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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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19 pages, 1107 KiB  
Article
A Novel Harmonic Clocking Scheme for Concurrent N-Path Reception in Wireless and GNSS Applications
by Dina Ibrahim, Mohamed Helaoui, Naser El-Sheimy and Fadhel Ghannouchi
Electronics 2025, 14(15), 3091; https://doi.org/10.3390/electronics14153091 - 1 Aug 2025
Viewed by 342
Abstract
This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, [...] Read more.
This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of −55dBm and a conversion gain varying from −2.5dB to −9dB depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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20 pages, 2341 KiB  
Article
Magnetic Field Measurement of Various Types of Vehicles, Including Electric Vehicles
by Hiromichi Fukui, Norihiro Minami, Masatoshi Tanezaki, Shinichi Muroya and Chiyoji Ohkubo
Electronics 2025, 14(15), 2936; https://doi.org/10.3390/electronics14152936 - 23 Jul 2025
Viewed by 975
Abstract
Since around the year 2000, following the introduction of electric vehicles (EVs) to the market, some people have expressed concerns about the level of magnetic flux density (MFD) inside vehicles. In 2013, we reported the results of MFD measurements for electric vehicles (EVs), [...] Read more.
Since around the year 2000, following the introduction of electric vehicles (EVs) to the market, some people have expressed concerns about the level of magnetic flux density (MFD) inside vehicles. In 2013, we reported the results of MFD measurements for electric vehicles (EVs), hybrid electric vehicles (HEVs), and internal combustion engine vehicles (ICEVs). However, those 2013 measurements were conducted using a chassis dynamometer, and no measurements were taken during actual driving. In recent years, with the rapid global spread of EVs and plug-in hybrid electric vehicles (PHEVs), the international standard IEC 62764-1:2022, which defines methods for measuring magnetic fields (MF) in vehicles, has been issued. In response, and for the first time, we conducted new MF measurements on current Japanese vehicle models in accordance with the international standard IEC 62764-1:2022, identifying the MFD levels and their sources at various positions within EVs, PHEVs, and ICEVs. The measured MFD values in all vehicle types were below the reference levels recommended by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for public exposure. Furthermore, we performed comparative measurements with the MF data obtained in 2013 and confirmed that the MF levels remained similar. These findings are expected to provide valuable insights for risk communication with the public regarding electromagnetic fields, particularly for those concerned about MF exposure inside electrified vehicles. Full article
(This article belongs to the Special Issue Innovations in Electromagnetic Field Measurements and Applications)
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10 pages, 700 KiB  
Article
Neurocognitive Foundations of Memory Retention in AR and VR Cultural Heritage Experiences
by Paula Srdanović, Tibor Skala and Marko Maričević
Electronics 2025, 14(15), 2920; https://doi.org/10.3390/electronics14152920 - 22 Jul 2025
Viewed by 362
Abstract
Immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as powerful tools in cultural heritage education and preservation. Building on prior work that demonstrated the effectiveness of gamified XR applications in engaging users with heritage content and drawing on [...] Read more.
Immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as powerful tools in cultural heritage education and preservation. Building on prior work that demonstrated the effectiveness of gamified XR applications in engaging users with heritage content and drawing on existing studies in neuroscience and cognitive psychology, this study explores how immersive experiences support multisensory integration, emotional engagement, and spatial presence—all of which contribute to the deeper encoding and recall of heritage narratives. Through a theoretical lens supported by the empirical literature, we argue that the interactive and embodied nature of AR/VR aligns with principles of cognitive load theory, dual coding theory, and affective neuroscience, supporting enhanced learning and memory consolidation. This paper aims to bridge the gap between technological innovation and cognitive understanding in cultural heritage dissemination, identifying concrete design principles for memory-driven digital heritage experiences. While promising, these approaches also raise important ethical considerations, including accessibility, cultural representation, and inclusivity—factors essential for equitable digital heritage dissemination. Full article
(This article belongs to the Special Issue Metaverse, Digital Twins and AI, 3rd Edition)
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28 pages, 2518 KiB  
Article
Enhancing Keyword Spotting via NLP-Based Re-Ranking: Leveraging Semantic Relevance Feedback in the Handwritten Domain
by Stergios Papazis, Angelos P. Giotis and Christophoros Nikou
Electronics 2025, 14(14), 2900; https://doi.org/10.3390/electronics14142900 - 20 Jul 2025
Viewed by 450
Abstract
Handwritten Keyword Spotting (KWS) remains a challenging task, particularly in segmentation-free scenarios where word images must be retrieved and ranked based on their similarity to a query without relying on prior page-level segmentation. Traditional KWS methods primarily focus on visual similarity, often overlooking [...] Read more.
Handwritten Keyword Spotting (KWS) remains a challenging task, particularly in segmentation-free scenarios where word images must be retrieved and ranked based on their similarity to a query without relying on prior page-level segmentation. Traditional KWS methods primarily focus on visual similarity, often overlooking the underlying semantic relationships between words. In this work, we propose a novel NLP-driven re-ranking approach that refines the initial ranked lists produced by state-of-the-art KWS models. By leveraging semantic embeddings from pre-trained BERT-like Large Language Models (LLMs, e.g., RoBERTa, MPNet, and MiniLM), we introduce a relevance feedback mechanism that improves both verbatim and semantic keyword spotting. Our framework operates in two stages: (1) projecting retrieved word image transcriptions into a semantic space via LLMs and (2) re-ranking the retrieval list using a weighted combination of semantic and exact relevance scores based on pairwise similarities with the query. We evaluate our approach on the widely used George Washington (GW) and IAM collections using two cutting-edge segmentation-free KWS models, which are further integrated into our proposed pipeline. Our results show consistent gains in Mean Average Precision (mAP), with improvements of up to 2.3% (from 94.3% to 96.6%) on GW and 3% (from 79.15% to 82.12%) on IAM. Even when mAP gains are smaller, qualitative improvements emerge: semantically relevant but inexact matches are retrieved more frequently without compromising exact match recall. We further examine the effect of fine-tuning transformer-based OCR (TrOCR) models on historical GW data to align textual and visual features more effectively. Overall, our findings suggest that semantic feedback can enhance retrieval effectiveness in KWS pipelines, paving the way for lightweight hybrid vision-language approaches in handwritten document analysis. Full article
(This article belongs to the Special Issue AI Synergy: Vision, Language, and Modality)
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27 pages, 5012 KiB  
Article
Optimizing FPGA Resource Allocation in SDR Remote Laboratories via Partial Reconfiguration
by Zhiyun Zhang and Rania Hussein
Electronics 2025, 14(14), 2908; https://doi.org/10.3390/electronics14142908 - 20 Jul 2025
Viewed by 535
Abstract
In wireless communications and radio frequency courses, Software-Defined Radios (SDRs) offer students hands-on experience with software-based signal processing on programmable hardware platforms such as Field Programmable Gate Arrays (FPGAs). While some remote SDR laboratories enable students to access real hardware, they typically lack [...] Read more.
