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Volume 13
Issue 18
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Electronics, Volume 13, Issue 18 (September-2 2024) – 205 articles

Cover Story (view full-size image): This research presents a methodology for indoor positioning using Visible Light Communication (VLC), addressing the limitations of traditional satellite-based systems. It integrates trilateration with a semi-spherical array of photodiodes to enhance positional accuracy and orientation estimation. Using multiple LEDs as transmitters, the system achieves an average position error of below 3 cm and an angular accuracy within 10 degrees, demonstrating robustness even with an obstructed line-of-sight. These results show the potential for improving indoor positioning accuracy and reliability. View this paper
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22 pages, 494 KiB  
Article
Vehicle Trajectory Prediction Based on Adaptive Edge Generation
by He Ren and Yanyan Zhang
Electronics 2024, 13(18), 3787; https://doi.org/10.3390/electronics13183787 - 23 Sep 2024
Viewed by 939
Abstract
With the rapid evolution of intelligent driving technology, vehicle trajectory prediction has become a pivotal technique for enhancing road safety and traffic efficiency. In this domain, high-definition vector maps and graph neural networks (GNNs) play a vital role, supporting precise vehicle positioning and [...] Read more.
With the rapid evolution of intelligent driving technology, vehicle trajectory prediction has become a pivotal technique for enhancing road safety and traffic efficiency. In this domain, high-definition vector maps and graph neural networks (GNNs) play a vital role, supporting precise vehicle positioning and optimizing path planning, thereby improving the performance of intelligent driving systems. However, high-definition vector maps and traditional GNNs still encounter several challenges in trajectory prediction, such as high computational resource demands, long training times, and limited modeling capabilities for dynamic traffic environments and complex interactions. To address these challenges, this paper proposes an adaptive edge generator method, this method dynamically constructs and optimizes the connections between nodes in the GNN architecture, effectively enhancing the accuracy and efficiency of trajectory prediction. Specifically, we classify nodes into dynamic and static nodes based on their attributes, and devise differentiated edge construction strategies accordingly. For dynamic nodes, we introduce a relative angle factor, enabling the attention model to comprehensively consider the distance and intersection status between nodes, resulting in more accurate computation of edge weights. For static nodes, we utilize a length threshold to assess the feasibility of establishing connections between vehicles and lane lines, determining whether a connection should be established. Through this approach, we successfully reduce the algorithmic complexity, increase computational speed, and maintain high trajectory prediction accuracy. Tests on the Argoverse motion prediction dataset demonstrate that trajectory prediction utilizing the adaptive edge generator achieves an average displacement error (ADE) of 0.6681, a final displacement error (FDE) of 0.9864, and a miss rate (MR) of 0.0952. Furthermore, the model parameters are significantly reduced, validating the effectiveness of the proposed vehicle trajectory prediction method based on the adaptive edge generator. Full article
(This article belongs to the Section Artificial Intelligence)
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23 pages, 1115 KiB  
Article
A Smart Contract Vulnerability Detection Method Based on Heterogeneous Contract Semantic Graphs and Pre-Training Techniques
by Jie Zhang, Gehao Lu and Jia Yu
Electronics 2024, 13(18), 3786; https://doi.org/10.3390/electronics13183786 - 23 Sep 2024
Viewed by 874
Abstract
The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic [...] Read more.
The use of smart contracts in areas such as finance, supply chain management, and the Internet of Things has significantly advanced blockchain technology. However, once deployed on the blockchain, smart contracts cannot be modified or revoked. Any vulnerabilities can lead to severe economic losses and data breaches, making pre-deployment vulnerability detection critically important. Traditional smart contract vulnerability detection methods suffer from low accuracy and limited reusability across different scenarios. To enhance detection capabilities, this paper proposes a smart contract vulnerability detection method based on heterogeneous contract semantic graphs and pre-training techniques. Compared to the conventional graph structures used in existing methods, heterogeneous contract semantic graphs contain richer contract information. By integrating these with pre-trained models, our method exhibits stronger vulnerability capture and generalization capabilities. Experimental results show that this method has improved the accuracy, recall, precision, and F1 value in the detection of four widely existing and harmful smart contract vulnerabilities compared with existing methods, which greatly improves the detection ability of smart contract vulnerabilities. Full article
(This article belongs to the Special Issue Security, Privacy, Confidentiality and Trust in Blockchain)
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12 pages, 4318 KiB  
Article
Contact State Recognition for Dual Peg-in-Hole Assembly of Tightly Coupled Dual Manipulator
by Jiawei Zhang, Chengchao Bai, Jifeng Guo, Zhengai Cheng and Ying Chen
Electronics 2024, 13(18), 3785; https://doi.org/10.3390/electronics13183785 - 23 Sep 2024
Viewed by 671
Abstract
Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes [...] Read more.
Contact state recognition is a critical technology for enhancing the robustness of robotic assembly tasks. There have been many studies on contact state recognition for single-manipulator, single peg-in-hole assembly tasks. However, as the number of pegs and holes increases, the contact state becomes significantly more complex. Additionally, when a tightly coupled multi-manipulator is required, the estimation errors in the contact forces between pegs and holes make contact state recognition challenging. The current state recognition methods have not been tested in such tasks. This paper tested Support Vector Machine (SVM) and several neural network models on these tasks and analyzed the recognition accuracy, precision, recall, and F1 score. An ablation experiment was carried out to test the contributions of force, image, and position to the recognition performance. The experimental results show that SVM has better performance than the neural network models. However, when the size of the dataset is limited, SVM still faces generalization issues. By applying heuristic action, this paper proposes a two-stage recognition strategy that can improve the recognition success rate of the SVM. Full article
(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))
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14 pages, 8502 KiB  
Article
Dual-Branch Colorization Network for Unpaired Infrared Images Based on High-Level Semantic Features and Multiscale Residual Attention
by Tong Jiang, Junqi Bai, Lin Xiao, Tingting Liu, Xiaodong Kuang, Yuan Liu, Xiubao Sui and Qian Chen
Electronics 2024, 13(18), 3784; https://doi.org/10.3390/electronics13183784 - 23 Sep 2024
Viewed by 659
Abstract
The infrared image colorization technique overcomes the limitation of grayscale characteristics of infrared images and achieves cross-modal conversion between infrared and visible images. Aiming at the problem of lack of infrared-visible pairing data, existing studies usually adopt unsupervised learning methods based on contrastive [...] Read more.
