Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Zero-FVeinNet: Optimizing Finger Vein Recognition with Shallow CNNs and Zero-Shuffle Attention for Low-Computational Devices
Electronics 2024, 13(9), 1751; https://doi.org/10.3390/electronics13091751 - 01 May 2024
Abstract
In the context of increasing reliance on mobile devices, robust personal security solutions are critical. This paper presents Zero-FVeinNet, an innovative, lightweight convolutional neural network (CNN) tailored for finger vein recognition on mobile and embedded devices, which are typically resource-constrained. The model integrates
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In the context of increasing reliance on mobile devices, robust personal security solutions are critical. This paper presents Zero-FVeinNet, an innovative, lightweight convolutional neural network (CNN) tailored for finger vein recognition on mobile and embedded devices, which are typically resource-constrained. The model integrates cutting-edge features such as Zero-Shuffle Coordinate Attention and a blur pool layer, enhancing architectural efficiency and recognition accuracy under various imaging conditions. A notable reduction in computational demands is achieved through an optimized design involving only 0.3 M parameters, thereby enabling faster processing and reduced energy consumption, which is essential for mobile applications. An empirical evaluation on several leading public finger vein datasets demonstrates that Zero-FVeinNet not only outperforms traditional biometric systems in speed and efficiency but also establishes new standards in biometric identity verification. The Zero-FVeinNet achieves a Correct Identification Rate (CIR) of 99.9% on the FV-USM dataset, with a similarly high accuracy on other datasets. This paper underscores the potential of Zero-FVeinNet to significantly enhance security features on mobile devices by merging high accuracy with operational efficiency, paving the way for advanced biometric verification technologies.
Full article
(This article belongs to the Special Issue Emerging Artificial Intelligence Technologies and Applications)
Open AccessCommunication
Objective Video Quality Assessment Method for Object Recognition Tasks
by
Mikołaj Leszczuk, Lucjan Janowski, Jakub Nawała and Atanas Boev
Electronics 2024, 13(9), 1750; https://doi.org/10.3390/electronics13091750 - 01 May 2024
Abstract
In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision
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In the field of video quality assessment for object recognition tasks, accurately predicting the impact of different quality factors on recognition algorithms remains a significant challenge. Our study introduces a novel evaluation framework designed to address this gap by focussing on machine vision rather than human perceptual quality metrics. We used advanced machine learning models and custom Video Quality Indicators to enhance the predictive accuracy of object recognition performance under various conditions. Our results indicate a model performance, achieving a mean square error (MSE) of 672.4 and a correlation coefficient of 0.77, which underscores the effectiveness of our approach in real-world scenarios. These findings highlight not only the robustness of our methodology but also its potential applicability in critical areas such as surveillance and telemedicine.
Full article
(This article belongs to the Special Issue Machine Learning, Image Analysis and IoT Applications in Industry)
Open AccessArticle
Refining Localized Attention Features with Multi-Scale Relationships for Enhanced Deepfake Detection in Spatial-Frequency Domain
by
Yuan Gao, Yu Zhang, Ping Zeng and Yingjie Ma
Electronics 2024, 13(9), 1749; https://doi.org/10.3390/electronics13091749 - 01 May 2024
Abstract
The rapid advancement of deep learning and large-scale AI models has simplified the creation and manipulation of deepfake technologies, which generate, edit, and replace faces in images and videos. This gradual ease of use has turned the malicious application of forged faces into
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The rapid advancement of deep learning and large-scale AI models has simplified the creation and manipulation of deepfake technologies, which generate, edit, and replace faces in images and videos. This gradual ease of use has turned the malicious application of forged faces into a significant threat, complicating the task of deepfake detection. Despite the notable success of current deepfake detection methods, which predominantly employ data-driven CNN classification models, these methods exhibit limited generalization capabilities and insufficient robustness against novel data unseen during training. To tackle these challenges, this paper introduces a novel detection framework, ReLAF-Net. This framework employs a restricted self-attention mechanism that applies self-attention to deep CNN features flexibly, facilitating the learning of local relationships and inter-regional dependencies at both fine-grained and global levels. This attention mechanism has a modular design that can be seamlessly integrated into CNN networks to improve overall detection performance. Additionally, we propose an adaptive local frequency feature extraction algorithm that decomposes RGB images into fine-grained frequency domains in a data-driven manner, effectively isolating fake indicators in the frequency space. Moreover, an attention-based channel fusion strategy is developed to amalgamate RGB and frequency information, achieving a comprehensive facial representation. Tested on the high-quality version of the FaceForensics++ dataset, our method attained a detection accuracy of 97.92%, outperforming other approaches. Cross-dataset validation on Celeb-DF, DFDC, and DFD confirms the robust generalizability, offering a new solution for detecting high-quality deepfake videos.
