Electronics doi: 10.3390/electronics15081578

Authors: Peijun Qin Long Liu Hongjuan Wang Siyuan Ma Cui Chen Zixuan Han Mingzhi Cheng

Digital stylization of Dunhuang murals can support cultural heritage revitalization by transferring their distinctive aesthetics to modern images, but existing methods face practical limitations. Transformer-based models can yield high visual quality, but often at a prohibitive computational cost. In contrast, standard state space models (SSMs) are more efficient but tend to incur issues such as semantic loss, inconsistent stylization, and an undesired coupling between color and structure when processing the complex textures of historical murals. To address these issues, we propose Dh-Mamba, a hierarchical visual Mamba framework tailored for high-fidelity Dunhuang mural style transfer. Dh-Mamba introduces a CrossMamba in-context style injection mechanism. This mechanism prefixes the style token sequence to the content sequence, which enables globally consistent style propagation as a persistent memory and retains linear-time efficiency. We also designed two additional components: a Modulated Style Perception Module (Δt) and an Orthogonal Decoupled HSV Modulator. The former adaptively regulates texture injection based on style complexity. The latter models mineral pigment palettes and mitigates oxidation-related artifacts by disentangling hue, saturation, and value. Experiments on a custom Dunhuang dataset show that Dh-Mamba improves content preservation and produces more natural mural textures than recent state-of-the-art methods; multiple quantitative metrics corroborate these gains. With 20.04 million parameters, Dh-Mamba provides a resource-efficient solution suitable for deployment in resource-constrained terminal applications for cultural heritage preservation.

Electronics doi: 10.3390/electronics15081576

Authors: Grzegorz Rzym Zbigniew Duliński Rafał Stankiewicz Piotr Wydrych

This work presents an enhancement to the Dynamic Traffic Management (DTM) framework aimed at reducing signaling overhead between SDN controllers in multi-domain cloud environments. This extension is based on the ability to transmit information regarding the amount of balanced traffic and the optimal transfer pattern. In the baseline periodic mode, the system regularly exchanges the compensation vector (C→) and the reference pattern (R→). To minimize communication, we define non-periodic modes that restrict C→ updates and eliminate R→ transmission entirely. Within these restricted signaling modes, we further distinguish between reactive and proactive operational schemes. Our experimental results demonstrate that reducing the visibility of optimization goals (R→ and only sign of C→) and cutting signaling frequency in this manner maintains a comparable level of cost-efficiency. Specifically, the initial evaluation shows that DTM typically decreases transit costs by 8% to 15%, with maximum savings reaching up to 29% when compared to the worst-case default BGP path scenario. These findings suggest that the DTM mechanism can maintain its economic efficiency even with significantly reduced inter-domain coordination.

Electronics doi: 10.3390/electronics15081577

Authors: Yi Zhang Hongguang Wang Xinan Pan Qianyi Wang

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches.

Electronics doi: 10.3390/electronics15081574

Authors: Habib Benbouhenni Nicu Bizon

This paper presents an intelligent cascaded fractional-order proportional–integral (CFO-PI) control strategy optimized using a genetic algorithm (GA) for a 1.5 MW DFIG-based multi-rotor wind turbine (MRWT) system. The primary objective is to enhance operational performance and power quality. The proposed method is evaluated against the conventional direct power control scheme using a traditional PI regulator (DPC-PI) to demonstrate its effectiveness. Comparative analysis shows substantial performance improvements achieved by the CFO-PI approach. Specifically, active power ripple is reduced by 61.71% compared to DPC-PI, resulting in smoother power delivery and improved grid compatibility. In addition, the steady-state error of active power decreases by 72.60%, indicating improved tracking accuracy. For reactive power, a 52.03% reduction in ripple is observed, while current ripple is reduced by approximately 56%, reflecting enhanced waveform quality. These results highlight the CFO-PI controller’s capability to maintain better power quality and steady-state performance relative to conventional DPC-PI. Overall, the GA-optimized CFO-PI controller provides a promising alternative for improving dynamic performance and power quality in DFIG-based MRWT systems.

Electronics doi: 10.3390/electronics15081575

Authors: Aris van Ieperen Stijn Derammelaere Ben Minnaert

Wireless power transfer (WPT) continues to gain momentum across diverse applications, from milliwatt biomedical implants to tens-of-kilowatts electric vehicle charging. Within short-distance WPT, inductive wireless power transfer (IPT) and capacitive wireless power transfer (CPT) are the two dominant approaches, each with distinct advantages and limitations. This paper surveys the recent experimental progress in IPT and CPT reported in 133 peer-reviewed publications between 2020 and 2025. The survey focuses on system-level demonstrations that include quantitative performance metrics, with particular emphasis on DC-DC efficiency. Key parameters, such as power level, operating frequency, transfer distance, and coupler area, are systematically compared. The survey reveals that IPT remains dominant in very high-power and larger-gap realizations, while CPT has expanded beyond its traditionally short-gap applications and now competes directly with IPT across a wide range of power levels. Both techniques routinely achieve efficiencies exceeding 90% under diverse operating conditions, underscoring their growing maturity and potential to address future WPT demands. The presented data reveal measurable shifts in achievable power and efficiency in the last decade, reflecting the maturation of CPT and the influence of wide-bandgap power electronics. These findings establish an updated data-driven performance envelope derived from experimentally demonstrated systems, providing a reference for future experimental and modeling studies in short-range WPT.

Electronics doi: 10.3390/electronics15081573

Authors: Wenxin Lei Xiaochuan Ming Jiyang Gao

Acquiring decorative patterns from glossy ceramic surfaces is challenging because specular reflections often obscure fine details and reduce the reliability of subsequent digital analysis. Although existing highlight-removal methods, including data-driven and single-image enhancement approaches, have improved restoration quality in generic scenes, they are not fully suited to glossy ceramic documentation because they often rely on scene priors, large paired datasets, or post hoc enhancement alone while paying limited attention to acquisition-side optimization for reflective cultural objects. This article presents a portable multimodal imaging system and a processing framework for ceramic pattern acquisition, highlight suppression, and acquisition optimization. Multimodal images captured under different illumination and polarization configurations are first geometrically registered, after which specular regions are localized by jointly exploiting polarization and intensity cues, followed by highlight suppression and perceptual appearance restoration to improve pattern visibility while preserving visual authenticity. Experimental results indicates that warm illumination with 0° polarization is more suitable for warm-toned ceramics or ceramics with large-area patterns, whereas uniform illumination with 45° polarization is more suitable for cool-toned ceramics and ceramics with sparse patterns; additionally, cool illumination with 90° polarization yields the highest average score across the dataset, indicating stronger robustness across diverse samples. The proposed system is portable, supports wireless image transmission, and integrates adjustable illumination with a servo-driven polarizer, thereby providing a practical solution for high-quality digital documentation of glossy ceramic patterns.

Electronics doi: 10.3390/electronics15081572

Authors: Lingzi Zhu Bo Zhao Rao Peng

The rapid advancement of artificial intelligence relies on massive high-quality data, yet increasingly stringent data privacy regulations have exacerbated the problem of data silos. Federated learning enables collaborative training under privacy protection by exchanging model parameters rather than transmitting raw data. Nevertheless, its traditional centralized architecture still suffers from limitations such as single points of failure, lack of trust, and insufficient incentives. The integration of blockchain and federated learning opens new pathways for decentralized, auditable, and secure machine learning systems. This paper systematically reviews research progress in blockchain-enabled federated learning, analyzing technological evolution from three perspectives: system architecture, incentive mechanisms, and privacy enhancement. It further explores critical challenges including efficiency bottlenecks, storage overhead, and the inherent tension between transparency and privacy, while identifying key research directions for building scalable, efficient, and trustworthy decentralized learning systems.

Electronics doi: 10.3390/electronics15081571

Authors: Antonio Manno Gaetano Palumbo

This paper, after an overview of most of the improved Domino Logic topologies with keeper, provides an in-depth and comprehensive comparison of the simplest keeper architectures: the Delayed Keeper, the Conditional Keeper, the Split Keeper and their combined variants. A design strategy for setting the keeper aspect ratio to satisfy a target noise immunity requirement is presented in the paper. All the considered topologies are then evaluated through extensive Monte Carlo simulations, assessing delay and its standard deviation, power consumption and Power–Delay–Product, as well as noise immunity and sensitivity to layout-dependent parasitics, implementing a wide set of logic gates in a 28 nm CMOS technology. A further comparison of the topologies, when the gates are cascaded to realize a simple Datapath, suggests that the Split Keeper, while being the simplest topology, generally provides a very favorable speed–power trade-off. In particular, although the speed advantage with respect to the more complex Delayed topologies is marginal, it generally results in less than half the power consumption and PDP.

Electronics doi: 10.3390/electronics15081570

Authors: Yichao Fei Youfeng Zhao Wenrui Liu Fei Wu Shangdong Liu Xinyu Zhu Yimu Ji Pingsheng Jia

With the proliferation of edge computing in IoT and smart security, there is a growing demand for large-scale encrypted traffic anomaly detection. However, the opaque nature of encrypted traffic makes it difficult for traditional detection methods to balance efficiency and accuracy. To address this challenge, this paper proposes FMTF, a Multi-Scale Feature Fusion method based on Federated Learning for encrypted traffic anomaly detection. FMTF constructs graph structures at three scales—spatial, statistical, and content—to comprehensively characterize traffic features. At the spatial scale, communication graphs are constructed based on host-to-host IP interactions, where each node represents the IP address of a host and edges capture the communication relationships between them. The statistical scale builds traffic statistic graphs based on interactions between port numbers, with nodes representing individual ports and edge weights corresponding to the lengths of transmitted packets. At the content scale, byte-level traffic graphs are generated, where nodes represent pairs of bytes extracted from the traffic data, and edges are weighted using pointwise mutual information (PMI) to reflect the statistical association between byte occurrences. To extract and fuse these multi-scale features, FMTF employs the Graph Attention Network (GAT), enhancing the model’s traffic representation capability. Furthermore, to reduce raw-data exposure in distributed edge environments, FMTF integrates a federated learning framework. In this framework, edge devices train models locally based on their multi-scale traffic features and periodically share model parameters with a central server for aggregation, thereby optimizing the global model without exposing raw data. Experimental results demonstrate that FMTF maintains efficient and accurate anomaly detection performance even under limited computing resources, offering a practical and effective solution for encrypted traffic identification and network security protection in edge computing environments.

Electronics doi: 10.3390/electronics15081569

Authors: Yuanqing Ding Fanliang Bu Hanming Zhai

Deepfake technology poses significant threats to information authenticity and social trust, necessitating effective detection methods. However, existing detection approaches predominantly rely on high-complexity network architectures that, while accurate in controlled environments, suffer from prohibitive computational costs that hinder deployment in resource-constrained scenarios such as social media platforms. To address this efficiency-accuracy dilemma, we propose a lightweight face forgery detection method that systematically learns multi-scale forgery traces. Our approach features a four-stage lightweight architecture that hierarchically extracts features from local textures to global semantics, mimicking the human visual system. Within each stage, a multi-scale dynamic perception mechanism divides feature channels into parallel groups equipped with lightweight attention modules to capture forgery cues spanning pixel-level anomalies, local structures, regional patterns, and semantic inconsistencies. Furthermore, rather than relying on conventional feature fusion that risks suppressing subtle artifacts, we introduce a novel Context-Guided Dynamic Convolution. This mechanism uses mid-level spatial anomalies as active anchors to dynamically modulate high-level semantic filters, with the goal of mitigating the disconnect between semantic content and forgery evidence. Our model achieves strong performance, yielding an AUC of 91.98% on FaceForensics++ and 93.50% on DeepFake Detection Challenge, outperforming current state-of-the-art lightweight methods. Furthermore, compared to heavy Vision Transformers, our model achieves a superior performance-efficiency trade-off, requiring only 3.06 M parameters and 1.36 G FLOPs, making it highly suitable for real-time, resource-constrained deployment.

Electronics doi: 10.3390/electronics15081567

Authors: Zaid A. Abdul Hassain Malik J. Farhan Taha A. Elwi

In this paper, a novel multiband microwave resonator is proposed and investigated for non-invasive glucose sensing applications. The structure is based on a compact, planar spiral-like geometry fed by a Coplanar waveguide (CPW) transmission line, designed to support multiple resonant modes through nested concentric rings. A full electromagnetic model was developed to predict the resonance behavior analytically, achieving excellent agreement with Computer Simulated Technology (CST) simulations across four resonant frequencies (2.7, 6.44, 8.0, and 12.8 GHz). The sensor demonstrated high glucose sensitivity at multiple frequencies, with peak values reaching 0.05 dB/mg/dL and 0.038 dB/mg/dL at 10.1 GHz and 6.22 GHz, respectively. To enhance conformability and skin contact, the antenna was further transformed into a semi-cylindrical flexible form suitable for finger-wrapping. Despite the mechanical deformation, the structure preserved its resonance while offering enhanced near-field interaction with biological tissues. The folded sensor achieved a sensitivity of 0.032 dB/mg/dL at 5.25 GHz and a peak gain of 6.05 dB, validating its robustness for wearable deployment. The clear correlation between reflection magnitude and glucose level (with R > 0.99) confirms the sensor’s potential as a passive, multiband, and non-invasive glucose monitoring platform. The physics-informed residual deep learning framework significantly enhances prediction accuracy, achieving an RMSE of 0.28 mg/dL, MARD of 0.13%, and confining 100% of both training and holdout predictions within the <5% ISO-like risk region, thereby ensuring robust and clinically reliable non-invasive glucose estimation.

