Electronics

14 pages, 3251 KiB

Open AccessCommunication

Design and Optimization of a Miniaturized Wireless Power Transfer System Using Matching Media for Efficiency Enhancement at 1.6 GHz

by Aftab Ahmad, Ashfaq Ahmad and Dong-You Choi

Electronics 2025, 14(14), 2918; https://doi.org/10.3390/electronics14142918 - 21 Jul 2025

Viewed by 340

Abstract

This paper presents the design and performance analysis of a compact wireless power transfer (WPT) system operating at 1.6 GHz. The transmitter (Tx) structure consists of a circular slot and a circular radiating element, excited from the backside of the substrate, while the [...] Read more.

This paper presents the design and performance analysis of a compact wireless power transfer (WPT) system operating at 1.6 GHz. The transmitter (Tx) structure consists of a circular slot and a circular radiating element, excited from the backside of the substrate, while the receiver (Rx) comprises a slotted patch antenna miniaturized using two vertical vias. The initial power transfer efficiency (PTE), represented by the transmission coefficient

S_{21}

, was measured to be −31 dB with a 25 mm separation between Tx and Rx. To enhance the efficiency of the system, a dielectric matching media (MM) was introduced between the transmitter and receiver. Through the implementation of the MM, the PTE improved significantly, with

S_{21}

increasing to −24 dB. A parametric study was conducted by varying the thickness of the MM from 1 mm to 10 mm and the relative permittivity (

ε_{r}

) from 5 to 30. The results demonstrate that both the thickness and dielectric constant of the MM play a crucial role in improving the coupling and overall efficiency of the WPT system. The optimal configuration was achieved with a matching media thickness of 10 mm and a relative permittivity of 25, which yielded the best improvement in transmission performance. This work offers a practical approach to enhance near-field WPT efficiency using simple matching structures and is particularly relevant for compact and low-profile energy transfer applications. Full article

(This article belongs to the Special Issue Advances in Low Power Circuit and System Design and Applications)

► Show Figures

Figure 1

43 pages, 2108 KiB

Open AccessFeature PaperArticle

FIGS: A Realistic Intrusion-Detection Framework for Highly Imbalanced IoT Environments

by Zeynab Anbiaee, Sajjad Dadkhah and Ali A. Ghorbani

Electronics 2025, 14(14), 2917; https://doi.org/10.3390/electronics14142917 - 21 Jul 2025

Viewed by 376

Abstract

The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems [...] Read more.

The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead. Full article

(This article belongs to the Special Issue Network Security and Cryptography Applications)

► Show Figures

Figure 1

19 pages, 1406 KiB

Open AccessArticle

A Comparative Study of Dimensionality Reduction Methods for Accurate and Efficient Inverter Fault Detection in Grid-Connected Solar Photovoltaic Systems

by Shahid Tufail and Arif I. Sarwat

Electronics 2025, 14(14), 2916; https://doi.org/10.3390/electronics14142916 - 21 Jul 2025

Viewed by 265

Abstract

The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection [...] Read more.

The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection presents interesting prospects in accuracy and responsiveness. By streamlining data complexity and allowing faster and more effective fault diagnosis, dimensionality reduction methods play vital role. Using dimensionality reduction and ML techniques, this work explores inverter fault detection in GCPV systems. Photovoltaic inverter operational data was normalized and preprocessed. In the next step, dimensionality reduction using Principal Component Analysis (PCA) and autoencoder-based feature extraction were explored. For ML training four classifiers which include Random Forest (RF), logistic regression (LR), decision tree (DT), and K-Nearest Neighbors (KNN) were used. Trained on the whole standardized dataset, the RF model routinely produced the greatest accuracy of 99.87%, so efficiently capturing complicated feature interactions but requiring large processing resources and time of 36.47sec. LR model showed reduction in accuracy, but very fast training time compared to other models. Further, PCA greatly lowered computing demands, especially improving inference speed for LR and KNN. High accuracy of 99.23% across all models was maintained by autoencoder-derived features. Full article

(This article belongs to the Special Issue Machine Learning Applications in Predictive Monitoring of Power Grid Stability and Resiliency Enhancement)

► Show Figures

Figure 1

35 pages, 3265 KiB

Open AccessArticle

Cyber Edge: Current State of Cybersecurity in Aotearoa-New Zealand, Opportunities, and Challenges

by Md. Rajib Hasan, Nurul I. Sarkar, Noor H. S. Alani and Raymond Lutui

Electronics 2025, 14(14), 2915; https://doi.org/10.3390/electronics14142915 - 21 Jul 2025

Viewed by 376

Abstract

This study investigates the cybersecurity landscape of Aotearoa-New Zealand through a culturally grounded lens, focusing on the integration of Indigenous Māori values into cybersecurity frameworks. In response to escalating cyber threats, the research adopts a mixed-methods and interdisciplinary approach—combining surveys, focus groups, and [...] Read more.

This study investigates the cybersecurity landscape of Aotearoa-New Zealand through a culturally grounded lens, focusing on the integration of Indigenous Māori values into cybersecurity frameworks. In response to escalating cyber threats, the research adopts a mixed-methods and interdisciplinary approach—combining surveys, focus groups, and case studies—to explore how cultural principles such as whanaungatanga (collective responsibility) and manaakitanga (care and respect) influence digital safety practices. The findings demonstrate that culturally informed strategies enhance trust, resilience, and community engagement, particularly in rural and underserved Māori communities. Quantitative analysis revealed that 63% of urban participants correctly identified phishing attempts compared to 38% of rural participants, highlighting a significant urban–rural awareness gap. Additionally, over 72% of Māori respondents indicated that cybersecurity messaging was more effective when delivered through familiar cultural channels, such as marae networks or iwi-led training programmes. Focus groups reinforced this, with participants noting stronger retention and behavioural change when cyber risks were communicated using Māori metaphors, language, or values-based analogies. The study also confirms that culturally grounded interventions—such as incorporating Māori motifs (e.g., koru, poutama) into secure interface design and using iwi structures to disseminate best practices—can align with international standards like NIST CSF and ISO 27001. This compatibility enhances stakeholder buy-in and demonstrates universal applicability in multicultural contexts. Key challenges identified include a cybersecurity talent shortage in remote areas, difficulties integrating Indigenous perspectives into mainstream policy, and persistent barriers from the digital divide. The research advocates for cross-sector collaboration among government, private industry, and Indigenous communities to co-develop inclusive, resilient cybersecurity ecosystems. Based on the UTAUT and New Zealand’s cybersecurity vision “Secure Together—Tō Tātou Korowai Manaaki 2023–2028,” this study provides a model for small nations and multicultural societies to create robust, inclusive cybersecurity frameworks. Full article

