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
Improving Election Integrity: Blockchain and Byzantine Generals Problem Theory in Vote Systems
Electronics 2024, 13(10), 1853; https://doi.org/10.3390/electronics13101853 (registering DOI) - 9 May 2024
Abstract
In the digital age, maintaining election integrity is critical, especially in Africa, where the security of electronic elections is often questioned. This study presents a blockchain-based vote counting and validation (BBVV) system developed using a mixed methods approach that combines stakeholder questionnaires to
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In the digital age, maintaining election integrity is critical, especially in Africa, where the security of electronic elections is often questioned. This study presents a blockchain-based vote counting and validation (BBVV) system developed using a mixed methods approach that combines stakeholder questionnaires to capture system specification and randomized historical election data analysis, following the Design Science Research strategy. Using the theory of the Byzantine General Problem, the BBVV protocol is proposed, which provides an accurate local count of votes at polling stations before national aggregation. The system was tested with randomized historical election data on the Algorand blockchain TestNet and confirmed that a local consensus on the vote count could be reached before it is added to the national tally on the blockchain. Our results show that in the cases where consensus was reached, this was the instance in only about 5% of the voting scenarios, with only 10% of the total vote being considered valid due to the strict consensus requirements. In addition, significant discrepancies were found between officials, with no consensus reached in 95% of cases which was due to the rogue values generated by a randomized dataset. The performance of the BBVV system was evaluated using transaction metrics, saturation, throughput, traffic, and latency to assess its efficiency, scalability, and reliability. The results suggest that blockchain technology can significantly improve the integrity of elections by ensuring a transparent, secure, and accurate vote-counting process. Future work will focus on improving the adaptability and scalability of the BBVV system for different electoral situations.
Full article
Open AccessArticle
Hierarchical Vector-Quantized Variational Autoencoder and Vector Credibility Mechanism for High-Quality Image Inpainting
by
Cheng Li, Dan Xu and Kuai Chen
Electronics 2024, 13(10), 1852; https://doi.org/10.3390/electronics13101852 (registering DOI) - 9 May 2024
Abstract
Image inpainting infers the missing areas of a corrupted image according to the information of the undamaged part. Many existing image inpainting methods can generate plausible inpainted results from damaged images with the fast-developed deep-learning technology. However, they still suffer from over-smoothed textures
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Image inpainting infers the missing areas of a corrupted image according to the information of the undamaged part. Many existing image inpainting methods can generate plausible inpainted results from damaged images with the fast-developed deep-learning technology. However, they still suffer from over-smoothed textures or textural distortion in the cases of complex textural details or large damaged areas. To restore textures at a fine-grained level, we propose an image inpainting method based on a hierarchical VQ-VAE with a vector credibility mechanism. It first trains the hierarchical VQ-VAE with ground truth images to update two codebooks and to obtain two corresponding vector collections containing information on ground truth images. The two vector collections are fed to a decoder to generate the corresponding high-fidelity outputs. An encoder then is trained with the corresponding damaged image. It generates vector collections approximating the ground truth by the help of the prior knowledge provided by the codebooks. After that, the two vector collections pass through the decoder from the hierarchical VQ-VAE to produce the inpainted results. In addition, we apply a vector credibility mechanism to promote vector collections from damaged images and approximate vector collections from ground truth images. To further improve the inpainting result, we apply a refinement network, which uses residual blocks with different dilation rates to acquire both global information and local textural details. Extensive experiments conducted on several datasets demonstrate that our method outperforms the state-of-the-art ones.
Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Image and Video Processing)
Open AccessArticle
Interleaved High Voltage Gain DC-DC Converter with Winding-Cross-Coupled Inductors and Voltage Multiplier Cells for Photovoltaic Systems
by
Shin-Ju Chen, Sung-Pei Yang, Chao-Ming Huang, Sin-Da Li and Cheng-Hsuan Chiu
Electronics 2024, 13(10), 1851; https://doi.org/10.3390/electronics13101851 - 9 May 2024
Abstract
An interleaved high voltage gain DC-DC converter with winding-cross-coupled inductors (WCCIs) and voltage multiplier cells is proposed for photovoltaic systems. The converter configuration is based on the interleaved boost converter integrating the diode-capacitor clamp circuits, the winding-cross-coupled inductors, and voltage multiplier cells to
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An interleaved high voltage gain DC-DC converter with winding-cross-coupled inductors (WCCIs) and voltage multiplier cells is proposed for photovoltaic systems. The converter configuration is based on the interleaved boost converter integrating the diode-capacitor clamp circuits, the winding-cross-coupled inductors, and voltage multiplier cells to increase the voltage gain and reduce the semiconductor voltage stresses. The equal current sharing of two phases is achieved with the help of the winding-cross-coupled inductors. The converter achieves high voltage gain while operating at a proper duty ratio. The low-voltage-rated MOSFETs with low on-resistance are available to reduce the conduction losses due to the low switch voltage stress. The leakage energy of the coupled inductors is recycled such that the voltage spikes on the power switches are avoided. The input current ripple is decreased due to the interleaved operation. The operating principle and steady-state analysis of the proposed converter are proposed in detail. The design guidelines of the proposed converter are given. In addition, the closed-loop controlled system of the proposed converter is designed to diminish the effect of the variations in input voltage and load on the output voltage. Finally, the experimental results of a 1000 W converter prototype with 36 V input and 400 V output are given to validate the theoretical analysis and the converter performance.
Full article
(This article belongs to the Special Issue Efficient and Reliable DC–DC Converters and Related Industrial Electronics)
Open AccessArticle
Design of Lumbar Rehabilitation Training System Based on Virtual Reality
by
Jiani Liu, Ping Shi and Hongliu Yu
Electronics 2024, 13(10), 1850; https://doi.org/10.3390/electronics13101850 - 9 May 2024
Abstract
A virtual reality-based lumbar rehabilitation training system is designed to address the increasing number of patients with low back pain (LBP) year by year. Attitude sensors are used to track lower back movement. In order to improve the effect of rehabilitation training, several
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A virtual reality-based lumbar rehabilitation training system is designed to address the increasing number of patients with low back pain (LBP) year by year. Attitude sensors are used to track lower back movement. In order to improve the effect of rehabilitation training, several virtual rehabilitation training games and assessment scenes are designed based on the Unity3D engine to complete different tasks from simple to complex. The goal is to increase patients’ interest in rehabilitation training. The experimental results verify the accuracy of rehabilitation data acquisition, real-time interactive communication, and the smooth operation of rehabilitation scenes.
Full article
(This article belongs to the Topic Simulations and Applications of Augmented and Virtual Reality, 2nd Edition)
Open AccessArticle
Value-Guided Adaptive Data Augmentation for Imbalanced Small Object Detection
by
Haipeng Wang, Chenhong Sui, Fuhao Jiang, Shuai Li, Hao Liu and Ao Wang
Electronics 2024, 13(10), 1849; https://doi.org/10.3390/electronics13101849 - 9 May 2024
Abstract
Data augmentation is considered a promising technique to resolve the imbalance of large and small objects. Unfortunately, most existing methods augment all small objects indiscriminately, regardless of their learnability and proportion. This tends to result in wasteful enlargement for many weak, low-information objects
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Data augmentation is considered a promising technique to resolve the imbalance of large and small objects. Unfortunately, most existing methods augment all small objects indiscriminately, regardless of their learnability and proportion. This tends to result in wasteful enlargement for many weak, low-information objects but under-augmentation for rare and learnable objects. To this end, we propose a value-guided adaptive data augmentation for scale- and proportion-imbalanced small object detection (ValCopy-Paste). Specifically, we first develop a non-learning object value criteria to determine whether one object should be expanded. Both scale-based learnability and quantity-based necessity are involved in this criteria. Then, the value distribution of objects in the dataset can be further constructed on the basis of the relevant object values. This helps to ensure that those uncommon, learnable objects that deserve enhancement are more likely to be enhanced. Additionally, we propose to enhance the data by pasting the sampled objects into relatively smooth portions of fresh background images, rather than arbitrary areas of any background images. This helps to boost data diversity while reducing the interference from complicated backgrounds. Evidently, our method does not require sophisticated training and just depends on the size and distribution of the objects in the dataset. Extensive experiments on MS COCO 2017 and PASCAL VOC 2012 demonstrate that our method achieves better performance than state-of-the-art methods.
