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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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22 pages, 1612 KB  
Article
Lightweight 1D-CNN-Based Battery State-of-Charge Estimation and Hardware Development
by Seungbum Kang, Yoonjae Lee, Gahyeon Jang and Seongsoo Lee
Electronics 2026, 15(3), 704; https://doi.org/10.3390/electronics15030704 - 6 Feb 2026
Cited by 2 | Viewed by 918
Abstract
This paper presents the FPGA implementation and verification of a lightweight one-dimensional convolutional neural network (1D-CNN) pipeline for real-time battery state-of-charge (SoC) estimation in automotive battery management systems. The proposed model employs separable 1D convolution and global average pooling, and applies aggressive structured [...] Read more.
This paper presents the FPGA implementation and verification of a lightweight one-dimensional convolutional neural network (1D-CNN) pipeline for real-time battery state-of-charge (SoC) estimation in automotive battery management systems. The proposed model employs separable 1D convolution and global average pooling, and applies aggressive structured pruning to reduce the number of parameters from 3121 to 358, representing an 88.5% reduction, without significant accuracy loss. Using quantization-aware training (QAT), the network is trained and executed in INT8, which reduces weight storage to one-quarter of the 32-bit baseline while maintaining high estimation accuracy with a Mean Absolute Error (MAE) of 0.0172. The hardware adopts a time-multiplexed single MAC architecture with FSM control, occupying 98,410 gates under a 28 nm process. Evaluations on an FPGA testbed with representative drive-cycle inputs show that the proposed INT8 pipeline achieves performance comparable to the floating-point reference with negligible precision drop, demonstrating its suitability for in-vehicle BMS deployment. Full article
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37 pages, 501 KB  
Article
Comparative Analysis of Attribute-Based Encryption Schemes for Special Internet of Things Applications
by Łukasz Pióro, Krzysztof Kanciak and Zbigniew Zieliński
Electronics 2026, 15(3), 697; https://doi.org/10.3390/electronics15030697 - 5 Feb 2026
Cited by 3 | Viewed by 1273
Abstract
Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, [...] Read more.
Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, and context-aware security management in modern Internet of Things (IoT) applications. ABAC controls access based on attributes associated with users, devices, resources, and environmental conditions rather than fixed roles, making it highly adaptable to the complex and heterogeneous nature of IoT ecosystems. ABE can significantly improve the security and manageability of modern military IoT systems. Nevertheless, its practical implementation requires obtaining a range of performance data and assessing the additional overhead, particularly regarding data transmission efficiency. This paper provides a comparative analysis of the performance of two cryptographic schemes for attribute-based encryption in the context of special Internet of Things (IoT) applications. This applies to special environments, both military and civilian, where infrastructure is unreliable and dynamic and decisions must be made locally and in near-real time. From a security perspective, there is a need for strong authentication, precise access control, and a zero-trust approach at the network edge as well. The CIRCL scheme, based on traditional pairing-based ABE (CP-ABE), is compared with the newer Covercrypt scheme, a hybrid key encapsulation mechanism with access control (KEMAC) that provides quantum resistance. The main goal is to determine which scheme scales better and meets the performance requirements for two different scenarios: large corporate networks (where scalability is key) and tactical edge networks (where minimal bandwidth and post-quantum security are paramount). The benchmark results are used to compare the operating costs in detail, such as the key generation time, message encryption and decryption times, public key size, and cipher overhead, showing that Covercrypt provides a reduction in ciphertext overhead in tactical scenarios, while CIRCL offers faster decryption throughput in large-scale enterprise environments. It is concluded that the optimal choice depends on the specific constraints of the operating environment. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
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25 pages, 7527 KB  
Article
Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT
by Matthew Boeding, Michael Hempel, Hamid Sharif and Juan Lopez, Jr.
Electronics 2026, 15(3), 692; https://doi.org/10.3390/electronics15030692 - 5 Feb 2026
Cited by 1 | Viewed by 791
Abstract
The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly [...] Read more.
The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures. Full article
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28 pages, 8751 KB  
Article
LiDAR–RADAR Sensor Fusion and Telemetry Data Integration for Obstacle Detection Along the UAS Navigation Trajectory
by Luigi Farina, Francesco Lo Caso, Gennaro Ariante, Aniello Menichino, Michele Inverno, Vittorio Di Vito, Salvatore Ponte and Giuseppe Del Core
Electronics 2026, 15(3), 685; https://doi.org/10.3390/electronics15030685 - 4 Feb 2026
Cited by 1 | Viewed by 1552
Abstract
Today, the use of UASs (unmanned aerial systems) is rapidly expanding across civil, military, and scientific applications. The deployment of drones in close proximity to urban areas is becoming increasingly common, particularly during missions conducted beyond visual line of sight (BVLOS) or in [...] Read more.
Today, the use of UASs (unmanned aerial systems) is rapidly expanding across civil, military, and scientific applications. The deployment of drones in close proximity to urban areas is becoming increasingly common, particularly during missions conducted beyond visual line of sight (BVLOS) or in fully autonomous modes. Advancements in technology have enabled the development of systems and platforms that no longer require a human operator onboard and are equipped with progressively higher levels of autonomy. Therefore, enhancing onboard systems is crucial to ensure a high level of operational safety, particularly during missions conducted in harsh and complex environments, such as urban and suburban areas, where the presence of a large number of static and dynamic obstacles, including pedestrians, vehicles, and other aircraft, is pervasive. In this context, the implementation and integration of multiple onboard devices and sensors represent the core focus of this work, with the objective of improving perception, navigation, and safety capabilities for autonomous UAV operations. In particular, communication channels, hardware integration, and data fusion techniques have been implemented and evaluated to improve system performance and situational awareness. This work presents the hardware and software integration of LiDAR and radar sensors with a Pixhawk autopilot and a Raspberry Pi companion computer, aimed at developing obstacle detection applications. Full article
(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, 2nd Edition)
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18 pages, 538 KB  
Article
Enhancing Vehicle IoT Security with PQC: A Lightweight Approach for Encrypted Sensor Data Transmission
by Jackson Diaz-Gorrin and Candido Caballero-Gil
Electronics 2026, 15(3), 684; https://doi.org/10.3390/electronics15030684 - 4 Feb 2026
Viewed by 1095
Abstract
Cybersecurity threats are evolving constantly, and the arrival of quantum computing raises serious doubts about whether today’s cryptographic methods will hold up over time. This concern has motivated interest in algorithms designed to resist future attacks, with CRYSTALS-Kyber emerging as a practical candidate [...] Read more.
Cybersecurity threats are evolving constantly, and the arrival of quantum computing raises serious doubts about whether today’s cryptographic methods will hold up over time. This concern has motivated interest in algorithms designed to resist future attacks, with CRYSTALS-Kyber emerging as a practical candidate and forming the basis of an NIST post-quantum standard. This study focuses on protecting data exchanged between a vehicle sensor suite and cloud services over the Message Queuing Telemetry Transport protocol. Performance must remain acceptable; therefore, attention centers on lightweight and efficient execution while leveraging the board’s hardware capabilities to keep latency and resource usage low. Adding this layer of post-quantum encryption helps limit the exposure of critical telemetry and control data to sophisticated adversaries. It also aims to preserve integrity and confidentiality in vehicular communications as the Internet of Things becomes increasingly connected. This approach maintains a practical balance between forward-looking security and real-world deployability. Full article
(This article belongs to the Special Issue New Technologies in Applied Cryptography and Network Security)
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27 pages, 1148 KB  
Article
Preference-Aligned Ride-Sharing Repositioning via a Two-Stage Bilevel RLHF Framework
by Ruihan Li and Vaneet Aggarwal
Electronics 2026, 15(3), 669; https://doi.org/10.3390/electronics15030669 - 3 Feb 2026
Viewed by 578
Abstract
Vehicle repositioning is essential for improving efficiency and service quality in ride-sharing platforms, yet existing approaches typically optimize proxy rewards that fail to reflect human-centered preferences such as wait time, service coverage, and unnecessary empty travel. We propose the first two-stage Bilevel Reinforcement [...] Read more.
Vehicle repositioning is essential for improving efficiency and service quality in ride-sharing platforms, yet existing approaches typically optimize proxy rewards that fail to reflect human-centered preferences such as wait time, service coverage, and unnecessary empty travel. We propose the first two-stage Bilevel Reinforcement Learning (RL) from Human Feedback (RLHF) framework for preference-aligned vehicle repositioning. In Stage 1, a value-based Deep Q-Network (DQN)-RLHF warm start learns an initial preference-aligned reward model and stable reference policy, mitigating the reward-model drift and cold-start instability that arise when applying on-policy RLHF directly. In Stage 2, a Kullback–Leibler (KL)-regularized Proximal Policy Optimization (PPO)-RLHF algorithm, equipped with action masking, behavioral-cloning anchoring, and alternating forward–reverse KL, fine-tunes the repositioning policy using either Large Language Model (LLM)-generated or rubric-based preference labels. We develop and compare two coordination schemes, pure alternating (PPO-Alternating) and k-step alternating (PPO-k-step), demonstrating that both yield consistent improvements across all tested arrival scales. Empirically, our framework reduces wait time and empty-mile ratio while improving served rate, without inducing trade-offs or reducing platform profit. These results show that human preference alignment can be stably and effectively incorporated into large-scale ride-sharing repositioning. Full article
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24 pages, 29852 KB  
Article
Dual-Axis Transformer-GNN Framework for Touchless Finger Location Sensing by Using Wi-Fi Channel State Information
by Minseok Koo and Jaesung Park
Electronics 2026, 15(3), 565; https://doi.org/10.3390/electronics15030565 - 28 Jan 2026
Cited by 1 | Viewed by 636
Abstract
Camera, lidar, and wearable-based gesture recognition technologies face practical limitations such as lighting sensitivity, occlusion, hardware cost, and user inconvenience. Wi-Fi channel state information (CSI) can be used as a contactless alternative to capture subtle signal variations caused by human motion. However, existing [...] Read more.
