You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.

Search for Articles:

Title / Keyword

Author / Affiliation / Email

Journal

Article Type

Advanced Search

Section

Special Issue

Volume

Issue

Number

Page

Logical OperatorOperator

Search Text

Search Type

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, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Physics, Applied) / CiteScore - Q2 (Control and Systems Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2024).
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, Microwave, Network, Signals, Software and Microelectronics.

Impact Factor: 2.6 (2023); 5-Year Impact Factor: 2.6 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2079-9292

Latest Articles

12 pages, 539 KiB

Open AccessArticle

Parameter-Efficient Tuning for Object Tracking by Migrating Pre-Trained Decoders

by Ruijuan Zhang, Li Wang and Song Yang

Electronics 2024, 13(23), 4621; https://doi.org/10.3390/electronics13234621 (registering DOI) - 22 Nov 2024

Video object tracking has taken advantage of pre-trained weights on large-scale datasets. However, most trackers fully fine-tune all the backbone’s parameters for adjusting to tracking-specific representations, where the utilization rate of parameter adjustment is inefficient. In this paper, we aim to explore whether [...] Read more.

Video object tracking has taken advantage of pre-trained weights on large-scale datasets. However, most trackers fully fine-tune all the backbone’s parameters for adjusting to tracking-specific representations, where the utilization rate of parameter adjustment is inefficient. In this paper, we aim to explore whether a better balance can be achieved between parameter efficiency and tracking performance, and fully utilize the weight advantage of training on large-scale datasets. There are two main differences from a normal tracking paradigm: (i) We freeze the pre-trained weights of the backbone and add a dynamic adapter structure for every transformer block for tuning. (ii) We migrate the pre-trained decoder blocks to the tracking head for better generalization and localization. Extensive experiments are conducted on both mainstream challenging datasets and datasets for special scenarios or targets such as night-time and transparent objects. With the full utilization of pre-training knowledge, we found through experiments that a few tuned parameters can compensate for the gap between the pre-trained representation and the tracking-specific representation, especially for large backbones. Even better performance and generalization can be achieved. For instance, our AdaDe-B256 tracker achieves 49.5 AUC on the LaSOT

_{ext}

which contains 150 sequences. Full article

(This article belongs to the Special Issue Selected Papers from the 7th Asian Conference on Artificial Intelligence Technology (ACAIT 2023))

► Show Figures

Figure 1

18 pages, 824 KiB

Open AccessArticle

Anti-Disturbance Target Tracking Control of Auxiliary Unmanned Ground Vehicles for Physical Education

by Lei Liu and Wei Yin

Electronics 2024, 13(23), 4620; https://doi.org/10.3390/electronics13234620 - 22 Nov 2024

The auxiliary unmanned ground vehicle (AUGV) for physical education can significantly enhance the continuity and safety of training and competitions. However, obstacles and area boundary constraints present substantial challenges to the efficiency of the AUGV. This paper proposes an anti-disturbance target tracking control [...] Read more.

The auxiliary unmanned ground vehicle (AUGV) for physical education can significantly enhance the continuity and safety of training and competitions. However, obstacles and area boundary constraints present substantial challenges to the efficiency of the AUGV. This paper proposes an anti-disturbance target tracking control strategy for AUGV, enabling rapid tracking of out-of-bounds balls. In the guidance layer, we design safety constraints based on the exponentially stabilizing control Lyapunov function (ES-CLF) position constraint and control barrier function (CBF), and solve the expected convergence velocity guidance law through quadratic programming. Additionally, the expected motion direction of AUGV is determined using the expected combined velocity. In the control layer, we employ a nonlinear tracking differentiators (NLTD) to achieve finite-time estimation of the derivative of the guidance velocity signal, and observed the model parameter uncertainty and external environmental disturbances through a fixed time disturbance observer. Finally, a fixed-time control strategy is developed to achieve precise target tracking. Stability analysis and simulation results confirm the effectiveness of the proposed AUGV target tracking control strategy and the safety collision avoidance method. Full article

(This article belongs to the Section Systems & Control Engineering)

23 pages, 5281 KiB

Open AccessArticle

A Multiscale Adaptive Fusion Network for Modular Multilevel Converter Fault Diagnosis

by Longzhang Ke, Guozhen Hu, Zhi Liu, Yuqing Yang, Qianju Cheng and Peng Zhang

Electronics 2024, 13(23), 4619; https://doi.org/10.3390/electronics13234619 - 22 Nov 2024

Modular Multilevel Converters (MMCs) play a crucial role in new energy grid connection and renewable energy conversion systems due to the significant merits of good modularity, flexible scalability, and lower operating loss. However, reliability is a significant challenge for MMCs, which consist of [...] Read more.

