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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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25 pages, 7503 KiB  
Article
EMF Exposure of Workers Due to 5G Private Networks in Smart Industries
by Peter Gajšek, Christos Apostolidis, David Plets, Theodoros Samaras and Blaž Valič
Electronics 2025, 14(13), 2662; https://doi.org/10.3390/electronics14132662 - 30 Jun 2025
Abstract
5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) [...] Read more.
5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article
(This article belongs to the Special Issue Innovations in Electromagnetic Field Measurements and Applications)
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35 pages, 10153 KiB  
Article
EnvMat: A Network for Simultaneous Generation of PBR Maps and Environment Maps from a Single Image
by SeongYeon Oh, Moonryul Jung and Taehoon Kim
Electronics 2025, 14(13), 2554; https://doi.org/10.3390/electronics14132554 - 24 Jun 2025
Viewed by 158
Abstract
Generative neural networks have expanded from text and image generation to creating realistic 3D graphics, which are critical for immersive virtual environments. Physically Based Rendering (PBR)—crucial for realistic 3D graphics—depends on PBR maps, environment (env) maps for lighting, and camera viewpoints. Current research [...] Read more.
Generative neural networks have expanded from text and image generation to creating realistic 3D graphics, which are critical for immersive virtual environments. Physically Based Rendering (PBR)—crucial for realistic 3D graphics—depends on PBR maps, environment (env) maps for lighting, and camera viewpoints. Current research mainly generates PBR maps separately, often using fixed env maps and camera poses. This limitation reduces visual consistency and immersion in 3D spaces. Addressing this, we propose EnvMat, a diffusion-based model that simultaneously generates PBR and env maps. EnvMat uses two Variational Autoencoders (VAEs) for map reconstruction and a Latent Diffusion UNet. Experimental results show that EnvMat surpasses the existing methods in preserving visual accuracy, as validated through metrics like L-PIPS, MS-SSIM, and CIEDE2000. Full article
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence)
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14 pages, 5002 KiB  
Article
A Hexagonal Bi-Isotropic Honeycomb in PCB
by Ismael Barba, Óscar Fernández, Álvaro Gómez-Gómez, Ana Grande and Ana Cristina López-Cabeceira
Electronics 2025, 14(13), 2521; https://doi.org/10.3390/electronics14132521 - 21 Jun 2025
Viewed by 170
Abstract
In this study we explored the chiral behavior of a honeycomb-like chiral metamaterial with a negative Poisson’s ratio. This type of structure is widely used in sectors such as construction and packaging, but is not as common in electromagnetics/electrical engineering. Moreover, in contrast [...] Read more.
In this study we explored the chiral behavior of a honeycomb-like chiral metamaterial with a negative Poisson’s ratio. This type of structure is widely used in sectors such as construction and packaging, but is not as common in electromagnetics/electrical engineering. Moreover, in contrast with typical layer-by-layer chiral metamaterial structures, which are usually formed by metallic patterns with C4 symmetry, this hexachiral structure presents C6 symmetry. The aim of this paper is analyzing the electromagnetic behavior of this kind of auxetic metamaterial with special attention to its chiral behavior. This structure is analyzed by means of measurements and simulations of its reflection and transmission responses (scattering parameters) in different configurations, showing that a dual-layer configuration with conjugated faces provides high electromagnetic activity (gyrotropy) with low losses. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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39 pages, 14267 KiB  
Review
Smart Precision Weeding in Agriculture Using 5IR Technologies
by Chaw Thiri San and Vijay Kakani
Electronics 2025, 14(13), 2517; https://doi.org/10.3390/electronics14132517 - 20 Jun 2025
Viewed by 349
Abstract
The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), [...] Read more.
The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), 5G connectivity, and edge computing technologies. This review discusses a comprehensive analysis of the traditional and contemporary weeding techniques, thereby focusing on the technological innovations paving way for the smart systems. Primarily, this work investigates the application of 5IR technologies in weed detection and decision-making with particular emphasis on the role of the aspects such as AI-driven models, drone-robot integration, GPS-guided practices, and intelligent sensor networks. Additionally, the work outlines key commercial solutions, sustainability metrics, data-driven decision support systems, and blockchain traceable practices. The prominent challenges in the context of global agricultural equity pertaining to cost, scalability, policy alignment, and adoption barriers in accordance to the low-resource environments are discussed in this study. The paper concludes with strategic recommendations and future research directions, highlighting the potential of 5IR technologies on the smart precision weeding. Full article
(This article belongs to the Special Issue Harnessing AI and Fifth Industrial Revolution (5IR) Technologies for the Future of Automation)
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28 pages, 39576 KiB  
Article
Generalized Maximum Delay Estimation for Enhanced Channel Estimation in IEEE 802.11p/OFDM Systems
by Kyunbyoung Ko and Sungmook Lim
Electronics 2025, 14(12), 2404; https://doi.org/10.3390/electronics14122404 - 12 Jun 2025
Viewed by 181
Abstract
This paper proposes a generalized maximum access delay time (MADT) estimation method for orthogonal frequency division multiplexing (OFDM) systems operating over multipath fading channels. The proposed approach derives a novel log-likelihood ratio (LLR) formulation by exploiting the correlation characteristics introduced by the cyclic [...] Read more.
This paper proposes a generalized maximum access delay time (MADT) estimation method for orthogonal frequency division multiplexing (OFDM) systems operating over multipath fading channels. The proposed approach derives a novel log-likelihood ratio (LLR) formulation by exploiting the correlation characteristics introduced by the cyclic prefix (CP) in received OFDM symbols, thereby enabling the efficient approximation of the maximum likelihood (ML) MADT estimation. A key contribution of this study is represented by the unification and generalization of existing MADT estimation methods by explicitly formulating the bias term associated with the geometric mean. Within this framework, a previously reported scheme is shown to be a special case of the proposed method. The effectiveness of the proposed MADT estimator is evaluated in terms of correct and good detection probabilities, illustrating not only improved detection accuracy but also robustness across varying channel conditions, in comparison with existing methods. Furthermore, the estimator is applied to both noise-canceling channel estimation (NCCE) and time-domain least squares (TDLS) methods, and its practical effectiveness is verified in IEEE 802.11p/OFDM system scenarios relevant to vehicle-to-everything (V2X) communications. Simulation results confirm that when integrated with NCCE and TDLS, the proposed estimator closely approaches the performance bound of ideal MADT estimation. Full article
(This article belongs to the Special Issue Future Prospects of Wireless Transmission Based on Engineering Electrical Electronic and Telecommunications)
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27 pages, 774 KiB  
Article
GNSS Spoofing Detection Based on Wavelets and Machine Learning
by Katarina Babić, Marta Balić and Dinko Begušić
Electronics 2025, 14(12), 2391; https://doi.org/10.3390/electronics14122391 - 11 Jun 2025
Viewed by 352
Abstract
Global Navigation Satellite Systems (GNSSs) are widely used for positioning, timing, and navigation services. Such widespread usage makes them exposed to various threats including malicious attacks such as spoofing attacks. The availability of low-cost devices such as software-defined radios enhances the viability of [...] Read more.
Global Navigation Satellite Systems (GNSSs) are widely used for positioning, timing, and navigation services. Such widespread usage makes them exposed to various threats including malicious attacks such as spoofing attacks. The availability of low-cost devices such as software-defined radios enhances the viability of performing such attacks. Efficient spoofing detection is of essential importance for the mitigation of such attacks. Although various methods have been proposed for that purpose it is still an important research topic. In this paper, we investigate the spoofing detection method based on the integrated usage of discrete wavelet transform (DWT) and machine learning (ML) techniques and propose efficient solutions. A series of experiments using different wavelets and machine learning techniques for Global Positioning System (GPS) and Galileo systems are performed. Moreover, the impact of the usage of different types of training data are explored. Following the computational complexity analysis, the potential for complexity reduction is investigated and computationally efficient solutions proposed. The obtained results show the efficacy of the proposed approach. Full article
(This article belongs to the Special Issue Smart Antennas and Systems for 5G and Beyond: Latest Advances and Prospects)
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17 pages, 439 KiB  
Article
MultiAVSR: Robust Speech Recognition via Supervised Multi-Task Audio–Visual Learning
by Shad Torrie, Kimi Wright and Dah-Jye Lee
Electronics 2025, 14(12), 2310; https://doi.org/10.3390/electronics14122310 - 6 Jun 2025
Viewed by 475
Abstract
Speech recognition approaches typically fall into three categories: audio, visual, and audio–visual. Visual speech recognition, or lip reading, is the most difficult because visual cues are ambiguous and data is scarce. To address these challenges, we present a new multi-task audio–visual speech recognition, [...] Read more.
Speech recognition approaches typically fall into three categories: audio, visual, and audio–visual. Visual speech recognition, or lip reading, is the most difficult because visual cues are ambiguous and data is scarce. To address these challenges, we present a new multi-task audio–visual speech recognition, or MultiAVSR, framework for training a model on all three types of speech recognition simultaneously primarily to improve visual speech recognition. Unlike prior works which use separate models or complex semi-supervision, our framework employs a supervised multi-task hybrid Connectionist Temporal Classification/Attention loss cutting training exaFLOPs to just 18% of that required by semi-supervised multitask models. MultiAVSR achieves state-of-the-art visual speech recognition word error rate of 21.0% on the LRS3-TED dataset. Furthermore, it exhibits robust generalization capabilities, achieving a remarkable 44.7% word error rate on the WildVSR dataset. Our framework also demonstrates reduced dependency on external language models, which is critical for real-time visual speech recognition. For the audio and audio–visual tasks, our framework improves the robustness under various noisy environments with average relative word error rate improvements of 16% and 31%, respectively. These improvements across the three tasks illustrate the robust results our supervised multi-task speech recognition framework enables. Full article
(This article belongs to the Special Issue Advances in Information, Intelligence, Systems and Applications)
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21 pages, 2333 KiB  
Article
Human-Centric Depth Estimation: A Hybrid Approach with Minimal Data
by Yuhyun Kim, Heejin Ahn, Taeseop Kim, Byungtae Ahn and Dong-Geol Choi
Electronics 2025, 14(11), 2283; https://doi.org/10.3390/electronics14112283 - 4 Jun 2025
Viewed by 444
Abstract
This study presents a novel system for accurate camera-to-person distance estimation in CCTV environments. To address the limitations of existing approaches—which often require extensive training data and lack object-level precision—we propose a hybrid framework that integrates SAM’s zero-shot segmentation with monocular depth estimation. [...] Read more.
