Pub Date : 2024-09-16DOI: 10.3390/electronics13183679
Seunghwan Seol, Yongcheol Kim, Minho Kim, Jaehak Chung
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.
{"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}
Pub Date : 2024-09-16DOI: 10.3390/electronics13183682
Jianfei Zhang, Zhiming Qiao
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.
{"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}
Pub Date : 2024-09-15DOI: 10.3390/electronics13183669
Yi Liu, Yiming Yin, Jia Deng, Weimin Li, Zhichao Peng
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.
由于可用库数量庞大，记住软件库组件并掌握其应用编程接口（API）对程序员来说是一项艰巨的任务。根据代码上下文预测后续 API 的 API 补全工具对于提高开发效率至关重要。然而，现有的应用程序接口补全技术面临着限制其性能的特定弱点。基于模式的代码完成方法依赖于统计信息，在提取 API 序列的常见使用模式方面表现出色。但是，它们往往难以捕捉到周围代码的语义。相比之下，基于深度学习的方法在理解代码语义方面表现出色，但可能会遗漏某些基于模式的方法可以轻松识别的常见用法。我们对克服这些挑战的见解基于这两类方法之间的互补性。本文提出了一种完成 API 的组合方法，旨在利用基于模式的方法和基于深度学习的方法的优势。其基本思想是利用基于置信度的选择器来确定应采用哪种方法来生成预测。只有当特定模式的频率超过预先设定的阈值时，才会应用基于模式的方法，而在其他情况下，将利用深度学习模型生成 API 完成结果。结果表明，在大规模实验中，我们的方法显著提高了准确率和平均倒数等级（MRR），凸显了其实用性。
{"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}
Pub Date : 2024-09-15DOI: 10.3390/electronics13183670
Sunghae Jun
Technology keyword analysis (TKA) requires a different approach compared to general keyword analysis. While general keyword analysis identifies relationships between keywords, technology keyword analysis must find cause–effect relationships between technology keywords. Because the development of new technologies depends on previously researched and developed technologies, we need to build a causal inference model, in which the previously developed technology is the cause and the newly developed technology is the effect. In this paper, we propose a technology keyword analysis method using casual inference modeling. To understand the causal relationships between technology keywords, we constructed a graphical causal model combining a graph structure with causal inference. To show how the proposed model can be applied to the practical domains, we collected the patent documents related to the digital therapeutics technology from the world patent databases and analyzed them by the graphical causal model. We expect that our research contributes to various aspects of technology management, such as research and development planning.
{"title":"Technology Keyword Analysis Using Graphical Causal Models","authors":"Sunghae Jun","doi":"10.3390/electronics13183670","DOIUrl":"https://doi.org/10.3390/electronics13183670","url":null,"abstract":"Technology keyword analysis (TKA) requires a different approach compared to general keyword analysis. While general keyword analysis identifies relationships between keywords, technology keyword analysis must find cause–effect relationships between technology keywords. Because the development of new technologies depends on previously researched and developed technologies, we need to build a causal inference model, in which the previously developed technology is the cause and the newly developed technology is the effect. In this paper, we propose a technology keyword analysis method using casual inference modeling. To understand the causal relationships between technology keywords, we constructed a graphical causal model combining a graph structure with causal inference. To show how the proposed model can be applied to the practical domains, we collected the patent documents related to the digital therapeutics technology from the world patent databases and analyzed them by the graphical causal model. We expect that our research contributes to various aspects of technology management, such as research and development planning.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"3 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259548","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}
Pub Date : 2024-09-15DOI: 10.3390/electronics13183667
Ying Ma, Hongjie Lin, Wei Chen, Weijie Chen, Qianting Wang
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.
{"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}
Pub Date : 2024-09-15DOI: 10.3390/electronics13183668
Padmanabhan Balasubramanian, Douglas L. Maskell
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.
{"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}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183658
Ruru Liu, Liping Xu, Tao Zeng, Tao Luo, Mengfei Wang, Yuming Zhou, Chunpeng Chen, Shuo Zhao
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.
{"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}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183661
Ahmad Esmaeil Abbasi, Agostino Marcello Mangini, Maria Pia Fanti
Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.
{"title":"Object and Pedestrian Detection on Road in Foggy Weather Conditions by Hyperparameterized YOLOv8 Model","authors":"Ahmad Esmaeil Abbasi, Agostino Marcello Mangini, Maria Pia Fanti","doi":"10.3390/electronics13183661","DOIUrl":"https://doi.org/10.3390/electronics13183661","url":null,"abstract":"Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"77 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259553","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}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183659
Ivan Vajs, Srđan Brkić, Predrag Ivaniš, Dejan Drajic
The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.
{"title":"Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks","authors":"Ivan Vajs, Srđan Brkić, Predrag Ivaniš, Dejan Drajic","doi":"10.3390/electronics13183659","DOIUrl":"https://doi.org/10.3390/electronics13183659","url":null,"abstract":"The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.","PeriodicalId":11646,"journal":{"name":"Electronics","volume":"21 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259552","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}
Pub Date : 2024-09-14DOI: 10.3390/electronics13183662
Krzysztof Górecki, Paweł Górecki
This article proposes an electrothermal averaged model of a half-bridge DC–DC converter containing a power module. This kind of model enables the computation of characteristics of DC–DC converters using DC analysis. The form of the elaborated model is presented. Both the electrical and thermal properties of the analyzed DC–DC converter are included in this model. This is the first averaged electrothermal model of a DC–DC converter which makes it possible to compute the junction temperature of all the semiconductor devices and magnetic components. The accuracy of the model was experimentally verified in a wide range of switching frequencies and output currents. Particularly, the influence of mutual thermal couplings between the transistors contained in the considered module on the characteristics of the converter and the junction temperature of the transistors is analyzed.
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