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Parallel Implementation of the CCSDS Turbo Decoder on GPU

China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

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Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1022-1038页

作者： Xiao Xue Deyu Zhou Xiangning Yu Gang Wang Juanjuan Li Xia Xie Lizhen Cui Fei-Yue Wang the School of Computer Software College of Intelligence and ComputingTianjin UniversityTianjin 300350China the School of Software and the Joint SDUNTU Centre for Artificial Intelligence Research(C-FAIR) Shandong UniversityJinan 250101China the Information School Xi’an University of Financial and EconomicsXi’an 710100China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Hainan University Haikou 570228China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 1001901China

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

关键词： Agent-based modeling computational experiments cyber-physical-social systems(CPSS) generative deduction generative experiments meta model

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Time-synchronized control with the least upper bound of fixed settling time

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Journal of automation and Intelligence 2025年第2期4卷 98-107页

作者： Jiang, Wanyue Ge, Shuzhi Sam Ji, Ruihang Li, Dongyu The Institute for Future & Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore School of Cyber Science and Technology Beihang University Beijing 100191 China

This paper introduces time-synchronized convergence in fixed-time control, where all system states converge to the origin at the same time before a fixed time instant. Sufficient Lyapunov conditions are derived for fixed-time synchronized control (FTSC). An enhanced estimation method for synchronized settling time (ST) is proposed, with an explicit formula for its least upper bound (LUB), which reduces overestimation compared to existing methods. A switching-based technique is incorporated into the controller to avoid singularities while maintaining compatibility with the time-synchronized design. Simulation results validate the fixed-time synchronization properties and the improved ST estimation, demonstrating smoother output trajectories and reduced energy consumption. © 2025 The Authors

关键词： Fixed-time control Settling-time estimation Time-synchronized convergence

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High-Performance Image Compression and Reconstruction Using Lightweight Deep Neural Networks

High-Performance Image Compression and Reconstruction Using ...

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2024 Academic Conference of China Instrument and control Society, ACCIS 2024

作者： Gao, Shuhua Qin, Zekun Jiang, Xingyu Wang, Zhengfang Xu, Wenjun Wang, Jing School of Control Science and Engineering Shandong University Jinan China National University of Singapore Electrical and Computer Engineering Singapore Peng Cheng Laboratory Research Institute of MA&EI Shenzhen China

ISBN: (纸本)9798350350326

In many remote areas, tunnels, and underground spaces, network bandwidth is extremely limited. However, with the development of high-resolution cameras, the size of image data has significantly increased, making it challenging to frequently transmit large-scale images for detection and monitoring purposes. To address this issue, this paper proposes an image compression and reconstruction method based on lightweight neural networks, utilizing a dual-module deep learning architecture comprising a Compressed Convolutional Neural Network (CCNN) and a Reconstructed Convolutional Neural Network (RCNN). The lightweight design facilitates easy deployment on edge devices. The CCNN module compresses the original image for fast network transmission, and upon reception, the RCNN module reconstructs the image. This approach significantly reduces the transmission payload and enhances speed under limited bandwidth conditions. Furthermore, the developed method outperforms the classic JPEG algorithm across four representative metrics as evaluated on a widely-used public dataset. To further optimize the CCNN for edge deployment, a model distillation technique is employed, reducing the model size by approximately 70% with negligible performance impact. In summary, the proposed deep neural network model offers high performance and a low memory footprint for image compression and reconstruction, making it particularly suitable for resourcelimited edge devices and bandwidth-constrained applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution 19th

Collaborative Decision-Making Processes Analysis of Service ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Yan, Xiangpei Xue, Xiao Peng, Chao Liu, Donghua Feng, Zhiyong Xiao, Wang School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China China Waterborne Transport Research Institute Tianjin China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031545306

With the collaboration of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. As a novel service organization model, the Service Ecosystem (SE) can provide more sophisticated, precise, and thorough services and has attracted widespread attention. However, it also brings negative effects such as involution, and information cocoon room. Thus, how to analyze the collaborative decision-making mechanism between the SE regulation algorithm and the crowd intelligence group, exploring the reasons behind the negative effects, and finding effective intervention strategies have become problems in this field. To solve the challenges, we propose a Computational Experiments-based method Decision-making processes Analysis model in SE, namely CEDA. The proposed CEDA model consists of three modules: the autonomous evolution mechanism module, the learning evolution mechanism module, and the collaborative decision-making analysis module. Among them, the computational experiments can provide a customized test environment for the analysis of collaborative decision-making processes and find out the appropriate intervention strategy. Finally, the validity of the CEDA model is verified through the case of academic ecosystem involution. The results show that computational experiments can provide new ideas and paths for collaborative decision-making processes analysis. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Ecosystems

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control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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Wi-Fi Positioning Method Based on Convolutional Autoencoder and Device Heterogeneity Compensation 12th

Wi-Fi Positioning Method Based on Convolutional Autoencoder ...

