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

Journal Description

Electronics

Electronics is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Electrical and Electronic) / CiteScore - Q1 (Electrical and Electronic Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Electronics include: Magnetism, Microwave, Network, Signals, Software and Microelectronics.

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

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

17 pages, 9122 KiB

Open AccessArticle

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

by Takafumi Fujimoto and Chai-Eu Guan

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

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

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

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20 pages, 2781 KiB

Open AccessArticle

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

by Mohamed Salah Dahassa and Nadjet Zioui

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

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

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

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

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13 pages, 2352 KiB

Open AccessArticle

Research on Improving the Avalanche Current Limit of Parallel SiC MOSFETs

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

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

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

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

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28 pages, 8607 KiB

Open AccessArticle

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

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

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

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

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

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

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16 pages, 1475 KiB

Open AccessArticle

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

by Neda Miteva, Lior Sima and Kfir Jack Dagan

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

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

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

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

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25 pages, 547 KiB

Open AccessArticle

An Interaction–Engagement–Intention Model: How Artificial Intelligence and Augmented Reality Transform the User–Platform Interaction Paradigm

by Zian Shah Kabir and Kyeong Kang

Electronics 2025, 14(12), 2499; https://doi.org/10.3390/electronics14122499 - 19 Jun 2025

Interaction with mobile platforms changes users’ emotional and cognitive engagements through various stimuli cues that respond to behavioural intentions. Emerging technologies such as artificial intelligence (AI) and augmented reality (AR) foster more engagements and transform a new user–platform interaction paradigm in the e-commerce [...] Read more.

Interaction with mobile platforms changes users’ emotional and cognitive engagements through various stimuli cues that respond to behavioural intentions. Emerging technologies such as artificial intelligence (AI) and augmented reality (AR) foster more engagements and transform a new user–platform interaction paradigm in the e-commerce industry. This study signifies the effects of artificial intelligence and augmented reality in assessing user experience for mobile platforms. In this paper, we develop an interaction–engagement–intention model that considers users’ continuance intention based on perceived user experience. The proposed model uniquely explains a nuanced understanding of how the user–platform interactions evolve interactivity, product fit, artificial intelligence-driven recommendation, and online reviews in perceiving spatial presence and subjective norm. This paper explores the importance of attitude and trust as emotional states that influence the user’s behavioural responses. We validate the consequences of user–platform interactions toward continuance intention by conducting an online questionnaire survey and assessing user experience in augmented reality environments. The results contribute to adopting the co-created values of user–platform interactions through cognitive and emotional engagements that affect users’ continuance intention. The platform industry can apply the research outcomes by considering user experience and its implications to enhance the platforms’ capability with a broader aspect. Full article

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21 pages, 1889 KiB

Open AccessArticle

Optimizing Glioblastoma Multiforme Diagnosis: Semantic Segmentation and Survival Modeling Using MRI and Genotypic Data

by Yu-Hung Tsai, Wen-Yu Cheng, Bo-Hua Huang, Chiung-Chyi Shen and Meng-Hsiun Tsai

Electronics 2025, 14(12), 2498; https://doi.org/10.3390/electronics14122498 - 19 Jun 2025

Glioblastoma multiforme (GBM) is the most aggressive and common primary brain tumor. Magnetic resonance imaging (MRI) provides detailed visualization of tumor morphology, edema, and necrosis. However, manually segmenting GBM from MRI scans is time-consuming, subjective, and prone to inter-observer variability. Therefore, automated and [...] Read more.