Full article
(This article belongs to the Special Issue Deep Learning in Image Processing and Computer Vision)
Open AccessArticle
Enhancing Moisture-Induced Defect Detection in Insulated Steel Pipes through Infrared Thermography and Hybrid Dataset
by
Reza Khoshkbary Rezayiye, Clemente Ibarra-Castanedo and Xavier Maldague
Electronics 2024, 13(9), 1748; https://doi.org/10.3390/electronics13091748 - 01 May 2024
Abstract
It is crucial to accurately detect moisture-induced defects in steel pipe insulation in order to combat corrosion under insulation (CUI). This study enhances the capabilities of infrared thermography (IRT) by integrating it with top-performing machine learning models renowned for their effectiveness in image
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It is crucial to accurately detect moisture-induced defects in steel pipe insulation in order to combat corrosion under insulation (CUI). This study enhances the capabilities of infrared thermography (IRT) by integrating it with top-performing machine learning models renowned for their effectiveness in image segmentation tasks. A novel methodology was developed to enrich machine learning training, incorporating synthetic datasets generated via finite element method (FEM) simulations with experimental data. The performance of four advanced models—UNet, UNet++, DeepLabV3+, and FPN—was evaluated. These models demonstrated significant enhancements in defect detection capabilities, with notable improvements observed in FPN, which exhibited a mean intersection over union (IoU) increase from 0.78 to 0.94, a reduction in loss from 0.19 to 0.06, and an F1 score increase from 0.92 to 0.96 when trained on hybrid datasets compared to those trained solely on real data. The results highlight the benefits of integrating synthetic and experimental data, effectively overcoming the challenges of limited dataset sizes, and significantly improving the models’ accuracy and generalization capabilities in identifying defects. This approach marks a significant advancement in industrial maintenance and inspection, offering a precise, reliable, and scalable solution to managing the risks associated with CUI.
Full article
(This article belongs to the Special Issue Adversarial Machine Learning: Attacks, Defenses and Security)
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Open AccessArticle
AliasClassifier: A High-Performance Router Alias Classifier
by
Yuancheng Xie, Zhaoxin Zhang, Enhao Chen and Ning Li
Electronics 2024, 13(9), 1747; https://doi.org/10.3390/electronics13091747 - 01 May 2024
Abstract
The task of router alias resolution for IPv4 networks presents a formidable challenge in the realm of router-level topology inference. Despite the considerable potential exhibited by machine-learning-based alias-resolution methods for IPv4 networks, several constraints impede their effectiveness. These constraints include a low discovery
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The task of router alias resolution for IPv4 networks presents a formidable challenge in the realm of router-level topology inference. Despite the considerable potential exhibited by machine-learning-based alias-resolution methods for IPv4 networks, several constraints impede their effectiveness. These constraints include a low discovery rate of aliased IPs, a failure to account for router aggregation, and a dearth of valid features in current schemes. In this study, we introduce a novel alias resolver, AliasClassifier, which is based on the Random Forest model and the alias triangulation algorithm. This innovative model identifies the key six features from a set of four prevalent routing behaviors that are typically employed to distinguish aliased IPs from non-alienated IPs. Subsequently, the AliasClassifier aggregates aliased IP pairs into routers using an alias triangulation algorithm. Experimental results demonstrate that AliasClassifier excels in discovering aliased IPs in IPv4 networks, boasting a resolution accuracy as high as 94.8% and a recall rate of 40.4%. Its comprehensive performance significantly surpasses that of state-of-the-art alias resolvers such as TreeNET, MLAR, and APPLE. Furthermore, as a typical centralized alias parser, AliasClassifier’s deployment cost is remarkably low. Consequently, AliasClassifier emerges as an ideal tool for router alias resolution in large-scale IPv4 networks.
