Electronics

20 pages, 3082 KiB

Open AccessArticle

Operator-Based Fractional-Order Nonlinear Robust Control for the Spiral Heat Exchanger Identified by Particle Swarm Optimization

by Guanqiang Dong and Mingcong Deng

Electronics 2022, 11(17), 2800; https://doi.org/10.3390/electronics11172800 - 05 Sep 2022

Cited by 1 | Viewed by 1246

Abstract

Fractional-order calculus and derivative is extended from integral-order calculus and derivative. This paper investigates a nonlinear robust control problem using fractional order and operator theory. In order to improve the tracking performance and antidisturbance ability, operator- and fractional-order-based nonlinear robust control for the [...] Read more.

Fractional-order calculus and derivative is extended from integral-order calculus and derivative. This paper investigates a nonlinear robust control problem using fractional order and operator theory. In order to improve the tracking performance and antidisturbance ability, operator- and fractional-order-based nonlinear robust control for the spiral counter-flow heat exchanger described by the parallel fractional-order model (PFOM) is proposed. The parallel fractional-order model for the spiral counter-flow heat exchanger was identified by particle swarm optimization (PSO) and the parameters of a fractional-order PID (FOPID) controller were optimized by the PSO. First, the parallel fractional-order mathematical model for a spiral counter-flow heat exchanger plant was identified by PSO. Second, a fractional-order PID controller and operator controller for the spiral heat exchanger were designed under the identified parallel fractional-order mathematical model. Third, the parameters of the operator and fractional-order PID were optimized by PSO. Then, tracking and antidisturbance performance of the control system were analyzed. Finally, comparisons of two control schemes were performed, and the effectiveness illustrated. Full article

(This article belongs to the Special Issue Fractional-Order Circuits & Systems Design and Applications)

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19 pages, 4195 KiB

Open AccessArticle

Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery

by Chandra Shekhar Yadav, Monoj Kumar Pradhan, Syam Machinathu Parambil Gangadharan, Jitendra Kumar Chaudhary, Jagendra Singh, Arfat Ahmad Khan, Mohd Anul Haq, Ahmed Alhussen, Chitapong Wechtaisong, Hazra Imran, Zamil S. Alzamil and Himansu Sekhar Pattanayak

Electronics 2022, 11(17), 2799; https://doi.org/10.3390/electronics11172799 - 05 Sep 2022

Cited by 20 | Viewed by 2516

Abstract

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of [...] Read more.

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient. Full article

(This article belongs to the Section Computer Science & Engineering)

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10 pages, 2048 KiB

Open AccessArticle

Reconfigurable Intelligent Surface Physical Model in Channel Modeling

by Yiping Liu, Jianwu Dou, Yijun Cui, Yijian Chen, Jun Yang, Fan Qin and Yuxin Wang

Electronics 2022, 11(17), 2798; https://doi.org/10.3390/electronics11172798 - 05 Sep 2022

Cited by 1 | Viewed by 1818

Abstract

Reconfigurable intelligent surfaces (RISs) are one of the potential technologies for 6th generation (6G) mobile communication systems with superior electromagnetic (EM) wave-steering capability to effectively control the phase, amplitude, and polarization of the incident EM wave. An implementation-independent physical RIS model with key [...] Read more.

Reconfigurable intelligent surfaces (RISs) are one of the potential technologies for 6th generation (6G) mobile communication systems with superior electromagnetic (EM) wave-steering capability to effectively control the phase, amplitude, and polarization of the incident EM wave. An implementation-independent physical RIS model with key EM characteristics is especially crucial to RIS channel modeling considering the trade-off between complexity and accuracy. In this paper, a reflective RIS physical model is proposed to facilitate channel modeling in a system simulation. Based on the impinging EM wave of the last bounce to the RIS, the scattering field intensity of the target point is obtained using geometric optics and the electric field surface integration method of physical optics. The feasibility of the model is verified by a comparison of the simulation and test results. Full article

(This article belongs to the Special Issue Massive MIMO Technology for 5G and Beyond)

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11 pages, 3580 KiB

Open AccessArticle

A Multi-Agent-Based Defense System Design for Multiple Unmanned Surface Vehicles

by Shangyan Zhang, Weizhi Ran, Geng Liu, Yang Li and Yang Xu

Electronics 2022, 11(17), 2797; https://doi.org/10.3390/electronics11172797 - 05 Sep 2022

Cited by 5 | Viewed by 1518

Abstract

Defense systems are usually deployed to protect high-value targets or hot spots that are integral parts of the modern battlefield environment. However, in coastal defense operations (due to the variability of the maritime environment and the sustainability of combat), limited operational capabilities, the [...] Read more.

