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Traffic Congestion Control with Emergency Awareness and Optimized Communication Infrastructure using Reinforcement Learning and Non-Dominated Sorting Genetic Algorithm

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IEEE Access 2025年

作者： Al-Heety, Othman S. Zakaria, Zahriladha Abu-Khadrah, Ahmed Ismail, Mahamod Shakir, Mohammed Mudhafar Alani, Sameer Alsariera, Hussein Faculty of Electronic and Computer Engineering Durian Tunggal Malaysia Al-Balqa Applied University College of Engineering Technology Department of Electrical Engineering Amman Jordan Universiti Kebangsaan Malaysia UKM Bangi Department of Electrical Electronic and System Engineering Selangor Malaysia Faculty of Engineering and Electronic Engineering Batu Pahat Malaysia University of Anbar Computer Center Anbar5543 Iraq

Urban centers are grappling with increasing traffic congestion, which hampers mobility and reduces the effectiveness of emergency responses. Current traffic management systems struggle to dynamically prioritize emergency vehicles during peak congestion, highlighting the need for an advanced solution that improves both everyday traffic flow and emergency response efficiency. Traditional traffic light systems typically follow either fixed schedules ("Fixed"approach) or make real-time adjustments without achieving long-term efficiency ("Greedy"approach), often failing to effectively balance regular traffic dynamics with emergency needs. We propose the "Avg Log "method, an adaptive Q-learning strategy that enhances conventional traffic management systems by incorporating an emergency-aware module. This module uses real-time data to prioritize emergency vehicles and adjust traffic signals accordingly. Additionally, our approach employs a multi-objective optimization algorithm to strengthen communication infrastructure, optimizing latency, throughput, and reliability. Through simulations in a multi-intersection urban setting, we compared the performance of our method against the standard "Greedy"and "Fixed"approaches. Key metrics assessed included average waiting times, system throughput, and the efficiency of emergency vehicle passage. The "Avg Log"method outperformed traditional methods, showing superior long-term adaptability and learning. It demonstrated lower average waiting times and effectively managed emergency situations, significantly reducing delays for emergency vehicles compared to both control methods. This method provides an efficient balance between overall traffic flow and emergency prioritization, suggesting a scalable solution for complex urban environments. © 2025 IEEE.

关键词： Efficiency

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

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Light(Science & Applications) 2025年第3期14卷 746-756页

作者： Na Qian Defu Zhou Haowen Shu Ming Zhang Xingjun Wang Daoxin Dai Xiao Deng Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBejingChina State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhouChina

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic ***,we report an analog parallel processor(APP)based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog *** APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel *** photonic analog parallelism,data rate and data volume in each sequence are simultaneously compressed,which mitigates the requirement on each parallel computing ***,the fusion of the outputs from each computing core is equivalent to directly processing broadband *** the proof-of-concept experiment,two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated *** bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is *** wireless communication rate of 8 Gbit/s is also *** the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture,we anticipate that the proposed APP will accelerate the development of powerful optoelectronic processors as critical support for applications such as satellite networks and intelligent driving.

关键词： analog parallel processor sharing hardware platform multifunctional integrated systems establish full cooperation different functionalities analog parallel processor app based silicon photonic platform electronic circuitryherewe broadband signal

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A Novel Approach to Reduce A Visual Gap Using Inpainting GAN

A Novel Approach to Reduce A Visual Gap Using Inpainting GAN

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2023 IEEE International Conference on Consumer electronics-Asia, ICCE-Asia 2023

作者： Truong, Thanh Hien Lee, Tae-Ho Munasinghe, Viduranga Kim, Tae-Sung Kim, Jin-Sung Lee, Hyuk-Jae Seoul National University Electrical and Computer Engineering Korea Republic of Seoul National University Next-Generation Semiconductor Convergence and Open Sharing System Korea Republic of Sun Moon University Department of Electronic Engineering Korea Republic of

