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
Shaping Tomorrow: Anticipating Skills Requirements Based on the Integration of Artificial Intelligence in Business Organizations—A Foresight Analysis Using the Scenario Method
Electronics 2024, 13(11), 2198; https://doi.org/10.3390/electronics13112198 (registering DOI) - 4 Jun 2024
Abstract
This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths
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This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.
Full article
(This article belongs to the Special Issue Future Trends of Artificial Intelligence (AI) and Big Data)
Open AccessArticle
An Improved YOLOv5s Model for Building Detection
by
Jingyi Zhao, Yifan Li, Jing Cao, Yutai Gu, Yuanze Wu, Chong Chen and Yingying Wang
Electronics 2024, 13(11), 2197; https://doi.org/10.3390/electronics13112197 (registering DOI) - 4 Jun 2024
Abstract
With the continuous advancement of autonomous vehicle technology, the recognition of buildings becomes increasingly crucial. It enables autonomous vehicles to better comprehend their surrounding environment, facilitating safer navigation and decision-making processes. Therefore, it is significant to improve detection efficiency on edge devices. However,
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With the continuous advancement of autonomous vehicle technology, the recognition of buildings becomes increasingly crucial. It enables autonomous vehicles to better comprehend their surrounding environment, facilitating safer navigation and decision-making processes. Therefore, it is significant to improve detection efficiency on edge devices. However, building recognition faces problems such as severe occlusion and large size of detection models that cannot be deployed on edge devices. To solve these problems, a lightweight building recognition model based on YOLOv5s is proposed in this study. We first collected a building dataset from real scenes and the internet, and applied an improved GridMask data augmentation method to expand the dataset and reduce the impact of occlusion. To make the model lightweight, we pruned the model by the channel pruning method, which decreases the computational costs of the model. Furthermore, we used Mish as the activation function to help the model converge better in sparse training. Finally, comparing it to YOLOv5s (baseline), the experiments show that the improved model reduces the model size by 9.595 MB, and the [email protected] reaches 82.3%. This study will offer insights into lightweight building detection, demonstrating its significance in environmental perception, monitoring, and detection, particularly in the field of autonomous driving.
Full article
(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)
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Open AccessArticle
A Compact C-Band Multiple-Input Multiple-Output Circular Microstrip Patch Antenna Array with Octagonal Slotted Ground Plane and Neutralization Line for Improved Port Isolation in 5G Handheld Devices
by
Asad Ali Khan, Zhenyong Wang, Dezhi Li and Ali Ahmed
Electronics 2024, 13(11), 2196; https://doi.org/10.3390/electronics13112196 (registering DOI) - 4 Jun 2024
Abstract
In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom
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In this paper, an eight-port antenna array is presented for 5G handheld terminals to support multiple-input multiple-output (MIMO) operations. The reported design involves three layers: the top contains eight circular microstrip feed elements; the middle is a low-cost FR-4 substrate, and the bottom layer is a ground plane with four etched octagonal slots. Each resonating element is fed by 50-ohm sub-miniature connectors. To mitigate the detrimental effects of mutual coupling of ports and enhance overall isolation between the adjacent microstrip-fed circular patch elements, a neutralization line is strategically implemented between the feed lines of the antenna array. The design configuration involves two elements at each vertex of the printed circuit board (PCB). The overall dimensions of the PCB are 150 × 75 mm2. Each slot and its corresponding radiating elements exhibit linear dual polarization and diverse radiation patterns. The proposed antenna design achieves the required operating bandwidth of more than 1000 MHz spanning from 3 to 4.2 GHz, effectively covering all the upper C-band frequency range of 3.3 GHz to 4.2 GHz allocated for 5G n77 and n78 frequency range 1 (FR1). Required port isolation and lower envelop correlation coefficient (ECC) are achieved for the band of interest. The proposed design gives a peak gain of up to 4 dB for the said band. In addition to these results, degradation in the performance of the antenna array is also investigated during different operating modes of the handheld device. Measured results from the fabricated unit cell and whole array also have a good match with simulated results. On the whole, the proposed antenna possesses the potential to be used in 5G and the open radio access network (ORAN) compliant handheld devices.