Full article
(This article belongs to the Special Issue Emerging Trends in Advanced Video and Sequence Technology)
Open AccessArticle
Bio-Inspired Intelligent Swarm Confrontation Algorithm for a Complex Urban Scenario
by
He Cai, Yaoguo Luo, Huanli Gao and Guangbin Wang
Electronics 2024, 13(10), 1848; https://doi.org/10.3390/electronics13101848 - 9 May 2024
Abstract
This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of
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This paper considers the confrontation problem for two tank swarms of equal size and capability in a complex urban scenario. Based on the Unity platform (2022.3.20f1c1), the confrontation scenario is constructed featuring multiple crossing roads. Through the analysis of a substantial amount of biological data and wildlife videos regarding animal behavioral strategies during confrontations for hunting or food competition, two strategies are been utilized to design a novel bio-inspired intelligent swarm confrontation algorithm. The first one is the “fire concentration” strategy, which assigns a target for each tank in a way that the isolated opponent will be preferentially attacked with concentrated firepower. The second one is the “back and forth maneuver” strategy, which makes the tank tactically retreat after firing in order to avoid being hit when the shell is reloading. Two state-of-the-art swarm confrontation algorithms, namely the reinforcement learning algorithm and the assign nearest algorithm, are chosen as the opponents for the bio-inspired swarm confrontation algorithm proposed in this paper. Data of comprehensive confrontation tests show that the bio-inspired swarm confrontation algorithm has significant advantages over its opponents from the aspects of both win rate and efficiency. Moreover, we discuss how vital algorithm parameters would influence the performance indices.
Full article
(This article belongs to the Topic Agents and Multi-Agent Systems)
Open AccessArticle
Detecting Smell/Gas-Source Direction Using Output Voltage Characteristics of a CMOS Smell Sensor
by
Yoshihiro Asada, Kenichi Maeno, Kenichi Hashizume, Yusuke Yodo, Toshihiko Noda, Kazuaki Sawada and Masahiro Akiyama
Electronics 2024, 13(10), 1847; https://doi.org/10.3390/electronics13101847 - 9 May 2024
Abstract
Various organisms, such as dogs and moths, can locate their prey and mates by sensing their smells. Following this manner, if an engineering device with the capability to detect a smell or gas source is realized, it can have a wide range of
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Various organisms, such as dogs and moths, can locate their prey and mates by sensing their smells. Following this manner, if an engineering device with the capability to detect a smell or gas source is realized, it can have a wide range of potential applications, such as searching for landmines, locating gas leaks, and rapid detection of fire. A previous study on the estimation of smell and gas-flow direction successfully detected the smell/gas-source direction in low-wind-velocity environments using a semiconductor gas sensor array. However, some problems are generally associated with the use of semiconductor gas sensors due to the use of heaters. This study aimed to detect the location of a smell/gas source using an integrated CMOS smell sensor array, which operates at room temperature without a heater. The experiment showed that under ideal conditions, the order of gas responses and concentration gradient of the gas enabled the estimation of the direction of the smell/gas-source location on one side of the sensor.
Full article
(This article belongs to the Special Issue Science and Technology of Advanced Electronics, Sensing Systems and AI Applied to Society: Including Collections from the Latest Papers of KRIS 2023)
Open AccessArticle
Joint Transmit and Receive Beamforming Design for DPC-Based MIMO DFRC Systems
by
Chenhao Yang, Xin Wang and Wei Ni
Electronics 2024, 13(10), 1846; https://doi.org/10.3390/electronics13101846 - 9 May 2024
Abstract
This paper proposes an optimal beamforming strategy for a downlink multi-user multi-input–multi-output (MIMO) dual-function radar communication (DFRC) system with dirty paper coding (DPC) adopted at the transmitter. We aim to achieve the maximum weighted sum rate of communicating users while adhering to a
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This paper proposes an optimal beamforming strategy for a downlink multi-user multi-input–multi-output (MIMO) dual-function radar communication (DFRC) system with dirty paper coding (DPC) adopted at the transmitter. We aim to achieve the maximum weighted sum rate of communicating users while adhering to a predetermined transmit covariance constraint for radar performance assurance. To make the intended problem trackable, we leverage the equivalence of the weighted sum rate and the weighted minimum mean squared error (MMSE) to reframe the issue and devise a block coordinate descent (BCD) approach to iteratively calculate transmit and receive beamforming solutions. Through this methodology, we demonstrate that the optimal receive beamforming aligns with the traditional MMSE approach, whereas the optimal transmit beamforming design can be cast into a quadratic optimization problem defined on a complex Stiefel manifold. Based on the majorization–minimization (MM) method, an iterative algorithm is then developed to compute the optimal transmit beamforming design by solving a series of orthogonal Procrustes problems (OPPs) that admit closed-form optimal solutions. Numerical findings serve to validate the efficacy of our scheme. It is demonstrated that our approach can achieve at least 73% higher spectral efficiency than the existing methods in a high signal-to-noise ratio (SNR) regime.
