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Joint Offloading and Resource Allocation for Hybrid Cloud and Edge Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Huang, Chong Chen, Gaojie Xiao, Pei Xiao, Yue Han, Zhu Chambers, Jonathon A. State Key Laboratory of Optoelectronic Materials and Technologies Sun Yat-sen University Guangdong China Home for 5GIC & 6GIC University of Surrey GuildfordGU2 7XH United Kingdom The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering the University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Engineering University of Leicester LeicesterLE1 7RU United Kingdom

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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An Analysis of The NASA CSDA Bistatic Radar GNSS-R Dataset

An Analysis of The NASA CSDA Bistatic Radar GNSS-R Dataset

引用

International Conference on Electromagnetics in advanced Applications (ICEAA)

作者： Mohammad Al-Khaldi Joel T. Johnson Darren S. McKague Anthony Russel Dorina Twigg Dept. of Electrical and Computer Engineering Electro Science Lab The Ohio State University Columbus OH USA Department of Climate and Space Sciences and Engineering University of Michigan Ann Arbor MI USA Space Physics Research Laboratory University of Michigan Ann Arbor MI USA

The remote sensing of the Earth’s surface through a variety of spaceborne platforms has a long history of investigation. The selection of one sensing technology over another is typically governed by the geophysical products of interest and requirements of spatial and temporal sampling. Recent studies have highlighted the utility of GNSS-R (Global Navigation Satellite System-Reflectometry) measurements for the sensing of multiple geophysical properties. The modest cost of GNSS-R receive-only platforms also promotes the simultaneous launch of multiple receivers, further improving revisit times, and the L-band frequency used provides all weather operations given the insensitivity of L-band measurements to cloud cover and even heavy rain rates.

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Sensor Selection for Remote State Estimation with QoS Requirement Constraints

arXiv

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arXiv 2023年

作者： Yang, Huiwen Huang, Lingying Yang, Chao Mo, Yilin Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong School of Electrical and Electronic Engineering Nanyang Technological University Singapore Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Department of Automation and BNRist Tsinghua University Beijing China

In this paper, we study the sensor selection problem for remote state estimation under the Quality-of-Service (QoS) requirement constraints. Multiple sensors are employed to observe a linear time-invariant system, and their measurements should be transmitted to a remote estimator for state estimation. However, due to the limited communication resources and the QoS requirement constraints, only some of the sensors can be allowed to transmit their measurements. To estimate the system state as accurately as possible, it is essential to select sensors for transmission appropriately. We formulate the sensor selection problem as a non-convex optimization problem. It is difficult to solve such a problem and even to find a feasible solution. To obtain a solution which can achieve good estimation performance, we first reformulate and relax the formulated problem. Then, we propose an algorithm based on successive convex approximation (SCA) to solve the relaxed problem. By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution. Simulation results are presented to show the effectiveness of the proposed heuristic. Copyright © 2023, The Authors. All rights reserved.

关键词： Quality of service

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Deep imaging inside scattering media through virtual spatiotemporal wavefront shaping

arXiv

引用

arXiv 2023年

作者： Zhang, Yiwen Dinh, Minh Wang, Zeyu Zhang, Tianhao Chen, Tianhang Hsu, Chia Wei Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

The multiple scattering of light makes materials opaque and obstructs imaging. Wavefront shaping can reverse the scattering process, but imaging with physical wavefront shaping has limitations such as requiring physical guidestars, being restricted within a small isoplanatic volume, and relying on slow wavefront updates, with some approaches only working for planar targets outside the scattering media. Here, we introduce scattering matrix tomography (SMT): measure the hyperspectral scattering matrix of the sample, use it to digitally scan a synthesized confocal spatiotemporal focus and construct a volumetric image of the sample, and then use the tomograms as virtual guidestars in a nonconvex optimization to find the pulse shape, input wavefront, and output wavefront that can compensate for aberrations and scattering. SMT combines the strengths of wavefront shaping, spatiotemporal gating, and computational adaptive optics, eliminating physical guidestars and enabling digital double-path wavefront corrections tailored for every isoplanatic volume. We demonstrate sub-micron lateral resolution and one-micron axial resolution at one millimeter beneath ex vivo mouse brain tissue and over three transport mean free paths inside an opaque colloid, where existing imaging methods all fail due to the overwhelming multiple scattering. As a noninvasive and label-free method that works both inside and outside the scattering media, SMT may be applied broadly across medical imaging, biological science, device inspection, and colloidal physics. Copyright © 2023, The Authors. All rights reserved.