Full article
(This article belongs to the Special Issue Advanced Machine Learning Applications for Security, Privacy, and Reliability)
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Open AccessArticle
Causal Inference and Prefix Prompt Engineering Based on Text Generation Models for Financial Argument Analysis
by
Fei Ding, Xin Kang, Linhuang Wang, Yunong Wu, Satoshi Nakagawa and Fuji Ren
Electronics 2024, 13(9), 1746; https://doi.org/10.3390/electronics13091746 - 01 May 2024
Abstract
The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enhancing human initiatives. Despite its importance, current technologies
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The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enhancing human initiatives. Despite its importance, current technologies face significant challenges, including (1) low interpretability, (2) lack of precision and robustness, particularly in specialized fields like finance, and (3) the inability to deploy effectively on lightweight devices. To address these challenges, we introduce a framework uniquely designed to process and analyze massive volumes of argument data efficiently and accurately. This framework employs a text-to-text Transformer generation model as its backbone, utilizing multiple prompt engineering methods to fine-tune the model. These methods include Causal Inference from ChatGPT, which addresses the interpretability problem, and Prefix Instruction Fine-tuning as well as in-domain further pre-training, which tackle the issues of low robustness and accuracy. Ultimately, the proposed framework generates conditional outputs for specific tasks using different decoders, enabling deployment on consumer-grade devices. After conducting extensive experiments, our method achieves high accuracy, robustness, and interpretability across various tasks, including the highest F1 scores in the NTCIR-17 FinArg-1 tasks.
Full article
(This article belongs to the Section Artificial Intelligence)
Open AccessArticle
Multi-Channel Audio Completion Algorithm Based on Tensor Nuclear Norm
by
Lin Zhu, Lidong Yang, Yong Guo, Dawei Niu and Dandan Zhang
Electronics 2024, 13(9), 1745; https://doi.org/10.3390/electronics13091745 - 01 May 2024
Abstract
Multi-channel audio signals provide a better auditory sensation to the audience. However, missing data may occur in the collection, transmission, compression, or other processes of audio signals, resulting in audio quality degradation and affecting the auditory experience. As a result, the completeness of
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Multi-channel audio signals provide a better auditory sensation to the audience. However, missing data may occur in the collection, transmission, compression, or other processes of audio signals, resulting in audio quality degradation and affecting the auditory experience. As a result, the completeness of the audio signal has become a popular research topic in the field of signal processing. In this paper, the tensor nuclear norm is introduced into the audio signal completion algorithm, and the multi-channel audio signals with missing data are restored by using the completion algorithm based on the tensor nuclear norm. First of all, the multi-channel audio signals are preprocessed and are then transformed from the time domain to the frequency domain. Afterwards, the multi-channel audio with missing data is modeled to construct a third-order multi-channel audio tensor. In the next part, the tensor completion algorithm is used to complete the third-order tensor. The optimal solution of the convex optimization model of the tensor completion is obtained by using the convex relaxation technique and, ultimately, the data recovery of the multi-channel audio with data loss is accomplished. The experimental results of the tensor completion algorithm and the traditional matrix completion algorithm are compared using both objective and subjective indicators. The final result shows that the high-order tensor completion algorithm has a better completion ability and can restore the audio signal better.
Full article
(This article belongs to the Section Circuit and Signal Processing)
Open AccessArticle
SpikeExplorer: Hardware-Oriented Design Space Exploration for Spiking Neural Networks on FPGA
by
Dario Padovano, Alessio Carpegna, Alessandro Savino and Stefano Di Carlo
Electronics 2024, 13(9), 1744; https://doi.org/10.3390/electronics13091744 - 01 May 2024
Abstract
One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable
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One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs.
Full article
(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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Open AccessArticle
Fingerprint-Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface-Assisted Networks
by
Shiru Duan, Yuexia Zhang and Ruiqi Liu
Electronics 2024, 13(9), 1743; https://doi.org/10.3390/electronics13091743 - 01 May 2024
Abstract
The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and
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The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment.
