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Electronics, Volume 12, Issue 4 (February-2 2023) – 288 articles

Cover Story (view full-size image): Creating a new sensor for detecting gases is important for the safety of people. We present a unique platform constructed for depositing additional functional materials—a graphene oxide layer on a tapered optical fiber structure. That hybrid connection enables the detection of different gases as an in-line measurement. Our results present differences in light transmission for three different kinds of gasses: pure nitrogen, pure hydrogen, and a mixture of propane–butane. Measurements were provided in a wide range of 500–1800 nm to find the most sensitive areas, for which we are able to detect the absorption of gases by the layer and produce a simple, cheap, all-fiber sensor. View this paper
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21 pages, 12339 KiB  
Article
Stability Control Strategies for Bidirectional Energy Storage Converters Considering AC Constant Power Loads
by Xinbo Liu, Shi Wang, Xiaotong Song and Jinghua Zhou
Electronics 2023, 12(4), 1067; https://doi.org/10.3390/electronics12041067 - 20 Feb 2023
Cited by 1 | Viewed by 1159
Abstract
In islanded AC microgrids, negative impedance characteristics of AC constant power loads (AC CPLs) easily introduce large signal instability to the system, while energy storage systems sometimes compensate for the dynamic characteristics of AC CPLs, and increase the system stability. Although energy storage [...] Read more.
In islanded AC microgrids, negative impedance characteristics of AC constant power loads (AC CPLs) easily introduce large signal instability to the system, while energy storage systems sometimes compensate for the dynamic characteristics of AC CPLs, and increase the system stability. Although energy storage control techniques and characteristics have gained a lot of attention, few studies have derived quantitative design guidelines for energy storage systems from the aspect of stability improvement. In order to fill this gap, this paper proposes stability control strategies for bidirectional energy storage converters considering the characteristics of AC CPLs to guarantee large signal stability of islanded AC microgrids. The presented control techniques create quantitative limits for the DC bus voltage loop control parameters of the energy storage DC/DC converter and the integral control loop control parameter of the energy storage DC/AC converter, and also interpret the positive stability influence of energy storage systems and the negative stability influence of AC CPLs. The structure of the paper is as follows. Firstly, DQ coordinate transformation is adopted, and AC microgrid nonlinear models with the energy storage system in charging and discharging states are constructed. Then, large signal models are constructed depending on mixed potential theory. Stability control strategies for bidirectional energy storage converters are obtained, and AC CPLs power, storage system equivalent resistor, and micro power source power are all taken into account. Finally, based on simulation and experimental results, it is obvious that regulating the control parameters of the energy storage converter significantly increases the large signal stability of islanded AC microgrids without extra equipment. The method is very simple and easy to implement. Full article
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20 pages, 4108 KiB  
Article
The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network
by Pengju Yang, Taoyun Wang, Heng Yang, Chuipan Meng, Hao Zhang and Li Cheng
Electronics 2023, 12(4), 1066; https://doi.org/10.3390/electronics12041066 - 20 Feb 2023
Cited by 11 | Viewed by 1418
Abstract
With the widely application of electronic transformers in smart grids, transformer faults have become a pressing problem. However, reliable fault diagnosis of electronic current transformers (ECT) is still an open problem due to the complexity and diversity of fault types. In order to [...] Read more.
With the widely application of electronic transformers in smart grids, transformer faults have become a pressing problem. However, reliable fault diagnosis of electronic current transformers (ECT) is still an open problem due to the complexity and diversity of fault types. In order to solve this problem, this paper proposes an ECT fault diagnosis model based on radial basis function neural network (RBFNN) and optimizes the model parameters and the network size of RBFNN simultaneously via an improved whale optimization algorithm (WOA) to improve the classification accuracy and robustness of RBFNN. Since the classical WOA is easy to fall into a locally optimal performance, a hybrid multi-strategies WOA algorithm (CASAWOA) is proposed for further improvement in optimization performance. Firstly, we introduced the tent chaotic map strategy to improve the population diversity of WOA. Secondly, we introduced nonlinear convergence factor and adaptive inertia weight to enhance the exploitation ability of the WOA. Finally, on the premise of ensuring the convergence speed of the algorithm, we modified the simulated annealing mechanism in order to prevent premature convergence. The benchmark function tests show that the CASAWOA outperforms other state-of-the-art WOA algorithms in terms of convergence speed and exploration ability. Furthermore, to validate the performance of ECT fault diagnosis model based on CASAWOA-RBFNN, a comprehensive analysis of eight fault diagnosis methods is conducted based on the ECT fault samples collected from the detection circuit. The experimental results show that the CASAWOA-RBFNN achieves an accuracy of 97.77% in ECT fault diagnosis, which is 9.8% better than WOA-RBF and which shows promising engineering practicality. Full article
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15 pages, 934 KiB  
Article
A Semi-Fragile, Inner-Outer Block-Based Watermarking Method Using Scrambling and Frequency Domain Algorithms
by Ahmet Senol, Ersin Elbasi, Ahmet E. Topcu and Nour Mostafa
Electronics 2023, 12(4), 1065; https://doi.org/10.3390/electronics12041065 - 20 Feb 2023
Cited by 1 | Viewed by 1072
Abstract
Image watermarking is most often used to prove that an image belongs to someone and to make sure that the image is the same as was originally produced. The type of watermarking used for the detection of originality and tampering is known as [...] Read more.
