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arXiv 2024年

作者： Huang, Chung-Ming Wu, Yen-Hung Dao, Duy-Tuan Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Faculty of Electronics and Telecommunications Engineering The University of Danang-University of Science and Technology Viet Nam

In the context of 5G platoon communications, the Platoon Leader Vehicle (PLV) employs groupcasting to transmit control messages to Platoon Member Vehicles (PMVs). Due to the restricted transmission power for groupcasting, it may need to pick one PMV as the Platoon Relay Vehicle (PRV) to be responsible for re-groupcasting messages of PLVs. To optimize the usage of limited spectrum resources, resource sharing can adopted to enhance spectrum efficiency within the platoon. This study proposes a resource allocation method, which is called Resource Sharing for Platoon Groupcasting (RSPG), for platoon’s groupcasting based on transmission reliability. RSPG utilizes the tripartite matching to assign a subchannel to either a PLV or PRV that shares the assigned subchannel with the corresponding individual entity (IE), which does not belong to any platoon. The simulation results shown that the proposed method has the better performance in terms of the QoS satisfaction rate of IEs, the number of allocated subchannels for platoons, and spectral efficiency. © 2024, CC BY.

关键词： 5G mobile communication systems

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Occlusion-Aware Secure Device-to-Device Communication in Social Internet of Things Networks

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IEEE Transactions on Consumer electronics 2025年

作者： Chandra, Saurabh Prateek Arya, Rajeev Sharma, Rohit Almansour, Shahad Gandor, Michal National Institute of Technology Patna Wireless Sensor Networks Lab Department of Electronics and Communication Engineering Bihar Patna800005 India ABES Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India University of Hail Applied College Saudi Arabia Cracow University of Technology Faculty of Computer Science and Telecommunications Department of Computer Science Warszawska Krakow Poland

The Social Internet of Things (SIoT) framework facilitates the development of various consumer electronic devices such as smartphones, smart wearables, and smart wireless devices, enabling social relationships between electronic devices and humans. Human-centric intelligent Device-to-Device (D2D)-enabled SIoT networks provide essential services like traffic offloading and secure data rate, but face challenges from urban interference caused by obstacles;to mitigate this, we propose a user-driven approach that prioritizes social relationships to enhance network performance. We address occlusion challenges in urban areas by formulating a problem aimed at maximizing throughput and secrecy rate while minimizing power consumption, employing an intelligent resource allocation and spline-based power transmission scheme for efficient utilization. Field tests using Universal Software Radio Peripheral (USRP) and LabVIEW have been performed to validate the proposed method. The results show a throughput improvement of 10.93%, and a 27.86% enhancement in secrecy rate compared to other benchmark schemes. The comprehensive adoption of this algorithm can enable secure D2D communication for consumer electronics products driven by social IoT Networks. © 1975-2011 IEEE.

关键词： Radio equipment

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Enhancing IoT Network Security: A Double Decker Convolutional Neural Network with Brown-Bear Optimization for Intrusion Detection

Enhancing IoT Network Security: A Double Decker Convolutiona...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Abhay Chaturvedi Suhas S. J. L. Divya Shivani Ch. Raja Umang Soni Natrayan L Department of Electronics & Communication Engineering GLA University Mathura Uttar Pradesh India Department of Computer Science and Engineering (CSE) SJCE JSS Science and Technology University Mysore Karnataka India Department of Electronics and Communication Engineering CMR College of Engineering & Technology Hyderabad Telangana India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Hyderabad Telangana India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The extensive adoption of Internet of Things (IoT) networks has exponentially increased cybersecurity threats, necessitating effective intrusion detection mechanisms to secure connected devices. Traditional security mechanisms fail to detect sophisticated cyberattacks as IoT environments are dynamic in nature. Existing intrusion detection techniques suffer from high false alarms and low computational efficiency, making them impractical to apply in resource-constrained IoT networks. To overcome these challenges, this paper presents Enhancing IoT Network Security: A Double Decker Convolutional Neural Network with Brown-Bear Optimization for Intrusion Detection (BrBO-DD-CNN), which yields high detection efficiency and accuracy. This paper utilizes data from the NSL-KDD and BoT-IoT datasets, which are IoT network traffic and attack types. Raw data is preprocessed by Min-Max Zscore Normalization to remove duplicates and complete missing values. Planet Optimization selects significant features, reducing data complexity without losing key information. Preprocessed data is input to a Double Decker Convolutional Neural Network that detects low-level and high-level features to differentiate normal and malicious traffic. Brown-Bear Optimization optimizes model parameters, enhancing convergence and detection performance. This research yields an accuracy of 99.98% for NSL-KDD and 99.95% for BoT-IoT. Detection rate is 99.97% and 99.93%, and F1-score is 99.96% and 99.92%, respectively, proving its effectiveness. The results guarantee that BrBO-DD-CNN performs better than existing intrusion detection techniques, offering a highly accurate, efficient, and scalable intrusion detection mechanism to secure IoT networks against emerging cyberattacks.