Electronics doi: 10.3390/electronics15081568

Authors: Jarosław Joostberens Aurelia Rybak Aleksandra Rybak Piotr Toś Artur Kozłowski Leszek Kasprzyczak

This article presents the potential for maliciously influencing a control system by interfering with the program code of an industrial controller, using a temperature control system for a heating process based on resistive electric heating as an example. The presented attack scenarios are crucial for the energy efficiency of electric heating systems, which is related to the issue of cybersecurity in the area of energy security. The aim of this research was to demonstrate that a cyberattack involving the malicious manipulation of the setpoint can be carried out in a manner invisible to the heating process operator and be difficult to detect using classical time-domain control quality indicators (time-response specifications). The first involves incorporating proportional elements with mutually inverted gains into the input and output of a closed-loop system. The second method is based on adding an additional transfer function Gm(s) in parallel to the control system. The difference between the correct and manipulated setpoints is introduced into the input, and the output signal is added to the actual (hidden) value of the controlled variable. In the first method, at the moment of starting the control system, there is a difference between the apparent (falsified) value and the ambient temperature. In the second method, the inclusion of an additional Gm(s) ensures that the apparent (falsified) value of the controlled variable matches the temperature at the moment of starting the system. PID control enables achieving satisfactory control quality in heating processes, which are characterized by high inertia and time delays. Compared to classical PID regulation, advanced control methods can, under certain conditions, provide better performance in terms of quality indicators. However, due to their high computational complexity and sensitivity to model uncertainty—particularly in methods relying on accurate system identification—PID controllers continue to be widely used in industrial practice. For this reason, the present study focuses on a control system based on a PID controller as a practical solution. Based on the results, it was found that the most effective manipulation occurred within the range from 0.9 to 1.1 of the actual setpoint value for both the first and second method, using a model with Tm between 5 s and 30 s. In these cases, the quality indicators referenced to the nominal values, determined for the falsified control system responses to a step change in the setpoint, were as follows: overshoot—0.97 and 1.30 (method 1), and 0.90 and 1.10 (method 2 for 5 s), 0.75 and 1.30 (method 2 for 30 s); settling time—1.06 (method 1), and 0.98 and 1.17 (method 2 for 5 s), 0.85 and 1.14 (method 2 for 30 s). The settling times determined for the system’s response to a disturbance were: 1.00 and 1.15 (method 1), and 1.13 and 1.16 (method 2 for 5 s), 1.12 and 1.02 (method 2 for 30 s). Based on the conducted analysis, it was demonstrated that the relatively simple setpoint manipulation methods presented can effectively mask the impact of malicious interference on the temperature value in the control system of a heating process.

Electronics doi: 10.3390/electronics15081566

Authors: Hsiang-Hsi Wang Cheng-Chun Chang Xuan-Yang Lin Cheng-Hsien Yu Yu-Xiang Huang Wen-Long Chin

Signal synchronization is one of the core aspects of communication, ensuring that the receiver accurately decodes the signals transmitted by the sender. However, in the diverse application scenarios and broad spectrum range of 5G new radio (NR), the performance of traditional estimation algorithms often deteriorates as frequency offset increases and noise interference intensifies. This work focuses on the estimation of time offset, cell sector identifier (ID), and frequency offset in 5G mobile communication systems. We leverage the advanced learning capabilities and adaptability of a convolutional neural network (CNN) to optimize the estimation process. Additionally, we incorporate a long short-term memory (LSTM) network to capture the dynamic variations in time-varying channels. The results demonstrate that the proposed neural network exhibits significant advantages in estimation performance.

Electronics doi: 10.3390/electronics15081565

Authors: Longfei Chen Xiaoyuan Wang Jingheng Wang Han Zhang Chenyang Jiao Bin Wang Kai Feng Cheng Shen

Collecting a large number of dangerous omen scenarios from drivers’ first-person perspective is of great significance for training and improving end-to-end autonomous driving models. In this study, we aim at capturing driver-perspective scenarios when recognizing dangerous omens. Firstly, through the design and implementation of vehicle and virtual driving experiments, the electroencephalogram, electrocardiogram and eye movement data of the subjects are collected. Statistical tests are conducted to analyze the characteristic differences among drivers across three distinct states. It also reveals that the driver can perceive and distinguish the dangerous omen clearly. Secondly, the evolution law of drivers’ perception state is analyzed to accurately judge the time period of drivers’ dangerous omen perception. Thirdly, the Hidden Markov Model is used to build the driver perception state transition model, and then the model is calibrated and verified. Finally, the model is utilized to identify drivers’ dangerous omen perception states and extract the corresponding perspective objective scenarios, which can provide sufficient samples for training end-to-end autonomous driving models. This study is of great significance to enable the capability of vehicles to recognize dangerous omens, advancing end-to-end and other high-level autonomous driving technologies and further securing vehicle safety.

Electronics doi: 10.3390/electronics15081564

Authors: Qian Guo Anming Dong Sufang Li Jiguo Yu You Zhou

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods.

Electronics doi: 10.3390/electronics15081563

Authors: Yiqi Huang Qiang Zhao Miao Zhu Shuli Wen Bing Zhang

A triple phase shift (TPS) modulation strategy is proposed for a three-port active bridge (TAB) converter in shipboard zonal DC systems. Unlike traditional multi-port converters, the TAB realizes voltage conversion and bidirectional power conversion under TPS modulation. It exhibits superior performance in reducing control complexity, enhancing fault-tolerant capability, and extending the zero-voltage switching (ZVS) region under normal and fault operation modes. To further enhance its conversion efficiency, a deep reinforcement learning optimization approach based on the deep deterministic policy gradient (DDPG) algorithm is introduced to adaptively optimize TPS control parameters and minimize the overall power losses of the converter. To verify the proposed TPS modulation and DDPG-based optimization strategy for the TAB converter topology, a corresponding hardware prototype is built and experimentally tested under different operating conditions. Experimental results demonstrate that the TAB architecture with DDPG optimization effectively reduces current stress and power loss, boosting the converter’s maximum efficiency to 96.9% under normal mode and a 3% efficiency gain after fault isolation.

Electronics doi: 10.3390/electronics15081562

Authors: Yanping Lu Myun Kim

In response to the redundant spatio-temporal modeling and insufficient adaptation to dynamic decision-making in eye-tracking interaction of smart home interfaces, a smart home interface eye-tracking response optimization model based on spatio-temporal Transformer and gate control cross-attention is proposed. It adapts the physiological characteristics of eye-tracking jumps through dynamic sparse attention gating to compress computational redundancy and combines multi-objective reinforcement learning attention modulation to construct a closed-loop decision-making mechanism, optimizing interface parameters in real-time. Experiments showed that the model reduced eye-tracking trajectory prediction error by 23.7% compared to advanced benchmarks, increased the success rate of adapting to dynamic mutation scenarios to 89.2%, and controlled performance fluctuations within 2.3% under noise interference. In high-fidelity user testing, the accuracy of cross-task gaze transfer reached 93.4%, the failure rate of glare interference was optimized to 2.4%, and the user cognitive load index was reduced by 27.9%. Its resource consumption and energy consumption were reduced by 26.7% and 44.9%, respectively, while its posture deviation tolerance remained at 3.5°. The sparse spatio-temporal modeling of the spatio-temporal adaptive Transformer module and the enhanced gating mechanism of the hierarchical gated cross-attention module work together to break through the limitations of traditional methods in computational efficiency and dynamic feedback, providing high-precision and low-latency eye-tracking interaction solutions for smart home interface systems, and promoting the practical evolution of personalized human–machine collaborative control.

Electronics doi: 10.3390/electronics15081561

Authors: Tzu-Wei Wang Tai-You Chen Chien-Chia Chiu Berlin Chen Jeih-Weih Hung

This study presents HGRN2-based Flexible Dynamic Encoder Personal VAD (FDE-HGRN2), a recurrent framework for personal voice activity detection (PVAD). Building on the original LSTM-based FDE-RNN backbone, we replace all recurrent modules with the recently introduced HGRN2 gated linear RNN and adopt a cosine-annealing learning rate schedule to improve both detection accuracy and efficiency. HGRN2 uses gated linear recurrence with non-parametric state expansion, enlarging the recurrent state without increasing the number of trainable parameters and enabling more expressive long-range temporal modeling than conventional LSTMs. We evaluate FDE-HGRN2 on a LibriSpeech-derived PVAD benchmark, where multi-speaker mixtures are constructed by concatenating one to three speakers per utterance and randomly designating a target speaker, following established PVAD data construction practices to ensure direct comparability with prior work. The system uses 40-dimensional Mel-filterbank features as acoustic inputs and conditions the detector on 256-dimensional d-vector embeddings extracted from a pretrained speaker verification network. Experimental results show that FDE-HGRN2 consistently outperforms the original FDE-RNN baseline and several state-of-the-art PVAD models in terms of mean Average Precision and frame-level accuracy, while reducing the parameter count of the recurrent backbone by roughly 15% and yielding substantially smaller models than many competing systems. These findings indicate that HGRN2 provides a more temporally expressive and parameter-efficient alternative to LSTM for PVAD, offering a favorable accuracy–efficiency trade-off for real-world, deployment-oriented personalized speech interfaces.

Electronics doi: 10.3390/electronics15081560

Authors: Haoran Wang Zhongmeng Zhang Wenbang Long Haitao Pu

Silicon carbide (SiC) power converters offer superior switching performance but generate severe broadband electromagnetic interference (EMI) that challenges regulatory compliance. Existing prediction methods face a fundamental trade-off between physical fidelity and computational efficiency, while conventional suppression strategies lack adaptability to varying operating conditions. This paper proposes a frequency-band-aware physics-informed generative adversarial network (FBA-PIGAN) that integrates electromagnetic domain knowledge into data-driven generative modeling for joint EMI prediction and adaptive suppression in SiC power converters. The framework employs a Wasserstein GAN with gradient penalty as the adversarial backbone and introduces feature-wise linear modulation (FiLM) to inject converter operating parameters into the generator through learned affine transformations. A hierarchical physics-informed loss function enforces three frequency-dependent constraints, namely, harmonic structure consistency, parasitic resonance characterization, and high-frequency envelope regularization, coordinated by a curriculum-based weight-scheduling strategy. An end-to-end differentiable suppression module maps predicted spectra to optimal passive filter parameters through an analytically embedded transfer function. Experimental validation on a 10 kW SiC inverter platform with 5120 measured spectra across 32 operating conditions demonstrates that FBA-PIGAN achieves a mean spectral error of 2.1 dB, 93.8% peak frequency accuracy, and a physical consistency score of 0.93, improving prediction accuracy by 56% over conventional conditional GANs while maintaining sub-millisecond inference latency. The integrated suppression pipeline attains 19.2 dB average attenuation with 98.5% CISPR 25 compliance, and the framework generalizes to unseen operating conditions with only 19% performance degradation, compared with 56% for data-driven baselines.

Electronics doi: 10.3390/electronics15081559

Authors: Jethro Shell Sophy Smith

The visualisation of music has gained traction in both research and musical composition in recent years. The increased accessibility to immersive technologies, such as virtual reality (VR) and other forms of mixed reality (MR), lend themselves to the examination of how visualisation can impact the perception of audio virtual worlds. In this paper, we propose the MAVIS (Multi-stem Audio Visualisation in Immersive Spaces) design framework, an approach to generating a visualisation of multi-stem structured orchestral music in a virtual world. This research explores the impact on participants’ interaction with an orchestral musical composition through the use of a two framework iterations informed by use cases. The resulting final design structure outlined in this article points towards constructing multi-stem virtual orchestral experiences through three pillars: semantic consistency, spatial agency, and complexity control. Whilst this research serves to propose a design intervention, future work requires a more extensive participant testing approach, coupled with an exploration of additional multimodal analysis.

Electronics doi: 10.3390/electronics15081557

Authors: Ning Wang Wenxing Mu Yixuan An Tao Liu

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring.

Electronics doi: 10.3390/electronics15081558

Authors: Shaoming Qiu Lei He Hanhan Dang Chong Wang Han Yu Yuqi Chen

Unsupervised video anomaly detection (VAD) methods learn from normal data to identify anomalies by capturing pattern deviations. However, they often struggle to model multi-scale features and distinguish between normal and abnormal instances. To address these limitations, we propose a Multi-scale U-shaped Adaptive Clustering Learning (MS-UACL) framework. Built on the U-Net architecture, we redesign it as a 3D-encoder/2D-decoder autoencoder. In the encoder, we introduce a Dual-scale Feature Cascading Module (IDCN), which adopts a pseudo-branch fusion mechanism to systematically model multi-scale spatiotemporal features, thereby enhancing the model’s representational capability. To further enhance the distinction between normal and anomalous patterns, we propose an MLP-based Adaptive Clustering Algorithm (MLP-ACA). Specifically, MLP-ACA employs an initial mapping mechanism to align cluster centers with the underlying normal data distribution, facilitating more accurate feature reconstruction. Additionally, we introduce an adaptive clustering update strategy that optimizes cluster centers by tuning solely the parameters of the MLP. This enables the cluster centers to autonomously converge toward optimal feature representations, thereby accelerating clustering convergence and enhancing pattern separability. Extensive experiments on three benchmark datasets demonstrate that the proposed MS-UACL framework outperforms most existing methods on small- and medium-scale datasets.

Electronics doi: 10.3390/electronics15081556

Authors: Abdullah Fawaz Aljulayfi

Transforming a conventional university campus into a smart campus by leveraging modern technologies aims to deliver university services efficiently, effectively, and at low cost. Modern technologies enhance campus life by providing services, such as smart classrooms and campus security, on demand. Seamless service delivery requires reliable and efficient access to the services that take into consideration the dynamic contextual attributes related to, e.g., end-device mobility, latency sensitivity, and resource constraints. University staff, students, and visitors frequently submit different types of service requests on the move, which requires a robust orchestration framework capable of managing these requests across edge-cloud environments. The orchestration framework needs to intelligently distribute the workload, taking into consideration the latency sensitivity requirements and contextual conditions, including resource constraints. Therefore, a fuzzy-logic orchestration framework for smart-campus environments in edge-cloud architecture is proposed. The framework incorporates key factors, including user speed, resource utilization, and request delay sensitivity, in the decision-making process to satisfy both service consumers and service providers. It prioritizes latency-sensitive requests while simultaneously enhancing resource utilization efficiency. Simulation-based experimental results demonstrate the effectiveness of the proposed framework compared with benchmark approaches in orchestrating incoming workloads under several user and contextual conditions. Additionally, the results show that the proposed framework improves the execution rate by 30% compared to benchmark models and achieves more than double resource utilization efficiency.

