Electronics

27 pages, 5530 KiB

Open AccessEditor’s ChoiceArticle

Optoelectronic Devices Analytics: MachineLearning-Driven Models for Predicting the Performance of a Dye-Sensitized Solar Cell

by Emeka Harrison Onah, N. L. Lethole and P. Mukumba

Electronics 2025, 14(10), 1948; https://doi.org/10.3390/electronics14101948 - 10 May 2025

Viewed by 368

Abstract

Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant [...] Read more.

Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant attention due to their affordability, flexibility, and ability to function under low light conditions. The current research incorporates machine learning (ML) models to predict the performance of a modified Eu³⁺-doped Y₂WO₆/TiO₂ photo-electrode DSSC. Experimental data were collected from the “Dryad Repository Database” to feed into the models, and a detailed data visualization analysis was performed to study the trends in the datasets. The support vector regression (SVR) and Random Forest regression (RFR) models were applied to predict the short-circuit current density (J_sc) and maximum power (P_max) output of the device. Both models achieved reasonably accurate predictions, and the RFR model attained a better prediction response, with the percentage difference between the experimental data and model prediction being 0.73% and 1.01% for the J_sc and P_max respectively, while the SVR attained a percentage difference of 1.22% and 3.54% for the J_sc and P_max respectively. Full article

(This article belongs to the Special Issue Modeling and Design of Solar Cell Materials)

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24 pages, 1781 KiB

Open AccessEditor’s ChoiceArticle

Learning-Based MPC Leveraging SINDy for Vehicle Dynamics Estimation

by Francesco Paparazzo, Andrea Castoldi, Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Stefano Arrigoni and Francesco Braghin

Electronics 2025, 14(10), 1935; https://doi.org/10.3390/electronics14101935 - 9 May 2025

Viewed by 497

Abstract

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate [...] Read more.

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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23 pages, 3948 KiB

Open AccessEditor’s ChoiceArticle

A Dynamic Spatiotemporal Deep Learning Solution for Cloud–Edge Collaborative Industrial Control System Distributed Denial of Service Attack Detection

by Zhigang Cao, Bo Liu, Dongzhan Gao, Ding Zhou, Xiaopeng Han and Jiuxin Cao

Electronics 2025, 14(9), 1843; https://doi.org/10.3390/electronics14091843 - 30 Apr 2025

Viewed by 360

Abstract

With the continuous development of industrial intelligence, the integration of cyber–physical components creates a need for effective attack detection methods to mitigate potential DDoS threats. Although several DDoS attack detection modeling approaches have been proposed, few effectively incorporate the unique characteristics of industrial [...] Read more.

With the continuous development of industrial intelligence, the integration of cyber–physical components creates a need for effective attack detection methods to mitigate potential DDoS threats. Although several DDoS attack detection modeling approaches have been proposed, few effectively incorporate the unique characteristics of industrial control system (ICS) architectures and traffic patterns. This paper focuses on DDoS attack detection within cloud–edge collaborative ICSs and proposes a novel detection model called FedDynST. This model combines federated learning and deep learning to construct feature graphs of traffic data. Introducing dynamic and static adjacency matrices, this work reveals the interactions between long-term industrial traffic data and short-term anomalies associated with DDoS attacks. Convolutional neural networks are utilized to capture distinctive temporal features within industrial traffic, thereby improving the detection precision. Moreover, the model enables continuous optimization of the global detection framework through a federated learning-based distributed training and aggregation mechanism, ensuring the privacy and security of industrial client data. The effectiveness of the FedDynST model was validated on the CICDDoS2019 and Edge-IIoTset datasets. The simulation results validated the superiority of the proposed approach, and thus, demonstrated significant improvements in both detection accuracy and convergence. Full article

(This article belongs to the Section Artificial Intelligence)

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29 pages, 4136 KiB

Open AccessEditor’s ChoiceArticle

IoT-NTN with VLEO and LEO Satellite Constellations and LPWAN: A Comparative Study of LoRa, NB-IoT, and Mioty

by Changmin Lee, Taekhyun Kim, Chanhee Jung and Zizung Yoon

Electronics 2025, 14(9), 1798; https://doi.org/10.3390/electronics14091798 - 28 Apr 2025

Viewed by 496

Abstract

This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600km using a Genetic Algorithm (GA). [...] Read more.

This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600km using a Genetic Algorithm (GA). Focusing on three LPWAN technologies—LoRa, Narrowband IoT (NB-IoT), and Mioty—we evaluate their performance in terms of revisit time, data transmission volume, and economic efficiency. Results indicate that a 300 km VLEO constellation with LoRa achieves the shortest average revisit time and requires the fewest satellites, offering notable cost benefits. NB-IoT provides the highest data transmission volume. Mioty demonstrates strong scalability but necessitates a larger satellite count. These findings highlight the potential of VLEO satellites, particularly at 300 km, combined with LPWAN solutions for efficient and scalable IoT Non-Terrestrial Network (IoT-NTN) applications. Future work will explore multi-altitude simulations and hybrid LPWAN integration for further optimization. Full article

(This article belongs to the Special Issue Future Generation Non-Terrestrial Networks)

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17 pages, 6015 KiB

Open AccessEditor’s ChoiceArticle

Process Monitoring of One-Shot Drilling of Al/CFRP Aeronautical Stacks Using the 1DCAE-GMM Framework

by Giulio Mattera, Maria Grazia Marchesano, Alessandra Caggiano, Guido Guizzi and Luigi Nele

Electronics 2025, 14(9), 1777; https://doi.org/10.3390/electronics14091777 - 27 Apr 2025

Viewed by 306

Abstract

This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force [...] Read more.

This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force and torque signals at a 10 kHz sampling rate during the drilling process. These signals are employed for real-time process monitoring, focusing on material change detection and anomaly identification, where anomalies are defined as holes that fail to meet predefined quality criteria. An innovative approach based on unsupervised learning is proposed to enable automatic material change identification, signal segmentation, feature extraction, and hole quality assessment. Specifically, a semi-supervised approach based on a Gaussian Mixture Model (GMM) and 1D Convolutional AutoEncoder (1D-CAE) is employed to detect deviations from normal drilling conditions. The proposed method is benchmarked against state-of-the-art supervised techniques, including logistic regression (LR) and Support Vector Machines (SVMs). Results show that these traditional models struggle with class imbalance, leading to overfitting and limited generalisation, as reflected by the F1 scores of 0.78 and 0.75 for LR and SVM, respectively. In contrast, the proposed semi-supervised approach improves anomaly detection, achieving an F1 score of 0.87 by more effectively identifying poor-quality holes. This study demonstrates the potential of deep learning-based semi-supervised methods for intelligent process monitoring, enabling adaptive control in the drilling process of hybrid stacks and detecting anomalous holes. While the proposed approach effectively handles small and imbalanced datasets, further research into the application of generative AI could enhance performance, aiming for F1 scores above 0.90, thereby supporting adaptation in real industrial environments with high performance. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Intelligent Manufacturing)

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7 pages, 1890 KiB

Open AccessEditor’s ChoiceArticle

Investigation of Temperature-Dependent Gate Degradation in Normally-Off AlGaN/GaN High-Electron-Mobility Transistor p-GaN

by Jeonghyeok Yoon and Hyungtak Kim

Electronics 2025, 14(9), 1764; https://doi.org/10.3390/electronics14091764 - 26 Apr 2025

Viewed by 350

Abstract

The effect of temperature on gate degradation behavior was analyzed in Schottky-type p-GaN gate HEMTs under a positive gate voltage. TDDB measurements were conducted at various temperatures, revealing an accelerated gate failure rate at lower temperatures. A Weibull distribution analysis was employed to [...] Read more.

The effect of temperature on gate degradation behavior was analyzed in Schottky-type p-GaN gate HEMTs under a positive gate voltage. TDDB measurements were conducted at various temperatures, revealing an accelerated gate failure rate at lower temperatures. A Weibull distribution analysis was employed to predict the 10-year rated gate voltage, showing that the rated voltage at −10 °C is significantly lower than at 60 °C. Furthermore, the derived activation energy of −0.22 eV indicates that gate degradation intensifies in colder environments. Hole accumulation occurring at the p-GaN/AlGaN interface can promote degradation by facilitating electron injection and accelerating defect generation in the presence of strong electric fields. At higher temperatures, hole release mitigates charge accumulation, thereby extending device longevity. These findings highlight the necessity of reliability assessments for p-GaN gate HEMTs suitable for environments with low temperatures, including space and polar environments. Full article

(This article belongs to the Special Issue Recent Advances in GaN Power Devices)

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14 pages, 5030 KiB

Open AccessEditor’s ChoiceArticle

A Linearized Open-Loop MDAC with Memory Effect Compensation Technique for High-Speed Pipelined ADC Stage

by Jie Wu, Qiao Meng, Shaocong Guo, Gaojing Li, Jianxun Shao and Sha Li

Electronics 2025, 14(9), 1753; https://doi.org/10.3390/electronics14091753 - 25 Apr 2025

Viewed by 255

Abstract

This paper presents a prototype open-loop pipelined stage in a 45 nm CMOS process for supporting 1.8 GS/s and 10-bit design specifications of pipelined ADCs. In order to alleviate the severe non-linearity expressed by open-loop MDACs, an innovative current-mode harmonic compensation is proposed [...] Read more.

This paper presents a prototype open-loop pipelined stage in a 45 nm CMOS process for supporting 1.8 GS/s and 10-bit design specifications of pipelined ADCs. In order to alleviate the severe non-linearity expressed by open-loop MDACs, an innovative current-mode harmonic compensation is proposed to provide input related third harmonic terms to cancel non-linearity. In addition, an effective double-sampling scheme is optimized by modifying compensation timing and input of a residual amplifier so that the pipelined stage can be immune to memory effect and improve power efficiency. The memory effect compensation scheme can provide a 21 dB improvement on output SNDR of the double-sampling pipelined stage. The simulation results illustrate that the open-loop pipelined ADC stage achieves an output SNDR of at least 52 dB with 840 mV input amplitude and 240 fF load while consuming only 11.24 mW. Full article

(This article belongs to the Section Microelectronics)

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21 pages, 13080 KiB

Open AccessEditor’s ChoiceArticle

Color Normalization Through a Simulated Color Checker Using Generative Adversarial Networks

by Albert Siré Langa, Ramón Reig Bolaño, Sergi Grau Carrión and Ibon Uribe Elorrieta

Electronics 2025, 14(9), 1746; https://doi.org/10.3390/electronics14091746 - 25 Apr 2025

Viewed by 372

Abstract

Digital cameras often struggle to reproduce the true colors perceived by the human eye due to lighting geometry and illuminant color. This research proposes an innovative approach for color normalization in digital photographs. A machine learning algorithm combined with an external physical color [...] Read more.