In wireless communications and radio frequency courses, Software-Defined Radios (SDRs) offer students hands-on experience with software-based signal processing on programmable hardware platforms such as Field Programmable Gate Arrays (FPGAs). While some remote SDR laboratories enable students to access real hardware, they typically lack support for Partial Reconfiguration (PR)—a powerful FPGA capability that allows sections of a design to be reconfigured at runtime without disrupting the main system operation. This capability enhances real-time adaptability and optimizes resource utilization, making it highly relevant for modern SDR applications. This study addresses this gap by extending an existing SDR remote lab to support PR, enabling students to explore reconfigurable hardware design within a remote learning environment. Two integration architectures were developed: one based on a graphical user interface (UI) and another utilizing a command-line workflow, both accessible via a web browser. Preliminary experiments using Red Pitaya SDR platforms—reportedly the first use of these devices for educational PR exploration—examined the impact of PR on logic resource utilization and total power consumption across three levels of design complexity. These results were compared to equivalent static FPGA designs performing the same functionality without PR. By making PR experimentation accessible through a remote platform, this work enhances STEM education by bridging advanced FPGA techniques with practical learning. It will equip students with industry-relevant skills for developing agile, resource-efficient wireless systems and foster a deeper understanding of adaptive hardware design. Full article
(This article belongs to the Special Issue FPGA-Based Reconfigurable Embedded Systems)
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17 pages, 7597 KiB  
Article
Screen-Printed 1 × 4 Quasi-Yagi-Uda Antenna Array on Highly Flexible Transparent Substrate for the Emerging 5G Applications
by Matthieu Egels, Anton Venouil, Chaouki Hannachi, Philippe Pannier, Mohammed Benwadih and Christophe Serbutoviez
Electronics 2025, 14(14), 2850; https://doi.org/10.3390/electronics14142850 - 16 Jul 2025
Viewed by 301
Abstract
In the Internet of Things (IoT) era, the demand for cost-effective, flexible, wearable antennas and circuits has been growing. Accordingly, screen-printing techniques are becoming more popular due to their lower costs and high-volume manufacturing. This paper presents and investigates a full-screen-printed 1 × [...] Read more.
In the Internet of Things (IoT) era, the demand for cost-effective, flexible, wearable antennas and circuits has been growing. Accordingly, screen-printing techniques are becoming more popular due to their lower costs and high-volume manufacturing. This paper presents and investigates a full-screen-printed 1 × 4 Quasi-Yagi-Uda antenna array on a high-transparency flexible Zeonor thin-film substrate for emerging 26 GHz band (24.25–27.55 GHz) 5G applications. As part of this study, screen-printing implementation rules are developed by properly managing ink layer thickness on a transparent flexible Zeonor thin-film dielectric to achieve a decent antenna array performance. In addition, a screen-printing repeatability study has been carried out through a performance comparison of 24 antenna array samples manufactured by our research partner from CEA-Liten Grenoble. Despite the challenging antenna array screen printing at higher frequencies, the measured results show a good antenna performance as anticipated from the traditional subtractive printed circuit board (PCB) manufacturing process using standard substrates. It shows a wide-band matched input impedance from 22–28 GHz (i.e., 23% of relative band-width) and a maximum realized gain of 12.8 dB at 27 GHz. Full article
(This article belongs to the Special Issue Recent Advancements in Antenna Technologies: Novel Design Techniques and Emerging Applications)
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30 pages, 55073 KiB  
Review
Advances in Gecko-Inspired Climbing Robots: From Biology to Robotics—A Review
by Wenrui Xiang and Barmak Honarvar Shakibaei Asli
Electronics 2025, 14(14), 2810; https://doi.org/10.3390/electronics14142810 - 12 Jul 2025
Viewed by 808
Abstract
Wall-climbing robots have garnered significant attention for their ability to operate in hazardous environments. Among these, bioinspired gecko robots exhibit exceptional adaptability and climbing performance due to their flexible morphology and intelligent motion strategies. This review systematically analyzes studies published between 2000–2025, sourced [...] Read more.
Wall-climbing robots have garnered significant attention for their ability to operate in hazardous environments. Among these, bioinspired gecko robots exhibit exceptional adaptability and climbing performance due to their flexible morphology and intelligent motion strategies. This review systematically analyzes studies published between 2000–2025, sourced from IEEE Xplore, Web of Science, and Scopus databases, to explore the biological principles of gecko adhesion and locomotion. A structured literature review methodology is employed, through which representative climbing robots are systematically categorized based on spine flexibility (rigid vs. flexible) and attachment mechanisms (adhesive, suction, claw-based). We analyze various motion control strategies, from hierarchical architectures to advanced neural algorithms, with a focus on central pattern generator (CPG)-based systems. By synthesizing current research and technological advancements, this paper provides a roadmap for developing more efficient, adaptive, and intelligent wall-climbing robots, addressing key challenges and future directions in the field. Full article
(This article belongs to the Special Issue Robotics: From Technologies to Applications)
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15 pages, 4471 KiB  
Article
Reconfigurable Intelligent Surfaces with Dual-Band Dual-Polarization Capabilities for Arbitrary Beam Synthesis Beyond Beam Steering
by Moosung Kim, Geun-Yeong Jun and Minseok Kim
Electronics 2025, 14(14), 2812; https://doi.org/10.3390/electronics14142812 - 12 Jul 2025
Viewed by 471
Abstract
A surface-wave-assisted, dual-band, circularly polarized reconfigurable intelligent surface is proposed that allows arbitrary beam-shaping capability within the [4.35 GHz–4.5 GHz] and [11.8 GHz–12.3 GHz] frequency bands. In particular, alongside the proposed physical design of the surface, a genetic algorithm-based design framework is introduced [...] Read more.
