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 277

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 1190

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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25 pages, 22171 KB

Open AccessEditor’s ChoiceArticle

Physics-Informed Co-Optimization of Fuel-CellFlying Vehicle Propulsion and Control Systems with Onboard Catalysis

by Yifei Bao, Chaoyi Chen, Hao Zhang and Nuo Lei

Electronics 2025, 14(21), 4150; https://doi.org/10.3390/electronics14214150 - 23 Oct 2025

Viewed by 373

Abstract

Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end, this paper [...] Read more.

Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end, this paper introduces a novel optimization paradigm, termed physics-informed gradient-enhanced multi-objective optimization (PI-GEMO), to simultaneously optimize the ammonia decomposition unit (ADU) catalyst composition, powertrain sizing, and flight control parameters. The PI-GEMO framework leverages a physics-informed neural network (PINN) as a differentiable surrogate model, which is trained not only on sparse simulation data but also on the governing differential equations of the system. This enables the use of analytical gradient information extracted from the trained PINN via automatic differentiation to intelligently guide the evolutionary search process. A comprehensive case study on a flying vehicle demonstrates that the PI-GEMO framework not only discovers a superior set of Pareto-optimal solutions compared to traditional methods but also critically ensures the physical plausibility of the results. Full article

(This article belongs to the Special Issue Eco-Safe Intelligent Mobility Development and Application)

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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 684

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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36 pages, 552 KB

Open AccessEditor’s ChoiceReview

Review of Applications of Regression and Predictive Modeling in Wafer Manufacturing

by Hsuan-Yu Chen and Chiachung Chen

Electronics 2025, 14(20), 4083; https://doi.org/10.3390/electronics14204083 - 17 Oct 2025

Viewed by 1784

Abstract

Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can [...] Read more.

Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can lead to catastrophic yield loss, challenging traditional physics-based control methods. In response, the industry has increasingly adopted regression analysis and predictive modeling as essential analytical frameworks. Classical regression, long used to support design of experiments (DOE), process optimization, and yield analysis, has evolved to enable multivariate modeling, virtual metrology, and fault detection. Predictive modeling extends these capabilities through machine learning and AI, leveraging massive sensor and metrology data streams for real-time process monitoring, yield forecasting, and predictive maintenance. These data-driven tools are now tightly integrated into advanced process control (APC), digital twins, and automated decision-making systems, transforming fabs into agile, intelligent manufacturing environments. This review synthesizes foundational and emerging methods, industry applications, and case studies, emphasizing their role in advancing Industry 4.0 initiatives. Future directions include hybrid physics–ML models, explainable AI, and autonomous manufacturing. Together, regression and predictive modeling provide semiconductor fabs with a robust ecosystem for optimizing performance, minimizing costs, and accelerating innovation in an increasingly competitive, high-stakes industry. Full article

(This article belongs to the Special Issue Advances in Semiconductor Devices and Applications)

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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 689

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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16 pages, 6589 KB

Open AccessEditor’s ChoiceArticle

An Enhanced Steganography-Based Botnet Communication Method in BitTorrent

by Gyeonggeun Park, Youngho Cho and Gang Qu

Electronics 2025, 14(20), 4081; https://doi.org/10.3390/electronics14204081 - 17 Oct 2025

Viewed by 440

Abstract

In a botnet attack, significant damage can occur when an attacker gains control over a large number of compromised network devices. Botnets have evolved from traditional centralized architectures to decentralized Peer-to-Peer (P2P) and hybrid forms. Recently, a steganography-based botnet (Stego-botnet) has emerged, which [...] Read more.