Electronics doi: 10.3390/electronics15081555

Authors: Tieniu Chen Xiaozhou He Yunjiang Lou Houde Liu Kunfeng Zhang

Permanent magnet synchronous motor (PMSM)-based servo actuators are fundamental to high-performance electromechanical systems. However, in energy-sensitive aerospace applications, the impact of tracking error on system-level efficiency remains insufficiently quantified. This paper establishes an energy-oriented analytical framework linking PMSM tracking accuracy to vehicle-level energy consumption and flight range. By employing a specific mechanical energy formulation, we demonstrate that tracking deviations modify aerodynamic drag and introduce additional dissipative work. Specifically, the accumulated dissipation is shown to admit a lower bound proportional to the integral of the squared tracking error, from which a range degradation bound is derived. These results reveal that “tracking-error energy” imposes a fundamental limit on achievable flight distance. A Lyapunov-based analysis further proves that minimizing this error energy reduces total aerodynamic dissipation without requiring modifications to propulsion scheduling or guidance laws. Numerical simulations comparing a conventional sliding mode controller with an advanced fuzzy-adaptive nonsingular terminal sliding mode controller confirm that enhanced servo precision directly improves velocity retention and range performance. This framework offers practical insights for designing energy-aware PMSM control strategies in energy-constrained aerospace platforms.

Electronics doi: 10.3390/electronics15081554

Authors: Haruki Inoue Ryotaro Ishihara Jaesang Cha Chang-Jun Ahn

Spectrum scarcity has become a critical issue due to the rapid deployment of fifth-generation (5G) networks and the explosive growth of future wireless data traffic. Spectrally Efficient Frequency Division Multiplexing (SEFDM) is a promising technique to enhance spectral efficiency by compressing subcarrier spacing and allowing spectral overlap; however, it suffers from severe inter-carrier interference (ICI) caused by the loss of orthogonality. In particular, under Rayleigh fading channels, the combined effects of ICI and multipath fading lead to significant degradation in bit error rate (BER) performance. Conventional SEFDM systems employing a cyclic prefix (CP) encounter an unavoidable error floor due to residual interference stemming from non-orthogonality. On the other hand, while zero-padding (ZP)-based SEFDM offers superior multipath tolerance, further enhancement in communication quality is still desired. This paper proposes a novel receiver architecture utilizing sector antennas to spatially separate multipath components based on the angle of arrival (AoA). Furthermore, we investigate and compare sector selection algorithms specifically tailored for SEFDM systems. Simulation results demonstrate that the proposed method, employing a sector selection scheme based on the maximum channel response power, effectively suppresses inter-symbol interference (ISI) and improves BER performance for both CP-SEFDM and ZP-SEFDM. Furthermore, our quantitative evaluations confirm that the proposed architecture successfully achieves the theoretical maximum spectral efficiency even in higher-order modulation schemes (16QAM), while maintaining a low computational complexity compared to conventional spatial diversity techniques.

Electronics doi: 10.3390/electronics15081548

Authors: Jianyu Ren Yun Zhao Yiming Zhang Haolin Wang Hao Yang Yuxin Lu Ziwen Cai

Short-term load forecasting plays a pivotal role in modern power system operations yet it remains challenging due to the complex spatiotemporal dependencies in load data. This paper proposes a dual-head attention residual network (DARNet) that significantly advances STLF through three key innovations: (1) a hybrid encoder combining 1D-CNN and GRU architectures to simultaneously capture the local load patterns and long-term temporal dependencies, achieving a 28% better locality awareness than that of conventional approaches; (2) a novel dual-head attention mechanism that dynamically models both the inter-temporal relationships and cross-variable dependencies, reducing the feature engineering requirements; and (3) an autocorrelation-adjusted recursive forecasting framework that cuts the multi-step prediction error accumulation by 33% compared to that with standard seq2seq models. Extensive experiments on real-world datasets from three Chinese cities demonstrate DARNet’s superior performance, outperforming six state-of-the-art benchmarks by 21–35% across all of the evaluation metrics (MAPE, SMAPE, MAE, and RRSE) while maintaining robust generalization across different geographical regions and prediction horizons.

Electronics doi: 10.3390/electronics15081553

Authors: Zhenzhen Wu Jianli Guo Wei Hou Kun Zhou Kerang Cao Hoekyung Jung

Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in cross-unseen-crop scenarios. Cross-crop generalization for plant disease recognition requires models to identify known disease categories in crop domains never observed during training. A central challenge is that disease symptoms are strongly coupled with crop-specific appearance cues, which severely degrades generalization. Here, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition. The proposed method employs a dual-branch visual encoder to separately capture disease semantic representations and crop-domain representations, and it leverages a frozen CLIP text encoder to use disease and crop prompts for text-guided semantic anchoring. A semantic-anchor-only contrastive disentanglement strategy is further formulated under a hybrid label space, where crop-branch features are incorporated as stop-gradient hard negatives to suppress semantic–domain information leakage and strengthen the intra-class aggregation of the same disease across crops. Residual domain-discriminative cues are mitigated via domain-adversarial learning. During inference, only the disease branch is retained for classification, improving generalization while reducing deployment overhead. Experiments demonstrate that under the PlantVillage cross-crop setting, the method achieves 98.04% and 74.29% Top-1 accuracy on seen and unseen crop domains, respectively. Moreover, it attains 81.99% on a real-world field dataset of strawberry powdery mildew and 76.31% on a low-illumination degradation set, validating robustness under realistic imaging distribution shifts.

Electronics doi: 10.3390/electronics15081552

Authors: Emanuel Palacio Urrego Carlos D. Pabón Zapata Samuel García Bonilla Jesús M. López-Lezama Nicolás Muñoz-Galeano

The increasing penetration of renewable energy sources, distributed generation, and advanced control technologies has transformed microgrids into complex, dynamic systems that pose significant challenges for protection coordination. This paper presents a comprehensive bibliometric analysis of the scientific literature on protection strategies in modern microgrids. Using a curated dataset from the Scopus database, four types of analyses were conducted: trend topic analysis, dendrogram clustering, co-occurrence network mapping, and thematic map analysis. The trend topic analysis highlights the temporal evolution of specific topics. The dendrogram analysis reveals thematic groupings and highlights concepts that have received limited attention. The co-occurrence network analysis reveals interactions between terms, and the thematic map analysis identifies basic, niche, and motor themes, as well as emerging or declining themes. These insights provide a structured overview of current knowledge and potential future research directions in microgrid protection. This study serves as a valuable reference for researchers and practitioners aiming to understand and address the evolving challenges associated with protection coordination in modern microgrids.

Electronics doi: 10.3390/electronics15081550

Authors: Keun-Chang Kwak

Granular modeling has emerged as an interpretable framework for nonlinear system representation by constructing clusters of meaningful data units within the input and output domains. Unlike conventional neuro-fuzzy models that yield crisp outputs, granular models generate fuzzy-set-based outputs, preserving uncertainty information. However, traditional granular architectures rely on linear aggregation mechanisms, limiting their expressive power and structural adaptability. This paper proposes a novel framework termed Logic-Based Fuzzy Granular Networks (LFGNs), in which conventional granular models are enhanced through the incorporation of fuzzy logical neurons implementing AND–OR operations. The proposed logic-based structure enables nonlinear interactions among induced granules while maintaining interpretability. To further improve predictive performance, LFGNs are embedded into a boosting framework, forming a boosted LFGN in which each LFGN acts as a weak learner. Extensive simulation studies on benchmark datasets indicate that the proposed approach outperforms conventional granular models and the existing boosting method in terms of regression accuracy. The integration of logical neurons, boosting, and fuzzy granular models provides a unified and robust granular modeling framework.

Electronics doi: 10.3390/electronics15081551

Authors: Wenhao Zhang Lianheng Zou Yingying Xiong Peng Shi Xiao He

With the widespread application of deep neural networks across various fields, issues related to model security have become increasingly prevalent. Backdoor attacks, as a covert method of attack, can implant malicious behavior during the model training process, causing the model to perform predetermined tasks under specific trigger conditions. However, current backdoor attacks struggle to achieve a good balance between stealthiness and attack success rate, and there is an issue in which certain data transformation operations can negatively impact attack performance. To address these issues, this paper proposes a specialized backdoor attack method called Ditto. It first uses a boundary detection algorithm and a padding algorithm to determine the trigger’s insertion position. The trigger is then dynamically generated using a generative adversarial network, taking into account the texture features of the images. Subsequently, the trigger is applied to the images, and its level of stealthiness is adjusted. Compared to existing popular backdoor attack methods, the experimental results ensure a high level of stealthiness while also maintaining a high attack success rate and a high accuracy for clean data. Furthermore, our attack method exhibits considerable robustness and adaptability, demonstrating effective resistance against baseline backdoor defense techniques.

Electronics doi: 10.3390/electronics15081549

Authors: Yang Song Hongyu Sun Wenmin Song Xiangpeng Wang Fanqiang Lin

Stereo matching constitutes a critical technology in applications such as autonomous driving and robot navigation. Conventional algorithms, however, often encounter limitations in real-time performance and resource efficiency when deployed on embedded platforms. This paper presents a real-time stereo matching system implemented on a Field-Programmable Gate Array (FPGA), which is built around a lightweight, hardware-optimized dual-path Semi-Global Matching (SGM) algorithm. The proposed method simplifies the traditional eight-path cost aggregation into horizontal and vertical dual-path aggregation, substantially reducing hardware resource consumption while preserving matching accuracy. The system employs a pipelined architecture that integrates image capture, DDR3 caching, and HDMI display output. Experimental results demonstrate that under the configuration of a 5 × 5 matching window and a disparity range of 64, the system generates stable disparity maps at 60 frames per second, with total power consumption below 2.2 W and FPGA core logic utilization under 30%. Compared to the conventional eight-path SGM, the dual-path strategy incurs only a marginal 6% increase in average bad pixel rate on standard stereo datasets while reducing Block RAM (BRAM) usage by approximately 30%. This achieves an effective practical balance between accuracy, computational efficiency, and power consumption.

Electronics doi: 10.3390/electronics15081547

Authors: Yinuo Li Jiayuan Wang Zhouli Yuan Haiyu Zhang

Agricultural intelligent agent technology features autonomy in multimodal perception, scalability for cross-scenario collaboration and adaptability via closed-loop optimization, serving as a core technological pillar for industrial intelligent upgrading and refined production management. This paper systematically elucidates its technical essence and methodological framework, focusing on five key aspects: multimodal heterogeneous data perception and fusion, scenario-oriented knowledge modeling and dynamic memory, intelligent decision-making and planning, embodied artificial intelligence, and closed-loop feedback optimization. On this basis, the paper outlines its core agricultural applications in four domains: crop cultivation, efficient utilization of agricultural resources, intelligent upgrading of agricultural technologies and equipment, and collaborative governance of the entire agricultural industry chain. From an interdisciplinary “AI + Agriculture” perspective, the paper further analyzes its future development directions, aiming to provide insights for improving agricultural intelligent agent technologies and promoting their industrial application to accelerate agricultural intelligent transformation. This study constructs a three-dimensional integrated methodological framework encompassing technological analysis, application mapping and trend forecasting, systematically summarizes its agricultural application scenarios and technological evolution characteristics, enriches the theoretical system and methodological construction of agricultural intelligent agent research, and provides a reusable analytical paradigm for agricultural intelligent agent research and practice.

Electronics doi: 10.3390/electronics15081546

Authors: Kaishun Zhang Changhao Wang Tao Su

As a widely used computing substrate, the side-channel security of FPGAs has attracted considerable attention, yet a systematic understanding of how FPGA device types contribute to exploitable leakage remains limited. This work presents a device-centric evaluation that maps an S-box-like function onto common FPGA primitives, including look-up table (LUT), flip-flop (FF), block RAM (BRAM), and distributed RAM (LUTRAM), and assesses Correlation Power Analysis (CPA) outcomes under the Hamming Weight (HW) and Hamming Distance (HD) power models. The results show pronounced leakage differences across device types: FF- and BRAM-based implementations exhibit substantially stronger leakage than LUT- and LUTRAM-based ones, and they frequently achieve GE=0 in our configurations, while the HD model is generally more effective than the HW model in the performed CPA evaluations. Notably, FF-, BRAM-, and LUTRAM-based implementations can already be breakable starting from one instance under the HD model in our device-level tests, indicating that exploitable leakage may manifest in real FPGA applications. These device-level observations are further validated on a practical cipher by analyzing two SM4 encryption modules that differ only in the S-box implementation style; the BRAM-based design shows significantly stronger leakage than the LUT-based design, achieving GE=2.58 versus GE=78.3 at 10,000 traces. This work highlights the critical role of device selection and implementation style in FPGA side-channel security, and it provides practical insights for designing secure FPGA applications against power side-channel analysis.

Electronics doi: 10.3390/electronics15081545

Authors: Zbigniew Omiotek Natalia Krukar Aleksandra Olejarz Piotr Lichograj Miłosz Komada Magda Konieczna

The aim of this study was to develop an effective and fast tool to support the automatic segmentation of skin lesions, with particular emphasis on the precise differentiation between malignant and benign lesions. In response to the problem of high false positive rates in existing CAD systems, modern neural network architectures from the YOLO family (YOLOv8, YOLOv9, YOLOv11, YOLOv12, and YOLOv26) were used in this research. The models were trained and evaluated on a new, balanced dataset (7000 images) based on the ISIC archive, where the key innovation was the introduction of a dedicated background class representing healthy skin. Through a multi-stage, rigorous optimization process, it was demonstrated that the yolov11s-seg model is highly effective for this task. It achieved a strong balance between effectiveness and processing speed, obtaining an mAP@50 score of 0.840 and an overall precision of 0.852. From a clinical perspective, the model’s high sensitivity (85.9%) in detecting the most aggressive lesion, invasive melanoma (MI), is particularly noteworthy. Thanks to its extremely short inference time (only 4.8 ms), the proposed yolov11s-seg variant overcomes the limitations of heavy hybrid architecture, providing a stable and highly efficient solution showing significant potential for deployment in real-time medical mobile applications.

Electronics doi: 10.3390/electronics15081544

Authors: Shanxian Lin Yuichi Nagata Haichuan Yang

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable.

Electronics doi: 10.3390/electronics15081543

Authors: Jingyu Huang Chenhui Deng Bohua Yin Liping Zhang Li Han

Sub-15 nm line structures are key building blocks for advanced device prototyping, nanoscale electrodes, and lithography templates such as etch/deposition masks. Although ultrahigh-voltage (≥100 kV) electron-beam lithography (EBL) can more readily achieve extremely small critical dimensions, its tool and infrastructure requirements limit widespread adoption in many laboratories. In contrast, 30 kV field-emission SEM platforms are far more accessible; however, resolution-limit patterning at 30 kV is more sensitive to beam current, exposure dose, and development conditions, motivating the establishment of a reproducible process flow and a well-defined process window. Here, we investigate the resolution limit of isolated lines using a Zeiss Gemini 460 system operated at 30 kV and an in-house pattern generator with 950 k PMMA C2 resist. To demonstrate device-level applicability, we develop a stable lift-off process, and all critical dimensions are evaluated on metal lines after e-beam evaporation and lift-off. By screening beam current and scanning dose to build the dose-to-size relationship, we show that reducing beam current significantly improves the achievable minimum line width. Under 35 pA, using CD ≤ 15 nm as the criterion for sub-15 nm window extraction, the usable dose range is [700, 804.3] µC/cm2, corresponding to a dose latitude of ~14.9%. The best performance is obtained at 700 µC/cm2, yielding a transferred metal line width of 13.85 nm after lift-off. This work provides a practical resolution-limit process flow and a quantitative process window for performing sub-15 nm patterning on accessible 30 kV platforms, supported by product-level lift-off validation.