Image watermarking is most often used to prove that an image belongs to someone and to make sure that the image is the same as was originally produced. The type of watermarking used for the detection of originality and tampering is known as authentication-type watermarking. In this paper, a blind semi-fragile authentication watermarking method is introduced. Although the main concern in this paper is authenticating the image, watermarking for proving ownership is additionally implemented. The method considers the image as two main parts: an inner part and an outer part. The inner and outer parts are divided into non-overlapping blocks. The block size of the inner and outer part are different. The outer blocks have a greater area than the inner blocks so that their watermark-holding capacity is greater, providing enough robustness for semi-fragility. The method is semi-fragile and the watermarked image is authenticated despite the JPEG being compressed to 75% quality. The embedded watermark also survives innocent types of image operations, such as intensity adjustment, histogram equalization and gamma correction. Semi-fragile and selectively fragile authentication is valuable and in high demand specifically because it survives these innocent image operations while detecting ill-intentioned tampering. In this work, we embed a binary watermark into the inner and outer parts of images using a scrambling algorithm, discrete wavelet transform (DWT) and discrete cosine transform (DCT) in the blocks. The proposed methodology has high image quality after watermarking, with a PSNR value of 40.577, and high quality is also achieved after JPEG compression. The embedding process provides acceptable image quality after tamper attacks, including JPEG compression, Gaussian noise, average filtering, and scaling attacks with PSNR values greater than 29. Experimental results obtained show that the proposed semi-fragile watermarking algorithm is more robust, secure and resistant than other algorithms in the literature. Full article
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10 pages, 4182 KiB  
Communication
Negative Group Delay Metamaterials Based on Split-Ring Resonators and Their Application
by Zheng Liu, Jian Zhang, Xue Lei, Jun Gao, Zhijian Xu and Tianpeng Li
Electronics 2023, 12(4), 1064; https://doi.org/10.3390/electronics12041064 - 20 Feb 2023
Cited by 1 | Viewed by 1450
Abstract
In this report, negative group delay (NGD) metamaterials based on split-ring resonators (SRRs) are discussed. A theoretical analysis is proposed to calculate the equivalent circuit parameters, NGD values, and S21 amplitudes of two types of SRRs. Metamaterials made from tantalum nitride are simulated, [...] Read more.
In this report, negative group delay (NGD) metamaterials based on split-ring resonators (SRRs) are discussed. A theoretical analysis is proposed to calculate the equivalent circuit parameters, NGD values, and S21 amplitudes of two types of SRRs. Metamaterials made from tantalum nitride are simulated, and the parameters of the two types of SRRs are discussed. Prototypes of metamaterials were fabricated and tested. Measured real-world results were found to be consistent with theoretical and simulated predictions. For EC-SRR, a negative group delay of up to −0.1 ns was achieved at 12–13 GHz. For SR-SRR of the same size as the out ring of EC-SRR, a negative group delay of up to −0.04 ns was achieved, with a loss lower than 2.7 dB. The proposed SRRs were applied to continuous transverse stub (CTS) antenna to reduce the beam walk. The simulation shows that the beam walk can be reduced using the proposed metamaterial. Full article
(This article belongs to the Special Issue Metamaterials and Metasurfaces)
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12 pages, 3896 KiB  
Article
A Reconfigurable Setup for the On-Wafer Characterization of the Dynamic RON of 600 V GaN Switches at Variable Operating Regimes
by Alessio Alemanno, Alberto Maria Angelotti, Gian Piero Gibiino, Alberto Santarelli, Enrico Sangiorgi and Corrado Florian
Electronics 2023, 12(4), 1063; https://doi.org/10.3390/electronics12041063 - 20 Feb 2023
Cited by 2 | Viewed by 1225
Abstract
Charge-trapping mechanisms observed in high-voltage GaN switches are responsible for the degradation of power converter efficiency due to modulation of the effective dynamic ON-resistance (RON) with respect to its static value. Dynamic RON degradation is typically dependent [...] Read more.
Charge-trapping mechanisms observed in high-voltage GaN switches are responsible for the degradation of power converter efficiency due to modulation of the effective dynamic ON-resistance (RON) with respect to its static value. Dynamic RON degradation is typically dependent on the blocking voltage and the commutation frequency and is particularly significant in new technologies under development. The possibility to characterize this phenomenon on GaN switch samples directly on-wafer, under controlled operating conditions that resemble real operations of the DUT in a switching mode power converter is extremely valuable in the development phase of new technologies or for quality verification of production wafers. In this paper, we describe a setup that allows this characterization: dynamic RON degradation of on-wafer 600 V GaN switches is characterized as a function of the VDS blocking voltage, the VGS driving voltage, and at different temperatures. The dependency on the switching frequency is identified by measuring the current recovery of the switch after the application of blocking voltages of different durations. Full article
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31 pages, 3823 KiB  
Review
Multi-Objective Optimization Algorithms for a Hybrid AC/DC Microgrid Using RES: A Comprehensive Review
by Chinna Alluraiah Nallolla, Vijayapriya P, Dhanamjayulu Chittathuru and Sanjeevikumar Padmanaban
Electronics 2023, 12(4), 1062; https://doi.org/10.3390/electronics12041062 - 20 Feb 2023
Cited by 12 | Viewed by 3307
Abstract
Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, [...] Read more.
Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems. Full article
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14 pages, 13477 KiB  
Article
Vibration Energy Coupling Behavior of Rolling Mills under Double Disturbance Conditions
by Lidong Wang, Shen Wang, Xingdou Jia, Xiaoling Wang and Xiaoqiang Yan
Electronics 2023, 12(4), 1061; https://doi.org/10.3390/electronics12041061 - 20 Feb 2023
Viewed by 1139
Abstract
The operation of the world’s first multimode continuous casting and rolling F3 (3rd finishing mill stand) finishing mill was hampered by frequent vibrations. Mill vibrations were found to be caused by the transmission and coupling of vibration energy flow. In this study, an [...] Read more.
The operation of the world’s first multimode continuous casting and rolling F3 (3rd finishing mill stand) finishing mill was hampered by frequent vibrations. Mill vibrations were found to be caused by the transmission and coupling of vibration energy flow. In this study, an overall finite element model of the F3 stand is established based on the structural sound intensity method and harmonic response analysis method, and then, the intrinsic energy flow modes and energy flow harmonic response of the F3 stand are obtained. Further, the effects of the steady-state rolling force variation, preload torque variation, rolling force fluctuation, torque fluctuation, and its phase angle difference on the vibration energy flow of the mill are analyzed. Finally, the effects of the mill damping ratio, strip width, and strip modulus on the vibration energy flow under double dynamic load are discussed to reveal the inherent characteristics of the mill vibration energy flow. The results show that the vibration energy flow of the mill increases with the increase of strip modulus, rolling force, and moment fluctuation; the phase angle difference of rolling moment shows a “V” trend change on the vibration energy flow; the change of strip width has a greater effect on the vibration energy flow of the vertical system; and for the damping ratio of 0.01–0.1, the reduction of the vibration energy flow at all excitation frequencies is obvious. Full article
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15 pages, 3338 KiB  
Article
Application of Feature Pyramid Network and Feature Fusion Single Shot Multibox Detector for Real-Time Prostate Capsule Detection
by Shixiao Wu, Xinghuan Wang and Chengcheng Guo
Electronics 2023, 12(4), 1060; https://doi.org/10.3390/electronics12041060 - 20 Feb 2023
Cited by 4 | Viewed by 1163
Abstract
In the process of feature propagation, the low-level convolution layers of the forward feature propagation network lack semantic information, and information loss occurs when fine-grained information is transferred to higher-level convolution; therefore, multi-stage feature fusion networks are needed to solve the interaction between [...] Read more.