Digital cameras often struggle to reproduce the true colors perceived by the human eye due to lighting geometry and illuminant color. This research proposes an innovative approach for color normalization in digital photographs. A machine learning algorithm combined with an external physical color checker achieves color normalization. To address the limitations of relying on a physical color checker, our approach employs a generative adversarial network capable of replicating the color normalization process without the need for a physical reference. This network (GAN-CN-CC) incorporates a custom loss function specifically designed to minimize errors in color generation. The proposed algorithm yields the lowest coefficient of variation in the normalized median intensity (NMI), while maintaining a standard deviation comparable to that of conventional methods such as Gray World and Max-RGB. The algorithm eliminates the need for a color checker in color normalization, making it more practical in scenarios where inclusion of the checker is challenging. The proposed method has been fine-tuned and validated, demonstrating high effectiveness and adaptability. Full article

(This article belongs to the Special Issue Machine Learning in Data Analytics and Prediction)

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22 pages, 46829 KiB

Open AccessEditor’s ChoiceArticle

Waveshift 2.0: An Improved Physics-Driven Data Augmentation Strategy in Fine-Grained Image Classification

by Gent Imeraj and Hitoshi Iyatomi

Electronics 2025, 14(9), 1735; https://doi.org/10.3390/electronics14091735 - 24 Apr 2025

Viewed by 347

Abstract

This paper presents Waveshift Augmentation 2.0 (WS 2.0), an enhanced version of the previously proposed Waveshift Augmentation (WS 1.0), a novel data augmentation technique inspired by light propagation dynamics in optical systems. While WS 1.0 introduced phase-based wavefront transformations under the assumption of [...] Read more.

This paper presents Waveshift Augmentation 2.0 (WS 2.0), an enhanced version of the previously proposed Waveshift Augmentation (WS 1.0), a novel data augmentation technique inspired by light propagation dynamics in optical systems. While WS 1.0 introduced phase-based wavefront transformations under the assumption of an infinitesimally small aperture, WS 2.0 incorporates an additional aperture-dependent hyperparameter that models real-world optical attenuation. This refinement enables broader frequency modulation and greater diversity in image transformations while preserving compatibility with well-established data augmentation pipelines such as CLAHE, AugMix, and RandAugment. Evaluated across a wide range of tasks, including medical imaging, fine-grained object recognition, and grayscale image classification, WS 2.0 consistently outperformed both WS 1.0 and standard geometric augmentation. Notably, when benchmarked against geometric augmentation alone, it achieved average macro-F1 improvements of +1.48 (EfficientNetV2), +0.65 (ConvNeXt), and +0.73 (Swin Transformer), with gains of up to +9.32 points in medical datasets. These results demonstrate that WS 2.0 advances physics-based augmentation by enhancing generalization without sacrificing modularity or preprocessing efficiency, offering a scalable and realistic augmentation strategy for complex imaging domains. Full article

(This article belongs to the Special Issue New Trends in Computer Vision and Image Processing)

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11 pages, 1677 KiB

Open AccessEditor’s ChoiceArticle

A Novel Darlington Structure Power Switch Using a Vacuum Field Emission Transistor

by Yulong Ding, Yanlin Ke, Juncong She, Yu Zhang and Shaozhi Deng

Electronics 2025, 14(9), 1737; https://doi.org/10.3390/electronics14091737 - 24 Apr 2025

Viewed by 250

Abstract

This study proposes a power switch combining a vacuum field emission transistor (VFET) as a controlled transistor with a power bipolar Darlington transistor (DT) as an output transistor, termed the VFET–DT structure. Compared to the MOS–bipolar Darlington power switch, the VFET–DT structure achieves [...] Read more.

This study proposes a power switch combining a vacuum field emission transistor (VFET) as a controlled transistor with a power bipolar Darlington transistor (DT) as an output transistor, termed the VFET–DT structure. Compared to the MOS–bipolar Darlington power switch, the VFET–DT structure achieves an extremely low off-state leakage current and high-voltage withstanding capability due to the field emission mechanism of the VFET. It can also avoid the Miller effect that results from incorporating the load resistance into the feedback loop. The high gain and high-power capacity can be achieved due to the cascade of DT. The device’s typical electrical characteristics were theoretically investigated by simulation. The VFET–DT structure exhibited a high-power capacity of 20 A and 400 V with a minimum conduction voltage drop of 1.316 V and a switching frequency of 100 kHz. The results demonstrated that the combination of a vacuum transistor and a solid-state transistor combines the advantages of both and benefits the performance of the power switch. Full article

(This article belongs to the Special Issue Vacuum Electronics: From Micro to Nano)

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24 pages, 7088 KiB

Open AccessEditor’s ChoiceArticle

Ultra-Lightweight and Highly Efficient Pruned Binarised Neural Networks for Intrusion Detection in In-Vehicle Networks

by Auangkun Rangsikunpum, Sam Amiri and Luciano Ost

Electronics 2025, 14(9), 1710; https://doi.org/10.3390/electronics14091710 - 23 Apr 2025

Viewed by 437

Abstract

With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, [...] Read more.

With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, their heavy computational and power demands often limit real-world deployment. In this paper, we present an optimised IDS based on a Binarised Neural Network (BNN) that employs network pruning to eliminate redundant parameters, achieving up to a 91.07% reduction with only a 0.1% accuracy loss. The proposed approach incorporates a two-stage Coarse-to-Fine (C2F) framework, efficiently filtering normal traffic in the initial stage to minimise unnecessary processing. To assess its practical feasibility, we implement and compare the pruned IDS across CPU, GPU, and FPGA platforms. The experimental results indicate that, with the same model structure, the FPGA-based solution outperforms GPU and CPU implementations by up to 3.7× and 2.4× in speed, while achieving up to 7.4× and 3.8× greater energy efficiency, respectively. Among cutting-edge BNN-based IDSs, our ultra-lightweight FPGA-based C2F approach achieves the fastest average inference speed, showing a 3.3× to 12× improvement, while also outperforming them in accuracy and average F1 score, highlighting its potential for low-power, high-performance vehicle security. Full article

(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)

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25 pages, 6066 KiB

Open AccessEditor’s ChoiceArticle

CNN-Based Fault Classification in Induction Motors Using Feature Vector Images of Symmetrical Components

by Tae-Hong Min, Joong-Hyeok Lee and Byeong-Keun Choi

Electronics 2025, 14(8), 1679; https://doi.org/10.3390/electronics14081679 - 21 Apr 2025

Viewed by 592

Abstract

Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on [...] Read more.

Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on symmetrical components. Three-phase stator current signals are converted into zero, positive, and negative sequence components, and their time-domain feature vectors are systematically integrated into a single image representation. A Convolutional Neural Network (CNN) is then employed for fault classification. The proposed method is model-free, requiring no explicit motor model, which offers greater flexibility compared to model-based techniques. Validation experiments were conducted on a rotor kit test bench under seven different conditions (one healthy condition and six mechanical/electrical fault conditions), with fault severities chosen to reflect practical scenarios. The symmetrical components-based image classification method demonstrated superior performance, achieving 99.76% classification accuracy and outperforming a widely used Short-Time Fourier Transform (STFT)-based spectrogram approach. These findings highlight that integrating all symmetrical component information into one image effectively captures each fault’s distinct behavior, enabling reliable diagnostic outcomes. By leveraging the distinct variations in zero, positive, and negative components under fault conditions, the proposed method offers a powerful, accurate, and non-invasive framework for real-time motor fault diagnosis in industrial applications. Full article

(This article belongs to the Special Issue AI in Signal and Image Processing)

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21 pages, 13497 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral LiDAR for Subsea Exploration: System Design and Performance Evaluation

by Huijing Zhang, Linsheng Chen, Haohao Wu, Mei Zhou, Jiuying Chen, Zhichao Chen, Jian Hu, Yuwei Chen, Jinhu Wang, Yifang Niu, Meisong Liao, Xiaoxing Wang, Wanqiu Xu, Tianxing Wang and Shizi Yu

Electronics 2025, 14(8), 1539; https://doi.org/10.3390/electronics14081539 - 10 Apr 2025

Viewed by 374

Abstract

Hyperspectral LiDAR (HSL) is a promising active detection technique for underwater positioning and remote sensing, enabling the simultaneous acquisition of three-dimensional topographic and spectral information of underwater targets. This study presents an advanced underwater hyperspectral LiDAR (UDHSL) system with a spectral range of [...] Read more.

Hyperspectral LiDAR (HSL) is a promising active detection technique for underwater positioning and remote sensing, enabling the simultaneous acquisition of three-dimensional topographic and spectral information of underwater targets. This study presents an advanced underwater hyperspectral LiDAR (UDHSL) system with a spectral range of 450–700 nm, adjustable spectral bandwidth of 10–300 nm, and tunable repetition frequency of 50 kHz to 1 MHz. The system achieves high precision with a laser divergence angle of ≤1 mrad, pulse width of 7 ns, laser energy of 7.5 µJ, ranging resolution of 1.13 cm and ranging accuracy of 1.02 m@distance of 27 m. Hyperspectral point clouds spanning 11 bands (450–650 nm) are generated during 3D pool experiments. The distance-colored point clouds precisely align with the geometric characteristics of targets, the normalized intensity-colored point clouds across spectral bands exhibit discriminative capabilities for target identification, and the color-composite point clouds approximate the true colors of targets, collectively validating the system’s ability to concurrently acquire spectral and topographic data. These results underscore the potential of this technology for underwater exploration and positioning applications. Full article

(This article belongs to the Special Issue The Application of Lidars in Positioning Systems)

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21 pages, 2649 KiB

Open AccessEditor’s ChoiceArticle

A Novel Approach for Self-Driving Vehicle Longitudinal and Lateral Path-Following Control Using the Road Geometry Perception

by Felipe Barreno, Matilde Santos and Manuel Romana

Electronics 2025, 14(8), 1527; https://doi.org/10.3390/electronics14081527 - 10 Apr 2025

Viewed by 494

Abstract

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the [...] Read more.

