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Electronics
Volume 13
Issue 12
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Electronics, Volume 13, Issue 12 (June-2 2024) – 197 articles

Cover Story (view full-size image): In a multi-source permanent magnet synchronous motor (PMSM) drive, three distinct winding structures can be implemented: multi-sector, multi-three-phase, and highly coupled. However, due to variations in the magnetic coupling between windings, their low-frequency DC-link current ripple components differ. This paper presents a method to identify the phenomena associated with each low-frequency harmonic content. Three analytical models are developed for the DC current ripple induced by unbalanced winding, counter-electromotive force (back-EMF) harmonics, and aliasing effects, with the results validated through simulations. Experimental validation is conducted for highly coupled winding drives, demonstrating agreement with the analytical models and simulations. View this paper
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21 pages, 2147 KiB  
Article
TrustHealth: Enhancing eHealth Security with Blockchain and Trusted Execution Environments
by Jun Li, Xinman Luo and Hong Lei
Electronics 2024, 13(12), 2425; https://doi.org/10.3390/electronics13122425 - 20 Jun 2024
Viewed by 285
Abstract
The rapid growth of electronic health (eHealth) systems has led to serious security and privacy challenges, highlighting the critical importance of protecting sensitive healthcare data. Although researchers have employed blockchain to tackle data management and sharing within eHealth systems, substantial privacy concerns persist [...] Read more.
The rapid growth of electronic health (eHealth) systems has led to serious security and privacy challenges, highlighting the critical importance of protecting sensitive healthcare data. Although researchers have employed blockchain to tackle data management and sharing within eHealth systems, substantial privacy concerns persist as a primary challenge. In this paper, we introduce TrustHealth, a secure data sharing system that leverages trusted execution environment (TEE) and blockchain technology. TrustHealth leverages blockchain to design smart contracts to offer robust hashing protection for patients’ healthcare data. We provide a secure execution environment for SQLCipher, isolating all sensitive operations of healthcare data from the untrusted environment to ensure the confidentiality and integrity of the data. Additionally, we design a TEE-empowered session key generation protocol that enables secure authentication and key sharing for both parties involved in data sharing. Finally, we implement TrustHealth using Hyperledger Fabric and ARM TrustZone. Through security and performance evaluation, TrustHealth is shown to securely process massive encrypted data flows at a rate of 5000 records per second, affirming the feasibility of our proposed scheme. We believe that TrustHealth offers valuable guidelines for the design and implementation of similar systems, providing a valuable contribution to ensuring the privacy and security of eHealth systems. Full article
(This article belongs to the Special Issue Blockchain-Enabled Trust Management)
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25 pages, 6099 KiB  
Article
Adaptive Multi-Surface Sliding Mode Control with Radial Basis Function Neural Networks and Reinforcement Learning for Multirotor Slung Load Systems
by Clevon Peris, Michael Norton and Suiyang Khoo
Electronics 2024, 13(12), 2424; https://doi.org/10.3390/electronics13122424 - 20 Jun 2024
Viewed by 302
Abstract
While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, [...] Read more.
While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems. Full article
(This article belongs to the Special Issue UAV (Unmanned Aerial Vehicles) Networks: Recent Developments and Emerging Trends)
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20 pages, 4919 KiB  
Article
Mobile Robot Navigation Based on Noisy N-Step Dueling Double Deep Q-Network and Prioritized Experience Replay
by Wenjie Hu, Ye Zhou and Hann Woei Ho
Electronics 2024, 13(12), 2423; https://doi.org/10.3390/electronics13122423 - 20 Jun 2024
Viewed by 373
Abstract
Effective real-time autonomous navigation for mobile robots in static and dynamic environments has become a challenging and active research topic. Although the simultaneous localization and mapping (SLAM) algorithm offers a solution, it often heavily relies on complex global and local maps, resulting in [...] Read more.
Effective real-time autonomous navigation for mobile robots in static and dynamic environments has become a challenging and active research topic. Although the simultaneous localization and mapping (SLAM) algorithm offers a solution, it often heavily relies on complex global and local maps, resulting in significant computational demands, slower convergence rates, and prolonged training times. In response to these challenges, this paper presents a novel algorithm called PER-n2D3QN, which integrates prioritized experience replay, a noisy network with factorized Gaussian noise, n-step learning, and a dueling structure into a double deep Q-network. This combination enhances the efficiency of experience replay, facilitates exploration, and provides more accurate Q-value estimates, thereby significantly improving the performance of autonomous navigation for mobile robots. To further bolster the stability and robustness, meaningful improvements, such as target “soft” updates and the gradient clipping mechanism, are employed. Additionally, a novel and powerful target-oriented reshaping reward function is designed to expedite learning. The proposed model is validated through extensive experiments using the robot operating system (ROS) and Gazebo simulation environment. Furthermore, to more specifically reflect the complexity of the simulation environment, this paper presents a quantitative analysis of the simulation environment. The experimental results demonstrate that PER-n2D3QN exhibits heightened accuracy, accelerated convergence rates, and enhanced robustness in both static and dynamic scenarios. Full article
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17 pages, 8944 KiB  
Article
A Method for In-Loop Video Coding Restoration
by Carlos Salazar, Maria Trujillo and John W. Branch-Bedoya
Electronics 2024, 13(12), 2422; https://doi.org/10.3390/electronics13122422 - 20 Jun 2024
Viewed by 275
Abstract
In-loop restoration is a post-processing task aiming to reduce losses caused by the quantization and the inverse quantization phases in a video coding process. Emerging in-loop restoration methods, most of them based on deep learning, have reported higher quality gains than classical filters. [...] Read more.
