Electronics

15 pages, 4009 KB

Open AccessArticle

Design and Theoretical Analysis of a Hexagonal-Stacked MISO Electric Resonant Coupling Wireless Power Transfer Coupler

by Hong-Guk Bae and Sang-Wook Park

Electronics 2025, 14(17), 3568; https://doi.org/10.3390/electronics14173568 - 8 Sep 2025

This study presents the design of an optimal Electric Resonant Coupling Wireless Power Transfer (ER-WPT) coupler intended for multiple-input multiple-output (MIMO) systems. The proposed coupler features a hexagonal-stacked structure optimized for electric field coupling and consists of three transmitters and one receiver. Analysis [...] Read more.

This study presents the design of an optimal Electric Resonant Coupling Wireless Power Transfer (ER-WPT) coupler intended for multiple-input multiple-output (MIMO) systems. The proposed coupler features a hexagonal-stacked structure optimized for electric field coupling and consists of three transmitters and one receiver. Analysis of the electromagnetic characteristics in this 3-to-1 configuration can be extended to larger arrays. Theoretical analysis based on a practical equivalent circuit (PEC) model, which incorporates loss elements from measurement, is validated through comparison with 3D full-wave simulations and experimental results. Across three representative receiver positions, the summed transmission coefficient of the MISO structure reaches up to 0.90, while the PEC model agrees with measurements within a maximum deviation of 0.09, confirming high accuracy. Furthermore, the proposed structure demonstrates stable resonance characteristics near 6.78 MHz with reduced frequency shifts under different receiver positions. The key contributions of this work are the proposal of an efficient hexagonal-stacked MISO ER-WPT coupler and a validated equivalent circuit modeling approach that reflects real-world losses, providing a reliable basis for future multi-transmitter/multi-receiver Wireless Power Transfer systems. Full article

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58 pages, 7761 KB

Open AccessReview

Blockchain Consensus Mechanisms: A Comprehensive Review and Performance Analysis Framework

by Zhihua Shen, Qiang Qu and Xue-Bo Chen

Electronics 2025, 14(17), 3567; https://doi.org/10.3390/electronics14173567 - 8 Sep 2025

Abstract

In recent years, blockchain consensus mechanisms have evolved significantly from the original proof-of-work design, transitioning towards more efficient and scalable alternatives. This paper presents a comprehensive review and analysis framework for blockchain consensus mechanisms based on a systematic examination of 200+ publications. We [...] Read more.

In recent years, blockchain consensus mechanisms have evolved significantly from the original proof-of-work design, transitioning towards more efficient and scalable alternatives. This paper presents a comprehensive review and analysis framework for blockchain consensus mechanisms based on a systematic examination of 200+ publications. We categorize consensus mechanisms into four performance-oriented groups: high throughput, strong security, low energy, and flexible scaling, each addressing specific trade-offs in the blockchain trilemma of decentralization, security, and scalability. Through quantitative metrics including transactions per second, energy consumption, fault tolerance, and communication complexity, we evaluate mainstream mechanisms. Our findings reveal that no single consensus mechanism optimally satisfies all performance requirements, with each design involving explicit trade-offs. This paper provides researchers and practitioners with a structured framework for understanding these trade-offs and selecting appropriate consensus mechanisms for specific application contexts. Finally, we discussed future development trends, as well as regulatory and ethical considerations. Full article

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14 pages, 4246 KB

Open AccessArticle

PI-Based Current Constant Control with Ripple Component for Lifetime Extension of Lithium-Ion Battery

by Min-Ho Shin, Jin-Ho Lee and Jehyuk Won

Electronics 2025, 14(17), 3566; https://doi.org/10.3390/electronics14173566 - 8 Sep 2025

Abstract

This paper presents a proportional–integral (PI) control-based charging strategy that introduces a ripple component into the constant-current (CC) charging profile to regulate battery temperature and improve long-term performance. The proposed method is implemented within an on-board charger (OBC), where the ripple amplitude is [...] Read more.

This paper presents a proportional–integral (PI) control-based charging strategy that introduces a ripple component into the constant-current (CC) charging profile to regulate battery temperature and improve long-term performance. The proposed method is implemented within an on-board charger (OBC), where the ripple amplitude is adaptively adjusted based on battery temperature and internal resistance. While most prior studies focus on electrochemical characteristics, this work highlights the importance of analyzing current profiles from a power electronics and converter control perspective. The ripple magnitude is controlled in real time through gain tuning of the PI current controller, allowing temperature-aware charging. To validate the proposed method, experiments were conducted using a 11 kW OBC system and 70 Ah lithium-ion battery to examine the correlation between ripple amplitude and battery temperature rise, as well as its impact on internal resistance. The control strategy was evaluated under various thermal conditions and shown to be effective in mitigating temperature-related degradation through ripple-based modulation. Full article

(This article belongs to the Special Issue Advances in Power Electronics for Distributed Energy Sources: Topologies, Modeling and Control)

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17 pages, 4689 KB

Open AccessFeature PaperArticle

A Novel Compact Beamforming Network Based on Quasi-Twisted Branch Line Coupler for 5G Applications

by Fayyadh H. Ahmed and Salam K. Khamas

Electronics 2025, 14(17), 3565; https://doi.org/10.3390/electronics14173565 - 8 Sep 2025

Abstract

This paper presents a novel compact 4 × 4 Butler matrix (BM) employing a quasi-twisted branch line coupler (QBLC) as the unit cell to achieve enhanced bandwidth performance. The proposed BM integrates four QBLCs, a uniquely designed 0 dB crossover, and a 45° [...] Read more.