This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article

(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)

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14 pages, 7854 KiB

Open AccessEditor’s ChoiceArticle

Adaptive DC-Link Voltage Control for 22 kW, 40 kHz LLC Resonant Converter Considering Low-Frequency Voltage Ripple

by Roland Unruh, Joachim Böcker and Frank Schafmeister

Electronics 2025, 14(8), 1517; https://doi.org/10.3390/electronics14081517 - 9 Apr 2025

Viewed by 425

Abstract

The LLC converter achieves the highest efficiency in resonant operation. Conventionally, the input DC-link voltage is controlled to operate the LLC converter at resonance for the given operating point. However, the DC-link capacitor voltage shows a low-frequency voltage ripple (typically the second harmonic [...] Read more.

The LLC converter achieves the highest efficiency in resonant operation. Conventionally, the input DC-link voltage is controlled to operate the LLC converter at resonance for the given operating point. However, the DC-link capacitor voltage shows a low-frequency voltage ripple (typically the second harmonic of grid frequency) in cascaded converters so that the LLC has to adapt its switching frequency within the grid period. Conventionally, the LLC converter operates 50% of the time above the resonant frequency of 40 kHz and 50% below resonance. Both operating conditions cause additional losses. However, experimental measurements indicate that the below-resonance operation causes significantly higher losses than above-resonance operation due to much higher primary and secondary transformer currents. It is better to increase the DC-link voltage by 30% of the peak-to-peak low-frequency voltage ripple to mostly avoid below-resonance operation (i.e., from 650 V to 680 V in this case). With the proposed control, the LLC converter operates about 75% of time over resonance and only 25% of time below resonance. The overall efficiency increases from 97.66% to 97.7% for the average operating point with an 80% load current. This corresponds to a 2% total loss reduction. Finally, the peak resonance capacitor voltage decreases from 910 V to 790 V (−13%). Full article

(This article belongs to the Special Issue Innovative Technologies in Power Converters, 2nd Edition)

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15 pages, 5428 KiB

Open AccessEditor’s ChoiceArticle

Design and Modeling Guidelines for Auxiliary Voltage Sensing Windings in High-Voltage Transformers and Isolated Converters

by Elinor Ginzburg-Ganz, Dmitry Baimel, Juri Belikov and Yoash Levron

Electronics 2025, 14(8), 1519; https://doi.org/10.3390/electronics14081519 - 9 Apr 2025

Viewed by 294

Abstract

This paper provides guidelines for designing and modeling sensing coils in high-voltage, high-frequency transformers to enable a cost-efficient design of isolated converter topologies. The objective is to design a magnetic structure in which an additional sensing coil, placed on the main transformer, can [...] Read more.

This paper provides guidelines for designing and modeling sensing coils in high-voltage, high-frequency transformers to enable a cost-efficient design of isolated converter topologies. The objective is to design a magnetic structure in which an additional sensing coil, placed on the main transformer, can be used to precisely measure the voltage on the secondary, despite fast changes in the voltage and current. This is usually a challenging task since high-voltage transformers will always require considerable isolation, which will give rise to significant leakage fields, which in turn will distort the measurement, especially at high frequencies. Our main finding is that this problem can be avoided if the sensing winding is carefully routed to maintain a certain ratio between the transformer’s coupling coefficients, which is achieved by placing this winding in an area within the core in which the magnetic field is low. In principle, this leads to a linear relationship between the voltages of the secondary and sensing windings despite non-ideal leakage inductances. The results are demonstrated experimentally using a 10 kW transformer, with 60 kV isolation, demonstrating a coupling coefficient of about 0.99, which reflects an error of less than

1.5 %

in the sensed secondary voltage. Full article

(This article belongs to the Special Issue High-Voltage Technology and Its Applications)

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26 pages, 938 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Personalised Learning with a Context-Aware Intelligent Question-Answering System and Automated Frequently Asked Question Generation

by Eleonora Bernasconi, Domenico Redavid and Stefano Ferilli

Electronics 2025, 14(7), 1481; https://doi.org/10.3390/electronics14071481 - 7 Apr 2025

Viewed by 606

Abstract

The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of [...] Read more.

The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of student questions. This research focuses on developing a context-aware IQAS that leverages advanced Natural Language Processing (NLP) techniques and contextual information, including student learning history and educational content, to provide personalised support. This study also introduces a software tool that utilizes NLP techniques to automatically generate FAQs from educational materials. Employing a hybrid approach combining rule-based and machine learning techniques, the IQAS demonstrated high accuracy in interpreting and responding to a wide range of student queries. The software tool effectively automated the generation of FAQs, creating a valuable resource for personalised learning. The findings suggest that these tools can significantly improve student engagement, motivation, and learning outcomes, highlighting the potential of AI to transform education and pave the way for more personalised, adaptive, and effective learning environments. Full article

(This article belongs to the Special Issue Advances in Natural Language Processing and Their Applications)

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17 pages, 6487 KiB

Open AccessEditor’s ChoiceArticle

A Cost-Effective System for EMG/MMG Signal Acquisition

by Jerzy S. Witkowski and Andrzej Grobelny

Electronics 2025, 14(7), 1468; https://doi.org/10.3390/electronics14071468 - 5 Apr 2025

Viewed by 540

Abstract

This article presents a cost-effective, robust, and reliable system for EMG/MMG (electromyography/mechanomyography). Signals indicating muscle activity have numerous applications and are the subject of many studies. However, acquiring these signals is challenging. Commercial measurement systems are often expensive, limiting their accessibility. Therefore, the [...] Read more.

This article presents a cost-effective, robust, and reliable system for EMG/MMG (electromyography/mechanomyography). Signals indicating muscle activity have numerous applications and are the subject of many studies. However, acquiring these signals is challenging. Commercial measurement systems are often expensive, limiting their accessibility. Therefore, the primary goal of this project was to develop a simple and affordable system for simultaneous EMG and MMG data acquisition, offering efficiency comparable to commercial systems. The system consists of eight EMG/MMG probes, 16-bit analog-to-digital converters with 16 channels, and a microprocessor unit. Despite its multiple components, the system remains simple and user-friendly. This paper describes the construction of the EMG/MMG probe and analyzes the intrinsic noise of the preamplifier, as well as electromagnetic interference, particularly power line noise. The elimination of power line noise was carried out in two stages: first, using techniques known for electromagnetic compatibility (EMC), and second, by implementing a digital filter in the microprocessor system. The proposed solution enables direct data collection from eight EMG/MMG probes using any computer equipped with a USB interface. This interface facilitates both data transmission and power supply, making EMG/MMG data acquisition straightforward and efficient. Full article

(This article belongs to the Section Bioelectronics)

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28 pages, 2365 KiB

Open AccessEditor’s ChoiceArticle

Trustworthiness Optimisation Process: A Methodology for Assessing and Enhancing Trust in AI Systems

by Mattheos Fikardos, Katerina Lepenioti, Dimitris Apostolou and Gregoris Mentzas

Electronics 2025, 14(7), 1454; https://doi.org/10.3390/electronics14071454 - 3 Apr 2025

Viewed by 651

Abstract

The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monitored, and governed. For this reason, trustworthy [...] Read more.

The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monitored, and governed. For this reason, trustworthy AI (TAI) has received increased attention lately, primarily aiming to build trust between humans and AI. Due to the far-reaching socio-technical consequences of AI, organisations and government bodies have already started implementing frameworks and legislation for enforcing TAI, such as the European Union’s AI Act. Multiple approaches have evolved around TAI, covering different aspects of trustworthiness that include fairness, bias, explainability, robustness, accuracy, and more. Moreover, depending on the AI models and the stage of the AI system lifecycle, several methods and techniques can be used for each trustworthiness characteristic to assess potential risks and mitigate them. Deriving from all the above is the need for comprehensive tools and solutions that can help AI stakeholders follow TAI guidelines and adopt methods that practically increase trustworthiness. In this paper, we formulate and propose the Trustworthiness Optimisation Process (TOP), which operationalises TAI and brings together its procedural and technical approaches throughout the AI system lifecycle. It incorporates state-of-the-art enablers of trustworthiness such as documentation cards, risk management, and toolkits to find trustworthiness methods that increase the trustworthiness of a given AI system. To showcase the application of the proposed methodology, a case study is conducted, demonstrating how the fairness of an AI system can be increased. Full article

(This article belongs to the Special Issue Future Development of Trustworthy Artificial Intelligence and Applications)

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30 pages, 13767 KiB

Open AccessEditor’s ChoiceArticle

A Novel Transformerless Soft-Switching Symmetrical Bipolar Power Converter: Analysis, Design, Simulation and Validation

by Cristian Díaz-Martín, Eladio Durán Aranda, Fernando Alves da Silva and Sérgio André

Electronics 2025, 14(7), 1434; https://doi.org/10.3390/electronics14071434 - 2 Apr 2025

Viewed by 394

Abstract

In order to obtain acceptable efficiencies, hard-switching techniques and the converters that implement them must operate at relatively low frequencies (tens of kilohertz), which translate into converters of large size, weight, and volume, and therefore higher cost. To improve these characteristics, this work [...] Read more.

In order to obtain acceptable efficiencies, hard-switching techniques and the converters that implement them must operate at relatively low frequencies (tens of kilohertz), which translate into converters of large size, weight, and volume, and therefore higher cost. To improve these characteristics, this work introduces a new transformerless MHz-range DC–DC converter that provides symmetrical bipolar outputs. The developed topology uses a single grounded switch, achieves soft switching (ZVS) over a wide load range, and does not require the use of floating or isolated controllers, reducing cost, size, and complexity. The output voltages are self-regulated to maintain the same value, ensuring balanced bipolar operation. A comprehensive analysis, design, sizing, simulation, implementation and testing are provided on a 150 W prototype operating at a switching frequency of 1 MHz, with step-up and step-down capability and implemented with GaN FET. The evaluated configuration shows an efficiency close to 90% and high power density, making it suitable for compact designs in a variety of applications requiring reliable power management and high efficiency such as lighting, electric vehicles, or auxiliary power supplies. Full article

(This article belongs to the Special Issue Section Collection Series: New Horizons and Recent Advances in Power Electronics)

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23 pages, 14314 KiB

Open AccessEditor’s ChoiceArticle

RGB-D Camera-Based Human Head Motion Detection and Recognition System for Positron Emission Tomography Scanning

by Yixin Shan, Zikun Lu, Zhe Sun, Hao Liu, Jiangchang Xu, Yixing Sun and Xiaojun Chen

Electronics 2025, 14(7), 1441; https://doi.org/10.3390/electronics14071441 - 2 Apr 2025

Viewed by 476

Abstract

Positron emission tomography (PET) is one of the most advanced imaging diagnostic devices in the medical field, playing a crucial role in tumor diagnosis and treatment. However, patient motion during scanning can lead to motion artifacts, which affect diagnostic accuracy. This study aims [...] Read more.

