Electronics

25 pages, 7400 KiB

Open AccessArticle

OT Control and Integration of Mobile Robotic Networks

by Marco Mărieș and Mihai Olimpiu Tătar

Electronics 2025, 14(13), 2531; https://doi.org/10.3390/electronics14132531 (registering DOI) - 22 Jun 2025

This paper introduces a configuration and integration model for mobile robots deployed in emergency and special operations scenarios. The proposed method is designed for implementation within the operational technology (OT) domain, enforcing security protocols that ensure both data encryption and network isolation. The [...] Read more.

This paper introduces a configuration and integration model for mobile robots deployed in emergency and special operations scenarios. The proposed method is designed for implementation within the operational technology (OT) domain, enforcing security protocols that ensure both data encryption and network isolation. The primary objective is to establish a dedicated operational environment encompassing a command and control center where the robotic network server resides, alongside real-time data storage from network clients and remote control of field-deployed mobile robots. Building on this infrastructure, operational strategies are developed to enable an efficient robotic response in critical situations. By leveraging remote robotic networks, significant benefits are achieved in terms of personnel safety and mission efficiency, minimizing response time and reducing the risk of injury to human operators during hazardous interventions. Unlike generic IoT or IoRT systems, this work focuses on secure robotic integration within segmented OT infrastructures. The technologies employed create a synergistic system that ensures data integrity, encryption, and safe user interaction through a web-based interface. Additionally, the system includes mobile robots and a read-only application positioned within a demilitarized zone (DMZ), allowing for secure data monitoring without granting control access to the robotic network, thus enabling cyber-physical isolation and auditability. Full article

(This article belongs to the Special Issue Modeling and Control of Mobile Robots)

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28 pages, 5168 KiB

Open AccessArticle

GazeHand2: A Gaze-Driven Virtual Hand Interface with Improved Gaze Depth Control for Distant Object Interaction

by Jaejoon Jeong, Soo-Hyung Kim, Hyung-Jeong Yang, Gun Lee and Seungwon Kim

Electronics 2025, 14(13), 2530; https://doi.org/10.3390/electronics14132530 (registering DOI) - 22 Jun 2025

Abstract

Research on Virtual Reality (VR) interfaces for distant object interaction has been carried out to improve user experience. Since hand-only interfaces and gaze-only interfaces have limitations such as physical fatigue or restricted usage, VR interaction interfaces using both gaze and hand input have [...] Read more.

Research on Virtual Reality (VR) interfaces for distant object interaction has been carried out to improve user experience. Since hand-only interfaces and gaze-only interfaces have limitations such as physical fatigue or restricted usage, VR interaction interfaces using both gaze and hand input have been proposed. However, current gaze + hand interfaces still have restrictions such as difficulty in translating along the gaze ray direction, using less realistic methods, or limited rotation support. This study aims to design a new distant object interaction technique that supports hand-based interaction with high freedom of object interaction in immersive VR. In this study, we developed GazeHand2, a hand-based object interaction technique, which features a new depth control that enables free object manipulation in VR. Building on the strength of the original GazeHand, GazeHand2 can control the change rate of the gaze depth by using the relative position of the hand, allowing users to translate the object to any position. To validate our design, we conducted a user study on object manipulation, which compares it with other gaze + hand interfaces (Gaze+Pinch and ImplicitGaze). Result showed that, compared to other conditions, GazeHand2 reduced 39.3% to 54.3% of hand movements and 27.8% to 47.1% of head movements under 3 m and 5 m tasks. It also significantly increased overall user experiences (0.69 to 1.12 pt higher than Gaze+Pinch and 1.18 to 1.62 pt higher than ImplicitGaze). Furthermore, over half of the participants preferred GazeHand2 because it supports convenient and efficient object translation and hand-based realistic object manipulation. We concluded that GazeHand2 can support simple and effective distant object interaction with reduced physical fatigue and higher user experiences compared to other interfaces in immersive VR. We suggested future designs to improve interaction accuracy and user convenience for future works. Full article

(This article belongs to the Section Computer Science & Engineering)

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32 pages, 5110 KiB

Open AccessArticle

Using AI to Improve MIMO Antennas with SRR for 26 GHz by Analyzing Data

by Linda Chouikhi, Chaker Essid, Bassem Ben-Salah, Mongi Ben Moussa and Hedi Sakli

Electronics 2025, 14(13), 2529; https://doi.org/10.3390/electronics14132529 (registering DOI) - 22 Jun 2025

Abstract

This paper introduces a database-based design methodology aimed at optimizing a 26 GHz MIMO antenna system through machine learning (ML) techniques. The procedure is divided into two primary phases. Initially, a rectangular microstrip patch antenna is designed and enhanced using analytical models alongside [...] Read more.

