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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 networks 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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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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Cloud Det ection Using Super Pixel Classification and Semantic Segmentation

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Journal of computer science & Technology 2019年第3期34卷 622-633页

作者： Han Liu Hang Du Dan Zeng Qi Tian Key Laboratorij of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China Shanghai Institute of Advanced Communication and Data Science Shanghai UniversityShanghai 200444China Department of Computer Science The University of Texas at San AntonioSan AntonioU.S.A.

Cloud detection plays a very significant role in remote sensing image *** paper introduces a cloud detection method based on super pixel level classification and semantic ***,remote sensing images are segmented into super *** super pixels compose a super pixel level remote sensing image *** cloud detection is essentially a binary classification task,our database is labeled into four categories to improve the generalization ability:thick cloud,cirrus cloud,building,and other ***,the super pixel level database is used to train our cloud detection models based on convolution neural network(CNN)and deep *** fusion CNN is proposed considering super pixel level images contain less semantic information than normal *** full advantage of low-level features like color and texture information,it is more applicable for super pixel level ***,a distance metric is proposed to refine ambiguous super ***,an end-to-end cloud detection model based on semantic segmentation is *** model has no restrictions on the input size,and takes less *** results show that compared with other cloud detection *** proj)osed method acliieves better performance.

关键词： cloud detection convolution neural network deep forest semantic segmentation

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Enhancing Reliability in Federated mmWave networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition

arXiv

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arXiv 2023年

作者： Al-Quraan, Mohammad Zoha, Ahmed Centeno, Anthony Salameh, Haythem Bany Muhaidat, Sami Imran, Muhammad Ali Mohjazi, Lina James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Networks Communication Engineering Department Al Ain University Al Ain64141 United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid21163 Jordan KU Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track objects movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Dual-polarized wearable antenna/rectenna for full-duplex and MIMO simultaneous wireless information and power transfer (SWIPT)

TechRxiv

引用

TechRxiv 2021年

作者： Wagih, Mahmoud Hilton, Geoffrey S. Weddell, Alex S. Beeby, Steve The School of Electronics and Computer science University of Southampton SouthamptonSO17 1BJ United Kingdom The Communication Systems & Networks Group University of Bristol BristolBS8 1UB United Kingdom

Owing to the mobility of a wearable antenna and the unpredictable body-centric communications environment, dual-polarization antennas are essential for both communications and energy harvesting. This paper presents a dual-polarized four-port textile antenna/rectenna for wearable simultaneous wireless information and power transfer (SWIPT) applications. The proposed antenna utilizes dual ports for both off-body communication and energy harvesting from horizontal and vertical polarizations. The antenna maintains a 100 MHz bandwidth with an S11 under −10 dB around 2.4 GHz in the presence and absence of the human body, and at least 10 dB small-signal and large-signal isolation between all ports. The antenna maintains a 70–88% measured total efficiency and 8.4–9.6 dBi gain for various on-phantom positions across both communication ports. The measured mutual coupling is under −10 dB between co-polarized rectenna/antenna ports, and under −16 dB between orthogonally-polarized ports. A high RF to DC peak power conversion efficiency of over 70% (±5 %) is achieved with a broadside harvesting pattern. Based on the proposed antenna’s performance, SWIPT microstrip antennas can be adopted for both full-duplex and MIMO applications, significantly reducing the complexity of future battery-free networks for both wearable and non-wearable applications. © 2021, CC BY.

关键词： Energy harvesting

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Millimeter wave power transmission for compact and large-area wearable IoT devices based on a higher-order mode wearable antenna

TechRxiv

引用

TechRxiv 2021年

作者： Wagih, Mahmoud Hilton, Geoffrey S. Weddell, Alex S. Beeby, Steve The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom The Communication Systems & Networks Group University of Bristol BristolBS8 1UB United Kingdom

Owing to the shorter wavelength in the millimeter-wave (mmWave) spectrum, miniaturized antennas can receive power with a higher efficiency than UHF bands, promising sustainable mmWave-powered Internet of Things (IoT) devices. Nevertheless, the performance of a mmWave power receiver has not been compared, numerically or experimentally, to its sub-6 GHz counterpart. In this paper, the performance of mmWave-powered receivers is evaluated based on a novel wearable textile-based higher-order mode microstrip antenna, showing the benefits of wireless power transmission (WPT). Firstly, a broadband antenna is proposed maintaining a stable wearable measured bandwidth from 24.9 to 31.1 GHz, over three-fold improvement compared to a conventional patch. The proposed antenna has a measured 8.2 dBi co-polarized gain with the highest thickness-normalized efficiency of a wearable antenna. When evaluated for compact power receivers, the measured path gain shows that WPT at 26 GHz outperforms 2.4 GHz by 11 dB. A rectenna array based on the proposed antenna is then evaluated analytically showing the potential for up to 6.3× higher power reception compared to a UHF patch, based on the proposed antenna’s gain and an empirical path-loss model. Both use cases demonstrate that mmWave-powered rectennas are suitable for area-constrained and large-area wearable IoT applications. © 2021, CC BY.

关键词： Energy harvesting

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Numerical Analyzation of Distributed Feedback Lasers with Different Phase Shift Structures

Numerical Analyzation of Distributed Feedback Lasers with Di...