This study presents a novel system for accurate camera-to-person distance estimation in CCTV environments. To address the limitations of existing approaches—which often require extensive training data and lack object-level precision—we propose a hybrid framework that integrates SAM’s zero-shot segmentation with monocular depth estimation. Our method isolates human subjects from complex backgrounds and incorporates Kernel Density Estimation (KDE), log-space learning, and linear residual blocks to improve prediction accuracy. This approach is designed to resolve the non-linear mapping between visual features and metric distances. Evaluations on a custom dataset demonstrate a mean absolute error (MAE) of 0.65 m on 1612 test images, using only 30 training samples. Notably, the use of SAM for fine-grained segmentation significantly outperforms conventional bounding box methods, reducing the MAE from 0.82 m to 0.65 m. The proposed system offers immediate applicability to security surveillance and disaster response scenarios, with its minimal data requirements enhancing its practical deployability. Full article
(This article belongs to the Collection Computer Vision and Pattern Recognition Techniques)
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26 pages, 3695 KiB  
Article
Exploitability of Maritime Fleet-Based 5G Network Extension
by Riivo Pilvik, Tanel Jairus, Arvi Sadam, Kaidi Nõmmela, Kati Kõrbe Kaare and Johan Scholliers
Electronics 2025, 14(11), 2210; https://doi.org/10.3390/electronics14112210 - 29 May 2025
Viewed by 501
Abstract
This paper analyzes the exploitability, economic viability, and impact of fleet-based 5G network extensions implemented in maritime environments, focusing on the Baltic Sea and Mediterranean as a case study. Through cost–benefit analysis and business model validation, we demonstrate how multi-hop 5G connectivity can [...] Read more.
This paper analyzes the exploitability, economic viability, and impact of fleet-based 5G network extensions implemented in maritime environments, focusing on the Baltic Sea and Mediterranean as a case study. Through cost–benefit analysis and business model validation, we demonstrate how multi-hop 5G connectivity can reduce communication costs while improving service quality for maritime operators. Our findings indicate that implementing vessel-based 5G relay stations can achieve 80–90% coverage in key maritime corridors with a break-even period of 2–3 years. The study reveals that combining vessel-to-vessel relaying with strategic floating base stations can reduce connectivity costs by up to 40% compared to traditional satellite solutions, while enabling new revenue streams through premium services. We provide a detailed economic framework for evaluating similar implementations across different maritime routes and suggest policy recommendations for facilitating cross-border 5G maritime networks and introduce key use cases value creation for network extension. Full article
(This article belongs to the Special Issue Latest Trends in 5G/6G Wireless Communication)
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37 pages, 732 KiB  
Article
Document GraphRAG: Knowledge Graph Enhanced Retrieval Augmented Generation for Document Question Answering Within the Manufacturing Domain
by Simon Knollmeyer, Oğuz Caymazer and Daniel Grossmann
Electronics 2025, 14(11), 2102; https://doi.org/10.3390/electronics14112102 - 22 May 2025
Viewed by 2483
Abstract
Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and [...] Read more.
Retrieval-Augmented Generation (RAG) systems have shown significant potential for domain-specific Question Answering (QA) tasks, although persistent challenges in retrieval precision and context selection continue to hinder their effectiveness. This study introduces Document Graph RAG (GraphRAG), a novel framework that bolsters retrieval robustness and enhances answer generation by incorporating Knowledge Graphs (KGs) built upon a document’s intrinsic structure into the RAG pipeline. Through the application of the Design Science Research methodology, we systematically design, implement, and evaluate GraphRAG, leveraging graph-based document structuring and a keyword-based semantic linking mechanism to improve retrieval quality. The evaluation, conducted on well-established datasets including SQuAD, HotpotQA, and a newly developed manufacturing dataset, demonstrates consistent performance gains over a naive RAG baseline across both retrieval and generation metrics. The results indicate that GraphRAG improves Context Relevance metrics, with task-dependent optimizations for chunk size, keyword density, and top-k retrieval further enhancing performance. Notably, multi-hop questions benefit most from GraphRAG’s structured retrieval strategy, highlighting its advantages in complex reasoning tasks. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Intelligent Manufacturing)
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25 pages, 5209 KiB  
Article
Enhancing Indoor Positioning with GNSS-Aided In-Building Wireless Systems
by Shuya Zhou, Xinghe Chu and Zhaoming Lu
Electronics 2025, 14(10), 2079; https://doi.org/10.3390/electronics14102079 - 21 May 2025
Viewed by 379
Abstract
Wireless indoor positioning systems are challenged by the reliance on densely deployed hardware and exhaustive site surveys, leading to elevated deployment and maintenance costs that limit scalability. This paper introduces a novel positioning framework that enhances the existing In-Building Wireless (IBW) infrastructure by [...] Read more.
Wireless indoor positioning systems are challenged by the reliance on densely deployed hardware and exhaustive site surveys, leading to elevated deployment and maintenance costs that limit scalability. This paper introduces a novel positioning framework that enhances the existing In-Building Wireless (IBW) infrastructure by retransmitting Global Navigation Satellite System (GNSS) signals. Pseudorange residuals extracted from raw GNSS measurements, when mapped against known cable lengths, facilitate anchor identification and precise ranging. In parallel, directional and inertial measurements are derived from the channel state information (CSI) of cellular reference signals. Building upon these observations, we develop a Hybrid Adaptive Filter-Graph Fusion (HAF-GF) algorithm for high-precision positioning, wherein the adaptive filter modulates observation noise based on Line-of-Sight (LoS) conditions, while a factor graph optimization over multiple positional constraints ensures global consistency and accelerates convergence. Ray tracing-based simulations in a complex office environment validate the efficacy of the proposed approach, demonstrating a 30% improvement in positioning accuracy and at least a threefold increase in deployment efficiency compared to conventional methods. Full article
(This article belongs to the Special Issue Mobile Positioning and Tracking Using Wireless Networks)
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19 pages, 251 KiB  
Article
Defending Federated Learning from Collaborative Poisoning Attacks: A Clique-Based Detection Framework
by Dimitrios Anastasiadis and Ioannis Refanidis
Electronics 2025, 14(10), 2011; https://doi.org/10.3390/electronics14102011 - 15 May 2025
Viewed by 465
Abstract
Federated Learning (FL) systems are increasingly vulnerable to data poisoning attacks, in which malicious clients attempt to manipulate their training data in order to compromise the corresponding machine learning model. Existing detection techniques rely mostly on identifying clients who provide weight updates that [...] Read more.
Federated Learning (FL) systems are increasingly vulnerable to data poisoning attacks, in which malicious clients attempt to manipulate their training data in order to compromise the corresponding machine learning model. Existing detection techniques rely mostly on identifying clients who provide weight updates that significantly diverge from the average across multiple training rounds. In this work, we propose a Clique-Based Detection Framework (CBDF) that focuses on similarity patterns between client updates instead of their deviation. Specifically, we make use of the Euclidean distance to measure similarity between the weight update vectors of different clients over training iterations. Clients that provide consistently similar weight updates and exceed a predefined threshold are flagged as potential adversaries. Therefore, this method detects the coordination patterns of the attackers and uses them to strengthen FL systems against sophisticated, coordinated data poisoning attacks. We validate the effectiveness of this approach through extensive experimental evaluation. Moreover, we provide suggestions regarding fine-tuning hyperparameters to maximize the performance of the detection method. This approach represents a novel advancement in protecting FL models from malicious interference. Full article
(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)
21 pages, 1847 KiB  
Article
A Certificateless Aggregated Signcryption Scheme Based on Edge Computing in VANETs
by Wenfeng Zou, Qiang Guo and Xiaolan Xie
Electronics 2025, 14(10), 1993; https://doi.org/10.3390/electronics14101993 - 14 May 2025
Viewed by 304
Abstract
The development of Vehicle AD Hoc Networks (VANETs) has significantly enhanced the efficiency of intelligent transportation systems. Through real-time communication between vehicles and roadside units (RSUs), the immediate sharing of traffic information has been achieved. However, challenges such as network congestion, data privacy, [...] Read more.
The development of Vehicle AD Hoc Networks (VANETs) has significantly enhanced the efficiency of intelligent transportation systems. Through real-time communication between vehicles and roadside units (RSUs), the immediate sharing of traffic information has been achieved. However, challenges such as network congestion, data privacy, and low computing efficiency still exist. Data privacy is at risk of leakage due to the sensitivity of vehicle information, especially in a resource-constrained vehicle environment, where computing efficiency becomes a bottleneck restricting the development of VANETs. To address these challenges, this paper proposes a certificateless aggregated signcryption scheme based on edge computing. This scheme integrates online/offline encryption (OOE) technology and a pseudonym mechanism. It not only solves the problem of key escrow, generating part of the private key through collaboration between the user and the Key Generation Center (KGC), but also uses pseudonyms to protect the real identities of the vehicle and RSU, effectively preventing privacy leakage. This scheme eliminates bilinear pairing operations, significantly improves efficiency, and supports conditional traceability and revocation of malicious vehicles while maintaining anonymity. The completeness analysis shows that under the assumptions of calculating the Diffie–Hellman (CDH) and elliptic curve discrete logarithm problem (ECDLP), this scheme can meet the requirements of IND-CCA2 confidentiality and EUF-CMA non-forgeability. The performance evaluation further confirmed that, compared with the existing schemes, this scheme performed well in both computing and communication costs and was highly suitable for the resource-constrained VANET environment. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) Communication and Networking)
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17 pages, 5356 KiB  
Article
A Study on the Features for Multi-Target Dual-Camera Tracking and Re-Identification in a Comparatively Small Environment
by Jong-Chen Chen, Po-Sheng Chang and Yu-Ming Huang
Electronics 2025, 14(10), 1984; https://doi.org/10.3390/electronics14101984 - 13 May 2025
Viewed by 409
Abstract
Tracking across multiple cameras is a complex problem in computer vision. Its main challenges include camera calibration, occlusion handling, camera overlap and field of view, person re-identification, and data association. In this study, we designed a laboratory as a research environment that facilitates [...] Read more.
