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Electronics, Volume 12, Issue 15 (August-1 2023) – 170 articles

Cover Story (view full-size image): Energy harvesting bears the potential to provide lifetime power supply to wireless sensors. In this work, we investigate a model of an electromagnetic energy harvester, which converts vibrational energy into electrical energy. Such a device is composed of four permanent magnets oscillating along a coil. The model is implemented in a finite element-based simulation software. A compact model is derived from parametrized solutions of this model and integrated into a system-level simulation. Furthermore, matrix interpolation-based and algebraic parameterization-based parametric model order reduction methods are suggested for reducing the computational effort for the generation of the compact model. These techniques are applied to the design optimization of the harvester with respect to magnet dimensions. View this paper
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16 pages, 6723 KiB  
Article
Study on Parking Space Recognition Based on Improved Image Equalization and YOLOv5
by Xin Zhang, Wen Zhao and Yueqiu Jiang
Electronics 2023, 12(15), 3374; https://doi.org/10.3390/electronics12153374 - 7 Aug 2023
Cited by 1 | Viewed by 1133
Abstract
Parking space recognition is an important part in the process of automatic parking, and it is also a key issue in the research field of automatic parking technology. The parking space recognition process was studied based on vision and the YOLOv5 target detection [...] Read more.
Parking space recognition is an important part in the process of automatic parking, and it is also a key issue in the research field of automatic parking technology. The parking space recognition process was studied based on vision and the YOLOv5 target detection algorithm. Firstly, the fisheye camera around the body was calibrated using the Zhang Zhengyou calibration method, and then the corrected images captured by the camera were top-view transformed; then, the projected transformed images were stitched and fused in a unified coordinate system, and an improved image equalization processing fusion algorithm was used in order to improve the uneven image brightness in the parking space recognition process; after that, the fused images were input to the YOLOv5 target detection model for training and validation, and the results were compared with those of two other algorithms. Finally, the contours of the parking space were extracted based on OpenCV. The simulations and experiments proved that the brightness and sharpness of the fused images meet the requirements after image equalization, and the effectiveness of the parking space recognition method was also verified. Full article
(This article belongs to the Special Issue Computer Vision for Modern Vehicles)
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14 pages, 2035 KiB  
Article
Multi-Scenario Millimeter Wave Channel Measurements and Characteristic Analysis in Smart Warehouse at 28 GHz
by Hang Mi, Bo Ai, Ruisi He, Tong Wu, Xin Zhou, Zhangdui Zhong, Haoxiang Zhang and Ruifeng Chen
Electronics 2023, 12(15), 3373; https://doi.org/10.3390/electronics12153373 - 7 Aug 2023
Cited by 1 | Viewed by 1134
Abstract
Smart warehouses are revolutionizing traditional logistics operations by incorporating advanced technologies such as Internet of Things, robotics, and artificial intelligence. In these complex and dynamic environments, control and operation instructions need to be transmitted through wireless networks. Therefore, wireless communication plays a crucial [...] Read more.
Smart warehouses are revolutionizing traditional logistics operations by incorporating advanced technologies such as Internet of Things, robotics, and artificial intelligence. In these complex and dynamic environments, control and operation instructions need to be transmitted through wireless networks. Therefore, wireless communication plays a crucial role in enabling efficient and reliable operations. Meanwhile, channel measurements and modeling in smart warehouse scenarios are essential for understanding and optimizing wireless communication performance. By accurately characterizing radio channels, communication systems can be better designed and deployed to meet unique challenges in smart warehouse scenarios. In this paper, we present an overview of smart warehouse scenarios and explore channel characteristics in smart warehouse scenarios. We conducted a measurement campaign for millimeter wave radio channels in smart warehouse scenarios. A vector network analyzer-based channel sounder was exploited to record channel characteristics at 28 GHz. Based on the measurements, large-scale channel parameters, including path loss, root-mean-square (RMS) delay spread, and Rician K factor were investigated. The unique channel characteristics in smart warehouse scenarios were explored. Full article
(This article belongs to the Special Issue Channel Measurement, Modeling and Simulation of 6G)
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23 pages, 4391 KiB  
Article
TSM-CV: Twitter Sentiment Analysis for COVID-19 Vaccines Using Deep Learning
by Saleh Albahli and Marriam Nawaz
Electronics 2023, 12(15), 3372; https://doi.org/10.3390/electronics12153372 - 7 Aug 2023
Cited by 3 | Viewed by 1409
Abstract
The coronavirus epidemic has imposed a devastating impact on humans around the globe, causing profound anxiety, fear, and complex emotions and feelings. Vaccination against the new coronavirus has started, and people’s feelings are becoming more diverse and complicated. In the presented work, our [...] Read more.
The coronavirus epidemic has imposed a devastating impact on humans around the globe, causing profound anxiety, fear, and complex emotions and feelings. Vaccination against the new coronavirus has started, and people’s feelings are becoming more diverse and complicated. In the presented work, our goal is to use the deep learning (DL) technique to understand and elucidate their feelings. Due to the advancement of IT and internet facilities, people are socially connected to explain their emotions and sentiments. Among all social sites, Twitter is the most used platform among consumers and can assist scientists to comprehend people’s opinions related to anything. The major goal of this work is to understand the audience’s varying sentiments about the vaccination process by using data from Twitter. We have employed both the historic (All COVID-19 Vaccines Tweets Kaggle dataset) and real (tweets) data to analyze the people’s sentiments. Initially, a preprocessing step is applied to the input samples. Then, we use the FastText approach for computing semantically aware features. In the next step, we apply the Valence Aware Dictionary for sentiment Reasoner (VADER) method to assign the labels to the collected features as being positive, negative, or neutral. After this, a feature reduction step using the Non-Negative Matrix Factorization (NMF) approach is utilized to minimize the feature space. Finally, we have used the Random Multimodal Deep Learning (RMDL) classifier for sentiment prediction. We have confirmed through experimentation that our work is effective in examining the emotions of people toward the COVID-19 vaccines. The presented work has acquired an accuracy result of 94.81% which is showing the efficacy of our strategy. Other standard measures like precision, recall, F1-score, AUC, and confusion matrix are also reported to show the significance of our work. The work is aimed to improve public understanding of coronavirus vaccines which can help the health departments to stop the anti-vaccination leagues and motivate people to a booster dose of coronavirus. Full article
(This article belongs to the Special Issue Application of Data Mining in Social Media)
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14 pages, 462 KiB  
Article
Near-Field-to-Far-Field RCS Prediction Using Only Amplitude Estimation Technique Based on State Space Method
by Jinhai Huang, Jianjiang Zhou and Yao Deng
Electronics 2023, 12(15), 3371; https://doi.org/10.3390/electronics12153371 - 7 Aug 2023
Viewed by 926
Abstract
Measuring the radar cross-section (RCS) of a far-field (FF) target in engineering can be challenging, especially when remote measurement is difficult. To overcome this challenge, an FF RCS can be predicted by near-field (NF)-extrapolated transformation. However, due to the relative error between the [...] Read more.