Glioblastoma multiforme (GBM) is the most aggressive and common primary brain tumor. Magnetic resonance imaging (MRI) provides detailed visualization of tumor morphology, edema, and necrosis. However, manually segmenting GBM from MRI scans is time-consuming, subjective, and prone to inter-observer variability. Therefore, automated and reliable segmentation methods are crucial for improving diagnostic accuracy. This study employs an image semantic segmentation model to segment brain tumors in MRI scans of GBM patients. The MRI recall images include T1-weighted imaging (T1WI) and fluid-attenuated inversion recovery (FLAIR) sequences. To enhance the performance of the semantic segmentation model, image preprocessing techniques were applied before analyzing and comparing commonly used segmentation models. Additionally, a survival model was constructed using discrete genotype attributes of GBM patients. The results indicate that the DeepLabV3+ model achieved the highest accuracy for semantic segmentation, with an accuracy of 77.9% on T1WI image sequences, while the U-Net model achieved 80.1% accuracy on FLAIR image sequences. Furthermore, in constructing the survival model using a discrete attribute dataset, the dataset was divided into three subsets based on different missing value handling strategies. This study found that replacing missing values with 1 resulted in the highest accuracy, with the Bernoulli Bayesian model and the multinomial Bayesian model achieving an accuracy of 94.74%. This study integrates image preprocessing techniques and semantic segmentation models to improve the accuracy and efficiency of brain tumor segmentation while also developing a highly accurate survival model. The findings aim to assist physicians in saving time and facilitating preliminary diagnosis and analysis. Full article

(This article belongs to the Special Issue Image Segmentation, 2nd Edition)

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19 pages, 1433 KiB

Open AccessArticle

Cost-Optimised Machine Learning Model Comparison for Predictive Maintenance

by Yating Yang and Muhammad Zahid Iqbal

Electronics 2025, 14(12), 2497; https://doi.org/10.3390/electronics14122497 - 19 Jun 2025

Predictive maintenance is essential for reducing industrial downtime and costs, yet real-world datasets frequently encounter class imbalance and require cost-sensitive evaluation due to costly misclassification errors. This study utilises the SCANIA Component X dataset to advance predictive maintenance through machine learning, employing seven [...] Read more.

Predictive maintenance is essential for reducing industrial downtime and costs, yet real-world datasets frequently encounter class imbalance and require cost-sensitive evaluation due to costly misclassification errors. This study utilises the SCANIA Component X dataset to advance predictive maintenance through machine learning, employing seven supervised algorithms, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbours, Multi-Layer Perceptron, XGBoost, and LightGBM, trained on time-series features extracted via a sliding window approach. A bespoke cost-sensitive metric, aligned with SCANIA’s misclassification cost matrix, assesses model performance. Three imbalance mitigation strategies, downsampling, downsampling with SMOTETomek, and manual class weighting, were explored, with downsampling proving most effective. Random Forest and Support Vector Machine models achieved high accuracy and low misclassification costs, whilst a voting ensemble further enhanced cost efficiency. This research emphasises the critical role of cost-aware evaluation and imbalance handling, proposing an ensemble-based framework to improve predictive maintenance in industrial applications Full article

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29 pages, 5989 KiB

Open AccessArticle

Risk Analysis Method of Aviation Critical System Based on Bayesian Networks and Empirical Information Fusion

by Xiangjun Dang, Yongxuan Shao, Haoming Liu, Zhe Yang, Mingwen Zhong, Maohua Sun and Wu Deng

Electronics 2025, 14(12), 2496; https://doi.org/10.3390/electronics14122496 - 19 Jun 2025

The intrinsic hazards associated with high-pressure hydrogen, combined with electromechanical interactions in hybrid architectures, pose significant challenges in predicting potential system risks during the conceptual design phase. In this paper, a risk analysis methodology integrating systems theoretic process analysis (STPA), D-S evidence theory, [...] Read more.

The intrinsic hazards associated with high-pressure hydrogen, combined with electromechanical interactions in hybrid architectures, pose significant challenges in predicting potential system risks during the conceptual design phase. In this paper, a risk analysis methodology integrating systems theoretic process analysis (STPA), D-S evidence theory, and Bayesian networks (BN) is established. The approach employs STPA to identify unsafe control actions and analyze their loss scenarios. Subsequently, D-S evidence theory quantifies the likelihood of risk factors, while the BN model’s nodal uncertainties to construct a risk network identifying critical risk-inducing events. This methodology provides a comprehensive risk analysis process that identifies systemic risk elements, quantifies risk probabilities, and incorporates uncertainties for quantitative risk assessment. These insights inform risk-averse design decisions for hydrogen–electric hybrid powered aircraft. A case study demonstrates the framework’s effectiveness. The approach bridges theoretical risk analysis with early-stage engineering practice, delivering actionable guidance for advancing zero-emission aviation. Full article

