Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
A High-Voltage Pulse Modulator Composed of SiC MOSFETs/IGBTs in a Hybrid Connecting State
Electronics 2024, 13(11), 2108; https://doi.org/10.3390/electronics13112108 (registering DOI) - 29 May 2024
Abstract
In order to solve problems such as a slow switching speed, a high switching power, a loss of pure IGBT modulators, and the weak withstanding load short-circuit ability of pure SiC MOSFET modulators used for vacuum loads, this paper proposes a new scheme
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In order to solve problems such as a slow switching speed, a high switching power, a loss of pure IGBT modulators, and the weak withstanding load short-circuit ability of pure SiC MOSFET modulators used for vacuum loads, this paper proposes a new scheme for high-voltage pulse modulators based on SiC MOSFET/IGBT hybrid connecting circuits. It has a low power loss like the pure SiC MOSFET modulator and a strong withstanding load short-circuit ability like the pure IGBT modulator. Firstly, the principle circuit of the hybrid connecting modulator are discussed and chosen. And the basic working processes of the hybrid parallel-series modulator is described in detail. Secondly, three key points in this new scheme are analyzed and designed as follows: the static and dynamic voltage sharing; the actualizing of the ZVS process for IGBTs; the improvement of short-circuit protection for SiC MOSFETs. A modulator consisting of 16-stage 1200 V-SiC MOSFETs and 1200 V-IGBTs in hybrid parallel-series states is tested. Based on the sample circuit, the working data, such as high-voltage pulse waveforms of 10 kV/2 KHz/10 μs, static and dynamic voltage sharing, the driving control sequence, the U/I sequence of the IGBT, the short-circuit protection waveform, and the calculation, are obtained and discussed.
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(This article belongs to the Special Issue Advances in Pulsed-Power and High-Power Electronics)
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Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things
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Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè
Electronics 2024, 13(11), 2107; https://doi.org/10.3390/electronics13112107 (registering DOI) - 29 May 2024
Abstract
Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could
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Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could be adopted in the IoT scenario; however, most of these approaches represent the different dimensions of trust using scalar measures, then integrating these measures in a global trustworthiness value. In this paper, we discuss the limitation of this approach in the IoT context, highlighting the necessity of modeling complex trust relationships that cannot be captured by a vector-based model, and we propose a new trust model in which the trust perceived by an object with respect to another object is modeled by a directed, weighted graph whose vertices are trust dimensions and whose arcs represent relationships between trust dimensions. By using this new model, we provide the IoT community with the possibility of representing also situations in which an object does not know a trust dimension, e.g., reliability, but it is able to derive it from another one, e.g., honesty. The introduced model can represent any trust structure of the type illustrated above, in which several trust dimensions are mutually dependent.
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(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
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FLsM: Fuzzy Localization of Image Scenes Based on Large Models
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Weiyi Chen, Lingjuan Miao, Jinchao Gui, Yuhao Wang and Yiran Li
Electronics 2024, 13(11), 2106; https://doi.org/10.3390/electronics13112106 (registering DOI) - 29 May 2024
Abstract
This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in
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This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments.
