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
To (US)Be or Not to (US)Be: Discovering Malicious USB Peripherals through Neural Network-Driven Power Analysis
Electronics 2024, 13(11), 2117; https://doi.org/10.3390/electronics13112117 (registering DOI) - 29 May 2024
Abstract
Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the
[...] Read more.
Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score.
Full article
(This article belongs to the Special Issue Cyber Attacks: Threats and Security Solutions)
►
Show Figures
Open AccessArticle
Detection of Dangerous Human Behavior by Using Optical Flow and Hybrid Deep Learning
by
Laith Mohammed Salim and Yuksel Celik
Electronics 2024, 13(11), 2116; https://doi.org/10.3390/electronics13112116 (registering DOI) - 29 May 2024
Abstract
Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this
[...] Read more.
Dangerous human behavior in the driving sense may cause traffic accidents and even cause economic losses and casualties. Accurate identification of dangerous human behavior can prevent potential risks. To solve the problem of difficulty retaining the temporal characteristics of the existing data, this paper proposes a human behavior recognition model based on utilized optical flow and hybrid deep learning model-based 3D CNN-LSTM in stacked autoencoder and uses the abnormal behavior of humans in real traffic scenes to verify the proposed model. This model was tested using HMDB51 datasets and JAAD dataset and compared with the recent related works. For a quantitative test, the HMDB51 dataset was used to train and test models for human behavior. Experimental results show that the proposed model achieved good accuracy of about 86.86%, which outperforms recent works. For qualitative analysis, we depend on the initial annotations of walking movements in the JAAD dataset to streamline the annotating process to identify transitions, where we take into consideration flow direction, if it is cross-vehicle motion (to be dangerous) or if it is parallel to vehicle motion (to be of no danger). The results show that the model can effectively identify dangerous behaviors of humans and then test on the moving vehicle scene.
Full article
(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
►▼
Show Figures
Figure 1
Open AccessArticle
Multitask Learning for Concurrent Grading Diagnosis and Semi-Supervised Segmentation of Honeycomb Lung in CT Images
by
Yunyun Dong, Bingqian Yang and Xiufang Feng
Electronics 2024, 13(11), 2115; https://doi.org/10.3390/electronics13112115 (registering DOI) - 29 May 2024
Abstract
Honeycomb lung is a radiological manifestation of various lung diseases, seriously threatening patients’ lives worldwide. In clinical practice, the precise localization of lesions and assessment of their severity are crucial. However, accurate segmentation and grading are challenging for physicians due to the heavy
[...] Read more.
Honeycomb lung is a radiological manifestation of various lung diseases, seriously threatening patients’ lives worldwide. In clinical practice, the precise localization of lesions and assessment of their severity are crucial. However, accurate segmentation and grading are challenging for physicians due to the heavy annotation burden and diversity of honeycomb lungs. In this paper, we propose a multitask learning architecture for semi-supervised segmentation and grading diagnosis to achieve automatic localization and assessment of lesions. To the best of our knowledge, this is the first method that integrates a grading diagnosis task into honeycomb lung semi-supervised segmentation. Firstly, we adapt cross-learning to capture local features and long-range dependencies from the CNN and transformer. Secondly, considering the diversity of honeycomb lung lesions, the shape-edge aware constraint is designed to assist the model in locating lesions. Then, in order to better understand the different levels of information in the images, we develop global contrast and local contrast learning to enhance the model’s learning of semantic-level and pixel-level features. Lastly, aiming to improve the diagnostic accuracy, we propose a gradient thresholding algorithm to integrate the segmentation predictions into the grading diagnosis network. The experiment’s results based on the in-house honeycomb lung dataset demonstrate the superiority of our method. Compared to other methods, our approach achieves a state-of-the-art performance. In particular, in external data testing, our predictions are consistent with physicians in the majority of cases. In addition, the segmentation results based on the public Kvasir-SEG dataset also indicate that our method has good generalization ability.
Full article
(This article belongs to the Section Artificial Intelligence)
►▼
Show Figures
Figure 1
Open AccessArticle
Tomato Sorting System Based on Machine Vision
by
Lixin Hou, Zeye Liu, Jixuan You, Yandong Liu, Jingxuan Xiang, Jing Zhou and Yu Pan
Electronics 2024, 13(11), 2114; https://doi.org/10.3390/electronics13112114 (registering DOI) - 29 May 2024
Abstract
In the fresh tomato market, it is crucial to sort and sell tomatoes based on their quality. This is important to enhance the competitiveness and profitability of the market. However, the manual sorting process is subjective and inefficient. To address this issue, we
[...] Read more.