(This article belongs to the Special Issue Stability Analysis and Optimal Operation Strategy for Power Systems with High Renewable Energy Penetration)

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55 pages, 2065 KiB

Open AccessReview

Edge Intelligence: A Review of Deep Neural Network Inference in Resource-Limited Environments

by Dat Ngo, Hyun-Cheol Park and Bongsoon Kang

Electronics 2025, 14(12), 2495; https://doi.org/10.3390/electronics14122495 - 19 Jun 2025

Deploying deep neural networks (DNNs) in resource-limited environments—such as smartwatches, IoT nodes, and intelligent sensors—poses significant challenges due to constraints in memory, computing power, and energy budgets. This paper presents a comprehensive review of recent advances in accelerating DNN inference on edge platforms, [...] Read more.

Deploying deep neural networks (DNNs) in resource-limited environments—such as smartwatches, IoT nodes, and intelligent sensors—poses significant challenges due to constraints in memory, computing power, and energy budgets. This paper presents a comprehensive review of recent advances in accelerating DNN inference on edge platforms, with a focus on model compression, compiler optimizations, and hardware–software co-design. We analyze the trade-offs between latency, energy, and accuracy across various techniques, highlighting practical deployment strategies on real-world devices. In particular, we categorize existing frameworks based on their architectural targets and adaptation mechanisms and discuss open challenges such as runtime adaptability and hardware-aware scheduling. This review aims to guide the development of efficient and scalable edge intelligence solutions. Full article

(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)

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15 pages, 634 KiB

Open AccessArticle

Robust H_∞ Time-Varying Formation Tracking for Heterogeneous Multi-Agent Systems with Unknown Control Input

by Jichuan Liu, Song Yang, Chunxi Dong and Peng Song

Electronics 2025, 14(12), 2494; https://doi.org/10.3390/electronics14122494 - 19 Jun 2025

This paper studies the robust

H_{\infty}

time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multi-agent systems (MASs) with parameter uncertainties, external disturbances, and unknown leader inputs. The objective is to ensure that follower agents track the leader’s trajectory while achieving a desired [...] Read more.

This paper studies the robust

H_{\infty}

time-varying formation tracking (TVFT) problem for heterogeneous nonlinear multi-agent systems (MASs) with parameter uncertainties, external disturbances, and unknown leader inputs. The objective is to ensure that follower agents track the leader’s trajectory while achieving a desired time-varying formation, even under unmodeled dynamics and disturbances. Unlike existing methods that rely on global topology information or homogeneous system assumptions, an adaptive control protocol is proposed in full distribution, requiring no global topology information, and integrates nonlinear compensation terms to handle unknown leader inputs and parameter uncertainties. Based on the Lyapunov theory and laplacian matrix, a robust

H_{\infty}

TVFT criterion is developed. Finally, a numerical example is given to verify the theory. Full article

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25 pages, 2361 KiB

Open AccessArticle

Enhancing Bug Assignment with Developer-Specific Feature Extraction and Hybrid Deep Learning

by Geunseok Yang, Jinfeng Ji and Dongkyu Kim

Electronics 2025, 14(12), 2493; https://doi.org/10.3390/electronics14122493 - 19 Jun 2025

The increasing reliance on software in diverse domains has led to a surge in user-reported functional enhancements and unexpected bugs. In large-scale open-source projects like Eclipse and Mozilla, initial bug assignment frequently faces challenges, with approximately 50% of bug reports being reassigned due [...] Read more.

