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2023 International Conference on Ubiquitous Communication, Ucom 2023

作者： Chen, Yutong Shi, Haoran Weng, Qiyan Shi, Zhiguo College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province Hangzhou310027 China

ISBN: (纸本)9798350340433

Quick UDP Internet Connections (QUIC), an Internet protocol proposed by Google to replace TCP, is already used on YouTube and Facebook. With the popularization of new multimedia applications such as VR and the development of low-cost supporting equipment, the current QUIC protocol needs to further evolve to support new multimedia data and multicast applications. Therefore, it is a promising research direction to study how to apply QUIC protocol to multicast scenario and design congestion control algorithm in multicast scenario. The working mechanism of traditional network congestion control protocols is relatively simple, and they do not have the ability to adapt to different network environments. Based on the multicast QUIC protocol, this paper proposes a reinforcement learning congestion control algorithm based on Decaying epsilon-greedy and critic-actor methods. The simulation results show that the proposed algorithm can greatly reduce the packet loss rate and has good adaptability in the complex and changeable network environment. © 2023 IEEE.

关键词： Reinforcement learning

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An Economical-Efficient Resource Allocation Scheme for Leo Satellite Communication systems

An Economical-Efficient Resource Allocation Scheme for Leo S...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Ruxin Zhi Yuanyuan Ma Zhan Xu Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing

In this paper, we propose an economical-efficient downlink resource allocation scheme for the low-earth orbit (LEO) satellite communication system with cache-enabled terminals. The broadcast channel model with the incorporation of shadowed Rice fading is adopted and subsequently transformed into a finite-state Markov channel model. Primarily, we deduce the formulation for the attainable channel capacity pertaining to discrete-rate transmission, wherein adaptive modulation and coding are employed. Second, we define an economical efficiency (ECE) metering based on the system revenue and cost and derive the closed-form ECE. Finally, an optimization problem with maximizing the ECE is developed in the terms of transmit power of the satellite, allocation of caching memory, file grouping, and probability falling into each range of the channel state. This problem is nondeterministic polynomial hard (NP-hard) in essence and is thus difficult to solve. For solving it, we first optimize the allocation of caching memory and file grouping. Then, we propose a gradient strategy based heuristic algorithm to acquire the allocation of power and probability in each channel state. The numerical findings substantiate that the proposed scheme presents a substantial enhancement to the ECE of the LEO satellite communication system.

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The Methodology of Mask Detection Employing the YOLOv7 Framework

The Methodology of Mask Detection Employing the YOLOv7 Frame...

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Neural Networks, information and Communication Engineering (NNICE), International Conference on

作者： Fucheng Zhang Pinfang Tian School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-TimeIndustrial Systems Wuhan China

In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. This indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments.

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Continuous-variable quantum key distribution with on-chip light sources

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Photonics Research 2023年第4期11卷 504-516页

作者： LANG LI TAO WANG XINHANG LI PENG HUANG YUYAO GUO LIANGJUN LU LINJIE ZHOU GUIHUA ZENG State Key Laboratory of Advanced Optical Communication Systems and Networks Center for Quantum Sensing and Information ProcessingDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China Shanghai Research Center for Quantum Sciences Shanghai 201315China State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory of Navigation and Location ServicesDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu 314200China

Integrated quantum key distribution(QKD)systems based on photonic chips have high scalability and stability,and are promising for further construction of global quantum communications ***-chip quantum light sources are a critical component of a fully integrated QKD system;especially a continuous-variable QKD(CVQKD)system based on coherent detection,which has extremely high requirements for the light ***,for what we believe is the first time,we designed and fabricated two on-chip tunable lasers for CV-QKD,and demonstrated a high-performance system based on these *** of the high output power,fine tunability,and narrow linewidth,the involved on-chip lasers guarantee the accurate shot-noise-limited detection of quantum signals,center wavelength alignment of nonhomologous lasers,and suppression of untrusted excess *** system’s secret key rate can reach 0.75 Mb/s at a 50 km fiber distance,and the secure transmission distance can exceed 100 *** results mark a breakthrough toward building a fully integrated CV-QKD,and pave the way for a reliable and efficient terrestrial quantum-secure metropolitan area network.

关键词： quantum sources distribution

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Sora for foundation robots with parallel intelligence:three world models,three robotic systems

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Frontiers of information Technology & Electronic Engineering 2024年第7期25卷 917-923页

作者： Lili FAN Chao GUO Yonglin TIAN Hui ZHANG Jun ZHANG Fei-Yue WANG School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Electrical Engineering Wuhan UniversityWuhan 430072China

This paper outlines the initial steps and basic framework for developing foundation/infrastructure robots/robotics based on foundation models and parallel intelligence,as well as the potential applications of new art... 详细信息

关键词： artificial foundation intelligence

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Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

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IET Cyber-systems and Robotics 2022年第3期4卷 200-211页

作者： Yiwei Chong Jiaming Liang Tehuan Chen Chao Xu Changchun Pan College of Control Science and Engineering Zhejiang UniversityHangzhouZhejiangChina School of Mechanical Engineering and Mechanics Ningbo UniversityNingboZhejiangChina Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingboZhejiangChina The State Key Laboratory of Industrial Control Technology Institute of Cyber‐Systems and ControlZhejiang UniversityHangzhouZhejiangChina School of Electronic Information and Electrical Engineering Key Laboratory of Shanghai Navigation and Location Based ServicesShanghai Jiao Tong UniversityShanghaiZhejiangChina

Particle image velocimetry(PIV)is an essential method in experimental fluid *** recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of ***,the supervised learning of PIV is driven by large volumes of data with ground truth ***,the authors consider unsupervised PIV *** has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning *** authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical *** this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV *** authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic ***,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle *** a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.