In the process of feature propagation, the low-level convolution layers of the forward feature propagation network lack semantic information, and information loss occurs when fine-grained information is transferred to higher-level convolution; therefore, multi-stage feature fusion networks are needed to solve the interaction between low-level convolution layers and high-level convolution layers. Based on a two-way feature feedback network and feature fusion mechanism, we created a new object detection network called Feature Pyramid Network (FPN)-based Feature Fusion Single Shot Multibox Detector (FFSSD). A bottom-up and top-down architecture with lateral connections enhances the detector’s ability to extract features, then high-level multi-scale semantic feature maps are utilized to generate a feature pyramid network. The results show that the proposed network the mAP for prostate capsule image detection reaches 83.58%, providing real-time detection ability. The context interaction mechanism can transfer high-level semantic information to low-level convolution, and the resulting convolution after low-level and high-level fusion contains richer location and semantic information. Full article
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32 pages, 9302 KiB  
Article
A DDoS Attack Detection Method Based on Natural Selection of Features and Models
by Ruikui Ma, Xuebin Chen and Ran Zhai
Electronics 2023, 12(4), 1059; https://doi.org/10.3390/electronics12041059 - 20 Feb 2023
Cited by 5 | Viewed by 2703
Abstract
Distributed Denial of Service (DDoS) is still one of the main threats to network security today. Attackers are able to run DDoS in simple steps and with high efficiency to slow down or block users’ access to services. In this paper, we propose [...] Read more.
Distributed Denial of Service (DDoS) is still one of the main threats to network security today. Attackers are able to run DDoS in simple steps and with high efficiency to slow down or block users’ access to services. In this paper, we propose a framework based on feature and model selection (FAMS), which is used for detecting DDoS attacks with the aim of identifying the features and models with a high generalization capability, high prediction accuracy, and short prediction time. The FAMS framework is divided into four main phases. The first phase is data pre-processing, including operations such as feature coding, outlier processing, duplicate elimination, data balancing, and normalization. In the second stage, 79 features are extracted from the dataset and selected by the feature selection algorithms filter, wrapper, embedded, variance, mutual information, backward elimination, Lasso.L1, and random forest. The purpose of feature selection is to simplify the model, avoid dimensional disasters, reduce computational costs, and reduce the prediction time. The third stage is model selection, which aims to select the most ideal algorithm from GD, SVM, LR, RF, HVG, SVG, HVR, and SVR using a model selection algorithm for the selected 21 features, and the results show that RF is far ahead in all evaluation indexes compared to the other models. The fourth stage is model optimization, which aims to further improve the performance of the RF algorithm in detecting DDoS attacks by optimizing the parameters max_samples, max_depth, n_estimators for the initially selected RF by the RF optimization algorithm. Finally, by testing the 100,000 CIC-IDS2018, CIC-IDS2017, and CIC-DoS2016 synthetic datasets, the results show that all the results have achieved excellent performance in the same category. Moreover, the framework also shows an excellent generalization performance by testing over 1 million synthetic datasets and over 330,000 CIC-DDoS2019 datasets. Full article
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26 pages, 6770 KiB  
Article
Investigation of Recent Metaheuristics Based Selective Harmonic Elimination Problem for Different Levels of Multilevel Inverters
by Satılmış Ürgün, Halil Yiğit and Seyedali Mirjalili
Electronics 2023, 12(4), 1058; https://doi.org/10.3390/electronics12041058 - 20 Feb 2023
Cited by 6 | Viewed by 1700
Abstract
Multilevel inverters (MLI) are popular in high-power applications. MLIs are generally configured to have switches reduced by switching techniques that eliminate low-order harmonics. The selective harmonic elimination (SHE) method, which significantly reduces the number of switching, determines the optimal switching moments to obtain [...] Read more.
Multilevel inverters (MLI) are popular in high-power applications. MLIs are generally configured to have switches reduced by switching techniques that eliminate low-order harmonics. The selective harmonic elimination (SHE) method, which significantly reduces the number of switching, determines the optimal switching moments to obtain the desired output voltage and eliminates the desired harmonic components. To solve the SHE problem, classical methods are primarily employed. The disadvantages of such methods are the high probability of trapping in locally optimal solutions and their dependence on initial controlling parameters. One solution to overcome this problem is the use of metaheuristic algorithms. In this study, firstly, 22 metaheuristic algorithms with different sources of inspiration were used to solve the SHE problem at different levels of MLIs, and their performances were extensively analyzed. To reveal the method that offers the best solution, these algorithms were first applied to an 11-level MLI circuit, and six methods were determined as a result of the performance analysis. As a result of the evaluation, the outstanding methods were SPBO, BMO, GA, GWO, MFO, and SPSA. As a result of the application of superior methods to 7-, 11-, 15-, and 19-level MLIs according to the IEEE 519—2014 standard, it has been shown that BMO outperforms in 7-level MLI, GA in 11-level MLI, and SPBO in 15- and 19-level MLIs in terms of THD, while in terms of output voltage quality, GA in 7-level MLI, BMO in 11-level MLI, GA and SPSA in 15-level MLI, and SPSA in 19-level MLI come forward. Full article
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12 pages, 385 KiB  
Communication
Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining
by Wei Meng, Yidong Gu, Jianjun Bao, Li Gan, Tao Huang and Zhengmin Kong
Electronics 2023, 12(4), 1057; https://doi.org/10.3390/electronics12041057 - 20 Feb 2023
Cited by 1 | Viewed by 1101
Abstract
In underground mines, physical layer security (PLS) technology is a promising method for the effective and secure communication to monitor the mining process. Therefore, in this paper, we investigate the PLS of an amplify-and-forward relay-aided system in power monitoring and communication systems for [...] Read more.