Camera, lidar, and wearable-based gesture recognition technologies face practical limitations such as lighting sensitivity, occlusion, hardware cost, and user inconvenience. Wi-Fi channel state information (CSI) can be used as a contactless alternative to capture subtle signal variations caused by human motion. However, existing CSI-based methods are highly sensitive to domain shifts and often suffer notable performance degradation when applied to environments different from the training conditions. To address this issue, we propose a domain-robust touchless finger location sensing framework that operates reliably even in a single-link environment composed of commercial Wi-Fi devices. The proposed system applies preprocessing procedures to reduce noise and variability introduced by environmental factors and introduces a multi-domain segment combination strategy to increase the domain diversity during training. In addition, the dual-axis transformer learns temporal and spatial features independently, and the GNN-based integration module incorporates relationships among segments originating from different domains to produce more generalized representations. The proposed model is evaluated using CSI data collected from various users and days; experimental results show that the proposed method achieves an in-domain accuracy of 99.31% and outperforms the best baseline by approximately 4% and 3% in cross-user and cross-day evaluation settings, respectively, even in a single-link setting. Our work demonstrates a viable path for robust, calibration-free finger-level interaction using ubiquitous single-link Wi-Fi in real-world and constrained environments, providing a foundation for more reliable contactless interaction systems. Full article
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25 pages, 5185 KB  
Review
A Review of Routing and Resource Optimization in Quantum Networks
by Md. Shazzad Hossain Shaon and Mst Shapna Akter
Electronics 2026, 15(3), 557; https://doi.org/10.3390/electronics15030557 - 28 Jan 2026
Viewed by 1825
Abstract
Quantum computing is a new discipline that uses the ideas of quantum physics to do calculations that are not possible with conventional computers. Quantum bits, called qubits, could exist in superposition states, making them suitable for parallel processing in contrast to traditional bits. [...] Read more.
Quantum computing is a new discipline that uses the ideas of quantum physics to do calculations that are not possible with conventional computers. Quantum bits, called qubits, could exist in superposition states, making them suitable for parallel processing in contrast to traditional bits. When it comes to addressing complex challenges like proof simulation, optimization, and cryptography, quantum entanglement and quantum interference provide exponential improvements. This survey focuses on recent advances in entanglement routing, quantum key distribution (QKD), and qubit management for short- and long-distance quantum communication. It studies optimization approaches such as integer programming, reinforcement learning, and collaborative methods, evaluating their efficacy in terms of throughput, scalability, and fairness. Despite improvements, challenges remain in dynamic network adaptation, resource limits, and error correction. Addressing these difficulties necessitates the creation of hybrid quantum–classical algorithms for efficient resource allocation, hardware-aware designs to improve real-world deployment, and fault-tolerant architecture. Therefore, this survey suggests that future research focus on integrating quantum networks with existing classical infrastructure to improve security, dependability, and mainstream acceptance. This connection has significance for applications that require secure communication, financial transactions, and critical infrastructure protection. Full article
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29 pages, 2945 KB  
Article
Physics-Informed Neural Network for Denoising Images Using Nonlinear PDE
by Carlos Osorio Quero and Maria Liz Crespo
Electronics 2026, 15(3), 560; https://doi.org/10.3390/electronics15030560 - 28 Jan 2026
Cited by 1 | Viewed by 2571
Abstract
Noise remains a persistent limitation in coherent imaging systems, degrading image quality and hindering accurate interpretation in critical applications such as remote sensing, medical imaging, and non-destructive testing. This paper presents a physics-informed deep learning framework for effective image denoising under complex noise [...] Read more.
Noise remains a persistent limitation in coherent imaging systems, degrading image quality and hindering accurate interpretation in critical applications such as remote sensing, medical imaging, and non-destructive testing. This paper presents a physics-informed deep learning framework for effective image denoising under complex noise conditions. The proposed approach integrates nonlinear partial differential equations (PDEs), including the heat equation, diffusion models, MPMC, and the Zhichang Guo (ZG) method, into advanced neural network architectures such as ResUNet, UNet, U2Net, and Res2UNet. By embedding physical constraints directly into the training process, the framework couples data-driven learning with physics-based priors to enhance noise suppression and preserve structural details. Experimental evaluations across multiple datasets demonstrate that the proposed method consistently outperforms conventional denoising techniques, achieving higher PSNR, SSIM, ENL, and CNR values. These results confirm the effectiveness of combining physics-informed neural networks with deep architectures and highlight their potential for advanced image restoration in real-world, high-noise imaging scenarios. Full article
(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network: 2nd Edition)
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18 pages, 42966 KB  
Article
A Model-Based Design and Verification Framework for Virtual ECUs in Automotive Seat Control Systems
by Anna Yang, Woo Jin Han, Hyun Suk Cho, Dong-Woo Koh and Jae-Gon Kim
Electronics 2026, 15(3), 569; https://doi.org/10.3390/electronics15030569 - 28 Jan 2026
Viewed by 1559
Abstract
As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform [...] Read more.
As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform configuration, and vECU (Virtual Electronic Control Unit) execution into a single, repeatable development workflow. Control logic validated in Simulink is translated into AUTOSAR-compliant software, built into a QEMU (Quick EMUlator)-based vECU, and exercised in DRIM-SimHub using both virtual stimuli and a real sensor–actuator signal delivered through a dedicated I/O interface board. Using a seat–slide virtual limit controller as a representative case, the proposed workflow enables consistent reuse of the test scenarios across model-in-the-loop (MiL), software-in-the-loop (SiL), and virtual ECU stages, while preserving production-level timing behavior and the semantics of the AUTOSAR runtime. The experimental results show that the vECU accurately reproduces the PWM outputs, Hall sensor pulse timing, and limit–stop decisions of physical ECU, and that integration issues previously discovered only in HiL tests can be exposed much earlier. Overall, the workflow shortens verification cycles, improves the observability of timing-dependent behavior, and provides a practical basis for early validation in software-defined vehicle development. Full article
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26 pages, 48080 KB  
Article
Teleoperation of Dual-Arm Manipulators via VR Interfaces: A Framework Integrating Simulation and Real-World Control
by Alejandro Torrejón, Sergio Eslava, Jorge Calderón, Pedro Núñez and Pablo Bustos
Electronics 2026, 15(3), 572; https://doi.org/10.3390/electronics15030572 - 28 Jan 2026
Cited by 2 | Viewed by 2015
Abstract
We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each [...] Read more.
We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each arm’s tool point via VR controllers with natural depth perception and motion tracking. The same control interface is seamlessly extended to a physical dual-arm robot, enabling teleoperation within the same VR environment. Our architecture supports real-time bidirectional communication between the VR layer and both the simulator and hardware, enabling responsive control and feedback. We describe the system design and performance evaluation in both domains, demonstrating the viability of immersive VR as a unified interface for simulation and physical robot control. Full article
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24 pages, 1253 KB  
Article
Re-Evaluating Android Malware Detection: Tabular Features, Vision Models, and Ensembles
by Prajwal Hosahalli Dayananda and Zesheng Chen
Electronics 2026, 15(3), 544; https://doi.org/10.3390/electronics15030544 - 27 Jan 2026
Cited by 1 | Viewed by 1540
Abstract
Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular features and image-based representations have been explored for Android [...] Read more.
Static, machine learning-based malware detection is widely used in Android security products, where even small increases in false-positive rates can impose significant burdens on analysts and cause unacceptable disruptions for end users. Both tabular features and image-based representations have been explored for Android malware detection. However, existing public benchmark datasets do not provide paired tabular and image representations for the same samples, limiting direct comparisons between tabular models and vision-based models. This work investigates whether carefully engineered, domain-specific tabular features can match or surpass the performance of state-of-the-art deep vision models under strict false-positive-rate constraints, and whether ensemble approaches justify their additional complexity. To enable this analysis, we construct a large corpus of Android applications with paired static representations and evaluate six popular machine learning models on the exact same samples: two tabular models using EMBER features, two tabular models using extended EMBER features, and two vision-based models using malware images. Our results show that a LightGBM model trained on extended EMBER features outperforms all other evaluated models, as well as a state-of-the-art approach trained on a much larger dataset. Furthermore, we develop an ensemble model combining both tabular and vision-based detectors, which yields a modest performance improvement but at the cost of substantial additional computational and engineering overhead. Full article
(This article belongs to the Special Issue Feature Papers in Networks: 2025–2026 Edition)
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11 pages, 4271 KB  
Article
A Low-Power High-Precision Discrete-Time Delta–Sigma Modulator for Battery Management System
by Ying Li and Wenyuan Li
Electronics 2026, 15(3), 535; https://doi.org/10.3390/electronics15030535 - 26 Jan 2026
Viewed by 1453
Abstract
This paper presents a low-power high-precision Discrete-Time Delta–Sigma (DT-DS) analog-to-digital converter (ADC) for a Battery Management System (BMS), which is critical for monitoring key battery parameters such as voltage, current, and temperature. This design employs a second-order Cascade of Integrators FeedForward (CIFF) architecture [...] Read more.
