Electronics

18 pages, 2906 KiB

Open AccessArticle

Considering a mm-Wave Front-End Receiver and Quadrature Down-Converter for 18–40 GHz with Low Noise Figure and High Gain for an ESM System

by Yuseok Jeon and Hyunkyu Kim

Electronics 2025, 14(14), 2803; https://doi.org/10.3390/electronics14142803 - 11 Jul 2025

Abstract

In this paper, RF sub-modules with millimeter-wave functionality are considered and verified for designing an ultra-wideband receiver (18–40 GHz) required in the electronic support measure (ESM) field. The pre-design of an ultra-wideband super heterodyne receiver (SHR) requires a front-end module (FEM) with four [...] Read more.

In this paper, RF sub-modules with millimeter-wave functionality are considered and verified for designing an ultra-wideband receiver (18–40 GHz) required in the electronic support measure (ESM) field. The pre-design of an ultra-wideband super heterodyne receiver (SHR) requires a front-end module (FEM) with four units in the system. Each FEM has four channels with the same path, while the quadrature millimeter down-converter (QMDC) needs to have a converting function that uses a broadband mixer. The FEM includes the ability to provide built-in test (BIT) path functionality to the antenna ports prior to system field installation. Each path of the QMDC requires the consideration of several factors, such as down-converting, broadband gain flatness, and high isolation. As this is an RF module requiring high frequency and wideband characteristics, it is necessary to identify risk factors in advance within a predictable range. Accordingly, the blind-mate A (BMA) connector connection method, the phase-alignment test method in the down-conversion structure, and the LO signal, IF path inflow-blocking method were analyzed and designed. Full article

30 pages, 21055 KiB

Open AccessArticle

Precise Recognition of Gong-Che Score Characters Based on Deep Learning: Joint Optimization of YOLOv8m and SimAM/MSCAM

by Zhizhou He, Yuqian Zhang, Liumei Zhang and Yuanjiao Hu

Electronics 2025, 14(14), 2802; https://doi.org/10.3390/electronics14142802 - 11 Jul 2025

Abstract

In the field of music notation recognition, while the recognition technology for common notation systems such as staff notation has become quite mature, the recognition techniques for traditional Chinese notation systems like guqin tablature (jianzipu) and Kunqu opera gongchepu remain relatively underdeveloped. As [...] Read more.

In the field of music notation recognition, while the recognition technology for common notation systems such as staff notation has become quite mature, the recognition techniques for traditional Chinese notation systems like guqin tablature (jianzipu) and Kunqu opera gongchepu remain relatively underdeveloped. As an important carrier of China’s thousand-year musical culture, the digital preservation and inheritance of Kunqu opera’s Gongche notation hold significant cultural value and practical significance. By addressing the unique characteristics of Gongche notation, this study overcomes the limitations of Western staff notation recognition technologies. By constructing a deep learning model adapted to the morphology of Chinese character-style notation symbols, it provides technical support for establishing an intelligent processing system for Chinese musical documents, thereby promoting the innovative development and inheritance of traditional music in the era of artificial intelligence. This paper has constructed the LGRC2024 (Gong-che notation based on Lilu Qu Pu) dataset. It has also employed data augmentation operations such as image translation, rotation, and noise processing to enhance the diversity of the dataset. For the recognition of Gong-che notation, the YOLOv8 model was adopted, and the network performances of its lightweight (n) and medium-weight (m) versions were compared and analyzed. The superior-performing YOLOv8m was selected as the basic model. To further improve the model’s performance, SimAM, Triplet Attention, and Multi-scale Convolutional Attention Module (MSCAM) were introduced to optimize the model. The experimental results show that the accuracy of the basic YOLOv8m model increased from 65.9% to 78.2%. The improved models based on YOLOv8m achieved recognition accuracies of 80.4%, 81.8%, and 83.6%, respectively. Among them, the improved model with the MSCAM module demonstrated the best performance in all aspects. Full article