The increasing reliance on software in diverse domains has led to a surge in user-reported functional enhancements and unexpected bugs. In large-scale open-source projects like Eclipse and Mozilla, initial bug assignment frequently faces challenges, with approximately 50% of bug reports being reassigned due to the inability of the initially assigned developer to resolve the issue effectively. This reassignment process contributes to elevated software maintenance costs and delays in bug resolution. To address this, we propose a developer recommendation model that assigns the most suitable developer for a given bug report at the outset, thereby minimizing reassignment rates. Our approach combines a top-K feature selection algorithm tailored for each developer with a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to capture the nuanced patterns in bug reports and developer expertise. The model was evaluated on prominent open-source projects, including Google Chrome, Mozilla Core, and Mozilla Firefox. Experimental results show that the proposed model significantly outperforms baseline approaches, with an improvement in developer recommendation accuracy of approximately 0.3582 when comparing the best-performing configuration to the worst-performing configuration of our model. Furthermore, the baseline difference was reduced by approximately 0.1343. A statistical analysis confirms the significant performance improvement achieved by the proposed method over existing baselines. These findings underscore the potential of our model to enhance efficiency in bug resolution workflows, reduce maintenance costs, and improve overall software quality in open-source ecosystems. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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35 pages, 2826 KiB

Open AccessArticle

AI-Driven Anomaly Detection for Securing IoT Devices in 5G-Enabled Smart Cities

by Manuel J. C. S. Reis

Electronics 2025, 14(12), 2492; https://doi.org/10.3390/electronics14122492 - 19 Jun 2025

This paper proposes a novel AI-driven anomaly detection framework designed to enhance cybersecurity in IoT-enabled smart cities operating over 5G networks. While prior research has explored deep learning for anomaly detection, most existing systems rely on single-model architectures, employ centralized training, or lack [...] Read more.

This paper proposes a novel AI-driven anomaly detection framework designed to enhance cybersecurity in IoT-enabled smart cities operating over 5G networks. While prior research has explored deep learning for anomaly detection, most existing systems rely on single-model architectures, employ centralized training, or lack support for real-time, privacy-preserving deployment—limiting their scalability and robustness. To address these gaps, our system integrates a hybrid deep learning model combining autoencoders, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs) to detect spatial, temporal, and reconstruction-based anomalies. Additionally, we implement federated learning and edge AI to enable decentralized, privacy-preserving threat detection across distributed IoT nodes. The system is trained and evaluated using a combination of real-world (CICIDS2017, TON_IoT, UNSW-NB15) and synthetically generated attack data, including adversarial perturbations. Experimental results show our hybrid model achieves a precision of 97.5%, a recall of 96.2%, and an F1 score of 96.8%, significantly outperforming traditional IDS and standalone deep learning methods. These findings validate the framework’s effectiveness and scalability, making it suitable for real-time intrusion detection and autonomous threat mitigation in smart city environments. Full article

(This article belongs to the Special Issue New Insights of Internet of Things in Industry 4.0)

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19 pages, 3066 KiB

Open AccessArticle

A Convex Constraint Approach for High-Type Control Loop Design

by Chao Liu, Xiaoxia Qiu and Yao Mao

Electronics 2025, 14(12), 2491; https://doi.org/10.3390/electronics14122491 - 19 Jun 2025

This paper proposes a high-type control loop design method for LQR-LMI based on Lyapunov and polyhedral model theory. The high-type control loop design problem is simplified into a convex constraint problem, which achieves superior tracking performance. In this framework, the input amplitude of [...] Read more.

This paper proposes a high-type control loop design method for LQR-LMI based on Lyapunov and polyhedral model theory. The high-type control loop design problem is simplified into a convex constraint problem, which achieves superior tracking performance. In this framework, the input amplitude of the control signal, the poles of the closed-loop system, the suppression of external interference and the perturbation of internal parameters are considered, and the linear matrix inequality (LMI) method is effectively used to solve the problems. In this paper, the polyhedral model control theory is introduced to characterize the uncertainty of the system for the change of model parameters of the controlled plant. Aiming at the problem of external disturbance suppression, the

