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Electronics, Volume 14, Issue 3 (February-1 2025) – 240 articles

Cover Story (view full-size image): Energy harvesting technologies are becoming increasingly popular as sources of energy for Internet of Things (IoT) devices. Magnetic field energy harvesting (MFEH) from current-carrying components is a particularly promising technology for smart grid, infrastructure, and environmental monitoring applications. This paper presents a single-stage AC/DC power converter, a control architecture, and an energy harvester design that consists of a MOSFET full bridge used to actively rectify the induced voltage while providing a regulated output voltage. The compact design reduces the number of components required and improves thermal management, while the results demonstrate that the power converter provides a stable energy source and offers an alternative to self-powering smart grid IoT devices. View this paper
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22 pages, 7406 KiB  
Article
Analog Frontend for Big Data Compression and Instantaneous Failure Prediction in Power Management Systems
by Erez Sarig, Michael Evzelman and Mor Mordechai Peretz
Electronics 2025, 14(3), 641; https://doi.org/10.3390/electronics14030641 - 6 Feb 2025
Abstract
An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires [...] Read more.
An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires data collection from many wide-bandwidth signals within a system, as low-bandwidth information such as DC output voltage is of limited value for decision-making and failure prediction. Analog compression, data profiling, and anomaly detection methods enabled by the unique analog frontend are presented. The system significantly reduces the demand for high computational power, fast communication, and large storage space required for the task. A real-time compression ratio exceeding 100:1 was achieved by the experimental analog frontend, digitizing the analog signal at a rate of 135 MS/s with a 10-bit resolution. The motivation, existing solutions, performance metrics, and advantages of the analog frontend are demonstrated, along with the details of the circuit operation principle. The process of data collection, its intelligent processing using the analog frontend, and anomaly detection are simulated to validate the theoretical hypotheses. For experimental validation, a laboratory setup that includes a dedicated analog frontend prototype and step-down DC-DC converter was built and evaluated to demonstrate the robust performance in sampling and monitoring wide-bandwidth signals and smart data processing using analog frontend for quick decision-making. Full article
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24 pages, 1354 KiB  
Article
Multi-User Encrypted Machine Learning Based on Partially Homomorphic Encryption
by Shaoxiong Xie, Jun Ye and Wei Ou
Electronics 2025, 14(3), 640; https://doi.org/10.3390/electronics14030640 - 6 Feb 2025
Abstract
Machine-learning applications are becoming increasingly widespread. However, machine learning is highly dependent on high-quality, large-scale training data. Due to the limitations of data privacy and security, in order to accept more user data, users are required to participate in the computation themselves through [...] Read more.
Machine-learning applications are becoming increasingly widespread. However, machine learning is highly dependent on high-quality, large-scale training data. Due to the limitations of data privacy and security, in order to accept more user data, users are required to participate in the computation themselves through the secure use of secret keys. In this paper, we propose a multi-user encrypted machine-learning system based on partially homomorphic encryption, which can be realized for the purpose of supporting encrypted machine learning under multiple users. In this system, offline homomorphic computation is provided, so that users can support homomorphic computation without interacting with the cloud after locally executing encryption, and all computational parameters are computed in the initial and encryption phases. In this system, the isolation forest algorithm is modified appropriately so that its computation can be within the supported homomorphic computation methods. The comparison with other schemes in the comparison experiments reflects this scheme’s computational and communication advantages. In the application experiments, where anomaly detection is taken as the goal, the encrypted machine-learning system can provide more than 90% recall, illustrating this scheme’s usability. Full article
(This article belongs to the Section Computer Science & Engineering)
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34 pages, 14102 KiB  
Article
Adversarial Attacks on Supervised Energy-Based Anomaly Detection in Clean Water Systems
by Naghmeh Moradpoor, Ezra Abah, Andres Robles-Durazno and Leandros Maglaras
Electronics 2025, 14(3), 639; https://doi.org/10.3390/electronics14030639 - 6 Feb 2025
Abstract
Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity. The protection of these infrastructures is crucial, as nations depend on their operation and stability. However, cyberattacks on such [...] Read more.
Critical National Infrastructure includes large networks such as telecommunications, transportation, health services, police, nuclear power plants, and utilities like clean water, gas, and electricity. The protection of these infrastructures is crucial, as nations depend on their operation and stability. However, cyberattacks on such systems appear to be increasing in both frequency and severity. Various machine learning approaches have been employed for anomaly detection in Critical National Infrastructure, given their success in identifying both known and unknown attacks with high accuracy. Nevertheless, these systems are vulnerable to adversarial attacks. Hackers can manipulate the system and deceive the models, causing them to misclassify malicious events as benign, and vice versa. This paper evaluates the robustness of traditional machine learning techniques, such as Support Vector Machines (SVMs) and Logistic Regression (LR), as well as Artificial Neural Network (ANN) algorithms against adversarial attacks, using a novel dataset captured from a model of a clean water treatment system. Our methodology includes four attack categories: random label flipping, targeted label flipping, the Fast Gradient Sign Method (FGSM), and Jacobian-based Saliency Map Attack (JSMA). Our results show that, while some machine learning algorithms are more robust to adversarial attacks than others, a hacker can manipulate the dataset using these attack categories to disturb the machine learning-based anomaly detection system, allowing the attack to evade detection. Full article
(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)
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24 pages, 1713 KiB  
Article
A Performance Analysis of You Only Look Once Models for Deployment on Constrained Computational Edge Devices in Drone Applications
by Lucas Rey, Ana M. Bernardos, Andrzej D. Dobrzycki, David Carramiñana, Luca Bergesio, Juan A. Besada and José Ramón Casar
Electronics 2025, 14(3), 638; https://doi.org/10.3390/electronics14030638 - 6 Feb 2025
Abstract
Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of [...] Read more.
