You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.

Search for Articles:

Title / Keyword

Author / Affiliation / Email

Journal

Article Type

Advanced Search

Section

Special Issue

Volume

Issue

Number

Page

Logical OperatorOperator

Search Text

Search Type

Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data
Revolutionizing Urban Mobility: A Systematic Review of AI, IoT, and Predictive Analytics in Adaptive Traffic Control Systems for Road Networks
Low-Power Silicon-Based Frequency Dividers: An Overview
Forecasting Corporate Financial Performance Using Deep Learning with Environmental, Social, and Governance Data

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, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Electrical and Electronic Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2024).
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, Microwave, Network, Signals, Software and Microelectronics.

Impact Factor: 2.6 (2024); 5-Year Impact Factor: 2.6 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

19 pages, 3049 KiB

Open AccessArticle

Non-Isolated Ultra-High Step-Up DC-DC Converter Topology Using Coupled-Inductor-Based Inverting Buck-Boost and Voltage Multipliers

by Van-Tinh Duong, Zeeshan Waheed and Woojin Choi

Electronics 2025, 14(13), 2519; https://doi.org/10.3390/electronics14132519 - 20 Jun 2025

This paper introduces a non-isolated ultra-high voltage gain topology using the combination of the coupled-inductor-based inverting buck-boost converter (IBB) and voltage multiplier (VM) structure. In the proposed converter, an ultra-high step-up voltage gain can be achieved with a small duty cycle thanks to [...] Read more.

This paper introduces a non-isolated ultra-high voltage gain topology using the combination of the coupled-inductor-based inverting buck-boost converter (IBB) and voltage multiplier (VM) structure. In the proposed converter, an ultra-high step-up voltage gain can be achieved with a small duty cycle thanks to a coupled inductor and VMs. The voltage stress and the losses of the switches in the proposed converter are even less than other conventional topologies. Unlike other coupled-inductor topologies, a large voltage spike caused by the leakage inductance of the coupled inductor is smoothed by the capacitor in the voltage multiplier. In addition, zero-voltage switching (ZVS) turn-on for the switches and zero-current switching (ZCS) turn-off for the diodes can be achieved with the energy stored in the leakage inductance. A 360 W (40 V/380 V) prototype converter is implemented to prove the advantages of the proposed converter, with a maximum efficiency of 98.4%. Full article

(This article belongs to the Special Issue Advanced DC-DC Converter Topology Design, Control, Application)

► Show Figures

Figure 1

19 pages, 5086 KiB

Open AccessArticle

Expedited Near-Field Holographic Microwave Imaging with an Azimuthally Distributed Antenna Array

by Mona Heydari and Reza K. Amineh

Electronics 2025, 14(13), 2518; https://doi.org/10.3390/electronics14132518 - 20 Jun 2025

In this article, we propose a novel near-field holographic microwave imaging technique designed to accelerate the data acquisition process. The system employs a novel electronic switching mechanism utilizing two switching networks that virtually rotate the transmitting and receiving antennas along the azimuthal direction [...] Read more.

In this article, we propose a novel near-field holographic microwave imaging technique designed to accelerate the data acquisition process. The system employs a novel electronic switching mechanism utilizing two switching networks that virtually rotate the transmitting and receiving antennas along the azimuthal direction for efficient data collection. This minimizes the need for mechanical scanning of the antennas which, in turn, leads to faster data acquisition. To enhance the quality of the imaging outcome, the number of samples can be increased by combining only a few mechanical scanning steps with the electronic scanning. This data acquisition scheme leverages the system’s space-invariant property to enable convolution-based near-field holographic microwave image reconstruction. By capturing and processing scattered fields over a cylindrical aperture, the system achieves high-resolution imaging of concealed objects across multiple range positions. Both simulation and experimental results validate the effectiveness of the proposed approach in delivering high-quality imaging results. Its ability to provide faster and enhanced imaging outcomes highlights its potential for a wide range of applications, including biomedical imaging, security screening, and non-destructive testing of the materials. Full article

(This article belongs to the Special Issue RF, Microwave, and Millimeter Wave Devices and Circuits and Their Applications)

► Show Figures

Figure 1

39 pages, 14267 KiB

Open AccessReview

Smart Precision Weeding in Agriculture Using 5IR Technologies

by Chaw Thiri San and Vijay Kakani

Electronics 2025, 14(13), 2517; https://doi.org/10.3390/electronics14132517 - 20 Jun 2025

The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), [...] Read more.

