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
Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images
Electronics 2024, 13(11), 2164; https://doi.org/10.3390/electronics13112164 (registering DOI) - 1 Jun 2024
Abstract
In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly
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In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized.
Full article
(This article belongs to the Special Issue Parallel and Distributed Cloud, Edge and Fog Computing: Latest Advances and Prospects)
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Open AccessArticle
Enhancing Cross-Lingual Sarcasm Detection by a Prompt Learning Framework with Data Augmentation and Contrastive Learning
by
Tianbo An, Pingping Yan, Jiaai Zuo, Xing Jin, Mingliang Liu and Jingrui Wang
Electronics 2024, 13(11), 2163; https://doi.org/10.3390/electronics13112163 (registering DOI) - 1 Jun 2024
Abstract
Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of
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Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins.
Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)
Open AccessArticle
Security and Trust in the 6G Era: Risks and Mitigations
by
Giulio Tripi, Antonio Iacobelli, Lorenzo Rinieri and Marco Prandini
Electronics 2024, 13(11), 2162; https://doi.org/10.3390/electronics13112162 (registering DOI) - 1 Jun 2024
Abstract
The ubiquitous diffusion of connected devices in every context of the daily life of citizens, public bodies, and companies is stimulating the creation of new applications that require very high wireless communication performances. To fulfill this need, the sixth generation of communication standards
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The ubiquitous diffusion of connected devices in every context of the daily life of citizens, public bodies, and companies is stimulating the creation of new applications that require very high wireless communication performances. To fulfill this need, the sixth generation of communication standards (6G) is planned to roll out by 2030. While structuring this new standard, it is crucial to take into account the security aspects given the impact of the technologies that will rely on its reliability and resiliency. In this paper, we provide an overview of the technologies that will be used in 6G to achieve the required functional goals for the development of key applications. Then, we proceed to discuss the threats and the solutions to make the communications infrastructure secure and reliable, and finally, we elaborate on the concept of how to achieve trust in this scenario.
Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems, 2nd Edition)
Open AccessArticle
High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain
by
Slawomir Hanczewski, Maciej Stasiak and Michal Weissenberg
Electronics 2024, 13(11), 2161; https://doi.org/10.3390/electronics13112161 (registering DOI) - 1 Jun 2024
Abstract
The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models
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The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems.
Full article
(This article belongs to the Section Networks)
Open AccessArticle
Wireless Power Transfer System Model Reduction with Split Frequency Matching
by
Ke Wang, Qingyu Wu, Jing Peng and Hongchang Li
Electronics 2024, 13(11), 2160; https://doi.org/10.3390/electronics13112160 (registering DOI) - 1 Jun 2024
Abstract
Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise
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Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise mathematical formula. However, the model relies on the assumption of loose coupling and loses accuracy when the coupling becomes stronger. In this paper, a model reduction method with split frequency matching is proposed to improve model accuracy under relatively strong coupling conditions, which is suitable for most short-distance WPT applications, such as wireless electrical vehicle charging. Split frequency matching is achieved through a pair of conjugate equivalent mutual inductances, which are derived from the asymmetry characteristics of the full-order dynamic phasor model in the positive and negative frequency domains. The proposed model retains the advantages of the existing model while significantly improving the accuracy under strong coupling conditions. Its characteristics are verified by comparing the experimental results and model predictions under both large step changes and small-signal perturbations.
Full article
(This article belongs to the Special Issue Wireless Power Transfer Technology and Its Applications)
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Open AccessArticle
Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks
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George Brayshaw, Benjamin Ward-Cherrier and Martin J. Pearson
Electronics 2024, 13(11), 2159; https://doi.org/10.3390/electronics13112159 (registering DOI) - 1 Jun 2024
Abstract
The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic
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The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%).
