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A Novel Labeling Method for Radar Pulse Data With Complex Modulations and Parametric Overlaps

IEEE ACCESS 2025年 13卷 58978-58991页

作者： Wei, Youtan Shen, Yang Southwest Inst Elect Equipment Chengdu 610036 Peoples R China

This paper aims to solve a problem: using azimuthal information to label radar pulse data with complex inter-pulse modulations and overlaps on electromagnetic parameters. In these tough conditions, azimuthal information is the key for data labeling instead of parameter-based methods. According to the measurement principle, the azimuthal information from the identical emitter shares the same positional relationship between the radiation source and the receivers. We propose a transformation, the inv-Hough transform, to reveal this positional relationship. The data from the identical emitter show highly linearly correlated distributions in the transformed space. Through transformation, the problem becomes a clustering problem. Dirichlet Process Mixture Models (DPMM) are widely used to address clustering problems, as they can automatically estimate the number of clusters. However, the outliers deteriorate clustering performances. Therefore, two improvements based on DPMM are proposed: adding a pre-clustering step to improve the outlier adaptation and distributed inference to improve the federated learning capability. The experimental results show that our method outperforms parameter-based methods when complex interpulse modulations and overlaps on electromagnetic parameters occur. In addition, the pre-clustering step significantly improves outlier adaptation and parametric stability for our method. And the method can work in federated learning scenarios.

关键词： Labeling Radar Receivers Transforms Sorting Radar signal processing Federated learning Reconnaissance Mixture models Electromagnetics Azimuth distributed computing Dirichlet process mixture models federated learning Hough transform labeling pulse descriptive word radar signal processing

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High-Fidelity High-Resolution Regional Seismic Risk and Resilience Assessment of Large Building Inventories

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2025年第5期54卷 1376-1396页

作者： Dahal, Laxman Burton, Henry Zhong, Kuanshi Univ Calif Los Angeles Dept Civil & Environm Engn Los Angeles CA 90095 USA Univ Cincinnati Dept Civil & Architectural Engn & Construct Manage Los Angeles OH USA

Historically, building design codes have been governed by the life-safety performance standard with minimal emphasis on other metrics such as economic loss and downtime. However, recent advancements in earthquake engineering have put in motion a national-level initiative to explicitly codify functional recovery as a target performance objective. While the importance of this effort has been broadly acknowledged by engineers and policymakers, there is a need to develop efficient computational strategies for evaluating the performance and cost-benefit implications of these resilience-based standards at the regional scale. It is with this vision in mind that this paper makes two primary contributions. First, an efficient computational framework for performing high-fidelity high-resolution seismic risk and resilience assessments of large building inventories is proposed. The framework harnesses automation, optimization, and distributed computing to render high fidelity and resolution risk-based seismic assessments computationally feasible. Second, a new approach to performing stochastic event set-based evaluation building portfolios is proposed, which utilizes building-specific performance-based assessments that include site-specific ground motion selection. A case study is presented on a risk-based seismic loss and functional recovery evaluation of more than 15,000 residential woodframe buildings in the City of Los Angeles. By leveraging automation, optimization, and high-performance computing, we are able to reduce the runtime for such an assessment from months to days.

关键词： automation distributed computing high-fidelity high-resolution regional assessment resilience seismic risk

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A model for distributed in-network and near-edge computing with heterogeneous hardware

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 105卷 395-409页

作者： Cooke, Ryan A. Fahmy, Suhaib A. Univ Warwick Sch Engn Coventry W Midlands England

Applications that involve analysis of data from distributed networked data sources typically involve computation performed centrally in a datacenter or cloud environment, with some minor preprocessing potentially performed at the data sources. As these applications grow in scale, this centralized approach leads to potentially impractical bandwidth requirements and computational latencies. This has led to interest in edge computing, where processing is moved nearer to the data sources, and recently, in-network computing, where processing is done as data progresses through the network. This paper presents a model for reasoning about distributed computing at the edge and in the network, with support for heterogeneous hardware and alternative software and hardware accelerator implementations. Unlike previous distributed computing models, it considers the cost of computation for compute-intensive applications, supports a variety of hardware platforms, and considers a heterogeneous network. The model is flexible and easily extensible for a range of applications and scales, and considers a variety of metrics. We use the model to explore the key factors that influence where computational capability should be placed and what platforms should be considered for distributed applications. (C) 2019 Elsevier B.V. All rights reserved.

关键词： distributed computing Edge computing In-network computing Hardware acceleration

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Luby's MIS algorithms made self-stabilizing

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INFORMATION PROCESSING LETTERS 2025年 188卷

作者： Giakkoupis, George Turau, Volker Ziccardi, Isabella Univ Rennes Inria CNRS IRISA Rennes France Hamburg Univ Technol Inst Telemat Hamburg Germany Bocconi Univ BIDSA Milan Italy

We reconsider two well-known distributed randomized algorithms computing a maximal independent set, proposed in the seminal work of Luby (1986). We enhance these algorithms such that they become self-stabilizing without sacrificing their run-time, i.e., both stabilize in O (log n ) synchronous rounds with high probability on any n-node graph. The first algorithm gets along with three states, but needs to know an upper bound on the maximum degree. The second does not need any information about the graph, but uses a number of states that is linear in the node degree. Both algorithms use messages of logarithmic size.