H_{2} / H_{\infty}

control method is introduced into the system. These control methods provide the basis for the design of the high-type control loop. Compared with the simulation results of other optimization algorithms, the effectiveness and superiority of the controller parameter tuning rules in the proposed high-type control loop are verified. Full article

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12 pages, 2513 KiB

Open AccessArticle

Optoelectronic Memristor Based on ZnO/Cu₂O for Artificial Synapses and Visual System

by Chen Meng, Hongxin Liu, Tong Li, Jin Luo and Sijie Zhang

Electronics 2025, 14(12), 2490; https://doi.org/10.3390/electronics14122490 - 19 Jun 2025

The development of artificial intelligence has resulted in significant challenges to conventional von Neumann architectures, including the separation of storage and computation, and power consumption bottlenecks. The new generation of brain-like devices is accelerating its evolution in the direction of high-density integration and [...] Read more.

The development of artificial intelligence has resulted in significant challenges to conventional von Neumann architectures, including the separation of storage and computation, and power consumption bottlenecks. The new generation of brain-like devices is accelerating its evolution in the direction of high-density integration and integrated sensing, storage, and computing. The structural and information transmission similarity between memristors and biological synapses signifies their unique potential in sensing and memory. Therefore, memristors have become potential candidates for neural devices. In this paper, we have designed an optoelectronic memristor based on a ZnO/Cu₂O structure to achieve synaptic behavior through the modulation of electrical signals, demonstrating the recognition of a dataset by a neural network. Furthermore, the optical synaptic functions, such as short-term/long-term potentiation and learn-forget-relearn behavior, and advanced synaptic behavior of optoelectronic modulation, are successfully simulated. The mechanism of light-induced conductance enhancement is explained by the barrier change at the interface. This work explores a new pathway for constructing next-generation optoelectronic synaptic devices, which lays the foundation for future brain-like visual chips and intelligent perceptual devices. Full article

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19 pages, 9631 KiB

Open AccessArticle

Res2Former: Integrating Res2Net and Transformer for a Highly Efficient Speaker Verification System

by Defu Chen, Yunlong Zhou, Xianbao Wang, Sheng Xiang, Xiaohu Liu and Yijian Sang

Electronics 2025, 14(12), 2489; https://doi.org/10.3390/electronics14122489 - 19 Jun 2025

Speaker verification (SV) is an exceptionally effective method of biometric authentication. However, its performance is heavily influenced by the effectiveness of the extracted speaker features and their suitability for use in resource-limited environments. Transformer models and convolutional neural networks (CNNs), leveraging self-attention mechanisms, [...] Read more.

Speaker verification (SV) is an exceptionally effective method of biometric authentication. However, its performance is heavily influenced by the effectiveness of the extracted speaker features and their suitability for use in resource-limited environments. Transformer models and convolutional neural networks (CNNs), leveraging self-attention mechanisms, have demonstrated state-of-the-art performance in most Natural Language Processing (NLP) and Image Recognition tasks. However, previous studies indicate that standalone Transformer and CNN architectures present distinct challenges in speaker verification. Specifically, while Transformer models deliver good results, they fail to meet the requirements of low-resource scenarios and computational efficiency. On the other hand, CNNs perform well in resource-constrained environments but suffer from significantly reduced recognition accuracy. Several existing approaches, such as Conformer, combine Transformers and CNNs but still face challenges related to high resource consumption and low computational efficiency. To address these issues, we propose a novel solution that enhances the Transformer model by introducing multi-scale convolutional attention and a Global Response Normalization (GRN)-based feed-forward network, resulting in a lightweight backbone architecture called the lightweight simple transformer (LST). We further improve LST by incorporating the Res2Net structure from CNN, yielding the Res2Former model—a low-parameter, high—precision SV model. In Res2Former, we design and implement a time-frequency adaptive feature fusion(TAFF) mechanism that enables fine-grained feature propagation by fusing features at different depths at the frame level. Additionally, holistic fusion is employed for global feature propagation across the model. To enhance performance, multiple convergence methods are introduced, improving the overall efficacy of the SV system. Experimental results on the VoxCeleb1-O, VoxCeleb1-E, VoxCeleb1-H, and Cn-Celeb(E) datasets demonstrate that Res2Former achieves excellent performance, with the Large configuration attaining Equal Error Rate (EER)/Minimum Detection Cost Function (minDCF) scores of 0.81%/0.08, 0.98%/0.11, 1.81%/0.17, and 8.39%/0.46, respectively. Notably, the Base configuration of Res2Former, with only 1.73M parameters, also delivers competitive results. Full article