Modular Multilevel Converters (MMCs) play a crucial role in new energy grid connection and renewable energy conversion systems due to the significant merits of good modularity, flexible scalability, and lower operating loss. However, reliability is a significant challenge for MMCs, which consist of a large number of Insulated Gate Bipolar Transistors (IGBTs). Failures of the IGBTs in submodules (SMs) are a critical issue that affect the performance and operation of MMCs. The insufficient ability of convolutional neural networks to learn key fault features affects the accuracy of MMC fault diagnosis. To resolve this issue, this paper proposes a novel deep fault diagnosis framework named the Multiscale Adaptive Fusion Network (MSAFN) for MMC fault diagnosis. In the proposed MSAFN, the fault features of the raw current in an MMC are extracted by employing multiscale convolutional neural networks (CNNs) firstly, and then a channel attention mechanism is added to adaptively select the channel containing key features, so as to improve the fault diagnosis ability of the MMC in a noisy environment. Finally, the adaptive size of a one-dimensional CNN is adopted to adjust the weight of the feature channels of different scales, which are adaptively fused for fault diagnosis. Experimental validation is performed on two different MMC datasets. Experimental results confirm that the introduction of an attention mechanism of the multiscale feature adaptive fusion channel improves the recognition accuracy of the model by an average of

15.6 %

. Moreover, comparative experiments under different signal-to-noise ratios (SNRs) demonstrate that the MSAFN maintains accuracy levels above

96.7 %

, highlighting its excellent performance, particularly under noisy conditions. Full article

(This article belongs to the Section Power Electronics)

17 pages, 5441 KiB

Open AccessArticle

Parallel Fusion of Graph and Text with Semantic Enhancement for Commonsense Question Answering

by Jiachuang Zong, Zhao Li, Tong Chen, Liguo Zhang and Yiming Zhan

Electronics 2024, 13(23), 4618; https://doi.org/10.3390/electronics13234618 - 22 Nov 2024

Commonsense question answering (CSQA) is a challenging task in the field of knowledge graph question answering. It combines the context of the question with the relevant knowledge in the knowledge graph to reason and give an answer to the question. Existing CSQA models [...] Read more.

Commonsense question answering (CSQA) is a challenging task in the field of knowledge graph question answering. It combines the context of the question with the relevant knowledge in the knowledge graph to reason and give an answer to the question. Existing CSQA models combine pretrained language models and graph neural networks to process question context and knowledge graph information, respectively, and obtain each other’s information during the reasoning process to improve the accuracy of reasoning. However, the existing models do not fully utilize the textual representation and graph representation after reasoning to reason about the answer, and they do not give enough semantic representation to the edges during the reasoning process of the knowledge graph. Therefore, we propose a novel parallel fusion framework for text and knowledge graphs, using the fused global graph information to enhance the semantic information of reasoning answers. In addition, we enhance the relationship embedding by enriching the initial semantics and adjusting the initial weight distribution, thereby improving the reasoning ability of the graph neural network. We conducted experiments on two public datasets, CommonsenseQA and OpenBookQA, and found that our model is competitive when compared with other baseline models. Additionally, we validated the generalizability of our model on the MedQA-USMLE dataset. Full article

24 pages, 4221 KiB

Open AccessArticle

Dynamic Resource Aggregation Method Based on Statistical Capacity Distribution

by Yuexin Wang, Jiali You and Yang Li

Electronics 2024, 13(23), 4617; https://doi.org/10.3390/electronics13234617 - 22 Nov 2024

The computing network is a novel architecture that enables resource matching through the network. In distributed computing networks, computing resource management devices collect resource information and report it to network nodes. These nodes then broadcast the information to guide resource matching. One challenge [...] Read more.

The computing network is a novel architecture that enables resource matching through the network. In distributed computing networks, computing resource management devices collect resource information and report it to network nodes. These nodes then broadcast the information to guide resource matching. One challenge is efficiently aggregating and disseminating computing resource information, as directly reporting fully multi-dimensional data can cause excessive overhead, while overly simplified aggregation may reduce matching accuracy. Existing aggregation methods typically rely on static resource information, overlooking the heterogeneity and dynamics of computing resources that arise from variations in resource capabilities and fluctuations over time, leading to suboptimal matching decisions. In response, this study proposes a dynamic resource aggregation method based on statistical capacity distribution. By modeling the capacity distribution of computing nodes, this method captures dynamic resource information, enabling more precise resource matching. Additionally, constructing resource groups and calculating representative distributions effectively compress the volume of data announcements. Experiments and data analysis demonstrate that, compared to static resource matching methods, the proposed method improves matching accuracy by

48 %

. Furthermore, it reduces announcement overhead by approximately

77.1 %

compared to existing dynamic resource allocation methods. These findings provide an efficient solution for resource aggregation in distributed computing networks. Full article