This paper presents a novel compact 4 × 4 Butler matrix (BM) employing a quasi-twisted branch line coupler (QBLC) as the unit cell to achieve enhanced bandwidth performance. The proposed BM integrates four QBLCs, a uniquely designed 0 dB crossover, and a 45° phase shifter, all fabricated on a double-layer Rogers RO4003C substrate with a thickness of 0.8 mm, dielectric constant (εr) of 3.3, and a loss tangent of 0.0027. A common ground plane is used to separate the layers. Both simulation and experimental results indicate a reflection coefficient of approximately −6.5 dB at the resonant frequency of 6.5 GHz and isolation levels better than −20 dB at all ports. The system achieves output phase differences of ±13°, ±41°, ±61°, ±89°, and ±120° (±10°) at the designated frequencies. The BM occupies a compact area of 13.8 mm × 38.8 mm, achieving a 92.5% size reduction compared to conventional T-shaped BM structures. The design was modeled and simulated using CST Microwave Studio, with a strong correlation observed between simulated and measured results, validating the design’s reliability and effectiveness. Furthermore, the BM’s beamforming performance is evaluated by integrating it with a 1 × 4 microstrip antenna array. The measured return loss at all ports is below −10 dB at 6.5 GHz, and the system successfully achieves switched beam steering toward four distinct angles: −5°, +6°, +26°, −24°, +43, and −43 with antenna gains ranging from 7 to 10 dBi. Full article

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25 pages, 4415 KB

Open AccessFeature PaperArticle

Multi-Scale Dual Discriminator Generative Adversarial Network for Gas Leakage Detection

by Saif H. A. Al-Khazraji, Hafsa Iqbal, Jesús Belmar Rubio, Fernando García and Abdulla Al-Kaff

Electronics 2025, 14(17), 3564; https://doi.org/10.3390/electronics14173564 - 8 Sep 2025

Abstract

Gas leakages pose significant safety risks in urban environments and industrial sectors like the Oil and Gas Industry (OGI), leading to accidents, fatalities, and economic losses. This paper introduces a novel generative AI framework, the Multi-Scale Dual Discriminator Generative Adversarial Network (MSDD-GAN), designed [...] Read more.

Gas leakages pose significant safety risks in urban environments and industrial sectors like the Oil and Gas Industry (OGI), leading to accidents, fatalities, and economic losses. This paper introduces a novel generative AI framework, the Multi-Scale Dual Discriminator Generative Adversarial Network (MSDD-GAN), designed to detect and localize gas leaks by generating thermal images from RGB input images. The proposed method integrates three key innovations: (1) Attention-Guided Masking (AttMask) for precise gas leakage localization using saliency maps and a circular Region of Interest (ROI), enabling pixel-level validation; (2) Multi-scale input processing to enhance feature learning with limited data; and (3) Dual Discriminator to validate the thermal image realism and leakage localization accuracy. A comprehensive dataset from laboratory and industrial environment has been collected using a FLIR thermal camera. The MSDD-GAN demonstrated robust performance by generating thermal images with the gas leakage indications at a mean accuracy of

81.6 %

, outperforming baseline cGANs by leveraging a multi-scale generator and dual adversarial losses. By correlating ice formation in RGB images with the leakage indications in thermal images, the model addresses critical challenges of OGI applications, including data scarcity and validation reliability, offering a robust solution for continuous gas leak monitoring in pipeline. 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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67 pages, 2605 KB

Open AccessArticle

Polar Codes for 6G and Beyond Wireless Quantum Optical Communications

by Peter Jung, Kushtrim Dini, Faris Abdel Rehim and Hamza Almujahed

Electronics 2025, 14(17), 3563; https://doi.org/10.3390/electronics14173563 - 8 Sep 2025

Abstract

Wireless communication applications above 300 GHz need careful analog electronics design that takes into account the frequency-dependent nature of ohmic resistance at these frequencies. The cumbersome development of electronics brings quantum optical communication solutions for the sixth generation (6G) THz band located between [...] Read more.

Wireless communication applications above 300 GHz need careful analog electronics design that takes into account the frequency-dependent nature of ohmic resistance at these frequencies. The cumbersome development of electronics brings quantum optical communication solutions for the sixth generation (6G) THz band located between 300 GHz and 10 THz into focus. In this manuscript, the authors propose to replace the classical radio frequency based inner physical layer transceiver blocks used in classical channel coded short range wireless communication systems by wireless quantum optical communication concepts. In addition to discussing the resulting generic concept of the wireless quantum optical communications and illustrating optimum quantum data detection schemes, novel reduced state quantum data detection and novel Kohonen maps-based quantum data detection, will be addressed. All the considered quantum data detection schemes provide soft outputs required for the lowest possible block error ratio (BLER) at the output of the channel decoding. Furthermore, a novel polar codes design approach determining the polar sequence by appropriately combining already available polar sequences tailored for low BLER is presented for the first time after illustrating the basics of polar codes. In addition, turbo equalization for wireless quantum optical communications using polar codes will be presented, for the first time explicitly stating the generation of soft information associated with the codebits and introducing a novel scheme for the computation of extrinsic soft outputs to be used in the turbo equalization iterations. New simulation results emphasize the viability of the theoretical concepts. Full article

(This article belongs to the Special Issue Channel Coding and Measurements for 6G Wireless Communications)

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28 pages, 1433 KB

Open AccessArticle

Class-Adaptive Weighted Broad Learning System with Hybrid Memory Retention for Online Imbalanced Classification

by Jintao Huang, Yu Wang and Mengxin Wang

Electronics 2025, 14(17), 3562; https://doi.org/10.3390/electronics14173562 - 8 Sep 2025

Abstract

Data stream classification is a critical challenge in data mining, where models must rapidly adapt to evolving data distributions and concept drift in real time, while extreme learning machines offer fast training and strong generalization, most existing methods struggle to jointly address multi-class [...] Read more.