(This article belongs to the Special Issue New Advances in Embedded Software and Applications)

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21 pages, 4507 KiB

Open AccessArticle

GSTD-DETR: A Detection Algorithm for Small Space Targets Based on RT-DETR

by Yijian Zhang, Huichao Guo, Yang Zhao, Laixian Zhang, Chenglong Luan, Yingchun Li and Xiaoyu Zhang

Electronics 2025, 14(12), 2488; https://doi.org/10.3390/electronics14122488 - 19 Jun 2025

Ground-based optical equipment for detecting geostationary orbit space targets typically involves long-exposure imaging, facing challenges such as small and blurred target images, complex backgrounds, and star streaks obstructing the view. To address these issues, this study proposes a GSTD-DETR model based on Real-Time [...] Read more.

Ground-based optical equipment for detecting geostationary orbit space targets typically involves long-exposure imaging, facing challenges such as small and blurred target images, complex backgrounds, and star streaks obstructing the view. To address these issues, this study proposes a GSTD-DETR model based on Real-Time Detection Transformer (RT-DETR), which aims to balance model efficiency and detection accuracy. First, we introduce a Dynamic Cross-Stage Partial (DynCSP) backbone network for feature extraction and fusion, which enhances the network’s representational capability by reducing convolutional parameters and improving information exchange between channels. This effectively reduces the model’s parameter count and computational complexity. Second, we propose a ResFine model with a feature pyramid designed for small target detection, enhancing its ability to perceive small targets. Additionally, we improve the detection head and incorporate a Dynamic Multi-Channel Attention mechanism, which strengthens the focus on critical regions. Finally, we designed an Area-Weighted NWD loss function to improve detection accuracy. The experimental results show that compared to RT-DETR-r18, the GSTD-DETR model reduces the parameter count by 29.74% on the SpotGEO dataset. Its AP₅₀ and AP_50:95 improve by 1.3% and 4.9%, reaching 88.6% and 49.9%, respectively. The GSTD-DETR model demonstrates superior performance in the detection accuracy of faint and small space targets. Full article

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21 pages, 7734 KiB

Open AccessArticle

Thermal–Flow Coupling Simulation and Performance Analysis for Self-Starting Permanent Magnet Motors

by Jinhui Liu, Yunbo Shi, Yang Zheng and Minghui Wang

Electronics 2025, 14(12), 2487; https://doi.org/10.3390/electronics14122487 - 19 Jun 2025

In practical applications, the fully enclosed structure is always required by self-starting permanent magnet synchronous motors for safety. However, internal heat dissipation can be obstructed as a result, which affects operational reliability. To resolve the issue, this study takes a 3 kW self-starting [...] Read more.

In practical applications, the fully enclosed structure is always required by self-starting permanent magnet synchronous motors for safety. However, internal heat dissipation can be obstructed as a result, which affects operational reliability. To resolve the issue, this study takes a 3 kW self-starting permanent magnet synchronous motor as the research object. Based on fluid dynamics and fluid solid coupling heat transfer theory, the model is reasonably simplified according to the characteristics of the structure of motor cooling, and basic assumptions and boundary conditions are given to establish a three-dimensional, whole machine solution domain model. The finite element method is used to numerically analyze and calculate under rated conditions. The fluid flow characteristics, heat transfer characteristics, motion trajectories of the cooling medium on the surface of the external casing, fan, and internal stator and rotor domains, and winding ends are analyzed. Therefore, the internal rheological characteristics and temperature rise distribution law of the self-starting permanent magnet synchronous motor can be revealed. Based on the aforementioned research, a novel method to design the wind spur structure on the surface of the rotor end is proposed. By comparing the simulation results of the fluid field and temperature field of the motor under wind spur structures with different lengths and equidistant distributions in the circumferential direction of the rotor end, the influence of the convective heat characteristics can be systematically studied. Lastly, the accuracy of the calculation results and the rationality of the solution method are verified through experiments of temperature rise, and the flow temperature distribution characteristics of the motor can be optimized by the wind spur structure, which can be used in practical applications. Full article