Electronics doi: 10.3390/electronics15071542

Authors: Jaehyeok Cha Jisoo Jang Dongil Shin Dongkyoo Shin

Machine learning-based Network Intrusion Detection Systems (NIDSs) typically optimize uniform metrics such as accuracy and F1-score, overlooking the asymmetric cost structure of real-world security operations, where a missed attack (False Negative (FN)) far outweighs a false alarm (False Positive (FP)). We propose a cost-sensitive threshold optimization framework based on XGBoost, using a 10:1 FN-to-FP cost ratio derived from established cost models. We first demonstrate that the default threshold of 0.5 is suboptimal and that a globally optimized threshold of 0.08 substantially reduces total cost. However, a single global threshold cannot accommodate the heterogeneous detection characteristics of diverse attack types. We therefore introduce Per-Class Thresholding, which assigns independently optimized thresholds to each attack class. Evaluated on CIC-IDS2018 and UNSW-NB15 across five independent random seeds, our method achieves a 28.19% cost reduction over the Random Forest baseline on CIC-IDS2018, demonstrating that attack classes undetectable under the global threshold—including DDoS attack-LOIC-UDP (100%), DoS attacks-SlowHTTPTest (99.79%), and FTP-BruteForce (98.16%)—can achieve near-complete cost elimination through individual per-class threshold search. Cross-dataset validation on UNSW-NB15 further confirms that per-class thresholding consistently improves class-level detection, with cost reductions of 74.10% for Reconnaissance, 69.06% for Backdoor, and 54.42% for Analysis attacks. These results confirm that class-specific threshold calibration is essential for cost-effective intrusion detection.

Electronics doi: 10.3390/electronics15071541

Authors: Mei Sun Dongbing Zhang Yuyan Guo Xudong Zhai

Traditional VANET authentication schemes often face challenges such as centralization bottlenecks and the updating of vehicle keys or pseudonyms. This paper proposes a layered approach that divides VANET into regions, utilizing dual-blockchain to enable anonymous message authentication between vehicles and RSUs, as well as between vehicles within the VANET. Compared to traditional blockchain authentication methods, this paper introduces an approach that enhances authentication efficiency and ensures information security by establishing secure connections between private and consortium chains through a trusted authority (TA). By leveraging third-party public parameter updates, the automatic updating of private and public keys for VANET nodes is achieved without the need for certificate issuance and updates. This approach facilitates long-term anonymous authentication and communication between VANET nodes, reduces the frequency of authentication interactions, simplifies authentication processes, and lowers computational and communication costs. The proposed scheme is well-suited for practical VANET environments that require low authentication latency and robust large-scale privacy protection.

Electronics doi: 10.3390/electronics15071540

Authors: Aoba Morimoto Jaesang Cha Incheol Jeong Chang-Jun Ahn

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks.

Electronics doi: 10.3390/electronics15071539

Authors: Yuyang Li Zhigang Zhang Jian Shen

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R2) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R2 > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making.

Electronics doi: 10.3390/electronics15071537

Authors: Yi Wei Li Huang Ce Wang Xiong Yin Ce Wang

This study presents a heterogeneous integration micro-assembly process and circuit board packaging solution for GaN Schottky Barrier Diode (SBD) rectifiers, and innovatively constructs a hierarchical reinforcement learning strategy for optimizing SBD RF parameters. By establishing an optimization framework with the goal of efficiency in the load-input power two-dimensional space, a dual-layer optimization mechanism is employed: the high-level strategy dynamically selects optimization regions and parameter combinations, while the low-level strategy implements specific parameter adjustments. This approach effectively addresses the challenges of device parameter modeling and circuit design. Experimental data shows that the efficiency error for the SBD1 rectifier remains stable within 2%, and the average error for SBD2 is reduced to 1.5%. This method enables efficient and accurate optimization of RF parameters, providing a reliable technical pathway for the engineering application of Wireless Power Transmission systems.

Electronics doi: 10.3390/electronics15071538

Authors: Songyao Dou Ying Chen Yaobing Lu

Aiming to improve radar multi-target tracking (MTT) accuracy and association performance in complex scenarios involving dense clutter, missed detections, and maneuvering targets, an improved tracklet generation approach based on the expectation–maximization (EM) framework is proposed in which data association variables and motion model variables are jointly modeled as latent variables. These variables are estimated through iterative updates based on the loopy belief propagation (LBP) algorithm and the interacting multiple model (IMM) filtering and smoothing algorithms to generate high-confidence tracklets. Then, a delayed decision-making strategy based on the multi-hypothesis approach is employed to associate these tracklets into complete target trajectories. The resulting algorithm is named IMM-TrackletMHT. The performance of the IMM-TrackletMHT algorithm is evaluated and compared with several baseline algorithms in simulated scenarios under different clutter rates and detection probabilities. The simulation results demonstrate that the proposed algorithm consistently outperforms the baseline methods in terms of tracking accuracy, exhibits strong robustness to variations in the operating environment, and achieves higher computational efficiency in multi-scan measurement processing, thereby demonstrating the effectiveness and superiority of the proposed tracklet generation approach for maneuvering MTT.

Electronics doi: 10.3390/electronics15071536

Authors: Qing Wang Jian Yang Pingxin Wang Yaru Sheng Hongxia Zhu

Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity of large-scale grids. To address these issues, this paper proposes ST-ResGAT, a spatio-temporal residual graph attention framework for nonlinear state estimation under heterogeneous sensing conditions. The proposed method models the problem on an augmented heterogeneous factor graph, employs a reliability-aware heterogeneous graph attention mechanism with residual propagation to adaptively fuse measurements of different quality, and further refines the graph-based estimates through a lightweight LSTM post-processing module that exploits short-term temporal continuity. All datasets are generated using pandapower on the IEEE 30-bus, IEEE 118-bus, and IEEE 1354-bus benchmark systems to ensure full reproducibility of the experimental pipeline. Experimental results show that the proposed method consistently achieves lower estimation errors than WLS, DNN, GAT, and PINN baselines across all three systems, while also exhibiting more compact node-level error distributions and stronger spatial consistency. Multi-seed ablation studies further indicate that residual propagation, reliability-aware attention, and temporal refinement play complementary roles across different system scales. Robustness experiments additionally show that, under random measurement exclusion as well as bias, Gaussian, and mixed corrupted-measurement settings, ST-ResGAT exhibits smooth and progressive degradation, including on the newly added large-scale IEEE 1354-bus benchmark. These results suggest that the proposed framework is a promising direction for data-driven state estimation under controlled mixed-measurement benchmark conditions.

Electronics doi: 10.3390/electronics15071535

Authors: Haiyi Wu Kai Zhao Wei Yao Yong Xiong

Millimeter-wave (mmWave) radar point clouds offer a privacy-preserving solution for Human Activity Recognition (HAR), but their inherent sparsity and noise limit single-modal performance. While multimodal fusion mitigates this issue, existing methods often suffer from severe negative transfer during visual degradation and incur high computational costs, unsuitable for edge devices. To address these challenges, we propose Tac-Mamba, a lightweight cross-modal state space model. First, we introduce a topology-guided distillation scheme that uses a Spatial Mamba teacher to extract structural priors from visual skeletons. These priors are then explicitly distilled into a Point Transformer v3 (PTv3) radar student with a modality dropout strategy. We also developed a Trust-Aware Cross-Modal Attention (TACMA) module to prevent negative transfer. It evaluates the reliability of visual features through a SiLU-activated cross-modal bilinear interaction, smoothly degrading to a pure radar-driven fallback projection when visual inputs are corrupted. Finally, a Lightweight Temporal Mamba Block (LTMB) with a Zero-Parameter Cross-Gating (ZPCG) mechanism captures long-range kinematic dependencies with linear complexity. Experiments on the public MM-Fi dataset under strict cross-environment protocols demonstrate that Tac-Mamba achieves competitive accuracies of 95.37% (multimodal) and 87.54% (radar-only) with only 0.86M parameters and 1.89 ms inference latency. These results highlight the model’s exceptional robustness to modality missingness and its feasibility for edge deployment.

Electronics doi: 10.3390/electronics15071534

Authors: Lin Li Jichun Zhu Mingxing Jiang Jingli Fang

With the increasing demand for high-quality imaging in consumer electronics, image aesthetics assessment (IAA) has been widely applied to electronic cameras and display devices. Although the deformable attention mechanism has been introduced into IAA due to its perceptual capabilities, enabling models to refine attention regions by learning interest points and their corresponding offsets, existing methods often lack guidance from aesthetic composition features during the offset generation process, which limits their performance in aesthetic evaluation tasks. To address this issue, we propose a graph neural network (GNN)-guided deformable attention module that incorporates composition information into the generation of interest points by modeling image features as graphs and applying the GNN to guide interest point selection. In addition, we design an improved transformer model that employs neighborhood attention to further enhance IAA performance. We evaluate the proposed model on two aesthetic datasets, AVA and TAD66K, and the experimental results demonstrate its effectiveness in improving overall model performance.

Electronics doi: 10.3390/electronics15071533

Authors: Yang Xu Wanxiang Che

Mainstream Large Language Models (LLMs) work under the paradigm of Next-Token Prediction (NTP). Multi-Token Prediction (MTP) is motivated by higher decoding efficiency, extending NTP to enable LLMs to draft multiple tokens during each forward pass. However, existing MTP approaches pretrain MTP along with the target LLM, making it difficult to unlock MTP for LLMs without official support. In this work, we propose a post-hoc approach to training an MTP module for a target LLM, providing an efficient way to evolve the LLM from NTP to MTP. The proposed approach features two main characteristics. (1) No changes to the target LLM, since it is frozen during MTP training. (2) Efficient MTP training via self-distillation from the target LLM’s native NTP capability. Results show that our approach can post-hoc train a performant MTP module via lightweight pretraining.

Electronics doi: 10.3390/electronics15071532

Authors: Jongpil Youn Youngju Nam Euisin Lee

The rapidly growing data demands of autonomous driving and onboard multimedia services pose significant challenges to traditional roadside unit (RSU) based content delivery, particularly under limited cache capacity and coverage gaps. In the content-centric Internet of Vehicles (CIoV), vehicle-to-vehicle (V2V) precaching has emerged as an effective solution to mitigate these limitations. However, existing schemes rely on static precaching roles, leading to inefficiencies when a precaching vehicle initiates its own content request. To address this issue, we propose a dynamic relay assignment scheme (DRAS) that enables seamless role transitions without discarding cached data. Upon detecting such a role-transition event, the RSU assigns two new precaching vehicles to independently serve the original requester vehicle and the newly transitioned requester vehicle, ensuring continuous service. Furthermore, we extend this to an energy-efficient DRAS (EE-DRAS) that incorporates vehicle-to-infrastructure (V2I) and V2V transmission energy costs into the selection process, achieving a balanced trade-off between energy consumption and delivery efficiency. Extensive NS-3 simulations show that DRAS reduces average delay by up to 53% and improves throughput by 8% over existing baselines. EE-DRAS further reduces energy consumption by up to 66% while maintaining service fairness.

Electronics doi: 10.3390/electronics15071531

Authors: Qiuze Wu Qing Yang Dong Meng Xiaofei Yan

For the automation of Agrobacterium-mediated genetic transformation of tissue-cultured plantlets, accurate leaf vein segmentation is essential. The thin, low-contrast structure of leaf veins frequently leads to fragmented segmentation outputs, despite the proposal of various methodologies for vein segmentation. To address this issue, we propose Direction-Aware U-Net (DA-UNet), an improved U-Net architecture that incorporates a Direction-Aware Context Pooling (DACPool) module and Topology-aware Segmentation loss (TopoSeg loss). The DACPool module explicitly exploits vein orientation to aggregate directional contextual information, while the TopoSeg loss jointly optimizes pixel-level accuracy and topological continuity. DA-UNet achieves efficient leaf vein segmentation with improved continuity and structural integrity, according to evaluations on the self-constructed Tissue-Cultured Plantlet Vein Dataset 2025 (TCPVD2025). Comparative experiment results show that the improved model outperforms PSPNet, DeepLabV3+, U-Net, TransUNet, Swin-UNet, CCNet, and SegNeXt, as evidenced by Recall, Dice, and CONNECT scores of 71.35%, 69.08%, and −2.25, while maintaining competitive Precision of 66.98%. Ablation experiment results provide further evidence for the efficacy of the TopoSeg loss and the DACPool module. The results demonstrate the effectiveness of the proposed vein segmentation framework for generating outputs that are both accurate and structurally consistent, thus enabling reliable automated processes for plant genetic transformation.

Electronics doi: 10.3390/electronics15071530

Authors: Yueying Zhang Zhuangzhi Nie Chaowei Hu Shouguan Xiao Yuxi Wang Shuqing Yang Fanggang Wang

We investigate wheat lodging segmentation from UAV RGB imagery acquired over real production fields rather than controlled experimental sites. Besides pixel-level accuracy, our evaluation also emphasizes robustness under heterogeneous farmland conditions and deployment-oriented efficiency. We propose CASGNet, an edge-oriented segmentation network with a content-aware spatial gating mechanism that reweights intermediate features according to local structural variation. Instead of uniformly aggregating features, the module suppresses responses in homogeneous regions while preserving activation in structurally complex areas. In practice, this improves the continuity of irregular lodging shapes and reduces spurious responses in relatively homogeneous backgrounds. The dataset spans 46 farms across Jiaozuo, Jiyuan, and Luoyang, covering progressively fragmented farmland. Under a stricter mission-level data-isolation protocol, CASGNet achieves 94.4% mIoU and 90.38% IoU for the lodging class on the combined dataset. Under sequential regional adaptation, performance remains relatively stable in continuous parcels, and degradation is less severe than most compact baselines in highly fragmented landscapes. On Jetson Nano, CASGNet achieves 1.94 FPS embedded inference under the 5 W mode. Smaller networks achieve higher speed but show reduced structural continuity in complex scenes. The results indicate that CASGNet provides a favorable balance between structural fidelity and computational cost, while its robustness remains constrained by scene complexity.

