检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

arXiv

引用

arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

来源：评论

学校读者我要写书评

暂无评论

Finite-Blocklength Multi-Antenna Covert communication Aided By A UAV Relay

Finite-Blocklength Multi-Antenna Covert Communication Aided ...

引用

2021 IEEE Global communications Conference (GLOBECOM)

作者： Xinying Chen Min Sheng Nan Zhao Wei Xu Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland State Key Laboratory of Integrated Services Networks Xidian University Xi'an China National Mobile Communications Research Lab. Southeast University Nanjing Jiangsu China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781728181059

We propose a UAV-relayed covert communication scheme with finite blocklength to maximize the effective transmission rate from the transmitter to the legitimate receiver against a flying warden. The transmitter adopts the maximum ratio transmission and the relay performs Gaussian signaling transmission to cause uncertainty at the warden. First, the optimal detection thresholds are derived at the warden towards the transmitter and the relay, respectively. Then, the hovering location of the warden is optimized to maximize the summation of relative entropies from the transmitter and the relay, which can greatly threaten the covertness. With this worst covert situation, the blocklength and transmit power at the transmitter and the relay are jointly optimized under the constraint of the error detection probability to maximize the effective transmission rate from the transmitter to the legitimate receiver. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-relayed covert communication scheme.

关键词： Uncertainty Transmitters Conferences Receivers Autonomous aerial vehicles Entropy Global communication

来源：评论

学校读者我要写书评

暂无评论

Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images

arXiv

引用

arXiv 2021年

作者： Li, Jiaxuan Jin, Peiyao Zhu, Jianfeng Zou, Haidong Xu, Xun Tang, Min Zhou, Minwen Gan, Yu He, Jiangnan Ling, Yuye 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 General Hospital Shanghai Jiao Tong University Shanghai200080 China Department of Electrical and Computer Engineering The University of Alabama AL35487 United States State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we developed a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conducted experiments on human peripapillary retinal OCT images. The Dice score of the proposed segmentation network is 0.820 ± 0.001 and the pixel accuracy is 0.830 ± 0.002, both of which outperform those from other state-of-the-art techniques. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA communications

arXiv

引用

arXiv 2021年

作者： Shi, Zhaoyuan Yang, Helin Xie, Xianzhong Lu, Huabing Cai, Jun Ding, Zhiguo School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada Department of Electrical Engineering Princeton University PrincetonNJ08544 United States School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the nonconvexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Soliton bursts and deterministic dissipative Kerr soliton generation in auxiliary-assisted microcavities

引用

Light(Science & Applications) 2019年第1期8卷 739-748页

作者： Heng Zhou Yong Geng Wenwen Cui Shu-Wei Huang Qiang Zhou Kun Qiu Chee Wei Wong Key Lab of Optical Fiber Sensing and Communication Networks University of Electronic Science and Technology of China611731 ChengduChina Fang Lu Mesoscopic Optics and Quantum Electronics Laboratory University of CaliforniaLos AngelesCA 90095USA Department of Electrical Computerand Energy EngineeringUniversity of ColoradoBoulderCO 80309USA Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China611731 ChengduChina

Dissipative Kerr solitons in resonant frequency combs offer a promising route for ultrafast mode-locking,precision spectroscopy and time-frequency *** dynamics for the dissipative soliton generation,however,are intrinsically intertwined with thermal nonlinearities,limiting the soliton generation parameter map and statistical success probabilities of the solitary ***,via use of an auxiliary laser heating approach to suppress thermal dragging dynamics in dissipative soliton comb formation,we demonstrate stable Kerr soliton singlet formation and soliton ***,we access a new soliton existence range with an inverse-sloped Kerr soliton evolution—diminishing soliton energy with increasing pump ***,we achieve deterministic transitions from Turinglike comb patterns directly into the dissipative Kerr soliton singlet pulse bypassing the chaotic *** is achieved by avoiding subcomb overlaps at lower pump power,with near-identical singlet soliton comb generation over twenty ***,with the red-detuned pump entrance route enabled,we uncover unique spontaneous soliton bursts in the direct formation of low-noise optical frequency combs from continuum background *** burst dynamics are due to the rapid entry and mutual attraction of the pump laser into the cavity mode,aided by the auxiliary laser and matching well with our numerical *** by the auxiliary-assisted frequency comb dynamics,we demonstrate an application of automatic soliton comb recovery and long-term stabilization against strong external *** findings hold potential to expand the parameter space for ultrafast nonlinear dynamics and precision optical frequency comb stabilization.