The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), 5G connectivity, and edge computing technologies. This review discusses a comprehensive analysis of the traditional and contemporary weeding techniques, thereby focusing on the technological innovations paving way for the smart systems. Primarily, this work investigates the application of 5IR technologies in weed detection and decision-making with particular emphasis on the role of the aspects such as AI-driven models, drone-robot integration, GPS-guided practices, and intelligent sensor networks. Additionally, the work outlines key commercial solutions, sustainability metrics, data-driven decision support systems, and blockchain traceable practices. The prominent challenges in the context of global agricultural equity pertaining to cost, scalability, policy alignment, and adoption barriers in accordance to the low-resource environments are discussed in this study. The paper concludes with strategic recommendations and future research directions, highlighting the potential of 5IR technologies on the smart precision weeding. Full article

(This article belongs to the Special Issue Harnessing AI and Fifth Industrial Revolution (5IR) Technologies for the Future of Automation)

► Show Figures

Figure 1

17 pages, 2332 KiB

Open AccessArticle

Low Carrier–Frequency Ratio Luenberger Observer Based on Discrete Mathematical Model for SPMSMs

by Shuhan Guo, Yawen Jin and Wenguang Yang

Electronics 2025, 14(13), 2516; https://doi.org/10.3390/electronics14132516 - 20 Jun 2025

To address the issue of reduced observer accuracy under low carrier–frequency ratio (CFR) conditions in the sensorless control of high-speed motors, which limits system performance, this paper proposes a discrete mathematical modeling method for surface-mounted permanent magnet synchronous motors (SPMSMs). Based on this [...] Read more.

To address the issue of reduced observer accuracy under low carrier–frequency ratio (CFR) conditions in the sensorless control of high-speed motors, which limits system performance, this paper proposes a discrete mathematical modeling method for surface-mounted permanent magnet synchronous motors (SPMSMs). Based on this established accurate discrete motor model, the influence of low CFR on the phase estimation error of back electromotive force (EMF) is analyzed. Building on this foundation, an accurate discrete Luenberger observer (ALO) is designed, and a corresponding phase compensation control method is proposed. A motor drive control system comprising hardware, software, and experimental test setups is constructed. The experimental results demonstrate that, compared to the Euler model, the discrete mathematical model established by this method significantly improves position observation accuracy under low CFR conditions. Furthermore, compared to the traditional Luenberger observer (TLO), the estimation error of the proposed observer under a low CFR is reduced by approximately 85%. This approach exhibits high application value in the sensorless control of high-speed and high-frequency motors. Full article

(This article belongs to the Section Systems & Control Engineering)

► Show Figures

Graphical abstract

19 pages, 433 KiB

Open AccessArticle

Mutual Information-Oriented ISAC Beamforming Design for Large Dimensional Antenna Array

by Shanfeng Xu, Yanshuo Cheng, Siqiang Wang, Xinyi Wang, Zhong Zheng and Zesong Fei

Electronics 2025, 14(13), 2515; https://doi.org/10.3390/electronics14132515 - 20 Jun 2025

In this paper, we study the beamforming design for multiple-input multiple-output (MIMO) ISAC systems, with the weighted mutual information (MI) comprising sensing and communication perspectives adopted as the performance metric. In particular, the weighted sum of the communication mutual information and the sensing [...] Read more.

In this paper, we study the beamforming design for multiple-input multiple-output (MIMO) ISAC systems, with the weighted mutual information (MI) comprising sensing and communication perspectives adopted as the performance metric. In particular, the weighted sum of the communication mutual information and the sensing mutual information is shown to asymptotically converge to a deterministic limit when the number of transmitting and receiving antennas grow to infinity. This deterministic limit is derived by utilizing the operator-valued free probability theory. Subsequently, an efficient projected gradient ascent (PGA) algorithm is proposed to optimize the transmit beamforming matrix with the aim of maximizing the weighted asymptotic MI. Numerical results validate that the derived closed-form expression matches well with the Monte Carlo simulation results and the proposed optimization algorithm is able to improve the weighted asymptotic MI significantly. We also illustrate the trade-off between asymptotic sensing and asymptotic communication MI. Full article

(This article belongs to the Special Issue Intelligent Signal Processing for Integrated Sensing and Communications towards 6G)

24 pages, 6560 KiB

Open AccessArticle

Spatio-Temporal Attention-Based Deep Learning for Smart Grid Demand Prediction

by Muhammed Cavus and Adib Allahham

Electronics 2025, 14(13), 2514; https://doi.org/10.3390/electronics14132514 - 20 Jun 2025

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates [...] Read more.

