Electronics

18 pages, 1539 KB

Open AccessEditor’s ChoiceArticle

A Model of Output Power Control Method for Fault Ride-Through in a Single-Phase NPC Inverter-Based Power Conditioning System with IPOS DAB Converter and Battery

by Reo Emoto, Hiroaki Yamada and Tomokazu Mishima

Electronics 2025, 14(21), 4291; https://doi.org/10.3390/electronics14214291 - 31 Oct 2025

Viewed by 172

Abstract

Grid-tied inverters must satisfy fault ride-through (FRT) requirements to ensure grid stability during voltage disturbances. However, most existing FRT-related studies have focused on reactive current injection or voltage support functions, with few addressing how the active power reference should be dynamically controlled during [...] Read more.

Grid-tied inverters must satisfy fault ride-through (FRT) requirements to ensure grid stability during voltage disturbances. However, most existing FRT-related studies have focused on reactive current injection or voltage support functions, with few addressing how the active power reference should be dynamically controlled during voltage dips. In addition, few systems enable bidirectional power transfer or provide comprehensive verification under deep voltage dips. To address this issue, this paper proposes an output power control method for FRT in a single-phase neutral-point-clamped (NPC) inverter-based PCS consisting of an input-parallel output-series (IPOS) dual-active-bridge (DAB) converter and a battery. The proposed PCS dynamically reduces the output power reference according to the retained voltage while maintaining the inverter current within the rated limit, thereby ensuring stable operation. Computer simulations were conducted using Altair PSIM to verify the effectiveness of the proposed method. The results confirmed that the PCS satisfied the FRT requirements for all post-fault voltage levels. The injected current returned to its pre-fault value within 20 ms and 90 ms for 20% and 0% voltage dips, respectively, complying with the required recovery times. The proposed control method enhances grid resilience and maintains power quality in single-phase low-voltage distribution systems. Full article

(This article belongs to the Special Issue DC–DC Power Converter Technologies for Energy Storage Integration)

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33 pages, 22059 KB

Open AccessEditor’s ChoiceReview

Resistive Sensing in Soft Robotic Grippers: A Comprehensive Review of Strain, Tactile, and Ionic Sensors

by Donya Mostaghniyazdi and Shahab Edin Nodehi

Electronics 2025, 14(21), 4290; https://doi.org/10.3390/electronics14214290 - 31 Oct 2025

Viewed by 496

Abstract

Soft robotic grippers have emerged as crucial tools for safe and adaptive manipulation of delicate and different objects, enabled by their compliant structures. These grippers need embedded sensing that offers proprioceptive and exteroceptive feedback in order to function consistently. Resistive sensing is unique [...] Read more.

Soft robotic grippers have emerged as crucial tools for safe and adaptive manipulation of delicate and different objects, enabled by their compliant structures. These grippers need embedded sensing that offers proprioceptive and exteroceptive feedback in order to function consistently. Resistive sensing is unique among transduction processes since it is easy to use, scalable, and compatible with deformable materials. The three main classes of resistive sensors used in soft robotic grippers are systematically examined in this review: ionic sensors, which are emerging multimodal devices that can capture both mechanical and environmental cues; tactile sensors, which detect contact, pressure distribution, and slip; and strain sensors, which monitor deformation and actuation states. Their methods of operation, material systems, fabrication techniques, performance metrics, and integration plans are all compared in the survey. The results show that sensitivity, linearity, durability, and scalability are all trade-offs across sensor categories, with ionic sensors showing promise as a new development for multipurpose soft grippers. There is also a discussion of difficulties, including hysteresis, long-term stability, and signal processing complexity. In order to move resistive sensing from lab prototypes to reliable, practical applications in domains like healthcare, food handling, and human–robot collaboration, the review concludes that developments in hybrid material systems, additive manufacturing, and AI-enhanced signal interpretation will be crucial. Full article

(This article belongs to the Special Issue Advances in Environmental Sensing and Control for Mobile Robotics: Methods, Technologies, and Applications)

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29 pages, 1671 KB

Open AccessEditor’s ChoiceArticle

Towards Secure Legacy Manufacturing: A Policy-Driven Zero Trust Architecture Aligned with NIST CSF 2.0

by Cheon-Ho Min, Deuk-Hun Kim, Haomiao Yang and Jin Kwak

Electronics 2025, 14(20), 4109; https://doi.org/10.3390/electronics14204109 - 20 Oct 2025

Viewed by 481

Abstract

As smart manufacturing environments continue to evolve, operational technology systems are increasingly integrated with external networks and cloud-based platforms. However, many manufacturing facilities still use legacy systems running on end-of-support/life operating systems with discontinued security updates. It is difficult to mitigate the cyber [...] Read more.

As smart manufacturing environments continue to evolve, operational technology systems are increasingly integrated with external networks and cloud-based platforms. However, many manufacturing facilities still use legacy systems running on end-of-support/life operating systems with discontinued security updates. It is difficult to mitigate the cyber threats and risks for these systems using perimeter-based security models that isolate them from other networks. To address these constraints, a Zero Trust-based security architecture tailored for legacy manufacturing environments with practical field applicability is proposed. Our architecture builds upon the six core functions outlined in National Institute of Standards and Technology Cybersecurity Framework 2.0—identify, protect, detect, respond, recover, and govern—adapting them specifically to manufacturing environment security challenges. To achieve this, the architecture combines asset identification, policy-driven access control, secure SMB gateway transfers, automated anomaly detection and response, clean image recovery, and organizational governance procedures. This study validates the effectiveness and scalability of the proposed architecture through scenario-based simulations. When combining the EoSL defense hardening and gateway-based perimeter control, the architecture achieves approximately 99% overall threat suppression and a 98% reduction in critical-asset infection rates, demonstrating its strong resilience and scalability in large-scale legacy OT environments. Full article

(This article belongs to the Special Issue Industrial Process Control and Flexible Manufacturing Systems)

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25 pages, 2128 KB

Open AccessEditor’s ChoiceArticle

A Low-Cost UAV System and Dataset for Real-Time Weed Detection in Salad Crops

by Alina L. Machidon, Andraž Krašovec, Veljko Pejović, Daniele Latini, Sarathchandrakumar T. Sasidharan, Fabio Del Frate and Octavian M. Machidon

Electronics 2025, 14(20), 4082; https://doi.org/10.3390/electronics14204082 - 17 Oct 2025

Viewed by 459

Abstract

The global food crises and growing population necessitate efficient agricultural land use. Weeds cause up to 40% yield loss in major crops, resulting in over USD 100 billion in annual economic losses. Camera-equipped UAVs offer a solution for automatic weed detection, but the [...] Read more.

The global food crises and growing population necessitate efficient agricultural land use. Weeds cause up to 40% yield loss in major crops, resulting in over USD 100 billion in annual economic losses. Camera-equipped UAVs offer a solution for automatic weed detection, but the high computational and energy demands of deep learning models limit their use to expensive, high-end UAVs. In this paper, we present a low-cost UAV system built from off-the-shelf components, featuring a custom-designed on-board computing system based on the NVIDIA Jetson Nano. This system efficiently manages real-time image acquisition and inference using the energy-efficient Squeeze U-Net neural network for weed detection. Our approach ensures the pipeline operates in real time without affecting the drone’s flight autonomy. We also introduce the AgriAdapt dataset, a novel collection of 643 high-resolution aerial images of salad crops with weeds, which fills a key gap by providing realistic UAV data for benchmarking segmentation models under field conditions. Several deep learning models are trained and validated on the newly introduced AgriAdapt dataset, demonstrating its suitability for effective weed segmentation in UAV imagery. Quantitative results show that the dataset supports a range of architectures, from larger models such as DeepLabV3 to smaller, lightweight networks like Squeeze U-Net (with only 2.5 M parameters), achieving high accuracy (around 90%) across the board. These contributions distinguish our work from earlier UAV-based weed detection systems by combining a novel dataset with a comprehensive evaluation of accuracy, latency, and energy efficiency, thus directly targeting deep learning applications for real-time UAV deployment. Our results demonstrate the feasibility of deploying a low-cost, energy-efficient UAV system for real-time weed detection, making advanced agricultural technology more accessible and practical for widespread use. Full article

(This article belongs to the Special Issue Unmanned Aircraft Systems with Autonomous Navigation, 2nd Edition)

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15 pages, 5169 KB

Open AccessEditor’s ChoiceArticle

Twisting Soft Sleeve Actuator: Design and Experimental Evaluation

by Mohammed Abboodi and Marc Doumit

Electronics 2025, 14(20), 4020; https://doi.org/10.3390/electronics14204020 - 14 Oct 2025

Viewed by 432

Abstract

Soft wearable actuators must align with anatomical joints, conform to limb geometry, and operate at low pneumatic pressures. Yet most twisting mechanisms rely on bulky attachment interfaces and relatively high actuation pressures, limiting practicality in assistive applications. This study introduces the first Twisting [...] Read more.

Soft wearable actuators must align with anatomical joints, conform to limb geometry, and operate at low pneumatic pressures. Yet most twisting mechanisms rely on bulky attachment interfaces and relatively high actuation pressures, limiting practicality in assistive applications. This study introduces the first Twisting Soft Sleeve Actuator (TSSA), a self-contained, wearable actuator that produces controlled bidirectional torsion. The design integrates helically folded bellows with internal stabilization layers to suppress radial expansion and enhance torque transmission. The TSSA is fabricated from thermoplastic polyurethane using a Bowden-type fused filament fabrication (FFF) process optimized for airtightness and flexibility. Performance was characterized using a modular test platform that measured angular displacement and output force under positive pressure (up to 75 kPa) and vacuum (down to −85 kPa). A parametric study evaluated the effects of fold width, fold angle, wall thickness, and twist angle. Results demonstrate bidirectional, self-restoring torsion with clockwise rotation of approximately 30 degrees and a peak output force of about 40 N at 75 kPa, while reverse torsional motion occurred under vacuum actuation. The TSSA enables anatomically compatible, low-pressure torsion, supporting scalable, multi-degree-of-freedom sleeve systems for wearable robotics and rehabilitation. Full article

(This article belongs to the Special Issue Soft Robotics and Mechatronics: Advances in Design, Sensing, and Control)

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19 pages, 3266 KB

Open AccessEditor’s ChoiceArticle

Empirically Informed Multi-Agent Simulation of Distributed Energy Resource Adoption and Grid Overload Dynamics in Energy Communities

by Lu Cong, Kristoffer Christensen, Magnus Værbak, Bo Nørregaard Jørgensen and Zheng Grace Ma

Electronics 2025, 14(20), 4001; https://doi.org/10.3390/electronics14204001 - 13 Oct 2025

Viewed by 453

Abstract

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging [...] Read more.