Advancements in embedded systems and Artificial Intelligence (AI) have enhanced the capabilities of Unmanned Aircraft Vehicles (UAVs) in computer vision. However, the integration of AI techniques o-nboard drones is constrained by their processing capabilities. In this sense, this study evaluates the deployment of object detection models (YOLOv8n and YOLOv8s) on both resource-constrained edge devices and cloud environments. The objective is to carry out a comparative performance analysis using a representative real-time UAV image processing pipeline. Specifically, the NVIDIA Jetson Orin Nano, Orin NX, and Raspberry Pi 5 (RPI5) devices have been tested to measure their detection accuracy, inference speed, and energy consumption, and the effects of post-training quantization (PTQ). The results show that YOLOv8n surpasses YOLOv8s in its inference speed, achieving 52 FPS on the Jetson Orin NX and 65 fps with INT8 quantization. Conversely, the RPI5 failed to satisfy the real-time processing needs in spite of its suitability for low-energy consumption applications. An analysis of both the cloud-based and edge-based end-to-end processing times showed that increased communication latencies hindered real-time applications, revealing trade-offs between edge (low latency) and cloud processing (quick processing). Overall, these findings contribute to providing recommendations and optimization strategies for the deployment of AI models on UAVs. Full article
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18 pages, 1218 KiB  
Article
Knowledge Graph-Based Multi-Objective Recommendation for a 6G Air Interface: A Digital Twin Empowered Approach
by Yuan Li, Xinyao Wang, Zhong Zheng, Ming Zeng and Zesong Fei
Electronics 2025, 14(3), 637; https://doi.org/10.3390/electronics14030637 - 6 Feb 2025
Abstract
In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, [...] Read more.
In future sixth-generation (6G) communication systems, it is foreseen that complex communication scenarios and critical performance requirements will necessitate more flexible air interface configurations. Traditional air interface adaptation will no longer be applicable to 6G due to issues such as high computational complexity, sub-optimal trade-offs among multi-objective performance metrics, outdated configurations due to fast-varying channels, etc. In this paper, the relevant user behaviors, communication environment, and system are virtualized via the digital twinning technique. Then, a knowledge graph-based multi-objective recommendation framework is proposed to configure the digital twinning air interface to adapt to channel conditions, while balancing various service requirements. First, the knowledge graph is applied to reveal complex dependencies between the air interface and the service requirements, and more importantly, to reconcile possibly contradictory performance targets. Furthermore, the air interface configuration, empowered by the digital twin technique, is able to exploit predicted prior knowledge about user behavior and the channel characteristics, thus improving the utilization efficiency of wireless resources promptly. Moreover, the digital twin technique allows the candidate air interfaces to be virtually verified and compared with little effort. Finally, two case studies are presented to demonstrate the potential of the knowledge graph-based recommendation method for the digital twinning air interface. Full article
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35 pages, 7555 KiB  
Article
Performance Analysis of a Wireless Power Transfer System Employing the Joint MHN-IRS Technology
by Romans Kusnins, Kristaps Gailis, Janis Eidaks, Deniss Kolosovs, Ruslans Babajans, Darja Cirjulina and Dmitrijs Pikulins
Electronics 2025, 14(3), 636; https://doi.org/10.3390/electronics14030636 - 6 Feb 2025
Abstract
The present study is concerned with the power transfer efficiency enhancement using a combination of the multi-hop node (MHN) and the Intelligent Reflecting Surface (IRS)-based passive beamforming technologies. The primary objective is to ensure a high RF-DC converter power conversion efficiency (PCE) used [...] Read more.
The present study is concerned with the power transfer efficiency enhancement using a combination of the multi-hop node (MHN) and the Intelligent Reflecting Surface (IRS)-based passive beamforming technologies. The primary objective is to ensure a high RF-DC converter power conversion efficiency (PCE) used at the receiving end, which is difficult to achieve due to path loss and multi-path propagation. An electronically tunable reconfigurable reflectarray (RRA) designed to operate at the sub-GHz ISM band (865.5 MHz) is utilized to implement the IRS concept. Both the MHN and RRA were developed and studied in our earlier research. The RRA redirects the reflected power-carrying wave amplified by the MHN toward the intended receiver. It comprises two layers: the RF layer containing tunable phase shifters and the ground plane. Each phase shifter comprises two identical eight-shaped metal patches coupled by a pair of varactor diodes used to achieve the reflection phase tuning. The phase gradient method is used to synthesize the RRA phase profiles, ensuring different desired reflection angles. The RRA prototype, composed of 36 phase shifters, is employed in conjunction with the MHN equipped with two antennas and an amplifier. The RRA parameter optimization is accomplished by randomly varying the varactor diode voltages and measuring the corresponding received power levels until the power reflected in the desired direction is maximized. Two measurement scenarios are examined: power transmission without and with the MHN. In the first scenario, the received power is calculated and measured at several distinct beam steering angles for different distances between the Tx antenna and RRA. The same procedure is applied to different distances between the RRA and MHN in the second scenario. The effect of slight deviations in the operating frequency from the designed one (865.5 MHz) on the RRA performance is also examined. Additionally, the received power levels for both scenarios are estimated via full-wave analysis performed using the full-wave simulation software Ansys HFSS 2023 R1. A Huygens’ surface equivalence principle-based model decomposition method was developed and employed to reduce the CPU time. The calculated results are consistent with the measured ones. However, some discrepancies attributed to the adverse effect of RRA diode biasing lines, manufacturing tolerances, and imperfection of the indoor environment model are observed. Full article
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14 pages, 7899 KiB  
Article
Missing/Extra Via Check Algorithm for Advanced VLSI Analog Designs
by Marika Grochowska and Witold A. Pleskacz
Electronics 2025, 14(3), 635; https://doi.org/10.3390/electronics14030635 - 6 Feb 2025
Abstract
This paper presents an original algorithm and the application of a Via Check script implemented in the PVS/Pegasus Verification System Tool (Cadence). The algorithm was written in the physical verification language. Via Check is mainly looking for places in the layout where connections [...] Read more.
This paper presents an original algorithm and the application of a Via Check script implemented in the PVS/Pegasus Verification System Tool (Cadence). The algorithm was written in the physical verification language. Via Check is mainly looking for places in the layout where connections (vias) between metals within the same net are missing or could be reinforced. The designed tool was equipped with special user interface graphics to filter the obtained results for more convenient use. It was successfully used in many projects involving advanced submicron technologies like cmos65lp, cmos40lp, stios40nm, stios28nm, 16ff, and 12ff for almost two years. Its application supported by examples of the results from ongoing projects is also included in this publication. Full article
(This article belongs to the Special Issue Advances in RF, Analog, and Mixed Signal Circuits)
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18 pages, 10824 KiB  
Article
Pattern-Reconfigurable, Vertically Polarized, Wideband Electrically Small Huygens Source Antenna
by Yunlu Duan, Ming-Chun Tang, Mei Li, Zhehao Zhang, Qingli Lin and Richard W. Ziolkowski
Electronics 2025, 14(3), 634; https://doi.org/10.3390/electronics14030634 - 6 Feb 2025
Abstract
A pattern-reconfigurable, vertically polarized (VP), electrically small (ES), Huygens source antenna (HSA) is demonstrated. A custom-designed reconfigurable inverted-F structure is embedded in a hollowed-out cylindrical dielectric resonator (DR). It radiates VP electric dipole fields that excite the DR’s HEM11δ mode, which in [...] Read more.