关键词： Self-stabilization Fault tolerance distributed computing Maximal independent set (MIS)

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***: a scalable Julia toolbox for level set-based topology optimisation

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2025年第1期68卷 1-23页

作者： Wegert, Zachary J. Manyer, Jordi Mallon, Connor N. Badia, Santiago Challis, Vivien J. Queensland Univ Technol Sch Math Sci 2 George St Brisbane Qld 4000 Australia Monash Univ Sch Math Wellington Rd Clayton Vic 3800 Australia Monash Univ Dept Chem & Biol Engn Wellington Rd Clayton Vic 3800 Australia

In this paper, we present GridapTopOpt, an extendable framework for level set-based topology optimisation that can be readily distributed across a personal computer or high-performance computing cluster. The package is written in Julia and uses the Gridap package ecosystem for parallel finite element assembly from arbitrary weak formulations of partial differential equations (PDEs) along with the scalable solvers from the Portable and Extendable Toolkit for Scientific computing (PETSc). The resulting user interface is intuitive and easy-to-use, allowing for the implementation of a wide range of topology optimisation problems with a syntax that is near one-to-one with the mathematical notation. Furthermore, we implement automatic differentiation to help mitigate the bottleneck associated with the analytic derivation of sensitivities for complex problems. GridapTopOpt is capable of solving a range of benchmark and research topology optimisation problems with large numbers of degrees of freedom. This educational article demonstrates the usability and versatility of the package by describing the formulation and step-by-step implementation of several distinct topology optimisation problems. The driver scripts for these problems are provided and the package source code is available at https://***/zjwegert/***.

关键词： Topology optimisation Level set method distributed computing Automatic differentiation Julia

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Scalable and accurate online multivariate anomaly detection

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INFORMATION SYSTEMS 2025年 131卷

作者： Salles, Rebecca Lange, Benoit Akbarinia, Reza Masseglia, Florent Ogasawara, Eduardo Pacitti, Esther Natl Inst Res Digital Sci & Technol INRIA Montpellier France Univ Montpellier Montpellier France Fed Ctr Technol Educ Rio de Janeiro CEFET RJ Rio De Janeiro Brazil

The continuous monitoring of dynamic processes generates vast amounts of streaming multivariate time series data. Detecting anomalies within them is crucial for real-time identification of significant events, such as environmental phenomena, security breaches, or system failures, which can critically impact sensitive applications. Despite significant advances in univariate time series anomaly detection, scalable and efficient solutions for online detection in multivariate streams remain underexplored. This challenge becomes increasingly prominent with the growing volume and complexity of multivariate time series data in streaming scenarios. In this paper, we provide the first structured survey primarily focused on scalable and online anomaly detection techniques for multivariate time series, offering a comprehensive taxonomy. Additionally, we introduce the Online distributed Outlier Detection (2OD) methodology, a novel well-defined and repeatable process designed to benchmark the online and distributed execution of anomaly detection methods. Experimental results with both synthetic and real-world datasets, covering up to hundreds of millions of observations, demonstrate that a distributed approach can enable centralized algorithms to achieve significant computational efficiency gains, averaging tens and reaching up to hundreds in speedup, without compromising detection accuracy.

关键词： Multivariate Time series Online Anomaly detection Scalable Accurate detection distributed computing

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Pipeline parallelism with reduced network communication for efficient compute-intensive neural network training

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JOURNAL OF SUPERcomputing 2025年第5期81卷 1-22页

作者： Yu, Chanhee Park, Kyongseok Univ Sci & Technol Dept Big Data Sci 245 Daehak ro Daejeon 34112 South Korea Univ Sci & Technol Dept Appl AI 245 Daehak ro Daejeon 34112 South Korea Korea Inst Sci & Technol Informat Ctr Supercomp Technol Dev 245 Daehak ro Daejeon 34141 South Korea

Pipeline parallelism is a distributed method used to train deep neural networks and is suitable for tasks that consume large amounts of memory. However, this method entails a large overhead because of the dependency between devices for performing forward and backward steps using multiple accelerator devices. Although a method to remove forward step dependency through the all-to-all approach has been proposed for training compute-intensive models, it incurs a large overhead when training with many devices and is inefficient with respect to weight memory consumption. Alternatively, we propose a pipeline parallelism method that reduces both network communication using a self-generation concept and overhead by minimizing the weight memory used for acceleration. In a DarkNet53 training throughput experiment using six devices, the proposed method outperforms a baseline by approximately 63.7% in reduction of overhead and communication costs and achieves less memory consumption by approximately 17.0%.