The infrared image colorization technique overcomes the limitation of grayscale characteristics of infrared images and achieves cross-modal conversion between infrared and visible images. Aiming at the problem of lack of infrared-visible pairing data, existing studies usually adopt unsupervised learning methods based on contrastive loss. Due to significant differences between modalities, reliance on contrastive loss alone hampers the learning of accurate semantic features. In this paper, we propose DC-Net, which is a dual-branch contrastive learning network that combines perceptual features and multiscale residual attention for the unsupervised cross-modal transformation of infrared to visible images. The network comprises a patch-wise contrastive guidance branch (PwCGB) and a perceptual contrastive guidance branch (PCGB). PwCGB focuses on discerning feature similarities and variances across image patches, synergizing patch-wise contrastive loss with adversarial loss to adaptively learn local structure and texture. In addition, we design a multiscale residual attention generator to capture richer features and adaptively integrate multiscale information. PCGB introduces a novel perceptual contrastive loss that uses perceptual features from pre-trained VGG16 models as positive and negative samples. This helps the network align colorized infrared images with visible images in the high-level feature space, improving the semantic accuracy of the colorized infrared images. Our unsupervised infrared image colorization method achieves a PSNR of 16.833 and an SSIM of 0.584 on the thermal infrared dataset and a PSNR of 18.828 and an SSIM of 0.685 on the near-infrared dataset. Compared to existing algorithms, it demonstrates substantial improvements across all metrics, validating its effectiveness. Full article
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18 pages, 3587 KiB  
Article
Experimental Design of Steel Surface Defect Detection Based on MSFE-YOLO—An Improved YOLOV5 Algorithm with Multi-Scale Feature Extraction
by Lin Li, Ruopeng Zhang, Tunjun Xie, Yushan He, Hao Zhou and Yongzhong Zhang
Electronics 2024, 13(18), 3783; https://doi.org/10.3390/electronics13183783 - 23 Sep 2024
Viewed by 1177
Abstract
Integrating artificial intelligence (AI) technology into student training programs is strategically crucial for developing future professionals with both forward-thinking capabilities and practical skills. This paper uses steel surface defect detection as a case study to propose a simulation-based teaching method grounded in deep [...] Read more.
Integrating artificial intelligence (AI) technology into student training programs is strategically crucial for developing future professionals with both forward-thinking capabilities and practical skills. This paper uses steel surface defect detection as a case study to propose a simulation-based teaching method grounded in deep learning. The method encompasses the entire process from data preprocessing and model training to validation analysis and innovation optimization with the goal of deepening students’ understanding of AI technology and enhancing their ability to apply it to real-world scenarios. We have designed an experimental framework that incorporates the Efficient Multi-Scale Attention (EMA) mechanism into the Backbone network. This approach helps students understand the principles of feature extraction and the core functions of attention mechanisms. Additionally, we introduced a novel architecture—Convolution 3 Dilated Convolution X (C3DX)—into the Neck network. This architecture effectively expands the network’s receptive field, improves its ability to capture multi-scale information, and thus enhances defect detection accuracy. Furthermore, the implementation of the Efficient Intersection over Union (EIoU) loss function optimizes the bounding box predictions, further increasing the model’s accuracy and robustness. Overall, the teaching design not only ensures that the content remains at the cutting edge of technology but also emphasizes its practicality and operability. This approach enables students to effectively apply theoretical knowledge to real-world engineering projects. Full article
(This article belongs to the Section Artificial Intelligence)
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20 pages, 10701 KiB  
Article
Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study
by Dilliraj Ekambaram and Vijayakumar Ponnusamy
Electronics 2024, 13(18), 3782; https://doi.org/10.3390/electronics13183782 - 23 Sep 2024
Viewed by 1091
Abstract
In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately [...] Read more.
In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual’s overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system’s usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna .v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients’ engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts. Full article
(This article belongs to the Section Bioelectronics)
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23 pages, 3964 KiB  
Article
Geometry of Textual Data Augmentation: Insights from Large Language Models
by Sherry J. H. Feng, Edmund M-K. Lai and Weihua Li
Electronics 2024, 13(18), 3781; https://doi.org/10.3390/electronics13183781 - 23 Sep 2024
Viewed by 1131
Abstract
Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for [...] Read more.
Data augmentation is crucial for enhancing the performance of text classification models when labelled training data are scarce. For natural language processing (NLP) tasks, large language models (LLMs) are able to generate high-quality augmented data. But a fundamental understanding of the reasons for their effectiveness remains limited. This paper presents a geometric and topological perspective on textual data augmentation using LLMs. We compare the augmentation data generated by GPT-J with those generated through cosine similarity from Word2Vec and GloVe embeddings. Topological data analysis reveals that GPT-J generated data maintains label coherence. Convex hull analysis of such data represented by their two principal components shows that they lie within the spatial boundaries of the original training data. Delaunay triangulation reveals that increasing the number of augmented data points that are connected within these boundaries correlates with improved classification accuracy. These findings provide insights into the superior performance of LLMs in data augmentation. A framework for predicting the usefulness of augmentation data based on geometric properties could be formed based on these techniques. Full article
(This article belongs to the Special Issue Emerging Theory and Applications in Natural Language Processing)
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27 pages, 12821 KiB  
Article
FishDet-YOLO: Enhanced Underwater Fish Detection with Richer Gradient Flow and Long-Range Dependency Capture through Mamba-C2f
by Chen Yang, Jian Xiang, Xiaoyong Li and Yunjie Xie
Electronics 2024, 13(18), 3780; https://doi.org/10.3390/electronics13183780 - 23 Sep 2024
Viewed by 1198
Abstract
The fish detection task is an essential component of marine exploration, which helps scientists monitor fish population numbers and diversity and understand changes in fish behavior and habitat. It also plays a significant role in assessing the health of marine ecosystems, formulating conservation [...] Read more.