In-loop restoration is a post-processing task aiming to reduce losses caused by the quantization and the inverse quantization phases in a video coding process. Emerging in-loop restoration methods, most of them based on deep learning, have reported higher quality gains than classical filters. However, the complexity at the decoder side remains a challenge. The Sparse Restoration Method (SRM) is presented as a low-complexity method that utilizes sparse representation and Natural Scene Statistic metrics to enhance visual quality at the block level. Our method shows potential restoration benefits when applied to synthetic video sequences. Full article
(This article belongs to the Special Issue Image and Video Processing Based on Deep Learning)
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17 pages, 2252 KiB  
Article
Enhanced Multi-View Low-Rank Graph Optimization for Dimensionality Reduction
by Haohao Li and Huibing Wang
Electronics 2024, 13(12), 2421; https://doi.org/10.3390/electronics13122421 - 20 Jun 2024
Viewed by 275
Abstract
In the last decade, graph embedding-based dimensionality reduction for multi-view data has been extensively studied. However, constructing a high-quality graph for dimensionality reduction is still a significant challenge. Herein, we propose a new algorithm, named multi-view low-rank graph optimization for dimensionality reduction (MvLRGO), [...] Read more.
In the last decade, graph embedding-based dimensionality reduction for multi-view data has been extensively studied. However, constructing a high-quality graph for dimensionality reduction is still a significant challenge. Herein, we propose a new algorithm, named multi-view low-rank graph optimization for dimensionality reduction (MvLRGO), which integrates graph optimization with dimensionality reduction into one objective function in order to simultaneously determine the optimal subspace and graph. The subspace learning of each view is conducted independently by the general graph embedding framework. For graph construction, we exploit low-rank representation (LRR) to obtain reconstruction relationships as the affinity weight of the graph. Subsequently, the learned graph of each view is further optimized throughout the learning process to obtain the ideal assignment of relations. Moreover, to integrate information from multiple views, MvLRGO regularizes each of the view-specific optimal graphs such that they align with one another. Benefiting from this term, MvLRGO can achieve flexible multi-view communication without constraining the subspaces of all views to be the same. Various experimental results obtained with different datasets show that the proposed method outperforms many state-of-the-art multi-view and single-view dimensionality reduction algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Computer Vision: Technologies and Applications, 2nd Edition)
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22 pages, 819 KiB  
Article
A Novel Dataset and Approach for Adversarial Attack Detection in Connected and Automated Vehicles
by Tae Hoon Kim, Moez Krichen, Meznah A. Alamro and Gabreil Avelino Sampedro
Electronics 2024, 13(12), 2420; https://doi.org/10.3390/electronics13122420 - 20 Jun 2024
Viewed by 255
Abstract
Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such [...] Read more.
Adversarial attacks have received much attention as communication network applications rise in popularity. Connected and Automated Vehicles (CAVs) must be protected against adversarial attacks to ensure passenger and vehicle safety on the road. Nevertheless, CAVs are susceptible to several types of attacks, such as those that target intra- and inter-vehicle networks. These harmful attacks not only cause user privacy and confidentiality to be lost, but they also have more grave repercussions, such as physical harm and death. It is critical to precisely and quickly identify adversarial attacks to protect CAVs. This research proposes (1) a new dataset comprising three adversarial attacks in the CAV network traffic and normal traffic, (2) a two-phased adversarial attack detection technique named TAAD-CAV, where in the first phase, an ensemble voting classifier having three machine learning classifiers and one separate deep learning classifier is trained, and the output is used in the next phase. In the second phase, a meta classifier (i.e., Decision Tree is used as a meta classifier) is trained on the combined predictions from the previous phase to detect adversarial attacks. We preprocess the dataset by cleaning data, removing missing values, and adjusting the Z-score normalization. Evaluation metrics such as accuracy, recall, precision, F1-score, and confusion matrix are employed to evaluate and compare the performance of the proposed model. Results reveal that TAAD-CAV achieves the highest accuracy with a value of 70% compared with individual ML and DL classifiers. Full article
(This article belongs to the Special Issue Automotive Cyber Security)
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21 pages, 3252 KiB  
Article
UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings
by Tarek Berghout and Mohamed Benbouzid
Electronics 2024, 13(12), 2419; https://doi.org/10.3390/electronics13122419 - 20 Jun 2024
Viewed by 475
Abstract
Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions [...] Read more.
Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment. Full article
(This article belongs to the Special Issue New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems)
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18 pages, 6500 KiB  
Article
NSVDNet: Normalized Spatial-Variant Diffusion Network for Robust Image-Guided Depth Completion
by Jin Zeng and Qingpeng Zhu
Electronics 2024, 13(12), 2418; https://doi.org/10.3390/electronics13122418 - 20 Jun 2024
Viewed by 264
Abstract
Depth images captured by low-cost three-dimensional (3D) cameras are subject to low spatial density, requiring depth completion to improve 3D imaging quality. Image-guided depth completion aims at predicting dense depth images from extremely sparse depth measurements captured by depth sensors with the guidance [...] Read more.