In a botnet attack, significant damage can occur when an attacker gains control over a large number of compromised network devices. Botnets have evolved from traditional centralized architectures to decentralized Peer-to-Peer (P2P) and hybrid forms. Recently, a steganography-based botnet (Stego-botnet) has emerged, which conceals command and control (C&C) messages within cover media such as images or video files shared over social networking sites (SNS). This type of Stego-botnet can evade conventional detection systems, as identifying hidden messages embedded in media transmitted via SNS platforms is inherently challenging. However, the inherent file size limitations of SNS platforms restrict the achievable payload capacity of such Stego-botnets. Moreover, the centralized characteristics of conventional botnet architectures expose attackers to a higher risk of identification. To overcome these challenges, researchers have explored network steganography techniques leveraging P2P networks such as BitTorrent, Google Suggest, and Skype. Among these, a hidden communication method utilizing Bitfield messages in BitTorrent has been proposed, demonstrating improved concealment compared to prior studies. Nevertheless, existing approaches still fail to achieve sufficient payload capacity relative to traditional digital steganography techniques. In this study, we extend P2P-based network steganography methods—particularly within the BitTorrent protocol—to address these limitations. We propose a novel botnet C&C communication model that employs network steganography over BitTorrent and validate its feasibility through experimental implementation. Furthermore, our results show that the proposed Stego-botnet achieves a higher payload capacity and outperforms existing Stego-botnet models in terms of both efficiency and concealment performance. Full article

(This article belongs to the Special Issue Cybersecurity and Privacy in Internet-of-Things: Advances, Challenges, and Emerging Trends)

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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 531

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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24 pages, 1535 KB

Open AccessEditor’s ChoiceArticle

Enhanced Distributed Multimodal Federated Learning Framework for Privacy-Preserving IoMT Applications: E-DMFL

by Dagmawit Tadesse Aga and Madhuri Siddula

Electronics 2025, 14(20), 4024; https://doi.org/10.3390/electronics14204024 - 14 Oct 2025

Viewed by 643

Abstract

The rapid growth of Internet of Medical Things (IoMT) devices offers promising avenues for real-time, personalized healthcare while also introducing critical challenges related to data privacy, device heterogeneity, and deployment scalability. This paper presents E-DMFL (Enhanced Distributed Multimodal Federated Learning), an Enhanced Distributed [...] Read more.

The rapid growth of Internet of Medical Things (IoMT) devices offers promising avenues for real-time, personalized healthcare while also introducing critical challenges related to data privacy, device heterogeneity, and deployment scalability. This paper presents E-DMFL (Enhanced Distributed Multimodal Federated Learning), an Enhanced Distributed Multimodal Federated Learning framework designed to address these issues. Our approach combines systems analysis principles with intelligent model design, integrating PyTorch-based modular orchestration and TensorFlow-style data pipelines to enable multimodal edge-based training. E-DMFL incorporates gated attention fusion, differential privacy, Shapley-value-based modality selection, and peer-to-peer communication to facilitate secure and adaptive learning in non-IID environments. We evaluate the framework using the EarSAVAS dataset, which includes synchronized audio and motion signals from ear-worn sensors. E-DMFL achieves a test accuracy of 92.0% in just six communication rounds. The framework also supports energy-efficient and real-time deployment through quantization-aware training and battery-aware scheduling. These results demonstrate the potential of combining systems-level design with federated learning (FL) innovations to support practical, privacy-aware IoMT applications. Full article

(This article belongs to the Special Issue Wireless Artificial Intelligent Computing Systems and Applications (WASA))

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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 543

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 497

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 932

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 343

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, 643 KB

Open AccessEditor’s ChoiceArticle

Improving Physical Layer Security for Multi-Hop Transmissions in Underlay Cognitive Radio Networks with Various Eavesdropping Attacks

by Kyusung Shim and Beongku An

Electronics 2025, 14(19), 3867; https://doi.org/10.3390/electronics14193867 - 29 Sep 2025

Viewed by 328

Abstract

This paper investigates physical layer security (PHY-security) for multi-hop transmission in underlay cognitive radio networks under various eavesdropping attacks. To enhance secrecy performance, we propose two opportunistic scheduling schemes. The first scheme, called the minimal node selection (MNS) scheme, selects the node in [...] Read more.