(This article belongs to the Special Issue Intelligent Solutions for Network and Cyber Security)

► Show Figures

Figure 1

23 pages, 1150 KiB

Open AccessArticle

ECHO: Enhancing Linux Kernel Fuzzing via Call Stack-Aware Crash Deduplication

by Shuoyu Tao, Baoju Zhang and Qiang Zhang

Electronics 2025, 14(14), 2914; https://doi.org/10.3390/electronics14142914 - 21 Jul 2025

Viewed by 226

Abstract

Fuzz testing plays a key role in improving Linux kernel security, but large-scale fuzzing often generates a high number of crash reports, many of which are redundant. These duplicated reports burden triage efforts and delay the identification of truly impactful bugs. Syzkaller, a [...] Read more.

Fuzz testing plays a key role in improving Linux kernel security, but large-scale fuzzing often generates a high number of crash reports, many of which are redundant. These duplicated reports burden triage efforts and delay the identification of truly impactful bugs. Syzkaller, a widely used kernel fuzzer, clusters crashes using instruction pointers and sanitizer metadata. However, this heuristic may misgroup distinct issues or split similar ones caused by the same root cause. To address this, we present ECHO, a lightweight call stack-based deduplication tool that analyzes structural similarity among kernel stack traces. By computing the longest common subsequence (LCS) between normalized call stacks, ECHO groups semantically related crashes and improves post-fuzzing analysis. We integrate ECHO into the Syzkaller fuzzing workflow and use it to prioritize inputs that trigger deeper, previously untested kernel paths. Evaluated across multiple Linux kernel versions, ECHO improves average code coverage by 15.2% and discovers 20 previously unknown bugs, all reported to the Linux kernel community. Our results highlight that stack-aware crash grouping not only streamlines triage, but also enhances fuzzing efficiency by guiding seed selection toward unexplored execution paths. Full article

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

► Show Figures

Figure 1

34 pages, 6958 KiB

Open AccessArticle

Non-Intrusive Low-Cost IoT-Based Hardware System for Sustainable Predictive Maintenance of Industrial Pump Systems

by Sérgio Duarte Brito, Gonçalo José Azinheira, Jorge Filipe Semião, Nelson Manuel Sousa and Salvador Pérez Litrán

Electronics 2025, 14(14), 2913; https://doi.org/10.3390/electronics14142913 - 21 Jul 2025

Viewed by 259

Abstract

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and [...] Read more.

Industrial maintenance has shifted from reactive repairs and calendar-based servicing toward data-driven predictive strategies. This paper presents a non-intrusive, low-cost IoT hardware platform for sustainable predictive maintenance of rotating machinery. The system integrates an ESP32-S3 sensor node that captures vibration (100 kHz) and temperature data, performs local logging, and communicates wirelessly. An automated spectral band segmentation framework is introduced, comparing equal-energy, linear-width, nonlinear, clustering, and peak–valley partitioning methods, followed by a weighted feature scheme that emphasizes high-value bands. Three unsupervised one-class classifiers—transformer autoencoders, GANomaly, and Isolation Forest—are evaluated on these weighted spectral features. Experiments conducted on a custom pump test bench with controlled anomaly severities demonstrate strong anomaly classification performance across multiple configurations, supported by detailed threshold-characterization metrics. Among 150 model–segmentation configurations, 25 achieved perfect classification (100% precision, recall, and F1 score) with ROC-AUC = 1.0, 43 configurations achieved ≥90% accuracy, and the lowest-performing setup maintained 81.8% accuracy. The proposed end-to-end solution reduces the downtime, lowers maintenance costs, and extends the asset life, offering a scalable, predictive maintenance approach for diverse industrial settings. Full article

(This article belongs to the Special Issue Advances in Low Power Circuit and System Design and Applications)

► Show Figures

Figure 1

19 pages, 7168 KiB

Open AccessArticle

MTD-YOLO: An Improved YOLOv8-Based Rice Pest Detection Model

by Feng Zhang, Chuanzhao Tian, Xuewen Li, Na Yang, Yanting Zhang and Qikai Gao

Electronics 2025, 14(14), 2912; https://doi.org/10.3390/electronics14142912 - 21 Jul 2025

Viewed by 295

Abstract

The impact of insect pests on the yield and quality of rice is extremely significant, and accurate detection of insect pests is of crucial significance to safeguard rice production. However, traditional manual inspection methods are inefficient and subjective, while existing machine learning-based approaches [...] Read more.

The impact of insect pests on the yield and quality of rice is extremely significant, and accurate detection of insect pests is of crucial significance to safeguard rice production. However, traditional manual inspection methods are inefficient and subjective, while existing machine learning-based approaches still suffer from limited generalization and suboptimal accuracy. To address these challenges, this study proposes an improved rice pest detection model, MTD-YOLO, based on the YOLOv8 framework. First, the original backbone is replaced with MobileNetV3, which leverages optimized depthwise separable convolutions and the Hard-Swish activation function through neural architecture search, effectively reducing parameters while maintaining multiscale feature extraction capabilities. Second, a Cross Stage Partial module with Triplet Attention (C2f-T) module incorporating Triplet Attention is introduced to enhance the model’s focus on infested regions via a channel-patial dual-attention mechanism. In addition, a Dynamic Head (DyHead) is introduced to adaptively focus on pest morphological features using the scale–space–task triple-attention mechanism. The experiments were conducted using two datasets, Rice Pest1 and Rice Pest2. On Rice Pest1, the model achieved a precision of 92.5%, recall of 90.1%, mAP@0.5 of 90.0%, and mAP@[0.5:0.95] of 67.8%. On Rice Pest2, these metrics improved to 95.6%, 92.8%, 96.6%, and 82.5%, respectively. The experimental results demonstrate the high accuracy and efficiency of the model in the rice pest detection task, providing strong support for practical applications. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

23 pages, 4361 KiB

Open AccessArticle

ANHNE: Adaptive Multi-Hop Neighborhood Information Fusion for Heterogeneous Network Embedding

by Hanyu Xie, Hao Shao, Lunwen Wang and Changjian Song

Electronics 2025, 14(14), 2911; https://doi.org/10.3390/electronics14142911 - 21 Jul 2025

Viewed by 268

Abstract

Heterogeneous information network (HIN) embedding transforms multi-type nodes into low-dimensional vectors to preserve structural and semantic information for downstream tasks. However, it struggles with multiplex networks where nodes connect via diverse semantic paths (metapaths). Information fusion mainly improves the quality of node embedding [...] Read more.