Full article
(This article belongs to the Special Issue Neuromorphic Devices, Circuits, Systems and Their Applications)
Open AccessFeature PaperArticle
Enhancing Financial Time Series Prediction with Quantum-Enhanced Synthetic Data Generation: A Case Study on the S&P 500 Using a Quantum Wasserstein Generative Adversarial Network approach with a Gradient Penalty
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Filippo Orlandi, Enrico Barbierato and Alice Gatti
Electronics 2024, 13(11), 2158; https://doi.org/10.3390/electronics13112158 (registering DOI) - 1 Jun 2024
Abstract
This study introduces a novel Quantum Wasserstein Generative Adversarial Network approach with a Gradient Penalty (QWGAN-GP) model that leverages a quantum generator alongside a classical discriminator to synthetically generate time series data. This approach aims to accurately replicate the statistical properties of the
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This study introduces a novel Quantum Wasserstein Generative Adversarial Network approach with a Gradient Penalty (QWGAN-GP) model that leverages a quantum generator alongside a classical discriminator to synthetically generate time series data. This approach aims to accurately replicate the statistical properties of the S&P 500 index. The synthetic data generated by this model were compared to the original series using various metrics, including Wasserstein distance, Dynamic Time Warping (DTW) distance, and entropy measures, among others. The outcomes demonstrate the model’s robustness, with the generated data exhibiting a high degree of fidelity to the statistical characteristics of the original data. Additionally, this study explores the applicability of the synthetic time series in enhancing prediction models. An LSTM (Long-Short Term Memory)-based model was developed to evaluate the impact of incorporating synthetic data on forecasting accuracy, particularly focusing on general trends and extreme market events. The findings reveal that models trained on a mix of synthetic and real data significantly outperform those trained solely on historical data, improving predictive performance.
Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
Open AccessArticle
Design and Optimization of Coil for Transcutaneous Energy Transmission System
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Ruiming Wu, Haonan Li, Jiangyu Chen, Qi Le, Lijun Wang, Feng Huang and Yang Fu
Electronics 2024, 13(11), 2157; https://doi.org/10.3390/electronics13112157 (registering DOI) - 1 Jun 2024
Abstract
This article presents a coil couple-based transcutaneous energy transmission system (TETS) for wirelessly powering implanted artificial hearts. In the TETS, the performance of the system is commonly affected by the change in the position of the coupling coils, which are placed inside and
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This article presents a coil couple-based transcutaneous energy transmission system (TETS) for wirelessly powering implanted artificial hearts. In the TETS, the performance of the system is commonly affected by the change in the position of the coupling coils, which are placed inside and outside the skin. However, to some extent, the influence of coupling efficiency caused by misalignment can be reduced by optimizing the coil. Thus, different types of coils are designed in this paper for comparison. It has been found that the curved coil better fits the surface of the skin and provides better performance for the TETS. Various types of curved coils have been designed in response to observed bending deformations, dislocations, and other coupling variations in the curved coil couple. The numerical model of the TETS is established to analyze the effects of the different types of coils. Subsequently, a series of experiments are designed to evaluate the resilience to misalignment and to verify the heating of the coil under conditions of severe coupling misalignment. The results indicated that, in the case of misalignment of the coils used in artificial hearts, the curved transmission coil demonstrated superior efficiency and lower temperature rise compared to the planar coil.
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(This article belongs to the Topic Advanced Wireless Charging Technology)
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A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges
by
Abdul Majeed and Seong Oun Hwang
Electronics 2024, 13(11), 2156; https://doi.org/10.3390/electronics13112156 (registering DOI) - 1 Jun 2024
Abstract
Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been
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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.
Full article
(This article belongs to the Special Issue Big Data and Blockchain Technologies: Explorations, Solutions and Applications)
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Open AccessArticle
A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration
by
Hamed H. Aly
Electronics 2024, 13(11), 2155; https://doi.org/10.3390/electronics13112155 (registering DOI) - 1 Jun 2024
Abstract
Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining
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Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions. Integrating machine learning techniques with tidal power forecasting could greatly enhance the accuracy and reliability of predictions, which is crucial for efficient energy production and management. A hybrid approach combining different methods often yields better results than relying on individual techniques. The accuracy of tidal current power is very important, especially for smart grid applications. This work proposes hybrid adaptive neuro-fuzzy inference system (ANFIS) with the Kalman filter (KF) and a neuro-wavelet (WNN) for tidal current speed, direction, and power forecasting. The turbine used in this study is driven by a direct drive permanent magnet synchronous generator (DDPMSG). The predictions of individual and hybrid models including the ANFIS, the Kalman filter, and the WNN for tidal current speed and the power it generates are compared with another dataset as a way of validation which is the tidal currents direction. Also, other published work results in the literature are compared to the proposed work. Different hybrid models are proposed for smart grid integration. The results of this work indicate that the hybrid model of the WNN and the ANFIS for tidal current power or speed forecasting has the highest performance compared to all other models.