(This article belongs to the Special Issue New Trends in AI-Assisted Computer Vision)

36 pages, 5913 KiB

Open AccessArticle

Design and Temperature Control of a Novel Aeroponic Plant Growth Chamber

by Ali Guney and Oguzhan Cakir

Electronics 2025, 14(14), 2801; https://doi.org/10.3390/electronics14142801 - 11 Jul 2025

Abstract

It is projected that the world population will quadruple over the next century, and to meet future food demands, agricultural production will need to increase by 70%. Therefore, there has been a transition from traditional farming methods to autonomous modern agriculture. One such [...] Read more.

It is projected that the world population will quadruple over the next century, and to meet future food demands, agricultural production will need to increase by 70%. Therefore, there has been a transition from traditional farming methods to autonomous modern agriculture. One such modern technique is aeroponic farming, in which plants are grown without soil under controlled and hygienic conditions. In aeroponic farming, plants are significantly less affected by climatic conditions, infectious diseases, and biotic and abiotic stresses, such as pest infestations. Additionally, this method can reduce water, nutrient, and pesticide usage by 98%, 60%, and 100%, respectively, while increasing the yield by 45–75% compared to traditional farming. In this study, a three-dimensional industrial design of an innovative aeroponic plant growth chamber was presented for use by individuals, researchers, and professional growers. The proposed chamber design is modular and open to further innovation. Unlike existing chambers, it includes load cells that enable real-time monitoring of the fresh weight of the plant. Furthermore, cameras were integrated into the chamber to track plant growth and changes over time and weight. Additionally, RGB power LEDs were placed on the inner ceiling of the chamber to provide an optimal lighting intensity and spectrum based on the cultivated plant species. A customizable chamber design was introduced, allowing users to determine the growing tray and nutrient nozzles according to the type and quantity of plants. Finally, system models were developed for temperature control of the chamber. Temperature control was implemented using a proportional-integral-derivative controller optimized with particle swarm optimization, radial movement optimization, differential evolution, and mayfly optimization algorithms for the gain parameters. The simulation results indicate that the temperatures of the growing and feeding chambers in the cabinet reached a steady state within 260 s, with an offset error of no more than 0.5 °C. This result demonstrates the accuracy of the derived model and the effectiveness of the optimized controllers. Full article

(This article belongs to the Special Issue Intelligent and Autonomous Sensor System for Precision Agriculture)

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40 pages, 7773 KiB

Open AccessArticle

A Novel Llama 3-Based Prompt Engineering Platform for Textual Data Generation and Labeling

by Wedyan Salem Alsakran and Reham Alabduljabbar

Electronics 2025, 14(14), 2800; https://doi.org/10.3390/electronics14142800 - 11 Jul 2025

Abstract

With the growing demand for labeled textual data in Natural Language Processing (NLP), traditional data collection and annotation methods face significant challenges, such as high cost, limited scalability, and privacy constraints. This study presents a novel web-based platform that automates text data generation [...] Read more.

With the growing demand for labeled textual data in Natural Language Processing (NLP), traditional data collection and annotation methods face significant challenges, such as high cost, limited scalability, and privacy constraints. This study presents a novel web-based platform that automates text data generation and labeling by integrating Llama 3.3, an open-source large language model (LLM), with advanced prompt engineering techniques. A core contribution of this work is the Attributed Prompt Engineering Framework, which enables modular and configurable prompt templates for both data generation and labeling tasks. This framework combines zero-shot, few-shot, role-based, and chain-of-thought prompting strategies within a unified architecture to optimize output quality and control. Users can interactively configure prompt parameters and generate synthetic datasets or annotate raw data with minimal human intervention. We evaluated the platform using both benchmark datasets (AG News, Yelp, Amazon Reviews) and two fully synthetic datasets we generated (restaurant reviews and news articles). The system achieved 99% accuracy and F1-score on generated news article data, 98% accuracy and F1-score on generated restaurant review data, and 92%, 90%, and 89% accuracy and F1-scores on the benchmark labeling tasks for AG News, Yelp Reviews, and Amazon Reviews, respectively, demonstrating high effectiveness and generalizability. A usability study also confirmed the platform’s practicality for non-expert users. This work advances scalable NLP data pipeline design and provides a cost-effective alternative to manual annotation for supervised learning applications. Full article