► Show Figures

Figure 1

30 pages, 1831 KiB

Open AccessArticle

MultiTagging: A Vulnerable Smart Contract Labeling and Evaluation Framework

by Shikah J. Alsunaidi, Hamoud Aljamaan and Mohammad Hammoudeh

Electronics 2024, 13(23), 4616; https://doi.org/10.3390/electronics13234616 - 22 Nov 2024

Identifying vulnerabilities in Smart Contracts (SCs) is crucial, as they can lead to significant financial losses if exploited. Although various SC vulnerability identification methods exist, selecting the most effective approach remains challenging. This article examines these challenges and introduces solutions to enhance SC [...] Read more.

Identifying vulnerabilities in Smart Contracts (SCs) is crucial, as they can lead to significant financial losses if exploited. Although various SC vulnerability identification methods exist, selecting the most effective approach remains challenging. This article examines these challenges and introduces solutions to enhance SC vulnerability identification. It introduces MultiTagging, a modular SC multi-labeling framework designed to overcome limitations in existing SC vulnerability identification approaches. MultiTagging automates SC vulnerability tagging by parsing analysis reports and mapping tool-specific tags to standardized labels, including SC Weakness Classification (SWC) codes and Decentralized Application Security Project (DASP) ranks. Its mapping strategy and the proposed vulnerability taxonomy resolve tool-level labeling inconsistencies, where different tools use distinct labels for identical vulnerabilities. The framework integrates an evaluation module to assess SC vulnerability identification methods. MultiTagging enables both tool-based and vote-based SC vulnerability labeling. To improve labeling accuracy, the article proposes Power-based voting, a method that systematically defines voter roles and voting thresholds for each vulnerability. MultiTagging is used to evaluate labeling across six tools: MAIAN, Mythril, Semgrep, Slither, Solhint, and VeriSmart. The results reveal high coverage for Mythril, Slither, and Solhint, which identified eight, seven, and six DASP classes, respectively. Tool performance varied, underscoring the impracticality of relying on a single tool to identify all vulnerability classes. A comparative evaluation of Power-based voting and two threshold-based methods—AtLeastOne and Majority voting—shows that while voting methods can increase vulnerability identification coverage, they may also reduce detection performance. Power-based voting proved more effective than pure threshold-based methods across all vulnerability classes. Full article

► Show Figures

Figure 1

14 pages, 2708 KiB

Open AccessArticle

A Multi-Attack Adaptive Fake Face Detection Network Based on Global Feature Normalization

by Honggang Xie, Jia Liu, Zhiwei Chen, Kaiyuan Hou and Yuan Yao

Electronics 2024, 13(23), 4615; https://doi.org/10.3390/electronics13234615 - 22 Nov 2024

The advancement of deepfake technology has resulted in increasingly realistic forged faces, posing a challenge for existing fake face detection models, which often exhibit poor adaptability to complex and varied forgery techniques. To address this challenge, this paper proposes a fake face detection [...] Read more.

The advancement of deepfake technology has resulted in increasingly realistic forged faces, posing a challenge for existing fake face detection models, which often exhibit poor adaptability to complex and varied forgery techniques. To address this challenge, this paper proposes a fake face detection network that demonstrates high accuracy and generalization capabilities. A global normalization calibration module is designed to address the issue of ineffective feature extraction within the network, enhancing the model’s focus on key feature regions by establishing a mutual inhibition competition mechanism between different channels. This network maintains high accuracy even when facing various attack methods in both the objective and digital worlds. Experimental results indicate that the proposed method excels in both binary and multi-class classification tests on the CASIA-FASD dataset, the Celeb-DF dataset, and a self-constructed dataset. Specifically, for binary classification tasks, the accuracy reaches 98.19% on the CASIA-FASD dataset, 99.17% on the Celeb-DF dataset, and 98.03% on the self-constructed dataset. In comparison, other methods exhibit slightly inferior performance. For multi-class classification tasks, the proposed model achieves the best performance across all tested forgery types, demonstrating exceptional overall performance and adaptability. Full article

(This article belongs to the Section Artificial Intelligence)

► Show Figures

Figure 1

21 pages, 3720 KiB

Open AccessArticle

Simple and Effective Control System for Active AC Ripple Filtering Circuits

by Roshan Sharma, Masoud Karimi-Ghartemani and Umar Iqbal

Electronics 2024, 13(23), 4614; https://doi.org/10.3390/electronics13234614 - 22 Nov 2024

In systems where dc and ac subsystems are interacting, dc capacitors are used to balance (filter or decouple) the ac power pulsations. The required capacitance often poses a limitation in terms of physical footprint, cost, and/or reliability. Active ripple filtering circuits are widely [...] Read more.