A surface-wave-assisted, dual-band, circularly polarized reconfigurable intelligent surface is proposed that allows arbitrary beam-shaping capability within the [4.35 GHz–4.5 GHz] and [11.8 GHz–12.3 GHz] frequency bands. In particular, alongside the proposed physical design of the surface, a genetic algorithm-based design framework is introduced to enable the synthesis of complex radiation patterns beyond simple beam steering. It is shown that the phase profiles obtained from the proposed optimization scheme naturally lead to the excitation of surface waves, which facilitate arbitrary beam shaping by satisfying the local power conservation condition between the normally impinging and arbitrarily reflected waves. To physically construct the proposed surface, cascaded symmetric unit cells are employed to facilitate circular polarization operation and realize dual-band operation. Furthermore, varactor diodes are incorporated into the design of unit cells so that the reflection phase can be independently and continuously tuned across the two frequency bands, with a tuning range of 300 degrees. The versatility of the proposed surface is demonstrated through design examples that achieve (i) unidirectional beam steering, (ii) multi-directional beam steering, and (iii) sector-beam formation within each frequency band. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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22 pages, 2113 KiB  
Article
Tracking Control of Quadrotor Micro Aerial Vehicles Using Efficient Nonlinear Model Predictive Control with C/GMRES Optimization on Resource-Constrained Microcontrollers
by Dong-Min Lee, Jae-Hong Jung, Yeon-Su Sim and Gi-Woo Kim
Electronics 2025, 14(14), 2775; https://doi.org/10.3390/electronics14142775 - 10 Jul 2025
Viewed by 283
Abstract
This study investigates the tracking control of quadrotor micro aerial vehicles using nonlinear model predictive control (NMPC), with primary emphasis on the implementation of a real-time embedded control system. Apart from the limited memory size, one of the critical challenges is the limited [...] Read more.
This study investigates the tracking control of quadrotor micro aerial vehicles using nonlinear model predictive control (NMPC), with primary emphasis on the implementation of a real-time embedded control system. Apart from the limited memory size, one of the critical challenges is the limited processor speed on resource-constrained microcontroller units (MCUs). This technical issue becomes critical particularly when the maximum allowed computation time for real-time control exceeds 0.01 s, which is the typical sampling time required to ensure reliable control performance. To reduce the computational burden for NMPC, we first derive a nonlinear quadrotor model based on the quaternion number system rather than formulating nonlinear equations using conventional Euler angles. In addition, an implicit continuation generalized minimum residual optimization algorithm is designed for the fast computation of the optimal receding horizon control input. The proposed NMPC is extensively validated through rigorous simulations and experimental trials using Crazyflie 2.1®, an open-source flying development platform. Owing to the more precise prediction of the highly nonlinear quadrotor model, the proposed NMPC demonstrates that the tracking performance outperforms that of conventional linear MPCs. This study provides a basis and comprehensive guidelines for implementing the NMPC of nonlinear quadrotors on resource-constrained MCUs, with potential extensions to applications such as autonomous flight and obstacle avoidance. Full article
(This article belongs to the Section Systems & Control Engineering)
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18 pages, 3941 KiB  
Article
Method of Collaborative UAV Deployment: Carrier-Assisted Localization with Low-Resource Precision Touchdown
by Krzysztof Kaliszuk, Artur Kierzkowski and Bartłomiej Dziewoński
Electronics 2025, 14(13), 2726; https://doi.org/10.3390/electronics14132726 - 7 Jul 2025
Viewed by 391
Abstract
This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a [...] Read more.
This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article
(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, 2nd Edition)
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14 pages, 1835 KiB  
Article
Cybersecurity Applications of Near-Term Large Language Models
by Casimer DeCusatis, Raymond Tomo, Aurn Singh, Emile Khoury and Andrew Masone
Electronics 2025, 14(13), 2704; https://doi.org/10.3390/electronics14132704 - 4 Jul 2025
Viewed by 500
Abstract
This paper examines near-term generative large language models (GenLLM) for cybersecurity applications. We experimentally study three common use cases, namely the use of GenLLM as a digital assistant, analysts for threat hunting and incident response, and analysts for access management in zero trust [...] Read more.
This paper examines near-term generative large language models (GenLLM) for cybersecurity applications. We experimentally study three common use cases, namely the use of GenLLM as a digital assistant, analysts for threat hunting and incident response, and analysts for access management in zero trust systems. In particular, we establish that one of the most common GenLLMs, ChatGPT, can pass cybersecurity certification exams for security fundamentals, hacking and penetration testing, and mobile device security, as well as perform competitively in cybersecurity ethics assessments. We also identify issues associated with hallucinations in these environments. The ability of ChatGPT to analyze network scans and security logs is also evaluated. Finally, we attempt to jailbreak ChatGPT in order to assess its application to access management systems. Full article
(This article belongs to the Special Issue Advanced Machine Learning, Pattern Recognition, and Deep Learning Technologies: Methodologies and Applications, 2nd Edition)
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18 pages, 56511 KiB  
Article
A CMOS Current Reference with Novel Temperature Compensation Based on Geometry-Dependent Threshold Voltage Effects
by Francesco Gagliardi, Andrea Ria, Massimo Piotto and Paolo Bruschi
Electronics 2025, 14(13), 2698; https://doi.org/10.3390/electronics14132698 - 3 Jul 2025
Viewed by 386
Abstract
Next-generation smart sensing devices necessitate on-chip integration of power-efficient reference circuits. The latters are required to provide other circuit blocks with highly reliable bias signals, even in the presence of temperature shifts and supply voltage disturbances, while draining a small fraction of the [...] Read more.
Next-generation smart sensing devices necessitate on-chip integration of power-efficient reference circuits. The latters are required to provide other circuit blocks with highly reliable bias signals, even in the presence of temperature shifts and supply voltage disturbances, while draining a small fraction of the overall power budget. In particular, it is especially challenging to design current references with enhanced robustness and efficiency; hence, thorough exploration of novel architectures and design approaches is needed for this type of circuits. In this work, we propose a novel CMOS-only current reference, achieving temperature compensation by exploiting geometry dependences of the threshold voltage (specifically, the reverse short-channel effect and the narrow-channel effect). This allows reaching first-order temperature compensation within a single current reference core. Implemented in 0.18 µm CMOS, a version of the proposed current reference designed to deliver 141 nA (with 377 nW of total power consumption) achieved an average temperature coefficient equal to 194 ppm/°C (from −20 °C to 80 °C) and an average line sensitivity of −0.017%/V across post-layout statistical Monte Carlo simulations. Based on such findings, the newly proposed design methodology stands out as a noteworthy solution to design robust current references for power-constrained mixed-signal systems-on-chip. Full article
(This article belongs to the Section Microelectronics)
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8 pages, 443 KiB  
Article
A Simple Open-Loop Control Method for Optimizing Manufacturing Control Knobs Using Artificial Intelligence
by Sarah Marzen
Electronics 2025, 14(13), 2676; https://doi.org/10.3390/electronics14132676 - 2 Jul 2025
Viewed by 292
Abstract
Manufacturing processes are collecting a wealth of data on how operational knobs affect process efficiency and product quality. Yet, optimizing the adjustment of these knobs using artificial intelligence remains a challenge. We propose a simple open-loop control method for optimizing a manufacturing process, [...] Read more.