Defense systems are usually deployed to protect high-value targets or hot spots that are integral parts of the modern battlefield environment. However, in coastal defense operations (due to the variability of the maritime environment and the sustainability of combat), limited operational capabilities, the need for efficient coordination, and protracted combat are peculiarly challenging to meet by traditional manned fleets. In contrast, with lower costs, unmanned fleets can organize an autonomous defense against enemy targets that are capable of rapid response. This paper focuses on the typical defense scenario; we analyzed and modeled the objective functions of the intelligent defense system and propose a hierarchical distributed multi-agent-based system design scheme. Finally, to test the system’s performance, we established simulation verification experiments in a typical scenario and compared the system based on the traditional central architecture. The results show that, in a defense operation, the hierarchically-distributed multi-agent-based system shows improvements in system decision-making efficiency and interception effect. Full article

(This article belongs to the Special Issue Autonomous Unmanned Systems Technology: Selected Papers from ICAUS 2022 and 2023)

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Graphical abstract

8 pages, 888 KiB

Open AccessTechnical Note

Assessment of Exposure to Time-Varying Magnetic Fields in Magnetic Resonance Environments Using Pocket Dosimeters

by Giuseppe Acri, Carmelo Anfuso, Giuseppe Vermiglio and Valentina Hartwig

Electronics 2022, 11(17), 2796; https://doi.org/10.3390/electronics11172796 - 05 Sep 2022

Cited by 2 | Viewed by 1429

Abstract

Staff working in Magnetic Resonance environments are mainly exposed to the static and spatially heterogeneous magnetic field. Moreover, workers movements in such environments give slowly time-varying magnetic field that reflects in an induced electric field in conductive bodies, such as human bodies. It [...] Read more.

Staff working in Magnetic Resonance environments are mainly exposed to the static and spatially heterogeneous magnetic field. Moreover, workers movements in such environments give slowly time-varying magnetic field that reflects in an induced electric field in conductive bodies, such as human bodies. It is very important to have a practice method to personal exposure assessment, also to create a list of procedures and job descriptions at highest risk of exposure, to provide complete information for the workers. This is important especially for the “workers at particular risk”, such as pregnant workers or medical devices wearers. The purpose of this work is to measure the exposure of the staff to time-varying magnetic field in Magnetic Resonance clinical environments, using pocket dosimeter. We present here the assessment of exposure in two different working conditions relative to routine procedures for different kinds of workers. The obtained results show compliance with the safety limits imposed by regulation for controlled exposure conditions. However, during the activity of replacement of the oxygen sensor performed by a maintenance technician, some exposure parameters exceeded the limits, suggesting to pay attention with specific conditions to prevent vertigo or other sensory effects. Full article

(This article belongs to the Special Issue Advanced Applications of Magnetic Resonance in Biomedical Imaging)

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18 pages, 6156 KiB

Open AccessArticle

An Improved RandLa-Net Algorithm Incorporated with NDT for Automatic Classification and Extraction of Raw Point Cloud Data

by Zhongli Ma, Jiadi Li, Jiajia Liu, Yuehan Zeng, Yi Wan and Jinyu Zhang

Electronics 2022, 11(17), 2795; https://doi.org/10.3390/electronics11172795 - 05 Sep 2022

Cited by 2 | Viewed by 1767

Abstract

A high-definition map of the autonomous driving system was built with the target points of interest, which were extracted from a large amount of unordered raw point cloud data obtained by Lidar. In order to better obtain the target points of interest, this [...] Read more.

A high-definition map of the autonomous driving system was built with the target points of interest, which were extracted from a large amount of unordered raw point cloud data obtained by Lidar. In order to better obtain the target points of interest, this paper proposes an improved RandLa-Net algorithm incorporated with NDT registration, which can be used to automatically classify and extract large-scale raw point clouds. First, based on the NDT registration algorithm, the frame-by-frame raw point cloud data were converted into a point cloud global map; then, the RandLa-Net network combined random sampling with a local feature sampler is used to classify discrete points in the point cloud map point by point. Finally, the corresponding point cloud data were extracted for the labels of interest through numpy indexing. Experiments on public datasets senmatic3D and senmatickitti show that the method has excellent accuracy and processing speed for the classification and extraction of large-scale point cloud data acquired by Lidar. Full article

(This article belongs to the Special Issue Advances in Image Enhancement)