In the fresh tomato market, it is crucial to sort and sell tomatoes based on their quality. This is important to enhance the competitiveness and profitability of the market. However, the manual sorting process is subjective and inefficient. To address this issue, we have developed an automatic tomato sorting system that uses the Raspberry PI 4B as the control platform for the robot arm. This system has been integrated with a human–computer interaction interface sorting system. Our experimental results indicate that this sorting method has an accuracy rate of 99.1% and an efficiency of 1350 tomatoes per hour. This development is in line with modern agricultural mechanization and intelligence needs.
Full article
(This article belongs to the Section Artificial Intelligence)
►▼
Show Figures
Figure 1
Open AccessFeature PaperArticle
Enhanced Hyperspectral Sharpening through Improved Relative Spectral Response Characteristic (R-SRC) Estimation for Long-Range Surveillance Applications
by
Peter Yuen, Jonathan Piper, Catherine Yuen and Mehmet Cakir
Electronics 2024, 13(11), 2113; https://doi.org/10.3390/electronics13112113 (registering DOI) - 29 May 2024
Abstract
►▼
Show Figures
The fusion of low-spatial-resolution hyperspectral images (LRHSI) with high-spatial-resolution multispectral images (HRMSI) for super-resolution (SR), using coupled non-negative matrix factorization (CNMF), has been widely studied in the past few decades. However, the matching of spectral characteristics between the LRHSI and HRMSI, which is
[...] Read more.
The fusion of low-spatial-resolution hyperspectral images (LRHSI) with high-spatial-resolution multispectral images (HRMSI) for super-resolution (SR), using coupled non-negative matrix factorization (CNMF), has been widely studied in the past few decades. However, the matching of spectral characteristics between the LRHSI and HRMSI, which is required before they are jointly factorized, has rarely been studied. One objective of this work is to study how the relative spectral response characteristics (R-SRC) of the LRHSI and HRMSI can be better estimated, particularly when the SRC of the latter is unknown. To this end, three variants of enhanced R-SRC algorithms were proposed, and their effectiveness was assessed by applying them for sharpening data using CNMF. The quality of the output was assessed using the L1-norm-error (L1NE) and receiver operating characteristics (ROC) of target detections performed using the adaptive coherent estimator (ACE) algorithm. Experimental results obtained from two subsets of a real scene revealed a two- to three-fold reduction in the reconstruction error when the scenes were sharpened by the proposed R-SRC algorithms, in comparison with Yokoya’s original algorithm. Experiments also revealed that a much higher proportion (by one order of magnitude) of small targets of 0.015 occupancy in the LRHSI scene could be detected by the proposed R-SRC methods compared with the baseline algorithm, for an equal false alarm rate. These results may suggest the possibility of SR to allow long-range surveillance using low-cost HSI hardware, particularly when the remaining issues of the occurrence of large reconstruction errors and comparatively higher false alarm rate for ‘rare’ species in the scene can be understood and resolved in future research.
Full article
Figure 1
Open AccessArticle
Key-Point-Descriptor-Based Image Quality Evaluation in Photogrammetry Workflows
by
Dalius Matuzevičius, Vytautas Urbanavičius, Darius Miniotas, Šarūnas Mikučionis, Raimond Laptik and Andrius Ušinskas
Electronics 2024, 13(11), 2112; https://doi.org/10.3390/electronics13112112 (registering DOI) - 29 May 2024
Abstract
Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process
[...] Read more.
Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process by reducing data redundancy and computational demands. This study presents a novel approach to image quality evaluation tailored for photogrammetric applications that uses the key point descriptors typically encountered in image matching. Using a LightGBM ranker model, this research evaluates the effectiveness of key point descriptors such as SIFT, SURF, BRISK, ORB, KAZE, FREAK, and SuperPoint in predicting image quality. These descriptors are evaluated for their ability to indicate image quality based on the image patterns they capture. Experiments conducted on various publicly available image datasets show that descriptor-based methods outperform traditional no-reference image quality metrics such as BRISQUE, NIQE, PIQE, and BIQAA and a simple sharpness-based image quality evaluation method. The experimental results highlight the potential of using key-point-descriptor-based image quality evaluation methods to improve the photogrammetric workflow by selecting high-quality images for 3D modeling.
Full article
(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
►▼
Show Figures
Figure 1
Open AccessArticle
A Novel Source Code Representation Approach Based on Multi-Head Attention
by
Lei Xiao, Hao Zhong, Jianjian Liu, Kaiyu Zhang, Qizhen Xu and Le Chang
Electronics 2024, 13(11), 2111; https://doi.org/10.3390/electronics13112111 (registering DOI) - 29 May 2024
Abstract
Code classification and code clone detection are crucial for understanding and maintaining large software systems. Although deep learning surpasses traditional techniques in capturing the features of source code, existing models suffer from low processing power and high complexity. We propose a novel source
[...] Read more.