Depth images captured by low-cost three-dimensional (3D) cameras are subject to low spatial density, requiring depth completion to improve 3D imaging quality. Image-guided depth completion aims at predicting dense depth images from extremely sparse depth measurements captured by depth sensors with the guidance of aligned Red–Green–Blue (RGB) images. Recent approaches have achieved a remarkable improvement, but the performance will degrade severely due to the corruption in input sparse depth. To enhance robustness to input corruption, we propose a novel depth completion scheme based on a normalized spatial-variant diffusion network incorporating measurement uncertainty, which introduces the following contributions. First, we design a normalized spatial-variant diffusion (NSVD) scheme to apply spatially varying filters iteratively on the sparse depth conditioned on its certainty measure for excluding depth corruption in the diffusion. In addition, we integrate the NSVD module into the network design to enable end-to-end training of filter kernels and depth reliability, which further improves the structural detail preservation via the guidance of RGB semantic features. Furthermore, we apply the NSVD module hierarchically at multiple scales, which ensures global smoothness while preserving visually salient details. The experimental results validate the advantages of the proposed network over existing approaches with enhanced performance and noise robustness for depth completion in real-use scenarios. Full article
(This article belongs to the Special Issue Image Sensors and Companion Chips)
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21 pages, 5216 KiB  
Article
Temporal Knowledge Graph Reasoning Based on Entity Relationship Similarity Perception
by Siling Feng, Cong Zhou, Qian Liu, Xunyang Ji and Mengxing Huang
Electronics 2024, 13(12), 2417; https://doi.org/10.3390/electronics13122417 - 20 Jun 2024
Viewed by 292
Abstract
Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and static attributes, making it difficult for them [...] Read more.
Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and static attributes, making it difficult for them to effectively handle these temporal attributes. Therefore, these models have limitations in dealing with previously invisible entities that appear over time and the implicit associations of static attributes between entities. To address this issue, we propose a temporal knowledge graph reasoning model based on Entity Relationship Similarity Perception, known as ERSP. This model employs the similarity measurement method to capture the similarity features of entity relationships and static attributes, and then fuses these features to generate structural representations. Finally, we provide a decoder with entity relationship representation, static attribute representation, and structural representation information to form a quadruple. Experiments conducted on five common benchmark datasets show that ERSP surpasses the majority of TKG reasoning methods. Full article
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14 pages, 1027 KiB  
Article
Caching Method for Information-Centric Ad Hoc Networks Based on Content Popularity and Node Centrality
by Masaki Koide, Naoyuki Matsumoto and Tomofumi Matsuzawa
Electronics 2024, 13(12), 2416; https://doi.org/10.3390/electronics13122416 - 20 Jun 2024
Viewed by 351
Abstract
In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a [...] Read more.
In recent years, most internet communications have focused on accessing content such as video, web services, and audio. Conversely, traditional Internet communications are inefficient because they are primarily designed for data transfer between hosts. In response, Information-Centric Networking (ICN) has emerged as a content-oriented networking model. The impact of ICN in reducing the location dependency of data and its high compatibility with ad hoc networks has led to research on realizing Information-Centric ad hoc Networks (ICANET). There has also been extensive research into caching content in the network, which is one of the features of ICN. In static networks, methods have been proposed to cache highly popular content in nodes that are more likely to be used for shortest paths. However, in dynamic networks, content with high popularity should be cached on nodes that are more likely to reach all nodes, as missing nodes need to be taken into account. In this study, we propose a cache control scheme for content caching in ICANET that utilizes both content popularity and the closeness centrality of nodes within the ad hoc network as indicators. To realise the proposed method, a new packet flow based on the Pending Interest Table (PIT) and Content Store (CS) was implemented in the forwarding strategy of ICN. The experiments used ndnSIM, a protocol implementation of NDN based on Network Simulator3, which is widely used in wireless network research. The experimental results showed that the cache hit rate could be increased by up to 4.5% in situations with low content bias. In the same situation, the response delay was also reduced by up to 28.3%. Full article
(This article belongs to the Special Issue New Advances in Multi-Agent Systems: Control and Modelling)
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23 pages, 2784 KiB  
Article
Enhancing Steganography through Optimized Quantization Tables
by Rasa Brūzgienė, Algimantas Venčkauskas, Šarūnas Grigaliūnas and Jonas Petraška
Electronics 2024, 13(12), 2415; https://doi.org/10.3390/electronics13122415 - 20 Jun 2024
Viewed by 355
Abstract
This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, [...] Read more.
This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, capacity, and robustness. The novelty of this research lies in the introduction of the F5A method, which utilizes optimized quantization tables to significantly increase the capacity for concealed information while ensuring high-quality image retention and resistance to unauthorized content recovery. The F5A method integrates cryptographic keys and features to detect and prevent copyright infringement in real time. Experimental evaluations demonstrate that the F5A method improves the mean square error and peak signal-to-noise ratio indices by 1.716 and 1.121 times, respectively, compared to the traditional F5 algorithm. Additionally, it increases the steganographic capacity by up to 1.693 times for smaller images and 1.539 times for larger images. These results underscore the effectiveness of the F5A method in enhancing digital media security and copyright protection. Full article
(This article belongs to the Special Issue Data Security and Privacy: Challenges and Techniques)
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19 pages, 6987 KiB  
Article
Multistable Memristor Synapse-Based Coupled Bi-Hopfield Neuron Model: Dynamic Analysis, Microcontroller Implementation and Image Encryption
by Victor Kamdoum Tamba, Arsene Loic Mbanda Biamou, Viet-Thanh Pham and Giuseppe Grassi
Electronics 2024, 13(12), 2414; https://doi.org/10.3390/electronics13122414 - 20 Jun 2024
Viewed by 312
Abstract
The memristor, a revolutionary electronic component, mimics both neural synapses and electromagnetic induction phenomena. Recent study challenges are the development of effective neural models and discovering their dynamics. In this study, we propose a novel Hopfield neural network model leveraging multistable memristors, showcasing [...] Read more.