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(This article belongs to the Special Issue Advances in Social Bots)
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A Lightweight and Dynamic Feature Aggregation Method for Cotton Field Weed Detection Based on Enhanced YOLOv8
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Doudou Ren, Wenzhong Yang, Zhifeng Lu, Danny Chen, Wenxuan Su and Yihang Li
Electronics 2024, 13(11), 2105; https://doi.org/10.3390/electronics13112105 (registering DOI) - 29 May 2024
Abstract
Weed detection is closely related to agricultural production, but often faces the problems of leaf shading and limited computational resources. Therefore, this study proposes an improved weed detection algorithm based on YOLOv8. Firstly, the Dilated Feature Integration Block is designed to improve the
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Weed detection is closely related to agricultural production, but often faces the problems of leaf shading and limited computational resources. Therefore, this study proposes an improved weed detection algorithm based on YOLOv8. Firstly, the Dilated Feature Integration Block is designed to improve the feature extraction in the backbone network by introducing large kernel convolution and multi-scale dilation convolution, which utilizes information from different scales and levels. Secondly, to solve the problem of a large number of parameters in the feature fusion process of the Path Aggregation Feature Pyramid Network, a new feature fusion architecture multi-scale feature interaction network is designed, which achieves the high-level semantic information to guide the low-level semantic information through the attention mechanism. Finally, we propose a Dynamic Feature Aggregation Head to solve the problem that the YOLOv8 detection head cannot dynamically focus on important features. Comprehensive experiments on two publicly accessible datasets show that the proposed model outperforms the benchmark model, with mAP50 and mAP75 improving by 4.7% and 5.0%, and 5.3% and 3.3%, respectively, whereas the number of model parameters is only 6.62 M. This study illustrates the utility potential of the algorithm for weed detection in cotton fields, marking a significant advancement of artificial intelligence in agriculture.
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(This article belongs to the Section Artificial Intelligence)
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CMOS IC Solutions for the 77 GHz Radar Sensor in Automotive Applications
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Giuseppe Papotto, Alessandro Parisi, Alessandro Finocchiaro, Claudio Nocera, Andrea Cavarra, Alessandro Castorina and Giuseppe Palmisano
Electronics 2024, 13(11), 2104; https://doi.org/10.3390/electronics13112104 (registering DOI) - 28 May 2024
Abstract
This paper presents recent results on CMOS integrated circuits for automotive radar sensor applications in the 77 GHz frequency band. It is well demonstrated that nano-scale CMOS technologies are the best solution for the implementation of low-cost and high-performance mm-wave radar sensors since
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This paper presents recent results on CMOS integrated circuits for automotive radar sensor applications in the 77 GHz frequency band. It is well demonstrated that nano-scale CMOS technologies are the best solution for the implementation of low-cost and high-performance mm-wave radar sensors since they provide high integration level besides supporting high-speed digital processing. The present work is mainly focused on the RF front-end and summarizes the most stringent requirements of both short/medium- and long-range radar applications. After a brief introduction of the adopted technology, the paper addresses the critical building blocks of the receiver and transmitter chain while discussing crucial design aspects to meet the final performance. Specifically, effective circuit topologies are presented, which concern mixer, variable-gain amplifier, and filter for the receiver, as well as frequency doubler and power amplifier for the transmitter. Moreover, a voltage-controlled oscillator for a PLL efficiently covering the two radar bands is described. Finally, the circuit description is accompanied by experimental results of an integrated implementation in a 28 nm fully depleted silicon-on-insulator CMOS technology.
Full article
(This article belongs to the Special Issue Radar System and Radar Signal Processing)
Open AccessArticle
Benchmarking Android Malware Analysis Tools
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Javier Bermejo Higuera, Javier Morales Moreno, Juan Ramón Bermejo Higuera, Juan Antonio Sicilia Montalvo, Gustavo Javier Barreiro Martillo and Tomas Miguel Sureda Riera
Electronics 2024, 13(11), 2103; https://doi.org/10.3390/electronics13112103 - 28 May 2024
Abstract
Today, malware is arguably one of the biggest challenges organisations face from a cybersecurity standpoint, regardless of the types of devices used in the organisation. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper
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Today, malware is arguably one of the biggest challenges organisations face from a cybersecurity standpoint, regardless of the types of devices used in the organisation. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper presents research on the functionalities and performance of different malicious Android application package analysis tools, including one that uses machine learning techniques. In addition, it investigates how these tools streamline the detection, classification, and analysis of malicious Android Application Packages (APKs) for Android operating system devices. As a result of the research included in this article, it can be highlighted that the AndroPytool, a tool that uses machine learning (ML) techniques, obtained the best results with an accuracy of 0.986, so it can be affirmed that the tools that use artificial intelligence techniques used in this study are more efficient in terms of detection capacity. On the other hand, of the online tools analysed, Virustotal and Pithus obtained the best results. Based on the above, new approaches can be suggested in the specification, design, and development of new tools that help to analyse, from a cybersecurity point of view, the code of applications developed for this environment.