Measuring the radar cross-section (RCS) of a far-field (FF) target in engineering can be challenging, especially when remote measurement is difficult. To overcome this challenge, an FF RCS can be predicted by near-field (NF)-extrapolated transformation. However, due to the relative error between the theoretical and measured electric field (E-field) values in a NF, the extrapolation calculation of a FF can be carried out by correcting the NF amplitude. This paper proposes the use of the state space method (SSM) to estimate the amplitude-only of NF E-fields for improving the prediction accuracy of FFs. The simulation results demonstrate that the SSM can estimate NF amplitude, which can be transformed into a FF, and which can lead to improved prediction accuracy when compared to reference-FF-calculated and to circular-NF-to-FF-transform-(CNFFFT)-calculated RCSs. Full article
(This article belongs to the Special Issue Advancements in Radar Signal Processing)
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18 pages, 18460 KiB  
Article
A Study on Pavement Classification and Recognition Based on VGGNet-16 Transfer Learning
by Junyi Zou, Wenbin Guo and Feng Wang
Electronics 2023, 12(15), 3370; https://doi.org/10.3390/electronics12153370 - 7 Aug 2023
Cited by 2 | Viewed by 1051
Abstract
The types of road surfaces on which intelligent connected cars operate are complicated and varied, and current research lacks the achievement of real-time and reasonably high accuracy for road surface categorization. In this research, we provide a deep learning-based technique for classifying and [...] Read more.
The types of road surfaces on which intelligent connected cars operate are complicated and varied, and current research lacks the achievement of real-time and reasonably high accuracy for road surface categorization. In this research, we provide a deep learning-based technique for classifying and identifying road surfaces that makes use of an improved (VGGNet-16) model, in conjunction with a transfer learning strategy, to gather data from the road surface in front of the car using an on-board camera. To accurately classify data based on obtained road surface photos, the dataset is first preprocessed, then pretrained weights are frozen, and the network is initialized using transfer learning parameters. In order to explore the accuracy analysis of the various models regarding the identification of six types of road surfaces, comparisons were made via the VGG16, AlexNet, InceptionV3, and ResNet50 models, using the same parameter values. The experimental findings demonstrate that the improved VGGNet-16 model, combined with the transfer learning approach, achieves 96.87% accuracy for the classification and recognition of pavements, demonstrating the improved network model’s superior accuracy for these tasks. Additionally, the driving recorder of the vehicle may be used as the sensor to complete pavement detection, which has significant financial advantages. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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19 pages, 874 KiB  
Article
A Novel Container Placement Mechanism Based on Whale Optimization Algorithm for CaaS Clouds
by Abdulelah Alwabel
Electronics 2023, 12(15), 3369; https://doi.org/10.3390/electronics12153369 - 7 Aug 2023
Viewed by 923
Abstract
Advancements in container technology can improve the efficiency of cloud systems by reducing the initiation time of virtual machines (VMs) and improving portability. Therefore, many cloud service providers offer cloud services based on the container as a service (CaaS) model. Container placement (CP) [...] Read more.
Advancements in container technology can improve the efficiency of cloud systems by reducing the initiation time of virtual machines (VMs) and improving portability. Therefore, many cloud service providers offer cloud services based on the container as a service (CaaS) model. Container placement (CP) is a mechanism that allocates containers to a pool of VMs by mapping new containers to VMs and simultaneously considering VM placements on physical machines. The CP mechanism can serve several purposes, such as reducing power consumption and optimizing resource availability. This study presents directed container placement (DCP), a novel policy for placing containers in CaaS cloud systems. DCP extends the whale optimization algorithm, an optimization technique aimed at reducing the power consumption in cloud systems with a minimum effect on the overall performance. The proposed mechanism is evaluated against established methods, namely, improved genetic algorithm and discrete whale optimization using two criteria: energy savings and search time. The experiments demonstrate that DCP consumes approximately 78% less power and reduces the search time by approximately 50% in homogeneous clouds. In addition, DCP saves power by approximately 85% and reduces the search time by approximately 30% in heterogeneous clouds. Full article
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23 pages, 4527 KiB  
Article
Self-Regulated Learning and Active Feedback of MOOC Learners Supported by the Intervention Strategy of a Learning Analytics System
by Ruth Cobos
Electronics 2023, 12(15), 3368; https://doi.org/10.3390/electronics12153368 - 7 Aug 2023
Cited by 1 | Viewed by 1719
Abstract
MOOCs offer great learning opportunities, but they also present several challenges for learners that hinder them from successfully completing MOOCs. To address these challenges, edX-LIMS (System for Learning Intervention and its Monitoring for edX MOOCs) was developed. It is a learning analytics system [...] Read more.
MOOCs offer great learning opportunities, but they also present several challenges for learners that hinder them from successfully completing MOOCs. To address these challenges, edX-LIMS (System for Learning Intervention and its Monitoring for edX MOOCs) was developed. It is a learning analytics system that supports an intervention strategy (based on learners’ interactions with the MOOC) to provide feedback to learners through web-based Learner Dashboards. Additionally, edX-LIMS provides a web-based Instructor Dashboard for instructors to monitor their learners. In this article, an enhanced version of the aforementioned system called edX-LIMS+ is presented. This upgrade introduces new services that enhance both the learners’ and instructors’ dashboards with a particular focus on self-regulated learning. Moreover, the system detects learners’ problems to guide them and assist instructors in better monitoring learners and providing necessary support. The results obtained from the use of this new version (through learners’ interactions and opinions about their dashboards) demonstrate that the feedback provided has been significantly improved, offering more valuable information to learners and enhancing their perception of both the dashboard and the intervention strategy supported by the system. Additionally, the majority of learners agreed with their detected problems, thereby enabling instructors to enhance interventions and support learners’ learning processes. Full article
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21 pages, 2292 KiB  
Article
An Explainable Fake News Analysis Method with Stance Information
by Lu Yuan, Hao Shen, Lei Shi, Nanchang Cheng and Hangshun Jiang
Electronics 2023, 12(15), 3367; https://doi.org/10.3390/electronics12153367 - 7 Aug 2023
Cited by 2 | Viewed by 1404
Abstract
The high level of technological development has enabled fake news to spread faster than real news in cyberspace, leading to significant impacts on the balance and sustainability of current and future social systems. At present, collecting fake news data and using artificial intelligence [...] Read more.