The rapid proliferation of residential electric vehicles (EVs), rooftop photovoltaics (PVs), and behind-the-meter batteries is transforming energy communities while introducing new operational stresses to local distribution grids. Short-duration transformer overloads, often overlooked in conventional hourly or optimization-based planning models, can accelerate asset aging before voltage limits are reached. This study introduces a second-by-second, multi-agent-based simulation (MABS) framework that couples empirically calibrated Distributed Energy Resource (DER) adoption trajectories with real-time-price (RTP)–driven household charging decisions. Using a real 160-household feeder in Denmark (2024–2025), five progressively integrated DER scenarios are evaluated, ranging from EV-only adoption to fully synchronized EV–PV–battery coupling. Results reveal that uncoordinated EV charging under RTP shifts demand to early-morning hours, causing the first transformer overload within four months. PV deployment alone offers limited relief, while adding batteries delays overload onset by 55 days. Only fully coordinated EV–PV–battery adoption postponed the first overload by three months and reduced total overload hours in 2025 by 39%. The core novelty of this work lies in combining empirically grounded adoption behavior, second-level temporal fidelity, and agent-based grid dynamics to expose transient overload mechanisms invisible to coarser models. The framework provides a diagnostic and planning tool for distribution system operators to evaluate tariff designs, bundled incentives, and coordinated DER deployment strategies that enhance transformer longevity and grid resilience in future energy communities. Full article

(This article belongs to the Special Issue Wind and Renewable Energy Generation and Integration)

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20 pages, 794 KB

Open AccessEditor’s ChoiceArticle

Replay-Based Domain Incremental Learning for Cross-User Gesture Recognition in Robot Task Allocation

by Kanchon Kanti Podder, Pritom Dutta and Jian Zhang

Electronics 2025, 14(19), 3946; https://doi.org/10.3390/electronics14193946 - 6 Oct 2025

Viewed by 431

Abstract

Reliable gesture interfaces are essential for coordinating distributed robot teams in the field. However, models trained in a single domain often perform poorly when confronted with new users, different sensors, or unfamiliar environments. To address this challenge, we propose a memory-efficient replay-based domain [...] Read more.

Reliable gesture interfaces are essential for coordinating distributed robot teams in the field. However, models trained in a single domain often perform poorly when confronted with new users, different sensors, or unfamiliar environments. To address this challenge, we propose a memory-efficient replay-based domain incremental learning (DIL) framework, ReDIaL, that adapts to sequential domain shifts while minimizing catastrophic forgetting. Our approach employs a frozen encoder to create a stable latent space and a clustering-based exemplar replay strategy to retain compact, representative samples from prior domains under strict memory constraints. We evaluate the framework on a multi-domain air-marshalling gesture recognition task, where an in-house dataset serves as the initial training domain and the NATOPS dataset provides 20 cross-user domains for sequential adaptation. During each adaptation step, training data from the current NATOPS subject is interleaved with stored exemplars to retain prior knowledge while accommodating new knowledge variability. Across 21 sequential domains, our approach attains

97.34 %

accuracy on the domain incremental setting, exceeding pooled fine-tuning (

91.87 %

), incremental fine-tuning (

80.92 %

), and Experience Replay (

94.20 %

) by

+ 5.47

,

+ 16.42

, and

+ 3.14

percentage points, respectively. Performance also approaches the joint-training upper bound (

98.18 %

), which represents the ideal case where data from all domains are available simultaneously. These results demonstrate that memory-efficient latent exemplar replay provides both strong adaptation and robust retention, enabling practical and trustworthy gesture-based human–robot interaction in dynamic real-world deployments. Full article

(This article belongs to the Special Issue Coordination and Communication of Multi-Robot Systems)

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13 pages, 20849 KB

Open AccessEditor’s ChoiceArticle

Real-Time True Wireless Stereo Wearing Detection Using a PPG Sensor with Edge AI

by Raehyeong Kim, Joungmin Park, Jaeseong Kim, Jongwon Oh and Seung Eun Lee

Electronics 2025, 14(19), 3911; https://doi.org/10.3390/electronics14193911 - 30 Sep 2025

Viewed by 727

Abstract

True wireless stereo (TWS) earbuds are evolving into multifunctional wearable devices, offering opportunities not only for audio streaming but also for health-related applications. A fundamental requirement for such devices is the ability to accurately detect whether they are being worn, yet conventional proximity [...] Read more.

True wireless stereo (TWS) earbuds are evolving into multifunctional wearable devices, offering opportunities not only for audio streaming but also for health-related applications. A fundamental requirement for such devices is the ability to accurately detect whether they are being worn, yet conventional proximity sensors remain limited in both reliability and functionality. This work explores the use of photoplethysmography (PPG) sensors, which are widely applied in heart rate and blood oxygen monitoring, as an alternative solution for wearing detection. A PPG sensor was embedded into a TWS prototype to capture blood flow changes, and the wearing status was classified in real time using a lightweight k-nearest neighbor (k-NN) algorithm on an edge AI processor. Experimental evaluation showed that incorporating a validity check enhanced classification performance, achieving F1 scores above 0.95 across all wearing conditions. These results indicate that PPG-based sensing can serve as a robust alternative to proximity sensors and expand the capabilities of TWS devices. Full article

(This article belongs to the Special Issue Machine Learning in Electronic and Biomedical Engineering, 3rd Edition)

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24 pages, 4993 KB

Open AccessEditor’s ChoiceArticle

Skeletal Image Features Based Collaborative Teleoperation Control of the Double Robotic Manipulators

by Hsiu-Ming Wu and Shih-Hsun Wei

Electronics 2025, 14(19), 3897; https://doi.org/10.3390/electronics14193897 - 30 Sep 2025

Viewed by 268

Abstract

In this study, a vision-based remote and synchronized control scheme is proposed for the double six-DOF robotic manipulators. Using an Intel RealSense D435 depth camera and MediaPipe skeletal image feature technique, the operator’s 3D hand pose is captured and mapped to the robot’s [...] Read more.

In this study, a vision-based remote and synchronized control scheme is proposed for the double six-DOF robotic manipulators. Using an Intel RealSense D435 depth camera and MediaPipe skeletal image feature technique, the operator’s 3D hand pose is captured and mapped to the robot’s workspace via coordinate transformation. Inverse kinematics is then applied to compute the necessary joint angles for synchronized motion control. Implemented on double robotic manipulators with the MoveIt framework, the system successfully achieves a collaborative teleoperation control task to transfer an object from a robotic manipulator to another one. Further, moving average filtering techniques are used to enhance trajectory smoothness and stability. The framework demonstrates the feasibility and effectiveness of non-contact, vision-guided multi-robot control for applications in teleoperation, smart manufacturing, and education. Full article

(This article belongs to the Section Systems & Control Engineering)

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20 pages, 3174 KB

Open AccessEditor’s ChoiceArticle

Techno-Economic Optimization of a Grid-Connected Hybrid-Storage-Based Photovoltaic System for Distributed Buildings

by Tao Ma, Bo Wang, Cangbin Dai, Muhammad Shahzad Javed and Tao Zhang

Electronics 2025, 14(19), 3843; https://doi.org/10.3390/electronics14193843 - 28 Sep 2025

Viewed by 416

Abstract

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected [...] Read more.

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

(This article belongs to the Section Systems & Control Engineering)

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14 pages, 331 KB

Open AccessEditor’s ChoiceArticle

Flow Matching for Simulation-Based Inference: Design Choices and Implications

by Massimiliano Giordano Orsini, Alessio Ferone, Laura Inno, Angelo Casolaro and Antonio Maratea

Electronics 2025, 14(19), 3833; https://doi.org/10.3390/electronics14193833 - 27 Sep 2025

Viewed by 878

Abstract

Inverse problems are ubiquitous across many scientific fields, and involve the determination of the causes or parameters of a system from observations of its effects or outputs. These problems have been deeply studied through the use of simulated data, thereby under the lens [...] Read more.

Inverse problems are ubiquitous across many scientific fields, and involve the determination of the causes or parameters of a system from observations of its effects or outputs. These problems have been deeply studied through the use of simulated data, thereby under the lens of simulation-based inference. Recently, the natural combination of Continuous Normalizing Flows (CNFs) and Flow Matching Posterior Estimation (FMPE) has emerged as a novel, powerful, and scalable posterior estimator, capable of inferring the distribution of free parameters in a significantly reduced computational time compared to conventional techniques. While CNFs provide substantial flexibility in designing machine learning solutions, modeling decisions during their implementation can strongly influence predictive performance. To the best of our knowledge, no prior work has systematically analyzed how such modeling choices affect the robustness of posterior estimates in this framework. The aim of this work is to address this research gap by investigating the sensitivity of CNFs trained with FMPE under different modeling decisions, including data preprocessing, noise conditioning, and noisy observations. As a case study, we consider atmospheric retrieval of exoplanets and perform an extensive experimental campaign on the Ariel Data Challenge 2023 dataset. Through a comprehensive posterior evaluation framework, we demonstrate that (i) Z-score normalization outperforms min–max scaling across tasks; (ii) noise conditioning improves accuracy, coverage, and uncertainty estimation; and (iii) noisy observations significantly degrade predictive performance, thus underscoring reduced robustness under the assumed noise conditions. Full article

(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)

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31 pages, 1002 KB

Open AccessEditor’s ChoiceArticle

Strengthening Small Object Detection in Adapted RT-DETR Through Robust Enhancements

by Manav Madan and Christoph Reich

Electronics 2025, 14(19), 3830; https://doi.org/10.3390/electronics14193830 - 27 Sep 2025

Viewed by 1980

Abstract

RT-DETR (Real-Time DEtection TRansformer) has recently emerged as a promising model for object detection in images, yet its performance on small objects remains limited, particularly in terms of robustness. While various approaches have been explored, developing effective solutions for reliable small object detection [...] Read more.

RT-DETR (Real-Time DEtection TRansformer) has recently emerged as a promising model for object detection in images, yet its performance on small objects remains limited, particularly in terms of robustness. While various approaches have been explored, developing effective solutions for reliable small object detection remains a significant challenge. This paper introduces an adapted variant of RT-DETR, specifically designed to enhance robustness in small object detection. The model was first designed on one dataset and subsequently transferred to others to validate generalization. Key contributions include replacing components of the feed-forward neural network (FFNN) within a hybrid encoder with Hebbian, randomized, and Oja-inspired layers; introducing a modified loss function; and applying multi-scale feature fusion with fuzzy attention to refine encoder representations. The proposed model is evaluated on the Al-Cast Detection X-ray dataset, which contains small components from high-pressure die-casting machines, and the PCB quality inspection dataset, which features tiny hole anomalies. The results show that the optimized model achieves an mAP of 0.513 for small objects—an improvement from the 0.389 of the baseline RT-DETR model on the Al-Cast dataset—confirming its effectiveness. In addition, this paper contributes a mini-literature review of recent RT-DETR enhancements, situating our work within current research trends and providing context for future development. Full article

(This article belongs to the Special Issue Applications of Computer Vision, 3rd Edition)

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31 pages, 18957 KB

Open AccessEditor’s ChoiceArticle

Hierarchical Hybrid Control and Communication Topology Optimization in DC Microgrids for Enhanced Performance

by Yuxuan Tang, Azeddine Houari, Lin Guan and Abdelhakim Saim

Electronics 2025, 14(19), 3797; https://doi.org/10.3390/electronics14193797 - 25 Sep 2025

Viewed by 391

Abstract

Bus voltage regulation and accurate power sharing constitute two pivotal control objectives in DC microgrids. The conventional droop control method inherently suffers from steady-state voltage deviation. Centralized control introduces vulnerability to single-point failures, with significantly degraded stability under abnormal operating conditions. Distributed control [...] Read more.

