Electronics最新文献_第5页

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":"19 1","pages":""},"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":"374 1","pages":""},"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

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":"46 1","pages":""},"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

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":"6 1","pages":""},"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

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":"31 1","pages":""},"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

Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering 基于时态特征聚类的捕食螨栽培环境参数预测

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-15 DOI: 10.3390/electronics13183667

Ying Ma, Hongjie Lin, Wei Chen, Weijie Chen, Qianting Wang

{"title":"Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering","authors":"Ying Ma, Hongjie Lin, Wei Chen, Weijie Chen, Qianting Wang","doi":"10.3390/electronics13183667","DOIUrl":"https://doi.org/10.3390/electronics13183667","url":null,"abstract":"With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259505","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 New Carry Look-Ahead Adder Architecture Optimized for Speed and Energy 优化速度和能耗的新型前向携带加法器架构

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-15 DOI: 10.3390/electronics13183668

Padmanabhan Balasubramanian, Douglas L. Maskell

{"title":"A New Carry Look-Ahead Adder Architecture Optimized for Speed and Energy","authors":"Padmanabhan Balasubramanian, Douglas L. Maskell","doi":"10.3390/electronics13183668","DOIUrl":"https://doi.org/10.3390/electronics13183668","url":null,"abstract":"We introduce a new carry look-ahead adder (NCLA) architecture that employs non-uniform-size carry look-ahead adder (CLA) modules, in contrast to the conventional CLA (CCLA) architecture, which utilizes uniform-size CLA modules. We adopted two strategies for the implementation of the NCLA. Our novel approach enables improved speed and energy efficiency for the NCLA architecture compared to the CCLA architecture without incurring significant area and power penalties. Various adders were implemented to demonstrate the advantages of NCLA, ranging from the slower ripple carry adder to the widely regarded fastest parallel-prefix adder viz. the Kogge–Stone adder, and their performance metrics were compared. The 32-bit addition was used as an example, with the adders implemented using a semi-custom design method and a 28 nm CMOS standard cell library. Synthesis results show that the NCLA architecture offers substantial improvements in design metrics compared to its high-speed counterparts. Specifically, an NCLA achieved (i) a 14.7% reduction in delay and a 13.4% reduction in energy compared to an optimized CCLA, while occupying slightly more area; (ii) a 42.1% reduction in delay and a 58.3% reduction in energy compared to a conditional sum adder, with an 8% increase in the area; (iii) a 14.7% reduction in delay and a 37.7% reduction in energy compared to an optimized carry select adder, while requiring 37% less area; and (iv) a 20.2% reduction in energy and a 55.4% reduction in area compared to the Kogge–Stone adder.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"9 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259546","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 Novel Short-Term PM2.5 Forecasting Approach Using Secondary Decomposition and a Hybrid Deep Learning Model 利用二次分解和混合深度学习模型的新型短期 PM2.5 预测方法

IF 2.9 3区工程技术

Electronics Pub Date : 2024-09-14 DOI: 10.3390/electronics13183658

Ruru Liu, Liping Xu, Tao Zeng, Tao Luo, Mengfei Wang, Yuming Zhou, Chunpeng Chen, Shuo Zhao

{"title":"A Novel Short-Term PM2.5 Forecasting Approach Using Secondary Decomposition and a Hybrid Deep Learning Model","authors":"Ruru Liu, Liping Xu, Tao Zeng, Tao Luo, Mengfei Wang, Yuming Zhou, Chunpeng Chen, Shuo Zhao","doi":"10.3390/electronics13183658","DOIUrl":"https://doi.org/10.3390/electronics13183658","url":null,"abstract":"PM2.5 pollution poses an important threat to the atmospheric environment and human health. To precisely forecast PM2.5 concentration, this study presents an innovative combined model: EMD-SE-GWO-VMD-ZCR-CNN-LSTM. First, empirical mode decomposition (EMD) is used to decompose PM2.5, and sample entropy (SE) is used to assess the subsequence complexity. Secondly, the hyperparameters of variational mode decomposition (VMD) are optimized by Gray Wolf Optimization (GWO) algorithm, and the complex subsequences are decomposed twice. Next, the sequences are divided into high-frequency and low-frequency parts by using the zero crossing rate (ZCR); the high-frequency sequences are predicted by a convolutional neural network (CNN), and the low-frequency sequences are predicted by a long short-term memory network (LSTM). Finally, the predicted values of the high-frequency and low-frequency sequences are reconstructed to obtain the final results. The experiment was conducted based on the data of 1009A, 1010A, and 1011A from three air quality monitoring stations in the Beijing area. The results indicate that the R2 value of the designed model increased by 2.63%, 0.59%, and 1.88% on average in the three air quality monitoring stations, respectively, compared with the other single model and the mixed model, which verified the significant advantages of the proposed model.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"23 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259550","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