The fish detection task is an essential component of marine exploration, which helps scientists monitor fish population numbers and diversity and understand changes in fish behavior and habitat. It also plays a significant role in assessing the health of marine ecosystems, formulating conservation measures, and maintaining biodiversity. However, there are two main issues with current fish detection algorithms. First, the lighting conditions underwater are significantly different from those on land. In addition, light scattering and absorption in water trigger uneven illumination, color distortion, and reduced contrast in images. The accuracy of detection algorithms can be affected by these lighting variations. Second, the wide variation of fish species in shape, color, and size brings about some challenges. As some fish have complex textures or camouflage features, it is difficult to differentiate them using current detection algorithms. To address these issues, we propose a fish detection algorithm—FishDet-YOLO—through improvement in the YOLOv8 algorithm. To tackle the complexities of underwater environments, we design an Underwater Enhancement Module network (UEM) that can be jointly trained with YOLO. The UEM enhances the details of underwater images via end-to-end training with YOLO. To address the diversity of fish species, we leverage the Mamba model’s capability for long-distance dependencies without increasing computational complexity and integrate it with the C2f from YOLOv8 to create the Mamba-C2f. Through this design, the adaptability in handling complex fish detection tasks is improved. In addition, the RUOD and DUO public datasets are used to train and evaluate FishDet-YOLO. FishDet-YOLO achieves mAP scores of 89.5% and 88.8% on the test sets of RUOD and DUO, respectively, marking an improvement of 8% and 8.2% over YOLOv8. It also surpasses recent state-of-the-art general object detection and underwater fish detection algorithms. Full article
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10 pages, 217 KiB  
Editorial
Applications of Computer Vision, 2nd Edition
by Eva Cernadas
Electronics 2024, 13(18), 3779; https://doi.org/10.3390/electronics13183779 - 23 Sep 2024
Viewed by 784
Abstract
Computer vision (CV) is a broad term mainly used to refer to processing image and video data [...] Full article
(This article belongs to the Special Issue Applications of Computer Vision, 2nd Edition)
23 pages, 7033 KiB  
Article
Diagnosis of DC-DC Converter Semiconductor Faults Based on the Second-Order Derivative of the Converter Input Current
by Fernando Bento and Antonio J. Marques Cardoso
Electronics 2024, 13(18), 3778; https://doi.org/10.3390/electronics13183778 - 23 Sep 2024
Viewed by 647
Abstract
The deployment of DC microgrids presents an excellent opportunity to enhance energy efficiency in buildings. Among other components, DC-DC converters play a crucial role in ensuring the interface between the microgrid and its energy generation, storage, and consumption components. However, the reliability of [...] Read more.
The deployment of DC microgrids presents an excellent opportunity to enhance energy efficiency in buildings. Among other components, DC-DC converters play a crucial role in ensuring the interface between the microgrid and its energy generation, storage, and consumption components. However, the reliability of these energy conversion solutions remains somewhat limited. Adopting strategies for accurate monitoring and diagnostics of the DC-DC converter topologies that best suit each equipment’s constraints is, therefore, of critical relevance. Solutions available in the literature concerning fault diagnostics on DC-DC converters do not consider the application of such converters in the household and tertiary sector environments and associated constraints—cost effectiveness, robustness against parameter uncertainty of the converter model, and obviation of the need for historical data. On this basis, this paper presents a simple and effective fault diagnostic strategy, based on a time-domain analysis of the second-order derivative of the converter input current. Its implementation is straightforward and can be integrated into the pre-installed converter control unit. The unique features of the fault diagnostic algorithm show good results for a broad range of operating points, along with insensitivity against load transients and supply voltage fluctuations. Full article
(This article belongs to the Section Industrial Electronics)
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19 pages, 3061 KiB  
Article
Improved Control Strategy for Dual-PWM Converter Based on Equivalent Input Disturbance
by Zixin Huang, Wei Wang, Chengsong Yu and Junjie Lu
Electronics 2024, 13(18), 3777; https://doi.org/10.3390/electronics13183777 - 23 Sep 2024
Viewed by 660
Abstract
Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based [...] Read more.
Aiming at the problems of jittering waveforms and poor power quality caused by external disturbances during the operation of a dual-pulse-width-modulation (PWM) converter, an improved terminal sliding mode control and an improved active disturbance rejection control (ADRC) are investigated. The method is based on mathematical models of grid-side and machine-side converters to design the controllers separately, and the balance between the two sides is maintained by the capacitor voltage. An improved terminal fuzzy sliding mode control and equivalent input disturbance (EID)-error-estimation-based active disturbance rejection control are presented on the grid side to improve the voltage response rate, and an improved support vector modulation (SVM)–direct torque control (DTC)–ADRC method is developed on the motor side to improve the robustness against disturbances. Finally, theoretical simulation experiments are built in MATLAB R2023a/Simulink to verify the effectiveness and superiority of this method. Full article
(This article belongs to the Special Issue Advanced Control Strategies and Applications of Multi-Agent Systems)
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12 pages, 570 KiB  
Article
Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables
by Hesham Omran
Electronics 2024, 13(18), 3776; https://doi.org/10.3390/electronics13183776 - 23 Sep 2024
Viewed by 1738
Abstract
Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that [...] Read more.
Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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15 pages, 3502 KiB  
Article
Evaluation of Haptic Textures for Tangible Interfaces for the Tactile Internet
by Nikolaos Tzimos, George Voutsakelis, Sotirios Kontogiannis and Georgios Kokkonis
Electronics 2024, 13(18), 3775; https://doi.org/10.3390/electronics13183775 - 23 Sep 2024
Viewed by 767
Abstract
Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information [...] Read more.
Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures. Full article
(This article belongs to the Section Computer Science & Engineering)
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9 pages, 2681 KiB  
Communication
A 28/56 Gb/s NRZ/PAM-4 Dual-Mode Transmitter with Eye-Opening Enhancement in 28 nm CMOS
by Jonghyeok Won and Jintae Kim
Electronics 2024, 13(18), 3774; https://doi.org/10.3390/electronics13183774 - 23 Sep 2024
Viewed by 721
Abstract
This paper presents a non-return-to-zero (NRZ)/4-level pulse amplitude modulation (PAM-4) dual-mode wireline transmitter with an eye-opening enhancement technique to improve horizontal eye-opening. With the eye-enhancement pulse generator and the auxiliary pull-up device in the tail-less current-mode driver, the worst-case horizontal eye-opening increased by [...] Read more.
This paper presents a non-return-to-zero (NRZ)/4-level pulse amplitude modulation (PAM-4) dual-mode wireline transmitter with an eye-opening enhancement technique to improve horizontal eye-opening. With the eye-enhancement pulse generator and the auxiliary pull-up device in the tail-less current-mode driver, the worst-case horizontal eye-opening increased by 30% in the PAM-4 eye diagram. The power efficiency of the NRZ mode was also improved by completely turning off the LSB path in the differential data path, resulting in only a 31% power efficiency degradation, which is far lower than that of the prior dual-mode transmitters. Fabricated in 28 nm CMOS, the transmitter achieves power efficiency of 1.4 pJ/bit at 56 Gb/s in PAM-4 mode and 1.84 pJ/bit at 28 Gb/s in NRZ mode, respectively. Full article
(This article belongs to the Section Microelectronics)
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12 pages, 3199 KiB  
Article
Construction and Explanation Analysis of a Hypotension Risk Prediction Model in Hemodialysis Based on Machine Learning
by Mingwei Zhang and Tianyi Zhang
Electronics 2024, 13(18), 3773; https://doi.org/10.3390/electronics13183773 - 23 Sep 2024
Viewed by 720
Abstract
Objective. To establish a risk prediction model for intradialytic hypotension (IDH) in maintenance hemodialysis (MHD) patients and to analyze the explainability of the risk prediction model. Methods. A total of 2,228,650 hemodialysis records of 1075 MHD patients were selected as the research objects. [...] Read more.
Objective. To establish a risk prediction model for intradialytic hypotension (IDH) in maintenance hemodialysis (MHD) patients and to analyze the explainability of the risk prediction model. Methods. A total of 2,228,650 hemodialysis records of 1075 MHD patients were selected as the research objects. Thirteen important clinical features including demographic features and clinical features were screened, the blood pressure measured before hemodialysis was collected, then an IDH risk prediction model during hemodialysis was established based on a machine learning algorithm. The contribution of each feature to the risk prediction of IDH was measured based on the Gini evaluation index. The TreeSHAP method was used to provide global and individual explanations for the IDH risk prediction model. Results. Hemodialysis duration, pre-dialysis mean arterial pressure, and pre-dialysis systolic blood pressure were the most important predictive variables for the occurrence of IDH during hemodialysis in MHD patients. The best IDH risk prediction model based on machine learning had an accuracy of 0.92 (95% CI 0.90–0.94) and an AUC of 0.95 (95% CI 0.94–0.96), indicating that machine learning has a good effect on the prediction of IDH during hemodialysis treatment. Our research innovatively achieved IDH risk prediction during the entire hemodialysis period based on blood pressure before the start of hemodialysis and other clinical features, thus enabling the medical team to quickly adjust hemodialysis prescriptions or initiate treatment for timely management and prevention of IDH. Global and individual explanations of the IDH risk prediction model can help hemodialysis medical staff understand the overall prediction mechanism of the model, discover prediction outliers, and identify potential biases or errors in the model. Conclusions. The IDH risk prediction model has definite clinical value in actual hemodialysis treatment. Full article
(This article belongs to the Section Bioelectronics)
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14 pages, 5427 KiB  
Article
Eddy Current Mechanism Model for Dynamic Magnetic Field in Ferromagnetic Metal Structures
by Chao Zuo, Zhipeng Lai, Zuoshuai Wang, Jianxun Wang, Hanchen Xiao, Wentie Yang, Pan Geng and Meng Chen
Electronics 2024, 13(18), 3772; https://doi.org/10.3390/electronics13183772 - 23 Sep 2024
Viewed by 683
Abstract
The degaussing process is crucial for ensuring magnetic protection in ships. It involves the application of oscillating and attenuating magnetic fields to eliminate residual magnetism in the ship’s structure. However, this process can lead to the generation of distorted magnetic fields within the [...] Read more.