The high level of technological development has enabled fake news to spread faster than real news in cyberspace, leading to significant impacts on the balance and sustainability of current and future social systems. At present, collecting fake news data and using artificial intelligence to detect fake news have an important impact on building a more sustainable and resilient society. Existing methods for detecting fake news have two main limitations: they focus only on the classification of news authenticity, neglecting the semantics between stance information and news authenticity. No cognitive-related information is involved, and there are not enough data on stance classification and news true-false classification for the study. Therefore, we propose a fake news analysis method based on stance information for explainable fake news detection. To make better use of news data, we construct a fake news dataset built on cognitive information. The dataset primarily consists of stance labels, along with true-false labels. We also introduce stance information to further improve news falsity analysis. To better explain the relationship between fake news and stance, we use propensity score matching for causal inference to calculate the correlation between stance information and true-false classification. The experiment result shows that the propensity score matching for causal inference yielded a negative correlation between stance consistency and fake news classification. Full article
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18 pages, 6985 KiB  
Article
Phase Stabilization of a Terahertz Wave Using Mach–Zehnder Interference Detection
by Amalina Athira Ibrahim, Bo Li, Shenghong Ye, Takashi Shiramizu, Hanwei Chen, Yuya Mikami and Kazutoshi Kato
Electronics 2023, 12(15), 3366; https://doi.org/10.3390/electronics12153366 - 7 Aug 2023
Viewed by 1030
Abstract
As a high-frequency carrier, the terahertz (THz) wave is essential for achieving high-data-rate wireless transmission due to its ultra-wide bandwidth. Phase stabilization becomes crucial to enable phase-shift-based multilevel modulation for high-speed data transmission. We developed a Mach–Zehnder interferometric phase stabilization technique for photomixing, [...] Read more.
As a high-frequency carrier, the terahertz (THz) wave is essential for achieving high-data-rate wireless transmission due to its ultra-wide bandwidth. Phase stabilization becomes crucial to enable phase-shift-based multilevel modulation for high-speed data transmission. We developed a Mach–Zehnder interferometric phase stabilization technique for photomixing, which has proved a promising method for phase-stable continuous THz-wave generation. However, this method faced inefficiencies in generating phase-modulated THz waves due to the impact of the phase modulator on the phase stabilization system. By photomixing, which is one of the promising methods for generating THz waves, the phase of the generated THz waves can be controlled in the optical domain so that the stability of the generated THz wave can be controlled by photonics technologies. Thus, we devised a new phase stabilization approach using backward-directional lightwave, which is overlapped with the THz wave generation system. This study presented a conceptual and experimental framework for stabilizing the phase differences of optical carrier signals. We compared the optical domain and transmission performances between forward-directional and backward-directional phase stabilization methods. Remarkably, our results demonstrated error-free transmission at a modulation frequency of 3 Gbit/s and higher. Full article
(This article belongs to the Special Issue Green Communications and Networks)
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16 pages, 6273 KiB  
Article
Design of a Fuel Cell Test System with Fault Identification
by Shusheng Xiong, Zhankuan Wu and Junjie Cheng
Electronics 2023, 12(15), 3365; https://doi.org/10.3390/electronics12153365 - 7 Aug 2023
Viewed by 1400
Abstract
With the growing concerns over the energy crisis and environmental pollution, fuel cells have attracted increasing attention. Proton exchange membrane fuel cells (PEMFCs) have promising prospects due to their economic efficiency, low noise, and minimal environmental pollution. However, the existing commercial testing systems [...] Read more.
With the growing concerns over the energy crisis and environmental pollution, fuel cells have attracted increasing attention. Proton exchange membrane fuel cells (PEMFCs) have promising prospects due to their economic efficiency, low noise, and minimal environmental pollution. However, the existing commercial testing systems for PEMFCs suffer from limited functionalities and lack of scalability. In this study, we propose the design of a testing platform specifically tailored for water-cooled PEMFCs with a power greater than 1 kW. The functionality of the testing platform is verified through static and dynamic testing, demonstrating its compliance with the required standards. Furthermore, a fault diagnosis model for fuel cell stacks is developed based on the back-propagation (BP) neural network, achieving an overall accuracy rate of over 95% for fault classification. Full article
(This article belongs to the Section Electronic Materials)
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26 pages, 10696 KiB  
Article
Rule-Based Architectural Design Pattern Recognition with GPT Models
by Zoltán Richárd Jánki and Vilmos Bilicki
Electronics 2023, 12(15), 3364; https://doi.org/10.3390/electronics12153364 - 6 Aug 2023
Cited by 1 | Viewed by 1950
Abstract
Architectural design patterns are essential in software development because they offer proven solutions to large-scale structural problems in software systems and enable developers to create software that is more maintainable, scalable, and comprehensible. Model-View-Whatever (MVW) design patterns are prevalent in many areas of [...] Read more.