This paper investigates physical layer security (PHY-security) for multi-hop transmission in underlay cognitive radio networks under various eavesdropping attacks. To enhance secrecy performance, we propose two opportunistic scheduling schemes. The first scheme, called the minimal node selection (MNS) scheme, selects the node in each cluster that minimizes the eavesdropper’s channel capacity. The second scheme, named the optimal node selection (ONS) scheme, chooses the node that maximizes secrecy capacity by using both the main and eavesdropper channel information. To reveal the relationship between network parameters and secrecy performance, we derive closed-form expressions for the secrecy outage probability (SOP) under different scheduling schemes and eavesdropping scenarios. Numerical results show that the ONS scheme provides the most robust secrecy performance among the considered schemes. Furthermore, we analyze the impact of key network parameters on secrecy performance. In detail, although the proposed ONS scheme requires more channel information than the MNS scheme, under a 20 dB interference threshold, the secrecy performance of the ONS scheme is 15% more robust than that of the MNS scheme. Full article

(This article belongs to the Section Networks)

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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 481

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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19 pages, 3437 KB

Open AccessEditor’s ChoiceArticle

Comparing CNN and ViT for Open-Set Face Recognition

by Ander Galván, Mariví Higuero, Ane Sanz, Asier Atutxa, Eduardo Jacob and Mario Saavedra

Electronics 2025, 14(19), 3840; https://doi.org/10.3390/electronics14193840 - 27 Sep 2025

Viewed by 1150

Abstract

At present, there is growing interest in automated biometric identification applications. For these, it is crucial to have a system capable of accurately identifying a specific group of people while also detecting individuals who do not belong to that group. In face identification [...] Read more.

At present, there is growing interest in automated biometric identification applications. For these, it is crucial to have a system capable of accurately identifying a specific group of people while also detecting individuals who do not belong to that group. In face identification models that use Deep Learning (DL) techniques, this context is referred to as Open-Set Recognition (OSR), which is the focus of this work. This scenario presents a substantial challenge for this type of system, as it involves the need to effectively identify unknown individuals who were not part of the system’s training data. In this context, where the accuracy of this type of system is considered crucial, selecting the model to be used in each scenario becomes key. It is within this context that our work arises. Here, we present the results of a rigorous comparative analysis examining the precision of some of the most widely used models today for face identification, specifically some Convolutional Neural Network (CNN) models compared with a Vision Transformer (ViT) model. All models were pre-trained on the same large dataset and evaluated in an OSR scenario. The results show that ViT achieves the highest precision, outperforming CNN baselines and demonstrating better generalization for unknown identities. These findings support recent evidence that ViT is a promising alternative to CNN for this type of application. Full article

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

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14 pages, 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 1208

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 2931

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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20 pages, 2856 KB

Open AccessEditor’s ChoiceArticle

Privacy-Preserving Federated Review Analytics with Data Quality Optimization for Heterogeneous IoT Platforms

by Jiantao Xu, Liu Jin and Chunhua Su

Electronics 2025, 14(19), 3816; https://doi.org/10.3390/electronics14193816 - 26 Sep 2025

Viewed by 618

Abstract

The proliferation of Internet of Things (IoT) devices has created a distributed ecosystem where users generate vast amounts of review data across heterogeneous platforms, from smart home assistants to connected vehicles. This data is crucial for service improvement but is plagued by fake [...] Read more.

The proliferation of Internet of Things (IoT) devices has created a distributed ecosystem where users generate vast amounts of review data across heterogeneous platforms, from smart home assistants to connected vehicles. This data is crucial for service improvement but is plagued by fake reviews, data quality inconsistencies, and significant privacy risks. Traditional centralized analytics fail in this landscape due to data privacy regulations and the sheer scale of distributed data. To address this, we propose FedDQ, a federated learning framework for Privacy-Preserving Federated Review Analytics with Data Quality Optimization. FedDQ introduces a multi-faceted data quality assessment module that operates locally on each IoT device, evaluating review data based on textual coherence, behavioral patterns, and cross-modal consistency without exposing raw data. These quality scores are then used to orchestrate a quality-aware aggregation mechanism at the server, prioritizing contributions from high-quality, reliable clients. Furthermore, our framework incorporates differential privacy and models system heterogeneity to ensure robustness and practical applicability in resource-constrained IoT environments. Extensive experiments on multiple real-world datasets show that FedDQ significantly outperforms baseline federated learning methods in accuracy, convergence speed, and resilience to data poisoning attacks, achieving up to a 13.8% improvement in F1-score under highly heterogeneous and noisy conditions while preserving user privacy. Full article