Full article
(This article belongs to the Section Microwave and Wireless Communications)
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Open AccessArticle
Intelligent Geo-Tour Route Recommendation Algorithm Based on Feature Text Mining and Spatial Accessibility Model
by
Xiao Zhou, Zheng Zhang, Xinjian Liang and Mingzhan Su
Electronics 2024, 13(10), 1845; https://doi.org/10.3390/electronics13101845 - 9 May 2024
Abstract
In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering
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In view of the problems in planning and recommending tour routes, this paper constructs a feature text mining (FTM) method and spatial accessibility model (SAM) as the key factors for scenic spot recommendation (SSR) and tour route recommendation (TRR). The scenic spot clustering algorithm (SSCA) based on FTM was constructed by tourists’ text evaluation data mining. Considering the spatial attributes of scenic spots, the scenic spot topology tree algorithm (SSTTA) based on dynamic buffer spatial accessibility (DBSA) was constructed. The optimal scenic spots were recommended based on interest matching and spatial accessibility optimization. As to the recommended scenic spots, this paper proposes an optimal tour route recommendation algorithm (TRRA) based on SSTTA, which aims to determine the optimal adjacent section path structure tree (ASPST) with the lowest cost under travel constraints and transportation modes. The experiment verifies that the proposed algorithm can recommend scenic spots that match tourists’ interests and have optimal spatial accessibility, and the optimal tour routes with the lowest costs under certain travel constraints. Compared with the searched sub-optimal tour routes, the optimal tour route recommended by the proposed algorithm produces the lowest travel costs, and all the scenic spots in the tour route meet the tourists’ interests. Compared with the commonly used BDMA and GDMA methods, the proposed algorithm can determine the optimal routes with lower travel costs.
Full article
(This article belongs to the Special Issue Advances in Intelligent Systems and Networks, 2nd Edition)
Open AccessArticle
An Improved Suppression Method of AC Transient Overvoltage for Line Commuted Converter Based High Voltage Direct Current Considering AC-DC System Coupling
by
Jinxin Ouyang, Yujie Chen, Xinyu Pan and Yanbo Diao
Electronics 2024, 13(10), 1844; https://doi.org/10.3390/electronics13101844 - 9 May 2024
Abstract
Commutation failures in line commuted converter-based high voltage direct current (LCC-HVDC) transmission systems leads to an increase in the converter bus voltage of the rectifier station, thus resulting in AC transient overvoltage in the sending-end grid. The transient overvoltage could lead to the
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Commutation failures in line commuted converter-based high voltage direct current (LCC-HVDC) transmission systems leads to an increase in the converter bus voltage of the rectifier station, thus resulting in AC transient overvoltage in the sending-end grid. The transient overvoltage could lead to the disconnection of renewable energy generation and threaten the stable operation of the sending-end grid. However, the influences of the coupling between AC and DC systems caused by the interaction between the active and reactive power of the sending-end grid, the AC bus voltage of the rectifier station, and the DC current are ignored. The AC transient overvoltage cannot be accurately suppressed. Therefore, in this study, the transient voltage characteristics of the rectifier station under a commutation failure of the inverter station are analyzed. The influence of LCC-HVDC control on the AC bus voltage of a rectifier station through the active and reactive power of the rectifier station is analyzed. A dynamic model of the AC bus voltage of a rectifier station under an AC-DC system coupling is established. The calculation method of the command value of the DC current of the rectifier station is proposed by a predictive control model, and an improved suppression method for AC transient overvoltage is proposed. The case studies show that the accuracy and effectiveness of the suppression of AC transient overvoltage are improved by considering the coupling between AC and DC systems.