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(This article belongs to the Special Issue Power Delivery Technologies)
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Open AccessArticle
Graph-Based Modeling and Optimization of WPT Systems for EVs
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Matthew J. Hansen, Greg Droge and Abhilash Kamineni
Electronics 2024, 13(11), 2154; https://doi.org/10.3390/electronics13112154 (registering DOI) - 31 May 2024
Abstract
A model of a system of wireless power transfer (WPT) pads is developed, where each WPT pad is modeled as a node and the coupling between pads is modeled as graph edges. This modeling approach is generalized to admit primary, secondary, and booster
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A model of a system of wireless power transfer (WPT) pads is developed, where each WPT pad is modeled as a node and the coupling between pads is modeled as graph edges. This modeling approach is generalized to admit primary, secondary, and booster coils, where power can flow among the pads and a pad can fill multiple roles. An excitation in one pad induces voltage and current in all neighboring pads, causing each pad to act as both a booster coil and either a transmitter or a receiver. Power flow through the entire system can be modeled with the graph structure; the power flow can then be optimized by alternating the phases of the WPT excitations to maximize power transfer. An example is shown where exploiting the graph-based WPT system modeling increases total energy transfer by 25% compared to another method. This increase occurs without altering the geometry of the pads or the magnitude of the pad excitations.
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Open AccessArticle
Fault Diagnosis Model for Bearings under Multiple Operating Conditions Based on Feature Parameterization Weighting
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Linghui Meng, Jinyang Xie, Zhenwei Zhou and Yiqiang Chen
Electronics 2024, 13(11), 2153; https://doi.org/10.3390/electronics13112153 - 31 May 2024
Abstract
As a core component of automobile transmission, rolling bearings play a main role in the safety and reliability of vehicles. Existing diagnostic models often treat all features equally after feature extraction, without effectively distinguishing the importance of fault features, resulting in low accuracy
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As a core component of automobile transmission, rolling bearings play a main role in the safety and reliability of vehicles. Existing diagnostic models often treat all features equally after feature extraction, without effectively distinguishing the importance of fault features, resulting in low accuracy and poor robustness in bearing fault diagnosis. To address this issue, a fault diagnosis model for bearings under multiple operating conditions based on feature parameterization weighting is proposed. The model utilizes a feature parameterization weighting module to categorize faults into two classes based on differences in means and implements different feature processing methods. The experimental results validate that the proposed feature parameterization weighting module effectively improves the diagnostic accuracy of the model by 8.95%. In terms of noise resistance, on two multi-condition datasets, the proposed diagnostic model achieves diagnostic accuracy of 98.79% and 98.36%. The diagnostic accuracy is improved by 15.7% and 22.48%, which indicates that the model has strong anti-noise ability.
Full article
(This article belongs to the Special Issue Recent Advances in Electrified Vehicles and Transportation Electrification)
Open AccessArticle
Collaborative Design of Pulsed-Power Generator Based on SiC Drift Step Recovery Diode
by
Jingkai Guo, Yahui Chen, Yu Zhang, Lejia Sun, Yu Zhou, Qingwen Song, Xiaoyan Tang and Yuming Zhang
Electronics 2024, 13(11), 2152; https://doi.org/10.3390/electronics13112152 - 31 May 2024
Abstract
Despite the extensively researched physical principles, numerous published simulations on SiC drift step recovery diodes (SiC DSRD) and the practical implementation of SiC DSRD-based pulses, there are few kinds of research focusing on collaborative design between a SiC DSRD and its driving circuit.