Heterogeneous information network (HIN) embedding transforms multi-type nodes into low-dimensional vectors to preserve structural and semantic information for downstream tasks. However, it struggles with multiplex networks where nodes connect via diverse semantic paths (metapaths). Information fusion mainly improves the quality of node embedding by fully exploiting the structure and hidden information within the network. Current metapath-based methods ignore information from intermediate nodes along paths, depend on manually defined metapaths, and overlook implicit relationships between nodes sharing similar attributes. Our objective is to develop an adaptive framework that overcomes limitations in existing metapath-based embedding (incomplete information aggregation, manual path dependency, and ignorance of latent semantics) to learn more discriminative embeddings. We propose an adaptive multi-hop neighbor information fusion model for heterogeneous network embedding (ANHNE), which: (1) autonomously extracts composite metapaths (weighted combinations of relations) via a multipath aggregation matrix to mine hierarchical semantics of varying lengths for task-specific scenarios; (2) projects heterogeneous nodes into a unified space and employs hierarchical attention to selectively fuse neighborhood features across metapath hierarchies; and (3) enhances semantics by identifying potential node correlations via cosine similarity to construct implicit connections, enriching network structure with latent information. Extensive experimental results on multiple datasets show that ANHNE achieves more precise embeddings than comparable baseline models. Full article

(This article belongs to the Special Issue Advances in Learning on Graphs and Information Networks)

► Show Figures

Figure 1

18 pages, 7477 KiB

Open AccessArticle

A Three-Layer Sequential Model Predictive Current Control for NNPC Four-Level Inverters with Low Common-Mode Voltage

by Liyu Dai, Wujie Chao, Chaoping Deng, Junwei Huang, Yihan Wang, Minxin Lin and Tao Jin

Electronics 2025, 14(14), 2910; https://doi.org/10.3390/electronics14142910 - 21 Jul 2025

Viewed by 283

Abstract

The four-level nested neutral point clamped (4L-NNPC) inverter has recently become a promising solution for renewable energy generation, e.g., wind and photovoltaic power. The NNPC inverter can stabilize the flying capacitor (FC) voltages of each bridge through redundant switch states (RSSs). This paper [...] Read more.

The four-level nested neutral point clamped (4L-NNPC) inverter has recently become a promising solution for renewable energy generation, e.g., wind and photovoltaic power. The NNPC inverter can stabilize the flying capacitor (FC) voltages of each bridge through redundant switch states (RSSs). This paper presents an improved three-layer sequential model predictive control (3LS-MPC) method for 4L-NNPCs. This method eliminates weighting factors and removes the switch states that generate high common-mode voltage (CMV). Before selecting the optimal vector, we disable certain switch states which affect the FC voltages, continuing to deviate from the desired value. Then, adopting a two-stage optimal vector selection method, we select the optimal sector based on six specific vectors and choose the optimal vector from the seven vectors in the optimal sector. The feasibility of this method was verified in Matlab/Simulink and the prototype. The experimental results show that compared with classical FCS-MPC, the proposed 3LS-MPC method reduces the common-mode voltage and has better harmonic quality and more stable FCs voltages. Full article

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

► Show Figures

Figure 1

17 pages, 13873 KiB

Open AccessArticle

A Passivity-Based Control Integrated with Virtual DC Motor Strategy for Boost Converters Feeding Constant Power Loads

by Mingyang Ou, Pingping Gong, Huajie Guo and Gaoxiang Li

Electronics 2025, 14(14), 2909; https://doi.org/10.3390/electronics14142909 - 21 Jul 2025

Viewed by 282

Abstract

This article proposes a nonlinear control strategy to address the voltage instability issue caused by the boost converter with an uncertain constant power load (CPL). This strategy combines a passivity-based controller (PBC) with a virtual DC motor controller (VDCM). Initially, a PBC is [...] Read more.

This article proposes a nonlinear control strategy to address the voltage instability issue caused by the boost converter with an uncertain constant power load (CPL). This strategy combines a passivity-based controller (PBC) with a virtual DC motor controller (VDCM). Initially, a PBC is designed for the boost converter, which enhances the robustness of the converter with CPL perturbations in the DC bus voltage. To overcome the limitations of PBC, including steady-state errors resulting from variations in load or input voltage, the VDCM is incorporated, simulating the characteristics of a DC motor. This addition improves the system’s inertia and damping, making it more stable and significantly enhancing its dynamic performance. The efficacy and stability analysis of the proposed control strategy is validated through both simulation and experimentation. Full article

(This article belongs to the Special Issue Advanced Control Techniques for Power Converter and Drives)

► Show Figures

Figure 1

27 pages, 5012 KiB

Open AccessArticle

Optimizing FPGA Resource Allocation in SDR Remote Laboratories via Partial Reconfiguration

by Zhiyun Zhang and Rania Hussein

Electronics 2025, 14(14), 2908; https://doi.org/10.3390/electronics14142908 - 20 Jul 2025

Viewed by 380

Abstract

In wireless communications and radio frequency courses, Software-Defined Radios (SDRs) offer students hands-on experience with software-based signal processing on programmable hardware platforms such as Field Programmable Gate Arrays (FPGAs). While some remote SDR laboratories enable students to access real hardware, they typically lack [...] Read more.