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Open AccessArticle
Analytical Solutions of the 3-DOF Gyroscope Model
by
Izabela Krzysztofik and Slawomir Blasiak
Electronics 2024, 13(10), 1843; https://doi.org/10.3390/electronics13101843 - 9 May 2024
Abstract
The motion of a rigid body (a gyroscope) is one of the key issues in classical mechanics. It remains a significant challenge, as evidenced by its extensive practical implementations in various scientific disciplines and engineering operations. It is important to obtain analytical solutions,
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The motion of a rigid body (a gyroscope) is one of the key issues in classical mechanics. It remains a significant challenge, as evidenced by its extensive practical implementations in various scientific disciplines and engineering operations. It is important to obtain analytical solutions, as they provide solutions that depend directly on the system’s parameters, which can be definitively interpreted. The coupling of numerical and analytical solutions allows for a more precise representation of the real phenomenon. The main objective of the article was to formulate analytical solutions for the motion of a Cardan suspension gyroscope subjected to controlling torque moments. Analytical solutions for the proposed mathematical model were developed using the Laplace transform and Green’s function. Subsequently, they were validated by numerical tests. The obtained analytical solutions are universally applicable, regardless of the type of controlling moments.
Full article
(This article belongs to the Section Systems & Control Engineering)
Open AccessArticle
Multi-User Detection Based on Improved Cheetah Optimization Algorithm
by
Shuang Chen, Yuanfa Ji and Xiyan Sun
Electronics 2024, 13(10), 1842; https://doi.org/10.3390/electronics13101842 - 9 May 2024
Abstract
Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual
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Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual update mechanism of the Cheetah Optimizer (CO) algorithm using a nonlinear strategy to improve the uniformity and discretization of the individual search range, and then dynamically introduces a differential evolutionary algorithm into the improved selection mechanism of the CO algorithm, which is utilized to fine-tune the solution space and maintain the local diversity during the fast search process. Simulation results demonstrate that this detection algorithm not only realizes fast convergence with a very low bit error rate (BER) at eight iterations but also has obvious advantages in terms of noise immunity, resistance to far and near effects, communication capacity, etc., which greatly improves the speed and accuracy of optimal position solving for multi-user detection and can achieve the purpose of accurate solving in complex environments.
Full article
(This article belongs to the Section Circuit and Signal Processing)
Open AccessArticle
Microstrip Quasi-Elliptic Absorptive Bandpass Filter with Ultra-Wide Reflectionless Range and Compact Size
by
Awei Zhang, Jinping Xu, Zhiqiang Liu and Yuwei Zhang
Electronics 2024, 13(10), 1841; https://doi.org/10.3390/electronics13101841 - 9 May 2024
Abstract
Absorptive bandpass filters (ABPFs) are highly attractive in modern microwave communication systems due to their ability to internally absorb the harmful stopband RF-power reflections. This paper reports an approach to designing quasi-elliptic ABPFs with ultra-wide reflectionless range, enhanced selectivity, and compact size. The
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Absorptive bandpass filters (ABPFs) are highly attractive in modern microwave communication systems due to their ability to internally absorb the harmful stopband RF-power reflections. This paper reports an approach to designing quasi-elliptic ABPFs with ultra-wide reflectionless range, enhanced selectivity, and compact size. The method is realized based on a fourth-order quasi-elliptic absorptive lowpass filter (ALPF) prototype with a simplified structure. This ALPF prototype exhibits both good impedance-matching over the whole normalized frequency domain and an adjustable transmission zero close to the passband. By applying an equivalent impedance transformer model, a coupled-line-based ABPF scheme is devised from the ALPF prototype, which eliminates conventional dispersive transmission line inverters, resulting in an ultra-wide reflectionless range and a compact size. Closed-form equations are derived to support the filter synthesis. A 2.45 GHz microstrip ABPF with 30% fractional bandwidth is designed for verification. The measured minimum in-band insertion loss is 0.83 dB and the reflectionless range of return loss better than 10 dB is from DC to 12.88 GHz. Both the upper and lower stopband suppression exceed 20 dB, with the upper stopband extending up to 6.80 GHz. The upper and lower out-of-band roll-off rates are 93.9 and 121.4 dB/GHz, respectively. The overall circuit size is 0.12 λg2.