In underground mines, physical layer security (PLS) technology is a promising method for the effective and secure communication to monitor the mining process. Therefore, in this paper, we investigate the PLS of an amplify-and-forward relay-aided system in power monitoring and communication systems for mining, with the consideration of multiple eavesdroppers. Explicitly, we propose a PLS scheme of cooperative jamming and precoding for a full-duplex system considering imperfect channel state information. To maximize the secrecy rate of the communications, an effective block coordinate descent algorithm is used to design the precoding and jamming matrix at both the source and the relay. Furthermore, the effectiveness and convergence of the proposed scheme with high channel state information uncertainty have been proven. Full article
(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems)
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11 pages, 5783 KiB  
Communication
A 3.4–3.6 GHz High-Selectivity Filter Chip Based on Film Bulk Acoustic Resonator Technology
by Qinghua Yang, Yao Xu, Yongle Wu, Weimin Wang and Zhiguo Lai
Electronics 2023, 12(4), 1056; https://doi.org/10.3390/electronics12041056 - 20 Feb 2023
Cited by 4 | Viewed by 1896
Abstract
The development of mobile 5G technology poses new challenges for high-frequency and high-performance filters. However, current commercial acoustic wave filters mainly focus on 4G LTE, which operates below 3 GHz. It is necessary to accelerate research on high-frequency acoustic wave filters. A high-selectivity [...] Read more.
The development of mobile 5G technology poses new challenges for high-frequency and high-performance filters. However, current commercial acoustic wave filters mainly focus on 4G LTE, which operates below 3 GHz. It is necessary to accelerate research on high-frequency acoustic wave filters. A high-selectivity film bulk acoustic resonator (FBAR) filter chip for the 3.4–3.6 GHz range was designed and fabricated in this paper. The design procedure includes FBAR parameter fitting, filter schematic analysis, and the generation principle of transmission zeros (TZs). The measured results show that the filter chip is of high roll-off and stopband suppression. Most of the stopband suppression is better than 35 dB. Finally, error analysis was conducted, and FBAR parameters were modified after testing for future filter design work. Full article
(This article belongs to the Section Circuit and Signal Processing)
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18 pages, 4682 KiB  
Article
An Improved Vision Transformer Network with a Residual Convolution Block for Bamboo Resource Image Identification
by Qing Zou, Xiu Jin, Yi Song, Lianglong Wang, Shaowen Li, Yuan Rao, Xiaodan Zhang and Qijuan Gao
Electronics 2023, 12(4), 1055; https://doi.org/10.3390/electronics12041055 - 20 Feb 2023
Viewed by 1941
Abstract
Bamboo is an important economic crop with up to a large number of species. The distribution of bamboo species is wide; therefore, it is difficult to collect images and make the recognition model of a bamboo species with few amount of images. In [...] Read more.
Bamboo is an important economic crop with up to a large number of species. The distribution of bamboo species is wide; therefore, it is difficult to collect images and make the recognition model of a bamboo species with few amount of images. In this paper, nineteen species of bamboo with a total of 3220 images are collected and divided into a training dataset, a validation dataset and a test dataset. The main structure of a residual vision transformer algorithm named ReVI is improved by combining the convolution and residual mechanisms with a vision transformer network (ViT). This experiment explores the effect of reducing the amount of bamboo training data on the performance of ReVI and ViT on the bamboo dataset. The ReVI has a better generalization of a deep model with small-scale bamboo training data than ViT. The performances of each bamboo species under the ReVI, ViT, ResNet18, VGG16, Densenet121, Xception were then compared, which showed that ReVI performed the best, with an average accuracy of 90.21%, and the reasons for the poor performance of some species are discussed. It was found that ReVI offered the efficient identification of bamboo species with few images. Therefore, the ReVI algorithm proposed in this manuscript offers the possibility of accurate and intelligent classification and recognition of bamboo resource images. Full article
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11 pages, 723 KiB  
Article
Noise2Clean: Cross-Device Side-Channel Traces Denoising with Unsupervised Deep Learning
by Honggang Yu, Mei Wang, Xiyu Song, Haoqi Shan, Hongbing Qiu, Junyi Wang and Kaichen Yang
Electronics 2023, 12(4), 1054; https://doi.org/10.3390/electronics12041054 - 20 Feb 2023
Cited by 3 | Viewed by 1614
Abstract
Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim [...] Read more.
Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim devices. Despite this success, recent works have demonstrated that certain countermeasures, such as random delay interrupts or clock jitters, would make these attacks more complex and less practical in real-world scenarios. To address this challenge, we present a novel denoising scheme that exploits the U-Net model to pre-process SCA traces for “noises” (i.e., countermeasures) removal. Specifically, we first pre-train the U-Net model on the paired noisy-clean profiling traces to obtain suitable parameters. This model is then fine-tuned on the noisy-only traces collected from the attacking device. The well-trained model will be finally deployed on the attacking device to remove the noises (i.e., countermeasures) from the measured power traces. In particular, a new inductive transfer learning method is also utilized in our scheme to transfer knowledge learned from the source domain (i.e., profiling device) to the target domain (i.e., attacking device) to improve the model’s generalization ability. During our experimental evaluations, we conduct a detailed analysis of various countermeasures separately or combined and show that the proposed denoising model outperforms current state-of-the-art work by a large margin, e.g., a reduction of at least 30% in computation costs and 5× in guessing entropy. Full article
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16 pages, 7599 KiB  
Article
Depth-Based Dynamic Sampling of Neural Radiation Fields
by Jie Wang, Jiangjian Xiao, Xiaolu Zhang, Xiaolin Xu, Tianxing Jin and Zhijia Jin
Electronics 2023, 12(4), 1053; https://doi.org/10.3390/electronics12041053 - 20 Feb 2023
Cited by 1 | Viewed by 1852
Abstract
Although the NeRF approach can achieve outstanding view synthesis, it is limited in practical use because it requires many views (hundreds) for training. With only a few input views, the Depth-DYN NeRF that we propose can accurately match the shape. First, we adopted [...] Read more.