Data stream classification is a critical challenge in data mining, where models must rapidly adapt to evolving data distributions and concept drift in real time, while extreme learning machines offer fast training and strong generalization, most existing methods struggle to jointly address multi-class imbalance, concept drift, and the high cost of label acquisition in streaming settings. In this paper, we present the Adaptive Broad Learning System for Online Imbalanced Classification (ABLS-OIC), which introduces three core innovations: (1) a Class-Adaptive Weight Matrix (CAWM) that dynamically adjusts sample weights according to class distribution, sample density, and difficulty; (2) a Hybrid Memory Retention Mechanism (HMRM) that selectively retains representative samples based on importance and diversity; and (3) a Multi-Objective Adaptive Optimization Framework (MAOF) that balances classification accuracy, class balance, and computational efficiency. Extensive experiments on ten benchmark datasets with varying imbalance ratios and drift patterns show that ABLS-OIC consistently outperforms state-of-the-art methods, with improvements of 5.9% in G-mean, 6.3% in F1-score, and 3.4% in AUC. Furthermore, a real-world credit fraud detection case study demonstrates the practical effectiveness of ABLS-OIC, highlighting its value for early detection of rare but critical events in dynamic, high-stakes applications. Full article

(This article belongs to the Special Issue Advances in Data Mining and Its Applications)

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1 pages, 126 KB

Open AccessRetraction

RETRACTED: Syah et al. Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm. Electronics 2021, 10, 2214

by Rahmad Syah, Afshin Davarpanah, Marischa Elveny, Ashish Kumar Karmaker, Mahyuddin K. M. Nasution and Md. Alamgir Hossain

Electronics 2025, 14(17), 3561; https://doi.org/10.3390/electronics14173561 - 8 Sep 2025

Abstract

The journal retracts the article, “Forecasting Daily Electricity Price by Hybrid Model of Fractional Wavelet Transform, Feature Selection, Support Vector Machine and Optimization Algorithm” [...] Full article

22 pages, 2118 KB

Open AccessArticle

Two-Stage Robust Optimization for Bi-Level Game-Based Scheduling of CCHP Microgrid Integrated with Hydrogen Refueling Station

by Ji Li, Weiqing Wang, Zhi Yuan and Xiaoqiang Ding

Electronics 2025, 14(17), 3560; https://doi.org/10.3390/electronics14173560 - 7 Sep 2025

Viewed by 404

Abstract

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and [...] Read more.

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and load sides. Therefore, this paper proposes a two-stage robust optimization for bi-level game-based scheduling of a CCHP microgrid integrated with an HRS. Initially, a bi-level game structure comprising a CCHP microgrid and an HRS is established. The upper layer microgrid can coordinate scheduling and the step carbon trading mechanism, thereby ensuring low-carbon economic operation. In addition, the lower layer hydrogenation station can adjust the hydrogen production plan according to dynamic electricity price information. Subsequently, a two-stage robust optimization model addresses the uncertainty issues associated with wind turbine (WT) power, photovoltaic (PV) power, and multi-load scenarios. Finally, the model’s duality problem and linearization problem are solved by the Karush–Kuhn–Tucker (KKT) condition, Big-M method, strong duality theory, and column and constraint generation (C&CG) algorithm. The simulation results demonstrate that the strategy reduces the cost of both CCHP microgrid and HRS, exhibits strong robustness, reduces carbon emissions, and can provide a useful reference for the coordinated operation of the microgrid. Full article

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26 pages, 1560 KB

Open AccessArticle

Classifying Tooth Loss and Assessing Risk Factors in U.S. Adults: A Machine Learning Analysis of BRFSS 2022 Data

by Sanket Salvi, Giang Vu, Varadraj Gurupur and Christian King

Electronics 2025, 14(17), 3559; https://doi.org/10.3390/electronics14173559 - 7 Sep 2025

Viewed by 160

Abstract

Dental care is a well-established marker of both oral and systemic health, driven by behavioral, socioeconomic, and geographic factors. This study aimed to develop and evaluate machine learning models to classify the presence and severity of permanent tooth loss in U.S. adults using [...] Read more.

Dental care is a well-established marker of both oral and systemic health, driven by behavioral, socioeconomic, and geographic factors. This study aimed to develop and evaluate machine learning models to classify the presence and severity of permanent tooth loss in U.S. adults using the 2022 Behavioral Risk Factor Surveillance System (BRFSS) dataset. We analyzed responses from 365,803 adults after recoding demographic, behavioral, socioeconomic, and access variables. Ten supervised classifiers were trained and evaluated using stratified 80/20 train–test splits, with ANOVA-based selection for the binary task and Pearson correlation plus engineered features for the multiclass task. Performance was assessed by accuracy, AUC, precision, recall, and specificity. For binary classification (any loss vs. none), XGBoost achieved the highest performance (AUC = 0.786; accuracy = 71.4%), with CatBoost close behind (AUC = 0.711). For multiclass severity (none, 1–5, 6+, all teeth removed), an ensemble of gradient-boosting models achieved strong discrimination (macro-AUC = 0.752). Key predictors included age, smoking, education, income, and general health. These findings demonstrate that large-scale survey–based ML models can support oral health surveillance by identifying high-risk groups and informing targeted prevention strategies. Full article

(This article belongs to the Special Issue Machine Learning in Electronic and Biomedical Engineering, 3rd Edition)

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27 pages, 2027 KB

Open AccessArticle

Comparative Analysis of SDN and Blockchain Integration in P2P Streaming Networks for Secure and Reliable Communication

by Aisha Mohmmed Alshiky, Maher Ali Khemakhem, Fathy Eassa and Ahmed Alzahrani

Electronics 2025, 14(17), 3558; https://doi.org/10.3390/electronics14173558 - 7 Sep 2025

Viewed by 226

Abstract

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) [...] Read more.

Rapid advancements in peer-to-peer (P2P) streaming technologies have significantly impacted digital communication, enabling scalable, decentralized, and real-time content distribution. Despite these advancements, challenges persist, including dynamic topology management, high latency, security vulnerabilities, and unfair resource sharing (e.g., free rider). While software-defined networking (SDN) and blockchain individually address aspects of these limitations, their combined potential for comprehensive optimization remains underexplored. This study proposes a distributed SDN (DSDN) architecture enhanced with blockchain support to provide secure, scalable, and reliable P2P video streaming. We identified research gaps through critical analysis of the literature. We systematically compared traditional P2P, SDN-enhanced, and hybrid architectures across six performance metrics: latency, throughput, packet loss, authentication accuracy, packet delivery ratio, and control overhead. Simulations with 200 peers demonstrate that the proposed hybrid SDN–blockchain framework achieves a latency of 140 ms, a throughput of 340 Mbps, an authentication accuracy of 98%, a packet delivery ratio of 97.8%, a packet loss ratio of 2.2%, and a control overhead of 9.3%, outperforming state-of-the-art solutions such as NodeMaps, the reinforcement learning-based routing framework (RL-RF), and content delivery networks-P2P networks (CDN-P2P). This work establishes a scalable and attack-resilient foundation for next-generation P2P streaming. Full article

