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Electronics, Volume 14, Issue 20 (October-2 2025) – 161 articles

Cover Story (view full-size image): Grant-free access is promising for supporting massive random access with low latency. In grant-free access, the length of pilots used for joint activity detection and channel estimation (JADCE) is limited by the duration of access slot and becomes the bottleneck for system performance, especially for base-station with multiple antennas. The inter-user interference (IUI) caused by non-orthogonal pilots is random owing to random access and it affects the JADCE performance. This paper focuses on pilot design to reduce pilot overhead for grant-free access. Compared with the existing designs, which reduce the mean IUI to users, the proposed pilot design exploits the IUI distribution to shorten its tail and consequently improves JADCE performance. View this paper
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18 pages, 10539 KB  
Article
Coal Shearer Drum Detection in Underground Mines Based on DCS-YOLO
by Tao Hu, Jinbo Qiu, Libo Zheng, Zehai Yu and Cong Liu
Electronics 2025, 14(20), 4132; https://doi.org/10.3390/electronics14204132 - 21 Oct 2025
Viewed by 295
Abstract
To address the challenges of low illumination, heavy dust, and severe occlusion in fully mechanized mining faces, this paper proposes a shearer drum detection algorithm named DCS-YOLO. To enhance the model’s ability to effectively capture features under drum deformation and occlusion, a C3k2_DCNv4 [...] Read more.
To address the challenges of low illumination, heavy dust, and severe occlusion in fully mechanized mining faces, this paper proposes a shearer drum detection algorithm named DCS-YOLO. To enhance the model’s ability to effectively capture features under drum deformation and occlusion, a C3k2_DCNv4 module based on deformable convolution (DCNv4) is incorporated into the network. This module adaptively adjusts convolution sampling points according to the drum’s size and position, enabling efficient and precise multi-scale feature extraction. To overcome the limitations of conventional convolution in global feature modeling, a convolution and attention fusion module (CAFM) is constructed, which combines lightweight convolution with attention mechanisms to selectively reweight feature maps at different resolutions. Under low-light conditions, the Shape-IoU loss function is employed to achieve accurate regression of irregular drum boundaries while considering both positional and shape similarity. In addition, GSConv is adopted to achieve model lightweighting while maintaining efficient feature extraction capability. Experiments were conducted on a dataset built from shearer drum images collected in underground coal mines. The results demonstrate that, compared with YOLOv11n, the proposed method reduces Params and Flops by 7.7% and 4.6%, respectively, while improving precision, recall, mAP@0.5, and mAP@0.5:0.95 by 2.9%, 3.2%, 1.1%, and 3.3%, respectively. These findings highlight the significant advantages of the proposed approach in both model lightweighting and detection performance. Full article
(This article belongs to the Section Artificial Intelligence)
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13 pages, 1789 KB  
Article
Simplification of Indirect Resonant Switched-Capacitor Converter Based on State-Space Average Model Method
by Yihe Wang, Dejun Ba, Yuxin Niu, Xinran Chen, Qi Cao and Xiaofeng Lyu
Electronics 2025, 14(20), 4131; https://doi.org/10.3390/electronics14204131 - 21 Oct 2025
Viewed by 199
Abstract
This paper simplifies indirect resonant switched-capacitor (ReSC) converters using the state-space average model method. The operation principles of the 4:1 and 5:1 ReSC converters derived from the Dickson (4:1) circuit are analyzed, and the corresponding state-space average matrices are derived based on their [...] Read more.
This paper simplifies indirect resonant switched-capacitor (ReSC) converters using the state-space average model method. The operation principles of the 4:1 and 5:1 ReSC converters derived from the Dickson (4:1) circuit are analyzed, and the corresponding state-space average matrices are derived based on their equivalent circuits. The resonant inductor of the specific resonant branch is eliminated by analyzing the composition of the state-variable matrix, thereby obtaining the simplified topologies of 4:1 and 5:1 indirect ReSC converters. The simplified topologies are simulated and experimentally verified. The results prove the correctness of the state-space average modeling method and the effectiveness of the simplified topologies. Full article
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22 pages, 1845 KB  
Article
Subset-Aware Dual-Teacher Knowledge Distillation with Hybrid Scoring for Human Activity Recognition
by Young-Jin Park and Hui-Sup Cho
Electronics 2025, 14(20), 4130; https://doi.org/10.3390/electronics14204130 - 21 Oct 2025
Viewed by 248
Abstract
Human Activity Recognition (HAR) is a key technology with applications in healthcare, security, smart environments, and sports analytics. Despite advances in deep learning, challenges remain in building models that are both efficient and generalizable due to the large scale and variability of video [...] Read more.
Human Activity Recognition (HAR) is a key technology with applications in healthcare, security, smart environments, and sports analytics. Despite advances in deep learning, challenges remain in building models that are both efficient and generalizable due to the large scale and variability of video data. To address these issues, we propose a novel Dual-Teacher Knowledge Distillation (DTKD) framework tailored for HAR. The framework introduces three main contributions. First, we define static and dynamic activity classes in an objective and reproducible manner using optical-flow-based indicators, establishing a quantitative classification scheme based on motion characteristics. Second, we develop subset-specialized teacher models and design a hybrid scoring mechanism that combines teacher confidence with cross-entropy loss. This enables dynamic weighting of teacher contributions, allowing the student to adaptively balance knowledge transfer across heterogeneous activities. Third, we provide a comprehensive evaluation on the UCF101 and HMDB51 benchmarks. Experimental results show that DTKD consistently outperforms baseline models and achieves balanced improvements across both static and dynamic subsets. These findings validate the effectiveness of combining subset-aware teacher specialization with hybrid scoring. The proposed approach improves recognition accuracy and robustness, offering practical value for real-world HAR applications such as driver monitoring, healthcare, and surveillance. Full article
(This article belongs to the Special Issue Deep Learning Applications on Human Activity Recognition)
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27 pages, 7033 KB  
Article
Network Traffic Prediction for Multiple Providers in Digital Twin-Assisted NFV-Enabled Network
by Ying Hu, Ben Liu, Jianyong Li and Linlin Jia
Electronics 2025, 14(20), 4129; https://doi.org/10.3390/electronics14204129 - 21 Oct 2025
Viewed by 214
Abstract
This manuscript investigates the network traffic prediction problem, with the aim of predicting network traffic on a network function virtualization (NFV)-enabled and digital twin (DT)-assisted physical network for network service providers and network resource providers. It faces several key challenges like data privacy [...] Read more.