This paper presents a low-power high-precision Discrete-Time Delta–Sigma (DT-DS) analog-to-digital converter (ADC) for a Battery Management System (BMS), which is critical for monitoring key battery parameters such as voltage, current, and temperature. This design employs a second-order Cascade of Integrators FeedForward (CIFF) architecture using a hybrid chopping technique to effectively suppress 1/f noise and offset. Fabricated in a 180 nm Bipolar-CMOS-DMOS (BCD) process, the ADC achieves a peak signal-to-noise ratio (SNR) of 91.2 dB and a peak signal-to-noise-and-distortion ratio (SNDR) of 90.6 dB within a 600 Hz bandwidth, while consuming only 35 µA from a 1.8 V supply. This corresponds to a figure-of-merit (FoM) of 160.4 dB, calculated based on the SNDR, bandwidth, and power dissipation. Full article
(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles, Volume 2)
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18 pages, 1408 KB  
Article
Joint Effect of Signal Strength, Bitrate, and Topology on Video Playback Delays of 802.11ax Gigabit Wi-Fi
by Nurul I. Sarkar and Sonia Gul
Electronics 2026, 15(3), 531; https://doi.org/10.3390/electronics15030531 - 26 Jan 2026
Viewed by 972
Abstract
This paper presents a performance evaluation of IEEE 802.11ax (Wi-Fi 6) networks using a combination of real-world testbed measurements and simulation-based analysis. The paper investigates the combined effect of received signal strength (RSSI), application bitrate, and network topology on video playback delays of [...] Read more.
This paper presents a performance evaluation of IEEE 802.11ax (Wi-Fi 6) networks using a combination of real-world testbed measurements and simulation-based analysis. The paper investigates the combined effect of received signal strength (RSSI), application bitrate, and network topology on video playback delays of 802.11ax. The effect of frequency band and client density on system performance is also investigated. Testbed measurements and field experiments were conducted in indoor environments using dual-band (2.4 GHz and 5 GHz) ad hoc and infrastructure network configurations. OMNeT++ based simulations are conducted to explore scalability by increasing the number of wireless clients. The results obtained show that the infrastructure-based deployments provide more stable video playback than the ad hoc network, particularly under varying RSSI conditions. While the 5 GHz band delivers higher throughput at a short range, the 2.4 GHz band offers improved coverage at reduced system performance. The simulation results further demonstrate significant degradation in throughput and latency as client density increases. To contextualize the observed performance, a baseline comparison with 802.11ac is incorporated, highlighting the relative improvements and remaining limitations of 802.11ax within the evaluated signal and load conditions. The findings provide practical deployment insights for video-centric wireless networks and inform the optimization of next-generation Wi-Fi deployments. Full article
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20 pages, 1385 KB  
Article
Development of an IoT System for Acquisition of Data and Control Based on External Battery State of Charge
by Aleksandar Valentinov Hristov, Daniela Gotseva, Roumen Ivanov Trifonov and Jelena Petrovic
Electronics 2026, 15(3), 502; https://doi.org/10.3390/electronics15030502 - 23 Jan 2026
Cited by 1 | Viewed by 1086
Abstract
In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with [...] Read more.
In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article
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13 pages, 2210 KB  
Article
High-Throughput Control-Data Acquisition for Multicore MCU-Based Real-Time Control Systems Using Double Buffering over Ethernet
by Seung-Hun Lee, Duc M. Tran and Joon-Young Choi
Electronics 2026, 15(2), 469; https://doi.org/10.3390/electronics15020469 - 22 Jan 2026
Viewed by 846
Abstract
For the design, implementation, performance optimization, and predictive maintenance of high-speed real-time control systems with sub-millisecond control periods, the capability to acquire large volumes of high-rate control data in real time is required without interfering with normal control operation that is repeatedly executed [...] Read more.
For the design, implementation, performance optimization, and predictive maintenance of high-speed real-time control systems with sub-millisecond control periods, the capability to acquire large volumes of high-rate control data in real time is required without interfering with normal control operation that is repeatedly executed in each extremely short control cycle. In this study, we propose a control-data acquisition method for high-speed real-time control systems with sub-millisecond control periods, in which control data are transferred to an external host device via Ethernet in real time. To enable the transmission of high-rate control data without disturbing the real-time control operation, a multicore microcontroller unit (MCU) is adopted, where the control task and the data transmission task are executed on separately assigned central processing unit (CPU) cores. Furthermore, by applying a double-buffering algorithm, continuous Ethernet communication without intermediate waiting time is achieved, resulting in a substantial improvement in transmission throughput. Using a control card based on TI’s multicore MCU TMS320F28388D, which consists of dual digital signal processor cores and one connectivity manager (CM) core, the proposed control-data acquisition method is implemented and an actual experimental environment is constructed. Experimental results show that the double-buffering transmission achieves a maximum throughput of 94.2 Mbps on a 100 Mbps Fast Ethernet link, providing a 38.5% improvement over the single-buffering case and verifying the high performance and efficiency of the proposed data acquisition method. Full article
(This article belongs to the Section Industrial Electronics)
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28 pages, 2192 KB  
Article
AptEVS: Adaptive Edge-and-Vehicle Scheduling for Hierarchical Federated Learning over Vehicular Networks
by Yu Tian, Nina Wang, Zongshuai Zhang, Wenhao Zou, Liangjie Zhao, Shiyao Liu and Lin Tian
Electronics 2026, 15(2), 479; https://doi.org/10.3390/electronics15020479 - 22 Jan 2026
Viewed by 522
Abstract
Hierarchical federated learning (HFL) has emerged as a promising paradigm for distributed machine learning over vehicular networks. Despite recent advances in vehicle selection and resource allocation, most still adopt a fixed Edge-and-Vehicle Scheduling (EVS) configuration that keeps the number of participating edge nodes [...] Read more.
Hierarchical federated learning (HFL) has emerged as a promising paradigm for distributed machine learning over vehicular networks. Despite recent advances in vehicle selection and resource allocation, most still adopt a fixed Edge-and-Vehicle Scheduling (EVS) configuration that keeps the number of participating edge nodes and vehicles per node constant across training rounds. However, given the diverse training tasks and dynamic vehicular environments, our experiments confirm that such static configurations struggle to efficiently meet the task-specific requirements across model accuracy, time delay, and energy consumption. To address this, we first formulate a unified, long-term training cost metric that balances these conflicting objectives. We then propose AptEVS, an adaptive scheduling framework based on deep reinforcement learning (DRL), designed to minimize this cost. The core of AptEVS is its phase-aware design, which adapts the scheduling strategy by first identifying the current training phase and then switching to specialized strategies accordingly. Extensive simulations demonstrate that AptEVS learns an effective scheduling policy online from scratch, consistently outperforming baselines and and reducing the long-term training cost by up to 66.0%. Our findings demonstrate that phase-aware DRL is both feasible and highly effective for resource scheduling over complex vehicular networks. Full article
(This article belongs to the Special Issue Technology of Mobile Ad Hoc Networks)
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26 pages, 3088 KB  
Article
A Human-Centered Visual Cognitive Framework for Traffic Pair Crossing Identification in Human–Machine Teaming
by Bufan Liu, Sun Woh Lye, Terry Liang Khin Teo and Hong Jie Wee
Electronics 2026, 15(2), 477; https://doi.org/10.3390/electronics15020477 - 22 Jan 2026
Cited by 1 | Viewed by 557
Abstract
Human–machine teaming (HMT) in air traffic management (ATM) promises safer, more efficient operations by combining human expertise in decision-making with machine efficiency in data processing, where traffic pair crossing identification is crucial for effective conflict detection and resolution by recognizing aircraft pairs that [...] Read more.
Human–machine teaming (HMT) in air traffic management (ATM) promises safer, more efficient operations by combining human expertise in decision-making with machine efficiency in data processing, where traffic pair crossing identification is crucial for effective conflict detection and resolution by recognizing aircraft pairs that may lead to conflict. To facilitate this goal, this paper presents a four-phase cognitive framework to enhance HMT for monitoring traffic pairs at crossing points through a human-centered, visual-based approach. The visual cognitive framework integrates three data streams—eye-tracking metrics, mouse-over actions, and issued radar commands—to capture the traffic context from the controller’s perspective. A target pair identification method is designed to generate potential conflict pairs. Controller behavior is then modeled using a sighting timeline, yielding insights to develop the cognitive mechanism. Using air traffic crossing-conflict monitoring in en route airspace as a case study, the framework successfully captures the state of controllers’ monitoring and awareness behavior through tests on five target flight pairs under various crossing conditions. Specifically, aware monitoring activities are characterized by higher fixation count on either flight across a 10 min window, with 53% to 100% of visual input activities occurring between 8 to 7 and 3 to 2 min before crossing, ensuring timely conflict management. Furthermore, the study quantifies the effect of crossing geometry, whereby narrow-angle crossings (21 degrees) require significantly higher monitoring intensity (15 paired sightings) compared to wide or moderate angle crossings. These results indicate that controllers exhibit distinct monitoring and awareness behaviors when identifying and managing conflicts across the different test pairs, demonstrating the effectiveness and applicability of the proposed visual cognitive framework. Full article
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28 pages, 1241 KB  
Article
Joint Learning for Metaphor Detection and Interpretation Based on Gloss Interpretation
by Yanan Liu, Hai Wan and Jinxia Lin
Electronics 2026, 15(2), 456; https://doi.org/10.3390/electronics15020456 - 21 Jan 2026
Viewed by 810
Abstract
Metaphor is ubiquitous in daily communication and makes language expression more vivid. Identifying metaphorical words, known as metaphor detection, is crucial for capturing the real meaning of a sentence. As an important step of metaphorical understanding, the correct interpretation of metaphorical words [...] Read more.