关键词： neural network particle image velocimetry unsupervised learning

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Study on Oracle Bone Image Preprocessing Based on Fusion of Denoising and Enhancement Techniques: Improving Text Detection Accuracy 7

Study on Oracle Bone Image Preprocessing Based on Fusion of ...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Yang, Hankun Zhu, Bowei Zhang, Yanmin Beiming, Li Yantai Research Institute of Harbin Engineering University Yantai China Beijing Institute of Astronautical Systems Engineering Beijing China Wuhan Second Ship Design and Research Institute Key Laboratory of Marine Electromagnetic Detection and Control China Harbin Engineering University College of Information and Communication Engineering Yantai China

ISBN: (纸本)9798350384437

This paper proposes a method of preprocessing oracle bone script images based on the fusion of denoising and enhancement techniques, aiming to improve text detection accuracy. By integrating multiple denoising techniques, we effectively eliminate speckles and noise from the images, and enhance image quality by adjusting contrast and sharpening, making oracle bone characters clearer. Subsequently, we utilize YOLOv8 object detection technology to detect text regions in oracle bone images, ensuring efficient subsequent text segmentation and analysis. Through this study, we have laid a solid foundation for future research on oracle bone scripts and provided reliable technical solutions to address the challenges posed by complex images. Experimental results show that the Box(P) after preprocessing improves by approximately 2.65% compared to the unprocessed, validating the effectiveness of the fusion preprocessing method proposed in this study in practical applications. We look forward to using this method to explore the deeper meanings of more oracle bone texts, advancing the exploration and understanding of ancient civilizations. © 2024 IEEE.

关键词： Image denoising

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Dynamic Event-Triggered Model Predictive control for Multiple Platoons under Non-Ideal Communications

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IEEE Internet of Things Journal 2025年

作者： Han, Qiaoni Xu, Chengfei Wang, Xiaocheng Yang, Hongjiu Zuo, Zhiqiang Yang, Bo Guan, Xinping Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China Tianjin Normal University Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission College of Electronic and Communication Engineering Tianjin300387 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

The uncertainty of wireless communication seriously affects the control performance of the platoon. In this paper, a model predictive control (MPC) method based on dynamic event-triggering is proposed to address the impact of non-ideal communications on multi-vehicle systems. Firstly, a hierarchical platoon architecture combining backbone layer and sub-platoon layers is proposed to achieve efficient coordination among multiple platoons, and a communication-aware control mechanism is designed for each vehicle to effectively mitigate the adverse effects of non-ideal communication scenarios. Secondly, a dynamic event-triggering mechanism is established for each vehicle, which is related to time-varying delay and random packet loss parameters, so as to effectively reduce the frequency of MPC solutions and data transmission. Additionally, based on different communication topologies, distributed model predictive controllers are suggested for backbone layer and sub-platoon layer to achieve precise and comfortable tracking. Then, sufficient conditions are provided to ensure the recursive feasibility of the MPC algorithm and the stability of the platoon system. Finally, extensive simulations are conducted, and the results demonstrate the effectiveness of the proposed algorithm in terms of control performance and low computational complexity under non-ideal communication conditions. © 2014 IEEE.

关键词： Predictive control systems

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Pyramid Pooling-based Local Profiles for Graph Classification

Pyramid Pooling-based Local Profiles for Graph Classificatio...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Lou, Yang Li, Junli College of Computer Science Sichuan Normal University Chengdu610066 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan

ISBN: (纸本)9798350337020

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods;2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks. © 2023 IEEE.

关键词： graph classification graph neural network Graph representation learning pyramid pooling

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MmWave MIMO: An LAMP-Based Network with Deep Residual Learning Combining the Prior Channel information for Beamspace Sparse Channel Estimation 21

MmWave MIMO: An LAMP-Based Network with Deep Residual Learni...

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21st IEEE International Conference on Communication Technology, ICCT 2021

作者： Ji, Qinwen Zhu, Chunhua Guo, Xinying Henan University of Technology Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Key Lab. of Grain Information Processing and Control School of Information Science and Engineering Zhengzhou China

ISBN: (纸本)9781665432061

In mmWave MIMO systems relying on lens antenna arrays, beamspace sparse channel estimation is one of the key technologies to improve system performance. One new beamspace sparse channel estimation scheme with deep residual learning is proposed, which introduces the channel prior information following the Gaussian mixture distribution and the residual processing unit. Firstly, the prior-aided GM-LAMP network is employed to obtain a preliminary estimation;then the deep residual learning network is designed to reduce the impact of channel noise. Compared with existing methods, the proposed algorithm can improve estimation accuracy with the lower computational complexity. © 2021 IEEE.

关键词： MIMO systems

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