(This article belongs to the Topic Energy Management and Efficiency in Electric Motors, Drives, Power Converters and Related Systems)

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14 pages, 3406 KiB

Open AccessArticle

Development and Evaluation of a Novel Mixed Reality-Based Surgical Navigation System for Distal Locking of Intramedullary Nails

by Fei Lyu, Puxun Tu, Xingguang Tao and Huixiang Wang

Electronics 2025, 14(12), 2486; https://doi.org/10.3390/electronics14122486 - 19 Jun 2025

Intramedullary nailing (IMN) is the gold standard for fixing mid-shaft fractures of long bones, but distal locking remains a challenging procedure. This study aims to develop and evaluate a novel mixed reality (MR)-based surgical navigation system for distal locking of IMN through phantom [...] Read more.

Intramedullary nailing (IMN) is the gold standard for fixing mid-shaft fractures of long bones, but distal locking remains a challenging procedure. This study aims to develop and evaluate a novel mixed reality (MR)-based surgical navigation system for distal locking of IMN through phantom experiments. Twelve bone models closely replicating the mechanical properties, anatomy, and density of human tibial bone were utilized. Six orthopedic surgeons participated in the phantom experiments using both MR and traditional electromagnetic (EM) navigation systems. Effectiveness was evaluated using postoperative fluoroscopic imaging and the time taken for distal locking. Compared to the EM navigation system, the MR system significantly reduced distal locking time (81.54 ± 6.06 vs. 132.67 ± 6.45 s per screw) and achieved a higher success rate (23/24 vs. 21/24 screws accurately placed), but the difference in terms of success rate is not statistically significant. The MR-based navigation system for distal locking of IMN is time-efficient, accurate, and shows high potential for enhancing surgical precision in orthopedic procedures. Full article

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

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12 pages, 7323 KiB

Open AccessArticle

WinEdge: Low-Power Winograd CNN Execution with Transposed MRAM for Edge Devices

by Milad Ashtari Gargari, Sepehr Tabrizchi and Arman Roohi

Electronics 2025, 14(12), 2485; https://doi.org/10.3390/electronics14122485 - 19 Jun 2025

This paper presents a novel transposed MRAM architecture (WinEdge) specifically optimized for Winograd convolution acceleration in edge computing devices. Leveraging Magnetic Tunnel Junctions (MTJs) with Spin Hall Effect (SHE)-assisted Spin-Transfer Torque (STT) writing, the proposed design enables a single SHE current to simultaneously [...] Read more.

This paper presents a novel transposed MRAM architecture (WinEdge) specifically optimized for Winograd convolution acceleration in edge computing devices. Leveraging Magnetic Tunnel Junctions (MTJs) with Spin Hall Effect (SHE)-assisted Spin-Transfer Torque (STT) writing, the proposed design enables a single SHE current to simultaneously write data to four MTJs, substantially reducing power consumption. Additionally, the integration of stacked MTJs significantly improves storage density. The proposed WinEdge efficiently supports both standard and transposed data access modes regardless of bit-width, achieving up to 36% lower power, 47% reduced energy consumption, and 28% faster processing speed compared to existing designs. Simulations conducted in 45 nm CMOS technology validate its superiority over conventional SRAM-based solutions for convolutional neural network (CNN) acceleration in resource-constrained edge environments. Full article

(This article belongs to the Special Issue Emerging Computing Paradigms for Efficient Edge AI Acceleration)

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