The memristor, a revolutionary electronic component, mimics both neural synapses and electromagnetic induction phenomena. Recent study challenges are the development of effective neural models and discovering their dynamics. In this study, we propose a novel Hopfield neural network model leveraging multistable memristors, showcasing its efficacy in encoding biomedical images. We investigate the equilibrium states and dynamic behaviors of our designed model through comprehensive numerical simulations, revealing a rich array of phenomena including periodic orbits, chaotic dynamics, and homogeneous coexisting attractors. The practical realization of our model is achieved using a microcontroller, with experimental results demonstrating strong agreement with theoretical analyses. Furthermore, harnessing the chaos inherent in the neural network, we develop a robust biomedical image encryption technique, validated through rigorous computational performance tests. Full article
(This article belongs to the Special Issue Recent Advances in Chaotic Systems and Their Security Applications, 2nd edition)
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17 pages, 22915 KiB  
Article
Road Surface Defect Detection Algorithm Based on YOLOv8
by Zhen Sun, Lingxi Zhu, Su Qin, Yongbo Yu, Ruiwen Ju and Qingdang Li
Electronics 2024, 13(12), 2413; https://doi.org/10.3390/electronics13122413 - 20 Jun 2024
Viewed by 341
Abstract
In maintaining roads and ensuring safety, promptly detecting and repairing pavement defects is crucial. However, conventional detection methods demand substantial manpower, incur high costs, and suffer from low efficiency. To enhance road maintenance efficiency and reduce costs, we propose an improved algorithm based [...] Read more.
In maintaining roads and ensuring safety, promptly detecting and repairing pavement defects is crucial. However, conventional detection methods demand substantial manpower, incur high costs, and suffer from low efficiency. To enhance road maintenance efficiency and reduce costs, we propose an improved algorithm based on YOLOv8. Our method incorporates several key enhancements. First, we replace conventional convolutions with a module composed of spatial-to-depth layers and nonstrided convolution layers (SPD-Conv) in the network backbone, enhancing the capability of recognizing small-sized defects. Second, we replace the neck of YOLOv8 with the neck of the ASF-YOLO network to fully integrate spatial and scale features, improving multiscale feature extraction capability. Additionally, we introduce the FasterNet block from the FasterNet network into C2f to minimize redundant computations. Furthermore, we utilize Wise-IoU (WIoU) to optimize the model’s loss function, which accounts for the quality factors of objects more effectively, enabling adaptive learning adjustments based on samples of varying qualities. Our model was evaluated on the RDD2022 road damage dataset, demonstrating significant improvements over the baseline model. Specifically, with a 2.8% improvement in mAP and a detection speed reaching 43 FPS, our method proves to be highly effective in real-time road damage detection tasks. Full article
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19 pages, 8962 KiB  
Article
Video Colorization Based on Variational Autoencoder
by Guangzi Zhang, Xiaolin Hong, Yan Liu, Yulin Qian and Xingquan Cai
Electronics 2024, 13(12), 2412; https://doi.org/10.3390/electronics13122412 - 20 Jun 2024
Viewed by 241
Abstract
This paper introduces a variational autoencoder network designed for video colorization using reference images, addressing the challenge of colorizing black-and-white videos. Although recent techniques perform well in some scenarios, they often struggle with color inconsistencies and artifacts in videos that feature complex scenes [...] Read more.
This paper introduces a variational autoencoder network designed for video colorization using reference images, addressing the challenge of colorizing black-and-white videos. Although recent techniques perform well in some scenarios, they often struggle with color inconsistencies and artifacts in videos that feature complex scenes and long durations. To tackle this, we propose a variational autoencoder framework that incorporates spatio-temporal information for efficient video colorization. To improve temporal consistency, we unify semantic correspondence with color propagation, allowing for simultaneous guidance in colorizing grayscale video frames. Additionally, the variational autoencoder learns spatio-temporal feature representations by mapping video frames into a latent space through an encoder network. The decoder network then transforms these latent features back into color images. Compared to traditional coloring methods, our approach accurately captures temporal relationships between video frames, providing precise colorization while ensuring video consistency. To further enhance video quality, we apply a specialized loss function that constrains the generated output, ensuring that the colorized video remains spatio-temporally consistent and natural. Experimental results demonstrate that our method significantly improves the video colorization process. Full article
(This article belongs to the Special Issue Image/Video Processing and Encoding for Contemporary Applications)
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32 pages, 18590 KiB  
Article
Data-Driven Machine Fault Diagnosis of Multisensor Vibration Data Using Synchrosqueezed Transform and Time-Frequency Image Recognition with Convolutional Neural Network
by Dominik Łuczak
Electronics 2024, 13(12), 2411; https://doi.org/10.3390/electronics13122411 - 20 Jun 2024
Viewed by 401
Abstract
Accurate vibration classification using inertial measurement unit (IMU) data is critical for various applications such as condition monitoring and fault diagnosis. This study proposes a novel convolutional neural network (CNN) based approach, the IMU6DoF-SST-CNN in six variants, for robust vibration classification. The method [...] Read more.