关键词： Accuracy Planets Intrusion detection Telecommunication traffic Network security NSL-KDD Internet of Things Convolutional neural networks computer crime Optimization

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High Efficiency Dual-Active-Bridge Converter with Triple-Phase-Shift Control for Battery Charger of Electric Vehicles

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Energies 2024年第2期17卷 354页

作者： Kuo, Shih-Hao Chiu, Huang-Jen Chiang, Che-Wei Huang, Ta-Wei Chang, Yu-Chen Bachman, Serafin Piasecki, Szymon Jasinski, Marek Turzyński, Marek Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Institute of Control and Industrial Electronics Warsaw University of Technology Warsaw00-662 Poland Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdansk80-233 Poland

An optimal modulation scheme with triple-phase-shift (TPS) control could increase the efficiency in the entire load range for a dual-active-bridge (DAB) converter under wide output voltage range conditions. Therefore, this study proposes a convergent approach to TPS mode selection, coupled with an optimal modulation scheme, ensuring the circuit’s efficiency over the entire range in the realm of a high-power and high-efficiency battery charger for electric vehicles. The convergent approach to TPS mode selection also reduces the numerous cases for small-signal analysis through general average modeling. After verifying the small-signal models under various voltage transfer ratios and load conditions to verify the stability, a converter prototype with a rated power of 15 kW is built and tested. Thus, a peak efficiency of 97.7% can be achieved. © 2024 by the authors.

关键词： Charging (batteries)

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Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Priya K. Priyadharshini R. Santhana Krishnan J. Relin Francis Raj Imran Javeed Settu A. Srinivasan Department of Computer Science and Engineering Ramco Institute of Technology India Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development.

关键词： Adaptive systems Smart cities Roads Traffic control Predictive models Air quality Real-time systems Fuels Time factors Traffic congestion

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Reinforcement Learning Based Technique for Interference Management in UAV Aided HetNets 10

Reinforcement Learning Based Technique for Interference Mana...

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10th International Japan-Africa Conference on electronics, Communications, and Computations, JAC-ECC 2022

作者： Hamden, Nehal Nasser, Ahmed Selim, Mohamed Y. Elsabrouty, Maha Egyptian Customs Authority Ministry of Finance Egypt Suez Canal University Faculty of Engineering Department of Electronics and Communication Egypt Iowa State University Electrical and Computer Engineering Department Ames United States Egypt Japan University of Science and Technology Department of Electronics and Communication Egypt

ISBN: (纸本)9781665464635

In this paper, we consider the sum rate maximization problem in a downlink unmanned aerial vehicles (UAV) assisted heterogeneous networks (HetNets) to mitigate existing interference. We propose to manage the interference by jointly optimizing the position of the UAV as well as power allocation. The proposed optimization problem is non-convex due to the non-linearity in the objective function and the constraints. We propose a near-optimal solution that breaks down the problem into two consecutive problems. First, we proposed a reinforcement learning-based technique to solve the UAV positioning problem. Then, a particle swarm optimization (PSO) based technique is applied to the power allocation problem. Simulation results demonstrate the efficiency of the proposed algorithm. © 2022 IEEE.

关键词： Heterogeneous networks

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A Perspective Analysis of Optical Biosensors in Machine Learning Applications

A Perspective Analysis of Optical Biosensors in Machine Lear...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： B. Naveen Kumar M. V. Panduranga Rao Preeta Sharan K. R. Varun Suresh Kallam M. Kumaresan Department of Computer Science and Engineering Faculty of Engineering and Technology Jain (Deemed-to-be-University) Bengaluru India Department of Electronics and Communication Engineering The Oxford College of Engineering Bengaluru India Department of Mechanical Engineering The Oxford college of Engineering Bengaluru India

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light’s wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques.

