Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Control and Systems Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Reservoir Optimization Scheduling Driven by Knowledge Graphs
Electronics 2024, 13(12), 2283; https://doi.org/10.3390/electronics13122283 (registering DOI) - 11 Jun 2024
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As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds,
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As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds, existing optimization models and algorithms are typically applicable only to specific watershed environments. This specificity results in a “one watershed, one model” limitation. Consequently, optimization of different watersheds usually requires manual reconstruction of models and algorithms. This process is not only time-consuming but also limits the versatility and flexibility of the algorithms. To address this issue, this paper proposes a knowledge graph-driven method for reservoir optimization scheduling. By improving genetic algorithms, this method allows for the automatic construction of optimization models tailored to specific watershed characteristics based on knowledge graphs. This approach reduces the dependency of the optimization model on manual modeling. It also integrates hydrodynamic simulations within the watershed to ensure the effectiveness and practicality of the genetic algorithms. Furthermore, this paper has developed an algorithm that directly converts optimized reservoir outflow into actionable dispatch instructions. This method has been applied in the Pihe River Basin, optimizing flood control and resource management strategies according to different seasonal demands. It demonstrates high flexibility and effectiveness under varying hydrological conditions, significantly enhancing the operational efficiency of reservoir management.
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Open AccessArticle
Embedding Enhancement Method for LightGCN in Recommendation Information Systems
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Sangmin Lee, Junho Ahn and Namgi Kim
Electronics 2024, 13(12), 2282; https://doi.org/10.3390/electronics13122282 (registering DOI) - 11 Jun 2024
Abstract
In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an
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In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an increase in the amount of data and slow learning and inference speeds occur due to an increase in the amount of computation. To overcome these problems, this study focused on optimizing LightGCN, the basic structure of the graph-convolution-network-based recommendation system. To improve this, techniques and structures were proposed. We propose an embedding enhancement method to strengthen the robustness of embedding and a non-combination structure to overcome LightGCN’s weight sum structure through this method. To verify the proposed method, we have demonstrated its effectiveness through experiments using the SELFRec library on various datasets, such as Yelp2018, MovieLens-1M, FilmTrust, and Douban-book. Mainly, significant performance improvements were observed in key indicators, such as Precision, Recall, NDCG, and Hit Ratio in Yelp2018 and Douban-book datasets. These results suggest that the proposed methods effectively improved the recommendation performance and learning efficiency of the LightGCN model, and the improvement of LightGCN, which is most widely used as a backbone network, makes an important contribution to the entire field of GCN-based recommendation systems. Therefore, in this study, we improved the learning method of the existing LightGCN and changed the weight sum structure to surpass the existing accuracy.
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(This article belongs to the Special Issue Future Trends and Challenges of Ubiquitous Computing and Smart Systems)
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Improving Single-Image Super-Resolution with Dilated Attention
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Xinyu Zhang, Boyuan Cheng, Xiaosong Yang, Zhidong Xiao, Jianjun Zhang and Lihua You
Electronics 2024, 13(12), 2281; https://doi.org/10.3390/electronics13122281 (registering DOI) - 11 Jun 2024
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Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range
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Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range dependencies, and transformer-based techniques suffer from computational complexity. To tackle these problems, this paper proposes a novel method called dilated attention-based single-image super-resolution (DAIR). It comprises three components: low-level feature extraction, multi-scale dilated transformer block (MDTB), and high-quality image reconstruction. A convolutional layer is used to extract the base features from low-resolution images, which lays the foundation for subsequent processing. Dilated attention is introduced to MDTB to enhance its ability to capture image features at different scales and ensure superior image details and structure recovery. After that, MDTB refines these features to extract multi-scale global attributes and effectively grasps images’ long-distance relationships and features across multiple scales. Finally, low-level features obtained from feature extraction and multi-scale global features obtained from MDTB are aggregated to reconstruct high-resolution images. The comparison with existing methods validates the efficacy of the proposed method and demonstrates its advantage in improving image resolution and quality.