This manuscript investigates the network traffic prediction problem, with the aim of predicting network traffic on a network function virtualization (NFV)-enabled and digital twin (DT)-assisted physical network for network service providers and network resource providers. It faces several key challenges like data privacy and different variation patterns of network traffic for multiple service function chain (SFC) requests. In view of this, we address the network traffic prediction problem by jointly considering the above key challenges in this manuscript. Specifically, we formulate the virtual network function (VNF) migration and SFC placement problems as integer linear programming (ILP) that aim to maximize acceptance revenues, minimize network resource costs, minimize energy consumption, and minimize migration cost. Then, we define the Markov Decision Process (MDP) for the network traffic prediction problem, and propose a model and algorithm to solve the problem. The simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve a better performance. Full article
(This article belongs to the Special Issue IoT Based Intelligent Communications: Modelling, Practice and Applications)
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29 pages, 1018 KB  
Article
Explainable Bilingual Medical-Question-Answering Model Using Ensemble Learning Technique
by Abdul Rahaman Wahab Sait and Yazeed Alkhurayyif
Electronics 2025, 14(20), 4128; https://doi.org/10.3390/electronics14204128 - 21 Oct 2025
Viewed by 246
Abstract
Accessing reliable medical information is a major challenge for healthcare professionals due to limited accessibility to real-time medical data sources. The study’s objectives are maximization of response accuracy with minimal latency and enhancement of the model’s interpretability. An explainable bilingual medical-question-answering system (MQAS) [...] Read more.
Accessing reliable medical information is a major challenge for healthcare professionals due to limited accessibility to real-time medical data sources. The study’s objectives are maximization of response accuracy with minimal latency and enhancement of the model’s interpretability. An explainable bilingual medical-question-answering system (MQAS) is introduced to improve accessibility and trust in healthcare information retrieval. Using knowledge-aware networks (KANs), retrieval augmented generation (RAG), and linked open data (LOD), a synthetic bilingual dataset is generated. Through the application of a synthetic dataset and Bayesian optimization HyperBand (BOHB)-based hyperparameter optimization, the performance of GPT-Neo and RoBERTa models is fine-tuned. The outcomes of GPT-Neo and RoBERTa are ensembled using the weighted majority voting approach, while Shapley Additive ExPlanation (SHAP) value provides interpretability and transparency. The proposed model is trained and evaluated using diverse medical-question-answering datasets, demonstrating superior performance over baseline models. It achieves a generalization accuracy of 90.58%, an F1-score of 89.62%, and a BLEU score of 0.80 with a low inference time of 3.4 s per query. The findings underscore the model’s potential in delivering accurate, bilingual, and explainable medical responses. This study establishes a foundation for building multilingual healthcare information systems, promoting inclusive and equitable access to medical information. Full article
(This article belongs to the Special Issue The Future of AI-Generated Content（AIGC）)
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22 pages, 662 KB  
Article
Multi-Chain Fusion Reasoning for Knowledge Graph Link Prediction
by Shaonian Huang, Peilin Li, Huanran Wang and Zhixin Chen
Electronics 2025, 14(20), 4127; https://doi.org/10.3390/electronics14204127 - 21 Oct 2025
Viewed by 318
Abstract
The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the reasoning process. Aiming at the above problems, this paper proposes a multi-chain [...] Read more.
The knowledge graph link prediction task currently faces challenges such as insufficient semantic fusion of structured knowledge and unstructured text, limited representation learning of long-tailed entities, and insufficient interpretability of the reasoning process. Aiming at the above problems, this paper proposes a multi-chain fusion reasoning framework to realize accurate link prediction. First, a dual retrieval mechanism based on semantic similarity metrics and embedded feature matching is designed to construct a high-confidence candidate entity set; second, entity-attribute chains, entity-relationship chains, and historical context chains are established by integrating context information from external knowledge bases to generate a candidate entity set. Finally, a self-consistency scoring method fusing type constraints and semantic space alignment is proposed to realize the joint validation of structural rationality and semantic relevance of candidate entities. Experiments on two public datasets show that the method in this paper fully utilizes the ability of multi-chain reasoning and significantly improves the accuracy of knowledge graph link prediction. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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25 pages, 5852 KB  
Article
ADEmono-SLAM: Absolute Depth Estimation for Monocular Visual Simultaneous Localization and Mapping in Complex Environments
by Kaijun Zhou, Zifei Yu, Xiancheng Zhou, Ping Tan, Yunpeng Yin and Huanxin Luo
Electronics 2025, 14(20), 4126; https://doi.org/10.3390/electronics14204126 - 21 Oct 2025
Viewed by 459
Abstract
Aiming to address the problems of scale uncertainty and dynamic object interference in monocular visual simultaneous localization and mapping (SLAM), this paper proposes an absolute depth estimation network-based monocular visual SLAM method, namely, ADEmono-SLAM. Firstly, some detail features including oriented fast and rotated [...] Read more.
Aiming to address the problems of scale uncertainty and dynamic object interference in monocular visual simultaneous localization and mapping (SLAM), this paper proposes an absolute depth estimation network-based monocular visual SLAM method, namely, ADEmono-SLAM. Firstly, some detail features including oriented fast and rotated brief (ORB) features of input image are extracted. An object depth map is obtained through an absolute depth estimation network, and some reliable feature points are obtained by a dynamic interference filtering algorithm. Through these operations, the potential dynamic interference points are eliminated. Secondly, the absolute depth image is obtained by using the monocular depth estimation network, in which a dynamic point elimination algorithm using target detection is designed to eliminate dynamic interference points. Finally, the camera poses and map information are obtained by static feature point matching optimization. Thus, the remote points are randomly filtered by combining the depth values of the feature points. Experiments on the karlsruhe institute of technology and toyota technological institute (KITTI) dataset, technical university of munich (TUM) dataset, and mobile robot platform show that the proposed method can obtain sparse maps with absolute scale and improve the pose estimation accuracy of monocular SLAM in various scenarios. Compared with existing methods, the maximum error is reduced by about 80%, which provides an effective method or idea for the application of monocular SLAM in the complex environment. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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19 pages, 4123 KB  
Article
A Feature-Enhancement 6D Pose Estimation Method for Weakly Textured and Occluded Targets
by Xiaoqing Liu, Kaijun Zhou, Qingyuan Zeng and Peng Li
Electronics 2025, 14(20), 4125; https://doi.org/10.3390/electronics14204125 - 21 Oct 2025
Viewed by 328
Abstract
To achieve real-time and accurate pose estimation for weakly textured or occluded targets, this study proposes a feature-enhancement 6D pose estimation method based on DenseFusion. Firstly, in the image feature extraction stage, skip connections and attention modules, which could effectively fuse deep and [...] Read more.