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Despite the extensively researched physical principles, numerous published simulations on SiC drift step recovery diodes (SiC DSRD) and the practical implementation of SiC DSRD-based pulses, there are few kinds of research focusing on collaborative design between a SiC DSRD and its driving circuit. In this paper, a collaborative design method of a SiC DSRD and its driving circuit are presented. In addition, a detailed simulation is conducted to verify design considerations and to analyze the impact of driving parameter changes on the output pulse waveform. A pulse generator prototype with a self-developed SiC DSRD is implemented. The experimental results show that the circuit can output a peak voltage of 790 V on a matching load of 50 Ω, with a rise time of 520 ps (20%~80%), and can work at a 1 MHz repetition frequency rate with good stability.
Full article
(This article belongs to the Special Issue Wide-Bandgap Device Application: Devices, Circuits, and Drivers)
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Open AccessArticle
Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities
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Andra Sandu, Liviu-Adrian Cotfas, Aurelia Stănescu and Camelia Delcea
Electronics 2024, 13(11), 2151; https://doi.org/10.3390/electronics13112151 - 31 May 2024
Abstract
In recent years, the research community has increasingly embraced topics related to smart cities, recognizing their potential to enhance residents’ quality of life and create sustainable, efficient urban environments through the integration of diverse systems and services. Concurrently, recommender systems have demonstrated continued
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In recent years, the research community has increasingly embraced topics related to smart cities, recognizing their potential to enhance residents’ quality of life and create sustainable, efficient urban environments through the integration of diverse systems and services. Concurrently, recommender systems have demonstrated continued improvement in accuracy, delivering more precise recommendations for items or content and aiding users in decision-making processes. This paper explores the utilization of recommender systems in the context of smart cities by analyzing a dataset comprised of papers indexed in the ISI Web of Science database. Through bibliometric analysis, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors were extracted. The findings revealed an average annual scientific production growth of 25.85%. Additionally, an n-gram analysis across keywords, abstracts, titles, and keywords plus, along with a review of selected papers, enriched the analysis. The insights gained from these efforts offer valuable perspectives, contribute to identifying pertinent issues, and provide guidance on trends in this evolving field. The importance of recommender systems in the context of smart cities lies in their ability to enhance urban living by providing personalized and efficient recommendations, optimizing resource utilization, improving decision-making processes, and ultimately contributing to a more sustainable and intelligent urban environment.
Full article
(This article belongs to the Special Issue Advances and Challenges of Recommender Systems in Smart City)
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Open AccessArticle
Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications
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Lei Chen, Baoping Cheng, Haotian Zhu, Haowen Qin, Lihua Deng and Lei Luo
Electronics 2024, 13(11), 2150; https://doi.org/10.3390/electronics13112150 - 31 May 2024
Abstract
Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase is a great challenge for power-constrained applications, such as Internet
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Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase is a great challenge for power-constrained applications, such as Internet of video things. In the case of intra coding, VVC utilizes the brute-force recursive search for both the partition structure of the coding unit (CU), which is based on the quadtree with nested multi-type tree (QTMT), and 67 intra prediction modes, compared to 35 in HEVC. As a result, we offer optimization strategies for CU partition decision and intra coding modes to lessen the computational overhead. Regarding the high complexity of the CU partition process, first, CUs are categorized as simple, fuzzy, and complex based on their texture characteristics. Then, we train two random forest classifiers to speed up the RDO-based brute-force recursive search process. One of the classifiers directly predicts the optimal partition modes for simple and complex CUs, while another classifier determines the early termination of the partition process for fuzzy CUs. Meanwhile, to reduce the complexity of intra mode prediction, a fast hierarchical intra mode search method is designed based on the texture features of CUs, including texture complexity, texture direction, and texture context information. Extensive experimental findings demonstrate that the proposed approach reduces complexity by up to 77% compared to the latest VVC reference software (VTM-23.1). Additionally, an average coding time saving of 70% is achieved with only a 1.65% increase in BDBR. Furthermore, when compared to state-of-the-art methods, the proposed method also achieves the largest time saving with comparable BDBR loss. These findings indicate that our method is superior to other up-to-date methods in terms of lowering VVC intra coding complexity, which provides an elective solution for power-constrained applications.