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with an Attentive Long Short-Term Memory (LSTM) network to enhance forecasting performance in renewable-integrated smart grids. The CNN component captures spatial dependencies from multivariate inputs, comprising meteorological variables and generation data, while the LSTM module models temporal correlations in historical load patterns. An embedded attention mechanism dynamically weights input sequences, enabling the model to prioritise the most influential time steps, thereby improving its interpretability and robustness during demand fluctuations. ST-CALNet was trained and evaluated using real-world datasets that include electricity consumption, solar photovoltaic (PV) output, and wind generation. Experimental evaluation demonstrated that the model achieved a mean absolute error (MAE) of 0.0494, root mean squared error (RMSE) of 0.0832, and a coefficient of determination (R²) of 0.4376 for electricity demand forecasting. For PV and wind generation, the model attained MAE values of 0.0134 and 0.0141, respectively. Comparative analysis against baseline models confirmed ST-CALNet’s superior predictive accuracy, particularly in minimising absolute and percentage-based errors. Temporal and regime-based error analysis validated the model’s resilience under high-variability conditions such as peak load periods, while visualisation of attention scores offered insights into the model’s temporal focus. These findings underscore the potential of ST-CALNet for deployment in intelligent energy systems, supporting more adaptive, transparent, and dependable forecasting within smart grid infrastructures. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems, 2nd Edition)

► Show Figures

Figure 1

26 pages, 5691 KiB

Open AccessArticle

A Participatory Design Approach to Designing Educational Interventions for Science Students Using Socially Assistive Robots

by Mahmoud Mohamed Hussien Ahmed, Mohammad Nehal Hasnine and Bipin Indurkhya

Electronics 2025, 14(13), 2513; https://doi.org/10.3390/electronics14132513 - 20 Jun 2025

We present here an approach to the deployment of social robots in a science laboratory to monitor the behavior of students with respect to safety regulations to prevent accidents. Our vision is that the social robot should act as a friendly companion for [...] Read more.

We present here an approach to the deployment of social robots in a science laboratory to monitor the behavior of students with respect to safety regulations to prevent accidents. Our vision is that the social robot should act as a friendly companion for students and encourage them to follow safe laboratory practices. Towards this goal, we developed a Laboratory Safety Assistant Framework (LSA) using a Misty II Plus robot and designed three dashboards within it as interventions. This LSA framework was evaluated using a participatory design (PD) study with twenty university students (eleven from Japan and nine from Egypt). For this study, we designed a questionnaire that contains 42 questions on the prior knowledge of students about socially assistive robots and their expectations about how socially assistive robots can create a secure environment in the scientific laboratory. The chi-square test revealed that there are no differences between groups in their perceptions of using Misty II to achieve safety inside science laboratories. In their perception of the capabilities of social robots and the sharing of feelings, students believe that using social robots like Misty II inside the science laboratory can make the lab safe and decrease risk inside the science laboratory without using the three dashboards of the LSA framework. However, the Wilcoxon signed-rank test revealed that there is a significant improvement in students’ perceptions ((Median = 106.5, Z = −2.39, p < 0.05, r = 0.53)) between students’ expectations of using social robots to achieve safety in scientific laboratories before and after they interacted with the social robot and knew about the feasibility of the three dashboards we designed. Furthermore, the t-test revealed participants’ experiences of sharing feelings with a social robot, and the intervention suggested by the LSA framework was to design a system integrating this into a social robot to enhance safety within the scientific laboratory (t (19) = 3.39, p = 0.003). Full article

(This article belongs to the Section Artificial Intelligence)

51 pages, 2801 KiB

Open AccessReview

A Review on Federated Learning Architectures for Privacy-Preserving AI: Lightweight and Secure Cloud–Edge–End Collaboration

by Shanhao Zhan, Lianfen Huang, Gaoyu Luo, Shaolong Zheng, Zhibin Gao and Han-Chieh Chao

Electronics 2025, 14(13), 2512; https://doi.org/10.3390/electronics14132512 - 20 Jun 2025

Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper [...] Read more.

Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper provides a system review of the state-of-the-art techniques and future research directions in FL, with a focus on addressing these challenges in resource-constrained environments by a cloud–edge–end collaboration FL architecture. We first introduce the foundations of cloud–edge–end collaboration and FL. We then discuss the key technical challenges. Next, we delve into the pillars of trustworthy AI in the federated context, covering robustness, fairness, and explainability. We propose a dimension reconstruction of trusted AI and analyze the foundations of each trustworthiness pillar. Furthermore, we present a lightweight FL framework for resource-constrained edge–end devices, analyzing the core contradictions and proposing optimization paradigms. Finally, we highlight advanced topics and future research directions to provide valuable insights into the field. Full article

(This article belongs to the Special Issue Security and Privacy in Networks and Multimedia, 2nd Edition)