Metaphor is ubiquitous in daily communication and makes language expression more vivid. Identifying metaphorical words, known as metaphor detection, is crucial for capturing the real meaning of a sentence. As an important step of metaphorical understanding, the correct interpretation of metaphorical words directly affects metaphor detection. This article investigates how to use metaphor interpretation to enhance metaphor detection. Since previous approaches for metaphor interpretation are coarse-grained or constrained by ambiguous meanings of substitute words, we propose a different interpretation mechanism that explains metaphorical words by means of gloss-based interpretations. To comprehensively explore the optimal joint strategy, we go beyond previous work by designing diverse model architectures. We investigate both classification and sequence labeling paradigms, incorporating distinct component designs based on MIP and SPV theories. Furthermore, we integrate Part-of-Speech tags and external knowledge to further refine the feature representation. All methods utilize pre-trained language models to encode text and capture semantic information of the text. Since this mechanism involves both metaphor detection and metaphor interpretation but there is a lack of datasets annotated for both tasks, we have enhanced three datasets with glosses for metaphor detection: one Chinese dataset (PSUCMC) and two English datasets (TroFi and VUA). Experimental results demonstrate that the proposed joint methods are superior to or at least comparable to state-of-the-art methods on the three enhanced datasets. Results confirm that joint learning of metaphor detection and gloss-based interpretation makes metaphor detection more accurate. Full article
(This article belongs to the Section Artificial Intelligence)
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31 pages, 3765 KB  
Article
Rain Detection in Solar Insecticidal Lamp IoTs Systems Based on Multivariate Wireless Signal Feature Learning
by Lingxun Liu, Lei Shu, Yiling Xu, Kailiang Li, Ru Han, Qin Su and Jiarui Fang
Electronics 2026, 15(2), 465; https://doi.org/10.3390/electronics15020465 - 21 Jan 2026
Cited by 1 | Viewed by 567
Abstract
Solar insecticidal lamp Internet of Things (SIL-IoTs) systems are widely deployed in agricultural environments, where accurate and timely rain-detection is crucial for system stability and energy-efficient operation. However, existing rain-sensing solutions rely on additional hardware, leading to increased cost and maintenance complexity. This [...] Read more.
Solar insecticidal lamp Internet of Things (SIL-IoTs) systems are widely deployed in agricultural environments, where accurate and timely rain-detection is crucial for system stability and energy-efficient operation. However, existing rain-sensing solutions rely on additional hardware, leading to increased cost and maintenance complexity. This study proposes a hardware-free rain detection method based on multivariate wireless signal feature learning, using LTE communication data. A large-scale primary dataset containing 11.84 million valid samples was collected from a real farmland SIL-IoTs deployment in Nanjing, recording RSRP, RSRQ, and RSSI at 1 Hz. To address signal heterogeneity, a signal-strength stratification strategy and a dual-rate EWMA-based adaptive signal-leveling mechanism were introduced. Four machine-learning models—Logistic Regression, Random Forest, XGBoost, and LightGBM—were trained and evaluated using both the primary dataset and an external test dataset collected in Changsha and Dongguan. Experimental results show that XGBoost achieves the highest detection accuracy, whereas LightGBM provides a favorable trade-off between performance and computational cost. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC indicates that all metrics exceed 0.975. The proposed method demonstrates strong accuracy, robustness, and cross-regional generalization, providing a practical and scalable solution for rain detection in agricultural IoT systems without additional sensing hardware. Full article
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18 pages, 3705 KB  
Article
Cross-Platform Multi-Modal Transfer Learning Framework for Cyberbullying Detection
by Weiqi Zhang, Chengzu Dong, Aiting Yao, Asef Nazari and Anuroop Gaddam
Electronics 2026, 15(2), 442; https://doi.org/10.3390/electronics15020442 - 20 Jan 2026
Viewed by 1060
Abstract
Cyberbullying and hate speech increasingly appear in multi-modal social media posts, where images and text are combined in diverse and fast changing ways across platforms. These posts differ in style, vocabulary and layout, and labeled data are sparse and noisy, which makes it [...] Read more.
Cyberbullying and hate speech increasingly appear in multi-modal social media posts, where images and text are combined in diverse and fast changing ways across platforms. These posts differ in style, vocabulary and layout, and labeled data are sparse and noisy, which makes it difficult to train detectors that are both reliable and deployable under tight computational budgets. Many high performing systems rely on large vision language backbones, full parameter fine tuning, online retrieval or model ensembles, which raises training and inference costs. We present a parameter efficient cross-platform multi-modal transfer learning framework for cyberbullying and hateful content detection. Our framework has three components. First, we perform domain adaptive pretraining of a compact ViLT backbone on in domain image-text corpora. Second, we apply parameter efficient fine tuning that updates only bias terms, a small subset of LayerNorm parameters and the classification head, leaving the inference computation graph unchanged. Third, we use noise aware knowledge distillation from a stronger teacher built from pretrained text and CLIP based image-text encoders, where only high confidence, temperature scaled predictions are used as soft labels during training, and teacher models and any retrieval components are used only offline. We evaluate primarily on Hateful Memes and use IMDB as an auxiliary text only benchmark to show that the deployment aware PEFT + offline-KD recipe can still be applied when other modalities are unavailable. On Hateful Memes, our student updates only 0.11% of parameters and retain about 96% of the AUROC of full fine-tuning. Full article
(This article belongs to the Special Issue Data Privacy and Protection in IoT Systems)
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28 pages, 2865 KB  
Article
Reliability Assessment of Power System Microgrid Using Fault Tree Analysis: Qualitative and Quantitative Analysis
by Shravan Kumar Akula and Hossein Salehfar
Electronics 2026, 15(2), 433; https://doi.org/10.3390/electronics15020433 - 19 Jan 2026
Cited by 3 | Viewed by 1077
Abstract
Renewable energy sources account for approximately one-quarter of the total electric power generating capacity in the United States. These sources increase system complexity, with potential negative impacts caused by their inherent variability. A microgrid, a decentralized local grid, offers an excellent solution for [...] Read more.
Renewable energy sources account for approximately one-quarter of the total electric power generating capacity in the United States. These sources increase system complexity, with potential negative impacts caused by their inherent variability. A microgrid, a decentralized local grid, offers an excellent solution for integrating these sources into the system’s generation mix in a cost-effective and efficient manner. This paper presents a comprehensive fault tree analysis for the reliability assessment of microgrids, ensuring their safe operation. In this work, fault tree analysis of a microgrid in grid-tied mode with solar, wind, and battery energy storage systems is performed, and the results are reported. The analyses and calculations are performed using the Relyence software suite. The fault tree analysis was performed using various calculation methods, including exact (conventional fault tree analysis), simulation (Monte Carlo simulation), cut-set summation, Esary–Proschan, and cross-product. Once these analyses were completed, the results were compared with the ‘exact’ method as the base case. Critical risk measures, such as unavailability, conditional failure intensity, failure frequency, mean unavailability, number of failures, and minimal cut-sets, were documented and compared. Importance measures, such as marginal or Birnbaum, criticality, diagnostic, risk achievement, and risk reduction worth, were also computed and tabulated. Details of all cut-sets and the probability of failure are presented. The calculated importance measures would help microgrid operators focus on events that yield the greatest system improvements and maintain an acceptable range of risk levels to ensure safe operation and improved system reliability. Full article
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29 pages, 7700 KB  
Article
Secure and Decentralised Swarm Authentication Using Hardware Security Primitives
by Sagir Muhammad Ahmad and Barmak Honarvar Shakibaei Asli
Electronics 2026, 15(2), 423; https://doi.org/10.3390/electronics15020423 - 18 Jan 2026
Viewed by 940
Abstract
Autonomous drone swarms are increasingly deployed in critical domains such as infrastructure inspection, environmental monitoring, and emergency response. While their distributed operation enables scalability and resilience, it also introduces new vulnerabilities, particularly in authentication and trust establishment. Conventional cryptographic solutions, including public key [...] Read more.
