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Proceedings of the 18th USENIX Conference on Operating systems Design and Implementation

作者： Hanze Zhang Ke Cheng Rong Chen Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China and Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (纸本)9781939133403

Distributed lock services are extensively utilized in distributed systems to serialize concurrent accesses to shared resources. The need for fast and scalable lock services has become more pronounced with decreasing task execution times and expanding dataset scales. However, traditional lock managers, reliant on server CPUs to handle lock requests, experience significant queuing delays in lock grant latency. Advanced network hardware (e.g. programmable switches) presents an avenue to manage locks without queuing delays due to their high packet processing power. Nevertheless, their constrained memory capacity restricts the manageable lock scale, thereby limiting their effect in large-scale *** paper presents FISSLOCK, a fast and scalable distributed lock service that exploits the programmable switch to improve (tail) latency and peak throughput for millions of locks. The key idea behind FISSLOCK is the concept of lock fission, which decouples lock management into grant decision and participant maintenance. FISSLOCK leverages the programmable switch to decide lock grants synchronously and relies on servers to maintain participants (i.e., holders and waiters) asynchronously. By using the programmable switch for routing, FISSLOCK enables on-demand fine-grained lock migration, thereby reducing the lock grant and release delays. FISSLOCK carefully designs and implements grant decision procedure on the programmable switch, supporting over one million locks. Evaluation using various benchmarks and a real-world application shows the efficiency of FISSLOCK. Compared to the state-of-the-art switch-based approach (NetLock), FISSLOCK cuts up to 79.1% (from 43.0%) of median lock grant time in the microbenchmark and improves transaction throughput for TATP and TPC-C by 1.76× and 2.28×, respectively.

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Development of a Hypersonic Entry Test Platform

Development of a Hypersonic Entry Test Platform

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Johnson, Breanna J. Johnson, Kate E. Jang, Peter Steffes, Stephen R. Fritz, Matthew P. Hawke, Veronica Garcia, Joseph A. Morr, Donald E. NASA Johnson Space Center Flight Mechanics and Trajectory Design Branch HoustonTX77058 United States NASA Johnson Space Center Applied Aeroscience and CFD Branch HoustonTX77058 United States Aerodyne Industries Applied Aeroscience and CFD Branch HoustonTX77058 United States Charles Stark Draper Laboratory Navigation Algorithms and Software Space GNC Group HoustonTX77058 United States Science Technology Corporation Systems Analysis Office Mountain ViewCA United States NASA Ames Research Center NASA Systems Analysis Office Mountain ViewCA94035 United States Millennium Engineering and Integration Company ArlingtonVA22202 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

NASA is designing and building a low-cost, scalable, rapidly developed test platform to lower the barrier to entry for entry, descent, and landing (EDL) technologies. This HyperSonic Testing for Entry Platform, HyperSTEP, will enable the low-cost flight demonstration of hypersonic entry technologies, leveraging a publishable government reference design to further enable future government and industry landers. The HyperSTEP vehicle leverages a lifting body shape that was originally designed for a NASA human-class Mars mission concept vehicle, the CobraMRV. Lifting body vehicles offer increased volume efficiency and greater range control capabilities over capsules, making them an attractive class of vehicles to further develop for small to large payload return missions from the Moon, Mars, and beyond. Progress has been made in the development of the HyperSTEP vehicle in the areas of aerodynamics, aerothermodynamics, structural design and packaging, propulsion, guidance navigation and control (GN&C), decelerators (i.e., parachutes), and the thermal protection system (TPS). This paper details progress made in refining the mission concept of operations (ConOps) for the first notional missions and the development of the aerodynamic and aerothermodynamic databases, GN&C, and parachute system, as the project moves into its build and test phase. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： NASA

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A GNN-Based Learning Approach for Energy Optimization in Relay-Assisted IoT Networks

A GNN-Based Learning Approach for Energy Optimization in Rel...

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IEEE Conference on Wireless Communications and Networking

作者： Huapeng Yang Han Ji Zhangqin Huang Xiping Wu Beijing Engineering Research Center for IoT Software and Systems College of Computer Science Beijing University of Technology Beijing China School of Electrical and Electronic Engineering University College Dublin Dublin Ireland The National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Minimizing energy consumption is critical for the long-range (LoRa) Internet of Things (IoT) networks, to extend the battery lifetime of end devices (EDs) while reducing the maintenance cost. To overcome the excessive computational complexity required by the traditional optimization methods, learning approaches such as deep neural network (DNN) and reinforcement learning (RL) have been researched in wireless networks, where star topologies are widely employed. In contrast, LoRa usually deploys relays to assist the connection between the EDs and the gateway (GW), leading to a much more complex network topology. Consequently, DNN and RL would become less effective in LoRa, since those methods are difficult to learn the complex network topology constructed by LoRa. In this paper, we propose a learning method based on graph neural network (GNN), which is known for its prominent ability to capture and represent intricate graph-structural dependencies, to tackle the energy optimization problem for relay-assisted LoRa. Specifically, the multi-hop LoRa network is modeled as a directed graph, with channel state information (CSI) defined as node features, while the spreading factor and transmission power are deemed labels. A hierarchical message aggregation mechanism is proposed to effectively capture the multi-hop structural dependencies, followed by the process of inductive learning. Results show that against conventional optimization algorithms, the proposed method can achieve near-optimal energy consumption with a gap of 10% to 16%, while reducing the runtime by about six orders of magnitude. Compared to DNN, the GNN-based model can provide an energy saving of up to 32%, at a similar level of inference time.