► Show Figures

Figure 1

27 pages, 3926 KiB

Open AccessArticle

A Multi-Source Embedding-Based Named Entity Recognition Model for Knowledge Graph and Its Application to On-Site Operation Violations in Power Grid Systems

by Lingwen Meng, Yulin Wang, Guobang Ban, Yuanjun Huang, Xinshan Zhu and Shumei Zhang

Electronics 2025, 14(13), 2511; https://doi.org/10.3390/electronics14132511 - 20 Jun 2025

With the increasing complexity of power grid field operations, frequent operational violations have emerged as a major concern in the domain of power grid field operation safety. To support dispatchers in accurately identifying and addressing violation risks, this paper introduces a profiling approach [...] Read more.

With the increasing complexity of power grid field operations, frequent operational violations have emerged as a major concern in the domain of power grid field operation safety. To support dispatchers in accurately identifying and addressing violation risks, this paper introduces a profiling approach for power grid field operation violations based on knowledge graph techniques. The method enables deep modeling and structured representation of violation behaviors. In the structured data processing phase, statistical analysis is conducted based on predefined rules, and mutual information is employed to quantify the contribution of various operational factors to violations. At the municipal bureau level, statistical modeling of violation characteristics is performed to support regional risk assessment. For unstructured textual data, a multi-source embedding-based named entity recognition (NER) model is developed, incorporating domain-specific power lexicon information to enhance the extraction of key entities. High-weight domain terms related to violations are further identified using the TF-IDF algorithm to characterize typical violation behaviors. Based on the extracted entities and relationships, a knowledge graph of field operation violations is constructed, providing a computable and inferable semantic representation of operational scenarios. Finally, visualization techniques are applied to present the structural patterns and distributional features of violations, offering graph-based support for violation risk analysis and dispatch decision-making. Experimental results demonstrate that the proposed method effectively identifies critical features of violation behaviors and provides a structured foundation for intelligent decision support in power grid operation management. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

► Show Figures

Figure 1

13 pages, 3487 KiB

Open AccessArticle

Inverse Design of Microstrip Antennas Based on Deep Learning

by Shiyang Chen, Guang-Hua Sun and Kaixu Wang

Electronics 2025, 14(13), 2510; https://doi.org/10.3390/electronics14132510 - 20 Jun 2025

This paper introduces a novel inverse design framework that combines pixelated microstrip antenna modeling, convolutional neural network (CNN), and binary particle swarm optimization (BPSO) to automate the process of generating antenna structures from specified performance targets. The framework operates through a streamlined workflow: [...] Read more.

This paper introduces a novel inverse design framework that combines pixelated microstrip antenna modeling, convolutional neural network (CNN), and binary particle swarm optimization (BPSO) to automate the process of generating antenna structures from specified performance targets. The framework operates through a streamlined workflow: the radiating patch is discretized into a 10 × 10 binary matrix to enable combinatorial design space exploration; a CNN is trained on 150,000 simulated datasets to predict S-parameters as a surrogate for time-consuming electromagnetic simulations; and BPSO optimizes pixel states guided by a fitness function that minimizes reflection coefficients at target frequencies. By representing the patch as binary pixels, the approach exponentially expands the design space from traditional parametric limits to about 10³⁰ combinatorial possibilities, overcoming the inefficiencies of manual trial-and-error design. Comparative studies with genetic algorithms (GAs) and simulated annealing (SA) demonstrate that the BPSO-CNN framework achieves faster convergence and lower S11 error at target frequencies. This work not only advances the state of the art in intelligent antenna design but also provides a scalable paradigm for automated electromagnetic device optimization. Full article

► Show Figures

Figure 1

23 pages, 1784 KiB

Open AccessArticle

Signal-Specific and Signal-Independent Features for Real-Time Beat-by-Beat ECG Classification with AI for Cardiac Abnormality Detection

by I Hua Tsai and Bashir I. Morshed

Electronics 2025, 14(13), 2509; https://doi.org/10.3390/electronics14132509 - 20 Jun 2025

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone [...] Read more.

ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article

(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)

► Show Figures

Figure 1

22 pages, 2436 KiB

Open AccessArticle

An Efficient Sparse Synthetic Aperture Radar Imaging Method Based on L₁-Norm and Total Variation Regularization

by Zhiqi Gao, Huiying Ma, Pingping Huang, Wei Xu, Weixian Tan and Zhixia Wu

Electronics 2025, 14(13), 2508; https://doi.org/10.3390/electronics14132508 - 20 Jun 2025

The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition and processing have become increasingly prominent. However, traditional SAR imaging models are limited [...] Read more.

