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Electronics, Volume 11, Issue 23 (December-1 2022) – 228 articles

Cover Story (view full-size image): This paper proposes an energy management system (EMS) for a photovoltaic (PV) grid-connected charging station with a battery energy storage system (BESS). The main objective of this EMS is to manage the energy delivered to the electric vehicle (EV), considering the price and CO2 emissions due to the grid's connection. This paper also analyses the performance of an actual charging station installed at the University of Trieste under the proposed EMS considering three main aspects, economic, environmental, and energy, for one month of data. The results show that due to the proposed optimization, the new energy profile guarantees reductions of 32% in emissions and 29% in energy costs. View this paper
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31 pages, 10865 KiB  
Article
Using Machine Learning and Software-Defined Networking to Detect and Mitigate DDoS Attacks in Fiber-Optic Networks
by Sulaiman Alwabisi, Ridha Ouni and Kashif Saleem
Electronics 2022, 11(23), 4065; https://doi.org/10.3390/electronics11234065 - 06 Dec 2022
Cited by 7 | Viewed by 2429
Abstract
Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used [...] Read more.
Fiber optic networks (FONs) are considered the backbone of telecom companies worldwide. However, the network elements of FONs are scattered over a wide area and managed through a centralized controller based on intelligent devices and the internet of things (IoT), with actuators used to perform specific tasks at remote locations. During the COVID-19 pandemic, many telecom companies advised their employees to manage the network using the public internet (e.g., working from home while connected to an IoT network). Theses IoT devices mostly have weak security algorithms that are easily taken-over by hackers, and therefore can generate Distributed Denial of Service (DDoS) attacks in FONs. A DDoS attack is one of the most severe cyberattack types, and can negatively affect the stability and quality of managing networks. Nowadays, software-defined networks (SDN) constitute a new approach that simplifies how the network can be managed through a centralized controller. Moreover, machine learning algorithms allow the detection of incoming malicious traffic with high accuracy. Therefore, combining SDN and ML approaches can lead to detecting and stopping DDoS attacks quickly and efficiently, especially compared to traditional methods. In this paper, we evaluated six ML models: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, and Random Forest. The accuracy reached 100% while detecting DDoS attacks in FON with two approaches: (1) using SVM with three features (SOS, SSIP, and RPF) and (2) using Random Forest with five features (SOS, SSIP, RPF, SDFP, and SDFB). The training time for the first approach was 14.3 s, whereas the second approach only requires 0.18 s; hence, the second approach was utilized for deployment. Full article
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17 pages, 12528 KiB  
Article
X-Band Active Phased Array Antenna Using Dual-Port Waveguide for High-Power Microwave Applications
by Rong Liu, Naizhi Wang, Tong Li, Ruoqiao Zhang and Hongchao Wu
Electronics 2022, 11(23), 4064; https://doi.org/10.3390/electronics11234064 - 06 Dec 2022
Viewed by 2418
Abstract
An X-band active phased array horn antenna with high power capacity and high peak power is proposed in this paper. At the horn aperture, the baffles are loaded to suppress higher-order modes and eliminate blind spots during beam scanning. Straight walls are added [...] Read more.
An X-band active phased array horn antenna with high power capacity and high peak power is proposed in this paper. At the horn aperture, the baffles are loaded to suppress higher-order modes and eliminate blind spots during beam scanning. Straight walls are added to improve impedance matching. Considering that the peak power that T/R modules can provide is very limited, the proposal of a dual-port waveguide breaks through the bottleneck of the power capacity of a single-port input for the first time. The proposed curved dual-port waveguide is used to connect the horn antenna and the T/R module, which is verified to improve the power capacity of the overall internal structure. Simulated and measured results show that VSWR ≤ 2 in the frequency range of 7.5–8.5 GHz. There is no grating lobe in the ±10° scanning range and the maximum gain drop does not exceed 0.4 dB. The power capacity of the proposed HPM array is 56.34 MW. The phased array antenna has the characteristics of flexible scanning, small size, and high gain, and can be applied in high-power microwave systems. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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13 pages, 3767 KiB  
Article
Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks
by Teguh Indra Bayu, Yung-Fa Huang and Jeang-Kuo Chen
Electronics 2022, 11(23), 4063; https://doi.org/10.3390/electronics11234063 - 06 Dec 2022
Cited by 2 | Viewed by 1218
Abstract
Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability (Prk) variable [...] Read more.
Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability (Prk) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed Prk value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal Prk value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable Prk. The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively. Full article
(This article belongs to the Special Issue Advances in Wireless Networks and Mobile Systems)
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9 pages, 2212 KiB  
Article
Photoluminescence Properties of InAs Quantum Dots Overgrown by a Low-Temperature GaAs Layer under Different Arsenic Pressures
by Sergey Balakirev, Natalia Chernenko, Natalia Kryzhanovskaya, Nikita Shandyba, Danil Kirichenko, Anna Dragunova, Sergey Komarov, Alexey Zhukov and Maxim Solodovnik
Electronics 2022, 11(23), 4062; https://doi.org/10.3390/electronics11234062 - 06 Dec 2022
Cited by 4 | Viewed by 1384
Abstract
We studied the influence of the arsenic pressure during low-temperature GaAs overgrowth of InAs quantum dots on their optical properties. In the photoluminescence spectrum of quantum dots overgrown at a high arsenic pressure, we observed a single broad line corresponding to unimodal size [...] Read more.
We studied the influence of the arsenic pressure during low-temperature GaAs overgrowth of InAs quantum dots on their optical properties. In the photoluminescence spectrum of quantum dots overgrown at a high arsenic pressure, we observed a single broad line corresponding to unimodal size distribution of quantum dots. Meanwhile, two distinct peaks (~1080 and ~1150 nm) at larger wavelengths are found in the spectra of samples with quantum dots overgrown at a low arsenic pressure. We attributed this phenomenon to the high-pressure suppression of atom diffusion between InAs islands at the overgrowth stage, which makes it possible to preserve the initial unimodal size distribution of quantum dots. The same overgrowth of quantum dots at the low arsenic pressure induces intensive mass transfer, which leads to the formation of arrays of quantum dots with larger sizes. Integrated photoluminescence intensity at 300 K is found to be lower for quantum dots overgrown at the higher arsenic pressure. However, a difference in the photoluminescence intensity for the high- and low-pressure overgrowths is not so significant for a temperature of 77 K. This indicates that excess arsenic incorporates into the capping layer at high arsenic pressures and creates numerous nonradiative recombination centers, diminishing the photoluminescence intensity. Full article
(This article belongs to the Special Issue Quantum and Optoelectronic Devices, Circuits and Systems)
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14 pages, 1454 KiB  
Article
Energy Saving Implementation in Hydraulic Press Using Industrial Internet of Things (IIoT)
by Sumit, Deepali Gupta, Sapna Juneja, Ali Nauman, Yasir Hamid, Inam Ullah, Taejoon Kim, Elsayed Mohamed Tag eldin and Nivin A. Ghamry
Electronics 2022, 11(23), 4061; https://doi.org/10.3390/electronics11234061 - 06 Dec 2022
Cited by 6 | Viewed by 2531
Abstract
With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for [...] Read more.