Accurate vibration classification using inertial measurement unit (IMU) data is critical for various applications such as condition monitoring and fault diagnosis. This study proposes a novel convolutional neural network (CNN) based approach, the IMU6DoF-SST-CNN in six variants, for robust vibration classification. The method utilizes Fourier synchrosqueezed transform (FSST) and wavelet synchrosqueezed transform (WSST) for time-frequency analysis, effectively capturing the temporal and spectral characteristics of the vibration data. Additionally, was used the IMU6DoF-SST-CNN to explore three different fusion strategies for sensor data to combine information from the IMU’s multiple axes, allowing the CNN to learn from complementary information across various axes. The efficacy of the proposed method was validated using three datasets. The first dataset consisted of constant fan velocity data (three classes: idle, normal operation, and fault) at 200 Hz. The second dataset contained variable fan velocity data (also with three classes: normal operation, fault 1, and fault 2) at 2000 Hz. Finally, a third dataset of Case Western Reserve University (CWRU) comprised bearing fault data with thirteen classes, sampled at 12 kHz. The proposed method achieved a perfect validation accuracy for the investigated vibration classification task. While all variants of the method achieved high accuracy, a trade-off between training speed and image generation efficiency was observed. Furthermore, FSST demonstrated superior localization capabilities compared to traditional methods like continuous wavelet transform (CWT) and short-time Fourier transform (STFT), as confirmed by image representations and interpretability analysis. This improved localization allows the CNN to effectively capture transient features associated with faults, leading to more accurate vibration classification. Overall, this study presents a promising and efficient approach for vibration classification using IMU data with the proposed IMU6DoF-SST-CNN method. The best result was obtained for IMU6DoF-SST-CNN with FSST and sensor-type fusion. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Based Pattern Recognition)
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18 pages, 3164 KiB  
Article
Cough Detection Using Acceleration Signals and Deep Learning Techniques
by Daniel Sanchez-Morillo, Diego Sales-Lerida, Blanca Priego-Torres and Antonio León-Jiménez
Electronics 2024, 13(12), 2410; https://doi.org/10.3390/electronics13122410 - 20 Jun 2024
Viewed by 301
Abstract
Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective [...] Read more.
Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions. Full article
(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)
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12 pages, 3939 KiB  
Article
5G Reconfigurable Intelligent Surface TDOA Localization Algorithm
by Changbao Liu and Yuexia Zhang
Electronics 2024, 13(12), 2409; https://doi.org/10.3390/electronics13122409 - 20 Jun 2024
Viewed by 314
Abstract
In everyday life, 5G-based localization technology is commonly used, but non-line-of-sight (NLOS) environments can block the propagation of the localization signal, thus preventing localization. In order to solve this problem, this paper proposes a reconfigurable intelligent surface non-line-of-sight time difference of arrival (TDOA) [...] Read more.
In everyday life, 5G-based localization technology is commonly used, but non-line-of-sight (NLOS) environments can block the propagation of the localization signal, thus preventing localization. In order to solve this problem, this paper proposes a reconfigurable intelligent surface non-line-of-sight time difference of arrival (TDOA) localization (RNTL) algorithm. Firstly, a model of a reflective-surface-based intelligent localization (RBP) system is constructed, which utilizes multiple RISs deployed in the air to reflect signals. Secondly, in order to reduce the localization error, this paper establishes the optimization problem of minimizing the distance between each estimated coordinate and the actual coordinate and solves it via the piecewise linear chaotic map–gray wolf optimization algorithm (PWLCM-GWO). Finally, the simulation results show that the RNTL algorithm significantly outperforms the traditional gray wolf optimization and particle swarm optimization algorithms in different signal-to-noise ratios, and the localization errors are reduced by 46% and 53.5%, respectively. Full article
(This article belongs to the Special Issue Intelligent Resource Allocation Methods for Next Generation Wireless Cellular Systems)
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16 pages, 1345 KiB  
Article
A Haptic Braille Keyboard Layout for Smartphone Applications
by Georgios Voutsakelis, Nikolaos Tzimos, Georgios Kokkonis and Sotirios Kontogiannis
Electronics 2024, 13(12), 2408; https://doi.org/10.3390/electronics13122408 - 20 Jun 2024
Viewed by 388
Abstract
Though most people are capable of performing many tasks regardless of cognitive or physical challenges, some individuals, especially those with visual impairments, must rely on others to perform even basic tasks. The chance of them interacting with a computing device is minimal, except [...] Read more.