(This article belongs to the Special Issue Advanced Natural Language Processing Technology and Applications)

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29 pages, 1234 KiB

Open AccessArticle

Automatic Detection of the CaRS Framework in Scholarly Writing Using Natural Language Processing

by Olajide Omotola, Nonso Nnamoko, Charles Lam, Ioannis Korkontzelos, Callum Altham and Joseph Barrowclough

Electronics 2025, 14(14), 2799; https://doi.org/10.3390/electronics14142799 - 11 Jul 2025

Abstract

Many academic introductions suffer from inconsistencies and a lack of comprehensive structure, often failing to effectively outline the core elements of the research. This not only impacts the clarity and readability of the article but also hinders the communication of its significance and [...] Read more.

Many academic introductions suffer from inconsistencies and a lack of comprehensive structure, often failing to effectively outline the core elements of the research. This not only impacts the clarity and readability of the article but also hinders the communication of its significance and objectives to the intended audience. This study aims to automate the CaRS (Creating a Research Space) model using machine learning and natural language processing techniques. We conducted a series of experiments using a custom-developed corpus of 50 biology research article introductions, annotated with rhetorical moves and steps. The dataset was used to evaluate the performance of four classification algorithms: Prototypical Network (PN), Support Vector Machines (SVM), Naïve Bayes (NB), and Random Forest (RF); in combination with six embedding models: Word2Vec, GloVe, BERT, GPT-2, Llama-3.2-3B, and TEv3-small. Multiple experiments were carried out to assess performance at both the move and step levels using 5-fold cross-validation. Evaluation metrics included accuracy and weighted F1-score, with comprehensive results provided. Results show that the SVM classifier, when paired with Llama-3.2-3B embeddings, consistently achieved the highest performance across multiple tasks when trained on preprocessed dataset, with 79% accuracy and weighted F1-score on rhetorical moves and strong results on M2 steps (75% accuracy and weighted F1-score). While other combinations showed promise, particularly NB and RF with newer embeddings, none matched the consistency of the SVM–Llama pairing. Compared to existing benchmarks, our model achieves similar or better performance; however, direct comparison is limited due to differences in datasets and experimental setups. Despite the unavailability of the benchmark dataset, our findings indicate that SVM is an effective choice for rhetorical classification, even in few-shot learning scenarios. Full article

(This article belongs to the Special Issue Emerging Theory and Applications in Natural Language Processing, 2nd Edition)

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26 pages, 2573 KiB

Open AccessArticle

Two-Layer Robust Optimization Scheduling Strategy for Active Distribution Network Considering Electricity-Carbon Coupling

by Yiteng Xu, Chenxing Yang, Zijie Liu, Yaxian Zheng, Yuechi Liu and Haiteng Han

Electronics 2025, 14(14), 2798; https://doi.org/10.3390/electronics14142798 - 11 Jul 2025

Abstract

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into [...] Read more.

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

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22 pages, 12122 KiB

Open AccessArticle

RA3T: An Innovative Region-Aligned 3D Transformer for Self-Supervised Sim-to-Real Adaptation in Low-Altitude UAV Vision

by Xingrao Ma, Jie Xie, Di Shao, Aiting Yao and Chengzu Dong

Electronics 2025, 14(14), 2797; https://doi.org/10.3390/electronics14142797 - 11 Jul 2025

Abstract

Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework [...] Read more.

Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework that enables robust Sim-to-Real adaptation. Specifically, we first develop a dual-branch strategy for self-supervised feature learning, integrating Masked Autoencoders and contrastive learning. This approach extracts domain-invariant representations from unlabeled simulated imagery to enhance robustness against occlusion while reducing annotation dependency. Leveraging these learned features, we then introduce a 3D Transformer fusion module that unifies multi-view RGB and LiDAR point clouds through cross-modal attention. By explicitly modeling spatial layouts and height differentials, this component significantly improves recognition of small and occluded targets in complex low-altitude environments. To address persistent fine-grained domain shifts, we finally design region-level adversarial calibration that deploys local discriminators on partitioned feature maps. This mechanism directly aligns texture, shadow, and illumination discrepancies which challenge conventional global alignment methods. Extensive experiments on UAV benchmarks VisDrone and DOTA demonstrate the effectiveness of RA3T. The framework achieves +5.1% mAP on VisDrone and +7.4% mAP on DOTA over the 2D adversarial baseline, particularly on small objects and sparse occlusions, while maintaining real-time performance of 17 FPS at 1024 × 1024 resolution on an RTX 4080 GPU. Visual analysis confirms that the synergistic integration of 3D geometric encoding and local adversarial alignment effectively mitigates domain gaps caused by uneven illumination and perspective variations, establishing an efficient pathway for simulation-to-reality UAV perception. Full article

(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)

21 pages, 3250 KiB

Open AccessArticle

Deploying Optimized Deep Vision Models for Eyeglasses Detection on Low-Power Platforms

by Henrikas Giedra, Tomyslav Sledevič and Dalius Matuzevičius

Electronics 2025, 14(14), 2796; https://doi.org/10.3390/electronics14142796 - 11 Jul 2025

Abstract

This research addresses the optimization and deployment of convolutional neural networks for eyeglasses detection on low-power edge devices. Multiple convolutional neural network architectures were trained and evaluated using the FFHQ dataset, which contains annotated eyeglasses in the context of faces with diverse facial [...] Read more.

This research addresses the optimization and deployment of convolutional neural networks for eyeglasses detection on low-power edge devices. Multiple convolutional neural network architectures were trained and evaluated using the FFHQ dataset, which contains annotated eyeglasses in the context of faces with diverse facial features and eyewear styles. Several post-training quantization techniques, including Float16, dynamic range, and full integer quantization, were applied to reduce model size and computational demand while preserving detection accuracy. The impact of model architecture and quantization methods on detection accuracy and inference latency was systematically evaluated. The optimized models were deployed and benchmarked on Raspberry Pi 5 and NVIDIA Jetson Orin Nano platforms. Experimental results show that full integer quantization reduces model size by up to 75% while maintaining competitive detection accuracy. Among the evaluated models, MobileNet architectures achieved the most favorable balance between inference speed and accuracy, demonstrating their suitability for real-time eyeglasses detection in resource-constrained environments. These findings enable efficient on-device eyeglasses detection, supporting applications such as virtual try-ons and IoT-based facial analysis systems. Full article

(This article belongs to the Special Issue Convolutional Neural Networks and Vision Applications, 4th Edition)

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28 pages, 781 KiB

Open AccessArticle

Unlinkable Revocation Lists for Qualified Electronic Attestations: A Blockchain-Based Framework

by Emil Bureacă, Răzvan-Andrei Leancă, Ionuț Ciobanu, Andrei Brînzea and Iulian Aciobăniței

Electronics 2025, 14(14), 2795; https://doi.org/10.3390/electronics14142795 - 11 Jul 2025

Abstract

The use of Verifiable Credentials under the new eIDAS Regulation introduces privacy concerns, particularly during revocation status checks. This paper proposes a privacy-preserving revocation mechanism tailored to the European Digital Identity Wallet and its Architecture and Reference Framework. Our method publishes a daily [...] Read more.