This paper introduces a database-based design methodology aimed at optimizing a 26 GHz MIMO antenna system through machine learning (ML) techniques. The procedure is divided into two primary phases. Initially, a rectangular microstrip patch antenna is designed and enhanced using analytical models alongside ML algorithms that are trained on a detailed dataset of geometric parameters. This yields effective impedance matching (S11 < −45 dB) and a high gain (~6.64 dBi), which serve as the foundation for the MIMO structure. In the second phase, split ring resonator (SRR) configurations are integrated between the antenna elements to reduce mutual coupling. A specialized dataset, featuring varied dimensions of SRR, quantities of unit cells, and spatial placements, is utilized to train Random Forest models that forecast arrangements achieving optimal isolation (S21 < −40 dB) while maintaining low reflection losses. Additionally, a secondary dataset is constructed to investigate the best strategies for SRR placement, ensuring an optimal balance between isolation and return loss. The ultimate MIMO design is validated via comprehensive full-wave electromagnetic simulations and experimental measurements. The proposed system exhibits noteworthy performance enhancements, including an envelope correlation coefficient (ECC) < 0.005, diversity gain (DG) ≈ 9.99 dB, channel capacity loss (CCL) < 0.3 bits/s/Hz, total active reflection coefficient (TARC) < −30 dB, radiation efficiency exceeding 80%, and a maximum gain increase up to 10.22 dB. The close correlation between predicted and measured outcomes validates the effectiveness of the ML-driven approach in expediting antenna optimization for 5G and future applications. Full article

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29 pages, 1602 KiB

Open AccessArticle

A Recommender System Model for Presentation Advisor Application Based on Multi-Tower Neural Network and Utility-Based Scoring

by Maria Vlahova-Takova and Milena Lazarova

Electronics 2025, 14(13), 2528; https://doi.org/10.3390/electronics14132528 (registering DOI) - 22 Jun 2025

Abstract

Delivering compelling presentations is a critical skill across academic, professional, and public domains—yet many presenters struggle with structuring content, maintaining visual consistency, and engaging their audience effectively. Existing tools offer isolated support for design or delivery but fail to promote long-term skill development. [...] Read more.

Delivering compelling presentations is a critical skill across academic, professional, and public domains—yet many presenters struggle with structuring content, maintaining visual consistency, and engaging their audience effectively. Existing tools offer isolated support for design or delivery but fail to promote long-term skill development. This paper presents a novel intelligent application, the Presentation Advisor application, powered by a personalized recommendation engine that goes beyond fixing slide content and visualization, enabling users to build presentation competence. The recommendation engine leverages a model based on hybrid multi-tower neural network architecture enhanced with temporal encoding, problem sequence modeling, and utility-based scoring to deliver adaptive context-aware feedback. Unlike current tools, the presented system analyzes user-submitted presentations to detect common issues and delivers curated educational content tailored to user preferences, presentation types, and audiences. The system also incorporates strategic cold-start mitigation, ensuring high-quality recommendations even for new users or unseen content. Comprehensive experimental evaluations demonstrate that the suggested model significantly outperforms content-based filtering, collaborative filtering, autoencoders, and reinforcement learning approaches across both accuracy and personalization metrics. By combining cutting-edge recommendation techniques with a pedagogical framework, the Presentation Advisor application enables users not only to improve individual presentations but to become consistently better presenters over time. Full article

(This article belongs to the Section Computer Science & Engineering)

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28 pages, 6169 KiB

Open AccessArticle

FairChain: A Trusted and Transparent Blockchain-Based Ecosystem for Drug Development for Nagoya Protocol Implementation

by Shada AlSalamah, Shaima A. Alnehmi, Anfal A. Abanumai, Asmaa H. Alnashri, Sara S. Alduhim, Norah A. Alnamlah, Khulood AlGhamdi, Haytham A. Sheerah, Sara A. Alsalamah and Hessah A. Alsalamah

Electronics 2025, 14(13), 2527; https://doi.org/10.3390/electronics14132527 (registering DOI) - 22 Jun 2025

Abstract

The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a [...] Read more.