Bus voltage regulation and accurate power sharing constitute two pivotal control objectives in DC microgrids. The conventional droop control method inherently suffers from steady-state voltage deviation. Centralized control introduces vulnerability to single-point failures, with significantly degraded stability under abnormal operating conditions. Distributed control strategies mitigate this vulnerability but require careful balancing between control effectiveness and communication costs. Therefore, this paper proposes a hybrid hierarchical control architecture integrating multiple control strategies to achieve near-zero steady-state deviation voltage regulation and precise power sharing in DC microgrids. Capitalizing on the complementary advantages of different control methods, an operation-condition-adaptive hierarchical control (OCAHC) strategy is proposed. The proposed method improves reliability over centralized control under communication failures, and achieves better performance than distributed control under normal conditions. With a fault-detection logic module, the OCAHC framework enables automatic switching to maintain high control performance across different operating scenarios. For the inherent trade-off between consensus algorithm performance and communication costs, a communication topology optimization model is established with communication cost as the objective, subject to constraints including communication intensity, algorithm convergence under both normal and N-1 conditions, and control performance requirements. An accelerated optimization approach employing node-degree computation and equivalent topology reduction is proposed to enhance computational efficiency. Finally, case studies on a DC microgrid with five DGs verify the effectiveness of the proposed model and methods. Full article

(This article belongs to the Special Issue Power Electronics Controllers for Power System)

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28 pages, 29247 KB

Open AccessEditor’s ChoiceArticle

Channel Capacity Analysis of Partial-CSI SWIPT Opportunistic Amplify-and-Forward (OAF) Relaying over Rayleigh Fading

by Kyunbyoung Ko and Seokil Song

Electronics 2025, 14(19), 3791; https://doi.org/10.3390/electronics14193791 - 24 Sep 2025

Viewed by 255

Abstract

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the [...] Read more.

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy-harvesting parts. By modeling the partial channel state information (P-CSI)-based SWIPT OAF system as an equivalent non-SWIPT OAF configuration, a semi-lower bound and a new upper bound on the ergodic channel capacity are derived. A refined approximation is then obtained by averaging these bounds, yielding a simple yet accurate analytical estimate of the true capacity. Simulation results confirm that the proposed approximations closely track the actual performance across a wide range of signal-to-noise ratios (SNRs) and relay configurations. They further demonstrate that SR-based relay selection provides higher capacity than RD-based selection, primarily due to its direct influence on energy harvesting efficiency at the relay. In addition, diversity advantages manifest mainly as SNR improvements, rather than as gains in diversity order. The proposed framework thus serves as a practical and insightful tool for the capacity analysis and design of SWIPT-enabled cooperative networks, with direct relevance to energy-constrained Internet of Things (IoT) and wireless sensor applications. Full article

(This article belongs to the Special Issue Applications of Image Processing and Sensor Systems)

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30 pages, 1641 KB

Open AccessEditor’s ChoiceReview

Sensing-Assisted Communication for mmWave Networks: A Review of Techniques, Applications, and Future Directions

by Ruba Mahmoud, Daniel Castanheira, Adão Silva and Atílio Gameiro

Electronics 2025, 14(19), 3787; https://doi.org/10.3390/electronics14193787 - 24 Sep 2025

Viewed by 878

Abstract

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, [...] Read more.

The emergence of 6G wireless systems marks a paradigm shift toward intelligent, context-aware networks that can adapt in real-time to their environment. Within this landscape, Sensing-Assisted Communication (SAC) emerges as a key enabler, integrating perception into the communication control loop to enhance reliability, beamforming accuracy, and system responsiveness. Unlike prior surveys that treat SAC as a subfunction of Integrated Sensing and Communication (ISAC), this work offers the first dedicated review of SAC in Millimeter-Wave (mmWave) and Sub-Terahertz (Sub-THz) systems, where directional links and channel variability present core challenges. SAC encompasses a diverse set of methods that enable wireless systems to dynamically adapt to environmental changes and channel conditions in real time. Recent studies demonstrate up to 80% reduction in beam training overhead and significant gains in latency and mobility resilience. Applications include predictive beamforming, blockage mitigation, and low-latency Unmanned Aerial Vehicle (UAV) and vehicular communication. This review unifies the SAC landscape and outlines future directions in standardization, Artificial Intelligence (AI) integration, and cooperative sensing for next-generation wireless networks. Full article

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

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19 pages, 4708 KB

Open AccessEditor’s ChoiceArticle

Physical-Layer Encryption for Terahertz Wireless Communication via Logical AND Operation of Dual Beams

by Yoshiki Kamiura, Shinji Iwamoto, Yuya Mikami and Kazutoshi Kato

Electronics 2025, 14(19), 3762; https://doi.org/10.3390/electronics14193762 - 23 Sep 2025

Viewed by 376

Abstract

This paper proposes and experimentally demonstrates a novel physical-layer encryption scheme for terahertz (THz) wireless communication based on a logical AND operation between dual THz beams transmitted from spatially separated sources. Unlike previous studies, confined to chip-scale or waveguide configurations, our approach validates [...] Read more.

This paper proposes and experimentally demonstrates a novel physical-layer encryption scheme for terahertz (THz) wireless communication based on a logical AND operation between dual THz beams transmitted from spatially separated sources. Unlike previous studies, confined to chip-scale or waveguide configurations, our approach validates the concept under free-space transmission, thereby highlighting its applicability to real wireless environments. The system utilizes uni-traveling carrier photodiodes (UTC-PDs) to generate independent THz carriers, and coherent detection combined with envelope extraction enables analog-domain realization of the AND operation. Experimental results confirm successful decryption at data rates up to 1.5 Gbit/s, achieving bit error rates (BERs) below the forward error correction threshold (e.g., 3.13 × 10⁻¹⁰ at 500 Mbit/s). Furthermore, spatial mapping and simulation show strong agreement with measurements, yielding a predictive accuracy of approximately 84% and validating spatial selectivity as a security feature. These findings establish the novelty of applying dual-beam logical operations for secure THz transmission and provide a foundation for scalable, low-complexity physical-layer security in next-generation wireless networks. Full article

(This article belongs to the Special Issue Security for Integrated Sensing and Communication in Next-Generation Wireless Communication)

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15 pages, 673 KB

Open AccessEditor’s ChoiceArticle

Integrating and Benchmarking KpqC in TLS/X.509

by Minjoo Sim, Gyeongju Song, Siwoo Eum, Minwoo Lee, Seyoung Yoon, Anubhab Baksi and Hwajeong Seo

Electronics 2025, 14(18), 3717; https://doi.org/10.3390/electronics14183717 - 19 Sep 2025

Viewed by 950

Abstract

Advances in quantum computing pose a fundamental threat to classical public-key cryptosystems, including RSA and elliptic-curve cryptography (ECC), which form the foundation for authentication and key exchange in the Transport Layer Security (TLS) protocol. In response to these emerging threats, Korea launched the [...] Read more.

Advances in quantum computing pose a fundamental threat to classical public-key cryptosystems, including RSA and elliptic-curve cryptography (ECC), which form the foundation for authentication and key exchange in the Transport Layer Security (TLS) protocol. In response to these emerging threats, Korea launched the KpqC (Korea Post-Quantum Cryptography) project in 2021 to design, evaluate, and standardize domestic PQC algorithms. To the best of our knowledge, this is the first systematic evaluation of the finalized Korean PQC algorithms (HAETAE, AIMer, SMAUG-T, NTRU+) within a production-grade TLS/X.509 stack, enabling direct comparison against NIST PQC and ECC baselines. To contextualize KpqC performance, we further compare against NIST-standardized PQC algorithms and classical ECC baselines. Our evaluation examines both static overhead (certificate size) and dynamic overhead (TLS 1.3 handshake latency) across computation-bound (localhost) and network-bound (LAN) scenarios, including embedded device and hybrid TLS configurations. Our results show that KpqC certificates are approximately 4.6–48.8× larger than ECC counterparts and generally exceed NIST PQC sizes. In computation-bound tests, both NIST PQC (ML-KEM) and KpqC hybrids exhibited similar handshake latency increases of approximately 8–9× relative to ECC. In network-bound tests, the difference between the two families was negligible, with relative overhead typically around 30–41%. These findings offer practical guidance for balancing security level, key size, packet size, and latency and support phased PQC migration strategies in real-world TLS deployments. Full article

(This article belongs to the Special Issue Trends in Information Systems and Security)

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31 pages, 914 KB

Open AccessEditor’s ChoiceReview

A Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal Implications

by Seyed Mahmoud Sajjadi Mohammadabadi, Burak Cem Kara, Can Eyupoglu, Can Uzay, Mehmet Serkan Tosun and Oktay Karakuş

Electronics 2025, 14(18), 3580; https://doi.org/10.3390/electronics14183580 - 9 Sep 2025

Viewed by 3959

Abstract

This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish [...] Read more.

This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish this, we trace the evolution of language models and describe core approaches, including parameter-efficient fine-tuning (PEFT). The methodology involves a thorough survey of real-world LLM applications across the scientific, engineering, healthcare, and creative sectors, coupled with a review of current benchmarks. Our findings indicate that high training and inference costs are shaping market structures, raising economic and labor concerns, while also underscoring a persistent need for human oversight in assessment. Key trends include the development of unified multimodal architectures capable of processing varied data inputs and the emergence of agentic systems that exhibit complex behaviors such as tool use and planning. We identify critical open problems, such as detectability, data contamination, generalization, and benchmark diversity. Ultimately, we conclude that overcoming these complex technical, economic, and social challenges necessitates collaborative advancements in adaptation, evaluation, infrastructure, and governance. Full article

(This article belongs to the Section Artificial Intelligence)

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17 pages, 1335 KB

Open AccessEditor’s ChoiceArticle

User Authentication Using Graph Neural Networks (GNNs) for Adapting to Dynamic and Evolving User Patterns

by Hyun-Sik Choi

Electronics 2025, 14(18), 3570; https://doi.org/10.3390/electronics14183570 - 9 Sep 2025

Cited by 1 | Viewed by 644

Abstract

With recent advancements in digital environments, user authentication is becoming increasingly important. Traditional authentication methods such as passwords and PINs suffer from inherent limitations, including vulnerability to theft, guessing, and replay attacks. Consequently, there has been a growing body of research on more [...] Read more.