Full article
Open AccessArticle
Ergodic Rate Analysis for Full-Duplex and Half-Duplex Networks with Energy Harvesting
by
Bin Zhong, Liang Chen and Zhongshan Zhang
Electronics 2024, 13(11), 2195; https://doi.org/10.3390/electronics13112195 - 4 Jun 2024
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Considering energy harvesting, the ergodic data rates for both in band full-duplex (FD) and half-duplex (HD) wireless communications were studied. The analytic expressions of downlink and uplink ergodic rates for the proposed system were first derived with independent and identically distributed (i.i.d.) Rayleigh
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Considering energy harvesting, the ergodic data rates for both in band full-duplex (FD) and half-duplex (HD) wireless communications were studied. The analytic expressions of downlink and uplink ergodic rates for the proposed system were first derived with independent and identically distributed (i.i.d.) Rayleigh fading link. It was revealed that the uplink data rate can be improved by decreasing the downlink data rate. Furthermore, the uplink/downlink data rates are also shown to be influenced by some significance parameters, for example, the power split parameter and signal-to-noise ratio (SNR) (i.e., PS/σ2) of each link. Additionally, unlike the HD, the proposed FD node is capable of harvesting energy during the communication process; however, this is at the cost of performance loss induced by the residual self-interference (RSI), which is caused by the essence of simultaneous uplink and downlink transmissions in a single frequency band.
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Open AccessReview
A Review of the Antenna Field Regions
by
Amedeo Capozzoli, Claudio Curcio, Francesco D’Agostino and Angelo Liseno
Electronics 2024, 13(11), 2194; https://doi.org/10.3390/electronics13112194 - 4 Jun 2024
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We review the field regions and their boundaries around an electromagnetic source. We consider the cases of sources whose dimensions are comparable or larger than the wavelength, of planar sources/apertures, and of sources whose dimensions are small with respect to the wavelength and
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We review the field regions and their boundaries around an electromagnetic source. We consider the cases of sources whose dimensions are comparable or larger than the wavelength, of planar sources/apertures, and of sources whose dimensions are small with respect to the wavelength and the criteria involving the strength of the reactive components of the electromagnetic field with respect to the radiative ones. The Fraunhofer and the Fresnel Regions are detailed, along with references to the paraxial approximation for planar apertures. The near-field and intermediate regions are also discussed. We review the standard boundaries between the regions. However, the standard boundaries are not clearly marked, nor are the regions uniquely defined. Accordingly, we also discuss different criteria that have been proposed during the years, which depend on the application and typically rely on numerical arguments, but are not necessarily universally accepted.
Full article
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Open AccessArticle
Continuous Time Simulation and System-Level Model of a MVDC Distribution Grid Including SST and MMC-Based AFE
by
Daniel Siemaszko and Mauro Carpita
Electronics 2024, 13(11), 2193; https://doi.org/10.3390/electronics13112193 - 4 Jun 2024
Abstract
Medium-voltage DC (MVDC) technology has gained increasing attention in recent years. Power electronics devices dominate these grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be
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Medium-voltage DC (MVDC) technology has gained increasing attention in recent years. Power electronics devices dominate these grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system-level control model of such a modern MVDC distribution grid. The aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/−10 kV MVDC grid case study, including a solid-state transformer (SST)- and modular multilevel converter (MMC)-based active front end (AFE). An additional aim of this paper is to supply educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.
Full article
(This article belongs to the Special Issue Multi-Level Power Converters Systems)
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Open AccessArticle
A Novel CNFET SRAM-Based Compute-In-Memory for BNN Considering Chirality and Nanotubes
by
Youngbae Kim, Nader Alnatsheh, Nandakishor Yadav, Jaeik Cho, Heeyoung Jo and Kyuwon Ken Choi
Electronics 2024, 13(11), 2192; https://doi.org/10.3390/electronics13112192 - 4 Jun 2024
Abstract
As AI models grow in complexity to enhance accuracy, supporting hardware encounters challenges such as heightened power consumption and diminished processing speed due to high throughput demands. Compute-in-memory (CIM) technology emerges as a promising solution. Furthermore, carbon nanotube field-effect transistors (CNFETs) show significant
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As AI models grow in complexity to enhance accuracy, supporting hardware encounters challenges such as heightened power consumption and diminished processing speed due to high throughput demands. Compute-in-memory (CIM) technology emerges as a promising solution. Furthermore, carbon nanotube field-effect transistors (CNFETs) show significant potential in bolstering CIM technology. Despite advancements in silicon semiconductor technology, CNFETs pose as formidable competitors, offering advantages in reliability, performance, and power efficiency. This is particularly pertinent given the ongoing challenges posed by the reduction in silicon feature size. We proposed an ultra-low-power architecture leveraging CNFETs for Binary Neural Networks (BNNs), featuring an advanced state-of-the-art 8T SRAM bit cell and CNFET model to optimize performance in intricate AI computations. Through meticulous optimization, we fine-tune the CNFET model by adjusting tube counts and chiral vectors, as well as optimizing transistor ratios for SRAM transistors and nanotube diameters. SPICE simulation in 32 nm CNFET technology facilitates the determination of optimal transistor ratios and chiral vectors across various nanotube diameters under a 0.9 V supply voltage. Comparative analysis with conventional FinFET-based CIM structures underscores the superior performance of our CNFET SRAM-based CIM design, boasting a 99% reduction in power consumption and a 91.2% decrease in delay compared to state-of-the-art designs.
