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
Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis
Electronics 2024, 13(10), 1902; https://doi.org/10.3390/electronics13101902 (registering DOI) - 13 May 2024
Abstract
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The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust
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The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.
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Open AccessArticle
BPA: A Novel Blockchain-Based Privacy-Preserving Authentication Scheme for the Internet of Vehicles
by
Jie Li, Yuanyuan Lin, Yibing Li, Yan Zhuang and Yangjie Cao
Electronics 2024, 13(10), 1901; https://doi.org/10.3390/electronics13101901 (registering DOI) - 13 May 2024
Abstract
The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process
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The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process in the IoV. In this study, we designed a blockchain-based conditional privacy-preserving authentication scheme for the IoV (BPA). Our scheme implements zero-knowledge proof (ZKP) to verify the identities of vehicles, which moves the authentication process down to the Roadside Units (RSUs) and achieves decentralized authentication at the edge nodes. Moreover, blockchain technology is utilized to synchronize a consistent ledger across all RSUs for recording and disseminating vehicle authentication states, which enhances the overall authentication process efficiency. We provide a theoretical analysis asserting that the BPA ensures enhanced security and effectively protects the privacy of all participating vehicles. Experimental evaluations confirm that our scheme outperforms existing solutions in terms of the computational and communication overhead.
Full article
(This article belongs to the Special Issue Advanced Techniques in Computing and Security, 2nd Edition)
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Open AccessArticle
g2D-Net: Efficient Dehazing with Second-Order Gated Units
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Jia Jia, Zhibo Wang and Jeongik Min
Electronics 2024, 13(10), 1900; https://doi.org/10.3390/electronics13101900 (registering DOI) - 12 May 2024
Abstract
Image dehazing aims to reconstruct potentially clear images from corresponding images corrupted by haze. With the rapid development of deep learning-related technologies, dehazing methods based on deep convolutional neural networks have gradually become mainstream. We note that existing dehazing methods often accompany an
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Image dehazing aims to reconstruct potentially clear images from corresponding images corrupted by haze. With the rapid development of deep learning-related technologies, dehazing methods based on deep convolutional neural networks have gradually become mainstream. We note that existing dehazing methods often accompany an increase in computational overhead while improving the performance of dehazing. We propose a novel lightweight dehazing neural network to balance performance and efficiency: the g2D-Net. The g2D-Net borrows the design ideas of input-adaptive and long-range information interaction from Vision Transformers and introduces two kinds of convolutional blocks, i.e., the g2D Block and the FFT-g2D Block. Specifically, the g2D Block is a residual block with second-order gated units, which inherit the input-adaptive property of a gated unit and can realize the second-order interaction of spatial information. The FFT-g2D Block is a variant of the g2D Block, which efficiently extracts the global features of the feature maps through fast Fourier convolution and fuses them with local features. In addition, we employ the SK Fusion layer to improve the cascade fusion layer in a traditional U-Net, thus introducing the channel attention mechanism and dynamically fusing information from different paths. We conducted comparative experiments on five benchmark datasets, and the results demonstrate that the g2D-Net achieves impressive dehazing performance with relatively low complexity.
Full article
(This article belongs to the Section Artificial Intelligence)
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Open AccessArticle
Optimal Dispatching of Microgrids with Development of Prosumers Sharing Energy Storage
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Fei Li, Kai Su, Xianshan Li and Binqiao Zhang
Electronics 2024, 13(10), 1899; https://doi.org/10.3390/electronics13101899 (registering DOI) - 12 May 2024
Abstract
The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a
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The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers’ complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member’s energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved.
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(This article belongs to the Topic Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems, 2nd Volume)
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Open AccessCommunication
A High-Gain Metallic-Via-Loaded Antipodal Vivaldi Antenna for Millimeter-Wave Application
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Jun Li, Junjie Huang, Hongli He and Yanjie Wang
Electronics 2024, 13(10), 1898; https://doi.org/10.3390/electronics13101898 (registering DOI) - 12 May 2024
Abstract
This paper presents a miniaturized-structure high-gain antipodal Vivaldi antenna (AVA) operating in the millimeter-wave (mm-wave) band. A gradient-length microstrip-patch-based director is utilized on the flares of the AVA to enhance gain. Additionally, an array of metallic vias is incorporated along the lateral and
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This paper presents a miniaturized-structure high-gain antipodal Vivaldi antenna (AVA) operating in the millimeter-wave (mm-wave) band. A gradient-length microstrip-patch-based director is utilized on the flares of the AVA to enhance gain. Additionally, an array of metallic vias is incorporated along the lateral and horizontal edges of the antenna for further gain enhancement and bandwidth extension. Based on the proposed structure, the AVA can achieve a peak gain of 11.9 dBi over a relative bandwidth of 71.24% within 16.5–36.6 GHz as measured, while the electrical dimension is only 1.54 × 2.69 × 0.07 . The measured results show good agreement with the simulated ones. Owning the characteristics of being high-gain and ultra-wideband, and having a compact size, the proposed AVA can be a competitive candidate for future millimeter-wave communication.