A pattern-reconfigurable, vertically polarized (VP), electrically small (ES), Huygens source antenna (HSA) is demonstrated. A custom-designed reconfigurable inverted-F structure is embedded in a hollowed-out cylindrical dielectric resonator (DR). It radiates VP electric dipole fields that excite the DR’s HEM11δ mode, which in turn acts as an orthogonal magnetic dipole radiator. The HSA’s unidirectional properties are thus formed. It becomes low-profile and electrically small through a significant lowering of its operational frequency band by loading the DR’s top surface with a metallic disk. The entire 360° azimuth range is covered by each of the HSA’s four 90° reconfigurable states, emitting a unidirectional wide beam. A prototype was fabricated and tested. The measured results, which are in good agreement with their simulated values, demonstrate that the developed wideband Huygens source antenna, with its 0.085 λL low profile and its 0.20 λL × 0.20 λL compact transverse dimensions, hence, electrically small size with ka = 0.89, exhibits a wide 14.1% fractional impedance bandwidth and a 6.1 dBi peak realized gain in all four of its pattern-reconfigurable states. Full article
(This article belongs to the Special Issue Antennas for IoT Devices)
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20 pages, 432 KiB  
Article
Virtual Machine Placement in Edge Computing Based on Multi-Objective Reinforcement Learning
by Shanwen Yi, Shengyi Hong, Yao Qin, Hua Wang and Naili Liu
Electronics 2025, 14(3), 633; https://doi.org/10.3390/electronics14030633 - 6 Feb 2025
Abstract
With the popularization of internet of things (IoT), the energy consumption of mobile edge computing (MEC) servers is also on the rise. Some important IoT applications, such as autonomous driving, smart manufacturing, and smart wearables, have high real-time requirements, making it imperative for [...] Read more.
With the popularization of internet of things (IoT), the energy consumption of mobile edge computing (MEC) servers is also on the rise. Some important IoT applications, such as autonomous driving, smart manufacturing, and smart wearables, have high real-time requirements, making it imperative for edge computing to reduce task response latency. Virtual machine (VM) placement can effectively reduce the response latency of VM requests and the energy consumption of MEC servers. However, the existing work does not consider the selection of weighting coefficients for the optimization objectives and the feasibility of the solution. Besides, these algorithms scalarize the objective functions without considering the order-of-magnitude difference between objectives. To overcome the above problems, the article proposes an algorithm called EVMPRL for VM placement in edge computing based on reinforcement learning (RL). Our aim is to find the Pareto approximate solution set that achieves the trade-off between the response latency of VM requests and the energy consumption of MEC servers. EVMPRL is based on the Chebyshev scalarization function, which is able to efficiently solve the problem of selecting weighting coefficients for objectives. EVMPRL can always search for solutions in the feasible domain, which can be guaranteed by selecting the servers that can satisfy the current VM request as the next action. Furthermore, EVMPRL scalarizes the Q-values instead of the objective functions, thus avoiding the problem in previous work where the order-of-magnitude difference between the optimization objectives makes the impact of an objective function on the final result too small. Finally, we conduct experiments to prove that EVMPRL is superior to the state-of-the-art algorithm in terms of objectives and the solution set quality. Full article
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27 pages, 3511 KiB  
Article
Study on a User Preference Conversational Recommender Based on a Knowledge Graph
by Ganglong Duan, Shanshan Xie and Yutong Du
Electronics 2025, 14(3), 632; https://doi.org/10.3390/electronics14030632 - 6 Feb 2025
Abstract
In the era of information explosion, as a form of personalized recommendation, dialogue recommendation systems provide users with personalized recommendation services through natural language interaction. However, in the face of complex user preferences, the traditional dialogue recommendation system has the problem of a [...] Read more.
In the era of information explosion, as a form of personalized recommendation, dialogue recommendation systems provide users with personalized recommendation services through natural language interaction. However, in the face of complex user preferences, the traditional dialogue recommendation system has the problem of a poor recommendation effect. To solve these problems, this paper proposes a user preference dialogue recommendation algorithm (KGCR) based on a knowledge graph, which aims to enhance the understanding of user preferences through the semantic information of the knowledge graph and improve the relevance and accuracy of recommendations. This paper proposes a personalized conversation recommendation algorithm framework for user preference modeling. The framework uses a bilinear model attention mechanism and self-attention hierarchical coding structure to model user preferences to rank and recommend candidate items. By introducing rich user-related information, the recommendation results are not only more in line with users’ individual preferences but also have better diversity, effectively reducing the negative impact of information cocoons and other phenomena. At the same time, the experimental results on the open dataset prove the effectiveness and accuracy of the proposed model in the personalized conversation recommendation task. Full article
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24 pages, 3415 KiB  
Article
Future-Proofing EU-27 Energy Policies with AI: Analyzing and Forecasting Fossil Fuel Trends
by Cristiana Tudor, Robert Sova, Pavlos Stamatiou, Vasileios Vlachos and Persefoni Polychronidou
Electronics 2025, 14(3), 631; https://doi.org/10.3390/electronics14030631 - 6 Feb 2025
Abstract
The energy sector plays a pivotal role in economic development, societal progress, and environmental sustainability, yet heavy reliance on fossil fuels remains a major challenge for achieving climate neutrality. Within this context, the European Union (EU-27) has committed to ambitious climate goals, including [...] Read more.
The energy sector plays a pivotal role in economic development, societal progress, and environmental sustainability, yet heavy reliance on fossil fuels remains a major challenge for achieving climate neutrality. Within this context, the European Union (EU-27) has committed to ambitious climate goals, including achieving carbon neutrality by 2050, making it a critical region for studying energy transition. This study analyzes the determinants of fossil fuels’ share (SFF) in final energy consumption at the aggregate EU-27 level over a 19-year period (2004–2022) and forecasts trends in the region’s energy transition through 2030. Using a random forest (RF) regressor, complex nonlinear relationships between SFF and six key predictors—GDP, population, industrial production, CO2 emissions, renewable energy share (SRE), and energy intensity—were modeled. Model interpretability was enhanced through Shapley additive explanations (SHAP) and partial dependence plots (PDPs), revealing CO2 emissions and SRE as the dominant predictors with opposing effects on SFF. Interaction effects highlighted the synergistic role of emission reduction and renewable energy adoption in minimizing fossil fuel reliance. GDP, while less influential overall, exhibited a significant negative relationship with SFF during early growth stages. Forecasts indicate a steady decline in fossil fuel reliance, from 1.8% in 2022 to 1.33% by 2030, supporting the EU’s climate objectives by emphasizing the importance of renewable energy adoption and emission control. This study demonstrates the transformative potential of machine learning and explainable AI (XAI) techniques in providing actionable insights to advance the EU-27’s sustainability journey. Full article
(This article belongs to the Section Artificial Intelligence)
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14 pages, 2845 KiB  
Article
Application of Voltage Optimization Strategy for Rotary Power Flow Controllers in Loop Closing of Distribution Networks
by Wenqiang Xie, Yubo Yuan, Xian Zheng, Hui Chen, Jian Liu and Chenyu Zhang
Electronics 2025, 14(3), 630; https://doi.org/10.3390/electronics14030630 - 6 Feb 2025
Abstract
To mitigate voltage limit issues in the operation of a novel electromagnetic voltage regulation device, this paper presents a flexible loop-closing control strategy with voltage optimization. The approach uses a two-stage path optimization: in the first stage, the voltage phase at the loop-closing [...] Read more.