To achieve real-time and accurate pose estimation for weakly textured or occluded targets, this study proposes a feature-enhancement 6D pose estimation method based on DenseFusion. Firstly, in the image feature extraction stage, skip connections and attention modules, which could effectively fuse deep and shallow features, are introduced to enhance the richness and effectiveness of image features. Secondly, in the point cloud feature extraction stage, PointNet is applied to the initial feature extraction of the point cloud. Then, the K-nearest neighbor method and the Pool globalization method are applied to obtain richer point cloud features. Subsequently, in the dense feature fusion stage, an adaptive feature selection module is introduced to further preserve and enhance effective features. Finally, we add a supervision network to the original pose estimation network to enhance the training results. The results of the experiment show that the improved method performs significantly better than classic methods in both the LineMOD dataset and Occlusion LineMOD dataset, and all enhancements improve the real-time performance and accuracy of pose estimation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)
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27 pages, 12490 KB  
Article
Fast CU Division Algorithm for Different Occupancy Types of CUs in Geometric Videos
by Nana Li, Tiantian Zhang, Jinchao Zhao and Qiuwen Zhang
Electronics 2025, 14(20), 4124; https://doi.org/10.3390/electronics14204124 - 21 Oct 2025
Viewed by 261
Abstract
Video-based point cloud compression (V-PCC) is a 3D point cloud compression standard that first projects the point cloud from 3D space onto 2D space, thereby generating geometric and attribute videos, and then encodes the geometric and attribute videos using high-efficiency video coding (HEVC). [...] Read more.
Video-based point cloud compression (V-PCC) is a 3D point cloud compression standard that first projects the point cloud from 3D space onto 2D space, thereby generating geometric and attribute videos, and then encodes the geometric and attribute videos using high-efficiency video coding (HEVC). In the whole coding process, the coding of geometric videos is extremely time-consuming, mainly because the division of geometric video coding units has high computational complexity. In order to effectively reduce the coding complexity of geometric videos in video-based point cloud compression, we propose a fast segmentation algorithm based on the occupancy type of coding units. First, the CUs are divided into three categories—unoccupied, partially occupied, and fully occupied—based on the occupancy graph. For unoccupied CUs, the segmentation is terminated immediately; for partially occupied CUs, a geometric visual perception factor is designed based on their spatial depth variation characteristics, thus realizing early depth range skipping based on visual sensitivity; and, for fully occupied CUs, a lightweight fully connected network is used to make the fast segmentation decision. The experimental results show that, under the full intra-frame configuration, this algorithm significantly reduces the coding time complexity while almost maintaining the coding quality; i.e., the BD rate of D1 and D2 only increases by an average of 0.11% and 0.28% under the total coding rate, where the geometric video coding time saving reaches up to 58.71% and the overall V-PCC coding time saving reaches up to 53.96%. Full article
(This article belongs to the Section Computer Science & Engineering)
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21 pages, 4464 KB  
Article
Chest X-Ray Medical Report Generation Using a CNN—Transformer Model with Maximum Attention
by Mei-Hua Hsih, Shih-Po Lin and Chen-Chiung Hsieh
Electronics 2025, 14(20), 4123; https://doi.org/10.3390/electronics14204123 - 21 Oct 2025
Viewed by 277
Abstract
Medical imaging, particularly chest X-rays, plays a vital role in radiological diagnosis. However, interpreting these images and generating detailed diagnostic reports is a time-consuming task for clinicians. To address this challenge, this study proposes an automated image captioning framework for chest X-ray images, [...] Read more.
Medical imaging, particularly chest X-rays, plays a vital role in radiological diagnosis. However, interpreting these images and generating detailed diagnostic reports is a time-consuming task for clinicians. To address this challenge, this study proposes an automated image captioning framework for chest X-ray images, aiming to reduce clinical workload and enhance diagnostic efficiency. The proposed approach employs convolutional neural networks (CNNs) for visual feature extraction and a modified Transformer architecture—referred to as the Medical Transformer—for structured report generation. Three CNN models, namely InceptionV3, ResNet152V2, and Inception–ResNetV2, were evaluated as feature extractors. The attention mechanisms, Bahdanau, Luong, and scaled dot product, were activated by ReLU or Tanh functions to identify the optimal configuration, i.e., the maximum attention is used. Experiments were conducted using the Indiana University Chest X-ray dataset, which contains 7466 images paired with corresponding diagnostic reports. The proposed approach employs image augmentation to accommodate input variability, utilizes Inception–ResNetV2 for feature extraction, and integrates the Medical Transformer with maximum attention mechanisms to achieve optimal performance in medical report generation. Evaluation metrics include BLEU (BLEU-1 to BLEU-4 scores of 0.720, 0.669, 0.648, and 0.600, respectively), METEOR (0.741), and BERTScore (FBERT = 0.787), demonstrating superior performance compared to baseline models and the state of the art. These results validate the effectiveness of the proposed Medical Transformer framework in generating accurate and clinically relevant medical image captions. Full article
(This article belongs to the Special Issue Digital Signal and Image Processing for Multimedia Technology)
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29 pages, 549 KB  
Article
Catch Me If You Can: Rogue AI Detection and Correction at Scale
by Fatemeh Stodt, Jan Stodt, Mohammed Alshawki, Javad Salimi Sratakhti and Christoph Reich
Electronics 2025, 14(20), 4122; https://doi.org/10.3390/electronics14204122 - 21 Oct 2025
Viewed by 367
Abstract
Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation [...] Read more.
Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation Battery (SEB), a standardized probe suite for measuring deceptive reporting across large language models (LLMs), reinforcement-learning agents, vision-only classifiers, multimodal encoders, state-space models, and diffusion models. SEB uses Bayesian inference tasks with persona-controlled instructions, schema-constrained outputs, deterministic decoding where supported, and a probe mix (near-threshold, repeats, neutralized, cross-checks). Estimates use clustered bootstrap intervals, and significance is assessed with a logistic regression by architecture; a mixed-effects analysis is planned once the per-round agent/episode traces are exported. On the latest pre-correction runs, SEB shows a consistent cross-architecture pattern in deception rates: ViT 80.0%, CLIP 15.0%, Mamba 10.0%, RL agents 10.0%, Stable Diffusion 10.0%, and LLMs 5.0% (20 scenarios/architecture). A logistic regression on per-scenario flags finds a significant overall architecture effect (likelihood-ratio test vs. intercept-only: χ2(5)=41.56, p=7.22×10−8). Holm-adjusted contrasts indicate ViT is significantly higher than all other architectures in this snapshot; the remaining pairs are not significant. Post-correction acceptance decisions are evaluated separately using residual deception and override rates under SEB-Correct. Latency varies by architecture (sub-second to minutes), enabling pre-deployment screening broadly and real-time auditing for low-latency classes. Results indicate that SEB-Detect deception flags are not confined to any one paradigm, that distinct architectures can converge to similar levels under a common interface, and that reporting interfaces and incentive framing are central levers for mitigation. We operationalize “deception” as reward-sensitive misreport flags, and we separate detection from intervention via a correction wrapper (SEB-Correct), supporting principled acceptance decisions for deployment. Full article
(This article belongs to the Special Issue Trends and Prospects in AI-Empowered Information Systems and Technologies)
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16 pages, 663 KB  
Article
SAIL-Y: A Socioeconomic and Gender-Aware Career Recommender System
by Enrique J. Delahoz-Domínguez and Raquel Hijón-Neira
Electronics 2025, 14(20), 4121; https://doi.org/10.3390/electronics14204121 - 21 Oct 2025
Viewed by 280
Abstract
This study presents SAIL-Y (Sailing Artificial Intelligence for Learning in Youth), a novel gender-focused recommender system designed to promote female participation in STEM careers through data-driven guidance. Drawing inspiration from the metaphor of an academic journey as a voyage, SAIL-Y functions as a [...] Read more.
This study presents SAIL-Y (Sailing Artificial Intelligence for Learning in Youth), a novel gender-focused recommender system designed to promote female participation in STEM careers through data-driven guidance. Drawing inspiration from the metaphor of an academic journey as a voyage, SAIL-Y functions as a digital compass—leveraging socioeconomic profiles and standardised test results (Saber 11, Colombia) to help students navigate career decisions in high-impact academic fields. SAIL-Y integrates multiple machine learning strategies, including collaborative filtering, bootstrapped data augmentation to rebalance gender representation, and socioeconomic-aware conditioning, to generate personalised and bias-controlled career recommendations. The system is explicitly designed to skew recommendations toward STEM disciplines for female students, countering systemic underrepresentation in these fields. Using a dataset of 332,933 Colombian students (2010–2021), we evaluate the performance of different recommendation architectures under the SAIL-Y framework. The results show that a gender-oriented recommender design increases the proportion of STEM career recommendations for female students by up to 25% compared to reference models. Beyond technical contributions, this work proposes an ethically aligned paradigm for educational recommender systems—one that empowers rather than merely predicts. SAIL-Y is thus envisioned as both a methodological tool and a socio-educational intervention, supporting more equitable academic journeys for future generations. Full article
(This article belongs to the Special Issue Context-Aware Computing and Smart Recommender Systems in the IoT, 3rd Edition)
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18 pages, 2381 KB  
Article
Overcoming Data Scarcity in Non-Contact Respiratory Monitoring: A DTW-Enhanced CNN-LSTM Approach
by Ju O Kim and Deokwoo Lee
Electronics 2025, 14(20), 4120; https://doi.org/10.3390/electronics14204120 - 21 Oct 2025
Viewed by 313
Abstract
This study investigates non-contact respiratory pattern classification using Ultra-Wideband (UWB) radar sensors and deep learning. A CNN-LSTM hybrid architecture was developed combining spatial feature extraction through convolutional layers with temporal pattern recognition via LSTM networks. To address data scarcity in the minority class, [...] Read more.
This study investigates non-contact respiratory pattern classification using Ultra-Wideband (UWB) radar sensors and deep learning. A CNN-LSTM hybrid architecture was developed combining spatial feature extraction through convolutional layers with temporal pattern recognition via LSTM networks. To address data scarcity in the minority class, a two-stage augmentation strategy incorporating Dynamic Time Warping-based SMOTE-TS was implemented. The experimental evaluation utilized 700 respiratory recordings from seven healthy volunteers performing controlled breathing exercises. Under controlled laboratory conditions, the system achieved 94.3% accuracy and 0.969 AUC, with an average inference time of 45.3 ms per sample (SD: 8.7 ms), demonstrating computational feasibility for real-time applications. This preliminary investigation establishes technical proof-of-concept, though validation with clinical populations remains necessary before medical deployment. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Biomedical Data Processing)
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27 pages, 879 KB  
Review
A Literature Review of Automated Roadside Parking Monitoring Using Artificial Intelligence Algorithms
by Christina Georgopoulou and Panagiotis Papantoniou
Electronics 2025, 14(20), 4119; https://doi.org/10.3390/electronics14204119 - 21 Oct 2025
Viewed by 539
Abstract
The issue of parking has been a major concern in urban centers, primarily due to the increasing demand and daily traffic congestion. This paper endeavors to explore, process, and evaluate the existing literature on parking space detection methodologies, integrating photogrammetric techniques with deep [...] Read more.
The issue of parking has been a major concern in urban centers, primarily due to the increasing demand and daily traffic congestion. This paper endeavors to explore, process, and evaluate the existing literature on parking space detection methodologies, integrating photogrammetric techniques with deep learning models. Towards that end, various existing systems, applications, and models that have been studied were evaluated, and their impact on different test cases was assessed. The literature review was based on the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). Results indicated that smart parking systems significantly enhance dynamic parking management by leveraging deep learning techniques, particularly convolutional neural networks (CNNs). These systems process visual data from monitoring sources to generate statistics, diagrams, and maps that highlight occupied and available parking spaces, allowing for more efficient parking management and improved traffic flow. These methods contributed to improved urban mobility by providing real-time information to drivers about parking conditions along their routes. This not only enhanced convenience but also supported the development of smarter and more sustainable urban transportation solutions. Full article
(This article belongs to the Special Issue Automated Driving Systems: Latest Advances and Prospects)
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15 pages, 897 KB  
Article
Effects and Compensation of High-Speed Motion in ISAR Imaging
by Zhou Wu and Junfeng Wang
Electronics 2025, 14(20), 4118; https://doi.org/10.3390/electronics14204118 - 21 Oct 2025
Viewed by 212
Abstract
Traditional ISAR imaging algorithms are based on the “stop-and-go” assumption and lack theoretical analysis, accurate simulation, and effective compensation regarding the high-speed motion of the target or the platform. In response to this issue, a theoretical analysis of the high-speed motion of the [...] Read more.
