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Editor’s Choice Articles

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

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25 pages, 2352 KB  
Article
High-Frequency Link Analysis of Enhanced Power Factor in Active Bridge-Based Multilevel Converters
by Morteza Dezhbord, Fazal Ur Rehman, Amir Ghasemian and Carlo Cecati
Electronics 2025, 14(17), 3551; https://doi.org/10.3390/electronics14173551 - 6 Sep 2025
Viewed by 486
Abstract
Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, [...] Read more.
Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, power factor performance, and power loss characteristics. A high-frequency link analysis framework is developed to characterize voltage, current, and power transfer waveforms, providing insight into reactive power generation and its impact on overall efficiency. By introducing a modulation-based control approach, the proposed converters significantly reduce circulating currents and enhance the power factor, particularly under varying phase-shift conditions. Compared to quadruple active bridge topologies, the discussed multilevel architectures offer reduced transformer complexity and improved power quality, making them suitable for demanding applications such as electric vehicles and aerospace systems. Full article
(This article belongs to the Special Issue Advanced DC-DC Converter Topology Design, Control, Application)
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24 pages, 6566 KB  
Article
Milepost-to-Vehicle Monocular Depth Estimation with Boundary Calibration and Geometric Optimization
by Enhua Zhang, Tao Ma, Handuo Yang, Jiaqi Li, Zhiwei Xie and Zheng Tong
Electronics 2025, 14(17), 3446; https://doi.org/10.3390/electronics14173446 - 29 Aug 2025
Viewed by 446
Abstract
Milepost-assisted positioning estimates the distance between a vehicle-mounted camera and a milepost as a reference position for autonomous driving. However, the accuracy of monocular metric depth estimation is compromised by camera installation angle, milepost inclination, and image occlusions. To solve the problems, this [...] Read more.
Milepost-assisted positioning estimates the distance between a vehicle-mounted camera and a milepost as a reference position for autonomous driving. However, the accuracy of monocular metric depth estimation is compromised by camera installation angle, milepost inclination, and image occlusions. To solve the problems, this paper proposes a two-stage monocular metric depth estimation with boundary calibration and geometric optimization. In the first stage, the method detects a milepost in one frame of a video and computes a metric depth map of the milepost region by a monocular depth estimation model. In the second stage, in order to mitigate the effects of road surface undulation and occlusion, we propose geometric optimization with road plane fitting and a multi-frame fusion strategy. An experiment using pairwise images and depth measurement demonstrates that the proposed method exceeds other state-of-the-art methods with an absolute relative error of 0.055 and root mean square error of 3.421. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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28 pages, 17193 KB  
Article
Radio Propagation Characteristics in Several Application Scenarios at 285 GHz Terahertz Band
by Jinhyung Oh and Jong Ho Kim
Electronics 2025, 14(17), 3419; https://doi.org/10.3390/electronics14173419 - 27 Aug 2025
Viewed by 333
Abstract
In this paper, we have derived R.M.S. delay spread characteristics and models according to the influence of antenna beam width in the mobile kiosk data download environment, the inter-rack communication environment in the data center, the intra-device communication environment, and the experimental laboratory [...] Read more.
In this paper, we have derived R.M.S. delay spread characteristics and models according to the influence of antenna beam width in the mobile kiosk data download environment, the inter-rack communication environment in the data center, the intra-device communication environment, and the experimental laboratory measurement environment scenario in the 275 GHz to 295 GHz bands. The measurement system used in this paper used a vector network analyzer and a frequency expander to derive the statistical characteristics of terahertz frequency band signals, and used antennas with different beamwidths for measurement. It is confirmed that the distribution of delay spread varies depending on the beam width of the antenna used for measurement and the type of measurement scenario. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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36 pages, 14352 KB  
Article
NRXR-ID: Two-Factor Authentication (2FA) in VR Using Near-Range Extended Reality and Smartphones
by Aiur Nanzatov, Lourdes Peña-Castillo and Oscar Meruvia-Pastor
Electronics 2025, 14(17), 3368; https://doi.org/10.3390/electronics14173368 - 25 Aug 2025
Viewed by 455
Abstract
Two-factor authentication (2FA) has become widely adopted as an efficient and secure way of validating someone’s identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their [...] Read more.
Two-factor authentication (2FA) has become widely adopted as an efficient and secure way of validating someone’s identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their real-world surroundings. We present NRXR-ID, a technique to implement two-factor authentication while using extended reality systems and smartphones. The proposed method allows users to complete an authentication challenge using their smartphones without removing their HMD. We performed a user study in which we explored four types of challenges for users, including a novel checkers-style challenge. Users responded to these challenges under three different configurations, including a technique that uses a smartphone to support gaze-based selection without the use of a VR controller. A 4 × 3 within-subjects design allowed us to study all of the proposed variations. We collected performance metrics along with user experience questionnaires containing subjective impressions from thirty participants. Results suggest that the checkers-style visual matching challenge was the most preferred option, followed by the challenge involving entering a digital PIN submitted via the smartphone. Participants were fastest at solving the digital PIN challenge, with an average of 12.35 ± 5 s, followed by the Checkers challenge with 13.85 ± 5.29 s, then the CAPTCHA-style challenge with 14.36 ± 7.5 s, whereas the alphanumeric password took almost twice as long, averaging 32.71 ± 16.44 s. The checkers-style challenge performed consistently across all conditions with no significant differences (p = 0.185), making it robust to different implementation choices. Full article
(This article belongs to the Special Issue Emerging Technologies in Augmented, Virtual, and Mixed Reality: Advancing Human Experience in Digital and Physical Environments)
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21 pages, 5469 KB  
Article
Radio Frequency Passive Tagging System Enabling Object Recognition and Alignment by Robotic Hands
by Armin Gharibi, Mahmoud Tavakoli, André F. Silva, Filippo Costa and Simone Genovesi
Electronics 2025, 14(17), 3381; https://doi.org/10.3390/electronics14173381 - 25 Aug 2025
Viewed by 1130
Abstract
Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch [...] Read more.
Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article
(This article belongs to the Special Issue Pushing Boundaries: Innovations in Robotics, Artificial Intelligence, and Extended Reality)
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19 pages, 1127 KB  
Article
Movable Wireless Sensor-Enabled Waterway Surveillance with Enhanced Coverage Using Multi-Layer Perceptron and Reinforced Learning
by Minsoo Kim and Hyunbum Kim
Electronics 2025, 14(16), 3295; https://doi.org/10.3390/electronics14163295 - 19 Aug 2025
Viewed by 329
Abstract
Waterway networking environments present unique challenges due to their dynamic nature, including vessel movement, water flow, and varying water quality. These challenges render traditional static surveillance systems inadequate for effective monitoring. This study proposes a novel wireless sensor-enabled surveillance and monitoring framework tailored [...] Read more.