The use of Verifiable Credentials under the new eIDAS Regulation introduces privacy concerns, particularly during revocation status checks. This paper proposes a privacy-preserving revocation mechanism tailored to the European Digital Identity Wallet and its Architecture and Reference Framework. Our method publishes a daily randomized revocation list as a cascaded Bloom filter, enhancing unlinkability by randomizing revocation indexes derived from ARF guidelines. The implementation extends open-source components developed by the European Committee. This work demonstrates a practical, privacy-centric approach to revocation in digital identity systems, supporting the advancement of privacy-preserving technologies. Full article

(This article belongs to the Special Issue Advanced Research in Technology and Information Systems, 2nd Edition)

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31 pages, 529 KiB

Open AccessReview

Advances and Challenges in Respiratory Sound Analysis: A Technique Review Based on the ICBHI2017 Database

by Shaode Yu, Jieyang Yu, Lijun Chen, Bing Zhu, Xiaokun Liang, Yaoqin Xie and Qiurui Sun

Electronics 2025, 14(14), 2794; https://doi.org/10.3390/electronics14142794 - 11 Jul 2025

Abstract

Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biomedical and Health Informatics 2017 (ICBHI2017) database stands as one of the most authoritative [...] Read more.

Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biomedical and Health Informatics 2017 (ICBHI2017) database stands as one of the most authoritative open-access RSA datasets. This review systematically examines 135 technical publications utilizing the database, and a comprehensive and timely summary of RSA methodologies is offered for researchers and practitioners in this field. Specifically, this review covers signal processing techniques including data resampling, augmentation, normalization, and filtering; feature extraction approaches spanning time-domain, frequency-domain, joint time–frequency analysis, and deep feature representation from pre-trained models; and classification methods for adventitious sound (AS) categorization and pathological state (PS) recognition. Current achievements for AS and PS classification are summarized across studies using official and custom data splits. Despite promising technique advancements, several challenges remain unresolved. These include a severe class imbalance in the dataset, limited exploration of advanced data augmentation techniques and foundation models, a lack of model interpretability, and insufficient generalization studies across clinical settings. Future directions involve multi-modal data fusion, the development of standardized processing workflows, interpretable artificial intelligence, and integration with broader clinical data sources to enhance diagnostic performance and clinical applicability. Full article

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15 pages, 5752 KiB

Open AccessArticle

Coordinated Control of Grid-Forming Inverters for Adaptive Harmonic Mitigation and Dynamic Overcurrent Control

by Khaliqur Rahman, Jun Hashimoto, Kunio Koseki, Dai Orihara and Taha Selim Ustun

Electronics 2025, 14(14), 2793; https://doi.org/10.3390/electronics14142793 - 11 Jul 2025

Abstract

This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt [...] Read more.

This paper proposes a coordinated control strategy for grid-forming inverters (GFMs) to address two critical challenges in evolving power systems. These are the active harmonic mitigation under nonlinear loading conditions and dynamic overcurrent control during grid disturbances. The proposed framework integrates a shunt active filter (SAF) mechanism within the GFM control structure to achieve a real-time suppression of harmonic distortions from the inverter and grid currents. In parallel, a virtual impedance-based dynamic current limiting strategy is incorporated to constrain fault current magnitudes, ensuring the protection of power electronic components and maintaining system stability. The SAF operates in a current-injection mode aligned with harmonic components, derived via instantaneous reference frame transformations and selective harmonic extraction. The virtual impedance control (VIC) dynamically modulates the inverter’s output impedance profile based on grid conditions, enabling adaptive response during fault transients to limit overcurrent stress. A detailed analysis is performed for the coordinated control of the grid-forming inverter. Supported by simulations and analytical methods, the approach ensures system stability while addressing overcurrent limitations and active harmonic filtering under nonlinear load conditions. This establishes a viable solution for the next-generation inverter-dominated power systems where reliability, power quality, and fault resilience are paramount. Full article

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17 pages, 1472 KiB

Open AccessArticle

A Wallboard Outsourcing Recommendation Method Based on Dual-Channel Neural Networks and Probabilistic Matrix Factorization

by Hongen Yang, Shanhui Liu, Yangzhen Cao, Yuanyang Wang and Chaoyang Li

Electronics 2025, 14(14), 2792; https://doi.org/10.3390/electronics14142792 - 11 Jul 2025

Abstract

Wallboard outsourcing is a critical task in cloud-based manufacturing, where demand enterprises seek suitable suppliers for machining services through online platforms. However, the recommendation process faces significant challenges, including sparse rating data, unstructured textual descriptions from suppliers, and complex, non-linear user preferences. To [...] Read more.