In wireless communications and radio frequency courses, Software-Defined Radios (SDRs) offer students hands-on experience with software-based signal processing on programmable hardware platforms such as Field Programmable Gate Arrays (FPGAs). While some remote SDR laboratories enable students to access real hardware, they typically lack support for Partial Reconfiguration (PR)—a powerful FPGA capability that allows sections of a design to be reconfigured at runtime without disrupting the main system operation. This capability enhances real-time adaptability and optimizes resource utilization, making it highly relevant for modern SDR applications. This study addresses this gap by extending an existing SDR remote lab to support PR, enabling students to explore reconfigurable hardware design within a remote learning environment. Two integration architectures were developed: one based on a graphical user interface (UI) and another utilizing a command-line workflow, both accessible via a web browser. Preliminary experiments using Red Pitaya SDR platforms—reportedly the first use of these devices for educational PR exploration—examined the impact of PR on logic resource utilization and total power consumption across three levels of design complexity. These results were compared to equivalent static FPGA designs performing the same functionality without PR. By making PR experimentation accessible through a remote platform, this work enhances STEM education by bridging advanced FPGA techniques with practical learning. It will equip students with industry-relevant skills for developing agile, resource-efficient wireless systems and foster a deeper understanding of adaptive hardware design. Full article

(This article belongs to the Special Issue FPGA-Based Reconfigurable Embedded Systems)

► Show Figures

Figure 1

20 pages, 3790 KiB

Open AccessArticle

Adaptive Distributed Type-2 Fuzzy Dynamic Event-Triggered Formation Control for Switched Nonlinear Multi-Agent System with Actuator Faults

by Cheng-Qin Ben, Xiao-Yu Zhang and Ji-Hong Gu

Electronics 2025, 14(14), 2907; https://doi.org/10.3390/electronics14142907 - 20 Jul 2025

Viewed by 268

Abstract

The adaptive distributed type-2 fuzzy dynamic event-triggered (DET) formation control problem of switched nonlinear multi-agent systems (SNMASs) with actuator faults is addressed in this study. Each agent has a switching subsystem and the switching method of each subsystem is heterogeneous. Interval type-2 fuzzy [...] Read more.

The adaptive distributed type-2 fuzzy dynamic event-triggered (DET) formation control problem of switched nonlinear multi-agent systems (SNMASs) with actuator faults is addressed in this study. Each agent has a switching subsystem and the switching method of each subsystem is heterogeneous. Interval type-2 fuzzy logic systems (T2FLSs) are adopted to handle uncertain nonlinearities. To conserve communication resources (UCRs), a novel distributed DET controller with an event triggering mechanism is proposed. Additionally, Zeno behavior is excluded. Then, the formation objective can be achieved with a designed common Lyapunov function (CLF). Finally, simulation results confirm the validity of the proposed scheme. Full article

(This article belongs to the Special Issue Advanced Wireless Technologies for Next-G Networks: Antennas, Circuits, and Systems)

► Show Figures

Figure 1

18 pages, 2562 KiB

Open AccessArticle

Data-Driven Predictive Modelling of Lifestyle Risk Factors for Cardiovascular Health

by Solomon Agyiri Kissi, Md Golam Muttaquee Talukder and Muhammad Zahid Iqbal

Electronics 2025, 14(14), 2906; https://doi.org/10.3390/electronics14142906 - 20 Jul 2025

Viewed by 579

Abstract

Cardiovascular disease (CVD) remains the foremost global cause of mortality, driven significantly by modifiable lifestyle factors. This study employs a data-driven approach to identify and evaluate these risk factors using advanced machine learning techniques. Analysing a large publicly available dataset of over 300,000 [...] Read more.

Cardiovascular disease (CVD) remains the foremost global cause of mortality, driven significantly by modifiable lifestyle factors. This study employs a data-driven approach to identify and evaluate these risk factors using advanced machine learning techniques. Analysing a large publicly available dataset of over 300,000 adult health records containing lifestyle behaviours, clinical risk factors, and self-reported health indicators, this research implemented traditional classifiers, ensemble methods, and deep learning architectures to examine the impact of behaviours such as smoking, diet, physical activity, and alcohol consumption on CVD risk. The Random Forest model demonstrated superior performance, achieving high accuracy, recall, and ROC-AUC scores. To demonstrate real-world utility, the model was deployed as an interactive Streamlit web application. This tool allows individuals to input lifestyle and health data to receive real-time CVD risk predictions, offering a novel, user-friendly prototype that bridges machine learning insights with personalised digital health engagement. This tool can facilitate personalised health monitoring and supports early detection by providing actionable insights. The findings underscore the efficacy of predictive modelling in informing targeted interventions and public health strategies. By bridging advanced analytics with practical applications, this research offers a scalable framework for reducing CVD burden, paving the way for precision medicine and improved population health outcomes through data-driven decision-making. Full article

(This article belongs to the Special Issue Smart Bioelectronics, Wearable Systems and E-Health)

► Show Figures

Figure 1

25 pages, 579 KiB

Open AccessArticle

An Internet Messenger Using Post-Quantum Cryptography Algorithms Based on Isogenies of Elliptic Curves

by Beniamin Jankowski, Kamil Szydłowski, Marcin Niemiec and Piotr Chołda

Electronics 2025, 14(14), 2905; https://doi.org/10.3390/electronics14142905 - 20 Jul 2025

Viewed by 417

Abstract

This paper presents the design and implementation of an Internet-based instant messaging application that leverages post-quantum cryptographic algorithms founded on isogenies of elliptic curves. The system employs the CSIDH cryptosystem for key exchange and SeaSign for digital signatures, integrating these with the X3DH [...] Read more.

This paper presents the design and implementation of an Internet-based instant messaging application that leverages post-quantum cryptographic algorithms founded on isogenies of elliptic curves. The system employs the CSIDH cryptosystem for key exchange and SeaSign for digital signatures, integrating these with the X3DH and Double-Ratchet protocols to enable end-to-end encryption for both text messages and binary file transfers. Key generation is supported for new users upon registration, ensuring robust cryptographic foundations from the outset. The performance of the CSIDH and SeaSign algorithms is evaluated at various security levels using a Python-based prototype, providing practical benchmarks. By combining isogeny-based cryptographic schemes with widely adopted secure messaging protocols, this work presents an illustration of a selected quantum-resistant communication solution and offers insights into the feasibility and practicality of deploying such protocols in real-world applications. Full article

(This article belongs to the Special Issue Network Security and Cryptography Applications)

► Show Figures

Figure 1

26 pages, 3275 KiB

Open AccessArticle

Detection of Critical Links for Improving Network Resilience

by Nusin Akram, Onur Ugurlu, İlker Kocabaş and Orhan Dagdeviren

Electronics 2025, 14(14), 2904; https://doi.org/10.3390/electronics14142904 - 20 Jul 2025

Viewed by 264

Abstract

Identifying and eliminating critical links in multi-hop networks is essential for enhancing overall network resilience. In this study, we propose a novel algorithm to detect links that significantly impact the pairwise connectivity of multi-hop networks. We formulate the critical link detection problem as [...] Read more.