Tracking across multiple cameras is a complex problem in computer vision. Its main challenges include camera calibration, occlusion handling, camera overlap and field of view, person re-identification, and data association. In this study, we designed a laboratory as a research environment that facilitates our exploration of some of the above challenging issues. This study uses stereo camera calibration and key point detection to reconstruct the three-dimensional key points of the person being tracked, thereby performing person-tracking tasks. The results show that the dual cameras’ 3D spatial tracking method can have a relatively better continuous monitoring effect than a single camera alone. This study adopts four ways to evaluate person similarity, which can effectively reduce the unnecessary identity generation of persons. However, using all four methods simultaneously may not produce better results than a specific assessment method alone due to differences in people’s activity situations. Full article
(This article belongs to the Collection Computer Vision and Pattern Recognition Techniques)
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43 pages, 10194 KiB  
Article
Fuzzy Rules for Explaining Deep Neural Network Decisions (FuzRED)
by Anna L. Buczak, Benjamin D. Baugher and Katie Zaback
Electronics 2025, 14(10), 1965; https://doi.org/10.3390/electronics14101965 - 12 May 2025
Viewed by 384
Abstract
This paper introduces a novel approach to explainable artificial intelligence (XAI) that enhances interpretability by combining local insights from Shapley additive explanations (SHAP)—a widely adopted XAI tool—with global explanations expressed as fuzzy association rules. By employing fuzzy association rules, our method enables AI [...] Read more.
This paper introduces a novel approach to explainable artificial intelligence (XAI) that enhances interpretability by combining local insights from Shapley additive explanations (SHAP)—a widely adopted XAI tool—with global explanations expressed as fuzzy association rules. By employing fuzzy association rules, our method enables AI systems to generate explanations that closely resemble human reasoning, delivering intuitive and comprehensible insights into system behavior. We present the FuzRED methodology and evaluate its performance on models trained across three diverse datasets: two classification tasks (spam identification and phishing link detection), and one reinforcement learning task involving robot navigation. Compared to the Anchors method FuzRED offers at least one order of magnitude faster execution time (minutes vs. hours) while producing easily interpretable rules that enhance human understanding of AI decision making. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence: Concepts, Techniques, Analytics and Applications)
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28 pages, 587 KiB  
Article
A Privacy-Preserving Authentication Scheme Using PUF and Biometrics for IoT-Enabled Smart Cities
by Chaeeon Kim, Seunghwan Son and Youngho Park
Electronics 2025, 14(10), 1953; https://doi.org/10.3390/electronics14101953 - 11 May 2025
Cited by 1 | Viewed by 363
Abstract
With the advancement of communication technology, smart cities can provide remote services to users using mobile devices and Internet of Things (IoT) sensors in real time. However, the collected data in smart cities include sensitive personal information and data transmitted over public wireless [...] Read more.
With the advancement of communication technology, smart cities can provide remote services to users using mobile devices and Internet of Things (IoT) sensors in real time. However, the collected data in smart cities include sensitive personal information and data transmitted over public wireless channels, leaving the network vulnerable to security attacks. Thus, robust and secure authentication is critical to verify legitimate users and prevent malicious attacks. This paper reviews a recent authentication scheme for smart cities and identifies its susceptibilities to attacks, including insider attacks, sensor node capture, user impersonation, and random number leakage. We propose a secure and privacy-preserving authentication scheme for smart cities to resolve these security weaknesses. The scheme enables mutual authentication by incorporating biometric features to verify identity and using the physical unclonable function to prevent physical attacks. We evaluate the security of the proposed scheme via informal and formal analyses, including Burrows–Abadi–Needham logic, the real-or-random model, and the Automated Validation of Internet Security Protocols and Applications simulation tool. Finally, we compare the performance, demonstrating that the proposed scheme has better efficiency and security than existing schemes. Consequently, the proposed scheme is suitable for resource-constrained IoT-enabled smart cities. Full article
(This article belongs to the Special Issue Intelligent Solutions for Network and Cyber Security)
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27 pages, 5530 KiB  
Article
Optoelectronic Devices Analytics: MachineLearning-Driven Models for Predicting the Performance of a Dye-Sensitized Solar Cell
by Emeka Harrison Onah, N. L. Lethole and P. Mukumba
Electronics 2025, 14(10), 1948; https://doi.org/10.3390/electronics14101948 - 10 May 2025
Viewed by 503
Abstract
Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant [...] Read more.
Optoelectronic devices, which combine optics and electronics, are vital for converting light energy into electrical energy. Various solar cell technologies, such as dye-sensitized solar cells (DSSCs), silicon solar cells, and perovskite solar cells, among others, belong to this category. DSSCs have gained significant attention due to their affordability, flexibility, and ability to function under low light conditions. The current research incorporates machine learning (ML) models to predict the performance of a modified Eu3+-doped Y2WO6/TiO2 photo-electrode DSSC. Experimental data were collected from the “Dryad Repository Database” to feed into the models, and a detailed data visualization analysis was performed to study the trends in the datasets. The support vector regression (SVR) and Random Forest regression (RFR) models were applied to predict the short-circuit current density (Jsc) and maximum power (Pmax) output of the device. Both models achieved reasonably accurate predictions, and the RFR model attained a better prediction response, with the percentage difference between the experimental data and model prediction being 0.73% and 1.01% for the Jsc and Pmax respectively, while the SVR attained a percentage difference of 1.22% and 3.54% for the Jsc and Pmax respectively. Full article
(This article belongs to the Special Issue Modeling and Design of Solar Cell Materials)
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26 pages, 1312 KiB  
Article
Advanced Intelligent Frame Generation Algorithm for Differentiated QoS Requirements in Advanced Orbiting Systems
by Jiahui Peng, Jun Chen, Huaifeng Shi and Yibing Feng
Electronics 2025, 14(10), 1939; https://doi.org/10.3390/electronics14101939 - 9 May 2025
Viewed by 330
Abstract
In order to efficiently transmit spatial data with diversified service types, this paper proposes an Advanced Intelligent Framing Algorithm (AIFG) for Advanced Orbiting System (AOS), based on the virtual channel multiplexing AOS technology, aiming to meet differentiated quality of service (QoS) requirements. To [...] Read more.
In order to efficiently transmit spatial data with diversified service types, this paper proposes an Advanced Intelligent Framing Algorithm (AIFG) for Advanced Orbiting System (AOS), based on the virtual channel multiplexing AOS technology, aiming to meet differentiated quality of service (QoS) requirements. To enable the timely transmission of delay-sensitive services, the optimal time threshold for framing such services is modeled as a Markov decision process (MDP). The algorithm utilizes Proximal Policy Optimization (PPO), achieving an optimal solution based on performance metrics such as frame multiplexing efficiency, average framing time, and average packet delay. To improve the multiplexing efficiency of non-delay-sensitive services, this type of traffic is transmitted only after completely filling a frame. Simulation results show that, compared to traditional frame generation algorithms, the AIFG algorithm reduces the average queuing delay of services by an average of 32%. Moreover, the AIFG algorithm increases throughput by an average of 32%, and improves multiplexing efficiency by an average of 61%. Thus, the AIFG algorithm balances the transmission requirements of both real-time and non-real-time services, and enhances the QoS of the AOS. Full article
(This article belongs to the Special Issue Future Generation Non-Terrestrial Networks)
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24 pages, 1781 KiB  
Article
Learning-Based MPC Leveraging SINDy for Vehicle Dynamics Estimation
by Francesco Paparazzo, Andrea Castoldi, Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Stefano Arrigoni and Francesco Braghin
Electronics 2025, 14(10), 1935; https://doi.org/10.3390/electronics14101935 - 9 May 2025
Cited by 1 | Viewed by 849
Abstract
Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate [...] Read more.
Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems. Full article
(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)
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23 pages, 3948 KiB  
Article
A Dynamic Spatiotemporal Deep Learning Solution for Cloud–Edge Collaborative Industrial Control System Distributed Denial of Service Attack Detection
by Zhigang Cao, Bo Liu, Dongzhan Gao, Ding Zhou, Xiaopeng Han and Jiuxin Cao
Electronics 2025, 14(9), 1843; https://doi.org/10.3390/electronics14091843 - 30 Apr 2025
Viewed by 491
Abstract
With the continuous development of industrial intelligence, the integration of cyber–physical components creates a need for effective attack detection methods to mitigate potential DDoS threats. Although several DDoS attack detection modeling approaches have been proposed, few effectively incorporate the unique characteristics of industrial [...] Read more.
With the continuous development of industrial intelligence, the integration of cyber–physical components creates a need for effective attack detection methods to mitigate potential DDoS threats. Although several DDoS attack detection modeling approaches have been proposed, few effectively incorporate the unique characteristics of industrial control system (ICS) architectures and traffic patterns. This paper focuses on DDoS attack detection within cloud–edge collaborative ICSs and proposes a novel detection model called FedDynST. This model combines federated learning and deep learning to construct feature graphs of traffic data. Introducing dynamic and static adjacency matrices, this work reveals the interactions between long-term industrial traffic data and short-term anomalies associated with DDoS attacks. Convolutional neural networks are utilized to capture distinctive temporal features within industrial traffic, thereby improving the detection precision. Moreover, the model enables continuous optimization of the global detection framework through a federated learning-based distributed training and aggregation mechanism, ensuring the privacy and security of industrial client data. The effectiveness of the FedDynST model was validated on the CICDDoS2019 and Edge-IIoTset datasets. The simulation results validated the superiority of the proposed approach, and thus, demonstrated significant improvements in both detection accuracy and convergence. Full article
(This article belongs to the Section Artificial Intelligence)
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27 pages, 1864 KiB  
Article
Enhancing Multi-Factor Authentication for Mobile Devices Through Cryptographic Zero-Knowledge Protocols
by Thomas Segkoulis and Konstantinos Limniotis
Electronics 2025, 14(9), 1846; https://doi.org/10.3390/electronics14091846 - 30 Apr 2025
Viewed by 638
Abstract
During the last few years, smart mobile devices have constituted an indispensable part of our lives, being a main element for many daily activities. However, it is well known that several security and privacy concerns still occur, especially taking into account their role [...] Read more.
