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Foreign object debris detection in lane images using deep learning methodology

PeerJ computer Science 2025年 11卷 1-16页

作者： Priyadharsini, S. Bhuvaneshwara, Raja K. Kousi, Krishnan T Senthil, Kumar Jagatheesaperumal Bader, Fahad Alkhamees Mohammad, Mehedi Hassan Department of Computer Science and Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi India Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Background: Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal systems of planes, potentially leading to flight crashes. Surveys indicate that FOD-related damage costs over $4 billion annually, affecting airlines, airport tenants, and passengers. Current FOD clearance involves high-cost radars and significant manpower, and existing radar and camera-based surveillance methods are expensive to install. Methods: This work proposes a video-based deep learning methodology to address the high cost of radar-based FOD detection. The proposed system consists of two modules for FOD detection: object classification and object localization. The classification module categorizes FOD into specific types of foreign objects. In the object localization module, these classified objects are pinpointed in video frames. Results: The proposed system was experimentally tested with a large video dataset and compared with existing methods. The results demonstrated improved accuracy and robustness, allowing the FOD clearance team to quickly detect and remove foreign objects, thereby enhancing the safety and efficiency of airport runway operations. © 2025 S et al.

关键词： Airport runways

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Mask-Aware Transformer for Crowd Counting 27th

Mask-Aware Transformer for Crowd Counting

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jad, Sarah Torki, Marwan Khalafallah, Ayman Computer and Systems Engineering Department Alexandria University Alexandria Egypt

ISBN: (纸本)9783031784552

Crowd counting problem is a challenging task in computer vision and image analysis. It has many applications in the real world such as crowd management, public safety, and urban planning. Our proposal in this paper is a mask-aware transformer-based network for crowd counting that uses the background/foreground mask information to improve density regression accuracy. Our backbone network is a pyramid vision transformer. Our proposed Mask-aware transformer (M-Trans) takes into consideration the background/foreground mask information. We further improve the performance by applying a greedy ensemble strategy. Our experimental evaluation shows that our mask-aware network achieves state-of-the-art performance on standard benchmarking datasets for crowd counting such as ShanghaiTech and UCF-QNRF datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Urban planning

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Generalized Regenerating Codes and Node Repair on Graphs

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IEEE Transactions on Information Theory 2025年第3期71卷 1613-1630页

作者： Patra, Adway Barg, Alexander University of Maryland Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD20742 United States

We consider regenerating codes in distributed storage systems where connections between the nodes are constrained by a graph. In this problem, the failed node downloads the information stored at a subset of vertices of the graph for the purpose of recovering the lost data. Compared to the standard setting, regenerating codes on graphs address two additional features. The repair information is moved across the network, and the cost of node repair is determined by the graphical distance from the helper nodes to the failed node. Accordingly, the helpers far away from the failed node may be expected to contribute less data for repair than the nodes in the neighborhood of that node. We analyze regenerating codes with nonuniform download for repair on graphs. Moreover, in the process of repair, the information moved from the helpers to the failed node may be combined through intermediate processing, reducing the repair bandwidth. We derive lower bounds for communication complexity of node repair on graphs, including repair schemes with nonuniform download and intermediate processing, and construct codes that attain these bounds. Additionally, some of the nodes may act as adversaries, introducing errors into the data moved in the network. For repair on graphs in the presence of adversarial nodes, we construct codes that support node repair and error correction in systematic nodes. © 1963-2012 IEEE.

关键词： Maintenance engineering Codes Costs Network coding IP networks Bandwidth Symbols Encoding Vectors Standards

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Bridging the Digital Divide with FANETs: Toolset for Optimized Microservices Deployment 28

Bridging the Digital Divide with FANETs: Toolset for Optimiz...