Identifying and eliminating critical links in multi-hop networks is essential for enhancing overall network resilience. In this study, we propose a novel algorithm to detect links that significantly impact the pairwise connectivity of multi-hop networks. We formulate the critical link detection problem as minimizing pairwise connectivity subject to a total edge weight constraint c. The proposed method first computes the maximum flow between neighboring nodes to evaluate strong connections, and then progressively contracts these nodes to expose weaker connections. Throughout this iterative process, the algorithm records previously identified flows to minimize redundant flow computations. At each step, it also keeps track of the cut sets that reduce the network’s pairwise connectivity. Ultimately, it selects the subset of these cut sets whose removal minimizes pairwise connectivity while satisfying the total weight constraint c. This approach consistently identifies fewer yet more impactful critical edges than traditional Min-Cut or Greedy strategies. We evaluate the performance of our method against existing algorithms across various network sizes and node degrees. Experimental results show that the proposed method consistently discovers more influential edges and achieves a 34–38% reduction in pairwise connectivity, outperforming Greedy (22–24%), Min-Cut (24–32%), and Degree-based (12–19%) methods. Full article

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

► Show Figures

Figure 1

13 pages, 6558 KiB

Open AccessArticle

Efficient Optimization Method for Designing Defected Ground Structure-Based Common-Mode Filters

by Ook Chung, Jongheun Lee, Suhyoun Song, Hogeun Yoo and Jaehoon Lee

Electronics 2025, 14(14), 2903; https://doi.org/10.3390/electronics14142903 - 20 Jul 2025

Viewed by 288

Abstract

An efficient optimization method for designing defected ground structure (DGS)-based common-mode filters (CMFs) is proposed, utilizing equation-based transmission line models integrated with a genetic algorithm (GA). Designing an optimal DGS-based CMF using full-wave simulation tools is time-consuming due to its process-intensive nature. The [...] Read more.

An efficient optimization method for designing defected ground structure (DGS)-based common-mode filters (CMFs) is proposed, utilizing equation-based transmission line models integrated with a genetic algorithm (GA). Designing an optimal DGS-based CMF using full-wave simulation tools is time-consuming due to its process-intensive nature. The proposed optimization method implements transmission line theory to allow for direct S-parameter calculation, enabling integration with an optimization algorithm to identify optimal parameters within a confined 5 mm × 10 mm design space. This work demonstrates a compact asymmetric DGS design to illustrate the method’s capability. The resulting compact asymmetric DGS-based CMF achieves wideband common-mode suppression with a –10 dB bandwidth from 3.18 GHz to 12.89 GHz. The optimization method significantly reduces design time by minimizing the need for lengthy and repetitive full-wave simulations. The measured S-parameters of the fabricated CMF closely match the simulated results, validating the model’s accuracy. Compared with traditional methods for designing DGS-based CMFs, the proposed method utilizes transmission line theory to optimize the design efficiently, providing a practical and efficient solution. Full article

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

► Show Figures

Figure 1

20 pages, 5656 KiB

Open AccessArticle

A Quantitative Analysis Framework for Investigating the Impact of Variable Interactions on the Dynamic Characteristics of Complex Nonlinear Systems

by Yiming Tang, Chongru Liu and Chenbo Su

Electronics 2025, 14(14), 2902; https://doi.org/10.3390/electronics14142902 - 20 Jul 2025

Viewed by 202

Abstract

The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a [...] Read more.

The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a unified nonlinear modal analysis framework integrating second-order analytical solutions with novel nonlinear indices. Validated across diverse systems (DC microgrids and grid-connected PV), the framework yields significant findings: (1) second-order solutions outperform linearization in capturing critical oscillation/damping distortions under realistic disturbances, essential for fault analysis; (2) nonlinear effects induce modal dominance inversion and generate governing composite modes; (3) key interaction mechanisms are quantified, revealing distinct voltage regulation pathways in DC microgrids and multi-path dynamics driving DC voltage fluctuations. This approach provides a systematic foundation for dynamic characteristic assessment and directly informs control design for power electronics-dominated grids. Full article

► Show Figures

Figure 1

18 pages, 6739 KiB

Open AccessArticle

Analytical Modeling of an Ironless Axial Flux Machine for Sizing Purposes

by Víctor Ballestín-Bernad, Guillermo Sanz-Sánchez, Jesús Sergio Artal-Sevil and José Antonio Domínguez-Navarro

Electronics 2025, 14(14), 2901; https://doi.org/10.3390/electronics14142901 - 20 Jul 2025

Viewed by 198

Abstract

This paper presents a novel analytical model of a double-stator single-rotor (DSSR) ironless axial flux machine (IAFM), with no iron either in the rotor or in the stator, that has cylindrical magnets in the rotor. The model is based on sizing equations that [...] Read more.