Full article
(This article belongs to the Special Issue Advanced Wireless Technologies for Next-G Networks: Antennas, Circuits, and Systems)
Open AccessArticle
On Embedding Implementations in Text Ranking and Classification Employing Graphs
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Nikitas-Rigas Kalogeropoulos, Dimitris Ioannou, Dionysios Stathopoulos and Christos Makris
Electronics 2024, 13(10), 1897; https://doi.org/10.3390/electronics13101897 (registering DOI) - 12 May 2024
Abstract
This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed
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This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed into a graph representation. The proposed enhancement involves augmenting the edges of these graphs with embeddings, which can be pretrained or generated using Word2Vec and GloVe models. Additionally, an alternative aspect of our proposed model consists of the Node2Vec embedding technique, which is applied to a graph created at the collection level through the extension of the set-based model, providing edges based on the graph’s structural information. Core decomposition is utilized as a method for pruning the graph. As a byproduct of our information retrieval model, we explore text classification techniques based on our approach. Node2Vec embeddings are generated by our graphs and are applied in order to represent the different documents in our collections that have undergone various preprocessing methods. We compare the graph-based embeddings with the Doc2Vec and Word2Vec representations to elaborate on whether our approach can be implemented on topic classification problems. For that reason, we then train popular classifiers on the document embeddings obtained from each model.
Full article
(This article belongs to the Special Issue Machine Learning Advances and Applications on Natural Language Processing (NLP))
Open AccessArticle
Short-Term Air Traffic Flow Prediction Based on CEEMD-LSTM of Bayesian Optimization and Differential Processing
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Rui Zhou, Shuang Qiu, Ming Li, Shuangjie Meng and Qiang Zhang
Electronics 2024, 13(10), 1896; https://doi.org/10.3390/electronics13101896 (registering DOI) - 12 May 2024
Abstract
With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term
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With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow prediction, this paper proposes a short-term air traffic flow prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) of the Bayesian optimization algorithm and data differential processing. Initially, the model performs CEEMD on the short-term air traffic flow series. Subsequently, to improve prediction accuracy, the data differencing is employed to stabilize the time series. Finally, the smoothed sequences are, respectively, input into the LSTM network model optimized by the Bayesian optimization algorithm for prediction. After data reconstruction, the final short-term flow prediction result is obtained. The model proposed in this paper is verified by using the data from Shanghai Pudong International Airport. The results show that the evaluation indexes of the prediction accuracy and fitting degree of the model, RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 (Coefficient of Determination), are 0.336, 0.239, and 97.535%, respectively. Compared to other classical time-series prediction models, the prediction accuracy is greatly improved, which can provide a useful reference for short-term air traffic flow prediction.
Full article
(This article belongs to the Special Issue Application of Time Series Analysis and Forecasting in Computer Science)
Open AccessArticle
Transferring Black-Box Decision Making to a White-Box Model
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Bojan Žlahtič, Jernej Završnik, Helena Blažun Vošner and Peter Kokol
Electronics 2024, 13(10), 1895; https://doi.org/10.3390/electronics13101895 (registering DOI) - 12 May 2024
Abstract
In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed
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In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.
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(This article belongs to the Special Issue Explainable and Interpretable AI)
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Open AccessSystematic Review
IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities
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Elisha Elikem Kofi Senoo, Lia Anggraini, Jacqueline Asor Kumi, Luna Bunga Karolina, Ebenezer Akansah, Hafeez Ayo Sulyman, Israel Mendonça and Masayoshi Aritsugi
Electronics 2024, 13(10), 1894; https://doi.org/10.3390/electronics13101894 (registering DOI) - 11 May 2024
Abstract
The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a
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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.