关键词： Wavefronts

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Toward Lung Ultrasound Automation: Fully Autonomous Robotic Longitudinal and Transverse Scans Along Intercostal Spaces

IEEE Transactions on Medical Robotics and Bionics

引用

IEEE Transactions on Medical Robotics and Bionics 2025年第2期7卷 768-781页

作者： Long Lei Yingbai Hu Zixing Jiang Juzheng Miao Xiao Luo Yu Zhang Qiong Wang Shujun Wang Zheng Li Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China Department of Surgery The Chinese University of Hong Kong Hong Kong China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Biomedical Engineering The Hong Kong Polytechnic University Hong Kong China Department of Surgery Chow Yuk Ho Technology Centre for Innovative Medicine Li Ka Shing Institute of Health Sciences and Multiscale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong China

Lung ultrasound scanning is essential for diagnosing lung diseases. The scan effectiveness critically depends on both longitudinal and transverse scans through intercostal spaces to reduce rib shadowing interference, as well as maintaining the probe perpendicular to pleura for pathological artifact generation. Achieving this level of scan quality often depends heavily on the experience of doctors. Robotic ultrasound scanning shows promise, but currently lacks a direct path planning method for intercostal scanning, and probe orientation does not consider imaging differences between lungs and solid organs. In this paper, we aim to fully automate two fundamental operations in lung ultrasound scanning: longitudinal and transverse scans. We propose pioneering path planning methods along intercostal spaces and innovative solutions for adaptive probe posture adjustment using real-time pleural line feedback, specifically addressing the unique characteristics of lung ultrasound scanning. This ensures the acquisition of high-quality, diagnostically meaningful ultrasound images. In addition, we develop a robotic lung ultrasound system to validate the proposed methods. Extensive experimental results on two volunteers and a chest phantom confirm the efficacy of our methods, and demonstrate the system’s feasibility in automated lung ultrasound examinations. Our work lays a solid foundation for automated robotic complete lung scanning.

关键词： Ultrasonic imaging Lungs Probes Robots Robot kinematics Medical robotics Ribs Medical services Surface morphology Computed tomography

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Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation

arXiv

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arXiv 2022年

作者： Winnicki, Anna Srikant, R. Coordinated Science Laboratory The Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States C3.ai Digital Transformation Institute United States

We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation algorithms along with state-of-the-art techniques that are useful for very large MDPs, including lookahead, function approximation, and gradient descent. Specifically, we analyze two algorithms;the first algorithm involves a least squares approach where a new set of weights associated with feature vectors is obtained via least squares minimization at each iteration and the second algorithm involves a two-time-scale stochastic approximation algorithm taking several steps of gradient descent towards the least squares solution before obtaining the next iterate using a stochastic approximation algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Markov processes

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Wheat Rust Disease Segmentation from Ground Imagery

Wheat Rust Disease Segmentation from Ground Imagery

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Hirra Anwar Muhammad Jawad Khan Muhammad Fayyaz Ajmal Saeed Mian Faisal Shafait School of Mechanical and Manufacturing Engineering NUST Islamabad Pakistan Department of Electrical Engineering Prince Sattam Bin Abdul Aziz University Al-Kharj Saudi Arabia Crop Diseases Research Institute National Agriculture Research Center Islamabad Pakistan University of Western Australia Perth Australia School of Electrical Engineering & Computer Science NUST Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence Islamabad Pakistan