With the growing cost of electrical energy, the necessity of energy-saving implementation in industries based on energy audits has become a major focus area. Energy audit results indicate energy-saving potential in an application and require the physical presence of the auditor’s team for monitoring and analyzing the energy consumption data. The use of Industrial Internet of Things (IIoT) for remote data monitoring and analysis is growing and new industrial applications based on IIoT are being developed and used by various industrial sectors. Possibilities of a mixed method of physical and remote energy audit using IIoT in industrial applications and its advantages as proposed in this research work needs to be explored. Existing hydraulic press machines running with direct online starter (DOL) can be run with variable speed drive (VSD) for energy saving but this requires an extensive energy audit. Key electrical and operational parameters of the hydraulic pump motor were monitored and analyzed remotely using IIoT in this research work by operating the hydraulic press with DOL and VSD motor control methods one by one. The input power factor of the hydraulic pump motor showed an improvement from 0.79 in DOL control to 0.9 in VSD control at different motor loads. The hydraulic pump motor starting current showed a reduction of 84% with VSD control. The hydraulic pump motor’s continuous current was reduced by 40% and 65% during the loading and unloading cycle, respectively, with VSD control. Electrical consumption was reduced by 24% as a result of operating the hydraulic pump motor at 35 Hz with VSD control without impacting the performance of the hydraulic press. These results indicated more efficient control by changing to VSD control in comparison with DOL control. A combination of physical and remote energy audits as performed in this research work using the proposed IIoT framework can be utilized for implementing energy saving in hydraulic presses thus motivating industries to adopt available more energy-efficient technologies at a faster pace. Full article
(This article belongs to the Special Issue Cyborgs in Industrial Internet of Things: Security and Privacy)
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14 pages, 19358 KiB  
Article
RefinePose: Towards More Refined Human Pose Estimation
by Hao Dong, Guodong Wang, Chenglizhao Chen and Xinyue Zhang
Electronics 2022, 11(23), 4060; https://doi.org/10.3390/electronics11234060 - 06 Dec 2022
Cited by 2 | Viewed by 2442
Abstract
Human pose estimation is a very important research topic in computer vision and attracts more and more researchers. Recently, ViTPose based on heatmap representation refreshed the state of the art for pose estimation methods. However, we find that ViTPose still has room for [...] Read more.
Human pose estimation is a very important research topic in computer vision and attracts more and more researchers. Recently, ViTPose based on heatmap representation refreshed the state of the art for pose estimation methods. However, we find that ViTPose still has room for improvement in our experiments. On the one hand, the PatchEmbedding module of ViTPose uses a convolutional layer with a stride of 14 × 14 to downsample the input image, resulting in the loss of a significant amount of feature information. On the other hand, the two decoding methods (Classical Decoder and Simple Decoder) used by ViTPose are not refined enough: transpose convolution in the Classical Decoder produces the inherent chessboard effect; the upsampling factor in the Simple Decoder is too large, resulting in the blurry heatmap. To this end, we propose a novel pose estimation method based on ViTPose, termed RefinePose. In RefinePose, we design the GradualEmbedding module and Fusion Decoder, respectively, to solve the above problems. More specifically, the GradualEmbedding module only downsamples the image to 1/2 of the original size in each downsampling stage, and it reduces the input image to a fixed size (16 × 112 in ViTPose) through multiple downsampling stages. At the same time, we fuse the outputs of max pooling layers and convolutional layers in each downsampling stage, which retains more meaningful feature information. In the decoding stage, the Fusion Decoder designed by us combines bilinear interpolation with max unpooling layers, and gradually upsamples the feature maps to restore the predicted heatmap. In addition, we also design the FeatureAggregation module to aggregate features after sampling (upsampling and downsampling). We validate the RefinePose on the COCO dataset. The experiments show that RefinePose has achieved better performance than ViTPose. Full article
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18 pages, 6732 KiB  
Article
Human Emotions Recognition, Analysis and Transformation by the Bioenergy Field in Smart Grid Using Image Processing
by Gunjan Chhabra, Edeh Michael Onyema, Sunil Kumar, Maganti Goutham, Sridhar Mandapati and Celestine Iwendi
Electronics 2022, 11(23), 4059; https://doi.org/10.3390/electronics11234059 - 06 Dec 2022
Cited by 12 | Viewed by 3960
Abstract
The passage of electric signals throughout the human body produces an electromagnetic field, known as the human biofield, which carries information about a person’s psychological health. The human biofield can be rehabilitated by using healing techniques such as sound therapy and many others [...] Read more.
The passage of electric signals throughout the human body produces an electromagnetic field, known as the human biofield, which carries information about a person’s psychological health. The human biofield can be rehabilitated by using healing techniques such as sound therapy and many others in a smart grid. However, psychiatrists and psychologists often face difficulties in clarifying the mental state of a patient in a quantifiable form. Therefore, the objective of this research work was to transform human emotions using sound healing therapy and produce visible results, confirming the transformation. The present research was based on the amalgamation of image processing and machine learning techniques, including a real-time aura-visualization interpretation and an emotion-detection classifier. The experimental results highlight the effectiveness of healing emotions through the aforementioned techniques. The accuracy of the proposed method, specifically, the module combining both emotion and aura, was determined to be ~88%. Additionally, the participants’ feedbacks were recorded and analyzed based on the prediction capability of the proposed module and their overall satisfaction. The participants were strongly satisfied with the prediction capability (~81%) of the proposed module and future recommendations (~84%). The results indicate the positive impact of sound therapy on emotions and the biofield. In the future, experimentation using different therapies and integrating more advanced techniques are anticipated to open new gateways in healthcare. Full article
(This article belongs to the Special Issue Internet of Things for Smart Grid)
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19 pages, 4142 KiB  
Article
Deep Learning Architecture for Flight Flow Spatiotemporal Prediction in Airport Network
by Haipei Zang, Jinfu Zhu and Qiang Gao
Electronics 2022, 11(23), 4058; https://doi.org/10.3390/electronics11234058 - 06 Dec 2022
Cited by 4 | Viewed by 1249
Abstract
Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basis for airport resource allocation. With the help of [...] Read more.