This paper presents a novel analytical model of a double-stator single-rotor (DSSR) ironless axial flux machine (IAFM), with no iron either in the rotor or in the stator, that has cylindrical magnets in the rotor. The model is based on sizing equations that include the peak no-load flux density as a determining parameter, and then static simulations using the finite element method show that the 3D magnetic field created by cylindrical magnets can be generally fitted with an empirical function. The analytical model is validated throughout this work with finite element simulations and experiments over a prototype, showing a good agreement. It is stated that the integration of the magnetic field for different rotor positions, using the empirical approach presented here, gives accurate results regarding the back-electromotive force waveform and harmonics, with a reduced computation time and effort compared to the finite element method and avoiding complex formulations of previous analytical models. Moreover, this straightforward approach facilitates the design and comparison of IAFMs with other machine topologies, as sizing equations and magnetic circuits developed for conventional electrical machines are not valid for IAFMs, because, here, the magnetic field circulates entirely through air due to the absence of ferromagnetic materials. Furthermore, the scope of this paper is limited to a DSSR-IAFM, but the method can be directly applied to single-sided IAFMs and could be refined to deal with single-stator double-rotor IAFMs. Full article

(This article belongs to the Special Issue Advanced Design in Electrical Machines)

► Show Figures

Figure 1

28 pages, 2518 KiB

Open AccessArticle

Enhancing Keyword Spotting via NLP-Based Re-Ranking: Leveraging Semantic Relevance Feedback in the Handwritten Domain

by Stergios Papazis, Angelos P. Giotis and Christophoros Nikou

Electronics 2025, 14(14), 2900; https://doi.org/10.3390/electronics14142900 - 20 Jul 2025

Viewed by 326

Abstract

Handwritten Keyword Spotting (KWS) remains a challenging task, particularly in segmentation-free scenarios where word images must be retrieved and ranked based on their similarity to a query without relying on prior page-level segmentation. Traditional KWS methods primarily focus on visual similarity, often overlooking [...] Read more.

Handwritten Keyword Spotting (KWS) remains a challenging task, particularly in segmentation-free scenarios where word images must be retrieved and ranked based on their similarity to a query without relying on prior page-level segmentation. Traditional KWS methods primarily focus on visual similarity, often overlooking the underlying semantic relationships between words. In this work, we propose a novel NLP-driven re-ranking approach that refines the initial ranked lists produced by state-of-the-art KWS models. By leveraging semantic embeddings from pre-trained BERT-like Large Language Models (LLMs, e.g., RoBERTa, MPNet, and MiniLM), we introduce a relevance feedback mechanism that improves both verbatim and semantic keyword spotting. Our framework operates in two stages: (1) projecting retrieved word image transcriptions into a semantic space via LLMs and (2) re-ranking the retrieval list using a weighted combination of semantic and exact relevance scores based on pairwise similarities with the query. We evaluate our approach on the widely used George Washington (GW) and IAM collections using two cutting-edge segmentation-free KWS models, which are further integrated into our proposed pipeline. Our results show consistent gains in Mean Average Precision (mAP), with improvements of up to

2.3 %

(from

94.3 %

to

96.6 %

) on GW and

3 %

(from

79.15 %

to

82.12 %

) on IAM. Even when mAP gains are smaller, qualitative improvements emerge: semantically relevant but inexact matches are retrieved more frequently without compromising exact match recall. We further examine the effect of fine-tuning transformer-based OCR (TrOCR) models on historical GW data to align textual and visual features more effectively. Overall, our findings suggest that semantic feedback can enhance retrieval effectiveness in KWS pipelines, paving the way for lightweight hybrid vision-language approaches in handwritten document analysis. Full article

(This article belongs to the Special Issue AI Synergy: Vision, Language, and Modality)

► Show Figures

Figure 1

18 pages, 1956 KiB

Open AccessArticle

Two Novel Quantum Steganography Algorithms Based on LSB for Multichannel Floating-Point Quantum Representation of Digital Signals

by Meiyu Xu, Dayong Lu, Youlin Shang, Muhua Liu and Songtao Guo

Electronics 2025, 14(14), 2899; https://doi.org/10.3390/electronics14142899 - 20 Jul 2025

Viewed by 203

Abstract

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the [...] Read more.

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process. Full article

(This article belongs to the Special Issue New Sights in Quantum Computing: Circuits, Algorithms, and Applications)

► Show Figures

Figure 1

17 pages, 2893 KiB

Open AccessArticle

Insulator Defect Detection Based on Improved YOLO11n Algorithm Under Complex Environmental Conditions

by Shoutian Dong, Yiqi Qin, Benrui Li, Qi Zhang and Yu Zhao

Electronics 2025, 14(14), 2898; https://doi.org/10.3390/electronics14142898 - 20 Jul 2025

Viewed by 374

Abstract

Detecting defects in transmission line insulators is crucial to prevent power grid failures as power systems continue to expand. This study introduces YOL011n-SSA, an enhanced insulator defect detection technique method that addresses the challenges of effectively identifying flaws in complex environments. First, this [...] Read more.

Detecting defects in transmission line insulators is crucial to prevent power grid failures as power systems continue to expand. This study introduces YOL011n-SSA, an enhanced insulator defect detection technique method that addresses the challenges of effectively identifying flaws in complex environments. First, this study incorporates the StarNet network into the backbone of the model. By stacking multiple layers of star operations, the model reduces both parameter count and model size, improving its adaptability to real-time object detection tasks. Secondly, the SOPN feature pyramid network is introduced into the neck part of the model. By optimizing the multi-scale feature fusion of the richer information obtained after expanding the channel dimension, the detection efficiency for low-resolution images and small objects is improved. Then, the ADown module was adopted to improve the backbone and neck parts of the model. It effectively reduces parameter count and significantly lowers the computational cost by implementing downsampling operations between different layers of the feature map, thereby enhancing the practicality of the model. Meanwhile, by introducing the NWD to improve the evaluation index of the loss function, the detection model’s capability in assessing the similarities among various small-object defects is enhanced. Experimental results were obtained using an expanded dataset based on a public dataset, incorporating three types of insulator defects under complex environmental conditions. The results demonstrate that the YOLO11n-SSA algorithm achieved an mAP@0.5 of 0.919, an mAP@0.5:0.95 of 70.7%, a precision of 0.95, and a recall of 0.875, representing improvements of 3.9%, 5.5%, 2%, and 5.7%, respectively, when compared to the original YOLO1ln method. The detection time per image is 0.0134 s. Compared to other mainstream algorithms, the YOLO11n-SSA algorithm demonstrates superior detection accuracy and real-time performance. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

20 pages, 1647 KiB

Open AccessArticle

Research on the Enhancement of Provincial AC/DC Ultra-High Voltage Power Grid Security Based on WGAN-GP

by Zheng Shi, Yonghao Zhang, Zesheng Hu, Yao Wang, Yan Liang, Jiaojiao Deng, Jie Chen and Dingguo An

Electronics 2025, 14(14), 2897; https://doi.org/10.3390/electronics14142897 - 19 Jul 2025

Viewed by 235

Abstract

With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power [...] Read more.