The coronavirus pandemic has spread globally, affecting over 700 million people and resulting in over 7 million deaths. In response, global pharmaceutical companies and disease control centers have urgently sought effective treatments and vaccines. However, the rise of counterfeit drugs has become a significant concern amid this urgency. To standardize the legal provision and usage of genetic resources, the United Nations Development Program (UNDP) introduced the Nagoya Protocol. Despite advancements in drug research, the production process remains tedious, complex and vulnerable to fraud. FairChain addresses this pressing challenge by creating a transparent ecosystem that builds trust among all stakeholders throughout the Drug Development Life Cycle (DDLC) by using decentralized, immutable, and transparent blockchain technology. This makes FairChain the first digital health tool to implement the principles of the UNDP’s Nagoya Protocol among all stakeholders throughout all DDLC stages, starting with sample collection, to discovery and development, to preclinical research, to clinical development, to regulator review, and ending with post-market monitoring. Therefore, FairChain allows pharmaceutical companies to document the entire drug production process, landowners to monitor bio-samples from their land, doctors to share clinical research, and regulatory agencies such as the Food and Drug Authority to oversee samples and authorize production. FairChain should enhance transparency, foster trust and efficiency, and ensure a fair and traceable DDLC. To date, no blockchain-based framework has addressed the integration of traceability, auditability, and Nagoya Protocol compliance within a unified system architecture. This paper introduces FairChain, a system that formalizes these requirements in a modular, policy-aligned, and verifiable digital trust infrastructure. Full article

(This article belongs to the Section Computer Science & Engineering)

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16 pages, 1058 KiB

Open AccessArticle

Multi-Scale Context Enhancement Network with Local–Global Synergy Modeling Strategy for Semantic Segmentation on Remote Sensing Images

by Qibing Ma, Hongning Liu, Yifan Jin and Xinyue Liu

Electronics 2025, 14(13), 2526; https://doi.org/10.3390/electronics14132526 (registering DOI) - 21 Jun 2025

Abstract

Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, and ecological monitoring. In complex geographic scenes, low target-background discriminability in overhead views [...] Read more.

Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, and ecological monitoring. In complex geographic scenes, low target-background discriminability in overhead views (e.g., indistinct boundaries, ambiguous textures, and low contrast) significantly complicates local–global information modeling and results in blurred boundaries and classification errors in model predictions. To address this issue, in this paper, we proposed a novel Multi-Scale Local–Global Mamba Feature Pyramid Network (MLMFPN) through designing a local–global information synergy modeling strategy, and guided and enhanced the cross-scale contextual information interaction in the feature fusion process to obtain quality semantic features to be used as cues for precise semantic reasoning. The proposed MLMFPN comprises two core components: Local–Global Align Mamba Fusion (LGAMF) and Context-Aware Cross-attention Interaction Module (CCIM). Specifically, LGAMF designs a local-enhanced global information modeling through asymmetric convolution for synergistic modeling of the receptive fields in vertical and horizontal directions, and further introduces the Vision Mamba structure to facilitate local–global information fusion. CCIM introduces positional encoding and cross-attention mechanisms to enrich the global-spatial semantics representation during multi-scale context information interaction, thereby achieving refined segmentation. The proposed methods are evaluated on the ISPRS Potsdam and Vaihingen datasets and the outperformance in the results verifies the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)

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35 pages, 2102 KiB

Open AccessArticle

Enhancing Spectrum Utilization in Cognitive Radio Networks Using Reinforcement Learning with Snake Optimizer: A Meta-Heuristic Approach

by Haider Farhi, Abderraouf Messai and Tarek Berghout

Electronics 2025, 14(13), 2525; https://doi.org/10.3390/electronics14132525 (registering DOI) - 21 Jun 2025

Abstract

The rapid development of sixth-generation mobile communication systems has brought about significant advancements in both Quality of Service (QoS) and Quality of Experience (QoE) for users, largely due to the extremely high data rates and a diverse range of service offerings. However, these [...] Read more.