Positron emission tomography (PET) is one of the most advanced imaging diagnostic devices in the medical field, playing a crucial role in tumor diagnosis and treatment. However, patient motion during scanning can lead to motion artifacts, which affect diagnostic accuracy. This study aims to develop a head motion monitoring system to identify and select images with excessive motion and corresponding periods. The system, based on an RGB-D structured-light camera, implements facial feature point detection, 3D information acquisition, and head motion monitoring, along with a user interaction software. Through phantom experiments and volunteer experiments, the system’s performance was tested under various conditions, including stillness, pitch movement, yaw movement, and comprehensive movement. Experimental results show that the system’s translational error is less than 2.5 mm, rotational error is less than 2.0°, and it can output motion monitoring results within 10 s after the PET scanning, meeting clinical accuracy requirements and showing significant potential for clinical application. Full article

(This article belongs to the Special Issue Medical Robots: Safety, Performance and Improvement)

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26 pages, 8883 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Machine Learning Techniques in VSLAM for Robust Autonomous Unmanned Aerial Vehicle Navigation

by Hussam Rostum and József Vásárhelyi

Electronics 2025, 14(7), 1440; https://doi.org/10.3390/electronics14071440 - 2 Apr 2025

Viewed by 446

Abstract

This study introduces a visual SLAM real-time system designed for small indoor environments. The system demonstrates resilience against significant motion clutter and supports wide-baseline loop closing, re-localization, and automatic initialization. Leveraging state-of-the-art algorithms, the approach presented in this article utilizes adapted Oriented FAST [...] Read more.

This study introduces a visual SLAM real-time system designed for small indoor environments. The system demonstrates resilience against significant motion clutter and supports wide-baseline loop closing, re-localization, and automatic initialization. Leveraging state-of-the-art algorithms, the approach presented in this article utilizes adapted Oriented FAST and Rotated BRIEF features for tracking, mapping, re-localization, and loop closing. In addition, the research uses an adaptive threshold to find putative feature matches that provide efficient map initialization and accurate tracking. The assignment is to process visual information from the camera of a DJI Tello drone for the construction of an indoor map and the estimation of the trajectory of the camera. In a ’survival of the fittest’ style, the algorithms selectively pick adaptive points and keyframes for reconstruction. This leads to robustness and a concise traceable map that develops as scene content emerges, making lifelong operation possible. The results give an improvement in the RMSE for the adaptive ORB algorithm and the adaptive threshold (3.280). However, the standard ORB algorithm failed to achieve the mapping process. Full article

(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)

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22 pages, 14590 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Carrier-Based Implementation of SVPWM for a Three-Level Simplified Neutral Point Clamped Inverter with XOR Logic Gates

by Zifan Lin, Wenxiang Du, Yang Bai, Herbert Ho Ching Iu, Tyrone Fernando and Xinan Zhang

Electronics 2025, 14(7), 1408; https://doi.org/10.3390/electronics14071408 - 31 Mar 2025

Viewed by 416

Abstract

The three-level simplified neutral point clamped (3L-SNPC) inverter has received increasing attention in recent years due to its potential applications in electrical drives and smart grids with renewable energy integration. However, most existing research has primarily focused on control development, with limited studies [...] Read more.

The three-level simplified neutral point clamped (3L-SNPC) inverter has received increasing attention in recent years due to its potential applications in electrical drives and smart grids with renewable energy integration. However, most existing research has primarily focused on control development, with limited studies investigating modulation strategies or analyzing inverter losses under varying operating conditions. These aspects are critical for practical industrial applications. To address this gap, this paper proposes a novel carrier-based space vector pulse width modulation (CB-SVPWM) strategy for the 3L-SNPC inverter, aimed at simplifying PWM implementation and reducing cost. The proposed modulation strategy is experimentally evaluated by comparing inverter losses and total harmonic distortion with those of the conventional three-level neutral point clamped (3L-NPC) inverter under an equivalent carrier-based modulation scheme. A comprehensive comparative analysis is conducted across the full modulation range to demonstrate the effectiveness of the proposed approach, achieving a 13.2% reduction in total power loss, a 33.6% improvement in execution time, and maintaining a comparable weighted total harmonic distortion (WTHD) with a deviation within 0.04% of the conventional 3L-NPC inverter. Full article

(This article belongs to the Special Issue Control and Optimization of Power Converters and Drives)

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22 pages, 10075 KiB

Open AccessEditor’s ChoiceArticle

Open Data-Driven Reconstruction of Power Distribution Grid: A Land Use-Based Approach

by Mohannad Babli, Tobias Gebhard and Eva Brucherseifer

Electronics 2025, 14(7), 1414; https://doi.org/10.3390/electronics14071414 - 31 Mar 2025

Viewed by 411

Abstract

Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring [...] Read more.

Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring approximate data for urgent decisions. However, traditional grid reconstruction approaches often rely on incomplete data, expert knowledge, or closed datasets, limiting their utility during emergencies. This study proposes a novel automated method for reconstructing medium-voltage (MV) power grids. The novelty of the proposed method lies in combining OpenStreetMap energy and land-use data in a unified and automated framework, thereby reducing the need for expert input. The proposed method employs a systematic aggregation of data, an estimation of energy demand, and the application of algorithmic techniques to generate synthetic MV grid models that functionally represent real networks, capturing key topological features. The resulting outputs include visual representations to support decision-makers in simulating "what-if” scenarios and ensuring rapid operational awareness. In a step toward eliminating reliance on proprietary data, our approach broadens access to critical infrastructure insights across diverse urban contexts, contributing to critical infrastructure resilience and potentially supporting both energy system research and crisis management. A case study demonstrates that a medium-sized city’s MV grid can be reconstructed in minutes without expert knowledge or geographically constrained datasets, underscoring the method’s deployment potential and practical value for emergency scenarios. Full article

(This article belongs to the Special Issue Unlocking Data’s Potential: Artificial Intelligence and Visual Analytics in the Modern Age)

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28 pages, 10273 KiB

Open AccessEditor’s ChoiceArticle

Design and Analysis of 15-Level and 25-Level Asymmetrical Multilevel Inverter Topologies

by Prasad Kumar Bandahalli Mallappa, Guillermo Velasco-Quesada and Herminio Martínez-García

Electronics 2025, 14(7), 1416; https://doi.org/10.3390/electronics14071416 - 31 Mar 2025

Viewed by 395

Abstract

This study aims to minimize component requirements by presenting a novel topology for a single-phase 15-level asymmetrical multilevel inverter. Utilizing an H-bridge configuration, the proposed design achieves a maximum 15-level output voltage using asymmetrical DC sources. The initial 15-level inverter structure is further [...] Read more.

This study aims to minimize component requirements by presenting a novel topology for a single-phase 15-level asymmetrical multilevel inverter. Utilizing an H-bridge configuration, the proposed design achieves a maximum 15-level output voltage using asymmetrical DC sources. The initial 15-level inverter structure is further enhanced to support a 25-level variant suitable for renewable energy applications, effectively reducing system costs and size. However, the increased component count in multilevel inverters poses reliability challenges, particularly concerning total harmonic distortion reduction, which remains a focal point for researchers. Various parameters, including total standing voltage, multilevel inverter cost function, and power loss, are analyzed for both the proposed 15-level and the expanded 25-level multilevel inverters. This study contributes a new topology for a single-phase 15-level asymmetrical multilevel inverter, optimizing component usage and paving the way for renewable energy integration. Despite the advantages of multilevel inverters, addressing reliability concerns related to total harmonic distortion reduction remains crucial for future advancements in this domain. Full article

(This article belongs to the Special Issue Power Electronics and Renewable Energy System)

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17 pages, 4274 KiB

Open AccessEditor’s ChoiceArticle

Quantifying the Benefits of Hybrid Energy Harvesting from Natural Sources

by Antonietta Simone, Pasquale Marino, Roberto Greco and Alessandro Lo Schiavo

Electronics 2025, 14(7), 1400; https://doi.org/10.3390/electronics14071400 - 30 Mar 2025

Viewed by 336

Abstract

The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of [...] Read more.

The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of hybrid energy harvesters, which utilize multiple energy sources, compared to single-source harvesters. The analysis leverages a real-world dataset collected from a meteorological station in Cervinara, Southern Italy. The measured data are processed to estimate the energy that can be recovered from solar, wind, and rain sources using energy harvesters designed to supply low-power electronic devices. The available energy serves as the basis for optimizing the sizing of a hybrid energy harvester that effectively integrates the aforementioned energy sources. The system sizing, carried out under the constraint of ensuring a continuous and uninterrupted power supply to the load, quantifies the benefits of using a hybrid harvester over a single-source harvester. The results show that one of the main advantages of the hybrid solution is the reduction in the size of the storage device, enabling the replacement of rechargeable batteries with supercapacitors, which offer both environmental and reliability benefits. Full article

(This article belongs to the Special Issue New Applications of Wireless Sensor Networks: Focus on Structural Health Monitoring and Condition Monitoring of Industrial Machinery)

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47 pages, 7373 KiB

Open AccessEditor’s ChoiceArticle

AI and Evolutionary Computation for Intelligent Aviation Health Monitoring

by Igor Kabashkin

Electronics 2025, 14(7), 1369; https://doi.org/10.3390/electronics14071369 - 29 Mar 2025

Viewed by 501

Abstract

This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework [...] Read more.

This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework employs deep learning models for fault detection, achieving about 97% classification accuracy with an F1-score of 0.97, while remaining useful life prediction yields an R² score of 0.89 with a mean absolute error of 9.8 h. Evolutionary algorithms optimize maintenance strategies, reducing downtime and costs by up to 22% compared to traditional methods. The methodology includes robust data processing protocols, feature engineering techniques, and a modular system architecture supporting real-time monitoring and decision-making. Simulation experiments demonstrate the framework’s effectiveness in balancing maintenance objectives while maintaining high reliability. The research provides practical implementation guidelines and addresses key challenges in computational efficiency, data quality, and system integration. The results show significant improvements in maintenance planning efficiency and system reliability compared to traditional approaches. The framework’s modular design enables scalability and adaptation to various aircraft systems, offering broader applications in complex technical system maintenance. Full article

(This article belongs to the Special Issue Advancements in AI-Driven Cybersecurity and Securing AI Systems)

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27 pages, 10156 KiB

Open AccessEditor’s ChoiceArticle

A Distributed Time-of-Flight Sensor System for Autonomous Vehicles: Architecture, Sensor Fusion, and Spiking Neural Network Perception

by Edgars Lielamurs, Ibrahim Sayed, Andrejs Cvetkovs, Rihards Novickis, Anatolijs Zencovs, Maksis Celitans, Andis Bizuns, George Dimitrakopoulos, Jochen Koszescha and Kaspars Ozols

Electronics 2025, 14(7), 1375; https://doi.org/10.3390/electronics14071375 - 29 Mar 2025

Viewed by 630

Abstract

Mechanically scanning LiDAR imaging sensors are abundantly used in applications ranging from basic safety assistance to high-level automated driving, offering excellent spatial resolution and full surround-view coverage in most scenarios. However, their complex optomechanical structure introduces limitations, namely limited mounting options and blind [...] Read more.