(This article belongs to the Special Issue Emerging IoT Sensor Network Technologies and Applications)

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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 452

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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23 pages, 3690 KB

Open AccessEditor’s ChoiceArticle

Reliability and Performance Evaluation of IoT-Based Gas Leakage Detection Systems for Residential Environments

by Elia Landi, Lorenzo Parri, David Baldo, Stefano Parrino, Tunahan Vatansever, Ada Fort, Marco Mugnaini and Valerio Vignoli

Electronics 2025, 14(19), 3798; https://doi.org/10.3390/electronics14193798 - 25 Sep 2025

Viewed by 1092

Abstract

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study [...] Read more.

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study evaluates the node’s long-term robustness and stability under both realistic and accelerated operating conditions. The system employs a dual-sensor strategy in which the VOC sensor acts as a sentinel, activating the pellistor only when necessary, thereby optimizing power consumption and extending battery life. BLE and LoRa communication capabilities support flexible deployment and real-time data transmission. To ensure suitability for safety-critical applications, we conducted comprehensive reliability testing, including accelerated life tests and environmental stress testing in compliance with IEC 60068 standards. The results confirm the system’s ability to maintain consistent performance and data integrity under thermal, mechanical, and chemical stress, demonstrating its robustness for prolonged operation in demanding environments. Overall, this work underscores the importance of rigorous reliability validation for IoT-based safety devices and positions the proposed solution as a significant step toward enhancing residential gas safety, with potential applications in broader industrial monitoring scenarios. Full article

(This article belongs to the Special Issue High-Performance Gas Sensors: Materials, Technologies and Applications)

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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 1110

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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46 pages, 4133 KB

Open AccessEditor’s ChoiceReview

Flux-Weakening Control Methods for Permanent Magnet Synchronous Machines in Electric Vehicles at High Speed

by Samer Alwaqfi, Mohamad Alzayed and Hicham Chaoui

Electronics 2025, 14(19), 3779; https://doi.org/10.3390/electronics14193779 - 24 Sep 2025

Viewed by 3346

Abstract

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive [...] Read more.

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive Force (EMF) increases proportionally with the motor’s rotational speed, it must be carefully controlled at high speeds. Flux-weakening (FW) control is required to avoid excessive electromagnetic flux beyond the power source and inverter’s voltage restrictions. This paper aims to compare various FW control strategies and analyze their effectiveness in maximizing the speed of PMSMs in EV applications while ensuring stable and reliable performance. Various FW approaches, such as voltage-based control, current-based control, and advanced predictive control methods, are examined to determine how each method balances speed enhancement with torque output and efficiency. In addition, other control strategies are crucial for optimizing the performance of PMSMs in electric vehicles. Among the most popular methods for controlling torque and speed in PMSMs are Field-Oriented Control (FOC), Direct Torque Control (DTC), and Vector Current Control (VCC). Each control technique has advantages and is frequently cited in the literature as a crucial instrument for improving EV motor control. This article provides a comprehensive evaluation of FW methods, highlighting their respective advantages and disadvantages by synthesizing the findings of numerous studies. In addition to outlining future research directions in FW control for EV applications, this study provides essential insights and valuable suggestions to help select FW control techniques for various PMSM types and operating conditions. Full article

(This article belongs to the Special Issue Advanced Control and Power Electronics for Electric Vehicles)

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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 278