To mitigate voltage limit issues in the operation of a novel electromagnetic voltage regulation device, this paper presents a flexible loop-closing control strategy with voltage optimization. The approach uses a two-stage path optimization: in the first stage, the voltage phase at the loop-closing point is adjusted to ensure smooth operation, while in the second stage, the voltage magnitude is optimized to prevent voltage limits and achieve seamless regulation. By integrating phase angle difference calculations with coordinated rotation angle control, the simulation results show that this strategy reduces loop-closing current by approximately 95.87% compared to direct loop closing, decreases voltage fluctuations by around 50.0% compared to traditional methods, and shortens operation time by 40.14%. This approach significantly enhances system stability and response speed, effectively addressing the issue of excessive loop-closing current caused by voltage deviations at distribution network tie switches. Full article
(This article belongs to the Special Issue Power Electronics in Renewable Systems)
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14 pages, 2933 KiB  
Article
Detection of Cyclic Variation in Heart Rate (CVHR) During Sleep Using a Ring-Type Silicon Sensor and Evaluation of Intra-Weekly Variability
by Emi Yuda, Hiroyuki Edamatsu, Kenji Hosomi and Junichiro Hayano
Electronics 2025, 14(3), 629; https://doi.org/10.3390/electronics14030629 - 6 Feb 2025
Abstract
Patients with sleep apnea syndrome (SAS) have a risk of stroke that is more than three times higher than that of healthy individuals. Early detection and appropriate treatment are crucial for preventing serious complications, and detecting cyclic variation in heart rate (CVHR) plays [...] Read more.
Patients with sleep apnea syndrome (SAS) have a risk of stroke that is more than three times higher than that of healthy individuals. Early detection and appropriate treatment are crucial for preventing serious complications, and detecting cyclic variation in heart rate (CVHR) plays a key role in early diagnosis. This study investigated the feasibility of detecting CVHR during sleep using a wearable, comfortable device and evaluated the ability to assess weekly fluctuations. Heart rate, blood oxygen saturation, and bio-acceleration were measured for seven consecutive nights in eight healthy subjects (45.7 ± 10.1 years old). The CVHR values obtained using a ring-type sensor were compared to those derived from the apnea–hypopnea index (AHI) measured with a Holter ECG. The results revealed that CVHR values measured with the ring-type sensor were higher than those measured with the Holter monitor. Although correction is required, the ring-type sensor successfully detected intra-weekly fluctuations. These findings suggest that a ring-type sensor could be a practical tool for monitoring CVHR and identifying weekly trends in a comfortable, non-invasive manner. Full article
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17 pages, 4014 KiB  
Article
High-Resistance Grounding Fault Location in High-Voltage Direct Current Transmission Systems Based on Deep Residual Shrinkage Network
by Ping Huang, Junlin Huang, Shengquan Huang, Guoting Yang and Zhipeng Wu
Electronics 2025, 14(3), 628; https://doi.org/10.3390/electronics14030628 - 5 Feb 2025
Abstract
Due to the precision limitations of traditional fault location methods in identifying grounding faults in High-Voltage Direct Current (HVDC) transmission systems and considering the high occurrence probability of high-resistance grounding faults in practical engineering scenarios coupled with the sampling accuracy constraints of actual [...] Read more.
Due to the precision limitations of traditional fault location methods in identifying grounding faults in High-Voltage Direct Current (HVDC) transmission systems and considering the high occurrence probability of high-resistance grounding faults in practical engineering scenarios coupled with the sampling accuracy constraints of actual equipment, this article introduces a novel approach for high-resistance grounding fault location in HVDC transmission lines. This method integrates Variational Mode Decomposition (VMD) and Deep Residual Shrinkage Network (DRSN). Initially, VMD is employed to decompose double-ended high-resistance grounding fault signals, extracting the corresponding Intrinsic Mode Functions (IMF). These IMF signals are then preprocessed to construct the input data for the DRSN model. Upon training, the model outputs the precise fault location. To validate the effectiveness of the proposed method, a ±800 kV bipolar HVDC transmission system model is established using PSCAD/EMTDC version 4.6.2 software for simulating high-resistance grounding faults. The sampling accuracy of the model’s output signals is set to 10 kHz, aligning closely with actual engineering equipment specifications. Comprehensive simulation experiments and anti-interference analyses are conducted on the DRSN model. The results demonstrate that the fault location method based on the DRSN exhibits high accuracy in locating high-resistance grounding faults, with a maximum error of less than 1.5 km, even when considering factors such as engineering sampling frequency, fault types, and signal noise. Full article
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34 pages, 3129 KiB  
Article
Social-Aware Link Reliability Prediction Model Based Minimum Delay Routing for CR-VANETs
by Jing Wang, Wenshi Dan, Hong Li, Lingyu Yan, Aoxue Mei and Xing Tang
Electronics 2025, 14(3), 627; https://doi.org/10.3390/electronics14030627 - 5 Feb 2025
Abstract
Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic [...] Read more.
Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic topology changes and frequent disruptions of links can lead to large end-to-end delays. To address this issue, we propose the social-based minimum end-to-end delay routing (SMED) algorithm, which leverages the social attributes of both primary and secondary users to reduce end-to-end delay and packet loss. We analyze the influencing factors of vehicle communication in urban road segments and at intersections, formulate the end-to-end delay minimization problem as a nonlinear integer programming problem, and utilize two sub-algorithms to solve this problem. Simulation results show that, compared to the intersection delay-aware routing algorithm (IDRA) and the expected path duration maximization routing algorithm (EPDMR), our method demonstrates significant improvements in both end-to-end delay and packet loss rate. Specifically, the SMED routing algorithm achieved an average reduction of 11.7% in end-to-end delay compared to EPDMR and 25.0% compared to IDRA. Additionally, it lowered the packet loss rate by 24.9% on average compared to EPDMR and 32.5% compared to IDRA. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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17 pages, 2479 KiB  
Article
A Study on the Factors Influencing Rank Prediction in PlayerUnknown’s Battlegrounds
by Ji-Na Lee and Ji-Yeoun Lee
Electronics 2025, 14(3), 626; https://doi.org/10.3390/electronics14030626 - 5 Feb 2025
Abstract
This study analyzes the key factors influencing player rank prediction in PlayerUnknown’s Battlegrounds (PUBG), using machine learning models to evaluate in-game performance. By examining variables such as “walkDistance”, “boosts”, and “weaponsAcquired”, the study identifies these as critical predictors, with “walkDistance” emerging [...] Read more.
This study analyzes the key factors influencing player rank prediction in PlayerUnknown’s Battlegrounds (PUBG), using machine learning models to evaluate in-game performance. By examining variables such as “walkDistance”, “boosts”, and “weaponsAcquired”, the study identifies these as critical predictors, with “walkDistance” emerging as the most significant across all match types. Utilizing models including random forest (RF), gradient descent (GD), extreme gradient boosting (XGBoost), and feedforward neural network (FNN), the analysis reveals performance variation by match type: XGBoost achieves the highest accuracy in solo matches (88.07%), GD performs best in duo matches (84.75%), and RF records the highest accuracy in squad matches (78.21%). These findings provide valuable insights for game developers in balancing gameplay and offer personalized strategic recommendations for players. Future research may enhance predictive performance by incorporating additional variables and exploring alternative models applicable to PUBG and similar battle royale games. Full article
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17 pages, 2674 KiB  
Article
Research on Predictive Maintenance Methods for Current Transformers with Iron Core Structures
by Huan Hu, Kang Xu, Xianya Zhang, Fangjing Li, Lingling Zhu, Rui Xu and Deng Li
Electronics 2025, 14(3), 625; https://doi.org/10.3390/electronics14030625 - 5 Feb 2025
Abstract
The reliable operation of power systems is heavily dependent on effective maintenance strategies for critical equipment. Current maintenance methods are typically categorized into corrective, preventive, and predictive approaches. While corrective maintenance often results in significant downtime and preventive maintenance can be inefficient, predictive [...] Read more.
The reliable operation of power systems is heavily dependent on effective maintenance strategies for critical equipment. Current maintenance methods are typically categorized into corrective, preventive, and predictive approaches. While corrective maintenance often results in significant downtime and preventive maintenance can be inefficient, predictive maintenance emerges as a promising technique for accurately forecasting faults. In this study, we investigated the diagnosis and prediction of fault states, specifically single-phase short circuit (1HCF) and double-phase short circuit (2HCF) faults, using monitoring data from current transformers in 110 kV substations. We proposed a predictive maintenance method for current transformers based on core-type structures, which integrates wavelet transform to extract multi-level frequency domain features, employs feature selection techniques (including the Spearman correlation coefficient and mutual information) to identify key predictive features, and utilizes Random Forest classifiers for fault state prediction. Experimental results demonstrate an overall prediction accuracy of 94%. Full article
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12 pages, 3928 KiB  
Article
Evaluation of a 1200 V Polarization Super Junction GaN Field-Effect Transistor in Cascode Configuration
by Alireza Sheikhan, E. M. Sankara Narayanan, Hiroji Kawai, Shuichi Yagi and Hironobu Narui
Electronics 2025, 14(3), 624; https://doi.org/10.3390/electronics14030624 - 5 Feb 2025
Abstract
GaN HEMTs based on polarization super junction (PSJ) technology offer significant improvements in efficiency and power density over conventional silicon (Si) devices due to their excellent material characteristics, which enable fast switching edges and lower specific on-resistance. However, due to the presence of [...] Read more.
GaN HEMTs based on polarization super junction (PSJ) technology offer significant improvements in efficiency and power density over conventional silicon (Si) devices due to their excellent material characteristics, which enable fast switching edges and lower specific on-resistance. However, due to the presence of an uninterrupted channel between drain and source at zero gate bias, these devices have normally-on characteristics. In this paper, the performance of a 1200 V GaN FET utilizing PSJ technology in cascode configuration is reported. The device working principle, characteristics, and switching behavior are experimentally demonstrated. The results show that cascoded GaN FETs utilizing the PSJ concept are highly promising for power device applications. Full article
(This article belongs to the Special Issue GaN-Based Electronic Materials and Devices)
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29 pages, 4144 KiB  
Article
Physical-Unclonable-Function-Based Lightweight Anonymous Authentication Protocol for Smart Grid
by Yu Guo, Lifeng Li, Xu Jin, Chunyan An, Chenyu Wang and Hairui Huang
Electronics 2025, 14(3), 623; https://doi.org/10.3390/electronics14030623 - 5 Feb 2025
Abstract
In the Internet of Everything era of Web 3.0, smart grid (SG) technology is also developing towards intelligent interconnection of terminal devices. However, in the smart grid scenario, security issues are particularly prominent, especially the openness of wireless sensor networks. Sensor nodes are [...] Read more.
In the Internet of Everything era of Web 3.0, smart grid (SG) technology is also developing towards intelligent interconnection of terminal devices. However, in the smart grid scenario, security issues are particularly prominent, especially the openness of wireless sensor networks. Sensor nodes are vulnerable to attacks and other security threats, which makes confirming the legitimacy of access identity and ensuring the secure transmission of data an urgent problem to be solved. At present, although a variety of authentication schemes for smart grid nodes have been proposed, most of them have problems. For example, some cannot achieve forward security. Therefore, this paper aims to solve this problem and proposes a lightweight anonymous authentication protocol based on physical unclonable functions (PUFs), which can implement mutual authentication and session key agreement between gateway nodes and sensor nodes. Compared to five state-of-the-art schemes in security and performance, the proposed scheme achieves all eight of the listed security requirements with lightweight calculation overhead, communication overhead, and storage overhead. Full article
(This article belongs to the Special Issue Applied Cryptography and Practical Cryptoanalysis for Web 3.0)
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24 pages, 4942 KiB  
Article
Identification and Localization Study of Grounding System Defects in Cross-Bonded Cables
by Qiying Zhang, Kunsheng Li, Lian Chen, Jian Luo and Zhongyong Zhao
Electronics 2025, 14(3), 622; https://doi.org/10.3390/electronics14030622 - 5 Feb 2025
Abstract
Cross-bonded cables improve transmission efficiency by optimizing the grounding method. However, due to the complexity of their grounding system, they are prone to multiple types of defects, making defect state identification more challenging. Additionally, accurately locating sheath damage defects becomes more difficult in [...] Read more.