Traffic flow prediction is a significant component for the new generation intelligent transportation. In the field of air transportation, accurate prediction of airport flight flow can help airlines schedule flights and provide a decision-making basis for airport resource allocation. With the help of Deep Learning technology, this paper focuses on the characteristics of flight flow easily disturbed by environmental factors, studies the spatiotemporal dependence between flight flows, and predicts the spatiotemporal distribution of flight flows from the airport network level. We proposed a deep learning architecture named ATFSTNP, which combining the residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM). Based big data analytics of air traffic management, this method takes the spatiotemporal causal relationship between weather impact and flight flow as the core, and deeply mines the complex spatiotemporal relationship of flight flow. The model’s methodologies are improved from the practical application level, and extensive experiments conducted on the China’s flight operation dataset. The results illustrate that the improved model has significant advantages in predicting the flight flow under weather affect. Even in the complex and variable external environment, the model can still accurately predict the spatiotemporal distribution of the airport network flight flow, with strong robustness. Full article
(This article belongs to the Special Issue Big Data Analytics, Emerging Technologies and Its Applications)
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28 pages, 20127 KiB  
Article
High-Frequency Forecasting of Stock Volatility Based on Model Fusion and a Feature Reconstruction Neural Network
by Zhiwei Shi, Zhifeng Wu, Shuaiwei Shi, Chengzhi Mao, Yingqiao Wang and Laiqi Zhao
Electronics 2022, 11(23), 4057; https://doi.org/10.3390/electronics11234057 - 06 Dec 2022
Cited by 2 | Viewed by 1694
Abstract
Stock volatility is an important measure of financial risk. Due to the complexity and variability of financial markets, time series forecasting in the financial field is extremely challenging. This paper proposes a “model fusion learning algorithm” and a “feature reconstruction neural network” to [...] Read more.
Stock volatility is an important measure of financial risk. Due to the complexity and variability of financial markets, time series forecasting in the financial field is extremely challenging. This paper proposes a “model fusion learning algorithm” and a “feature reconstruction neural network” to forecast the future 10 min volatility of 112 stocks from different industries over the past three years. The results show that the model in this paper has higher fitting accuracy and generalization ability than the traditional model (CART, MLR, LightGBM, etc.). This study found that the “model fusion learning algorithm” can be well applied to financial data modeling; the “feature reconstruction neural network” can well-model data sets with fewer features. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies and Applications)
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24 pages, 1837 KiB  
Article
Multi-UAV Clustered NOMA for Covert Communications: Joint Resource Allocation and Trajectory Optimization
by Xiaofei Qin, Xu Wu, Mudi Xiong, Ye Liu and Yue Zhang
Electronics 2022, 11(23), 4056; https://doi.org/10.3390/electronics11234056 - 06 Dec 2022
Cited by 2 | Viewed by 1307
Abstract
Due to strong survivability and flexible scheduling, multi-UAV (Unmanned Aerial Vehicle)-assisted communication networks have been widely used in civil and military fields. However, the open accessibility of wireless channels brings a huge risk of privacy disclosure to UAV-based networks. This paper considers a [...] Read more.
Due to strong survivability and flexible scheduling, multi-UAV (Unmanned Aerial Vehicle)-assisted communication networks have been widely used in civil and military fields. However, the open accessibility of wireless channels brings a huge risk of privacy disclosure to UAV-based networks. This paper considers a multi-UAV-assisted covert communication system based on Wireless Powered Communication (WPC) and Clustered-Non-Orthogonal-Multiple-Access (C-NOMA), aiming to hide the transmission behavior between UAVs and legitimate ground users (LGUs). Specifically, the UAVs serve as aerial base stations to provide services to LGUs, while avoiding detection by the ground warden. In order to improve the considered covert communication performance, the average uplink covert rate of all clusters in each slot is maximized by jointly optimizing the cluster scheduling variable, subslot allocation, LGU transmit power and multi-UAV trajectory subject to covertness constraints. The original problem is a mixed integer non-convex problem, which are typically difficult to solve directly. To solve this challenge, this paper decouples it into four sub-problems and solves the sub-problems by alternating iterations until the objective function converges. The simulation results show that the proposed multi-UAV-assisted covert communication scheme can effectively improve the average uplink covert rate of all clusters compared with the benchmark schemes. Full article
(This article belongs to the Special Issue Satellite-Terrestrial Integrated Internet of Things)
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15 pages, 5299 KiB  
Article
Experimental Validation and Applications of mm-Wave 8 × 8 Antenna-in-Package (AiP) Array Platform
by Yifa Li, Wei Fan, Huaqiang Gao and Fengchun Zhang
Electronics 2022, 11(23), 4055; https://doi.org/10.3390/electronics11234055 - 06 Dec 2022
Viewed by 1157
Abstract
Phased array antennas play an indispensable role in millimeter-wave (mmWave) communications. The Antenna-in-package (AiP) system combines advanced antenna and packaging technology, making it highly valuable for various cellular, radar and automative applications. The benefits it brings in terms of small size, low development [...] Read more.
Phased array antennas play an indispensable role in millimeter-wave (mmWave) communications. The Antenna-in-package (AiP) system combines advanced antenna and packaging technology, making it highly valuable for various cellular, radar and automative applications. The benefits it brings in terms of small size, low development costs, low power consumption and fast beam-steering capability further drive the vast deployment of phased array antennas in mmWave systems. In this paper, an 8 × 8 AiP experimental platform is presented and its operating performance is measured and analyzed. Further, two application examples of the AiP are presented, namely, a platform for investigating the phased array calibration performance of different methods, and an AiP-based channel sounder for channel characterization. The performance of the channel sounder is verified by analysing the angle of arrival (AoA), angle of departure (AoD) and propagation delay of the measured dominant propagation components (DPCs). Full article
(This article belongs to the Special Issue Massive MIMO Technology for 5G and Beyond)
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14 pages, 2624 KiB  
Article
ABC-ANN Based Indoor Position Estimation Using Preprocessed RSSI
by Muhammed Fahri Unlersen
Electronics 2022, 11(23), 4054; https://doi.org/10.3390/electronics11234054 - 06 Dec 2022
Cited by 3 | Viewed by 1429
Abstract
The widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three preprocessed states (raw, preprocessed with the [...] Read more.