Full article
(This article belongs to the Special Issue New Advances in Navigation and Positioning Systems)
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Open AccessArticle
Research on Frequency Discrimination Method Using Multiplicative-Integral and Linear Transformation Network
by
Pengcheng Wang, Sen Yan and Xiuhua Li
Electronics 2024, 13(9), 1742; https://doi.org/10.3390/electronics13091742 - 01 May 2024
Abstract
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In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency
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In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency modulation signal are accurately analyzed by the linear transformation network to restore the original modulation signal. Compared with the phase discriminator, the simulation results show that this method has a higher frequency discrimination bandwidth. In addition, this method has better anti-noise performance, and the frequency discrimination distortion caused by noise with a different Signal-to-Noise Ratio is reduced by 33.80% on average compared with the phase discriminator. What is more, the carrier center frequency error has little influence on the frequency discrimination quality of this method, which solves the problem that most common frequency discriminators are seriously affected by the carrier center frequency error. This method requires a low accuracy of carrier center frequency, which makes it extremely suitable for digital frequency discrimination technology and can meet the needs of various frequency discrimination occasions.
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Open AccessArticle
A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors
by
Shoudong Xu, Jinxin Ouyang, Jiyu Chen and Xiaofu Xiong
Electronics 2024, 13(9), 1741; https://doi.org/10.3390/electronics13091741 - 01 May 2024
Abstract
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To ensure safe and stable operation, accurate fault localization within active distribution networks is required, and this has attracted much attention. Influenced by many factors such as the control strategy, control performance, initial state of the distributed generators, and distribution network topology, it
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To ensure safe and stable operation, accurate fault localization within active distribution networks is required, and this has attracted much attention. Influenced by many factors such as the control strategy, control performance, initial state of the distributed generators, and distribution network topology, it is still difficult to reliably locate complex and variable single-phase short-circuit faults relying only on a single feature quantity, while localization methods incorporating intelligent algorithms are affected by the choice of a priori samples and the fact that the solution process is a black-box model. To address this challenge, in this work, an expression for the single-phase short-circuit current vector of a distribution network containing distributed generators is derived, and the differences in magnitude and phase angle of the short-circuit current vectors upstream and downstream of the fault point are analyzed. Based on measurement theory, a fault confidence distribution function that reacts to the relative size of the current magnitude difference and phase angle difference is established, and the fusion fault confidence of the short-circuit current vector is constructed with the help of evidence theory. Finally, a method of locating single-phase short-circuit faults in distribution networks that contain distributed generators is proposed. The simulation results show that the ratio of the fusion fault confidence of the short-circuit current vector between faulted and non-faulted sections under the influence of different distributed generator capacities, fault locations, and transition resistances differ significantly. The proposed single-phase short-circuit fault localization method is both adaptive and physically interpretable and has clear boundaries, sound sensitivity, and engineering practicability.
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Open AccessArticle
Digitally Controlled Hybrid Switching Step-Up Converter
by
Evelyn-Astrid Lovasz, Dan Lascu and Septimiu Lica
Electronics 2024, 13(9), 1740; https://doi.org/10.3390/electronics13091740 - 01 May 2024
Abstract
This paper focuses on the digital closed-loop design for a step-up converter with hybrid switching. For this purpose, for the first time, the control-to-output small-signal transfer function of a hybrid switching converter is determined in the rational form. Based on it, a type
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This paper focuses on the digital closed-loop design for a step-up converter with hybrid switching. For this purpose, for the first time, the control-to-output small-signal transfer function of a hybrid switching converter is determined in the rational form. Based on it, a type 3 analog controller is designed, and then, its digitized counterpart is found, and the digital controller is designed using a digital signal processor. The closed-loop operation is then validated both through simulation and practical implementation.
Full article
(This article belongs to the Special Issue Analysis and Control Techniques in Power-Electronic-Based Power System)
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Open AccessArticle
Enhancing Human Activity Recognition with Siamese Networks: A Comparative Study of Contrastive and Triplet Learning Approaches
by
Byung-Rae Cha and Binod Vaidya
Electronics 2024, 13(9), 1739; https://doi.org/10.3390/electronics13091739 - 01 May 2024
Abstract
This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR’s growing importance in healthcare, sports, and smart
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This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR’s growing importance in healthcare, sports, and smart environments, the need for advanced models capable of accurately recognizing and classifying complex human activities has become paramount. Addressing this, we have introduced a Siamese network architecture integrated with convolutional neural networks (CNNs) for spatial feature extraction, bidirectional LSTM (Bi-LSTM) for temporal dependency capture, and attention mechanisms to prioritize salient features. Employing both contrastive and triplet loss functions, we meticulously analyze the impact of these learning approaches on the network’s ability to generate discriminative embeddings for HAR tasks. Through extensive experimentation, the study reveals that Siamese networks, particularly those utilizing triplet loss functions, demonstrate superior performance in activity recognition accuracy and F1 scores compared with baseline deep learning models. The inclusion of a stacking meta-classifier further amplifies classification efficacy, showcasing the robustness and adaptability of our proposed model. Conclusively, our findings underscore the potential of Siamese networks with advanced learning paradigms in enhancing HAR systems, paving the way for future research in model optimization and application expansion.
