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

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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

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

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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

Multi-antenna covert communications with random access protocol

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arXiv 2019年

作者： Zhang, Weile Zhao, Nan Zhang, Shun Richard Yu, F. School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China School of Information and Communication Engineering Dalian University of Technology Dalian China State Key Lab of Integrated Services Networks Xidian University Xi'an Shaanxi710071 China Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

In this paper, we consider the issue of covert communications with random access protocol. We consider that the legitimate user Bob has no priori knowledge about packet arrival time and thus employs data-aided frame detection based on reference sequence. The warden user Willie tries to detect this covert communication by using a radiometer. The detection performance analysis is provided for both Bob and Willie under Rayleigh fading channel. It is demonstrated that the covert performance can benefit from the increase of the frame length or the number of receive antennas from frame detection perspective. Numerical results are provided to verify the proposed studies. Copyright © 2019, The Authors. All rights reserved.

关键词： Receiving antennas

93 GHz Wireless Transmission based on a Fully Packaged mm-Wave Band Optical Clock Generator

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93 GHz Wireless Transmission based on a Fully Packaged mm-Wa...

International Topical Meeting on Microwave Photonics (MWP)

作者： Alberto Montanaro Claudio Porzi Fawad Ahmad Marco Chiesa Antonio D’Errico Alessandra Bigongiari Aina Serrano Rodrigo Federico Camponeschi Marco Romagnoli Antonella Bogoni Antonio Malacarne Photonic Networks and Technologies National Lab (PNTLab) National Inter-University Consortium for Telecommunications (CNIT) Pisa Italy Telecommunications Computer Engineering and Photonics Institute (TeCIP) Scuola Superiore Sant’Anna Pisa Italy CamGraPhIC s.r.l. Pisa Italy Ericsson Research Pisa Italy

The mm-wave frequency spectrum offers the possibility to afford the increasing demand of high data-rate communication links. Microwave-photonics techniques can be advantageously used for reconfigurable generation of very high frequency clocks with superior phase noise performance with respect to purely electrical techniques, with the additional functionality of remote distribution of the signal through fiber-optics or free-space optic links. Here we present a fully packaged optoelectronic assembly of a CMOS-compatible silicon-based photonic integrated circuit for optical frequency comb generation with reconfigurable free spectral range given by an RF driving local oscillator. Through optical filtering and photodetection, a 98 GHz electrical clock has been derived and employed for successful 93 GHz wireless transmission of complex modulation formats carrying up to 4 Gb/s data rate, demonstrating the suitability of the solution for 5G and next generation 6G mobile networks.

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Time-Division Based Integrated Sensing, Communication, and Computing in Integrated Satellite-Terrestrial networks

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arXiv 2023年

作者： Zhu, Xiangming Wang, Hua Yang, Zhaohui Pham, Quoc-Viet The Research Institute of Intelligent Networks Zhejiang Lab Hangzhou311121 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Statistics Trinity College Dublin University of Dublin Dublin 2 D02 PN40 Ireland

In this paper, we investigate time-division based framework for integrated sensing, communication, and computing in integrated satellite-terrestrial networks. We consider a scenario, where Internet-of-Things devices on the ground operate with sensing and communication in a time-division manner, and can process the sensing results locally, at the edge, or in the cloud via the satellite communication link. Based on the proposed framework, we formulate a multi-dimensional optimization problem to maximize the utility performance of sensing, communication, and computing abilities. After decomposing the original optimization problem into two subproblems, we first derive the closed-form solution of the optimal task partitioning strategy for terrestrial users and satellite users. Then, we develop the joint subframe allocation and task partitioning strategy to optimize the overall performance, by means of which the Pareto optimal solutions can be obtained along the Pareto frontier. Extensive simulations are provided to demonstrated the effectiveness of the proposed strategy, which is 10% to 60% superior compared with the benchmarks. Also, the trade-off between the multidimensional resource and multi-functional performance is analyzed from the perspective of network design. Copyright © 2023, The Authors. All rights reserved.

关键词： Pareto principle

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization

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arXiv 2022年

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. School of Science and Engineering Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Wireless Technology Lab Laboratories 2012 Huawei China Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target's direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound optimization

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Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao ...

IEEE Globecom Workshops

作者： Xianxin Song Jie Xu Fan Liu Tony Xiao Han Yonina C. Eldar School of Science and Engineering Future Network of Intelligence Institute and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong (Shenzhen) China Department of Electrical and Electronic Engineering Southern University of Science and Technology China wireless Technology Lab 2012 Laboratories Huawei China Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel

ISBN: (纸本)9781665459761

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target in its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. The AP aims to estimate the target’s direction-of-arrival (DoA) with respect to the IRS, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the CramérRao lower bound (CRLB) on estimation error. Towards this end, we first obtain the CRLB expression for estimating the DoA in closed form. Next, we optimize the joint beamforming design to minimize the CRLB, via alternating optimization, semi-definite relaxation, and successive convex approximation. Numerical results show that the proposed design based on CRLB minimization achieves improved sensing performance in terms of mean squared error, as compared to the traditional schemes with signal-to-noise ratio maximization and separate beamforming.

关键词： Wireless communication Wireless sensor networks Estimation error Direction-of-arrival estimation Array signal processing Minimization Sensors

Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT networks

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arXiv 2023年

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Hou, Ronghui School of Cyber Engineering Xidian University Xi’an710071 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China State Key Lab of Integrated Service Networks Information Science Institute Xidian University Shaanxi Xi’an710071 China

Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the UAV’s limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered UAV. Each SN generates status updates at stochastic intervals, while the UAV collects and subsequently transmits these status updates to a central data center. Furthermore, the UAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the UAV’s offloading policy, as well as the continuous control of UAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Age-optimal trajectory planning for UAV-assisted data collection

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arXiv 2018年

作者： Liu, Juan Wang, Xijun Bai, Bo Dai, Huaiyu College of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China State Key Laboratory of Integrated Service Networks Institute of Information Science Xidian University Xi'an Shaanxi710071 China Future Network Theory Lab 2012 Labs Huawei Technologies Co. Ltd. Shatin N. T. Hong Kong Department of Electrical and Computer Engineering North Carolina State University United States

Unmanned aerial vehicle (UAV)-aided data collection is a new and promising application in many practical scenarios. In this work, we study the age-optimal trajectory planning problem in UAV-enabled wireless sensor networks, where a UAV is dispatched to collect data from the ground sensor nodes (SNs). The age of information (AoI) collected from each SN is characterized by the data uploading time and the time elapsed since the UAV leaves this SN. We attempt to design two age-optimal trajectories, referred to as the Max-AoI-optimal and Ave-AoI-optimal trajectories, respectively. The Max-AoI-optimal trajectory planning is to minimize the age of the 'oldest' sensed information among the SNs. The Ave-AoI-optimal trajectory planning is to minimize the average AoI of all the SNs. Then, we show that each age-optimal flight trajectory corresponds to a shortest Hamiltonian path in the wireless sensor network where the distance between any two SNs represents the amount of inter-visit time. The dynamic programming (DP) method and genetic algorithm (GA) are adopted to find the two different age-optimal trajectories. Simulation results validate the effectiveness of the proposed methods, and show how the UAV's trajectory is affected by the two AoI metrics. Copyright © 2018, The Authors. All rights reserved.

关键词： Sensor nodes