(This article belongs to the Section Computer Science & Engineering)

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20 pages, 2404 KB

Open AccessArticle

Optimal Capacity Configuration of Multi-Type Renewable Energy in Islanded LCC-HVDC Transmission Systems

by Yuxuan Tao, Qing Wang, Chengbin Hu, Kuangyu Chen, Chunsheng Guo and Jianquan Liao

Electronics 2025, 14(17), 3557; https://doi.org/10.3390/electronics14173557 - 7 Sep 2025

Viewed by 174

Abstract

The islanded line-commutated-converter-based high-voltage direct-current (LCC-HVDC) transmission system is becoming a key solution for delivering multiple types of clean energy from large-scale renewable energy bases, including wind power, photovoltaic power, hydropower, and energy storage. However, the high penetration of renewable sources significantly increases [...] Read more.

The islanded line-commutated-converter-based high-voltage direct-current (LCC-HVDC) transmission system is becoming a key solution for delivering multiple types of clean energy from large-scale renewable energy bases, including wind power, photovoltaic power, hydropower, and energy storage. However, the high penetration of renewable sources significantly increases the risks of frequency fluctuations and voltage violations due to their inherent volatility and uncertainty, posing serious challenges to system stability. To enhance the integration capacity of clean energy and ensure the stable operation of islanded systems, this paper proposes a maximum capacity optimization method tailored for islanded DC transmission involving multiple energy types. A K-medoids clustering algorithm is applied to historical data to extract typical wind and photovoltaic output scenarios, and a virtual balancing node is introduced. Subsequently, an active power droop control strategy and reactive power regulation are applied to enhance system frequency and voltage stability. Finally, the capacities of wind, photovoltaic, and energy storage systems are jointly optimized using particle swarm optimization. Simulation results demonstrate that the proposed approach can accurately determine the maximum allowable integration of wind and photovoltaic power while satisfying system operational constraints, and effectively reduce the required energy storage capacity. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 10200 KB

Open AccessArticle

Real-Time Driver State Detection Using mmWave Radar: A Spatiotemporal Fusion Network for Behavior Monitoring on Edge Platforms

by Shih-Pang Tseng, Wun-Yang Wu, Jhing-Fa Wang and Dawei Tao

Electronics 2025, 14(17), 3556; https://doi.org/10.3390/electronics14173556 - 7 Sep 2025

Viewed by 247

Abstract

Fatigue and distracted driving are among the leading causes of traffic accidents, highlighting the importance of developing efficient and non-intrusive driver monitoring systems. Traditional camera-based methods are often limited by lighting variations, occlusions, and privacy concerns. In contrast, millimeter-wave (mmWave) radar offers a [...] Read more.

Fatigue and distracted driving are among the leading causes of traffic accidents, highlighting the importance of developing efficient and non-intrusive driver monitoring systems. Traditional camera-based methods are often limited by lighting variations, occlusions, and privacy concerns. In contrast, millimeter-wave (mmWave) radar offers a non-contact, privacy-preserving, and environment-robust solution, providing a forward-looking alternative. This study introduces a novel deep learning model, RTSFN (radar-based temporal-spatial fusion network), which simultaneously analyzes the temporal motion changes and spatial posture features of the driver. RTSFN incorporates a cross-gated fusion mechanism that dynamically integrates multi-modal information, enhancing feature complementarity and stabilizing behavior recognition. Experimental results show that RTSFN effectively detects dangerous driving states with an average F1 score of 94% and recognizes specific high-risk behaviors with an average F1 score of 97% and can run in real-time on edge devices such as the NVIDIA Jetson Orin Nano, demonstrating its strong potential for deployment in intelligent transportation and in-vehicle safety systems. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) Based Radar Detection and Recognition in Complex Electromagnetic Environments)

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23 pages, 3220 KB

Open AccessArticle

Robust Observer Design for the Longitudinal Dynamics of a Fixed-Wing Aircraft

by Uygar Gunes, Artun Sel, Erdi Sayar and Cosku Kasnakoglu

Electronics 2025, 14(17), 3555; https://doi.org/10.3390/electronics14173555 - 7 Sep 2025

Viewed by 221

Abstract

This paper presents a novel control-based observer (CbO) framework for robust state and disturbance estimation in the longitudinal dynamics of fixed-wing aircraft. In this approach, the observer design problem is recast as an equivalent control problem, enabling the use of advanced control techniques [...] Read more.

This paper presents a novel control-based observer (CbO) framework for robust state and disturbance estimation in the longitudinal dynamics of fixed-wing aircraft. In this approach, the observer design problem is recast as an equivalent control problem, enabling the use of advanced control techniques for observer synthesis. Within the proposed framework, the estimation of both system states and unknown disturbance inputs is achieved by integrating disturbance rejection capabilities into the control sub-block of the observer. This integration ensures that the output mismatch between the plant and observer model is minimized, even in the presence of modeling uncertainties and external disturbances. Two observer designs are developed: (i) an

H_{\infty}

-CbO, formulated as an

H_{\infty}

control problem around a linearized model at a nominal operating point, and (ii) a robust

H_{\infty}

-CbO, which extends the design to account for significant model nonlinearities and variations by incorporating multiple operating points and optimizing for the worst-case estimation error. The longitudinal dynamics of a fixed-wing aircraft are derived and linearized to provide the basis for observer design. The performance of the proposed observers is evaluated through comprehensive simulation studies under three scenarios: operation at the nominal point, operation around neighboring points, and comparison with conventional linear observers. Simulation results demonstrate that the proposed observer offers superior robustness and accuracy in estimating both states and external disturbances, particularly in the presence of model uncertainties and varying flight conditions. Full article

(This article belongs to the Special Issue Control and Navigation of Robotics and Unmanned Aerial Vehicles)

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17 pages, 2128 KB

Open AccessArticle

Vision-Based Highway Lane Extraction from UAV Imagery: A Deep Learning and Geometric Constraints Approach

by Jin Wang, Guangjun He, Xiuwang Dai, Feng Wang and Yanxin Zhang

Electronics 2025, 14(17), 3554; https://doi.org/10.3390/electronics14173554 - 6 Sep 2025

Viewed by 260

Abstract

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of [...] Read more.