Although the NeRF approach can achieve outstanding view synthesis, it is limited in practical use because it requires many views (hundreds) for training. With only a few input views, the Depth-DYN NeRF that we propose can accurately match the shape. First, we adopted the ip_basic depth-completion method, which can recover the complete depth map from sparse radar depth data. Then, we further designed the Depth-DYN MLP network architecture, which uses a dense depth prior to constraining the NeRF optimization and combines the depthloss to supervise the Depth-DYN MLP network. When compared to the color-only supervised-based NeRF, the Depth-DYN MLP network can better recover the geometric structure of the model and reduce the appearance of shadows. To further ensure that the depth depicted along the rays intersecting these 3D points is close to the measured depth, we dynamically modified the sample space based on the depth of each pixel point. Depth-DYN NeRF considerably outperforms depth NeRF and other sparse view versions when there are a few input views. Using only 10–20 photos to render high-quality images on the new view, our strategy was tested and confirmed on a variety of benchmark datasets. Compared with NeRF, we obtained better image quality (NeRF average at 22.47 dB vs. our 27.296 dB). Full article
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21 pages, 1392 KiB  
Article
Countermeasuring MITM Attacks in Solar-Powered PON-Based FiWi Access Networks
by Polyxeni Tsompanoglou, Antonios Iliadis, Konstantinos Kantelis, Sophia Petridou and Petros Nicopolitidis
Electronics 2023, 12(4), 1052; https://doi.org/10.3390/electronics12041052 - 20 Feb 2023
Viewed by 1108
Abstract
Solar power (SP) passive optical network (PON)-based fiber-wireless (FiWi) access systems are becoming increasingly popular as they provide coverage to rural and urban areas where no power grid exists. Secure operation of such networks which includes solar- and/or battery-powered devices, is crucial for [...] Read more.
Solar power (SP) passive optical network (PON)-based fiber-wireless (FiWi) access systems are becoming increasingly popular as they provide coverage to rural and urban areas where no power grid exists. Secure operation of such networks which includes solar- and/or battery-powered devices, is crucial for anticipating potential network issues and prolong the life of the network operation. Since optical network units (ONUs) may be powered by SP-charged batteries, energy awareness becomes an important issue, particularly when it comes to reducing ONUs’ energy consumption and allowing them to operate in off-grid remote areas. With the PON as the fixed part of these networks, the optical line terminal (OLT) informs the ONUs through a message exchange mechanism when no traffic is present, allowing them to transition to a low-power-consumption sleep mode. However, man-in-the-middle (MITM) attacks pose a serious threat to the message exchange mechanisms, which can eventually drain the energy of battery-powered ONUs resulting in their shutdown. Consequently, this paper introduces two novel mechanisms for reducing ONU energy consumption, namely the wake-up and time-out mechanisms, which can be used to mitigate the effectiveness of MITM attacks that may seek to affect the unit’s operation due to battery drain. The formal verification results show that these goals were effectively achieved. Full article
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15 pages, 4445 KiB  
Article
An Unmanned Aerial Vehicle (UAV) System for Disaster and Crisis Management in Smart Cities
by Wedad Alawad, Nadhir Ben Halima and Layla Aziz
Electronics 2023, 12(4), 1051; https://doi.org/10.3390/electronics12041051 - 20 Feb 2023
Cited by 13 | Viewed by 4245
Abstract
Over the course of the last decade, the unmanned aerial vehicle (UAV) research community has received a significant amount of attention. Emergency response operations, such as those that follow a natural disaster, are one of the civil applications that could benefit from the [...] Read more.
Over the course of the last decade, the unmanned aerial vehicle (UAV) research community has received a significant amount of attention. Emergency response operations, such as those that follow a natural disaster, are one of the civil applications that could benefit from the use of UAVs in disaster and crisis management. In the event of a catastrophic event, it would be extremely beneficial for both victims and first responders to have access to a UAV network that is capable of deploying independently and offering communication services. However, when working with complicated situations, one of the most difficult things is coming up with exploratory paths for the networks involved. A crisis and disaster management system using a swarm optimization algorithm (SOA) is proposed to assist in disaster and crisis management. In this system, the UAV search and rescue team follows the strategy called the delay tolerant network, which has the ability to explore. The proposed approach is able to find the global maximum in the search space without ever settling for a suboptimal solution. This work has two primary objectives: the first is to investigate a potential disaster zone, and the second is to direct the UAV to a number of victim groups that were found during the investigation phase. For the purpose of performing a characterization, performance metrics such as delay, throughput, performance rate, and path loss have been analyzed. The results show the superiority of the performance over the existing work. Full article
(This article belongs to the Section Artificial Intelligence)
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13 pages, 2000 KiB  
Article
Introducing Artificial/Computational Intelligence-Derived Non-Parametric Transfer Functions for the Implementation of Dynamic Circular Economy Decision-Making Systems
by Minghua Yu and Junjun Zheng
Electronics 2023, 12(4), 1050; https://doi.org/10.3390/electronics12041050 - 20 Feb 2023
Viewed by 1086
Abstract
With the development of science and technology, resource consumption has increasingly become a social problem. The circular economy is an economic form of a comprehensive utilization of resources. At present, China’s circular economy industry has begun to develop towards intensification, scale and specialization. [...] Read more.