Architectural design patterns are essential in software development because they offer proven solutions to large-scale structural problems in software systems and enable developers to create software that is more maintainable, scalable, and comprehensible. Model-View-Whatever (MVW) design patterns are prevalent in many areas of software development, but their use in Web development is on the rise. There are numerous subtypes of MVW design patterns applicable to Web systems, but there is no exhaustive listing of them. Additionally, it is unclear how these subtypes can be utilized in contemporary Web development, as their usage is typically unconscious. Here, we discuss and define the most prevalent MVW design patterns used in Web development, as well as provide Angular framework examples and guidance on when to employ a particular design pattern. On the premise of the primary characteristics of design patterns, we created a rule system that large language models (LLMs) can comprehend without doubt. Here, we demonstrate how effectively Generative Pre-trained Transformer (GPT) models can identify various design patterns based on our principles and verify the quality of our recommendations. Together, our solution and GPT models constitute an effective natural language processing (NLP) solution capable of detecting MVW design patterns in Angular projects with an average accuracy of 90%. Full article
(This article belongs to the Special Issue Machine Learning (ML) and Software Engineering)
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15 pages, 448 KiB  
Article
Improving Question Answering over Knowledge Graphs with a Chunked Learning Network
by Zicheng Zuo, Zhenfang Zhu, Wenqing Wu, Wenling Wang, Jiangtao Qi and Linghui Zhong
Electronics 2023, 12(15), 3363; https://doi.org/10.3390/electronics12153363 - 6 Aug 2023
Cited by 1 | Viewed by 1626
Abstract
The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly [...] Read more.
The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly understanding the semantics of a problem is difficult for machines. On the other hand, the growing knowledge graph will inevitably lead to information retrieval errors. Specifically, the question-answering task has three difficulties: word abbreviation, object complement, and entity ambiguity. An object complement means that different entities share the same predicate, and entity ambiguity means that words have different meanings in different contexts. To solve these problems, we propose a novel method named the Chunked Learning Network. It uses different models according to different scenarios to obtain a vector representation of the topic entity and relation in the question. The answer entity representation that yields the closest fact triplet, according to a joint distance metric, is returned as the answer. For sentences with an object complement, we use dependency parsing to construct dependency relationships between words to obtain more accurate vector representations. Experiments demonstrate the effectiveness of our method. Full article
(This article belongs to the Special Issue Advances in Intelligent Data Analysis and Its Applications)
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20 pages, 395 KiB  
Article
Enhancing Performance and Security in the Metaverse: Latency Reduction Using Trust and Reputation Management
by Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren and Byung-Seo Kim
Electronics 2023, 12(15), 3362; https://doi.org/10.3390/electronics12153362 - 6 Aug 2023
Cited by 2 | Viewed by 1377
Abstract
In the rapidly evolving landscape of distributed systems, security stands as a significant challenge, especially in the face of network node attacks. Such threats introduce profound complexities into the dynamics of security protocols, trust management, and resource allocation, issues further amplified by the [...] Read more.
In the rapidly evolving landscape of distributed systems, security stands as a significant challenge, especially in the face of network node attacks. Such threats introduce profound complexities into the dynamics of security protocols, trust management, and resource allocation, issues further amplified by the metaverse’s exponential growth. This paper proposes an innovative solution, offering unique technical contributions to address these multi-faceted challenges. We unveil a trust-based resource allocation framework designed to facilitate the secure and efficient sharing of computational resources within the metaverse. This system has the potential to markedly diminish latency, thereby enhancing overall performance. In parallel, we introduce a reputation system that systematically monitors latency across a spectrum of metaverse entities, providing valuable insights for making informed resource allocation decisions. Moreover, we advocate for a decentralized trust management system, specifically designed to withstand potential security breaches without reliance on a centralized authority. This significantly fortifies both system security and user trust. Alongside this, we unveil an inventive proof-of-trust consensus mechanism that fosters trust and collaboration among metaverse entities during resource allocation, thereby cultivating a more secure ecosystem. Our proposed model poses a robust challenge to malicious entities, and it substantially bolsters the security architecture. The simulation results lend substantial credence to the effectiveness of our approach, demonstrating significant improvements in latency reduction, scalability, and the detection of malicious nodes, thereby outperforming existing methodologies. Full article
(This article belongs to the Special Issue Modern Cybersecurity: Theory, Technologies and Applications)
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21 pages, 1494 KiB  
Article
A Practical Non-Profiled Deep-Learning-Based Power Analysis with Hybrid-Supervised Neural Networks
by Fancong Kong, Xiaohua Wang, Kangran Pu, Jingqi Zhang and Hua Dang
Electronics 2023, 12(15), 3361; https://doi.org/10.3390/electronics12153361 - 6 Aug 2023
Viewed by 1218
Abstract
With the rapid advancement of deep learning, the neural network has become the primary approach for non-profiled side-channel attacks. Nevertheless, challenges arise in practical applications due to noise in collected power traces and the substantial amount of data required for training deep learning [...] Read more.
With the rapid advancement of deep learning, the neural network has become the primary approach for non-profiled side-channel attacks. Nevertheless, challenges arise in practical applications due to noise in collected power traces and the substantial amount of data required for training deep learning neural networks. Additionally, acquiring measuring equipment with exceptionally high sampling rates is difficult for average researchers, further obstructing the analysis process. To address these challenges, in this paper, we propose a novel architecture for non-profiled differential deep learning analysis, employing a hybrid-supervised neural network. The architecture incorporates a self-supervised autoencoder to enhance the features of power traces before they are utilized as training data for the supervised neural network. Experimental results demonstrate that the proposed architecture not only outperforms traditional differential deep learning networks by providing a more obvious distinction, but it also achieves key discrimination with reduced computational costs. Furthermore, the architecture is evaluated using small-scale and downsampled datasets, confirming its ability recover correct keys under such conditions. Moreover, the altered architecture designed for data resynchronization was proved to have the ability to distinguish the correct key from small-scale and desynchronized datasets. Full article
(This article belongs to the Special Issue Computer-Aided Design for Hardware Security and Trust)
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23 pages, 3912 KiB  
Article
Image Sampling Based on Dominant Color Component for Computer Vision
by Saisai Wang, Jiashuai Cui, Fan Li and Liejun Wang
Electronics 2023, 12(15), 3360; https://doi.org/10.3390/electronics12153360 - 6 Aug 2023
Viewed by 1106
Abstract
Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately [...] Read more.