Full article
(This article belongs to the Special Issue Blockchain-Based Cryptography, Privacy-Preserving and Cybersecurity Systems)
Open AccessArticle
Learning to Diagnose: Meta-Learning for Efficient Adaptation in Few-Shot AIOps Scenarios
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Yunfeng Duan, Haotong Bao, Guotao Bai, Yadong Wei, Kaiwen Xue, Zhangzheng You, Yuantian Zhang, Bin Liu, Jiaxing Chen, Shenhuan Wang and Zhonghong Ou
Electronics 2024, 13(11), 2102; https://doi.org/10.3390/electronics13112102 - 28 May 2024
Abstract
With the advancement of technologies like 5G, cloud computing, and microservices, the complexity of network management systems and the variety of technical components have greatly increased. This rise in complexity has rendered traditional operations and maintenance methods inadequate for current monitoring and maintenance
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With the advancement of technologies like 5G, cloud computing, and microservices, the complexity of network management systems and the variety of technical components have greatly increased. This rise in complexity has rendered traditional operations and maintenance methods inadequate for current monitoring and maintenance demands. Consequently, artificial intelligence for IT operations (AIOps), which harnesses AI and big data technologies, has emerged as a solution. AIOps plays a crucial role in enhancing service quality and customer satisfaction, boosting engineering productivity, and reducing operational costs. This article delves into the primary tasks involved in AIOps, such as anomaly detection, and log fault analysis and classification. A significant challenge identified in many AIOps tasks is the scarcity of fault sample data, indicating a natural alignment of these tasks with few-shot learning. Inspired by model-agnostic meta-learning (MAML), we propose a new anomaly detector, MAML-KAD, for application in various AIOps tasks. Observations confirm that meta-learning algorithms effectively enhance AIOps tasks, showcasing the wide-ranging application prospects of meta-learning algorithms in the field of AIOps. Moreover, we introduced an AIOps platform that embeds meta-learning within its diagnostic core and features streamlined log collection, caching, and alerting to automate the AIOps workflow.
Full article
(This article belongs to the Special Issue Applied Artificial Intelligence Approach: Intelligent Data Processing and Mining with Online Behaviors)
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Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting
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Yun Zhang, Bin Yang, Xi Zhao, Shiqian Wu, Bin Luo and Liangpei Zhang
Electronics 2024, 13(11), 2101; https://doi.org/10.3390/electronics13112101 - 28 May 2024
Abstract
Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric
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Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we leverage this consensus among outliers in quantized residual preference space by extending energy minimization to combine model error and spatial smoothness for outlier detection. The outlier detection process based on energy minimization follows an alternate sampling and labeling framework. Subsequently, an ordinary energy minimization method is employed to optimize inlier labels, thereby following the alternate sampling and labeling framework. Experimental results demonstrate that the energy minimization-based outlier detection method effectively identifies most outliers in the data. Additionally, the proposed energy minimization-based inlier segmentation accurately segments inliers into different models. Overall, the performance of the proposed method surpasses that of most state-of-the-art methods.
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(This article belongs to the Special Issue Computational Imaging and Its Application)
Open AccessArticle
Stochastic and Extreme Scenario Generation of Wind Power and Supply–Demand Balance Analysis Considering Wind Power–Temperature Correlation
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Fan Li, Dong Liu, Ke Sun, Shidong Hong, Fangzheng Peng, Cheng Zhang, Taikun Tao and Boyu Qin
Electronics 2024, 13(11), 2100; https://doi.org/10.3390/electronics13112100 - 28 May 2024
Abstract
In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand
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In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results.