Full article
(This article belongs to the Special Issue The Future of IoT: Advanced AI Based IoT Technologies and Applications)
Open AccessArticle
ECHO: Energy-Efficient Computation Harnessing Online Arithmetic—An MSDF-Based Accelerator for DNN Inference
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Muhammad Sohail Ibrahim, Muhammad Usman and Jeong-A Lee
Electronics 2024, 13(10), 1893; https://doi.org/10.3390/electronics13101893 (registering DOI) - 11 May 2024
Abstract
Deep neural network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of
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Deep neural network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO, an accelerator for DNN inference designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/most significant digit first (MSDF) arithmetic, which performs computations in a digit-serial manner. The MSDF digit-serial computation of online arithmetic enables overlapped computation of successive operations, leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction in energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. The experimental results demonstrate promising power and performance improvements compared to contemporary methods. In particular, the proposed design achieved average improvements in power consumption of up to , , and for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of , , and were observed when comparing the proposed design to conventional bit-serial designs for the VGG-16, ResNet-18, and ResNet-50 models, respectively.
Full article
(This article belongs to the Section Circuit and Signal Processing)
Open AccessArticle
Optimizing an Autonomous Robot’s Path to Increase Movement Speed
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Damian Gorgoteanu, Cristian Molder, Vlad-Gabriel Popescu, Lucian Ștefăniță Grigore and Ionica Oncioiu
Electronics 2024, 13(10), 1892; https://doi.org/10.3390/electronics13101892 (registering DOI) - 11 May 2024
Abstract
The goal of this study is to address the challenges associated with identifying and planning a mobile land robot’s path to optimize its speed in a stationary environment. Our focus was on devising routes that navigate around obstacles in various spatial arrangements. To
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The goal of this study is to address the challenges associated with identifying and planning a mobile land robot’s path to optimize its speed in a stationary environment. Our focus was on devising routes that navigate around obstacles in various spatial arrangements. To achieve this, we employed MATLAB R2023b for trajectory simulation and optimization. On-board data processing was conducted, while obstacle detection relied on the omnidirectional video processing system integrated into the robot. Odometry was facilitated by engine encoders and optical flow sensors. Additionally, an external video system was utilized to verify the experimental data pertaining to the robot’s movement. Last but not least, the algorithms and hardware equipment used enabled the robot to go along the path at greater speeds. Limiting the amount of time and energy required to travel allowed us to avoid obstacles.
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(This article belongs to the Special Issue Control Systems for Autonomous Vehicles)
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Open AccessArticle
A Causality-Aware Perspective on Domain Generalization via Domain Intervention
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Youjia Shao, Shaohui Wang and Wencang Zhao
Electronics 2024, 13(10), 1891; https://doi.org/10.3390/electronics13101891 (registering DOI) - 11 May 2024
Abstract
Most mainstream statistical models will achieve poor performance in Out-Of-Distribution (OOD) generalization. This is because these models tend to learn the spurious correlation between data and will collapse when the domain shift exists. If we want artificial intelligence (AI) to make great strides
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Most mainstream statistical models will achieve poor performance in Out-Of-Distribution (OOD) generalization. This is because these models tend to learn the spurious correlation between data and will collapse when the domain shift exists. If we want artificial intelligence (AI) to make great strides in real life, the current focus needs to be shifted to the OOD problem of deep learning models to explore the generalization ability under unknown environments. Domain generalization (DG) focusing on OOD generalization is proposed, which is able to transfer the knowledge extracted from multiple source domains to the unseen target domain. We are inspired by intuitive thinking about human intelligence relying on causality. Unlike relying on plain probability correlations, we apply a novel causal perspective to DG, which can improve the OOD generalization ability of the trained model by mining the invariant causal mechanism. Firstly, we construct the inclusive causal graph for most DG tasks through stepwise causal analysis based on the data generation process in the natural environment and introduce the reasonable Structural Causal Model (SCM). Secondly, based on counterfactual inference, causal semantic representation learning with domain intervention (CSRDN) is proposed to train a robust model. In this regard, we generate counterfactual representations for different domain interventions, which can help the model learn causal semantics and develop generalization capacity. At the same time, we seek the Pareto optimal solution in the optimization process based on the loss function to obtain a more advanced training model. Extensive experimental results of Rotated MNIST and PACS as well as VLCS datasets verify the effectiveness of the proposed CSRDN. The proposed method can integrate causal inference into domain generalization by enhancing interpretability and applicability and brings a boost to challenging OOD generalization problems.