The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition and processing have become increasingly prominent. However, traditional SAR imaging models are limited by their large demand for data sampling and slow image reconstruction speeds, which is particularly prominent in large-scale scene applications. To overcome these limitations, this study proposes an innovative L₁-Total Variation (TV) regularization sparse SAR imaging algorithm. The submitted algorithm constructs an imaging operator and an echo simulation operator to achieve decoupling in the azimuth and range dimensions, respectively, as well as to reduce the requirement for sampling data. In addition, a Newton acceleration iterative method is introduced to the optimization process, aiming to accelerate the speed of image reconstruction. Comparative analysis and experimental validation indicate that the proposed sparse SAR imaging algorithm outperforms conventional methods in resolution, target localization, and clutter suppression. The results suggest strong potential for rapid scene reconstruction and real-time monitoring in complex environments. Full article

► Show Figures

Figure 1

15 pages, 5002 KiB

Open AccessArticle

Leveraging Machine Learning for Optimal Pilgrim Crowd Management

by Roaa Alzahrani and Nahlah Algethami

Electronics 2025, 14(13), 2507; https://doi.org/10.3390/electronics14132507 - 20 Jun 2025

The Hajj pilgrimage involves high crowd density within limited time and space, making traditional crowd control methods insufficient for real-time alerts or predictive safety measures. This research proposes a machine learning-based system to enhance crowd management by detecting abnormal behavior and forecasting future [...] Read more.

The Hajj pilgrimage involves high crowd density within limited time and space, making traditional crowd control methods insufficient for real-time alerts or predictive safety measures. This research proposes a machine learning-based system to enhance crowd management by detecting abnormal behavior and forecasting future conditions. The study utilizes the Hajjv2 dataset, which consists of annotated video frames capturing various crowd behaviors across multiple Hajj locations. After data preprocessing and feature extraction, including crowd density, speed, direction, and object area, two models are employed: the Isolation Forest algorithm for anomaly detection and a Long Short-Term Memory (LSTM) neural network for forecasting crowd behavior. The system integrates the results of both models to issue real-time alerts based on predefined thresholds. Evaluation results indicate that the Isolation Forest model achieved an average accuracy of 91% across all test sets, effectively identifying abnormal movement patterns. The LSTM model produced reliable predictions of average crowd speed with a low Mean Squared Error (MSE) of 0.000439. Together, these models form a robust alert mechanism that enables early identification of risks. In summary, this study presents an intelligent, scalable solution for enhancing crowd safety during the Hajj. It illustrates the practical value of machine learning in enabling proactive and informed crowd management strategies. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

18 pages, 4367 KiB

Open AccessArticle

Three-Dimensional Phase-Space Design and Simulation of a Broadband THz Transmission Line Using Wigner Optics and Ray Tracing

by Jacob Gerasimov, Emmanuel Bender, Moshe Sitbon, Egor Dyunin and Michael Gerasimov

Electronics 2025, 14(13), 2506; https://doi.org/10.3390/electronics14132506 - 20 Jun 2025

Designing a transmission line (TL) for a widely tunable, broadband terahertz radiation source presents substantial challenges due to the complexity of beam dynamics and spectral characteristics. Here, we investigate the propagation of the most significant radiation modes expected to traverse the TL, intended [...] Read more.

Designing a transmission line (TL) for a widely tunable, broadband terahertz radiation source presents substantial challenges due to the complexity of beam dynamics and spectral characteristics. Here, we investigate the propagation of the most significant radiation modes expected to traverse the TL, intended for integration with an advanced particle accelerator currently under construction at the Schlesinger Family Center for Compact Accelerators, Radiation Sources and Applications. The total electromagnetic field at the source output is expressed in the frequency domain via cavity eigenmodes and transformed into an optical field representation using the Wigner distribution function (WDF). This formulation enables physically consistent modeling within the constraints of geometric optics and Wigner formalism of the spatiotemporal evolution of the radiation during propagation. The initial TL design is developed and optimized based on this representation. A 3D space–frequency analysis tool for pulsed radiation, based on the WDF, was implemented to characterize field behavior and guide system development. Complementary ray tracing simulations were conducted using the Zemax Optic Studio platform, supporting the assessment of optical feasibility through simulation and system feasibility. Full article

(This article belongs to the Special Issue 3D Computer Vision and 3D Reconstruction)

► Show Figures

Figure 1

25 pages, 1382 KiB

Open AccessArticle

Joint Spoofing Detection Algorithm Based on Dual Control Charts and Robust Estimation

by Lunlong Zhong, Xu Yuan and Wenjing Yue

Electronics 2025, 14(13), 2505; https://doi.org/10.3390/electronics14132505 - 20 Jun 2025

To address the issue that existing GNSS spoofing detection methods are not suitable for intermittent minor spoofing detection and spoofing duration identification, this paper theoretically analyzes the shortcomings of existing detection algorithms in terms of minor spoofing termination detection performance, and proposes comprehensively [...] Read more.