In systems where dc and ac subsystems are interacting, dc capacitors are used to balance (filter or decouple) the ac power pulsations. The required capacitance often poses a limitation in terms of physical footprint, cost, and/or reliability. Active ripple filtering circuits are widely studied in the literature for reducing the size of the capacitor needed by using power electronic switches, passive circuit components, and a control system. The passive circuit components include a substantially smaller dc capacitor at a reasonably higher or lower voltage, and an inductor for filtering the switching ripples. The control systems presented in the literature for such systems are complex and need multiple sensors and are hardly plug-and-play and universal. This paper proposes a simple yet effective control system which uses only one voltage sensor. The circuit accurately emulates a large capacitor within a wide frequency bandwidth in a plug-and-play manner. The paper explains the details of the proposed control system and presents simulation and experimental results to illustrate its properties. Full article

(This article belongs to the Special Issue New Trends in Power Electronics for Microgrids)

► Show Figures

Figure 1

28 pages, 3796 KiB

Open AccessArticle

Adaptive Dynamic Shuffle Convolutional Parallel Network for Image Super-Resolution

by Yiting Long, Haoyu Ruan, Hui Zhao, Yi Liu, Lei Zhu, Chengyuan Zhang and Xinghui Zhu

Electronics 2024, 13(23), 4613; https://doi.org/10.3390/electronics13234613 - 22 Nov 2024

Image super-resolution has experienced significant advancements with the emergence of deep learning technology. However, deploying highly complex super-resolution networks on resource-constrained devices poses a challenge due to their substantial computational requirements. This paper presents the Adaptive Dynamic Shuffle Convolutional [...] Read more.

Image super-resolution has experienced significant advancements with the emergence of deep learning technology. However, deploying highly complex super-resolution networks on resource-constrained devices poses a challenge due to their substantial computational requirements. This paper presents the Adaptive Dynamic Shuffle Convolutional Parallel Network (ADSCPN), a novel lightweight super-resolution model designed to achieve an optimal balance between computational efficiency and image reconstruction quality. The ADSCPN framework employs large-kernel parallel depthwise separable convolutions, dynamic convolutions, and an enhanced attention mechanism to optimize feature extraction and improve detail preservation. Extensive evaluations on standard benchmark datasets demonstrate that ADSCPN achieves state-of-the-art performance while significantly reducing computational complexity, making it well-suited for practical applications on devices with limited computational resources. Full article

(This article belongs to the Special Issue Big Model Techniques for Image Processing)

► Show Figures

Figure 1

15 pages, 2256 KiB

Open AccessArticle

Multitasking Feature Selection Using a Clonal Selection Algorithm for High-Dimensional Microarray Data

by Yi Wang, Dan Luo and Jian Yao

Electronics 2024, 13(23), 4612; https://doi.org/10.3390/electronics13234612 - 22 Nov 2024

Effective gene feature selection is critical for enhancing the interpretability and accuracy of genetic data analysis, particularly in the realm of disease prediction and precision medicine. Most evolutionary feature selection algorithms tend to become stuck in local optima and incur high computational costs, [...] Read more.

Effective gene feature selection is critical for enhancing the interpretability and accuracy of genetic data analysis, particularly in the realm of disease prediction and precision medicine. Most evolutionary feature selection algorithms tend to become stuck in local optima and incur high computational costs, particularly when dealing with the complex and high-dimensional nature of genetic data. To address these issues, this study proposes a multitasking feature selection method based on clone selection for high-dimensional microarray data, which identifies optimal features by transferring useful knowledge across two related tasks derived from the same microarray dataset. First, a dual-task generation strategy is designed, where one task selects features based on the Relief-F method, and the other task is generated from the original features. Second, a new mutation operator is introduced to share useful information between the multiple tasks. Finally, an improved clonal selection algorithm is proposed to strengthen the global and local search abilities. The experimental results on six high-dimensional microarray datasets demonstrate that our method significantly outperforms four state-of-the-art feature selection methods, highlighting its effectiveness and efficiency in tackling complex feature selection problems. Full article

(This article belongs to the Special Issue Knowledge Information Extraction Research)

► Show Figures

Figure 1

22 pages, 2169 KiB

Open AccessArticle

Leveraging AI for Network Threat Detection—A Conceptual Overview

by Muhammad Ali Paracha, Syed Usman Jamil, Khurram Shahzad, Muhammad Arif Khan and Abdul Rasheed

Electronics 2024, 13(23), 4611; https://doi.org/10.3390/electronics13234611 - 22 Nov 2024

Network forensics is commonly used to identify and analyse evidence of any illegal or unauthorised activity in a given network. The collected information can be used for preventive measures against potential cyber attacks and serve as evidence acceptable in legal proceedings. Several conventional [...] Read more.