Electronics doi: 10.3390/electronics15071528

Authors: Sai Zhao Caisen Chen Shuai He

With the rapid advancement of location-based services, next Point-of-Interest (POI) recommendation has emerged as a critical task in personalized mobility modeling and recommendation systems. It aims to predict users’ future locations based on their historical trajectories, thereby enhancing the personalization and intelligence of recommendation systems. Despite the promising progress, two key challenges remain insufficiently addressed. First, many existing methods overlook the dynamic evolution of user trajectories across multiple perspectives, resulting in entangled representations that fail to capture user intent accurately. Second, they often ignore the latent synergy across diverse perspectives, which limits the effective utilization of complementary information for recommendation. To address these issues, we propose a novel framework called MRHL. MRHL constructs multiple hypergraphs to represent distinct views of user behavior, including interaction frequency, time decay, and geographical proximity. An enhanced hypergraph convolutional network is employed to effectively model the high-order relationships within them. We propose a cascaded enhancement fusion mechanism that progressively integrates multi-view hypergraph representations to enrich the semantic information of user representations. In addition, a multi-relational contrastive learning strategy is developed to capture the consistent signals across different views, thereby enhancing the robustness and discriminative capability of user and POI representations. Extensive experiments on three public datasets consistently demonstrate that MRHL outperforms a range of strong baselines.

Electronics doi: 10.3390/electronics15071529

Authors: Yongji Wu Weiqi Liu

This work introduces a trimming technique based on eTrim technology to minimize both the input-referred offset voltage and its temperature drift in the operational amplifiers. The proposed low-voltage op-amp utilizes the body effect to maintain a constant bandwidth across the rail-to-rail input common-mode range under low supply voltages. During input common-mode transitions, the current in the folded cascode stage remains stable, ensuring a robust output stage. Furthermore, a specialized gain-boosting structure enhances the low-frequency gain while preventing occasional latch-up during low-voltage power-up. A pin-multiplexing scheme is employed for trimming data input, thereby eliminating the need for dedicated trimming pins and mitigating post-package parameter variations. At room temperature, a constant-current injection mechanism reduces the DC offset to microvolt levels. At high temperature, temperature-compensated current injection cancels the first-order drift component. Implemented in a low-voltage operational amplifier, post-layout simulation results demonstrate that with a 100-pF capacitive load, the amplifier achieves a gain–bandwidth product exceeding 10 MHz, a low-frequency gain greater than 140 dB, and an input-referred noise of 2.54 µVp-p for the P-channel input and 3.95 µVp-p for the N-channel input. The trimming process reduces the residual offset to the microvolt range and effectively suppresses offset drift, ensuring accurate offset compensation across the specified temperature range.

Electronics doi: 10.3390/electronics15071524

Authors: Wonbin Son Hyungjoon Kim

Conventional file management systems primarily rely on structured metadata such as filenames, file extensions, and creation dates to manage and organize files. However, such metadata alone fails to capture the actual content or semantic meaning of a file, often leading to results misaligned with user intent. To overcome these limitations, we developed the Content-based File Classification and Organization System (CFCOS), which integrates a Large Language Model (LLM) to perform content-aware file analysis. The LLM generates semantic summaries of file contents and classifies files into meaningful categories based on composition-derived criteria, enabling organization strategies that go beyond rigid, rule-based methods. Through a range of evaluations, we analyze how CFCOS addresses key limitations of conventional file management systems and characterize the properties of LLM-based approaches to content-aware file organization. Furthermore, these results suggest that our approach can be generalized beyond file systems, enabling the semantic and personalized transformation of existing services through prompt engineering.

Electronics doi: 10.3390/electronics15071527

Authors: Jianpei Zhao Ruihua Li Hanqing Wang Jie Jiang Bo Hu

The gimbal servo system in a control moment gyroscope (CMG) is critical for high-precision spacecraft attitude control, where comprehensive performance testing and evaluation are essential for ensuring spacecraft reliability and service life. Traditional motor testbenches exhibit limitations, whereas the electric motor emulator (EME) based on power electronic converters is a promising alternative for testing extreme operating conditions, such as ultra-low speed operation and fault scenarios. However, existing EME control methods suffer from limited system bandwidth and insufficient emulation accuracy, which limits their applicability. To address these issues, this paper proposes an improved current control strategy for the ultra-low speed permanent magnet synchronous motor (PMSM) emulator. First, a mathematical model of the EME based on the topology of the voltage source converter is established. Then, based on the deadbeat control concept, a deadbeat predictive current control (DPCC) strategy is developed to enhance the dynamic performance. Furthermore, to suppress the parameter mismatch disturbance, an optimization scheme based on a cascaded extended state observer (CESO) is introduced. The first-stage ESO is applied to estimate and compensate for total disturbances, while the second-stage ESO is a supplement to suppress the remaining disturbances in the EME system, which improves the robustness of the DPCC controller. Finally, the effectiveness of the improved emulation accuracy of the proposed method is verified through experiments.

Electronics doi: 10.3390/electronics15071525

Authors: Huanchun Yang Hongshe Dang Xuande Zhang Quanping Chen

To address the reduced accuracy of stereo matching networks near object boundaries and disparity discontinuities, a double cost–volume stereo matching network with entropy-difference-guided fusion is proposed. The proposed network was built based on RAFT-Stereo. It employs a pretrained backbone to extract multi-scale features and uses deformable attention for cross-scale feature fusion. A shallow image-guided branch was used to generate pixel-wise constraint information to limit the magnitude of sampling offsets and alleviate cross-structure sampling. Based on the extracted features, a group-wise correlation cost–volume and a normalized correlation cost–volume were constructed. Both cost–volumes were regularized by 3D Hourglass networks, and a structure-consistent intra-scale aggregation module was introduced during the regularization of the group-wise correlation cost–volume. The two aggregated results were then fused by the entropy-difference-guided fusion module to obtain the final cost–volume. The experimental results show the effectiveness of the proposed network in the Scene Flow, KITTI, and ETH3D datasets, achieving an endpoint error of 0.45 px and a >3 px error rate of 2.41% on the Scene Flow dataset.

Electronics doi: 10.3390/electronics15071526

Authors: Haihang Gu Fujia Sun Shuhao Hou Shuangyuan Wang

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor.

Electronics doi: 10.3390/electronics15071523

Authors: Lan Ma Pei Hu Jeng-Shyang Pan

Particle swarm optimization (PSO) improves classification performance and reduces computational complexity in feature selection. However, it frequently experiences from premature convergence and insufficient exploration. To address these constraints, this paper suggests an adaptive binary PSO (ABPSO) algorithm specifically designed for feature selection. First, an adaptive transfer function and two adaptive learning coefficients are introduced to achieve a better balance between exploration and exploitation during the search process. Second, a hybrid learning mechanism that integrates personal best, global best, and elite solutions is utilized to enhance population diversity. Finally, a simulated annealing (SA)–based local search strategy is employed to further refine candidate solutions and improve convergence behavior. Experimental results demonstrate that ABPSO outperforms binary PSO (BPSO), harris hawks optimization (HHO), whale optimization algorithm (WOA), and ant colony optimization (ACO) in classification accuracy. In particular, ABPSO achieves the lowest classification error rates on the Dermatology (0.0106), Ionosphere (0.0705), Lung (0.1521), Sonar (0.0996), Spambase (0.0758), Statlog (0.1446), and Wine (0.0280) datasets.

Electronics doi: 10.3390/electronics15071522

Authors: Aysha Ebrahim

Non-orthogonal multiple access (NOMA) has become one of the main enabling technologies for next-generation cellular networks. The ability to allocate multiple users on the same frequency resources simultaneously leads to improved spectral efficiency. This paper examines power allocation and user pairing for NOMA networks with an objective to enhance the sum spectral efficiency (sum capacity, bps/Hz) while guaranteeing the target rate of the far user. Two benchmark methods were used to evaluate the performance of the proposed scheme: (1) fixed power allocation, in which fixed power coefficients are allocated to the near and far users, and (2) random power allocation, where random coefficients are assigned to the users. However, these static methods fail to adapt to instantaneous channel conditions and may lead to reduced performance for the weak user and inefficient power utilization. To manage these limitations, a novel interference-limited power allocation (IL-PA) scheme is proposed. In the IL-PA, the power allocation coefficients are dynamically allocated to users according to an interference threshold. The proposed scheme guarantees that the interference induced by the near user does not exceed a predefined interference threshold; thus, the target rate of the far user is achieved. The proposed interference threshold is derived theoretically to enhance the overall system capacity and optimize the signal-to-interference-plus-noise ratio (SINR). Additionally, a user pairing scheme, which separates users into two groups according to their channel gains, is proposed to reduce complexity while preserving good performance. The simulation results show that the proposed power allocation and user pairing scheme outperforms the benchmark methods in terms of overall capacity.

Electronics doi: 10.3390/electronics15071521

Authors: Qianran Zhang Weiqing Wang Fangkai Wang Xudong Wang Pufeng Chen

This paper presents two broadband four-way power dividers based on a novel T-coil topology, operating in the 22–32 GHz band (covering the K/Ka bands). Type I adopts a cascaded power division structure, while Type II employs a direct-feed integrated architecture. The innovation lies in the introduction of isolating capacitors at the input and output ports, which significantly shortens the critical transmission line lengths in both topologies. This effectively reduces the equivalent inductance and raises the self-resonant frequency, achieving wideband response while maintaining structural simplicity, compact size, and ease of integration. Both circuits were fabricated using a standard 45 nm CMOS process. The measured core chip areas (excluding pads) are only 0.125 mm2 for Type I and 0.066 mm2 for Type II, demonstrating excellent integration density. Through even-mode and odd-mode theoretical analysis and full-wave electromagnetic simulation verification, both power dividers exhibit good impedance matching and port isolation across the target frequency band. Measurement results further confirm their performance: across the entire 22–32 GHz band, both power dividers achieve a return loss better than 11 dB and isolation exceeding 15 dB; the insertion loss is 1.1–1.4 dB for Type I and 0.8–1.3 dB for Type II; the amplitude imbalance is below ±0.3 dB and ±0.1 dB, respectively; and the phase imbalance is less than ±5° and ±3°, respectively. All measured data show good agreement with simulation results. In summary, Type I offers advantages in layout flexibility and isolation performance, while Type II excels in insertion loss and chip size. Both provide practical circuit solutions for broadband, high-performance, and compact power division systems.

Electronics doi: 10.3390/electronics15071520

Authors: Kitty Kioskli Eleni Seralidou Wissam Mallouli Dimitrios Koutras Pedro Tomás Dimitrios Kallergis

Artificial intelligence (AI) is increasingly integrated into cybersecurity tools to improve threat detection, anomaly identification, and incident response. However, organisations, particularly small- and medium-sized enterprises (SMEs), often struggle to discover, evaluate, and effectively use AI-enabled cybersecurity solutions due to skills gaps, usability challenges, and fragmented tool ecosystems. This paper presents the advaNced cybErsecurity awaReness ecOsystem for SMEs (NERO), a human-centric cybersecurity ecosystem that combines a cybersecurity marketplace with a competency-based training and awareness platform to support the practical adoption of advanced cybersecurity technologies. The NERO Marketplace enables structured discovery, comparison, and assessment of cybersecurity tools based on usability, operational relevance, and competency alignment. Complementing this, the NERO Training Platform delivers modular, multi-modal training aligned with the European Cybersecurity Skills Framework (ECSF) to develop the human competencies required to operate advanced cybersecurity systems. This study contributes a socio-technical framework that addresses the gap between AI tool availability and organisational readiness through ECSF role-based competency mapping and iterative design-based evaluation. The platform targets technical roles like Cybersecurity Implementer to ensure training is aligned with the operational requirements of critical infrastructure protection. Results from cross-sector SME training activities show measurable improvements in cybersecurity awareness, knowledge, and user satisfaction, with knowledge gains exceeding 30% in some modules. Finally, the paper provides a structural mapping of these cross-sectoral results to the maritime defence domain, specifically addressing legacy OT systems and intermittent connectivity constraints.

Electronics doi: 10.3390/electronics15071519

Authors: Mahbuba Ferdowsi Nour Moustafa Marwa Keshk Benjamin Turnbull

The Internet of Things (IoT) plays an increasingly central role in healthcare by enabling continuous patient monitoring, remote diagnosis, and data-driven clinical decision-making through interconnected medical devices and sensing infrastructures. Despite these advances, IoT healthcare systems remain constrained by persistent challenges related to data privacy, computational efficiency, scalability, and regulatory compliance. Federated learning (FL) reduces reliance on centralised data aggregation but remains vulnerable to inference-based privacy risks, while edge-oriented approaches are limited by device heterogeneity and restricted computational and energy resources; the deployment of large language models (LLMs) further exacerbates concerns surrounding privacy exposure, communication overhead, and practical feasibility. This study introduces Trustworthy Intelligence (TI) as a guiding framework for privacy-preserving distributed intelligence in IoT healthcare, explicitly integrating predictive performance, privacy protection, and deployment-oriented system design. Within this framework, split learning (SL) is examined as a core architectural mechanism and extended to support split-aware LLM integration across heterogeneous devices, supported by a structured taxonomy spanning architectural configurations, system adaptation strategies, and evaluation considerations. The study establishes a systematic mapping between SL design choices and representative healthcare scenarios, including wearable monitoring, multi-modal data fusion, clinical text analytics, and cross-institutional collaboration, and analyses key technical challenges such as activation-level privacy leakage, early-round vulnerability, reconstruction risks, and communication–computation trade-offs. An energy- and resource-aware adaptive cut layer selection strategy is outlined to support efficient deployment across devices with varying capabilities. A proof-of-concept experimental evaluation compares the proposed SL–LLM framework with centralised learning (CL), federated learning (FL), and conventional SL in terms of training latency, communication overhead, model accuracy, and privacy exposure under realistic IoT constraints, providing system-level evidence for the applicability of the TI framework in distributed healthcare environments and outlining directions for clinically viable and regulation-aligned IoT healthcare intelligence.