The rapid development of sixth-generation mobile communication systems has brought about significant advancements in both Quality of Service (QoS) and Quality of Experience (QoE) for users, largely due to the extremely high data rates and a diverse range of service offerings. However, these advancements have also introduced challenges, especially concerning the growing demand for a wireless spectrum and the limited availability of resources. Various efforts have been made and research has attempted to tackle this issue such as the use of Cognitive Radio Networks (CRNs), which allows opportunistic spectrum access and intelligent resource management. This work demonstrate a new method in the optimization of allocation resource in CRNs based on the Snake Optimizer (SO) along with reinforcement learning (RL), which is an effective meta-heuristic algorithm that simulates snake cloning behavior. SO is tested over three different scenarios with varying numbers of secondary users (SUs), primary users (PUs), and frequency bands available. The obtained results reveal that the proposed approach is able to largely satisfy the aforementioned requirements and ensures high spectrum utilization efficiency and low collision rates, which eventually lead to the maximum possible spectral capacity. The study also demonstrates that SO is versatile and resilient and thus indicates its capability of serving as an effective method for augmenting resource management in next-generation wireless communication systems. Full article

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18 pages, 5182 KiB

Open AccessFeature PaperArticle

Nominalization of Split DC Link Voltage Dynamics in Three-Phase Three-Level Converters Operating Under Arbitrary Power Factor with Restricted Zero-Sequence Component

by Yan Vule and Alon Kuperman

Electronics 2025, 14(13), 2524; https://doi.org/10.3390/electronics14132524 (registering DOI) - 21 Jun 2025

Abstract

The paper focuses on linearization of split DC link voltage dynamics and balancing their respective average values in three-phase three-level AC/DC converters. It was recently demonstrated that both AC-side current magnitude and operating power factor impact the dynamics of partial DC link voltage [...] Read more.

The paper focuses on linearization of split DC link voltage dynamics and balancing their respective average values in three-phase three-level AC/DC converters. It was recently demonstrated that both AC-side current magnitude and operating power factor impact the dynamics of partial DC link voltage difference, imposing the time-varying behavior of split DC link voltages when a typical linear time-invariant compensator, e.g., proportional or proportional–integrative, is utilized. Consequently, robust split DC link voltage balancing loops would be beneficial. The case of a bandwidth-restricted (DC in a steady state) zero-sequence component employed as a control signal to equalize average partial DC link voltages is considered in this work. It is proposed to nominalize the dynamics of partial DC link voltage difference by means of a linear disturbance observer based on a frequency-selective filter so that the modified dynamics become linear and nearly nominal from a compensator point of view. As a result, the closed-loop response becomes time-invariant—a desirable characteristic of any practical system. Simulations validate the proposed methodology applied to a 10 kVA T-type converter model. Full article

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

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36 pages, 122050 KiB

Open AccessArticle

GAML-YOLO: A Precise Detection Algorithm for Extracting Key Features from Complex Environments

by Lihu Pan, Zhiyang Xue and Kaiqiang Zhang

Electronics 2025, 14(13), 2523; https://doi.org/10.3390/electronics14132523 (registering DOI) - 21 Jun 2025

Abstract

This study addresses three major challenges in non-motorized vehicle rider helmet detection: multi-spectral interference between the helmet and hair color (HSV spatial similarity > 0.82), target occlusion in high-density traffic flows (with peak density reaching 11.7 vehicles/frame), and perception degradation under complex weather [...] Read more.

This study addresses three major challenges in non-motorized vehicle rider helmet detection: multi-spectral interference between the helmet and hair color (HSV spatial similarity > 0.82), target occlusion in high-density traffic flows (with peak density reaching 11.7 vehicles/frame), and perception degradation under complex weather conditions (such as overcast, foggy, and strong light interference). To tackle these issues, we developed the GMAL-YOLO detection algorithm. This algorithm enhances feature representation by constructing a Feature-Enhanced Neck Network (FENN) that integrates both global and local features. It employs the Global Mamba Architecture Enhancement (GMET) to reduce parameter size while strengthening global context capturing ability. It also incorporates Multi-Scale Spatial Pyramid Pooling (MSPP) combined with multi-scale feature extraction to improve the model’s robustness. The enhanced channel attention mechanism with self-attention (ECAM) is designed to enhance local feature extraction and stabilize deep feature learning through partial convolution and residual learning, resulting in a 13.04% improvement in detection precision under occlusion scenarios. Furthermore, the model’s convergence speed and localization precision are optimized using the modified Enhanced Precision-IoU loss function(EP-IoU). Experimental results demonstrate that GMAL-YOLO outperforms existing algorithms on the self-constructed HelmetVision dataset and public datasets. Specifically, in extreme scenarios, the false detection rate is reduced by 17.3%, and detection precision in occluded scenes is improved by 13.6%, providing an effective technical solution for intelligent traffic surveillance. Full article