Mechanically scanning LiDAR imaging sensors are abundantly used in applications ranging from basic safety assistance to high-level automated driving, offering excellent spatial resolution and full surround-view coverage in most scenarios. However, their complex optomechanical structure introduces limitations, namely limited mounting options and blind zones, especially in elongated vehicles. To mitigate these challenges, we propose a distributed Time-of-Flight (ToF) sensor system with a flexible hardware–software architecture designed for multi-sensor synchronous triggering and fusion. We formalize the sensor triggering, interference mitigation scheme, data aggregation and fusion procedures and highlight challenges in achieving accurate global registration with current state-of-the-art methods. The resulting surround view visual information is then applied to Spiking Neural Network (SNN)-based object detection and probabilistic occupancy grid mapping (OGM) for enhanced environmental awareness. The proposed system is demonstrated on a test vehicle, achieving coverage of blind zones in a range of 0.5–6 m with a scalable and reconfigurable sensor mounting setup. Using seven ToF sensors, we can achieve a 10 Hz synchronized frame rate, with a 360° point cloud registration and fusion latency below 40 ms. We collected real-world driving data to evaluate the system, achieving 65% mean Average Precision (mAP) in object detection with our SNN. Overall, this work presents a replacement or addition to LiDAR in future high-level automation tasks, offering improved coverage and system integration. Full article

(This article belongs to the Section Electrical and Autonomous Vehicles)

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18 pages, 10434 KiB

Open AccessEditor’s ChoiceArticle

Frequency-Domain Masking and Spatial Interaction for Generalizable Deepfake Detection

by Xinyu Luo and Yu Wang

Electronics 2025, 14(7), 1302; https://doi.org/10.3390/electronics14071302 - 26 Mar 2025

Viewed by 1139

Abstract

Over the past few years, the rapid development of deepfake technology based on generative models has posed a significant threat to the field of information security. Despite the notable progress in deepfake-detection methods based on the spatial domain, the detection capability of the [...] Read more.

Over the past few years, the rapid development of deepfake technology based on generative models has posed a significant threat to the field of information security. Despite the notable progress in deepfake-detection methods based on the spatial domain, the detection capability of the models drops sharply when dealing with low-quality images. Moreover, the effectiveness of detection relies on the realism of the forged images and the specific traces inherent to particular forgery techniques, which often weakens the models’ generalization ability. To address this issue, we propose the Frequency-Domain Masking and Spatial Interaction (FMSI) model. The FMSI model innovatively introduces masked image modeling in frequency-domain processing. This prevents the model from focusing too much on specific frequency-domain features and enhances its generalization ability. We design a high-frequency information convolution module for spatial and channel dimensions to help the model capture subtle forgery traces more effectively. Also, we creatively design a dual stream architecture for frequency-domain and spatial-domain information interaction and overcome single-domain detection limitations. Our model is tested on three public benchmark datasets (FaceForensics++, Celeb-DF, and WildDeepfake) through intra-domain and cross-domain experiments. The detection and generalization capabilities of the model are evaluated using the AUC and EER metrics. The experimental results demonstrate that our model not only possesses high detection capability but also exhibits excellent generalization ability. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Image Classification)

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26 pages, 13614 KiB

Open AccessEditor’s ChoiceArticle

Through-Hole Buck Converters for Fast Prototyping: A Comparative Study

by Jose Vicente Muñoz, Luis M. Nieto-Nieto, Luis Pulido-Lopez, Juan D. Aguilar-Peña and Angel Gaspar Gonzalez-Rodriguez

Electronics 2025, 14(7), 1273; https://doi.org/10.3390/electronics14071273 - 24 Mar 2025

Viewed by 360

Abstract

The increasing demand for emerging applications like IoT or drones has boosted the interest of industry and academia in DC-DC converters. Due to their high performance, non-isolated buck DC-DC converters have become one of the most common configurations for covering the power demand [...] Read more.

The increasing demand for emerging applications like IoT or drones has boosted the interest of industry and academia in DC-DC converters. Due to their high performance, non-isolated buck DC-DC converters have become one of the most common configurations for covering the power demand of portable devices. The current trend focuses on manufacturing these integrated circuits (IC) using surface-mount technology packaging. However, this technology presents disadvantages compared to through-hole devices in pursuing a quick functional circuit. This work aims to guide designers in choosing the most suitable integrated THT buck converter to develop a fast prototype. A comparative market analysis was conducted considering five integrated chip manufacturers to identify the most adequate ICs for this purpose. Then, a comparative experimental study focused on the buck converter LM2576-ADJ by Texas Instruments was carried out. The analysis aims to determine the performance of this IC mounted in a breadboard and stripboard compared to a demonstration board based on SMT technology provided by the manufacturer. Despite their shortcomings, these quick implementations performed remarkably well regarding, among others, line regulation and load regulation (0.37% and –0.33%, respectively), as well as efficiency (up to 79.9%), which indicates that their electrical response was not compromised. Full article

(This article belongs to the Special Issue Power Electronics and Its Applications in Power System)

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12 pages, 4320 KiB

Open AccessEditor’s ChoiceArticle

Two-Dimensional Fin-Shaped Carbon Nanotube Field Emission Structure with High Current Density Capability

by Xiaoyu Qin, Yulong Ding, Jun Jiang, Junzhong Liang, Yanlin Ke, Juncong She, Yu Zhang and Shaozhi Deng

Electronics 2025, 14(7), 1268; https://doi.org/10.3390/electronics14071268 - 24 Mar 2025

Viewed by 332

Abstract

A vacuum electron device requires a high-performance electron source that provides high current and current density. A carbon nanotube (CNT) field emission cold cathode is the optimal choice. To achieve its higher emission current capacity, its macroscale and microscale structures should be combined. [...] Read more.

A vacuum electron device requires a high-performance electron source that provides high current and current density. A carbon nanotube (CNT) field emission cold cathode is the optimal choice. To achieve its higher emission current capacity, its macroscale and microscale structures should be combined. Here, a two-dimensional fin-shaped CNT field emission structure is proposed, integrating a macroscale CNT fin with billions of nanoscale nanotubes. The fin contributes two-dimensional heat dissipation paths, and the nanotubes provide a high field enhancement factor, both of which enhance the high-current field emission characteristics. A model combining macro- and microstructures was simulated to optimize the structure and fin-shaped array parameters. The calculation of the field enhancement factor of the compound structure is proposed. It was also determined that the fin-shaped array configuration can be densely arranged without field screen effects, thereby enhancing the emission area efficiency. The fin-shaped CNT emitter and array emitters with different parameters were fabricated by laser ablation, which demonstrated superior field emission characteristics. A 16.55 mA pulsing emission current, 1103.33 A/cm² current density, and 6.13% current fluctuation were achieved in a single fin-shaped CNT emitter. An 87.29 mA pulsing emission current, 0.349 A/cm² current density, and 1.9% current fluctuation were achieved in a fin-shaped CNT array. The results demonstrate that the high-current field emission electron source can be realized in a well-designed emission structure that bridges the nanoscale emitter and macroscale structure. Full article

(This article belongs to the Special Issue Vacuum Electronics: From Micro to Nano)

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15 pages, 3813 KiB

Open AccessEditor’s ChoiceArticle

Dual-Gate Metal-Oxide-Semiconductor Transistors: Nanoscale Channel Length Scaling and Performance Optimization

by Huajian Zheng, Zhuohang Ye, Baiquan Liu, Mengye Wang, Li Zhang and Chuan Liu

Electronics 2025, 14(7), 1257; https://doi.org/10.3390/electronics14071257 - 22 Mar 2025

Viewed by 552

Abstract

Dual-gate metal-oxide-semiconductor transistors have attracted considerable interest due to their high threshold voltage control capability, higher drain current, and the ability to alleviate the impact of carrier surface scattering at the channel/dielectric interface. However, their applications in the monolithic integration of scaled devices [...] Read more.

Dual-gate metal-oxide-semiconductor transistors have attracted considerable interest due to their high threshold voltage control capability, higher drain current, and the ability to alleviate the impact of carrier surface scattering at the channel/dielectric interface. However, their applications in the monolithic integration of scaled devices encounter challenges stemming from the interaction between the pre-treated channel layer and its covering dielectric. Here, we demonstrate the successful realization of a scaled back-end-of-line (BEOL) compatible dual-gate indium–gallium–zinc oxide (IGZO) transistor with a channel length (L_ch) scaled down to 150 nm and a channel thickness (T_ch) of 4.2 nm. After precisely adjusting the metal ratio to In_0.24Ga_0.58Zn_0.18O and employing O₃ as an oxygen precursor for the deposition of Al₂O₃ as the top-gate dielectric layer, a high maximum current of 1.384 mA was attained under top-gate control, while a high current of 1.956 mA was achieved under bottom-gate control. Additionally, a high current on/off ratio (I_on/off > 10⁹) was achieved for the dual gate. Careful calculations reveal that the field-effective mobility (μ_eff) reaches 11.68 cm²V⁻¹s⁻¹ under top-gate control and 22.46 cm²V⁻¹s⁻¹ under bottom-gate control. We demonstrate excellent dual-gate low-voltage modulation performance, with a high current switch ratio of 3 × 10⁵ at L_ch = 300 nm and 2 × 10⁴ at L_ch = 150 nm achieved by only 1 V modulation voltage, accompanied by a normalized current variation higher than 10⁶. Overall, our devices show the remarkable electrical performance characteristics, highlighting their potential applications in high-performance electronic circuits. Full article

(This article belongs to the Special Issue Optoelectronics, Energy and Integration)

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18 pages, 1584 KiB

Open AccessEditor’s ChoiceArticle

Robust Sensorless PMSM Control with Improved Back-EMF Observer and Adaptive Parameter Estimation

by Ayyoub Zeghlache, Ali Djerioui, Hemza Mekki, Samir Zeghlache and Mohamed Fouad Benkhoris

Electronics 2025, 14(7), 1238; https://doi.org/10.3390/electronics14071238 - 21 Mar 2025

Viewed by 953

Abstract

This paper presents an enhanced sensorless control strategy for permanent magnet synchronous motors (PMSMs) by improving back-electromotive force (back-EMF) estimation and control robustness. An improved back-EMF extended state observer (ESO) is proposed, incorporating back-EMF differentiation to compensate for DC position error without requiring [...] Read more.