During the last few years, smart mobile devices have constituted an indispensable part of our lives, being a main element for many daily activities. However, it is well known that several security and privacy concerns still occur, especially taking into account their role as an authentication factor for many users’ applications. This paper focuses on multi-factor authentication methods based on mobile devices, proposing a new user authentication scheme based on cryptographic zero-knowledge protocols. This new approach aims to enhance, with minimal effort and cost, any existing authentication method by offering an additional authentication factor based on a unique device identifier through an intuitive and adaptable solution that can be seamlessly integrated into any mobile system, thus providing an additional authentication layer. The ultimate goal is to bridge the gap between ease of use and strengthening security without disrupting the existing infrastructure. A security analysis of the new scheme is presented, whereas an implementation illustrates its effectiveness. It is also shown that this approach is in line with relevant legal data protection and privacy requirements. Full article
(This article belongs to the Special Issue Emerging Topics in Wireless Security and Privacy towards 6G Networks)
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29 pages, 4136 KiB  
Article
IoT-NTN with VLEO and LEO Satellite Constellations and LPWAN: A Comparative Study of LoRa, NB-IoT, and Mioty
by Changmin Lee, Taekhyun Kim, Chanhee Jung and Zizung Yoon
Electronics 2025, 14(9), 1798; https://doi.org/10.3390/electronics14091798 - 28 Apr 2025
Viewed by 693
Abstract
This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600km using a Genetic Algorithm (GA). [...] Read more.
This study investigates the optimization of satellite constellations for Low-Power, Wide-Area Network (LPWAN)-based Internet of Things (IoT) communications in Very Low Earth Orbit (VLEO) at 200 km and 300 km altitudes and Low Earth Orbit (LEO) at 600km using a Genetic Algorithm (GA). Focusing on three LPWAN technologies—LoRa, Narrowband IoT (NB-IoT), and Mioty—we evaluate their performance in terms of revisit time, data transmission volume, and economic efficiency. Results indicate that a 300 km VLEO constellation with LoRa achieves the shortest average revisit time and requires the fewest satellites, offering notable cost benefits. NB-IoT provides the highest data transmission volume. Mioty demonstrates strong scalability but necessitates a larger satellite count. These findings highlight the potential of VLEO satellites, particularly at 300 km, combined with LPWAN solutions for efficient and scalable IoT Non-Terrestrial Network (IoT-NTN) applications. Future work will explore multi-altitude simulations and hybrid LPWAN integration for further optimization. Full article
(This article belongs to the Special Issue Future Generation Non-Terrestrial Networks)
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17 pages, 6015 KiB  
Article
Process Monitoring of One-Shot Drilling of Al/CFRP Aeronautical Stacks Using the 1DCAE-GMM Framework
by Giulio Mattera, Maria Grazia Marchesano, Alessandra Caggiano, Guido Guizzi and Luigi Nele
Electronics 2025, 14(9), 1777; https://doi.org/10.3390/electronics14091777 - 27 Apr 2025
Viewed by 396
Abstract
This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force [...] Read more.
This study explores advanced process monitoring for one-shot drilling of aeronautical stacks made of aluminium 2024 and carbon fibre-reinforced polymer (CFRP) laminates using a 4.8 mm diameter drilling tool and unsupervised machine learning techniques. An experimental campaign is conducted to collect thrust force and torque signals at a 10 kHz sampling rate during the drilling process. These signals are employed for real-time process monitoring, focusing on material change detection and anomaly identification, where anomalies are defined as holes that fail to meet predefined quality criteria. An innovative approach based on unsupervised learning is proposed to enable automatic material change identification, signal segmentation, feature extraction, and hole quality assessment. Specifically, a semi-supervised approach based on a Gaussian Mixture Model (GMM) and 1D Convolutional AutoEncoder (1D-CAE) is employed to detect deviations from normal drilling conditions. The proposed method is benchmarked against state-of-the-art supervised techniques, including logistic regression (LR) and Support Vector Machines (SVMs). Results show that these traditional models struggle with class imbalance, leading to overfitting and limited generalisation, as reflected by the F1 scores of 0.78 and 0.75 for LR and SVM, respectively. In contrast, the proposed semi-supervised approach improves anomaly detection, achieving an F1 score of 0.87 by more effectively identifying poor-quality holes. This study demonstrates the potential of deep learning-based semi-supervised methods for intelligent process monitoring, enabling adaptive control in the drilling process of hybrid stacks and detecting anomalous holes. While the proposed approach effectively handles small and imbalanced datasets, further research into the application of generative AI could enhance performance, aiming for F1 scores above 0.90, thereby supporting adaptation in real industrial environments with high performance. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Intelligent Manufacturing)
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7 pages, 1890 KiB  
Article
Investigation of Temperature-Dependent Gate Degradation in Normally-Off AlGaN/GaN High-Electron-Mobility Transistor p-GaN
by Jeonghyeok Yoon and Hyungtak Kim
Electronics 2025, 14(9), 1764; https://doi.org/10.3390/electronics14091764 - 26 Apr 2025
Viewed by 449
Abstract
The effect of temperature on gate degradation behavior was analyzed in Schottky-type p-GaN gate HEMTs under a positive gate voltage. TDDB measurements were conducted at various temperatures, revealing an accelerated gate failure rate at lower temperatures. A Weibull distribution analysis was employed to [...] Read more.
The effect of temperature on gate degradation behavior was analyzed in Schottky-type p-GaN gate HEMTs under a positive gate voltage. TDDB measurements were conducted at various temperatures, revealing an accelerated gate failure rate at lower temperatures. A Weibull distribution analysis was employed to predict the 10-year rated gate voltage, showing that the rated voltage at −10 °C is significantly lower than at 60 °C. Furthermore, the derived activation energy of −0.22 eV indicates that gate degradation intensifies in colder environments. Hole accumulation occurring at the p-GaN/AlGaN interface can promote degradation by facilitating electron injection and accelerating defect generation in the presence of strong electric fields. At higher temperatures, hole release mitigates charge accumulation, thereby extending device longevity. These findings highlight the necessity of reliability assessments for p-GaN gate HEMTs suitable for environments with low temperatures, including space and polar environments. Full article
(This article belongs to the Special Issue Recent Advances in GaN Power Devices)
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14 pages, 5030 KiB  
Article
A Linearized Open-Loop MDAC with Memory Effect Compensation Technique for High-Speed Pipelined ADC Stage
by Jie Wu, Qiao Meng, Shaocong Guo, Gaojing Li, Jianxun Shao and Sha Li
Electronics 2025, 14(9), 1753; https://doi.org/10.3390/electronics14091753 - 25 Apr 2025
Viewed by 331
Abstract
This paper presents a prototype open-loop pipelined stage in a 45 nm CMOS process for supporting 1.8 GS/s and 10-bit design specifications of pipelined ADCs. In order to alleviate the severe non-linearity expressed by open-loop MDACs, an innovative current-mode harmonic compensation is proposed [...] Read more.
This paper presents a prototype open-loop pipelined stage in a 45 nm CMOS process for supporting 1.8 GS/s and 10-bit design specifications of pipelined ADCs. In order to alleviate the severe non-linearity expressed by open-loop MDACs, an innovative current-mode harmonic compensation is proposed to provide input related third harmonic terms to cancel non-linearity. In addition, an effective double-sampling scheme is optimized by modifying compensation timing and input of a residual amplifier so that the pipelined stage can be immune to memory effect and improve power efficiency. The memory effect compensation scheme can provide a 21 dB improvement on output SNDR of the double-sampling pipelined stage. The simulation results illustrate that the open-loop pipelined ADC stage achieves an output SNDR of at least 52 dB with 840 mV input amplitude and 240 fF load while consuming only 11.24 mW. Full article
(This article belongs to the Section Microelectronics)
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21 pages, 13080 KiB  
Article
Color Normalization Through a Simulated Color Checker Using Generative Adversarial Networks
by Albert Siré Langa, Ramón Reig Bolaño, Sergi Grau Carrión and Ibon Uribe Elorrieta
Electronics 2025, 14(9), 1746; https://doi.org/10.3390/electronics14091746 - 25 Apr 2025
Viewed by 474
Abstract
Digital cameras often struggle to reproduce the true colors perceived by the human eye due to lighting geometry and illuminant color. This research proposes an innovative approach for color normalization in digital photographs. A machine learning algorithm combined with an external physical color [...] Read more.
Digital cameras often struggle to reproduce the true colors perceived by the human eye due to lighting geometry and illuminant color. This research proposes an innovative approach for color normalization in digital photographs. A machine learning algorithm combined with an external physical color checker achieves color normalization. To address the limitations of relying on a physical color checker, our approach employs a generative adversarial network capable of replicating the color normalization process without the need for a physical reference. This network (GAN-CN-CC) incorporates a custom loss function specifically designed to minimize errors in color generation. The proposed algorithm yields the lowest coefficient of variation in the normalized median intensity (NMI), while maintaining a standard deviation comparable to that of conventional methods such as Gray World and Max-RGB. The algorithm eliminates the need for a color checker in color normalization, making it more practical in scenarios where inclusion of the checker is challenging. The proposed method has been fine-tuned and validated, demonstrating high effectiveness and adaptability. Full article
(This article belongs to the Special Issue Machine Learning in Data Analytics and Prediction)
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14 pages, 5999 KiB  
Article
Frequency-Selective Surface Based 360-Degree Beam-Steerable Cavity Antenna for UAV Swarm Coordination
by Mashrur Zawad, Chandana Kolluru, Sohel Rana, Kalyan C. Durbhakula and Mohamed Z. M. Hamdalla
Electronics 2025, 14(9), 1725; https://doi.org/10.3390/electronics14091725 - 24 Apr 2025
Viewed by 470
Abstract
A swarm of unmanned aerial vehicles (UAVs) often rely on exceptional wireless coverage of embedded or flush-mounted antennas or arrays, especially in long-range communication. While arrays offer significant range and beam steerability control, they often suffer from size, weight, and power (SWaP) limitations. [...] Read more.
