Electronics最新文献_第4页

Functional Exercise Induces Adaptations in Muscle Oxygen Saturation in Division One Collegiate Butterfly Swimmers: A Randomized Controlled Trial 功能性锻炼诱导甲组大学生蝶泳运动员肌肉氧饱和度的适应性变化：随机对照试验

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183680

Jack Grotke, Austin Alcantara, Joe Amitrano, Dhruv R. Seshadri

引用次数: 0

Low-Voltage Water Pump System Based on Permanent Magnet Synchronous Motor 基于永磁同步电机的低压水泵系统

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183674

Xinrong Jin, Leifu Zhou, Tingting Lang, Yanbing Jiang

引用次数: 0

Securing Federated Learning: Approaches, Mechanisms and Opportunities 确保联合学习：方法、机制和机遇

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183675

Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim, Ali Raad

{"title":"Securing Federated Learning: Approaches, Mechanisms and Opportunities","authors":"Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim, Ali Raad","doi":"10.3390/electronics13183675","DOIUrl":"https://doi.org/10.3390/electronics13183675","url":null,"abstract":"With the ability to analyze data, artificial intelligence technology and its offshoots have made difficult tasks easier. The tools of these technologies are now used in almost every aspect of life. For example, Machine Learning (ML), an offshoot of artificial intelligence, has become the focus of interest for researchers in industry, education, healthcare and other disciplines and has proven to be as efficient as, and in some cases better than, experts in answering various problems. However, the obstacles to ML’s progress are still being explored, and Federated Learning (FL) has been presented as a solution to the problems of privacy and confidentiality. In the FL approach, users do not disclose their data throughout the learning process, which improves privacy and security. In this article, we look at the security and privacy concepts of FL and the threats and attacks it faces. We also address the security measures used in FL aggregation procedures. In addition, we examine and discuss the use of homomorphic encryption to protect FL data exchange, as well as other security strategies. Finally, we discuss security and privacy concepts in FL and what additional improvements could be made in this context to increase the efficiency of FL algorithms.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters 促进 5G 基站与雷达高度计共存的护带保护计划

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183681

Jiaqi Li, Seung-Hoon Hwang

引用次数: 0

A Hessian-Based Deep Learning Preprocessing Method for Coronary Angiography Image Analysis 基于黑森深度学习的冠状动脉造影图像分析预处理方法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183676

Yanjun Li, Takaaki Yoshimura, Yuto Horima, Hiroyuki Sugimori

{"title":"A Hessian-Based Deep Learning Preprocessing Method for Coronary Angiography Image Analysis","authors":"Yanjun Li, Takaaki Yoshimura, Yuto Horima, Hiroyuki Sugimori","doi":"10.3390/electronics13183676","DOIUrl":"https://doi.org/10.3390/electronics13183676","url":null,"abstract":"Leveraging its high accuracy and stability, deep-learning-based coronary artery detection technology has been extensively utilized in diagnosing coronary artery diseases. However, traditional algorithms for localizing coronary stenosis often fall short when detecting stenosis in branch vessels, which can pose significant health risks due to factors like imaging angles and uneven contrast agent distribution. To tackle these challenges, we propose a preprocessing method that integrates Hessian-based vascular enhancement and image fusion as prerequisites for deep learning. This approach enhances fuzzy features in coronary angiography images, thereby increasing the neural network’s sensitivity to stenosis characteristics. We assessed the effectiveness of this method using the latest deep learning networks, such as YOLOv10, YOLOv9, and RT-DETR, across various evaluation metrics. Our results show that our method improves AP50 accuracy by 4.84% and 5.07% on RT-DETR R101 and YOLOv10-X, respectively, compared to images without special pre-processing. Furthermore, our analysis of different imaging angles on stenosis localization detection indicates that the left coronary artery zero is the most suitable for detecting stenosis with a AP50(%) value of 90.5. The experimental results have revealed that the proposed method is effective as a preprocessing technique for deep-learning-based coronary angiography image processing and enhances the model’s ability to identify stenosis in small blood vessels.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections 用于网络钓鱼攻击检测的增强型 K-Means 聚类算法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183677

Abdallah Al-Sabbagh, Khalil Hamze, Samiya Khan, Mahmoud Elkhodr

{"title":"An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections","authors":"Abdallah Al-Sabbagh, Khalil Hamze, Samiya Khan, Mahmoud Elkhodr","doi":"10.3390/electronics13183677","DOIUrl":"https://doi.org/10.3390/electronics13183677","url":null,"abstract":"Phishing attacks continue to pose a significant threat to cybersecurity, employing increasingly sophisticated techniques to deceive victims into revealing sensitive information or downloading malware. This paper presents a comprehensive study on the application of Machine Learning (ML) techniques for identifying phishing websites, with a focus on enhancing detection accuracy and efficiency. We propose an approach that integrates the CfsSubsetEval attribute evaluator with the K-Means Clustering algorithm to improve phishing detection capabilities. Our method was evaluated using datasets of varying sizes (2000, 7000, and 10,000 samples) from a publicly available repository. Simulation results demonstrate that our approach achieves an accuracy of 89.2% on the 2000-sample dataset, outperforming the traditional kernel K-Means algorithm, which achieved an accuracy of 51.5%. Further analysis using precision, recall, and F1-score metrics corroborates the effectiveness of our method. We also discuss the scalability and real-world applicability of our approach, addressing limitations and proposing future research directions. This study contributes to the ongoing efforts to develop robust, efficient, and adaptable phishing detection systems in the face of evolving cyber threats.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SoFL: Clustered Federated Learning Based on Dual Clustering for Heterogeneous Data SoFL：基于双重聚类的异构数据聚类联合学习