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16 pages, 3833 KiB

Open AccessArticle

Fault-Tolerant Operation of Photovoltaic Systems Using Quasi-Z-Source Boost Converters: A Hardware-in-the-Loop Validation with Typhoon HIL

by Basit Ali, Mothana S. A. Al Sunjury, Adnan Ashraf, Mohammad Meraj and Pietro Tricoli

Electronics 2025, 14(13), 2522; https://doi.org/10.3390/electronics14132522 (registering DOI) - 21 Jun 2025

Abstract

Photovoltaic (PV) systems are prone to different types of faults, primarily electrical faults such as line-to-ground (L-G) and line-to-line (L-L) faults, which can significantly reduce system performance, efficiency, and lead to increased power losses. Moreover, mechanical damage caused by environmental stressors (such as [...] Read more.

Photovoltaic (PV) systems are prone to different types of faults, primarily electrical faults such as line-to-ground (L-G) and line-to-line (L-L) faults, which can significantly reduce system performance, efficiency, and lead to increased power losses. Moreover, mechanical damage caused by environmental stressors (such as wind, hail, or temperature variations), aging, or improper installation also contribute to system degradation. This study specifically focuses on electrical faults and proposes a method that not only enables the isolation of faulty modules but also ensures the uninterrupted operation of the remaining healthy modules and also assists in the localization of faults. Unlike benchmarked techniques-based boost converters, the Quasi-Z-Source Boost Converter (QZBC) topology offers improved voltage boosting with high gain values, reduced component stress, and enhanced reliability when the PV system is undergoing fault identification and localization algorithms. A 600-watt PV system connected with a Quasi-Z-Source Boost Converter was implemented and tested under different fault conditions using a hardware-in-the-loop (HIL) setup with Typhoon HIL. All the component values of the QZBC were calculated based on the system requirements rather than assumed, ensuring both practical feasibility and design accuracy. The experimental results show that the converter achieved an efficiency of over 96% under electrical-fault conditions, confirming the effectiveness of the quasi-Z-source boost converter in maintaining a stable power output when the PV system is undergoing fault identification and localization algorithms. The study further highlights the benefits of HIL-based testing for evaluating PV-system resilience and fault-handling capabilities in real-time conditions using a Typhoon HIL 404 environment. Full article

(This article belongs to the Special Issue Compatibility, Power Electronics and Power Engineering)

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14 pages, 5002 KiB

Open AccessArticle

A Hexagonal Bi-Isotropic Honeycomb in PCB

by Ismael Barba, Óscar Fernández, Álvaro Gómez-Gómez, Ana Grande and Ana Cristina López-Cabeceira

Electronics 2025, 14(13), 2521; https://doi.org/10.3390/electronics14132521 (registering DOI) - 21 Jun 2025

Abstract

In this study we explored the chiral behavior of a honeycomb-like chiral metamaterial with a negative Poisson’s ratio. This type of structure is widely used in sectors such as construction and packaging, but is not as common in electromagnetics/electrical engineering. Moreover, in contrast [...] Read more.

In this study we explored the chiral behavior of a honeycomb-like chiral metamaterial with a negative Poisson’s ratio. This type of structure is widely used in sectors such as construction and packaging, but is not as common in electromagnetics/electrical engineering. Moreover, in contrast with typical layer-by-layer chiral metamaterial structures, which are usually formed by metallic patterns with C4 symmetry, this hexachiral structure presents C6 symmetry. The aim of this paper is analyzing the electromagnetic behavior of this kind of auxetic metamaterial with special attention to its chiral behavior. This structure is analyzed by means of measurements and simulations of its reflection and transmission responses (scattering parameters) in different configurations, showing that a dual-layer configuration with conjugated faces provides high electromagnetic activity (gyrotropy) with low losses. Full article

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

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14 pages, 11409 KiB

Open AccessArticle

Automatic Parallel Parking System Design with Fuzzy Control and LiDAR Detection

by Jung-Shan Lin, Hao-Jheng Wu and Jeih-Weih Hung

Electronics 2025, 14(13), 2520; https://doi.org/10.3390/electronics14132520 (registering DOI) - 21 Jun 2025

Abstract

This paper presents a self-driving system for automatic parallel parking, integrating obstacle avoidance for enhanced safety. The vehicle platform employs three primary sensors—a web camera, a Zed depth camera, and LiDAR—to perceive its surroundings, including sidewalks and potential obstacles. By processing camera and [...] Read more.