The degaussing process is crucial for ensuring magnetic protection in ships. It involves the application of oscillating and attenuating magnetic fields to eliminate residual magnetism in the ship’s structure. However, this process can lead to the generation of distorted magnetic fields within the ship’s cabin, posing a potential threat to electronic equipment performance. Therefore, it is essential to have a comprehensive understanding of the dynamic magnetic field response in ship structures to develop effective degaussing systems. To address this need, this paper proposes an eddy current model for analyzing the dynamic magnetic field response in ferromagnetic metal structures. This model focuses on the role of eddy currents in shaping the magnetic field response and provides valuable insights into the underlying mechanisms. Using the proposed eddy current model, the effects of key system parameters such as thickness, conductivity, and the length-scale of the ship structure can be analytically investigated. This analysis helps in understanding how these parameters influence the dynamic magnetic field response and aids in the design and optimization of degaussing systems. The effectiveness and applicability of the proposed eddy current model are demonstrated through comprehensive investigations involving two simulation cases of varying complexity. The model accurately predicts the changing trends of the dynamic magnetic field response, as confirmed through finite element simulations. This validation highlights the model’s ability to reproduce simulation results accurately and its potential as a powerful tool for analyzing and optimizing dynamic magnetic field responses. In summary, the proposed eddy current model represents a significant advancement in the field. It provides a valuable theoretical framework for understanding and analyzing the dynamic magnetic field response in ferromagnetic metal structures. By offering insights into the underlying mechanisms and the influence of key parameters, this research contributes to the development of improved degaussing systems and enhances the overall magnetic protection capabilities of ships. Full article
(This article belongs to the Special Issue Pulsed Magnetic Fields and Its Applications)
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17 pages, 5704 KiB  
Article
OAR-UNet: Enhancing Long-Distance Dependencies for Head and Neck OAR Segmentation
by Kuankuan Peng, Danyu Zhou and Shihua Gong
Electronics 2024, 13(18), 3771; https://doi.org/10.3390/electronics13183771 - 23 Sep 2024
Viewed by 624
Abstract
Accurate segmentation of organs at risk (OARs) is a crucial step in the precise planning of radiotherapy for head and neck tumors. However, manual segmentation methods using CT images, which are still predominantly applied in clinical settings, are inefficient and expensive. Additionally, existing [...] Read more.
Accurate segmentation of organs at risk (OARs) is a crucial step in the precise planning of radiotherapy for head and neck tumors. However, manual segmentation methods using CT images, which are still predominantly applied in clinical settings, are inefficient and expensive. Additionally, existing segmentation methods struggle with small organs and have difficulty managing the complex interdependencies between organs. To address these issues, this study proposed an OAR-UNet segmentation method based on a U-shaped architecture with two key designs. To tackle the challenge of segmenting small organs, a Local Feature Perception Module (LFPM) is developed to enhance the sensitivity of the method to subtle structures. Furthermore, a Cross-shaped Transformer Block (CSTB) with a cross-shaped attention mechanism is introduced to improve the ability of the model to capture and process long-distance dependency information. To accelerate the convergence of the Transformer, we designed a Local Encoding Module (LEM) based on depthwise separable convolutions. In our experimental evaluation, we utilized two publicly available datasets, SegRap2023 and PDDCA, achieving Dice coefficients of 78.22% and 89.42%, respectively. These results demonstrate that our method outperforms both previous classic methods and state-of-the-art (SOTA) methods. Full article
(This article belongs to the Section Bioelectronics)
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21 pages, 8044 KiB  
Article
Multi-Trajectory Planning Control Strategy for Hydropower Plant Bridge Crane Based on Evaluation Algorithm
by Tiehua Chen, Ming Xu, Guangxin Wu, Shihao Dong and Xinze Liu
Electronics 2024, 13(18), 3770; https://doi.org/10.3390/electronics13183770 - 23 Sep 2024
Viewed by 705
Abstract
Currently, the research on crane trajectory planning mostly aims to, first, plan the trajectories of the crane and the trolley, and then to use a trial-and-error method or optimization algorithm to iteratively calculate the optimal trajectory parameters under the control of the optimal [...] Read more.
Currently, the research on crane trajectory planning mostly aims to, first, plan the trajectories of the crane and the trolley, and then to use a trial-and-error method or optimization algorithm to iteratively calculate the optimal trajectory parameters under the control of the optimal trajectory parameters to achieve the suppression of the swing angle. However, research on the fusion application of multi-trajectory planning algorithms is very rare. In addition, the existing methods are not suitable for the special operation control of hydropower plant bridge cranes. Based on the application scenario of hydropower plant bridge cranes, this paper proposes a comprehensive multi-trajectory control strategy based on the entropy weight technique for order preference, similarly to the ideal solution (TOPSIS) evaluation method. Specifically, the kinematic analysis of the crane is carried out and the trajectory evaluation index system is established. Secondly, under the walking constraint condition, four different trajectory planning algorithms are used to obtain the crane trajectory curve. In order to ensure the accuracy and comprehensiveness of the evaluation, the evaluation data are obtained through the Adams motion simulation platform. Finally, based on the entropy weight TOPSIS evaluation method, the optimal walking trajectory for each displacement is selected. The simulation and experimental results show that the evaluation method can select the optimal trajectory based on the motion characteristics of the trajectory algorithm in different displacement conditions, effectively reducing the load swing during the walking process of the crane and improving the positioning accuracy. Full article
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20 pages, 6747 KiB  
Article
Meta-Hybrid: Integrate Meta-Learning to Enhance Class Imbalance Graph Learning
by Liming Ran, Hongyu Sun, Lanqi Gao, Yanhua Dong and Yang Lu
Electronics 2024, 13(18), 3769; https://doi.org/10.3390/electronics13183769 - 22 Sep 2024
Viewed by 920
Abstract
The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully [...] Read more.
The class imbalance problem is a significant challenge in node classification tasks. Since majority class samples dominate imbalanced data, the model tends to favor the majority class, resulting in insufficient ability to identify minority classes. Evaluation indicators such as accuracy may not fully reflect the model’s performance. To solve these undesirable effects, we propose a framework for synthesizing minority class samples, GraphSHX, to balance the number of samples of different classes, and integrate the XGBoost model for node classification prediction during the training process. Conventional graph neural networks (GNNs) yielded unsatisfactory results, possibly due to the limited number of newly generated nodes. Therefore, we introduce a meta-mechanism to deal with small-sample problems, and employ the meta-learning approach to enhance performance on small-sample tasks by learning from a large number of tasks. An empirical evaluation of node classification on six publicly available datasets demonstrated that our balanced data set method outperforms existing optimal loss repair methods and synthetic node methods. The addition of the XGBoost model and meta-learning improves the accuracy by more than 5% to 10%, with the overall accuracy of the improved model being 15% higher than that of the baseline method. Full article
(This article belongs to the Special Issue Celebrating the 100th Anniversary of Yunnan University—Securing Mobile Edge Computing: Challenges and Solutions for Edge Architectures)
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23 pages, 5405 KiB  
Article
Iterative Removal of G-PCC Attribute Compression Artifacts Based on a Graph Neural Network
by Zhouyan He, Wenming Yang, Lijun Li and Rui Bai
Electronics 2024, 13(18), 3768; https://doi.org/10.3390/electronics13183768 - 22 Sep 2024
Viewed by 774
Abstract
As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion challenges, such as the encoding of attribute information [...] Read more.