With recent advancements in digital environments, user authentication is becoming increasingly important. Traditional authentication methods such as passwords and PINs suffer from inherent limitations, including vulnerability to theft, guessing, and replay attacks. Consequently, there has been a growing body of research on more accurate and efficient user authentication methods. One such approach involves the use of biometric signals to enhance security. However, biometric methods face significant challenges in ensuring stable authentication accuracy, primarily due to variations in the user’s environment, physical activity, and health conditions. To address these issues, this paper proposes a biometric-signal-based user authentication system using graph neural networks (GNNs). The feasibility of the proposed system was evaluated using an electromyogram (EMG) dataset specifically constructed by Chosun University for user authentication research. GNNs have demonstrated exceptional performance in modeling the relationships among complex data and attracted attention in various fields. Specifically, GNNs are well-suited for modeling user behavioral patterns while considering temporal and spatial relationships, making them an ideal method for adapting to dynamic and evolving user patterns. Unlike traditional neural networks, GNNs can dynamically learn and adapt to changes or evolutions in user behavioral patterns over time. This paper describes the design and implementation of a user authentication system using GNNs with an EMG dataset and discusses how the system can adapt to dynamic and changing user patterns. Full article

(This article belongs to the Section Artificial Intelligence)

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14 pages, 4246 KB

Open AccessEditor’s ChoiceArticle

PI-Based Current Constant Control with Ripple Component for Lifetime Extension of Lithium-Ion Battery

by Min-Ho Shin, Jin-Ho Lee and Jehyuk Won

Electronics 2025, 14(17), 3566; https://doi.org/10.3390/electronics14173566 - 8 Sep 2025

Viewed by 606

Abstract

This paper presents a proportional–integral (PI) control-based charging strategy that introduces a ripple component into the constant-current (CC) charging profile to regulate battery temperature and improve long-term performance. The proposed method is implemented within an on-board charger (OBC), where the ripple amplitude is [...] Read more.

This paper presents a proportional–integral (PI) control-based charging strategy that introduces a ripple component into the constant-current (CC) charging profile to regulate battery temperature and improve long-term performance. The proposed method is implemented within an on-board charger (OBC), where the ripple amplitude is adaptively adjusted based on battery temperature and internal resistance. While most prior studies focus on electrochemical characteristics, this work highlights the importance of analyzing current profiles from a power electronics and converter control perspective. The ripple magnitude is controlled in real time through gain tuning of the PI current controller, allowing temperature-aware charging. To validate the proposed method, experiments were conducted using a 11 kW OBC system and 70 Ah lithium-ion battery to examine the correlation between ripple amplitude and battery temperature rise, as well as its impact on internal resistance. The control strategy was evaluated under various thermal conditions and shown to be effective in mitigating temperature-related degradation through ripple-based modulation. Full article

(This article belongs to the Special Issue Advances in Power Electronics for Distributed Energy Sources: Topologies, Modeling and Control)

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25 pages, 4415 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Multi-Scale Dual Discriminator Generative Adversarial Network for Gas Leakage Detection

by Saif H. A. Al-Khazraji, Hafsa Iqbal, Jesús Belmar Rubio, Fernando García and Abdulla Al-Kaff

Electronics 2025, 14(17), 3564; https://doi.org/10.3390/electronics14173564 - 8 Sep 2025

Viewed by 660

Abstract

Gas leakages pose significant safety risks in urban environments and industrial sectors like the Oil and Gas Industry (OGI), leading to accidents, fatalities, and economic losses. This paper introduces a novel generative AI framework, the Multi-Scale Dual Discriminator Generative Adversarial Network (MSDD-GAN), designed [...] Read more.

Gas leakages pose significant safety risks in urban environments and industrial sectors like the Oil and Gas Industry (OGI), leading to accidents, fatalities, and economic losses. This paper introduces a novel generative AI framework, the Multi-Scale Dual Discriminator Generative Adversarial Network (MSDD-GAN), designed to detect and localize gas leaks by generating thermal images from RGB input images. The proposed method integrates three key innovations: (1) Attention-Guided Masking (AttMask) for precise gas leakage localization using saliency maps and a circular Region of Interest (ROI), enabling pixel-level validation; (2) Multi-scale input processing to enhance feature learning with limited data; and (3) Dual Discriminator to validate the thermal image realism and leakage localization accuracy. A comprehensive dataset from laboratory and industrial environment has been collected using a FLIR thermal camera. The MSDD-GAN demonstrated robust performance by generating thermal images with the gas leakage indications at a mean accuracy of

81.6 %

, outperforming baseline cGANs by leveraging a multi-scale generator and dual adversarial losses. By correlating ice formation in RGB images with the leakage indications in thermal images, the model addresses critical challenges of OGI applications, including data scarcity and validation reliability, offering a robust solution for continuous gas leak monitoring in pipeline. Full article

(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems, 2nd Edition)

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67 pages, 2605 KB

Open AccessEditor’s ChoiceArticle

Polar Codes for 6G and Beyond Wireless Quantum Optical Communications

by Peter Jung, Kushtrim Dini, Faris Abdel Rehim and Hamza Almujahed

Electronics 2025, 14(17), 3563; https://doi.org/10.3390/electronics14173563 - 8 Sep 2025

Viewed by 576

Abstract

Wireless communication applications above 300 GHz need careful analog electronics design that takes into account the frequency-dependent nature of ohmic resistance at these frequencies. The cumbersome development of electronics brings quantum optical communication solutions for the sixth generation (6G) THz band located between [...] Read more.

Wireless communication applications above 300 GHz need careful analog electronics design that takes into account the frequency-dependent nature of ohmic resistance at these frequencies. The cumbersome development of electronics brings quantum optical communication solutions for the sixth generation (6G) THz band located between 300 GHz and 10 THz into focus. In this manuscript, the authors propose to replace the classical radio frequency based inner physical layer transceiver blocks used in classical channel coded short range wireless communication systems by wireless quantum optical communication concepts. In addition to discussing the resulting generic concept of the wireless quantum optical communications and illustrating optimum quantum data detection schemes, novel reduced state quantum data detection and novel Kohonen maps-based quantum data detection, will be addressed. All the considered quantum data detection schemes provide soft outputs required for the lowest possible block error ratio (BLER) at the output of the channel decoding. Furthermore, a novel polar codes design approach determining the polar sequence by appropriately combining already available polar sequences tailored for low BLER is presented for the first time after illustrating the basics of polar codes. In addition, turbo equalization for wireless quantum optical communications using polar codes will be presented, for the first time explicitly stating the generation of soft information associated with the codebits and introducing a novel scheme for the computation of extrinsic soft outputs to be used in the turbo equalization iterations. New simulation results emphasize the viability of the theoretical concepts. Full article

(This article belongs to the Special Issue Channel Coding and Measurements for 6G Wireless Communications)

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25 pages, 2352 KB

Open AccessEditor’s ChoiceArticle

High-Frequency Link Analysis of Enhanced Power Factor in Active Bridge-Based Multilevel Converters

by Morteza Dezhbord, Fazal Ur Rehman, Amir Ghasemian and Carlo Cecati

Electronics 2025, 14(17), 3551; https://doi.org/10.3390/electronics14173551 - 6 Sep 2025

Viewed by 691

Abstract

Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, [...] Read more.

Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, power factor performance, and power loss characteristics. A high-frequency link analysis framework is developed to characterize voltage, current, and power transfer waveforms, providing insight into reactive power generation and its impact on overall efficiency. By introducing a modulation-based control approach, the proposed converters significantly reduce circulating currents and enhance the power factor, particularly under varying phase-shift conditions. Compared to quadruple active bridge topologies, the discussed multilevel architectures offer reduced transformer complexity and improved power quality, making them suitable for demanding applications such as electric vehicles and aerospace systems. Full article

(This article belongs to the Special Issue Advanced DC-DC Converter Topology Design, Control, Application)

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22 pages, 4937 KB

Open AccessEditor’s ChoiceArticle

Multimodal AI for UAV: Vision–Language Models in Human– Machine Collaboration

by Maroš Krupáš, Ľubomír Urblík and Iveta Zolotová

Electronics 2025, 14(17), 3548; https://doi.org/10.3390/electronics14173548 - 6 Sep 2025

Cited by 1 | Viewed by 1906

Abstract

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. [...] Read more.

Recent advances in multimodal large language models (MLLMs)—particularly vision– language models (VLMs)—introduce new possibilities for integrating visual perception with natural-language understanding in human–machine collaboration (HMC). Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments, where adaptive autonomy and intuitive interaction are essential. Traditional UAV autonomy has relied mainly on visual perception or preprogrammed planning, offering limited adaptability and explainability. This study introduces a novel reference architecture, the multimodal AI–HMC system, based on which a dedicated UAV use case architecture was instantiated and experimentally validated in a controlled laboratory environment. The architecture integrates VLM-powered reasoning, real-time depth estimation, and natural-language interfaces, enabling UAVs to perform context-aware actions while providing transparent explanations. Unlike prior approaches, the system generates navigation commands while also communicating the underlying rationale and associated confidence levels, thereby enhancing situational awareness and fostering user trust. The architecture was implemented in a real-time UAV navigation platform and evaluated through laboratory trials. Quantitative results showed a 70% task success rate in single-obstacle navigation and 50% in a cluttered scenario, with safe obstacle avoidance at flight speeds of up to 0.6 m/s. Users approved 90% of the generated instructions and rated explanations as significantly clearer and more informative when confidence visualization was included. These findings demonstrate the novelty and feasibility of embedding VLMs into UAV systems, advancing explainable, human-centric autonomy and establishing a foundation for future multimodal AI applications in HMC, including robotics. Full article

(This article belongs to the Special Issue Human-Robot Interaction and Collaboration for Effective Solutions to Real-World Problems)

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12 pages, 2618 KB

Open AccessEditor’s ChoiceArticle

Modeling S-Band Communication Window Using Random Distributed Raman Laser Amplifier

by Paweł Rosa

Electronics 2025, 14(17), 3527; https://doi.org/10.3390/electronics14173527 - 4 Sep 2025

Viewed by 524

Abstract

This study simulates an open-cavity random distributed Raman amplifier for optimal performance across a 5 THz S-band spectrum (196.2–201.1 THz; 1490.76–1527.99 nm), evaluating its capacity via a 50-channel WDM grid with 100 GHz spacing. The primary Raman pump wavelength was tuned from 1318 [...] Read more.

This study simulates an open-cavity random distributed Raman amplifier for optimal performance across a 5 THz S-band spectrum (196.2–201.1 THz; 1490.76–1527.99 nm), evaluating its capacity via a 50-channel WDM grid with 100 GHz spacing. The primary Raman pump wavelength was tuned from 1318 to 1344 nm to identify the optimal point. A Fiber Bragg Grating (FBG), placed at the end of a 60 km single-mode fiber and upshifted 88 nm from the pump, enhances efficiency by transferring energy to the amplified signal, minimizing power variation. Results yield < 2 dB gain ripple across channels using raw Raman amplification without flattening filters with minor degradation from residual channels, confirming the DRA design’s viability for high-density S-band optical communication expansion. Full article

(This article belongs to the Special Issue New Trends and Methods in Communication Systems, 2nd Edition)

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23 pages, 5852 KB

Open AccessEditor’s ChoiceArticle

Symbol Synchronization for Optical Intrabody Nanocommunication Using Noncoherent Detection

by Pankaj Singh and Sung-Yoon Jung

Electronics 2025, 14(17), 3537; https://doi.org/10.3390/electronics14173537 - 4 Sep 2025

Viewed by 852

Abstract

Optical intrabody wireless nanosensor networks (OiWNSNs) enable groundbreaking biomedical applications via optical nanocommunication within biological tissues. Synchronization is critical for accurate data recovery in these energy- and size-constrained nanonetworks. In this study, we investigate timing synchronization in a highly dispersive and noisy intravascular [...] Read more.