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(This article belongs to the Section Microwave and Wireless Communications)
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Open AccessArticle
Study of Acoustic Emission from the Gate of Gallium Nitride High Electron Mobility Transistors
by
Bartłomiej K. Paszkiewicz, Bogdan Paszkiewicz and Andrzej Dziedzic
Electronics 2024, 13(10), 1840; https://doi.org/10.3390/electronics13101840 - 9 May 2024
Abstract
Nitrides are the leading semiconductor material used for the fabrication of high electron mobility transistors (HEMTs). They exhibit piezoelectric properties, which, coupled with their high mechanical stiffness, expand their versatile applications into the fabrication of piezoelectric devices. Today, due to advances in device
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Nitrides are the leading semiconductor material used for the fabrication of high electron mobility transistors (HEMTs). They exhibit piezoelectric properties, which, coupled with their high mechanical stiffness, expand their versatile applications into the fabrication of piezoelectric devices. Today, due to advances in device technology that result in a reduction in the size of individual transistor elements and due to increased structural complexity (e.g., multi-gate transistors), the integration of piezoelectric materials into HEMTs leads to an interesting occurrence, namely acoustic emission from the transistor gate due to piezoelectric effects. This could affect the device’s performance, reliability, and durability. However, this phenomenon has not yet been comprehensively described. This paper aims to examine this overlooked aspect of AlGaN/GaN HEMT operation, that is, the acoustic emission from the gate region of the device induced by piezoelectric effects. For this purpose, dedicated test structures were designed, consisting of two narrow 1.7 μm-wide metallization strips placed at distances ranging from 5 μm to 200 μm fabricated in AlGaN/GaN heterostructures to simulate and examine the gate behavior of the HEMT transistor. For comparison, the test device structures were also fabricated on sapphire, which is not a piezoelectric material. Measurements of acoustic and electrical interactions in the microwave range were carried out using the “on wafer” method with Picoprobe’s signal–ground–signal (SGS)-type microwave probes. The dependence of reflectance |S11| and transmittance |S21| vs. frequency was investigated, and the coupling capacitance was determined. An equivalent circuit model of the test structure was developed, and finite element method simulation was performed to study the distribution of the acoustic wave in the nitride layers and substrate for different frequencies using Comsol Multiphysics software. At frequencies up to 2–3 GHz, the formation of volume waves and a surface wave, capable of propagating over long distances (in the order of tens of micrometers) was observed. At higher frequencies, the resulting distribution of displacements as a result of numerous reflections and interferences was more complicated. However, there was always the possibility of a surface wave occurrence, even at large distances from the excitation source. At small gate distances, electrical interactions dominate. Above 100 µm, electrical interactions are comparable to acoustic ones. With further increases in distance, weakly attenuated surface waves will dominate.
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(This article belongs to the Topic Advances in Microelectronics and Semiconductor Engineering)
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Open AccessArticle
Analysis and Prevention of AI-Based Phishing Email Attacks
by
Chibuike Samuel Eze and Lior Shamir
Electronics 2024, 13(10), 1839; https://doi.org/10.3390/electronics13101839 - 9 May 2024
Abstract
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Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format
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Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.
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Open AccessArticle
A Single-Output-Filter Double Dual Ćuk Converter
by
Hector R. Robles-Campos, Julio C. Rosas-Caro, Antonio Valderrabano-Gonzalez and Johnny Posada
Electronics 2024, 13(10), 1838; https://doi.org/10.3390/electronics13101838 - 9 May 2024
Abstract
This study introduces an innovative version of a recently studied converter. A Double Dual Ćuk Converter was recently studied with advantages like the possibility of designing it for achieving a low-input current ripple. The proposed converter, called the Improved Double Dual Ćuk Converter,
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This study introduces an innovative version of a recently studied converter. A Double Dual Ćuk Converter was recently studied with advantages like the possibility of designing it for achieving a low-input current ripple. The proposed converter, called the Improved Double Dual Ćuk Converter, maintains the advantages of the former one, and it is characterized by requiring one less capacitor and inductor than its predecessor. This allows addressing the challenge of optimizing the topology to reduce component count without compromising the operation; this work proposes an efficient design methodology based on theoretical analysis and experimental validation. Results demonstrate that the improved topology not only retains the advantages of the previous version, including high efficiency and robustness, but also enhances power density by reducing the number of components. These advancements open new possibilities for applications requiring compact and efficient power converters, such as renewable energy systems, electric vehicles, and portable power supply systems. This work underscores the importance of continuous innovation in power converter design and lays the groundwork for future research aimed at optimizing converter topologies. A detailed discussion of the operating principles and modeling of the converter is provided. Furthermore, simulation outcomes highlighting differences in steady-state duration, output voltage, input current ripple, and operational efficiency are shared. The results from an experimental test bench are also presented to corroborate the efficacy of the improved converter.