A swarm of unmanned aerial vehicles (UAVs) often rely on exceptional wireless coverage of embedded or flush-mounted antennas or arrays, especially in long-range communication. While arrays offer significant range and beam steerability control, they often suffer from size, weight, and power (SWaP) limitations. On the other hand, achieving a wideband, high-gain, and beam-steerable response from a single antenna is highly desired for its compact SWaP characteristics. In this study, a cube-shaped cavity antenna excited by a monopole feed is designed, fabricated, and measured. The proposed antenna operates from 4.1 to 5.56 GHz with a 30.22% fractional bandwidth and a peak gain of 8 dBi. In addition, a frequency-selective surface (FSS) is developed to replace the metallic faces of the cavity, enabling 360° electronic beam steerability. Thermal analysis of the FSS-based cavity design is conducted to determine its maximum power handling capability, revealing a maximum power handling capability of 1.3 KW continuous. In addition, the maximum rating currents of the FSS diodes can be reached only at 165 W, limiting the maximum power handling to only 165 W in the case of using the diodes used in this analysis. The antenna prototype is successfully fabricated, and the radiation pattern is experimentally measured, showing a strong agreement between the simulated and measured results. The electronic steerability of the proposed antenna indicates its suitability for 5G new radio and UAV applications. Full article
(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
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22 pages, 46829 KiB  
Article
Waveshift 2.0: An Improved Physics-Driven Data Augmentation Strategy in Fine-Grained Image Classification
by Gent Imeraj and Hitoshi Iyatomi
Electronics 2025, 14(9), 1735; https://doi.org/10.3390/electronics14091735 - 24 Apr 2025
Viewed by 449
Abstract
This paper presents Waveshift Augmentation 2.0 (WS 2.0), an enhanced version of the previously proposed Waveshift Augmentation (WS 1.0), a novel data augmentation technique inspired by light propagation dynamics in optical systems. While WS 1.0 introduced phase-based wavefront transformations under the assumption of [...] Read more.
This paper presents Waveshift Augmentation 2.0 (WS 2.0), an enhanced version of the previously proposed Waveshift Augmentation (WS 1.0), a novel data augmentation technique inspired by light propagation dynamics in optical systems. While WS 1.0 introduced phase-based wavefront transformations under the assumption of an infinitesimally small aperture, WS 2.0 incorporates an additional aperture-dependent hyperparameter that models real-world optical attenuation. This refinement enables broader frequency modulation and greater diversity in image transformations while preserving compatibility with well-established data augmentation pipelines such as CLAHE, AugMix, and RandAugment. Evaluated across a wide range of tasks, including medical imaging, fine-grained object recognition, and grayscale image classification, WS 2.0 consistently outperformed both WS 1.0 and standard geometric augmentation. Notably, when benchmarked against geometric augmentation alone, it achieved average macro-F1 improvements of +1.48 (EfficientNetV2), +0.65 (ConvNeXt), and +0.73 (Swin Transformer), with gains of up to +9.32 points in medical datasets. These results demonstrate that WS 2.0 advances physics-based augmentation by enhancing generalization without sacrificing modularity or preprocessing efficiency, offering a scalable and realistic augmentation strategy for complex imaging domains. Full article
(This article belongs to the Special Issue New Trends in Computer Vision and Image Processing)
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11 pages, 1677 KiB  
Article
A Novel Darlington Structure Power Switch Using a Vacuum Field Emission Transistor
by Yulong Ding, Yanlin Ke, Juncong She, Yu Zhang and Shaozhi Deng
Electronics 2025, 14(9), 1737; https://doi.org/10.3390/electronics14091737 - 24 Apr 2025
Viewed by 313
Abstract
This study proposes a power switch combining a vacuum field emission transistor (VFET) as a controlled transistor with a power bipolar Darlington transistor (DT) as an output transistor, termed the VFET–DT structure. Compared to the MOS–bipolar Darlington power switch, the VFET–DT structure achieves [...] Read more.
This study proposes a power switch combining a vacuum field emission transistor (VFET) as a controlled transistor with a power bipolar Darlington transistor (DT) as an output transistor, termed the VFET–DT structure. Compared to the MOS–bipolar Darlington power switch, the VFET–DT structure achieves an extremely low off-state leakage current and high-voltage withstanding capability due to the field emission mechanism of the VFET. It can also avoid the Miller effect that results from incorporating the load resistance into the feedback loop. The high gain and high-power capacity can be achieved due to the cascade of DT. The device’s typical electrical characteristics were theoretically investigated by simulation. The VFET–DT structure exhibited a high-power capacity of 20 A and 400 V with a minimum conduction voltage drop of 1.316 V and a switching frequency of 100 kHz. The results demonstrated that the combination of a vacuum transistor and a solid-state transistor combines the advantages of both and benefits the performance of the power switch. Full article
(This article belongs to the Special Issue Vacuum Electronics: From Micro to Nano)
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24 pages, 7088 KiB  
Article
Ultra-Lightweight and Highly Efficient Pruned Binarised Neural Networks for Intrusion Detection in In-Vehicle Networks
by Auangkun Rangsikunpum, Sam Amiri and Luciano Ost
Electronics 2025, 14(9), 1710; https://doi.org/10.3390/electronics14091710 - 23 Apr 2025
Cited by 1 | Viewed by 551
Abstract
With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, [...] Read more.
With the rapid evolution toward autonomous vehicles, securing in-vehicle communications is more critical than ever. The widely used Controller Area Network (CAN) protocol lacks built-in security, leaving vehicles vulnerable to cyberattacks. Although machine learning-based Intrusion Detection Systems (IDSs) can achieve high detection accuracy, their heavy computational and power demands often limit real-world deployment. In this paper, we present an optimised IDS based on a Binarised Neural Network (BNN) that employs network pruning to eliminate redundant parameters, achieving up to a 91.07% reduction with only a 0.1% accuracy loss. The proposed approach incorporates a two-stage Coarse-to-Fine (C2F) framework, efficiently filtering normal traffic in the initial stage to minimise unnecessary processing. To assess its practical feasibility, we implement and compare the pruned IDS across CPU, GPU, and FPGA platforms. The experimental results indicate that, with the same model structure, the FPGA-based solution outperforms GPU and CPU implementations by up to 3.7× and 2.4× in speed, while achieving up to 7.4× and 3.8× greater energy efficiency, respectively. Among cutting-edge BNN-based IDSs, our ultra-lightweight FPGA-based C2F approach achieves the fastest average inference speed, showing a 3.3× to 12× improvement, while also outperforming them in accuracy and average F1 score, highlighting its potential for low-power, high-performance vehicle security. Full article
(This article belongs to the Special Issue Recent Advances in Intrusion Detection Systems Using Machine Learning)
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16 pages, 973 KiB  
Article
Compensator with Weighted Input Parameters for Automatic Ball-Balancing Mechatronic System
by Ján Šefčík, Štefan Chamraz and Katarína Žáková
Electronics 2025, 14(9), 1695; https://doi.org/10.3390/electronics14091695 - 22 Apr 2025
Viewed by 304
Abstract
This paper presents an Automatic Ball-Balancing Mechatronic System (ABMS) with a lever transmission, which provides higher positioning accuracy for the ball. The system was identified by a double integrator, and the results confirmed the suitability of the chosen mathematical model. Then, we designed [...] Read more.
This paper presents an Automatic Ball-Balancing Mechatronic System (ABMS) with a lever transmission, which provides higher positioning accuracy for the ball. The system was identified by a double integrator, and the results confirmed the suitability of the chosen mathematical model. Then, we designed and tested a new compensator with weighted input parameters, which was firstly successfully implemented in real time on a microprocessor platform. Both simulation and then also experimental results demonstrated that the proposed controller provides stable and accurate control of the system under various step changes in the input signal. Full article
(This article belongs to the Special Issue Advanced PID Control: Theory and Applications Towards a Smarter PID Controller)
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25 pages, 6066 KiB  
Article
CNN-Based Fault Classification in Induction Motors Using Feature Vector Images of Symmetrical Components
by Tae-Hong Min, Joong-Hyeok Lee and Byeong-Keun Choi
Electronics 2025, 14(8), 1679; https://doi.org/10.3390/electronics14081679 - 21 Apr 2025
Cited by 2 | Viewed by 808
Abstract
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on [...] Read more.
Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on symmetrical components. Three-phase stator current signals are converted into zero, positive, and negative sequence components, and their time-domain feature vectors are systematically integrated into a single image representation. A Convolutional Neural Network (CNN) is then employed for fault classification. The proposed method is model-free, requiring no explicit motor model, which offers greater flexibility compared to model-based techniques. Validation experiments were conducted on a rotor kit test bench under seven different conditions (one healthy condition and six mechanical/electrical fault conditions), with fault severities chosen to reflect practical scenarios. The symmetrical components-based image classification method demonstrated superior performance, achieving 99.76% classification accuracy and outperforming a widely used Short-Time Fourier Transform (STFT)-based spectrogram approach. These findings highlight that integrating all symmetrical component information into one image effectively captures each fault’s distinct behavior, enabling reliable diagnostic outcomes. By leveraging the distinct variations in zero, positive, and negative components under fault conditions, the proposed method offers a powerful, accurate, and non-invasive framework for real-time motor fault diagnosis in industrial applications. Full article
(This article belongs to the Special Issue AI in Signal and Image Processing)
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26 pages, 5683 KiB  
Article
V2X Network-Based Enhanced Cooperative Autonomous Driving for Urban Clusters in Real Time: A Model for Control, Optimization and Security
by Minseong Yoon, Dongjun Seo, Soyoung Kim and Keecheon Kim
Electronics 2025, 14(8), 1629; https://doi.org/10.3390/electronics14081629 - 17 Apr 2025
Viewed by 850
Abstract
For the commercialization of connected vehicles and smart cities, extensive research is carried out on autonomous driving, Vehicle-to-Everything (V2X) communication, and platooning. However, limitations remain, such as restrictions to highway environments, and studies are conducted separately due to challenges in ensuring reliability and [...] Read more.