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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20 pages, 3944 KB

Open AccessEditor’s ChoiceArticle

Performance Analysis and Security Preservation of DSRC in V2X Networks

by Muhammad Saad Sohail, Giancarlo Portomauro, Giovanni Battista Gaggero, Fabio Patrone and Mario Marchese

Electronics 2025, 14(19), 3786; https://doi.org/10.3390/electronics14193786 - 24 Sep 2025

Cited by 1 | Viewed by 823

Abstract

Protecting communications within vehicular networks is of paramount importance, particularly when data are transmitted using wireless ad-hoc technologies such as Dedicated Short-Range Communications (DSRC). Vulnerabilities in Vehicle-to-Everything (V2X) communications, especially along highways, pose significant risks, such as unauthorized interception or alteration of vehicle [...] Read more.

Protecting communications within vehicular networks is of paramount importance, particularly when data are transmitted using wireless ad-hoc technologies such as Dedicated Short-Range Communications (DSRC). Vulnerabilities in Vehicle-to-Everything (V2X) communications, especially along highways, pose significant risks, such as unauthorized interception or alteration of vehicle data. This study proposes a Software-Defined Radio (SDR)-based tool designed to assess the protection level of V2X communication systems against cyber attacks. The proposed tool can emulate both reception and transmission of IEEE 802.11p packets while testing DSRC implementation and robustness. The results of this investigation offer valuable contributions toward shaping cybersecurity strategies and frameworks designed to protect the integrity of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. Full article

(This article belongs to the Special Issue Computer Networking Security and Privacy)

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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 451

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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39 pages, 2251 KB

Open AccessEditor’s ChoiceArticle

Real-Time Phishing Detection for Brand Protection Using Temporal Convolutional Network-Driven URL Sequence Modeling

by Marie-Laure E. Alorvor and Sajjad Dadkhah

Electronics 2025, 14(18), 3746; https://doi.org/10.3390/electronics14183746 - 22 Sep 2025

Viewed by 1323

Abstract

Phishing, especially brand impersonation attacks, is a critical cybersecurity threat that harms user trust and organization security. This paper establishes a lightweight model for real-time detection that relies on URL-only sequences, addressing limitations for multimodal methods that leverage HTML, images, or metadata. This [...] Read more.

Phishing, especially brand impersonation attacks, is a critical cybersecurity threat that harms user trust and organization security. This paper establishes a lightweight model for real-time detection that relies on URL-only sequences, addressing limitations for multimodal methods that leverage HTML, images, or metadata. This approach is based on a Temporal Convolutional Network with Attention (TCNWithAttention) that utilizes character-level URLs to capture both local and long-range dependencies, while providing interpretability with attention visualization and Shapley additive explanations (SHAP). The model was trained and tested on the balanced GramBeddings dataset (800,000 URLs) and validated on the PhiUSIIL dataset of real-world phishing URLs. The model achieved 97.54% accuracy on the GramBeddings dataset, and 81% recall on the PhiUSIIL dataset. The model demonstrated strong generalization, fast inference, and CPU-only deployability. It outperformed CNN, BiLSTM and BERT baselines. Explanations highlighted phishing indicators, such as deceptive subdomains, brand impersonation, and suspicious tokens. It also affirmed real patterns in the legitimate domains. To our knowledge, a Streamlit application to facilitate single and batch URL analysis and log feedback to maintain usability is the first phishing detection framework to integrate TCN, attention, and SHAP, bridging academic innovation with practical cybersecurity techniques. Full article

(This article belongs to the Special Issue Emerging Technologies for Network Security and Anomaly Detection)

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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 1211

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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17 pages, 1816 KB

Open AccessEditor’s ChoiceArticle

Welcome to the Machine (WTTM): A Cybersecurity Framework for the Automotive Sector

by Enrico Picano and Massimo Fontana

Electronics 2025, 14(18), 3645; https://doi.org/10.3390/electronics14183645 - 15 Sep 2025

Viewed by 910

Abstract

Cybersecurity has become a critical concern in the automotive sector, where the increasing connectivity and complexity of modern vehicles—particularly in the context of autonomous driving—have significantly expanded the attack surface. In response to these challenges, this paper presents the Welcome To The Machine [...] Read more.