Full article
(This article belongs to the Special Issue Advances in Image Processing and Computer Vision Based on Machine Learning)
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Open AccessArticle
Vehicle–Pedestrian Detection Method Based on Improved YOLOv8
by
Bo Wang, Yuan-Yuan Li, Weijie Xu, Huawei Wang and Li Hu
Electronics 2024, 13(11), 2149; https://doi.org/10.3390/electronics13112149 - 31 May 2024
Abstract
The YOLO series of target detection networks are widely used in transportation targets due to the advantages of high detection accuracy and good real-time performance. However, it also has some limitations, such as poor detection in scenes with large-scale variations, a large number
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The YOLO series of target detection networks are widely used in transportation targets due to the advantages of high detection accuracy and good real-time performance. However, it also has some limitations, such as poor detection in scenes with large-scale variations, a large number of computational resources being consumed, and occupation of more storage space. To address these issues, this study uses the YOLOv8n model as the benchmark and makes the following four improvements: (1) embedding the BiFormer attention mechanism in the Neck layer to capture the associations and dependencies between the features more efficiently; (2) adding a 160 × 160 small-scale target detection header in the Head layer of the network to enhance the pedestrian and motorcycle detection capability; (3) adopting a weighted bidirectional feature pyramid structure to enhance the feature fusion capability of the network; and (4) making WIoUv3 as a loss function to enhance the focus on common quality anchor frames. Based on the improvement strategies, the evaluation metrics of the model have improved significantly. Compared to the original YOLOv8n, the mAP reaches 95.9%, representing an increase of 4.7 percentage points, and the mAP50:95 reaches 74.5%, reflecting an improvement of 6.2 percentage points.
Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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Open AccessArticle
Enhancing IoT Security: Optimizing Anomaly Detection through Machine Learning
by
Maria Balega, Waleed Farag, Xin-Wen Wu, Soundararajan Ezekiel and Zaryn Good
Electronics 2024, 13(11), 2148; https://doi.org/10.3390/electronics13112148 - 31 May 2024
Abstract
As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of
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As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVMs), and Deep Convolutional Neural Networks (DCNNs). Identifying optimal anomaly detection models for IoT anomaly detection is challenging due to diverse IoT applications and dynamic IoT networking environments. It is of vital importance to evaluate ML-powered anomaly detection models using multiple datasets collected from different environments. We utilized three reputable datasets to benchmark the aforementioned machine learning methods, namely, IoT-23, NSL-KDD, and TON_IoT. Our results show that XGBoost outperformed both the SVM and DCNN, achieving accuracies of up to 99.98%. Moreover, XGBoost proved to be the most computationally efficient method; the model performed 717.75 times faster than the SVM and significantly faster than the DCNN in terms of training times. The research results have been further confirmed by using our real-world IoT data collected from an IoT testbed consisting of physical devices that we recently built.
Full article
(This article belongs to the Special Issue Machine Learning for Cybersecurity: Threat Detection and Mitigation)
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Open AccessArticle
Development of Control System for a Prefabricated Board Transfer Palletizer Based on S7-1500 PLC
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Jinjiao Zhang, Jing Xie, Daode Zhang and Yi Li
Electronics 2024, 13(11), 2147; https://doi.org/10.3390/electronics13112147 - 30 May 2024
Abstract
A palletizing machine is extensively utilized in the production of prefabricated boards. However, due to its large and complex system, as well as its low level of automation, the development of the control system of current transfer palletizing machines has proven challenging. To
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A palletizing machine is extensively utilized in the production of prefabricated boards. However, due to its large and complex system, as well as its low level of automation, the development of the control system of current transfer palletizing machines has proven challenging. To address these issues, a palletizer control system based on S7-1500 PLC has been designed. This design encompasses the hardware electrical system, software control system, and human–machine interaction system for the palletizer. A structured programming strategy has been adopted to simplify the system and enhance its expansion compatibility while improving efficiency and automation levels.