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications. Full article

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25 pages, 21209 KB

Open AccessArticle

Hyperspectral Image Classification Using a Spectral-Cube Gated Harmony Network

by Nana Li, Wentao Shen and Qiuwen Zhang

Electronics 2025, 14(17), 3553; https://doi.org/10.3390/electronics14173553 - 6 Sep 2025

Viewed by 200

Abstract

In recent years, hybrid models that integrate Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) have achieved significant improvements in hyperspectral image classification (HSIC). Nevertheless, their complex architectures often lead to computational redundancy and inefficient feature fusion, particularly struggling to balance global modeling [...] Read more.

In recent years, hybrid models that integrate Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) have achieved significant improvements in hyperspectral image classification (HSIC). Nevertheless, their complex architectures often lead to computational redundancy and inefficient feature fusion, particularly struggling to balance global modeling and local detail extraction in high-dimensional spectral data. To solve these issues, this paper proposes a Spectral-Cube Gated Harmony Network (SCGHN) that achieves efficient spectral–spatial joint feature modeling through a dynamic gating mechanism and hierarchical feature decoupling strategy. There are three primary innovative contributions of this paper as follows: Firstly, we design a Spectral Cooperative Parallel Convolution (SCPC) module that combines dynamic gating in the spectral dimension and spatial deformable convolution. This module adopts a dual-path parallel architecture that adaptively enhances key bands and captures local textures, thereby significantly improving feature discriminability at mixed ground object boundaries. Secondly, we propose a Dual-Gated Fusion (DGF) module that achieves cross-scale contextual complementarity through group convolution and lightweight attention, thereby enhancing hierarchical semantic representations with significantly lower computational complexity. Finally, by means of the coordinated design of 3D convolution and lightweight classification decision blocks, we construct an end-to-end lightweight framework that effectively alleviates the structural redundancy issues of traditional hybrid models. Extensive experiments on three standard hyperspectral datasets reveal that our SCGHN requires fewer parameters and exhibits lower computational complexity as compared with some existing HSIC methods. Full article

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22 pages, 7349 KB

Open AccessFeature PaperReview

Non-Equilibrium Quantum Materials for Electronics

by Giuliano Chiriacò

Electronics 2025, 14(17), 3552; https://doi.org/10.3390/electronics14173552 - 6 Sep 2025

Viewed by 302

Abstract

We review recent experimental advances in non-equilibrium quantum materials, focusing on current- and light-driven systems, transient and metastable phases, and non-equilibrium steady states. Emphasis is placed on current-driven phases and photoinduced control of quantum orders such as superconductivity, charge density waves, and ferroelectricity. [...] Read more.

We review recent experimental advances in non-equilibrium quantum materials, focusing on current- and light-driven systems, transient and metastable phases, and non-equilibrium steady states. Emphasis is placed on current-driven phases and photoinduced control of quantum orders such as superconductivity, charge density waves, and ferroelectricity. We briefly outline the most relevant experimental results and discuss implications for future quantum and electronic technologies. Full article

(This article belongs to the Special Issue Feature Papers in Electronic Materials)

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25 pages, 2352 KB

Open AccessEditor’s ChoiceArticle

High-Frequency Link Analysis of Enhanced Power Factor in Active Bridge-Based Multilevel Converters

by Morteza Dezhbord, Fazal Ur Rehman, Amir Ghasemian and Carlo Cecati

Electronics 2025, 14(17), 3551; https://doi.org/10.3390/electronics14173551 - 6 Sep 2025

Viewed by 267

Abstract

Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, [...] Read more.

Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, power factor performance, and power loss characteristics. A high-frequency link analysis framework is developed to characterize voltage, current, and power transfer waveforms, providing insight into reactive power generation and its impact on overall efficiency. By introducing a modulation-based control approach, the proposed converters significantly reduce circulating currents and enhance the power factor, particularly under varying phase-shift conditions. Compared to quadruple active bridge topologies, the discussed multilevel architectures offer reduced transformer complexity and improved power quality, making them suitable for demanding applications such as electric vehicles and aerospace systems. Full article

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

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23 pages, 2699 KB

Open AccessFeature PaperArticle

Leveraging Visual Side Information in Recommender Systems via Vision Transformer Architectures

by Arturo Álvarez-Sánchez, Diego M. Jiménez-Bravo, María N. Moreno-García, Sergio García González and David Cruz García

Electronics 2025, 14(17), 3550; https://doi.org/10.3390/electronics14173550 - 6 Sep 2025

Viewed by 257

Abstract

Recommender systems are essential tools in the digital age, helping users discover products, content, and services across platforms like streaming services, online stores, and social networks. Traditionally, these systems have relied on methods such as collaborative filtering, content-based, and knowledge-based approaches, using data [...] Read more.

Recommender systems are essential tools in the digital age, helping users discover products, content, and services across platforms like streaming services, online stores, and social networks. Traditionally, these systems have relied on methods such as collaborative filtering, content-based, and knowledge-based approaches, using data like user–item interactions and demographic details. With the rise of big data, an increasing amount of “side information”, like contextual data, social behavior, and metadata, has become available, enabling more personalized and effective recommendations. This work provides a comparative analysis of traditional recommender systems and newer models incorporating side information, particularly visual features, to determine whether integrating such data improves recommendation quality. By evaluating the benefits and limitations of using complex formats like visual content, this work aims to contribute to the development of more robust and adaptive recommender systems, offering insights for future research in the field. Full article

(This article belongs to the Special Issue Application of Data Mining in Social Media)

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14 pages, 2677 KB

Open AccessArticle

Spatial Monitoring of I/O Interconnection Nets in Flip-Chip Packages

by Emmanuel Bender, Moshe Sitbon, Tsuriel Avraham and Michael Gerasimov

Electronics 2025, 14(17), 3549; https://doi.org/10.3390/electronics14173549 - 6 Sep 2025

Viewed by 249

Abstract

Here, we introduce a novel method for the real-time spatial monitoring of I/O interconnection nets in flip-flop packages. Resistance changes in 39 I/O nets are observed simultaneously to produce a spatial profile of the relative degradations of the solder ball joints, interconnection lines, [...] Read more.