Waterway networking environments present unique challenges due to their dynamic nature, including vessel movement, water flow, and varying water quality. These challenges render traditional static surveillance systems inadequate for effective monitoring. This study proposes a novel wireless sensor-enabled surveillance and monitoring framework tailored to waterway conditions, integrating a two-phase approach with a Movement Phase and a Deployment Phase. In the Movement Phase, a Multi-Layer Perceptron (MLP) guides sensors efficiently toward a designated target area, minimizing travel time and computational complexity. Subsequently, the Deployment Phase utilizes reinforcement learning (RL) to arrange sensors within the target area, optimizing coverage while minimizing overlap between sensing regions. By addressing the unique requirements of waterways, the proposed framework ensures both efficient sensor mobility and resource utilization. Experimental evaluations demonstrate the framework’s effectiveness in achieving high coverage and minimal overlap, with comparable performance to traditional clustering algorithms such as K-Means. The results confirm that the proposed approach achieves flexible, scalable, and computationally efficient monitoring tailored to waterway environments. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
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24 pages, 1219 KB  
Article
Asset Discovery in Critical Infrastructures: An LLM-Based Approach
by Luigi Coppolino, Antonio Iannaccone, Roberto Nardone and Alfredo Petruolo
Electronics 2025, 14(16), 3267; https://doi.org/10.3390/electronics14163267 - 17 Aug 2025
Viewed by 539
Abstract
Asset discovery in critical infrastructures, and in particular within industrial control systems, constitutes a fundamental cybersecurity function. Ensuring accurate and comprehensive asset visibility while maintaining operational continuity represents an ongoing challenge. Existing methodologies rely on deterministic tools that apply fixed fingerprinting strategies and [...] Read more.
Asset discovery in critical infrastructures, and in particular within industrial control systems, constitutes a fundamental cybersecurity function. Ensuring accurate and comprehensive asset visibility while maintaining operational continuity represents an ongoing challenge. Existing methodologies rely on deterministic tools that apply fixed fingerprinting strategies and lack the capacity for contextual reasoning. Such approaches often fail to adapt to the heterogeneous architectures and dynamic configurations characteristic of modern critical infrastructures. This work introduces an architecture based on a Mixture of Experts model designed to overcome these limitations. The proposed framework combines multiple specialized modules to perform automated asset discovery, integrating passive and active software probes with physical sensors. This design enables the system to adapt to different operational scenarios and to classify discovered assets according to functional and security-relevant attributes. A proof-of-concept implementation is also presented, along with experimental results that demonstrate the feasibility of the proposed approach. The outcomes indicate that our LLM-based approach can support the development of non-intrusive asset management solutions, strengthening the cybersecurity posture of critical infrastructure systems. Full article
(This article belongs to the Special Issue Advanced Monitoring of Smart Critical Infrastructures)
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20 pages, 421 KB  
Article
RISC-V Address-Encoded Byte Order Extension
by David Guerrero, Jorge Juan-Chico, German Cano-Quiveu, Paulino Ruiz-de-Clavijo, Julian Viejo and Enrique Ostua
Electronics 2025, 14(16), 3257; https://doi.org/10.3390/electronics14163257 - 16 Aug 2025
Viewed by 284
Abstract
In some cases, computer systems need to handle both little-endian and big-endian data, even if it differs from their native endianness. This paper proposes an RISC-V extension that makes it possible to remove the overhead introduced when dealing with foreign-endian data. It can [...] Read more.
In some cases, computer systems need to handle both little-endian and big-endian data, even if it differs from their native endianness. This paper proposes an RISC-V extension that makes it possible to remove the overhead introduced when dealing with foreign-endian data. It can be implemented with little engineering effort and a negligible impact on performance and hardware resources. Our results demonstrate that the extension can reduce the overhead of foreign-endian data processing by 62% or 37% compared to software-based solutions that use the base Instruction Set Architecture (ISA) or current bit manipulation extensions, respectively. This performance boost has the potential to benefit both new and legacy software once compiler and library support have been put in place. Full article
(This article belongs to the Special Issue High-Performance Computer Architecture)
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33 pages, 7587 KB  
Article
A Fractional-Order State Estimation Method for Supercapacitor Energy Storage
by Arsalan Rasoolzadeh, Sayed Amir Hashemi and Majid Pahlevani
Electronics 2025, 14(16), 3231; https://doi.org/10.3390/electronics14163231 - 14 Aug 2025
Viewed by 383
Abstract
Supercapacitors (SCs) are emerging as a dependable energy storage technology in industrial applications, valued for their high power output and exceptional longevity. In high-power applications, SCs are not used as single cells but are configured in a series–parallel combination to form a bank. [...] Read more.
Supercapacitors (SCs) are emerging as a dependable energy storage technology in industrial applications, valued for their high power output and exceptional longevity. In high-power applications, SCs are not used as single cells but are configured in a series–parallel combination to form a bank. Accurate state-of-charge estimation is essential for effective energy management in power systems employing SC banks. This work presents a novel state estimation approach for SC banks. First, a dynamic model of an SC bank is derived by applying a fractional-order Thévenin equivalent circuit to a single-cell SC. Then, an observability analysis is conducted, which reveals that the system is empirically weakly observable. This is the fundamental challenge for state-of-the-art observers to robustly perform state estimation. To address this challenge, an implicitly regularized observer is developed based on generalized parameter estimation techniques. The performance of the proposed observer is benchmarked against a fractional-order extended Kalman filter using experimental data. The results demonstrate that incorporating a regularization law into the observer dynamics effectively mitigates observability limitations, offering a robust solution for the SC bank state estimation. Full article
(This article belongs to the Special Issue Hybrid Energy Harvesting Systems: New Developments and Applications)
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27 pages, 3770 KB  
Article
Precision Time Interval Generator Based on CMOS Counters and Integration with IoT Timing Systems
by Nebojša Andrijević, Zoran Lovreković, Vladan Radivojević, Svetlana Živković Radeta and Hadžib Salkić
Electronics 2025, 14(16), 3201; https://doi.org/10.3390/electronics14163201 - 12 Aug 2025
Viewed by 735
Abstract
Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor [...] Read more.
Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor (CMOS) logic counters (Integrated Circuit (IC) IC 7493 and IC 4017) and inverter-based crystal oscillators (IC 74LS04). The proposed system enables frequency division from 1 MHz down to 1 Hz through a cascade of binary and Johnson counters, enhanced with digitally controlled multiplexers for output signal selection. Unlike conventional timing systems relying on expensive Field-Programmable Gate Array (FPGA) or Global Navigation Satellite System (GNSS)-based synchronization, this approach offers a robust, locally controlled reference clock suitable for IoT nodes without network access. The hardware is integrated with Arduino and ESP32 microcontrollers via General-Purpose Input/Output (GPIO) level interfacing, supporting real-time timestamping, deterministic task execution, and microsecond-level synchronization. The system was validated through Python-based simulations incorporating Gaussian jitter models, as well as real-time experimental measurements using Arduino’s micros() function. Results demonstrated stable pulse generation with timing deviations consistently below ±3 µs across various frequency modes. A comparative analysis confirms the advantages of this CMOS-based timing solution over Real-Time Clock (RTC), Network Time Protocol (NTP), and Global Positioning System (GPS)-based methods in terms of local autonomy, cost, and integration simplicity. This work provides a practical and scalable time reference architecture for educational, industrial, and distributed applications, establishing a new bridge between classical digital circuit design and modern Internet of Things (IoT) timing requirements. Full article
(This article belongs to the Section Circuit and Signal Processing)
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32 pages, 8208 KB  
Review
General Overview of Antennas for Unmanned Aerial Vehicles: A Review
by Sara Reis, Fábio Silva, Daniel Albuquerque and Pedro Pinho
Electronics 2025, 14(16), 3205; https://doi.org/10.3390/electronics14163205 - 12 Aug 2025
Viewed by 1010
Abstract
Unmanned Aerial Vehicles (UAVs), commonly known as drones, are becoming increasingly important in multiple areas and various applications, including communication, detection, and monitoring. This review paper examines the development of antennas for UAVs, with a particular focus on miniaturization techniques, polarization strategies, and [...] Read more.