Image sampling is a fundamental technique for image compression, which greatly improves the efficiency of image storage, transmission, and applications. However, existing sampling algorithms primarily consider human visual perception and discard irrelevant information based on subjective preferences. Unfortunately, these methods may not adequately meet the demands of computer vision tasks and can even lead to redundancy because of the different preferences between human and computer. To tackle this issue, this paper investigates the key features of computer vision. Based on our findings, we propose an image sampling method based on the dominant color component (ISDCC). In this method, we utilize a grayscale image to preserve the essential structural information for computer vision. Then, we construct a concise color feature map based on the dominant channel of pixels. This approach provides relevant color information for computer vision tasks. We conducted experimental evaluations using well-known benchmark datasets. The results demonstrate that ISDCC adapts effectively to computer vision requirements, significantly reducing the amount of data needed. Furthermore, our method has a minimal impact on the performance of mainstream computer vision algorithms across various tasks. Compared to other sampling approaches, our proposed method exhibits clear advantages by achieving superior results with less data usage. Full article
(This article belongs to the Topic Computer Vision and Image Processing)
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18 pages, 9331 KiB  
Article
CAS-UNet: A Retinal Segmentation Method Based on Attention
by Zeyu You, Haiping Yu, Zhuohan Xiao, Tao Peng and Yinzhen Wei
Electronics 2023, 12(15), 3359; https://doi.org/10.3390/electronics12153359 - 6 Aug 2023
Cited by 4 | Viewed by 1577
Abstract
Retinal vessel segmentation is an important task in medical image analysis that can aid doctors in diagnosing various eye diseases. However, due to the complexity and blurred boundaries of retinal vessel structures, existing methods face many challenges in practical applications. To overcome these [...] Read more.
Retinal vessel segmentation is an important task in medical image analysis that can aid doctors in diagnosing various eye diseases. However, due to the complexity and blurred boundaries of retinal vessel structures, existing methods face many challenges in practical applications. To overcome these challenges, this paper proposes a retina vessel segmentation algorithm based on an attention mechanism, called CAS-UNet. Firstly, the Cross-Fusion Channel Attention mechanism is introduced, and the Structured Convolutional Attention block is used to replace the original convolutional block of U-Net to achieve channel enhancement for retinal blood vessels. Secondly, an Additive Attention Gate is added to the skip-connection layer of the network to achieve spatial enhancement for retinal blood vessels. Finally, the SoftPool pooling method is used to reduce information loss. Experimental results using the CHASEDB1 and DRIVE datasets show that the proposed algorithm achieves an accuracy of 96.68% and 95.86%, and a sensitivity of 83.21% and 83.75%, respectively. The proposed CAS-UNet thus outperforms the existing U-Net-based classic algorithms. Full article
(This article belongs to the Special Issue Application of Machine Learning in Graphics and Images)
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20 pages, 3869 KiB  
Article
An Improved Particle Swarm Optimization Algorithm for the Urban Transit Routing Problem
by Vasileios Kourepinis, Christina Iliopoulou, Ioannis X. Tassopoulos, Chrysanthi Aroniadi and Grigorios N. Beligiannis
Electronics 2023, 12(15), 3358; https://doi.org/10.3390/electronics12153358 - 6 Aug 2023
Cited by 1 | Viewed by 1107
Abstract
The Urban Transit Routing Problem (UTRP) is a challenging discrete problem that revolves around designing efficient routes for public transport systems. It falls under the category of NP-hard problems, characterized by its complexity and numerous constraints. Evaluating potential route sets for feasibility is [...] Read more.
The Urban Transit Routing Problem (UTRP) is a challenging discrete problem that revolves around designing efficient routes for public transport systems. It falls under the category of NP-hard problems, characterized by its complexity and numerous constraints. Evaluating potential route sets for feasibility is a demanding and time-consuming task, often resulting in the rejection of many solutions. Given its difficulty, metaheuristic methods, such as swarm intelligence algorithms, are considered highly suitable for addressing the UTRP. However, the effectiveness of these methods depends heavily on appropriately adapting them to discrete problems, as well as employing suitable initialization procedures and solution-evaluation methods. In this study, a new variant of the particle swarm optimization algorithm is proposed as an efficient solution approach for the UTRP. We present an improved initialization function and improved modification operators, along with a post-optimization routine to further improve solutions. The algorithm’s performance is then compared to the state of the art using Mandl’s widely recognized benchmark, a standard for evaluating UTRP solutions. By comparing the generated solutions to published results from 10 studies on Mandl’s benchmark network, we demonstrate that the developed algorithm outperforms existing techniques, providing superior outcomes. Full article
(This article belongs to the Special Issue Advanced Research in Technology and Information Systems)
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18 pages, 391 KiB  
Article
Global Prescribed-Time Stabilization of Input-Quantized Nonlinear Systems via State-Scale Transformation
by Xin Guo, Wenhui Zhang and Fangzheng Gao
Electronics 2023, 12(15), 3357; https://doi.org/10.3390/electronics12153357 - 5 Aug 2023
Cited by 1 | Viewed by 871
Abstract
The problem of global prescribed-time stabilization is reported in this paper for a kind of uncertain nonlinear system in power normal form. Compared with related work, the distinct characteristics of this study are that the system under consideration has an input-quantized actuator, and [...] Read more.
The problem of global prescribed-time stabilization is reported in this paper for a kind of uncertain nonlinear system in power normal form. Compared with related work, the distinct characteristics of this study are that the system under consideration has an input-quantized actuator, and the prescribed time convergence of the system states is wanted. To meet these special requirements, a novel state-scaling transformation (SST) is firstly given to convert the prescribed-time stabilization of original systems to the asymptotic stabilization of the transformed one. Then, under the new framework of equivalent transformation, a quantized state feedback controller that ensures the achievement of the performance requirements is developed by using a power integrator (API) technique. Finally, the simulation results of a liquid-level system are provided to confirm the efficacy of the proposed approach. Full article
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22 pages, 6139 KiB  
Article
An Industrial Load Classification Method Based on a Two-Stage Feature Selection Strategy and an Improved MPA-KELM Classifier: A Chinese Cement Plant Case
by Mengran Zhou, Ziwei Zhu, Feng Hu, Kai Bian and Wenhao Lai
Electronics 2023, 12(15), 3356; https://doi.org/10.3390/electronics12153356 - 5 Aug 2023
Cited by 2 | Viewed by 1135
Abstract
Accurately identifying industrial loads helps to accelerate the construction of new power systems and is crucial to today’s smart grid development. Therefore, this paper proposes an industrial load classification method based on two-stage feature selection combined with an improved marine predator algorithm (IMPA)-optimized [...] Read more.