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Open AccessArticle
An FPGA-Accelerated CNN with Parallelized Sum Pooling for Onboard Realtime Routing in Dynamic Low-Orbit Satellite Networks
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Hyeonwoo Kim, Juhyeon Park, Heoncheol Lee, Dongshik Won and Myonghun Han
Electronics 2024, 13(12), 2280; https://doi.org/10.3390/electronics13122280 (registering DOI) - 11 Jun 2024
Abstract
This paper addresses the problem of real-time onboard routing for dynamic low earth orbit (LEO) satellite networks. It is difficult to apply general routing algorithms to dynamic LEO networks due to the frequent changes in satellite topology caused by the disconnection between moving
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This paper addresses the problem of real-time onboard routing for dynamic low earth orbit (LEO) satellite networks. It is difficult to apply general routing algorithms to dynamic LEO networks due to the frequent changes in satellite topology caused by the disconnection between moving satellites. Deep reinforcement learning (DRL) models trained by various dynamic networks can be considered. However, since the inference process with the DRL model requires too long a computation time due to multiple convolutional layer operations, it is not practical to apply to a real-time on-board computer (OBC) with limited computing resources. To solve the problem, this paper proposes a practical co-design method with heterogeneous processors to parallelize and accelerate a part of the multiple convolutional layer operations on a field-programmable gate array (FPGA). The proposed method was tested with a real heterogeneous processor-based OBC and showed that the proposed method was about 3.10 times faster than the conventional method while achieving the same routing results.
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(This article belongs to the Special Issue FPGAs Explored: Pioneering Methods, Theories and Their Applications in Reconfigurable Computing)
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Open AccessArticle
Blockchain-Based Control Plane Attack Detection Mechanisms for Multi-Controller Software-Defined Networks
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Abrar Alkhamisi, Iyad Katib and Seyed M. Buhari
Electronics 2024, 13(12), 2279; https://doi.org/10.3390/electronics13122279 (registering DOI) - 11 Jun 2024
Abstract
A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility
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A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and malicious activities on the MC-SDN structure. The security holes in the MC-SDN structure significantly impact network survivability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, this work intended to design blockchain-based controller security (BCS) that exploits the advantages of immutable and distributed ledger technology among multiple controllers and securely manages the controller communications against various attacks. Thereby, it enables the controllers to maintain consistent network view and accurate flow tables among themselves and also neglects the controller failure issues. Finally, the experimental results of the proposed BCS approach demonstrated superior performance under various scenarios, such as attack detection, number of attackers, number of controllers, and number of compromised controllers, by applying different performance metrics.
Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)
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Open AccessArticle
An Advanced Methodology for Crystal System Detection in Li-Ion Batteries
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Nikola Anđelić and Sandi Baressi Šegota
Electronics 2024, 13(12), 2278; https://doi.org/10.3390/electronics13122278 (registering DOI) - 10 Jun 2024
Abstract
Detecting the crystal system of lithium-ion batteries is crucial for optimizing their performance and safety. Understanding the arrangement of atoms or ions within the battery’s electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides
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Detecting the crystal system of lithium-ion batteries is crucial for optimizing their performance and safety. Understanding the arrangement of atoms or ions within the battery’s electrodes and electrolyte allows for improvements in energy density, cycling stability, and safety features. This knowledge also guides material design and fabrication techniques, driving advancements in battery technology for various applications. In this paper, a publicly available dataset was utilized to develop mathematical equations (MEs) using a genetic programming symbolic classifier (GPSC) to determine the type of crystal structure in Li-ion batteries with a high classification performance. The dataset consists of three different classes transformed into three binary classification datasets using a one-versus-rest approach. Since the target variable of each dataset variation is imbalanced, several oversampling techniques were employed to achieve balanced dataset variations. The GPSC was trained on these balanced dataset variations using a five-fold cross-validation (5FCV) process, and the optimal GPSC hyperparameter values were searched for using a random hyperparameter value search (RHVS) method. The goal was to find the optimal combination of GPSC hyperparameter values to achieve the highest classification performance. After obtaining MEs using the GPSC with the highest classification performance, they were combined and tested on initial binary classification dataset variations. Based on the conducted investigation, the ensemble of MEs could detect the crystal system of Li-ion batteries with a high classification accuracy (1.0).