Here, we introduce a novel method for the real-time spatial monitoring of I/O interconnection nets in flip-flop packages. Resistance changes in 39 I/O nets are observed simultaneously to produce a spatial profile of the relative degradations of the solder ball joints, interconnection lines, and transistor gates. Location-specific TTF profiles are generated from the degradation data to show the impact of the I/O nets in the context of their placement on the chip. The system succeeds in formulating a clear trend of resistance increase even in relatively mild constant temperature stress conditions. Test results of four temperatures from 80 °C to 120 °C show a dominant degradation pattern strongly influenced by BTI aging demonstrating an acute vulnerability in the pass gates to voltage and temperature stress. The proposed compact spatial monitor solution can be integrated into virtually all chip orientations. The outcome of this study can assist in foreseeing system vulnerabilities in a large spectrum of packaging and advanced packaging orientations in field applications. Full article

(This article belongs to the Special Issue Advances in Hardware Security Research)

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22 pages, 4937 KB

Open AccessArticle

Multimodal AI for UAV: Vision–Language Models in Human– Machine Collaboration

by Maroš Krupáš, Ľubomír Urblík and Iveta Zolotová

Electronics 2025, 14(17), 3548; https://doi.org/10.3390/electronics14173548 - 6 Sep 2025

Viewed by 419

Abstract

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. [...] Read more.

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article

(This article belongs to the Special Issue Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems)

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16 pages, 1705 KB

Open AccessArticle

MLEDNet: Multi-Directional Learnable Edge Information-Assisted Dense Nested Network for Infrared Small Target Detection

by Yong Li, Wenjie Kang, Wei Zhao and Xuchong Liu

Electronics 2025, 14(17), 3547; https://doi.org/10.3390/electronics14173547 - 6 Sep 2025

Viewed by 357

Abstract

Infrared small target detection (IRSTD) remains a critical yet challenging task due to the inherent low signal-to-noise ratio, weak target features, and complex backgrounds prevalent in infrared images. Existing methods often struggle to effectively capture the subtle edge features of targets and suppress [...] Read more.

Infrared small target detection (IRSTD) remains a critical yet challenging task due to the inherent low signal-to-noise ratio, weak target features, and complex backgrounds prevalent in infrared images. Existing methods often struggle to effectively capture the subtle edge features of targets and suppress background clutter simultaneously. To address these limitations, this study proposed a novel Multi-directional Learnable Edge-assisted Dense Nested Attention Network (MLEDNet). Firstly, we propose a multi-directional learnable edge extraction module (MLEEM), which is designed to capture rich directional edge information. The extracted multi-directional edge features are hierarchically integrated into the dense nested attention module (DNAM) to significantly enhance the model’s capability in discerning the crucial edge features of infrared small targets. Then, we design a feature fusion module guided by residual channel spatial attention (ResCSAM-FFM). This module leverages spatio-channel contextual cues to intelligently fuse features across different levels output by the DNAM, effectively enhancing target representation while robustly suppressing complex background interferences. By combining the MLEEM and the ResCSAM-FFM within a dense nested attention framework, we present a new model named MLEDNet. Extensive experiments conducted on benchmark datasets NUDT-SIRST and NUAA-SIRST demonstrate that the proposed MLEDNet achieves superior performance compared to state-of-the-art methods. Full article

(This article belongs to the Special Issue Deep Learning-Based Object Detection/Classification)

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15 pages, 2671 KB

Open AccessArticle

A Novel Integrated IMU-UWB Framework for Walking Trajectory Estimation in Non-Line-of-Sight Scenarios Involving Turning Gait

by Haonan Jia, Tongrui Peng, Wenchao Zhang, Qifei Fan, Zhikang Zhong, Hongsheng Li and Xinyao Hu

Electronics 2025, 14(17), 3546; https://doi.org/10.3390/electronics14173546 - 5 Sep 2025

Viewed by 309

Abstract

Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and [...] Read more.

Accurate walking trajectory estimation is critical for monitoring activity levels in healthcare and occupational safety applications. Ultra-Wideband (UWB) technology has emerged as a key solution for indoor human activity and trajectory tracking. However, its performance is fundamentally limited by Non-Line-of-Sight (NLOS) errors and kinematic drift during turns. To address these challenges, this study introduces a novel integrated IMU-UWB framework for walking trajectory estimation in NLOS scenarios involving turning gait. The algorithm integrates an error-state Kalman filter (ESKF) and a phase-aware turning correction module. Experiments were carried out to evaluate the effectiveness of this framework. The results show that the presented framework demonstrates significant improvements in walking trajectory estimation, with a smaller mean absolute error (7.0 cm) and a higher correlation coefficient, compared to the traditional methods. By effectively mitigating both NLOS-induced ranging errors and turn-related drift, this system enables reliable indoor tracking for healthcare monitoring, industrial safety, and consumer navigation applications. Full article

(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)

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13 pages, 4154 KB

Open AccessArticle

An E-Band High-Precision Active Phase Shifter Based on Inductive Compensation and Series Peaking Enhancement Techniques

by Lingtao Jiang, Bing Cai, Shangyao Huang, Xianfeng Que and Yanjie Wang

Electronics 2025, 14(17), 3545; https://doi.org/10.3390/electronics14173545 - 5 Sep 2025

Viewed by 236

Abstract

This paper presents the design and implementation of a 6-bit high-precision active vector-sum phase shifter (PS) operating in the E-band, fabricated using a 40 nm CMOS process. To generate high-quality in-phase and quadrature (I/Q) signals, a folded transformer-based quadrature generator circuit (QGC) [...] Read more.