Though most people are capable of performing many tasks regardless of cognitive or physical challenges, some individuals, especially those with visual impairments, must rely on others to perform even basic tasks. The chance of them interacting with a computing device is minimal, except for speech recognition technology, which is quite complicated. Additionally, it has become apparent that mainstream devices are gaining more acceptance among people with vision problems compared to traditional assistive devices. To address this, we developed the Haptic Braille Keyboard Android application to help vision-impaired users interact more easily with devices such as smartphones and tablets. The academic novelty of the application lies in its customization capabilities, which maximize the Quality of Experience for the user. The application allows users to place the Braille buttons in their desired layout for convenience. Users can move and position the virtual buttons on the screen to create a layout for text entry based on the Braille writing system. For this purpose, we conducted extensive testing and experimentation to determine which of the two commonly used Braille layouts is most user-friendly. This work can help visually impaired users interact with smartphones and tablets more easily and independently, making communication less challenging. Full article
(This article belongs to the Special Issue Haptic Systems and the Tactile Internet: Design and Applications)
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9 pages, 6100 KiB  
Article
An 18–40 GHz Ridge Waveguide Magic-T Using Stepped Conducting T-Junction Transition
by Wenchao Peng, Chao Li, Hailong Wang and Longfang Ye
Electronics 2024, 13(12), 2407; https://doi.org/10.3390/electronics13122407 - 20 Jun 2024
Viewed by 338
Abstract
This paper presents a wideband ridge waveguide Magic-T based on an E- and an H-arm waveguide power divider using a stepped conducting T-junction transition. The Magic-T is designed to cover the full band of 18–40 GHz with a relative bandwidth of 76%. This [...] Read more.
This paper presents a wideband ridge waveguide Magic-T based on an E- and an H-arm waveguide power divider using a stepped conducting T-junction transition. The Magic-T is designed to cover the full band of 18–40 GHz with a relative bandwidth of 76%. This bandwidth cannot be achieved by a Magic-T based on a conventional rectangular waveguide. Thus, the ridge waveguide ports have been employed and an 18–40 GHz ridge waveguide Magic-T has been fabricated and measured. It is demonstrated that the proposed Magic-T achieves better than 0.3 dB insertion loss and 15 dB return loss, less than 0.2 dB magnitude imbalance, and 2.5° phase imbalance. In addition, the measured data show good agreement with the simulation results, and high isolation is also obtained. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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18 pages, 4504 KiB  
Article
LACTNet: A Lightweight Real-Time Semantic Segmentation Network Based on an Aggregated Convolutional Neural Network and Transformer
by Xiangyue Zhang, Hexiao Li, Jingyu Ru, Peng Ji and Chengdong Wu
Electronics 2024, 13(12), 2406; https://doi.org/10.3390/electronics13122406 - 19 Jun 2024
Viewed by 314
Abstract
Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN [...] Read more.
Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN architectures for the real-time processing of local and global contextual features. LACTNet is designed with a lightweight Transformer, which integrates a specially designed gated convolutional feedforward network, to establish feature dependencies across distant regions. A Lightweight Average Feature Bottleneck (LAFB) module is designed to effectively capture spatial detail information within the features, thereby enhancing segmentation accuracy. To address the issue of spatial feature loss in the decoder, a long skip-connection approach is employed through the designed Feature Fusion Enhancement Module (FFEM), which enhances the integrity of spatial features and the feature interaction capability in the decoder. LACTNet is evaluated on two datasets, achieving a segmentation accuracy of 74.8% mIoU and a frame rate of 90 FPS on the Cityscapes dataset, and a segmentation accuracy of 71.8% mIoU with a frame rate of 126 FPS on the CamVid dataset. Full article
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19 pages, 859 KiB  
Article
Mitigation of Adversarial Attacks in 5G Networks with a Robust Intrusion Detection System Based on Extremely Randomized Trees and Infinite Feature Selection
by Gianmarco Baldini
Electronics 2024, 13(12), 2405; https://doi.org/10.3390/electronics13122405 - 19 Jun 2024
Viewed by 379
Abstract
Intrusion Detection Systems (IDSs) are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs [...] Read more.
Intrusion Detection Systems (IDSs) are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs presented in the research literature is based on Machine Learning (ML) and Deep Learning (DL) elements, but they may be prone to adversarial attacks, which may undermine the overall performance of the IDS algorithm. This paper proposes a novel IDS focused on the detection of cybersecurity attacks in 5G networks, which addresses in a simple but effective way two specific adversarial attacks: (1) tampering of the labeled set used to train the ML algorithm, (2) modification of the features in the training data set. The approach is based on the combination of two algorithms, which have been introduced recently in the research literature. The first algorithm is the Extremely Randomized Tree (ERT) algorithm, which enhances the capability of Decision Tree (DT) and Random Forest (RF) algorithms to perform classification in data sets, which are unbalanced and of large size as IDS data sets usually are (legitimate traffic messages are more numerous than attack related messages). The second algorithm is the recently introduced Infinite Feature Selection algorithm, which is used to optimize the choice of the hyper-parameter defined in the approach and improve the overall computing efficiency. The result of the application of the proposed approach on a recently published 5G IDS data set proves its robustness against adversarial attacks with different degrees of severity calculated as the percentage of the tampered data set samples. Full article
(This article belongs to the Special Issue Machine Learning and Cybersecurity—Trends and Future Challenges)
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23 pages, 4379 KiB  
Article
Enhancing Multi-Class Attack Detection in Graph Neural Network through Feature Rearrangement
by Hong-Dang Le and Minho Park
Electronics 2024, 13(12), 2404; https://doi.org/10.3390/electronics13122404 - 19 Jun 2024
Viewed by 425
Abstract
As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing system security necessitates the implementation of multi-class intrusion detection [...] Read more.