Cross-bonded cables improve transmission efficiency by optimizing the grounding method. However, due to the complexity of their grounding system, they are prone to multiple types of defects, making defect state identification more challenging. Additionally, accurately locating sheath damage defects becomes more difficult in cases of high transition resistance. To address these issues, this paper constructs a distributed parameter circuit model for cross-bonded cables and proposes a particle swarm optimization support vector machine (PSO-SVM) defect classification model based on the sheath voltage and current phase angle and amplitude characteristics. This model effectively classifies 25 types of grounding system states. Furthermore, for two types of defects—open joints and sheath damage short circuits—this paper proposes an accurate segment-based location method based on fault impedance characteristics, using zero-crossing problems to achieve efficient localization. The results show that the distributed parameter circuit model for cross-bonded cables is feasible for simulating electrical quantities, as confirmed by both simulation and real-world applications. The defect classification model achieves an accuracy of over 97%. Under low transition resistance, the defect localization accuracy exceeds 95.4%, and the localization performance is significantly improved under high transition resistance. Additionally, the defect localization method is more sensitive to variations in cable segment length and grounding resistance impedance but less affected by fluctuations in core voltage and current. Full article
(This article belongs to the Special Issue Advanced Online Monitoring and Fault Diagnosis of Power Equipment)
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23 pages, 883 KiB  
Article
SAM-PAY: A Location-Based Authentication Method for Mobile Environments
by Diana Gratiela Berbecaru
Electronics 2025, 14(3), 621; https://doi.org/10.3390/electronics14030621 - 5 Feb 2025
Abstract
Wireless, satellite, and mobile networks are increasingly used in application scenarios to provide advanced services to mobile or nomadic devices. For example, to authenticate mobile users while obtaining access to remote services, a two-factor authentication mechanism is typically used, e.g., based on the [...] Read more.
Wireless, satellite, and mobile networks are increasingly used in application scenarios to provide advanced services to mobile or nomadic devices. For example, to authenticate mobile users while obtaining access to remote services, a two-factor authentication mechanism is typically used, e.g., based on the ownership of a personal mobile phone, device, or (smart)card and the knowledge of a (static) username and password. Nevertheless, two-factor authentication is considered roughly “adequate” for security problems encountered today on the Internet and even less for ubiquitous or mobile environments. To increase the authentication level, several authentication methods of different classes may be combined to achieve more reliable user identification. In particular, location technologies allow ubiquitous applications to better exploit the (physical) location information in the authentication process. Consequently, in security applications based on multiple authentication factors, an additional authentication factor could be the location information protected for integrity against undesired modification. We present the SAM-PAY authentication method, which combines different authentication factors to obtain a more reliable user identification. The mechanism is based on the use of a (location-aware) device, the location information certified by a trusted external party, such as a component or element in a telecom network, and the knowledge of data, like a static PIN and a dynamically generated one-time password. We also describe the design and implementation of a real case scenario exploiting our SAM-PAY method, namely the refueling service at a self-service gas station. The test-bed put in place for this service demonstrates the feasibility and effectiveness of the SAM-PAY method in open mobile environments. Full article
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15 pages, 7877 KiB  
Article
Optimized Watermelon Scion Leaf Segmentation Model Based on Hungarian Algorithm and Information Theory
by Yi Zhu, Qingcang Yu and Zihao Xu
Electronics 2025, 14(3), 620; https://doi.org/10.3390/electronics14030620 - 5 Feb 2025
Abstract
In the fully automated grafting process of watermelon seedlings, it is crucial to ensure that the scion’s cotyledons maintain a perpendicular orientation with the rootstock cotyledons. To achieve precise segmentation of watermelon scion cotyledons and accurately extract parameters, such as cotyledon orientation angles, [...] Read more.
In the fully automated grafting process of watermelon seedlings, it is crucial to ensure that the scion’s cotyledons maintain a perpendicular orientation with the rootstock cotyledons. To achieve precise segmentation of watermelon scion cotyledons and accurately extract parameters, such as cotyledon orientation angles, this study introduces enhancements to the Mask2Former network, aiming to improve segmentation accuracy for watermelon scion cotyledons. Specifically, two innovative modules are designed. Taking Swin-Former as the backbone, an Optimal Feature Re-ranking (OFR) module based on the Hungarian Algorithm is devised to re-rank the feature maps obtained from the feature extraction process. Grounded in information theory, the amount of information in semantic segmentation tasks is quantified as Shannon entropy, enabling the model to perceive the information distribution of the feature maps and dynamically adjust the output features. Experimental results demonstrate that the improved model achieves mIoU, mDice, mPrecision, and mRecall scores of 97.44%, 98.70%, 98.20%, and 99.21%, respectively, greatly outperforming Mask2Former, FCNN, and DeepLabv3. Furthermore, the enhanced network exhibits superior accuracy in low signal-to-noise ratio environments, highlighting its robustness in complex scenarios. This study provides a high-precision solution for agricultural automation in the watermelon industry, contributing to the development of fully automated grafting machines. Full article
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13 pages, 2006 KiB  
Article
Load Rejection Overvoltage Suppression and Parameter Design Method of UHV AC Transmission Line
by Guanqun Sun, Wang Ma, Yingge Wang, Dian Xu, Haiguang Liu, Rusi Chen and Yixing Ding
Electronics 2025, 14(3), 619; https://doi.org/10.3390/electronics14030619 - 5 Feb 2025
Abstract
UHV (ultra-high voltage) by instant AC transmission system is accompanied by huge reactive power transmission. When the load drops sharply, it is easy to produce serious power frequency overvoltage, which is also defined as load rejection overvoltage. This paper makes an in-depth analysis [...] Read more.
UHV (ultra-high voltage) by instant AC transmission system is accompanied by huge reactive power transmission. When the load drops sharply, it is easy to produce serious power frequency overvoltage, which is also defined as load rejection overvoltage. This paper makes an in-depth analysis from the perspective of voltage increase caused by instantaneous load unloading, and obtains the causes and key influencing factors of load rejection overvoltage. Taking the UHV AC transmission line of a practical project as an example, the suppression effect of the suppression strategy represented by the installation of opening resistance and shunt reactor on the load rejection overvoltage is analyzed. The simulation results show that the above method has an obvious inhibitory effect on load rejection overvoltage. Based on the optimal suppression principle, the optional interval range of the opening resistance and shunt reactor parameters are designed. Full article
(This article belongs to the Special Issue Advanced Online Monitoring and Fault Diagnosis of Power Equipment)
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22 pages, 9442 KiB  
Article
A Novel Approach for Robust Automatic Modulation Recognition Based on Reversible Column Networks
by Dan Jing, Tao Xu, Liang Han, Hongfei Yin, Liangchao Li, Yan Zhang, Ming Li, Mian Pan and Liang Guo
Electronics 2025, 14(3), 618; https://doi.org/10.3390/electronics14030618 - 5 Feb 2025
Abstract
Automatic Modulation Recognition (AMR) technology, as a key component of intelligent wireless communication, has significant military and civilian value, and there is an urgent need to research relevant algorithms to quickly and effectively identify the modulation type of signals. However, existing models often [...] Read more.