The widespread use of mobile devices has popularized the idea of indoor navigation. The Wi-Fi fingerprint method is emerging as an important alternative indoor positioning method for GPS usage difficulties. This study utilizes RSSI signals with three preprocessed states (raw, preprocessed with the path loss adapted, and exponential transformed) to train and test an artificial neural network (ANN). A systematic approach to the determination of neuron numbers in the hidden layers and activation functions of ANN is provided. The ANN is trained by the artificial bee colony algorithm. Five ML methods have been employed for estimation. The best performance has been achieved with ABC-ANN by the path loss adapted database with the MAE of 1.01 m. The estimation done using processed RSSI values has better performance than raw RSSI values. In addition, 33% less error occurs with the mentioned method compared to the data set source study. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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20 pages, 6058 KiB  
Article
Deep Feature Extraction for Detection of COVID-19 Using Deep Learning
by Arisa Rafiq, Muhammad Imran, Mousa Alhajlah, Awais Mahmood, Tehmina Karamat, Muhammad Haneef and Ashwaq Alhajlah
Electronics 2022, 11(23), 4053; https://doi.org/10.3390/electronics11234053 - 06 Dec 2022
Cited by 1 | Viewed by 2229
Abstract
SARS-CoV-2, a severe acute respiratory syndrome that is related to COVID-19, is a novel type of influenza virus that has infected the entire international community. It has created severe health and safety concerns all over the globe. Identifying the outbreak in the initial [...] Read more.
SARS-CoV-2, a severe acute respiratory syndrome that is related to COVID-19, is a novel type of influenza virus that has infected the entire international community. It has created severe health and safety concerns all over the globe. Identifying the outbreak in the initial phase may aid successful recovery. The rapid and exact identification of COVID-19 limits the risk of spreading this fatal disease. Patients with COVID-19 have distinctive radiographic characteristics on chest X-rays and CT scans. CXR images can be used for people with COVID-19 to diagnose their disease early. This research was focused on the deep feature extraction, accurate detection, and prediction of COVID-19 from X-ray images. The proposed concatenated CNN model is based on deep learning models (Xception and ResNet101) for CXR images. For the extraction of features, CNN models (Xception and ResNet101) were utilized, and then these features were combined using a concatenated model technique. In the proposed scheme, the particle swarm optimization method is applied to the concatenated features that provide optimal features from an overall feature vector. The selection of these optimal features helps to decrease the classification period. To evaluate the performance of the proposed approach, experiments were conducted with CXR images. Datasets of CXR images were collected from three different sources. The results demonstrated the efficiency of the proposed scheme for detecting COVID-19 with average accuracies of 99.77%, 99.72%, and 99.73% for datasets 1, 2 and 3, respectively. Moreover, the proposed model also achieved average COVID-19 sensitivities of 96.6%, 97.18%, and 98.88% for datasets 1, 2, and 3, respectively. The maximum overall accuracy of all classes—normal, pneumonia, and COVID-19—was about 98.02%. Full article
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22 pages, 5025 KiB  
Article
Rate Control Technology for Next Generation Video Coding Overview and Future Perspective
by Hao Zeng, Jun Xu, Shuqian He, Zhengjie Deng and Chun Shi
Electronics 2022, 11(23), 4052; https://doi.org/10.3390/electronics11234052 - 06 Dec 2022
Cited by 4 | Viewed by 2179
Abstract
Video data have become the main data traffic on the Internet, and their traffic is increasing explosively every year, thus increasing the pressure of video transmission. Video coding technology has become the key to compressing original videos. As an indispensable technology, rate control [...] Read more.
Video data have become the main data traffic on the Internet, and their traffic is increasing explosively every year, thus increasing the pressure of video transmission. Video coding technology has become the key to compressing original videos. As an indispensable technology, rate control plays an important role in stabilizing video stream transmission. Rate control (RC) is part of rate distortion optimization (RDO) whose job is to find the optimal solution based on balancing rate and distortion. It not only needs to consider the buffer and network status but also adjust the corresponding bit rate according to the video content. This paper reviews the related technologies of rate control under high efficiency video coding (HEVC) and versatile video coding (VVC) standards so that subsequent researchers can quickly understand the field and promote the development of rate control algorithms. Firstly, the paper summarizes the various aspects of RC, including basic principles, rate-distortion models, major processes, and performance criteria. Secondly, the paper surveys, in detail, the research progress in the field of rate control and analyzes several mainstream research directions. Thirdly, we carry out relevant experiments on the standard reference software and analyze and discuss the experimental results of the existing studies. Finally, we look ahead to the future trends of rate control and provide feasible improvement suggestions. Full article
(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)
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20 pages, 866 KiB  
Article
Probabilistic Analysis of an RL Circuit Transient Response under Inductor Failure Conditions
by Muhammad Farooq-i-Azam, Zeashan Hameed Khan, Syed Raheel Hassan and Rameez Asif
Electronics 2022, 11(23), 4051; https://doi.org/10.3390/electronics11234051 - 06 Dec 2022
Cited by 1 | Viewed by 1651
Abstract
We apply probability theory for the analysis of the exponentially decaying transient response of a resistor inductor electric circuit with partially known value of the inductance due to its failure. The inductance is known to be within a continuous interval, and the exact [...] Read more.