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Open AccessArticle
A Multi-Frequency Low-Coupling MIMO Antenna Based on Metasurface
by
Guangpu Tang, Tong Xiao, Lifeng Cao, Runsheng Cheng, Chengguo Liu, Lifeng Huang and Xin Xu
Electronics 2024, 13(11), 2146; https://doi.org/10.3390/electronics13112146 - 30 May 2024
Abstract
In this paper, a multi-frequency MIMO antenna for 5G and Wi-Fi 6E is presented. The antenna uses a cosine-shape monopole and split-ring resonator (SRR) structure for tri-band radiation, and frequency band expansion is achieved through SRR, folded split-ring resonators (FSRR) and Archimedean spiral
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In this paper, a multi-frequency MIMO antenna for 5G and Wi-Fi 6E is presented. The antenna uses a cosine-shape monopole and split-ring resonator (SRR) structure for tri-band radiation, and frequency band expansion is achieved through SRR, folded split-ring resonators (FSRR) and Archimedean spiral metasurfaces for decoupling, with which a combination of surface wave and space wave decoupling is achieved. The Archimedean spiral metasurface unit can achieve space wave decoupling in the tri-band. By adopting the method of combining space wave decoupling and surface wave decoupling, the miniature antenna is achieved. The measured results closely align with the simulated results. Specifically, maintaining a reflection coefficient of −10 dB, the measured results indicate an increase in isolation of 3.5 dB, 36.47 dB, and 6.42 dB for the frequency bands of 3.45–3.55 GHz, 5.7–5.9 GHz, and 6.75–7 GHz, respectively. Additionally, the MIMO antenna demonstrates an average efficiency of approximately 89%, with an average envelope correlation coefficient (ECC) of 0.0025. Furthermore, the antenna’s peak gain increases by 4.3 dB at 3.5 GHz, 3.8 dB at 5.8 GHz, and 1.9 dB at 6.9 GHz upon integrating the metasurface. The proposed method and structure are anticipated to contribute significantly to decoupling in multi-frequency MIMO antennas.
Full article
(This article belongs to the Section Microwave and Wireless Communications)
Open AccessArticle
Supervised-Learning-Based Method for Restoring Subsurface Shallow-Layer Q Factor Distribution
by
Danfeng Zang, Jian Li, Chuankun Li, Mingxing Ma, Chenli Guo and Jiangang Wang
Electronics 2024, 13(11), 2145; https://doi.org/10.3390/electronics13112145 - 30 May 2024
Abstract
The distribution of shallow subsurface quality factors (Q) is a crucial indicator for assessing the integrity of subsurface structures and serves as a primary parameter for evaluating the attenuation characteristics of seismic waves propagating through subsurface media. As the complexity of underground spaces
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The distribution of shallow subsurface quality factors (Q) is a crucial indicator for assessing the integrity of subsurface structures and serves as a primary parameter for evaluating the attenuation characteristics of seismic waves propagating through subsurface media. As the complexity of underground spaces increases, regions expand, and testing environments diversify, the survivability of test nodes is compromised, resulting in sparse effective seismic data with a low signal-to-noise ratio (SNR). Within the confined area defined by the source and sensor placement, only the Q factor along the wave propagation path can be estimated with relative accuracy. Estimating the Q factor in other parts of the area is challenging. Additionally, in recent years, deep neural networks have been employed to address the issue of missing values in seismic data; however, these methods typically require large datasets to train networks that can effectively fit the data, making them less applicable to our specific problem. In response to this challenge, we have developed a supervised learning method for the restoration of shallow subsurface Q factor distributions. The process begins with the construction of an incomplete labeled data volume, followed by the application of a block-based data augmentation technique to enrich the training samples and train the network. The uniformly partitioned initial data are then fed into the trained network to obtain output data, which are subsequently combined to form a complete Q factor data volume. We have validated this training approach using various networks, all yielding favorable results. Additionally, we compared our method with a data augmentation approach that involves creating random masks, demonstrating that our method reduces the mean absolute percentage error (MAPE) by 5%.
Full article
(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)
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