Full article
(This article belongs to the Section Industrial Electronics)
Open AccessArticle
Understanding Learner Satisfaction in Virtual Learning Environments: Serial Mediation Effects of Cognitive and Social-Emotional Factors
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Xin Yin, Jiakai Zhang, Gege Li and Heng Luo
Electronics 2024, 13(12), 2277; https://doi.org/10.3390/electronics13122277 - 10 Jun 2024
Abstract
This study explored the relationship between technology acceptance and learning satisfaction within a virtual learning environment (VLE) with cognitive presence, cognitive engagement, social presence, and emotional engagement as mediators. A total of 237 university students participated and completed a questionnaire after studying in
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This study explored the relationship between technology acceptance and learning satisfaction within a virtual learning environment (VLE) with cognitive presence, cognitive engagement, social presence, and emotional engagement as mediators. A total of 237 university students participated and completed a questionnaire after studying in the Virbela VLE. The results revealed direct and indirect links between technology acceptance and virtual learning satisfaction. The mediation analysis showed the critical mediating roles of cognitive presence and emotional engagement in fostering satisfaction. There also appeared to be a sequential mediating pathway from technology acceptance to learning satisfaction through social presence and emotional engagement. Notably, cognitive engagement and social presence did not have a significant mediating effect on satisfaction. These results provide a supplementary perspective on how technological, cognitive, and emotional factors can enhance student satisfaction in VLEs. The study concludes with several implications for future research and practice of VLEs in higher education.
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(This article belongs to the Special Issue Use of Extended Reality (XR) Spectrum for Education and Training: Trends, Applications and Impact)
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P2P Federated Learning Based on Node Segmentation with Privacy Protection for IoV
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Jia Zhao, Yating Guo, Bokai Yang and Yanchun Wang
Electronics 2024, 13(12), 2276; https://doi.org/10.3390/electronics13122276 - 10 Jun 2024
Abstract
The current usage of federated learning in applications relies on the existence of servers. To address the inability to conduct federated learning for IoV (Internet of Vehicles) applications in serverless areas, a P2P (peer-to-peer) architecture for federated learning is proposed in this paper.
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The current usage of federated learning in applications relies on the existence of servers. To address the inability to conduct federated learning for IoV (Internet of Vehicles) applications in serverless areas, a P2P (peer-to-peer) architecture for federated learning is proposed in this paper. Following node segmentation based on limited subgraph diameters, an edge aggregation mode is employed to propagate models inwardly, and a mode for propagating the model inward to the C-node (center node) while aggregating is proposed. Simultaneously, a personalized differential privacy scheme was designed under this architecture. Through experimentation and verification, the approach proposed in this paper demonstrates the combination of both security and usability.
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(This article belongs to the Special Issue Network Security Management in Heterogeneous Networks)
Open AccessArticle
Effective Denoising Algorithms for Converting Indoor Blueprints Using a 3D Laser Scanner
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Sehyeon Yoon, Sanghyun Choi and Jhonghyun An
Electronics 2024, 13(12), 2275; https://doi.org/10.3390/electronics13122275 - 10 Jun 2024
Abstract
This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to
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This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity.