Wallboard outsourcing is a critical task in cloud-based manufacturing, where demand enterprises seek suitable suppliers for machining services through online platforms. However, the recommendation process faces significant challenges, including sparse rating data, unstructured textual descriptions from suppliers, and complex, non-linear user preferences. To address these issues, this paper proposes AttVAE-PMF, a novel recommendation method based on dual-channel neural networks and probabilistic matrix factorization. Specifically, an attention-enhanced long short-term memory (LSTM) is employed to extract semantic features from free-text supplier descriptions, while a variational autoencoder (VAE) is used to model latent preferences from sparse demand-side ratings. These two types of latent representations are then fused via probabilistic matrix factorization (PMF) to complete the rating matrix and infer enterprise preferences. Experiments conducted on both the wallboard dataset and the MovieLens-100K dataset demonstrate that AttVAE-PMF outperforms baseline methods—including PMF, DLCRS, and SSAERec—in terms of convergence speed and robustness to data sparsity, validating its effectiveness in handling sparse and heterogeneous information in wallboard outsourcing recommendation scenarios. Full article

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13 pages, 2498 KiB

Open AccessArticle

Evaluation of Dynamic On-Resistance and Trapping Effects in GaN on Si HEMTs Using Rectangular Gate Voltage Pulses

by Pasquale Cusumano, Alessandro Sirchia and Flavio Vella

Electronics 2025, 14(14), 2791; https://doi.org/10.3390/electronics14142791 - 11 Jul 2025

Abstract

Dynamic on-resistance (R_ON) of commercial GaN on Si normally off high-electron-mobility transistor (HEMT) devices is a very important parameter because it is responsible for conduction losses that limit the power conversion efficiency of high-power switching converters. Due to charge trapping effects, [...] Read more.

Dynamic on-resistance (R_ON) of commercial GaN on Si normally off high-electron-mobility transistor (HEMT) devices is a very important parameter because it is responsible for conduction losses that limit the power conversion efficiency of high-power switching converters. Due to charge trapping effects, dynamic R_ON is always higher than in DC, a behavior known as current collapse. To study how short-time dynamics of charge trapping and release affects R_ON we use rectangular 0–5 V gate voltage pulses with durations in the 1 μs to 100 μs range. Measurements are first carried out for single pulses of increasing duration, and it is found that R_ON depends on both pulse duration and drain current I_D, being higher at shorter pulse durations and lower I_D. For a train of five pulses, R_ON decreases with pulse number, reaching a steady state after a time interval of 100 μs. The response to a five pulses train is compared to that of a square-wave signal to study the time evolution of R_ON toward a dynamic steady state. The DC R_ON is also measured, and it is a factor of ten smaller than dynamic R_ON at the same I_D. This confirms that a reduction in trapped charges takes place in DC as compared to the square-wave switching operation. Additional off-state stress tests at V_DS = 55 V reveal the presence of residual surface traps in the drain access region, leading to four times increase in R_ON in comparison to pristine devices. Finally, the dynamic R_ON is also measured by the double-pulse test (DPT) technique with inductive load, giving a good agreement with results from single-pulse measurements. Full article

(This article belongs to the Special Issue GaN Technology’s Role in Next Generation Electronics Circuits and Power Applications)

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17 pages, 3698 KiB

Open AccessArticle

A Novel Fault Diagnosis Method for Rolling Bearings Based on Spectral Kurtosis and LS-SVM

by Lianyou Lai, Weijian Xu and Zhongzhe Song

Electronics 2025, 14(14), 2790; https://doi.org/10.3390/electronics14142790 - 11 Jul 2025

Abstract

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise [...] Read more.