Accurately identifying industrial loads helps to accelerate the construction of new power systems and is crucial to today’s smart grid development. Therefore, this paper proposes an industrial load classification method based on two-stage feature selection combined with an improved marine predator algorithm (IMPA)-optimized kernel extreme learning machine (KELM). First, the time- and frequency-domain features of electrical equipment (active and reactive power) are extracted from the power data after data cleaning, and the initial feature pool is established. Next, a two-stage feature selection algorithm is proposed to generate the smallest features, leading to superior classification accuracy. In the initial selection phase, each feature weight is calculated using ReliefF technology, and the features with smaller weights are removed to obtain the candidate feature set. In the reselection stage, the k-nearest neighbor classifier (KNN) based on the MPA is designed to obtain the superior combination of features from the candidate feature set concerning the classification accuracy and the number of feature inputs. Third, the IMPA-KELM classifier is developed as a load identification model. The MPA improvement strategy includes self-mapping to generate chaotic sequence initialization and boundary mutation operations. Compared with the MPA, IMPA has a faster convergence speed and more robust global search capability. In this paper, actual data from the cement industry within China are used as a research case. The experimental results show that after two-stage feature selection, the initial feature set reduces the feature dimensionality from 58 dimensions to 3 dimensions, which is 5.17% of the original. In addition, the proposed IMPA-KELM has the highest overall recognition accuracy of 93.39% compared to the other models. The effectiveness and feasibility of the proposed method are demonstrated. Full article
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19 pages, 865 KiB  
Article
Supervised Dimensionality Reduction of Proportional Data Using Exponential Family Distributions
by Walid Masoudimansour and Nizar Bouguila
Electronics 2023, 12(15), 3355; https://doi.org/10.3390/electronics12153355 - 5 Aug 2023
Viewed by 859
Abstract
Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ill-conditioned second-order statistics matrices. They also do not deal with multi-modal data properly since they construct neighborhood graphs that do not discriminate between multi-modal [...] Read more.
Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ill-conditioned second-order statistics matrices. They also do not deal with multi-modal data properly since they construct neighborhood graphs that do not discriminate between multi-modal classes of data and single-modal ones. In this paper, a novel method that mitigates the above problems is proposed. In this method, assuming the data is from two classes, they are projected into the low-dimensional space in the first step which removes sparsity from the data and reduces the time complexity of any operation drastically afterwards. These projected data are modeled using a mixture of exponential family distributions for each class, allowing the modeling of multi-modal data. A measure for the similarity between the two projected classes is used as an objective function for constructing an optimization problem, which is then solved using a heuristic search algorithm to find the best separating projection. The conducted experiments show that the proposed method outperforms the rest of the compared algorithms and provides a robust effective solution to the problem of dimensionality reduction even in the presence of multi-modal and sparse data. Full article
(This article belongs to the Special Issue Data Push and Data Mining in the Age of Artificial Intelligence)
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18 pages, 513 KiB  
Article
Cascading and Ensemble Techniques in Deep Learning
by I. de Zarzà, J. de Curtò, Enrique Hernández-Orallo and Carlos T. Calafate
Electronics 2023, 12(15), 3354; https://doi.org/10.3390/electronics12153354 - 5 Aug 2023
Cited by 3 | Viewed by 2884
Abstract
In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions [...] Read more.
In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications)
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19 pages, 4391 KiB  
Article
Spatial Modeling of Air Pollution Using Data Fusion
by Adrian Dudek and Jerzy Baranowski
Electronics 2023, 12(15), 3353; https://doi.org/10.3390/electronics12153353 - 5 Aug 2023
Viewed by 1277
Abstract
Air pollution is a widespread issue. One approach to predicting air pollution levels in specific locations is through the development of mathematical models. Spatial models are one such category, and they can be optimized using calculation methods like the INLA (integrated nested Laplace [...] Read more.
Air pollution is a widespread issue. One approach to predicting air pollution levels in specific locations is through the development of mathematical models. Spatial models are one such category, and they can be optimized using calculation methods like the INLA (integrated nested Laplace approximation) package. It streamlines the complex computational process by combining the Laplace approximation and numerical integration to approximate the model and provides a computationally efficient alternative to traditional MCMC (Markov chain Monte Carlo) methods for Bayesian inference in complex hierarchical models. Another crucial aspect is obtaining data for this type of problem. Relying only on official or professional monitoring stations can pose challenges, so it is advisable to employ data fusion techniques and integrate data from various sensors, including amateur ones. Moreover, when modeling spatial air pollution, careful consideration should be given to factors such as the range of impact and potential obstacles that may affect a pollutant’s dispersion. This study showcases the utilization of INLA spatial modeling and data fusion to address multiple problems, such as pollution in industrial facilities and urban areas. The results show promise for resolving such problems with the proposed algorithms. Full article
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10 pages, 6912 KiB  
Communication
185–215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
by Ruibing Dong and Chengwu You
Electronics 2023, 12(15), 3352; https://doi.org/10.3390/electronics12153352 - 5 Aug 2023
Viewed by 1090
Abstract
This paper presents a 220 GHz × 2 amplifier–doubler chain composed of a rat-race balun, a 6-stage driver amplifier, and a frequency doubler. The presented amplifier–doubler chain was fabricated in commercial 40 nm bulk CMOS technology. The maximum cutoff frequency fmax for [...] Read more.
This paper presents a 220 GHz × 2 amplifier–doubler chain composed of a rat-race balun, a 6-stage driver amplifier, and a frequency doubler. The presented amplifier–doubler chain was fabricated in commercial 40 nm bulk CMOS technology. The maximum cutoff frequency fmax for the NMOS transistor produced by this manufacturing process was 290 GHz. The saturation output power of the six-stage driver amplifier at 110 GHz was 11.5 dBm. The transistor of the frequency doubler consisted of a single-row interleaved Poly-Diffusion Contact balancing structure. Theoretically, the single-row interleaved Poly-Diffusion Contact balancing structure was able to effectively avoid parasitic components. The simulated results demonstrate that the presented structure achieves a higher output than the conventional designs. Based on these measured results, the presented amplifier–doubler chain provides a peak output power of 7.9 dBm at 200 GHz and a 3-dB bandwidth of 30 GHz. Based on the comparison with other reported results, the presented amplifier–doubler chain provides the highest output power among reported frequency doublers fabricated in CMOS technology. Full article
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21 pages, 13187 KiB  
Article
Potential of Low-Cost Light Detection and Ranging (LiDAR) Sensors: Case Studies for Enhancing Visitor Experience at a Science Museum
by Nobuyuki Umezu, Shohei Koizumi, Kohki Nakagawa and Saku Nishida
Electronics 2023, 12(15), 3351; https://doi.org/10.3390/electronics12153351 - 5 Aug 2023
Cited by 2 | Viewed by 1798
Abstract
A low-cost light detection and ranging (LiDAR) device has several advantages including being able to perform a wide range of angle measurements, less privacy concerns, and robustness to illumination variance owing to its use of infrared (IR) light. In this study, to enhance [...] Read more.