引用

Asia communications and Photonics Conference and Exhibition (ACP)

作者： Bo Wang Peiqi Zhou Xingjun Wang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engi-neering and Computer Science Peking University Beijing China Institute of Microelectronics School of Electronics Engineering and Computer Science Peking University Beijing China Frontier Science Center for Nano-optoelectronics Peking University Beijing China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We optimize phase shift structures of distributed feedback (DFB) lasers. For the multi-distributed phase shift laser, the output slope can reach up to 51.90% when the optimal cavity length is 2.2 mm.

ISBN: (纸本)9781957171005

We optimize phase shift structures of distributed feedback (DFB) lasers. For the multi-distributed phase shift laser, the output slope can reach up to 51.90% when the optimal cavity length is 2.2 mm.

关键词： Laser theory Asia Distributed feedback devices Waveguide lasers Laser feedback Silicon nitride Erbium

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Performance of Secure UAV Transmission: Delay-Secrecy Analysis with Channel Uncertainty

Performance of Secure UAV Transmission: Delay-Secrecy Analys...

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IEEE Conference on Wireless communications and Networking

作者： Muyu Mei Qinghai Yang Mingwu Yao Meng Qin Kyung Sup Kwak School of Telecom. Engineering Xidian University Shaanxi China Inha University Korea Science and Technology on Communication Networks Laboratory Shijiazhuang China Shenzhen Pengcheng Laboratory Shenzhen Guangdong China School of Electronics and Computer Engineering Peking University

ISBN: (纸本)9781728195056;9781728195063

Unmanned aerial vehicles (UAV) wireless communications have attracted great interests in 5G networks due to its high mobility, on-demand deployment and low cost. However, it arises new serious concerns about the malicious eavesdropping attacks against UAV communications. In this paper, we study the UAV transmission with a wiretap Rayleigh fading channel, over which UAVs transmit data to a target receiver in an unsafe environment with multiple eavesdroppers. A secure transmission scheme is proposed for satisfying various performance requirements including secrecy and transmission latency, considering the unavailability of wiretappers' instantaneous channel state information (CSI). In particular, secrecy performance is measured by the derivation of secure transmission probability (STP) by physical layer security (PLS) technique. A novel stochastic-network-calculus (SNC) approach is proposed to analyze the service capability of the wiretap channel and as well calculate the latency bounds, whilst obtaining the internal relationship between secrecy and latency. Simulation results verify the theoretical performance bounds, which provide a guidance for designing secure transmission strategies with various performance requirements.

关键词： Fading channels Wireless communication Uncertainty Monte Carlo methods Simulation Receivers Physical layer security

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A portable rotational sensor based on the dual-polarization fiber optic gyroscope

A portable rotational sensor based on the dual-polarization ...

引用

Asia communications and Photonics Conference and Exhibition (ACP)

作者： Yuwen Cao Lanxin Zhu Tong Zhou Chunxia Yang Yanjun Chen Zhengbin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China Youfu Technology Co. Ltd Hangzhou China

A portable rotational sensor based on a modified dual-polarization fiber optic gyroscope is demonstrated, in which the excess relative intensity noise is effectively com-pensated achieving an enhanced sensitivity of 7... 详细信息

ISBN: (纸本)9781957171005

关键词： Optical fiber sensors Sensitivity Seismology Prototypes Optical fiber communication Thermal noise Gyroscopes

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Dynamic Scheduling for Delay-Critical Packets in a Networked Control System Using WirelessHART

Dynamic Scheduling for Delay-Critical Packets in a Networked...

引用

IEEE International Conference on communications (ICC)

作者： Samuele Zoppi Jaya Prakash Champati James Gross Wolfgang Kellerer Chair of Communication Networks Technical University of Munich Germany School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In future industrial scenarios, Wireless Sensor networks (WSN) are envisioned to support the traffic of Networked Control Systems (NCS). WirelessHART is a prevalent WSN protocol that uses the Time Slotted Channel Hopping (TSCH) medium access to cope with the delay and reliability requirements of NCS in the harsh industrial environment. In TSCH, time slots and frequencies can be scheduled by a network coordinator to provide Quality of Service (QoS). In contrast to previous works that consider the end-to-end delay requirement of a flow of packets, we focus on a finite sequence of time-critical packets. These packets may belong to a time-critical message whose latency could significantly impact the NCS. Given an end-to-end delay deadline, our objective is to minimize the Delay Violation Probability (DVP) for a finite sequence of packets by dynamically scheduling the time slots in each frame. This is a challenging task as DVP depends on the instantaneous state of the network and requires its transient analysis. In this work, we model the wireless NCS as a two-queue lossy wireless network and propose the first transient analysis of DVP for a finite sequence of time-critical packets. Noting that DVP cannot be directly used for dynamic resource allocation, we propose a heuristic algorithm by relating DVP with the network's throughput. The proposed heuristic maximizes the expected throughput, is computed by solving a finite-horizon Markov Decision Process (MDP), and can be implemented at the network coordinator. Using simulation we demonstrate that the MDP-based heuristic achieves lower DVP compared to the classical MaxWeight and Weighted-Fair Queuing.

关键词： Delays Wireless sensor networks Reliability Dynamic scheduling Time factors Time division multiple access Quality of service

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