With the development of science and technology, resource consumption has increasingly become a social problem. The circular economy is an economic form of a comprehensive utilization of resources. At present, China’s circular economy industry has begun to develop towards intensification, scale and specialization. With the deepening of research on the value of a comprehensive utilization of resources, the traditional economic field has undergone tremendous changes. According to the current situation of circular economy industry, an intelligent decision-making system is designed using artificial intelligence technology. Empirically, using the circular economy model and combining the sequence parameters to determine the sequence parameter indicators of the scientific and technological innovation subsystem and the energy saving and emission reduction subsystem, the correlation analysis of each indicator is carried out, and the order degree of the two subsystems is obtained respectively, so as to obtain for the enterprise the degree of synergy of the evolution system of the circular economy synergy of industries and industrial clusters. After repeated exploratory tests, it is found that the calculation has achieved over 98% of the precision of an astute direction, and has specific use and value because of the enormous number of preparing modes and trial dependability. Making a specific commitment to the circular economy is a trusted and logical solution. Full article
(This article belongs to the Section Networks)
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10 pages, 4350 KiB  
Communication
Correlation of Crystal Defects with Device Performance of AlGaN/GaN High-Electron-Mobility Transistors Fabricated on Silicon and Sapphire Substrates
by Sakhone Pharkphoumy, Vallivedu Janardhanam, Tae-Hoon Jang, Kyu-Hwan Shim and Chel-Jong Choi
Electronics 2023, 12(4), 1049; https://doi.org/10.3390/electronics12041049 - 20 Feb 2023
Cited by 3 | Viewed by 2080
Abstract
Herein, the performance of AlGaN/GaN high-electron-mobility transistor (HEMT) devices fabricated on Si and sapphire substrates is investigated. The drain current of the AlGaN/GaN HEMT fabricated on sapphire and Si substrates improved from 155 and 150 mA/mm to 290 and 232 mA/mm, respectively, at [...] Read more.
Herein, the performance of AlGaN/GaN high-electron-mobility transistor (HEMT) devices fabricated on Si and sapphire substrates is investigated. The drain current of the AlGaN/GaN HEMT fabricated on sapphire and Si substrates improved from 155 and 150 mA/mm to 290 and 232 mA/mm, respectively, at VGS = 0 V after SiO2 passivation. This could be owing to the improvement in the two-dimensional electron gas charge and reduction in electron injection into the surface traps. The SiO2 passivation resulted in the augmentation of breakdown voltage from 245 and 415 V to 400 and 425 V for the AlGaN/GaN HEMTs fabricated on Si and sapphire substrates, respectively, implying the effectiveness of SiO2 passivation. The lower transconductance of the AlGaN/GaN HEMT fabricated on the Si substrate can be ascribed to the higher self-heating effect in Si. The X-ray rocking curve measurements demonstrated that the AlGaN/GaN heterostructures grown on sapphire exhibited a full-width half maximum of 368 arcsec against 703 arcsec for the one grown on Si substrate, implying a better crystalline quality of the AlGaN/GaN heterostructure grown on sapphire. The AlGaN/GaN HEMT fabricated on the sapphire substrate exhibited better performance characteristics than that on the Si substrate, owing to the high crystalline quality and improved surface. Full article
(This article belongs to the Special Issue State of the Art and Future Trends in Low and High Power Electronics)
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16 pages, 563 KiB  
Article
ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media
by Malik Almaliki, Abdulqader M. Almars, Ibrahim Gad and El-Sayed Atlam
Electronics 2023, 12(4), 1048; https://doi.org/10.3390/electronics12041048 - 20 Feb 2023
Cited by 11 | Viewed by 3222
Abstract
Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared [...] Read more.
Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models. Full article
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20 pages, 5725 KiB  
Article
Generating the Generator: A User-Driven and Template-Based Approach towards Analog Layout Automation
by Benjamin Prautsch, Uwe Eichler and Uwe Hatnik
Electronics 2023, 12(4), 1047; https://doi.org/10.3390/electronics12041047 - 20 Feb 2023
Viewed by 2339
Abstract
Various analog design automation attempts have addressed the shortcomings of the still largely manual and, thus, inefficient and risky analog design approach. These methods can roughly be divided into synthesis and procedural generation. An important key aspect has, however, rarely been considered: usability. [...] Read more.
Various analog design automation attempts have addressed the shortcomings of the still largely manual and, thus, inefficient and risky analog design approach. These methods can roughly be divided into synthesis and procedural generation. An important key aspect has, however, rarely been considered: usability. While synthesis requires sophisticated constraints, procedural generators require expert programmers. Both prevent users from adopting the respective method. Thus, we propose a new approach to automatically create procedural generators in a user-driven way. First, analog generators, which also create symbols and layouts, are utilized during schematic entry to encapsulate common analog building blocks. Second, automatic code creation builds a hierarchical generator for all views with the schematic as input. Third, the approach links the building block generators with the layout through an object-oriented template library that is accessible through generator parameters, allowing the user to control the arrangement. No programming is required to reach this state. We believe that our approach will significantly ease the transition of analog designers to procedural generation. At the same time, the templates allow for a “bridge” to open frameworks and synthesis approaches so that the methodologies can be both better spread and combined. This way, comprehensive frameworks of both synthesis-based and procedural-based analog automation methods can be built in a user-driven way, and designers are enabled to gain early layout insight and ease IP reusability. Full article
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15 pages, 1145 KiB  
Article
Multi-Stage Ensemble-Based System for Glaucomatous Optic Neuropathy Diagnosis in Fundus Images
by Carlos A. Vásquez-Rochín, Miguel E. Martínez-Rosas, Humberto Cervantes de Ávila, Gerardo Romo-Cárdenas, Priscy A. Luque-Morales and Manuel M. Miranda-Velasco
Electronics 2023, 12(4), 1046; https://doi.org/10.3390/electronics12041046 - 20 Feb 2023
Cited by 1 | Viewed by 1083
Abstract
Recent developments in Computer-aided Diagnosis (CAD) systems as a countermeasure to the increasing number of untreated cases of eye diseases related to visual impairment (such as diabetic retinopathy or age-related macular degeneration) have the potential to yield in low-to-mid income countries a comfortable [...] Read more.