With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power grid dispatching, is essential for maintaining the grid’s long-term stable operation. Traditional fault diagnosis methods encounter challenges such as limited samples and data quality issues under complex operating conditions. To overcome these problems, this study proposes a fault sample data enhancement method based on the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Firstly, a simulation model of the AC/DC hybrid system is constructed to obtain the original fault sample data. Then, through the adoption of the Wasserstein distance measure and the gradient penalty strategy, an improved WGAN-GP architecture suitable for feature learning of the AC/DC hybrid system is designed. Finally, by comparing the fault diagnosis performance of different data models, the proposed method achieves up to 100% accuracy on certain fault types and improves the average accuracy by 6.3% compared to SMOTE and vanilla GAN, particularly under limited-sample conditions. These results confirm that the proposed approach can effectively extract fault characteristics from complex fault data. Full article

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

► Show Figures

Figure 1

5 pages, 149 KiB

Open AccessEditorial

Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition

by Chang Cai, Yaqing Chi and Li Cai

Electronics 2025, 14(14), 2896; https://doi.org/10.3390/electronics14142896 - 19 Jul 2025

Viewed by 217

Abstract

In recent years, the expanding use of advanced electronics in harsh radiation environments has heightened reliability demands, prompting extensive research into radiation-effect modeling and advanced radiation hardening design methodologies that serve these applications [...] Full article

(This article belongs to the Special Issue Radiation Effects of Advanced Electronic Devices and Circuits, 2nd Edition)

23 pages, 1711 KiB

Open AccessArticle

ScaL2Chain: Towards a Scalable Protocol for Multi-Chain Decentralized Applications

by Haonan Yang, Zuobin Ying, Jianping Cai and Runjie Yang

Electronics 2025, 14(14), 2895; https://doi.org/10.3390/electronics14142895 - 19 Jul 2025

Viewed by 478

Abstract

During the last decade, the blockchain landscape has rapidly evolved, fostering the development of decentralized applications (DApps) that utilize cross-chain interactions. Although existing technologies have enhanced transaction processing and introduced interoperability solutions, scalability challenges persist, undermining their effectiveness. In particular, traditional cross-chain DApp [...] Read more.

During the last decade, the blockchain landscape has rapidly evolved, fostering the development of decentralized applications (DApps) that utilize cross-chain interactions. Although existing technologies have enhanced transaction processing and introduced interoperability solutions, scalability challenges persist, undermining their effectiveness. In particular, traditional cross-chain DApp interaction protocols experience performance bottlenecks due to their dependence on on-chain validation mechanisms, resulting in increased latency and computational costs. To address these issues, this paper presents the ScaL2Chain protocol, which is designed to facilitate efficient and secure cross-chain transactions for DApps. ScaL2Chain leverages off-chain technologies, such as payment channels, to enable participants to conduct transactions with a minimal on-chain footprint. By implementing an innovative state verification mechanism, ScaL2Chain guarantees high performance, confidentiality, and transaction integrity. Our empirical evaluations indicate that ScaL2Chain significantly outperforms existing solutions in terms of transaction throughput. Specifically, compared to baseline systems, ScaL2Chain achieves a 7.9-times to 8.4-times improvement in permissionless environments and a 1.9-times to 35.8-times improvement in permissioned environments under workloads with 4-64 DApps and varying cross-chain transaction ratios (0–100%). Full article

(This article belongs to the Special Issue Advancements in Distributed Intelligent Security Through AI-Driven Solutions)

► Show Figures

Figure 1

19 pages, 12234 KiB

Open AccessArticle

Non-Singular Terminal Sliding Mode Control for a Three-Phase Inverter Connected to an Ultra-Weak Grid

by Abdullah M. Noman, Abu Sufyan, Mohsin Jamil and Sulaiman Z. Almutairi

Electronics 2025, 14(14), 2894; https://doi.org/10.3390/electronics14142894 - 19 Jul 2025

Viewed by 179

Abstract

The quality of a grid-injected current in LCL-type grid-connected inverters (GCI) degrades under ultra-weak grid conditions, posing serious challenges to the stability of the GCI system. For this purpose, the sliding mode control (SMC) approach has been utilized to integrate DC energy seamlessly [...] Read more.

The quality of a grid-injected current in LCL-type grid-connected inverters (GCI) degrades under ultra-weak grid conditions, posing serious challenges to the stability of the GCI system. For this purpose, the sliding mode control (SMC) approach has been utilized to integrate DC energy seamlessly into the grid. The control performance of a GCI equipped with an LCL filter is greatly reduced when it is operating in a power grid with varying impedance and fluctuating grid voltages, which may result in poor current quality and possible instability in the system. A non-singular double integral terminal sliding mode (DIT-SMC) control is presented in this paper for a three-phase GCI with an LCL filter. The proposed method is presented in the α, β frame of reference without adopting an active or passive damping approach, reducing the computational burden. MATLAB/Simulink Version R2023b is leveraged to simulate the mathematical model of the proposed control system. The capability of the DIT-SMC method is validated through the OPAL-RT hardware-in-loop (HIL) platform. The effectiveness of the proposed method is first compared with SMC and integral terminal SMC, and then the DIT-SMC method is rigorously analyzed under resonance frequency events, grid impedance variation, and grid voltage distortions. It is demonstrated by the experimental results that the proposed control is highly effective in delivering a high-quality current into the grid, in spite of the simultaneous occurrence of power grid impedance variations in 6 mH and large voltage distortions. Full article

(This article belongs to the Topic Power Electronics Converters, 2nd Edition)

► Show Figures

Figure 1

20 pages, 4119 KiB

Open AccessArticle

Research on Pole-to-Ground Fault Ride-Through Strategy for Hybrid Half-Wave Alternating MMC

by Yanru Ding, Yi Wang, Yuhua Gao, Zimeng Su, Xiaoyu Song, Xiaoyin Wu and Yilei Gu

Electronics 2025, 14(14), 2893; https://doi.org/10.3390/electronics14142893 - 19 Jul 2025

Viewed by 260

Abstract

Considering the lightweight requirement of modular multilevel converter (MMC), the implementation of arm multiplexing significantly improves submodule utilization and achieves remarkable lightweight performance. However, the challenges of overvoltage and energy imbalance during pole-to-ground fault still exist. To address these issues, this paper proposes [...] Read more.