Cybersecurity has become a critical concern in the automotive sector, where the increasing connectivity and complexity of modern vehicles—particularly in the context of autonomous driving—have significantly expanded the attack surface. In response to these challenges, this paper presents the Welcome To The Machine (WTTM) framework, developed to support proactive and structured cyber risk management throughout the entire vehicle lifecycle. Specifically tailored to the automotive domain, the framework encompasses four core actions: detection, analysis, response, and remediation. A central element of WTTM is the WTTM Questionnaire, designed to assess the organizational cybersecurity maturity of automotive manufacturers and suppliers. The questionnaire addresses six key areas: Governance, Risk Management, Concept and Design, Security Requirements, Validation and Testing, and Supply Chain. This paper focuses on the development and validation of WTTM-Q. Statistical validation was performed using responses from 43 participants, demonstrating high internal consistency (Cronbach’s alpha > 0.70) and strong construct validity (CFI = 0.94, RMSEA = 0.061). A supervised classifier (XGBoost), trained on 115 hypothetical response configurations, was employed to predict a priori risk classes, achieving 78% accuracy and a ROC AUC of 0.84. The WTTM framework, supported by a Vehicle Security Operations Center, provides a scalable, standards-aligned solution for enhancing cybersecurity in the automotive industry. Full article

(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)

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25 pages, 6041 KB

Open AccessEditor’s ChoiceArticle

A Dynamic Bridge Architecture for Efficient Interoperability Between AUTOSAR Adaptive and ROS2

by Suhong Kim, Hyeongju Choi, Suhaeng Lee, Minseo Kim, Hyunseo Shin and Changjoo Moon

Electronics 2025, 14(18), 3635; https://doi.org/10.3390/electronics14183635 - 14 Sep 2025

Viewed by 1181

Abstract

The automotive industry is undergoing a transition toward Software-Defined Vehicles (SDVs), necessitating the integration of AUTOSAR Adaptive, a standard for vehicle control, with ROS2, a platform for autonomous driving research. However, current static bridge approaches present notable limitations, chiefly regarding unnecessary resource consumption [...] Read more.

The automotive industry is undergoing a transition toward Software-Defined Vehicles (SDVs), necessitating the integration of AUTOSAR Adaptive, a standard for vehicle control, with ROS2, a platform for autonomous driving research. However, current static bridge approaches present notable limitations, chiefly regarding unnecessary resource consumption and compatibility issues with Quality of Service (QoS). To tackle these challenges, in this paper, we put forward a dynamic bridge architecture consisting of three components: a Discovery Manager, a Bridge Manager, and a Message Router. The proposed dynamic SOME/IP-DDS bridge dynamically detects service discovery events from the SOME/IP and DDS domains in real time, allowing for the creation and destruction of communication entities as needed. Additionally, it automatically manages QoS settings to ensure that they remain compatible. The experimental results indicate that this architecture maintains a stable latency even with a growing number of connections, demonstrating high scalability while also reducing memory usage during idle periods compared to static methods. Moreover, real-world assessments using an autonomous driving robot confirm its real-time applicability by reliably relaying sensor data to Autoware with minimal end-to-end latency. This research contributes to expediting the integration of autonomous driving exploration and production vehicle platforms by offering a more efficient and robust interoperability solution. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicular Networks)

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15 pages, 2947 KB

Open AccessEditor’s ChoiceArticle

Visible-Light Spectroscopy and Laser Scattering for Screening Brewed Coffee Types Using a Low-Cost Portable Platform

by Eleftheria Maliaritsi, Georgios Violakis and Evangelos Hristoforou

Electronics 2025, 14(18), 3625; https://doi.org/10.3390/electronics14183625 - 12 Sep 2025

Viewed by 545

Abstract

Visible-light spectroscopy has long been used to assess various quality indicators in coffee, from green beans to brewed beverages. High-end absorption spectroscopy systems can identify chemical compounds, monitor roasting chemistry, and support flavor profiling. Despite advances in low-cost spectroscopy, such techniques are rarely [...] Read more.