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(This article belongs to the Special Issue Computer Vision Applications for Autonomous Vehicles)
Open AccessArticle
GNN-Based Network Traffic Analysis for the Detection of Sequential Attacks in IoT
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Tanzeela Altaf, Xu Wang, Wei Ni, Guangsheng Yu, Ren Ping Liu and Robin Braun
Electronics 2024, 13(12), 2274; https://doi.org/10.3390/electronics13122274 - 10 Jun 2024
Abstract
This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex
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This research introduces a novel framework utilizing a sequential gated graph convolutional neural network (GGCN) designed specifically for botnet detection within Internet of Things (IoT) network environments. By capitalizing on the strengths of graph neural networks (GNNs) to represent network traffic as complex graph structures, our approach adeptly handles the temporal dynamics inherent to botnet attacks. Key to our approach is the development of a time-stamped multi-edge graph structure that uncovers subtle temporal patterns and hidden relationships in network flows, critical for recognizing botnet behaviors. Moreover, our sequential graph learning framework incorporates time-sequenced edges and multi-edged structures into a two-layered gated graph model, which is optimized with specialized message-passing layers and aggregation functions to address the challenges of time-series traffic data effectively. Our comparative analysis with the state of the art reveals that our sequential gated graph convolutional neural network achieves substantial improvements in detecting IoT botnets. The proposed GGCN model consistently outperforms the conventional model, achieving improvements in accuracy ranging from marginal to substantial—0.01% for BoT IoT and up to 25% for Mirai. Moreover, our empirical analysis underscores the GGCN’s enhanced capabilities, particularly in binary classification tasks, on imbalanced datasets. These findings highlight the model’s ability to effectively navigate and manage the varying complexity and characteristics of IoT security threats across different datasets.
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(This article belongs to the Collection Graph Machine Learning)
Open AccessArticle
Image to Label to Answer: An Efficient Framework for Enhanced Clinical Applications in Medical Visual Question Answering
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Jianfeng Wang, Kah Phooi Seng, Yi Shen, Li-Minn Ang and Difeng Huang
Electronics 2024, 13(12), 2273; https://doi.org/10.3390/electronics13122273 - 10 Jun 2024
Abstract
Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering
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Medical Visual Question Answering (Med-VQA) faces significant limitations in application development due to sparse and challenging data acquisition. Existing approaches focus on multi-modal learning to equip models with medical image inference and natural language understanding, but this worsens data scarcity in Med-VQA, hindering clinical application and advancement. This paper proposes the ITLTA framework for Med-VQA, designed based on field requirements. ITLTA combines multi-label learning of medical images with the language understanding and reasoning capabilities of large language models (LLMs) to achieve zero-shot learning, meeting natural language module needs without end-to-end training. This approach reduces deployment costs and training data requirements, allowing LLMs to function as flexible, plug-and-play modules. To enhance multi-label classification accuracy, the framework uses external medical image data for pretraining, integrated with a joint feature and label attention mechanism. This configuration ensures robust performance and applicability, even with limited data. Additionally, the framework clarifies the decision-making process for visual labels and question prompts, enhancing the interpretability of Med-VQA. Validated on the VQA-Med 2019 dataset, our method demonstrates superior effectiveness compared to existing methods, confirming its outstanding performance for enhanced clinical applications.
Full article
(This article belongs to the Special Issue Emerging Topics in Artificial Intelligence (AI): Architectures and Techniques for Real-World Applications)
Open AccessReview
Exploring Android Obfuscators and Deobfuscators: An Empirical Investigation
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Shouki A. Ebad and Abdulbasit A. Darem
Electronics 2024, 13(12), 2272; https://doi.org/10.3390/electronics13122272 - 10 Jun 2024
Abstract
Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically
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Researchers have proposed different obfuscation transformations supported by numerous smartphone protection tools (obfuscators and deobfuscators). However, there is a need for a comprehensive study to empirically characterize these tools that belong to different categories of transformations. We propose a property-based framework to systematically classify twenty cutting-edge tools according to their features, analysis type, programming language support, licensing, applied obfuscation transformations, and general technical drawbacks. Our analysis predominantly reveals that very few tools work at the dynamic level, and most tools (which are static-based) work for Java or Java-based ecosystems (e.g., Android). The findings also show that the widespread adoption of renaming transformations is followed by formatting and code injection. In addition, this paper pinpoints the technical shortcomings of each tool; some of these drawbacks are common in static-based analyzers (e.g., resource consumption), and other drawbacks have negative effects on the experiment conducted by students (e.g., a third-party library involved). According to these critical limitations, we provide some timely recommendations for further research. This study can assist not only Android developers and researchers to improve the overall health of their apps but also the managers of computer science and cybersecurity academic programs to embed suitable obfuscation tools in their curricula.