This paper presents a self-driving system for automatic parallel parking, integrating obstacle avoidance for enhanced safety. The vehicle platform employs three primary sensors—a web camera, a Zed depth camera, and LiDAR—to perceive its surroundings, including sidewalks and potential obstacles. By processing camera and LiDAR data, the system determines the vehicle’s position and assesses parking space availability, with LiDAR also aiding in malfunction detection. The system operates in three stages: parking space identification, path planning using geometric circles, and fine-tuning with fuzzy control if misalignment is detected. Experimental results, evaluated visually in a model-scale setup, confirm the system’s ability to achieve smooth and reliable parallel parking maneuvers. Quantitative performance metrics, such as precise parking accuracy or total execution time, were not recorded in this study but will be included in future work to further support the system’s effectiveness. Full article

(This article belongs to the Special Issue Research on Deep Learning and Human-Robot Collaboration)

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19 pages, 3049 KiB

Open AccessArticle

Non-Isolated Ultra-High Step-Up DC-DC Converter Topology Using Coupled-Inductor-Based Inverting Buck-Boost and Voltage Multipliers

by Van-Tinh Duong, Zeeshan Waheed and Woojin Choi

Electronics 2025, 14(13), 2519; https://doi.org/10.3390/electronics14132519 - 20 Jun 2025

Abstract

This paper introduces a non-isolated ultra-high voltage gain topology using the combination of the coupled-inductor-based inverting buck-boost converter (IBB) and voltage multiplier (VM) structure. In the proposed converter, an ultra-high step-up voltage gain can be achieved with a small duty cycle thanks to [...] Read more.

This paper introduces a non-isolated ultra-high voltage gain topology using the combination of the coupled-inductor-based inverting buck-boost converter (IBB) and voltage multiplier (VM) structure. In the proposed converter, an ultra-high step-up voltage gain can be achieved with a small duty cycle thanks to a coupled inductor and VMs. The voltage stress and the losses of the switches in the proposed converter are even less than other conventional topologies. Unlike other coupled-inductor topologies, a large voltage spike caused by the leakage inductance of the coupled inductor is smoothed by the capacitor in the voltage multiplier. In addition, zero-voltage switching (ZVS) turn-on for the switches and zero-current switching (ZCS) turn-off for the diodes can be achieved with the energy stored in the leakage inductance. A 360 W (40 V/380 V) prototype converter is implemented to prove the advantages of the proposed converter, with a maximum efficiency of 98.4%. Full article

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

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19 pages, 5086 KiB

Open AccessArticle

Expedited Near-Field Holographic Microwave Imaging with an Azimuthally Distributed Antenna Array

by Mona Heydari and Reza K. Amineh

Electronics 2025, 14(13), 2518; https://doi.org/10.3390/electronics14132518 - 20 Jun 2025

Abstract

In this article, we propose a novel near-field holographic microwave imaging technique designed to accelerate the data acquisition process. The system employs a novel electronic switching mechanism utilizing two switching networks that virtually rotate the transmitting and receiving antennas along the azimuthal direction [...] Read more.

In this article, we propose a novel near-field holographic microwave imaging technique designed to accelerate the data acquisition process. The system employs a novel electronic switching mechanism utilizing two switching networks that virtually rotate the transmitting and receiving antennas along the azimuthal direction for efficient data collection. This minimizes the need for mechanical scanning of the antennas which, in turn, leads to faster data acquisition. To enhance the quality of the imaging outcome, the number of samples can be increased by combining only a few mechanical scanning steps with the electronic scanning. This data acquisition scheme leverages the system’s space-invariant property to enable convolution-based near-field holographic microwave image reconstruction. By capturing and processing scattered fields over a cylindrical aperture, the system achieves high-resolution imaging of concealed objects across multiple range positions. Both simulation and experimental results validate the effectiveness of the proposed approach in delivering high-quality imaging results. Its ability to provide faster and enhanced imaging outcomes highlights its potential for a wide range of applications, including biomedical imaging, security screening, and non-destructive testing of the materials. Full article

(This article belongs to the Special Issue RF, Microwave, and Millimeter Wave Devices and Circuits and Their Applications)

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39 pages, 14267 KiB

Open AccessReview

Smart Precision Weeding in Agriculture Using 5IR Technologies

by Chaw Thiri San and Vijay Kakani

Electronics 2025, 14(13), 2517; https://doi.org/10.3390/electronics14132517 - 20 Jun 2025

Abstract

The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), [...] Read more.