Optical intrabody wireless nanosensor networks (OiWNSNs) enable groundbreaking biomedical applications via optical nanocommunication within biological tissues. Synchronization is critical for accurate data recovery in these energy- and size-constrained nanonetworks. In this study, we investigate timing synchronization in a highly dispersive and noisy intravascular optical channel, particularly under an on–off keying preamble comprising Gaussian optical pulses. We proposed a synchronization scheme based on the repetitive transmission of a preamble and noncoherent detection using continuous-time moving average filters with multiple integrator windows. The simulation results reveal that increasing the communication distance degrades the synchronization performance. To counter this degradation, we can increase the number of preamble repetitions, which markedly improves the system reliability and timing accuracy due to the averaging gain, although the performance saturates due to the dispersion floor inherent in the blood channel. Moreover, we found that low-resolution nanoreceivers with fewer integrators outperform high-resolution designs in dispersive environments, as they mitigate the energy-splitting problem due to pulse broadening. This tradeoff between temporal resolution and robustness highlights the importance of channel-aware receiver design. Overall, this study provides key insights into the physical layer design of OiWNSNs and offers practical guidelines for achieving robust synchronization under realistic biological conditions. Full article

(This article belongs to the Special Issue Intelligent Communication Technologies for Health and Biomedical Applications)

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15 pages, 6085 KB

Open AccessEditor’s ChoiceArticle

AFCN: An Attention-Based Fusion Consistency Network for Facial Emotion Recognition

by Qi Wei, Hao Pei and Shasha Mao

Electronics 2025, 14(17), 3523; https://doi.org/10.3390/electronics14173523 - 3 Sep 2025

Viewed by 603

Abstract

Due to the local similarities between different facial expressions and the subjective influences of annotators, large-scale facial expression datasets contain significant label noise. Recognition-based noisy labels are a key challenge in the field of deep facial expression recognition (FER). Based on this, this [...] Read more.

Due to the local similarities between different facial expressions and the subjective influences of annotators, large-scale facial expression datasets contain significant label noise. Recognition-based noisy labels are a key challenge in the field of deep facial expression recognition (FER). Based on this, this paper proposes a simple and effective attention-based fusion consistency network (AFCN), which suppresses the impact of uncertainty and prevents deep networks from overemphasising local features. Specifically, the AFCN comprises four modules: a sample certainty analysis module, a label correction module, an attention fusion module, and a fusion consistency learning module. Among these, the sample certainty analysis module is designed to calculate the certainty of each input facial expression image; the label correction module re-labels samples with low certainty based on the model’s prediction results; the attention fusion module identifies all possible key regions of facial expressions and fuses them; the fusion consistency learning module constrains the model to maintain consistency between the regions of interest for the actual labels of facial expressions and the fusion of all possible key regions of facial expressions. This guides the model to perceive and learn global facial expression features and prevents it from incorrectly classifying expressions based solely on local features associated with noisy labels. Experiments are conducted on multiple noisy datasets to validate the effectiveness of the proposed method. The experimental results illustrate that the proposed method outperforms current state-of-the-art methods, achieving a 3.03% accuracy improvement on the 30% noisy RAF-DB dataset in particular. Full article

(This article belongs to the Special Issue Deep Learning for Facial Emotion Analysis and Human Activity Recognition)

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24 pages, 6566 KB

Open AccessEditor’s ChoiceArticle

Milepost-to-Vehicle Monocular Depth Estimation with Boundary Calibration and Geometric Optimization

by Enhua Zhang, Tao Ma, Handuo Yang, Jiaqi Li, Zhiwei Xie and Zheng Tong

Electronics 2025, 14(17), 3446; https://doi.org/10.3390/electronics14173446 - 29 Aug 2025

Viewed by 733

Abstract

Milepost-assisted positioning estimates the distance between a vehicle-mounted camera and a milepost as a reference position for autonomous driving. However, the accuracy of monocular metric depth estimation is compromised by camera installation angle, milepost inclination, and image occlusions. To solve the problems, this [...] Read more.

Milepost-assisted positioning estimates the distance between a vehicle-mounted camera and a milepost as a reference position for autonomous driving. However, the accuracy of monocular metric depth estimation is compromised by camera installation angle, milepost inclination, and image occlusions. To solve the problems, this paper proposes a two-stage monocular metric depth estimation with boundary calibration and geometric optimization. In the first stage, the method detects a milepost in one frame of a video and computes a metric depth map of the milepost region by a monocular depth estimation model. In the second stage, in order to mitigate the effects of road surface undulation and occlusion, we propose geometric optimization with road plane fitting and a multi-frame fusion strategy. An experiment using pairwise images and depth measurement demonstrates that the proposed method exceeds other state-of-the-art methods with an absolute relative error of 0.055 and root mean square error of 3.421. Full article

(This article belongs to the Section Electrical and Autonomous Vehicles)

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28 pages, 17193 KB

Open AccessEditor’s ChoiceArticle

Radio Propagation Characteristics in Several Application Scenarios at 285 GHz Terahertz Band

by Jinhyung Oh and Jong Ho Kim

Electronics 2025, 14(17), 3419; https://doi.org/10.3390/electronics14173419 - 27 Aug 2025

Viewed by 451

Abstract

In this paper, we have derived R.M.S. delay spread characteristics and models according to the influence of antenna beam width in the mobile kiosk data download environment, the inter-rack communication environment in the data center, the intra-device communication environment, and the experimental laboratory [...] Read more.

In this paper, we have derived R.M.S. delay spread characteristics and models according to the influence of antenna beam width in the mobile kiosk data download environment, the inter-rack communication environment in the data center, the intra-device communication environment, and the experimental laboratory measurement environment scenario in the 275 GHz to 295 GHz bands. The measurement system used in this paper used a vector network analyzer and a frequency expander to derive the statistical characteristics of terahertz frequency band signals, and used antennas with different beamwidths for measurement. It is confirmed that the distribution of delay spread varies depending on the beam width of the antenna used for measurement and the type of measurement scenario. Full article

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

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33 pages, 1397 KB

Open AccessEditor’s ChoiceArticle

Enhancing Agent-Based Negotiation Strategies via Transfer Learning

by Siqi Chen and Gerhard Weiss

Electronics 2025, 14(17), 3391; https://doi.org/10.3390/electronics14173391 - 26 Aug 2025

Cited by 1 | Viewed by 1116

Abstract

While negotiating agents have achieved remarkable success, one critical challenge that remains unresolved is the inherent inefficiency of learning negotiation strategies from scratch when encountering previously unencountered opponents. To address this limitation, Transfer Learning (TL) emerges as a promising solution, leveraging knowledge acquired [...] Read more.

While negotiating agents have achieved remarkable success, one critical challenge that remains unresolved is the inherent inefficiency of learning negotiation strategies from scratch when encountering previously unencountered opponents. To address this limitation, Transfer Learning (TL) emerges as a promising solution, leveraging knowledge acquired from prior tasks to accelerate learning and enhance adaptability in new negotiation contexts. This study introduces Transfer Learning-based Negotiating Agent (TLNAgent), a novel framework enabling autonomous negotiating agents to systematically leverage knowledge from pretrained source policies. The proposed transfer mechanism not only enhances negotiation performance but also substantially accelerates policy adaptation in unfamiliar negotiation environments. TLNAgent integrates three core components: (1) a negotiation module that interacts with opponents; (2) a critic module that determines whether to activate the transfer process and selects which source policies to transfer; and (3) a transfer module that facilitates knowledge integration between source and target policies. Specifically, the negotiation module interacts with opponents during the negotiation to execute core decision-making processes; in addition, it trains new policies using reinforcement learning. The critic module serves dual critical functions: (1) it dynamically triggers the transfer module according to interaction analysis; and (2) it selects the source policies via its adaptation model. The transfer module establishes lateral parameter-level connections between source and target policy networks, facilitating systematic knowledge transfer while ensuring training stability. Empirical findings from our extensive experiments indicate that transfer learning considerably enhances both the efficiency and utility of outcomes in cross-domain negotiation tasks. The proposed framework attains superior performance when compared to the state-of-the-art negotiating agents from the Automated Negotiating Agents Competition (ANAC). Full article

(This article belongs to the Special Issue Advancements in Autonomous Agents and Multi-Agent Systems)

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21 pages, 5469 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Radio Frequency Passive Tagging System Enabling Object Recognition and Alignment by Robotic Hands

by Armin Gharibi, Mahmoud Tavakoli, André F. Silva, Filippo Costa and Simone Genovesi

Electronics 2025, 14(17), 3381; https://doi.org/10.3390/electronics14173381 - 25 Aug 2025

Viewed by 1308

Abstract

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch [...] Read more.

Robotic hands require reliable and precise sensing systems to achieve accurate object recognition and manipulation, particularly in environments where vision- or capacitive-based approaches face limitations such as poor lighting, dust, reflective surfaces, or non-metallic materials. This paper presents a novel radiofrequency (RF) pre-touch sensing system that enables robust localization and orientation estimation of objects prior to grasping. The system integrates a compact coplanar waveguide (CPW) probe with fully passive chipless RF resonator tags fabricated using a patented flexible and stretchable conductive ink through additive manufacturing. This approach provides a low-cost, durable, and highly adaptable solution that operates effectively across diverse object geometries and environmental conditions. The experimental results demonstrate that the proposed RF sensor maintains stable performance under varying distances, orientations, and inter-tag spacings, showing robustness where traditional methods may fail. By combining compact design, cost-effectiveness, and reliable near-field sensing independent of an object or lighting, this work establishes RF sensing as a practical and scalable alternative to optical and capacitive systems. The proposed method advances robotic perception by offering enhanced precision, resilience, and integration potential for industrial automation, warehouse handling, and collaborative robotics. Full article

(This article belongs to the Special Issue Pushing Boundaries: Innovations in Robotics, Artificial Intelligence, and Extended Reality)

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36 pages, 14352 KB

Open AccessEditor’s ChoiceArticle

NRXR-ID: Two-Factor Authentication (2FA) in VR Using Near-Range Extended Reality and Smartphones

by Aiur Nanzatov, Lourdes Peña-Castillo and Oscar Meruvia-Pastor

Electronics 2025, 14(17), 3368; https://doi.org/10.3390/electronics14173368 - 25 Aug 2025

Viewed by 765

Abstract

Two-factor authentication (2FA) has become widely adopted as an efficient and secure way of validating someone’s identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their [...] Read more.