Full article
(This article belongs to the Section Microelectronics)
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Open AccessArticle
Speech Emotion Recognition Using Dual-Stream Representation and Cross-Attention Fusion
by
Shaode Yu, Jiajian Meng, Wenqing Fan, Ye Chen, Bing Zhu, Hang Yu, Yaoqin Xie and Qiuirui Sun
Electronics 2024, 13(11), 2191; https://doi.org/10.3390/electronics13112191 - 4 Jun 2024
Abstract
Speech emotion recognition (SER) aims to recognize human emotions through in-depth analysis of audio signals. However, it remains challenging to encode emotional cues and to fuse the encoded cues effectively. In this study, dual-stream representation is developed, and both full training and fine-tuning
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Speech emotion recognition (SER) aims to recognize human emotions through in-depth analysis of audio signals. However, it remains challenging to encode emotional cues and to fuse the encoded cues effectively. In this study, dual-stream representation is developed, and both full training and fine-tuning of different deep networks are employed for encoding emotion patterns. Specifically, a cross-attention fusion (CAF) module is designed to integrate the dual-stream output for emotion recognition. Using different dual-stream encoders (fully training a text processing network and fine-tuning a pre-trained large language network), the CAF module is compared to other three fusion modules on three databases. The SER performance is quantified with weighted accuracy (WA), unweighted accuracy (UA), and F1-score (F1S). The experimental results suggest that the CAF outperforms the other three modules and leads to promising performance on the databases (EmoDB: WA, 97.20%; UA, 97.21%; F1S, 0.8804; IEMOCAP: WA, 69.65%; UA, 70.88%; F1S, 0.7084; RAVDESS: WA, 81.86%; UA, 82.75.21%; F1S, 0.8284). It is also found that fine-tuning a pre-trained large language network achieves superior representation than fully training a text processing network. In a future study, improved SER performance could be achieved through the development of a multi-stream representation of emotional cues and the incorporation of a multi-branch fusion mechanism for emotion recognition.
Full article
(This article belongs to the Special Issue Applied AI in Emotion Recognition)
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Open AccessArticle
Design and Analysis of a High-Gain, Low-Noise, and Low-Power Analog Front End for Electrocardiogram Acquisition in 45 nm Technology Using gm/ID Method
by
Md. Zubair Alam Emon, Khosru Mohammad Salim and Md. Iqbal Bahar Chowdhury
Electronics 2024, 13(11), 2190; https://doi.org/10.3390/electronics13112190 - 4 Jun 2024
Abstract
In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth
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In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter—all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the methodology. Both the pre-layout and post-layout simulation are carried out. The layout consumes an area of 0.00628 mm2 without the resistors and capacitors. Analysis of various simulation results are carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz, with a variable gain between 44 and 58 dB, a notch depth of −56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of −59.65 dB (less than 1%), an input-referred noise spectral density of <34 Vrms/ at the pass-band, a dynamic range of 52.71 dB, and a total power consumption of 10.88 μW with a supply of ±0.6 V. Hence, the AFE exhibits the promise of high-quality signal acquisition capability required for portable ECG detection systems in modern healthcare.