This paper presents an enhanced sensorless control strategy for permanent magnet synchronous motors (PMSMs) by improving back-electromotive force (back-EMF) estimation and control robustness. An improved back-EMF extended state observer (ESO) is proposed, incorporating back-EMF differentiation to compensate for DC position error without requiring an increased observer bandwidth. Furthermore, an ESO-based quadrature phase-locked loop (QPLL) is developed to improve position tracking accuracy and enhance the robustness of the speed loop sliding mode controller (SMC) against unknown disturbances. To address parameter uncertainties in the back-EMF observer and current controller, a recursive least squares (RLSs) algorithm with an adaptive forgetting factor is introduced, providing a balance between adaptation speed and noise suppression. Simulation results validate the proposed approach, demonstrating improved estimation accuracy, disturbance rejection, and overall robustness in sensorless PMSM control. Full article

(This article belongs to the Special Issue Power Electronics in Renewable Systems)

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23 pages, 1716 KiB

Open AccessEditor’s ChoiceArticle

Knowledge Translator: Cross-Lingual Course Video Text Style Transform via Imposed Sequential Attention Networks

by Jingyi Zhang, Bocheng Zhao, Wenxing Zhang and Qiguang Miao

Electronics 2025, 14(6), 1213; https://doi.org/10.3390/electronics14061213 - 19 Mar 2025

Cited by 1 | Viewed by 334

Abstract

Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different [...] Read more.

Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different countries and regions. However, current MOOC video processing solutions rely excessively on manual operations, resulting in low efficiency and difficulty in meeting the urgent requirement for large-scale content translation. Key technical challenges include the accurate localization of embedded text in complex video frames, maintaining style consistency across languages, and preserving text readability and visual quality during translation. Existing methods often struggle with handling diverse text styles, background interference, and language-specific typographic variations. In view of this, this paper proposes an innovative cross-language style transfer algorithm that integrates advanced techniques such as attention mechanisms, latent space mapping, and adaptive instance normalization. Specifically, the algorithm first utilizes attention mechanisms to accurately locate the position of each text in the image, ensuring that subsequent processing can be targeted at specific text areas. Subsequently, by extracting features corresponding to this location information, the algorithm can ensure accurate matching of styles and text features, achieving an effective style transfer. Additionally, this paper introduces a new color loss function aimed at ensuring the consistency of text colors before and after style transfer, further enhancing the visual quality of edited images. Through extensive experimental verification, the algorithm proposed in this paper demonstrated excellent performance on both synthetic and real-world datasets. Compared with existing methods, the algorithm exhibited significant advantages in multiple image evaluation metrics, and the proposed method achieved a 2% improvement in the FID metric and a 20% improvement in the IS metric on relevant datasets compared to SOTA methods. Additionally, both the proposed method and the introduced dataset, PTTEXT, will be made publicly available upon the acceptance of the paper. For additional details, please refer to the project URL, which will be made public after the paper has been accepted. Full article

(This article belongs to the Special Issue Applications of Computational Intelligence, 3rd Edition)

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13 pages, 2923 KiB

Open AccessEditor’s ChoiceArticle

Programmable Gain Amplifier with Programmable Bandwidth for Ultrasound Imaging Application

by István Kovács, Paul Coste and Marius Neag

Electronics 2025, 14(6), 1186; https://doi.org/10.3390/electronics14061186 - 18 Mar 2025

Viewed by 439

Abstract

This paper presents a low-power, fully differential, programmable gain amplifier (PGA) for ultrasound receiver analog front-ends (AFE). It consists of a programmable attenuator implemented by a capacitive voltage divider and a closed-loop amplifier based on a differential difference amplifier (DDA). A suitable sizing [...] Read more.

This paper presents a low-power, fully differential, programmable gain amplifier (PGA) for ultrasound receiver analog front-ends (AFE). It consists of a programmable attenuator implemented by a capacitive voltage divider and a closed-loop amplifier based on a differential difference amplifier (DDA). A suitable sizing strategy provides orthogonal control over gain and bandwidth. The PGA was designed using a standard 180 nm CMOS process. The gain value can be set between −18 dB and +20 dB in 2 dB steps; the bandwidth can be programmed independently of gain, to values from 5 MHz to 20 MHz, in 5 MHz steps; it draws 600 µA from a 1.8 V supply line. It achieves a differential output swing of 0.8 V peak-to-peak differential with no more than 1.7% total harmonic distortion (THD) and an input-referred noise density of 22 nV/√Hz at 10 MHz, measured at the gain of 20 dB. The PGA exhibits high input impedance and low output resistance for easy integration within the AFE signal chain. The digitally controlled gain and bandwidth make this PGA suitable for ultrasound imaging applications requiring precise time gain compensation and adjustable frequency response and/or additional anti-aliasing filtering. Full article

(This article belongs to the Special Issue Innovations in Digital and Analog Electronic Systems for Next-Generation Devices)

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23 pages, 4940 KiB

Open AccessEditor’s ChoiceArticle

A Wearable Device Employing Biomedical Sensors for Advanced Therapeutics: Enhancing Stroke Rehabilitation

by Gabriella Spinelli, Kimon Panayotou Ennes, Laura Chauvet, Cherry Kilbride, Marvellous Jesutoye and Victor Harabari

Electronics 2025, 14(6), 1171; https://doi.org/10.3390/electronics14061171 - 17 Mar 2025

Viewed by 1285

Abstract

Stroke is a leading cause of disability worldwide. The long-term effects of a stroke depend on the location and size of the affected brain area, resulting in diverse disabilities and experiences for survivors. More than 70% of people experiencing stroke suffer upper-limb dysfunction, [...] Read more.

Stroke is a leading cause of disability worldwide. The long-term effects of a stroke depend on the location and size of the affected brain area, resulting in diverse disabilities and experiences for survivors. More than 70% of people experiencing stroke suffer upper-limb dysfunction, which can significantly limit independence in daily life. The growing strain on national healthcare resources, coupled with the rising demand for personalised, home-based rehabilitation, along with increased familiarity with digital technologies, has set the stage for developing an advanced therapeutics system consisting of a wearable solution aimed at complementing current stroke rehabilitation to enhance recovery outcomes. Through a user-centred approach, supported by primary and secondary research, this study has developed an advanced prototype integrating electromyography smart sensors, functional electrical stimulation, and virtual reality technologies in a closed-loop system that is capable of supporting personalised recovery journeys. The outcome is a more engaging and accessible rehabilitation experience, designed and evaluated through the participation of stroke survivors. This paper presents the design of the therapeutic platform, feedback from stroke survivors, and considerations regarding the integration of the proposed technology across the stroke pathway, from early days in a hospital to later stage rehabilitation in the community. Full article

(This article belongs to the Special Issue Advances in AI, IoT and Smart Sensors for Digital Healthcare Applications)

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16 pages, 8381 KiB

Open AccessEditor’s ChoiceArticle

DJPETE-SLAM: Object-Level SLAM System Based on Distributed Joint Pose Estimation and Texture Editing

by Chaofeng Yuan, Dan Wang, Zhi Li, Yuelei Xu and Zhaoxiang Zhang

Electronics 2025, 14(6), 1181; https://doi.org/10.3390/electronics14061181 - 17 Mar 2025

Viewed by 368

Abstract

Object-level SLAM is a new development direction in SLAM technology. To better understand the scene, it not only focuses on building an environmental map and robot localization but also emphasizes identifying, tracking, and constructing specific objects in the environment. To address the issues [...] Read more.

Object-level SLAM is a new development direction in SLAM technology. To better understand the scene, it not only focuses on building an environmental map and robot localization but also emphasizes identifying, tracking, and constructing specific objects in the environment. To address the issues of localization and pose estimation caused by spatial geometric feature distortion of objects in complex application scenarios, we propose a distributed joint pose estimation optimization method. This method, based on globally dense fused features, provides accurate global feature representation and employs an iterative optimization algorithm within the algorithm framework for pose refinement. Simultaneously, it completes visual localization and object state optimization through a joint factor graph algorithm. Finally, by employing parallel processing, it achieves precise optimization of localization and object pose, effectively solving the optimization error drift problem and realizing accurate visual localization and object pose estimation. Full article

(This article belongs to the Special Issue Point Cloud-Based 3D Reconstruction and Visualization)

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16 pages, 9045 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Stabilization of Signal Decomposition Based on Frequency Entrainment Phenomena

by Keina Kitaura, Takashi Kusaka, Koji Shimatani and Takayuki Tanaka

Electronics 2025, 14(6), 1163; https://doi.org/10.3390/electronics14061163 - 16 Mar 2025

Viewed by 393

Abstract

With advancements in the miniaturization and lightweight design of computers and electronic devices, wearable sensors are now widely utilized in fields such as healthcare and medicine. Signals obtained from wearable sensors often contain a mix of biological signals and noise. Typically, wearable sensor [...] Read more.

With advancements in the miniaturization and lightweight design of computers and electronic devices, wearable sensors are now widely utilized in fields such as healthcare and medicine. Signals obtained from wearable sensors often contain a mix of biological signals and noise. Typically, wearable sensor measurements focus on a single signal of interest (SoI), treating other signals as noise. While methods for separating multiple signals exist, the stable tracking of frequency variations during signal separation remains an unresolved challenge. Biological signal and human motion measurements often face issues such as noise, temporal disconnections, dropouts, and frequency variations. To address these challenges, we developed a method that can stably separate and extract SoI from measurement data. We demonstrated the effectiveness of the proposed method through simulations replicating common measurement issues. By applying the method, we show that SoI frequency estimates can be obtained with a high accuracy. Furthermore, we confirm that the method can separate multiple SoIs from a single measurement dataset, highlighting its utility. Finally, we validate that the proposed method can reliably extract multiple SoIs, such as heart rate, walking rhythm, and breathing rate, from actual data measured using stretch sensors, achieving results consistent with simulations. Full article

(This article belongs to the Special Issue Wearable Device Design and Its Latest Applications)

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13 pages, 2536 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Image Classification in Memristor-Based Neural Networks: A Comparative Study of Software and Hardware Models Using RRAM Crossbars

by Hassen Aziza

Electronics 2025, 14(6), 1125; https://doi.org/10.3390/electronics14061125 - 12 Mar 2025

Viewed by 837

Abstract

Vector–matrix multiplication (VMM), which dominates the computational workload in neural networks, accounts for over 99% of all operations, particularly in Convolutional Neural Networks (CNNs). These operations, consisting of multiply-and-accumulate (MAC) functions, are straightforward but demand massive parallelism, often involving billions of operations per [...] Read more.