To address the issue that existing GNSS spoofing detection methods are not suitable for intermittent minor spoofing detection and spoofing duration identification, this paper theoretically analyzes the shortcomings of existing detection algorithms in terms of minor spoofing termination detection performance, and proposes comprehensively utilizing two types of control charts and robust estimation to detect the spoofing end moment, laying a foundation for spoofing duration identification and intermittent minor spoofing detection. The Shewhart control chart-based spoofing detection algorithm (M1) is proposed to achieve rapid spoofing termination detection, serving as one of the baseline algorithms for the joint algorithm. The strengths and weaknesses of the two baseline algorithms (M1 and existing EWMA control chart and robust estimation-based detection algorithm (M2)) in minor spoofing detection are analyzed. Under the robust estimation mechanism, a joint spoofing detection metric that can effectively indicate spoofing termination is constructed by combining their respective spoofing test statistics; then, anomaly detection on the joint detection metric is performed based on sample quantiles to identify the spoofing end moment. The experimental results under various typical abrupt spoofing and slowly varying spoofing scenarios demonstrate that the proposed joint spoofing detection algorithm based on dual control charts and robust estimation satisfies the spoofing alert time requirements specified by the International Civil Aviation Organization (ICAO) for the cruise phase. Compared with existing detection algorithms, the joint algorithm maintains excellent spoofing initiation detection performance while significantly improving both the speed and accuracy of spoofing termination detection. This effectively integrates the advantages of the two baseline algorithms and compensates for their individual limitations when operating independently. Upon timely and effective detection of the start and end moments of minor spoofing, it becomes possible to achieve spoofing duration identification and intermittent minor spoofing detection. Full article

(This article belongs to the Section Microwave and Wireless Communications)

► Show Figures

Figure 1

17 pages, 9122 KiB

Open AccessArticle

A Printed Hybrid-Mode Antenna for Dual-Band Circular Polarization with Flexible Frequency Ratio

by Takafumi Fujimoto and Chai-Eu Guan

Electronics 2025, 14(13), 2504; https://doi.org/10.3390/electronics14132504 - 20 Jun 2025

In this paper, a printed hybrid-mode antenna for dual-band circular polarization (CP) is proposed. In the proposed antenna, one T-shaped element is fed by a coplanar waveguide and one L-shaped element is loaded to the ground plane. The relationship between the antenna’s geometric [...] Read more.

In this paper, a printed hybrid-mode antenna for dual-band circular polarization (CP) is proposed. In the proposed antenna, one T-shaped element is fed by a coplanar waveguide and one L-shaped element is loaded to the ground plane. The relationship between the antenna’s geometric parameters and the circular polarization characteristic (axial ratio) is examined through electric current distribution and radiation field components. In addition, the antenna’s resonant modes are investigated through characteristic mode analysis (CMA). Through parametric studies, the range of two frequency ratios is explored, revealing that the antenna operates as a dual-band single-sense CP antenna, even in ranges where the two frequency ratios (the ratio of high frequency to low frequency) are smaller compared to antennas in other studies. The proposed antenna has a frequency ratio of less than 1.5 between the two frequencies and can be flexibly designed. The proposed antenna is designed for the 2.5 GHz band and 3.5 GHz band. The measured bandwidths of 10 dB impedance with a 3 dB axial ratio are 2.35–2.52 GHz and 3.36–3.71 GHz, respectively. Full article

► Show Figures

Figure 1

20 pages, 2781 KiB

Open AccessArticle

Optimal Control-Based Grover’s Algorithm for a Six-Jointed Articulated Robotic Arm

by Mohamed Salah Dahassa and Nadjet Zioui

Electronics 2025, 14(13), 2503; https://doi.org/10.3390/electronics14132503 - 20 Jun 2025

This paper introduces a novel theoretical framework that reformulates optimal control as a quantum search problem using Grover’s algorithm, leveraging its quadratic speedup. Specifically, the method encodes all possible control inputs into a quantum superposition state and uses a reference value interpreted as [...] Read more.