This paper presents the design and implementation of a 6-bit high-precision active vector-sum phase shifter (PS) operating in the E-band, fabricated using a 40 nm CMOS process. To generate high-quality in-phase and quadrature (I/Q) signals, a folded transformer-based quadrature generator circuit (QGC) employing inductive compensation is developed. Additionally, the series peaking enhancement technique is applied to improve overall gain and effectively extend the bandwidth. Measurement results demonstrate that the phase shifter achieves a 3 dB bandwidth from 72.3 GHz to 82.3 GHz. Within this range, the measured RMS phase error is merely 1.78–2.55 degrees without calibration, and the RMS gain error is 0.6–0.75 dB. The core area of the proposed phase shifter is 940 μm × 280 μm, and it consumes 57.2 mW of power with a 1.1 V supply. Full article

(This article belongs to the Collection Advanced Design Techniques and EDA Methodologies for Analog, RF and MM-Wave Circuit Design)

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15 pages, 4274 KB

Open AccessArticle

Active and Reactive Power Optimal Control of Grid-Connected BDFG-Based Wind Turbines Considering Power Loss

by Wenna Wang, Liangyi Zhang, Sheng Hu, Defu Cai, Haiguang Liu, Dian Xu, Luyu Ma and Jinrui Tang

Electronics 2025, 14(17), 3544; https://doi.org/10.3390/electronics14173544 - 5 Sep 2025

Viewed by 186

Abstract

Power loss can influence the accuracy of maximum power point tracking (MPPT) control and the efficiency of a brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). Because power loss is related to both the active power reference and reactive power reference of BDFG, [...] Read more.

Power loss can influence the accuracy of maximum power point tracking (MPPT) control and the efficiency of a brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). Because power loss is related to both the active power reference and reactive power reference of BDFG, this article proposes active and reactive power optimal control of BDFGWT by considering power loss. Firstly, the mathematical model of BDFGWT, including the wind turbine, BDFG, and back-to-back converter, is established. Then, an active and reactive power optimal control strategy is proposed. In proposed control, the accurate active power reference of power winding (PW) is calculated by considering the active power loss of BDFG; in this way, proposed MPPT control can capture more wind power compared to traditional MPPT control, ignoring the power losses, thus improving the efficiency of BDFGWT. Furthermore, on the basis of the model of BDFG, the relations between reactive power and total active loss are analyzed, and the optimal reactive power control reference to minimize the active power loss is determined. Finally, in order to verify the validity of the proposed control, 2 MW BDFGWT has been constructed, and the proposed method was studied to make a comparison. The results verify that proposed control can maximize the utilization of wind energy, minimize the power loss of the BDFGWT system, and output maximal active power to the power grid. Full article

(This article belongs to the Special Issue Advances in Renewable Energy and Electricity Generation)

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30 pages, 1596 KB

Open AccessArticle

Network-Aware Smart Scheduling for Semi-Automated Ceramic Production via Improved Discrete Hippopotamus Optimization

by Qi Zhang, Changtian Zhang, Man Yao, Xiwang Guo, Shujin Qin, Haibin Zhu, Liang Qi and Bin Hu

Electronics 2025, 14(17), 3543; https://doi.org/10.3390/electronics14173543 - 5 Sep 2025

Viewed by 231

Abstract

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus [...] Read more.

The increasing integration of automation and intelligent sensing technologies in daily-use ceramic manufacturing poses new challenges for efficient scheduling under hybrid flow-shop and shared-kiln constraints. To address these challenges, this study proposes a Mixed-Integer Linear Programming (MILP) model and an Improved Discrete Hippopotamus Optimization (IDHO) algorithm designed for smart, network-aware production environments. The MILP formulation captures key practical features such as batch processing, no-idle kiln constraints, and machine re-entry dynamics. The IDHO algorithm enhances global search performance via segment-based encoding, nonlinear population reduction, and operation-specific mutation strategies, while a parallel evaluation framework accelerates computational efficiency, making the solution viable for industrial-scale, time-sensitive scenarios. The experimental results from 12 benchmark cases demonstrate that IDHO achieves superior performance over six representative metaheuristics (e.g., PSO, GWO, Jaya, DBO), with an average ARPD of 1.04%, statistically significant improvements (p < 0.05), and large effect sizes (Cohen’s d > 0.8). Compared to the commercial solver CPLEX, IDHO provides near-optimal results with substantially lower runtime. The proposed approach contributes to the development of intelligent networked scheduling systems for cyber-physical manufacturing environments, enabling responsive, scalable, and data-driven optimization in smart sensing-enabled production settings. Full article

(This article belongs to the Section Networks)

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25 pages, 6156 KB

Open AccessArticle

A Personalized 3D-Printed Smart Splint with Integrated Sensors and IoT-Based Control: A Proof-of-Concept Study for Distal Radius Fracture Management

by Yufeng Ma, Haoran Tang, Baojian Wang, Jiashuo Luo and Xiliang Liu

Electronics 2025, 14(17), 3542; https://doi.org/10.3390/electronics14173542 - 5 Sep 2025

Viewed by 195

Abstract

Conventional static fixation for distal radius fractures (DRF) is clinically challenging, with methods often leading to complications such as malunion and pressure-related injuries. These issues stem from uncontrolled pressure and a lack of real-time biomechanical feedback, resulting in suboptimal functional recovery. To overcome [...] Read more.