Full article
(This article belongs to the Special Issue Recent Advances in Wireless Ad Hoc and Sensor Networks)
Open AccessArticle
Personalized Federated Learning Incorporating Adaptive Model Pruning at the Edge
by
Yueying Zhou, Gaoxiang Duan, Tianchen Qiu, Lin Zhang, Li Tian, Xiaoying Zheng and Yongxin Zhu
Electronics 2024, 13(9), 1738; https://doi.org/10.3390/electronics13091738 - 01 May 2024
Abstract
Edge devices employing federated learning encounter several obstacles, including (1) the non-independent and identically distributed (Non-IID) nature of client data, (2) limitations due to communication bottlenecks, and (3) constraints on computational resources. To surmount the Non-IID data challenge, personalized federated learning has been
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Edge devices employing federated learning encounter several obstacles, including (1) the non-independent and identically distributed (Non-IID) nature of client data, (2) limitations due to communication bottlenecks, and (3) constraints on computational resources. To surmount the Non-IID data challenge, personalized federated learning has been introduced, which involves training tailored networks at the edge; nevertheless, these methods often exhibit inconsistency in performance. In response to these concerns, a novel framework for personalized federated learning that incorporates adaptive pruning of edge-side data is proposed in this paper. This approach, through a two-staged pruning process, creates customized models while ensuring strong generalization capabilities. Concurrently, by utilizing sparse models, it significantly condenses the model parameters, markedly diminishing both the computational burden and communication overhead on edge nodes. This method achieves a remarkable compression ratio of 3.7% on the Non-IID dataset FEMNIST, with the training accuracy remaining nearly unaffected. Furthermore, the total training duration is reduced by 46.4% when compared with the standard baseline method.
Full article
(This article belongs to the Special Issue AI for Edge Computing)
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Open AccessArticle
Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss
by
Xiaoya Cao, Wenting Wang, Zhenya Chen, Xin Wang and Ming Yang
Electronics 2024, 13(9), 1737; https://doi.org/10.3390/electronics13091737 - 01 May 2024
Abstract
This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with
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This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions.
Full article
(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Artificial Intelligence of Things (AIoT))
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Open AccessArticle
Enhanced Vascular Bifurcations Mapping: Refining Fundus Image Registration
by
Jesús Eduardo Ochoa-Astorga, Linni Wang, Weiwei Du and Yahui Peng
Electronics 2024, 13(9), 1736; https://doi.org/10.3390/electronics13091736 - 01 May 2024
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Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting
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Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing Fundus Image Vessel Segmentation (FIVES) dataset for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.
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Open AccessArticle
Multi-Electrode EMG Spatial-Filter Implementation Based on Current Conveyors
by
Federico N. Guerrero, Valentín A. Catacora, Alfio Dario Grasso and Gaetano Palumbo
Electronics 2024, 13(9), 1735; https://doi.org/10.3390/electronics13091735 - 01 May 2024
Abstract
In this work, a circuit topology for the implementation of a multi-electrode superficial electromyography (EMG) front-end is presented based on a type II current conveyor (CCII). The presented topology provides a feasible way to implement an amplifier capable of measuring several electrode locations
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In this work, a circuit topology for the implementation of a multi-electrode superficial electromyography (EMG) front-end is presented based on a type II current conveyor (CCII). The presented topology provides a feasible way to implement an amplifier capable of measuring several electrode locations and obtaining the signal of interest for posterior acquisition. In particular, a five-electrode normal double differential (NDD) EMG spatial filter is demonstrated. The signal modes necessary for the analysis of the circuit are derived, the respective rejection ratios are obtained, and the noise characteristic is calculated. A board-level electrode is implemented as a proof of concept, achieving a gain equal to 28 dB, a bandwidth of 17 Hz to 578 Hz, a noise voltage linked to the input of 3.7 and a common-mode rejection ratio higher than 95 dB at interference frequencies. The topology was validated after using it as an active electrode in experimental EMG measurements with an NDD dry-contact electrode in a flexible printed circuit board.