A low-cost light detection and ranging (LiDAR) device has several advantages including being able to perform a wide range of angle measurements, less privacy concerns, and robustness to illumination variance owing to its use of infrared (IR) light. In this study, to enhance the visitor experience at a science museum, three case studies using low-cost LiDAR sensors are presented: (1) an interactive floor projection to learn about the phases of the Moon; (2) an information kiosk with touchless interaction and visitor tracking; and (3) a visitor tracking box with horizontal and vertical scanning. The proposed kiosk system uses a mirror to reflect a portion of the scanning plane of the LiDAR sensor, to allow the capture of touchless interactions, track visitor positions, and count the number of nearby visitors. The visitor tracking box also uses two detection planes reflected by a mirror: the vertical plane is for counting visitors crossing the scanning plane and the horizontal plane is for tracking visitor positions to generate the corresponding heat maps for the visualization of museum hotspots. A series of evaluation experiments were conducted at a science museum, whereby an accuracy of 85% was obtained to estimate the number of visitors, with an accuracy increasing in counting people taller than 140 cm. The interactive floor received a visitor rating of 4.3–4.4 on a scale of 1–5. Full article
(This article belongs to the Special Issue XRiM: XR Technologies in Future Museums)
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17 pages, 18286 KiB  
Article
Safety Assessment for Full Flight between Beidou Radio Determination Satellite Service Airborne Equipment and 5G System
by Wantong Chen, Yuyin Tian, Shuguang Sun and Ruihua Liu
Electronics 2023, 12(15), 3350; https://doi.org/10.3390/electronics12153350 - 4 Aug 2023
Cited by 1 | Viewed by 939
Abstract
The Beidou Radio Determination Satellite Service (RDSS) is an advantageous service of the Beidou system. However, due to the weak landing power of Beidou RDSS signals and an operating frequency close to the 5G frequency, the system is vulnerable to interference from ground [...] Read more.
The Beidou Radio Determination Satellite Service (RDSS) is an advantageous service of the Beidou system. However, due to the weak landing power of Beidou RDSS signals and an operating frequency close to the 5G frequency, the system is vulnerable to interference from ground signals. In this paper, from the perspective of civil aviation safety, different evaluation indicators are used for the takeoff and cruise phases, respectively, to study the impact caused by adjacent frequency interference on airborne Beidou RDSS equipment. In the takeoff phase, accurate aircraft position information is obtained by processing real trajectory files. Deterministic analysis methods are used to determine the safety distance for the coexistence of the two systems. During the cruise phase, ground-based 5G base stations have less influence on the airborne RDSS receiver due to the high flight altitude, so the main consideration is electromagnetic compatibility between the airborne Beidou RDSS system and the 5G ATG system. By establishing a Boeing 737–800 simulation model, the antenna isolation degree is used as the evaluation index, and a reasonable antenna layout is given according to the evaluation results. In this study, the theoretical simulation and real flight data are combined to summarise the exact range of adjacent frequency influence during the takeoff phase and a reasonable antenna layout during the cruise phase. Full article
(This article belongs to the Special Issue Electromagnetic Interference and Compatibility, Volume III)
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15 pages, 3315 KiB  
Article
An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection
by Ga Xiang, Chen Shi and Yangsen Zhang
Electronics 2023, 12(15), 3349; https://doi.org/10.3390/electronics12153349 - 4 Aug 2023
Cited by 5 | Viewed by 1719
Abstract
Advanced Persistent Threat (APT) seriously threatens a nation’s cyberspace security. Current defense technologies are typically unable to detect it effectively since APT attack is complex and the signatures for detection are not clear. To enhance the understanding of APT attacks, in this paper, [...] Read more.
Advanced Persistent Threat (APT) seriously threatens a nation’s cyberspace security. Current defense technologies are typically unable to detect it effectively since APT attack is complex and the signatures for detection are not clear. To enhance the understanding of APT attacks, in this paper, a novel approach for extracting APT attack events from web texts is proposed. First, the APT event types and event schema are defined. Secondly, an APT attack event extraction dataset in Chinese is constructed. Finally, an APT attack event extraction model based on the BERT-BiGRU-CRF architecture is proposed. Comparative experiments are conducted with ERNIE, BERT, and BERT-BiGRU-CRF models, and the results show that the APT attack event extraction model based on BERT-BiGRU-CRF achieves the highest F1 value, indicating the best extraction performance. Currently, there is seldom APT event extraction research, the work in this paper contributes a new method to Cyber Threat Intelligence (CTI) analysis. By considering the multi-stages, complexity of APT attacks, and the data source from huge credible web texts, the APT event extraction method enhances the understanding of APT attacks and is helpful to improve APT attack detection capabilities. Full article
(This article belongs to the Special Issue AI-Driven Network Security and Privacy)
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19 pages, 9416 KiB  
Article
GMDNet: An Irregular Pavement Crack Segmentation Method Based on Multi-Scale Convolutional Attention Aggregation
by Yawei Qi, Fang Wan, Guangbo Lei, Wei Liu, Li Xu, Zhiwei Ye and Wen Zhou
Electronics 2023, 12(15), 3348; https://doi.org/10.3390/electronics12153348 - 4 Aug 2023
Cited by 1 | Viewed by 1038
Abstract
Pavement cracks are the primary type of distress that cause road damage, and deep-learning-based pavement crack segmentation is a critical technology for current pavement maintenance and management. To address the issues of segmentation discontinuity and poor performance in the segmentation of irregular cracks [...] Read more.
