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FPGA-based Real-time High-resolution Image Processing for rocket on-board processing

FPGA-based Real-time High-resolution Image Processing for ro...

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international conference on Computer Research and Development (ICCRD)

作者： Hui Zhou Xiaofeng Wang YuJia Xie Yongxiang Cao Yu Tang Yongchun Huo Huan Wen Xiongbo Zhao Chaoran Li Beijing Aerospace Automatic Control Institute National key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing China Computer Science and Engineering Beihang University Beijing China National Superior College for Engineers Beihang University Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

the structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. this paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. this approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. the overall computing efficiency, excluding the first layer, exceeded 90%. the peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for

关键词： YOLO Rockets Power demand Convolution Multicore processing Reviews Scalability Accelerator architectures Real-time systems Field programmable gate arrays

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A Column Sparse Controller Design for distributed Networked Control Systems 8th

A Column Sparse Controller Design for Distributed Networked...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Guan, Yanpeng Yin, Hongjie Sun, Chao Yao, Qingsheng Shanxi University Taiyuan030013 China Kunming Shipborne Equipment Research and Test Center Kunming650216 China

ISBN: (纸本)9789819602933

this paper presents a distributed structured controller design method in the case of finite number of subsystems with sensor failures. the constraint on the number of sensor failures is represented by a cardinality constraint on the controller gain matrix. A mixed integer programming method is employed to deal with this type of column cardinality constraint. By using the Lyapunov functional method, we present a sufficient condition that guarantees the H∞ asymptotic stability of the closed-loop system. And a column sparse controller design method is presented in the form of linear matrix inequalities. Finally, a numerical example is provided to illustrate the effectiveness of the developed approach. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lyapunov functions

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Stealthy False Data Injection Attacks Design and Scalable Detection in DC Microgrids 8th

Stealthy False Data Injection Attacks Design and Scalable D...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wu, Zhihua Peng, Chen Tian, Engang Shanghai University Shanghai200444 China University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819602247

the main purpose of this article is to design a stealthy false data injection (FDI) attack and corresponding scalable detection mechanism for DC Microgrids. Firstly, a DC microgrid model is established that includes disturbances, measurement noise, and FDI attacks. then, a novel FDI attack is designed to disrupt voltage balance and evade detectors based on unknown input observers (UIOs). Subsequently, a scalable detection mechanism combining distributed observers and UIOs is proposed to detect the novel attack, which does not rely on global system information (such as fixed topology) and therefore does not require global redesign when DGUs are plugged and unplugged. Finally, the theoretical results are verified by the SimPowerSystems Toolbox. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Lightweight distributed Ledger technology and Consensus Algorithms for D2D Communication 4th

Lightweight Distributed Ledger Technology and Consensus Alg...

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4th international conference on Advanced Network Technologies and intelligent computing, ANTIC 2024

作者： Kumari, Richa Singh, Kushall Pal Jangir, Sandeep Tyagi, Dinesh Kumar Battula, Ramesh Babu Malaviya National Institute of Technology Rajasthan Jaipur India

ISBN: (纸本)9783031837821

Device-to-device (D2D) communication is an emerging technology in Beyond-5G (B5G) networks, offering seamless connections and energy-efficient communication. However, D2D communication faces significant security challenges, including data protection, user privacy, and efficient resource sharing and management. distributed ledger technology (DLT) has emerged as a promising solution to address these concerns by preserving anonymity, context privacy, and data secrecy in mobile D2D communications. Additionally, DLT incentivizes honest D2D users to share resources through reward mechanisms. this research paper presents the type of DLT and explains the benefits and application of DAG-based DLT in D2D communication. Furthermore, a comprehensive analysis and comparison of consensus algorithms is provided, along with the implementation of one DAG-based DLT tailored by the proof-of-context (PoC) and proof-of-equipment (PoE) consensus algorithms. the reputation score of D2D users is identified to enhance the efficiency and reliability of the D2D communication network. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： distributed ledger

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Feature Selection and Classification Using an Ensemble Learning Model on Leh Ladakh Water Data 12th

Feature Selection and Classification Using an Ensemble Lea...