Network forensics is commonly used to identify and analyse evidence of any illegal or unauthorised activity in a given network. The collected information can be used for preventive measures against potential cyber attacks and serve as evidence acceptable in legal proceedings. Several conventional tools and techniques are available to identify and collect such pieces of evidence; however, most of them require expensive commercial resources, longer investigation times, and costly human expertise. Due to modern networks’ diverse and heterogeneous nature, forensic operations through conventional means become a cumbersome and challenging process. This calls for a new look at how network forensics is approached, considering contemporary approaches to network analysis. In this work, we explore artificial intelligence (AI) techniques based on contemporary machine learning (ML) algorithms such as deep learning (DL) to conduct network forensics. We also propose an investigation model based on AI/ML techniques that can analyse network traffic and behavioural patterns to identify any prior or potential cyber attacks. The proposed AI-based network forensics model speeds up the investigation process, boosting network monitoring without human intervention. This also aims to provide timely and accurate information to network administrators for quick and effective decisions, enabling them to avoid and circumvent future cyber attacks. Full article

(This article belongs to the Special Issue Artificial Intelligence for IoT Systems)

► Show Figures

Figure 1

19 pages, 4031 KiB

Open AccessArticle

MSTrans: Multi-Scale Transformer for Building Extraction from HR Remote Sensing Images

by Fei Yang, Fenlong Jiang, Jianzhao Li and Lei Lu

Electronics 2024, 13(23), 4610; https://doi.org/10.3390/electronics13234610 - 22 Nov 2024

Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on [...] Read more.

Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on convolutional neural networks (CNNs) and Transformers have begun to be of interest in BE, and have made some progress. However, the BE methods based on CNNs are limited by the difficulty in capturing global long-range relationships, while Transformer-based methods are often not detailed enough for pixel-level annotation tasks because they focus on global information. To conquer the limitations, a multi-scale Transformer (MSTrans) is proposed for BE from high-resolution remote sensing images. In the proposed MSTrans, we develop a plug-and-play multi-scale Transformer (MST) module based on atrous spatial pyramid pooling (ASPP). The MST module can effectively capture tokens of different scales through the Transformer encoder and Transformer decoder. This can enhance multi-scale feature extraction of buildings, thereby improving the BE performance. Experiments on three real and challenging BE datasets verify the effectiveness of the proposed MSTrans. While the proposed approach may not achieve the highest Precision and Recall accuracies compared with the seven benchmark methods, it improves the overall metrics F1 and mIoU by 0.4% and 1.67%, respectively. Full article

(This article belongs to the Special Issue Emerging Technologies in Computational Intelligence)

► Show Figures

Figure 1

16 pages, 1109 KiB

Open AccessArticle

A Receiver-Driven Named Data Networking (NDN) Congestion Control Method Based on Reinforcement Learning

by Ruijuan Zheng, Bohan Zhang, Xuhui Zhao, Lin Wang and Qingtao Wu

Electronics 2024, 13(23), 4609; https://doi.org/10.3390/electronics13234609 - 22 Nov 2024

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information [...] Read more.

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information (timeouts, NACKs) within NDN, consumers are unable to access the network congestion status from this information in a timely manner. To address the issues above, this paper combines the Q-learning algorithm with the NDN architecture, proposing Q-NDN. In Q-NDN, consumers can dynamically adjust the congestion window (cwnd) through the real-time monitoring of network status, leveraging the Q-learning algorithm, achieving automatic congestion control for the NDN architecture. Additionally, this paper introduces content popularity-based traffic scheduling for multi-user scenarioswhich adjusts the transmission rates of content with different popularity levels to maintain a dynamic balance in the network. The experimental results show that Q-NDN can converge quickly, make full use of bandwidth resources, and keep the packet loss rate to 0 in the basic network topology. In competing network topologies, Q-NDN can rapidly address conflict issues, efficiently utilize bandwidth resources, and maintain a relatively low packet loss rate. Full article

► Show Figures

Figure 1

15 pages, 789 KiB

Open AccessArticle

EiGC: An Event-Induced Graph with Constraints for Event Causality Identification

by Xi Zeng, Zhixin Bai, Ke Qin and Guangchun Luo

Electronics 2024, 13(23), 4608; https://doi.org/10.3390/electronics13234608 - 22 Nov 2024

Event causality identification (ECI) focuses on detecting causal relationships between events within a document. Existing approaches typically treat each event-mention pair independently, overlooking the relational dynamics and potential conflicts among event causalities. To tackle this challenge, we propose the Event-induced [...] Read more.