Pavement cracks are the primary type of distress that cause road damage, and deep-learning-based pavement crack segmentation is a critical technology for current pavement maintenance and management. To address the issues of segmentation discontinuity and poor performance in the segmentation of irregular cracks faced by current semantic segmentation models, this paper proposes an irregular pavement crack segmentation method based on multi-scale convolutional attention aggregation. In this approach, GhostNet is first introduced as the model backbone network for reducing parameter count, with dynamic convolution enhancing GhostNet’s feature extraction capability. Next, a multi-scale convolutional attention aggregation module is proposed to cause the model to focus more on crack features and thus improve the segmentation effect on irregular cracks. Finally, a progressive up-sampling structure is used to enrich the feature information by gradually fusing feature maps of different depths to enhance the continuity of segmentation results. The experimental results on the HGCrack dataset show that GMDNet has a lighter model structure and higher segmentation accuracy than the mainstream semantic segmentation algorithms, achieving 75.16% of MIoU and 84.43% of F1 score, with only 7.67 M parameters. Therefore, the GMDNet proposed in this paper can accurately and efficiently segment irregular cracks on pavements that are more suitable for pavement crack segmentation scenarios in practical applications. Full article
(This article belongs to the Special Issue Computer Vision for Modern Vehicles)
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14 pages, 1581 KiB  
Article
A Viscoelastic Model to Evidence Reduced Upper-Limb-Swing Capabilities during Gait for Parkinson’s Disease-Affected Subjects
by Luca Pietrosanti, Cristiano Maria Verrelli, Franco Giannini, Antonio Suppa, Francesco Fattapposta, Alessandro Zampogna, Martina Patera, Viviana Rosati and Giovanni Saggio
Electronics 2023, 12(15), 3347; https://doi.org/10.3390/electronics12153347 - 4 Aug 2023
Viewed by 770
Abstract
Parkinson’s disease (PD) is a chronic neurodegenerative disorder with high worldwide prevalence that manifests with muscle rigidity, tremor, postural instability, and slowness of movement. These motor symptoms are mainly evaluated by clinicians via direct observations of patients and, as such, can potentially be [...] Read more.
Parkinson’s disease (PD) is a chronic neurodegenerative disorder with high worldwide prevalence that manifests with muscle rigidity, tremor, postural instability, and slowness of movement. These motor symptoms are mainly evaluated by clinicians via direct observations of patients and, as such, can potentially be influenced by personal biases and inter- and intra-rater differences. In order to provide more objective assessments, researchers have been developing technology-based systems aimed at objective measurements of motor symptoms, among which are the reduced and/or trembling swings of the lower limbs during gait tests, resulting in data that are potentially prone to more objective evaluations. Within this frame, although the swings of the upper limbs during walking are likewise important, no efforts have been made to reveal their support significance. To fill this lack, this work concerns a technology-based assessment of the forearm-swing capabilities of PD patients with respect to their healthy counterparts. This was obtained by adopting a viscoelastic model validated via measurements during gait tests tackled as an inverse dynamic problem aimed at determining the torque forces acting on the forearms. The obtained results evidence differences in the forearm movements during gait tests of healthy subjects and PD patients with different pathology levels, and, in particular, we evidenced how the worsening of the disease can cause the worsening of the mechanical support offered by the forearm’s swing to the walking process. Full article
(This article belongs to the Special Issue Wearable Sensing Devices and Technology)
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20 pages, 4711 KiB  
Article
The Extraction of Foreground Regions of the Moving Objects Based on Spatio-Temporal Information under a Static Camera
by Yugui Zhang, Lina Yu, Shuang Li, Gang Wang, Xin Jiang and Wenfa Li
Electronics 2023, 12(15), 3346; https://doi.org/10.3390/electronics12153346 - 4 Aug 2023
Viewed by 938
Abstract
The rapid development of computer vision technology provides a basic guarantee for public security reliance on video surveillance. In current video surveillance based on static cameras, accurate and quick extractions of foreground regions of moving objects enable quicker analysis of the behavior of [...] Read more.
The rapid development of computer vision technology provides a basic guarantee for public security reliance on video surveillance. In current video surveillance based on static cameras, accurate and quick extractions of foreground regions of moving objects enable quicker analysis of the behavior of meaningful objects and thus improve the intelligent analysis level of video surveillance. However, there would always occur false detection in the extraction of foreground regions, because of the shaking of tree branches and leaves in the scene and the “ghosting” area caused by the delayed updating of the background model. To solve this problem, this paper proposes a method for the extraction of foreground regions by using spatio-temporal information. This method can accurately extract foreground regions of moving objects by utilizing the difference and complementarity between spatial domain methods and temporal domain methods and further in combination with image processing technology. Specifically, the foreground regions of moving objects can be extracted by the morphological processing of the combination of the spatial information and the morphologically processed temporal information in the video. The experimental results show that the proposed method for the extraction of foreground regions of moving objects in view of the spatio-temporal information can reduce false detections caused by the shaking of tree branches and leaves, and thus effectively extract foreground regions of moving objects. Full article
(This article belongs to the Section Artificial Intelligence)
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20 pages, 43883 KiB  
Review
Review of the Legacy and Future of IEC 61850 Protocols Encompassing Substation Automation System
by Shantanu Kumar, Ahmed Abu-Siada, Narottam Das and Syed Islam
Electronics 2023, 12(15), 3345; https://doi.org/10.3390/electronics12153345 - 4 Aug 2023
Cited by 5 | Viewed by 2250
Abstract
Communication protocols play a pivotal role in the substation automation system as they carry critical information related to asset control, automation, protection, and monitoring. Substation legacy protocols run the assets’ bulk data on multiple wires over long distances. These data packets pass through [...] Read more.
Communication protocols play a pivotal role in the substation automation system as they carry critical information related to asset control, automation, protection, and monitoring. Substation legacy protocols run the assets’ bulk data on multiple wires over long distances. These data packets pass through multiple nodes, which makes the identification of the location and type of various malfunctions a challenging and time-consuming task. As downtime of substations is of high importance from a regulatory and compliance point of view, utilities are motivated to revisit the overall scheme and redesign a new system that features flexibility, adaptability, interoperability, and high accuracy. This paper presents a comprehensive review of various legacy protocols and highlights the path forward for a new protocol laid down as per the IEC 61850 standard. The IEC 61850 protocol is expected to be user-friendly, employ fiber optics instead of conventional copper wires, facilitate the application of non-conventional instrument transformers, and connect Ethernet wires to multiple intelligent electronic devices. However, deployment of smart protocols in future substations is not a straightforward process as it requires careful planning, shutdown and foreseeable issues related to interface with proprietary vendor equipment. Along with the technical issues of communication, future smart protocols call for advanced personnel and engineering skills to embrace the new technology. Full article
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