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12th international conference on Frontiers of intelligent computing: theory and Applications, FICTA 2024

作者： Sahu, Nitesh Kumar Sahu, Mridu Golchha, Roopa Jhariya, D.C. Shrivastav, Mayank Kumar Singh, Chandan Department of Information Technology National Institute of Technology Raipur Chhattisgarh India Department of Applied Geology National Institute of Technology Raipur Chhattisgarh India Department of Civil Engineering National Institute of Technology Raipur Chhattisgarh India

ISBN: (纸本)9789819601387

the problem of safe drinking water is the biggest challenge faced by Leh, which is the northernmost part of India. Leh City is facing a problem of non-potable water and water scarcity. To address this problem, there is a need for effective water harvesting techniques and water quality management. this study examines water quality parameters: pH, temperature, potassium, calcium, chloride, magnesium, total hardness, and alkalinity for total dissolved solids prediction. the proposed framework utilizes interpolation techniques to increase the dataset population and investigate relevant features using mutual information importance and recursive feature elimination. A machine-learning model called Random Forest is used to predict the total dissolved solids. Random forest with RFE achieved an R2 value of 0.991 with a lower MAE of 0.0128 and an MSE of 0.0003, outperforming random forest with mutual information, which achieved an R2 value of 0.990 with a lower MAE of 0.014 and an MSE of 0.0004. this finding indicates that the Random Forest model with recursive feature elimination is more suitable for the Leh Ladakh data set. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision trees

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distributed MPC Compensation-Based LFC for Heterogeneous Multi-area Interconnected Power Systems 8th

Distributed MPC Compensation-Based LFC for Heterogeneous Mul...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Chi, Xiaobo Li, Xinyi Liu, Xiaolin Jia, Xinchun School of Automation and Software Engineering Shanxi University Taiyuan030031 China School of Mathematical Sciences Shanxi University Taiyuan030006 China The Key Laboratory of Complex Systems and Data Science Ministry of Education Taiyuan030006 China

ISBN: (纸本)9789819602247

this paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) with thermal and wind power generations, considering both inter-area and intra-area transmission delays and random packet losses induced by communication networks. Firstly, a heterogeneous framework of MAIPSs is constructed, where a networked distributed model predictive control (DMPC) scheme is proposed to compensate for transmission delays and random packet losses. Based on this framework, we then developed a distributed closed-loop LFC system model for heterogeneous MAIPS. Subsequently, a Luenberger observer is constructed to estimate the system state of the local area, and a networked feedback controller is designed to ensure stability with a certain H∞ performance level for the networked MAIPSs. Moreover, stability conditions and controller design criteria are provided by using the Lyapunov-Krasovskii stability theory. Finally, the control performance of the proposed method is verified through an example of a two-area interconnected power system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Predictive control systems

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Development of Blockchain Integrated Agricultural Land Lease Framework with a Voting-Based Consensus Protocol Using Event-B 4th

Development of Blockchain Integrated Agricultural Land Leas...

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4th international conference on Advanced Network Technologies and intelligent computing, ANTIC 2024

作者： Verma, Sudhani Yadav, Divakar Chandra, Girish Department of Computer Science and Engineering Institute of Engineering and Technology AKTU-Jankipuram Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9783031837821

India’s agriculture sector, contributes 18% to the national GDP, remains largely unorganized. this study proposes a blockchain-based solution to digitize land lease contracts, aiming to reduce risks of document forgery and fraud. the proposed system has transactions such as stamp procurement, registration of land and users etc. which are validated and stored using blockchain. To ensure fairness in execution of transactions, a voting-based consensus algorithm is also proposed. Compared to conventional consensus methods, the proposed algorithm achieves a 39% increase in throughput by reducing message exchange overhead. the proposed algorithm is formally verified using Event-B. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blockchain Consensus Protocols distributed Ledger technology (DLT) Event-B Formal Methods

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Task Scheduling in distributed Real-Time Systems Using Hybrid Model Based on ACO-GA 4th

Task Scheduling in Distributed Real-Time Systems Using Hybr...