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12th International Conference on Signal and Information Processing, Networking and computers, ICSINC 2024

作者： Wang, WenFeng Zhang, Zhang Guo, Liangliang Feng, Meijuan Wang, Qu School of Automation Science and Electrical Engineering University of Science and Technology Beijing Beijing100083 China China Electronics Standardization Institute Beijing100007 China Department of Energy and Control Engineering Shanxi Information Industry Technology Research Institute Co. LTD Taiyuan030012 China

ISBN: (纸本)9789819649662

As location-based service technology continues to develop, people's demand for positioning technology is increasing. Satellite positioning technology can be widely used for positioning in outdoor environments. However, due to the weak penetration ability of satellite signals into buildings, the application of this technology in indoor environments faces many limitations. Therefore, there is an urgent need to conduct in-depth research on reliable indoor positioning technology. Among commonly used indoor positioning technologies, indoor positioning technology based on WLAN signals has the advantages of wide coverage, low deployment cost, easy installation, and non-line-of-sight propagation, so it shows excellent universality. This article proposes a Wi-Fi Positioning Method Based on convolutional autoencoder and device heterogeneity compensation. Compared with traditional Wi-Fi positioning methods such as SVM, the positioning accuracy has been greatly improved and can meet daily low-cost, high-precision indoor positioning needs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Code division multiple access

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Implicit Scene Context-Aware Interactive Trajectory Prediction for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5461-5477页

作者： Lan, Wenxing Li, Dachuan Hao, Qi Zhao, Dezong Tian, Bin Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China

The accurate prediction of behaviors of surrounding traffic participants is critical for autonomous vehicles (AV). How to fully encode both explicit (e.g., map structure and road geometry) and implicit scene context information (e.g., traffic rules) within complex scenarios is still challenging. In this work, we propose an implicit scene context-aware trajectory prediction framework (the PRISC-Net, Prediction with Implicit Scene Context) for accurate and interactive behavior forecasting. The novelty of the proposed approach includes: 1) development of a behavior prediction framework that takes advantage of both model- and learning-based approaches to fully encode scene context information while modeling complex interactions;2) development of a candidate path target predictor that utilizes explicit and implicit scene context information for candidate path target prediction, along with a motion planning-based generator that generates kinematic feasible candidate trajectories;3) integration of the proposed target predictor and trajectory generator with a learning-based evaluator to capture complex agent-agent and agent-scene interactions and output accurate predictions. Experiment results based on vehicle behavior datasets and real-world road tests show that the proposed approaches outperform state-of-the-art methods in terms of prediction accuracy and scene context compliance. © 2016 IEEE.

关键词： Trajectories

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Semi-Markov Telecommunication Networks Model

Semi-Markov Telecommunication Networks Model

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2024 International Conference on Actual Problems of Electron Devices engineering, APEDE 2024

作者： Svetlov, Mikhail S. L’vov, Alexey A. Martirosyan, Arman M. Martirosyan, David M. Bagaev, Igor S. Khasanov, Semen T. Laboratory of Control and Automation Problems in Mechanical Engineering Institute of Precision Mechanics and Control of RAS Saratov Russia Institute of Institute of Electronic Engineering and Instrumentation Yuri Gagarin State Technical University of Saratov Saratov Russia Institute of Applied Information Technologies and Communications Yuri Gagarin State Technical University of Saratov Saratov Russia Phystech-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9798350393316

This paper proposes a semi-Markov model of telecommunication network (TCN). The variant of dynamic traffic adaptive control of queuing system as a special case of TCN is considered. The main purpose of control is to minimize the average cost per unit of time to service the incoming flow of information (packets). This takes into account the different bandwidth of the channels, the processing speed of information in the channel and the information capacity of the buffers. The approach to the organization of dynamic control taking into account noise immunity (information reliability) and information security is discussed. © 2024 IEEE.

关键词： Bandwidth

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Data augmentation method for light guide plate based on improved CycleGAN

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Optoelectronics Letters 2024年

作者： GONG Yefei YAN Chao XIAO Ming LU Mingli GAO Hua School of Electrical and Automation Engineering，Changshu Institute of Technology Jiangsu Engineering Research Center of Industrial Robot Complex Process Intelligent Control School of Electrical Engineering， Yancheng Institute of Technology Wuxi Novo Automation Technology Corp

An improved CycleGAN network method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production， as well as the problem of minor defects. Two optimizations are made to the generator of CycleGAN: 1) Fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features. 2) Combine self attention mechanism with residual network structure to replace the original residual module. Qualitative and quantitative experiments were conducted to compare different data augmentation methods， and the results showed that the defect images of the LGP generated by the improved network were more realistic， and the accuracy of the YOLOv5 detection network for the LGP was improved by 5.6%， proving the effectiveness and accuracy of the proposed method.

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