关键词： Energy consumption Runtime Wireless networks LoRa Artificial neural networks Spread spectrum communication Complex networks Graph neural networks Topology Internet of Things

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EcoWeedNet: A Lightweight and Automated Weed Detection Method for Sustainable Next-Generation Agricultural Consumer Electronics

arXiv

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arXiv 2025年

作者： Khater, Omar H. Siddiqui, Abdul Jabbar Hossain, M. Shamim Dhahran31261 Saudi Arabia SDAIAKFUPM Joint Research Center on Artificial Intelligence IRC for Intelligent Secure Systems Department of Computer Engineering KFUPM Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Sustainable agriculture plays a crucial role in ensuring world food security for consumers. A critical challenge faced by sustainable precision agriculture is weed growth, as weeds share essential resources with the crops, such as water, soil nutrients, and sunlight, which notably affect crop yields. The traditional methods employed to combat weeds include the usage of chemical herbicides and manual weed removal methods However, these could damage the environment and pose health hazards. The adoption of automated computer vision technologies and ground agricultural consumer electronic vehicles in precision agriculture offers sustainable, low-carbon solutions. However, prior works suffer from issues such as low accuracy and precision and high computational expense. This work proposes EcoWeedNet, a novel model with enhanced weed detection performance without adding significant computational complexity, aligning with the goals of low-carbon agricultural practices. Additionally, our model is lightweight and optimal for deployment on ground-based consumer electronic agricultural vehicles and robots. The effectiveness of the proposed model is demonstrated through comprehensive experiments on the CottonWeedDet12 benchmark dataset reflecting real-world scenarios. EcoWeedNet achieves performance close to that of large models yet with much fewer parameters. (approximately 4.21% of the parameters and 6.59% of the GFLOPs of YOLOv4). This work contributes effectively to the development of automated weed detection methods for next-generation agricultural consumer electronics featuring lower energy consumption and lower carbon footprint. This work paves the way forward for sustainable agricultural consumer technologies. Copyright © 2025, The Authors. All rights reserved.

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YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

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IEEE Transactions on Intelligent Transportation systems 2025年

作者： Munir, Adnan Siddiqui, Abdul Jabbar Hossain, M. Shamim El-Maleh, Aiman Computer Engineering Department Dhahran31261 Saudi Arabia KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence IRC for Intelligent Secure Systems and Computer Engineering Department Dhahran31261 Saudi Arabia King Saud University College of Computer and Information Sciences Research Chair of Pervasive and Mobile Computing Department of Software Engineering Riyadh12372 Saudi Arabia KFUPM Computer Engineering Department Information and Computer Science Department IRC for Intelligent Secure Systems Dhahran31261 Saudi Arabia

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. © 2000-2011 IEEE.

关键词： Drones

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Keystone bacteria dynamics in chronic obstructive pulmonary disease (COPD): Towards differential diagnosis and probiotic candidates

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Heliyon 2025年第4期11卷 e42719页

作者： KavianFar, Azadeh Taherkhani, Hamidreza Lanjanian, Hossein Aminnezhad, Sargol Ahmadi, Ali Azimzadeh, Sadegh Masoudi-Nejad, Ali Laboratory of Systems Biology and Bioinformatics (LBB) Department of Bioinformatics Kish International Campus University of Tehran Kish Island Iran Software engineering department engineering faculty Istanbul Topkapi University Istanbul Türkiye Laboratory of Systems Biology and Bioinformatics (LBB) Institute of Biochemistry and Biophysics University of Tehran Tehran Iran Molecular Biology Research Center Systems Biology and Poisonings Institute Tehran Iran Chemical Injuries Research Center Systems Biology and Poisonings Institute Tehran Iran