Unmanned Aerial Vehicles (UAVs), commonly known as drones, are becoming increasingly important in multiple areas and various applications, including communication, detection, and monitoring. This review paper examines the development of antennas for UAVs, with a particular focus on miniaturization techniques, polarization strategies, and beamforming solutions. It explores both structural and material-based methods, such as meander lines, slots, high-dielectric substrates, and metasurfaces, which aim to make the antenna more compact without compromising performance. Different antenna types including dipole, monopole, horn, vivaldi, and microstrip patch are explored to identify solutions that meet performance standards while respecting UAV constraints. In terms of polarization strategies, these are often implemented in the feeding network to achieve linear or circular polarization, and beamforming techniques like beam-steering and beam-switching enhance communication efficiency by improving signal directionality. Future research should focus on more lightweight, structurally integrated, and reconfigurable apertures that push miniaturization through conformal substrates and programmable metasurfaces, extending efficient operation from 5/6 GHz into the sub-THz regime and supporting agile beamforming for dense UAV swarms. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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12 pages, 1878 KB  
Article
Blind Source Separation for Joint Communication and Sensing in Time-Varying IBFD MIMO Systems
by Siyao Li, Conrad Prisby and Thomas Yang
Electronics 2025, 14(16), 3200; https://doi.org/10.3390/electronics14163200 - 12 Aug 2025
Viewed by 343
Abstract
This paper presents a blind source separation (BSS)-based framework for joint communication and sensing (JCAS) in in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems operating under time-varying channel conditions. Conventionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of [...] Read more.
This paper presents a blind source separation (BSS)-based framework for joint communication and sensing (JCAS) in in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems operating under time-varying channel conditions. Conventionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of interest (SOI). Under the JCAS paradigm, however, this high-power SI signal presents an opportunity for efficient sensing. Since each transceiver node has access to the original SI signal, its environmental reflections can be exploited to estimate channel conditions and detect changes, without requiring dedicated radar waveforms. We propose a blind source separation (BSS)-based framework to simultaneously perform self-interference cancellation (SIC) and extract sensing information in IBFD MIMO settings. The approach applies the Fast Independent Component Analysis (FastICA) algorithm in dynamic scenarios to separate the SI and SOI signals while enabling simultaneous signal recovery and channel estimation. Simulation results quantify the trade-off between estimation accuracy and channel dynamics, demonstrating that while FastICA is effective, its performance is fundamentally limited by a frame size optimized for the rate of channel variation. Specifically, in static channels, the signal-to-residual-error ratio (SRER) exceeds 22 dB with 500-symbol frames, whereas for moderately time-varying channels, performance degrades significantly for frames longer than 150 symbols, with SRER dropping below 4 dB. 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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17 pages, 4285 KB  
Article
3D-Printed Circular Horn Antenna with Dielectric Lens for Focused RF Energy Delivery
by Aviad Michael and Nezah Balal
Electronics 2025, 14(16), 3191; https://doi.org/10.3390/electronics14163191 - 11 Aug 2025
Viewed by 523
Abstract
This paper presents the design, simulation, and fabrication of a horn antenna integrated with a dielectric lens for focusing RF energy at 10 GHz. The antenna system combines established electromagnetic principles with 3D printing techniques to produce a cost-effective alternative to commercial focusing [...] Read more.
This paper presents the design, simulation, and fabrication of a horn antenna integrated with a dielectric lens for focusing RF energy at 10 GHz. The antenna system combines established electromagnetic principles with 3D printing techniques to produce a cost-effective alternative to commercial focusing antennas. The design methodology employs the lensmaker’s formula and Snell’s law to determine lens curvature for achieving a specified focal length of 100 mm. COMSOL Multiphysics simulations indicate that adding a PTFE lens increases power density concentration compared to a standard horn antenna, with a simulated focal point at approximately 100 mm. Surface roughness analysis based on the Rayleigh criterion supports 3D printing suitability for this application. Experimental validation includes radiation pattern measurements of the antenna without the lens and power density measurements versus distance with the lens, both showing good agreement with simulation results. The measured focal length was 95±5 mm, closely matching simulation predictions. This work presents an approach for implementing focused RF delivery solutions for medical treatments, wireless power transfer, and precision sensing at significantly lower costs than commercial alternatives. Full article
(This article belongs to the Special Issue Futuristic Antennas: Sustainable, Efficient, Reconfigurable, and Intelligent Design)
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20 pages, 6602 KB  
Article
A DC-Link Current Pulsation Compensator Based on a Triple-Active Bridge Converter Topology
by Karol Fatyga and Mariusz Zdanowski
Electronics 2025, 14(16), 3196; https://doi.org/10.3390/electronics14163196 - 11 Aug 2025
Viewed by 338
Abstract
This paper presents a method of compensating the AC pulsation appearing in the DC-link of a four-wire AC/DC converter operating with asymmetric output currents. If such a converter is operating with an electrochemical energy storage system, the AC component can cause several issues [...] Read more.
This paper presents a method of compensating the AC pulsation appearing in the DC-link of a four-wire AC/DC converter operating with asymmetric output currents. If such a converter is operating with an electrochemical energy storage system, the AC component can cause several issues for the battery. In order to solve this problem, a DC/DC converter is used to redirect the AC component into a capacitor bank. The triple-active bridge (TAB) converter is selected for this purpose. The converter is modeled using a reduced-order modelling approach, and the appropriate control loop is designed. The experimental setup is built and tested with a modelled DC-link, with emulated pulsation. The average AC component reduction on the battery port of 98.3% is achieved. Full article
(This article belongs to the Special Issue The Hybrid AC-DC Power System Coordinated Control and Operation Technology, 2nd Edition)
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17 pages, 3359 KB  
Article
Automated Generation of Test Scenarios for Autonomous Driving Using LLMs
by Aaron Agyapong Danso and Ulrich Büker
Electronics 2025, 14(16), 3177; https://doi.org/10.3390/electronics14163177 - 10 Aug 2025
Viewed by 1792
Abstract
This paper introduces an approach that leverages large language models (LLMs) to convert detailed descriptions of an Operational Design Domain (ODD) into realistic, executable simulation scenarios for testing autonomous vehicles. The method combines model-based and data-driven techniques to decompose ODDs into three key [...] Read more.
This paper introduces an approach that leverages large language models (LLMs) to convert detailed descriptions of an Operational Design Domain (ODD) into realistic, executable simulation scenarios for testing autonomous vehicles. The method combines model-based and data-driven techniques to decompose ODDs into three key components: environmental, scenery, and dynamic elements. It then applies prompt engineering to generate ScenarioRunner scripts compatible with CARLA. The model-based component guides the LLM using structured prompts and a “Tree of Thoughts” strategy to outline the scenario, while a data-driven refinement process, drawing inspiration from red teaming, enhances the accuracy and robustness of the generated scripts over time. Experimental results show that while static components, such as weather and road layouts, are well captured, dynamic elements like vehicle and pedestrian behavior require further refinement. Overall, this approach not only reduces the manual effort involved in creating simulation scenarios but also identifies key challenges and opportunities for advancing safer and more adaptive autonomous driving systems. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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26 pages, 819 KB  
Review
A Survey of Analog Computing for Domain-Specific Accelerators
by Leonid Belostotski, Asif Uddin, Arjuna Madanayake and Soumyajit Mandal
Electronics 2025, 14(16), 3159; https://doi.org/10.3390/electronics14163159 - 8 Aug 2025
Viewed by 1797
Abstract
Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, [...] Read more.
Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed. Full article
(This article belongs to the Special Issue Emerging Techniques and Applications for Analog and Mixed-Signal Integrated Circuits)
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15 pages, 8291 KB  
Article
Two-Stage Power Delivery Architecture Using Hybrid Converters for Data Centers and Telecommunication Systems
by Ratul Das and Hanh-Phuc Le
Electronics 2025, 14(16), 3169; https://doi.org/10.3390/electronics14163169 - 8 Aug 2025
Viewed by 380
Abstract
This paper presents a new power delivery architecture to bring AC distribution voltages to core levels for computing loads using only two conversion stages with new converter topologies to potentially replace the traditional four-stage structure in the development of new data centers. This [...] Read more.
This paper presents a new power delivery architecture to bring AC distribution voltages to core levels for computing loads using only two conversion stages with new converter topologies to potentially replace the traditional four-stage structure in the development of new data centers. This paper also includes new converters as solutions to the proposed two stages. A new switched capacitor (SC)-based AC-DC converter is proposed for the first stage and demonstrated for an intermediate bus with 90 V–110 VAC to 48–60 VDC conversion and power factor correction. The second stage also includes an SC-based hybrid converter with multi-phase operation suitable for power delivery for core voltages of up to ~1 V with a high current density. This work also reports a new phase sequence for the second stage for an extended output voltage range. Individually, the first stage was measured at 96.1% peak efficiency for output currents ranging from 0 to 4.5 A, while the second stage achieved 90.7% peak efficiency with a load range of 0–220 A at 1V. The measured peak power densities were 73 W/in3 for the first stage and 2020 W/in3 for the second stage. In combination, the direct conversion from ~110 VAC to 1 VDC led to a peak efficiency of 84.1% at 50 A, and this setup has been tested with output currents of up to 160 A, where the efficiency was 73.5%. Full article
(This article belongs to the Special Issue Applications, Control and Design of Power Electronics Converters)
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20 pages, 9514 KB  
Article
The Behavior of an IoT Sensor Monitoring System Using a 5G Network and Its Challenges in 6G Networking
by Georgios Gkagkas, Vasiliki Karamerou, Angelos Michalas, Michael Dossis and Dimitrios J. Vergados
Electronics 2025, 14(16), 3167; https://doi.org/10.3390/electronics14163167 - 8 Aug 2025
Viewed by 601
Abstract
The recent advances in 5G and beyond wireless networking have enabled the possibility of using the cellular network as the infrastructure for wireless sensor networks, due to the high bandwidth availability and the reduced cost per data unit. In this paper, we perform [...] Read more.
The recent advances in 5G and beyond wireless networking have enabled the possibility of using the cellular network as the infrastructure for wireless sensor networks, due to the high bandwidth availability and the reduced cost per data unit. In this paper, we perform an evaluation of the 5G infrastructure for sensor networks in order to quantify the performance in terms of energy efficiency and bandwidth within a testing environment. We used an ESP32 sensor with BLE-connected sensing devices for environmental conditions, and a Raspberry Pi with the Waveshare SIM8200EA-M2 5G module for cellular connectivity. We measured the power usage of each component of the system, in real conditions, as well as the power consumption for different bandwidth usage scenarios, and the end-to-end delay of the system. The results showed that the system is capable of achieving the required delay and bandwidth; however, the energy efficiency of the specific setup leaves room for improvement. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
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21 pages, 4525 KB  
Article
MAFUZZ: Adaptive Gradient-Guided Fuzz Testing for Satellite Internet Ground Terminals
by Ang Cao, Yongli Zhao, Xiaodan Yan, Wei Wang, Jian Yang, Yuanjian Zhang and Ruiqi Liu
Electronics 2025, 14(16), 3168; https://doi.org/10.3390/electronics14163168 - 8 Aug 2025
Viewed by 386
Abstract
With the proliferation of satellite internet systems, such as Starlink and OneWeb, ground terminals have become critical for ensuring end-user connectivity. However, the security of Satellite Internet Ground Terminals (SIGTs) remains underexplored. These Linux-based embedded systems are vulnerable to advanced attacks due to [...] Read more.
With the proliferation of satellite internet systems, such as Starlink and OneWeb, ground terminals have become critical for ensuring end-user connectivity. However, the security of Satellite Internet Ground Terminals (SIGTs) remains underexplored. These Linux-based embedded systems are vulnerable to advanced attacks due to limited source code access and immature protection mechanisms. This paper presents MAFUZZ, an adaptive fuzzing framework guided by neural network gradients to uncover hidden vulnerabilities in SIGT binaries. MAFUZZ uses a lightweight machine learning model to identify input bytes that influence program behavior and applies gradient-based mutation accordingly. It also integrates an adaptive Havoc mechanism to enhance path diversity. We compare MAFUZZ with NEUZZ, a neural fuzzing tool that uses program smoothing to guide mutation through a static model. Our experiments on real-world Linux binaries show that MAFUZZ improves path coverage by an average of 17.4% over NEUZZ, demonstrating its effectiveness in vulnerability discovery and its practical value for securing satellite terminal software. Full article
(This article belongs to the Special Issue Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios)
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14 pages, 24112 KB  
Article
ImpactAlert: Pedestrian-Carried Vehicle Collision Alert System
by Raghav Rawat, Caspar Lant, Haowen Yuan and Dennis Shasha
Electronics 2025, 14(15), 3133; https://doi.org/10.3390/electronics14153133 - 6 Aug 2025
Viewed by 604
Abstract
The ImpactAlert system is a chest-mounted system that detects objects that are likely to hit a pedestrian and alerts that pedestrian. The primary use cases are visually impaired pedestrians or pedestrians who need to be warned about vehicles or other pedestrians coming from [...] Read more.
The ImpactAlert system is a chest-mounted system that detects objects that are likely to hit a pedestrian and alerts that pedestrian. The primary use cases are visually impaired pedestrians or pedestrians who need to be warned about vehicles or other pedestrians coming from unseen directions. This paper argues for the need for such a system, the design and algorithms of ImpactAlert, and experiments carried out in varied urban environments, ranging from densely crowded to semi-urban in the United States, India and China. ImpactAlert makes use of a LiDAR camera found on a commercial wireless phone, processes the data over several frames to evaluate the time to impact and speed of potential threats. When ImpactAlert determines a threat meets the criteria set by the user, it sends warning signals through an output device to warn a pedestrian. The output device can be an audible warning and/or a low-cost smart cane that vibrates when danger approaches. Our experiments in urban and semi-urban environments show that (i) ImpactAlert can avoid nearly all false negatives (when an alarm should be sent and it isn’t) and (ii) enjoys a low false positive rate. The net result is an effective low cost system to alert pedestrians in an urban environment. Full article
(This article belongs to the Special Issue Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions)
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11 pages, 5939 KB  
Article
Low-Cost Phased Array with Enhanced Gain at the Largest Deflection Angle
by Haotian Wen, Hansheng Su, Yan Wen, Xin Ma and Deshuang Zhao
Electronics 2025, 14(15), 3111; https://doi.org/10.3390/electronics14153111 - 5 Aug 2025
Viewed by 566
Abstract
This paper presents a low-cost 1-bit phased array operating at 17 GHz (Ku band) with an enhanced scanning gain at the largest deflection angle to extend the beam coverage for ground target detection. The phased array is designed using 16 (2 × 8) [...] Read more.