For the commercialization of connected vehicles and smart cities, extensive research is carried out on autonomous driving, Vehicle-to-Everything (V2X) communication, and platooning. However, limitations remain, such as restrictions to highway environments, and studies are conducted separately due to challenges in ensuring reliability and real-time performance under external influences. This paper proposes a cooperative autonomous driving system based on V2X network implemented in the CARLA simulator, which simulates an urban environment to optimize vehicle-embedded systems and ensure safety and real-time performance. First, the proposed Throttle–Steer–Brake (TSB) driving technique reduces the computational overhead for following vehicles by utilizing the control commands of a leading vehicle. Second, a V2X network is designed to support object perception, cluster escape, and joining. Third, an urban perception system is developed and validated for safety. Finally, pseudonymized vehicle identifiers, Advanced Encryption Standard (AES), and the Edwards-curve Digital Signature Algorithm (EdDSA) are employed for data reliability and security. The system is validated in processing time and accuracy, confirming feasibility for real-world application. TSB driving demonstrates a computation speed approximately 466 times faster than conventional waypoints-based driving. Accurate urban perception and V2X communication enable safe cluster escape and joining, establishing a foundation for cooperative autonomous driving with improved safety and real-time capabilities. Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems, 2nd Edition)
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21 pages, 779 KiB  
Review
Optically Transparent Antennas for 5G and Beyond: A Review
by Bernardo Dominguez, Fábio Silva, Amit Baghel, Daniel Albuquerque and Pedro Pinho
Electronics 2025, 14(8), 1616; https://doi.org/10.3390/electronics14081616 - 16 Apr 2025
Viewed by 813
Abstract
As wireless communication technology advances towards faster and higher transmission rates such as Fifth Generation (5G) and beyond, the need for multiple access points increases. The growing demand for access points often results in them occupying any available surface area and potentially disrupting [...] Read more.
As wireless communication technology advances towards faster and higher transmission rates such as Fifth Generation (5G) and beyond, the need for multiple access points increases. The growing demand for access points often results in them occupying any available surface area and potentially disrupting the existing scenery. In order to address this issue, Optically Transparent Antennas (OTAs) emerge as an optimal solution for balancing the aesthetics of a specific setting with the desired communication system requirements. These antennas can be integrated into various infrastructures without interfering with the design of the objects on which they are installed. Research on the techniques and materials for OTA fabrication, which is proposed as a solution to the 5G wireless communication demand for access points, is presented. This work will highlight key antenna characteristics such as gain, bandwidth, efficiency, and transparency, and how the materials used for OTA implementation influence these parameters. Techniques like Metal Mesh (MM), Transparent Conductive Film (TCF), and Transparent Conductive Oxide (TCO) will be explained. The performance of the OTAs will be analyzed based on gain, bandwidth, transparency, and efficiency. This paper also addresses the challenges and limitations associated with OTAs. Finally, it confirms that OTAs offer a compelling solution for this scenario by balancing aesthetics with high antenna performance, making them an innovation for future wireless networks. Full article
(This article belongs to the Special Issue Smart Antennas and Systems for 5G and Beyond: Latest Advances and Prospects)
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19 pages, 3648 KiB  
Article
Design of an Experimental Test Rig for Shrouded and Open Rotors for Small Rotary Wing Unmanned Aerial System
by Abdallah Dayhoum, Alejandro Ramirez-Serrano and Robert J. Martinuzzi
Electronics 2025, 14(8), 1584; https://doi.org/10.3390/electronics14081584 - 14 Apr 2025
Viewed by 412
Abstract
This study details the design and testing of a custom test rig for evaluating the performance of both open and shrouded rotors. The rig includes a two-axis load cell that is directly connected to the rotor to measure the rotor thrust separated from [...] Read more.
This study details the design and testing of a custom test rig for evaluating the performance of both open and shrouded rotors. The rig includes a two-axis load cell that is directly connected to the rotor to measure the rotor thrust separated from the total thrust when testing shrouded rotors and ensure accurate torque measurements, independent of external structural influences. Moreover, a main load cell is used to measure the total thrust for both configurations (open and shrouded rotor), as it is connected to the entire setup. Rotor RPM is monitored by capturing the voltage frequency from the BLDC motor, controlled using a Pololu Maestro Controller through the electronic speed controller. A shunt resistance is used to calculate the current through the electric Brushless Direct Current (BLDC) motor and by measuring the voltage, the electric power is calculated. By combining both mechanical and electrical power measurements, the BLDC motor’s efficiency is calculated. Automated data collection is conducted using National Instruments DAQ systems, with averaged measurements of thrust, torque, RPM, current, and voltage. Two rotors are tested to obtain performance data for both open and shrouded configurations. Additionally, a computational study is carried out to account for the aerodynamic effects of the rig’s structural elements. Uncertainty analysis is employed to assess the reliability of the experimental results by quantifying the numerical errors associated with both random and systematic errors encountered during the rotor’s performance evaluation. Full article
(This article belongs to the Special Issue Recent Advances in Robotics and Automation Systems)
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21 pages, 2649 KiB  
Article
A Novel Approach for Self-Driving Vehicle Longitudinal and Lateral Path-Following Control Using the Road Geometry Perception
by Felipe Barreno, Matilde Santos and Manuel Romana
Electronics 2025, 14(8), 1527; https://doi.org/10.3390/electronics14081527 - 10 Apr 2025
Viewed by 646
Abstract
This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the [...] Read more.
This study proposes an advanced intelligent vehicle path-following control system using deep reinforcement learning, with a particular focus on the role of road geometry perception in motion planning and control. The system is structured around a three-degree-of-freedom (3-DOF) vehicle model, which facilitates the extraction of critical dynamic features necessary for robust control. The longitudinal control architecture integrates a Deep Deterministic Policy Gradient (DDPG) agent to optimise longitudinal velocity and acceleration, while lateral vehicle control is handled by a Deep Q-Network (DQN). To enhance situational awareness and adaptability, the system incorporates key input variables, including ego vehicle speed, speed error, lateral deviation, lateral error, and safety distance to the preceding vehicle, all in the context of road geometry and vehicle dynamics. In addition, the influence of road curvature is embedded into the control framework through perceived acceleration (sensed by vehicle occupants), allowing for more accurate and responsive adaptation to varying road conditions. The vehicle control system is tested in a simulated environment with a lead car in front with realistic speed profiles. The system outputs continuous values for acceleration and steering angle. The results of this study suggest that the proposed intelligent control system not only improves driver assistance but also has potential applications in autonomous driving. This framework contributes to the development of more autonomous, efficient, safety-aware, and comfortable vehicle control systems. Full article
(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles)
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21 pages, 13497 KiB  
Article
Hyperspectral LiDAR for Subsea Exploration: System Design and Performance Evaluation
by Huijing Zhang, Linsheng Chen, Haohao Wu, Mei Zhou, Jiuying Chen, Zhichao Chen, Jian Hu, Yuwei Chen, Jinhu Wang, Yifang Niu, Meisong Liao, Xiaoxing Wang, Wanqiu Xu, Tianxing Wang and Shizi Yu
Electronics 2025, 14(8), 1539; https://doi.org/10.3390/electronics14081539 - 10 Apr 2025
Cited by 1 | Viewed by 458
Abstract
Hyperspectral LiDAR (HSL) is a promising active detection technique for underwater positioning and remote sensing, enabling the simultaneous acquisition of three-dimensional topographic and spectral information of underwater targets. This study presents an advanced underwater hyperspectral LiDAR (UDHSL) system with a spectral range of [...] Read more.
Hyperspectral LiDAR (HSL) is a promising active detection technique for underwater positioning and remote sensing, enabling the simultaneous acquisition of three-dimensional topographic and spectral information of underwater targets. This study presents an advanced underwater hyperspectral LiDAR (UDHSL) system with a spectral range of 450–700 nm, adjustable spectral bandwidth of 10–300 nm, and tunable repetition frequency of 50 kHz to 1 MHz. The system achieves high precision with a laser divergence angle of ≤1 mrad, pulse width of 7 ns, laser energy of 7.5 µJ, ranging resolution of 1.13 cm and ranging accuracy of 1.02 m@distance of 27 m. Hyperspectral point clouds spanning 11 bands (450–650 nm) are generated during 3D pool experiments. The distance-colored point clouds precisely align with the geometric characteristics of targets, the normalized intensity-colored point clouds across spectral bands exhibit discriminative capabilities for target identification, and the color-composite point clouds approximate the true colors of targets, collectively validating the system’s ability to concurrently acquire spectral and topographic data. These results underscore the potential of this technology for underwater exploration and positioning applications. Full article
(This article belongs to the Special Issue The Application of Lidars in Positioning Systems)
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14 pages, 7854 KiB  
Article
Adaptive DC-Link Voltage Control for 22 kW, 40 kHz LLC Resonant Converter Considering Low-Frequency Voltage Ripple
by Roland Unruh, Joachim Böcker and Frank Schafmeister
Electronics 2025, 14(8), 1517; https://doi.org/10.3390/electronics14081517 - 9 Apr 2025
Viewed by 516
Abstract
The LLC converter achieves the highest efficiency in resonant operation. Conventionally, the input DC-link voltage is controlled to operate the LLC converter at resonance for the given operating point. However, the DC-link capacitor voltage shows a low-frequency voltage ripple (typically the second harmonic [...] Read more.
The LLC converter achieves the highest efficiency in resonant operation. Conventionally, the input DC-link voltage is controlled to operate the LLC converter at resonance for the given operating point. However, the DC-link capacitor voltage shows a low-frequency voltage ripple (typically the second harmonic of grid frequency) in cascaded converters so that the LLC has to adapt its switching frequency within the grid period. Conventionally, the LLC converter operates 50% of the time above the resonant frequency of 40 kHz and 50% below resonance. Both operating conditions cause additional losses. However, experimental measurements indicate that the below-resonance operation causes significantly higher losses than above-resonance operation due to much higher primary and secondary transformer currents. It is better to increase the DC-link voltage by 30% of the peak-to-peak low-frequency voltage ripple to mostly avoid below-resonance operation (i.e., from 650 V to 680 V in this case). With the proposed control, the LLC converter operates about 75% of time over resonance and only 25% of time below resonance. The overall efficiency increases from 97.66% to 97.7% for the average operating point with an 80% load current. This corresponds to a 2% total loss reduction. Finally, the peak resonance capacitor voltage decreases from 910 V to 790 V (−13%). Full article
(This article belongs to the Special Issue Innovative Technologies in Power Converters, 2nd Edition)
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15 pages, 5428 KiB  
Article
Design and Modeling Guidelines for Auxiliary Voltage Sensing Windings in High-Voltage Transformers and Isolated Converters
by Elinor Ginzburg-Ganz, Dmitry Baimel, Juri Belikov and Yoash Levron
Electronics 2025, 14(8), 1519; https://doi.org/10.3390/electronics14081519 - 9 Apr 2025
Viewed by 358
Abstract
This paper provides guidelines for designing and modeling sensing coils in high-voltage, high-frequency transformers to enable a cost-efficient design of isolated converter topologies. The objective is to design a magnetic structure in which an additional sensing coil, placed on the main transformer, can [...] Read more.