Manufacturing processes are collecting a wealth of data on how operational knobs affect process efficiency and product quality. Yet, optimizing the adjustment of these knobs using artificial intelligence remains a challenge. We propose a simple open-loop control method for optimizing a manufacturing process, with pharmaceutical applications in mind, using artificial intelligence. The first step involves fitting a simple supervised learning model to manufacturing data—typically an artificial neural network with universal approximation guarantees—so that operational knobs (such as concentrations and temperatures) can be used to predict process efficiency (e.g., time-to-product) and/or product quality (e.g., yield or quality score). Assuming the supervised learning model works well, we can perform typical optimization procedures, like gradient ascent, to increase efficiency and product quality. We test this on a publicly available dataset for wine and suggest new values for wine parameters that should produce a higher-quality wine with a greater probability. The result is a setting for the manufacturing knobs that optimizes the product using basic artificial intelligence. This method can be further enhanced by incorporating more advanced and recent AI applications for anomaly and defect detection in manufacturing processes. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence (AI) for Engineering Applications)
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21 pages, 3136 KiB  
Article
Negative Expressions by Social Robots and Their Effects on Persuasive Behaviors
by Chinenye Augustine Ajibo, Carlos Toshinori Ishi and Hiroshi Ishiguro
Electronics 2025, 14(13), 2667; https://doi.org/10.3390/electronics14132667 - 1 Jul 2025
Viewed by 729
Abstract
The ability to effectively engineer robots with appropriate social behaviors that conform to acceptable social norms and with the potential to influence human behavior remains a challenging area in robotics. Given this, we sought to provide insights into “what can be considered a [...] Read more.
The ability to effectively engineer robots with appropriate social behaviors that conform to acceptable social norms and with the potential to influence human behavior remains a challenging area in robotics. Given this, we sought to provide insights into “what can be considered a socially appropriate and effective behavior for robots charged with enforcing social compliance of various magnitudes”. To this end, we investigate how social robots can be equipped with context-inspired persuasive behaviors for human–robot interaction. For this, we conducted three separate studies. In the first, we explored how the android robot “ERICA” can be furnished with negative persuasive behaviors using a video-based within-subjects design with N = 50 participants. Through a video-based experiment employing a mixed-subjects design with N = 98 participants, we investigated how the context of norm violation and individual user traits affected perceptions of the robot’s persuasive behaviors in the second study. Lastly, we investigated the effect of the robot’s appearance on the perception of its persuasive behaviors, considering two humanoids (ERICA and CommU) through a within-subjects design with N = 100 participants. Findings from these studies generally revealed that the robot could be equipped with appropriate and effective context-sensitive persuasive behaviors for human–robot interaction. Specifically, the more assertive behaviors (displeasure and anger) of the agent were found to be effective (p < 0.01) as a response to a situation of repeated violation after an initial positive persuasion. Additionally, the appropriateness of these behaviors was found to be influenced by the severity of the violation. Specifically, negative behaviors were preferred for persuasion in situations where the violation affects other people (p < 0.01), as in the COVID-19 adherence and smoking prohibition scenarios. Our results also revealed that the preference for the negative behaviors of the robots varied with users’ traits, specifically compliance awareness (CA), agreeableness (AG), and the robot’s embodiment. The current findings provide insights into how social agents can be equipped with appropriate and effective context-aware persuasive behaviors. It also suggests the relevance of a cognitive-based approach in designing social agents, particularly those deployed in sensitive social contexts. Full article
(This article belongs to the Special Issue Advancements in Robotics: Perception, Manipulation, and Interaction)
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23 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
Viewed by 479
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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22 pages, 2815 KiB  
Article
Multi-Layer Cryptosystem Using Reversible Cellular Automata
by George Cosmin Stănică and Petre Anghelescu
Electronics 2025, 14(13), 2627; https://doi.org/10.3390/electronics14132627 - 29 Jun 2025
Viewed by 390
Abstract
The growing need for adaptable and efficient hardware-based encryption methods has led to increased interest in unconventional models such as cellular automata (CA). This study presents the hardware design and the field programmable gate array (FPGA)-based implementation of a multi-layer symmetric block encryption [...] Read more.
The growing need for adaptable and efficient hardware-based encryption methods has led to increased interest in unconventional models such as cellular automata (CA). This study presents the hardware design and the field programmable gate array (FPGA)-based implementation of a multi-layer symmetric block encryption algorithm built on the principles of reversible cellular automata (RCA). The algorithm operates on 128-bit plaintext blocks processed over iterative rounds and integrates five RCA components, each assigned with specific transformation roles to ensure high data diffusion. A 256-bit secret key that governs the rule configuration yields a vast keyspace, significantly enhancing resistance to brute-force attacks. Key elements such as rule-based evolution, neighborhood radius, and hybrid cellular automata for random state generation are also integrated into the hardware logic. All cryptographic components, including initialization, encryption logic, and control, are built exclusively using CA, ensuring design consistency and low complexity. The cryptosystem takes advantage of the localized interactions and naturally parallel CA structure, which align with the architecture of FPGA devices, making them a suitable platform for implementing such encryption schemes. The results demonstrate the feasibility of deploying multi-layer RCA encryption schemes on reconfigurable devices and provide a viable path toward efficient and secure hardware-level encryption systems. Full article
(This article belongs to the Section Computer Science & Engineering)
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26 pages, 2296 KiB  
Article
Novel Design of Three-Channel Bilateral Teleoperation with Communication Delay Using Wave Variable Compensators
by Bo Yang, Chao Liu, Lei Zhang, Long Teng, Jiawei Tian, Siyuan Xu and Wenfeng Zheng
Electronics 2025, 14(13), 2595; https://doi.org/10.3390/electronics14132595 - 27 Jun 2025
Viewed by 387
Abstract
Bilateral teleoperation systems have been widely used in many fields of robotics, such as industrial manipulation, medical treatment, space exploration, and deep-sea operation. Delays in communication, known as an inevitable issues in practical implementation, especially for long-distance operations and challenging communication situations, can [...] Read more.