Visible-light spectroscopy has long been used to assess various quality indicators in coffee, from green beans to brewed beverages. High-end absorption spectroscopy systems can identify chemical compounds, monitor roasting chemistry, and support flavor profiling. Despite advances in low-cost spectroscopy, such techniques are rarely applied during coffee-drink preparation. Most coffee shops, instead, rely on simple refractometers to measure total dissolved solids (TDS) as a proxy for beverage strength. This study explores a portable, low-cost screening system that integrates visible absorption-transmittance, laser-induced scattering, and fluorescence spectroscopy to estimate brew strength and investigate potential differentiation between coffee-drink types. Experiments were conducted on four common drink preparations. A dual-region exponential decay model was applied to absorption-transmittance spectra, while laser-scattered light imaging revealed distinctive color patterns across samples. The results demonstrate the feasibility of optical fingerprinting as a non-invasive tool to support quality assessment in brewed coffee. Full article

(This article belongs to the Special Issue Section Collection Series: Recent Advances in Optoelectronics from Lab to Industry, 2nd Edition)

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16 pages, 5561 KB

Open AccessEditor’s ChoiceArticle

Smooth and Robust Path-Tracking Control for Automated Vehicles: From Theory to Real-World Applications

by Karin Festl, Selim Solmaz and Daniel Watzenig

Electronics 2025, 14(18), 3588; https://doi.org/10.3390/electronics14183588 - 10 Sep 2025

Viewed by 675

Abstract

Path tracking is a fundamental challenge in the development of automated driving systems, requiring precise control of vehicle motion while ensuring smooth and stable actuation signals. Advancements in this field often lead to increasingly complex control solutions that demand significant computational effort and [...] Read more.

Path tracking is a fundamental challenge in the development of automated driving systems, requiring precise control of vehicle motion while ensuring smooth and stable actuation signals. Advancements in this field often lead to increasingly complex control solutions that demand significant computational effort and are difficult to parameterize. A novel variable structure path-tracking control approach that is based on the geometrically optimal solution of a Dubins car offers a promising solution to this challenge. The controller generates an n-smooth and differentially bounded steering angle, and with n + 1 parameters, it can be tuned towards performance, robustness, or low magnitude of the steering angle derivatives. In prior work, this controller demonstrated its performance, robustness, and tunablity in various simulations. In this contribution, we address the challenges of implementing this controller in a real vehicle, including system dead time, low sampling rates, and discontinuous paths. Key adaptations are proposed to ensure robust performance under these conditions. The controller is integrated into a comprehensive automated driving system, incorporating planning and velocity control, and evaluated during an overtaking maneuver (double-lane change) in a real-world setting. Experimental results show that the implemented controller successfully handles system dead time and path discontinuities, achieving consistent tracking errors of less than

0.3

m. Full article

(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)

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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 5819

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 774

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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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 675

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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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 715

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 762

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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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 734

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, 4938 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 2483

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 553

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 894

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 652

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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22 pages, 1672 KB

Open AccessEditor’s ChoiceArticle

Optimizing Robotic Disassembly-Assembly Line Balancing with Directional Switching Time via an Improved Q(λ) Algorithm in IoT-Enabled Smart Manufacturing

by Qi Zhang, Yang Xing, Man Yao, Xiwang Guo, Shujin Qin, Haibin Zhu, Liang Qi and Bin Hu

Electronics 2025, 14(17), 3499; https://doi.org/10.3390/electronics14173499 - 1 Sep 2025

Cited by 1 | Viewed by 931

Abstract

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across [...] Read more.