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Learning Features Using an optimized Artificial Neural Network for Breast Cancer Diagnosis

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SN computer Science 2022年第3期3卷 229页

作者： AlShourbaji, Ibrahim Kachare, Pramod Zogaan, Waleed Muhammad, L.J. Abualigah, Laith Department of Computer and Network Engineering Jazan University Jazan 45142 Saudi Arabia Department of Electronics and Telecommunication Engineering Ramrao Adik Institute of Technology Nerul Maharashtra Navi Mumbai 400706 India Department of Computer Science Jazan University Jazan 45142 Saudi Arabia Department of Computer Science Faculty of Science Federal University of Kashere P.M.B. 0182 Gombe Nigeria Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Breast cancer (BC) has been one of the significant causes of death worldwide, and its early detection can play a vital role in increasing the survival rate of this disease. This paper suggests a novel feature learning method for BC diagnosis using the artificial neural network (ANN) by optimizing the hidden layers. It uses a systematic hyper-parameter search method to optimize number of hidden layers, neurons in the hidden layers, activation functions, learning rate, batch size, and training epochs of the ANN. The number of hidden layers and the number of neurons in the hidden layer are investigated using t-distributed stochastic neighbor embedding (t-SNE) technique to specify the best hyper-parameters that achieved the highest testing accuracy while avoiding model overfitting. Comparative analysis showed that the proposed method achieved higher performance than the ANN-based models that utilizes particle swarm optimization (PSO), multi-verse optimizer (MVO), BAT algorithm, and firefly algorithm (FFA) as feature selection (FS) methods, with the best classification accuracy of 0.9948, sensitivity of 0.9815, and specificity of 0.9882 for 10 iterations of holdout cross-validation. The performance of the proposed method is also comparable with several earlier reported state-of-the-art models in the literature. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： ANN Brest cancer Classification Feature selection Machine learning

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Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

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computers, Materials & Continua 2022年第9期72卷 4509-4523页

作者： Elias Hossain Md.Shazzad Hossain Md.Selim Hossain Sabila Al Jannat Moontahina Huda Sameer Alsharif Osama S.Faragallah Mahmoud M.A.Eid Ahmed Nabih Zaki Rashed Department of Software Engineering Daffodil International UniversityDhaka 1207Bangladesh Department of Computing and Information System Daffodil International UniversityDhaka 1207Bangladesh Department of Computer Science&Engineering BRAC UniversityDhaka 1212Bangladesh Department of Information and Communication Engineering Bangladesh University of Professionals(BUP)Dhaka 1216Bangladesh Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering College of EngineeringTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenouf 32951Egypt

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional MagneticResonance Image (MRI) and Computed Tomography (CT) scans utilizing3D U-Net Design and ResNet50, taken after by conventional classificationstrategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, andthe 3D U-Net scored 97.99% among the different methods of deep *** is to be mentioned that traditional Convolutional Neural Network (CNN)gives 97.90% accuracy on top of the 3D MRI. In expansion, the imagefusion approach combines the multimodal images and makes a fused image toextricate more highlights from the medical images. Other than that, we haveidentified the loss function by utilizing several dice measurements approachand received Dice Result on top of a specific test case. The average mean scoreof dice coefficient and soft dice loss for three test cases was 0.0980. At thesame time, for two test cases, the sensitivity and specification were recordedto be 0.0211 and 0.5867 using patch level predictions. On the other hand,a software integration pipeline was integrated to deploy the concentratedmodel into the webserver for accessing it from the software system using theRepresentational state transfer (REST) API. Eventually, the suggested modelswere validated through the Area Under the Curve–Receiver CharacteristicOperator (AUC–ROC) curve and Confusion Matrix and compared with theexisting research articles to understand the underlying problem. ThroughComparative Analysis, we have extracted meaningful insights regarding braintumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imagingmodalities.

关键词： Brain cancer segmentation 3D U-Net ResNet50 Dice measurement ROC-AUC

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Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

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computers, Materials & Continua 2022年第8期72卷 3481-3492页

作者： Harsh Goud Prakash Chandra Sharma Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Narendra Singh Yadav Pankaj Swarnkar Manoj Gupta Laxmi Chand Indian Institute of Information Technology NagpurIndia School of Computing&Information Technology Manipal UniversityJaipurRajasthanIndia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Maulana Azad National Institute of Technology BhopalIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Department of Computer Science and Engineering Hanyang UniversitySeongdong-guSeoul04763Korea

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging *** is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical *** this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain *** controller is used to control Mobile Robot System(MRS)at the required set *** FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system *** FF is touted as providing an easy,reliable,and efficient tuning technique for PID *** most suitable ideal performance is accomplished with FF-PID,according to the display in the time ***,the observed response is compared to those received by applying GA and conventional off-line tuning *** comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.

关键词： Metaheuristic algorithm genetic algorithm PID controller mobile robot system firefly algorithm

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