Bilateral teleoperation systems have been widely used in many fields of robotics, such as industrial manipulation, medical treatment, space exploration, and deep-sea operation. Delays in communication, known as an inevitable issues in practical implementation, especially for long-distance operations and challenging communication situations, can destroy system passivity and potentially lead to system failure. In this work, we address the time-delayed three-channel teleoperation design problem to guarantee system passivity and achieve high transparency simultaneously. To realize this, the three-channel teleoperation structure is first reformulated to form a two-channel-like architecture. Then, the wave variable technique is used to handle the communication delay and guarantee system passivity. Two novel wave variable compensators are proposed to achieve delay-minimized system transparency, and energy reservoirs are employed to monitor and regulate the energy introduced via these compensators to preserve overall system passivity. Numerical studies confirm that the proposed method significantly improves both kinematic and force tracking performance, achieving near-perfect correspondence with only a single-trip delay. Quantitative analyses using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Dynamic Time Warping (DTW) metrics show substantial error reductions compared to conventional wave variable and direct transmission-based three-channel teleoperation approaches. Moreover, statistical validation via the Mann–Whitney U test further confirms the significance of these improvements in system performance. The proposed design guarantees passivity with any passive human operator and environment without requiring restrictive assumptions, offering a robust and generalizable solution for teleoperation tasks with communication time delay. Full article
(This article belongs to the Special Issue Intelligent Perception and Control for Robotics)
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27 pages, 990 KiB  
Article
Developing IQJournalism: An Intelligent Advisor for Predicting the Perceived Quality in Greek News Articles
by Catherine Sotirakou, Panagiotis Germanakos, Anastasia Karampela and Constantinos Mourlas
Electronics 2025, 14(13), 2552; https://doi.org/10.3390/electronics14132552 - 24 Jun 2025
Viewed by 347
Abstract
Technological developments and the integration of social media into journalistic practices have transformed the media landscape, changing how information is gathered, produced, and shared. This evolution poses challenges, including the lack of clear guidelines and practical tools for ensuring the quality of digital [...] Read more.
Technological developments and the integration of social media into journalistic practices have transformed the media landscape, changing how information is gathered, produced, and shared. This evolution poses challenges, including the lack of clear guidelines and practical tools for ensuring the quality of digital news content. To address these issues, IQJournalism, an intelligent quality prediction advisor, was developed. This paper outlines the methodology for the development of IQJournalism, a platform that leverages advanced AI technologies to process Greek news articles and provide real-time editing recommendations on various dimensions, including language quality, subjectivity level, emotionality, entertainment, and social media engagement. First, a qualitative study was conducted through semi-structured, in-depth interviews with 20 experts, academic researchers and media professionals to identify indicators of perceived quality in journalism. These insights were then transformed into measurable features, which served as training data for explainable machine learning-based models for quality categorization and prediction. Finally, the IQJournalism platform was designed following a user-centered iterative process that included prototyping, testing, and redesigning. The innovative approach aims to serve as a valuable tool for improving journalistic quality, contributing to more reliable and engaging online news content. Importantly, the platform is not limited to the journalistic sector, but can also be used to optimize content in various areas, such as marketing, political, and strategic communication, supporting editors seeking to improve the quality and impact of their writing. Full article
(This article belongs to the Special Issue Advances in HCI Research)
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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 362
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 277
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
Cited by 1 | Viewed by 1031
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, 8777 KiB  
Article
Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
by Cristian Vidan, Andrei Avram, Lucian Grigorie, Grigore Cican and Mihai Nacu
Electronics 2025, 14(12), 2473; https://doi.org/10.3390/electronics14122473 - 18 Jun 2025
Viewed by 574
Abstract
This study investigates the electromagnetic performance of two carbon fiber monopole antennas integrated into a UAV copter frame, with emphasis on design adaptation, impedance matching, and propagation behavior. A comprehensive experimental campaign was conducted to characterize key parameters such as center frequency, bandwidth, [...] Read more.
This study investigates the electromagnetic performance of two carbon fiber monopole antennas integrated into a UAV copter frame, with emphasis on design adaptation, impedance matching, and propagation behavior. A comprehensive experimental campaign was conducted to characterize key parameters such as center frequency, bandwidth, gain, VSWR, and S11. Both antennas exhibited dual-band resonance at approximately 381 MHz and 1.19 GHz, each achieving a 500 MHz bandwidth where VSWR ≤ 2. The modified antenna achieved a minimum reflection coefficient of –14.6 dB and a VSWR of 1.95 at 381.45 MHz, closely aligning with theoretical predictions. Gain deviations between measured (0.15–0.19 dBi) and calculated (0.19 dBi) values remained within 0.04 dB, while received power fluctuations did not exceed 1.3 dB under standard test conditions despite the composite material’s finite conductivity. Free-space link-budget tests at 0.5 m and 2 m of separation revealed received-power deviations of 0.9 dB and 1.3 dB, respectively, corroborating the Friis model. Radiation pattern measurements in both azimuth and elevation planes confirmed good directional behavior, with minor side lobe variations, where Antenna A displayed variations between 270° and 330° in azimuth, while Antenna B remained more uniform. A 90° polarization mismatch led to a 15 dBm signal drop, and environmental obstructions caused losses of 9.4 dB, 12.6 dB, and 18.3 dB, respectively, demonstrating the system’s sensitivity to alignment and surroundings. Additionally, signal strength changes observed in a Two-Ray propagation setup validated the importance of ground reflection effects. Small-scale fading analysis at 5 m LOS indicated a Rician-distributed envelope with mean attenuation of 53.96 dB, σdB = 5.57 dB, and a two-sigma interval spanning 42.82 dB to 65.11 dB; the fitted K-factor confirmed the dominance of the LOS component. The findings confirm that carbon fiber UAV frames can serve as effective directional antenna supports, providing proper alignment and tuning. These results support the future integration of lightweight, structure-embedded antennas in UAV systems, with potential benefits in communication efficiency, stealth, and design simplification. Full article
(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, 2nd Edition)
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23 pages, 3277 KiB  
Article
Behaviour-Based Digital Twin for Electro-Pneumatic Actuator: Modelling, Simulation, and Validation Through Virtual Commissioning
by Roman Ruzarovsky, Tibor Horak, Richard Skypala, Roman Zelník, Martin Csekei, Ján Šido, Eduard Nemlaha and Michal Kopček
Electronics 2025, 14(12), 2434; https://doi.org/10.3390/electronics14122434 - 14 Jun 2025
Viewed by 691
Abstract
A digital twin is an effective tool for the design, testing, and validation of control strategies for electro-pneumatic actuators in industrial automation. This study presents and compares three different digital twin models of a pneumatic cylinder with varying levels of physical fidelity—from basic [...] Read more.
