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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Baicai Li Xiaoqiang Xiao Shun Tang Weixun Ning State Key Laboratory of High Performance Computing Computer School National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781665428408

Wireless rechargeable sensor networks (WRSN) can prolong the lifetime of wireless sensor networks (WSN). In WRSN, mobile charger (MC) is used to charge low-power sensor node (SN). How to plan an efficient charging path for MC is a NP-hard problem. The task is to plan a path for MC that can not only charge the SN requesting charging in time, but also reduce the driving distance, so as to reduce the energy loss of MC. In this paper, policy gradient path planning (PGPP) algorithm based on deep reinforcement learning is proposed to solve this problem. In PGPP, the SNs in WRSN are treated as the environment, and the base station and MC are seen as the sensors and actuators of the agent. A reward function which can measure the energy efficiency of MC is designed, and the policy gradient algorithm is used to learn the strategy. Simulation experiments show that compared with other MC charging path planning algorithms, PGPP algorithm has certain advantages in the speed of path planning and the energy efficiency of WRSN.

关键词： Wireless communication Wireless sensor networks Base stations Actuators NP-hard problem Simulation Reinforcement learning

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WaveFormer: A Wavelet Transformer for Parkinson Disease’s Retinal Layer Segmentation in OCT

WaveFormer: A Wavelet Transformer for Parkinson Disease’s R...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yanlin Chen Xiaoqing Zhang Tianao Wang Chen Tang Haili Ye Jiang Liu Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou China The Oujiang Laboratory The Affiliated Eye Hospital Wenzhou Medical University Wenzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Pathology symptoms of Parkinson disease (PD) are different from those of retinal diseases in the retinal layers, which are subtle. However, segmenting pathology information of PD from retinal layers automatically based on optical coherence tomography (OCT) images has not been studied before. Although existing Transformer-based segmentation methods have achieved good segmentation results, they have limitations in capturing local context information. Convolutional neural networks (CNNs) can construct local context dependencies among pixels, which is complementary to Transformers. Particularly, edge information extraction is significant for accurate retinal layer segmentation, which is ignored by both Transformers and CNNs but can be captured by frequency domain learning methods. To fully leverage the advantages of Transformers, CNNs, and frequency domain learning methods, we propose a Wavelet Transformer (WaveFormer) for retinal layer segmentation based on OCT images. In the WaveFormer, we design a Wavelet Spatial Attention block to exploit the potential of frequency information. Based on these advantages, WaveFormer can be data-efficient in limited OCT images of PD. The experimental results on the OCT-PD segmentation dataset show that our WaveFormer outperforms existing Transformers and CNNs. For example, WaveFormer outperforms Swin-UNet by 3.41% of IoU.

关键词： Learning systems Image segmentation Pathology Wavelet domain Image edge detection Frequency-domain analysis Transformers

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Near 100%spectral-purity photons from reconfigurable micro-rings

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Chinese Physics B 2020年第11期29卷 135-139页

作者： Pingyu Zhu Yingwen Liu Chao Wu Shichuan Xue Xinyao Yu Qilin Zheng Yang Wang Xiaogang Qiang Junjie Wu Ping Xu Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology AMSBeijing 100071China National Laboratory of Solid State Microstructures and School of Physics Nanjing UniversityNanjing 210093China

We propose an on-chip reconfigurable micro-ring to engineer the spectral-purity of *** micro-ring resonator is designed to be coupled by one or two asymmetric Mach-Zehnder interferometers and the coupling coefficients hence the quality-factors of the pump and the converted photons can be dynamically changed by the interferometer’s internal *** calculate the joint-spectrum function and obtain the spectral-purity of photons and Schmidt number under different *** show that it is a dynamical method to adjust the spectral-purity and can optimize the spectralpurity of photons up to near 100%.The condition for high-spectral-purity photons is ensured by the micro-ring itself,so it overcomes the trade-off between spectral purity and brightness in the traditional post-filtering *** scheme is robust to fabrication variations and can be successfully applied in different fabrication labs and different *** high-spectral-purity photons will be beneficial for quantum information processing like Boson sampling and other quantum algorithms.

关键词： spectral-purity micro-ring Mach–Zehnder interferometer

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Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

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arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Hybrid plasmonic-photonic microcavity for enhanced light-matter interaction

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Science Bulletin 2022年第12期67卷 1205-1208页

作者： Xiao Xiong Yun-Feng Xiao Institute of High Performance Computing Agency for ScienceTechnologyand Research(A^(*)STAR)Singapore 138632Singapore State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of PhysicsPeking UniversityBeijing 100871China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China

Light-matter interaction describes how photons are absorbed,emitted,or scattered by a distribution of charges(e.g.,electrons in solids),which is governed by the fine-structure constantα≈137^(-1) in nature,thus intri... 详细信息

关键词：光与物质相互作用 scattered governed

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A Robust and Learning Approach for Multi-Phase Aerial Search with UAVs

A Robust and Learning Approach for Multi-Phase Aerial Search...

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作者： Zhongxuan Cai Minglong Li Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense Technology

Unmanned aerial vehicles(UAVs) have been attracting more and more attention in the research and industry field. Aerial search is a common mission and is intrinsically fit for UAVs, e.g. disaster rescue, remote sensing and environmental monitoring. With the improvement of UAV hardware and software, UAVs tend to achieve better autonomy and accomplish more complex tasks. However,current UAV aerial search is usually hardcoded, which limits their adaptability, autonomy and robustness in realistic scenarios. In this paper, we propose to address this problem by leveraging reinforcement learning(RL) and a recent control architecture, behavior trees(BTs). We develop robust and adaptive UAV systems that can automatically conduct multi-phase complex aerial search, including search, communication and refueling. Experimental results in a3 D robot simulator verify the effectiveness and robustness of the proposed approach, which achieves better performance than the baseline.