This paper introduces a novel theoretical framework that reformulates optimal control as a quantum search problem using Grover’s algorithm, leveraging its quadratic speedup. Specifically, the method encodes all possible control inputs into a quantum superposition state and uses a reference value interpreted as a candidate minimum to evaluate which inputs yield a lower control cost. To guide the search, we integrate a quantum comparator circuit to identify the inputs below this reference, and quantum counting to estimate their number. The reference is iteratively updated using a sigmoid-based rule until only one input satisfies the condition, thereby ensuring convergence to the global minimum within the discretized control space. Although full quantum implementation is currently infeasible due to oracle complexity and hardware limitations, we simulate the process using a classical controller as a pseudo-oracle to illustrate the algorithmic structure. This work does not aim to demonstrate performance gains but rather to establish a foundational method for embedding control synthesis within Grover-based quantum circuits. The framework paves the way for scalable quantum control systems once hardware resources permit full realization. Full article

(This article belongs to the Special Issue Quantum Computation and Its Applications)

► Show Figures

Figure 1

13 pages, 2352 KiB

Open AccessArticle

Research on Improving the Avalanche Current Limit of Parallel SiC MOSFETs

by Hua Mao, Binbing Wu, Xinsheng Lan, Yalong Xia, Junjie Chen and Lei Tang

Electronics 2025, 14(13), 2502; https://doi.org/10.3390/electronics14132502 - 20 Jun 2025

The transient overvoltage caused by coupling of loop inductance during rapid turn off of a silicon carbide metal-oxide-semiconductor field-effect transistor (SiC MOSFET) can easily induce avalanche breakdown. Meanwhile, the instantaneous high-density heat flux generated by energy dissipation can create significant electrothermal coupling stress, [...] Read more.

The transient overvoltage caused by coupling of loop inductance during rapid turn off of a silicon carbide metal-oxide-semiconductor field-effect transistor (SiC MOSFET) can easily induce avalanche breakdown. Meanwhile, the instantaneous high-density heat flux generated by energy dissipation can create significant electrothermal coupling stress, potentially leading to device failure under severe conditions. To address the issue that the multi-chip parallel structure of power modules cannot linearly enhance avalanche withstand capability, an innovative device screening method based on parameter matching is proposed in this paper. The effectiveness of the proposed solution is verified through experiments, with the total current limit of dual-tube parallel devices and three-tube parallel devices achieving 1.9 times and 2.4 times that of single-tube devices, respectively. This research is of great significance for improving safe and reliable operation of the system. Full article

► Show Figures

Figure 1

28 pages, 8607 KiB

Open AccessArticle

Analysis of Grid-Connected Damping Characteristics of Virtual Synchronous Generator and Improvement Strategies

by Xudong Cao, Ruogu Zhang, Jun Li, Li Ji, Xueliang Wei, Jile Geng and Bowen Li

Electronics 2025, 14(12), 2501; https://doi.org/10.3390/electronics14122501 - 19 Jun 2025

Focused on the contradiction between the steady-state error of active power and the dynamic oscillation caused by the virtual damping characteristics of the virtual synchronous generator (VSG) under disturbances during grid-connected operation, this article proposes an adaptive virtual inertia regulation and compensation method [...] Read more.

Focused on the contradiction between the steady-state error of active power and the dynamic oscillation caused by the virtual damping characteristics of the virtual synchronous generator (VSG) under disturbances during grid-connected operation, this article proposes an adaptive virtual inertia regulation and compensation method (PFFCVSG_AJ) based on an active power differential feedforward compensation strategy (PFFCVSG). Firstly, this article presents the working and control principles of VSG, analyzing its control mechanisms through a small-signal model. Models for VSG’s active power, reactive power, and virtual impedance components are established, with particular focus on the impact of the damping coefficient on active power regulation. Based on the PFFCVSG, an adaptive virtual inertia adjustment method is introduced to resolve the inherent inertia deficiency in PFFCVSG control, the influence of the moment of inertia on PFFCVSG is theoretically analyzed, and a dynamic adjustment mechanism for moment of inertia is developed based on the rate of change in frequency (RoCoF). Finally, simulation validation using MATLAB/Simulink (MathWorks, R2022b, Natick, MA, USA) demonstrates that the proposed PFFCVSG_AJ strategy effectively eliminates active power steady-state deviation, suppresses active power dynamic oscillation, and mitigates the frequency overshoot issue prevalent in traditional PFFCVSG. Experimental verification is conducted via a TMS320F28378DPTPS-based control platform, confirming the algorithm’s effectiveness under sudden load variations, and that the power quality of the power grid is not affected under the premise of efficient grid connection. Full article

(This article belongs to the Special Issue New Trends in Power Electronics for Microgrids)

► Show Figures

Figure 1

16 pages, 1475 KiB

Open AccessArticle

Power Loss Calculation in Oil-Type Distribution Transformers Supplying Nonlinear Loads

by Neda Miteva, Lior Sima and Kfir Jack Dagan

Electronics 2025, 14(12), 2500; https://doi.org/10.3390/electronics14122500 - 19 Jun 2025

Power transformers are the most vital component in the electric grid. Their loss calculation is critical to transformer asset management and reflects on both operation and techno-economic assessment. Acknowledging the above, this paper presents an application of a novel loss calculation method to [...] Read more.