The rise of smart precision weeding driven by Fifth Industrial Revolution (5IR) technologies symbolizes a quantum leap in sustainable agriculture. The modern weeding systems are becoming promisingly efficient, intelligently autonomous, and environmentally responsible by introducing artificial intelligence (AI), robotics, Internet of Things (IoT), 5G connectivity, and edge computing technologies. This review discusses a comprehensive analysis of the traditional and contemporary weeding techniques, thereby focusing on the technological innovations paving way for the smart systems. Primarily, this work investigates the application of 5IR technologies in weed detection and decision-making with particular emphasis on the role of the aspects such as AI-driven models, drone-robot integration, GPS-guided practices, and intelligent sensor networks. Additionally, the work outlines key commercial solutions, sustainability metrics, data-driven decision support systems, and blockchain traceable practices. The prominent challenges in the context of global agricultural equity pertaining to cost, scalability, policy alignment, and adoption barriers in accordance to the low-resource environments are discussed in this study. The paper concludes with strategic recommendations and future research directions, highlighting the potential of 5IR technologies on the smart precision weeding. Full article

(This article belongs to the Special Issue Harnessing AI and Fifth Industrial Revolution (5IR) Technologies for the Future of Automation)

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17 pages, 2332 KiB

Open AccessArticle

Low Carrier–Frequency Ratio Luenberger Observer Based on Discrete Mathematical Model for SPMSMs

by Shuhan Guo, Yawen Jin and Wenguang Yang

Electronics 2025, 14(13), 2516; https://doi.org/10.3390/electronics14132516 - 20 Jun 2025

Abstract

To address the issue of reduced observer accuracy under low carrier–frequency ratio (CFR) conditions in the sensorless control of high-speed motors, which limits system performance, this paper proposes a discrete mathematical modeling method for surface-mounted permanent magnet synchronous motors (SPMSMs). Based on this [...] Read more.

To address the issue of reduced observer accuracy under low carrier–frequency ratio (CFR) conditions in the sensorless control of high-speed motors, which limits system performance, this paper proposes a discrete mathematical modeling method for surface-mounted permanent magnet synchronous motors (SPMSMs). Based on this established accurate discrete motor model, the influence of low CFR on the phase estimation error of back electromotive force (EMF) is analyzed. Building on this foundation, an accurate discrete Luenberger observer (ALO) is designed, and a corresponding phase compensation control method is proposed. A motor drive control system comprising hardware, software, and experimental test setups is constructed. The experimental results demonstrate that, compared to the Euler model, the discrete mathematical model established by this method significantly improves position observation accuracy under low CFR conditions. Furthermore, compared to the traditional Luenberger observer (TLO), the estimation error of the proposed observer under a low CFR is reduced by approximately 85%. This approach exhibits high application value in the sensorless control of high-speed and high-frequency motors. Full article

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

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Graphical abstract

19 pages, 437 KiB

Open AccessArticle

Mutual Information-Oriented ISAC Beamforming Design for Large Dimensional Antenna Array

by Shanfeng Xu, Yanshuo Cheng, Siqiang Wang, Xinyi Wang, Zhong Zheng and Zesong Fei

Electronics 2025, 14(13), 2515; https://doi.org/10.3390/electronics14132515 - 20 Jun 2025

Abstract

In this paper, we study the beamforming design for multiple-input multiple-output (MIMO) ISAC systems, with the weighted mutual information (MI) comprising sensing and communication perspectives adopted as the performance metric. In particular, the weighted sum of the communication mutual information and the sensing [...] Read more.

In this paper, we study the beamforming design for multiple-input multiple-output (MIMO) ISAC systems, with the weighted mutual information (MI) comprising sensing and communication perspectives adopted as the performance metric. In particular, the weighted sum of the communication mutual information and the sensing mutual information is shown to asymptotically converge to a deterministic limit when the number of transmitting and receiving antennas grow to infinity. This deterministic limit is derived by utilizing the operator-valued free probability theory. Subsequently, an efficient projected gradient ascent (PGA) algorithm is proposed to optimize the transmit beamforming matrix with the aim of maximizing the weighted asymptotic MI. Numerical results validate that the derived closed-form expression matches well with the Monte Carlo simulation results and the proposed optimization algorithm is able to improve the weighted asymptotic MI significantly. We also illustrate the trade-off between asymptotic sensing and asymptotic communication MI. Full article

(This article belongs to the Special Issue Intelligent Signal Processing for Integrated Sensing and Communications towards 6G)

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24 pages, 6560 KiB

Open AccessArticle

Spatio-Temporal Attention-Based Deep Learning for Smart Grid Demand Prediction

by Muhammed Cavus and Adib Allahham

Electronics 2025, 14(13), 2514; https://doi.org/10.3390/electronics14132514 - 20 Jun 2025

Abstract

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates [...] Read more.