As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion challenges, such as the encoding of attribute information may lead to spatial detail loss and visible artifacts, which negatively impact visual quality. To address these challenges, this paper proposes an iterative removal method for attribute compression artifacts based on a graph neural network. First, the geometric coordinates of the PCs are used to construct a graph that accurately reflects the spatial structure, with the PC attributes treated as signals on the graph’s vertices. Adaptive graph convolution is then employed to dynamically focus on the areas most affected by compression, while a bi-branch attention block is used to restore high-frequency details. To maintain overall visual quality, a spatial consistency mechanism is applied to the recovered PCs. Additionally, an iterative strategy is introduced to correct systematic distortions, such as additive bias, introduced during compression. The experimental results demonstrate that the proposed method produces finer and more realistic visual details, compared to state-of-the-art techniques for PC attribute compression artifact removal. Furthermore, the proposed method significantly reduces the network runtime, enhancing processing efficiency. Full article
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15 pages, 13243 KiB  
Article
Three-Dimensional Probe Mispositioning Errors Compensation: A Feasibility Study in the Non-Redundant Helicoidal Near to Far-Field Transformation Case
by Francesco D’Agostino, Flaminio Ferrara, Claudio Gennarelli, Rocco Guerriero, Massimo Migliozzi, Luigi Pascarella and Giovanni Riccio
Electronics 2024, 13(18), 3767; https://doi.org/10.3390/electronics13183767 - 22 Sep 2024
Viewed by 575
Abstract
A feasibility study on the compensation of 3D mispositioning errors of the probe occurring in the characterization of a long antenna, via a non-redundant (NR) near to far-field (NTFF) transformation with helicoidal scan, is conducted in this article. Such types of errors can [...] Read more.
A feasibility study on the compensation of 3D mispositioning errors of the probe occurring in the characterization of a long antenna, via a non-redundant (NR) near to far-field (NTFF) transformation with helicoidal scan, is conducted in this article. Such types of errors can result from imperfections in the rail driving the linear motion of the probe and from an imprecise synchronization of the linear and rotational movements of the probe and the antenna when drawing the scan helix. To correct them, an approach, which proceeds through two steps, is proposed. The former step uses a technique called cylindo rical wave (CW) correction for compensating the phase of the near-field (NF) samples, which, owing to the rail imperfections, result in not being acquired over the measurement cylinder surface. The latter exploits an iterative scheme to restore the samples at the sampling points required by the adopted NR representation along the scan helix from those obtained by applying the CW correction technique and impaired by 2D mispositioning errors. The so compensated NF samples are then effectively recovered via a 2D optimal sampling interpolation (OSI) scheme to accurately obtain the input data required to carry out the standard cylindrical NTFF transformation. The OSI representation is determined here by assuming a long antenna under test as enclosed in a prolate ellipsoid or cylinder ending into two hemispheres (cigar) in order to make, depending on the particular geometry of the considered antenna, the representation effectively non-redundant. The reported numerical simulation results show the capability of the proposed approach to compensate even severe 3D mispositioning errors, thus enabling its usage in a real measurement scenario. Full article
(This article belongs to the Special Issue Feature Papers in Microwave and Wireless Communications Section)
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21 pages, 3299 KiB  
Article
A Voltage Equalization Strategy for Series-Connected SiC MOSFET Applications
by Peng Li, Jialin Liu, Shikai Sun, Wenhao Yang, Yuyin Sun and Yuming Zhang
Electronics 2024, 13(18), 3766; https://doi.org/10.3390/electronics13183766 - 22 Sep 2024
Viewed by 705
Abstract
A novel clamped voltage equalization strategy is presented for series-connected Silicon Carbide (SiC) Metal–Oxide semiconductor Field-Effect transistors (MOSFETs) in this paper. Differences in device parameters and circuit asymmetry result in the uneven voltage distribution of series-connected SiC MOSFETs, which threatens the safe operation [...] Read more.
A novel clamped voltage equalization strategy is presented for series-connected Silicon Carbide (SiC) Metal–Oxide semiconductor Field-Effect transistors (MOSFETs) in this paper. Differences in device parameters and circuit asymmetry result in the uneven voltage distribution of series-connected SiC MOSFETs, which threatens the safe operation of the circuit. Dynamic voltage equalization is difficult to achieve due to the fast switching speed of SiC MOSFETs. This paper analyzes the switching characteristics and dynamic voltage equalization characteristics of SiC MOSFETs. Based on the analysis, an energy recovery strategy based on the clamping auxiliary circuit is proposed. A 2.8 kW (50 KHz) prototype is fabricated and tested to verify the strategy. Measurement results show that the maximum voltage stress is suppressed from 600 V to less than 320 V in the experimental condition. Full article
(This article belongs to the Special Issue Wide-Bandgap Device Application: Devices, Circuits, and Drivers)
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28 pages, 1094 KiB  
Article
Efficient Convolutional Neural Networks Utilizing Fine-Grained Fast Fourier Transforms
by Yulin Zhang, Feipeng Li, Haoke Xu, Xiaoming Li and Shan Jiang
Electronics 2024, 13(18), 3765; https://doi.org/10.3390/electronics13183765 - 22 Sep 2024
Viewed by 1163
Abstract
Convolutional Neural Networks (CNNs) are among the most prevalent deep learning techniques employed across various domains. The computational complexity of CNNs is largely attributed to the convolution operations. These operations are computationally demanding and significantly impact overall model performance. Traditional CNN implementations convert [...] Read more.