With the growing adoption of circular economy principles in manufacturing, efficient disassembly and reassembly of end-of-life (EOL) products has become a key challenge in smart factories. This paper addresses the Disassembly and Assembly Line Balancing Problem (DALBP), which involves scheduling robotic tasks across workstations while minimizing total operation time and accounting for directional switching time between disassembly and assembly phases. To solve this problem, we propose an improved reinforcement learning algorithm, IQ(

λ

), which extends the classical Q(

λ

) method by incorporating eligibility trace decay, a dynamic Action Table mechanism to handle non-conflicting parallel tasks, and switching-aware reward shaping to penalize inefficient task transitions. Compared with standard Q(

λ

), these modifications enhance the algorithm’s global search capability, accelerate convergence, and improve solution quality in complex DALBP scenarios. While the current implementation does not deploy live IoT infrastructure, the architecture is modular and designed to support future extensions involving edge-cloud coordination, trust-aware optimization, and privacy-preserving learning in Industrial Internet of Things (IIoT) environments. Four real-world disassembly-assembly cases (flashlight, copier, battery, and hammer drill) are used to evaluate the algorithm’s effectiveness. Experimental results show that IQ(

λ

) consistently outperforms traditional Q-learning, Q(

λ

), and Sarsa in terms of solution quality, convergence speed, and robustness. Furthermore, ablation studies and sensitivity analysis confirm the importance of the algorithm’s core design components. This work provides a scalable and extensible framework for intelligent scheduling in cyber-physical manufacturing systems and lays a foundation for future integration with secure, IoT-connected environments. Full article

(This article belongs to the Section Networks)

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17 pages, 2871 KB

Open AccessEditor’s ChoiceArticle

Cu₂O Nanowire Chemiresistors for Detection of Organophosphorus CWA Simulants

by Jaroslav Otta, Jan Mišek, Ladislav Fišer, Jan Kejzlar, Martin Hruška, Jaromír Kukal and Martin Vrňata

Electronics 2025, 14(17), 3478; https://doi.org/10.3390/electronics14173478 - 31 Aug 2025

Viewed by 3537

Abstract

Rapid on-site detection of chemical warfare agents (CWAs) is vital for security and environmental monitoring. In this work, copper(I) oxide (Cu₂O) nanowire (NW) chemiresistors were investigated as gas sensors for low-concentration organophosphorus chemical warfare agent (CWA) simulants. The NWs were hydrothermally [...] Read more.

Rapid on-site detection of chemical warfare agents (CWAs) is vital for security and environmental monitoring. In this work, copper(I) oxide (Cu₂O) nanowire (NW) chemiresistors were investigated as gas sensors for low-concentration organophosphorus chemical warfare agent (CWA) simulants. The NWs were hydrothermally synthesized and deposited onto microheater platforms, enabling them to operate at elevated working temperatures. Their sensing performance was tested against a range of vapor-phase simulants, including dimethyl methylphosphonate (DMMP), triethyl phosphate (TEP), diethyl ethylphosphonate (DEEP), diphenyl phosphoryl chloride (DPPCl), parathion, diethyl phosphite (DEP), diethyl adipate (DEA), and cyanogen chloride (ClCN). Fully oxidized P(V) simulants (DMMP, DEEP, TEP) produced modest, predominantly reversible responses (~3–6% RR). On the contrary, DPPCl and DEP induced the strongest relative responses (RR −94.67% and >200%, respectively), accompanied by irreversible surface modification as revealed by SEM and EDS. ClCN produced a substantial but reversible negative response (RR −9.5%), consistent with transient oxidative interactions. Surface poisoning was confirmed after exposure to DEP and DPPCl, which left phosphorus or chlorine residues on the Cu₂O surface. These results highlight both the promise and limitations of Cu₂O NW chemiresistors for selective CWA detection. Full article

(This article belongs to the Special Issue High-Performance Gas Sensors: Materials, Technologies and Applications)

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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 847

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 481

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 1338

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 1372

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 860

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, 7716 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 1178

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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