Power transformers are the most vital component in the electric grid. Their loss calculation is critical to transformer asset management and reflects on both operation and techno-economic assessment. Acknowledging the above, this paper presents an application of a novel loss calculation method to oil-type transformers supplying nonlinear loads. Unlike the methodology presented in std. C57.110-2018, the applied approach evaluates transformer loss components relying solely on readily available technical data. The method was experimentally validated using a full-scale 250kVA oil-type distribution transformer. Experimental results show close agreement with the theoretical model and feature errors smaller than 3%. Full article

(This article belongs to the Special Issue New Trends in Power Electronics for Microgrids)

► Show Figures

Figure 1

More Articles...

Submit to Electronics Review for Electronics

Journal Menu

Journal Browser

► Journal Browser

Highly Accessed Articles

View More...

Latest Books

More Books and Reprints...

E-Mail Alert

News

30 April 2025
Richard DiMarchi and Rolf Müller Share the 2024 Tu Youyou Award

19 June 2025
Electronics | Highly Cited Papers in 2024 in the “Power Electronics” Section

12 June 2025
Electronics Receives an Increased CiteScore of 6.1

More News & Announcements...

Topics

Propose a Topic

Topic in Applied Sciences, Electronics, Future Internet, Machines, Systems, Technologies, Biomimetics

Theoretical and Applied Problems in Human-Computer Intelligent Systems Topic Editors: Jiahui Yu, Charlie Yang, Zhenyu Wen, Dalin Zhou, Dongxu Gao, Changting Lin
Deadline: 30 June 2025

Topic in Applied Sciences, Electronics, JSAN, Photonics, Sensors, Telecom

Machine Learning in Communication Systems and Networks, 2nd Edition Topic Editors: Yichuang Sun, Haeyoung Lee, Oluyomi Simpson
Deadline: 20 July 2025

Topic in Energies, Applied Sciences, Electronics, Vehicles, Eng, WEVJ

Advances in Electric Vehicle Charging Systems and Vehicle-to-Grid Technology Topic Editors: Ching Chuen Chan, Kwok Tong Chau, Wei Liu
Deadline: 1 September 2025

Topic in Applied Sciences, Electricity, Electronics, Energies, Sensors

Power System Protection Topic Editors: Seyed Morteza Alizadeh, Akhtar Kalam
Deadline: 30 September 2025

More Topics

Conferences

Announce Your Conference

17–19 November 2025 11th International Symposium on Sensor Science

16–18 March 2026 Nanomaterials 2026: Innovations and Future Perspectives

24–26 September 2025 5th International Conference on Novel & Intelligent Digital Systems

More Conferences...

Special Issues

Propose a Special Issue

Special Issue in Electronics

Data Retrieval and Data Mining Guest Editors: Yangjun Chen, Carson K. Leung
Deadline: 25 June 2025

Special Issue in Electronics

Advances in Human-Computer Interaction: Challenges and Opportunities Guest Editors: Deepti Aggarwal, Sophie Mckenzie
Deadline: 30 June 2025

Special Issue in Electronics

Soft Robotics and Its Applications Guest Editors: Hongliang Ren, Yang Yang
Deadline: 30 June 2025

Special Issue in Electronics

Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition Guest Editors: Chang Cai, Yaqing Chi, Li Cai
Deadline: 30 June 2025

More Special Issues

Topical Collections

Topical Collection in Electronics

Advance Technologies of Navigation for Intelligent Vehicles Collection Editors: Jinwen Hu, Wei Meng, Shenghai Yuan

Topical Collection in Electronics

Electronics for Agriculture Collection Editors: Lei Shu, Simon Pearson, Ye Liu, Mohamed Amine Ferrag, Leandros Maglaras

Topical Collection in Electronics

Collaborative Artificial Systems Collection Editors: Jesús Ángel Román Gallego, María-Luisa Pérez-Delgado, María Concepción Vega Hernández, Alfonso Jose Lopez Rivero, Daniel Hernández De la Iglesia

Topical Collection in Electronics

Graph Machine Learning Collection Editor: Gemma Piella

More Topical Collections

Back to TopTop