Autonomous drone swarms are increasingly deployed in critical domains such as infrastructure inspection, environmental monitoring, and emergency response. While their distributed operation enables scalability and resilience, it also introduces new vulnerabilities, particularly in authentication and trust establishment. Conventional cryptographic solutions, including public key infrastructures (PKI) and symmetric key protocols, impose computational and connectivity requirements unsuited to resource-constrained and external infrastructure-free swarm deployments. In this paper, we present a decentralized authentication scheme rooted in hardware security primitives (HSPs); specifically, Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs). The protocol leverages master-initiated token broadcasting, iterative HSP seed evolution, randomized response delays, and statistical trust evaluation to detect cloning, replay, and impersonation attacks without reliance on centralized authorities or pre-distributed keys. Simulation studies demonstrate that the scheme achieves lightweight operation, rapid anomaly detection, and robustness against wireless interference, making it well-suited for real-time swarm systems. Full article
(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, 2nd Edition)
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44 pages, 996 KB  
Article
Adaptive Hybrid Consensus Engine for V2X Blockchain: Real-Time Entropy-Driven Control for High Energy Efficiency and Sub-100 ms Latency
by Rubén Juárez and Fernando Rodríguez-Sela
Electronics 2026, 15(2), 417; https://doi.org/10.3390/electronics15020417 - 17 Jan 2026
Cited by 1 | Viewed by 762
Abstract
We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as [...] Read more.
We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy Hspatial over occupancy bins (both on [0,1])—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, Ecrypto=ehnhash+esignsig, and coherence is tracked using the LCP-normalized definition Dledger(t) computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes (S0.8), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility (v=30 m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below 0.07 at high spatial disorder. The main evaluation is limited to N100 vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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21 pages, 321 KB  
Review
Privacy-Preserving Protocols in Smart Cities and Industrial IoT: Challenges, Trends, and Future Directions
by Manuel José Cabral dos Santos Reis
Electronics 2026, 15(2), 399; https://doi.org/10.3390/electronics15020399 - 16 Jan 2026
Cited by 2 | Viewed by 1545
Abstract
The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive [...] Read more.
The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive data across heterogeneous and resource-constrained networks. This review explores the current landscape of privacy-preserving protocols designed for Smart City and IIoT infrastructures. We examine state-of-the-art approaches including lightweight cryptographic schemes, secure data aggregation, anonymous communication protocols, and blockchain-based frameworks. The paper also analyzes practical trade-offs between security, latency, and computational overhead in real-world deployments. Open research challenges such as secure interoperability, privacy in federated learning, and resilience against AI-driven cyberattacks are discussed. Finally, the paper outlines promising research directions and technologies that can enable scalable, secure, and privacy-aware network infrastructures for future urban and industrial ecosystems. Full article
(This article belongs to the Special Issue Computer Networking Security and Privacy)
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31 pages, 643 KB  
Systematic Review
The Use of Business Intelligence and Analytics in Electric Vehicle Technology: A Comprehensive Survey
by Alexandra Bousia
Electronics 2026, 15(2), 366; https://doi.org/10.3390/electronics15020366 - 14 Jan 2026
Cited by 3 | Viewed by 1744
Abstract
The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more [...] Read more.
The emerging urbanization and the extensive increase of the transportation sector are responsible for the significant increase in carbon dioxide emissions. Therefore, replacing traditional cars with Electric Vehicles (EVs) is a promising solution, offering a clearer alternative. EVs are becoming more and more well-known and are being quickly used worldwide. However, the exponential rise in EV sales has also raised a number of issues, which are becoming important and demanding. These challenges include the need of driving security, the battery degradation, the inadequate infrastructure for charging EVs, and the uneven energy distribution. In order for EVs to reach their full potential, intelligent systems and innovative technologies need to be introduced in the field of EVs. This is where business intelligence (BI) can be employed, along with artificial intelligence (AI), data analytics, and machine learning. In this paper, we provide a comprehensive survey on the use of BI strategies in the EV transportation sector. We first introduce the EVs and charging station technologies. Then, research works on the application of BI and data analysis techniques in EV technology are reviewed to further understand the challenges and open issues for the research and industry community. Moreover, related works on accident analysis, battery health prediction, charging station analysis, intelligent infrastructure, locating charging stations analysis, and autonomous driving are investigated. This survey systematically reviews 75 peer-reviewed studies published between 2020 and 2025. Finally, we discuss the fundamental limitations and the future open challenges in the aforementioned topics. Full article
(This article belongs to the Special Issue Electronic Architecture for Autonomous Vehicles)
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28 pages, 22992 KB  
Article
Domain Knowledge-Infused Synthetic Data Generation for LLM-Based ICS Intrusion Detection: Mitigating Data Scarcity and Imbalance
by Seokhyun Ann, Hongeun Kim, Suhyeon Park, Seong-je Cho, Joonmo Kim and Harksu Cho
Electronics 2026, 15(2), 371; https://doi.org/10.3390/electronics15020371 - 14 Jan 2026
Cited by 1 | Viewed by 1751
Abstract
Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of [...] Read more.
Industrial control systems (ICSs) are increasingly interconnected with enterprise IT networks and remote services, which expands the attack surface of operational technology (OT) environments. However, collecting sufficient attack traffic from real OT/ICS networks is difficult, and the resulting scarcity and class imbalance of malicious data hinder the development of intrusion detection systems (IDSs). At the same time, large language models (LLMs) have shown promise for security analytics when system events are expressed in natural language. This study investigates an LLM-based network IDS for a smart-factory OT/ICS environment and proposes a synthetic data generation method that injects domain knowledge into attack samples. Using the ICSSIM simulator, we construct a bottle-filling smart factory, implement six MITRE ATT&CK for ICS-based attack scenarios, capture Modbus/TCP traffic, and convert each request–response pair into a natural-language description of network behavior. We then generate synthetic attack descriptions with GPT by combining (1) statistical properties of normal traffic, (2) MITRE ATT&CK for ICS tactics and techniques, and (3) expert knowledge obtained from executing the attacks in ICSSIM. The Llama 3.1 8B Instruct model is fine-tuned with QLoRA on a seven-class classification task (Benign vs. six attack types) and evaluated on a test set composed exclusively of real ICSSIM traffic. Experimental results show that synthetic data generated only from statistical information, or from statistics plus MITRE descriptions, yield limited performance, whereas incorporating environment-specific expert knowledge is associated with substantially higher performance on our ICSSIM-based expanded test set (100% accuracy in binary detection and 96.49% accuracy with a macro F1-score of 0.958 in attack-type classification). Overall, these findings suggest that domain-knowledge-infused synthetic data and natural-language traffic representations can support LLM-based IDSs in OT/ICS smart-factory settings; however, further validation on larger and more diverse datasets is needed to confirm generality. Full article
(This article belongs to the Special Issue AI-Enhanced Security: Advancing Threat Detection and Defense)
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29 pages, 2829 KB  
Article
Real-Time Deterministic Lane Detection on CPU-Only Embedded Systems via Binary Line Segment Filtering
by Shang-En Tsai, Shih-Ming Yang and Chia-Han Hsieh
Electronics 2026, 15(2), 351; https://doi.org/10.3390/electronics15020351 - 13 Jan 2026
Cited by 2 | Viewed by 1370
Abstract
The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the [...] Read more.
The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F1-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article
(This article belongs to the Special Issue Feature Papers in Electrical and Autonomous Vehicles, Volume 2)
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34 pages, 12645 KB  
Article
Multimodal Intelligent Perception at an Intersection: Pedestrian and Vehicle Flow Dynamics Using a Pipeline-Based Traffic Analysis System
by Bao Rong Chang, Hsiu-Fen Tsai and Chen-Chia Chen
Electronics 2026, 15(2), 353; https://doi.org/10.3390/electronics15020353 - 13 Jan 2026
Cited by 3 | Viewed by 1018
Abstract
Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing [...] Read more.
Traditional automated monitoring systems adopted for Intersection Traffic Control still face challenges, including high costs, maintenance difficulties, insufficient coverage, poor multimodal data integration, and limited traffic information analysis. To address these issues, the study proposes a sovereign AI-driven Smart Transportation governance approach, developing a mobile AI solution equipped with multimodal perception, task decomposition, memory, reasoning, and multi-agent collaboration capabilities. The proposed system integrates computer vision, multi-object tracking, natural language processing, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to construct a Pipeline-based Traffic Analysis System (PTAS). The PTAS can produce real-time statistics on pedestrian and vehicle flows at intersections, incorporating potential risk factors such as traffic accidents, construction activities, and weather conditions for multimodal data fusion analysis, thereby providing forward-looking traffic insights. Experimental results demonstrate that the enhanced DuCRG-YOLOv11n pre-trained model, equipped with our proposed new activation function βsilu, can accurately identify various vehicle types in object detection, achieving a frame rate of 68.25 FPS and a precision of 91.4%. Combined with ByteTrack, it can track over 90% of vehicles in medium- to low-density traffic scenarios, obtaining a 0.719 in MOTA and a 0.08735 in MOTP. In traffic flow analysis, the RAG of Vertex AI, combined with Claude Sonnet 4 LLMs, provides a more comprehensive view, precisely interpreting the causes of peak-hour congestion and effectively compensating for missing data through contextual explanations. The proposed method can enhance the efficiency of urban traffic regulation and optimizes decision support in intelligent transportation systems. Full article
(This article belongs to the Special Issue Interactive Design for Autonomous Driving Vehicles)
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23 pages, 3086 KB  
Article
MARL-Driven Decentralized Crowdsourcing Logistics for Time-Critical Multi-UAV Networks
by Juhyeong Han and Hyunbum Kim
Electronics 2026, 15(2), 331; https://doi.org/10.3390/electronics15020331 - 12 Jan 2026
Cited by 1 | Viewed by 649
Abstract
Centralized UAV logistics controllers can achieve strong navigation performance in controlled settings, but they do not capture key deployment factors in crowdsourcing-enabled emergency logistics, where heterogeneous UAV owners participate with unreliability and dropout, and incentive expenditure and fairness must be accounted for. This [...] Read more.