Full article
(This article belongs to the Special Issue Object Detection, Segmentation and Categorization in Artificial Intelligence)
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Open AccessArticle
Simulation Analysis of Phase Jitter in Differential Sampling of AC Waveforms Based on the Programmable Josephson Voltage Standard
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Yanping Wang, Xiaogang Sun, Jianting Zhao, Kunli Zhou, Yunfeng Lu, Jifeng Qu, Pengcheng Hu and Qing He
Electronics 2024, 13(10), 1890; https://doi.org/10.3390/electronics13101890 (registering DOI) - 11 May 2024
Abstract
The effect of phase jitter on differential sampling using the programmable Josephson voltage standard (PJVS) system is studied in this paper. A phase jitter model is established for the measured signal, and compensation coefficients for phase jitter removal are derived for three different
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The effect of phase jitter on differential sampling using the programmable Josephson voltage standard (PJVS) system is studied in this paper. A phase jitter model is established for the measured signal, and compensation coefficients for phase jitter removal are derived for three different post-processing methods based on the discrete Fourier transform algorithm (DFT). Based on our analysis, the phase jitter compensation coefficients are determined by the phase jitter angle distribution and harmonic order. Furthermore, after analyzing and simulating various common distributions, the phase jitter compensation coefficients have been verified. The simulation shows that when the standard deviation of the phase jitter angle is 20 ns, and the frequency of the measuring waveform is 3.46 kHz, the influence of the phase jitter is 1 × 10−7. The results of the simulation indicate that, in the differential sampling of AC waveforms using a PJVS system, phase jitter is one of the error terms for an uncertainty budget that cannot be neglected, particularly as the frequency of the measured waveforms increases.
Full article
Open AccessArticle
Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks
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Chang Shu, Yinhui Luo and Fang Liu
Electronics 2024, 13(10), 1889; https://doi.org/10.3390/electronics13101889 (registering DOI) - 11 May 2024
Abstract
D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge
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D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks.
Full article
(This article belongs to the Special Issue Edge Computing for 5G and Internet of Things)
Open AccessArticle
Flexible Deployment of Machine Learning Inference Pipelines in the Cloud–Edge–IoT Continuum
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Karolina Bogacka, Piotr Sowiński, Anastasiya Danilenka, Francisco Mahedero Biot, Katarzyna Wasielewska-Michniewska, Maria Ganzha, Marcin Paprzycki and Carlos E. Palau
Electronics 2024, 13(10), 1888; https://doi.org/10.3390/electronics13101888 (registering DOI) - 11 May 2024
Abstract
Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced,
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Currently, deploying machine learning workloads in the Cloud–Edge–IoT continuum is challenging due to the wide variety of available hardware platforms, stringent performance requirements, and the heterogeneity of the workloads themselves. To alleviate this, a novel, flexible approach for machine learning inference is introduced, which is suitable for deployment in diverse environments—including edge devices. The proposed solution has a modular design and is compatible with a wide range of user-defined machine learning pipelines. To improve energy efficiency and scalability, a high-performance communication protocol for inference is propounded, along with a scale-out mechanism based on a load balancer. The inference service plugs into the ASSIST-IoT reference architecture, thus taking advantage of its other components. The solution was evaluated in two scenarios closely emulating real-life use cases, with demanding workloads and requirements constituting several different deployment scenarios. The results from the evaluation show that the proposed software meets the high throughput and low latency of inference requirements of the use cases while effectively adapting to the available hardware. The code and documentation, in addition to the data used in the evaluation, were open-sourced to foster adoption of the solution.