Accurate short-term load forecasting is vital for the reliable and efficient operation of smart grids, particularly under the uncertainty introduced by variable renewable energy sources (RESs) such as solar and wind. This study introduces ST-CALNet, a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with an Attentive Long Short-Term Memory (LSTM) network to enhance forecasting performance in renewable-integrated smart grids. The CNN component captures spatial dependencies from multivariate inputs, comprising meteorological variables and generation data, while the LSTM module models temporal correlations in historical load patterns. An embedded attention mechanism dynamically weights input sequences, enabling the model to prioritise the most influential time steps, thereby improving its interpretability and robustness during demand fluctuations. ST-CALNet was trained and evaluated using real-world datasets that include electricity consumption, solar photovoltaic (PV) output, and wind generation. Experimental evaluation demonstrated that the model achieved a mean absolute error (MAE) of 0.0494, root mean squared error (RMSE) of 0.0832, and a coefficient of determination (R²) of 0.4376 for electricity demand forecasting. For PV and wind generation, the model attained MAE values of 0.0134 and 0.0141, respectively. Comparative analysis against baseline models confirmed ST-CALNet’s superior predictive accuracy, particularly in minimising absolute and percentage-based errors. Temporal and regime-based error analysis validated the model’s resilience under high-variability conditions such as peak load periods, while visualisation of attention scores offered insights into the model’s temporal focus. These findings underscore the potential of ST-CALNet for deployment in intelligent energy systems, supporting more adaptive, transparent, and dependable forecasting within smart grid infrastructures. Full article

(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems, 2nd Edition)

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26 pages, 2780 KiB

Open AccessArticle

A Participatory Design Approach to Designing Educational Interventions for Science Students Using Socially Assistive Robots

by Mahmoud Mohamed Hussien Ahmed, Mohammad Nehal Hasnine and Bipin Indurkhya

Electronics 2025, 14(13), 2513; https://doi.org/10.3390/electronics14132513 - 20 Jun 2025

Abstract

We present here an approach to the deployment of social robots in a science laboratory to monitor the behavior of students with respect to safety regulations to prevent accidents. Our vision is that the social robot should act as a friendly companion for [...] Read more.

We present here an approach to the deployment of social robots in a science laboratory to monitor the behavior of students with respect to safety regulations to prevent accidents. Our vision is that the social robot should act as a friendly companion for students and encourage them to follow safe laboratory practices. Towards this goal, we developed a Laboratory Safety Assistant Framework (LSA) using a Misty II Plus robot and designed three dashboards within it as interventions. This LSA framework was evaluated using a participatory design (PD) study with twenty university students (eleven from Japan and nine from Egypt). For this study, we designed a questionnaire that contains 42 questions on the prior knowledge of students about socially assistive robots and their expectations about how socially assistive robots can create a secure environment in the scientific laboratory. The chi-square test revealed that there are no differences between groups in their perceptions of using Misty II to achieve safety inside science laboratories. In their perception of the capabilities of social robots and the sharing of feelings, students believe that using social robots like Misty II inside the science laboratory can make the lab safe and decrease risk inside the science laboratory without using the three dashboards of the LSA framework. However, the Wilcoxon signed-rank test revealed that there is a significant improvement in students’ perceptions ((Median = 106.5, Z = −2.39, p < 0.05, r = 0.53)) between students’ expectations of using social robots to achieve safety in scientific laboratories before and after they interacted with the social robot and knew about the feasibility of the three dashboards we designed. Furthermore, the t-test revealed participants’ experiences of sharing feelings with a social robot, and the intervention suggested by the LSA framework was to design a system integrating this into a social robot to enhance safety within the scientific laboratory (t (19) = 3.39, p = 0.003). Full article

(This article belongs to the Section Artificial Intelligence)

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