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(This article belongs to the Special Issue Advances in Software Engineering and Programming Languages)
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Open AccessArticle
HR-YOLO: A Multi-Branch Network Model for Helmet Detection Combined with High-Resolution Network and YOLOv5
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Yuanfeng Lian, Jing Li, Shaohua Dong and Xingtao Li
Electronics 2024, 13(12), 2271; https://doi.org/10.3390/electronics13122271 - 10 Jun 2024
Abstract
Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep
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Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively.
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(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images, 2nd Edition)
Open AccessArticle
A Closer Look at the Statistical Behavior of a Chaotic System with Message Inclusion for Cryptographic Applications
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Adina Elena Lupu (Blaj) and Adriana Vlad
Electronics 2024, 13(12), 2270; https://doi.org/10.3390/electronics13122270 - 10 Jun 2024
Abstract
One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion
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One technique, especially in chaos-based cryptographic applications, is to include the message in the evolution of the dynamical system. This paper aims to find out if and to what extent the statistical behavior of the chaotic system is affected by the message inclusion in its dynamic evolution. The study is illustrated by the dynamical system described by the logistic map in cryptographic applications based on images. The evaluation of the statistical behavior was performed on an original scheme proposed. The Monte Carlo analysis of the applied Kolmogorov–Smirnov statistical test revealed that the dynamical system in the processing scheme with message inclusion does not modify its proper statistical behavior (revealed by definition relation). This was possible due to the proposed scheme designed. Namely, this scheme contains a decision switch which, supported by an appropriate choice of the magnitude of the scaling factor, ensures that the values of the dynamical system are maintained in the definition domain. The proposed framework for analyzing the statistical properties and for preserving the dynamical system behavior is one main contribution of this research. The message inclusion scheme also provides an enhancement with cryptographic mixing functions applied internally; the statistical behavior of the dynamical system is also analyzed in this case. Thus, the paper contributes to the theoretical complex characterization of the dynamical system considering also the message inclusion case.
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(This article belongs to the Special Issue Recent Advances in Chaotic Systems and Their Security Applications, 2nd edition)
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Polarization-Based Two-Stage Image Dehazing in a Low-Light Environment
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Xin Zhang, Xia Wang, Changda Yan, Gangcheng Jiao and Huiyang He
Electronics 2024, 13(12), 2269; https://doi.org/10.3390/electronics13122269 - 10 Jun 2024
Abstract
Fog, as a common weather condition, severely affects the visual quality of images. Polarization-based dehazing techniques can effectively produce clear results by utilizing the atmospheric polarization transmission model. However, current polarization-based dehazing methods are only suitable for scenes with strong illumination, such as
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Fog, as a common weather condition, severely affects the visual quality of images. Polarization-based dehazing techniques can effectively produce clear results by utilizing the atmospheric polarization transmission model. However, current polarization-based dehazing methods are only suitable for scenes with strong illumination, such as daytime scenes, and cannot be applied to low-light scenes. Due to the insufficient illumination at night and the differences in polarization characteristics between it and sunlight, polarization images captured in a low-light environment can suffer from loss of polarization and intensity information. Therefore, this paper proposes a two-stage low-light image dehazing method based on polarization. We firstly construct a polarization-based low-light enhancement module to remove noise interference in polarization images and improve image brightness. Then, we design a low-light polarization dehazing module, which combines the polarization characteristics of the scene and objects to remove fog, thereby restoring the intensity and polarization information of the scene and improving image contrast. For network training, we generate a simulation dataset for low-light polarization dehazing. We also collect a low-light polarization hazy dataset to test the performance of our method. Experimental results indicate that our proposed method can achieve the best dehazing effect.