Full article
(This article belongs to the Section Bioelectronics)
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Open AccessArticle
A Simple Thermal Model for Junction and Hot Spot Temperature Estimation of 650 V GaN HEMT during Short Circuit
by
Simone Palazzo, Annunziata Sanseverino, Giovanni Canale Parola, Emanuele Martano, Francesco Velardi and Giovanni Busatto
Electronics 2024, 13(11), 2189; https://doi.org/10.3390/electronics13112189 - 4 Jun 2024
Abstract
Temperature is a critical parameter for the GaN HEMT as it sharply impacts the electrical characteristics of the device more than for SiC or Si MOSFETs. Either when designing a power converter or testing a device for reliability and robustness characterizations, it is
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Temperature is a critical parameter for the GaN HEMT as it sharply impacts the electrical characteristics of the device more than for SiC or Si MOSFETs. Either when designing a power converter or testing a device for reliability and robustness characterizations, it is essential to estimate the junction temperature of the device. For this aim, manufacturers provide compact models to simulate the device in SPICE-based simulators. These models provide the junction temperature, which is considered uniform along the channel. We demonstrate through two-dimensional numerical simulations that this approach is not suitable when the device undergoes high electrothermal stress, such as during short circuit (SC), when the temperature distribution along the channel is strongly not uniform. Based on numerical simulations and experimental measurements on a 650 V/4 A GaN HEMT, we derived a thermal network suitable for SPICE simulations to correctly compute the junction temperature and the SC current, even if not providing information about the possible failure of the device due to the formation of a local hot spot. For this reason, we used a second thermal network to estimate the maximum temperature reached inside the device, whose results are in good agreement with the experimental observed failures.
Full article
(This article belongs to the Special Issue Nitride Semiconductor Devices and Applications)
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Open AccessArticle
Impact of Solder Voids on IGBT Thermal Behavior: A Multi-Methodological Approach
by
Omid Alavi, Ward De Ceuninck and Michaël Daenen
Electronics 2024, 13(11), 2188; https://doi.org/10.3390/electronics13112188 - 4 Jun 2024
Abstract
This study investigates the thermal behavior of Insulated Gate Bipolar Transistors (IGBTs) with a focus on the influence of solder voids within the device. Utilizing a combination of Finite Element Method (FEM) simulations, X-ray imaging, and SEM-EDX analysis, we accurately modeled the internal
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This study investigates the thermal behavior of Insulated Gate Bipolar Transistors (IGBTs) with a focus on the influence of solder voids within the device. Utilizing a combination of Finite Element Method (FEM) simulations, X-ray imaging, and SEM-EDX analysis, we accurately modeled the internal structure of IGBTs to assess the impact of void characteristics on thermal resistance. The findings reveal that the presence and characteristics of solder voids—particularly their size, number, and distribution—significantly affect the thermal resistance of IGBT devices. Experimental measurements validate the FEM model’s accuracy, confirming that voids disrupt the heat flow path, which can lead to increased thermal resistance and potential device failure. Five regression models, including Gaussian process regression (GPR) and neural networks, were employed to predict the thermal resistance based on void characteristics, with the GPR models demonstrating superior performance. The optimal GPR RQ model consistently provided accurate predictions with an RMSE of 0.0050 and R2 of 0.9728. Using the void percentage as the only input parameter for the regression models significantly impacted the prediction accuracy, showing the importance of the void extraction method. This study shows the necessity of minimizing solder voids and offers a robust methodological framework for a better prediction of the reliability of IGBTs.
Full article
(This article belongs to the Special Issue Advances in Power Converter Design, Control and Applications)
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Open AccessArticle
Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction
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Zhiqi Gao, Wei Deng, Pingping Huang, Wei Xu and Weixian Tan
Electronics 2024, 13(11), 2187; https://doi.org/10.3390/electronics13112187 (registering DOI) - 4 Jun 2024
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Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary
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Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance.
Full article
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Open AccessArticle
Real-Time Implementation of Three-Phase Z Packed U-Cell Modular Multilevel Grid-Connected Converter Using CPU and FPGA
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Sandy Atanalian, Fadia Sebaaly, Rawad Zgheib and Kamal AL-Haddad
Electronics 2024, 13(11), 2186; https://doi.org/10.3390/electronics13112186 (registering DOI) - 4 Jun 2024
Abstract
The Modular Multilevel Converter (MMC) is a promising converter for medium-/high voltage applications due to its various features. The waveform quality could be enhanced further by expanding the number of generated voltage levels, which increases the number of submodules (SMs); however, this improvement
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The Modular Multilevel Converter (MMC) is a promising converter for medium-/high voltage applications due to its various features. The waveform quality could be enhanced further by expanding the number of generated voltage levels, which increases the number of submodules (SMs); however, this improvement enlarges the size and cost of the converter, posing a persistent challenge. Hence, there exists a trade-off between power quality and the size and complexity of the converter. To verify the performance of such a complex converter and to validate the effectiveness of the control system, especially in the absence of a physical system, Real-Time (RT) simulation becomes crucial. However, the large number of components of a MMC creates important numerical challenges and computational difficulties in RT simulation. This paper proposes a grid-connected MMC employing a Z Packed U-Cell converter as a SM to generate a higher number of voltage levels while minimizing the required number of SMs. The ZPUC-MMC is implemented on an FPGA-based RT simulation platform using Electric Hardware Solver to reduce computational burden and simulation time, while improving the accuracy of the obtained results. Conventional controllers of MMCs are applied to assess the effectiveness and robustness of the proposed system during steady-state and dynamic operations.