Full article
(This article belongs to the Special Issue Towards Efficient and Reliable AI at the Edge)
Open AccessArticle
Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System
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Jiaxin Xue, Yupeng Li, Yichao Zhang, Xiao Wu and Yanyue Zhang
Electronics 2024, 13(10), 1887; https://doi.org/10.3390/electronics13101887 (registering DOI) - 11 May 2024
Abstract
With the development of the Internet and information technology, optical fiber communication systems need to meet people’s information demand for large capacity and high speed. High-order phase modulation and channel multiplexing can improve the capacity and data rate of optical fiber communication systems,
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With the development of the Internet and information technology, optical fiber communication systems need to meet people’s information demand for large capacity and high speed. High-order phase modulation and channel multiplexing can improve the capacity and data rate of optical fiber communication systems, but they also bring the problem of bit error. To improve the transmission quality and reliability of optical fiber communication systems, forward error correction (FEC) coding techniques are commonly used, which serve as the fundamental approach to enhance the quality and reliability of fiber optic communication systems, ensuring that the received data remain accurate and reliable. The FEC in optical fiber communication systems is divided into three generations. The first generation FEC is mainly hard decision codewords, represented as RS code. The second generation FEC is mainly cascaded code, which stands for interleaved cascaded code. The third generation of FEC mainly refers to soft decision codes, which are represented as low-density parity-check (LDPC) codes. As a kind of FEC, LDPC codes stand out as pivotal contributors in the field of optical communication and have gained remarkable attention due to exceptional error correction performance and low decoding complexity. Based on IEEE802.16e standard, LDPC code with specific code length and rate is compiled and simulated in MATLAB and VPItransmissionMaker 10.1 and successfully incorporated into polarization multiplexed differential quadrature phase shift keying (PM-DQPSK) coherent optical transmission system. The simulation results indicate that the bit error rate (BER) can be reduced to 10−3 when the optical signal-to-noise ratio (OSNR) reaches 14.2 dB, and the BER experiences a reduction by nearly three orders of magnitude when the OSNR is 17.2 dB. These findings underscore the efficacy of LDPC codes in significantly improving the performance of optical communication systems, particularly in scenarios demanding robust error correction capabilities. This study provides valuable, significant results regarding the potential of LDPC codes for enhancing the reliability of optical transmission in real-world applications.
Full article
(This article belongs to the Special Issue Optical Fiber Communication: Prospects and Applications)
Open AccessArticle
Design of a 3-Bit Circularly Polarized Reconfigurable Reflectarray
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Zhe Chen, Chenlu Huang, Xinmi Yang, Xiaoming Yan, Xianqi Lin and Yedi Zhou
Electronics 2024, 13(10), 1886; https://doi.org/10.3390/electronics13101886 (registering DOI) - 11 May 2024
Abstract
In this paper, a 3-bit circularly polarized reconfigurable reflectarray is proposed. The array consists of 64 units in an 8 × 8 configuration, with each unit containing a circular metal patch loaded with phase-delay lines and eight PIN diodes. To independently control each
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In this paper, a 3-bit circularly polarized reconfigurable reflectarray is proposed. The array consists of 64 units in an 8 × 8 configuration, with each unit containing a circular metal patch loaded with phase-delay lines and eight PIN diodes. To independently control each unit, a corresponding DC control circuit was designed and tested with the array. In the bandwidth of 3.43–3.71 GHz, the circularly polarized reconfigurable reflectarray achieved a gain of 16 dB, an aperture efficiency of 27%, an axial ratio of ≤3 dB, an operating bandwidth of 8%, and a beam scanning range of ±60°. The circularly polarized reconfigurable reflectarray can also achieve a good dual-beam radiation performance after testing. The 3-bit circularly polarized reconfigurable reflectarray proposed in this paper offers several advantages, including low loss, high aperture efficiency, a wide beam scanning range, and excellent stability in wide-angle oblique incidence. It has potential applications in low-cost phased array, satellite communications, and deep space exploration.