关键词： distributed computing Model parallelism Synchronous pipeline parallelism Deep learning

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On the computational power of energy-constrained mobile robots

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INFORMATION AND COMPUTATION 2025年 303卷

作者： Buchin, Kevin Flocchini, Paola Kostitsyna, Irina Peters, Tom Santoro, Nicola Wada, Koichi Tech Univ Dortmund Dortmund Germany Univ Ottawa Ottawa ON Canada KBR NASA Ames Res Ctr Moffett Field CA USA TU Eindhoven Eindhoven Netherlands Carleton Univ Ottawa ON Canada Hosei Univ Tokyo Japan

We consider distributed systems of autonomous robots operating in the plane under synchronous Look-Compute-Move (LCM) cycles. Prior research on four distinct models assumes robots have unlimited energy. We remove this assumption and investigate systems where robots have limited but renewable energy, requiring inactivity for energy restoration. We analyze the computational impact of this constraint, fully characterizing the relationship between energy-restricted and unrestricted robots. Surprisingly, we show that energy constraints can enhance computational power. Additionally, we study how memory persistence and communication capabilities influence computation under energy constraints. By comparing the four models in this setting, we establish a complete characterization of their computational relationships. A key insight is that energy-limited robots can be modeled as unlimited-energy robots controlled by an adversarial activation scheduler. This provides a novel equivalence framework for analyzing energy-constrained distributed systems. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： distributed computing Mobile computational entities Energy constraint Synchronous adversarial schedulers

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Adaptive incremental transfer learning for efficient performance modeling of big data workloads

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 166卷

作者： Garralda-Barrio, Mariano Eiras-Franco, Carlos Bolon-Canedo, Veronica Univ A Coruna CITIC La Coruna Spain

The rise of data-intensive scalable computing systems, such as Apache Spark, has transformed data processing by enabling the efficient manipulation of large datasets across machine clusters. However, system configuration to optimize performance remains a challenge. This paper introduces an adaptive incremental transfer learning approach to predicting workload execution times. By integrating both unsupervised and supervised learning, we develop models that adapt incrementally to new workloads and configurations. To guide the optimal selection of relevant workloads, the model employs the coefficient of distance variation (CdV) and the coefficient of quality correlation (CqC), combined in the exploration-exploitation balance coefficient (EEBC). Comprehensive evaluations demonstrate the robustness and reliability of our model for performance modeling in Spark applications, with average improvements of up to 31% over state-of-the-art methods. This research contributes to efficient performance tuning systems by enabling transfer learning from historical workloads to new, previously unseen workloads. The full source code is openly available.

关键词： Performance modeling Big data Machine learning Apache spark distributed computing

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Blockchain-Based Federated Learning Methodologies in Smart Environments for Drone Technology

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第0期

作者： Chalapathi, Mukkoti Maruthi Venkata Sreenivasulu, K. Jeya, R. Faheem, Muhammad Mohana, R. Madana Khan, Arfat Ahmad Ramana, Kadiyala VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India Anantha Lakshmi Inst Technol & Sci Dept Comp Sci & Engn Anantapuramu Andhra Pradesh India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Chennai 603203 Tamil Nadu India VTT Tech Res Ctr Finland Espoo 04150 Finland Stanley Coll Engn & Technol Women Dept Comp Sci & Engn Hyderabad 500001 Telangana India Khon Kaen Univ Coll Comp Dept Comp Sci Khon Kaen 40002 Thailand Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad 500075 Telangana India

High-security transactions are stored in a chain of blocks using blockchain technology. Security and privacy concerns may be addressed by using blockchain technology. Federated learning is a paradigm for increasing data mining accuracy and precision by ensuring data privacy and security for both internet of things (IoT) devices and users in smart environments. Algorithms for dealing with limited training data and avoiding a particular model are included in the proposed model. Drones are indeed being researched and proactively employed in emergency situations, as well as catastrophic and high-casualty situations. Governance, security, flying circumstances, security and privacy, authorization, confidentiality, and specifics around the creation, maintenance, and operation of a medical drone network are now obstacles to extending their usage in emergency medicine and emergency medical service (EMS). In this paper, we present the more effective FL to protect the data privacy of drones, which involves doing local and global parameter updates for drones and exchanging training parameters concerning fog nodes, rather than sending drone raw data to the cloud. Even so, eavesdropping and analyzing parameters that are uploaded during the training procedure might still provide ground eavesdroppers with information on drone privacy and operations. Specifically, in this work, we examine how to optimize the power management strategies to optimize all the required parameters of FL security cost while being bound by battery usage of drone capacity and the necessity for quality of service (QoS) (i.e., required training time). Extensive simulations were conducted, and the results demonstrate that the proposed Secure Federated Power Control (SFPC) can effectively improve utilities for drones, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes. (c) 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LL

关键词： distributed computing sustainable society blockchain federated learning internet of things

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