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4th international conference on Advanced Network Technologies and intelligent computing, ANTIC 2024

作者： Sharma, Anchal Sharma, Sangeeta thakur, Sanat Kumar, Ankush Mahato, Dharmendra Prasad National Institute of Technology Hamirpur Himachal Pradesh Hamirpur177 005 India

ISBN: (纸本)9783031837821

Task scheduling in distributed real-time systems (DRTS) is a complex problem due to the heterogeneous nature of processors and the need for efficient resource utilization. this paper presents a novel hybrid model combining Ant Colony Optimization (ACO) and Genetic Algorithm (GA) to address the task scheduling problem. the proposed model aims to minimize system cost and response time while maximizing system reliability by leveraging the exploration capabilities of ACO and the solution refinement strength of GA. the hybrid algorithm operates in two phases: the first phase uses ACO to generate initial task assignments by dynamically exploring the search space based on pheromone trails and heuristic information. In the second phase, GA refines these solutions through crossover and mutation operators optimizing the task allocation further. Experimental results demonstrate that the proposed ACO-GA hybrid model significantly improves task scheduling performance compared to standalone algorithms by achieving better resource utilization, by reducing execution time, and by enhancing the system reliability. this approach is especially effective for real-time applications where optimal task assignment is critical to system performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant colony optimization

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An Adaptive Discrete Human Learning Optimization for TSP-Like Problems 8th

An Adaptive Discrete Human Learning Optimization for TSP-Lik...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Ling Kang, Xing Wang, Mengzhen Yao, Jun Pfaender, Fabien Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200444 China Sino-European School of Technology Shanghai University Shanghai200444 China

ISBN: (纸本)9789819602933

Human learning optimization (HLO) is a novel inborn binary meta-heuristic algorithm inspired by a simple human learning model. Although HLO, as a binary algorithm, can directly solve discrete (integer) problems, the efficiency may be significantly spoiled for the large-scale discrete problems due to the curse of dimension. therefore, an adaptive discrete human learning optimization for TSP-like problems (ADHLOT) is proposed in which the random learning operator, individual learning operator, social learning operator and the re-learning operation of HLO are extended or re-designed to improve the efficiency of the algorithm for TSP-like problems. Furthermore, an adaptive strategy is used in ADHLOT to dynamically adjust the social learning rate for achieving a better balance between exploration and exploitation. Finally, ADHLOT is evaluated by solving TSP benchmark problems and compared with recent meta-heuristic algorithms. the experimental results show that the ADHLOT has obvious advantages and shows promising potential for applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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NOBGP: A Novel Optimized Balanced Graph Partitioning Algorithm 19th

NOBGP: A Novel Optimized Balanced Graph Partitioning Algorit...

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19th CCF conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Chen, Jiebin Hu, Ziqiang Ye, Renjie Zhang, Qishan Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai China

ISBN: (纸本)9789819623723

Large-scale graphs have become prevalent with the advent of the big data era. distributed graph computing systems are commonly used for processing and analyzing large-scale graphs, with graph partitioning being a key prerequisite for their efficient computation. Graph partitioning aims to balance the load across partitions while minimizing the number of cut-edges. Moreover, it should achieve high efficiency and scalability. However, the existing popular graph partitioning algorithms do not fully take into account the internal topology of real-world graphs, which affects the final partition quality and convergence. Meanwhile, they easily fall into the local optimum due to partition load constraints. this paper introduces a Novel Optimized Balanced Graph Partitioning algorithm (NOBGP). First, we propose an initialization strategy based on label propagation of core vertices to achieve initial partitions with good locality and accelerate convergence. Second, we optimize the label propagation process to ensure balanced partitions and propose a probability-based disruption strategy to avoid the local optimum. We implement NOBGP on the distributed graph computing framework GraphX. Extensive experimental results on real-world graphs show that the proposed algorithm is scalable and performs better than the existing algorithms. We also run PageRank and Louvain applications using the graph partitioning results to demonstrate the efficiency of our algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph algorithms

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