Full article
(This article belongs to the Special Issue AI-Based Power System Stability and Control Analysis)
Open AccessArticle
An Integrated DQN and RF Packet Routing Framework for the V2X Network
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Chin-En Yen, Yu-Siang Jhang, Yu-Hsuan Hsieh, Yu-Cheng Chen, Chunghui Kuo and Ing-Chau Chang
Electronics 2024, 13(11), 2099; https://doi.org/10.3390/electronics13112099 - 28 May 2024
Abstract
With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic
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With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.
Full article
(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles)
Open AccessArticle
Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
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Chen Wang, Lin Zheng, Gang Wang, Zhiwei Liu and Chao Yang
Electronics 2024, 13(11), 2098; https://doi.org/10.3390/electronics13112098 - 28 May 2024
Abstract
With the rapid growth of satellite Internet of Things (SIoT) services, existing frequency band resources are insufficient to meet future business demands. To effectively address this issue, it is necessary to enhance the utilization of existing frequency resources. However, idle frequency resources are
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With the rapid growth of satellite Internet of Things (SIoT) services, existing frequency band resources are insufficient to meet future business demands. To effectively address this issue, it is necessary to enhance the utilization of existing frequency resources. However, idle frequency resources are typically scattered across multiple bands and vary in bandwidth size. Direct Spectrum Division Transmission (DSDT), dividing a complete signal into sub-spectrum signals for transmission in idle frequency bands, can take the use of fragmented spectrum resources for satellite communication. Nevertheless, the performance of DSDT depends heavily on accurate synchronization toward multiple sub-spectrums. In this paper, an algorithm for error synchronization tracking and compensation is proposed by utilizing the focusing nature of constellation. All sub-spectrums are weighed by the minimum Euclidean distance of the constellation to compensate for amplitude–frequency–phase errors simultaneously. Simulations and experimental verification demonstrate synchronization performance and feasibility of proposed method in a multi-radio frequency channels environment.
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(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
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Advantages and Pitfalls of Dataset Condensation: An Approach to Keyword Spotting with Time-Frequency Representations
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Pedro Henrique Pereira, Wesley Beccaro and Miguel Arjona Ramírez
Electronics 2024, 13(11), 2097; https://doi.org/10.3390/electronics13112097 - 28 May 2024
Abstract
With the exponential growth of data, the need for efficient techniques to extract relevant information from datasets becomes increasingly imperative. Reducing the training data can be useful for applications wherein storage space or computational resources are limited. In this work, we explore the
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With the exponential growth of data, the need for efficient techniques to extract relevant information from datasets becomes increasingly imperative. Reducing the training data can be useful for applications wherein storage space or computational resources are limited. In this work, we explore the concept of data condensation (DC) in the context of keyword spotting systems (KWS). Using deep learning architectures and time-frequency speech representations, we have obtained condensed speech signal representations using gradient matching with Efficient Synthetic-Data Parameterization. From a series of classification experiments, we analyze the models and condensed data performances in terms of accuracy and number of data per class. We also present results using cross-model techniques, wherein models are trained with condensed data obtained from a different architecture. Our findings demonstrate the potential of data condensation in the context of the speech domain for reducing the size of datasets while retaining their most important information and maintaining high accuracy for the model trained with the condensed dataset. We have obtained an accuracy of 80.75% with 30 condensed speech representations per class with ConvNet, representing an addition of 24.9% in absolute terms when compared to 30 random samples from the original training dataset. However, we demonstrate the limitations of this approach in the cross-model tests. We also highlight the challenges and opportunities for further improving the accuracy of condensed data obtained and trained with different neural network architectures.
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(This article belongs to the Special Issue Automated Methods for Speech Processing and Recognition)
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WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer
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Qingyao Zeng, Dapeng Xiong, Zhongwang Wu, Kechang Qian, Yu Wang and Yinghao Su
Electronics 2024, 13(11), 2096; https://doi.org/10.3390/electronics13112096 - 28 May 2024
Abstract
As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward
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As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.