Traditional ISAR imaging algorithms are based on the “stop-and-go” assumption and lack theoretical analysis, accurate simulation, and effective compensation regarding the high-speed motion of the target or the platform. In response to this issue, a theoretical analysis of the high-speed motion of the target or the platform in ISAR imaging is first conducted, indicating that when a chirp is transmitted, the echo from a scatterer can be approximated as a chirp with its central frequency and chirp rate changed, and this will lead to the shift and the blurring of the scatterer in range. A method is then proposed to estimate the central frequency and the chirp rate, which are used to adjust the central frequency and the chirp rate of the matched filter. The central frequency is estimated to maximize the normalized correlation of the amplitude spectrum and its nominal form, and the chirp rate is derived from the central frequency. Moreover, in order to show the rationality of our theoretical analysis and the effectiveness of our compensation method, a scheme is presented to simulate the received signal under the high-speed motion of the target or the platform. This scheme assumes that the target and the platform move continuously with time and reflects the effects of the high-speed motion on the received signal accurately. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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12 pages, 6063 KB  
Article
Prex-NetII: Attention-Based Back-Projection Network for Light Field Reconstruction
by Dong-Myung Kim and Jae-Won Suh
Electronics 2025, 14(20), 4117; https://doi.org/10.3390/electronics14204117 - 21 Oct 2025
Viewed by 216
Abstract
We propose an attention-based back-projection network that enhances light field reconstruction quality by modeling inter-view dependencies. The network uses pixel shuffle to efficiently extract initial features. Spatial attention focuses on important regions while capturing inter-view dependencies. Skip connections in the refinement network improve [...] Read more.
We propose an attention-based back-projection network that enhances light field reconstruction quality by modeling inter-view dependencies. The network uses pixel shuffle to efficiently extract initial features. Spatial attention focuses on important regions while capturing inter-view dependencies. Skip connections in the refinement network improve stability and reconstruction performance. In addition, channel attention within the projection blocks enhances structural representation across views. The proposed method reconstructs high-quality light field images not only in general scenes but also in complex scenes containing occlusions and reflections. The experimental results show that the proposed method outperforms existing approaches. Full article
(This article belongs to the Special Issue Image Processing Based on Convolution Neural Network: 2nd Edition)
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13 pages, 317 KB  
Article
Enhancing JPEG XL’s Weighted Average Predictor: Genetic Algorithm Optimization of Expanded Sub-Predictor Ensemble
by Xavier Hill Roy and Mahmoud R. El-Sakka
Electronics 2025, 14(20), 4116; https://doi.org/10.3390/electronics14204116 - 21 Oct 2025
Viewed by 253
Abstract
Lossless image compression relies heavily on prediction algorithms to reduce spatial redundancy before entropy coding. The JPEG XL standard employs a weighted average predictor that combines four sub-predictors with adaptive weighting; however, it uses fixed initial scaling factors regardless of the image content. [...] Read more.
Lossless image compression relies heavily on prediction algorithms to reduce spatial redundancy before entropy coding. The JPEG XL standard employs a weighted average predictor that combines four sub-predictors with adaptive weighting; however, it uses fixed initial scaling factors regardless of the image content. This study introduces WOP8 (weighted optimization predictor for 8 sub-predictors), which extends the predictor diversity and optimizes initial weights using a genetic algorithm. Four additional predictors were incorporated—adaptive MED (JPEG-LS), enhanced adaptive median, Paeth (PNG), and GAP-based (CALIC)—forming an eight-predictor ensemble. A genetic algorithm with a population of 30 and 24 generations optimized the weight configurations by minimizing the compressed file size of the training data. Experiments were conducted on the Kodak and Tecnick datasets to evaluate performance and generalizability. The Kodak color dataset showed notable gains: with the weighted average predictor in isolation, WOP8 achieved a 0.24 BPP reduction (2.7% improvement) at high effort levels. Under standard JPEG XL operation mode, improvements were minor but consistent. These results confirm the value of targeted predictor optimization and demonstrate that genetic algorithms can effectively discover dataset-specific weighting patterns, offering a foundation for future component-level enhancements in JPEG XL. Full article
(This article belongs to the Special Issue Artificial Intelligence in Graphics and Images)
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20 pages, 7704 KB  
Article
Seamless User-Generated Content Processing for Smart Media: Delivering QoE-Aware Live Media with YOLO-Based Bib Number Recognition
by Alberto del Rio, Álvaro Llorente, Sofia Ortiz-Arce, Maria Belesioti, George Pappas, Alejandro Muñiz, Luis M. Contreras and Dimitris Christopoulos
Electronics 2025, 14(20), 4115; https://doi.org/10.3390/electronics14204115 - 21 Oct 2025
Viewed by 314
Abstract
The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, [...] Read more.
The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow. The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%. Full article
(This article belongs to the Special Issue Recent Advances and Challenges in IoT, Cloud and Edge Coexistence, 2nd Edition)
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1 pages, 128 KB  
Retraction
RETRACTED: Pandian et al. Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis. Electronics 2022, 11, 2968
by M. Thurai Pandian, Kuldeep Chouhan, B. Muthu Kumar, Jatindra Kumar Dash, N. Z. Jhanjhi, Ashraf Osman Ibrahim and Anas W. Abulfaraj
Electronics 2025, 14(20), 4114; https://doi.org/10.3390/electronics14204114 - 21 Oct 2025
Viewed by 183
Abstract
The journal retracts the article “Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis” [...] Full article
24 pages, 1841 KB  
Article
A Framework for the Configuration and Operation of EV/FCEV Fast-Charging Stations Integrated with DERs Under Uncertainty
by Leon Fidele Nishimwe H., Kyung-Min Song and Sung-Guk Yoon
Electronics 2025, 14(20), 4113; https://doi.org/10.3390/electronics14204113 - 20 Oct 2025
Viewed by 260
Abstract
The integration of electric vehicles (EVs) and fuel-cell electric vehicles (FCEVs) requires accessible and profitable facilities for fast charging. To promote fast-charging stations (FCSs), a systematic analysis that encompasses both planning and operation is required, including the incorporation of multi-energy resources and uncertainty. [...] Read more.
The integration of electric vehicles (EVs) and fuel-cell electric vehicles (FCEVs) requires accessible and profitable facilities for fast charging. To promote fast-charging stations (FCSs), a systematic analysis that encompasses both planning and operation is required, including the incorporation of multi-energy resources and uncertainty. This paper presents an optimization framework that addresses a joint strategy for the configuration and operation of an EV/FCEV fast-charging station (FCS) integrated with distributed energy resources (DERs) and hydrogen systems. The framework incorporates uncertainties related to solar photovoltaic (PV) generation and demand for EVs/FCEVs. The proposed joint strategy comprises a four-phase decision-making framework. Phase 1 involves modeling EV/FECE demand, while Phase 2 focuses on determining an optimal long-term infrastructure configuration. Subsequently, in Phase 3, the operator optimizes daily power scheduling to maximize profit. A real-time uncertainty update is then executed in Phase 4 upon the realization of uncertainty. The proposed optimization framework, formulated as mixed-integer quadratic programming (MIQP), considers configuration investment, operational, maintenance, and penalty costs for excessive grid power usage. A heuristic algorithm is proposed to solve this problem. It yields good results with significantly less computational complexity. A case study shows that under the most adverse conditions, the proposed joint strategy increases the FCS owner’s profit by 3.32% compared with the deterministic benchmark. Full article
(This article belongs to the Special Issue Advanced Research in Technology and Information Systems, 2nd Edition)
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38 pages, 3954 KB  
Article
Geospatial Feature-Based Path Loss Prediction at 1800 MHz in Covenant University Campus with Tree Ensembles, Kernel-Based Methods, and a Shallow Neural Network
by Marta Moreno-Cuevas, José Lorente-López, José-Víctor Rodríguez, Ignacio Rodríguez-Rodríguez and Concepción Sanchis-Borrás
Electronics 2025, 14(20), 4112; https://doi.org/10.3390/electronics14204112 - 20 Oct 2025
Viewed by 312
Abstract
This paper investigates within-scene path loss prediction at 1.8 GHz in a smart-campus micro-urban environment using multivariate machine-learning (ML) models. We leverage an open measurement campaign from Covenant University (Nigeria) comprising three routes with per-sample geospatial predictors—longitude, latitude, altitude, elevation, Tx–Rx distance, and [...] Read more.
This paper investigates within-scene path loss prediction at 1.8 GHz in a smart-campus micro-urban environment using multivariate machine-learning (ML) models. We leverage an open measurement campaign from Covenant University (Nigeria) comprising three routes with per-sample geospatial predictors—longitude, latitude, altitude, elevation, Tx–Rx distance, and clutter height—and train Random Forests (RF), Gradient Boosting (GB), Support Vector Regression (SVR), Gaussian Processes (GP), and a shallow neural network (NN). A unified pipeline with 5-fold cross-validation (CV), seeded reproducibility, and Optuna-driven hyperparameter search is adopted; performance is reported as RMSE/MAE/R2 (mean ± sd). To contextualize feature reliability, we include Pearson correlation heatmaps and Variance Inflation Factors (VIFs), a systematic ablation of predictors, and TreeSHAP beeswarm analyses on held-out splits. We also evaluate spatially aware validation (blocked CV within route and leave-one-route-out checks) to mitigate optimism due to spatial autocorrelation. Results show that multivariate ML consistently outperforms classical empirical formulas (COST-231, ECC-33) in this campus setting, with RF achieving the lowest errors across routes (RMSE ≈ 2.14/2.16/2.95 dB for X/Y/Z, respectively), while GB ranks second and kernel methods (SVR/GP) and the NN trail closely behind. Ablation confirms that distance plus coordinates drive the largest gains, with terrain/clutter providing route-dependent refinements. SHAP analyses align with these findings, highlighting stable, interpretable contributions of geospatial covariates. Spatial CV increases absolute errors moderately but preserves model ranking, supporting the robustness of conclusions. Overall, scenario-aware, multivariate ML yields material accuracy gains for smart-campus planning at 1.8 GHz. Full article
(This article belongs to the Special Issue Artificial Intelligence in Radio Channel Modelling: Progress and Challenges)
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24 pages, 3990 KB  
Article
An Adaptive PID Controller for Longitudinal Velocity and Yaw Rate Tracking of Autonomous Mobility Based on RLS with Multiple Constraints
by Jeongwoo Lee and Kwangseok Oh
Electronics 2025, 14(20), 4111; https://doi.org/10.3390/electronics14204111 - 20 Oct 2025
Viewed by 283
Abstract
Recently, various forms and purposes of autonomous mobility have been widely developed and commercialized. To control the various iterations of shaped and purposeful mobility, control technology that can adapt to the dynamic characteristics of the mobility and environmental changes is essential. This study [...] Read more.
Recently, various forms and purposes of autonomous mobility have been widely developed and commercialized. To control the various iterations of shaped and purposeful mobility, control technology that can adapt to the dynamic characteristics of the mobility and environmental changes is essential. This study presents an adaptive proportional–integral–derivative (PID) controller for longitudinal velocity and yaw rate tracking in autonomous mobility, addressing the aforementioned issue. To design the adaptive PID controller, error dynamics have been designed using error and control input with two coefficients. It is designed that the two coefficients are estimated in real time by recursive least squares with multiple constraints and forgetting factors. The estimated coefficients are used to compute PI gains based on the Lyapunov direct method with constant derivative gain. Multiple constraints, such as value and rate limits, have been incorporated into the RLS algorithm to enhance the control stability. The performance evaluation is conducted through the co-simulation of MATLAB/Simulink and CarMaker under integrated control scenarios, such as longitudinal velocity and yaw rate tracking, for mobility. Full article
(This article belongs to the Special Issue Automated Driving Systems: Latest Advances and Prospects)
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23 pages, 1540 KB  
Article
A Hierarchical Step-by-Step Multi-Objective Genetic Optimization for Multi-Port Composite Flux-Modulated Machines
by Zheng Cai, Jincheng Yu, Fei Zhao and Yixiao Luo
Electronics 2025, 14(20), 4110; https://doi.org/10.3390/electronics14204110 - 20 Oct 2025
Viewed by 167
Abstract
This paper presents a hierarchical and step-by-step multi-objective genetic optimization method for the multi-port composite flux-modulated (MP-CFM) machine, aiming to propose a simpler and high-accuracy optimization strategy for such multi-port composite machines. As a specialized machine well-suited for hybrid power systems, the optimization [...] Read more.