Vector–matrix multiplication (VMM), which dominates the computational workload in neural networks, accounts for over 99% of all operations, particularly in Convolutional Neural Networks (CNNs). These operations, consisting of multiply-and-accumulate (MAC) functions, are straightforward but demand massive parallelism, often involving billions of operations per layer. This computational demand negatively affects processing time, energy consumption, and memory bandwidth due to frequent external memory access. To efficiently address these challenges, this paper investigates the implementation of a full neural network for image classification, using TensorFlow as a software baseline, and compares it with a hardware counterpart mapped onto resistive RAM-based crossbar arrays, a practical implementation of the memristor concept. By leveraging the inherent ability of RRAM crossbars to perform VMMs in a single step, we demonstrate how RRAM-based neural networks can achieve efficient in-memory analog computing. To ensure realistic and practical results, the hardware implemented utilizes RRAM memory cells characterized through silicon measurements. Furthermore, the design exclusively considers positive weights and biases to minimize the area overhead, resulting in a lightweight hardware solution. This approach achieves an energy consumption of 190 fJ/MAC operation for the crossbar array, highlighting its efficiency in power-constrained applications despite a drop in the prediction confidence of 27.5% compared to the software approach. Full article

(This article belongs to the Special Issue Intelligent Computing Technology Based on New Types of Memristors)

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16 pages, 4985 KiB

Open AccessEditor’s ChoiceArticle

Maximum Harvesting Power Algorithm in Magnetic Energy Harvester Considering Different Temperatures

by Yujun Shin and Bumjin Park

Electronics 2025, 14(6), 1085; https://doi.org/10.3390/electronics14061085 - 10 Mar 2025

Viewed by 660

Abstract

A major challenge for practical magnetic energy harvesting (MEH) applications is achieving stable harvested power with high power density under a wide range of temperature variation. The amount of power harvested from the MEH is sensitive to ambient temperature because the characteristics of [...] Read more.

A major challenge for practical magnetic energy harvesting (MEH) applications is achieving stable harvested power with high power density under a wide range of temperature variation. The amount of power harvested from the MEH is sensitive to ambient temperature because the characteristics of the magnetic material are greatly affected by temperature. From a practical point of view, previous studies have limitations because they do not consider thermal effects at all. In this paper, a novel control algorithm form maximum harvesting power in MEH is proposed by considering dynamic changes in temperature for the first time. In order to tackle this problem, a temperature-dependent B-H curve model is proposed, which considers the effect of temperature variation on the magnetic core. This study is the first to integrate thermal effects at the design stage of MEH. Theoretical analysis using the proposed B-H curve model demonstrates that the nonlinear behavior of magnetic materials can be accurately predicted under varying temperature conditions. Based on the above analysis, it was possible to extract the maximum harvested power while predicting shifts in the magnetic saturation point across a wide temperature range. Experimental results validate the effectiveness of the proposed design method, achieving a 26.5% higher power density compared to conventional methods that neglect thermal effects. Full article

(This article belongs to the Special Issue Energy Harvesting and Energy Storage Systems, 3rd Edition)

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13 pages, 2441 KiB

Open AccessEditor’s ChoiceArticle

Investigation of Source/Drain Height Variation and Its Impacts on FinFET and GAA Nanosheet FET

by Mingyu Ma, Cong Li, Jianghao Ma, Wangjun Yang, Haokun Li, Hailong You and M. Jamal Deen

Electronics 2025, 14(6), 1091; https://doi.org/10.3390/electronics14061091 - 10 Mar 2025

Viewed by 875

Abstract

As semiconductor technology and process nodes advance, three-dimensional devices like FinFET and NSFET are increasingly becoming the primary choice, replacing planar MOSFETs. However, the complex manufacturing processes and high process sensitivity of three-dimensional devices at advanced process nodes inevitably cause significant deviations from [...] Read more.

As semiconductor technology and process nodes advance, three-dimensional devices like FinFET and NSFET are increasingly becoming the primary choice, replacing planar MOSFETs. However, the complex manufacturing processes and high process sensitivity of three-dimensional devices at advanced process nodes inevitably cause significant deviations from the ideal structure during actual fabrication, leading to notable changes in their electrical characteristics. This paper investigates the impact of source/drain region height fluctuations caused by etching and epitaxial growth variations on the electrical characteristics of FinFET and NSFET devices, as well as their related circuits. The electrical characteristics when height variations occur in single and multiple electrodes indicate that, although NSFET and FinFET generally exhibit similar properties such as a decrease in the ON-state current when the source/drain height is reduced, the independent nature of the nanosheets in NSFET and the unidirectional conduction of Schottky contact resistance cause significant differences in their electrical characteristics. Additionally, the related circuit-level simulations show that height fluctuations in the source/drain regions of devices can significantly impact circuit characteristics, including voltage and delay, and in severe cases, they may even lead to circuit failure. Full article

(This article belongs to the Section Semiconductor Devices)

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41 pages, 1522 KiB

Open AccessEditor’s ChoiceReview

Radiator Enablers for Wireless Communication Evolution

by Apostolos-Christos Tsafaras, Panagiotis Mpatargias, Adamantios Karakilidis, Georgios Giouros, Ioannis Gavriilidis, Vasileios Katsinelis, Georgios Sarinakis and Theodoros Kaifas

Electronics 2025, 14(6), 1081; https://doi.org/10.3390/electronics14061081 - 9 Mar 2025

Viewed by 3111

Abstract

The general objective of the work is to propose, examine, and study the innovations needed, providing a roadmap in order to place the next generation of wireless communication vision and concepts into technological reach. The main trends and directions are identified; relative challenges [...] Read more.

The general objective of the work is to propose, examine, and study the innovations needed, providing a roadmap in order to place the next generation of wireless communication vision and concepts into technological reach. The main trends and directions are identified; relative challenges are addressed; and needed solutions are anticipated, proposed, and evaluated. In detail, to address the role of the antenna system in the wireless communication evolution, in the work at hand, we examine the challenges addressed by the increase in the degrees of freedom of the radiator systems. Specifically, we study the increase in the degrees of freedom provided by gMIMO, reconfigurable intelligence surfaces (RIS), holographic metasurfaces, and orbital angular momentum (OAM). Then, we thoroughly examine the impact that those potent technologies deliver to the mmWave, satellite, and THz wireless communications systems. Full article

(This article belongs to the Special Issue State-of-the-Art Antenna Technology for Advanced Wireless Systems)

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21 pages, 10373 KiB

Open AccessEditor’s ChoiceArticle

A 75 kW Medium-Frequency Transformer Design Based in Inductive Power Transfer (IPT) for Medium-Voltage Solid-State Transformer Applications

by Juan Blanco-Ortiz, Eduardo García-Martínez, Ignacio González-Prieto and Mario J. Duran

Electronics 2025, 14(6), 1059; https://doi.org/10.3390/electronics14061059 - 7 Mar 2025

Viewed by 778

Abstract

Solid-State Transformers (SSTs) enable significant improvements in size and functionality compared to conventional power transformers. However, one of the key challenges in Solid-State Transformer design is achieving reliable insulation between the high-voltage and low-voltage sections. This proposal presents the design and optimization of [...] Read more.

Solid-State Transformers (SSTs) enable significant improvements in size and functionality compared to conventional power transformers. However, one of the key challenges in Solid-State Transformer design is achieving reliable insulation between the high-voltage and low-voltage sections. This proposal presents the design and optimization of a high-insulation Medium-Frequency Transformer (MFT) for 66 kV grids operating at 50 kHz and delivering up to 75 kW for SST applications using Inductive Power Transfer (IPT) technology. A fixed 50 mm gap between the primary and secondary windings is filled with dielectric oil to enhance insulation. The proposed IPT system employs a double-D coil design developed through iterative 2D and 3D finite element method simulations to optimize the magnetic circuit, thereby significantly reducing stray flux and losses. Notably, the double-D configuration reduces enclosure losses from 269.6 W, observed in a rectangular coil design, to 4.38 W, resulting in an overall system loss reduction of 42.4% while maintaining the electrical parameters required for zero-voltage switching operation. These advancements address the critical limitations in conventional Medium-Frequency Transformers by providing enhanced insulation and improved thermal management. The proposed IPT-based design offers a low-loss solution with easy thermal management for solid-state transformer applications in high-voltage grids. Full article

(This article belongs to the Special Issue Advanced Control Techniques for Power Electronics: Addressing Challenges in Renewable Energy System Applications)

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31 pages, 875 KiB

Open AccessEditor’s ChoiceArticle

Hierarchical Traffic Engineering in 3D Networks Using QoS-Aware Graph-Based Deep Reinforcement Learning

by Robert Kołakowski, Lechosław Tomaszewski, Rafał Tępiński and Sławomir Kukliński

Electronics 2025, 14(5), 1045; https://doi.org/10.3390/electronics14051045 - 6 Mar 2025

Viewed by 811

Abstract

Ubiquitous connectivity is envisioned through the integration of terrestrial (TNs) and non-terrestrial networks (NTNs). However, NTNs face multiple routing and Quality of Service (QoS) provisioning challenges due to the mobility of network nodes. Distributed Software-Defined Networking (SDN) combined with Multi-Agent Deep Reinforcement Learning [...] Read more.