Code classification and code clone detection are crucial for understanding and maintaining large software systems. Although deep learning surpasses traditional techniques in capturing the features of source code, existing models suffer from low processing power and high complexity. We propose a novel source code representation method based on the multi-head attention mechanism (SCRMHA). SCRMHA captures the vector representation of entire code segments, enabling it to focus on different positions of the input sequence, capture richer semantic information, and simultaneously process different aspects and relationships of the sequence. Moreover, it can calculate multiple attention heads in parallel, speeding up the computational process. We evaluate SCRMHA on both the standard dataset and an actual industrial dataset, and analyze the differences between these two datasets. Experiment results in code classification and clone detection tasks show that SCRMHA consumes less time and reduces complexity by about one-third compared with traditional source code feature representation methods. The results demonstrate that SCRMHA reduces the computational complexity and time consumption of the model while maintaining accuracy.
Full article
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications)
►▼
Show Figures
Figure 1
Open AccessArticle
Development of Gold Inks for Inkjet Printing of Gas Sensors Electrodes on Plastic Support
by
Bastien Le Porcher, Mathilde Rieu and Jean-Paul Viricelle
Electronics 2024, 13(11), 2110; https://doi.org/10.3390/electronics13112110 (registering DOI) - 29 May 2024
Abstract
Among the conventional inks used for inkjet printing, metals, oxides, or polymers have been deposited in order to form functional coatings. Gold is one of the most used metals for electrode fabrication in the gas sensor field due to its inert behavior when
[...] Read more.
Among the conventional inks used for inkjet printing, metals, oxides, or polymers have been deposited in order to form functional coatings. Gold is one of the most used metals for electrode fabrication in the gas sensor field due to its inert behavior when exposed to reactive gases and conductive properties. However, only a few commercial gold inks are commercially available, and the combination of excessive price, a high minimum purchase quantity, and an unknown composition renders the actual products unappealing. To meet these shortcomings, gold inks were formulated with different solvents in order to reach sufficient properties for the inkjet printing process, such as surface tension and viscosity. On the one hand, gold ink was developed using a gold nanoparticle (AuNP) solution as the metal. This ink was optimized from nanoparticle synthesis, with the ink formulation obtaining a 32 mN·m−1 surface tension and 11.2 mPa·s viscosity in order to be inkjet-printed onto polyimide foil. On the other hand, a particle-free ink, called a precursor based of ink, was also developed. In this case, ink was made by solubilizing gold salt in aqueous medium in order to reach jettable properties. Surface tension was measured at 32 mN·m−1 while viscosity was 14.0 mPa·s. Then, printing and deposition parameters were optimized in order to obtain a highly conductive gold coating. The measured resistivity was 2 × 10−7 Ω·m which is close to the bulk gold conductive value. These coatings could be used for the fabrication of various devices in different working fields.
Full article
(This article belongs to the Section Microelectronics)
►▼
Show Figures
Figure 1
Open AccessArticle
Investigation of Hydrogen Flux Influence on InGaP Layer and Device Uniformity
by
Shangyu Yang, Ning Guo, Siqi Zhao, Yunkai Li, Moyu Wei, Yang Zhang and Xingfang Liu
Electronics 2024, 13(11), 2109; https://doi.org/10.3390/electronics13112109 - 29 May 2024
Abstract
►▼
Show Figures
In this study, we conduct a comprehensive examination of the influence of hydrogen (H2) carrier gas flux on the uniformity of epitaxial layers, specifically focusing on the InGaP single layer and the full structure of the InGaP/GaAs heterojunction bipolar transistor (HBT).
[...] Read more.
In this study, we conduct a comprehensive examination of the influence of hydrogen (H2) carrier gas flux on the uniformity of epitaxial layers, specifically focusing on the InGaP single layer and the full structure of the InGaP/GaAs heterojunction bipolar transistor (HBT). The results show that an elevated flux of H2 carrier gas markedly facilitates the stabilization of layer uniformity. Optimal uniformity in epitaxial wafers is achievable at a suitable carrier gas flux. Furthermore, this study reveals a significant correlation between the uniformity of the InGaP single layer and the overall uniformity of HBT structures, indicating a consequential interdependence.
Full article
Figure 1
Open AccessArticle
A High-Voltage Pulse Modulator Composed of SiC MOSFETs/IGBTs in a Hybrid Connecting State
by
Zhuang Kang, Xiaofeng Xie, Yang Liu, Daibing Chen, Haitao Yuan, Liu Zhao, Hai Zhao, Chengliang Yang and Guiqiang Zheng
Electronics 2024, 13(11), 2108; https://doi.org/10.3390/electronics13112108 - 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
[...] Read more.
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.