Event causality identification (ECI) focuses on detecting causal relationships between events within a document. Existing approaches typically treat each event-mention pair independently, overlooking the relational dynamics and potential conflicts among event causalities. To tackle this challenge, we propose the Event-induced Graph with Constraints (EiGC), which models the complex event-level causal structures in a more realistic manner, facilitating comprehensive causal relation identification. To be more specific, we construct a graph based on diverse event-driven knowledge sources, such as coreference and co-occurrence relations. A graph convolutional network (GCN) is then employed to encode these structural features, effectively capturing both local and global dependencies between nodes. Additionally, we implement event-aware constraints through integer linear programming, incorporating the principles of uniqueness, non-reflexivity, and coreference consistency in event-causal relationships. This approach ensures logical consistency and prevents conflicts in the prediction outcomes. Experimental results on three widely used datasets illustrate that our proposed EiGC approach achieves excellent performance among all the baseline models. Full article

(This article belongs to the Special Issue New Advances in Affective Computing)

► Show Figures

Figure 1

22 pages, 5934 KiB

Open AccessArticle

Estimation of the Immunity of an AC/DC Converter of an LED Lamp to a Standardized Electromagnetic Surge

by Wiesław Sabat, Dariusz Klepacki, Kazimierz Kamuda, Kazimierz Kuryło and Piotr Jankowski-Mihułowicz

Electronics 2024, 13(23), 4607; https://doi.org/10.3390/electronics13234607 - 22 Nov 2024

The method for estimating the immunity of an AC/DC converter built in a commercial LED lamp to a 1.2/50 µs (8/20 µs) surge has been presented in this paper. A lamp with a direct drive LED inverter was selected to present the methodology [...] Read more.

The method for estimating the immunity of an AC/DC converter built in a commercial LED lamp to a 1.2/50 µs (8/20 µs) surge has been presented in this paper. A lamp with a direct drive LED inverter was selected to present the methodology for determining the coefficient of immunity of the test object to a standardized type of surge. The choice of this configuration was important for the testing process and presentation of the methodology to estimate the immunity coefficient of the tested system. In this work, the methodology for determining the deterministic immunity factor of the model inverter to a normalized type of disturbance was presented. Considerations were carried out for a 1.2/50 µs (8/20 µs) surge in accordance with the recommendations of the EN 61000-4-5:2014 standard. This conventional surge is used in laboratory practice to test the immunity of electronic and electrical systems and devices to disturbances that can be generated in the power grid during switching processes, short circuits, and direct and indirect lightning. In the first stage of testing on test benches, the intensity of damage to the integral components of a model inverter was examined with increasing levels of disturbance. Statistical measures characterizing their impact resistance were determined for each of the elements tested. Knowing their values, the value of this coefficient was finally determined for the lamp selected for testing, and the mechanism of its damage was analyzed. Full article

(This article belongs to the Special Issue New Horizons and Recent Advances of Power Electronics)

► Show Figures

Figure 1

22 pages, 1081 KiB

Open AccessArticle

Explainable Safety Argumentation for the Deployment of Automated Vehicles

by Patrick Weissensteiner and Georg Stettinger

Electronics 2024, 13(23), 4606; https://doi.org/10.3390/electronics13234606 - 22 Nov 2024

With over 1.6 million traffic deaths in 2016, automated vehicles equipped with automated driving systems (ADSs) have the potential to increase traffic safety by assuming human driving tasks within the operational design domain (ODD). However, safety validation is challenging due to the open-context [...] Read more.

With over 1.6 million traffic deaths in 2016, automated vehicles equipped with automated driving systems (ADSs) have the potential to increase traffic safety by assuming human driving tasks within the operational design domain (ODD). However, safety validation is challenging due to the open-context problem. Current strategies, such as pure driving and requirement-based testing, are insufficient. Scenario-based testing offers a solution but necessitates appropriate scenario selection, testing methods, and evaluation criteria. This paper builds upon a method to calculate the covered ODD using tested scenarios generated from logical scenarios, considering parameter discretisation uncertainty. Acceptance criteria for the safety argumentation are proposed based on parameter space coverage and variance introduced via discretisation, thus contributing to quantifying the residual risks of safety validation. The approach is demonstrated through two logical scenarios with probability density functions of the parameters generated using a trajectory dataset. These criteria can serve as risk acceptance criteria, providing comparability and explainable results. By developing a robust scenario-based testing approach, ADS safety can be validated, leading to increased traffic safety and reduced fatalities. Since ADSs incorporate AI models, this proposed validation strategy can be extended to AI systems across multiple domains for the respective assurance argument required for deployment. Full article

(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)

► Show Figures

Figure 1

20 pages, 6303 KiB

Open AccessArticle

Progressive Transmission Line Image Transmission and Recovery Algorithm Based on Hybrid Attention and Feature Fusion for Signal-Free Regions of Transmission Lines

by Xiu Ji, Xiao Yang, Zheyu Yue, Hongliu Yang and Haiyang Guo

Electronics 2024, 13(23), 4605; https://doi.org/10.3390/electronics13234605 - 22 Nov 2024

In this paper, a progressive image transmission and recovery algorithm based on hybrid attention mechanism and feature fusion is proposed, aiming to solve the challenge of monitoring the signal-less region of transmission lines. The method combines wavelet transform, Swin Transformer, and hybrid attention [...] Read more.