As network sizes grow, attack schemes not only become more varied but also increase in complexity. This diversification leads to a proliferation of attack variants, complicating the identification and differentiation of potential threats. Enhancing system security necessitates the implementation of multi-class intrusion detection systems. This approach enables the categorization of incoming network traffic into distinct intrusion types and illustrates the specific attack encountered within the Internet. Numerous studies have leveraged deep learning (DL) for Network-based Intrusion Detection Systems (NIDS), aiming to improve intrusion detection. Among these DL algorithms, Graph Neural Networks (GNN) stand out for their ability to efficiently process unstructured data, especially network traffic, making them particularly suitable for NIDS applications. Although NIDS usually monitors incoming and outgoing flows in a network, represented as edge features in graph format, traditional GNN studies only consider node features, overlooking edge features. This oversight can result in losing important flow data and diminish the system’s ability to detect attacks effectively. To address this limitation, our research makes several key contributions: (1) Emphasize the significance of edge features for enhancing GNN for multi-class intrusion detection, (2) Utilize port information, which is essential for identifying attacks but often overlooked during training, (3) Reorganize features embedded within the graph. By doing this, the graph can represent close to the actual network, which is the node showing endpoint identification information such as IP addresses and ports; the edge contains information related to flow such as Duration, Number of Packet/s, and Length…; (4) Compared to traditional methods, our experiments demonstrate significant performance improvements on both CIC-IDS-2017 (98.32%) and UNSW-NB15 (96.71%) datasets. Full article
(This article belongs to the Special Issue AI Security and Safety)
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14 pages, 3673 KiB  
Article
Bearing Fault Vibration Signal Denoising Based on Adaptive Denoising Autoencoder
by Haifei Lu, Kedong Zhou and Lei He
Electronics 2024, 13(12), 2403; https://doi.org/10.3390/electronics13122403 - 19 Jun 2024
Viewed by 275
Abstract
Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a [...] Read more.
Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an adaptive denoising autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE’s noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings. Full article
(This article belongs to the Special Issue Recent Advances in Signal Processing and Applications)
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19 pages, 1329 KiB  
Article
A Fast and Cost-Effective Calibration Strategy of Inter-Stage Residual Amplification Errors for Cyclic-Pipelined ADCs
by Jinge Ma, Yanjin Lyu, Guoao Liu and Yuanqi Hu
Electronics 2024, 13(12), 2402; https://doi.org/10.3390/electronics13122402 - 19 Jun 2024
Viewed by 238
Abstract
Due to nonideal residue amplification, the limited resolution of pipelined analog-to-digital converters (ADCs) has become a popular research topic for ADC designers. High-gain and high-speed amplifiers usually consume too much power for a decent ADC. Hence, this paper proposes a fast and cost-effective [...] Read more.
Due to nonideal residue amplification, the limited resolution of pipelined analog-to-digital converters (ADCs) has become a popular research topic for ADC designers. High-gain and high-speed amplifiers usually consume too much power for a decent ADC. Hence, this paper proposes a fast and cost-effective foreground calibration strategy for cyclic-pipelined ADCs. The calibration strategy compensates for the gain error due to inter-stage residual amplification, which alleviates the DC gain requirement for internal amplifiers. Unlike other digital calibrations, the proposed scheme is implemented with a cyclic-pipelined structure, and only one parameter needs to be calibrated, whose value can be feasibly calculated by the Fix-Point Iteration algorithm. The proposed calibration scheme is implemented in an area-efficient 16-bit, 2 MS/s cyclic-pipelined ADC, fabricated in 180 nm CMOS technology. The ADC is designed and realized by cycling a 6-bit sub-ADC four times with 1-bit redundancy each time. The calibration algorithm manages to recover the sampled data to 93.85 dB spurious free dynamic range (SFDR) even with a 57.8 dB-DC-gain amplifier. The total power consumption of ADC is 17.92 mW and it occupies an active area of 1.8 mm2. Full article
(This article belongs to the Collection Analog IC Design—Analog and RF Interface for Electronic Systems)
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20 pages, 4947 KiB  
Article
FPGA-Based Acceleration of Polar-Format Algorithm for Video Synthetic-Aperture Radar Imaging
by Dongmin Jeong, Myeongjin Lee, Wookyung Lee and Yunho Jung
Electronics 2024, 13(12), 2401; https://doi.org/10.3390/electronics13122401 - 19 Jun 2024
Viewed by 275
Abstract
This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the [...] Read more.
This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies. Full article
(This article belongs to the Special Issue System-on-Chip (SoC) and Field-Programmable Gate Array (FPGA) Design)
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12 pages, 371 KiB  
Article
A Hardware Trojan Diagnosis Method for Gate-Level Netlists Based on Graph Theory
by Hongxu Gao, Guangxi Zhai, Zeyu Li, Jia Zhou, Xiang Li and Quan Wang
Electronics 2024, 13(12), 2400; https://doi.org/10.3390/electronics13122400 - 19 Jun 2024
Viewed by 260
Abstract
With the increasing complexity of integrated circuit design, the threat of a hardware Trojan (HT) is becoming more and more prominent. At present, the research mainly focuses on the detection of HTs, but the amount of research on the diagnosis of HTs is [...] Read more.