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(This article belongs to the Section Circuit and Signal Processing)
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Open AccessArticle
CamGNN: Cascade Graph Neural Network for Camera Re-Localization
by
Li Wang, Jiale Jia, Hualin Dai and Guoyan Li
Electronics 2024, 13(9), 1734; https://doi.org/10.3390/electronics13091734 - 01 May 2024
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In response to the inaccurate positioning of traditional camera relocation methods in scenes with large-scale or severe viewpoint changes, this study proposes a camera relocation method based on a cascaded graph neural network to achieve accurate scene relocation. Firstly, the NetVLAD retrieval method,
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In response to the inaccurate positioning of traditional camera relocation methods in scenes with large-scale or severe viewpoint changes, this study proposes a camera relocation method based on a cascaded graph neural network to achieve accurate scene relocation. Firstly, the NetVLAD retrieval method, which has advantages in image feature representation and similarity calculation, is used to retrieve the most similar images to a given query image. Then, the feature pyramid is employed to extract features at different scales of these images, and the features at the same scale are treated as nodes of the graph neural network to construct a single-layer graph neural network structure. Secondly, a top–down connection is used to cascade the single-layer graph structures, where the information of nodes in the previous graph is fused into a message node to improve the accuracy of camera pose estimation. To better capture the topological relationships and spatial geometric constraints between images, an attention mechanism is introduced in the single-layer graph structure, which helps to effectively propagate information to the next graph during the cascading process, thereby enhancing the robustness of camera relocation. Experimental results on the public dataset 7-Scenes demonstrate that the proposed method can effectively improve the accuracy of camera absolute pose localization, with average translation and rotation errors of 0.19 m and 6.9°, respectively. Compared to other deep learning-based methods, the proposed method achieves more than 10% improvement in both average translation and rotation accuracy, demonstrating highly competitive localization precision.
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Open AccessArticle
Modeling a Solenoid Driver with Nonlinear Inductive Load Using Circuit Simulation and Magnetic Flux Measurement
by
Tobias Hofbauer and Frank Denk
Electronics 2024, 13(9), 1733; https://doi.org/10.3390/electronics13091733 - 01 May 2024
Abstract
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This paper describes the procedure for creating a electronic simulation model of a solenoid power electronic driver with a nonlinear inductive load. Furthermore, it discusses the electromagnetic interaction between the driver and the load example electromagnetic valve. The consideration of nonlinear effects in
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This paper describes the procedure for creating a electronic simulation model of a solenoid power electronic driver with a nonlinear inductive load. Furthermore, it discusses the electromagnetic interaction between the driver and the load example electromagnetic valve. The consideration of nonlinear effects in the power electronic components MOSFET and diode is particularly important to distinguish their effects from the nonlinear behaviour of the inductive load.
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Open AccessArticle
Optimizing Precision Material Handling: Elevating Performance and Safety through Enhanced Motion Control in Industrial Forklifts
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Fahim Faisal Amio, Neaz Ahmed, Soonyong Jeong, Insoo Jung and Kanghyun Nam
Electronics 2024, 13(9), 1732; https://doi.org/10.3390/electronics13091732 - 01 May 2024
Abstract
In adapting to the demands of this modernized landscape, a conventional human-operated forklift within an industrial or warehouse setting falls short. However, the adoption of autonomous forklifts remains a distant prospect for many companies, primarily due to the formidable implementation and switching costs
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In adapting to the demands of this modernized landscape, a conventional human-operated forklift within an industrial or warehouse setting falls short. However, the adoption of autonomous forklifts remains a distant prospect for many companies, primarily due to the formidable implementation and switching costs associated with artificial intelligence and complex control mechanisms. To bridge this gap, we present the development of a teleoperated forklift utilizing mecanum wheels for enhanced maneuverability. A key contribution of this work lies in the design of a novel synchronization method for the precise position control of the pallet carriers. This method surpasses the conventional independent and master–slave approaches, demonstrably achieving superior tracking and synchronization performance. Also, a model-based velocity control algorithm was designed for the mecanum wheels to facilitate the mobility of the system. The forklift was successfully able to carry a maximum load of 300 kg. For the comparison of the tracking and synchronization performance, the independent and master–slave methods were also applied to the system. The proposed method showed better performance compared to other structures.
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(This article belongs to the Special Issue Control and Applications of Intelligent Robotic System)
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