This paper provides guidelines for designing and modeling sensing coils in high-voltage, high-frequency transformers to enable a cost-efficient design of isolated converter topologies. The objective is to design a magnetic structure in which an additional sensing coil, placed on the main transformer, can be used to precisely measure the voltage on the secondary, despite fast changes in the voltage and current. This is usually a challenging task since high-voltage transformers will always require considerable isolation, which will give rise to significant leakage fields, which in turn will distort the measurement, especially at high frequencies. Our main finding is that this problem can be avoided if the sensing winding is carefully routed to maintain a certain ratio between the transformer’s coupling coefficients, which is achieved by placing this winding in an area within the core in which the magnetic field is low. In principle, this leads to a linear relationship between the voltages of the secondary and sensing windings despite non-ideal leakage inductances. The results are demonstrated experimentally using a 10 kW transformer, with 60 kV isolation, demonstrating a coupling coefficient of about 0.99, which reflects an error of less than 1.5% in the sensed secondary voltage. Full article
(This article belongs to the Special Issue High-Voltage Technology and Its Applications)
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26 pages, 938 KiB  
Article
Enhancing Personalised Learning with a Context-Aware Intelligent Question-Answering System and Automated Frequently Asked Question Generation
by Eleonora Bernasconi, Domenico Redavid and Stefano Ferilli
Electronics 2025, 14(7), 1481; https://doi.org/10.3390/electronics14071481 - 7 Apr 2025
Cited by 1 | Viewed by 751
Abstract
The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of [...] Read more.
The increasing integration of Artificial Intelligence (AI) in education has led to the development of innovative tools like Intelligent Question-Answering Systems (IQASs), aiming to revolutionize traditional learning paradigms. However, many existing IQAS struggle with the nuances of natural language and the complexities of student questions. This research focuses on developing a context-aware IQAS that leverages advanced Natural Language Processing (NLP) techniques and contextual information, including student learning history and educational content, to provide personalised support. This study also introduces a software tool that utilizes NLP techniques to automatically generate FAQs from educational materials. Employing a hybrid approach combining rule-based and machine learning techniques, the IQAS demonstrated high accuracy in interpreting and responding to a wide range of student queries. The software tool effectively automated the generation of FAQs, creating a valuable resource for personalised learning. The findings suggest that these tools can significantly improve student engagement, motivation, and learning outcomes, highlighting the potential of AI to transform education and pave the way for more personalised, adaptive, and effective learning environments. Full article
(This article belongs to the Special Issue Advances in Natural Language Processing and Their Applications)
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17 pages, 6487 KiB  
Article
A Cost-Effective System for EMG/MMG Signal Acquisition
by Jerzy S. Witkowski and Andrzej Grobelny
Electronics 2025, 14(7), 1468; https://doi.org/10.3390/electronics14071468 - 5 Apr 2025
Viewed by 713
Abstract
This article presents a cost-effective, robust, and reliable system for EMG/MMG (electromyography/mechanomyography). Signals indicating muscle activity have numerous applications and are the subject of many studies. However, acquiring these signals is challenging. Commercial measurement systems are often expensive, limiting their accessibility. Therefore, the [...] Read more.
This article presents a cost-effective, robust, and reliable system for EMG/MMG (electromyography/mechanomyography). Signals indicating muscle activity have numerous applications and are the subject of many studies. However, acquiring these signals is challenging. Commercial measurement systems are often expensive, limiting their accessibility. Therefore, the primary goal of this project was to develop a simple and affordable system for simultaneous EMG and MMG data acquisition, offering efficiency comparable to commercial systems. The system consists of eight EMG/MMG probes, 16-bit analog-to-digital converters with 16 channels, and a microprocessor unit. Despite its multiple components, the system remains simple and user-friendly. This paper describes the construction of the EMG/MMG probe and analyzes the intrinsic noise of the preamplifier, as well as electromagnetic interference, particularly power line noise. The elimination of power line noise was carried out in two stages: first, using techniques known for electromagnetic compatibility (EMC), and second, by implementing a digital filter in the microprocessor system. The proposed solution enables direct data collection from eight EMG/MMG probes using any computer equipped with a USB interface. This interface facilitates both data transmission and power supply, making EMG/MMG data acquisition straightforward and efficient. Full article
(This article belongs to the Section Bioelectronics)
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28 pages, 2365 KiB  
Article
Trustworthiness Optimisation Process: A Methodology for Assessing and Enhancing Trust in AI Systems
by Mattheos Fikardos, Katerina Lepenioti, Dimitris Apostolou and Gregoris Mentzas
Electronics 2025, 14(7), 1454; https://doi.org/10.3390/electronics14071454 - 3 Apr 2025
Viewed by 817
Abstract
The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monitored, and governed. For this reason, trustworthy [...] Read more.
The emerging capabilities of artificial intelligence (AI) and the systems that employ them have reached a point where they are integrated into critical decision-making processes, making it paramount to change and adjust how they are evaluated, monitored, and governed. For this reason, trustworthy AI (TAI) has received increased attention lately, primarily aiming to build trust between humans and AI. Due to the far-reaching socio-technical consequences of AI, organisations and government bodies have already started implementing frameworks and legislation for enforcing TAI, such as the European Union’s AI Act. Multiple approaches have evolved around TAI, covering different aspects of trustworthiness that include fairness, bias, explainability, robustness, accuracy, and more. Moreover, depending on the AI models and the stage of the AI system lifecycle, several methods and techniques can be used for each trustworthiness characteristic to assess potential risks and mitigate them. Deriving from all the above is the need for comprehensive tools and solutions that can help AI stakeholders follow TAI guidelines and adopt methods that practically increase trustworthiness. In this paper, we formulate and propose the Trustworthiness Optimisation Process (TOP), which operationalises TAI and brings together its procedural and technical approaches throughout the AI system lifecycle. It incorporates state-of-the-art enablers of trustworthiness such as documentation cards, risk management, and toolkits to find trustworthiness methods that increase the trustworthiness of a given AI system. To showcase the application of the proposed methodology, a case study is conducted, demonstrating how the fairness of an AI system can be increased. Full article
(This article belongs to the Special Issue Future Development of Trustworthy Artificial Intelligence and Applications)
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23 pages, 14314 KiB  
Article
RGB-D Camera-Based Human Head Motion Detection and Recognition System for Positron Emission Tomography Scanning
by Yixin Shan, Zikun Lu, Zhe Sun, Hao Liu, Jiangchang Xu, Yixing Sun and Xiaojun Chen
Electronics 2025, 14(7), 1441; https://doi.org/10.3390/electronics14071441 - 2 Apr 2025
Viewed by 586
Abstract
Positron emission tomography (PET) is one of the most advanced imaging diagnostic devices in the medical field, playing a crucial role in tumor diagnosis and treatment. However, patient motion during scanning can lead to motion artifacts, which affect diagnostic accuracy. This study aims [...] Read more.
Positron emission tomography (PET) is one of the most advanced imaging diagnostic devices in the medical field, playing a crucial role in tumor diagnosis and treatment. However, patient motion during scanning can lead to motion artifacts, which affect diagnostic accuracy. This study aims to develop a head motion monitoring system to identify and select images with excessive motion and corresponding periods. The system, based on an RGB-D structured-light camera, implements facial feature point detection, 3D information acquisition, and head motion monitoring, along with a user interaction software. Through phantom experiments and volunteer experiments, the system’s performance was tested under various conditions, including stillness, pitch movement, yaw movement, and comprehensive movement. Experimental results show that the system’s translational error is less than 2.5 mm, rotational error is less than 2.0°, and it can output motion monitoring results within 10 s after the PET scanning, meeting clinical accuracy requirements and showing significant potential for clinical application. Full article
(This article belongs to the Special Issue Medical Robots: Safety, Performance and Improvement)
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26 pages, 8883 KiB  
Article
Enhancing Machine Learning Techniques in VSLAM for Robust Autonomous Unmanned Aerial Vehicle Navigation
by Hussam Rostum and József Vásárhelyi
Electronics 2025, 14(7), 1440; https://doi.org/10.3390/electronics14071440 - 2 Apr 2025
Viewed by 536
Abstract
This study introduces a visual SLAM real-time system designed for small indoor environments. The system demonstrates resilience against significant motion clutter and supports wide-baseline loop closing, re-localization, and automatic initialization. Leveraging state-of-the-art algorithms, the approach presented in this article utilizes adapted Oriented FAST [...] Read more.
This study introduces a visual SLAM real-time system designed for small indoor environments. The system demonstrates resilience against significant motion clutter and supports wide-baseline loop closing, re-localization, and automatic initialization. Leveraging state-of-the-art algorithms, the approach presented in this article utilizes adapted Oriented FAST and Rotated BRIEF features for tracking, mapping, re-localization, and loop closing. In addition, the research uses an adaptive threshold to find putative feature matches that provide efficient map initialization and accurate tracking. The assignment is to process visual information from the camera of a DJI Tello drone for the construction of an indoor map and the estimation of the trajectory of the camera. In a ’survival of the fittest’ style, the algorithms selectively pick adaptive points and keyframes for reconstruction. This leads to robustness and a concise traceable map that develops as scene content emerges, making lifelong operation possible. The results give an improvement in the RMSE for the adaptive ORB algorithm and the adaptive threshold (3.280). However, the standard ORB algorithm failed to achieve the mapping process. Full article
(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)
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30 pages, 13767 KiB  
Article
A Novel Transformerless Soft-Switching Symmetrical Bipolar Power Converter: Analysis, Design, Simulation and Validation
by Cristian Díaz-Martín, Eladio Durán Aranda, Fernando Alves da Silva and Sérgio André
Electronics 2025, 14(7), 1434; https://doi.org/10.3390/electronics14071434 - 2 Apr 2025
Viewed by 455
Abstract
In order to obtain acceptable efficiencies, hard-switching techniques and the converters that implement them must operate at relatively low frequencies (tens of kilohertz), which translate into converters of large size, weight, and volume, and therefore higher cost. To improve these characteristics, this work [...] Read more.