A digital twin is an effective tool for the design, testing, and validation of control strategies for electro-pneumatic actuators in industrial automation. This study presents and compares three different digital twin models of a pneumatic cylinder with varying levels of physical fidelity—from basic discrete control, through analogue control without pneumatic dynamics, to a complex model simulating pressure, friction, and airflow. The experiments were conducted using the Siemens NX Mechatronics Concept Designer, integrated with the SIMIT emulation platform and a PLC control system via the standardized OPC UA protocol. The main objective was to evaluate simulation accuracy, model flexibility for testing various control strategies, and the ability of the digital twin to reflect changes in PLC algorithms. The results showed that while simple models are suitable for verifying basic logic, only advanced models can realistically replicate the dynamic behaviour of pneumatic systems, including delay phases and pressure influence. A comparison with the experimental study by Jiménez confirmed a strong correlation between the simulated and actual actuator behaviour. In future work, the developed control algorithm will be connected to a physical cylinder to further validate the models and refine control strategies under real-world conditions. Full article
(This article belongs to the Special Issue Digital Twinning: Trends Challenging the Future)
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44 pages, 5969 KiB  
Article
iRisk: Towards Responsible AI-Powered Automated Driving by Assessing Crash Risk and Prevention
by Naomi Y. Mbelekani and Klaus Bengler
Electronics 2025, 14(12), 2433; https://doi.org/10.3390/electronics14122433 - 14 Jun 2025
Viewed by 765
Abstract
Advanced technology systems and neuroelectronics for crash risk assessment and anticipation may be a promising field for advancing responsible automated driving on urban roads. In principle, there are prospects of an artificially intelligent (AI)-powered automated vehicle (AV) system that tracks the degree of [...] Read more.
Advanced technology systems and neuroelectronics for crash risk assessment and anticipation may be a promising field for advancing responsible automated driving on urban roads. In principle, there are prospects of an artificially intelligent (AI)-powered automated vehicle (AV) system that tracks the degree of perceived crash risk (as either low, mid, or high) and perceived safety. As a result, communicating (verbally or nonverbally) this information to the user based on human factor aspects should be reflected. As humans and vehicle automation systems are prone to error, we need to design advanced information and communication technologies that monitor risks and act as a mediator when necessary. One possible approach is towards designing a crash risk classification and management system. This would be through responsible AI that monitors the user’s mental states associated with risk-taking behaviour and communicates this information to the user, in conjunction with the driving environment and AV states. This concept is based on a literature review and industry experts’ perspectives on designing advanced technology systems that support users in preventing crash risk encounters due to long-term effects. Equally, learning strategies for responsible automated driving on urban roads were designed. In a sense, this paper offers the reader a meticulous discussion on conceptualising a safety-inspired ‘ergonomically responsible AI’ concept in the form of an intelligent risk assessment system (iRisk) and an AI-powered Risk information Human–Machine Interface (AI rHMI) as a useful concept for responsible automated driving and safe human–automation interaction. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications in Computer Science and Engineering: 2nd Edition)
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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 301
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 728
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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23 pages, 1022 KiB  
Article
Optimizing Local Explainability in Robotic Grasp Failure Prediction
by Cagla Acun, Ali Ashary, Dan O. Popa and Olfa Nasraoui
Electronics 2025, 14(12), 2363; https://doi.org/10.3390/electronics14122363 - 9 Jun 2025
Cited by 1 | Viewed by 453
Abstract
This paper presents a local explainability mechanism for robotic grasp failure prediction that enhances machine learning transparency at the instance level. Building upon pre hoc explainability concepts, we develop a neighborhood-based optimization approach that leverages the Jensen–Shannon divergence to ensure fidelity between predictor [...] Read more.
This paper presents a local explainability mechanism for robotic grasp failure prediction that enhances machine learning transparency at the instance level. Building upon pre hoc explainability concepts, we develop a neighborhood-based optimization approach that leverages the Jensen–Shannon divergence to ensure fidelity between predictor and explainer models at a local level. Unlike traditional post hoc methods such as LIME, our local in-training explainability framework directly optimizes the predictor model during training, then fine-tunes the pre-trained explainer for each test instance within its local neighborhood. Experiments with Shadow’s Smart Grasping System demonstrate that our approach maintains black-box-level prediction accuracy while providing faithful local explanations with significantly improved point fidelity, neighborhood fidelity, and stability compared to LIME. In addition, our approach addresses the critical need for transparent and reliable grasp failure prediction systems by providing explanations consistent with the model’s local behavior, thereby enhancing trust in autonomous robotic grasping systems. Our analysis also shows that the proposed framework generates explanations more efficiently, requiring substantially less computational time than post hoc methods. Through a detailed examination of neighborhood size effects and explanation quality, we further demonstrate how users can select appropriate local neighborhoods to balance explanation quality and computational cost. Full article
(This article belongs to the Special Issue Robotics: From Technologies to Applications)
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24 pages, 4340 KiB  
Article
Real-Time Mobile Application for Translating Portuguese Sign Language to Text Using Machine Learning
by Gonçalo Fonseca, Gonçalo Marques, Pedro Albuquerque Santos and Rui Jesus
Electronics 2025, 14(12), 2351; https://doi.org/10.3390/electronics14122351 - 8 Jun 2025
Cited by 1 | Viewed by 1209
Abstract
Communication barriers between deaf and hearing individuals present significant challenges to social inclusion, highlighting the need for effective technological aids. This study aimed to bridge this gap by developing a mobile system for the real-time translation of Portuguese Sign Language (LGP) alphabet gestures [...] Read more.
Communication barriers between deaf and hearing individuals present significant challenges to social inclusion, highlighting the need for effective technological aids. This study aimed to bridge this gap by developing a mobile system for the real-time translation of Portuguese Sign Language (LGP) alphabet gestures into text, addressing a specific technological void for LGP. The core of the solution is a mobile application integrating two distinct machine learning approaches trained on a custom LGP dataset: firstly, a Convolutional Neural Network (CNN) optimized with TensorFlow Lite for efficient, on-device image classification, enabling offline use; secondly, a method utilizing MediaPipe for hand landmark extraction from the camera feed, with classification performed by a server-side Multilayer Perceptron (MLP). Evaluation tests confirmed that both approaches could recognize LGP alphabet gestures with good accuracy (F1-scores of approximately 76% for the CNN and 77% for the MediaPipe+MLP) and processing speed (1 to 2 s per gesture on high-end devices using the CNN and 3 to 5 s under typical network conditions using MediaPipe+MLP), facilitating efficient real-time translation, though performance trade-offs regarding speed versus accuracy under different conditions were observed. The implementation of this dual-method system provides crucial flexibility, adapting to varying network conditions and device capabilities, and offers a scalable foundation for future expansion to include more complex gestures. This work delivers a practical tool that may contribute to improve communication accessibility and the societal integration of the deaf community in Portugal. Full article
(This article belongs to the Special Issue Virtual Reality Applications in Enhancing Human Lives)
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18 pages, 3282 KiB  
Article
Hardware Accelerator for Approximation-Based Softmax and Layer Normalization in Transformers
by Raehyeong Kim, Dayoung Lee, Jinyeol Kim, Joungmin Park and Seung Eun Lee
Electronics 2025, 14(12), 2337; https://doi.org/10.3390/electronics14122337 - 7 Jun 2025
Viewed by 1625
Abstract
Transformer-based models have achieved remarkable success across various AI tasks, but their growing complexity has led to significant computational and memory demands. While most optimization efforts have focused on linear operations such as matrix multiplications, non-linear functions like Softmax and layer normalization (LayerNorm) [...] Read more.