This paper presents a hierarchical and step-by-step multi-objective genetic optimization method for the multi-port composite flux-modulated (MP-CFM) machine, aiming to propose a simpler and high-accuracy optimization strategy for such multi-port composite machines. As a specialized machine well-suited for hybrid power systems, the optimization design is innovatively conducted based on an analysis of the fundamental operating principles and working modes of the MP-CFM machines. Specifically, considering the complex structure of such composite machines, sensitivity analysis is employed, applying differentiated strategies based on parameter sensitiveness evaluation. Furthermore, to ensure the rationality of the optimization results, and also to reduce computational cost and improve convergence, the optimization is artfully developed hierarchically with multi-steps, in accordance with the multi-modes of the machine. Specific optimization objectives and variables are defined, respectively, for each mode to enhance the optimization efficiency. Finite element analysis results demonstrate the effectiveness of the proposed optimization strategy for such MF-CFM machines for hybrid power system applications. Full article
(This article belongs to the Special Issue Advances in Permanent Magnet Synchronous Machines: Design, Control, and Applications)
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29 pages, 1671 KB  
Article
Towards Secure Legacy Manufacturing: A Policy-Driven Zero Trust Architecture Aligned with NIST CSF 2.0
by Cheon-Ho Min, Deuk-Hun Kim, Haomiao Yang and Jin Kwak
Electronics 2025, 14(20), 4109; https://doi.org/10.3390/electronics14204109 - 20 Oct 2025
Viewed by 389
Abstract
As smart manufacturing environments continue to evolve, operational technology systems are increasingly integrated with external networks and cloud-based platforms. However, many manufacturing facilities still use legacy systems running on end-of-support/life operating systems with discontinued security updates. It is difficult to mitigate the cyber [...] Read more.
As smart manufacturing environments continue to evolve, operational technology systems are increasingly integrated with external networks and cloud-based platforms. However, many manufacturing facilities still use legacy systems running on end-of-support/life operating systems with discontinued security updates. It is difficult to mitigate the cyber threats and risks for these systems using perimeter-based security models that isolate them from other networks. To address these constraints, a Zero Trust-based security architecture tailored for legacy manufacturing environments with practical field applicability is proposed. Our architecture builds upon the six core functions outlined in National Institute of Standards and Technology Cybersecurity Framework 2.0—identify, protect, detect, respond, recover, and govern—adapting them specifically to manufacturing environment security challenges. To achieve this, the architecture combines asset identification, policy-driven access control, secure SMB gateway transfers, automated anomaly detection and response, clean image recovery, and organizational governance procedures. This study validates the effectiveness and scalability of the proposed architecture through scenario-based simulations. When combining the EoSL defense hardening and gateway-based perimeter control, the architecture achieves approximately 99% overall threat suppression and a 98% reduction in critical-asset infection rates, demonstrating its strong resilience and scalability in large-scale legacy OT environments. Full article
(This article belongs to the Special Issue Industrial Process Control and Flexible Manufacturing Systems)
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21 pages, 1266 KB  
Article
A Novel Gating Adversarial Imputation Method for High-Fidelity Restoration of Missing Electrical Disturbance Data
by Lidan Chen, Guangxu Feng and Lei Wang
Electronics 2025, 14(20), 4108; https://doi.org/10.3390/electronics14204108 - 20 Oct 2025
Viewed by 195
Abstract
The ongoing evolution of cyber-physical power systems renders them susceptible to frequent and multifaceted electrical disturbances. Critically, missingness resulting from cascading cyber-physical failures severely impedes the ability to accurately monitor and diagnose these electrical disturbances. To address this serious challenge, this paper proposes [...] Read more.
The ongoing evolution of cyber-physical power systems renders them susceptible to frequent and multifaceted electrical disturbances. Critically, missingness resulting from cascading cyber-physical failures severely impedes the ability to accurately monitor and diagnose these electrical disturbances. To address this serious challenge, this paper proposes a novel gating adversarial imputation (GAI) framework specially tailored for the high-fidelity restoration of missing electrical disturbance data. The proposed GAI efficiently introduces the latest gating mechanism into a stability-improved adversarial imputation process, enabling robust feature representation while maintaining high imputation accuracy. To validate its efficacy, a synthetic dataset encompassing 15 distinct disturbance types is constructed based on precise mathematical equations and standard missingness. A comprehensive experimental evaluation demonstrates that the proposed GAI consistently outperforms five representative imputation benchmarks across all tested missing percentages. Moreover, GAI effectively preserves the original critical characteristics during data recovery, thereby enhancing accurate system monitoring and operational security. Full article
(This article belongs to the Special Issue Cyber-Physical System Applications in Smart Power and Microgrids)
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26 pages, 11507 KB  
Article
PLD-DETR: A Method for Defect Inspection of Power Transmission Lines
by Jianing Chen, Xin Zhang, Dawei Feng, Jiahao Li and Liang Zhu
Electronics 2025, 14(20), 4107; https://doi.org/10.3390/electronics14204107 - 20 Oct 2025
Viewed by 370
Abstract
Unmanned Aerial Vehicle (UAV)-based computer vision has emerged as a crucial approach for transmission line defect detection. However, transmission lines contain multi-scale components in complex environments, thereby complicating the accurate extraction of multi-scale features and necessitating a careful balance between model complexity with [...] Read more.
Unmanned Aerial Vehicle (UAV)-based computer vision has emerged as a crucial approach for transmission line defect detection. However, transmission lines contain multi-scale components in complex environments, thereby complicating the accurate extraction of multi-scale features and necessitating a careful balance between model complexity with detection accuracy. This paper proposes a Transformer-based framework called Power Line Defect Detection Transformer (PLD-DETR). To simultaneously capture shallow texture and deep semantic information while avoiding single-path limitations, a dual-domain selection mechanism block is designed as the backbone network, enabling collaborative feature extraction at different levels. Subsequently, an adaptive sparse self-attention mechanism is introduced to dynamically adjust attention weights for improved processing of critical feature regions, aiming to enhance attention to semantically rich regions and reduce background interference. Finally, we construct a multi-branch auxiliary bidirectional feature pyramid network to address information loss in traditional feature fusion. It fuses multi-scale features from four backbone layers through top-down and bottom-up bidirectional information flow, significantly improving feature representation capability. While maintaining model lightness, experimental results demonstrate that PLD-DETR achieves 2.7%, 7.01%, and 5.58% improvements in AP50, AP75, and AP50–95, respectively, compared to the baseline model. Compared with other transmission line defect detection methods, PLD-DETR demonstrates superior performance in both accuracy and efficiency Full article
(This article belongs to the Special Issue AI Applications for Smart Grid)
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23 pages, 884 KB  
Article
Large Language Models for Structured Information Processing in Construction and Facility Management
by Kyrylo Buga, Ratko Tesic, Elif Koyuncu and Thomas Hanne
Electronics 2025, 14(20), 4106; https://doi.org/10.3390/electronics14204106 - 20 Oct 2025
Viewed by 292
Abstract
This study examines how the integration of structured information affects the performance of large language models (LLMs) in the context of facility management. The aim is to determine to what extent structured data such as maintenance schedules, room information, and asset inventories can [...] Read more.