Full article
(This article belongs to the Special Issue Advances in Pulsed-Power and High-Power Electronics)
►▼
Show Figures
Figure 1
Open AccessArticle
Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things
by
Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè
Electronics 2024, 13(11), 2107; https://doi.org/10.3390/electronics13112107 - 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
[...] Read more.
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.
Full article
(This article belongs to the Special Issue Security and Trust in Internet of Things and Edge Computing)
►▼
Show Figures
Figure 1
Open AccessArticle
FLsM: Fuzzy Localization of Image Scenes Based on Large Models
by
Weiyi Chen, Lingjuan Miao, Jinchao Gui, Yuhao Wang and Yiran Li
Electronics 2024, 13(11), 2106; https://doi.org/10.3390/electronics13112106 - 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
[...] Read more.
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.
Full article
(This article belongs to the Special Issue Advances in Social Bots)
►▼
Show Figures
Figure 1
Open AccessArticle
A Lightweight and Dynamic Feature Aggregation Method for Cotton Field Weed Detection Based on Enhanced YOLOv8
by
Doudou Ren, Wenzhong Yang, Zhifeng Lu, Danny Chen, Wenxuan Su and Yihang Li
Electronics 2024, 13(11), 2105; https://doi.org/10.3390/electronics13112105 - 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
[...] Read more.
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.
Full article
(This article belongs to the Section Artificial Intelligence)
►▼
Show Figures
Figure 1
Open AccessReview
CMOS IC Solutions for the 77 GHz Radar Sensor in Automotive Applications
by
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 - 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
[...] Read more.
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
by
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
[...] Read more.
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
by
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
[...] Read more.
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)
►▼
Show Figures
Figure 1
Open AccessArticle
Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting
by
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
[...] Read more.
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.
Full article
(This article belongs to the Special Issue Computational Imaging and Its Application)
►▼
Show Figures
Figure 1
Open AccessArticle
Stochastic and Extreme Scenario Generation of Wind Power and Supply–Demand Balance Analysis Considering Wind Power–Temperature Correlation
by
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 (registering DOI) - 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
[...] Read more.
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)
►▼
Show Figures
Figure 1
Open AccessArticle
An Integrated DQN and RF Packet Routing Framework for the V2X Network
by
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
[...] Read more.
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
by
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
[...] Read more.
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.
Full article
(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
►▼
Show Figures
Figure 1
Journal Menu
► ▼ Journal Menu-
- Electronics Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Energies, Materials, Electronics, Machines, WEVJ
Advanced Electrical Machine Design and Optimization Ⅱ
Topic Editors: Youguang Guo, Gang Lei, Xin BaDeadline: 31 May 2024
Topic in
Applied Sciences, Electricity, Electronics, Energies, Sensors
Power System Protection
Topic Editors: Seyed Morteza Alizadeh, Akhtar KalamDeadline: 20 June 2024
Topic in
Drones, Electronics, Future Internet, Information, Mathematics
Future Internet Architecture: Difficulties and Opportunities
Topic Editors: Peiying Zhang, Haotong Cao, Keping YuDeadline: 30 June 2024
Topic in
Applied Sciences, Electronics, Photonics, Remote Sensing, Technologies
Emerging Terahertz Technologies for Integrated Sensing and Communication
Topic Editors: Jianjun Ma, Xiue Bao, Bin Li, Suman MukherjeeDeadline: 31 July 2024
Conferences
Special Issues
Special Issue in
Electronics
Network Intrusion Detection Using Deep Learning
Guest Editor: Harald VrankenDeadline: 31 May 2024
Special Issue in
Electronics
Satellite-Terrestrial Integrated Internet of Things
Guest Editors: Min Jia, Zhenyu Na, Xin Liu, Lexi XuDeadline: 15 June 2024
Special Issue in
Electronics
Modeling and Optimization of Energy Efficiency in the Light of Energy Security
Guest Editors: Aurelia Rybak, Aleksandra Rybak, Jarosław JoostberensDeadline: 1 July 2024
Special Issue in
Electronics
Advances in Human-Machine Interaction, Artificial Intelligence, and Robotics
Guest Editors: Juan Ernesto Solanes Galbis, Luis Gracia, Jaime Valls MiroDeadline: 15 July 2024
Topical Collections
Topical Collection in
Electronics
Application of Advanced Computing, Control and Processing in Engineering
Collection Editors: Sudip Chakraborty, Robertas Damaševičius, Sergio Greco
Topical Collection in
Electronics
Instrumentation, Noise, Reliability
Collection Editor: Graziella Scandurra
Topical Collection in
Electronics
Computer Vision and Pattern Recognition Techniques
Collection Editor: Donghyeon Cho
Topical Collection in
Electronics
Deep Learning for Computer Vision: Algorithms, Theory and Application
Collection Editors: Jungong Han, Guiguang Ding