In order to obtain acceptable efficiencies, hard-switching techniques and the converters that implement them must operate at relatively low frequencies (tens of kilohertz), which translate into converters of large size, weight, and volume, and therefore higher cost. To improve these characteristics, this work introduces a new transformerless MHz-range DC–DC converter that provides symmetrical bipolar outputs. The developed topology uses a single grounded switch, achieves soft switching (ZVS) over a wide load range, and does not require the use of floating or isolated controllers, reducing cost, size, and complexity. The output voltages are self-regulated to maintain the same value, ensuring balanced bipolar operation. A comprehensive analysis, design, sizing, simulation, implementation and testing are provided on a 150 W prototype operating at a switching frequency of 1 MHz, with step-up and step-down capability and implemented with GaN FET. The evaluated configuration shows an efficiency close to 90% and high power density, making it suitable for compact designs in a variety of applications requiring reliable power management and high efficiency such as lighting, electric vehicles, or auxiliary power supplies. Full article
(This article belongs to the Special Issue Section Collection Series: New Horizons and Recent Advances in Power Electronics)
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28 pages, 10273 KiB  
Article
Design and Analysis of 15-Level and 25-Level Asymmetrical Multilevel Inverter Topologies
by Prasad Kumar Bandahalli Mallappa, Guillermo Velasco-Quesada and Herminio Martínez-García
Electronics 2025, 14(7), 1416; https://doi.org/10.3390/electronics14071416 - 31 Mar 2025
Cited by 1 | Viewed by 486
Abstract
This study aims to minimize component requirements by presenting a novel topology for a single-phase 15-level asymmetrical multilevel inverter. Utilizing an H-bridge configuration, the proposed design achieves a maximum 15-level output voltage using asymmetrical DC sources. The initial 15-level inverter structure is further [...] Read more.
This study aims to minimize component requirements by presenting a novel topology for a single-phase 15-level asymmetrical multilevel inverter. Utilizing an H-bridge configuration, the proposed design achieves a maximum 15-level output voltage using asymmetrical DC sources. The initial 15-level inverter structure is further enhanced to support a 25-level variant suitable for renewable energy applications, effectively reducing system costs and size. However, the increased component count in multilevel inverters poses reliability challenges, particularly concerning total harmonic distortion reduction, which remains a focal point for researchers. Various parameters, including total standing voltage, multilevel inverter cost function, and power loss, are analyzed for both the proposed 15-level and the expanded 25-level multilevel inverters. This study contributes a new topology for a single-phase 15-level asymmetrical multilevel inverter, optimizing component usage and paving the way for renewable energy integration. Despite the advantages of multilevel inverters, addressing reliability concerns related to total harmonic distortion reduction remains crucial for future advancements in this domain. Full article
(This article belongs to the Special Issue Power Electronics and Renewable Energy System)
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22 pages, 14590 KiB  
Article
Carrier-Based Implementation of SVPWM for a Three-Level Simplified Neutral Point Clamped Inverter with XOR Logic Gates
by Zifan Lin, Wenxiang Du, Yang Bai, Herbert Ho Ching Iu, Tyrone Fernando and Xinan Zhang
Electronics 2025, 14(7), 1408; https://doi.org/10.3390/electronics14071408 - 31 Mar 2025
Viewed by 555
Abstract
The three-level simplified neutral point clamped (3L-SNPC) inverter has received increasing attention in recent years due to its potential applications in electrical drives and smart grids with renewable energy integration. However, most existing research has primarily focused on control development, with limited studies [...] Read more.
The three-level simplified neutral point clamped (3L-SNPC) inverter has received increasing attention in recent years due to its potential applications in electrical drives and smart grids with renewable energy integration. However, most existing research has primarily focused on control development, with limited studies investigating modulation strategies or analyzing inverter losses under varying operating conditions. These aspects are critical for practical industrial applications. To address this gap, this paper proposes a novel carrier-based space vector pulse width modulation (CB-SVPWM) strategy for the 3L-SNPC inverter, aimed at simplifying PWM implementation and reducing cost. The proposed modulation strategy is experimentally evaluated by comparing inverter losses and total harmonic distortion with those of the conventional three-level neutral point clamped (3L-NPC) inverter under an equivalent carrier-based modulation scheme. A comprehensive comparative analysis is conducted across the full modulation range to demonstrate the effectiveness of the proposed approach, achieving a 13.2% reduction in total power loss, a 33.6% improvement in execution time, and maintaining a comparable weighted total harmonic distortion (WTHD) with a deviation within 0.04% of the conventional 3L-NPC inverter. Full article
(This article belongs to the Special Issue Control and Optimization of Power Converters and Drives)
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22 pages, 10075 KiB  
Article
Open Data-Driven Reconstruction of Power Distribution Grid: A Land Use-Based Approach
by Mohannad Babli, Tobias Gebhard and Eva Brucherseifer
Electronics 2025, 14(7), 1414; https://doi.org/10.3390/electronics14071414 - 31 Mar 2025
Viewed by 502
Abstract
Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring [...] Read more.
Disruptive events and the rapid evolution of urban energy systems highlight the need for robust methods to reconstruct critical infrastructure networks. Comprehensive, up-to-date power grid representations are essential for both researchers developing methods for analysing and optimising power systems and first responders requiring approximate data for urgent decisions. However, traditional grid reconstruction approaches often rely on incomplete data, expert knowledge, or closed datasets, limiting their utility during emergencies. This study proposes a novel automated method for reconstructing medium-voltage (MV) power grids. The novelty of the proposed method lies in combining OpenStreetMap energy and land-use data in a unified and automated framework, thereby reducing the need for expert input. The proposed method employs a systematic aggregation of data, an estimation of energy demand, and the application of algorithmic techniques to generate synthetic MV grid models that functionally represent real networks, capturing key topological features. The resulting outputs include visual representations to support decision-makers in simulating "what-if” scenarios and ensuring rapid operational awareness. In a step toward eliminating reliance on proprietary data, our approach broadens access to critical infrastructure insights across diverse urban contexts, contributing to critical infrastructure resilience and potentially supporting both energy system research and crisis management. A case study demonstrates that a medium-sized city’s MV grid can be reconstructed in minutes without expert knowledge or geographically constrained datasets, underscoring the method’s deployment potential and practical value for emergency scenarios. Full article
(This article belongs to the Special Issue Unlocking Data’s Potential: Artificial Intelligence and Visual Analytics in the Modern Age)
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17 pages, 4274 KiB  
Article
Quantifying the Benefits of Hybrid Energy Harvesting from Natural Sources
by Antonietta Simone, Pasquale Marino, Roberto Greco and Alessandro Lo Schiavo
Electronics 2025, 14(7), 1400; https://doi.org/10.3390/electronics14071400 - 30 Mar 2025
Viewed by 402
Abstract
The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of [...] Read more.
The increasing demand for self-powered sensors and wireless sensor networks, particularly for environmental and structural health monitoring applications, is driving the need for energy harvesting from natural sources. To fill a gap in the scientific literature, this study quantitatively investigates the advantages of hybrid energy harvesters, which utilize multiple energy sources, compared to single-source harvesters. The analysis leverages a real-world dataset collected from a meteorological station in Cervinara, Southern Italy. The measured data are processed to estimate the energy that can be recovered from solar, wind, and rain sources using energy harvesters designed to supply low-power electronic devices. The available energy serves as the basis for optimizing the sizing of a hybrid energy harvester that effectively integrates the aforementioned energy sources. The system sizing, carried out under the constraint of ensuring a continuous and uninterrupted power supply to the load, quantifies the benefits of using a hybrid harvester over a single-source harvester. The results show that one of the main advantages of the hybrid solution is the reduction in the size of the storage device, enabling the replacement of rechargeable batteries with supercapacitors, which offer both environmental and reliability benefits. Full article
(This article belongs to the Special Issue New Applications of Wireless Sensor Networks: Focus on Structural Health Monitoring and Condition Monitoring of Industrial Machinery)
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47 pages, 7373 KiB  
Article
AI and Evolutionary Computation for Intelligent Aviation Health Monitoring
by Igor Kabashkin
Electronics 2025, 14(7), 1369; https://doi.org/10.3390/electronics14071369 - 29 Mar 2025
Cited by 1 | Viewed by 674
Abstract
This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework [...] Read more.
This paper presents a novel framework integrating evolutionary computation and artificial intelligence for aircraft health monitoring and management systems. The research addresses critical challenges in modern aircraft maintenance through a comprehensive approach combining real-time fault detection, predictive maintenance, and multi-objective optimization. The framework employs deep learning models for fault detection, achieving about 97% classification accuracy with an F1-score of 0.97, while remaining useful life prediction yields an R2 score of 0.89 with a mean absolute error of 9.8 h. Evolutionary algorithms optimize maintenance strategies, reducing downtime and costs by up to 22% compared to traditional methods. The methodology includes robust data processing protocols, feature engineering techniques, and a modular system architecture supporting real-time monitoring and decision-making. Simulation experiments demonstrate the framework’s effectiveness in balancing maintenance objectives while maintaining high reliability. The research provides practical implementation guidelines and addresses key challenges in computational efficiency, data quality, and system integration. The results show significant improvements in maintenance planning efficiency and system reliability compared to traditional approaches. The framework’s modular design enables scalability and adaptation to various aircraft systems, offering broader applications in complex technical system maintenance. Full article
(This article belongs to the Special Issue Advancements in AI-Driven Cybersecurity and Securing AI Systems)
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