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
Developing Different Test Conditions to Verify the Robustness and Versatility of Robotic Arms Controlled by Evolutionary Algorithms
Electronics 2024, 13(11), 2130; https://doi.org/10.3390/electronics13112130 (registering DOI) - 29 May 2024
Abstract
In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using
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In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using it to learn to write. The other robotic arm is tested on how well it can function as a solar tracker and how precisely it can function as an energy harvester. On the basis of these tests, the robotic arm’s mechanical structure, electronics, and software are put to the test. The software is based on evolutionary software that implements genetic algorithms. The entire command system is also ported to FPGAs (to hardware) to increase speed and response time.
Full article
(This article belongs to the Section Industrial Electronics)
Open AccessArticle
Rapid Beam Tracking Using Power Measurement for Terahertz Communications
by
Xiaodan He, Changming Zhang, Chi Lu and Xianbin Yu
Electronics 2024, 13(11), 2129; https://doi.org/10.3390/electronics13112129 (registering DOI) - 29 May 2024
Abstract
With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow
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With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow beams are expected to be adopted, making beam tracking significant for robust communications. In this paper, a tracking method based on power measurement is proposed, consisting of beam status monitoring, recognition of the deviation direction, and movement toward the optimal angle. By observing the change in the received signal power, beam misalignment is first checked, and whether the misalignment is out of tracking range is also determined. Then, the deviation direction is recognized by comparing the received power variations in the candidate directions, and the beam angle is adjusted accordingly until it reaches the optimal angle. With a small scanning range, the deviation direction is recognized in a short duration, allowing for rapid beam tracking. Numerical results indicate that the alignment error is competitively low and stable in the proposed beam tracking method, and its technical superiority is particularly dominant in situations involving variable motion at high speeds.
Full article
(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications)
Open AccessArticle
Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints
by
Spyridon Anogiatis, Panagiotis S. Trakas and Charalampos P. Bechlioulis
Electronics 2024, 13(11), 2128; https://doi.org/10.3390/electronics13112128 (registering DOI) - 29 May 2024
Abstract
This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative
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This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.
Full article
(This article belongs to the Special Issue Path Planning for Mobile Robots, 2nd Edition)
Open AccessArticle
Parameter Optimization of Josephson Parametric Amplifiers Using a Heuristic Search Algorithm for Axion Haloscope Search
by
Younggeun Kim, Junu Jeong, Sungwoo Youn, Sungjae Bae, Arjan F. van Loo, Yasunobu Nakamura, Sergey Uchaikin and Yannis K. Semertzidis
Electronics 2024, 13(11), 2127; https://doi.org/10.3390/electronics13112127 (registering DOI) - 29 May 2024
Abstract
The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known
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The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known for its quantum-limited noise characteristics, has been incorporated into the detection system to capture the weakly interacting axion signals. However, the performance of the JPA can be influenced by its environment, leading to the potential unreliability of a predefined parameter set obtained in a specific laboratory setting. Furthermore, conducting a broadband search requires the consecutive characterization of the amplifier across different tuning frequencies. To ensure more reliable measurements, we utilize the Nelder–Mead technique as a numerical search method to dynamically determine the optimal operating conditions. This heuristic search algorithm explores the multidimensional parameter space of the JPA, optimizing critical characteristics such as gain and noise temperature to maximize signal-to-noise ratios for a given experimental setup. Our study presents a comprehensive analysis of the properties of a flux-driven JPA to demonstrate the effectiveness of the algorithm. This approach contributes to ongoing efforts in axion dark matter research by offering an efficient method to enhance axion detection sensitivity through the optimized utilization of JPAs.
Full article
(This article belongs to the Special Issue Recent Advances and Applications in New Detectors)
Open AccessArticle
Vulnerability Assessment and Topology Reconstruction of Task Chains in UAV Networks
by
Qingfeng Yue, Jinglei Li, Zijia Huang, Xiaoyu Xie and Qinghai Yang
Electronics 2024, 13(11), 2126; https://doi.org/10.3390/electronics13112126 (registering DOI) - 29 May 2024
Abstract
With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such
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With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such as area reconnaissance and fire rescue, any attack on critical UAVs can greatly disrupt the execution of the entire task chain by causing equipment damage or connectivity disruption. To ensure network resilience post attack, identifying vulnerable nodes in the UAV network becomes crucial. In this paper, a Vulnerability-based Topology Reconstruction Mechanism (VUTRM) is proposed to rank the importance of nodes in task chains and formulate a topology reconstruction. It consists of two parts: the first part is a Multi-metric Node Vulnerability Assessment Algorithm (MENVAL) used to rank the importance of nodes in task chains, and the second part is a Node Importance-based Topology Reconstruction Algorithm (NITRA) used to reconstruct the UAV network with the obtained node ranking. Finally, simulations carried out with simulation software demonstrate that our proposed method accurately identifies network vulnerabilities and promptly implements effective reconstruction measures to minimize network damage.