In this paper, a progressive image transmission and recovery algorithm based on hybrid attention mechanism and feature fusion is proposed, aiming to solve the challenge of monitoring the signal-less region of transmission lines. The method combines wavelet transform, Swin Transformer, and hybrid attention module with the Pixel Shuffle upsampling mechanism to achieve a balance between quality and efficiency of image transmission in a low bandwidth environment. Initial preview is achieved by prioritizing the transmission of low-frequency subbands through wavelet transform, followed by dynamic optimization of the weight allocation of key features using a hybrid attention and local window multi-scale self-attention mechanism, and further enhancement of the resolution of the decoded image through Pixel Shuffle upsampling. Experimental results show that the algorithm significantly outperforms existing methods in terms of image quality (PSNR, SSIM), transmission efficiency, and bandwidth utilization, proving its superior adaptability and effectiveness in surveillance scenarios in signal-free regions. Full article

► Show Figures

Figure 1

20 pages, 4822 KiB

Open AccessArticle

Networking 3 K Two-Qubit Logic Gate Quantum Processors to Approach 1 Billion Logic Gate Performance

by Daniel Guidotti, Xiaoli Ma and Gee-Kung Chang

Electronics 2024, 13(23), 4604; https://doi.org/10.3390/electronics13234604 - 22 Nov 2024

Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths, each pair having negligible spin-orbit fine structure splitting, each pair being channeled into [...] Read more.

Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths, each pair having negligible spin-orbit fine structure splitting, each pair being channeled into single mode pig-tail optical fibers. Entangled photons carry quantum state information among distributed quantum servers via I/O ports having two functions: the unconditionally secure distribution of decryption keys to decrypt publicly distributed, encrypted classical bit streams as input to generate corresponding qubit excitations and to convert a stream of quantum nondemolition measurements of qubit states into a classical bit stream. Outlined are key steps necessary to fabricate arrays of on-demand quantum dot sources of entangled photon pairs; the principles are (1) foundry fabrication of arrays of isolated quantum dots, (2) generation of localized sub-surface shear strain in a semiconductor stack, (3) a cryogenic anvil cell, (4) channeling entangled photons into single-mode optical fibers, (5) unconditionally secure decryption key distribution over the fiber network, (6) resonant excitation of a Josephson tunnel junction qubits from classical bits, and (7) conversion of quantum nondemolition measurements of qubit states into a classical bit. Full article

(This article belongs to the Special Issue Advances in Signals and Systems Research)

► Show Figures

Figure 1

1 pages, 138 KiB

Open AccessRetraction

RETRACTED: Lei et al. Thyristor Aging-State-Evaluation Method Based on State Information and Tensor Domain Theory. Electronics 2021, 10, 2700

by Zhaoyu Lei, Jianyi Guo, Yingfu Tian, Jiemin Yang, Yinwu Xiong, Jie Zhang, Ben Shang and Youping Fan

Electronics 2024, 13(23), 4603; https://doi.org/10.3390/electronics13234603 - 22 Nov 2024

The journal retracts the article titled “Thyristor Aging-State-Evaluation Method Based on State In-formation and Tensor Domain Theory” [...] Full article

9 pages, 4517 KiB

Open AccessArticle

Band Alignment of AlN/InGaZnO Heterojunction for Thin-Film Transistor Application

by Hongpeng Zhang, Tianli Huang, Rongjun Cao, Chen Wang, Bo Peng, Jibao Wu, Shaochong Wang, Kunwei Zheng, Renxu Jia, Yuming Zhang and Hongyi Zhang

Electronics 2024, 13(23), 4602; https://doi.org/10.3390/electronics13234602 - 22 Nov 2024

Uncrystallized indium-gallium-zinc-oxide (InGaZnO) thin-film transistors (TFTs) combined with an aluminum nitride (AlN) dielectric have been used to promote performance and steadiness. However, the high deposition temperature of AlN films limits their application in InGaZnO flexible TFTs. In this work, AlN layers were deposited [...] Read more.