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(This article belongs to the Special Issue Advanced Technologies in Power Electronics and Electric Drives)
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Open AccessArticle
A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting
by
Hua Fu, Junnan Zhang and Sen Xie
Electronics 2024, 13(10), 1837; https://doi.org/10.3390/electronics13101837 - 9 May 2024
Abstract
Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the
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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.
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(This article belongs to the Topic Advances in Power Science and Technology)
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Open AccessArticle
Adaptive Multi-Criteria Selection for Efficient Resource Allocation in Frugal Heterogeneous Hadoop Clusters
by
Basit Qureshi
Electronics 2024, 13(10), 1836; https://doi.org/10.3390/electronics13101836 - 9 May 2024
Abstract
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Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop scheduling is
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Efficient resource allocation is crucial in clusters with frugal Single-Board Computers (SBCs) possessing limited computational resources. These clusters are increasingly being deployed in edge computing environments in resource-constrained settings where energy efficiency and cost-effectiveness are paramount. A major challenge in Hadoop scheduling is load balancing, as frugal nodes within the cluster can become overwhelmed, resulting in degraded performance and frequent occurrences of out-of-memory errors, ultimately leading to job failures. In this study, we introduce an Adaptive Multi-criteria Selection for Efficient Resource Allocation (AMS-ERA) in Frugal Heterogeneous Hadoop Clusters. Our criterion considers CPU, memory, and disk requirements for jobs and aligns the requirements with available resources in the cluster for optimal resource allocation. To validate our approach, we deploy a heterogeneous SBC-based cluster consisting of 11 SBC nodes and conduct several experiments to evaluate the performance using Hadoop wordcount and terasort benchmark for various workload settings. The results are compared to the Hadoop-Fair, FOG, and IDaPS scheduling strategies. Our results demonstrate a significant improvement in performance with the proposed AMS-ERA, reducing execution time by 27.2%, 17.4%, and 7.6%, respectively, using terasort and wordcount benchmarks.
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Open AccessEditorial
Application of Computational Electromagnetics Techniques and Artificial Intelligence in the Engineering
by
Rui Li and Le Xu
Electronics 2024, 13(10), 1835; https://doi.org/10.3390/electronics13101835 - 9 May 2024
Abstract
Since the establishment of Maxwell’s equations in the 19th century, computational electromagnetics has undergone a century of stable development [...]
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(This article belongs to the Special Issue Application of Computational Electromagnetics Techniques and Artificial Intelligence in the Engineering)
Open AccessArticle
A Foam Line Position Detection Algorithm for A/O Pool Based on YOLOv5
by
Yubin Xu, Yihao Wu and Yinzhang Guo
Electronics 2024, 13(10), 1834; https://doi.org/10.3390/electronics13101834 - 9 May 2024
Abstract
During the biochemical pretreatment process of leachate in urban landfill sites, if the foam in the A/O pool is not promptly addressed, it can lead to overflow, posing hazards to the surrounding environment and personnel. Therefore, a real-time foam line detection algorithm based
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During the biochemical pretreatment process of leachate in urban landfill sites, if the foam in the A/O pool is not promptly addressed, it can lead to overflow, posing hazards to the surrounding environment and personnel. Therefore, a real-time foam line detection algorithm based on YOLOv5x was proposed, which enhances feature information and improves anchor box regression prediction to accurately detect the position of foam lines. Firstly, in the preprocessing stage, employing a rectangular box to simultaneously label the foam line and the edge of the A/O pool within the same region, enhances the feature information of the foam line. Then, the C3NAM module was proposed, which applies weight sparse penalties to attention modules in the feature extraction section, to enhance the capability of extracting foam line features. Subsequently, a B-SPPCSPC module was proposed to enhance the fusion of shallow and deep feature information, addressing the issue of susceptibility to background interference during foam line detection. Next, the Focal_EIOU was introduced to ameliorate the issue of class imbalance in detection, providing more accurate bounding box predictions. Lastly, optimizing the detection layer scale improves the detection performance for smaller targets. The experimental results demonstrate that the accuracy of this algorithm reaches 98.9%, and the recall reaches 88.1%, with a detection frame rate of 26.2 frames per second, which can meet the actual detection requirements of real-world application scenarios.
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(This article belongs to the Special Issue Deep Learning-Based Object Detection/Classification)
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