关键词： Unmanned aerial vehicles reinforcement learning behavior trees aerial search

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Joint UAV Deployment and Edge Association for Energy-Efficient Federated Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Wu, Tao Li, Maomao Qu, Yuben Wang, Hongjun Wei, Zhenhua Cao, Jiannong National University of Defense Technology Hefei230009 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Xi'an Research Institute of High Technology Xi'an710025 China The HongKong Polytechnic University Department of Computing Hong Kong Hong Kong

Recently, federated learning (FL) has become a promising distributed learning paradigm that caters to the recent trend of pushing intelligence from the cloud to the edge. Nevertheless, communication bottlenecks and device dropout can lead to inefficient FL in the large network scale, where massive devices cannot be accessed with severely limited network resources. Inspired by the Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC), we propose the multi-UAV assisted FL design to provide the intermediate model aggregation in the sky. Specifically, we study the problem of joint UAV dePloyment and edge aSsociation (UPS) to minimize the overall energy consumption, which concerns UAV deployment, edge association, and resource allocation. Unfortunately, solving this problem is non-trivial, due to its infinite search space and the complex coupling among mixed optimization variables. To tackle this difficulty, we exploit the FL bundle generation method to reduce candidate locations of UAVs from infinite to finite. Then, we decompose the initial problem and devise an alternating optimization-based algorithm to achieve the optimal resource allocation in the closed form. On this basis, we design a greedy-based approximation algorithm with logN performance guarantee for UAV deployment and edge association. Extensive simulations are conducted to validate the effectiveness of our proposed solution. Compared with five benchmarks, our proposed algorithm can significantly reduce the overall training energy consumption under the training time constraint, while always maintaining better training performance under different parameter settings. © 2015 IEEE.

关键词： Federated learning

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Numerical Study of Fluid-Structure Interaction Dynamics under high-explosive Detonation on Massively Parallel Computers 22

Numerical Study of Fluid-Structure Interaction Dynamics unde...

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22nd IEEE International Conference on high performance computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Zhang, Sen Guo, Xiao-Wei Li, Chao Liu, Yi Zhao, Ran Yang, Canqun Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728176499

Fluid-structure interaction dynamics under high-explosie detonation is widely used in engineering application, and its numerical simulation is also challenging due to the violent reaction of the blast. Based on the open source software and coupling library, we developed a solver to support the numerical simulation of fluid-structure interaction dynamics under high-explosie detonation, and carried out a case analysis. In the test case, the motion process of perpendicular elastic flap under high-explosive detonation is selected and the simulation time is 5ms. The transient results of the whole simulation process are given. We also tested the case in parallel, using two fluid meshes, that is, 972,000 cells (Mesh I) and 2,405,000 cells (Mesh II), and the scale of structure mesh is 2,000 cells. The results show that in the case of Mesh I, the maximum speedup ratio is 5.47 and the efficiency is 15.71% when the number of processes is 32. In the case of Mesh II, the maximum speedup ratio is 10.91 and the efficiency is 17.05% when the number of processes is 64. © 2020 IEEE.

关键词： Fluid structure interaction

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A Cost-Efficient Platform Design for Distributed UAV Swarm Research

A Cost-Efficient Platform Design for Distributed UAV Swarm R...

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作者： Zhongxuan Cai Xuefeng Chang Minglong Li Institute for Quantum Information &State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense Technology

Unmanned Aerial Vehicles(UAVs) have been attracting more and more attention in research and education. Specifically, Swarm intelligence is a promising future technology of UAVs and the frontier of multi-agent system research. It has the characteristics of low individual cost, strong system flexibility and robustness, and has great potential in many tasks. However, due to the constraints of research conditions and cost, most of the current researches on large-scale swarm UAVs are carried out in the simulation environment. Building a low-cost open-source software and hardware platform for swarm UAVs is an important basis for promoting researches on swarm UAVs and multi-agent systems. In this paper,we propose a design of a UAV platform with common cost-efficient hardware and a rich open-source software ecosystem, and provide a software solution for swarm robots based on the open-source robot operating system ROS. These software packages support the rapid programming development of swarm behaviors and different communication topology. Experiments have been conducted for typical UAV tasks like flocking and formation, indicating the effectiveness of the proposed platform.

关键词： Unmanned aerial vehicles UAV platform for research UAV hardware and software

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Verifying ReLU Neural Networks from a Model Checking Perspective

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Journal of Computer Science & technology 2020年第6期35卷 1365-1381页

作者： Wan-Wei Liu Fu Song Tang-Hao-Ran Zhang Ji Wang College of Computer Science National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai 201210China State Key Laboratory for High Performance Computing Changsha 410073China

Neural networks, as an important computing model, have a wide application in artificial intelligence (AI) domain. From the perspective of computer science, such a computing model requires a formal description of its behaviors, particularly the relation between input and output. In addition, such specifications ought to be verified automatically. ReLU (rectified linear unit) neural networks are intensively used in practice. In this paper, we present ReLU Temporal Logic (ReTL), whose semantics is defined with respect to ReLU neural networks, which could specify value-related properties about the network. We show that the model checking algorithm for theΣ2∪Π2 fragment of ReTL, which can express properties such as output reachability, is decidable in EXPSPACE. We have also implemented our algorithm with a prototype tool, and experimental results demonstrate the feasibility of the presented model checking approach.

关键词： model checking rectified linear unit neural(ReLU)network temporal logic

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