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28th Conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： García-Gil, Santiago García-López, Andrés Frejo-Martín, Sergio Murillo, Juan Manuel Galán-Jiménez, Jaime Department of Computer Systems and Telematics Engineering University of Extremadura Spain

ISBN: (纸本)9798331542399

Closing the connectivity gap between rural and urban areas is a challenging task. However, it is an excellent opportunity for digital innovation and for boosting the development and implementation of new networking technologies. In this demo proposal, we introduce two software utilities that, when used in conjunction, ease the design and evaluation of networking scenarios based on Flying Ad Hoc Networks (FANETs). They also allow the implementation and application of multiple optimization techniques to solve the microservices deployment problem for MSA-based applications in UAV networks. The evaluation of such deployments of MSA-based applications is addressed under a network emulator. Both software tools aim to provide a framework to improve the Quality of Service (QoS) of Unmanned Aerial Vehicles (UAV) based networks that allows the network operator to choose (and compare) the best method for each situation. © 2025 IEEE.

关键词： Application programs

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Interaction-aware Trust Management Scheme for IoT systems With Machine Learning-Based Attack Detection

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IEEE Internet of Things Journal 2025年第11期12卷 17169-17182页

作者： Farhat, Ali Eldosouky, Abdelrahman Ibnkahla, Mohamed Matrawy, Ashraf Carleton University Department of Systems and Computer Engineering OttawaON Canada Carleton University School of Information Technology OttawaON Canada

The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this paper proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an Artificial Neural Network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme's robustness. © 2025 IEEE.

关键词： Gateways (computer networks)

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Distributed Attention-Enabled Multi-Agent Reinforcement Learning Based Frequency Regulation of Power systems

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IEEE Transactions on Power systems 2025年第3期40卷 2427-2437页

作者： Zhao, Yunzheng Liu, Tao Hill, David J. University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia

This paper develops a new distributed attention-enabled multi-agent reinforcement learning method for frequency regulation of power systems. Specifically, the controller of each generator is modelled as an agent, and the reward and observation are designed based on the characteristics of power systems. All the agents learn their own control policies in the offline training phase and generate frequency control signals in the online execution phase. The target of the proposed algorithm is to conduct both offline training and online frequency control in a distributed way. To achieve this goal, two distributed information-sharing mechanisms are proposed based on the different global information to be discovered. First, a consensus-based reward-sharing mechanism is designed to estimate the globally averaged reward. Second, a distributed observation-sharing scheme is developed to discover the global observation information. Furthermore, the attention strategy is embedded in the observation-sharing scheme to help agents adaptively adjust the importance of observations from different neighbors. With these two mechanisms, a new distributed attention-enabled proximal policy optimization (DAPPO) based method is proposed to achieve model-free frequency control. Simulation results on the IEEE 39-bus system and the NPCC 140-bus system demonstrate that the proposed DAPPO achieves stable offline training and effective online frequency control. © 1969-2012 IEEE.

关键词： Reinforcement learning

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Latency Bounds for TSN Scheduling in the Presence of Clock Synchronization

IEEE Networking Letters

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IEEE Networking Letters 2025年第1期7卷 41-45页

作者： Ghosh, Aviroop Yousefi, Saleh Kunz, Thomas Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Urmia University Department of Electrical and Computer Engineering Urmia57561-51818 Iran

The IEEE 802.1Qbv (80.21Qbv) standard is designed for traffic requiring deterministic and bounded latencies through strict periodic time synchronization, as specified by IEEE 802.1AS standard. However, internal clock drift in devices causes timing misalignment, introducing further challenges to 802.1Qbv scheduling. Existing solutions, using either complex optimization approaches or non-trivial scheduling heuristics, address this by scheduling frame transmissions only once they are guaranteed to have been fully received, even in the presence of clock drifts. However, this approach introduces additional delays that can impact deadline requirements. This letter analytically derives tight end-to-end latency bounds, allowing us to determine if stream deadlines for a given network will be violated without the need to solve for any scheduling algorithms. It also proposes an approach that results in tighter bounds based on information collected from the synchronization process. The analytical results are compared with simulation results, confirming their validity. © 2019 IEEE.

关键词： Synchronization Clocks Delays Standards Schedules Logic gates Scheduling algorithms Payloads Jitter Data models

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