, Kan State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University China

In this paper, we present our recent works on graphene based integrated devices by using the thermo-optic effect. We demonstrated an all-optical switch with a response time constant of 253.0 ns and a switching energy of ~50 nJ. Furthermore, we implemented a nanobeam based tunable optical filter with a tuning efficiency ~1.5 nm/mW by using a graphene micro-heater. © 201. advanced Electromagnetics Symposium. All rights reserved.

关键词： Graphene

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Topologically Protected Entanglement Emitters

Topologically Protected Entanglement Emitters

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Dai, Tianxiang Ao, Yutian Bao, Jueming Mao, Jun Chi, Yulin Fu, Zhaorong You, Yilong Chen, Xiaojiong Zhai, Chonghao Tang, Bo Yang, Yan Li, Zhihua Yuan, Luqi Gao, Fei Lin, Xiao Thompson, Mark G. O'Brien, Jeremy L. Li, Yan Hu, Xiaoyong Gong, Qihuang Wang, Jianwei State Key Laboratory for Mesoscopic Physics School of Physics Peking University Beijing100871 China Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Quantum Engineering Technology Labs H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Physics The University of Western Australia Perth6009 Australia Frontiers Science Center for Nano-Optoelectronics Collaborative Innovation Center of Quantum Matter Peking University Beijing100871 China Collaborative Innovation Center of Extreme Optics Shanxi University Shanxi Taiyuan030006 China Peking University Yangtze Delta Institute of Optoelectronics Jiangsu Nantong226010 China

ISBN: (纸本)9781557528209

We report topologically-protected entanglement emitters, that emit topological Einstein-Podolsky-Rosen state and multiphoton entangled state from a plug-and-play silicon-photonic chip in ambient conditions. The device emulating a photonic anomalous Floquet insulator allows the generation of four-photon topological entangled states at nontrivial edge modes. © Optica Publishing Group 2022, © 2022 The Author(s)

关键词： Silicon photonics

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Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images

arXiv

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arXiv 2022年

作者： Tang, Yaoqi Li, Yufan Liu, Hongshan Li, Jiaxuan Jin, Peiyao Gan, Yu Ling, Yuye Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering The University of Alabama AL35487 United States Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University Shanghai200080 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder {1. have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented. © 2022, CC BY.

关键词： Image fusion

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A GCN-assisted deep learning method for peripapillary retinal layer segmentation in OCT images 25

A GCN-assisted deep learning method for peripapillary retina...

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optical Coherence Tomography and Coherence Domain optical Methods in Biomedicine XXV 2021

作者： Li, Jiaxuan Ling, Yuye He, Jiangnan Jin, Peiyao Zhu, Jianfeng Zou, Haidong Xu, Xun Shao, Shuo Gan, Yu Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China Department of Preventative Ophthalmology Shanghai Eye Disease Prevention and Treatment Center Shanghai Eye Hospital Shanghai200040 China Department of Ophthalmology Shanghai Jiao Tong University Shanghai General Hospital Shanghai200080 China Department of Electrical and Computer Engineering The University of Alabama AL35487 United States Department of Electronic Engineering State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781510640962

ISBN: (纸本)9781510640955

Accurate retinal layer segmentation, especially the peripapillary retinal nerve fiber layer (RNFL) is critical for the diagnosis of ophthalmic diseases. However, due to the complex morphologies of the peripapillary region, most of the existing methods focus on segmenting the macular region and could not be directly applied to the peripapillary retinal optical coherence tomography (OCT) images. In this paper, we propose a novel graph convolutional network (GCN)-assisted segmentation framework based on a U-shape neural network for peripapillary retinal layer segmentation in OCT images. We argue that the strictly stratified structure of retina layers in addition to the centered optic disc is an ideal objective for GCN. Specifically, a graph reasoning block is inserted between the encoder and decoder of the U-shape neural network to conduct spatial reasoning. In this way, the peripapillary retina in OCT images is segmented into nine layers including RNFL. The proposed method was trained and tested on our collected dataset of peripapillary retinal OCT images. Experimental results showed that our segmentation method outperformed other {1.-of-the-art methods. In particular, compared with ReLayNet, the average and RNFL Dice coefficients are improved by 1.2% and 2.6%, respectively. © 2021.SPIE.

关键词： optical tomography

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On-Chip Mode-Division Multiplexing with Modal Crosstalk Mitigation

On-Chip Mode-Division Multiplexing with Modal Crosstalk Miti...

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optical Fiber {1. (OFC) Conference

作者： Yetian Huang Ruihuan Zhang Haoshuo Chen Hanzi Huang Qingming Zhu Yu He Yuan Yuan Yingxiong Song Nicolas K. Fontaine Roland Ryf Yong Zhang Yikai Su Min Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China Nokia Bell Labs Holmdel NJ USA

We experimentally demonstrate modal crosstalk mitigation over an on-chip mode-division multiplexing link employing low-coherence matched detection. 20-Gbaud QPSK and 8-PSK mode-multiplexed signals are successfully tra... 详细信息

ISBN: (数字)9781943580712

ISBN: (纸本)9781728167626

关键词： Modal crosstalk Mode division multiplexing optical directional couplers Phase recovery Signal processing Signal recovery

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Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution

arXiv

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arXiv 2021年

作者： Dong, Zhenxing Cao, Hong Shen, Wang Gan, Yu Ling, Yuye Zhai, Guangtao Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Department of Electrical and Computer Engineering University of Alabama TuscaloosaAL35401 United States State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University China

Current learning-based single image super-resolution (SISR) algorithms underperform on real data due to the deviation in the assumed degradation process from that in the real-world scenario. Conventional degradation processes consider applying blur, noise, and downsampling (typically bicubic downsampling) on high-resolution (HR) images to synthesize low-resolution (LR) counterparts. However, few works on degradation modelling have taken the physical aspects of the optical imaging system into consideration. In this paper, we analyze the imaging system optically and exploit the characteristics of the real-world LR-HR pairs in the spatial frequency domain. We formulate a real-world physics-inspired degradation model by considering both optics and sensor degradation;The physical degradation of an imaging system is modelled as a low-pass filter, whose cut-off frequency is dictated by the object distance, the focal length of the lens, and the pixel size of the image sensor. In particular, we propose to use a convolutional neural network (CNN) to learn the cutoff frequency of real-world degradation process. The learned network is then applied to synthesize LR images from unpaired HR images. The synthetic HR-LR image pairs are later used to train an SISR network. We evaluate the effectiveness and generalization capability of the proposed degradation model on real-world images captured by different imaging systems. Experimental results showcase that the SISR network trained by using our synthetic data performs favorably against the network using the traditional degradation model. Moreover, our results are comparable to that obtained by the same network trained by using real-world LR-HR pairs, which are challenging to obtain in real scenes. © 2021. CC BY.

关键词： Cutoff frequency

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