Uncrystallized indium-gallium-zinc-oxide (InGaZnO) thin-film transistors (TFTs) combined with an aluminum nitride (AlN) dielectric have been used to promote performance and steadiness. However, the high deposition temperature of AlN films limits their application in InGaZnO flexible TFTs. In this work, AlN layers were deposited via low-temperature plasma-enhanced atomic layer deposition (PEALD), and InGaZnO films were fabricated via high-power impulse magnetron sputtering (HIPIMS). The band alignment of the AlN/InGaZnO heterojunction was studied using the X-ray photoemission spectrum and ultraviolet visible transmittance spectrum. It was found that the AlN/InGaZnO system exhibited a staggered band alignment with a valence band offset ΔE_v of −1.25 ± 0.05 eV and a conduction band offset ΔE_c of 4.01 ± 0.05 eV. The results imply that PEALD AlN could be more useful for surface passivation than a gate dielectric to promote InGaZnO device reliability under atmospheric exposure. Full article

(This article belongs to the Special Issue Analog/Mixed Signal Integrated Circuit Design)

► Show Figures

Figure 1

More Articles...

Submit to Electronics Review for Electronics

Journal Menu

Journal Browser

► Journal Browser

Highly Accessed Articles

View More...

Latest Books

More Books and Reprints...

E-Mail Alert

News

21 November 2024
Meet Us at the IEEE Region 10 Conference 2024 (TENCON 2024), 1–4 December 2024, Marina Bay, Singapore

14 November 2024
Electronics | Highly Cited Papers in 2023 in the Section “Optoelectronics”

13 November 2024
Meet Us at the 38^th IEEE International Conference on Micro Electro Mechanical Systems (IEEE MEMS 2025), 19–23 January 2025, Kaohsiung, Taiwan

More News & Announcements...

Topics

Propose a Topic

Topic in Computation, Electronics, Energies, Sensors, Sustainability, WEVJ

Modern Power Systems and Units Topic Editors: Jan Taler, Ali Cemal Benim, Sławomir Grądziel, Marek Majdak, Moghtada Mobedi, Tomasz Sobota, Dawid Taler, Bohdan Węglowski
Deadline: 30 November 2024

Topic in Electricity, Electronics, Energies, Processes, Sustainability

Integration of Renewable Energy Topic Editors: Xiandong Ma, Mohamed Benbouzid, Sinisa Durovic, Hao Chen
Deadline: 31 December 2024

Topic in Energies, Mathematics, Electronics, Smart Cities, Designs

Power System Modeling and Control, 2nd Volume Topic Editors: Andrea Bonfiglio, Andrea Mazza
Deadline: 31 January 2025

Topic in Energies, Applied Sciences, Electronics, JLPEA, Designs

Application of Innovative Power Electronic Technologies, 2nd Edition Topic Editors: Ching-Ming Lai, Yitao Liu
Deadline: 10 February 2025

More Topics

Conferences

Announce Your Conference

22–26 November 2024 2024 International Conference on Science and Engineering of Electronics (ICSEE'2024)

19–22 November 2024 The 16th Asian Conference on Organic Electronics (ACOE 2024)

20–22 November 2024 ISM 2024 International Conference on Industry 4.0 and Smart Manufacturing

More Conferences...

Special Issues

Propose a Special Issue

Special Issue in Electronics

New Advances in Visual Computing and Virtual Reality, 2nd Edition Guest Editors: Hai Huang, Na Jiang, Yuehua Wang
Deadline: 25 November 2024

Special Issue in Electronics

Advancement on Smart Vehicles and Smart Travel Guest Editors: Marco Del-Coco, Sergio Trilles Oliver, Pierluigi Carcagni, Ditsuhi Iskandaryan, Xin Wang
Deadline: 30 November 2024

Special Issue in Electronics

The Future of IoT: Advanced AI Based IoT Technologies and Applications Guest Editors: Laith Farhan, Xingwang Li, Waled Gheth
Deadline: 30 November 2024

Special Issue in Electronics

Selected Papers from XXXVIII Conference on Design of Circuits and Integrated Systems—DCIS 2023 Guest Editors: Francisco Javier González-Cañete, Martín González García, Antonio Lopez-Martin
Deadline: 30 November 2024

More Special Issues

Topical Collections

Topical Collection in Electronics

Image and Video Analysis and Understanding Collection Editors: Leonardo Galteri, Claudio Ferrari, Stefanos Kollias

Topical Collection in Electronics

Advance Technologies of Navigation for Intelligent Vehicles Collection Editors: Jinwen Hu, Wei Meng, Shenghai Yuan

Topical Collection in Electronics

Computer Vision and Pattern Recognition Techniques Collection Editor: Donghyeon Cho

Topical Collection in Electronics

Graph Machine Learning Collection Editor: Gemma Piella

More Topical Collections

Back to TopTop