Centralized UAV logistics controllers can achieve strong navigation performance in controlled settings, but they do not capture key deployment factors in crowdsourcing-enabled emergency logistics, where heterogeneous UAV owners participate with unreliability and dropout, and incentive expenditure and fairness must be accounted for. This paper presents a decentralized crowdsourcing multi-UAV emergency logistics framework on an edge-orchestrated architecture that (i) performs urgency-aware dispatch under distance/energy/payload constraints, (ii) tracks reliability and participation dynamics under stress (unreliable agents and dropout), and (iii) quantifies incentive feasibility via total payment and payment inequality (Gini). We adopt a hybrid decision design in which PPO/DQN policies provide real-time navigation/control, while GA/ACO act as planning-level route refinement modules (not reinforcement learning) to improve global candidate quality under safety constraints. We evaluate the framework in a controlled grid-world simulator and explicitly report stress-matched re-evaluation results under matched stress settings, where applicable. In the nominal comparison, centralized DQN attains high navigation-centric success (e.g., 0.970 ± 0.095) with short reach steps, but it omits incentives by construction, whereas the proposed crowdsourcing method reports measurable payment and fairness outcomes (e.g., payment and Gini) and remains evaluable under unreliability and dropout sweeps. We further provide a utility decomposition that attributes negative-utility regimes primarily to collision-related costs and secondarily to incentive expenditure, clarifying the operational trade-off between mission value, safety risk, and incentive cost. Overall, the results indicate that navigation-only baselines can appear strong when participation economics are ignored, while a deployable crowdsourcing system must explicitly expose incentive/fairness and robustness characteristics under stress. Full article
(This article belongs to the Special Issue Parallel and Distributed Computing for Emerging Applications)
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33 pages, 4488 KB  
Article
New Fuzzy Aggregators for Ordered Fuzzy Numbers for Trend and Uncertainty Analysis
by Miroslaw Kozielski, Piotr Prokopowicz and Dariusz Mikolajewski
Electronics 2026, 15(2), 309; https://doi.org/10.3390/electronics15020309 - 10 Jan 2026
Viewed by 445
Abstract
Decision-making under uncertainty, especially when dealing with incomplete or linguistically described data, remains a significant challenge in various fields of science and industry. The increasing complexity of real-world problems necessitates the development of mathematical models and data processing techniques that effectively address uncertainty [...] Read more.
Decision-making under uncertainty, especially when dealing with incomplete or linguistically described data, remains a significant challenge in various fields of science and industry. The increasing complexity of real-world problems necessitates the development of mathematical models and data processing techniques that effectively address uncertainty and incompleteness. Aggregators play a key role in solving these problems, particularly in fuzzy systems, where they constitute fundamental tools for decision-making, data analysis, and information fusion. Aggregation functions have been extensively studied and applied in many fields of science and engineering. Recent research has explored their usefulness in fuzzy control systems, highlighting both their advantages and limitations. One promising approach is the use of ordered fuzzy numbers (OFNs), which can represent directional tendencies in data. Previous studies have introduced the property of direction sensitivity and the corresponding determinant parameter, which enables the analysis of correspondence between OFNs and facilitates inference operations. The aim of this paper is to examine existing aggregate functions for fuzzy set numbers and assess their suitability within OFNs. By analyzing the properties, theoretical foundations, and practical applications of these functions, we aim to identify a suitable aggregation operator that complies with the principles of OFN while ensuring consistency and efficiency in decision-making based on fuzzy structures. This paper introduces a novel aggregation approach that preserves the expected mathematical properties while incorporating the directional components inherent to OFN. The proposed method aims to improve the robustness and interpretability of fuzzy reasoning systems under uncertainty. Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks, 2nd Edition)
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35 pages, 1875 KB  
Review
FPGA-Accelerated ECG Analysis: Narrative Review of Signal Processing, ML/DL Models, and Design Optimizations
by Laura-Ioana Mihăilă, Claudia-Georgiana Barbura, Paul Faragó, Sorin Hintea, Botond Sandor Kirei and Albert Fazakas
Electronics 2026, 15(2), 301; https://doi.org/10.3390/electronics15020301 - 9 Jan 2026
Cited by 3 | Viewed by 2525
Abstract
Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time [...] Read more.
Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article
(This article belongs to the Special Issue Emerging Biomedical Electronics)
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24 pages, 3204 KB  
Article
AMUSE++: A Mamba-Enhanced Speech Enhancement Framework with Bi-Directional and Advanced Front-End Modeling
by Tsung-Jung Li, Berlin Chen and Jeih-Weih Hung
Electronics 2026, 15(2), 282; https://doi.org/10.3390/electronics15020282 - 8 Jan 2026
Cited by 1 | Viewed by 1530
Abstract
This study presents AMUSE++, an advanced speech enhancement framework that extends the MUSE++ model by redesigning its core Mamba module with two major improvements. First, the originally unidirectional one-dimensional (1D) Mamba is transformed into a bi-directional architecture to capture temporal dependencies more effectively. [...] Read more.
This study presents AMUSE++, an advanced speech enhancement framework that extends the MUSE++ model by redesigning its core Mamba module with two major improvements. First, the originally unidirectional one-dimensional (1D) Mamba is transformed into a bi-directional architecture to capture temporal dependencies more effectively. Second, this module is extended to a two-dimensional (2D) structure that jointly models both time and frequency dimensions, capturing richer speech features essential for enhancement tasks. In addition to these structural changes, we propose a Preliminary Denoising Module (PDM) as an advanced front-end, which is composed of multiple cascaded 2D bi-directional Mamba Blocks designed to preprocess and denoise input speech features before the main enhancement stage. Extensive experiments on the VoiceBank+DEMAND dataset demonstrate that AMUSE++ significantly outperforms both the backbone MUSE++ across a variety of objective speech enhancement metrics, including improvements in perceptual quality and intelligibility. These results confirm that the combination of bi-directionality, two-dimensional modeling, and an enhanced denoising frontend provides a powerful approach for tackling challenging noisy speech scenarios. AMUSE++ thus represents a notable advancement in neural speech enhancement architectures, paving the way for more effective and robust speech enhancement systems in real-world applications. Full article
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31 pages, 13729 KB  
Article
Stage-Wise SOH Prediction Using an Improved Random Forest Regression Algorithm
by Wei Xiao, Jun Jia, Wensheng Gao, Haibo Li, Hong Xu, Weidong Zhong and Ke He
Electronics 2026, 15(2), 287; https://doi.org/10.3390/electronics15020287 - 8 Jan 2026
Cited by 1 | Viewed by 700
Abstract
In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation [...] Read more.
In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability pAA was defined. Based on the correlation statistics between HFs, kHF, the SOH, and pAA, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article
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15 pages, 3153 KB  
Article
Decentralized Q-Learning for Multi-UAV Post-Disaster Communication: A Robotarium-Based Evaluation Across Urban Environments
by Udhaya Mugil Damodarin, Cristian Valenti, Sergio Spanò, Riccardo La Cesa, Luca Di Nunzio and Gian Carlo Cardarilli
Electronics 2026, 15(1), 242; https://doi.org/10.3390/electronics15010242 - 5 Jan 2026
Cited by 6 | Viewed by 788
Abstract
Large-scale disasters such as earthquakes and floods often cause the collapse of terrestrial communication networks, isolating affected communities and disrupting rescue coordination. Unmanned aerial vehicles (UAVs) can serve as rapid-deployment aerial relays to restore connectivity in such emergencies. This work presents a decentralized [...] Read more.
Large-scale disasters such as earthquakes and floods often cause the collapse of terrestrial communication networks, isolating affected communities and disrupting rescue coordination. Unmanned aerial vehicles (UAVs) can serve as rapid-deployment aerial relays to restore connectivity in such emergencies. This work presents a decentralized Q-learning framework in which each UAV operates as an independent agent that learns to maintain reliable two-hop links between mobile ground users. The framework integrates user mobility, UAV–user assignment, multi-UAV coordination, and failure tracking to enhance adaptability under dynamic conditions. The system is implemented and evaluated on the Robotarium platform, with propagation modeled using the Al-Hourani air-to-ground path loss formulation. Experiments conducted across Suburban, Dense Urban, and Highrise Urban environments show throughput gains of up to 20% compared with random placement baselines while maintaining failure rates below 5%. These results demonstrate that decentralized learning offers a scalable and resilient foundation for UAV-assisted emergency communication in environments where conventional infrastructure is unavailable. Full article
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34 pages, 3066 KB  
Article
Underwater Antenna Technologies with Emphasis on Submarine and Autonomous Underwater Vehicles (AUVs)
by Dimitrios G. Arnaoutoglou, Tzichat M. Empliouk, Dimitrios-Naoum Papamoschou, Yiannis Kyriacou, Andreas Papanastasiou, Theodoros N. F. Kaifas and George A. Kyriacou
Electronics 2026, 15(1), 219; https://doi.org/10.3390/electronics15010219 - 2 Jan 2026
Cited by 1 | Viewed by 2303
Abstract
Following the persistent evolution of terrestrial 5G wireless systems, a new field of underwater communication has emerged for various related applications like environmental monitoring, underwater mining, and marine research. However, establishing reliable high-speed underwater networks remains notoriously difficult due to the severe RF [...] Read more.