Transformer-based models have achieved remarkable success across various AI tasks, but their growing complexity has led to significant computational and memory demands. While most optimization efforts have focused on linear operations such as matrix multiplications, non-linear functions like Softmax and layer normalization (LayerNorm) are increasingly dominating inference latency, especially for long sequences and high-dimensional inputs. To address this emerging bottleneck, we present a hardware accelerator that jointly approximates these non-linear functions using piecewise linear approximation for the exponential in Softmax and Newton–Raphson iteration for the square root in LayerNorm. The proposed unified architecture dynamically switches operation modes while reusing hardware resources. The proposed accelerator was implemented on a Xilinx VU37P FPGA and evaluated with BERT and GPT-2 models. Experimental results demonstrate speedups of up to 7.6× for Softmax and 2.0× for LayerNorm, while maintaining less than 1% accuracy degradation on classification tasks with conservative approximation settings. However, generation tasks showed greater sensitivity to approximation, underscoring the need for task-specific tuning. Full article
(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)
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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 967
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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27 pages, 3471 KiB  
Article
Control of a Dumper Vehicle with a Trailer Using Partial Feedback Linearization
by Jaume Franch, Jose-Manuel Rodriguez-Fortun and Rafael Herguedas
Electronics 2025, 14(11), 2293; https://doi.org/10.3390/electronics14112293 - 4 Jun 2025
Viewed by 467
Abstract
The control of vehicles towing trailers is of significant interest to industry due to their wide-ranging applications across various sectors. Trailers play essential roles in logistics, mining, and other fields. This study focuses on the control of a dumper with a trailer specifically [...] Read more.
The control of vehicles towing trailers is of significant interest to industry due to their wide-ranging applications across various sectors. Trailers play essential roles in logistics, mining, and other fields. This study focuses on the control of a dumper with a trailer specifically used for the monitoring of terrain stability in mining operations. The trailer is equipped with a radar system for detecting potential ground shifts that could jeopardize fieldwork safety. While numerous studies have addressed the control of Ackerman vehicles and trailers, this dumper presents a unique challenge due to its rear-axle steering mechanism. Due to this configuration, which has not been extensively studied in the literature, although the differential flatness of the system is proven, computation of the flat outputs leads to a system of partial differential equations that cannot be solved analytically. For this reason, this paper examines partial feedback linearization to facilitate control and proposes a solution for trajectory tracking that also stabilizes jack-knifing tendencies between the vehicle and trailer. The designed control system was successfully validated in a virtual environment. Full article
(This article belongs to the Special Issue Control and Design of Intelligent Robots)
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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 788
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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18 pages, 864 KiB  
Article
Gamification and User Experience in Fake News Detection on Tourism in Primary Education
by Androniki Koutsikou and Nikos Antonopoulos
Electronics 2025, 14(11), 2200; https://doi.org/10.3390/electronics14112200 - 29 May 2025
Viewed by 822
Abstract
The concept of gaming is universal and familiar to students worldwide. Gamification involves integrating game elements and mechanics into non-game environments, making it a valuable tool for enhancing user engagement and motivation in Human–Computer Interaction. This approach is particularly valuable for primary school [...] Read more.
The concept of gaming is universal and familiar to students worldwide. Gamification involves integrating game elements and mechanics into non-game environments, making it a valuable tool for enhancing user engagement and motivation in Human–Computer Interaction. This approach is particularly valuable for primary school education. Students are exposed to a great deal of information daily. This contains several inaccuracies and misinformation regarding the tourism sector. Our research is being conducted as part of the Computer Science course to help students aged 9 to 12 understand the concept of fake news in the context of tourism. Bilingual students brought valuable perspectives to the classroom, especially during discussions about cultural representation and media bias. Incorporating intercultural communication into learning activities helped these students enhance their language and critical thinking skills while navigating various cultural contexts. We used an application with gamification elements to engage the students and enhance their learning experience. We evaluated user experience and usability using quantitative methods through questionnaires. The results revealed that students found the application easy to use and had a positive experience with it. This study assessed the effectiveness of the educational intervention by comparing pre-test and post-test scores on a Likert scale on four key questions. The intervention was largely successful in enhancing student outcomes. These findings suggest that participants not only maintained stable information literacy behaviors over time but also showed improvements in critical evaluation and skepticism. Full article
(This article belongs to the Section Electronic Multimedia)
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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 866
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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17 pages, 1941 KiB  
Article
MMER-LMF: Multi-Modal Emotion Recognition in Lightweight Modality Fusion
by Eun-Hee Kim, Myung-Jin Lim and Ju-Hyun Shin
Electronics 2025, 14(11), 2139; https://doi.org/10.3390/electronics14112139 - 24 May 2025
Viewed by 711
Abstract
Recently, multimodal approaches that combine various modalities have been attracting attention to recognizing emotions more accurately. Although multimodal fusion delivers strong performance, it is computationally intensive and difficult to handle in real time. In addition, there is a fundamental lack of large-scale emotional [...] Read more.
Recently, multimodal approaches that combine various modalities have been attracting attention to recognizing emotions more accurately. Although multimodal fusion delivers strong performance, it is computationally intensive and difficult to handle in real time. In addition, there is a fundamental lack of large-scale emotional datasets for learning. In particular, Korean emotional datasets have fewer resources available than English-speaking datasets, thereby limiting the generalization capability of emotion recognition models. In this study, we propose a more lightweight modality fusion method, MMER-LMF, to overcome the lack of Korean emotional datasets and improve emotional recognition performance while reducing model training complexity. To this end, we suggest three algorithms that fuse emotion scores based on the reliability of each model, including text emotion scores extracted using a pre-trained large-scale language model and video emotion scores extracted based on a 3D CNN model. Each algorithm showed similar classification performance except for slight differences in disgust emotion performance with confidence-based weight adjustment, correlation coefficient utilization, and the Dempster–Shafer Theory-based combination method. The accuracy was 80% and the recall was 79%, which is higher than 58% using text modality and 72% using video modality. This is a superior result in terms of learning complexity and performance compared to previous studies using Korean datasets. Full article
(This article belongs to the Special Issue Modeling of Multimodal Speech Recognition and Language Processing)
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12 pages, 870 KiB  
Article
An Improved Strategy for Data Layout in Convolution Operations on FPGA-Based Multi-Memory Accelerators
by Yongchang Wang and Hongzhi Zhao
Electronics 2025, 14(11), 2127; https://doi.org/10.3390/electronics14112127 - 23 May 2025
Viewed by 491
Abstract
Convolutional Neural Networks (CNNs) are fundamental to modern AI applications but often suffer from significant memory bottlenecks due to non-contiguous access patterns during convolution operations. Although previous work has optimized data layouts at the software level, hardware-level solutions for multi-memory accelerators remain underexplored. [...] Read more.