Two-factor authentication (2FA) has become widely adopted as an efficient and secure way of validating someone’s identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their real-world surroundings. We present NRXR-ID, a technique to implement two-factor authentication while using extended reality systems and smartphones. The proposed method allows users to complete an authentication challenge using their smartphones without removing their HMD. We performed a user study in which we explored four types of challenges for users, including a novel checkers-style challenge. Users responded to these challenges under three different configurations, including a technique that uses a smartphone to support gaze-based selection without the use of a VR controller. A 4 × 3 within-subjects design allowed us to study all of the proposed variations. We collected performance metrics along with user experience questionnaires containing subjective impressions from thirty participants. Results suggest that the checkers-style visual matching challenge was the most preferred option, followed by the challenge involving entering a digital PIN submitted via the smartphone. Participants were fastest at solving the digital PIN challenge, with an average of 12.35 ± 5 s, followed by the Checkers challenge with 13.85 ± 5.29 s, then the CAPTCHA-style challenge with 14.36 ± 7.5 s, whereas the alphanumeric password took almost twice as long, averaging 32.71 ± 16.44 s. The checkers-style challenge performed consistently across all conditions with no significant differences (p = 0.185), making it robust to different implementation choices. Full article

(This article belongs to the Special Issue Emerging Technologies in Augmented, Virtual, and Mixed Reality: Advancing Human Experience in Digital and Physical Environments)

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12 pages, 7715 KB

Open AccessEditor’s ChoiceArticle

Hardware Accelerator Design by Using RT-Level Power Optimization Techniques on FPGA for Future AI Mobile Applications

by Achyuth Gundrapally, Yatrik Ashish Shah, Sai Manohar Vemuri and Kyuwon (Ken) Choi

Electronics 2025, 14(16), 3317; https://doi.org/10.3390/electronics14163317 - 20 Aug 2025

Viewed by 1036

Abstract

In resource-constrained edge environments—such as mobile devices, IoT systems, and electric vehicles—energy-efficient Convolution Neural Network (CNN) accelerators on mobile Field Programmable Gate Arrays (FPGAs) are gaining significant attention for real-time object detection tasks. This paper presents a low-power implementation of the Tiny YOLOv4 [...] Read more.

In resource-constrained edge environments—such as mobile devices, IoT systems, and electric vehicles—energy-efficient Convolution Neural Network (CNN) accelerators on mobile Field Programmable Gate Arrays (FPGAs) are gaining significant attention for real-time object detection tasks. This paper presents a low-power implementation of the Tiny YOLOv4 object detection model on the Xilinx ZCU104 FPGA platform by using Register Transfer Level (RTL) optimization techniques. We proposed three RTL techniques in the paper: (i) Local Explicit Clock Enable (LECE), (ii) operand isolation, and (iii) Enhanced Clock Gating (ECG). A novel low-power design of Multiply-Accumulate (MAC) operations, which is one of the main components in the AI algorithm, was proposed to eliminate redundant signal switching activities. The Tiny YOLOv4 model, trained on the COCO dataset, was quantized and compiled using the Tensil tool-chain for fixed-point inference deployment. Post-implementation evaluation using Vivado 2022.2 demonstrates around 29.4% reduction in total on-chip power. Our design supports real-time detection throughput while maintaining high accuracy, making it ideal for deployment in battery-constrained environments such as drones, surveillance systems, and autonomous vehicles. These results highlight the effectiveness of RTL-level power optimization for scalable and sustainable edge AI deployment. Full article

(This article belongs to the Special Issue Hardware Acceleration for Machine Learning)

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24 pages, 7251 KB

Open AccessEditor’s ChoiceArticle

WTCMC: A Hyperspectral Image Classification Network Based on Wavelet Transform Combining Mamba and Convolutional Neural Networks

by Guanchen Liu, Qiang Zhang, Xueying Sun and Yishuang Zhao

Electronics 2025, 14(16), 3301; https://doi.org/10.3390/electronics14163301 - 20 Aug 2025

Viewed by 835

Abstract

Hyperspectral images are rich in spectral and spatial information. However, their high dimensionality and complexity pose significant challenges for effective feature extraction. Specifically, the performance of existing models for hyperspectral image (HSI) classification remains constrained by spectral redundancy among adjacent bands, misclassification at [...] Read more.

Hyperspectral images are rich in spectral and spatial information. However, their high dimensionality and complexity pose significant challenges for effective feature extraction. Specifically, the performance of existing models for hyperspectral image (HSI) classification remains constrained by spectral redundancy among adjacent bands, misclassification at object boundaries, and significant noise in hyperspectral data. To address these challenges, we propose WTCMC—a novel hyperspectral image classification network based on wavelet transform combining Mamba and convolutional neural networks. To establish robust shallow spatial–spectral relationships, we introduce a shallow feature extraction module (SFE) at the initial stage of the network. To enable the comprehensive and efficient capture of both spectral and spatial characteristics, our architecture incorporates a low-frequency spectral Mamba module (LFSM) and a high-frequency multi-scale convolution module (HFMC). The wavelet transform suppresses noise for LFSM and enhances fine spatial and contour features for HFMC. Furthermore, we devise a spectral–spatial complementary fusion module (SCF) that selectively preserves the most discriminative spectral and spatial features. Experimental results demonstrate that the proposed WTCMC network attains overall accuracies (OA) of 98.94%, 98.67%, and 97.50% on the Pavia University (PU), Botswana (BS), and Indian Pines (IP) datasets, respectively, outperforming the compared state-of-the-art methods. Full article

(This article belongs to the Section Artificial Intelligence)

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19 pages, 991 KB

Open AccessEditor’s ChoiceArticle

Enhancing Machine Learning-Based DDoS Detection Through Hyperparameter Optimization

by Shao-Rui Chen, Shiang-Jiun Chen and Wen-Bin Hsieh

Electronics 2025, 14(16), 3319; https://doi.org/10.3390/electronics14163319 - 20 Aug 2025

Cited by 1 | Viewed by 1716

Abstract

In recent years, the occurrence and complexity of Distributed Denial of Service (DDoS) attacks have escalated significantly, posing threats to the availability, performance, and security of networked systems. With the rapid progression of Artificial Intelligence (AI) and Machine Learning (ML) technologies, attackers can [...] Read more.

In recent years, the occurrence and complexity of Distributed Denial of Service (DDoS) attacks have escalated significantly, posing threats to the availability, performance, and security of networked systems. With the rapid progression of Artificial Intelligence (AI) and Machine Learning (ML) technologies, attackers can leverage intelligent tools to automate and amplify DDoS attacks with minimal human intervention. The increasing sophistication of such attacks highlights the pressing need for more robust and precise detection methodologies. This research proposes a method to enhance the effectiveness of ML models in detecting DDoS attacks based on hyperparameter tuning. By optimizing model parameters, the proposed approach is going to enhance the performance of ML models in identifying DDoS attacks. The CIC-DDoS2019 dataset is utilized in this study as it offers a comprehensive set of real-world DDoS attack scenarios across various protocols and services. The proposed methodology comprises key stages, including data preprocessing, data splitting, and model training, validation, and testing. Three ML models are trained and tuned using an adaptive GridSearchCV (Cross Validation) strategy to identify optimal parameter configurations. The results demonstrate that our method significantly improves performance and efficiency compared with the general GridSearchCV. The SVM model achieves 99.87% testing accuracy and requires approximately 28% less execution time than the general GridSearchCV. The LR model achieves 99.6830% testing accuracy with an execution time of 16.90 s, maintaining the same testing accuracy but reducing the execution time by about 22.8%. The KNN model achieves 99.8395% testing accuracy and 2388.89 s of execution time, also preserving accuracy while decreasing the execution time by approximately 63%. These results indicate that our approach enhances DDoS detection performance and efficiency, offering novel insights into the practical application of hyperparameter tuning for improving ML model performance in real-world scenarios. Full article

(This article belongs to the Special Issue Advancements in AI-Driven Cybersecurity and Securing AI Systems)

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19 pages, 1127 KB

Open AccessEditor’s ChoiceArticle

Movable Wireless Sensor-Enabled Waterway Surveillance with Enhanced Coverage Using Multi-Layer Perceptron and Reinforced Learning

by Minsoo Kim and Hyunbum Kim

Electronics 2025, 14(16), 3295; https://doi.org/10.3390/electronics14163295 - 19 Aug 2025

Viewed by 430

Abstract

Waterway networking environments present unique challenges due to their dynamic nature, including vessel movement, water flow, and varying water quality. These challenges render traditional static surveillance systems inadequate for effective monitoring. This study proposes a novel wireless sensor-enabled surveillance and monitoring framework tailored [...] Read more.

Waterway networking environments present unique challenges due to their dynamic nature, including vessel movement, water flow, and varying water quality. These challenges render traditional static surveillance systems inadequate for effective monitoring. This study proposes a novel wireless sensor-enabled surveillance and monitoring framework tailored to waterway conditions, integrating a two-phase approach with a Movement Phase and a Deployment Phase. In the Movement Phase, a Multi-Layer Perceptron (MLP) guides sensors efficiently toward a designated target area, minimizing travel time and computational complexity. Subsequently, the Deployment Phase utilizes reinforcement learning (RL) to arrange sensors within the target area, optimizing coverage while minimizing overlap between sensing regions. By addressing the unique requirements of waterways, the proposed framework ensures both efficient sensor mobility and resource utilization. Experimental evaluations demonstrate the framework’s effectiveness in achieving high coverage and minimal overlap, with comparable performance to traditional clustering algorithms such as K-Means. The results confirm that the proposed approach achieves flexible, scalable, and computationally efficient monitoring tailored to waterway environments. Full article

(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)

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24 pages, 1219 KB

Open AccessEditor’s ChoiceArticle

Asset Discovery in Critical Infrastructures: An LLM-Based Approach

by Luigi Coppolino, Antonio Iannaccone, Roberto Nardone and Alfredo Petruolo

Electronics 2025, 14(16), 3267; https://doi.org/10.3390/electronics14163267 - 17 Aug 2025

Cited by 1 | Viewed by 1170

Abstract

Asset discovery in critical infrastructures, and in particular within industrial control systems, constitutes a fundamental cybersecurity function. Ensuring accurate and comprehensive asset visibility while maintaining operational continuity represents an ongoing challenge. Existing methodologies rely on deterministic tools that apply fixed fingerprinting strategies and [...] Read more.

Asset discovery in critical infrastructures, and in particular within industrial control systems, constitutes a fundamental cybersecurity function. Ensuring accurate and comprehensive asset visibility while maintaining operational continuity represents an ongoing challenge. Existing methodologies rely on deterministic tools that apply fixed fingerprinting strategies and lack the capacity for contextual reasoning. Such approaches often fail to adapt to the heterogeneous architectures and dynamic configurations characteristic of modern critical infrastructures. This work introduces an architecture based on a Mixture of Experts model designed to overcome these limitations. The proposed framework combines multiple specialized modules to perform automated asset discovery, integrating passive and active software probes with physical sensors. This design enables the system to adapt to different operational scenarios and to classify discovered assets according to functional and security-relevant attributes. A proof-of-concept implementation is also presented, along with experimental results that demonstrate the feasibility of the proposed approach. The outcomes indicate that our LLM-based approach can support the development of non-intrusive asset management solutions, strengthening the cybersecurity posture of critical infrastructure systems. Full article

(This article belongs to the Special Issue Advanced Monitoring of Smart Critical Infrastructures)

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20 pages, 421 KB

Open AccessEditor’s ChoiceArticle

RISC-V Address-Encoded Byte Order Extension

by David Guerrero, Jorge Juan-Chico, German Cano-Quiveu, Paulino Ruiz-de-Clavijo, Julian Viejo and Enrique Ostua

Electronics 2025, 14(16), 3257; https://doi.org/10.3390/electronics14163257 - 16 Aug 2025

Viewed by 745

Abstract

In some cases, computer systems need to handle both little-endian and big-endian data, even if it differs from their native endianness. This paper proposes an RISC-V extension that makes it possible to remove the overhead introduced when dealing with foreign-endian data. It can [...] Read more.