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(This article belongs to the Special Issue Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks)
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IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
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Yi Qu, Sheng Wang, Tianshi Pan and Hui Feng
Electronics 2024, 13(11), 2095; https://doi.org/10.3390/electronics13112095 - 28 May 2024
Abstract
IPCBs (Intelligent Pseudolite Constellations based on high-altitude balloons) are a novel type of air-based pseudolite application with many advantages. Compared with ground-based pseudolites and traditional air-based pseudolites, IPCBs have a wider coverage and a lower energy requirement. Compared with LEO satellite constellations, IPCBs
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IPCBs (Intelligent Pseudolite Constellations based on high-altitude balloons) are a novel type of air-based pseudolite application with many advantages. Compared with ground-based pseudolites and traditional air-based pseudolites, IPCBs have a wider coverage and a lower energy requirement. Compared with LEO satellite constellations, IPCBs have a stronger signal, a lower cost, and a shorter deployment period. These merits give promising potential to IPCBs. In IPCB applications, one of the key factors is geometry configuration, which is deeply influenced by the balloon’s unique features. The basic idea of this paper is to pursue a strategy to improve IPCB geometry performance by using diverse winds at different altitudes and balloons’ capability of altering flight altitude intelligently. Starting with a brief introduction to IPCBs, this paper defines an indicator to assess IPCB geometry performance, an approach to adjust IPCB geometry configuration and an IPCB geometry configuration planning algorithm. Next, a series of simulations are implemented with an IPCB composed of six pseudolites in winds with/without a quasi-zero wind layer. Some IPCB geometry configurations are analyzed, and their geometry performances are compared. Simulation results show the effectiveness of the proposed algorithm and the influence of the quasi-zero wind layer on IPCB performance.
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(This article belongs to the Special Issue Advances in Social Bots)
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Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction
by
Hui-Ting Christine Lin and Vincent S. Tseng
Electronics 2024, 13(11), 2094; https://doi.org/10.3390/electronics13112094 - 28 May 2024
Abstract
In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate
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In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traffic analysis, the dynamic nature of traffic patterns frequently challenges the forecasting capabilities of existing models, especially when forecasting both immediate and future traffic conditions across various time horizons. Additionally, the area of long-term travel time forecasting still remains not fully explored in current research due to these complexities. In response to these challenges, this study introduces the Periodic Transformer Encoder (PTE). PTE is a Transformer-based model designed to enhance traffic time predictions by effectively capturing temporal dependencies across various horizons. Utilizing attention mechanisms, PTE learns from long-range periodic traffic data for handling both short-term and long-term fluctuations. Furthermore, PTE employs a streamlined encoder-only architecture that eliminates the need for a traditional decoder, thus significantly simplifying the model’s structure and reducing its computational demands. This architecture enhances both the training efficiency and the performance of direct travel time predictions. With these enhancements, PTE effectively tackles the challenges presented by dynamic traffic patterns, significantly improving prediction performance across multiple time horizons. Comprehensive evaluations on an extensive real-world traffic dataset demonstrate PTE’s superior performance in predicting travel times over multiple horizons compared to existing methods. PTE is notably effective in adapting to high-variability road segments and peak traffic hours. These results prove PTE’s effectiveness and robustness across diverse traffic environments, indicating its significant contribution to advancing traffic prediction capabilities within ITS.