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Wheat rust disease is a major agricultural threat, causing significant yield losses globally. Precision agriculture and autonomous robot systems represent pivotal advancements in modern farming and disease detection, leveraging technology to enhance crop management, optimize resource use, and improve productivity. These systems rely heavily on accurate and comprehensive data to train effectively. However, current datasets often fall short of meeting the specific needs of these advanced agricultural technologies. This paper addresses this gap by introducing a primary contribution in the form of an open-source dataset focused on wheat rust disease segmentation for precision agriculture. Open-source datasets in literature are either general plant disease datasets or wheat disease classification datasets. Our proposed wheat rust segmentation dataset, Focussed Rust Disease Images (FRDI), provides close-up views of disease manifestations on wheat leaves, capturing the natural variability and complexity of diseases in field conditions, thereby meeting the requirements of precision agriculture systems. This facilitates detailed analysis and effective training of deep learning models designed for disease detection and segmentation tasks for real-world agricultural environments. Furthermore, in our work we present an improved wheat rust disease detection and segmentation technique which is based on the concept of co-salient feature extraction for disease identification within complex and cluttered field images. By integrating localized and generic contextual information, our proposed approach effectively identifies key features and intricate patterns associated with wheat rust disease.

关键词： Precision agriculture Training Deep learning Representation learning Productivity Image segmentation Plant diseases Object detection Feature extraction Diseases

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

引用

arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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Heterogeneously coupled waveguides for pure single photon generation 13

Heterogeneously coupled waveguides for pure single photon ge...

引用

13th International Conference on Information Optics and Photonics, CIOP 2022

作者： Ding, Xiangyan Ma, Jing Tan, Liying Helmy, Amr S. Kang, Dongpeng School of Astronautics National Key Laboratory of Science and Technology on Tunable Laser Harbin Institute of Technology 92 West Dazhi Street Harbin150001 China The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S 3G4 Canada

ISBN: (纸本)9781510660632

Single-photon sources are the cornerstone of the optical quantum information process, including quantum computing, simulation, metrology, etc. The practical application requires these sources to be high purity and indistinguishability. A readily available approach is to use heralded single-photon sources to generate photon pairs via spontaneous parametric down-conversion (SPDC). However, the purity of photon pairs generated by SPDC is usually limited by the spectral correlations because of energy conservation. Extra spectral filters and group velocity matching (GVM) can be used to generate spectral uncorrelated photon pairs on-chip. However, the material dispersion of most materials cannot meet the GVM requirements, and extra components will reduce the integration. In this work, we develop a strategy to generate pure single photon on-chip by mode coupling in heterogeneously coupled waveguides. This strategy can tailor the group velocity directly, or shape biphoton wave function through a new degree of freedom that is the non-constant overlap between interacting modes. This allows the generation of high-purity single photons totally on-chip. Design examples based on lithium niobate waveguide are provided to generate spectrally pure photon pairs. This strategy is generic that it is not limited to any material and waveguide system, providing new possibilities in the generation of quantum photons. © 2022 SPIE.

关键词： Photons

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Deep-Neural-Network Discrimination of Multiplexed Superconducting-Qubit States

引用

Physical Review Applied 2022年第1期17卷 014024-014024页

作者： Benjamin Lienhard Antti Vepsäläinen Luke C.G. Govia Cole R. Hoffer Jack Y. Qiu Diego Ristè Matthew Ware David Kim Roni Winik Alexander Melville Bethany Niedzielski Jonilyn Yoder Guilhem J. Ribeill Thomas A. Ohki Hari K. Krovi Terry P. Orlando Simon Gustavsson William D. Oliver Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Quantum Engineering and Computing Group Raytheon BBN Technologies Cambridge Massachusetts 02138 USA MIT Lincoln Laboratory Lexington Massachusetts 02421 USA

Demonstrating a quantum computational advantage will require high-fidelity control and readout of multiqubit systems. As system size increases, multiplexed qubit readout becomes a practical necessity to limit the growth of resource overhead. Many contemporary qubit-state discriminators presume single-qubit operating conditions or require considerable computational effort, limiting their potential extensibility. Here, we present multiqubit readout using neural networks as state discriminators. We compare our approach to contemporary methods employed on a quantum device with five superconducting qubits and frequency-multiplexed readout. We find that fully connected feedforward neural networks increase the qubit-state-assignment fidelity for our system. Relative to contemporary discriminators, the assignment error rate is reduced by up to 25% due to the compensation of system-dependent nonidealities such as readout crosstalk, which is reduced by up to one order of magnitude. Our work demonstrates a potentially extensible building block for high-fidelity readout relevant to both near-term devices and future fault-tolerant systems.

关键词： Machine learning Measurement-based quantum computing Optoelectronics Quantum control Quantum information with solid state qubits Superconducting qubits Artificial neural networks Microwave techniques

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