Full article
(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)
Open AccessArticle
A High-Performance Non-Indexed Text Search System
by
Binh Kieu-Do-Nguyen, Tuan-Kiet Dang, Nguyen The Binh, Cuong Pham-Quoc, Huynh Phuc Nghi, Ngoc-Thinh Tran, Katsumi Inoue, Cong-Kha Pham and Trong-Thuc Hoang
Electronics 2024, 13(11), 2125; https://doi.org/10.3390/electronics13112125 (registering DOI) - 29 May 2024
Abstract
Full-text search has a wide range of applications, including tracking systems, computer vision, and natural language processing. Standard methods usually implement a two-phase procedure: indexing and retrieving, with the retrieval performance entirely dependent on the index efficiency. In most cases, the more powerful
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Full-text search has a wide range of applications, including tracking systems, computer vision, and natural language processing. Standard methods usually implement a two-phase procedure: indexing and retrieving, with the retrieval performance entirely dependent on the index efficiency. In most cases, the more powerful the index algorithm, the more memory and processing time are required. The amount of time and memory required to index a collection of documents is proportional to its overall size. In this paper, we propose a full-text search hardware implementation without the indexing phase, thus removing the time and memory requirements for indexing. Additionally, we propose an efficient design to leverage the parallel architecture of High Bandwidth Memory (HBM). To our knowledge, few (if not zero) researchers have integrated their full-text search system with an effective data access control on HBM. The functionality of the proposed system is verified on the Xilinx Alveo U50 Field-Programmable Gate Array (FPGA). The experimental results show that our system achieved a throughput of 8 Gigabytes per second, about 6697× speed-up compared to other software-based approaches.
Full article
(This article belongs to the Section Microelectronics)
Open AccessArticle
Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship
by
Gangui Yan, Yupeng Wang, Yuxing Fan, Cheng Yang and Lin Yue
Electronics 2024, 13(11), 2124; https://doi.org/10.3390/electronics13112124 (registering DOI) - 29 May 2024
Abstract
The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet
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The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet synchronous generators (PMSGs) connected to power systems poses a challenge when investigating small disturbance stability and instability mechanisms. This study establishes a model of the grid-connected PMSG system based on the voltage/power excitation–response relationship to describe the dynamic characteristics of the port of the PMSG, and the analysis of active and reactive response characteristics of PMSG lays the foundation for model simplification. Based on the unit model, a direct-drive wind farm aggregation equivalence method based on the excitation–response relationship is proposed. The equivalent model obtained by this method is suitable for the small disturbance stability analysis of direct-drive wind farms grid connected system, with good accuracy. The simulation verified the effectiveness of the aggregation model.
Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
Open AccessArticle
Deep Pre-Training Transformers for Scientific Paper Representation
by
Jihong Wang, Zhiguang Yang and Zhanglin Cheng
Electronics 2024, 13(11), 2123; https://doi.org/10.3390/electronics13112123 (registering DOI) - 29 May 2024
Abstract
In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain
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In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain of scientific paper analysis. Our method employs a novel neural network embedding technique that leverages textual components, such as keywords, titles, abstracts, and full texts, to represent papers in a vector space. By integrating recent advancements in text representation and unsupervised feature aggregation, SPBERT offers a sophisticated approach to encode essential information implicitly, thereby enhancing paper classification and literature retrieval tasks. We applied our method to several real-world academic datasets, demonstrating notable improvements over existing methods. The findings suggest that SPBERT not only provides a more effective representation of scientific papers but also facilitates a deeper understanding of large-scale academic data, paving the way for more informed and accurate scholarly analysis.
Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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Open AccessArticle
Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value
by
Berkay Emin, Akif Akgul, Fahrettin Horasan, Abdullah Gokyildirim, Haris Calgan and Christos Volos
Electronics 2024, 13(11), 2122; https://doi.org/10.3390/electronics13112122 (registering DOI) - 29 May 2024
Abstract
Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical
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Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional-order Arneodo (FOAR) system with cubic nonlinearity is conducted. This investigation involves the analysis of phase planes, bifurcation diagrams, Lyapunov exponential spectra, and spectral entropy. Numerical studies show that the Arneodo chaotic system exhibits chaotic behavior when the lowest fractional-order (FO) value is set to 0.55. In this context, the aim is to securely encrypt biomedical images based on the Arneodo chaotic system with the lowest FO value using the Nvidia Jetson Nano development board. However, though the lowest FO system offers enhanced security in biomedical image encryption due to its richer dynamic behaviors, it necessitates careful consideration of the trade-off between high memory requirements and increasing complexity in encryption algorithms. Within the scope of the study, a novel random number generator (RNG) is designed using the FOAR chaotic system. The randomness of the random numbers is proven by using internationally accepted NIST 800-22 and ENT test suites. A biomedical image encryption application is developed using pseudo-random numbers. The images obtained as a result of the application are evaluated with tests such as histogram, correlation, differential attack, and entropy analyses. As a result of the study, it has been shown that encryption and decryption of biomedical images can be successfully performed on a mobile Nvidia Jetson Nano development card in a secure and fast manner.
Full article
(This article belongs to the Special Issue Nonlinear Circuits and Systems: Latest Advances and Prospects)
Open AccessArticle
Few-Shot Image Classification Based on Swin Transformer + CSAM + EMD
by
Huadong Sun, Pengyi Zhang, Xu Zhang and Xiaowei Han
Electronics 2024, 13(11), 2121; https://doi.org/10.3390/electronics13112121 (registering DOI) - 29 May 2024
Abstract
In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global information and long-distance dependencies effectively. In order to
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In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global information and long-distance dependencies effectively. In order to address this problem, an innovative FSIC method is proposed in this paper, which is the integration of Swin Transformer and CSAM and Earth Mover’s Distance (EMD) technology (STCE). We utilize the Swin Transformer network for image feature extraction, and perform CSAM attention mechanism feature weighting on the output feature map, while we adopt the EMD algorithm to generate the optimal matching flow between the structural units, minimizing the matching cost. This approach allows for a more precise representation of the classification distance between images. We have conducted numerous experiments to validate the effectiveness of our algorithm. On three commonly used few-shot datasets, namely mini-ImageNet, tiered-ImageNet, and FC100, the accuracy of one-shot and five-shot has reached the state of the art (SOTA) in the FSIC; the mini-ImageNet achieves an accuracy of 98.65 ± 0.1% for one-shot and 99.6 ± 0.2% for five-shot tasks, while tiered ImageNet has an accuracy of 91.6 ± 0.1% for one-shot tasks and 96.55 ± 0.27% for five-shot tasks. For FC100, the accuracy is 64.1 ± 0.3% for one-shot tasks and 79.8 ± 0.69% for five-shot tasks. On two commonly used few-shot datasets, namely CUB, CIFAR-FS, CUB achieves an accuracy of 83.1 ± 0.4% for one-shot and 92.88 ± 0.4% for five-shot tasks, while CIFAR-FS achieves an accuracy of 86.95 ± 0.2% for one-shot and 94 ± 0.4% for five-shot tasks.
Full article
(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)
Open AccessArticle
Rolling Bearing Residual Useful Life Prediction Model Based on the Particle Swarm Optimization-Optimized Fusion of Convolutional Neural Network and Bidirectional Long–Short-Term Memory–Multihead Self-Attention
by
Jianzhong Yang, Xinggang Zhang, Song Liu, Ximing Yang and Shangfang Li
Electronics 2024, 13(11), 2120; https://doi.org/10.3390/electronics13112120 (registering DOI) - 29 May 2024
Abstract
In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes
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In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes particle swarm optimization (PSO) in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multihead self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. Initially, the original signals undergo correlation signal processing to calculate the features, such as standard deviation, variance, and kurtosis, to help identify the beginning location of the rolling bearing degradation stage. A new dataset is constructed with similar degradation trend features. Subsequently, the particle swarm optimization (PSO) algorithm is employed to find the optimal values of important hyperparameters in the model. Then, a convolutional neural network (CNN) is utilized to extract the deterioration features of rolling bearings in order to predict their remaining lifespan. The degradation features are inputted into the BiLSTM-MHSA network to facilitate the learning process and estimate the remaining lifespan of rolling bearings. Finally, the degradation features are converted to the remaining usable life (RUL) via the fully connected layer. The XJTU-SY rolling bearing accelerated life experimental dataset was used to verify the effectiveness of the proposed method by k-fold cross-validation. After comparing our model to the CNN-LSTM network model and other models, we found that our model can achieve reductions in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 9.27%, 6.76%, and 2.35%, respectively. Therefore, the experimental results demonstrate the model’s accuracy in forecasting remaining lifetime and support its ability to forecast breakdowns.
Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
Open AccessArticle
Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster
by
Bo Zhou, Xinwei Sun, Yunyang Xu and Wei Wei
Electronics 2024, 13(11), 2119; https://doi.org/10.3390/electronics13112119 (registering DOI) - 29 May 2024
Abstract
It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method
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It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.
Full article
(This article belongs to the Special Issue The Hybrid AC-DC Power System Coordinated Control and Operation Technology, Volume II)
Open AccessArticle
A State-Feedback Control Strategy Based on Grey Fast Finite-Time Sliding Mode Control for an H-Bridge Inverter with LC Filter Output
by
En-Chih Chang, Rong-Ching Wu, Heidi H. Chang and Chun-An Cheng
Electronics 2024, 13(11), 2118; https://doi.org/10.3390/electronics13112118 (registering DOI) - 29 May 2024
Abstract
An H-bridge inverter with LC (inductor-capacitor) filter output allows the conversion of DC (direct current) power to AC (alternating current) power that has been used in a variety of applications, such as uninterruptible power supplies, AC motor drives, and renewable energy source systems.
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An H-bridge inverter with LC (inductor-capacitor) filter output allows the conversion of DC (direct current) power to AC (alternating current) power that has been used in a variety of applications, such as uninterruptible power supplies, AC motor drives, and renewable energy source systems. The fast finite-time sliding mode control (FFTSMC) features acceleration of the system state towards the equilibrium position as well as conserving insensitivity against internal parameter fluctuations as well as external load disturbances falling within the predetermined bounds. However, the FFTSMC would potentially witness chattering or steady-state errors as indefinite margins come to be exaggerated or underestimated. The chattering in the sliding mode control practice is oscillatory defective behavior. It induces inefficient operation, higher switching power losses in the transistor circuits, as well as saturated actuators, thus impairing the inverter’s output energy efficiency and raising harmonic distortion. Therefore, this paper presents the H-bridge inverter with LC filter output, which is controlled by a grey prediction fast finite-time sliding mode trajectory tracking. A more highly accurate grey prediction model based on the centered approximation methodology is deployed to vanish the chattering as well as steady-state errors. Taking into account the union of grey prediction and FFTSMC, a feedback-controlled H-bridge inverter with LC filter output allows attaining a highly efficient as well as quality sine-wave output voltage. The presented state-feedback control strategy is robust, less complex, attains more rapid convergence, and is highly accurate. The design process, computer simulation, as well as experimental results of the proposed state-feedback control strategy established that the H-bridge inverter with LC filter output has the capability to exhibit fast dynamic response time as well as good steady-state tracking behavior of the output voltage under step-loading changes and nonlinear loading conditions.
Full article
(This article belongs to the Special Issue Innovative Technologies in Power Converters, Volume II)
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To (US)Be or Not to (US)Be: Discovering Malicious USB Peripherals through Neural Network-Driven Power Analysis
by
Koffi Anderson Koffi, Christos Smiliotopoulos, Constantinos Kolias and Georgios Kambourakis
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
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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)
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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 - 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
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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.
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(This article belongs to the Special Issue Machine Learning Techniques for Image Processing)
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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 - 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
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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.
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(This article belongs to the Section Artificial Intelligence)
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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 - 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
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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.
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(This article belongs to the Section Artificial Intelligence)
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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 - 29 May 2024
Abstract
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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
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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.
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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 - 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
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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.
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(This article belongs to the Special Issue IoT-Enabled Smart Devices and Systems in Smart Environments)
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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 - 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
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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)
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