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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Irfan Khan Mehedi Hasan Shamsul Islam Suman Reza Tuhin Reza Erteza Tawsif Efaz Department of Computer Science & Engineering Faridpur Engineering College Faridpur Bangladesh Division of Electrical & Computer Engineering Louisiana State University Baton Rouge Louisiana United States

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In Bangladesh, most four-legged intersections use a static-timed or manually-controlled traffic signaling approach, which creates irrefutable congestion and requires human involvement. This paper proposes an adaptive traffic control system utilizing the YOLO object detection framework within CNNs to address this issue. The system captures and frames images using cameras facing every road at a roundabout to detect and count vehicles in real-time for providing lights based on the traffic conditions, prioritizing emergency vehicles such as ambulances, firetrucks, and police-car while also maintaining queues. It sets the signal duration considering road condition. Using a dataset of 10,462 images across 12 classifications of automobiles and a transfer learning approach in YOLOv8, the model achieved a mAP50 score of 0.90. This automated method reasonably enhances traffic flow management by dynamically adjusting signal lights without human intervention.

关键词： YOLO Adaptation models Adaptive systems Roads Transfer learning Traffic control Cameras Real-time systems Vehicle dynamics Software development management

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Theoretical Modeling of Substrate-Integrated Impedance Surfaces

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IEEE Transactions on Antennas and Propagation 2025年

作者： Al-Khaibari, Asim Wu, Nanshu Bagci, Hakan Erricolo, Danilo Chen, Pai-Yen University of Illinois Chicago Department of Electrical and Computer Engineering ChicagoIL United States Electrical and Mathematical Science and Engineering Division Thuwal Saudi Arabia

Substrate-integrated impedance surfaces (SIIS) or substrate-integrated metasurfaces have recently been proposed to tailor the dispersion and eigenmodes of waveguides, resonators, and cavity antennas, as well as to develop reconfigurable epsilon-near-zero (ENZ) channel with enhanced supercoupling, electric field localization, and sensitivity to perturbations. Here, we present a compact analytical model for calculating the effective surface impedance of the capacitive SIIS formed by a one-dimensional (1D) array of blind vias embedded in a substrateintegrated waveguide (SIW). The analytical model is validated using numerical examples and simulations, achieving good accuracy even for SIISs consisting of the non-diluted and dense array of blind vias. Our theoretical results may be beneficial for SIIS-loading techniques and SIWs, which are critical to realizing the full potential of the next-generation microwave/millimeter-wave communication, sensing, and cavity quantum electrodynamics (cQED) systems. © 1963-2012 IEEE.

关键词： Microwave generation

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Large Language Model with Region-guided Referring and Grounding for CT Report Generation

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IEEE Transactions on Medical Imaging 2025年

作者： Chen, Zhixuan Bie, Yequan Jin, Haibo Chen, Hao Hong Kong University of Science and Technology University Department of Computer Science and Engineering Hong Kong Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Division of Life Science Hong Kong Hong Kong

Computed tomography (CT) report generation is crucial to assist radiologists in interpreting CT volumes, which can be time-consuming and labor-intensive. Existing methods primarily only consider the global features of the entire volume, making it struggle to focus on specific regions and potentially missing abnormalities. To address this issue, we propose Reg2RG, the first region-guided referring and grounding framework for CT report generation, which enhances diagnostic performance by focusing on anatomical regions within the volume. Specifically, we utilize masks from a universal segmentation module to capture local features for each referring region. A local feature decoupling (LFD) strategy is proposed to preserve the local high-resolution details with little computational overhead. Then the local features are integrated with global features to capture inter-regional relationships within a cohesive context. Moreover, we propose a novel region-report alignment (RRA) training strategy. It leverages the recognition of referring regions to guide the generation of region-specific reports, enhancing the model's referring and grounding capabilities while also improving the report's interpretability. A large language model (LLM) is further employed as the language decoder to generate reports from integrated visual features, facilitating region-level comprehension. Extensive experiments on two large-scale chest CT-report datasets demonstrate the superiority of our method, which outperforms several state-of-the-art methods in terms of both natural language generation and clinical efficacy metrics while preserving promising interpretability. The code is available at https://***/zhi-xuan-chen/Reg2RG. © 1982-2012 IEEE.

关键词： Report generators

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Scalable HPC Job Scheduling and Resource Management in SST 24

Scalable HPC Job Scheduling and Resource Management in SST

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Proceedings of the Winter Simulation Conference

作者： Abubeker Abdurahman Abrar Hossain Kevin A. Brown Kazutomo Yoshii Kishwar Ahmed Dept of Electrical Engineering and Computer Science The University of Toledo Toledo OH USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA

ISBN: (纸本)9798331534202

Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces a workflow management components that supports the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance.