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

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20 pages, 3802 KiB

Open AccessArticle

RT-DETR-FFD: A Knowledge Distillation-Enhanced Lightweight Model for Printed Fabric Defect Detection

by Gengliang Liang, Shijia Yu and Shuguang Han

Electronics 2025, 14(14), 2789; https://doi.org/10.3390/electronics14142789 - 11 Jul 2025

Abstract

Automated defect detection for printed fabric manufacturing faces critical challenges in balancing industrial-grade accuracy with real-time deployment efficiency. To address this, we propose RT-DETR-FFD, a knowledge-distilled detector optimized for printed fabric defect inspection. Firstly, the student model integrates a Fourier cross-stage mixer (FCSM). [...] Read more.

Automated defect detection for printed fabric manufacturing faces critical challenges in balancing industrial-grade accuracy with real-time deployment efficiency. To address this, we propose RT-DETR-FFD, a knowledge-distilled detector optimized for printed fabric defect inspection. Firstly, the student model integrates a Fourier cross-stage mixer (FCSM). This module disentangles defect features from periodic textile backgrounds through spectral decoupling. Secondly, we introduce FuseFlow-Net to enable dynamic multi-scale interaction, thereby enhancing discriminative feature representation. Additionally, a learnable positional encoding (LPE) module transcends rigid geometric constraints, strengthening contextual awareness. Furthermore, we design a dynamic correlation-guided loss (DCGLoss) for distillation optimization. Our loss leverages masked frequency-channel alignment and cross-domain fusion mechanisms to streamline knowledge transfer. Experiments demonstrate that the distilled model achieves an mAP@0.5 of 82.1%, surpassing the baseline RT-DETR-R18 by 6.3% while reducing parameters by 11.7%. This work establishes an effective paradigm for deploying high-precision defect detectors in resource-constrained industrial scenarios, advancing real-time quality control in textile manufacturing. Full article

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21 pages, 5060 KiB

Open AccessArticle

Enhancing Mine Safety with YOLOv8-DBDC: Real-Time PPE Detection for Miners

by Jun Yang, Haizhen Xie, Xiaolan Zhang, Jiayue Chen and Shulong Sun

Electronics 2025, 14(14), 2788; https://doi.org/10.3390/electronics14142788 - 11 Jul 2025

Abstract

In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for [...] Read more.

In the coal industry, miner safety is increasingly challenged by growing mining depths and complex environments. The failure to wear Personal Protective Equipment (PPE) is a frequent issue in accidents, threatening lives and reducing operational efficiency. Additionally, existing PPE datasets are inadequate for model training due to their small size, lack of diversity, and poor labeling. Current methods often struggle with the complexity of multi-scenario and multi-type PPE detection, especially under varying environmental conditions and with limited training data. In this paper, we propose a novel minersPPE dataset and an improved algorithm based on YOLOv8, enhanced with Dilated-CBAM (Dilated Convolutional Block Attention Module) and DBB (Diverse Branch Block) Detection Block (YOLOv8-DCDB), to address these challenges. The minersPPE dataset constructed in this paper includes 14 categories of protective equipment needed for various body parts of miners. To improve detection performance under complex lighting conditions and with varying PPE features, the algorithm incorporates the Dilated-CBAM module. Additionally, a multi-branch structured detection head is employed to effectively capture multi-scale features, especially enhancing the detection of small targets. To mitigate the class imbalance issue caused by the long-tail distribution in the dataset, we adopt a K-fold cross-validation strategy, optimizing the detection results. Compared to standard YOLOv8-based models, experiments on the minersPPE dataset demonstrate an 18.9% improvement in detection precision, verifying the effectiveness of the proposed YOLOv8-DCDB model in multi-scenario, multi-type PPE detection tasks. Full article