This study examines how the integration of structured information affects the performance of large language models (LLMs) in the context of facility management. The aim is to determine to what extent structured data such as maintenance schedules, room information, and asset inventories can improve the accuracy, correctness, and contextual relevance of LLM-generated responses. We focused on scenarios involving function calling of a database with building information. Three use cases were developed to reflect different combinations of structured and unstructured input and output. The research follows a design science methodology and includes the implementation of a modular testing prototype, incorporating empirical experiments using various LLMs (Gemini, Llama, Qwen, and Mistral). The evaluation pipeline consists of three steps: user query translation (natural language into SQL), query execution, and final response (translating the SQL query results into natural language). The evaluation was based on defined criteria such as SQL execution validity, semantic correctness, contextual relevance, and hallucination rate. The study found that the use cases involving function calling are mostly successful. The execution validity improved up to 67% when schema information is provided. Full article
(This article belongs to the Special Issue Deep Learning Approaches for Natural Language Processing)
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23 pages, 1684 KB  
Article
Method of Accelerated Low-Frequency Oscillation Analysis in Low-Inertia Power Systems Based on Orthogonal Decomposition
by Mihail Senyuk, Svetlana Beryozkina, Ismoil Odinaev, Inga Zicmane and Murodbek Safaraliev
Electronics 2025, 14(20), 4105; https://doi.org/10.3390/electronics14204105 - 20 Oct 2025
Viewed by 261
Abstract
The peculiarity of the functioning of modern electric power systems, caused by the presence of renewable energy sources, flexible control devices based on power electronics, and the reduction of the reserve of the transmission capacity of the electric network, increases the relevance of [...] Read more.
The peculiarity of the functioning of modern electric power systems, caused by the presence of renewable energy sources, flexible control devices based on power electronics, and the reduction of the reserve of the transmission capacity of the electric network, increases the relevance of identifying and damping low-frequency oscillations (LFOs) of the electrical mode. This paper presents a comparative analysis of methods for estimating the parameters of low-frequency oscillations. Their applicability limits are shown as well as their peculiarity associated with low adaptability, and time costs in assessing the parameters of the electrical mode with low-frequency oscillations are revealed. A method for the accelerated evaluation of low-frequency oscillation parameters is proposed, the delay of which is ¼ of the oscillation cycle. The method was tested on both synthetic and physical signals. In the first case, the source of data was a four-machine mathematical model of a power system. In the second case, signals of transient processes occurring in a real power system were used as physical data. The accuracy of the proposed method was obtained by calculating the difference between the original and reconstructed signals. As a result, calculated error values were obtained, describing the accuracy and efficiency of the proposed method. The proposed algorithm for estimating LFO parameters displayed an error value not exceeding 0.8% for both synthetic and physical data. Full article
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22 pages, 1749 KB  
Review
How to Conduct AI-Assisted (Large Language Model-Assisted) Content Analysis in Information Science and Cyber Security Research
by Monica Therese Whitty
Electronics 2025, 14(20), 4104; https://doi.org/10.3390/electronics14204104 - 20 Oct 2025
Viewed by 503
Abstract
The advent of Large Language Models (LLMs) has revolutionised natural language processing, providing unprecedented capabilities in text generation and analysis. This paper examines the utility of Artificial-Intelligence-assisted (AI-assisted) content analysis (CA), supported by LLMs, as a methodological tool for research in Information Science [...] Read more.
The advent of Large Language Models (LLMs) has revolutionised natural language processing, providing unprecedented capabilities in text generation and analysis. This paper examines the utility of Artificial-Intelligence-assisted (AI-assisted) content analysis (CA), supported by LLMs, as a methodological tool for research in Information Science (IS) and Cyber Security. It reviews current applications, methodological practices, and challenges, illustrating how LLMs can augment traditional approaches to qualitative data analysis. Key distinctions between CA and other qualitative methods are outlined, alongside the traditional steps involved in CA. To demonstrate relevance, examples from Information Science and Cyber Security are highlighted, along with a new example detailing the steps involved. A hybrid workflow is proposed that integrates human oversight with AI capabilities, grounded in the principles of Responsible AI. Within this model, human researchers remain central to guiding research design, interpretation, and ethical decision-making, while LLMs support efficiency and scalability. Both deductive and inductive AI-assisted frameworks are introduced. Overall, AI-assisted CA is presented as a valuable approach for advancing rigorous, replicable, and ethical scholarship in Information Science and Cyber Security. This paper contributes to prior LLM-assisted coding work, proposing that this hybrid model is preferred over a fully manual content analysis. Full article
(This article belongs to the Special Issue Trends in Information Systems and Security)
17 pages, 2166 KB  
Article
Blind Separation and Feature-Guided Modulation Recognition for Single-Channel Mixed Signals
by Zhiping Tan, Tianhui Fu, Xi Wu and Yixin Zhu
Electronics 2025, 14(20), 4103; https://doi.org/10.3390/electronics14204103 - 20 Oct 2025
Viewed by 366
Abstract
With increasingly scarce spectrum resources, frequency-domain signal overlap interference has become a critical issue, making multi-user modulation classification (MUMC) a significant challenge in wireless communications. Unlike single-user modulation classification (SUMC), MUMC suffers from feature degradation caused by signal aliasing, feature redundancy, and low [...] Read more.
With increasingly scarce spectrum resources, frequency-domain signal overlap interference has become a critical issue, making multi-user modulation classification (MUMC) a significant challenge in wireless communications. Unlike single-user modulation classification (SUMC), MUMC suffers from feature degradation caused by signal aliasing, feature redundancy, and low inter-class discriminability. To address these challenges, this paper proposes a collaborative “separation–recognition” framework. The framework begins by separating overlapping signals via a band partitioning and FastICA module to alleviate feature degradation. For the recognition phase, we design a dual-branch network: one branch extracts prior knowledge features, including amplitude, phase, and frequency, from the I/Q sequence and models their temporal dependencies using a bidirectional LSTM; the other branch learns deep hierarchical representations directly from the raw signal through multi-scale convolutional layers. The features from both branches are then adaptively fused using a gated fusion module. Experimental results show that the proposed method achieves superior performance over several baseline models across various signal conditions, validating the efficacy of the dual-branch architecture and the overall framework. Full article
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