Full article
(This article belongs to the Special Issue Advanced Wireless Technologies for Next-G Networks: Antennas, Circuits, and Systems)
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Open AccessArticle
Single and Mixed Sensory Anomaly Detection in Connected and Automated Vehicle Sensor Networks
by
Tae Hoon Kim, Stephen Ojo, Moez Krichen and Meznah A. Alamro
Electronics 2024, 13(10), 1885; https://doi.org/10.3390/electronics13101885 (registering DOI) - 11 May 2024
Abstract
Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of
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Connected and automated vehicles (CAVs), integrated with sensors, cameras, and communication networks, are transforming the transportation industry and providing new opportunities for consumers to enjoy personalized and seamless experiences. The fast proliferation of connected vehicles on the road and the growing trend of autonomous driving create vast amounts of data that need to be analyzed in real time. Anomaly detection in CAVs refers to identifying any unusual or unforeseen behavior in the data generated by vehicles’ various sensors and components. Anomaly detection aims to identify any unusual behavior that might indicate a problem or a malfunction in the vehicle. To identify and detect anomalies efficiently, a method must deal with noisy data, missing data, dynamic frequency data, and low- and high-magnitude data, and it must be accurate enough to detect anomalies in a dynamic sensor streaming environment. Therefore, this paper proposes a fast and efficient hard-voting-based technique named FT-HV, comprising three fine-tuned machine learning algorithms to detect and classify anomaly behavior in CAVs for single and mixed sensory datasets. In experiments, we evaluate our approach on the benchmark Sensor Anomaly dataset that contains data from various vehicle sensors at low and high magnitudes. Further, it contains single and mixed anomaly types that are challenging to detect and identify. The results reveal that the proposed approach outperforms existing solutions for detecting single anomaly types at low magnitudes and detecting mixed anomaly types in all settings. Furthermore, this research is envisioned to help detect and identify anomalies early and efficiently promote safer and more resilient CAVs.
Full article
(This article belongs to the Special Issue In-Vehicle Networking/Autonomous Vehicle Security for Internet of Things/Vehicles, 2nd Edition)
Open AccessArticle
Reversible Image Fragile Watermarking with Dual Tampering Detection
by
Cai Zhan, Lu Leng, Chin-Chen Chang and Ji-Hwei Horng
Electronics 2024, 13(10), 1884; https://doi.org/10.3390/electronics13101884 (registering DOI) - 11 May 2024
Abstract
The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile
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The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile watermarking is proposed. The insect matrix reversible embedding algorithm is used to embed the watermark into the cover image. The cover image can be fully recovered when the dual-fragile-watermarked images are not tampered with. This study adopts two recovery schemes and adaptively chooses the most appropriate scheme to recover tampered data according to the square errors between the tampered data and the recovered data of two watermarked images. Tampering coincidence may occur when a large region of the fragile-watermarked image is tampered with, and the recovery information corresponding to the tampered pixels may be missing. The tampering coincidence problem is solved using image-rendering techniques. The experimental results show that the PSNR value of the watermarked image obtained using our scheme can reach 46.37 dB, and the SSIM value is 0.9942. In addition, high-accuracy tampering detection is achieved.
Full article
(This article belongs to the Special Issue Recent Developments and Applications of Image Watermarking, 2nd Edition)
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Open AccessArticle
A Novel Direct Current Circuit Breaker with a Gradually Increasing Counter-Current
by
Jinchao Chen, Siyuan Liu, Jingyong Jin, Yifan Chen, Zhiyuan Liu and Yingsan Geng
Electronics 2024, 13(10), 1883; https://doi.org/10.3390/electronics13101883 (registering DOI) - 11 May 2024
Abstract
A reliable and cost-effective mechanical direct current circuit breaker (DCCB) is a promising solution for DC interruption. However, the typical mechanical DCCB has difficulty in interrupting a rated current, because the high oscillating current superimposed on the rated current generates a steep current
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A reliable and cost-effective mechanical direct current circuit breaker (DCCB) is a promising solution for DC interruption. However, the typical mechanical DCCB has difficulty in interrupting a rated current, because the high oscillating current superimposed on the rated current generates a steep current slope at current zero-crossing (CZC) points, which makes it difficult for the vacuum interrupter to extinguish the arc. The objective of this paper is to present a novel DCCB topology with a gradually increasing counter-current. It utilizes a full-controlled converter, a semi-controlled full bridge, and an LC oscillation branch to generate a gradually increasing counter-current, which is superimposed on any fault current and generates a smooth current slope at CZC points. The proposed DCCB topology is modeled with PSCAD, and the current slope and the initial transient interruption voltage (ITIV) at CZC are analyzed and compared with the typical mechanical DCCB. The results indicate that the current slope at CZC decreases by 57–84% in full-range current interruptions, and the ITIV can be reduced by the same extent. Additionally, the performance of the proposed DCCB is evaluated in a four-terminal HVDC system. A cost and performance comparison is conducted among the main topologies. The obtained results show that the proposed DCCB is a reliable solution for the multi-terminal HVDC system.
Full article
(This article belongs to the Special Issue Advanced Power Generation and Conversion Systems)
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13 May 2024
Meet Us at the 2024 IEEE International Symposium on Information Theory (ISIT 2024), 7–12 July 2024, Athens, Greece
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