ISBN: (纸本)9798350344318

Image blending represents a technique employed in image composition, aimed at creating a composite image that appears as natural and realistic as possible. The objective of image blending is to ensure that the transitions between objects in the image appear seamless and without any color distortion. Recently, numerous studies investigated image blending methods adopting deep learning-based image processing algorithms and contributed to generating natural blended images. Although the previous studies show remarkable performance in many cases, they suffer from quality drop when blending incompletely cropped object. This paper introduces a novel approach that effectively reduces the unnatural edges and the color distortion in such cases. In this study, an algorithm which called edge checking (EC) is proposed to identify and manage incompletely cropped regions. To address missing or masked parts of the object image and integrate them into the image blending process, an inpainting generative adversarial model are employed. © 2023 IEEE.

关键词： Generative adversarial networks

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Optimal Resource Allocation for U-Shaped Parallel Split Learning

Optimal Resource Allocation for U-Shaped Parallel Split Lear...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Lyu, Song Lin, Zheng Qu, Guanqiao Chen, Xianhao Huang, Xiaoxia Li, Pan The University of Hong Kong The Department of Electrical and Electronic Engineering Hong Kong School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen510275 China Case Western Reserve University The Department of Electrical Computer and System Engineering ClevelandOH44106 United States

ISBN: (纸本)9798350370218

Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks label privacy as the tail model (with the last layers) should be placed on the server. To overcome this limitation, one promising solution is to utilize U-shaped architecture to leave both early layers and last layers on the user side. In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks. In the proposed framework, multiple users communicate with an edge server for SL. We analyze the end-to-end delay of each client during the training process and design an efficient resource allocation algorithm, called LSCRA, which finds the optimal computing resource allocation and split layers. Our experimental results show the effectiveness of LSCRA and that U-shaped parallel split learning can achieve a similar performance with other SL baselines while preserving label privacy. © 2023 IEEE.

关键词： 5G/6G edge networks label privacy resource allocation split learning U-shaped network

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization

Revisiting Depth Completion from a Stereo Matching Perspecti...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Luca Bartolomei Matteo Poggi Andrea Conti Fabio Tosi Stefano Mattoccia Advanced Research Center on Electronic System (ARCES) Department of Computer Science and Engineering (DISI) University of Bologna Italy

ISBN: (数字)9798350362459

ISBN: (纸本)9798350362466

This paper proposes a new framework for depth completion robust against domain-shifting issues. It exploits the generalization capability of modern stereo networks to face depth completion, by processing fictitious stereo pairs obtained through a virtual pattern projection paradigm. Any stereo network or traditional stereo matcher can be seamlessly plugged into our framework, allowing for the deployment of a virtual stereo setup that is future-proof against advancement in the stereo field. Exhaustive experiments on cross-domain generalization support our claims. Hence, we argue that our framework can help depth completion to reach new deployment scenarios.

关键词： Training Three-dimensional displays Task analysis Faces

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Federated Online Adaptation for Deep Stereo

Federated Online Adaptation for Deep Stereo

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Matteo Poggi Fabio Tosi Department of Computer Science and Engineering (DISI) Advanced Research Center on Electronic System (ARCES) University of Bologna Italy

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a novel approach for adapting deep stereo networks in a collaborative manner. By building over principles of federated learning, we develop a distributed framework allowing for demanding the optimization process to a number of clients deployed in different environments. This makes it possible, for a deep stereo network running on resourced-constrained devices, to capitalize on the adaptation process carried out by other instances of the same architecture, and thus improve its accuracy in challenging environments even when it cannot carry out adaptation on its own. Experimental results show how federated adaptation performs equivalently to on-device adaptation, and even better when dealing with challenging environments.

关键词： Performance evaluation Adaptation models Accuracy Costs Federated learning Computational modeling Hardware

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system for coronavirus classification based on a new textural descriptor 21

System for coronavirus classification based on a new textura...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Boubakri, Fathia Gargouri, Norhene Masmoudi, Alima Damak Sfax Tunisia Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group Tunisia Sfax Engineering School University of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering P.O. Box 1169 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

关键词： COVID-19

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