Full article
(This article belongs to the Special Issue Data-Centric Artificial Intelligence: New Methods for Data Processing)
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Open AccessArticle
Optimal Voltage Recovery Learning Control for Microgrids with N-Distributed Generations via Hybrid Iteration Algorithm
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Lüeshi Li, Ruizhuo Song and Shuqing Dong
Electronics 2024, 13(11), 2093; https://doi.org/10.3390/electronics13112093 - 28 May 2024
Abstract
Considering that the nonlinearity and uncertainty of the microgrid model complicate the derivation and design of the optimal controller, an adaptive dynamic programming (ADP) algorithm is designed to solve the model-free non-zero-sum game. By combining the advantages of policy iteration and value iteration,
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Considering that the nonlinearity and uncertainty of the microgrid model complicate the derivation and design of the optimal controller, an adaptive dynamic programming (ADP) algorithm is designed to solve the model-free non-zero-sum game. By combining the advantages of policy iteration and value iteration, an optimal learning control scheme based on hybrid iteration is constructed to provide stringent real power sharing for the nonlinear and coupled microgrid systems with N-distributed generations. First, using non-zero-sum differential game strategy, a novel distributed secondary voltage recovery consensus optimal control protocol is built using a hybrid iteration method to realize the voltage recovery of microgrids. Then, the data of the system state and input are gathered along a dynamic system trajectory and a data-driven optimal controller learns the game solution without microgrid physics information, enhancing convenience and efficiency in practical applications. Furthermore, the convergence analysis is given in detail, and it is proved that the control protocol can converge to the optimal solution so as to ensure the stability of the voltage recovery of the microgrid system. Convergence analysis proves the convergence of the the protocol to the optimal solution, ensuring voltage recovery stability. Simulation results validate the feasibility and effectiveness of the proposed scheme.
Full article
(This article belongs to the Special Issue Intelligent Mobile Robotic Systems: Decision, Planning and Control)
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ICC-BiFormer: A Deep-Learning Model for Near-Earth Asteroid Detection via Image Compression and Local Feature Extraction
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Yiyang Guo, Yuan Liu and Ru Yang
Electronics 2024, 13(11), 2092; https://doi.org/10.3390/electronics13112092 - 28 May 2024
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Detecting near-Earth asteroids (NEAs) is crucial for research in solar system and planetary science. In recent year, deep-learning methods have almost dominated the task. Since NEAs represent only one-thousandth of the pixels in images, we proposed an ICC-BiFormer model that includes an image
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Detecting near-Earth asteroids (NEAs) is crucial for research in solar system and planetary science. In recent year, deep-learning methods have almost dominated the task. Since NEAs represent only one-thousandth of the pixels in images, we proposed an ICC-BiFormer model that includes an image compression and contrast enhancement block and a BiFormer model to capture local features in input images, which is different from previous models based on Convolutional Neural Network (CNN). Furthermore, we utilize a larger input size of the model, which corresponds to the side length of the input image matrix, and design a cropping algorithm to prevent NEAs from being truncated and better divide NEAs and satellites. We apply our ICC-BiFormer model into a dataset of approximately 20,000 streak and 40,000 non-streak images to train a binary classification model. The ICC-BiFormer achieves 99.88% accuracy, which is superior to existing models. Focusing on local features has been proven effective in detecting NEAs.
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Open AccessReview
Concept of the Intelligent Support of Decision Making for Manufacturing a 3D-Printed Hand Exoskeleton within Industry 4.0 and Industry 5.0 Paradigms
by
Izabela Rojek, Jakub Kopowski, Piotr Kotlarz, Janusz Dorożyński and Dariusz Mikołajewski
Electronics 2024, 13(11), 2091; https://doi.org/10.3390/electronics13112091 - 28 May 2024
Abstract
Supporting the decision-making process for the production of a 3D-printed hand exoskeleton within the Industry 4.0 and Industry 5.0 paradigms brings new concepts of manufacturing procedures for 3D-printed medical devices, including hand exoskeletons for clinical applications. The article focuses on current developments in
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Supporting the decision-making process for the production of a 3D-printed hand exoskeleton within the Industry 4.0 and Industry 5.0 paradigms brings new concepts of manufacturing procedures for 3D-printed medical devices, including hand exoskeletons for clinical applications. The article focuses on current developments in the design and manufacturing of hand exoskeletons and their future directions from the point of view of implementation within the Industry 4.0 and Industry 5.0 paradigms and applications in practice. Despite numerous publications on the subject of hand exoskeletons, many have not yet entered production and clinical application. The results of research on hand exoskeletons to date indicate that they achieve good therapeutic effects not only in terms of motor control, but also in a broader context: ensuring independence and preventing secondary motor changes. This makes interdisciplinary research on hand exoskeletons a key study influencing the future lives of patients with hand function deficits and the further work of physiotherapists. The main aim of this article is to check in what direction hand exoskeletons can be developed from a modern economic perspective and how decision support systems can accelerate these processes based on a literature review, expert opinions, and a case study.