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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Hierarchical Scheduling for Oil Spill SAR Image Detection in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Son, Seok Bin Park, Soohyun Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of

Oil spills represent significant environmental hazards in ocean ecosystems, requiring rapid and accurate detection and response mechanisms. Due to its efficacy, synthetic aperture radar (SAR) is an important tool for remote monitoring of ocean environments. However, operational constraints such as communication time between low Earth orbit (LEO) satellites and ground stations (GS), as well as finite buffers, pose challenges. To address these issues, this paper proposes a space-air-ground-integrated network (SAGIN) scenario comprising LEO satellites, high altitude platforms (HAPs), and ground stations (GSs), suitable for rapid and accurate oil spill detection using SAR images. In addition, a novel three-step algorithm is proposed to facilitate SAR image transmission effectively. The first step of the proposed algorithm focuses on maximizing the transmission of ocean SAR images from the LEO satellites to the HAPs. This optimization involves the neural Myerson auction (NMA)-based scheduler, a second-price auction mechanism with deep learning methodologies for truthfulness, distributed computation, and low computational complexity. In addition, the pathloss is also considered for effective communication system design. The second step algorithm aims to maximize data transmission efficiency from HAPs to GSs, employing scheduling strategies based on the Markov decision process (MDP). This algorithm enables optimal decision-making over time steps, considering the throughput of each GS and the age of the information (AoI) ratio. Finally, an image compression rate control algorithm based on Lyapunov optimization is employed at the GSs to enhance detection accuracy while ensuring queue stability. The performance of the proposed three-step algorithm is intensively evaluated against various benchmark algorithms, demonstrating its superiority in oil spill detection efficacy. © 1967-2012 IEEE.

关键词： Oil spills

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Joint Quantum Reinforcement Learning and Neural Myerson Auction for High-Quality Digital-Twin Services in Multi-Tier Networks

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IEEE Internet of Things Journal 2025年

作者： Park, Soohyun Kim, Gyu Seon Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul04310 Korea Republic of Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of

In order to build realistic digital-twin systems, this paper proposes a novel two-stage algorithm for high-quality digital-twin services in cloud-assisted multi-tier networks. In our proposed algorithm, the first stage is quantum multi-agent reinforcement learning (QMARL)-based scheduling for differentiated quality control of individual segments of digital-twin virtual objects in our cloud. As the number of segments selected by each edge increases, the edge's action dimension expands exponentially, posing significant challenges to learning with conventional MARL. To solve this problem, the quantum-inspired MARL-based scheduler is considered in order to reduce the scheduling action dimensions into a logarithmic-scale. For the scheduling formulation, age-of-information (AoI) is also considered for low-latency high-quality digital-twin services. Additionally, the second stage is for the fast and seamless distribution of differentiated quality-controlled segments of virtual objects. For this objective, each user requests its desired segments and one of nearby edges is selected. Among various approaches, this second stage considers second price auction for truthful and distributed computation. Furthermore, low-complexity computation can be realized by avoiding integer programming based computation which is NP-hard. The proposed two-stage algorithm achieves performance levels that are 8.33 and 1.18 times higher in terms of reward value in high dimensions and revenue, respectively, compared to other benchmarks. © 2014 IEEE.

关键词： Scheduling algorithms

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Understanding, Implementing, and Supporting Security Assurance Cases in Safety-Critical Domains

arXiv

引用

arXiv 2025年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, software, and connectivity have become integral to meeting market expectations. Regulatory bodies now require security assurance cases (SAC) to verify compliance, as demonstrated in ISO/SAE-21434 for automotive. However, existing approaches for creating SACs do not adequately address industry-specific constraints and requirements. In this thesis, we present CASCADE, an approach for creating SACs that aligns with ISO/SAE-21434 and integrates quality assurance measures. CASCADE is developed based on insights from industry needs and a systematic literature review. We explore various factors driving SAC adoption, both internal and external to companies in safety-critical domains, and identify gaps in the existing literature. Our approach addresses these gaps and focuses on asset-driven methodology and quality assurance. We provide an illustrative example and evaluate CASCADE's suitability and scalability in an automotive OEM. We evaluate the generalizability of CASCADE in the medical domain, highlighting its benefits and necessary adaptations. Furthermore, we support the creation and management of SACs by developing a machine-learning model to classify security-related requirements and investigating the management of security evidence. We identify deficiencies in evidence management practices and propose potential areas for automation. Finally, our work contributes to the advancement of security assurance practices and provides practical support for practitioners in creating and managing SACs. Copyright © 2025, The Authors. All rights reserved.

关键词： Automotive industry

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Enhancing Network Security Management in Water Systems using FM-based Attack Attribution

arXiv

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arXiv 2025年

作者： Avdalović, Aleksandar Khoury, Joseph Taha, Ahmad Bou-Harb, Elias Division of Computer Science and Engineering Louisiana State University United States Civil and Environmental Engineering Vanderbilt University United States

Water systems are vital components of modern infrastructure, yet they are increasingly susceptible to sophisticated cyber attacks with potentially dire consequences on public health and safety. While state-of-the-art machine learning techniques effectively detect anomalies, contemporary model-agnostic attack attribution methods using LIME, SHAP, and LEMNA are deemed impractical for large-scale, interdependent water systems. This is due to the intricate interconnectivity and dynamic interactions that define these complex environments. Such methods primarily emphasize individual feature importance while falling short of addressing the crucial sensor-actuator interactions in water systems, which limits their effectiveness in identifying root cause attacks. To this end, we propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks. For instance, an anomaly in an actuator pump activity can be attributed to a top root cause attack candidates, a list of water pressure sensors, which is derived from the underlying linear and quadratic effects captured by our approach. We validate our method using two real-world water system specific datasets, SWaT and WADI, demonstrating its superior performance over traditional attribution methods. In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA. Additionally, our approach maintains strong performance in single-feature attack scenarios, demonstrating versatility across different types of cyber attacks. Notably, our approach maintains a low computational overhead equating to an O(n) time complexity, making it suitable for real-time applications in critical water system infrastructure. Our work underscores the impor

关键词： Factorization

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