Automatic Modulation Recognition (AMR) technology, as a key component of intelligent wireless communication, has significant military and civilian value, and there is an urgent need to research relevant algorithms to quickly and effectively identify the modulation type of signals. However, existing models often suffer from issues such as neglecting the correlation between IQ components of signals, poor feature extraction capability, and difficulty in achieving an effective balance between detection performance and computational resource utilization. To address these issues, this article proposes an automatic modulation classification method based on convolutional neural networks (CNNs)—OD_SERCNET. To prevent feature loss or useful features from being compressed, a reversible column network (REVCOL) is used as the backbone network to ensure that the overall information remains unchanged when features are decoupled. At the same time, a novel IQ channel fusion network is designed to preprocess the input signal, fully exploring the correlation between IQ components of the same signal and improving the network’s feature extraction ability. In addition, to improve the network’s ability to capture global information, we have improved the original reversible fusion module by introducing an effective attention mechanism. Finally, the effectiveness of this method is validated using various datasets, and the simulation results show that the average accuracy of OD_SRCNET improves by 1–10% compared to other SOTA models, and we explore the optimal number of subnetworks, achieving a better balance between accuracy and computational resource utilization. Full article
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17 pages, 4256 KiB  
Article
Diagnosis of Wind Turbine Yaw System Based on Self-Attention–Long Short-Term Memory (LSTM)
by Canglin Song, Niaona Zhang, Jingting Shao, Yanbo Wang, Xinyu Liu and Changhong Jiang
Electronics 2025, 14(3), 617; https://doi.org/10.3390/electronics14030617 - 5 Feb 2025
Abstract
Addressing the challenges and significant risks associated with diagnosing faults in wind turbine yaw systems, along with the typically low diagnostic accuracy, this study introduces a Long Short-Term Memory (LSTM) neural network augmented by a self-attention mechanism (SAM) as a novel fault diagnosis [...] Read more.
Addressing the challenges and significant risks associated with diagnosing faults in wind turbine yaw systems, along with the typically low diagnostic accuracy, this study introduces a Long Short-Term Memory (LSTM) neural network augmented by a self-attention mechanism (SAM) as a novel fault diagnosis technique for wind turbine yaw systems. The method integrates the automatic weighting capability of the self-attention mechanism on input features with the advantage of LSTM in processing time series data, thereby effectively capturing key information and long-term dependencies in the operating data of the yawing system. This combination enhances the accuracy of fault feature extraction to more accurately identify various types of fault modes within the yawing system. Six types of feature parameters are extracted from the raw data collected by the SCADA (Supervisory Control And Data Acquisition) system of the wind turbine and are utilized as inputs for the diagnostic model. These parameters are then fed into the self-attention–LSTM neural network model to diagnose the health status of the yaw system, including yaw bearing damage, yaw gearbox failure, yaw motor failure, and sensor failure. The experimental results demonstrate that the accuracy of LSTM fault diagnosis, when enhanced with the self-attention mechanism, can reach 98.67% with an appropriate amount of training samples, verifying its significant advantages in terms of accuracy and stability of fault diagnosis. The proposed fault diagnosis method exhibits a better model fitting effect, strong generalization ability, and high accuracy compared to other methods, providing robust support for the reliable operation and maintenance of wind turbines. Full article
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22 pages, 1918 KiB  
Article
Data-Driven Dynamics Learning on Time Simulation of SF6 HVDC-GIS Conical Solid Insulators
by Kenji Urazaki Junior, Francesco Lucchini and Nicolò Marconato
Electronics 2025, 14(3), 616; https://doi.org/10.3390/electronics14030616 - 5 Feb 2025
Abstract
An HVDC-GIL system with a conical spacer in a radioactive environment is studied in this work using simulated data on COMSOL® Multiphysics. Electromagnetic simulations on a 2D model were performed with varying ion-pair generation rates and potential applied to the system. This [...] Read more.
An HVDC-GIL system with a conical spacer in a radioactive environment is studied in this work using simulated data on COMSOL® Multiphysics. Electromagnetic simulations on a 2D model were performed with varying ion-pair generation rates and potential applied to the system. This article explores machine learning methods to derive time to steady state, dark current, gas conductivity, and surface charge density expressions. The focus was on constructing symbolic representations, which could be interpretable and less prone to overfitting, using the symbolic regression (SR) and sparse identification of nonlinear dynamics (SINDy) algorithms. The study successfully derived the intended expressions, demonstrating the power of symbolic regression. Predictions of dark currents in the gas–ground electrode interface reported an absolute error and mean absolute percentage error (MAPE) of 1.04 × 104 pA and 0.01%, respectively. The solid–ground electrode interface reported an error of 8.99 × 105 pA and MAPE of 0.04%, showing strong agreement with simulation data. Expressions for time to steady state had a test error of approximately 110 h with MAPE of around 3%. Steady-state gas conductivity expression achieved an absolute error of 0.55 log(S/m) and MAPE of 1%. An interpretable equation was created with SINDy to model the time evolution of surface charge density, achieving a root mean squared error of 1.12 nC/m2/s across time-series data. These results demonstrate the capability of SR and SINDy to provide interpretable and computationally efficient alternatives to time-consuming numerical simulations of HVDC systems under radiation conditions. While the model provides useful insights, performance and practical applications of the expressions can improve with more diverse datasets, which might include experimental data in the future. Full article
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17 pages, 9213 KiB  
Article
Automated Transformer Selection for RFIC Design: Accelerating Development with a Comprehensive Database
by Jeffrey Torres-Clarke, Neda Mendoza-Calvo, Javier del Pino, Sunil Khemchandani and David Galante-Sempere
Electronics 2025, 14(3), 615; https://doi.org/10.3390/electronics14030615 - 5 Feb 2025
Abstract
The design of transformers, a key component of radio frequency integrated circuits (RFICs), is traditionally carried out through an iterative process involving extensive electromagnetic simulations. While process design kits (PDKs) offer tools based on interpolation or fitting equations to simplify parameter estimation, these [...] Read more.