关键词： soliton auxiliary dissipative

来源：评论

学校读者我要写书评

暂无评论

Beyond Fourier transform: Break the axial resolution limit in optical coherence tomography

arXiv

引用

arXiv 2020年

作者： Ling, Yuye Wang, Mengyuan Gan, Yu Yao, Xinwen Schmetterer, Leopold Zhou, Chuanqing Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering University of Alabama AL United States Singapore Institute for Health Technologies Nanyang Technological University Singapore Singapore Eye Research Institute Singapore National Eye Centre Singapore School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Institute of Biomedical Engineering Shenzhen Bay Lab Shenzhen China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

Optical coherence tomography (OCT) is a volumetric imaging modality that empowers clinicians and scientists to noninvasively visualize and examine the microscopic cross-sectional architectures of biological samples. By taking advantage of the coherence gating, OCT could achieve micrometer-level axial sectioning with high sensitivity. In the past decade, substantial efforts have been made to push the axial resolution of Fourier-domain OCT (FD-OCT) further into the sub-micron regime by physically extending the system's spectral bandwidth. Here, we offer a new perspective on this problem with the help of a new theoretical framework. We identified a key limitation on the reconstruction's axial resolution, which is induced by the Fourier transform-based reconstruction algorithm. To break this limit, we presented a novel optimization-based algorithm to recover the super-resolution image from ordinary FD-OCT measurements with proper physical assumptions. To prove the concept, we retrieved images at a resolution higher than the theoretical prediction in both phantoms and biological samples. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

A Mixed-Signal Framework for Modelling Fourier-Domain Optical Coherence Tomography

A Mixed-Signal Framework for Modelling Fourier-Domain Optica...

引用

Asia communications and Photonics Conference and Exhibition (ACP)

作者： Yuye Ling Mengyuan Wang Yu Gan Xinwen Yao Leopold Schmetterer Chuanqing Zhou Yikai Su John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China University of Alabama AL USA Singapore Eye Research Institute Singapore National Eye Centre Singapore School of Chemical and Biomedical Engineering Nanyang Technological University Singapore School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

We investigate the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its image formation. It turns out FD-OCT is a shift-variant system, if conventional IDFT is used to recons... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Optical coherence tomography Two dimensional displays Asia Optical fiber communication Image reconstruction Photonics

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning for fresh data collection in UAV-assisted IoT networks

arXiv

引用

arXiv 2020年

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Bai, Bo State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an Shaanxi710071 China Science and Technology on Communication Network Laboratory Shijiazhuang Hebei050081 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 865 Changning Road Shanghai200050 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China 2012 Labs Huawei Technologies Co. Ltd. Hong Kong Hong Kong

Due to the flexibility and low operational cost, dispatching unmanned aerial vehicles (UAVs) to collect information from distributed sensors is expected to be a promising solution in Internet of Things (IoT), especially for time-critical applications. How to maintain the information freshness is a challenging issue. In this paper, we investigate the fresh data collection problem in UAV-assisted IoT networks. Particularly, the UAV flies towards the sensors to collect status update packets within a given duration while maintaining a non-negative residual energy. We formulate a Markov Decision Process (MDP) to find the optimal flight trajectory of the UAV and transmission scheduling of the sensors that minimizes the weighted sum of the age of information (AoI). A UAV-assisted data collection algorithm based on deep reinforcement learning (DRL) is further proposed to overcome the curse of dimensionality. Extensive simulation results demonstrate that the proposed DRL-based algorithm can significantly reduce the weighted sum of the AoI compared to other baseline algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Time-varying massive MIMO channel estimation: Capturing, reconstruction and restoration

arXiv

引用

arXiv 2019年

作者： Li, Muye Zhang, Shun Zhao, Nan Zhang, Weile Wang, Xianbin State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Information and Communication Engineering Dalian University of Technology Dalian116024 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Engineering Western University LondonON Canada

On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： MIMO systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：