Ubiquitous connectivity is envisioned through the integration of terrestrial (TNs) and non-terrestrial networks (NTNs). However, NTNs face multiple routing and Quality of Service (QoS) provisioning challenges due to the mobility of network nodes. Distributed Software-Defined Networking (SDN) combined with Multi-Agent Deep Reinforcement Learning (MADRL) is widely used to introduce programmability and intelligent Traffic Engineering (TE) in TNs, yet applying DRL to NTNs is hindered by frequently changing state sizes, model scalability, and coordination issues. This paper introduces 3DQR, a novel TE framework that combines hierarchical multi-controller SDN, hierarchical MADRL based on Graph Neural Networks (GNNs), and network topology predictions for QoS path provisioning, effective load distribution, and flow rejection minimisation in future 3D networks. To enhance SDN scalability, introduced are metrics and path operations abstractions to facilitate domain agents coordination by the global agent. To the best of the authors’ knowledge, 3DQR is the first routing scheme to integrate MADRL and GNNs for optimising centralised routing and path allocation in SDN-based 3D mobile networks. The evaluations show up to a 14% reduction in flow rejection rate, a 50% improvement in traffic distribution, and effective QoS class prioritisation compared to baseline techniques. 3DQR also exhibits strong transfer capabilities, giving consistent performance gains in previously unseen environments. Full article

(This article belongs to the Special Issue Future Generation Non-Terrestrial Networks)

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22 pages, 1744 KiB

Open AccessEditor’s ChoiceArticle

Hybrid Long-Range–5G Multi-Sensor Platform for Predictive Maintenance for Ventilation Systems

by Praveen Mohanram and Robert H. Schmitt

Electronics 2025, 14(5), 1055; https://doi.org/10.3390/electronics14051055 - 6 Mar 2025

Viewed by 1079

Abstract

In this paper, we present a multi-sensor platform for predictive maintenance featuring hybrid long-range (LoRa) and 5G connectivity. This hybrid approach combines LoRa’s low-power transmission for energy efficiency with 5G’s real-time data capabilities. The hardware platform integrates multiple sensors to monitor machine health [...] Read more.

In this paper, we present a multi-sensor platform for predictive maintenance featuring hybrid long-range (LoRa) and 5G connectivity. This hybrid approach combines LoRa’s low-power transmission for energy efficiency with 5G’s real-time data capabilities. The hardware platform integrates multiple sensors to monitor machine health parameters, with data analyzed on the device using pre-trained AI models to assess the machine’s condition. Inferences are transmitted via LoRa to the operator for maintenance scheduling, while a cloud application tracks and stores sensor data. Periodic sensor data bursts are sent via 5G to update the AI model, which is then delivered back to the platform through over-the-air (OTA) updates. We provide a comprehensive overview of the hardware architecture, along with an in-depth analysis of the data generated by the sensors, and its processing methodology. However, the data analysis and the software for ventilation control and its predictive capabilities are not the focus of this paper and are not presented. Full article

(This article belongs to the Special Issue 5G Mobile Telecommunication Systems and Recent Advances)

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19 pages, 7225 KiB

Open AccessEditor’s ChoiceArticle

Utilization of MCU and Real-Time Simulator for Identifying Beatless Control for Six-Step Operation of Three-Phase Inverter

by Yongsu Han

Electronics 2025, 14(5), 1030; https://doi.org/10.3390/electronics14051030 - 5 Mar 2025

Viewed by 553

Abstract

In industries dealing with motor drive systems, the use of real-time simulators for validating control codes is becoming increasingly mandatory. This is particularly essential for systems with advanced control codes or complex microcontroller unit (MCU) register configurations, as this validation process helps prevent [...] Read more.

In industries dealing with motor drive systems, the use of real-time simulators for validating control codes is becoming increasingly mandatory. This is particularly essential for systems with advanced control codes or complex microcontroller unit (MCU) register configurations, as this validation process helps prevent accidents and shorten development time. This study presents a validation process using a real-time simulator for the beatless control of six-step operation. Six-step operation, when applied to high-speed drives, has a limitation on the number of samples per electrical rotation, which causes voltage errors. A representative of these voltage error phenomena is the beat phenomenon, resulting in torque ripple at the first harmonic and high current ripple. To mitigate this beat phenomenon, a synchronous PWM method is sometimes used. However, in practical industrial systems, it may not be feasible to synchronously adjust the inverter’s switching frequency with the rotation speed. This study proposes a beatless control method to eliminate the voltage errors caused by the beat phenomenon during six-step operation at a fixed switching frequency. The specific implementation of this control method is explained based on MCU timer register settings. While previous studies have only proposed beatless control methods, this paper goes further by implementing the proposed beatless method using the MCU (TMS320F28335) to generate gating signals and validating the implementation through simulation on a permanent magnet synchronous motor using a real-time simulator (Typhoon HIL). Full article

(This article belongs to the Special Issue Hardware in the Loop, Real-Time Simulation and Digital Control of Power Electronics and Drives, 2nd Edition)

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12 pages, 8620 KiB

Open AccessEditor’s ChoiceArticle

Picosecond-Level Synchronization over Optical Free Space Link Using White Rabbit

by Peng Zhang, Dong Hou, Ke Liu, Wenjian Zhou, Minghong Li and Lujun Fang

Electronics 2025, 14(5), 970; https://doi.org/10.3390/electronics14050970 - 28 Feb 2025

Viewed by 1863

Abstract

White Rabbit (WR) time synchronization has an accuracy up to a sub-nanosecond level. However, the current application scenario of WR is limited to wired transmission links. In this paper, we have proposed a time synchronization technique over a free space link using WR. [...] Read more.

White Rabbit (WR) time synchronization has an accuracy up to a sub-nanosecond level. However, the current application scenario of WR is limited to wired transmission links. In this paper, we have proposed a time synchronization technique over a free space link using WR. In the WR-based free space synchronization scheme, we replace the original WDM (Wavelength Division Multiplexing) with single-wavelength transmission to reduce the asymmetry of the path and design a high-power optical transceiver module to improve the transmission power. With the scheme, a free space synchronization experiment with a transmission distance of 50 m is conducted. The experimental results show that the RMS (root mean square) time drift of this free space synchronization system is 20.5 ps over a 24 h period, and the TDEV (Time Deviation) of the time synchronization is 14.3 ps at 1 s and 3.9 ps at 20,000 s. The experiment proves that it will be convenient to complete the free space time synchronization network between clock sites with the proposed technique in the future application of complex environments. Full article

(This article belongs to the Special Issue Applications of MEMS and QCM in Smart Sensor Systems)

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21 pages, 7796 KiB

Open AccessEditor’s ChoiceArticle

Electrical Response of Photovoltaic Power Cells to Cosmic Radiation in the Stratosphere

by Tomasz Aleksander Miś, Dominika Pytlak, Bartosz Kościanek, Korneliusz Szalkowski, Jakub Czerniej, Paulina Kucharczyk, Mikołaj Salamon, Marek Pąśko, Karolina Styrna, Sandra Wąsowska, Michał Gołąb, Paweł Urbański, Hubert Tronowski and Damian Legutko

Electronics 2025, 14(5), 991; https://doi.org/10.3390/electronics14050991 - 28 Feb 2025

Viewed by 609

Abstract

This article describes the CURiE (Composites and photovoltaics Undergoing Radiation Exposure) stratospheric experiment, which was designed and built in 2024 for the BEXUS 35 stratospheric flight campaign in Sweden. One of the main objectives of the experiment was to investigate the electric currents [...] Read more.

This article describes the CURiE (Composites and photovoltaics Undergoing Radiation Exposure) stratospheric experiment, which was designed and built in 2024 for the BEXUS 35 stratospheric flight campaign in Sweden. One of the main objectives of the experiment was to investigate the electric currents generated in polycrystalline photovoltaic panels, shielded from visible light, and exposed in stratospheric conditions to cosmic radiation. The experiment’s registered data correlate with the X-ray fluxes registered by the GOES satellites, which are presented with the inclusion of the atmosphere’s attenuation. A single voltage-generating event may have been linked to the impact of a high-energy proton. The article forms a basis for the next research with the exposed photovoltaics and the next generation of experiments involving novel radiation-proof panels. Full article

(This article belongs to the Special Issue Compatibility, Power Electronics and Power Engineering)

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23 pages, 10184 KiB

Open AccessEditor’s ChoiceReview

A Systematic Review on Advancement of Image Segmentation Techniques for Fault Detection Opportunities and Challenges

by Md Motiur Rahman, Saeka Rahman, Smriti Bhatt and Miad Faezipour

Electronics 2025, 14(5), 974; https://doi.org/10.3390/electronics14050974 - 28 Feb 2025

Viewed by 1810

Abstract

Fault and defect detection are critical for ensuring the safety, reliability, and quality of products and infrastructure across various industries. As traditional manual inspection methods face limitations in efficiency and accuracy, advancements in artificial intelligence, particularly image segmentation, have paved the way for [...] Read more.

Fault and defect detection are critical for ensuring the safety, reliability, and quality of products and infrastructure across various industries. As traditional manual inspection methods face limitations in efficiency and accuracy, advancements in artificial intelligence, particularly image segmentation, have paved the way for automated and precise fault detection processes. A significant gap exists in current research regarding the integration and comparative analysis of classical and modern segmentation approaches across diverse application domains. This study addresses this gap by providing a systematic review that bridges traditional segmentation techniques with cutting-edge deep learning methodologies. Unlike previous reviews that focus solely on isolated techniques or specific domains, this paper offers a holistic analysis of methodological innovations, application breadth, and emerging trends. Emphasis is placed on the integration of deep learning models, hybrid approaches, and advancements like attention mechanisms and lightweight architectures. Additionally, the review highlights critical challenges and proposes future research directions aimed at enhancing model scalability, robustness, and adaptability. This systematic review addresses gaps in the field and provides useful insights for academia and industry, making it a key reference in fault detection using image segmentation. Full article

(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)

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21 pages, 15561 KiB

Open AccessEditor’s ChoiceArticle

Semantic Communication on Digital Wireless Communication Systems

by Binhong Huang, Hao Chen, Cheng Guo, Xiaodong Xu, Nan Ma and Ping Zhang

Electronics 2025, 14(5), 956; https://doi.org/10.3390/electronics14050956 - 27 Feb 2025

Viewed by 820

Abstract

Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of [...] Read more.

Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of physical layer processing modules in end-to-end transmission are designed. Additionally, we develop a semantic communication simulator to implement and evaluate this framework. To optimize the performance of this framework, we introduce a novel physical layer metric, termed Integer Error Rate (IER), which provides a more suitable evaluation criterion for semantic communication compared to the conventional bit error rate (BER). On the basis of the IER, a minimum Manhattan distance constellation mapping scheme is proposed, which can improve the transmission quality of semantic communication under the same BER condition. Furthermore, we propose a hybrid joint source–channel coding (JSCC) and separate source–channel coding (SSCC) transmission scheme. This scheme decouples the semantic quantization output from the modulation order by segmenting the bits to be transmitted. Simulation results demonstrate that the hybrid JSCC/SSCC transmission scheme can improve the semantic performance, such as the Peak Signal-to-Noise Ratio (PSNR), in low Signal-to-Noise Ratio (SNR) environments while reducing bandwidth usage by up to 50% compared to the benchmark scheme. Full article

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

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