Recent developments in Computer-aided Diagnosis (CAD) systems as a countermeasure to the increasing number of untreated cases of eye diseases related to visual impairment (such as diabetic retinopathy or age-related macular degeneration) have the potential to yield in low-to-mid income countries a comfortable and accessible alternative to obtaining a general ophthalmological study necessary for follow-up medical attention. In this work, a multi-stage ensemble-based system for the diagnosis of glaucomatous optic neuropathy (GON) is proposed. GON diagnosis is based on a binary classification procedure working in conjunction with a multi-stage block based on image preprocessing and feature extraction. Our preliminary data show similar results compared to current studies considering metrics such as Accuracy, Sensitivity, Specificity, AUC (AUROC), F1score, and the use of Matthews Correlation Coefficient (MCC) as an additional performance metric is proposed. Full article
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13 pages, 12797 KiB  
Article
A Fully Integrated Solid-State Charge Detector with through Fused Silica Glass via Process
by Xiaomeng Wu, Liangjian Wen, Liqiang Cao, Guofu Cao, Gaosong Li, Yasheng Fu, Zhongyao Yu, Zhidan Fang and Qidong Wang
Electronics 2023, 12(4), 1045; https://doi.org/10.3390/electronics12041045 - 20 Feb 2023
Viewed by 1191
Abstract
A charge detector is a vital component in neutrino and dark matter detection. The integration of a charge collector in the form of flat pads and readout modules has been proposed as an optimization method as it can reduce noise and installation complexity. [...] Read more.
A charge detector is a vital component in neutrino and dark matter detection. The integration of a charge collector in the form of flat pads and readout modules has been proposed as an optimization method as it can reduce noise and installation complexity. As a substrate, fused silica glass has attracted considerable attention due to its low radioactive background properties. In this research, based on the application requirements of a high charge collection rate and low noise, the structure of the charge detector was designed using calculation and simulation methods. The entire manufacturing process is described. In addition, a novel through glass via (TGV) structure composed of a conformal metal layer and a photosensitive material that is easy to fabricate and has high morphological compatibility with via filling is proposed. The curing property of the new material was characterized. A fully integrated solid-state charge detector with 32 groups of TGVs was realized. Additionally, the electrical properties of key structures were tested and analyzed. Full article
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17 pages, 1538 KiB  
Article
A Machine Learning-Based Intrusion Detection System for IoT Electric Vehicle Charging Stations (EVCSs)
by Mohamed ElKashlan, Mahmoud Said Elsayed, Anca Delia Jurcut and Marianne Azer
Electronics 2023, 12(4), 1044; https://doi.org/10.3390/electronics12041044 - 20 Feb 2023
Cited by 12 | Viewed by 3559
Abstract
The demand for electric vehicles (EVs) is growing rapidly. This requires an ecosystem that meets the user’s needs while preserving security. The rich data obtained from electric vehicle stations are powered by the Internet of Things (IoT) ecosystem. This is achieved through us [...] Read more.
The demand for electric vehicles (EVs) is growing rapidly. This requires an ecosystem that meets the user’s needs while preserving security. The rich data obtained from electric vehicle stations are powered by the Internet of Things (IoT) ecosystem. This is achieved through us of electric vehicle charging station management systems (EVCSMSs). However, the risks associated with cyber-attacks on IoT systems are also increasing at the same pace. To help in finding malicious traffic, intrusion detection systems (IDSs) play a vital role in traditional IT systems. This paper proposes a classifier algorithm for detecting malicious traffic in the IoT environment using machine learning. The proposed system uses a real IoT dataset derived from real IoT traffic. Multiple classifying algorithms are evaluated. Results were obtained on both binary and multiclass traffic models. Using the proposed algorithm in the IoT-based IDS engine that serves electric vehicle charging stations will bring stability and eliminate a substantial number of cyberattacks that may disturb day-to-day life activities. Full article
(This article belongs to the Special Issue AI in Cybersecurity)
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16 pages, 405 KiB  
Article
APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection
by Zhibo Zhou, Yang Yang and Zhoujun Li
Electronics 2023, 12(4), 1043; https://doi.org/10.3390/electronics12041043 - 20 Feb 2023
Cited by 1 | Viewed by 1564
Abstract
Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic [...] Read more.
Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper. The fake news stance detection can contribute to detect a startling amount of fake news, which aims at evaluating the relevance between the headline and text bodies. There exists a significant difference between news article headline and text body, since headlines with several key phrases are usually much shorter than the text bodies. Such an information imbalance challenge may cause serious problems for the stance detection task. Furthermore, news article data in online social networks is usually exposed to various types of noise and can be contaminated, which poses more challenges for the stance detection task. In this paper, we propose a novel fake news stance detection model, namely Adversarial Pseudo-Siamese Network model (APSN), to solve these challenges. With coupled input components with imbalanced parameters, APSN can learn and compute feature vectors and similarity score of news article headlines and text bodies simultaneously. In addition, by adopting adversarial setting, besides the regular training set, a set of noisy training instances will be generated and fed to APSN in the learning process, which can significantly enhance the robustness of the model. Extensive experiments have been conducted on a real-world fake news dataset, and the experimental results reveal that the presented model exceeds compared suspicious information detection models with significant advantages. Full article
(This article belongs to the Section Artificial Intelligence)
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23 pages, 4544 KiB  
Article
Timeslot Scheduling with Reinforcement Learning Using a Double Deep Q-Network
by Jihye Ryu, Juhyeok Kwon, Jeong-Dong Ryoo, Taesik Cheung and Jinoo Joung
Electronics 2023, 12(4), 1042; https://doi.org/10.3390/electronics12041042 - 20 Feb 2023
Viewed by 1665
Abstract
Adopting reinforcement learning in the network scheduling area is getting more attention than ever because of its flexibility in adapting to the dynamic changes of network traffic and network status. In this study, a timeslot scheduling algorithm for traffic, with similar requirements but [...] Read more.