Full article
(This article belongs to the Special Issue Hardware in the Loop, Real-Time Simulation and Digital Control of Power Electronics and Drives, Volume II)
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Open AccessReview
A Review of Endogenous Security Research
by
Xiaoyu Liu, Haizhou Wang and Cuixia Li
Electronics 2024, 13(11), 2185; https://doi.org/10.3390/electronics13112185 - 3 Jun 2024
Abstract
The development of society has deepened the application of the Internet in production and daily life. At the same time, network security risks are becoming increasingly severe. For the security problems faced in cyberspace, most of the traditional defenses are currently focused on
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The development of society has deepened the application of the Internet in production and daily life. At the same time, network security risks are becoming increasingly severe. For the security problems faced in cyberspace, most of the traditional defenses are currently focused on blocking the discovered vulnerabilities. However, these methods not only rely on prior knowledge of vulnerabilities but also fail to address the security issues brought about by the protection program itself. In view of this, endogenous security, which emphasizes the importance of not relying on a priori knowledge and not bringing in new security problems, has received increasing attention. This review provides a detailed introduction to endogenous security and its related issues, which is lacking in the field of network security. Firstly, this paper outlines the detrimental effects of vulnerabilities, identifies issues within moving target defense, and contrasts it with mimic defense. Additionally, the concepts, models, principles, and application scenarios of endogenous security are introduced. Finally, the challenges encountered by this technology are comprehensively summarized, and potential future development trends are further explored.
Full article
(This article belongs to the Section Computer Science & Engineering)
Open AccessArticle
Underwater Image Enhancement Algorithm Based on Adversarial Training
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Monan Zhang, Yichen Li and Wenbin Yu
Electronics 2024, 13(11), 2184; https://doi.org/10.3390/electronics13112184 - 3 Jun 2024
Abstract
Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information
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Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.
Full article
Open AccessArticle
Temporal Attention for Few-Shot Concept Drift Detection in Streaming Data
by
Ximing Lin, Longtao Chang, Xiushan Nie and Fei Dong
Electronics 2024, 13(11), 2183; https://doi.org/10.3390/electronics13112183 - 3 Jun 2024
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Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way. These changes might be caused by changes in
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Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift is a phenomenon in which the statistical properties of a target domain change over time in an arbitrary way. These changes might be caused by changes in hidden variables that cannot be measured directly. With the onset of the big data era, domains such as social networks, meteorology, and finance are generating copious amounts of streaming data. Embedded within these data, the issue of concept drift can affect the attributes of streaming data in various ways, leading to a decline in the accuracy and performance of models. There is a pressing need for new models to re-adapt to the changes in streaming data. Traditional concept drift detection algorithms struggle to effectively capture and utilize the key feature points of concept drift within complex time series, thereby failing to maintain the accuracy and efficiency of the models. In light of these challenges, this study introduces a novel concept drift detection method that incorporates a temporal attention mechanism within a prototypical network. By integrating a temporal attention mechanism during the feature extraction process, our approach enhances the capability to process complex time series data, preserves temporal locality, strengthens the learning of key features, and reduces the amount of labeled data required. This method significantly improves the detection accuracy and efficiency of small sample streaming data by better capturing the local features of the data. Experiments conducted across multiple datasets demonstrate that this method exhibits comprehensive leading performance in terms of accuracy and F1-score, with excellent recall and precision, thereby validating its effectiveness in enhancing concept drift detection in streaming data.