Conventional static fixation for distal radius fractures (DRF) is clinically challenging, with methods often leading to complications such as malunion and pressure-related injuries. These issues stem from uncontrolled pressure and a lack of real-time biomechanical feedback, resulting in suboptimal functional recovery. To overcome these limitations, we engineered an intelligent, adaptive orthopedic device. The system is built on a patient-specific, 3D-printed architecture for a lightweight, personalized fit. It embeds an array of thin-film pressure sensors at critical anatomical sites to continuously quantify biomechanical forces. This data is transmitted via an Internet of Things (IoT) module to a cloud platform, enabling real-time remote monitoring by clinicians. The core innovation is a closed-loop feedback controller governed by a robust Interval Type-2 Fuzzy Logic (IT2-FLC) algorithm. This system autonomously adjusts servo-driven straps to dynamically regulate fixation pressure, adapting to changes in limb swelling. In a preliminary clinical evaluation, the group receiving the integrated treatment protocol, which included the smart splint and TCM herbal therapy, demonstrated superior anatomical restoration and functional recovery, evidenced by higher Cooney scores (91.65 vs. 83.15) and lower VAS pain scores. This proof-of-concept study validates a new paradigm for adaptive orthopedic devices, showing high potential for clinical translation. Full article

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38 pages, 5479 KB

Open AccessFeature PaperReview

Analog Design and Machine Learning: A Review

by Konstantinos G. Liakos and Fotis Plessas

Electronics 2025, 14(17), 3541; https://doi.org/10.3390/electronics14173541 - 5 Sep 2025

Viewed by 314

Abstract

Analog and mixed-signal integrated circuit (AMS-IC) designs remain a critical yet challenging aspect within electronic design automation (EDA), primarily due to the inherent complexity, nonlinear behavior, and increasing variability associated with advanced semiconductor technologies. Traditional manual and intuition-driven methodologies for AMS-ICs design, which [...] Read more.

Analog and mixed-signal integrated circuit (AMS-IC) designs remain a critical yet challenging aspect within electronic design automation (EDA), primarily due to the inherent complexity, nonlinear behavior, and increasing variability associated with advanced semiconductor technologies. Traditional manual and intuition-driven methodologies for AMS-ICs design, which rely heavily on iterative simulation loops and extensive designer experience, face significant limitations concerning efficiency, scalability, and reproducibility. Recently, machine learning (ML) techniques have emerged as powerful tools to address these challenges, offering significant enhancements in modeling, abstraction, optimization, and automation capabilities for AMS-ICs. This review systematically examines recent advancements in ML-driven methodologies applied to analog circuit design, specifically focusing on modeling techniques such as Bayesian inference and neural network (NN)-based surrogate models, optimization and sizing strategies, specification-driven predictive design, and artificial intelligence (AI)-assisted design automation for layout generation. Through an extensive survey of the existing literature, we analyze the effectiveness, strengths, and limitations of various ML approaches, identifying key trends and gaps within the current research landscape. Finally, the paper outlines potential future research directions aimed at advancing ML integration in AMS-ICs design, emphasizing the need for improved explainability, data availability, methodological rigor, and end-to-end automation. Full article

(This article belongs to the Special Issue Recent Advances in AI Hardware Design)

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26 pages, 5004 KB

Open AccessArticle

Effectiveness of Modern Models Belonging to the YOLO and Vision Transformer Architectures in Dangerous Items Detection

by Zbigniew Omiotek

Electronics 2025, 14(17), 3540; https://doi.org/10.3390/electronics14173540 - 5 Sep 2025

Viewed by 385

Abstract

The effectiveness of recently developed tools for detecting dangerous items is overestimated due to the low quality of the datasets used to build the models. The main drawbacks of these datasets include the unrepresentative range of conditions in which the items are presented, [...] Read more.

The effectiveness of recently developed tools for detecting dangerous items is overestimated due to the low quality of the datasets used to build the models. The main drawbacks of these datasets include the unrepresentative range of conditions in which the items are presented, the limited number of classes representing items being detected, and the small number of instances of items belonging to individual classes. To fill the gap in this area, a comprehensive dataset dedicated to the detection of items most used in various acts of public security violations has been built. The dataset includes items such as a machete, knife, baseball bat, rifle, and gun, which are presented in varying quality and under different environmental conditions. The specificity of the constructed dataset allows for more reliable results, which give a better idea of the effectiveness of item detection in real-world conditions. The collected dataset was used to build and compare the effectiveness of modern models for detecting items belonging to the YOLO and Vision Transformer (ViT) architectures. Based on a comprehensive analysis of the results, taking into account accuracy and performance, it turned out that the best results were achieved by the YOLOv11m model, for which Recall = 88.2%, Precision = 89.6%, mAP@50 = 91.8%, mAP@50–95 = 73.7%, Inference time = 1.9 ms. The test results make it possible to recommend this model for use in public security monitoring systems aimed at detecting potentially dangerous items. Full article

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

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18 pages, 3242 KB

Open AccessArticle

Synchronous Stability Analysis and Enhanced Control of Power Systems with Grid-Following and Grid-Forming Converters Considering Converter Distribution

by Xin Luo, Zhiying Chen, Fei Duan, Yilong He and Pengwei Sun

Electronics 2025, 14(17), 3539; https://doi.org/10.3390/electronics14173539 - 5 Sep 2025

Viewed by 348

Abstract

Under the backdrop of low-carbon energy transition, the increasing integration of grid-following (GFL) and grid-forming (GFM) converters into power systems is profoundly altering transient synchronous stability. A critical challenge lies in analyzing synchronous stability in grids with high penetration converters and improving converter [...] Read more.

Under the backdrop of low-carbon energy transition, the increasing integration of grid-following (GFL) and grid-forming (GFM) converters into power systems is profoundly altering transient synchronous stability. A critical challenge lies in analyzing synchronous stability in grids with high penetration converters and improving converter control strategies to enhance stability. This paper selects virtual synchronous generator (VSG)-based converters as representative GFM units to investigate synchronous stability and control in hybrid systems with both VSG and GFL converters. To simplify stability analysis, this study proposes a novel distribution scheme of power supplies based on an assessment of the ability of different sources to reshape synchronous stability. Specifically, synchronous generators (SGs) and GFL converters are located in the power sending area, while VSGs are deployed in the power receiving area. Under this configuration, synchronous risk is predominantly determined by the power-angle difference between VSGs and SGs. Subsequently, the mechanism by which voltage stability affects synchronous stability between SGs and VSGs is revealed. Furthermore, enhanced control strategies for both VSG and GFL converters are proposed which adjust their transient active/reactive power response characteristics to enhance synchronous stability between SGs and VSGs. Finally, the theoretical analysis and control strategies are validated through simulations on a multi-machine, two-area interconnected power system. Under the proposed enhanced control strategies for GFLs and VSGs, the first-swing power-angle amplitude between VSGs and SGs is reduced by 60% and 49%. Full article

(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated, 2nd Edition)

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