In some cases, computer systems need to handle both little-endian and big-endian data, even if it differs from their native endianness. This paper proposes an RISC-V extension that makes it possible to remove the overhead introduced when dealing with foreign-endian data. It can be implemented with little engineering effort and a negligible impact on performance and hardware resources. Our results demonstrate that the extension can reduce the overhead of foreign-endian data processing by 62% or 37% compared to software-based solutions that use the base Instruction Set Architecture (ISA) or current bit manipulation extensions, respectively. This performance boost has the potential to benefit both new and legacy software once compiler and library support have been put in place. Full article

(This article belongs to the Special Issue High-Performance Computer Architecture)

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33 pages, 7587 KB

Open AccessEditor’s ChoiceArticle

A Fractional-Order State Estimation Method for Supercapacitor Energy Storage

by Arsalan Rasoolzadeh, Sayed Amir Hashemi and Majid Pahlevani

Electronics 2025, 14(16), 3231; https://doi.org/10.3390/electronics14163231 - 14 Aug 2025

Cited by 1 | Viewed by 500

Abstract

Supercapacitors (SCs) are emerging as a dependable energy storage technology in industrial applications, valued for their high power output and exceptional longevity. In high-power applications, SCs are not used as single cells but are configured in a series–parallel combination to form a bank. [...] Read more.

Supercapacitors (SCs) are emerging as a dependable energy storage technology in industrial applications, valued for their high power output and exceptional longevity. In high-power applications, SCs are not used as single cells but are configured in a series–parallel combination to form a bank. Accurate state-of-charge estimation is essential for effective energy management in power systems employing SC banks. This work presents a novel state estimation approach for SC banks. First, a dynamic model of an SC bank is derived by applying a fractional-order Thévenin equivalent circuit to a single-cell SC. Then, an observability analysis is conducted, which reveals that the system is empirically weakly observable. This is the fundamental challenge for state-of-the-art observers to robustly perform state estimation. To address this challenge, an implicitly regularized observer is developed based on generalized parameter estimation techniques. The performance of the proposed observer is benchmarked against a fractional-order extended Kalman filter using experimental data. The results demonstrate that incorporating a regularization law into the observer dynamics effectively mitigates observability limitations, offering a robust solution for the SC bank state estimation. Full article

(This article belongs to the Special Issue Hybrid Energy Harvesting Systems: New Developments and Applications)

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32 pages, 8208 KB

Open AccessFeature PaperEditor’s ChoiceReview

General Overview of Antennas for Unmanned Aerial Vehicles: A Review

by Sara Reis, Fábio Silva, Daniel Albuquerque and Pedro Pinho

Electronics 2025, 14(16), 3205; https://doi.org/10.3390/electronics14163205 - 12 Aug 2025

Viewed by 2915

Abstract

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are becoming increasingly important in multiple areas and various applications, including communication, detection, and monitoring. This review paper examines the development of antennas for UAVs, with a particular focus on miniaturization techniques, polarization strategies, and [...] Read more.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are becoming increasingly important in multiple areas and various applications, including communication, detection, and monitoring. This review paper examines the development of antennas for UAVs, with a particular focus on miniaturization techniques, polarization strategies, and beamforming solutions. It explores both structural and material-based methods, such as meander lines, slots, high-dielectric substrates, and metasurfaces, which aim to make the antenna more compact without compromising performance. Different antenna types including dipole, monopole, horn, vivaldi, and microstrip patch are explored to identify solutions that meet performance standards while respecting UAV constraints. In terms of polarization strategies, these are often implemented in the feeding network to achieve linear or circular polarization, and beamforming techniques like beam-steering and beam-switching enhance communication efficiency by improving signal directionality. Future research should focus on more lightweight, structurally integrated, and reconfigurable apertures that push miniaturization through conformal substrates and programmable metasurfaces, extending efficient operation from 5/6 GHz into the sub-THz regime and supporting agile beamforming for dense UAV swarms. Full article

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

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27 pages, 3770 KB

Open AccessEditor’s ChoiceArticle

Precision Time Interval Generator Based on CMOS Counters and Integration with IoT Timing Systems

by Nebojša Andrijević, Zoran Lovreković, Vladan Radivojević, Svetlana Živković Radeta and Hadžib Salkić

Electronics 2025, 14(16), 3201; https://doi.org/10.3390/electronics14163201 - 12 Aug 2025

Viewed by 1080

Abstract

Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor [...] Read more.

Precise time interval generation is a cornerstone of modern measurement, automation, and distributed control systems, particularly within Internet of Things (IoT) architectures. This paper presents the design, implementation, and evaluation of a low-cost and high-precision time interval generator based on Complementary Metal-Oxide Semiconductor (CMOS) logic counters (Integrated Circuit (IC) IC 7493 and IC 4017) and inverter-based crystal oscillators (IC 74LS04). The proposed system enables frequency division from 1 MHz down to 1 Hz through a cascade of binary and Johnson counters, enhanced with digitally controlled multiplexers for output signal selection. Unlike conventional timing systems relying on expensive Field-Programmable Gate Array (FPGA) or Global Navigation Satellite System (GNSS)-based synchronization, this approach offers a robust, locally controlled reference clock suitable for IoT nodes without network access. The hardware is integrated with Arduino and ESP32 microcontrollers via General-Purpose Input/Output (GPIO) level interfacing, supporting real-time timestamping, deterministic task execution, and microsecond-level synchronization. The system was validated through Python-based simulations incorporating Gaussian jitter models, as well as real-time experimental measurements using Arduino’s micros() function. Results demonstrated stable pulse generation with timing deviations consistently below ±3 µs across various frequency modes. A comparative analysis confirms the advantages of this CMOS-based timing solution over Real-Time Clock (RTC), Network Time Protocol (NTP), and Global Positioning System (GPS)-based methods in terms of local autonomy, cost, and integration simplicity. This work provides a practical and scalable time reference architecture for educational, industrial, and distributed applications, establishing a new bridge between classical digital circuit design and modern Internet of Things (IoT) timing requirements. Full article

(This article belongs to the Section Circuit and Signal Processing)

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12 pages, 1878 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Blind Source Separation for Joint Communication and Sensing in Time-Varying IBFD MIMO Systems

by Siyao Li, Conrad Prisby and Thomas Yang

Electronics 2025, 14(16), 3200; https://doi.org/10.3390/electronics14163200 - 12 Aug 2025

Viewed by 573

Abstract

This paper presents a blind source separation (BSS)-based framework for joint communication and sensing (JCAS) in in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems operating under time-varying channel conditions. Conventionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of [...] Read more.

This paper presents a blind source separation (BSS)-based framework for joint communication and sensing (JCAS) in in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) systems operating under time-varying channel conditions. Conventionally, self-interference (SI) in IBFD systems is a major obstacle to recovering the signal of interest (SOI). Under the JCAS paradigm, however, this high-power SI signal presents an opportunity for efficient sensing. Since each transceiver node has access to the original SI signal, its environmental reflections can be exploited to estimate channel conditions and detect changes, without requiring dedicated radar waveforms. We propose a blind source separation (BSS)-based framework to simultaneously perform self-interference cancellation (SIC) and extract sensing information in IBFD MIMO settings. The approach applies the Fast Independent Component Analysis (FastICA) algorithm in dynamic scenarios to separate the SI and SOI signals while enabling simultaneous signal recovery and channel estimation. Simulation results quantify the trade-off between estimation accuracy and channel dynamics, demonstrating that while FastICA is effective, its performance is fundamentally limited by a frame size optimized for the rate of channel variation. Specifically, in static channels, the signal-to-residual-error ratio (SRER) exceeds 22 dB with 500-symbol frames, whereas for moderately time-varying channels, performance degrades significantly for frames longer than 150 symbols, with SRER dropping below 4 dB. Full article

(This article belongs to the Special Issue Future Prospects of Wireless Transmission Based on Engineering Electrical Electronic and Telecommunications)

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29 pages, 7072 KB

Open AccessEditor’s ChoiceArticle

DK-SMF: Domain Knowledge-Driven Semantic Modeling Framework for Service Robots

by Kyeongjin Joo, Yeseul Jeong, Seungwon Kwon, Minyoung Jeong, Haryeong Kim and Taeyong Kuc

Electronics 2025, 14(16), 3197; https://doi.org/10.3390/electronics14163197 - 11 Aug 2025

Viewed by 693

Abstract

Modern robotic systems are evolving toward conducting missions based on semantic knowledge. Such systems require environmental modeling as essential for successful mission execution. However, there is an inefficiency in that manual modeling is required whenever a new environment is given, and adaptive modeling [...] Read more.

Modern robotic systems are evolving toward conducting missions based on semantic knowledge. Such systems require environmental modeling as essential for successful mission execution. However, there is an inefficiency in that manual modeling is required whenever a new environment is given, and adaptive modeling that can adapt to the environment is needed. In this paper, we propose an integrated framework that enables autonomous environmental modeling for service robots by fusing domain knowledge with open-vocabulary-based Vision-Language Models (VLMs). When a robot is deployed in a new environment, it builds occupancy maps through autonomous exploration and extracts semantic information about objects and places. Furthermore, we introduce human–robot collaborative modeling beyond robot-only environmental modeling. The collected semantic information is stored in a structured database and utilized on demand. To verify the applicability of the proposed framework to service robots, experiments are conducted in a simulated home environment and a real-world indoor corridor. Through the experiments, the proposed framework achieved over 80% accuracy in semantic information extraction in both environments. Semantic information about various types of objects and places was extracted and stored in the database, demonstrating the effectiveness of DK-SMF for service robots. Full article

(This article belongs to the Section Systems & Control Engineering)

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20 pages, 6602 KB

Open AccessEditor’s ChoiceArticle

A DC-Link Current Pulsation Compensator Based on a Triple-Active Bridge Converter Topology

by Karol Fatyga and Mariusz Zdanowski

Electronics 2025, 14(16), 3196; https://doi.org/10.3390/electronics14163196 - 11 Aug 2025

Viewed by 471

Abstract

This paper presents a method of compensating the AC pulsation appearing in the DC-link of a four-wire AC/DC converter operating with asymmetric output currents. If such a converter is operating with an electrochemical energy storage system, the AC component can cause several issues [...] Read more.