This paper presents a low-cost 1-bit phased array operating at 17 GHz (Ku band) with an enhanced scanning gain at the largest deflection angle to extend the beam coverage for ground target detection. The phased array is designed using 16 (2 × 8) radiation-phase reconfigurable dipoles and a fixed-phase feeding network, achieving 1-bit beam steering via a direct current (DC) bias voltage of ±5 V. Measurement results demonstrate a peak gain of 9.2 dBi at a deflection angle of ±37°, with a 3 dB beamwidth of 94° across the scanning plane. Compared with conventional phased array radars with equivalent peak gains, the proposed design achieves a 16% increase in the detection range at the largest deflection angle. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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20 pages, 11318 KB  
Article
Addressing Challenges in Rds,on Measurement for Cloud-Connected Condition Monitoring in WBG Power Converter Applications
by Farzad Hosseinabadi, Sachin Kumar Bhoi, Hakan Polat, Sajib Chakraborty and Omar Hegazy
Electronics 2025, 14(15), 3093; https://doi.org/10.3390/electronics14153093 - 2 Aug 2025
Cited by 1 | Viewed by 370
Abstract
This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (Rds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, [...] Read more.
This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (Rds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and Rds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article
(This article belongs to the Section Industrial Electronics)
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19 pages, 1107 KB  
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 700
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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18 pages, 4863 KB  
Article
Evaluation of Explainable, Interpretable and Non-Interpretable Algorithms for Cyber Threat Detection
by José Ramón Trillo, Felipe González-López, Juan Antonio Morente-Molinera, Roberto Magán-Carrión and Pablo García-Sánchez
Electronics 2025, 14(15), 3073; https://doi.org/10.3390/electronics14153073 - 31 Jul 2025
Viewed by 460
Abstract
As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not [...] Read more.
As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not direct attacks—to evaluate and compare explainable, interpretable, and opaque machine learning models. Through advanced preprocessing and feature engineering, we examine the trade-off between model performance and transparency in the early detection of suspicious connections. We evaluate explainable ML-based models such as k-nearest neighbours, fuzzy algorithms, decision trees, and random forests, alongside interpretable models like naïve Bayes, support vector machines, and non-interpretable algorithms such as neural networks. Results show that neural networks achieve the highest performance, with a macro F1-score of 0.8786, but explainable models like HFER offer strong performance (macro F1-score = 0.6106) with greater interpretability. The choice of algorithm depends on project-specific needs: neural networks excel in accuracy, while explainable algorithms are preferred for resource efficiency and transparency, as stated in this work. This work underscores the importance of aligning cybersecurity strategies with operational requirements, providing insights into balancing performance with interpretability. Full article
(This article belongs to the Special Issue Network Security and Cryptography Applications)
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20 pages, 28928 KB  
Article
Evaluating the Effectiveness of Plantar Pressure Sensors for Fall Detection in Sloped Surfaces
by Tarek Mahmud, Rujan Kayastha, Krishna Kisi, Anne Hee Ngu and Sana Alamgeer
Electronics 2025, 14(15), 3003; https://doi.org/10.3390/electronics14153003 - 28 Jul 2025
Viewed by 554
Abstract
Falls are a major safety concern in physically demanding occupations such as roofing, where workers operate on inclined surfaces under unstable postures. While inertial measurement units (IMUs) are widely used in wearable fall detection systems, they often fail to capture early indicators of [...] Read more.
Falls are a major safety concern in physically demanding occupations such as roofing, where workers operate on inclined surfaces under unstable postures. While inertial measurement units (IMUs) are widely used in wearable fall detection systems, they often fail to capture early indicators of instability related to foot–ground interactions. This study evaluates the effectiveness of plantar pressure sensors, alone and combined with IMUs, for fall detection on sloped surfaces. We collected data in a controlled laboratory environment using a custom-built roof mockup with incline angles of 0°, 15°, and 30°. Participants performed roofing-relevant activities, including standing, walking, stooping, kneeling, and simulated fall events. Statistical features were extracted from synchronized IMU and plantar pressure data, and multiple machine learning models were trained and evaluated, including traditional classifiers and deep learning architectures, such as MLP and CNN. Our results show that integrating plantar pressure sensors significantly improves fall detection. A CNN using just three IMUs and two plantar pressure sensors achieved the highest F1 score of 0.88, outperforming the full 17-sensor IMU setup. These findings support the use of multimodal sensor fusion for developing efficient and accurate wearable systems for fall detection and physical health monitoring. Full article
(This article belongs to the Special Issue What Is Next in Wearable Computing for Mental and Physical Health Monitoring?)
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19 pages, 3365 KB  
Article
Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes
by Pretom Roy Ovi and Aryya Gangopadhyay
Electronics 2025, 14(15), 2970; https://doi.org/10.3390/electronics14152970 - 25 Jul 2025
Viewed by 822
Abstract
Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training process. Therefore, it is susceptible to [...] Read more.
Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances, adding noise to training instances, and adding out-of-distribution training instances in the local data to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, the prevention and detection of such data poisoning attacks is crucial to build a robust federated training framework. To address this, we propose a prevention strategy in federated learning, namely confident federated learning, to protect workers from such data poisoning attacks. Our proposed prevention strategy at first validates the label quality of local training samples by characterizing and identifying label errors in the local training data, and then excludes the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on both the image and audio domains, and our experimental results validated the robustness of our proposed confident federated learning in preventing the data poisoning attacks. Our proposed method can successfully detect the mislabeled training samples with above 85% accuracy and exclude those detected samples from the training set to prevent data poisoning attacks on the local workers. However, our prevention strategy can successfully prevent the attack locally in the presence of a certain percentage of poisonous samples. Beyond that percentage, the prevention strategy may not be effective in preventing attacks. In such cases, detection of the attacked workers is needed. So, in addition to the prevention strategy, we propose a novel detection strategy in the federated learning framework to detect the malicious workers under attack. We propose to create a class-wise cluster representation for every participating worker by utilizing the neuron activation maps of local models and analyze the resulting clusters to filter out the workers under attack before model aggregation. We experimentally demonstrated the efficacy of our proposed detection strategy in detecting workers affected by data poisoning attacks, along with the attack types, e.g., label-flipping or dirty labeling. In addition, our experimental results suggest that the global model could not converge even after a large number of training rounds in the presence of malicious workers, whereas after detecting the malicious workers with our proposed detection method and discarding them from model aggregation, we ensured that the global model achieved convergence within very few training rounds. Furthermore, our proposed approach stays robust under different data distributions and model sizes and does not require prior knowledge about the number of attackers in the system. Full article
(This article belongs to the Special Issue Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios)
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35 pages, 3157 KB  
Article
Federated Unlearning Framework for Digital Twin–Based Aviation Health Monitoring Under Sensor Drift and Data Corruption
by Igor Kabashkin
Electronics 2025, 14(15), 2968; https://doi.org/10.3390/electronics14152968 - 24 Jul 2025
Viewed by 788
Abstract
Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial [...] Read more.
Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial data once these have been integrated into global models. This paper proposes a novel FL–DT–FU framework that combines digital twin-based subsystem modeling, federated learning for collaborative training, and federated unlearning (FU) to support the post hoc correction of compromised model contributions. The architecture enables real-time monitoring through local DTs, secure model aggregation via FL, and targeted rollback using gradient subtraction, re-aggregation, or constrained retraining. A comprehensive simulation environment is developed to assess the impact of sensor drift, label noise, and adversarial updates across a federated fleet of aircraft. The experimental results demonstrate that FU methods restore up to 95% of model accuracy degraded by data corruption, significantly reducing false negative rates in early fault detection. The proposed system further supports auditability through cryptographic logging, aligning with aviation regulatory standards. This study establishes federated unlearning as a critical enabler for resilient, correctable, and trustworthy AI in next-generation AHM systems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)
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21 pages, 9379 KB  
Article
UDirEar: Heading Direction Tracking with Commercial UWB Earbud by Interaural Distance Calibration
by Minseok Kim, Younho Nam, Jinyou Kim and Young-Joo Suh
Electronics 2025, 14(15), 2940; https://doi.org/10.3390/electronics14152940 - 23 Jul 2025
Viewed by 500
Abstract
Accurate heading direction tracking is essential for immersive VR/AR, spatial audio rendering, and robotic navigation. Existing IMU-based methods suffer from drift and vibration artifacts, vision-based approaches require LoS and raise privacy concerns, and RF techniques often need dedicated infrastructure. We propose UDirEar, a [...] Read more.