Convolutional Neural Networks (CNNs) are among the most prevalent deep learning techniques employed across various domains. The computational complexity of CNNs is largely attributed to the convolution operations. These operations are computationally demanding and significantly impact overall model performance. Traditional CNN implementations convert convolutions into matrix operations via the im2col (image to column) technique, facilitating parallelization through advanced BLAS libraries. This study identifies and investigates a significant yet intricate pattern of data redundancy within the matrix-based representation of convolutions, a pattern that, while complex, presents opportunities for optimization. Through meticulous analysis of the redundancy inherent in the im2col approach, this paper introduces a mathematically succinct matrix representation for convolution, leading to the development of an optimized FFT-based convolution with finer FFT granularity. Benchmarking demonstrates that our approach achieves an average speedup of 14 times and a maximum speedup of 17 times compared to the regular FFT convolution. Similarly, it outperforms the Im2col+GEMM approach from NVIDIA’s cuDNN library, achieving an average speedup of three times and a maximum speedup of five times. Our FineGrained FFT convolution approach, when integrated into Caffe, a widely used deep learning framework, leads to significant performance gains. Evaluations using synthetic CNNs designed for real-world applications show an average speedup of 1.67 times. Furthermore, a modified VGG network variant achieves a speedup of 1.25 times. Full article
(This article belongs to the Topic Advances in Integrative AI, Machine Learning, and Big Data for Transformative Applications)
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17 pages, 3276 KiB  
Article
YOLOv8s-DDA: An Improved Small Traffic Sign Detection Algorithm Based on YOLOv8s
by Meiqi Niu, Yajun Chen, Jianying Li, Xiaoyang Qiu and Wenhao Cai
Electronics 2024, 13(18), 3764; https://doi.org/10.3390/electronics13183764 - 22 Sep 2024
Cited by 1 | Viewed by 809
Abstract
In the realm of traffic sign detection, challenges arise due to the small size of objects, complex scenes, varying scales of signs, and dispersed objects. To address these problems, this paper proposes a small object detection algorithm, YOLOv8s-DDA, for traffic signs based on [...] Read more.
In the realm of traffic sign detection, challenges arise due to the small size of objects, complex scenes, varying scales of signs, and dispersed objects. To address these problems, this paper proposes a small object detection algorithm, YOLOv8s-DDA, for traffic signs based on an improved YOLOv8s. Specifically, the C2f-DWR-DRB module is introduced, which utilizes an efficient two-step method to capture multi-scale contextual information and employs a dilated re-parameterization block to enhance feature extraction quality while maintaining computational efficiency. The neck network is improved by incorporating ideas from ASF-YOLO, enabling the fusion of multi-scale object features and significantly boosting small object detection capabilities. Finally, the original IoU is replaced with Wise-IoU to further improve detection accuracy. On the TT100K dataset, the YOLOv8s-DDA algorithm achieves [email protected] of 87.2%, [email protected]:0.95 of 68.3%, precision of 85.2%, and recall of 80.0%, with a 5.4% reduction in parameter count. The effectiveness of this algorithm is also validated on the publicly available Chinese traffic sign detection dataset, CCTSDB2021. Full article
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16 pages, 4295 KiB  
Article
Cloud-Edge Collaborative Optimization Based on Distributed UAV Network
by Jian Yang, Jinyu Tao, Cheng Wang and Qinghai Yang
Electronics 2024, 13(18), 3763; https://doi.org/10.3390/electronics13183763 - 22 Sep 2024
Viewed by 664
Abstract
With the continuous development of mobile communication technology, edge intelligence has received widespread attention from academia. However, when enabling edge intelligence in Unmanned Aerial Vehicle (UAV) networks where drones serve as edge devices, the problem of insufficient computing power often arises due to [...] Read more.
With the continuous development of mobile communication technology, edge intelligence has received widespread attention from academia. However, when enabling edge intelligence in Unmanned Aerial Vehicle (UAV) networks where drones serve as edge devices, the problem of insufficient computing power often arises due to limited storage and computing resources. In order to solve the problem of insufficient UAV computing power, this paper proposes a distributed cloud-edge collaborative optimization algorithm (DCECOA). The core idea of the DCECOA is to make full use of the local data of edge devices (i.e., UAVs) to optimize the neural network model more efficiently and achieve model volume compression. Compared with the traditional Taylor evaluation criterion, this algorithm consumes less resources on the communication uplink. The neural network model compressed by the proposed optimization algorithm can achieve higher performance under the same compression rate. Full article
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50 pages, 3825 KiB  
Systematic Review
Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis
by Constantinos Halkiopoulos and Evgenia Gkintoni
Electronics 2024, 13(18), 3762; https://doi.org/10.3390/electronics13183762 - 22 Sep 2024
Viewed by 14955
Abstract
This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from [...] Read more.
This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected from an initial pool of 818 records across several databases. The results indicate that AI can improve students’ performance, engagement, and motivation; at the same time, some challenges like bias and discrimination should be noted. The review covers the historic development of AI in education, its theoretical grounding, and its practical applications within PL and AA with high promise and ethical issues of AI-powered educational systems. Future directions are empirical validation of effectiveness and equity, development of algorithms that reduce bias, and exploration of ethical implications regarding data privacy. The review identifies the transformative potential of AI in developing personalized and adaptive learning (AL) environments, thus, it advocates continued development and exploration as a means to improve educational outcomes. Full article
(This article belongs to the Special Issue Cognitive Horizons: Exploring the Synergy of Artificial Intelligence in E-learning Environments)
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17 pages, 3843 KiB  
Article
SCS-YOLO: A Defect Detection Model for Cigarette Appearance
by Yingchao Ding, Hao Zhou, Hao Wu, Chenrui Ma and Guowu Yuan
Electronics 2024, 13(18), 3761; https://doi.org/10.3390/electronics13183761 - 22 Sep 2024
Viewed by 862
Abstract
Appearance defects significantly impact cigarette quality. However, in the current high-speed production lines, manual inspection and traditional methods are unable to satisfy the actual demands of inspection. Therefore, a real-time and high-precision defect detection model for cigarette appearance, SCS-YOLO, is presented. The model [...] Read more.