Preventing exacerbations in Chronic Obstructive Pulmonary Disease (COPD) is crucial due to the high mortality rate and the associated costs of hospitalization for patients during exacerbations. Despite the proven influence of the lung microbiome on disease control, the dynamics of bacterial communication in different stages of COPD remain unknown. This study aimed to propose a group of candidate bacteria for the differential diagnosis of different states of COPD based on the relative abundance correlation of bacteria in lung sputum samples. We compared microbiome data collected from 101 COPD patients in stable and exacerbation states, as well as 124 healthy controls from two separate general cohorts, to determine the major microbiome and keystone genera. To validate our findings, we utilized two additional distinct public datasets, each comprising 81 healthy subjects and 87 COPD patients in stable condition, exacerbation, and post-treatment phases. During COPD exacerbation, Porphyromonas, Clostridium, Moryella, and Megasphaera were identified as phenotype-specific keystone genera, while Prevotella, Streptococcus, Haemophilus, and Veillonella were consistently present across all datasets as core microbiome members. Changes in keystone genera during different COPD stages indicate rewiring of bacterial interactions, with increased keystone bacteria and network connectivity observed during dysbiosis and more severe COPD. Bifidobacterium showed probiotic potential, positively correlating with Lactobacillus during exacerbation, while Neisseria and Haemophilus increased in abundance, and negatively correlated with key probiotic bacteria. These findings indicate promising potential for the simultaneous use of Bifidobacterium along with Lactobacillus as a therapeutic candidate to prevent COPD exacerbations in lung health, underscoring the need for further research in future clinical studies. © 2025

关键词： Co-occurrence network COPD Differential correlation Keystone bacteria Pulmonology Respiratory microbiome

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A direct predictive DC-link voltage control with warm-starting iterative approach for three-level NPC BTB converter fed PMSG-based wind turbine systems

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Expert systems with Applications 2025年 283卷

作者： Ganesh, Mayilsamy Jeong, Jae Hoon Research Center for Wind Energy systems School of IT Information and Control Engineering Kunsan National University 588 Daehak-ro Gunsan-si Jeonbuk54150 Korea Republic of College of Computer and Software Kunsan National University 588 Daehak-ro Gunsan-si Jeonbuk54150 Korea Republic of

This article presents a direct predictive DC-link voltage control (DPVC) scheme for a three-level (3L) neutral point clamped (NPC) back-to-back (BTB) converter fed permanent magnet synchronous generator (PMSG)-based wind turbine system (WTS). The conventional 3L NPC BTB converter employs an external PI controller to regulate the DC-link voltage at the set point by generating power or current references for the cascaded finite control set (FCS) predictive control. The presented strategy incorporates the DC-link voltage regulation term into the power regulation term in the grid-side converter (GSC) cost function, identifying the optimum switching state that directly regulates the DC-link voltage at the set point. Through this, the required active power is transferred to the grid to maintain power balance, and the conventional cascaded control structure is eliminated. In addition, a warm-starting iterative approach (WSIA) is introduced to derive the switching states employed for each switching instant. This reduces computation in both the GSC and machine-side converter (MSC) control and inherently incorporates switching frequency optimization. The presented scheme is validated in 3 MW rated WTS simulations under various operating conditions, with comparative studies performed to demonstrate its significance. © 2025 Elsevier Ltd

关键词： Predictive control systems

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Comparative analysis of text mining and clustering techniques for assessing functional dependency between manual test cases

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software Quality Journal 2025年第2期33卷 1-36页

作者： Tahvili, Sahar Hatvani, Leo Felderer, Michael de Oliveira Neto, Francisco Gomes Afzal, Wasif Feldt, Robert Compute Platforms Engineering Unit Ericsson AB Stockholm Sweden Department of Industrial AI Systems Mälardalen University Västerås Sweden Institute of Software Technology German Aerospace Center Cologne Germany Department of Mathematics and Computer Science University of Cologne Cologne Germany Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Engineering University of Gothenburg Gothenburg Sweden Department of Computer Science and Engineering Blekinge Institute of Technology Karlskrona Sweden

Text mining techniques, particularly those leveraging machine learning for natural language processing, have gained significant attention for qualitative data analysis in software testing. However, their complexity and lack of transparency can pose challenges, especially in safety-critical domains where simpler, interpretable solutions are often preferred unless accuracy is heavily compromised. This study investigates the trade-offs between complexity, effort, accuracy, and utility in text mining and clustering techniques, focusing on their application for detecting functional dependencies among manual integration test cases in safety-critical systems. Using empirical data from an industrial testing project at ALSTOM Sweden, we evaluate various string distance methods, NCD compressors, and machine learning approaches. The results highlight the impact of preprocessing techniques, such as tokenization, and intrinsic factors, such as text length, on algorithm performance. Findings demonstrate how text mining and clustering can be optimized for safety-critical contexts, offering actionable insights for researchers and practitioners aiming to balance simplicity and effectiveness in their testing workflows.

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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CESFusion: Cross-Frequency Enhanced Spatial-Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China Engineering Research Center of Learning- Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Institute of Intelligent Media Computing Hangzhou Dianzi University Hangzhou310018 China

The fusion of hyperspectral and multispectral images involves integrating high spectral resolution hyperspectral image (HSI) and high spatial resolution multispectral image (MSI) to generate a HSI with high spatial and spectral resolution (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this paper, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency domain fusion module (CFFM) to perform global analysis through the Fourier transform and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 2025 IEEE.

关键词： Image fusion

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