Full article
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Open AccessArticle
RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8
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Yuanming Ding, Chen Jiang, Lin Song, Fei Liu and Yunrui Tao
Electronics 2024, 13(11), 2182; https://doi.org/10.3390/electronics13112182 - 3 Jun 2024
Abstract
Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems
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Currently, weed control robots that can accurately identify weeds and carry out removal work are gradually replacing traditional chemical weed control techniques. However, the computational and storage resources of the core processing equipment of weeding robots are limited. Aiming at the current problems of high computation and the high number of model parameters in weeding robots, this paper proposes a lightweight weed target detection model based on the improved YOLOv8 (You Only Look Once Version 8), called RVDR-YOLOv8 (Reversible Column Dilation-wise Residual). First, the backbone network is reconstructed based on RevCol (Reversible Column Networks). The unique reversible columnar structure of the new backbone network not only reduces the computational volume but also improves the model generalisation ability. Second, the C2fDWR module is designed using Dilation-wise Residual and integrated with the reconstructed backbone network, which improves the adaptive ability of the new backbone network RVDR and enhances the model’s recognition accuracy for occluded targets. Again, GSConv is introduced at the neck end instead of traditional convolution to reduce the complexity of computation and network structure while ensuring the model recognition accuracy. Finally, InnerMPDIoU is designed by combining MPDIoU with InnerIoU to improve the prediction accuracy of the model. The experimental results show that the computational complexity of the new model is reduced by 35.8%, the number of parameters is reduced by 35.4% and the model size is reduced by 30.2%, while the mAP50 and mAP50-95 values are improved by 1.7% and 1.1%, respectively, compared to YOLOv8. The overall performance of the new model is improved compared to models such as Faster R-CNN, SSD and RetinaNet. The new model proposed in this paper can achieve the accurate identification of weeds in farmland under the condition of limited hardware resources, which provides theoretical and technical support for the effective control of weeds in farmland.
Full article
(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)
Open AccessArticle
Robust Tensor Learning for Multi-View Spectral Clustering
by
Deyan Xie, Zibao Li, Yingkun Sun and Wei Song
Electronics 2024, 13(11), 2181; https://doi.org/10.3390/electronics13112181 - 3 Jun 2024
Abstract
Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single
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Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single view, compromising the clustering performance. Here, we design a robust tensor learning method for multi-view spectral clustering (RTL-MSC), which employs the weighted tensor nuclear norm to regularize the essential tensor for exploiting the high-order correlations underlying multiple views and adopts the nuclear norm to constrain each frontal slice of the essential tensor as the block diagonal matrix. Simultaneously, a novel column-wise sparse norm, namely, , is defined in RTL-MSC to measure the error tensor, making it sparser than the one derived by the norm. We design an effective optimization algorithm to solve the proposed model. Experiments on three widely used datasets demonstrate the superiority of our method.
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(This article belongs to the Special Issue Multi-Modal Learning for Multimedia Data Analysis and Applications)
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Open AccessArticle
The Real-Time Image Sequences-Based Stress Assessment Vision System for Mental Health
by
Mavlonbek Khomidov, Deokwoo Lee, Chang-Hyun Kim and Jong-Ha Lee
Electronics 2024, 13(11), 2180; https://doi.org/10.3390/electronics13112180 - 3 Jun 2024
Abstract
Early detection and prevention of stress is crucial because stress affects our vital signs like heart rate, blood pressure, skin temperature, respiratory rate, and heart rate variability. There are different ways to determine stress using different devices, such as the electrocardiogram (ECG), electrodermal
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Early detection and prevention of stress is crucial because stress affects our vital signs like heart rate, blood pressure, skin temperature, respiratory rate, and heart rate variability. There are different ways to determine stress using different devices, such as the electrocardiogram (ECG), electrodermal activity (EDA), the electroencephalogram (EEG), photoplethysmography (PPG), or a questionnaire-based method of stress assessment. In this study, we proposed a camera-based real-time stress detection system using remote photoplethysmography (rPPG). We trained different machine learning models using three datasets: the SWELL dataset, the PPG sensor dataset, and the last ECG and EEG-based stress dataset. The models with the highest predictive accuracy were used to classify stress based on HR and HRV features obtained from the face using a camera. HR and HRV estimations from the face were validated on the PURE public dataset and the custom dataset. In this study, it was observed that the random forest algorithm performs significantly better than other models, achieving an impressive 99% predictive accuracy in the SWELL dataset. In the second dataset, the logistic regression technique shows the best result, achieving an accuracy rate of 84.24%. In the last dataset, the ensemble model achieved an accuracy rate of 67%. We also checked the proposed algorithm in the process of public speaking to estimate stress in a real-time situation.
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(This article belongs to the Special Issue Applications of Artificial Intelligence in Image and Video Processing)
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Open AccessArticle
Grant-Free Random Access Enhanced by Massive MIMO and Non-Orthogonal Preambles
by
Hao Jiang, Hongming Chen, Hongming Hu and Jie Ding
Electronics 2024, 13(11), 2179; https://doi.org/10.3390/electronics13112179 - 3 Jun 2024
Abstract
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Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However,
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Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated.
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