Electronics doi: 10.3390/electronics15071517

Authors: Haotian Guo Ting Chen Xinke Xu Yuexin Qiu Jian Wu Lei Wang Huaichu Ye Xuwen Chen Ning Chen

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with reference block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration

Electronics doi: 10.3390/electronics15071518

Authors: Yu Zhang Yi Xu Jun Wang Haiqiang Hong

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work.

Electronics doi: 10.3390/electronics15071516

Authors: Xu Liu Junwu Zhu

Blockchain-based auctions often utilize smart contracts to automate auction rules, with much research focusing on enhancing privacy and fairness through cryptographic techniques. However, the authenticity of external data input into these systems is frequently overlooked. In particular, rational nodes may manipulate bidding data by submitting false types to maximize their utility, compromising market fairness and the reliability of auction outcomes. The aim of this study is to propose an alternative blockchain-based auction mechanism to incentivize nodes to report types honestly. We propose the Mixed-Node Advertising Auction (MNAA) mechanism for digital advertising auctions on blockchain systems. MNAA integrates quasi-linear and value maximization utility models to design allocation and pricing rules that eliminate nodes’ incentives to misreport their types, ensuring the authenticity of data submitted to the auction. To enhance efficiency, MNAA employs state channel technology and off-chain smart contracts, reducing main chain interactions. Theoretical analysis confirms that MNAA incentivizes truthful behavior and ensures security and correctness. Simulation results show that MNAA outperforms Generalized Second Price (GSP), Mixed Bidders with Private Classes (MPR), and Vickrey–Clarke–Grooves (VCG) auctions in terms of liquid social welfare (LSW), publisher revenue, and allocation efficiency, while also improving the transaction throughput and showing good performance in terms of transaction costs and latency.

Electronics doi: 10.3390/electronics15071515

Authors: Yordanis Alonso-Roque Francisco Marante Pablo Otero Alfonso Ariza

In this article, a new approach to the applicability of the self-complementarity concept in a classical two-port microstrip patch antenna element is presented. This was accomplished through an illustrative design and an electromagnetic analysis of a broadband two-port rectangular printed radiating element in transmission configuration. A calculated ultra-wide matching bandwidth up to approximately 11 GHz was achieved (BWsim-RL≥10 dB ≈ 11 GHz, fo = 5.5 GHz, i.e., BWsim-relative-matching ≈ 200%). One of the advantages of this topology is that only two degrees of freedom are needed to acquire a very wide impe-dance bandwidth: the length and the width of the slot. Full-wave analysis shows that sui-table combinations of the patch and slot dimensions allow to obtain the broadband mat-ching behavior. It has broadside radiation toward both hemispheres, which is conserved and considerably stable over a wide frequency range. Its linear polarization, radiation patterns, gain values, and radiation efficiency are adequate from 1 to 8 GHz (BWsim-radiation ≈ 7 GHz, fo [sim-rad] = 4.5 GHz, i.e., 63.6% of its BWsim-matching, and 156% of its fo [sim-rad]). Moreover, the gain and radiation efficiency exhibit very good flatness across wide frequency ranges. Measurements of S-parameters and radiation patterns validate the calculated results. The proposed antenna element is simple, compact, and light-weight. It has a very wide ope-ration bandwidth (7 GHz), its design is easy and flexible, and it is simple to manufacture. It could be used as a radiating element in different linear polarized antenna arrays.

Electronics doi: 10.3390/electronics15071514

Authors: Cecilia E. Chen Xuanyu Liu Limin Jia Bo Liang Yan Zhu Tong Wu

The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text Markup Language (CTML), an extensible markup language specifically designed to facilitate the automatic generation of smart contracts from multilingual contracts. CTML overcomes traditional method limitations by employing a two-stage transformation process: (1) Contract Abstraction and Markup: CTML redefines grammar rules and incorporates encoding extensions to transform multilingual contracts into structured, marked-up contracts. This process effectively abstracts the essential details of the original contract, enabling language-agnostic interpretation. (2) Domain-Specific Language (DSL) Translation and Smart Contract Code Generation: The marked-up contract is then seamlessly translated into a DSL program, capturing the legal concepts in a machine-readable format. Finally, the DSL program is automatically compiled into executable smart contract code, ready for deployment on the blockchain. The effectiveness of the proposed approach is demonstrated using a legal contract in both English and Chinese. Therefore, the CTML-based approach can automatically generate smart contracts from multilingual contracts, enabling a more inclusive and accessible smart contract ecosystem.

Electronics doi: 10.3390/electronics15071513

Authors: Shang-En Tsai Wei-Cheng Sun

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity.

Electronics doi: 10.3390/electronics15071512

Authors: Xuekai Li Lingguo Kong Yichen He Yikai Ren

Proton exchange membrane fuel cells (PEMFCs) have considerable potential for frequency support in grid-forming applications. However, their transient dispatchable power is nonlinearly influenced by operating conditions, such as the oxygen excess ratio and stack temperature, thereby weakening frequency support performance by delaying power compensation during disturbances. To address this issue, a coordinated control strategy for inertia support and active power compensation is proposed that explicitly accounts for operating-state effects. Based on a dynamic PEMFC model, the effects of the oxygen excess ratio and stack temperature on transient output capability are analyzed, and a jointly corrected inertia coefficient is introduced into the virtual synchronous generator (VSG) rotor motion equation to achieve adaptive adjustment of virtual inertia under varying operating conditions. In addition, model predictive control (MPC) is incorporated into the VSG control framework, and a performance index is formulated using weighted quadratic terms of frequency variation and input power, thereby enabling the compensation power to be determined online and the PEMFC power reference to be updated accordingly. Simulation results show that the proposed strategy can effectively suppress frequency fluctuations under disturbance conditions. Compared with Conventional PI-VSG, the maximum frequency deviation and the peak rate of change of frequency (ROCOF) are reduced by 49.1% and 62.1%, respectively.

Electronics doi: 10.3390/electronics15071511

Authors: Claudia Presicci Giulia Ballardini Giorgia Marchesi Paolo Robutti Matteo Moro Camilla Pierella Andrea Canessa Maura Casadio

Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals.

Electronics doi: 10.3390/electronics15071510

Authors: Teressa Talluri Eugene Kim Myeong-Hwan Hwang Amarnathvarma Angani Hyun-Rok Cha

To address collision risks in teleoperated mobile robotic vehicles, this study proposes a Human–Machine Interaction-based Advanced Smooth Shared Control (ASSC) system aimed at enhancing obstacle avoidance and achieving smooth shared control between human operators and the automation system. The ASSC system integrates a novel approach using predictive vectors to represent the vehicle’s heading position, automatically adjusting the steering position upon obstacle detection to ensure smooth collision avoidance without changing the driver’s perception. Feedback forces applied to the steering wheel are calculated through an artificial potential field algorithm. Twenty participants were invited to operate the vehicle, providing feedback on the ASSC system’s performance relative to conventional obstacle avoidance methods. Performance metrics such as the effects of communication delays, Time to Complete the Task (TTC), ASSC effectiveness, performance of the delay impact on the ASSC system, and the Number of Obstacle Collisions (NOC) are analyzed. The results demonstrate that the ASSC system significantly outperforms traditional obstacle avoidance methods, providing more precise control in teleoperation. Statistical analysis indicates that the ASSC system improves safety, comfort and operational performance by 12.8%. This research highlights the ASSC system as a promising solution for enhancing automation, safety, and human–machine interaction in teleoperated mobile robotic vehicles.

Electronics doi: 10.3390/electronics15071509

Authors: Wenhao Yang Ang Li Liyan Zhang Xiaoyao Qin

Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Although current deep learning approaches have improved efficiency, most remain confined to single tasks and lack a pathway to translate image recognition into quantifiable geological parameters. Moreover, these methods struggle with cross-scale feature extraction and accurate grain boundary localization in complex textures. To overcome these limitations, this study proposes a three-stage automated analysis framework integrating intelligent lithology identification, sandstone grain segmentation, and quantitative analysis of fabric parameters. To address scale discrepancies in lithology discrimination, Rock-PLionNet integrates a Partial-to-Whole Context Fusion (PWC-Fusion) module and the Lion optimizer, which mitigates cross-scale feature inconsistencies and enables accurate screening of target sandstone samples. Subsequently, to correct boundary deviations caused by low contrast and grain adhesion, the PetroSAM-CRF strategy integrates polarization-aware enhancement with dense conditional random field (DenseCRF)-based probabilistic refinement to extract precise grain contours. Based on these outputs, the framework automatically calculates key fabric parameters, including grain size and roundness. Experiments on 3290 original multi-source thin-section images show that Rock-PLionNet achieves a classification accuracy of 96.57% on the test set. Furthermore, PetroSAM-CRF reduces segmentation bias observed in general-purpose models under complex texture conditions, enabling accurate parameter estimation with a roundness error of 2.83%. Overall, this study presents an intelligent workflow linking microscopic image recognition with quantitative analysis of geological fabric parameters, providing a practical pathway for digital petrographic evaluation in hydrocarbon exploration.

Electronics doi: 10.3390/electronics15071508

Authors: Łukasz Grzymkowski Tymoteusz Cejrowski Tomasz P. Stefański

In this paper, we provide a thorough review of novel machine learning (ML) models for anomalous sound detection (ASD). We focus on deploying models to highly constrained, embedded systems and tiny ML, and using single-channel sound as the data input. The survey includes only the works published in 2020 and later. Researchers address the anomaly detection task in various ways, borrowing models and techniques from such fields as speech processing, audio generation, and even computer vision. However, it is not clear which of these are suitable for embedded systems, meeting their constraints such as memory or compute. To address that, we provide a deep analysis of these models and optimization techniques applied to meet the design criteria for embedded platforms. We consider both deep learning and classical ML methods. We define categories for the anomaly detection methods depending on the approach taken to provide a structure and simplify the comparison of methods. We aim to provide a guideline on how to develop ASD systems and how to efficiently deploy the models on the embedded platforms.

Electronics doi: 10.3390/electronics15071507

Authors: Bailin Zou Huiyi Liu

File fragment classification is crucial in digital forensics, aiding in the recovery and reconstruction of fragmented files, which serve as key evidence; while deep learning techniques have advanced in this area, challenges remain, particularly regarding the consideration of inter-file-type relationships and the granularity of classification. To overcome these challenges, we introduce a hierarchical classification approach that leverages an agglomerative hierarchical clustering algorithm combined with a dynamic adjustment mechanism, optimizing category distribution among leaf nodes. This structure is further enhanced by developing specific classifiers for each leaf node, tailored to its unique characteristics. Experimental results on the FFT-75 dataset show that our method achieves 76.3% accuracy in a 75-class scenario (512-byte blocks), surpassing the accuracy achieved with existing approaches. This method improves classification accuracy, addressing misclassification issues caused by excessive classification types.

Electronics doi: 10.3390/electronics15071506

Authors: Yu-Ling Lin Yi-Cheng Tu Yen-Sheng Chen

This paper presents a study of low-profile transmitarray antennas using two folded design approaches for microwave energy focusing. One approach realizes profile reduction through reflective phase compensation, whereas the other uses polarization-selective path folding. Prototypes are fabricated and measured, and their aperture performance is evaluated using gain, aperture efficiency, and first-sidelobe level as practical indicators of focusing quality and unwanted radiation outside the main beam. For the reflective phase-compensation design, dual-linear-polarized operation is maintained, and a height reduction of 52% is achieved. The measured broadside gain is reduced by 2.6–2.7 dB for x polarization and 1.6–1.7 dB for y polarization, while the first sidelobe increases by 3.7–6.6 dB for x polarization and by 5.1 dB in the y–z plane for y polarization. For the polarization-selective folded design, the feed-to-aperture distance is reduced from 165 mm to 43.5 mm, giving a compression factor of about 3.8. The measured peak gain is reduced by 3.4 dB, and the first sidelobe increases from −19.9 dB to −13.2 dB in the E-plane and from −16.8 dB to −12.9 dB in the H-plane. The comparison shows that reflective phase compensation is more suitable when dual-linear-polarized operation is required, whereas polarization-selective path folding is more suitable when stronger profile compression is prioritized and single-polarized operation is acceptable.

Electronics doi: 10.3390/electronics15071505

Authors: Tao Li Zehong Yan Junhua Ren Hongwu Gao

To meet the increasingly strict requirements of modern communication, radar detection and electronic measurement systems for wide-bandwidth, low-insertion-loss and high-isolation signal routing, this paper presents a 16 × 16 programmable switch matrix that simultaneously achieves wideband operation (DC-40 GHz), low insertion loss (≤0.9 dB maximum), high isolation (>50 dB typical), and systematic modular scalability, a combination not found in existing implementations. The matrix, constructed with high-quality coaxial switches and optimized RF circuitry and electromagnetic structures, provides flexible and stable single-pole multi-throw (SPMT) signal routing across an ultra-wide frequency range from DC to 40 GHz. The switch matrix features a modular architecture, integrating multiple RF switching units, drive control circuits, and communication interface modules. This architecture achieves minimal signal path depth while maintaining full connectivity between any input and output port, directly minimizing cumulative insertion loss. Through precise impedance matching design and isolation structure optimization, the system still exhibits outstanding transmission characteristics at the 40 GHz high-frequency end: typical insertion loss does not exceed 0.9 dB, and the isolation between channels is better than 50 dB, effectively ensuring the integrity of signals in complex multi-channel environments. To meet the requirements of automated testing and remote control, the equipment integrates dual communication interfaces (serial port/network port), supports the SCPI command set and TCP/IP protocol, and can be conveniently embedded in various test platforms to achieve instrument interconnection and test process automation. Experimental verification shows that this matrix exhibits excellent switching stability and signal consistency across the entire 40 GHz, with a switching action time of less than 10 ms. Furthermore, it is capable of real-time topology reconfiguration via a microcontroller or FPGA. These innovations collectively deliver a switch matrix that meets the demanding requirements of 5G communication, millimeter-wave radar, and aerospace defense systems—applications where bandwidth, signal integrity, and system flexibility are paramount.