Adopting reinforcement learning in the network scheduling area is getting more attention than ever because of its flexibility in adapting to the dynamic changes of network traffic and network status. In this study, a timeslot scheduling algorithm for traffic, with similar requirements but different priorities, is designed using a double deep q-network (DDQN), a reinforcement learning algorithm. To evaluate the behavior of the DDQN agent, a reward function is defined based on the difference between the estimated delay and the deadline of packets transmitted at the timeslot, and on the priority of packets. The simulation showed that the designed scheduling algorithm performs better than existing algorithms, such as the strict priority (SP) or weighted round robin (WRR) scheduler, in the sense that more packets arrived within the deadline. By using the proposed DDQN-based scheduler, it is expected that autonomous network scheduling can be realized in upcoming frameworks, such as time-sensitive or deterministic networking. Full article
(This article belongs to the Section Networks)
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14 pages, 1236 KiB  
Article
Using a Light-Weight CNN for Perfume Identification with An Integrated Handheld Electronic Nose
by Mengli Cao
Electronics 2023, 12(4), 1041; https://doi.org/10.3390/electronics12041041 - 20 Feb 2023
Viewed by 1230
Abstract
Exposing counterfeit perfume products is significant for protecting the legal profit of genuine perfume manufacturers and the health of perfume consumers. As a holistic solution to the problem of perfume identification (PI) using an electronic nose (EN), the methods based on convolutional neural [...] Read more.
Exposing counterfeit perfume products is significant for protecting the legal profit of genuine perfume manufacturers and the health of perfume consumers. As a holistic solution to the problem of perfume identification (PI) using an electronic nose (EN), the methods based on convolutional neural network (CNN) simplifies the inconvenient selection of methods and parameter values, which has traditionally complicated existing combinatory methods. However, existing CNN methods that can be used for EN-based PI were designed on the premise that the CNN model can be trained with plenty of computational resources in divide-body ENs. Aiming at PI with an integrated handheld EN, a novel light-weight CNN method, namely LwCNN, is presented for being entirely conducted on a resource-constrained NVDIA Jetson nano module. LwCNN utilizes a sequenced stack of two feature flattening layers, two one-dimensional (1D) convolutional layers, a 1D max-pooling layer, a feature dropout layer, and a fully connected layer. Extensive real experiments were conducted on an integrated handheld EN to the performance of LwCNN with those of four existing benchmark methods. Experimental results show that LwCNN obtained an average identification accuracy of 98.35% with model training time of about 26 s. Full article
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14 pages, 3207 KiB  
Article
Research on sEMG Feature Generation and Classification Performance Based on EBGAN
by Xia Zhang and Mingyu Ma
Electronics 2023, 12(4), 1040; https://doi.org/10.3390/electronics12041040 - 20 Feb 2023
Cited by 2 | Viewed by 1333
Abstract
Surface electromyography signal (sEMG) recognition technology requires a large number of samples to ensure the accuracy of the training results. However, sEMG signals generally have the problems of a small amount of data, complicated acquisition process and large environmental influence, which hinders the [...] Read more.
Surface electromyography signal (sEMG) recognition technology requires a large number of samples to ensure the accuracy of the training results. However, sEMG signals generally have the problems of a small amount of data, complicated acquisition process and large environmental influence, which hinders the improvement of the accuracy of sEMG classification. In order to improve the accuracy of sEMG classification, an sEMG feature generation method based on an energy generative adversarial network (EBGAN) is proposed in this paper for the first time. The energy concept is introduced into the discriminant network instead of the traditional binary judgment, and the distribution of the real EMG dataset is learned and captured by multiple fully connected layers, with similar sEMG data being generated. The experimental results show that, compared with other types of GAN networks, this method achieves a small maximum mean discrepancy in comparison with that of the original data. The experimental results using different typical classification models show that the data augmentation method proposed can effectively improve the classification accuracy of typical classification models, and the accuracy increase range is 1~5%. Full article
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11 pages, 3708 KiB  
Article
MWIRGAN: Unsupervised Visible-to-MWIR Image Translation with Generative Adversarial Network
by Mohammad Shahab Uddin, Chiman Kwan and Jiang Li
Electronics 2023, 12(4), 1039; https://doi.org/10.3390/electronics12041039 - 20 Feb 2023
Cited by 3 | Viewed by 1919
Abstract
Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed [...] Read more.
Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed a new model named MWIRGAN for visible-to-MWIR image translation in a fully unsupervised manner. We utilized a perceptual loss to leverage shape identification and location changes of the objects in the translation. The experimental results showed that MWIRGAN was capable of visible-to-MWIR image translation while preserving the object’s shape with proper enhancement in the translated images and outperformed several competing state-of-the-art models. In addition, we customized the proposed model to convert game-engine-generated (a commercial software) images to MWIR images. The quantitative results showed that our proposed method could effectively generate MWIR images from game-engine-generated images, greatly benefiting MWIR data augmentation. Full article
(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)
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15 pages, 3531 KiB  
Article
Studies on the Control of Dermanyssus gallinae via High-Voltage Impulse
by Takahisa Ueno, Yuma Mizobe, Junko Ninomiya, Takahiro Inoue, Takashi Furukawa and Takeshi Hatta
Electronics 2023, 12(4), 1038; https://doi.org/10.3390/electronics12041038 - 19 Feb 2023
Cited by 1 | Viewed by 1641
Abstract
Dermanyssus gallinae, a parasitic mite that subsists on the avian blood of chickens, poses a considerable threat to the poultry industry. D. gallinae infestation can result in a plethora of detrimental effects for the host birds, including decreased egg production and anemia. [...] Read more.
Dermanyssus gallinae, a parasitic mite that subsists on the avian blood of chickens, poses a considerable threat to the poultry industry. D. gallinae infestation can result in a plethora of detrimental effects for the host birds, including decreased egg production and anemia. Pyrethroid pesticides have been the primary means of combating this issue and have demonstrated high levels of efficacy. However, in recent years, D. gallinae has exhibited resistance to these chemicals, resulting in a marked decrease in their mortality; thus, an integrated control strategy in addition to the chemical use should be required for the sustainable control of this mite. This study confirms that D. gallinae can be effectively controlled through the utilization of high-voltage impulse discharges and that various electrical parameters possess optimal values that are required for mite control. The alterations in the body surface of the mite caused by high-voltage impulses were akin to those caused by heat, but no alteration in the elemental composition of the body surface was observed, suggesting a change in organization caused by currents flowing inside the exoskeleton. Comparatively, the mite control efficacy of high-voltage impulse was found to be substantially superior to that of ultraviolet light or ozone, with up to 95% more mites being killed in as little as 30 seconds. Full article
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