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183682

Jianfei Zhang, Zhiming Qiao

{"title":"SoFL: Clustered Federated Learning Based on Dual Clustering for Heterogeneous Data","authors":"Jianfei Zhang, Zhiming Qiao","doi":"10.3390/electronics13183682","DOIUrl":"https://doi.org/10.3390/electronics13183682","url":null,"abstract":"Federated Learning (FL) is an emerging privacy-preserving technology that enables training a global model beneficial to all participants without sharing their data. However, differences in data distributions among participants may undermine the stability and accuracy of the global model. To address this challenge, recent research proposes client clustering based on data distribution similarity, generating independent models for each cluster in order to enhance FL performance. Nevertheless, due to the uncertainty of participant identities, FL struggles to rapidly and accurately determine the clusters. Most of the existing algorithms distinguish clients by iterative clustering, which not only increases the computing cost of the server but also affects the convergence speed of the federation model. To address these shortcomings, in this paper, we propose a novel clustering-based FL method, SoFL. SoFL introduces SOM networks, improves the quality of cluster data, and eliminates redundant categories through secondary clustering, encouraging more similar clients to train together. Through this mechanism, SoFL completes the clustering task in one round of communication and speeds up the convergence of federated model training. Simulation results demonstrate that SoFL accurately and swiftly adapts to determine the clusters. In different non-IID settings, SoFL’s model accuracy improvements ranged from 9 to 18% compared to FedAvg and FedProx.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zadoff–Chu Sequence Pilot for Time and Frequency Synchronization in UWA OFDM System 用于 UWA OFDM 系统时间和频率同步的 Zadoff-Chu 序列先导

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-16 DOI: 10.3390/electronics13183679

Seunghwan Seol, Yongcheol Kim, Minho Kim, Jaehak Chung

{"title":"Zadoff–Chu Sequence Pilot for Time and Frequency Synchronization in UWA OFDM System","authors":"Seunghwan Seol, Yongcheol Kim, Minho Kim, Jaehak Chung","doi":"10.3390/electronics13183679","DOIUrl":"https://doi.org/10.3390/electronics13183679","url":null,"abstract":"In underwater communications for 6G, Doppler effects cause the coherent time to become similar to or shorter than the orthogonal frequency division multiplexing (OFDM) symbol length. Conventional time and frequency synchronization methods require additional training symbols for synchronization, which reduces the traffic data rate. This paper proposes the Zadoff–Chu sequence (ZCS) pilot-based OFDM for time and frequency synchronization. The proposed method transmits ZCS as a pilot for OFDM symbols and simultaneously transmits traffic data to increase the traffic data rate while estimating the CFO at each coherence time. For time–frequency synchronization, the correlation of the ZCS pilot is used to perform coarse and fine time and frequency synchronization in two stages. Since the traffic data cause interference with the correlation of ZCS pilots, we theoretically analyzed the relationship between the amount of traffic data and interference and verified it through computer simulations. The synchronization and BER performance of the proposed ZCS pilot-based OFDM were evaluated by conduction computer simulations and a practical ocean experiment. Compared to the methods of Ren, Yang, and Avrashi, the proposed method demonstrated a 6.3% to 14.3% increase in traffic data rate with similar BER performance and a 2 dB to 3.8 dB SNR gain for a 14.3% to 23.8% decrease in traffic data rate.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Combinatorial Strategy for API Completion: Deep Learning and Heuristics 完成 API 的组合策略：深度学习与启发式方法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-15 DOI: 10.3390/electronics13183669

Yi Liu, Yiming Yin, Jia Deng, Weimin Li, Zhichao Peng

{"title":"A Combinatorial Strategy for API Completion: Deep Learning and Heuristics","authors":"Yi Liu, Yiming Yin, Jia Deng, Weimin Li, Zhichao Peng","doi":"10.3390/electronics13183669","DOIUrl":"https://doi.org/10.3390/electronics13183669","url":null,"abstract":"Remembering software library components and mastering their application programming interfaces (APIs) is a daunting task for programmers, due to the sheer volume of available libraries. API completion tools, which predict subsequent APIs based on code context, are essential for improving development efficiency. Existing API completion techniques, however, face specific weaknesses that limit their performance. Pattern-based code completion methods that rely on statistical information excel in extracting common usage patterns of API sequences. However, they often struggle to capture the semantics of the surrounding code. In contrast, deep-learning-based approaches excel in understanding the semantics of the code but may miss certain common usages that can be easily identified by pattern-based methods. Our insight into overcoming these challenges is based on the complementarity between these two types of approaches. This paper proposes a combinatorial method of API completion that aims to exploit the strengths of both pattern-based and deep-learning-based approaches. The basic idea is to utilize a confidence-based selector to determine which type of approach should be utilized to generate predictions. Pattern-based approaches will only be applied if the frequency of a particular pattern exceeds a pre-defined threshold, while in other cases, deep learning models will be utilized to generate the API completion results. The results showed that our approach dramatically improved the accuracy and mean reciprocal rank (MRR) in large-scale experiments, highlighting its utility.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Technology Keyword Analysis Using Graphical Causal Models 利用图形因果模型进行技术关键词分析

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-15 DOI: 10.3390/electronics13183670

Sunghae Jun

引用次数: 0