Electronics doi: 10.3390/electronics15071504

Authors: Paweł Rydz Marek Natkaniec

Wi-Fi networks used in smart grids are essential for enabling communication between smart meters and data aggregation units. A key challenge, however, is the ability to hide the existence and traffic patterns of these communications, so that sensitive information exchanges cannot be easily detected or intercepted. Unfortunately, most existing solutions do not provide support for traffic prioritization and steganographic channel encryption. In this paper, we propose a novel covert channel with Quality of Service (QoS) and encryption support for smart grid environments based on the IEEE 802.11 standard. We introduce an original steganographic approach that leverages the backoff mechanism, the Enhanced Distributed Channel Access (EDCA) function, frame aggregation, and the StegoPaddingCipher algorithm. This design ensures QoS-aware traffic handling while enhancing security through encryption of the transmitted covert data. The proposed protocol was implemented and evaluated using the ns-3 simulator, where it achieved excellent performance results. The system maintained high efficiency even under heavily saturated network conditions with additional background traffic generated by other nodes. The proposed covert channel offers an innovative and secure method for transmitting substantial volumes of QoS-related data within smart grid environments.

Electronics doi: 10.3390/electronics15071503

Authors: Omar Alrumayh Abdulaziz Almutairi

The rapid growth of distributed energy resources (DERs) is transforming distribution networks and increasing the need for coordinated flexibility management to maintain secure and economically efficient operation. In this work, we examine how uncertainty in load demand and photovoltaic (PV) generation affects incentive-based flexibility coordination within a hierarchical three-level framework. The proposed architecture integrates household energy management systems (HEMSs), an aggregator responsible for incentive allocation, and a distribution system operator (DSO) model based on AC optimal power flow. To account for demand and PV variability, a Γ-budget-robust optimization approach is adopted. Also, an incentive–penalty mechanism is introduced to allocate compensation according to each prosumer’s actual flexibility contribution while promoting economic fairness. The entire framework is implemented in PYOMO and tested on the IEEE 33-bus distribution system. A comparative evaluation between deterministic and uncertainty-aware cases is conducted to quantify the cost of robustness and to analyze its influence on flexibility participation, incentive distribution, household net cost, and voltage regulation performance. The results indicate that uncertainty can lead to deviations from initially scheduled flexibility commitments, thereby triggering penalty signals during re-optimization and strengthening contractual compliance. Although the robust formulation results in a moderate increase in operational cost, it substantially improves voltage compliance and overall system reliability. Overall, the findings highlight the importance of explicitly incorporating uncertainty in multi-level flexibility coordination to ensure both technical consistency and practical enforceability in modern distribution networks.

Electronics doi: 10.3390/electronics15071502

Authors: Edison Tatayo Adrián Carrera Christian García Germán V. Arévalo Christian Tipantuña

This paper presents the performance analysis of a 100 Gbps long-reach passive optical network (LR-PON) based on intensity modulation and direct detection (IM-DD). The LR-PON is designed for low-complexity environments that reuse previously deployed infrastructure and extend coverage to rural areas. It features a point-to-multipoint PON topology with a 1:64 split and links up to 100 km long. The paper analyzes the impact of the booster amplifier, preamplifier, and chromatic-dispersion-compensating module on the bit error rate (BER) using OptSim simulations. The results demonstrate that the LR-PON, operating at 100 Gbps over a 100 km link and with losses over 3 dB over a legacy network, maintains acceptable BER levels in the order of 10−6, validating its viability as a scalable, efficient, and economical solution for optical access networks in suburban or rural areas in locations such as Quito city (Ecuador).

Electronics doi: 10.3390/electronics15071501

Authors: Yanan Ni Jiajia Chen Jiajia Jia Xinnian Guo

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over inland waters, where coherent scattering dominates and traditional ocean models produce large systematic biases. Unlike open oceans, inland waters are dominated by coherent scattering due to limited fetch, resulting in Delay-Doppler Maps (DDM) with highly concentrated energy and minimal spreading. These characteristics render conventional ocean-based retrieval models—built on incoherent scattering assumptions—often inadequate. To overcome this, we develop a lightweight convolutional neural network (CNN) tailored to the coherent regime, using raw CYGNSS DDM as input for end-to-end wind speed regression. Cross-seasonal validation over Lake Victoria and Lake Hongze shows that the model robustly captures wind-driven spatiotemporal patterns aligned with ERA5. Notably, ERA5 reanalysis winds exhibit uncertainties over inland waters, with a root mean square error (RMSE) of 1.5–2.5 m/s against in situ buoys. The model yields a low RMSE (<0.7 m/s) in reconstructing ERA5-resolved wind patterns. This work extends GNSS-R to inland waters, offering a lightweight, deployable remote sensing solution for wind energy and lake–atmosphere research.

Electronics doi: 10.3390/electronics15071500

Authors: Sichun Du Dingsheng He Zhang Luo

This paper proposes a novel three-layer parallel deep neural network (DNN) equalizer that achieves superior performance and resource efficiency compared to conventional decision feedback equalizers (DFEs). Evaluated in a 56 GBd PAM4 transmission system over a 306 mm channel, the proposed equalizer reduces the bit error rate (BER) by an order of magnitude relative to a first-order DFE while matching the performance of a second-order DFE, all with comparable computational complexity. Notably, the parallel architecture accelerates equalization processing without compromising BER performance and fundamentally eliminates the inherent feedback delay element in DFE structures, thereby overcoming a key timing bottleneck of traditional equalizers. Simulation results demonstrate that under similar power constraints, the DNN equalizer achieves higher throughput than its second-order DFE counterpart, highlighting its significant advantages in resource utilization efficiency.

Electronics doi: 10.3390/electronics15071498

Authors: Nursultan Meirambekuly Margulan Ibraimov Bakyt Khaniyev Beibit Karibayev Alisher Skabylov Nursultan Uzbekov Sungat Koishybay Timur Dautov Ainur Khaniyeva Bagdat Kozhakhmetova

Radio-frequency interference (RFI) poses a critical challenge for modern high-precision Global Navigation Satellite System (GNSS) applications, as both intentional and unintentional interference can significantly degrade positioning accuracy and reliability. With increasingly sophisticated interference sources, robust and computationally efficient automatic recognition methods are required for next-generation GNSS receivers. Although deep learning approaches show strong potential for interference detection, their high computational cost often limits deployment in resource-constrained navigation hardware. This paper proposes a hybrid deep learning architecture for radio interference recognition in high-precision GNSSs. The framework employs a dual-branch design integrating complementary signal representations. A Self-Organizing Operational Neural Network (Self-ONN) extracts nonlinear temporal features from raw one-dimensional signals, while a Vision Mamba state-space model processes two-dimensional time-frequency spectrograms obtained via Short-Time Fourier Transform (STFT). The fused features enable accurate classification of diverse interference types with high computational efficiency. Experiments on a synthetic dataset demonstrate that the proposed model achieves 99.83% accuracy and F1-score, outperforming ResNet18, VGG16, and Vision Transformer while reducing computational complexity by up to 42% and improving inference speed by up to 35%, supporting its applicability for intelligent interference monitoring in GNSS receivers.

Electronics doi: 10.3390/electronics15071499

Authors: Zigao Liu Zehui Yuan

Finite Rate of Innovation (FRI) sampling has been widely used to sampling parametric signals at sub-Nyquist sampling rates. Nevertheless, real-world systems generally handle real-valued signals, posing challenges for acquiring complex domain Fourier coefficients directly. To overcome this limitation, we propose a Chebyshev polynomial-based FRI sampling framework that enables processing entirely in the real domain. Projecting the FRI signal onto the Chebyshev basis and employing a improved annihilating filter reformulates the parameter estimation problem into a classical spectral estimation task. Furthermore, the integration of the discrete Hilbert transform allows for a further reduction in both sampling channels and total sample count. Numerical simulations validate the effectiveness of the proposed approach and the generalizability of FRI theory across different signal bases.

Electronics doi: 10.3390/electronics15071497

Authors: Wanqing Fu Honggui Deng Mingkang Qu Nanqing Zhou

Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input multiple-output (MIMO) systems further exacerbates power consumption and hardware costs. To address these challenges, this paper investigates RIS-assisted millimeter-wave (mmWave) MIMO systems with low-resolution analog-to-digital converters (ADCs). Exploiting the inherent sparsity of mmWave channels and considering the distortion introduced by low-resolution quantization, we propose a compressive sensing (CS)-based channel estimation scheme. Furthermore, to mitigate the effects of angular leakage, we introduce an energy capture orthogonal matching pursuit (ECOMP) algorithm. Simulation results demonstrate that the proposed scheme not only improves channel estimation accuracy but also reduces pilot overhead and power consumption, while maintaining enhanced stability in high signal-to-noise ratio (SNR) regimes.

Electronics doi: 10.3390/electronics15071496

Authors: Mario Del Prete Raffaele Solimene Loreto Di Donato Maria Antonia Maisto

Millimeter-wave and sub-millimeter-wave techniques are widely used in non-destructive testing of multilayered materials due to their ability to penetrate non-conductive media and resolve dielectric stratifications. However, conventional thickness estimation methods suffer from an inherent resolution limit dictated by the available frequency bandwidth. In this paper, a MUSIC-based approach is proposed to achieve super-resolution localization of echoes in the reflective response of the structure under test. The method exploits the sparsity of the reflective response, similarly to compressive sensing approaches, while providing improved reconstruction accuracy. Moreover, the proposed strategy enables the retrieval of dielectric permittivities and layer thicknesses without resorting to complex nonlinear fitting procedures. Finally, the method operates on magnitude-only data, with phase information recovered through an interferometric holographic technique, making the proposed framework well-suited for cost-effective industrial applications.

Electronics doi: 10.3390/electronics15071495

Authors: Rasool Seyghaly Jordi Garcia Xavi Masip-Bruin

The rapid growth of online learning platforms has intensified the need for privacy-aware methods that can analyze learner behavior without centralizing sensitive activity logs. This study presents a Federated Learning-Based Graph Representation Learning (FL-GRL) framework for online student performance analysis in distributed learning environments. Each learner is represented through a local Student Learning Knowledge Graph (SLKG) that captures typed interactions with courses, lessons, webinars, challenges, and forum activities. Graph Neural Networks (GNNs) are used to derive relation-aware embeddings from these local graphs, while federated learning supports collaborative model optimization without sharing raw data. A federated clustering stage is then used to identify soft learner groups with partially overlapping behavioral patterns that may support exploratory personalization and confidence-aware educational follow-up. The current experiments focus on the feasibility of privacy-aware graph-based analysis rather than on a complete supervised prediction benchmark. Results across the evaluated graph-based variants indicate that the proposed framework is operationally viable, preserves relational structure better than flat-feature formulations, and provides an interpretable basis for learner-group discovery in privacy-sensitive online education settings.

Electronics doi: 10.3390/electronics15071494

Authors: Zhijie Lin Yuewen Fan Yujie Chen Liyan Liang Yishuo Meng Jianfei Wang Chen Yang

The rapid growth of 5G necessitates wireless receivers capable of high-speed, low-latency communication under complex channel conditions. Traditional receivers struggle with the performance–complexity trade-off in massive MIMO systems, where linear detectors underperform and maximum likelihood (ML) detection becomes computationally prohibitive. Deep-learning-based model-driven approaches have demonstrated a favorable balance between detection performance and computational cost. However, despite their algorithmic promise, the transition of these learned detectors into practical, real-time systems is critically hampered by inefficient hardware mapping, resulting in suboptimal throughput, high resource overhead, and limited scalability. To bridge this gap, this paper presents LwAMP-Net, a dedicated FPGA accelerator for a lightweight learned AMP detector. We propose a modular and multi-mode hardware architecture for LwAMP-Net, featuring an outer-product-based dataflow that mitigates pipeline stalls and multi-mode processing elements that adapt to diverse computation patterns. These innovations jointly enhance computational parallelism and resource utilization on the FPGA. Implemented on a Xilinx XC7VX690T FPGA for a 128 × 8 MIMO system with 16QAM, the accelerator achieves a 49.2% higher normalized throughput per iteration, an 85.4% improvement in throughput per LUT slice, and a 12.7% improvement in throughput per DSP compared to the state-of-the-art methods. This work provides a complete architectural solution for deploying high-performance, hardware-efficient learned MIMO detectors in real-world systems.

Electronics doi: 10.3390/electronics15071493

Authors: Md Firoz Mahmud Zerin Nusrat W. David Pan

The rapid growth in large-scale image repositories over the past few years has made exact and near-duplicate images increasingly common, creating substantial redundancy that wastes storage resources and reduces retrieval efficiency in practical systems. Even though perceptual hashing and deep learning are promising deduplication strategies, the lack of standardized benchmarks complicates direct comparison. In this study, we conduct a unified, controlled evaluation of five commonly used methods, including four classical perceptual hashes (AHash, DHash, PHash, and WHash) and a CNN-based embedding model. We evaluate all methods on the UKBench and Amazon Berkeley Objects datasets using identical preprocessing, thresholds, and metrics, which include exact duplicates, near-duplicates, and geometrically transformed duplicates. Our experiments highlight a clear trade-off between speed and robustness. Hashing methods are computationally efficient and effective for exact matches, but perform poorly on near-duplicates and under geometric transformations, whereas the CNN model is significantly more robust across all duplicate types, but comes at a high computational cost. Based on these results, we outline practical recommendations for selecting deduplication strategies in large-scale applications. In addition, our evaluation setup serves as a reproducible baseline for future research in image similarity and large-scale deduplication.

Electronics doi: 10.3390/electronics15071492

Authors: Yimin Xu Ya Liu Xianbei Liu Bo Qu

In federated learning, multiple clients collaborate to train a global model without exchanging raw data, which addresses issues of data silos and the leakage of data privacy. However, existing federated learning schemes often suffer from high communication overhead and unreliable server-side aggregation. To address these limitations, this paper proposes a verifiable chained federated learning mechanism with Euclidean distance-based grouping, termed VDCG-FL. Grouping is used to improve communication efficiency, while verification ensures the accuracy of aggregated results. Unlike conventional approaches, VDCG-FL groups clients according to their Euclidean distance to the server, thereby reducing communication latency, avoiding long-distance transmissions, and enhancing the stability of model aggregation. Moreover, Lagrange interpolation is used for verification to ensure aggregation correctness while incurring significantly lower computational overhead than traditional cryptographic methods. Extensive experiments demonstrate that VDCG-FL improves aggregation stability under non-IID data distributions while simultaneously reducing communication overhead.