The design of transformers, a key component of radio frequency integrated circuits (RFICs), is traditionally carried out through an iterative process involving extensive electromagnetic simulations. While process design kits (PDKs) offer tools based on interpolation or fitting equations to simplify parameter estimation, these tools are restricted to standard geometries, leaving designers to manually simulate and optimize custom designs. This approach is inefficient and resource intensive. This paper proposes an automated process to generate a database containing the physical and electrical parameters of a wide range of transformers. This database is part of a tool designed to efficiently identify the desired transformer. To evaluate the tool’s effectiveness in reducing the time required for design, a millimeter-wave (mm-Wave) 69.4–74.2 GHz differential low-noise amplifier (LNA) is designed using GlobalFoundries 45 nm silicon-on-insulator (SOI) technology. This circuit demonstrates a noise figure (NF) of 4.1 dB, a gain of 10.1 dB, an input third-order intercept point (IIP3) of −10.78 dBm, and a power consumption of 4.7 mW from a 0.406 V DC supply. Moreover, the simulated performance achieves these specifications within a highly compact area of 0.12 mm2. The transformer selection process for the circuit takes only a few seconds, whereas the conventional method of manual transformer design and electromagnetic simulation would require a significantly greater amount of time. Full article
(This article belongs to the Special Issue New Advances in Semiconductor Devices/Circuits)
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10 pages, 4162 KiB  
Article
Simulation Design of an Electron Gun for Microchannel Plate Scrubbing
by Zengzhou Yi, Yuwei Xu and Jingjin Zhang
Electronics 2025, 14(3), 614; https://doi.org/10.3390/electronics14030614 - 5 Feb 2025
Abstract
The microchannel plate (MCP) is susceptible to the adsorption of substantial amounts of gas during its fabrication process. To mitigate this, a uniform electron source is essential for effective electron scrubbing and gas removal. Thermionic emission, a method of electron generation, can be [...] Read more.
The microchannel plate (MCP) is susceptible to the adsorption of substantial amounts of gas during its fabrication process. To mitigate this, a uniform electron source is essential for effective electron scrubbing and gas removal. Thermionic emission, a method of electron generation, can be employed to create the electron source. In this study, a flat spiral filament was designed and simulated using the CST Studio Suite electron simulation software to assess the cleaning performance of the electron gun. The impact of variations in electron gun parameters on the uniformity of the electron beam and current density was systematically analysed. The simulation results show that, with filament, grid, focusing sleeve, and anode voltages set to 200 V, 500 V, 250 V, and 300 V, respectively, a uniform electron beam with a diameter exceeding 30 mm can be achieved. In order to obtain the current density (5~50 nA/mm2) required for the MCP, the temperature of the filament should be 1800–2000 K through theoretical calculation. These findings offer valuable insights for designing a more efficient electron gun for MCP scrubbing. Full article
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21 pages, 1866 KiB  
Article
Development of a Common API for Multiple Ethernet Fieldbus Protocols in Embedded Slave Devices
by Donghyuk Kim and Joon-Young Choi
Electronics 2025, 14(3), 613; https://doi.org/10.3390/electronics14030613 - 5 Feb 2025
Abstract
Slave devices in Ethernet-based fieldbus networks often require extensive reprogramming of applications and replacement of protocol stacks and Ethernet drivers whenever the fieldbus protocol needs to be changed. To address this challenge, we develop a common application programming interface (API) and stack interfaces [...] Read more.
Slave devices in Ethernet-based fieldbus networks often require extensive reprogramming of applications and replacement of protocol stacks and Ethernet drivers whenever the fieldbus protocol needs to be changed. To address this challenge, we develop a common application programming interface (API) and stack interfaces that enable seamless protocol switching among EtherCAT, PROFINET, and EtherNet/IP without requiring protocol-specific code modifications. The real-time data exchange between the API and each protocol stack is realized in the stack interface by using the synchronization mechanism provided by FreeRTOS. The developed common API and stack interfaces facilitate the development of slave device applications that are universally compatible with multiple protocols, EtherCAT, PROFINET, and EtherNet/IP. Moreover, once a required protocol is selected in the integrated development environment (IDE) software before building the slave device firmware, the corresponding protocol stack and Ethernet drivers are automatically specified and the need to replace protocol stacks or Ethernet drivers is even eliminated when switching protocols. To validate the developed common API and stack interfaces, they were implemented on a slave device using TI’s TMDS243EVM board, and a fieldbus network was built by connecting the slave device to a master device executed by Beckhoff’s TwinCAT on a Windows PC. Experimental results confirmed the API’s functionality, reliability, and practical applicability in streamlining protocol management for Ethernet-based fieldbus networks. Full article
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17 pages, 2573 KiB  
Article
Rectifier Fault Diagnosis Based on Euclidean Norm Fusion Multi-Frequency Bands and Multi-Scale Permutation Entropy
by Jinping Liang and Xiangde Mao
Electronics 2025, 14(3), 612; https://doi.org/10.3390/electronics14030612 - 5 Feb 2025
Abstract
With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher power factor. However, due to the complex control [...] Read more.
With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher power factor. However, due to the complex control and harsh environment, it can easily fail. Faults can cause current and voltage distortion, harmonic increases and other problems, which can threaten the safety of the power system and the train. In order to ensure the stable operation of the rectifier, incidences of faults should be reduced. A fault diagnosis technique based on Euclidean norm fusion multi-frequency bands and multi-scale permutation entropy is proposed. Firstly, by the optimal wavelet function, information on the optimal multi-frequency bands of the fault signal is selected after wavelet packet decomposition. Secondly, the multi-scale permutation entropy of each frequency band is calculated, and multiple fault feature vectors are obtained for each frequency band. To reduce the classifier’s computational cost, the Euclidean norm is used to fuse the multi-scale permutation entropy into an entropy value, so that each frequency band uses an entropy value to characterize the fault information features. Finally, the optimal multi-frequency bands and multi-scale permutation entropy after fusion are used as the fault feature vector. In the simulation system, it is shown that the method’s average accuracy is 78.46%, 97.07%, and 99.45% when the SNR is 5 dB, 10 dB, and 15 dB, respectively. And the fusion of multi-scale permutation entropy can improve the accuracy, recall rate, precision, and F1 score and reduce the False Alarm Rate (FAR) and the Missing Alarm Rate (MAR). The results show that the fault diagnosis method has high diagnosis accuracy, is a simple feature fusion method, and has good robustness to working conditions and noise. Full article
(This article belongs to the Section Power Electronics)
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