Accurate heading direction tracking is essential for immersive VR/AR, spatial audio rendering, and robotic navigation. Existing IMU-based methods suffer from drift and vibration artifacts, vision-based approaches require LoS and raise privacy concerns, and RF techniques often need dedicated infrastructure. We propose UDirEar, a COTS UWB device-based system that estimates user heading using solely high-level UWB information like distance and unit direction. By initializing an EKF with each user’s constant interaural distance, UDirEar compensates for the earbuds’ roto-translational motion without additional sensors. We evaluate UDirEar on a step-motor-driven dummy head against an IMU-only baseline (MAE 30.8°), examining robustness across dummy head–initiator distances, elapsed time, EKF calibration conditions, and NLoS scenarios. UDirEar achieves a mean absolute error of 3.84° and maintains stable performance under all tested conditions. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
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20 pages, 2341 KB  
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 2508
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 KB  
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 675
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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43 pages, 2108 KB  
Article
FIGS: A Realistic Intrusion-Detection Framework for Highly Imbalanced IoT Environments
by Zeynab Anbiaee, Sajjad Dadkhah and Ali A. Ghorbani
Electronics 2025, 14(14), 2917; https://doi.org/10.3390/electronics14142917 - 21 Jul 2025
Viewed by 653
Abstract
The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems [...] Read more.
The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead. Full article
(This article belongs to the Special Issue Network Security and Cryptography Applications)
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19 pages, 1406 KB  
Article
A Comparative Study of Dimensionality Reduction Methods for Accurate and Efficient Inverter Fault Detection in Grid-Connected Solar Photovoltaic Systems
by Shahid Tufail and Arif I. Sarwat
Electronics 2025, 14(14), 2916; https://doi.org/10.3390/electronics14142916 - 21 Jul 2025
Viewed by 447
Abstract
The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection [...] Read more.
The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection presents interesting prospects in accuracy and responsiveness. By streamlining data complexity and allowing faster and more effective fault diagnosis, dimensionality reduction methods play vital role. Using dimensionality reduction and ML techniques, this work explores inverter fault detection in GCPV systems. Photovoltaic inverter operational data was normalized and preprocessed. In the next step, dimensionality reduction using Principal Component Analysis (PCA) and autoencoder-based feature extraction were explored. For ML training four classifiers which include Random Forest (RF), logistic regression (LR), decision tree (DT), and K-Nearest Neighbors (KNN) were used. Trained on the whole standardized dataset, the RF model routinely produced the greatest accuracy of 99.87%, so efficiently capturing complicated feature interactions but requiring large processing resources and time of 36.47sec. LR model showed reduction in accuracy, but very fast training time compared to other models. Further, PCA greatly lowered computing demands, especially improving inference speed for LR and KNN. High accuracy of 99.23% across all models was maintained by autoencoder-derived features. Full article
(This article belongs to the Special Issue Machine Learning Applications in Predictive Monitoring of Power Grid Stability and Resiliency Enhancement)
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28 pages, 2518 KB  
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 718
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 KB  
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 1123
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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16 pages, 2472 KB  
Article
Performance Evaluation of DAB-Based Partial- and Full-Power Processing for BESS in Support of Trolleybus Traction Grids
by Jiayi Geng, Rudolf Francesco Paternost, Sara Baldisserri, Mattia Ricco, Vitor Monteiro, Sheldon Williamson and Riccardo Mandrioli
Electronics 2025, 14(14), 2871; https://doi.org/10.3390/electronics14142871 - 18 Jul 2025
Viewed by 415
Abstract
The energy transition toward greater electrification leads to incentives in public transportation fed by catenary-powered networks. In this context, emerging technological devices such as in-motion-charging vehicles and electric vehicle charging points are expected to be operated while connected to trolleybus networks as part [...] Read more.
The energy transition toward greater electrification leads to incentives in public transportation fed by catenary-powered networks. In this context, emerging technological devices such as in-motion-charging vehicles and electric vehicle charging points are expected to be operated while connected to trolleybus networks as part of new electrification projects, resulting in a significant demand for power. To enable a significant increase in electric transportation without compromising technical compliance for voltage and current at grid systems, the implementation of stationary battery energy storage systems (BESSs) can be essential for new electrification projects. A key challenge for BESSs is the selection of the optimal converter topology for charging their batteries. Ideally, the chosen converter should offer the highest efficiency while minimizing size, weight, and cost. In this context, a modular dual-active-bridge converter, considering its operation as a full-power converter (FPC) and a partial-power converter (PPC) with module-shedding control, is analyzed in terms of operation efficiencies and thermal behavior. The goal is to clarify the advantages, disadvantages, challenges, and trade-offs of both power-processing techniques following future trends in the electric transportation sector. The results indicate that the PPC achieves an efficiency of 98.58% at the full load of 100 kW, which is 1.19% higher than that of FPC. Additionally, higher power density and cost effectiveness are confirmed for the PPC. Full article
(This article belongs to the Special Issue Integration of Electric Vehicle Chargers and Energy Storage Systems in Smart Grids)
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14 pages, 1179 KB  
Article
Dual-Core Hierarchical Fuzzing Framework for Efficient and Secure Firmware Over-the-Air
by Na-Hyun Kim, Jin-Min Lee and Il-Gu Lee
Electronics 2025, 14(14), 2886; https://doi.org/10.3390/electronics14142886 - 18 Jul 2025
Viewed by 368
Abstract
As the use of Internet of Things (IoT) devices becomes extensive, ensuring their security has become a critical issue for both individuals and organizations, particularly as these devices collect, transmit, and analyze diverse data. The firmware of IoT devices plays a key role [...] Read more.
As the use of Internet of Things (IoT) devices becomes extensive, ensuring their security has become a critical issue for both individuals and organizations, particularly as these devices collect, transmit, and analyze diverse data. The firmware of IoT devices plays a key role in ensuring system security; any vulnerabilities in the firmware can expose the system to threats such as hacking or malware infections. Consequently, fuzzing is used to analyze firmware vulnerabilities during the update process. However, conventional single-core and random fuzzing-based firmware vulnerability analysis techniques suffer from low efficiency, limited security, and high memory usage. Each time the firmware is updated, the entire file—including previously analyzed code—must be reanalyzed. Moreover, given that the firmware is not layered, unaffected code segments are redundantly reanalyzed. To address these limitations, this study proposes a dual-core-based hierarchical partial fuzzing technique for wireless networks using dual cores. Experimental results show that the proposed technique detects 11 more unique crashes within 300 s and finds 2435 more total crashes than that of the conventional scheme. It also reduces memory usage by 35 KiB. The proposed technique improves the speed, effectiveness, and reliability of firmware updates and vulnerability detection. Full article
(This article belongs to the Special Issue IoT Security in the Age of AI: Innovative Approaches and Technologies)
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24 pages, 6475 KB  
Review
Short-Circuit Detection and Protection Strategies for GaN E-HEMTs in High-Power Applications: A Review
by Haitz Gezala Rodero, David Garrido Díez, Iosu Aizpuru Larrañaga and Igor Baraia-Etxaburu
Electronics 2025, 14(14), 2875; https://doi.org/10.3390/electronics14142875 - 18 Jul 2025
Viewed by 949
Abstract
Gallium nitride (GaN) enhancement-mode high-electron-mobility transistors ( E-HEMTs) deliver superior performance compared to traditional silicon (Si) and silicon carbide (SiC) counterparts. Their faster switching speeds, lower on-state resistances, and higher operating frequencies enable more efficient and compact power converters. However, their integration into [...] Read more.