This paper presents a method of compensating the AC pulsation appearing in the DC-link of a four-wire AC/DC converter operating with asymmetric output currents. If such a converter is operating with an electrochemical energy storage system, the AC component can cause several issues for the battery. In order to solve this problem, a DC/DC converter is used to redirect the AC component into a capacitor bank. The triple-active bridge (TAB) converter is selected for this purpose. The converter is modeled using a reduced-order modelling approach, and the appropriate control loop is designed. The experimental setup is built and tested with a modelled DC-link, with emulated pulsation. The average AC component reduction on the battery port of 98.3% is achieved. Full article

(This article belongs to the Special Issue The Hybrid AC-DC Power System Coordinated Control and Operation Technology, 2nd Edition)

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17 pages, 4285 KB

Open AccessEditor’s ChoiceArticle

3D-Printed Circular Horn Antenna with Dielectric Lens for Focused RF Energy Delivery

by Aviad Michael and Nezah Balal

Electronics 2025, 14(16), 3191; https://doi.org/10.3390/electronics14163191 - 11 Aug 2025

Viewed by 1010

Abstract

This paper presents the design, simulation, and fabrication of a horn antenna integrated with a dielectric lens for focusing RF energy at 10 GHz. The antenna system combines established electromagnetic principles with 3D printing techniques to produce a cost-effective alternative to commercial focusing [...] Read more.

This paper presents the design, simulation, and fabrication of a horn antenna integrated with a dielectric lens for focusing RF energy at 10 GHz. The antenna system combines established electromagnetic principles with 3D printing techniques to produce a cost-effective alternative to commercial focusing antennas. The design methodology employs the lensmaker’s formula and Snell’s law to determine lens curvature for achieving a specified focal length of 100 mm. COMSOL Multiphysics simulations indicate that adding a PTFE lens increases power density concentration compared to a standard horn antenna, with a simulated focal point at approximately 100 mm. Surface roughness analysis based on the Rayleigh criterion supports 3D printing suitability for this application. Experimental validation includes radiation pattern measurements of the antenna without the lens and power density measurements versus distance with the lens, both showing good agreement with simulation results. The measured focal length was

95 \pm 5

mm, closely matching simulation predictions. This work presents an approach for implementing focused RF delivery solutions for medical treatments, wireless power transfer, and precision sensing at significantly lower costs than commercial alternatives. Full article

(This article belongs to the Special Issue Futuristic Antennas: Sustainable, Efficient, Reconfigurable, and Intelligent Design)

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17 pages, 3359 KB

Open AccessEditor’s ChoiceArticle

Automated Generation of Test Scenarios for Autonomous Driving Using LLMs

by Aaron Agyapong Danso and Ulrich Büker

Electronics 2025, 14(16), 3177; https://doi.org/10.3390/electronics14163177 - 10 Aug 2025

Cited by 1 | Viewed by 3356

Abstract

This paper introduces an approach that leverages large language models (LLMs) to convert detailed descriptions of an Operational Design Domain (ODD) into realistic, executable simulation scenarios for testing autonomous vehicles. The method combines model-based and data-driven techniques to decompose ODDs into three key [...] Read more.

This paper introduces an approach that leverages large language models (LLMs) to convert detailed descriptions of an Operational Design Domain (ODD) into realistic, executable simulation scenarios for testing autonomous vehicles. The method combines model-based and data-driven techniques to decompose ODDs into three key components: environmental, scenery, and dynamic elements. It then applies prompt engineering to generate ScenarioRunner scripts compatible with CARLA. The model-based component guides the LLM using structured prompts and a “Tree of Thoughts” strategy to outline the scenario, while a data-driven refinement process, drawing inspiration from red teaming, enhances the accuracy and robustness of the generated scripts over time. Experimental results show that while static components, such as weather and road layouts, are well captured, dynamic elements like vehicle and pedestrian behavior require further refinement. Overall, this approach not only reduces the manual effort involved in creating simulation scenarios but also identifies key challenges and opportunities for advancing safer and more adaptive autonomous driving systems. Full article

(This article belongs to the Special Issue Autonomous and Connected Vehicles)

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22 pages, 1750 KB

Open AccessEditor’s ChoiceArticle

Towards Energy Efficiency of HPC Data Centers: A Data-Driven Analytical Visualization Dashboard Prototype Approach

by Keith Lennor Veigas, Andrea Chinnici, Davide De Chiara and Marta Chinnici

Electronics 2025, 14(16), 3170; https://doi.org/10.3390/electronics14163170 - 8 Aug 2025

Cited by 1 | Viewed by 1637

Abstract

High-performance computing (HPC) data centers are experiencing rising energy consumption, despite the urgent need for increased efficiency. In this study, we develop an approach inspired by digital twins to enhance energy and thermal management in an HPC facility. We create a comprehensive framework [...] Read more.

High-performance computing (HPC) data centers are experiencing rising energy consumption, despite the urgent need for increased efficiency. In this study, we develop an approach inspired by digital twins to enhance energy and thermal management in an HPC facility. We create a comprehensive framework that incorporates a digital twin for the CRESCO7 supercomputer cluster at ENEA in Italy, integrating data-driven time series forecasting with an interactive analytical dashboard for resource prediction. We begin by reviewing relevant literature on digital twins and modern time series modeling techniques. After ingesting and cleansing sensor and job scheduling datasets, we perform exploratory and inferential analyses to understand key correlations. We then conduct descriptive statistical analyses and identify important features, which are used to train machine learning models for accurate short- and medium-term forecasts of power and temperature. These models feed into a simulated environment that provides real-time prediction metrics and a holistic “health score” for each node, all visualized in a dashboard built with Streamlit. The results demonstrate that a digital twin-based approach can help data center operators efficiently plan resources and maintenance, ultimately reducing the carbon footprint and improving energy efficiency. The proposed framework uniquely combines concepts inspired by digital twins with time series machine learning and interactive visualization for enhanced HPC energy planning. Key contributions include the novel integration of predictive models into a live virtual replica of the HPC cluster, employing a gradient-boosted tree-based LightGBM model. Our findings underscore the potential of data-driven digital twins to facilitate sustainable and intelligent management of HPC data centers. Full article

(This article belongs to the Special Issue High-Performance Computing for AI: Architecture, Systems, and Algorithms)

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21 pages, 4525 KB

Open AccessEditor’s ChoiceArticle

MAFUZZ: Adaptive Gradient-Guided Fuzz Testing for Satellite Internet Ground Terminals

by Ang Cao, Yongli Zhao, Xiaodan Yan, Wei Wang, Jian Yang, Yuanjian Zhang and Ruiqi Liu

Electronics 2025, 14(16), 3168; https://doi.org/10.3390/electronics14163168 - 8 Aug 2025

Viewed by 623

Abstract

With the proliferation of satellite internet systems, such as Starlink and OneWeb, ground terminals have become critical for ensuring end-user connectivity. However, the security of Satellite Internet Ground Terminals (SIGTs) remains underexplored. These Linux-based embedded systems are vulnerable to advanced attacks due to [...] Read more.

With the proliferation of satellite internet systems, such as Starlink and OneWeb, ground terminals have become critical for ensuring end-user connectivity. However, the security of Satellite Internet Ground Terminals (SIGTs) remains underexplored. These Linux-based embedded systems are vulnerable to advanced attacks due to limited source code access and immature protection mechanisms. This paper presents MAFUZZ, an adaptive fuzzing framework guided by neural network gradients to uncover hidden vulnerabilities in SIGT binaries. MAFUZZ uses a lightweight machine learning model to identify input bytes that influence program behavior and applies gradient-based mutation accordingly. It also integrates an adaptive Havoc mechanism to enhance path diversity. We compare MAFUZZ with NEUZZ, a neural fuzzing tool that uses program smoothing to guide mutation through a static model. Our experiments on real-world Linux binaries show that MAFUZZ improves path coverage by an average of 17.4% over NEUZZ, demonstrating its effectiveness in vulnerability discovery and its practical value for securing satellite terminal software. Full article

(This article belongs to the Special Issue Security Challenges and Opportunities of Artificial Intelligence/Big Data Scenarios)

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26 pages, 819 KB

Open AccessEditor’s ChoiceReview

A Survey of Analog Computing for Domain-Specific Accelerators

by Leonid Belostotski, Asif Uddin, Arjuna Madanayake and Soumyajit Mandal

Electronics 2025, 14(16), 3159; https://doi.org/10.3390/electronics14163159 - 8 Aug 2025

Viewed by 3948

Abstract

Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, [...] Read more.

Analog computing has re-emerged as a powerful tool for solving complex problems in various domains due to its energy efficiency and inherent parallelism. This paper summarizes recent advancements in analog computing, exploring discrete time and continuous time methods for solving combinatorial optimization problems, solving partial differential equations and systems of linear equations, accelerating machine learning (ML) inference, multi-beam beamforming, signal processing, quantum simulation, and statistical inference. We highlight CMOS implementations that leverage switched-capacitor, switched-current, and radio-frequency circuits, as well as non-CMOS implementations that leverage non-volatile memory, wave physics, and stochastic processes. These advancements demonstrate high-speed, energy-efficient computations for computational electromagnetics, finite-difference time-domain (FDTD) solvers, artificial intelligence (AI) inference engines, wireless systems, and related applications. Theoretical foundations, experimental validations, and potential future applications in high-performance computing and signal processing are also discussed. Full article

(This article belongs to the Special Issue Emerging Techniques and Applications for Analog and Mixed-Signal Integrated Circuits)

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15 pages, 8291 KB

Open AccessEditor’s ChoiceArticle

Two-Stage Power Delivery Architecture Using Hybrid Converters for Data Centers and Telecommunication Systems

by Ratul Das and Hanh-Phuc Le

Electronics 2025, 14(16), 3169; https://doi.org/10.3390/electronics14163169 - 8 Aug 2025

Viewed by 616

Abstract

This paper presents a new power delivery architecture to bring AC distribution voltages to core levels for computing loads using only two conversion stages with new converter topologies to potentially replace the traditional four-stage structure in the development of new data centers. This [...] Read more.

This paper presents a new power delivery architecture to bring AC distribution voltages to core levels for computing loads using only two conversion stages with new converter topologies to potentially replace the traditional four-stage structure in the development of new data centers. This paper also includes new converters as solutions to the proposed two stages. A new switched capacitor (SC)-based AC-DC converter is proposed for the first stage and demonstrated for an intermediate bus with 90 V–110 VAC to 48–60 VDC conversion and power factor correction. The second stage also includes an SC-based hybrid converter with multi-phase operation suitable for power delivery for core voltages of up to ~1 V with a high current density. This work also reports a new phase sequence for the second stage for an extended output voltage range. Individually, the first stage was measured at 96.1% peak efficiency for output currents ranging from 0 to 4.5 A, while the second stage achieved 90.7% peak efficiency with a load range of 0–220 A at 1V. The measured peak power densities were 73 W/in³ for the first stage and 2020 W/in³ for the second stage. In combination, the direct conversion from ~110 VAC to 1 VDC led to a peak efficiency of 84.1% at 50 A, and this setup has been tested with output currents of up to 160 A, where the efficiency was 73.5%. Full article

(This article belongs to the Special Issue Applications, Control and Design of Power Electronics Converters)

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