With the increasing complexity of integrated circuit design, the threat of a hardware Trojan (HT) is becoming more and more prominent. At present, the research mainly focuses on the detection of HTs, but the amount of research on the diagnosis of HTs is very small. The number of existing HT diagnosis methods is generally completed by detecting the HT nodes in the netlist. The main reason is the lack of consideration of the integrity of HTs, so the diagnosis accuracy is low. Based on the above reason, this paper proposes two implanted node search algorithms named layer-by-layer difference search (LDS) and layer-by-layer grouping difference search (LGDS). The LDS algorithm can greatly reduce the search time, and the LGDS algorithm can solve the problem of input node disorder. The two methods greatly reduce the number of nodes sorting and comparing, and therefore the time complexity is lower. Moreover, the relevance between implanted nodes is taken into account to improve the diagnosis rate. We completed experiments on an HT diagnosis; the HT implantation example is from Trust-Hub. The experimental results are shown as follows: (1) The average true positive rate (TPR) of the diagnosis using KNN, RF, or SVM with the LDS or LGDS algorithm is more than 93%, and the average true negative rate (TNR) is 100%. (2) The average proportion of implanted nodes obtained by the LDS or LGDS algorithm is more than 97%. The proposed method has a lower time complexity compared with other existing diagnosis methods, and the diagnosis time is shortened by nearly 75%. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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17 pages, 6524 KiB  
Article
Classification of Partial Discharge Sources in Ultra-High Frequency Using Signal Conditioning Circuit Phase-Resolved Partial Discharges and Machine Learning
by Almir Carlos dos Santos Júnior, Alexandre Jean René Serres, George Victor Rocha Xavier, Edson Guedes da Costa, Georgina Karla de Freitas Serres, Antonio Francisco Leite Neto, Itaiara Félix Carvalho, Luiz Augusto Medeiros Martins Nobrega and Pavlos Lazaridis
Electronics 2024, 13(12), 2399; https://doi.org/10.3390/electronics13122399 - 19 Jun 2024
Viewed by 335
Abstract
This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. [...] Read more.
This work presents a methodology for the generation and classification of phase-resolved partial discharge (PRPD) patterns based on the use of a printed UHF monopole antenna and signal conditioning circuit to reduce hardware requirements. For this purpose, the envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric discs with internal cavities in an oil cell, a potential transformer and tip–tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as a reference. After the acquisition of conditioned UHF signals, a digital signal filtering threshold technique was used, and peaks of partial discharge envelope pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector machine and decision tree algorithms. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work. Full article
(This article belongs to the Special Issue Advances in RF, Analog, and Mixed Signal Circuits)
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21 pages, 1914 KiB  
Article
An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning
by Hanan Saleh Alhaji, Yuksel Celik and Sanjay Goel
Electronics 2024, 13(12), 2398; https://doi.org/10.3390/electronics13122398 - 19 Jun 2024
Viewed by 265
Abstract
The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant [...] Read more.
The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI (XAI))
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18 pages, 1877 KiB  
Article
Geometry Optimization of Stratospheric Pseudolite Network for Navigation Applications
by Yi Qu, Sheng Wang, Hui Feng and Qiang Liu
Electronics 2024, 13(12), 2397; https://doi.org/10.3390/electronics13122397 - 19 Jun 2024
Viewed by 240
Abstract
A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research [...] Read more.
A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research about SPN geometry optimization has centered on geometric dilution of precision (GDOP). However, previous research rarely dealt with the topic of how SPN geometry configuration not only affects its GDOP, but also affects its energy balance. To obtain an optimal integrated performance, this paper employs the proportion of energy consumption in energy production as an indicator to assess SPN energy status and designs a composite indicator including GDOP and energy status to assess SPN geometry performance. Then, this paper proposes an SPN geometry optimization algorithm based on gray wolf optimization. Furthermore, this paper implements a series of simulations with an SPN composed of six SPs in a specific service area. Simulations show that the proposed algorithm can obtain SPN geometry solutions with good GDOP and energy balance performance. Also, simulations show that in the supposed scenarios and the specific area, a higher SP altitude can improve both GDOP and energy balance, while a lower SP latitude can improve SPN energy status. Full article
(This article belongs to the Special Issue Advances in Social Bots)
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26 pages, 10104 KiB  
Article
Validation Scores to Evaluate the Detection Capability of Sensor Systems Used for Autonomous Machines in Outdoor Environments
by Magnus Komesker, Christian Meltebrink, Stefan Ebenhöch, Yannick Zahner, Mirko Vlasic and Stefan Stiene
Electronics 2024, 13(12), 2396; https://doi.org/10.3390/electronics13122396 - 19 Jun 2024
Viewed by 500
Abstract
The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied [...] Read more.
The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied to outdoor machines. The scores quantify, in terms of safety, the suitability of the sensor for the intended implementation in an environmental perception system of (highly) automated machines. This was achieved by developing an extension to the new Real Environment Detection Area (REDA) method and linking the methodology with the sensor standard IEC/TS 62998-1. The extension includes point-by-point and statistic-based error evaluation which leads to the Usability-Score, Availability-Score, and Reliability-Score. By applying the principle in the agricultural sector using ISO 18497 and linking this with data from a real outdoor test stand, it was possible to show that the validation scores offer a generic approach to quantify the detection capability and express this in a machine manufacturer-oriented manner. The findings of this study have significant implications for the advancement of safety-related sensor systems integrated into machines operating in complex environments. In order to achieve full implementation, it is necessary to define in the standards which score is required for each Performance Level (PL). Full article
(This article belongs to the Special Issue Intelligent Sensor Systems Applied in Smart Agriculture)
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