Gallium nitride (GaN) enhancement-mode high-electron-mobility transistors ( E-HEMTs) deliver superior performance compared to traditional silicon (Si) and silicon carbide (SiC) counterparts. Their faster switching speeds, lower on-state resistances, and higher operating frequencies enable more efficient and compact power converters. However, their integration into high-power applications is limited by critical reliability concerns, particularly regarding their short-circuit (SC) withstand capability and overvoltage (OV) resilience. GaN devices typically exhibit SC withstand times of only a few hundred nanoseconds, needing ultrafast protection circuits, which conventional desaturation (DESAT) methods cannot adequately provide. Furthermore, their high switching transients increase the risk of false activation events. The lack of avalanche capability and the dynamic nature of GaN breakdown voltage exacerbate issues related to OV stress during fault conditions. Although SC-related behaviour in GaN devices has been previously studied, a focused and comprehensive review of protection strategies tailored to GaN technology remains lacking. This paper fills that gap by providing an in-depth analysis of SC and OV failure phenomena, coupled with a critical evaluation of current and next-generation protection schemes suitable for GaN-based high-power converters. Full article
(This article belongs to the Special Issue Advances in Semiconductor GaN and Applications)
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17 pages, 7597 KB  
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 470
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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4 pages, 149 KB  
Editorial
RF, Microwave, and Millimeter Wave Devices and Circuits and Their Applications
by Reza K. Amineh
Electronics 2025, 14(14), 2844; https://doi.org/10.3390/electronics14142844 - 16 Jul 2025
Viewed by 508
Abstract
The recent progress in the development of cost-effective, compact, and highly integrated high-frequency circuits in the RF, microwave, and millimeter-wave domains has significantly broadened the scope of these technologies across both traditional and emerging application areas [...] Full article
(This article belongs to the Special Issue RF, Microwave, and Millimeter Wave Devices and Circuits and Their Applications)
24 pages, 7849 KB  
Article
Face Desensitization for Autonomous Driving Based on Identity De-Identification of Generative Adversarial Networks
by Haojie Ji, Liangliang Tian, Jingyan Wang, Yuchi Yao and Jiangyue Wang
Electronics 2025, 14(14), 2843; https://doi.org/10.3390/electronics14142843 - 15 Jul 2025
Viewed by 487
Abstract
Automotive intelligent agents are increasingly collecting facial data for applications such as driver behavior monitoring and identity verification. These excessive collections of facial data bring serious risks of sensitive information leakage to autonomous driving. Facial information has been explicitly required to be anonymized, [...] Read more.
Automotive intelligent agents are increasingly collecting facial data for applications such as driver behavior monitoring and identity verification. These excessive collections of facial data bring serious risks of sensitive information leakage to autonomous driving. Facial information has been explicitly required to be anonymized, but the availability of most desensitized facial data is poor, which will greatly affect its application in autonomous driving. This paper proposes an automotive sensitive information anonymization method with high-quality generated facial images by considering the data availability under privacy protection. By comparing K-Same and Generative Adversarial Networks (GANs), this paper proposes a hierarchical self-attention mechanism in StyleGAN3 to enhance the feature perception of face images. The synchronous regularization of sample data is applied to optimize the loss function of the discriminator of StyleGAN3, thereby improving the convergence stability of the model. The experimental results demonstrate that the proposed facial desensitization model reduces the Frechet inception distance (FID) and structural similarity index measure (SSIM) by 95.8% and 24.3%, respectively. The image quality and privacy desensitization of the facial data generated by the StyleGAN3 model have been fully verified in this work. This research provides an efficient and robust facial privacy protection solution for autonomous driving, which is conducive to promoting the security guarantee of automotive data. Full article
(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)
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18 pages, 4058 KB  
Article
A Transferable DRL-Based Intelligent Secondary Frequency Control for Islanded Microgrids
by Sijia Li, Frede Blaabjerg and Amjad Anvari-Moghaddam
Electronics 2025, 14(14), 2826; https://doi.org/10.3390/electronics14142826 - 14 Jul 2025
Viewed by 431
Abstract
Frequency instability poses a significant challenge to the overall stability of islanded microgrid systems. Deep reinforcement learning (DRL)-based intelligent control strategies are drawing considerable attention for their ability to operate without the need for previous system dynamics information and the capacity for autonomous [...] Read more.
Frequency instability poses a significant challenge to the overall stability of islanded microgrid systems. Deep reinforcement learning (DRL)-based intelligent control strategies are drawing considerable attention for their ability to operate without the need for previous system dynamics information and the capacity for autonomous learning. This paper proposes an intelligent frequency secondary compensation solution that divides the traditional secondary frequency control into two layers. The first layer is based on a PID controller and the second layer is an intelligent controller based on DRL. To address the typically extensive training durations associated with DRL controllers, this paper integrates transfer learning, which significantly expedites the training process. This scheme improves control accuracy and reduces computational redundancy. Simulation tests are executed on an islanded microgrid with four distributed generators and an IEEE 13-bus system is utilized for further validation. Finally, the proposed method is validated on the OPAL-RT real-time test platform. The results demonstrate the superior performance of the proposed method. Full article
(This article belongs to the Special Issue Recent Advances in Control and Optimization in Microgrids)
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16 pages, 2050 KB  
Article
Analysis, Evaluation, and Prediction of Machine Learning-Based Animal Behavior Imitation
by Yu Qi, Siyu Xiong and Bo Wu
Electronics 2025, 14(14), 2816; https://doi.org/10.3390/electronics14142816 - 13 Jul 2025
Viewed by 559
Abstract
Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking [...] Read more.
Expressive imitation in the performing arts is typically trained through animal behavior imitation, aiming not only to reproduce action trajectories but also to recreate rhythm, style and emotional states. However, evaluation of such animal imitation behaviors relies heavily on teachers’ subjective judgments, lacking structured criteria, exhibiting low inter-rater consistency and being difficult to quantify. To enhance the objectivity and interpretability of the scoring process, this study develops a machine learning and structured pose data-based auxiliary evaluation framework for imitation quality. The proposed framework innovatively constructs three types of feature sets, namely baseline, ablation, and enhanced, and integrates recursive feature elimination with feature importance ranking to identify a stable and interpretable set of core structural features. This enables the training of machine learning models with strong capabilities in structured modeling and sensitivity to informative features. The analysis of the modeling results indicates that temporal–rhythm features play a significant role in score prediction and that only a small number of key feature values are required to model teachers’ ratings with high precision. The proposed framework not only lays a methodological foundation for standardized and AI-assisted evaluation in performing arts education but also expands the application boundaries of computer vision and machine learning in this field. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning in Image Analysis for Biological Systems)
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30 pages, 55073 KB  
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 1730
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 KB  
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 764
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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21 pages, 12122 KB  
Article
RA3T: An Innovative Region-Aligned 3D Transformer for Self-Supervised Sim-to-Real Adaptation in Low-Altitude UAV Vision
by Xingrao Ma, Jie Xie, Di Shao, Aiting Yao and Chengzu Dong
Electronics 2025, 14(14), 2797; https://doi.org/10.3390/electronics14142797 - 11 Jul 2025
Viewed by 420
Abstract
Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework [...] Read more.