Electronics doi: 10.3390/electronics15071491

Authors: Young-Jae Cho Na-Yeon Kim Kui-Jun Lee

The series resonant converter (SRC) is widely used in power conversion systems that require high efficiency and high-power density. However, under light-load conditions, the resonant current decreases, and a higher switching frequency is often required to regulate the output voltage, which leads to efficiency degradation. To mitigate this issue, phase-shift control can be applied to the SRC, and an appropriate small-signal model is essential for accurate dynamic analysis and controller design. Conventional extended describing function (EDF)-based small-signal models provide high accuracy, but their complex equivalent circuits make analytical derivation of the transfer functions difficult and limit intuitive physical interpretation. To overcome this limitation, this paper proposes a non-coupled third-order equivalent-circuit model for the phase-shift SRC. The proposed model reduces the complexity of the conventional EDF-based fifth-order model while preserving the essential low-frequency dynamic characteristics. By employing approximations based on the relationship between the modulation frequency and the switching frequency, together with the superposition principle and equivalent transformations, the model removes the coupling among state variables and enables analytical derivation of the transfer functions. The proposed model is verified through comparisons of the low-frequency small-signal frequency responses with the conventional fifth-order model, PLECS simulations, and experimental measurements.

Electronics doi: 10.3390/electronics15071490

Authors: Luca Notarianni Roberto Bagnato Anna Sabatini Giulia Di Tomaso Luca Vollero

Microcontroller-based embedded systems integrating multiple sensors are increasingly required to support continuous data acquisition, on-board processing, and long-term storage within tightly coupled hardware–software architectures. In such platforms, overall performance is often constrained not by computational capability but by storage I/O behavior, particularly under real-time constraints and concurrent workloads. This study presents a comprehensive empirical evaluation of eMMC storage performance on an STM32U5 microcontroller running the ThreadX RTOS. The proposed methodology combines multi-dimensional stress testing, controlled task concurrency (0–4 tasks), and long-duration aging analysis (90 h), together with timing variability assessment under electrical stress and interrupt-driven preemption. Both synthetic workloads and realistic sensor-node scenarios with heterogeneous and asynchronous access patterns are considered. The results highlight significant performance limitations, including up to 98% throughput degradation under four concurrent tasks and a nonlinear increase in metadata latency as free space decreases below 40% (from 10 ms to over 200 ms for file creation). Additionally, timing jitter increases by 2–5× under voltage variation and interrupt load. Based on these findings, practical firmware-level design guidelines are derived, including sector-aligned buffering, dedicated I/O task architectures, and proactive capacity management, enabling substantial improvements in throughput and latency. This study provides quantitative insights and reproducible methodologies for optimizing storage subsystems in multi-sensor embedded applications.

Electronics doi: 10.3390/electronics15071489

Authors: Georgios Chatzipetrakis Alexandros Skoulakis Ioannis Fitilis Emmanuel Antonidakis Michael Tatarakis John Chatzakis

A novel modular multilevel converter (MMC)-based spark-gap trigger generator for high-voltage pulsed-power applications has been developed and presented in this work. It fully exploits the inherent modularity of MMC topology to generate high-voltage trigger pulses in a flexible and scalable manner. A prototype based on insulated gate bipolar transistors (IGBTs) was constructed to effectively trigger the breakdown of the spark gaps of a Marx Bank consisting of four capacitors charged to 50 kV. It is characterized by a fast rise time and produces pulses of 15 kV with a duration of ~200 ns. Using semiconductors and foil capacitors, the new trigger generator successfully replaces the thyratron-based generator.

Electronics doi: 10.3390/electronics15071488

Authors: Yifan Pan Yuesheng Zhu

In real-world machine learning scenarios, training data are frequently weakly annotated and distributionally misaligned with deployment environments. Specifically, label ambiguity may arise when each instance is associated with a set of candidate labels, and distribution shifts between training and testing are common in practice. Although Partial Label Learning (PLL) and Unsupervised Domain Adaptation (UDA) have been extensively studied individually, they frequently co-occur in practice. For instance, in cross-hospital medical image analysis, datasets may exhibit both inconsistent diagnostic labels due to variations in expert interpretation (label ambiguity) and significant differences in imaging equipment or patient demographics (distribution shift). However, Partial-Label Unsupervised Domain Adaptation (PLUDA) has received limited attention as a unified problem. In this paper, a unified generalization bound is established for Partial-Label Unsupervised Domain Adaptation (PLUDA) and three critical limitations causing existing approaches to fail: ambiguity degree, ideal joint error, and model complexity remain uncontrolled. Motivated by these theoretical insights, we propose Dual-Smoothing over Manifold and Parameter (DSMP) to control all three factors. DSMP employs manifold-based representation smoothing via Laplacian smoothing based on adaptive multi-kernel RKHS similarity and candidate set refinement to address the three critical limitations. Moreover, DSMP leverages sharpness-aware parameter smoothing to ensure stable optimization under weak supervision through loss landscape flattening. Extensive experiments demonstrate that DSMP outperforms existing baselines, achieving superior cross-domain generalization from weakly labeled sources. This work provides theoretical insights and a principled solution to the previously underexplored yet practically important PLUDA problem.

Electronics doi: 10.3390/electronics15071487

Authors: Lin-Guo Gao Suxing Liu

Accurate classification of breast cancer histopathology images is essential for early diagnosis and effective clinical management. However, conventional deep learning models often exhibit performance degradation under limited labeled data and lack interpretability, which restricts their clinical applicability. To address these challenges, we propose GAFR-Net, a robust and interpretable Graph Attention and Fuzzy-Rule Network designed for histopathology image classification under scarce supervision (defined here as less than 10% labeled data). GAFR-Net constructs a similarity-driven graph to model inter-sample relationships and employs a multi-head graph attention mechanism to capture complex relational representations among heterogeneous tissue structures. Meanwhile, a differentiable fuzzy-rule module integrates intrinsic topological descriptors—such as node degree, clustering coefficient, and label consistency—into explicit and human-readable diagnostic rules. This architecture establishes transparent IF–THEN inference mappings that emulate the heuristic reasoning process of clinical experts, thereby enhancing model interpretability without relying on post-hoc explanation techniques. Extensive experiments conducted on three public benchmark datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that GAFR-Net consistently surpasses state-of-the-art methods across multiple magnifications and classification settings. These results highlight the strong generalization capability and practical potential of GAFR-Net as a trustworthy decision-support framework for weakly supervised medical image analysis.

Electronics doi: 10.3390/electronics15071486

Authors: Mustapha Asnoun Adel Rahoui Koussaila Mesbah Boussad Boukais David Frey Idris Sadli Seddik Bacha

The widespread development of medium-voltage and high-voltage direct current transmission systems has highlighted the modular multilevel converter (MMC) as a crucial enabling technology. However, the overall performance and lifetime of the MMC strongly depend on the integrity of its submodules (SMs), making online capacitance condition monitoring a critical requirement. Unlike recent related studies that rely on computationally heavy matrix-based algorithms or “black-box” artificial neural networks requiring massive offline training datasets, this paper proposes a parametric, adaptive linear neuron network. Mapped directly to the physical equations of the MMC, the method simultaneously exploits the arm current, SM switching state, and capacitor voltage to identify online parametric variations caused by aging or harsh conditions. The proposed scheme is fully non-intrusive, requiring no additional hardware sensors or signal injections, thereby reducing implementation complexity. The simulation results obtained in MATLAB/Simulink (vR2024b) demonstrate the method’s fast convergence and a quantified steady-state estimation error within ±1%. Furthermore, the estimator exhibits strong robustness under severe operating conditions, successfully maintaining accuracy during a 20% capacitance reduction, a 100% active power step variation, dc-link voltage fluctuations, measurement noise, grid unbalances, and harmonic perturbations.

Electronics doi: 10.3390/electronics15071485

Authors: Mao Ye Jianyu Wang

To solve the problems of high-precision attitude control and vibration isolation of satellite payloads, this paper conducts in-depth research on satellite attitude dynamics and maglev active vibration isolation control technology. A dual-super collaborative control scheme is proposed, which consists of payload module ultra-high precision and ultra-high stability control, relative position control of two modules, and service module attitude control. The target attitude and angular velocity obtained by maneuver path planning and attitude guidance are transmitted to the attitude and orbit control management unit, and the total control command torque is formed by combining feedback control and feedforward control, which is then distributed to each maglev actuator to realize high-precision control of the payload module. The architecture of the maglev vibration isolation system is designed, and its dynamic model is established based on the Newton–Euler equation. Meanwhile, the dynamic model of the maglev actuator is constructed, and the active control strategy is designed by adopting PID control. The models of output force and torque are established, system parameters are set for simulation analysis of dynamic responses such as displacement, attitude and electromagnetic force, and a 20% pull-bias robustness test is carried out. Simulation results show that the system has high isolation accuracy, stability, and can effectively suppress the interference and shaking of the platform and load, with strong robustness.

Electronics doi: 10.3390/electronics15071484

Authors: Nadjem Hadjer Djerfaf Fatima Boutejdar Ahmed

This paper presents the design and experimental validation of a compact low-pass filter based on a quasi-eight-shaped defected ground structure (DGS). The study begins with a single DGS resonator that perturbs the ground-plane current distribution, introducing additional effective inductance and capacitance. An equivalent circuit model is developed to provide physical insight into the resonant mechanism and to establish the relationship between the DGS geometry and the electromagnetic response. By incorporating microstrip stubs on the top layer, the resonant structure is transformed into a low-pass filtering configuration with improved passband characteristics. Subsequently, a higher-order topology composed of two identical quasi-eight DGS units and three microstrip stubs is implemented to significantly enhance the rejection performance and extend the stopband bandwidth. The fabricated prototype exhibits a measured cutoff frequency of approximately 2.1 GHz, with an insertion loss lower than 1 dB in the passband. A wide stopband extending from 2.8 GHz to 8 GHz is achieved, with attenuation exceeding 26 dB. The close agreement between the equivalent circuit model, full-wave electromagnetic simulations, and measured results confirms the effectiveness and physical consistency of the proposed design. Owing to its compact planar implementation and strong harmonic suppression capability, the proposed filter is suitable for microwave front-end and antenna applications.

Electronics doi: 10.3390/electronics15071482

Authors: Mert Yılmaz Erdal Çetkin Hakan Akça

Unmanned aerial vehicles, which are widely used today, require human assistance to meet their energy needs. This dependency disrupts autonomous operation. At this point, wireless power transfer technology offers a promising solution for full autonomy. These vehicles can be easily charged by contactless power transfer between magnetic couplers in seemingly impossible locations. Coupler configurations are critical due to the size constraints of these vehicles. In current studies, analyses of transfer efficiency are conducted using one or two parameters. In this study, in addition to the coupler configuration, the effects of air gap, duty cycle, and magnetic core on efficiency were analyzed together. The performance of couplers with rectangular, circular, and double-D configurations was investigated through comprehensive simulations and experiments. The AC and DC efficiencies of the wireless power transfer system were analyzed by performing 46 experiments, while the operating frequency of the system was between 95 and 105 kHz, the input power was around 250 W. Simulations of the system and couplers were performed in MATLAB and Ansys. In the experiments, the highest AC efficiency was 98.9%, and the DC efficiency was 86.7%. The error margins in MATLAB and Ansys models are less than 1% and 4%, respectively.

Electronics doi: 10.3390/electronics15071483

Authors: Xufeng Wu Zuowei Chen Hefang Jiang Shoukang Luo Yi Zhao Dongwei Zhao Peiyao Dang Jiajun Gao Lin Lin Hao Wang

The authors would like to make the following correction to their published paper [...]

Electronics doi: 10.3390/electronics15071481

Authors: Yang Cao Huimin Cai Haotian Zhu Sen Zhang Jun Hu

With the growing demand for trustworthy multi-party data sharing, federated learning has demonstrated broad potential in cross-entity collaborative modeling. However, it still faces challenges such as insufficient participant engagement, inaccurate contribution assessment, and the lack of dynamic profit-sharing mechanisms. Traditional incentive schemes, which typically rely on game-theoretic models or static rules, struggle to accommodate dynamic client participation and heterogeneous data distributions, thereby degrading the convergence efficiency and generalization performance of the global model. To address these issues, we propose a budget-aware closed-loop incentive allocation for federated learning with deep deterministic policy gradient (DDPG). The proposed approach constructs a DDPG-driven closed-loop framework in which the server manages system states, incentive decisions, and model aggregation, while clients autonomously adjust their data contribution levels. By formulating incentive allocation as a sequential decision-making problem, the mechanism jointly optimizes policy and value functions. A permutation method is introduced to ensure invariance to client ordering, and an Ornstein–Uhlenbeck process is employed to enhance exploration, thereby improving the adaptiveness and overall effectiveness of incentive allocation. Experimental results show that the proposed method significantly increases cumulative rewards and improves client data-sharing rates in high-dimensional dynamic environments. Compared with traditional fixed incentive schemes, the mechanism demonstrates clear advantages in adaptiveness, incentive effectiveness, and model performance.

Electronics doi: 10.3390/electronics15071480

Authors: Hannah R. Nolasco Andrew Vargo Chris Blakely Ko Watanabe Mark Armstrong Marco Stricker Koichi Kise

Commercially available wearable health devices (WHDs) carry the potential to decentralize healthcare systems. These devices can empower individuals with health knowledge by offering a low-cost and accessible way to monitor physical activity, sedentary behavior, cardiac health, and sleep. However, a lack of standardization in design, health, and safety regulations means that consumer-grade WHDs on the market vary in efficacy to affect positive behavior change in users, as user compliance alone does not indicate whether these devices actually influence wellbeing outcomes long term. We use a grounded theory analysis of the experiences of seven long-term informed users of the same wearable, the Oura Ring, to propose a substantive theory describing the tacit challenges that these users face in order to truly benefit from their device even after extended use. We provide recommendations as to how designers of wearable devices can facilitate the user’s journey to surpass these obstacles.

Electronics doi: 10.3390/electronics15071479

Authors: Zhibin Qin Changlei Feng Yue Zhang Fan Zhang Chen Lyu Shanshan Sun Ji Zhou

This study presents a TCAD model of a SiGe HBT designed for high-speed data transfer, with a cutoff frequency of 246.5 GHz and a β-value up to 416.7. Comprehensive single-event transient (SET) irradiation simulations were performed by injecting charges at different junctions with various angles. The influence of SET on data transfer was further evaluated at circuit level by loading the SET model from TCAD simulation into a high-speed laser diode driver circuit. Hence, this work employed a collector dummy structure in the designed HBT to build radiation-hardened devices. Simulation results indicate significant mitigation of the single-event transient current, which could be reduced to 10%, compared with non-hardened devices.