Following the persistent evolution of terrestrial 5G wireless systems, a new field of underwater communication has emerged for various related applications like environmental monitoring, underwater mining, and marine research. However, establishing reliable high-speed underwater networks remains notoriously difficult due to the severe RF attenuation in conductive seawater, which strictly limits range coverage. In this article, we focus on a comprehensive review of different antenna types for future underwater communication and sensing systems, evaluating their performance and suitability for Autonomous Underwater Vehicles (AUVs). We critically examine and compare distinct antenna technologies, including Magnetic Induction (MI) coils, electrically short dipoles, wideband traveling wave antennas, printed planar antennas, and novel magnetoelectric (ME) resonators. Specifically, these antennas are compared in terms of physical footprint, operating frequency, bandwidth, and realized gain, revealing the trade-offs between miniaturization and radiation efficiency. Our analysis aims to identify the benefits and weaknesses of the different antenna types while emphasizing the necessity of innovative antenna designs to overcome the fundamental propagation limits of the underwater channel. Full article
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12 pages, 1642 KB  
Article
Polarization-Shift Backscatter Identification for SWIPT-Based Battery-Free Sensor Nodes
by Taki E. Djidjekh and Alexandru Takacs
Electronics 2026, 15(1), 186; https://doi.org/10.3390/electronics15010186 - 31 Dec 2025
Viewed by 565
Abstract
Battery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocol-dependent security mechanisms cannot be employed, leaving BFSNs vulnerable to [...] Read more.
Battery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocol-dependent security mechanisms cannot be employed, leaving BFSNs vulnerable to replay, spoofing, and other security threats. This paper explores a protocol-independent security mechanism that enhances BFSN security by exploiting the power wave for controlled backscattering. The method introduces a Manchester-encoded digital private key generated by the BFSN’s low-power microcontroller and backscattered through a polarization-shifting module enabled by a fail-safe RF switch, thereby avoiding the need for a dedicated backscattering rectifier. A LoRaWAN-based BFSN integrating this add-on module was implemented to experimentally validate the approach. Results show successful extraction of the backscattered key with minimal energy overhead (approximately 95 µJ for a 3 ms identification sequence), while the original high-efficiency RF rectifier used for harvesting remains unmodified. The orthogonal polarization between the incoming and backscattered waves additionally reduces clutter and cross-jamming effects. These findings demonstrate that secure identification can be seamlessly incorporated into existing BFSNs without altering their core architecture, offering an easy-to-integrate and energy-efficient solution for improving security in SWIPT-based sensing systems. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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19 pages, 3394 KB  
Article
Wide Beam Analysis of Phased EM Surfaces
by Jiayue He, Fan Yang, Xiaotao Xu and Shenheng Xu
Electronics 2026, 15(1), 191; https://doi.org/10.3390/electronics15010191 - 31 Dec 2025
Viewed by 545
Abstract
Phased electromagnetic (EM) surfaces offer a versatile platform for beamforming, yet their application to wide-beam radiation—essential for broadcasting and target tracking—has been hindered by the absence of a foundational analytical model. This article establishes an effective model, quantitatively linking the maximum achievable beamwidth [...] Read more.
Phased electromagnetic (EM) surfaces offer a versatile platform for beamforming, yet their application to wide-beam radiation—essential for broadcasting and target tracking—has been hindered by the absence of a foundational analytical model. This article establishes an effective model, quantitatively linking the maximum achievable beamwidth to the surface’s core physical parameters. A direct scaling equation is first derived for an idealized continuous aperture, revealing a proportionality among beamwidth, the quadratic phase coefficient, and aperture size, which demonstrates the potential for quasi-omnidirectional coverage. The model is then extended to practical scenarios, showing that the main-lobe taper is directly controlled by the aperture amplitude taper, establishing a decoupling principle for independent control of beam shape and width. Finally, by modeling the array factor of a discrete aperture, the trade-off between element spacing and maximum beamwidth is quantified, providing clear design rules to prevent grating lobe distortion. This work provides an intuitive, physics-based foundation for the systematic design and performance prediction of wide-beam phased EM surfaces. Full article
(This article belongs to the Special Issue Advanced Antennas and Propagation for Next-Gen Wireless)
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19 pages, 1730 KB  
Article
Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters
by Minhaz Uddin Ahmed, Md Ohirul Qays, Stefan Lachowicz and Parvez Mahmud
Electronics 2026, 15(1), 177; https://doi.org/10.3390/electronics15010177 - 30 Dec 2025
Cited by 9 | Viewed by 1266
Abstract
The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address [...] Read more.
The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work. Full article
(This article belongs to the Special Issue Data-Related Challenges in Machine Learning: Theory and Application)
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21 pages, 1238 KB  
Review
Wi-Fi RSS Fingerprinting-Based Indoor Localization in Large Multi-Floor Buildings
by Inoj Neupane, Seyed Shahrestani and Chun Ruan
Electronics 2026, 15(1), 183; https://doi.org/10.3390/electronics15010183 - 30 Dec 2025
Cited by 1 | Viewed by 2478
Abstract
Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in [...] Read more.
Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article
(This article belongs to the Special Issue Machine Learning Approach for Prediction: Cross-Domain Applications)
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57 pages, 12554 KB  
Article
Multi-Fidelity Surrogate Models for Accelerated Multi-Objective Analog Circuit Design and Optimization
by Gianluca Cornetta, Abdellah Touhafi, Jorge Contreras and Alberto Zaragoza
Electronics 2026, 15(1), 105; https://doi.org/10.3390/electronics15010105 - 25 Dec 2025
Cited by 2 | Viewed by 2532
Abstract
This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on [...] Read more.
This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article
(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)
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47 pages, 6988 KB  
Article
A Hierarchical Predictive-Adaptive Control Framework for State-of-Charge Balancing in Mini-Grids Using Deep Reinforcement Learning
by Iacovos Ioannou, Saher Javaid, Yasuo Tan and Vasos Vassiliou
Electronics 2026, 15(1), 61; https://doi.org/10.3390/electronics15010061 - 23 Dec 2025
Cited by 2 | Viewed by 1205
Abstract
State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized [...] Read more.
State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article
(This article belongs to the Special Issue Smart Power System Optimization, Operation, and Control)
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28 pages, 1728 KB  
Article
A Lightweight Learning-Based Approach for Online Edge-to-Cloud Service Placement
by Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed and Calin Curescu
Electronics 2026, 15(1), 65; https://doi.org/10.3390/electronics15010065 - 23 Dec 2025
Cited by 2 | Viewed by 727
Abstract
The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity [...] Read more.
The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity for real-time decision-making. Because determining an optimal service placement in such networks is an NP-complete problem, the existing solutions rely on fast but suboptimal heuristics or computationally intensive metaheuristics. Neither approach meets the real-time demands of online scenarios, owing to its inefficiency or high computational overhead. In this study, we propose a lightweight learning-based approach for the online placement of services with multi-version components in edge-to-cloud computing. The proposed approach utilises a Shallow Neural Network (SNN) with both weight and power coefficients optimised using a Genetic Algorithm (GA). The use of an SNN ensures low computational overhead during the training phase and almost instant inference when deployed, making it well suited for real-time and online service placement in edge-to-cloud environments where rapid decision-making is crucial. The proposed method (SNN-GA) is specifically evaluated in AR/VR-based remote repair and maintenance scenarios, developed in collaboration with our industrial partner, and demonstrated robust performance and scalability across a wide range of problem sizes. The experimental results show that SNN-GA reduces the service response time by up to 27% compared to metaheuristics and 55% compared to heuristics at larger scales. It also achieves over 95% platform reliability, outperforming heuristics (which remain below 85%) and metaheuristics (which decrease to 90% at larger scales). Full article
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16 pages, 5350 KB  
Article
A Scalable Ultra-Compact 1.2 kV/100 A SiC 3D Packaged Half-Bridge Building Block
by Junhong Tong, Wei-Jung Hsu, Qingyun Huang and Alex Q. Huang
Electronics 2026, 15(1), 29; https://doi.org/10.3390/electronics15010029 - 22 Dec 2025
Viewed by 964
Abstract
This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than [...] Read more.