Appearance defects significantly impact cigarette quality. However, in the current high-speed production lines, manual inspection and traditional methods are unable to satisfy the actual demands of inspection. Therefore, a real-time and high-precision defect detection model for cigarette appearance, SCS-YOLO, is presented. The model integrates space-to-depth convolution (SPD-Conv), a convolutional block attention module (CBAM), and a self-calibrated convolutional module (SCConv). SPD-Conv replaces the pooling structure to enhance the granularity of feature information. CBAM improves the ability to pay attention to defect locations. Improved self-calibrated convolution broadens the network’s receptive field and feature fusion capability. Additionally, Complete IoU loss (CIoU) is replaced with Efficient IoU Loss (EIoU) to enhance model localization and mitigate sample imbalance. The experimental results show that the accuracy of SCS-YOLO is 95.5% and the mAP (mean average precision) value is 95.2%. Compared with the original model, the accuracy and mAP value of the SCS-YOLO model are improved by 4.0%. Furthermore, the model achieves a detection speed of 216 FPS, meeting cigarette production lines’ accuracy and speed demands. Our research will positively impact the real-time detection of appearance defects in cigarette production lines. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Computer Vision)
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12 pages, 2566 KiB  
Article
A Wideband Polarization-Reconfigurable Antenna Based on Fusion of TM10 and Transformed-TM20 Mode
by Xianjing Yuan, Siyuan Zheng, Binyun Yan and Weixing Sheng
Electronics 2024, 13(18), 3760; https://doi.org/10.3390/electronics13183760 - 22 Sep 2024
Viewed by 598
Abstract
A wideband polarization-reconfigurable microstrip antenna based on a mode-fusion mechanism is proposed. This simple antenna structure consists of a rectangular radiation patch and a ground, both with crossed slots. The slots crossing the ground are connected by eight PIN diodes and four capacitors [...] Read more.
A wideband polarization-reconfigurable microstrip antenna based on a mode-fusion mechanism is proposed. This simple antenna structure consists of a rectangular radiation patch and a ground, both with crossed slots. The slots crossing the ground are connected by eight PIN diodes and four capacitors such that two orthogonal linear-polarization radiation modes can be realized. The radiation patch is slotted such that a transformed TM20 mode is excited, realizing broadside radiation that the traditional TM20 mode is unable to. With fusion of the fundamental TM10 mode and the transformed-TM20 (T-TM20) mode, a wide bandwidth of 30.1% is achieved in two reconfigurable polarizations. The measured results agree well with the simulation results. The total efficiency of the proposed antenna is more than 80.0% over the bandwidth. Full article
(This article belongs to the Special Issue Advanced Wireless Technologies for Next-G Networks: Antennas, Circuits, and Systems)
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17 pages, 1049 KiB  
Article
Memory Grouping for the Built-In Self-Test of Three-Dimensional Integrated Circuits
by Shou-Yi Huang and Shih-Hsu Huang
Electronics 2024, 13(18), 3759; https://doi.org/10.3390/electronics13183759 - 22 Sep 2024
Viewed by 634
Abstract
As the complexity of circuit design continues to grow, the development of three-dimensional (3D) integrated circuit (IC) technology has become increasingly vital. While 3D ICs offer faster signal transmission speeds and lower power consumption compared with traditional two-dimensional (2D) ICs, they also pose [...] Read more.
As the complexity of circuit design continues to grow, the development of three-dimensional (3D) integrated circuit (IC) technology has become increasingly vital. While 3D ICs offer faster signal transmission speeds and lower power consumption compared with traditional two-dimensional (2D) ICs, they also pose greater challenges in manufacturing and testing. In memory testing, traditional 2D ICs require only a single testing stage, whereas 3D ICs involve both prebond and postbond testing stages, complicating the memory grouping process. Most existing memory grouping algorithms focus on testing 2D ICs. While one study addressed the memory grouping problem for 3D IC testing, it did not consider the impact of test scheduling. In contrast, our approach incorporates test scheduling into the memory grouping process, resulting in a reduction in BIST area overhead. Experimental results demonstrate that our method reduces built-in self-test circuit area overhead by an average of 10.28% compared with those in the existing literature. Full article
(This article belongs to the Special Issue Modeling, Optimization, and Automation for Complex Manufacturing System)
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29 pages, 2443 KiB  
Article
Assessing the Impact of Artificial Intelligence Tools on Employee Productivity: Insights from a Comprehensive Survey Analysis
by Sabina-Cristiana Necula, Doina Fotache and Emanuel Rieder
Electronics 2024, 13(18), 3758; https://doi.org/10.3390/electronics13183758 - 21 Sep 2024
Viewed by 5242
Abstract
This study provides a nuanced understanding of AI’s impact on productivity and employment using machine learning models and Bayesian Network Analysis. Data from 233 employees across various industries were analyzed using logistic regression, Random Forest, and XGBoost, with 5-fold cross-validation. The findings reveal [...] Read more.
This study provides a nuanced understanding of AI’s impact on productivity and employment using machine learning models and Bayesian Network Analysis. Data from 233 employees across various industries were analyzed using logistic regression, Random Forest, and XGBoost, with 5-fold cross-validation. The findings reveal that high levels of AI tool usage and integration within organizational workflows significantly enhance productivity, particularly among younger employees. A significant interaction between AI tools usage and integration (β = 0.4319, p < 0.001) further emphasizes the importance of comprehensive AI adoption. Bayesian Network Analysis highlights complex interdependencies between AI usage, innovation, and employee characteristics. This study confirms that strategic AI integration, along with targeted training programs and ethical frameworks, is essential for maximizing AI’s economic potential. Full article
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