Convolutional Neural Networks (CNNs) are fundamental to modern AI applications but often suffer from significant memory bottlenecks due to non-contiguous access patterns during convolution operations. Although previous work has optimized data layouts at the software level, hardware-level solutions for multi-memory accelerators remain underexplored. In this paper, we propose a hardware-level approach to mitigate memory row conflicts in FPGA-based CNN accelerators. Specifically, we introduce a dynamic DDR controller generated using Vivado 2019.1, which optimizes feature map allocation across memory banks and operates in conjunction with a multi-memory architecture to enable parallel access. Our method reduces row conflicts by up to 21% and improves throughput by 17% on the KCU1500 FPGA, with validation across YOLOv2, VGG16, and AlexNet. The key innovation lies in the layer-specific address mapping strategy and hardware-software co-design, providing a scalable and efficient solution for CNN inference across both edge and cloud platforms. Full article
(This article belongs to the Special Issue FPGA-Based Reconfigurable Embedded Systems)
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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 6413
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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18 pages, 11805 KiB  
Article
VL-PAW: A Vision–Language Dataset for Pear, Apple and Weed
by Gwang-Hyun Yu, Le Hoang Anh, Dang Thanh Vu, Jin Lee, Zahid Ur Rahman, Heon-Zoo Lee, Jung-An Jo and Jin-Young Kim
Electronics 2025, 14(10), 2087; https://doi.org/10.3390/electronics14102087 - 21 May 2025
Viewed by 638
Abstract
Vision–language models (VLMs) have achieved remarkable success in natural image domains, yet their potential remains underexplored in agriculture due to the lack of high-quality, joint image–text datasets. To address this limitation, we introduce VL-PAW (Vision–Language dataset for Pear, [...] Read more.
Vision–language models (VLMs) have achieved remarkable success in natural image domains, yet their potential remains underexplored in agriculture due to the lack of high-quality, joint image–text datasets. To address this limitation, we introduce VL-PAW (Vision–Language dataset for Pear, Apple, and Weed), a dataset comprising 3.9 K image–caption pairs for two key agricultural tasks: weed species classification and fruit inspection. We fine-tune the CLIP model on VL-PAW and gain several insights. First, the model demonstrates impressive zero-shot performance, achieving 98.21% accuracy in classifying coarse labels. Second, for fine-grained categories, the vision–language model outperforms vision-only models in both few-shot settings and entire dataset training (1-shot: 56.79%; 2-shot: 72.82%; 3-shot: 74.49%; 10-shot: 83.85%). Third, using intuitive captions enhances fine-grained fruit inspection performance compared to using class names alone. These findings demonstrate the applicability of VLMs in future agricultural querying systems. Full article
(This article belongs to the Collection Image and Video Analysis and Understanding)
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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
Cited by 1 | Viewed by 656
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 721
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 422
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 592
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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17 pages, 2738 KiB  
Article
Modeling of Phase-Interpolator-Based Clock and Data Recovery for High-Speed PAM-4 Serial Interfaces
by Alessio Cortiula, Davide Menin, Andrea Bandiziol, Francesco Driussi and Pierpaolo Palestri
Electronics 2025, 14(10), 1979; https://doi.org/10.3390/electronics14101979 - 13 May 2025
Viewed by 619
Abstract
We have employed a time-domain behavioral simulator to analyze how different design options for bang-bang Clock and Data Recovery (CDR) impact the Jitter Tolerance (JTOL) performance of High-Speed Serial Interfaces (HSSIs) with PAM-4 signaling. The simulator includes the effect of Inter-Symbol Interference (ISI) [...] Read more.
We have employed a time-domain behavioral simulator to analyze how different design options for bang-bang Clock and Data Recovery (CDR) impact the Jitter Tolerance (JTOL) performance of High-Speed Serial Interfaces (HSSIs) with PAM-4 signaling. The simulator includes the effect of Inter-Symbol Interference (ISI) due to the transmission channel, various equalization schemes and a detailed description of the CDR architecture. Many design options have been investigated, with particular focus on transition filtering and on the algorithm to identify the Early/Late (E/L) information from data and edge samples after deserialization. It has been found that if majority voting is employed to derive a single set of E/L information from an array of phase detectors working on deserialized data and edges, the different filtering strategies provide the same JTOL, meaning that one can avoid transition filtering and furthermore use a single edge sampler with a zero threshold, significantly simplifying the CDR architecture. Instead, if summation of the E/L information from deserialized data and edges is performed, the decision to use one or three thresholds for the edge sampling and the choice of whether to implement transition filtering both impact JTOL; however, better performance is achieved under these conditions than when employing majority voting on the deserialized E/L signals. Full article
(This article belongs to the Section Microelectronics)
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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 541
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 598
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
Cited by 1 | Viewed by 680
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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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 1410
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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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 407
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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14 pages, 4752 KiB  
Article
An Ultra-Wideband Low-Noise Amplifier with a New Cross-Coupling Noise-Canceling Technique for 28 nm CMOS Technology
by Yuanping Cui, Kaixue Ma and Kejie Hu
Electronics 2025, 14(10), 1904; https://doi.org/10.3390/electronics14101904 - 8 May 2025
Viewed by 875
Abstract
This paper presents an ultra-wideband low-noise amplifier (LNA) with a new cross-coupling noise-canceling technique for 28 nm CMOS technology. The entire LNA contains two stages. The first stage employs inductively coupled Gm-boosted technology, while the second stage is a novel asymmetric cross-coupling noise-canceling [...] Read more.
This paper presents an ultra-wideband low-noise amplifier (LNA) with a new cross-coupling noise-canceling technique for 28 nm CMOS technology. The entire LNA contains two stages. The first stage employs inductively coupled Gm-boosted technology, while the second stage is a novel asymmetric cross-coupling noise-canceling structure (ACCNCS). Through the introduction of these two key techniques, the LNA achieves balanced performance across a relative bandwidth of 56%. Input/output/inter-stage impedance matching uses a transformer-based network with series-parallel combinations of inductors and capacitors. The LNA is designed in a 28 nm CMOS process with a chip core area of 335 × 665 µm2. The operating frequency range is 26–46 GHz. Post-layout simulation results show that the peak gain of the LNA is 12.6 dB, and the noise figure is between 2.9 and 4.2 dB across the wideband range. At a center frequency of 36 GHz with a supply voltage (VDD) of 0.9 V, the input 1 dB compression point (IP1dB) is −7.6 dBm, while the power consumption is 22 mW. Full article
(This article belongs to the Section Microelectronics)
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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
Cited by 1 | Viewed by 613
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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