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(This article belongs to the Special Issue Advances in Digital Signal and Image Processing, Techniques, and Computations with Multidisciplinary Applications)
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Open AccessEditorial
Advanced Wireless Sensor Networks: Applications, Challenges and Research Trends
by
Dionisis Kandris and Eleftherios Anastasiadis
Electronics 2024, 13(12), 2268; https://doi.org/10.3390/electronics13122268 - 9 Jun 2024
Abstract
A typical wireless sensor network (WSN) contains wirelessly interconnected devices, called sensor nodes, which have sensing, processing, and communication abilities and are disseminated within an area of interest [...]
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(This article belongs to the Special Issue Advanced Wireless Sensor Networks: Applications, Challenges and Research Trends)
Open AccessArticle
Fusing Design and Machine Learning for Anomaly Detection in Water Treatment Plants
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Gauthama Raman and Aditya Mathur
Electronics 2024, 13(12), 2267; https://doi.org/10.3390/electronics13122267 - 9 Jun 2024
Abstract
Accurate detection of process anomalies is crucial for maintaining reliable operations in critical infrastructures such as water treatment plants. Traditional methods for creating anomaly detection systems in these facilities typically focus on either design-based strategies, which encompass physical and engineering aspects, or on
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Accurate detection of process anomalies is crucial for maintaining reliable operations in critical infrastructures such as water treatment plants. Traditional methods for creating anomaly detection systems in these facilities typically focus on either design-based strategies, which encompass physical and engineering aspects, or on data-driven models that utilize machine learning to interpret complex data patterns. Challenges in creating these detectors arise from factors such as dynamic operating conditions, lack of design knowledge, and the complex interdependencies among heterogeneous components. This paper proposes a novel fusion detector that combines the strengths of both design-based and machine learning approaches for accurate detection of process anomalies. The proposed methodology was implemented in an operational secure water treatment (SWaT) testbed, and its performance evaluated during the Critical Infrastructure Security Showdown (CISS) 2022 event. A comparative analysis against four commercially available anomaly detection systems that participated in the CISS 2022 event revealed that our fusion detector successfully detected 19 out of 22 attacks, demonstrating high accuracy with a low rate of false positives.
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(This article belongs to the Special Issue Advances in Predictive Maintenance for Critical Infrastructure)
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Open AccessArticle
Designing Multifunctional Multiferroic Composites for Advanced Electronic Applications
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Lilian Nunes Pereira, Julio Cesar Agreira Pastoril, Gustavo Sanguino Dias, Ivair Aparecido dos Santos, Ruyan Guo, Amar S. Bhalla and Luiz Fernando Cotica
Electronics 2024, 13(12), 2266; https://doi.org/10.3390/electronics13122266 - 9 Jun 2024
Abstract
This paper presents a novel approach for the fabrication of magnetoelectric composites aimed at enhancing cross-coupling between electrical and magnetic phases for potential applications in intelligent sensors and electronic components. Unlike previous methodologies known for their complexity and expense, our method offers a
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This paper presents a novel approach for the fabrication of magnetoelectric composites aimed at enhancing cross-coupling between electrical and magnetic phases for potential applications in intelligent sensors and electronic components. Unlike previous methodologies known for their complexity and expense, our method offers a simple and cost-effective assembly process conducted at room temperature, preserving the original properties of the components and avoiding undesired phases. The composites, composed of PZT fibers, cobalt (CoFe O ), and a polymeric resin, demonstrate the uniform distribution of PZT-5A fibers within the cobalt matrix, as demonstrated by scanning electron microscopy. Detailed morphological analyses reveal the interface characteristics crucial for determining overall performance. Dielectric measurements indicate stable behaviors, particularly when PZT-5A fibers are properly poled, showcasing potential applications in sensors or medical devices. Furthermore, H-dependence studies illustrate strong magnetoelectric interactions, suggesting promising avenues for enhancing coupling efficiency. Overall, this study lays the basic work for future optimization of composite composition and exploration of its long-term stability, offering valuable insights into the potential applications of magnetoelectric composites in various technological domains.