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(This article belongs to the Special Issue New Challenges of Decision Support Systems)
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Open AccessArticle
Design of a Sigma-Delta Analog-to-Digital Converter Cascade Decimation Filter
by
Mao Ye, Zitong Liu and Yiqiang Zhao
Electronics 2024, 13(11), 2090; https://doi.org/10.3390/electronics13112090 - 27 May 2024
Abstract
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As the current mainstream high-precision ADC architecture, sigma-delta ADC is extensively employed in a wide range of domains and applications. This paper presents the design of a highly efficient cascaded digital decimation filter for sigma-delta ADCs, emphasizing the suppression of high folding band
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As the current mainstream high-precision ADC architecture, sigma-delta ADC is extensively employed in a wide range of domains and applications. This paper presents the design of a highly efficient cascaded digital decimation filter for sigma-delta ADCs, emphasizing the suppression of high folding band noise and the achievement of a flat passband. Additionally, this study addresses the critical balance between filter performance and power consumption. An inserting zero (IZ) filter is incorporated into a cascaded integrator comb (CIC) filter to enhance aliasing suppression. The IZ filter and compensation filter are optimized using the particle swarm optimization (PSO) algorithm to achieve greater noise attenuation and smaller passband ripple. The designed filter achieves a noise attenuation of 93.4 dB in the folding band and exhibits an overall passband ripple of 0.0477 dB within a bandwidth of 20 KHz. To decrease the power consumption in the filter design, polyphase decomposition has been applied. The filter structure is implemented on an FPGA, processing a 5-bit stream from a 64-times oversampling rate and third-order sigma-delta modulator. The signal-to-noise ratio (SNR) of the output signal reaches 91.7 dB. For ASIC design, the filter utilizes 180 nm CMOS technology with a power consumption of 0.217 mW and occupies a layout area of 0.72 mm2. The post-layout simulation result indicates that the SNR remains at 91.7 dB.
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Open AccessArticle
A Robust CoS-PVNet Pose Estimation Network in Complex Scenarios
by
Jiu Yong, Xiaomei Lei, Jianwu Dang and Yangping Wang
Electronics 2024, 13(11), 2089; https://doi.org/10.3390/electronics13112089 - 27 May 2024
Abstract
Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors
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Object 6D pose estimation, as a key technology in applications such as augmented reality (AR), virtual reality (VR), robotics, and autonomous driving, requires the prediction of the 3D position and 3D pose of objects robustly from complex scene images. However, complex environmental factors such as occlusion, noise, weak texture, and lighting changes may affect the accuracy and robustness of object 6D pose estimation. We propose a robust CoS-PVNet (complex scenarios pixel-wise voting network) pose estimation network for complex scenes. By adding a pixel-weight layer based on the PVNet network, more accurate pixel point vectors are selected, and dilated convolution and adaptive weighting strategies are used to capture local and global contextual information of the input feature map. At the same time, the perspective-n-point localization algorithm is used to accurately locate 2D key points to solve the pose of 6D objects, and then, the transformation relationship matrix of 6D pose projection is solved. The research results indicate that on the LineMod and Occlusion LineMod datasets, CoS-PVNet has high accuracy and can achieve stable and robust 6D pose estimation even in complex scenes.
Full article
(This article belongs to the Special Issue Emerging Immersive Learning Technologies: Augmented and Virtual Reality)
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