We apply probability theory for the analysis of the exponentially decaying transient response of a resistor inductor electric circuit with partially known value of the inductance due to its failure. The inductance is known to be within a continuous interval, and the exact value is unknown, which may happen as a result of inductor faults due to a variety of factors—for example, when the circuit is deployed in an unusually harsh environment. We consider the inductance as a continuous uniform random variable for our analysis, and the transient voltage is treated as a derived random variable which is a function of the inductance random variable. Using this approach, a probability model of the transient voltage at the output terminals of the circuit is derived in terms of its cumulative distribution function and the probability density function. In our work, we further elaborate that the probability model of any other circuit parameter can also be obtained in a similar manner, or it can be derived from the transient voltage probability model. This is demonstrated by getting the model of a branch current from the probability distribution of the transient voltage. Usage of the probability model is demonstrated with the help of examples. The probability of the transient voltage falling in a certain interval at a given instant of time is evaluated. Similarly, the probability values of the branch current in different intervals are determined and analyzed. The derived probability model is checked for its validity and correctness as well. The model is found to be useful for probabilistic analysis of the circuit. Full article
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11 pages, 4419 KiB  
Article
A Joint Design of Radar Sensing, Wireless Power Transfer, and Communication Based on Reconfigurable Software Defined Radio
by Zhouyi Wu, Yasser Qaragoez, Vladimir Volskiy, Jiangtao Huangfu, Lixin Ran and Dominique Schreurs
Electronics 2022, 11(23), 4050; https://doi.org/10.3390/electronics11234050 - 06 Dec 2022
Cited by 1 | Viewed by 1792
Abstract
This paper proposes a compact three-mode base station capable of performing radar sensing, communication, and wireless power transfer (WPT) in collaboration with indoor sensor networks. With regard to the wireless sensor node, the base station transmits two-tone signals in the downlink to support [...] Read more.
This paper proposes a compact three-mode base station capable of performing radar sensing, communication, and wireless power transfer (WPT) in collaboration with indoor sensor networks. With regard to the wireless sensor node, the base station transmits two-tone signals in the downlink to support its operation and provides two-way communication. The sensor node sends uplink information through backscattering using the third order intermodulation (IM3) product of the rectification. In the radar mode, a single-tone continuous wave (CW) is used to monitor if there is a moving target in the static environment. If a speed is detected, the transmit signal to the node is stopped, while the single-tone CW excitation will continue until the speed of the target is zero, and then the base station transmits a stepped frequency continuous wave (SFCW) signal to measure the distance of the target. The repeat between the two radar waveforms continues until the target is undetectable within the detection range. The software defined radio PlutoSDR is adopted as the base station. The system can wirelessly supply power and bi-directionally communicate with a CO2 sensor node 2 m away. It gives a range resolution of 2.5 cm and a minimum detectable speed of 0.25 m/s in the radar mode. Full article
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19 pages, 6521 KiB  
Article
Scheduling Algorithm for Two-Workshop Production with the Time-Selective Strategy and Backtracking Strategy
by Xiaohuan Zhang, Zhen Wang, Dan Zhang and Tao Xu
Electronics 2022, 11(23), 4049; https://doi.org/10.3390/electronics11234049 - 06 Dec 2022
Viewed by 962
Abstract
To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy [...] Read more.
To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy and Backtracking Strategy is proposed. The scheduling order of each process in the process tree needs to be determined, which will be completed by the process sequence sequencing strategy. The scheduling plan also needs to be determined, which will be completed using the time-selective scheduling strategy for the two workshops. At the same time, the “reference time” is set for the current scheduling process. To find a better scheduling scheme, the “scheduling reference time” is recorded as T. If the time of the current scheduling process scheme is greater than T, the backtracking adjustment strategy will be used to track the process scheduling scheme. Finally, experiments show that the algorithm not only ensures the parallel processing of parallel processes but also effectively improves the tightness of serial processes and optimizes the results of integrated scheduling. Full article
(This article belongs to the Section Systems & Control Engineering)
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14 pages, 1468 KiB  
Article
Parallelism-Aware Channel Partition for Read/Write Interference Mitigation in Solid-State Drives
by Hyun Jo Lim, Dongkun Shin and Tae Hee Han
Electronics 2022, 11(23), 4048; https://doi.org/10.3390/electronics11234048 - 06 Dec 2022
Cited by 1 | Viewed by 1375
Abstract
The advancement of multi-level cell technology that enables storing multiple bits in a single NAND flash memory cell has increased the density and affordability of solid-state drives (SSDs). However, increased latency asymmetry between read and write (R/W) intensifies the severity of R/W interference, [...] Read more.
The advancement of multi-level cell technology that enables storing multiple bits in a single NAND flash memory cell has increased the density and affordability of solid-state drives (SSDs). However, increased latency asymmetry between read and write (R/W) intensifies the severity of R/W interference, so reads cannot be processed for a long time owing to the extended flash memory resource occupancy of writing. Existing flash translation layer (FTL)-level mitigation techniques can allocate flash memory resources in a balanced manner taking R/W interference into account; however, due to the inefficient utilization of parallel flash memory resources, the effect on performance enhancement is restrictive. From the perspectives of the predicted access pattern and available concurrency of flash memory resources, we propose a parallelism-aware channel partition (PACP) scheme that prevents SSD performance degradation caused by R/W interference. Moreover, an additional performance improvement is achieved by reallocating interference-vulnerable page using leveraged garbage collection (GC) migration. The evaluation results showed that compared with the existing solution, PACP reduced the average read latency by 11.6% and average write latency by 6.0%, with a negligible storage overhead. Full article
(This article belongs to the Section Computer Science & Engineering)
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14 pages, 2160 KiB  
Article
Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders
by Fazliddin Makhmudov, Alpamis Kutlimuratov, Farkhod Akhmedov, Mohamed S. Abdallah and Young-Im Cho
Electronics 2022, 11(23), 4047; https://doi.org/10.3390/electronics11234047 - 06 Dec 2022
Cited by 17 | Viewed by 2255
Abstract
Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsibilities in terms of modeling to improve performance. Therefore, in this [...] Read more.
Meticulous learning of human emotions through speech is an indispensable function of modern speech emotion recognition (SER) models. Consequently, deriving and interpreting various crucial speech features from raw speech data are complicated responsibilities in terms of modeling to improve performance. Therefore, in this study, we developed a novel SER model via attention-oriented parallel convolutional neural network (CNN) encoders that parallelly acquire important features that are used for emotion classification. Particularly, MFCC, paralinguistic, and speech spectrogram features were derived and encoded by designing different CNN architectures individually for the features, and the encoded features were fed to attention mechanisms for further representation, and then classified. Empirical veracity executed on EMO-DB and IEMOCAP open datasets, and the results showed that the proposed model is more efficient than the baseline models. Especially, weighted accuracy (WA) and unweighted accuracy (UA) of the proposed model were equal to 71.8% and 70.9% in EMO-DB dataset scenario, respectively. Moreover, WA and UA rates were 72.4% and 71.1% with the IEMOCAP dataset. Full article
(This article belongs to the Special Issue AI Technologies and Smart City)
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15 pages, 2987 KiB  
Article
VMD–RP–CSRN Based Fault Diagnosis Method for Rolling Bearings
by Yuanyuan Jiang and Jinyang Xie
Electronics 2022, 11(23), 4046; https://doi.org/10.3390/electronics11234046 - 06 Dec 2022
Cited by 1 | Viewed by 1101
Abstract
In response to the problems of low accuracy and poor noise immunity of the traditional fault diagnosis method for rolling bearing fault diagnosis due to the complex and variable operating conditions of rolling bearings and the large noise interference during bearing signal acquisition, [...] Read more.
In response to the problems of low accuracy and poor noise immunity of the traditional fault diagnosis method for rolling bearing fault diagnosis due to the complex and variable operating conditions of rolling bearings and the large noise interference during bearing signal acquisition, a rolling bearing fault diagnosis model based on VMD–RP–CSRN is proposed. Firstly, the initial feature extraction of the bearing signal is carried out by variational modal decomposition (VMD), which is then converted into a two-dimensional image with fault features by recurrent plot (RP) coding, and then the feature images are input to a channel split residual network (CSRN) for feature extraction and fault classification. In order to verify the accuracy and noise immunity of the proposed method for the diagnosis of bearing faults under complex working conditions, experiments on the selection of parameters in the CSRN model were conducted on the bearing dataset of Jiangnan University, and experiments on the diagnosis of bearing faults under complex working conditions and noise immunity of CSRN were carried out and compared with other commonly used methods. The proposed bearing fault diagnosis method based on VMD–RP–CSRN combines VMD and RP to retain the fault features in the original signal to the maximum extent and stress the hidden features in the signal. The proposed channel split operation realizes the extraction of hidden features by selecting the main operating channel of the three-channel feature image, and makes more fault features participate in the feature extraction of the diagnosis model. The experimental results demonstrate that the proposed method is at least 1.2% better than the comparison method, and has better noise immunity. In addition, experiments on the fault diagnosis capability of the model with different data set sizes and the diagnosis of variable speed bearing data by the model show that the proposed method has better generalization performance and diagnosis capability. Full article
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12 pages, 493 KiB  
Article
Multiple-Intelligent Reflective Surfaces (Multi-IRSs)-Based NOMA System
by Ziad Qais Al-Abbasi, Laith Farhan and Raad S. Alhumaima
Electronics 2022, 11(23), 4045; https://doi.org/10.3390/electronics11234045 - 06 Dec 2022
Cited by 3 | Viewed by 1414
Abstract
Recent research has introduced the notion of an Intelligent Reflective Surface (IRS) so as to boost the source–destination communication environment. IRS refers to a plane that consists of a number of passive elements that have a smaller size than the wavelength of the [...] Read more.
Recent research has introduced the notion of an Intelligent Reflective Surface (IRS) so as to boost the source–destination communication environment. IRS refers to a plane that consists of a number of passive elements that have a smaller size than the wavelength of the incident signal. Those elements use reflection to beam-form and to forward the incident radio signals toward the intended destination without power consumption. This article examines the performance of an IRS-based non-orthogonal multiple access (NOMA) system, and adopts the idea of using multiple IRS planes. It additionally investigates improving the performance of an IRS-NOMA combination through proposing various solutions that include optimizing the overall number of the IRS elements as a first step. After that, a closed form expression is derived for the considered IRS-NOMA system to determine the optimal number of the reflective elements. Secondly, the paper utilizes a new approach termed as multiple-IRS-based NOMA. This encompasses using multiple reflective planes (multi-IRS) along with IRS-NOMA, to boost the received signal characteristics, especially in situations where it is there a weak or no direct link between the source and the destination. An energy efficiency-spectral efficiency (EE-SE) trade-off is likewise presented, so as to have a complete view of the IRS-NOMA performance, along with the proposed solutions. The obtained simulation results depicts that IRS-NOMA is better than the existing orthogonal techniques. In addition, the results confirm that exploiting multiple IRSs offers a considerable gain in performance, as it enhances the conditions of the propagation environment. In specific, the considered case of a two IRS surfaces-assisted NOMA system offers higher performance levels than the case of a single IRS surface-assisted NOMA system. Full article
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13 pages, 4904 KiB  
Article
Classification of Plates and Trihedral Corner Reflectors Based on Linear Wavefront Phase-Modulated Beam
by Xiaodong Wang, Yi Zhang, Kaiqiang Zhu, Xiangdong Zhang and Houjun Sun
Electronics 2022, 11(23), 4044; https://doi.org/10.3390/electronics11234044 - 05 Dec 2022
Viewed by 1000
Abstract
Wavefront-modulated beams such as vortex beams have attracted much attention in the field of target recognition due to the introduced degrees of freedom. However, traditional wavefront-modulated beams are doughnut shaped, and are not suitable for radar detection or tracking. To solve this problem, [...] Read more.
Wavefront-modulated beams such as vortex beams have attracted much attention in the field of target recognition due to the introduced degrees of freedom. However, traditional wavefront-modulated beams are doughnut shaped, and are not suitable for radar detection or tracking. To solve this problem, a linear wavefront phase-modulated beam with a maximum radiation intensity in the center was proposed in a previous study. In this paper, we continue to study target characteristics under the linear wavefront phase-modulated beam. Through analysis of the target scattering based on the physical optics (PO) method, we find that a part of the monostatic or bistatic radar cross-section (RCS) of the target could be obtained by changing the phase gradient of the modulated beam. Taking this part of RCS for feature extraction, we recognize the plates and trihedral corner reflectors through the support vector machine (SVM) method. For data visualization, we use the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. The results show that the recognition probability of the plates and trihedral corner reflectors can reach 91% with an antenna array having an aperture of 20 wavelengths when the signal-to-noise ratio (SNR) is 20 dB, while the traditional plane beam cannot classify these two targets directly. Full article
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22 pages, 5556 KiB  
Article
Ensemble Learning-Enabled Security Anomaly Identification for IoT Cyber–Physical Power Systems
by Hongjun Zhao, Changjun Li, Xin Yin, Xiujun Li, Rui Zhou and Rong Fu
Electronics 2022, 11(23), 4043; https://doi.org/10.3390/electronics11234043 - 05 Dec 2022
Cited by 1 | Viewed by 865
Abstract
The public network access to smart grids has a great impact on the system‘s safe operation. With the rapid increase in Internet of Things (IoT) applications, cyber-attacks caused by multiple sources and flexible loads continue to rise, which results in equipment maloperation and [...] Read more.
The public network access to smart grids has a great impact on the system‘s safe operation. With the rapid increase in Internet of Things (IoT) applications, cyber-attacks caused by multiple sources and flexible loads continue to rise, which results in equipment maloperation and security hazard problems. In this paper, a novel ensemble learning algorithm (ELA)-enabled security anomaly identification technique is proposed. Firstly, the propagation process of typical cyber-attacks was analyzed to illustrate the impact on message transmission and power operation. Then, a feature matching identification method was designed according to the sequence sets under different situations. The classification rate of these abnormal attack behaviors was acquired thereafter, which could aid in the listing of the ranking of the consequences of abnormal attack behaviors. Moreover, the weights of training samples can be further updated according to the performance of weak learning error rates. Through a joint hardware platform, numerical results show that the proposed technique is effective and performs well in terms of situation anomaly identification. Full article
(This article belongs to the Special Issue Advances in Fault Detection/Diagnosis of Electrical Power Devices)
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20 pages, 5063 KiB  
Article
Large Signal Stability Criteria Combined with a 3D Region of Asymptotic Stability Method for Islanded AC/DC Hybrid Microgrids
by Xinbo Liu, Zhenkang Zhu, Junfu Shi, Xiaotong Song and Jinghua Zhou
Electronics 2022, 11(23), 4042; https://doi.org/10.3390/electronics11234042 - 05 Dec 2022
Viewed by 984
Abstract
Large disturbances frequently happen in isolated AC/DC Hybrid Microgrids. Unfortunately, constant power loads (CPLs) with negative impedance characteristics are equivalent to positive feedback, resulting in an increase in large disturbances. The system can easily become unstable. Consequently, large signal stability criteria are proposed [...] Read more.
Large disturbances frequently happen in isolated AC/DC Hybrid Microgrids. Unfortunately, constant power loads (CPLs) with negative impedance characteristics are equivalent to positive feedback, resulting in an increase in large disturbances. The system can easily become unstable. Consequently, large signal stability criteria are proposed in this paper. Combined with a three-dimensional region of asymptotic stability (3D RAS) method for islanded AC/DC Hybrid Microgrids, important parameters to increase stability margins were determined. Firstly, mixed potential theory was used to derive a large-signal stability criterion. The criteria gave constraints on filtering parameters, CPL power, power of the battery to charge and discharge, AC resistive loads, and DC bus voltage. Then, Lyapunov functions were constructed, and the Lasalle invariance principle was adopted to achieve 3D RAS. When large disturbances emerged, and simultaneously voltage and current varied in 3D RAS, the system always obtained stability and reached new steady-state equilibrium points. Finally, according to comparisons, bigger capacitances of the DC bus capacitor and the AC capacitor, larger battery discharging power and smaller charging power could significantly increase stability margins of islanded Microgrids. Simulations and experimental data have shown that the large signal stability criteria and the 3D RAS work. Full article
(This article belongs to the Special Issue Application of Power Electronics Technology in Energy System)
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27 pages, 561 KiB  
Article
An Intrusion Detection System for RPL-Based IoT Networks
by Eric Garcia Ribera, Brian Martinez Alvarez, Charisma Samuel, Philokypros P. Ioulianou and Vassilios G. Vassilakis
Electronics 2022, 11(23), 4041; https://doi.org/10.3390/electronics11234041 - 05 Dec 2022
Cited by 6 | Viewed by 1537
Abstract
The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking [...] Read more.
The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking infrastructure around the world via sensors, machine learning, and data analytics. However, the security of IoT devices has been proven to be a major concern. Almost a decade ago, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was designed to handle routing in IoT and IIoT. Since then, numerous types of attacks on RPL have been published. In this paper, a novel intrusion detection system (IDS) is designed and implemented for RPL-based IoT. The objective is to perform an accurate and efficient detection of various types of routing and denial-of-service (DoS) attacks such as version number attack, blackhole attack, and grayhole attack, and different variations of flooding attacks such as Hello flood attack, DIS attack, and DAO insider attack. To achieve this, different detection strategies are combined, taking advantage of the strengths of each individual strategy. In addition, the proposed IDS is experimentally evaluated by performing a deep analysis of the aforementioned attacks in order to study the impact caused. This evaluation also estimates the accuracy and effectiveness of the IDS performance when confronted with the considered attacks. The obtained results show high detection accuracy. Furthermore, the overhead introduced in terms of CPU usage and power consumption is negligible. In particular, the CPU usage overhead is less than 2% in all cases, whereas the average power consumption increase is no more than 0.5%, which can be considered an insignificant impact on the overall resource utilisation. Full article
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12 pages, 1930 KiB  
Communication
Advanced HEVC Screen Content Coding for MPEG Immersive Video
by Jarosław Samelak, Adrian Dziembowski and Dawid Mieloch
Electronics 2022, 11(23), 4040; https://doi.org/10.3390/electronics11234040 - 05 Dec 2022
Cited by 3 | Viewed by 1448
Abstract
This paper presents the modified HEVC Screen Content Coding (SCC) that was adapted to be more efficient as an internal video coding of the MPEG Immersive Video (MIV) codec. The basic, unmodified SCC is already known to be useful in such an application. [...] Read more.
This paper presents the modified HEVC Screen Content Coding (SCC) that was adapted to be more efficient as an internal video coding of the MPEG Immersive Video (MIV) codec. The basic, unmodified SCC is already known to be useful in such an application. However, in this paper, we propose three additional improvements to SCC to increase the efficiency of immersive video coding. First, we analyze using the quarter-pel accuracy in the intra block copy technique to provide a more effective search of the best candidate block to be copied in the encoding process. The second proposal is the use of tiles to allow inter-view prediction inside MIV atlases. The last proposed improvement is the addition of the MIV bitstream parser in the HEVC encoder that enables selecting the most efficient coding configuration depending on the type of currently encoded data. The experimental results show that the proposal increases the compression efficiency for natural content sequences by almost 7% and simultaneously decreases the computational time of encoding by more than 15%, making the proposal very valuable for further research on immersive video coding. Full article
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17 pages, 882 KiB  
Article
Cyclic Federated Learning Method Based on Distribution Information Sharing and Knowledge Distillation for Medical Data
by Liang Yu and Jianjun Huang
Electronics 2022, 11(23), 4039; https://doi.org/10.3390/electronics11234039 - 05 Dec 2022
Cited by 1 | Viewed by 1350
Abstract
Federated learning has been attracting increasing amounts of attention for its potential applications in disease diagnosis within the medical field due to privacy preservation and its ability to solve data silo problems. However, the inconsistent distributions of client-side data significantly degrade the performance [...] Read more.
Federated learning has been attracting increasing amounts of attention for its potential applications in disease diagnosis within the medical field due to privacy preservation and its ability to solve data silo problems. However, the inconsistent distributions of client-side data significantly degrade the performance of traditional federated learning. To eliminate the adverse effects of non-IID problems on federated learning performance on multiple medical institution datasets, this paper proposes a cyclic federated learning method based on distribution information sharing and knowledge distillation for medical data (CFL_DS_KD). The method is divided into two main phases. The first stage is an offline preparation process in which all clients train a generator model on local datasets and pass the generator to neighbouring clients to generate virtual shared data. The second stage is an online process that can also be mainly divided into two steps. The first step is a knowledge distillation learning process in which all clients first initialise the task model on the local datasets and share it with neighbouring clients. The clients then use the shared task model to guide the updating of their local task models on the virtual shared data. The second step simply re-updates the task model on the local datasets again and shares it with neighbouring clients. Our experiments on non-IID datasets demonstrated the superior performance of our proposed method compared to existing federated learning algorithms. Full article
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11 pages, 2705 KiB  
Article
A New Nano-Scale and Energy-Optimized Reversible Digital Circuit Based on Quantum Technology
by Saeid Seyedi, Nima Jafari Navimipour and Akira Otsuki
Electronics 2022, 11(23), 4038; https://doi.org/10.3390/electronics11234038 - 05 Dec 2022
Cited by 3 | Viewed by 1200
Abstract
A nano-scale quantum-dot cellular automaton (QCA) is one of the most promising replacements for CMOS technology. Despite the potential advantages of this technology, QCA circuits are frequently plagued by numerous forms of manufacturing faults (such as a missing cell, extra cell, displacement cell, [...] Read more.
A nano-scale quantum-dot cellular automaton (QCA) is one of the most promising replacements for CMOS technology. Despite the potential advantages of this technology, QCA circuits are frequently plagued by numerous forms of manufacturing faults (such as a missing cell, extra cell, displacement cell, and rotated cell), making them prone to failure. As a result, in QCA technology, the design of reversible circuits has received much attention. Reversible circuits are resistant to many kinds of faults due to their inherent properties and have the possibility of data reversibility, which is important. Therefore, this research proposes a new reversible gate, followed by a new 3 × 3 reversible gate. The proposed structure does not need rotated cells and only uses one layer, increasing the design’s manufacturability. QCADesigner-E and the Euler method on coherence vector (w/energy) are employed to simulate the proposed structure. The 3 × 3 reversible circuit consists of 21 cells that take up just 0.046 µm2. Compared to the existing QCA-based single-layer reversible circuit, the proposed reversible circuit minimizes cell count, area, and delay. Furthermore, the energy consumption is studied, confirming the optimal energy consumption pattern in the proposed circuit. The proposed reversible 3 × 3 circuit dissipates average energy of 1.36 (eV) and overall energy of 1.49 (eV). Finally, the quantum cost for implementing the reversible circuits indicates a lower value than that of all the other examined circuits. Full article
(This article belongs to the Special Issue Resource Sustainability for Energy and Electronics)
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21 pages, 4374 KiB  
Article
Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments
by Sungju Lee and Taikyeong Jeong
Electronics 2022, 11(23), 4037; https://doi.org/10.3390/electronics11234037 - 05 Dec 2022
Cited by 1 | Viewed by 1696
Abstract
The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose [...] Read more.
The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose two techniques for real-time interconnection and optimal resource allocation for a large-scale distributed system. In particular, to configure a CMP, we analyze the data transfer rate and utilization rate from the intelligent electronic device (IED), collecting the power production data to the individual controller. The details provided in this paper are used to design a sample value, i.e., raw data transfer, on the basis of the IEC 61850 protocol for mapping. The choice of sampled values is to attain the critical time requirement, data transmission of current transformers, voltage transformers, and protective relaying of less than 1 s without complicating the real-time implementation. Furthermore, in this paper, a way to determine the optimal number of physical resources (i.e., CPU, memory, and network) for a given system is discussed. CPU ranged from 0.9 to 0.98 while each cluster increased from 10 to 1000. With the same condition, memory utilized almost 100% utilization from 0.98 to 1. Lastly, the network utilization rate was 0.96 and peaked at 1 at most. Based on the results, we confirm that a large-scale distributed system can provide a seamless monitoring service to distribute messages for each IED, and this can provide a configuration for CMP without exceeding 100% utilization. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems and Applications in Industry 4.0)
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17 pages, 1828 KiB  
Article
Spectral Data Analysis for Forgery Detection in Official Documents: A Network-Based Approach
by Mohammed Abdulbasit Ali Al-Ameri, Bunyamin Ciylan and Basim Mahmood
Electronics 2022, 11(23), 4036; https://doi.org/10.3390/electronics11234036 - 05 Dec 2022
Cited by 5 | Viewed by 1992
Abstract
Despite the huge advances in digital communications in the last decade, physical documents are still the most common media for information transfer, especially in the official context. However, the readily available document processing devices and techniques (printers, scanners, etc.) facilitate the illegal manipulation [...] Read more.
Despite the huge advances in digital communications in the last decade, physical documents are still the most common media for information transfer, especially in the official context. However, the readily available document processing devices and techniques (printers, scanners, etc.) facilitate the illegal manipulation or imitation of original documents by forgers. Therefore, verification of the authenticity and detection of forgery is of paramount importance to all agencies receiving printed documents. We suggest an unsupervised forgery detection framework that can distinguish whether a document is forged based on the spectroscopy of the document’s ink. The spectra of the tested documents inks (original and questioned) were obtained using laser-induced breakdown spectroscopy (LIBS) technology. Then, a correlation matrix of the spectra was calculated for both the original and questioned documents together, which were then transformed into an adjacency matrix aiming at converting it into a weighted network under the concept of graph theory. Clustering algorithms were then applied to the network to determine the number of clusters. The proposed approach was tested under a variety of scenarios and different types of printers (e.g., inkjet, laser, and photocopiers) as well as different kinds of papers. The findings show that the proposed approach provided a high rate of accuracy in identifying forged documents and a high detection speed. It also provides a visual output that is easily interpretable to the non-expert, which provides great flexibility for real-world application. Full article
(This article belongs to the Section Computer Science & Engineering)
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