This work presents a highly compact and scalable 1.2-kV SiC MOSFET half-bridge building-block module enabled by a die-integrated 3D PCB packaging technology. Compared with conventional DBC-based or TO-247-based SiC half-bridge modules, the proposed design reduces the physical volume and weight by more than 90% while maintaining full compatibility with standard PCB manufacturing processes. The vertically laminated DC+/DC− conductors and symmetric PCB–die–PCB stack establish a tightly confined commutation loop, resulting in a measured power-loop inductance of 2.2 nH and a 3.8 nH gate-loop inductance—representing up to 94% and 89% reduction relative to discrete device implementations. Because the parasitic parameters are intrinsically well-balanced across replicated units and the mutual inductance between adjacent modules remains extremely small, the structure naturally supports current sharing during parallel operation. Thermal and insulation evaluations further confirm the suitability of copper filling via high-Tg laminated PCB substrates for high-power SiC applications, achieving withstand voltages exceeding twice the rated bus voltage. The proposed module is experimentally validated through finite-element parasitic extraction and 950 V double-pulse testing, demonstrating controlled dv/dt behavior and robust switching performance. This work establishes a manufacturable and parallel-friendly packaging approach for high-density SiC power conversion systems. Full article
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15 pages, 1613 KB  
Article
Exploring the Cognitive Capabilities of Large Language Models in Autonomous and Swarm Navigation Systems
by Dawid Ewald, Filip Rogowski, Marek Suśniak, Patryk Bartkowiak and Patryk Blumensztajn
Electronics 2026, 15(1), 35; https://doi.org/10.3390/electronics15010035 - 22 Dec 2025
Cited by 1 | Viewed by 1486
Abstract
The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research [...] Read more.
The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limitations of traditional rule-based approaches. The research investigates whether multimodal LLMs, specifically a customized version of LLaVA 7B (Large Language and Vision Assistant), can serve as a central decision-making unit for autonomous vehicles equipped with cameras and distance sensors. The developed prototype integrates a Raspberry Pi module for data acquisition and motor control with a main computational unit running the LLM via the Ollama platform. Communication between modules combines REST API for sensory data transfer and TCP sockets for real-time command exchange. Without fine-tuning, the system relies on advanced prompt engineering and context management to ensure consistent reasoning and structured JSON-based control outputs. Experimental results demonstrate that the model can interpret real-time visual and distance data to generate reliable driving commands and descriptive situational reasoning. These findings suggest that LLMs possess emerging cognitive abilities applicable to real-world robotic navigation and lay the groundwork for future swarm systems capable of cooperative exploration and decision-making in dynamic environments. These insights are particularly valuable for researchers in swarm robotics and developers of edge-AI systems seeking efficient, multimodal navigation solutions. Full article
(This article belongs to the Special Issue Data-Centric Artificial Intelligence: New Methods for Data Processing)
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33 pages, 894 KB  
Review
Impacts of Connected and Automated Driving: From Personal Acceptance to the Effects in Society: A Multi-Factor Review
by Nuria Herrero García, Nicoletta Matera, Michela Longo and Felipe Jiménez
Electronics 2026, 15(1), 27; https://doi.org/10.3390/electronics15010027 - 21 Dec 2025
Viewed by 1539
Abstract
This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel [...] Read more.
This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel approach, this paper analyses the parameters of user acceptance of technology and how these are reflected in the overall impacts of automated and connected driving. Thus, based on a behavioral intention to use the new technology model, we aim to analyze the state of the art of the overall impacts that may be correlated with individual interests. To this end, a multi-factor approach is applied and potential interactions between factors that may arise are studied in a holistic and quantitative assessment of their combined effects on transportation systems. This impact assessment is a significant challenge, as numerous factors come into play, leading to conflicting effects. Since there is no significant penetration of vehicles with medium or high levels of automation, conclusions are often obtained through simulations or estimates based on hypotheses that must be considered when analyzing the results and can lead to significant dispersion. The results confirm that these technologies can substantially improve road safety, traffic efficiency, and environmental performance. However, their large-scale deployment will critically depend on the establishment of coherent regulatory frameworks, infrastructural readiness, and societal acceptance. Comprehensive stakeholder collaboration, incorporating industry, regulatory authorities, and society, is essential to successfully address existing concerns, facilitate technological integration, and maximize the societal benefits of these transformative mobility systems. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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11 pages, 2336 KB  
Article
A Novel Feeding Technique for a Quadrifilar Helix Antenna
by Alessandro Di-Carlofelice, Emidio Di-Giampaolo and Piero Tognolatti
Electronics 2026, 15(1), 28; https://doi.org/10.3390/electronics15010028 - 21 Dec 2025
Viewed by 1217
Abstract
This paper proposes a novel method for feeding a half-turn quadrifilar helix antenna (QHA) operating in backfire mode. A self-phasing and self-supporting antenna is obtained using a specific method demonstrated numerically. Four straight parallel wires, by which a couple of short-circuited stubs are [...] Read more.
This paper proposes a novel method for feeding a half-turn quadrifilar helix antenna (QHA) operating in backfire mode. A self-phasing and self-supporting antenna is obtained using a specific method demonstrated numerically. Four straight parallel wires, by which a couple of short-circuited stubs are realized and connected in series with helix loops, constitute both the mast of the QHA and the feeding network. A prototype operating at 1 GHz is designed, realized, and measured. The results show a good axial ratio (measured cross-polar gain is about 25 dB below the co-polar one at the boresight) and good impedance matching over an adequately large frequency band. Full article
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14 pages, 4914 KB  
Article
Demonstration of 2D Optoelectronic THz-Wave Beam Steering
by Bo Li, Hussein Ssali, Yuanhao Li, Ming Che, Shenghong Ye, Yuya Mikami and Kazutoshi Kato
Electronics 2025, 14(24), 4980; https://doi.org/10.3390/electronics14244980 - 18 Dec 2025
Cited by 2 | Viewed by 781
Abstract
Advanced two-dimensional (2D) beam steering is essential for unlocking the full potential of terahertz (THz) systems in future 6G communications and high-resolution imaging. However, achieving wide-angle, high-speed, and high-precision 2D beam control within a compact THz platform remains a significant challenge. In this [...] Read more.
Advanced two-dimensional (2D) beam steering is essential for unlocking the full potential of terahertz (THz) systems in future 6G communications and high-resolution imaging. However, achieving wide-angle, high-speed, and high-precision 2D beam control within a compact THz platform remains a significant challenge. In this work, we experimentally demonstrate an optoelectronic 2×2 THz antenna array that enables flexible 2D beam steering, beam hopping, and beam scanning around the 300 GHz band. This work employs a 2×2 microstrip patch antenna (MPA) array directly driven by InGaAs/InP UTC-PDs on a silicon carbide (SiC) substrate. The relative phases of the four radiating elements are precisely programmed using an optical phased array (OPA), which provides fully decoupled and low-latency phase control in the optical domain. Experimentally, we demonstrate 2D beam steering and 2D beam hopping among three representative directions at a polar angle of 25 and azimuth angles of 60, 180, and 300. Furthermore, continuous 2D beam scanning at a fixed polar angle of 25 is achieved, enabling a full 360 azimuth sweep within 0.43 s while maintaining high beam quality. These results confirm that the proposed UTC-PD based 2×2 MPA array provides a practical and robust approach for 2D THz beam manipulation, and offers strong potential for future 6G wireless links and THz imaging applications. Full article
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22 pages, 2100 KB  
Article
A Novel Execution Time Prediction Scheme for Efficient Physical AI Resource Management
by Jin-Woo Kwon and Won-Tae Kim
Electronics 2025, 14(24), 4903; https://doi.org/10.3390/electronics14244903 - 13 Dec 2025
Cited by 1 | Viewed by 1005
Abstract
Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to [...] Read more.
Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to guarantee that tasks meet timing constraints. As a result, resource-aware execution time prediction becomes essential for efficient resource management in physical AI systems. However, existing methods typically assume specific environments or static resource usage and often fail to generalize to new environments. In this paper, we propose CARE-D (Calibration-Assisted Resource-aware Execution time prediction), which trains a deep neural network to model the nonlinear relationships among hardware characteristics, resource levels, and task features across environments. The model predicts the execution time of tasks under diverse hardware and dynamically allocated computing resources, using a few execution records from new environments. CARE-D applies few-history-based calibration using only 1 to k execution records from target environments to adjust predictions without retraining the model. Experiments show that CARE-D improves prediction accuracy by about 7.3% over zero-history predictors within a 10% relative error and outperforms regression and deep learning baselines, using only one to five records per target environment. Full article
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16 pages, 8281 KB  
Article
The Study on Real-Time RRT-Based Path Planning for UAVs Using a STM32 Microcontroller
by Shang-En Tsai, Shih-Ming Yang and Wei-Cheng Sun
Electronics 2025, 14(24), 4901; https://doi.org/10.3390/electronics14244901 - 12 Dec 2025
Cited by 1 | Viewed by 1864
Abstract
Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating [...] Read more.
Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating that real-time autonomous navigation can be achieved under low-power computation constraints. The proposed framework integrates a three-stage process—path pruning, Bézier curve smoothing, and iterative optimization—designed to minimize computational overhead while maintaining flight stability. By leveraging the STM32’s limited 72 MHz ARM Cortex-M3 core and 20 KB SRAM, the system performs all planning stages directly on the microcontroller without external computation. Experimental flight tests verify that the UAV can autonomously generate and follow smooth, collision-free trajectories across static obstacle fields with high tracking accuracy. The results confirm the feasibility of executing a full RRT-based planner on an STM32-class embedded platform, establishing a practical pathway for resource-efficient, onboard UAV autonomy. Full article
(This article belongs to the Section Systems & Control Engineering)
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