(This article belongs to the Special Issue Advances in Information Processing and Network Security)

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21 pages, 874 KiB

Open AccessArticle

Explainable Use of Foundation Models for Job Hiring

by Vishnu S. Pendyala, Neha Bais Thakur and Radhika Agarwal

Electronics 2025, 14(14), 2787; https://doi.org/10.3390/electronics14142787 - 11 Jul 2025

Abstract

Automating candidate shortlisting is a non-trivial task that stands to benefit substantially from advances in artificial intelligence. We evaluate a suite of foundation models such as Llama 2, Llama 3, Mixtral, Gemma-2b, Gemma-7b, Phi-3 Small, Phi-3 Mini, Zephyr, and Mistral-7b for their ability [...] Read more.

Automating candidate shortlisting is a non-trivial task that stands to benefit substantially from advances in artificial intelligence. We evaluate a suite of foundation models such as Llama 2, Llama 3, Mixtral, Gemma-2b, Gemma-7b, Phi-3 Small, Phi-3 Mini, Zephyr, and Mistral-7b for their ability to predict hiring outcomes in both zero-shot and few-shot settings. Using only features extracted from applicants’ submissions, these models, on average, achieved an AUC above 0.5 in zero-shot settings. Providing a few examples similar to the job applicants based on a nearest neighbor search improved the prediction rate marginally, indicating that the models perform competently even without task-specific fine-tuning. For Phi-3 Small and Mixtral, all reported performance metrics fell within the 95% confidence interval across evaluation strategies. Model outputs were interpreted quantitatively via post hoc explainability techniques and qualitatively through prompt engineering, revealing that decisions are largely attributable to knowledge acquired during pre-training. A task-specific MLP classifier trained solely on the provided dataset only outperformed the strongest foundation model (Zephyr in 5-shot setting) by approximately 3 percentage points on accuracy, but all the foundational models outperformed the baseline model by more than 15 percentage points on f1 and recall, underscoring the competitive strength of general-purpose language models in the hiring domain. Full article

(This article belongs to the Special Issue Innovative Applications of Large Language Models in Natural Language Processing (NLP))

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15 pages, 3706 KiB

Open AccessArticle

Short Circuit Withstand Time Screening of 1.2 kV Commercial SiC MOSFETs: A Non-Destructive Approach

by Monikuntala Bhattacharya, Hengyu Yu, Michael Jin, Shiva Houshmand, Jiashu Qian, Limeng Shi, Marvin H. White, Atsushi Shimbori and Anant K. Agarwal

Electronics 2025, 14(14), 2786; https://doi.org/10.3390/electronics14142786 - 10 Jul 2025

Abstract

SiC MOSFETs are becoming increasingly popular due to their superior material properties, but they lack the required reliability and ruggedness for safe applications. One of the biggest challenges in short-circuit (SC) reliability of the commercial devices and hence in the SC protection circuit [...] Read more.

SiC MOSFETs are becoming increasingly popular due to their superior material properties, but they lack the required reliability and ruggedness for safe applications. One of the biggest challenges in short-circuit (SC) reliability of the commercial devices and hence in the SC protection circuit design is the variability of SC withstand time (SCWT) among the devices from the same vendor, even with the same lot and batch number. In this work, a novel SC screening methodology has been presented to remove devices with lower SCWT from a pool of devices without damaging the reliable ones. The SC screening methodology has been developed using Sentaurus TCAD simulation, which is further verified using commercial devices. This work can potentially reduce field failure and, as a result, can enhance the reliability of the SiC MOSFETs in real-world applications. Full article

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24 pages, 9594 KiB

Open AccessArticle

Deep Learning Approaches for Skin Lesion Detection

by Jonathan Vieira, Fábio Mendonça and Fernando Morgado-Dias

Electronics 2025, 14(14), 2785; https://doi.org/10.3390/electronics14142785 - 10 Jul 2025

Abstract

Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated [...] Read more.

Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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