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(This article belongs to the Special Issue Advanced Materials for Intelligent Electronics)
Open AccessArticle
SIDGAN: Efficient Multi-Module Architecture for Single Image Defocus Deblurring
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Shenggui Ling, Hongmin Zhan and Lijia Cao
Electronics 2024, 13(12), 2265; https://doi.org/10.3390/electronics13122265 - 9 Jun 2024
Abstract
In recent years, with the rapid developments in deep learning and graphics processing units, learning-based defocus deblurring has made favorable achievements. However, the current methods are not effective in processing blurred images with a large depth of field. The greater the depth of
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In recent years, with the rapid developments in deep learning and graphics processing units, learning-based defocus deblurring has made favorable achievements. However, the current methods are not effective in processing blurred images with a large depth of field. The greater the depth of field, the blurrier the image, namely, the image contains large blurry regions and encounters severe blur. The fundamental reason for the unsatisfactory results is that it is difficult to extract effective features from the blurred images with large blurry regions. For this reason, a new FFEM (Fuzzy Feature Extraction Module) is proposed to enhance the encoder’s ability to extract features from images with large blurry regions. After using the FFEM during encoding, its PSNR (Peak Signal-to-Noise Ratio) is improved by 1.33% on the DPDD (Dual-Pixel Defocus Deblurring). Moreover, images with large blurry regions often cause the current algorithms to generate artifacts in their results. Therefore, a new module named ARM (Artifact Removal Module) is proposed in this work and employed during decoding. After utilizing the ARM during decoding, its PSNR is improved by 2.49% on the DPDD. After using the FFEM and the ARM simultaneously, compared to the latest algorithms, the PSNR of our method is improved by 3.29% on the DPDD. Following the previous research in this field, qualitative and quantitative experiments are conducted on the DPDD and the RealDOF (Real Depth of Field), and the experimental results indicate that our method surpasses the state-of-the-art algorithms in three objective metrics.
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(This article belongs to the Special Issue Artificial Intelligence in Image Processing and Computer Vision)
Open AccessArticle
Full-Duplex Unmanned Aerial Vehicle Communications for Cellular Spectral Efficiency Enhancement Utilizing Device-to-Device Underlaying Structure
by
Yuetian Zhou and Yang Li
Electronics 2024, 13(12), 2264; https://doi.org/10.3390/electronics13122264 - 9 Jun 2024
Abstract
Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One
[...] Read more.
Unmanned aerial vehicle (UAV) communications have gained recognition as a promising technology due to their unique characteristics of rapid deployment and flexible configuration. Meanwhile, device-to-device (D2D) and full-duplex (FD) technologies have emerged as promising methods for enhancing spectral efficiency and offloading traffic. One significant advantage of UAVs is their ability to partition suitable D2D pairs to increase cell capacity. In this paper, we present a novel network model in which UAVs are considered D2D pairs underlaying cellular networks, integrating FD into the communication links between UAVs to improve spectral efficiency. We then investigate a resource allocation problem for the proposed FD-UAV D2D underlaying structure model, with the objective of maximizing the system’s sum rate. Specifically, the UAVs in our model operate in full-duplex mode as D2D users (DUs), allowing the reuse of both the uplink and downlink subcarrier resources of cellular users (CUs). This optimization challenge is formulated as a mixed-integer nonlinear programming problem, known for its NP-hard and intractable nature. To address this issue, we propose a heuristic algorithm (HA) that decomposes the problem into two steps: power allocation and user pairing. The optimal power allocation is solved as a nonlinear programming problem by searching among a finite set, while the user pairing problem is addressed using the Kuhn–Munkres algorithm. The numerical results indicate that our proposed FD-MaxSumCell-HA (full-duplex UAVs maximizing the cell sum rate with a heuristic algorithm) scheme for FD-UAV D2D underlaying models outperforms HD-UAV underlaying cellular networks, with improved access rates for UAVs in FD-MaxSumCell-HA compared to HD-UAV networks.
Full article
(This article belongs to the Special Issue Innovative Technologies and Services for Unmanned Aerial Vehicles)
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