Low-altitude unmanned aerial vehicle (UAV) vision is critically hindered by the Sim-to-Real Gap, where models trained exclusively on simulation data degrade under real-world variations in lighting, texture, and weather. To address this problem, we propose RA3T (Region-Aligned 3D Transformer), a novel self-supervised framework that enables robust Sim-to-Real adaptation. Specifically, we first develop a dual-branch strategy for self-supervised feature learning, integrating Masked Autoencoders and contrastive learning. This approach extracts domain-invariant representations from unlabeled simulated imagery to enhance robustness against occlusion while reducing annotation dependency. Leveraging these learned features, we then introduce a 3D Transformer fusion module that unifies multi-view RGB and LiDAR point clouds through cross-modal attention. By explicitly modeling spatial layouts and height differentials, this component significantly improves recognition of small and occluded targets in complex low-altitude environments. To address persistent fine-grained domain shifts, we finally design region-level adversarial calibration that deploys local discriminators on partitioned feature maps. This mechanism directly aligns texture, shadow, and illumination discrepancies which challenge conventional global alignment methods. Extensive experiments on UAV benchmarks VisDrone and DOTA demonstrate the effectiveness of RA3T. The framework achieves +5.1% mAP on VisDrone and +7.4% mAP on DOTA over the 2D adversarial baseline, particularly on small objects and sparse occlusions, while maintaining real-time performance of 17 FPS at 1024 × 1024 resolution on an RTX 4080 GPU. Visual analysis confirms that the synergistic integration of 3D geometric encoding and local adversarial alignment effectively mitigates domain gaps caused by uneven illumination and perspective variations, establishing an efficient pathway for simulation-to-reality UAV perception. Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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27 pages, 1533 KB  
Article
Sound Source Localization Using Hybrid Convolutional Recurrent Neural Networks in Undesirable Conditions
by Bastian Estay Zamorano, Ali Dehghan Firoozabadi, Alessio Brutti, Pablo Adasme, David Zabala-Blanco, Pablo Palacios Játiva and Cesar A. Azurdia-Meza
Electronics 2025, 14(14), 2778; https://doi.org/10.3390/electronics14142778 - 10 Jul 2025
Viewed by 800
Abstract
Sound event localization and detection (SELD) is a fundamental task in spatial audio processing that involves identifying both the type and location of sound events in acoustic scenes. Current SELD models often struggle with low signal-to-noise ratios (SNRs) and high reverberation. This article [...] Read more.
Sound event localization and detection (SELD) is a fundamental task in spatial audio processing that involves identifying both the type and location of sound events in acoustic scenes. Current SELD models often struggle with low signal-to-noise ratios (SNRs) and high reverberation. This article addresses SELD by reformulating direction of arrival (DOA) estimation as a multi-class classification task, leveraging deep convolutional recurrent neural networks (CRNNs). We propose and evaluate two modified architectures: M-DOAnet, an optimized version of DOAnet for localization and tracking, and M-SELDnet, a modified version of SELDnet, which has been designed for joint SELD. Both modified models were rigorously evaluated on the STARSS23 dataset, which comprises 13-class, real-world indoor scenes totaling over 7 h of audio, using spectrograms and acoustic intensity maps from first-order Ambisonics (FOA) signals. M-DOAnet achieved exceptional localization (6.00° DOA error, 72.8% F1-score) and perfect tracking (100% MOTA with zero identity switches). It also demonstrated high computational efficiency, training in 4.5 h (164 s/epoch). In contrast, M-SELDnet delivered strong overall SELD performance (0.32 rad DOA error, 0.75 F1-score, 0.38 error rate, 0.20 SELD score), but with significantly higher resource demands, training in 45 h (1620 s/epoch). Our findings underscore a clear trade-off between model specialization and multifunctionality, providing practical insights for designing SELD systems in real-time and computationally constrained environments. Full article
(This article belongs to the Special Issue Recent Advances in Audio, Speech and Music Processing and Analysis, 2nd Edition)
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22 pages, 2113 KB  
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 494
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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22 pages, 2867 KB  
Article
Hierarchical Deep Reinforcement Learning-Based Path Planning with Underlying High-Order Control Lyapunov Function—Control Barrier Function—Quadratic Programming Collision Avoidance Path Tracking Control of Lane-Changing Maneuvers for Autonomous Vehicles
by Haochong Chen and Bilin Aksun-Guvenc
Electronics 2025, 14(14), 2776; https://doi.org/10.3390/electronics14142776 - 10 Jul 2025
Viewed by 854
Abstract
Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, [...] Read more.
Path planning and collision avoidance are essential components of an autonomous driving system (ADS), ensuring safe navigation in complex environments shared with other road users. High-quality planning and reliable obstacle avoidance strategies are essential for advancing the SAE autonomy level of autonomous vehicles, which can largely reduce the risk of traffic accidents. In daily driving scenarios, lane changing is a common maneuver used to avoid unexpected obstacles such as parked vehicles or suddenly appearing pedestrians. Notably, lane-changing behavior is also widely regarded as a key evaluation criterion in driver license examinations, highlighting its practical importance in real-world driving. Motivated by this observation, this paper aims to develop an autonomous lane-changing system capable of dynamically avoiding obstacles in multi-lane traffic environments. To achieve this objective, we propose a hierarchical decision-making and control framework in which a Double Deep Q-Network (DDQN) agent operates as the high-level planner to select lane-level maneuvers, while a High-Order Control Lyapunov Function–High-Order Control Barrier Function–based Quadratic Program (HOCLF-HOCBF-QP) serves as the low-level controller to ensure safe and stable trajectory tracking under dynamic constraints. Simulation studies are used to evaluate the planning efficiency and overall collision avoidance performance of the proposed hierarchical control framework. The results demonstrate that the system is capable of autonomously executing appropriate lane-changing maneuvers to avoid multiple obstacles in complex multi-lane traffic environments. In computational cost tests, the low-level controller operates at 100 Hz with an average solve time of 0.66 ms per step, and the high-level policy operates at 5 Hz with an average solve time of 0.60 ms per step. The results demonstrate real-time capability in autonomous driving systems. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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18 pages, 3941 KB  
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 526
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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23 pages, 728 KB  
Article
BASK: Backdoor Attack for Self-Supervised Encoders with Knowledge Distillation Survivability
by Yihong Zhang, Guojia Li, Yihui Zhang, Yan Cao, Mingyue Cao and Chengyao Xue
Electronics 2025, 14(13), 2724; https://doi.org/10.3390/electronics14132724 - 6 Jul 2025
Viewed by 739
Abstract
Backdoor attacks in self-supervised learning pose an increasing threat. Recent studies have shown that knowledge distillation can mitigate these attacks by altering feature representations. In response, we propose BASK, a novel backdoor attack that remains effective after distillation. BASK uses feature weighting and [...] Read more.
Backdoor attacks in self-supervised learning pose an increasing threat. Recent studies have shown that knowledge distillation can mitigate these attacks by altering feature representations. In response, we propose BASK, a novel backdoor attack that remains effective after distillation. BASK uses feature weighting and representation alignment strategies to implant persistent backdoors into the encoder’s feature space. This enables transferability to student models. We evaluated BASK on the CIFAR-10 and STL-10 datasets and compared it with existing self-supervised backdoor attacks under four advanced defenses: SEED, MKD, Neural Cleanse, and MiMiC. Our experimental results demonstrate that BASK maintains high attack success rates while preserving downstream task performance. This highlights the robustness of BASK and the limitations of current defense mechanisms. Full article
(This article belongs to the Special Issue Advancements in AI-Driven Cybersecurity and Securing AI Systems)
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