Considering the lightweight requirement of modular multilevel converter (MMC), the implementation of arm multiplexing significantly improves submodule utilization and achieves remarkable lightweight performance. However, the challenges of overvoltage and energy imbalance during pole-to-ground fault still exist. To address these issues, this paper proposes a hybrid half-wave alternating MMC (HHA-MMC) and presents its fault ride-through strategy. First, a transient equivalent model based on topology and operation principles is established to analyze fault characteristics. Depending on the arm’s alternative multiplexing feature, the half-wave shift non-blocking fault ride-through strategy is proposed to eliminate system overvoltage and fault current. Furthermore, to eliminate energy imbalance caused by asymmetric operation during non-blocking transients, dual-modulation energy balancing control based on the third-harmonic current and the phase-shifted angle is introduced. This strategy ensures capacitor voltage balance while maintaining 50% rated power transmission during the fault period. Finally, simulations and experiments demonstrate that the lightweight HHA-MMC successfully accomplishes non-blocking pole-to-ground fault ride-through with balanced arm energy distribution, effectively enhancing power supply reliability. Full article

(This article belongs to the Special Issue New Trends in Topology and Control of Converters for Power Electronics Dominated Grids)

► Show Figures

Figure 1

16 pages, 6343 KiB

Open AccessArticle

Smart Sensor Platform for MIMO Antennas with Gain and Isolation Enhancement Using Metamaterial

by Kranti Dhirajsinh Patil, Dinesh M. Yadav and Jayshri Kulkarni

Electronics 2025, 14(14), 2892; https://doi.org/10.3390/electronics14142892 - 19 Jul 2025

Viewed by 273

Abstract

In modern wireless communication systems, achieving high isolation and consistent signal gain is essential for optimizing Multiple-Input Multiple-Output (MIMO) antenna performance. This study presents a metamaterial-integrated smart sensor platform featuring a hexagonal two-element MIMO antenna designed to improve isolation and directive gain. Constructed [...] Read more.

In modern wireless communication systems, achieving high isolation and consistent signal gain is essential for optimizing Multiple-Input Multiple-Output (MIMO) antenna performance. This study presents a metamaterial-integrated smart sensor platform featuring a hexagonal two-element MIMO antenna designed to improve isolation and directive gain. Constructed on an FR4 substrate (1.6 mm thick), the proposed antenna configurations include a base hexagonal patch, an orthogonally oriented two-element system (TEH_OC), and further enhanced variants employing metamaterial arrays as the superstrate and reflector (TEH_OC_MTS and TEH_OC_MTR). The metamaterial structures significantly suppress mutual coupling, yielding superior diversity parameters such as Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), and Channel Capacity Loss (CCL). All configurations were fabricated and validated through comprehensive anechoic chamber measurements. The results demonstrate robust isolation and radiation performance across the 3 GHz and 5 GHz bands, making these antennas well-suited for deployment in compact, low-latency smart sensor networks operating in 5G and IoT environments. Full article

(This article belongs to the Special Issue Advances in MIMO Systems)

► Show Figures

Figure 1

16 pages, 2291 KiB

Open AccessArticle

Fixed Wireless Access in Flexible Environment: Problem Definition and Feasibility Check

by József Varga, Attila Hilt, Gábor Járó and Andrea Farkasvölgyi

Electronics 2025, 14(14), 2891; https://doi.org/10.3390/electronics14142891 - 19 Jul 2025

Viewed by 275

Abstract

This paper presents a problem definition and feasibility check for an algorithm to select a connection point in an existing fiber-optical access network topology that can be used to connect a new site, the planned location, via an E-band millimeter-wave radio link. [...] Read more.

This paper presents a problem definition and feasibility check for an algorithm to select a connection point in an existing fiber-optical access network topology that can be used to connect a new site, the planned location, via an E-band millimeter-wave radio link. The newly added fixed wireless access connections must meet end-to-end network requirements for availability, latency, and bandwidth. To accommodate highly dynamic service traffic patterns, requirements are defined with a suitable time granularity. Similarly, dynamic changes in available network capacity affect end-to-end availability, latency, and bandwidth. The proposed algorithm is designed to handle these dynamic changes both in the service requirements and in the available resources. Full article

(This article belongs to the Special Issue Mobile Networking: Latest Advances and Prospects)

► Show Figures

Figure 1

33 pages, 2299 KiB

Open AccessReview

Edge Intelligence in Urban Landscapes: Reviewing TinyML Applications for Connected and Sustainable Smart Cities

by Athanasios Trigkas, Dimitrios Piromalis and Panagiotis Papageorgas

Electronics 2025, 14(14), 2890; https://doi.org/10.3390/electronics14142890 - 19 Jul 2025

Viewed by 485

Abstract

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste [...] Read more.

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

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

► Show Figures

Figure 1

21 pages, 2522 KiB

Open AccessArticle

Using Convolutional Neural Networks and Pattern Matching for Digitization of Printed Circuit Diagrams

by Lukas Fuchs, Marc Diesse, Matthias Weber, Arif Rasim, Julian Feinauer and Volker Schmidt

Electronics 2025, 14(14), 2889; https://doi.org/10.3390/electronics14142889 - 19 Jul 2025

Viewed by 255

Abstract

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they [...] Read more.

The efficient and reliable maintenance and repair of industrial machinery depend critically on circuit diagrams, which serve as essential references for troubleshooting and must be updated when machinery is modified. However, many circuit diagrams are not available in structured, machine-readable format; instead, they often exist as unstructured PDF files, rendered images, or even photographs. Existing digitization methods often address isolated tasks, such as symbol detection, but fail to provide a comprehensive solution. This paper presents a novel pipeline for extracting the underlying graph structures of circuit diagrams, integrating image preprocessing, pattern matching, and graph extraction. A U-net model is employed for noise removal, followed by gray-box pattern matching for device classification, line detection by morphological operations, and a final graph extraction step to reconstruct circuit connectivity. A detailed error analysis highlights the strengths and limitations of each pipeline component. On a skewed test diagram from a scan with slight rotation, the proposed pipeline achieved a device detection accuracy of 88.46% with no false positives and a line detection accuracy of 94.7%. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Electronics, Volume 14, Issue 14 (July-2 2025) – 177 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI