Electronics

18 pages, 7762 KiB

Open AccessArticle

Miniaturized Patch Array Antenna Using CSRR Structures for 5G Millimeter-Wave Communication Systems

by Abderraoufe Zerrouk, Mohamed Lamine Tounsi, Tan Phu Vuong, Nicolas Corrao and Mustapha C. E. Yagoub

Electronics 2025, 14(9), 1834; https://doi.org/10.3390/electronics14091834 (registering DOI) - 29 Apr 2025

Abstract

This paper presents a novel design of a 28 GHz miniaturized 1 × 4 patch antenna array with a low profile configuration based on Complementary Split Ring Resonators (CSRRs). Along with a return loss of 45 dB and a bandwidth of 1.5 GHz, [...] Read more.

This paper presents a novel design of a 28 GHz miniaturized 1 × 4 patch antenna array with a low profile configuration based on Complementary Split Ring Resonators (CSRRs). Along with a return loss of 45 dB and a bandwidth of 1.5 GHz, the proposed structure exhibits low side lobes with a high gain of 13.7 dBi and an efficiency of 97%, as well as a beamwidth of 20° and 49° in the E and H-planes, respectively. With a compact size of 27 × 13 × 0.787 mm³, the good agreement between measured and simulated data makes the proposed array suitable for 5G millimeter-wave communication systems. Full article

(This article belongs to the Special Issue Advanced RF/Microwave Circuits and System for New Applications)

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16 pages, 746 KiB

Open AccessArticle

A Multi-Receiver Pulse Deinterleaving Method Based on SSC-DBSCAN and TDOA Mapping

by Jie Xue, Binbin Su, Yongcai Liu and Jin Meng

Electronics 2025, 14(9), 1833; https://doi.org/10.3390/electronics14091833 (registering DOI) - 29 Apr 2025

Abstract

Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature [...] Read more.

Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature of TDOA, providing the basis of deinterleaving. Then, a SSC (Sorting Skipping Clustering)-DBSCAN algorithm is proposed to achieve TDOA clustering by pre-sorting and traversing key points, which reduces the computational complexity. The TDOA mapping algorithm is further proposed to separate pulses and eliminate Cross-Pulse TDOAs simultaneously based on a one-time clustering result, which can significantly decrease the false alarm rate while avoiding clustering TDOA repeatedly. Simulation results show that the proposed method is capable of deinterleaving pulses of various PRI modulation modes and the performance remains excellent under multiple parameter settings. The running time and the false alarm rate have been reduced by at least 66% and 17%, respectively, compared with the existing methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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20 pages, 7342 KiB

Open AccessArticle

SOH Estimation Model Based on an Ensemble Hierarchical Extreme Learning Machine

by Yu He, Norasage Pattanadech, Kasian Sukemoke, Lin Chen and Lulu Li

Electronics 2025, 14(9), 1832; https://doi.org/10.3390/electronics14091832 (registering DOI) - 29 Apr 2025

Abstract

This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical [...] Read more.

This paper addresses the challenges of accurately estimating the state of health (SOH) of retired batteries, where factors such as limited historical data, non-linear degradation, and unstable parameters complicate the process. We propose a novel SOH estimation model based on an Integrated Hierarchical Extreme Learning Machine (I-HELM). The model minimizes reliance on historical data and reduces computational complexity by introducing health indicators derived from constant charging time and charging current area. The hierarchical structure of the Extreme Learning Machine (HELM) effectively captures the non-linear relationship between health indicators and battery capacity, improving estimation accuracy and learning efficiency. Additionally, integrating multiple HELM models enhances the stability and robustness of the results, making the approach more reliable across varying operational conditions. The proposed model is validated on experimental datasets collected from two Samsung battery packs, four Samsung single cells, and two Panasonic retired batteries under both constant-current and dynamic conditions. Experimental results demonstrate the superior performance of the model: the maximum error for Samsung battery cells and packs does not exceed 2.2% and 2.6%, respectively, with root mean square errors (RMSEs) below 1%. For Panasonic retired batteries, the maximum error remains under 3%. Full article

17 pages, 4549 KiB

Open AccessArticle

Tampering Detection in Absolute Moment Block Truncation Coding (AMBTC) Compressed Code Using Matrix Coding

by Yijie Lin, Ching-Chun Chang and Chin-Chen Chang

Electronics 2025, 14(9), 1831; https://doi.org/10.3390/electronics14091831 - 29 Apr 2025

Abstract

With the increasing use of digital image compression technology, ensuring data integrity and security within the compression domain has become a crucial area of research. Absolute moment block truncation coding (AMBTC), an efficient lossy compression algorithm, is widely used for low-bitrate image storage [...] Read more.

With the increasing use of digital image compression technology, ensuring data integrity and security within the compression domain has become a crucial area of research. Absolute moment block truncation coding (AMBTC), an efficient lossy compression algorithm, is widely used for low-bitrate image storage and transmission. However, existing studies have primarily focused on tamper detection for AMBTC compressed images, often overlooking the integrity of the AMBTC compressed code itself. To address this gap, this paper introduces a novel anti-tampering scheme specifically designed for AMBTC compressed code. The proposed scheme utilizes shuffle pairing to establish a one-to-one relationship between image blocks. The hash value, calculated as verification data from the original data of each block, is then embedded into the bitmap of its corresponding block using the matrix coding algorithm. Additionally, a tampering localization mechanism is incorporated to enhance the security of the compressed code without introducing additional redundancy. The experimental results demonstrate that the proposed scheme effectively detects tampering with high accuracy, providing protection for AMBTC compressed code. Full article

(This article belongs to the Special Issue Advancements in Distributed Intelligent Security Through AI-Driven Solutions)

28 pages, 2542 KiB

Open AccessArticle

Evaluating the Use of 360-Degree Video in Education

by Sam Kavanagh, Andrew Luxton-Reilly, Burkhard C. Wünsche, Beryl Plimmer and Sebastian Dunn

Electronics 2025, 14(9), 1830; https://doi.org/10.3390/electronics14091830 - 29 Apr 2025

Abstract

Virtual reality (VR) has existed in the realm of education for over half a century; however, it has never achieved widespread adoption. This was traditionally attributed to costs and usability problems associated with these technologies, but a new generation of consumer VR headsets [...] Read more.

Virtual reality (VR) has existed in the realm of education for over half a century; however, it has never achieved widespread adoption. This was traditionally attributed to costs and usability problems associated with these technologies, but a new generation of consumer VR headsets has helped mitigate these issues to a large degree. Arguably, the greater barrier is now the overhead involved in creating educational VR content, the process of which has remained largely unchanged. In this paper, we investigate the use of

360^{\circ}

video as an alternative way of producing educational VR content with a much lower barrier to entry. We report on the differences in user experience between

360^{\circ}

and standard desktop video. We also compare the short- and long-term learning retention of tertiary students who viewed the same video recordings but watched them in either

360^{\circ}

or standard video formats. Our results indicate that students retain an equal amount of information from either video format but perceive

360^{\circ}

video to be more enjoyable and engaging, and would prefer to use it as additional learning resources in their coursework. Full article

(This article belongs to the Special Issue Augmented Reality, Virtual Reality, and 3D Reconstruction)

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21 pages, 4675 KiB

Open AccessArticle

A Novel Hierarchical Optimal Scheduling and Coordination Control Method for Microgrid Based on Multi-Energy Complementarity

by Li Zhang, Zeyuan Ma, Chenhao Jia, Tao Zhang and Hongwei Zhang

Electronics 2025, 14(9), 1829; https://doi.org/10.3390/electronics14091829 - 29 Apr 2025

Abstract

To address the uncertainty of intermittent energy sources and enhance the economic efficiency and operational performance of microgrids, this paper proposes a novel three-layer coupled microgrid scheduling model based on the principles of model predictive control, optimized and solved using an improved dung [...] Read more.

To address the uncertainty of intermittent energy sources and enhance the economic efficiency and operational performance of microgrids, this paper proposes a novel three-layer coupled microgrid scheduling model based on the principles of model predictive control, optimized and solved using an improved dung beetle algorithm. Firstly, by comprehensively considering time-varying electricity prices and pollution protection costs, the model optimizes and mitigates the impact of uncertain factors in day-ahead scheduling, thereby constructing a new three-layer scheduling framework. Secondly, improvements to the traditional dung beetle algorithm, including population initialization, rolling behavior, and foraging behavior, are validated through simulations, demonstrating enhanced accuracy and convergence speed. Furthermore, the improved dung beetle algorithm is utilized to optimize the economic performance of the scheduling layer, determining optimal controls within the rolling control framework. Finally, through economic comparisons, rolling scheduling analysis, and control effectiveness experiments, this study demonstrates that the proposed model and algorithm significantly improve the environmental economics of microgrids while enhancing system controllability and stability. Full article

(This article belongs to the Topic Control and Optimization of Networked Microgrids)

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23 pages, 7669 KiB

Open AccessCommunication

YOLOv8-IDX: Optimized Deep Learning Model for Transmission Line Insulator-Defect Detection

by Umer Farooq, Fan Yang, Maryam Shahzadi, Umar Ali and Zhimin Li

Electronics 2025, 14(9), 1828; https://doi.org/10.3390/electronics14091828 - 29 Apr 2025

Abstract

Efficient insulator-defect detection in transmission lines is crucial for ensuring the reliability and safety of power systems. This study introduces YOLOv8-IDX (You Only Look Once v8—Insulator Defect eXtensions), an enhanced DL (Deep Learning) based model designed specifically for detecting defects in transmission line [...] Read more.

Efficient insulator-defect detection in transmission lines is crucial for ensuring the reliability and safety of power systems. This study introduces YOLOv8-IDX (You Only Look Once v8—Insulator Defect eXtensions), an enhanced DL (Deep Learning) based model designed specifically for detecting defects in transmission line insulators. The model builds upon the YOLOv8 framework, incorporating advanced modules, such as C3k2 in the backbone for enhanced feature extraction and C2fCIB in the neck for improved contextual understanding. These modifications aim to address the challenges of detecting small and complex defects under diverse environmental conditions. The results demonstrate that YOLOv8-IDX significantly outperforms the baseline YOLOv8 in terms of mean Average Precision (mAP) by 4.7% and 3.6% on the IDID and CPLID datasets, respectively, with F1 scores of 93.2 and 97.2 on the IDID and CPLID datasets, respectively. These findings underscore the model’s potential in automating power line inspections, reducing manual effort, and minimizing maintenance-related downtime. In conclusion, YOLOv8-IDX represents a step forward in leveraging DL and AI for smart grid applications, with implications for enhancing the reliability and efficiency of power transmission systems. Future work will focus on extending the model to multi-class defect detection and real-time deployment using UAV platforms. Full article

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38 pages, 1337 KiB

Open AccessArticle

Quantum-Enhanced Machine Learning for Cybersecurity: Evaluating Malicious URL Detection

by Lauren Eze, Umair B. Chaudhry and Hamid Jahankhani

Electronics 2025, 14(9), 1827; https://doi.org/10.3390/electronics14091827 - 29 Apr 2025

Abstract

The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and [...] Read more.

The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and scalability remain limited for emerging adversarial approaches. Quantum machine learning (QML) is a transformative strategy that relies on quantum computation and high-dimensional feature spaces to potentially overcome classical computational limitations. However, the accuracy of QML models such as QSVM and QCNN for URL detection in comparison to classical models remains unexplored. This study evaluates ML models (SVMs and CNNs) and QML models (QSVMs and QCNNs) on a dataset, employing data preprocessing techniques such as outliers, feature scaling and feature selection with ANOVA and PCA. Quantum models utilized ZZFeatureMap and ZFeatureMap for data encoding, to transfer original data to qubits. The achieved results showed that CNNs outperformed QCNNs and QSVMs outperformed SVMs in the performance evaluation, demonstrating a competitive potential of quantum computing. QML shows promise for cybersecurity, particularly given the QSVM’s kernel advantages, but current hardware limits the QCNN’s practicality. The significance of this research is to contribute to the growing body of knowledge in cybersecurity by providing a comparative analysis of classical and quantum ML models for classifying malicious URLs. Full article

(This article belongs to the Special Issue Natural Language Processing and Data Science Methods to Mitigate Digital Threats)

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33 pages, 2969 KiB

Open AccessArticle

Probabilistic Measurement of CTI Quality for Large Numbers of Unstructured CTI Products

by Georgios Sakellariou, Menelaos Katsantonis and Panagiotis Fouliras

Electronics 2025, 14(9), 1826; https://doi.org/10.3390/electronics14091826 - 29 Apr 2025

Abstract

This paper addresses the critical challenge of evaluating the quality of Cyber Threat Intelligence (CTI) products, particularly focusing on their relevance and actionability. As organizations increasingly rely on CTI to make cybersecurity decisions, the absence of CTI quality metrics challenges the assessment of [...] Read more.

This paper addresses the critical challenge of evaluating the quality of Cyber Threat Intelligence (CTI) products, particularly focusing on their relevance and actionability. As organizations increasingly rely on CTI to make cybersecurity decisions, the absence of CTI quality metrics challenges the assessment of intelligence quality. To address this gap, the article introduces two innovative metrics. Relevance (

R e

) and Actionability (

A c

) are designed to evaluate CTI products in relation to organizational information needs and defense mechanisms. Using probabilistic algorithms and data structures, these metrics provide a scalable approach for handling large numbers of unstructured CTI products. Experimental findings demonstrate the effectiveness of metrics in filtering and prioritizing CTI products, offering organizations a tool to prioritize their cybersecurity resources. Furthermore, experimental results demonstrate that, using the metrics, organizations can reduce candidate CTI products by several orders of magnitude, understand weaknesses in defining information needs, guide the application of CTI products, assess CTI products’ contribution to defense, and select CTI products from information sharing communities. In addition, the study has identified certain limitations, which open avenues for future research, including the real-time integration of CTI into organizational defense mechanisms. This work significantly contributes to standardizing the quality evaluation of CTI products and enhancing organizations’ cybersecurity posture. Full article

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

14 pages, 503 KiB

Open AccessArticle

High-Precision and Efficiency Hardware Implementation for GELU via Its Internal Symmetry

by Jianxin Huang, Yuling Wu, Mingyong Zhuang and Jianyang Zhou

Electronics 2025, 14(9), 1825; https://doi.org/10.3390/electronics14091825 - 29 Apr 2025

Abstract

The Gaussian Error Linear Unit (GELU), a crucial component of the transformer model, poses a significant challenge for hardware implementation. To address this issue, this paper proposes internal symmetry piecewise approximation (ISPA) and error peak search strategy (EPSS) for high-precision and high-efficiency implementation [...] Read more.

The Gaussian Error Linear Unit (GELU), a crucial component of the transformer model, poses a significant challenge for hardware implementation. To address this issue, this paper proposes internal symmetry piecewise approximation (ISPA) and error peak search strategy (EPSS) for high-precision and high-efficiency implementation of the GELU activation function. ISPA only approximates the positive axis of the erf in GELU and then leverages its internal symmetry to calculate the negative axis part. With ISPA, the mean square error (MSE) between the fitted result and the true value can reach

4.29 \times

10^{- 9}

with 16 parts of the approximation segment, outperforming the regular method, which achieves

1.19 \times

10^{- 6}

with 16 parts. Furthermore, EPSS can automatically find suitable and high-precision intervals for our piecewise approximation method. To evaluate the effectiveness of ISPA and EPSS, we conducted experiments on three different ViT models and observed negligible loss of prediction accuracy. The hardware implementation is on an XCZU9EG FPGA running at 450 MHz. Experimental results indicate that ISPA outperforms existing methods. Full article

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

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22 pages, 5827 KiB

Open AccessArticle

Synchronous Oscillation Suppression in Grid-Forming Converters Using Ultra-Local Model Predictive Control

by Zhen Wang, Ruixu Liu and Jinpeng Zhou

Electronics 2025, 14(9), 1824; https://doi.org/10.3390/electronics14091824 - 29 Apr 2025

Abstract

Grid-forming (GFM) converters play a vital role in future power systems due to their ability to independently establish voltage and frequency. However, their interaction with AC circuits may give rise to synchronous oscillations, which pose a threat to system stability and dynamic performance. [...] Read more.

Grid-forming (GFM) converters play a vital role in future power systems due to their ability to independently establish voltage and frequency. However, their interaction with AC circuits may give rise to synchronous oscillations, which pose a threat to system stability and dynamic performance. This paper investigates the issue of synchronous oscillations and proposes an ultra-local model predictive control strategy for their suppression. First, a small-signal power dynamic model is developed to analyze the mechanism behind these oscillations. It is revealed that this problem is related to the electromagnetic dynamics of power transfer and is strongly influenced by the line impedance characteristics. Then, a predictive control framework is formulated, which incorporates oscillation suppression into the control objective and enables the real-time optimization of the active power reference. To avoid reliance on detailed system models, an ultra-local modeling approach is introduced. In this framework, a fixed-time sliding mode observer is employed to estimate the system power dynamics in real time, enabling the prediction of future states without requiring grid-side parameters and facilitating the design of a model-free controller. Simulation results verify that the proposed method effectively mitigates synchronous oscillations while significantly enhancing system stability and robustness. Full article

17 pages, 7350 KiB

Open AccessArticle

Lightweight Network for Spoof Fingerprint Detection by Attention-Aggregated Receptive Field-Wise Feature

by Md Al Amin, Naim Reza and Ho Yub Jung

Electronics 2025, 14(9), 1823; https://doi.org/10.3390/electronics14091823 - 29 Apr 2025

Abstract

The spread of biometric systems utilizing fingerprints has increased the need for advanced spoof detection techniques, but training convolutional neural networks (CNNs) with the limited number of images available in fingerprint datasets poses significant challenges. In this paper, we propose a lightweight network [...] Read more.

The spread of biometric systems utilizing fingerprints has increased the need for advanced spoof detection techniques, but training convolutional neural networks (CNNs) with the limited number of images available in fingerprint datasets poses significant challenges. In this paper, we propose a lightweight network architecture which addresses the challenges inherent in small fingerprint datasets by employing a moderately deep network architecture which is sufficient for extracting essential features from fingerprint images. We apply a hyperbolic tangent activation to the final feature map, which has features from local receptive fields, and average the responses into a single value. Thus, our architecture reduces overfitting by increasing the number of effective labels during training. Additionally, the incorporation of the spatial attention module enhances feature representation, culminating in improved accuracy. The evaluation results show that the proposed model, with only 0.14 million parameters, outperforms existing techniques including lightweight models and transfer-learning-based models, achieving superior average test accuracies of 98.30% and 95.57% on the LivDet-2015 and -2017 datasets, respectively. It also delivers state-of-the-art cross-material performance, with corresponding average classification error values of 0.81% and 1.91%, making it highly effective for on-device fingerprint authentication. Full article

(This article belongs to the Special Issue Real-Time Audio, Video and Image Processing: Latest Advances and Prospects)

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25 pages, 28248 KiB

Open AccessArticle

Autism Spectrum Disorder Diagnosis Based on Attentional Feature Fusion Using NasNetMobile and DeiT Networks

by Zainab A. Altomi, Yasmin M. Alsakar, Mostafa M. El-Gayar, Mohammed Elmogy and Yasser M. Fouda

Electronics 2025, 14(9), 1822; https://doi.org/10.3390/electronics14091822 - 29 Apr 2025

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects social interactions, communication, and behavior. Prompt and precise diagnosis is essential for prompt support and intervention. In this study, a deep learning-based framework for diagnosing ASD using facial images has been proposed. The [...] Read more.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects social interactions, communication, and behavior. Prompt and precise diagnosis is essential for prompt support and intervention. In this study, a deep learning-based framework for diagnosing ASD using facial images has been proposed. The methodology begins with logarithmic transformation for image pre-processing, enhancing contrast and making subtle facial features more distinguishable. Next, feature extraction is performed using NasNetMobile and DeiT networks, where NasNetMobile captures high-level abstract patterns, and the DeiT network focuses on fine-grained facial characteristics relevant to ASD identification. The extracted features are then fused using attentional feature fusion, which adaptively assigns importance to the most discriminative features, ensuring an optimal representation. Finally, classification is conducted using bagging with a support vector machine (SVM) classifier employing a polynomial kernel, enhancing generalization and robustness. Experimental results validate the effectiveness of the proposed approach, achieving 95.77% recall, 95.67% precision, 95.66% F1-score, and 95.67% accuracy, demonstrating its strong potential for assisting in ASD diagnosis through facial image analysis. Full article

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24 pages, 8047 KiB

Open AccessArticle

High-Resolution Building Indicator Mapping Using Airborne LiDAR Data

by Fayez Tarsha Kurdi, Elżbieta Lewandowicz, Zahra Gharineiat and Jie Shan

Electronics 2025, 14(9), 1821; https://doi.org/10.3390/electronics14091821 - 29 Apr 2025

Abstract

Urban indicators established in spatial development plans should ensure the preservation of spatial order when introducing new construction investments. They should also harmonize with the existing urban structure and even modernize it toward sustainable development. When determining these indicators, the surrounding space is [...] Read more.

Urban indicators established in spatial development plans should ensure the preservation of spatial order when introducing new construction investments. They should also harmonize with the existing urban structure and even modernize it toward sustainable development. When determining these indicators, the surrounding space is analyzed. Conventionally, building indicators in the existing space are determined based on available documents, which usually comprise 2D spatial data such as large-scale maps or cadastral maps. This study aims to investigate the method of calculating building indicators using 3D urban building models that will be created from airborne Light Detection and Ranging (LiDAR) measurements. In the discussion of the results, indicators calculated based on LiDAR data are compared with the ones calculated from 2D cadastral data. The calculated 3D indicators correlate with the classically calculated indicators. The accuracy of the computed building area, volume, and other indicators depends on the LiDAR point cloud density and accuracy. The indicators calculated from the 3D data align with the new trends in defining Building Morphology Indicators (BMIs). Full article

(This article belongs to the Special Issue Point Cloud Data Processing and Applications)

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18 pages, 2777 KiB

Open AccessArticle

Chromosome Image Classification Based on Improved Differentiable Architecture Search

by Jianming Li, Changchang Zeng, Min Zhou, Zeyi Shang and Jiangang Zhu

Electronics 2025, 14(9), 1820; https://doi.org/10.3390/electronics14091820 - 29 Apr 2025

Abstract

Chromosomes are essential carriers of human genetic material, and karyotype diagnosis plays a crucial role in prenatal diagnostics, genetic disease identification, and medical research. Physicians rely heavily on karyotype images to diagnose potential abnormalities in chromosome numbers and structure. However, the process is [...] Read more.

Chromosomes are essential carriers of human genetic material, and karyotype diagnosis plays a crucial role in prenatal diagnostics, genetic disease identification, and medical research. Physicians rely heavily on karyotype images to diagnose potential abnormalities in chromosome numbers and structure. However, the process is tedious and challenging. To improve diagnostic efficiency and accuracy, artificial intelligence (AI) researchers have developed convolutional neural networks (CNNs) for chromosome image classification. Despite this progress, the gap between cytogeneticists and AI experts results in a time-consuming workflow. In this study, we propose a framework based on improved Differentiable Architecture Search (DARTS) to automatically design convolutional architectures for the classification task. The improvement strategies based on DARTS are implemented in two stages. First, a procedural approach was designed to comprehensively analyze the evolution of architectural parameters. Based on this analysis, the search space of the DARTS algorithm was refined, resulting in an optimized search space. Next, an entropy-based regularization term was incorporated into the supernetwork’s objective function to guide the algorithm in searching for a more effective architecture. Then, extensive experiments were conducted on CIFAR-10, ImageNet, and the Copenhagen datasets to evaluate the performance of the searched architecture in comparison with related works. The network composed of the searched architecture achieved accuracies of 97.27

\pm

0.05%, 75.40%, and 98.64% on the three datasets, respectively. These results demonstrate that the architecture is high-performing and the proposed framework for designing networks for chromosome classification is effective. Full article

(This article belongs to the Section Artificial Intelligence)

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20 pages, 6782 KiB

Open AccessArticle

Accelerating Millimeter-Wave Imaging: Automating Glow Discharge Detector Focal Plane Arrays with Chirped FMCW Radar for Rapid Measurement and Instrumentation Applications

by Arun Ramachandra Kurup, Daniel Rozban, Amir Abramovich, Yitzhak Yitzhaky and Natan Kopeika

Electronics 2025, 14(9), 1819; https://doi.org/10.3390/electronics14091819 - 29 Apr 2025

Abstract

This article presents an innovative integration of Glow Discharge Detector Focal Plane Arrays (GDD FPA) with Chirped Frequency Modulated Continuous Wave (FMCW) Radar, enhancing millimeter-wave (MMW) imaging. The cost-effective FPA design using GDDs as pixel elements forms the foundation of the system. We [...] Read more.

This article presents an innovative integration of Glow Discharge Detector Focal Plane Arrays (GDD FPA) with Chirped Frequency Modulated Continuous Wave (FMCW) Radar, enhancing millimeter-wave (MMW) imaging. The cost-effective FPA design using GDDs as pixel elements forms the foundation of the system. We investigate MMW effects on GDD discharge currents via basic data acquisition (DAQ) and implement a scanning mechanism with a step motor for sub-pixel imaging. The setup integrates an MMW source, optical components, a timer/counter, and an 8 × 8 FPA with 64 GDD, operating in electrical detection modes and processing signals using Fast Fourier Transform (FFT) algorithms. Recent advancements in millimeter-wave imaging have focused on improving image resolution and acquisition speed through various techniques, including lock-in amplifiers and electrical detection methods. However, these methods introduce complexity, cost, and extended acquisition times. Our approach mitigates these challenges by implementing a simplified FPA design that eliminates the need for external signal conditioning elements, providing faster and more efficient image acquisition. The primary contributions include significant improvements in the speed and automation of image acquisition achieved through a coordinated control mechanism for efficient row scanning. Compared to previous generations of GDD FPAs, this system achieves a notable reduction in image acquisition time by up to 75%, while maintaining high fidelity. These enhancements make the system particularly suitable for time-sensitive applications. Additionally, future research directions include the incorporation of 3D imaging using FMCW radar. Results from the FMCW measurements using the single GDD circuit demonstrate the system’s ability to accurately capture and process MMW radiation, even at low intensities. The combined strengths of GDD FPA and chirped FMCW radar underscore the system’s effectiveness in MMW detection, laying the groundwork for advanced MMW imaging capabilities across diverse applications. Full article

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30 pages, 19525 KiB

Open AccessArticle

Disease Monitoring and Characterization of Feeder Road Network Based on Improved YOLOv11

by Ying Fan, Kun Zhi, Haichao An, Runyin Gu, Xiaobing Ding and Jianhua Tang

Electronics 2025, 14(9), 1818; https://doi.org/10.3390/electronics14091818 - 29 Apr 2025

Abstract

In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the [...] Read more.

In response to the challenges of the low accuracy and high misdetection and omission rate of disease detection on feeder roads, an improved Rural-YOLO (SAConv-C2f+C2PSA_CAA+MCSAttention+WIOU) disease detection algorithm is proposed in this paper, which is an enhanced target detection framework based on the YOLOv11 architecture, for the identification of common diseases in the complex feeder road environment. The proposed methodology introduces four key innovations: (1) Switchable Atrous Convolution (SAConv) is introduced into the backbone network to enhance multiscale disease feature extraction under occlusion conditions; (2) Multi-Channel and Spatial Attention (MCSAttention) is constructed in the feature fusion process, and the weight distribution of multiscale diseases is adjusted through adaptive weight redistribution. By adjusting the weight distribution, the model’s sensitivity to subtle disease features is improved. To enhance its ability to discriminate between different disease types, Cross Stage Partial with Parallel Spatial Attention and Channel Adaptive Aggregation (C2PSA_CAA) is constructed at the end of the backbone network. (3) To mitigate category imbalance issues, Weighted Intersection over Union loss (WIoU_loss) is introduced, which helps optimize the bounding box regression process in disease detection and improve the detection of relevant diseases. Based on experimental validation, Rural-YOLO demonstrated superior performance with minimal computational overhead. Only 0.7 M additional parameters is required, and an 8.4% improvement in recall and a 7.8% increase in mAP₅₀ were achieved compared to the initial models. The optimized architecture also reduced the model size by 21%. The test results showed that the proposed model achieved 3.28 M parameters with a computational complexity of 5.0 GFLOPs, meeting the requirements for lightweight deployment scenarios. Cross-validation on multi-scenario public datasets was carried out, and the model’s robustness across diverse road conditions. In the quantitative experiments, the center skeleton method and the maximum internal tangent circle method were used to calculate crack width, and the pixel occupancy ratio method was used to assess the area damage degree of potholes and other diseases. The measurements were converted to actual physical dimensions using a calibrated scale of 0.081:1. Full article

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27 pages, 5953 KiB

Open AccessArticle

LiS-Net: A Brain-Inspired Framework for Event-Based End-to-End Steering Prediction

by Keyi Xu, Jiaxuan Liu, Shuo Wang, Erkang Cheng, Fang Zhao and Meng Li

Electronics 2025, 14(9), 1817; https://doi.org/10.3390/electronics14091817 - 29 Apr 2025

Abstract

The advancement of autonomous vehicles has shifted from modular pipeline architectures to end-to-end frameworks, enabling direct learning of control policies from sensory inputs. While frame-based RGB cameras are commonly utilized, they face challenges in dynamic environments, such as motion blur and varying illumination. [...] Read more.

The advancement of autonomous vehicles has shifted from modular pipeline architectures to end-to-end frameworks, enabling direct learning of control policies from sensory inputs. While frame-based RGB cameras are commonly utilized, they face challenges in dynamic environments, such as motion blur and varying illumination. Alternatively, event-based cameras, with their high temporal resolution and wide dynamic range, offer a promising solution. However, existing end-to-end models for event camera inputs are primarily constructed using traditional convolutional networks and time-sequence models (e.g., Recurrent Neural Networks, RNNs), which suffer from large parameter counts and excessive redundant computations. To address this gap, we propose LiS-Net, a novel framework that incorporates brain-inspired neural networks to construct the overall architecture, applying it to the task of end-to-end steering prediction. The core of LiS-Net is a liquid neural network, which is designed to simulate the behavior of C. elegans neurons for modeling purposes. By leveraging the strengths of event cameras and brain-inspired computation, LiS-Net achieves superior accuracy, smoothness, and efficiency. Specifically, LiS-Net outperforms existing models with the lowest RMSE and MAE, indicating better accuracy, while also maintaining the fewest number of neurons and achieving competitive FLOPs results, showcasing its computational efficiency. Experiments on the simulated EventScape dataset demonstrate its robustness, while validation on our self-collected dataset showcases its generalization capability. We also release the collected dataset comprising synchronized event cameras, RGB cameras, and GPS and CAN data. LiS-Net lays the foundation for scalable and efficient autonomous driving solutions by integrating bio-inspired sensors with brain-inspired computation. Full article

(This article belongs to the Section Artificial Intelligence)

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5 pages, 135 KiB

Open AccessEditorial

Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies

by Irfan-Ullah Awan, Amna Qureshi and Muhammad Shahwaiz Afaqui

Electronics 2025, 14(9), 1816; https://doi.org/10.3390/electronics14091816 - 29 Apr 2025

Abstract

The rapid development of future networking technologies, such as 5G, 6G, blockchain, the Internet of Things (IoT), cloud computing, and Software-Defined Networking (SDN) is set to revolutionize our methods of connection, communication, and data sharing [...] Full article

(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems in Future Networking Technologies)

18 pages, 2117 KiB

Open AccessArticle

Design of a New Busbar for VFTO Suppression and Analysis of the Suppression Effect

by Huan Wang, Xixiu Wu, Yinglong Diao, Xiwen Chen and Bolun Du

Electronics 2025, 14(9), 1815; https://doi.org/10.3390/electronics14091815 - 29 Apr 2025

Abstract

Very fast transient overvoltage (VFTO) is characterized by short wavefront time, high amplitude and wide frequency. VFTO poses a threat to the safe operation of power equipment. In order to suppress the harmful effects of VFTO transmission on power equipment, a new type [...] Read more.

Very fast transient overvoltage (VFTO) is characterized by short wavefront time, high amplitude and wide frequency. VFTO poses a threat to the safe operation of power equipment. In order to suppress the harmful effects of VFTO transmission on power equipment, a new type of busbar based on the structure of “inductance + resistance” has been designed, which is called a “damping busbar”. In this study, the equivalent circuit of a damping bus including spurious parameters was developed. These stray parameters are formed between the GIS enclosure and the damping bus, and between the disk insulators and the damping bus. A damping bus simulation model was established based on the equivalent circuit to carry out analysis of the relationship between the structural parameters of the damping bus and the inductance generated by the damping bus. The simulation results showed that the number of turns plays a decisive role in bus inductance, and the relationship between the number of turns and inductance is approximately linear. Comparative analysis of multiple waveforms was carried out before and after the addition of damping buses to the GIS on a 550 kV test rig. The test data showed that the average amplitude of VFTO decreased by 20.36% after the installation of the damping bus, the number of breakdowns decreased by about 66.7%, and there was no obvious high frequency in the measured waveform after installation. In short, the damping busbar had a good suppression effect on the amplitude and frequency of VFTO, and reduced the number of breakdowns. This technique provides a novel solution for VFTO suppression. Full article

15 pages, 18338 KiB

Open AccessArticle

A Graphene Nanoribbon Electrode-Based Porphyrin Molecular Device for DNA Sequencing

by Yong-Kang Li, Li-Ping Zhou, Xue-Feng Wang, Panagiotis Vasilopoulos, Wen-Long You and Yu-Shen Liu

Electronics 2025, 14(9), 1814; https://doi.org/10.3390/electronics14091814 - 29 Apr 2025

Abstract

We propose a DNA nucleobase sequencing device composed of zigzag graphene nanoribbon electrodes connected with a porphyrin molecule via carbon chains (GEPM). The connecting geometry between the nanoribbons with an even width number and the carbon chains is laterally symmetric to filter out [...] Read more.

We propose a DNA nucleobase sequencing device composed of zigzag graphene nanoribbon electrodes connected with a porphyrin molecule via carbon chains (GEPM). The connecting geometry between the nanoribbons with an even width number and the carbon chains is laterally symmetric to filter out electrons of specific modes. Various properties of the GEPM and of the GEPM + nucleobase systems, such as interaction energies, charge density differences, spin-differential electronic densities, and electric currents, are investigated using the density functional theory (DFT) combined with the non-equilibrium Green’s function (NEGF) method. The results show that the GEPM device holds promise for DNA sequencing with the measurement of the electric signals through it. The four nucleobases—adenine (A), cytosine (C), guanine (G), and thymine (T)—can be efficiently distinguished based on the conductance and current sensitivity when they are located on the porphyrin molecule of the GEPM device. The symmetry of the connecting geometry between the carbon chains and the nanoribbons selects Bloch states with specific symmetry to pass through the device and results in broad transmission valleys or gaps. In addition, the edge magnetism of graphene nanoribbons can further manipulate the transmission and then the sequencing effects. The device exhibits extremely high conductance sensitivity in the parallel magnetic configuration. This study explores the possible advantage of this technology compared with conventional nanopore sequencing devices and potentially expands the variety of available sequencing structures. Full article

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18 pages, 2206 KiB

Open AccessArticle

Multi-Knowledge-Enhanced Model for Korean Abstractive Text Summarization

by Kyoungsu Oh, Youngho Lee and Hyekyung Woo

Electronics 2025, 14(9), 1813; https://doi.org/10.3390/electronics14091813 - 29 Apr 2025

Abstract

Text summarization plays a crucial role in processing extensive textual data, particularly in low-resource languages such as Korean. However, abstractive summarization faces persistent challenges, including semantic distortion and inconsistency. This study addresses these limitations by proposing a multi-knowledge-enhanced abstractive summarization model tailored for [...] Read more.

Text summarization plays a crucial role in processing extensive textual data, particularly in low-resource languages such as Korean. However, abstractive summarization faces persistent challenges, including semantic distortion and inconsistency. This study addresses these limitations by proposing a multi-knowledge-enhanced abstractive summarization model tailored for Korean texts. The model integrates internal knowledge, specifically keywords and topics that are extracted using a context-aware BERT-based approach. Unlike traditional statistical extraction methods, our approach utilizes the semantic context to ensure that the internal knowledge is both diverse and representative. By employing a multi-head attention mechanism, the proposed model effectively integrates multiple types of internal knowledge with the original document embeddings. Experimental evaluations on Korean datasets (news and legal texts) demonstrate that our model significantly outperforms baseline methods, achieving notable improvements in lexical overlap, semantic consistency, and structural coherence, as evidenced by higher ROUGE and BERTScore metrics. Furthermore, the method maintains information consistency across diverse categories, including dates, quantities, and organizational details. These findings highlight the potential of context-aware multi-knowledge integration in enhancing Korean abstractive summarization and suggest promising directions for future research into broader knowledge-incorporation strategies. Full article

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13 pages, 2510 KiB

Open AccessArticle

How to Use Redundancy for Memory Reliability: Replace or Code?

by Hyosang Ju, Dong-Hyun Kong, Kijun Lee, Myung-Kyu Lee, Sunghye Cho and Sang-Hyo Kim

Electronics 2025, 14(9), 1812; https://doi.org/10.3390/electronics14091812 - 29 Apr 2025

Abstract

Modern digital systems rely on DRAM as main memory and flash-based SSDs for storage, forming the backbone of today’s computing infrastructure. As demands for faster processing and larger data services increase, the memory subsystems have become denser, pushing technologies to their physical limits [...] Read more.

Modern digital systems rely on DRAM as main memory and flash-based SSDs for storage, forming the backbone of today’s computing infrastructure. As demands for faster processing and larger data services increase, the memory subsystems have become denser, pushing technologies to their physical limits and increasing susceptibility to faults. To ensure data integrity, two complementary approaches are employed: replacement-based techniques, which map defective cells to redundant areas, and error-correcting code (ECC) methods, which dynamically detect and correct errors. This paper theoretically investigates the most efficient use of redundancy for DRAM reliability by categorizing detects into hard faults and soft errors. Each scenario is evaluated in terms of required redundancy and residual error rate, using finite-length channel coding capacity. We compare the ECC schemes with BCH codes, which are widely favored in on-die ECC applications due to their low latency and decoding complexity. Full article

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

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14 pages, 2772 KiB

Open AccessArticle

Critical Considerations for Observing Cross Quantum Capacitance in Electric-Double-Layer-Gated Transistors Based on Two-Dimensional Crystals

by Jacob D. Eisensmith, Pratik P. Dholabhai and Ke Xu

Electronics 2025, 14(9), 1811; https://doi.org/10.3390/electronics14091811 - 29 Apr 2025

Abstract

Cross quantum capacitance (CQC) has been proposed as an extension to traditional quantum capacitance (TQC) in systems where strong interfacial screening between spatially separated charge layers modifies the total capacitance—particularly in electric-double-layer-gated transistors (EDLTs) based on two-dimensional (2D) crystals. In this work, we [...] Read more.

Cross quantum capacitance (CQC) has been proposed as an extension to traditional quantum capacitance (TQC) in systems where strong interfacial screening between spatially separated charge layers modifies the total capacitance—particularly in electric-double-layer-gated transistors (EDLTs) based on two-dimensional (2D) crystals. In this work, we revisit a theoretical model of CQC to evaluate its relevance under experimentally realistic conditions. By systematically analyzing the model’s behavior across key parameter spaces, we identify the specific conditions under which CQC leads to the non-monotonic dependence of capacitance on inter-plate distance—a proposed experimental signature of CQC. However, we find that these conditions—requiring similar effective masses, high charge densities, and strong charge asymmetry—are highly restrictive and difficult to realize in typical EDLTs. Instead, we highlight a more experimentally accessible regime in which CQC enhances total capacitance beyond TQC predictions, even in the absence of non-monotonicity. These results clarify the limitations of the existing model and suggest concrete strategies for probing CQC in nanoscale devices, emphasizing the need for new theoretical frameworks that explicitly incorporate both ionic and electronic conductors. Full article

(This article belongs to the Special Issue Advanced System Architectures and AI-Driven Innovations for Next-Generation Computing)

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24 pages, 10988 KiB

Open AccessArticle

Neural Network Implementation for Fire Detection in Critical Infrastructures: A Comparative Analysis on Embedded Edge Devices

by Jon Aramendia, Andrea Cabrera, Jon Martín, Jose Ángel Gumiel and Koldo Basterretxea

Electronics 2025, 14(9), 1809; https://doi.org/10.3390/electronics14091809 - 29 Apr 2025

Abstract

This paper explores the application of artificial intelligence on edge devices to enhance security in critical infrastructures, with a specific focus on the use case of a battery-powered mobile system for fire detection in tunnels. The study leverages the YOLOv5 convolutional neural network [...] Read more.

This paper explores the application of artificial intelligence on edge devices to enhance security in critical infrastructures, with a specific focus on the use case of a battery-powered mobile system for fire detection in tunnels. The study leverages the YOLOv5 convolutional neural network (CNN) for real-time detection, focusing on a comparative analysis across three low-power platforms, NXP i.MX93, Xilinx Kria KV260, and NVIDIA Jetson Orin Nano, evaluating their performance in terms of detection accuracy (mAP), inference time, and energy consumption. The paper also presents a methodology for implementing neural networks on various platforms, aiming to provide a scalable approach to edge artificial intelligence (AI) deployment. The findings offer valuable insights into the trade-offs between computational efficiency and power consumption, guiding the selection of edge computing solutions in security-critical applications. Full article

(This article belongs to the Special Issue Computation Offloading for Mobile-Edge/Fog Computing)

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16 pages, 6945 KiB

Open AccessArticle

Lightweight Underwater Target Detection Algorithm Based on YOLOv8n

by Dengke Song and Hua Huo

Electronics 2025, 14(9), 1810; https://doi.org/10.3390/electronics14091810 - 28 Apr 2025

Abstract

To address the challenges in underwater target detection, such as complex environments, image blurring, and high model parameter counts and computational complexity, an improved lightweight detection algorithm, RDL-YOLO, is proposed. This algorithm incorporates multiple optimizations based on the YOLOv8n model. The introduction of [...] Read more.

To address the challenges in underwater target detection, such as complex environments, image blurring, and high model parameter counts and computational complexity, an improved lightweight detection algorithm, RDL-YOLO, is proposed. This algorithm incorporates multiple optimizations based on the YOLOv8n model. The introduction of the RFAConv module optimizes the backbone network, enhancing feature extraction capabilities under complex backgrounds. The DySample dynamic upsampling module is used to effectively improve the model’s ability to capture edge information. A lightweight detection head based on shared convolutions is designed to achieve model lightweighting. The combination of the normalized wasserstein distance (NWD) loss function and CIoU loss improves the detection accuracy for small targets. Experimental results on the UPRC (Underwater Robot Prototype Competition) and RUOD (Real-World Underwater Object Detection) datasets show that the improved algorithm achieves an average precision (mAP) increase of 1.4% and 1.0%, respectively, while reducing parameter count and computational complexity by 19.3% and 14.8%. Compared to other state-of-the-art underwater target detection algorithms, the proposed RDL-YOLO not only improves detection accuracy but also achieves model lightweighting, demonstrating superior applicability in resource-constrained underwater environments. Full article

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33 pages, 6915 KiB

Open AccessArticle

AI-Driven Resource Allocation and Auto-Scaling of VNFs in Edge-5G-IoT Ecosystems

by Rafael Moreno-Vozmediano, Eduardo Huedo, Rubén S. Montero and Ignacio M. Llorente

Electronics 2025, 14(9), 1808; https://doi.org/10.3390/electronics14091808 - 28 Apr 2025

Abstract

With the rapid expansion of edge-5G-IoT ecosystems, the need for intelligent and adaptive resource management strategies has become a critical challenge. In these environments, Virtualized Network Functions (VNFs) deployed at the network edge must handle highly dynamic workloads, making fixed resource allocation inefficient. [...] Read more.

With the rapid expansion of edge-5G-IoT ecosystems, the need for intelligent and adaptive resource management strategies has become a critical challenge. In these environments, Virtualized Network Functions (VNFs) deployed at the network edge must handle highly dynamic workloads, making fixed resource allocation inefficient. While over-provisioning can lead to unnecessary resource waste, an especially critical issue in edge environments with limited resources, under-provisioning can degrade performance and service quality. This paper presents an AI-based predictive auto-scaling framework designed to optimize resource allocation for VNFs in edge/5G-enabled IoT environments. The proposed approach evaluates and integrates different ML-based regression models to characterize VNF resource consumption, along with various forecasting methods to anticipate future workload fluctuations, enabling both vertical and horizontal auto-scaling. Extensive experiments with real-world traffic data demonstrate the effectiveness of our approach, showing significant improvements in resource efficiency compared to fixed allocation strategies. Full article

(This article belongs to the Special Issue Intelligent IoT Systems with Mobile/Multi-Access Edge Computing (MEC))

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15 pages, 1994 KiB

Open AccessArticle

A Hybrid Deep Learning and Feature Descriptor Approach for Partial Fingerprint Recognition

by Zhi-Sheng Chen, Chrisantonius, Farchan Hakim Raswa, Shang-Kuan Chen, Chung-I Huang, Kuo-Chen Li, Shih-Lun Chen, Yung-Hui Li and Jia-Ching Wang

Electronics 2025, 14(9), 1807; https://doi.org/10.3390/electronics14091807 - 28 Apr 2025

Abstract

Partial fingerprint recognition has emerged as a critical method for verifying user authenticity during mobile transactions. As a result, there is a pressing need to develop techniques that effectively and accurately authenticate users, even when the scanner only captures a limited area of [...] Read more.

Partial fingerprint recognition has emerged as a critical method for verifying user authenticity during mobile transactions. As a result, there is a pressing need to develop techniques that effectively and accurately authenticate users, even when the scanner only captures a limited area of the finger. A key challenge in partial fingerprint matching is the inevitable loss of features when a full fingerprint image is reduced to a partial one. To address this, we propose a method that integrates deep learning with feature descriptors for partial fingerprint matching. Specifically, our approach employs a Siamese Network based on a CNN architecture for deep learning, complemented by a SIFT-based feature descriptor to extract minimal yet significant features from the partial fingerprint. The final matching score is determined by combining the outputs from both methods, using a weighted scheme. The experimental results, obtained from varying image sizes, sufficient epochs, and different datasets, indicate that our combined method achieves an Equal Error Rate (EER) of approximately 4% for databases DB1 and DB3 in the FVC2002 dataset. Additionally, validation at FRR@FAR 1/50,000 yields results of about 6.36% and 8.11% for DB1 and DB2, respectively. These findings demonstrate the efficacy of our approach in partial fingerprint recognition. Future work could involve utilizing higher-resolution datasets to capture more detailed fingerprint features, such as pore structures, and exploring alternative deep learning techniques to further streamline the training process. Full article

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20 pages, 407 KiB

Open AccessArticle

Assessing the Measurement Invariance of the Human–Computer Trust Scale

by Gabriela Beltrão, Sonia Sousa and David Lamas

Electronics 2025, 14(9), 1806; https://doi.org/10.3390/electronics14091806 - 28 Apr 2025

Viewed by 25

Abstract

Trust in technology is a topic of growing importance in Human–Computer Interaction due to the growing impact of systems on daily lives. However, limited attention has been paid to how one’s national culture shapes their propensity to trust. This study addresses an existing [...] Read more.

Trust in technology is a topic of growing importance in Human–Computer Interaction due to the growing impact of systems on daily lives. However, limited attention has been paid to how one’s national culture shapes their propensity to trust. This study addresses an existing gap in trust in technology research by advancing towards a more accurate tool for quantitatively measuring propensity to trust across different contexts. We specifically evaluate the psychometric properties of the human–computer trust scale (HCTS) in Brazil, Singapore, Malaysia, Estonia, and Mongolia. To accomplish this, we used the Measurement Invariance of Composite Models (MICOM), a procedure that examines the equivalency of the instrument’s psychometric properties across different groups. Our results highlight the importance of rigorous validation processes when applying psychometric instruments in cross-cultural contexts, offering insights into the differences between the countries investigated and the procedure’s potential to investigate trust across different groups. Full article

(This article belongs to the Section Artificial Intelligence)

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25 pages, 5598 KiB

Open AccessArticle

Quad-Frequency Wide-Lane, Narrow-Lane and Hatch–Melbourne–Wübbena Combinations: The Beidou Case

by Daniele Borio, Melania Susi and Kinga Wȩzka

Electronics 2025, 14(9), 1805; https://doi.org/10.3390/electronics14091805 - 28 Apr 2025

Viewed by 36

Abstract

The pseudoranges of a Global Navigation Satellite System (GNSS) meta-signal can be reconstructed from the observations of its side-band components. More specifically, the Hatch–Melbourne–Wübbena (HMW) code-carrier combination is used to solve the ambiguity associated to the wide-lane carrier phase combination of the side-band [...] Read more.

The pseudoranges of a Global Navigation Satellite System (GNSS) meta-signal can be reconstructed from the observations of its side-band components. More specifically, the Hatch–Melbourne–Wübbena (HMW) code-carrier combination is used to solve the ambiguity associated to the wide-lane carrier phase combination of the side-band components, obtaining a high-accuracy pseudorange. The HMW and the wide-lane combinations thus play a key role in constructing meta-signal measurements. The theory of GNSS meta-signals was recently extended to the case with a number of components equal to a power of two. This theory can be used to generalize HMW and wide-lane combinations to the quad-frequency case. This is carried out through a Hadamard matrix of order four, which defines a narrow-lane and three wide-lane combinations. This paper characterizes meta-signal-inspired quad-frequency HMW and wide-lane measurements combinations using Beidou Navigation Satellite System (BDS) observations. Two professional Septentrio PolarRx5S multi-frequency, multi-constellation receivers were set up in a zero-baseline configuration and used to collect observables from all the BDS open frequencies. These measurements are used to characterize different quad-frequency HMW and wide-lane carrier combinations. Some of the combinations analyzed have large equivalent wavelengths and have the potential to enable single-epoch ambiguity resolution in scenarios where short convergence times are required. Full article

(This article belongs to the Special Issue Precision Positioning and Navigation Communication Systems)

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Electronics, Volume 14, Issue 9 (May-1 2025) – 141 articles

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