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16 pages, 5162 KiB

Open AccessArticle

JR-TFViT: A Lightweight Efficient Radar Jamming Recognition Network Based on Global Representation of the Time–Frequency Domain

by Bin Lang and Jian Gong

Electronics 2022, 11(17), 2794; https://doi.org/10.3390/electronics11172794 - 05 Sep 2022

Cited by 5 | Viewed by 1682

Abstract

Efficient jamming recognition capability is a prerequisite for radar anti-jamming and can enhance the survivability of radar in electronic warfare. Traditional recognition methods based on manually designed feature parameters have found it difficult to cope with the increasingly complex electromagnetic environment, and research [...] Read more.

Efficient jamming recognition capability is a prerequisite for radar anti-jamming and can enhance the survivability of radar in electronic warfare. Traditional recognition methods based on manually designed feature parameters have found it difficult to cope with the increasingly complex electromagnetic environment, and research combining deep learning to achieve jamming recognition is gradually increasing. However, existing research on radar jamming recognition based on deep learning can ignore the global representation in the jamming time–frequency domain data, while not paying enough attention to the problem of lightweighting the recognition network itself. Therefore, this paper proposes a lightweight jamming recognition network (JR-TFViT) that can fuse the global representation of jamming time–frequency domain data while combining the advantages of the Vision Transformer and a convolutional neural network (CNN). The global representation and local information in the jamming time–frequency data are fused with the assistance of the multi-head self-attention (MSA) mechanism in the transformer to improve the feature extraction capabilities of the recognition network. The model’s parameters are further decreased by modifying the standard convolutional operation mechanism and substituting the convolutional operation needed by the network with Ghost convolution, which has less parameters. The experimental results show that the JR-TFViT requires fewer model parameters while maintaining higher recognition performance than mainstream convolutional neural networks and lightweight CNNs. For 12 types of radar jamming, the JR-TFViT achieves 99.5% recognition accuracy at JNR = −6 dB with only 3.66 M model parameters. In addition, 98.9% recognition accuracy is maintained when the JR-TFViT parameter number is further compressed to 0.67 M. Full article

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11 pages, 1608 KiB

Open AccessArticle

A Typed Iteration Approach for Spoken Language Understanding

by Yali Pang, Peilin Yu and Zhichang Zhang

Electronics 2022, 11(17), 2793; https://doi.org/10.3390/electronics11172793 - 05 Sep 2022

Cited by 2 | Viewed by 1372

Abstract

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded [...] Read more.

A spoken language understanding (SLU) system usually involves two subtasks: intent detection (ID) and slot filling (SF). Recently, joint modeling of ID and SF has been empirically demonstrated to lead to improved performance. However, the existing joint models cannot explicitly use the encoded information of the two subtasks to realize mutual interaction, nor can they achieve the bidirectional connection between them. In this paper, we propose a typed abstraction mechanism to enhance the performance of intent detection by utilizing the encoded information of SF tasks. In addition, we design a typed iteration approach, which can achieve the bidirectional connection of the encoded information and mitigate the negative effects of error propagation. The experimental results on two public datasets ATIS and SNIPS present the superiority of our proposed approach over other baseline methods, indicating the effectiveness of the typed iteration approach. Full article

(This article belongs to the Special Issue Pattern Recognition and Machine Learning Applications)

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13 pages, 1030 KiB

Open AccessArticle

Prediction of Highway Blocking Loss Based on Ensemble Learning Fusion Model

by Honglie Guo, Jiahong Zhang, Jing Zhang and Yingna Li

Electronics 2022, 11(17), 2792; https://doi.org/10.3390/electronics11172792 - 05 Sep 2022

Cited by 2 | Viewed by 1087

Abstract

Road blocking events refer to road traffic blocking caused by landslides, debris flow, snow disasters, rolling stones and other factors. To predict road blocking events, the limit gradient lifting model (XGBoost), random forest regression model (RF regression) and support-vector regression model (SVR) are [...] Read more.

Road blocking events refer to road traffic blocking caused by landslides, debris flow, snow disasters, rolling stones and other factors. To predict road blocking events, the limit gradient lifting model (XGBoost), random forest regression model (RF regression) and support-vector regression model (SVR) are used as the prediction meta-models, and then the meta-models are fused by a logical regression algorithm to construct a road blocking loss prediction fusion model based on ensemble learning. The actual road blocking event data are used to train the model. Using the same blocking location and similar blocking loss characteristics between adjacent points to fill in the missing value and conducting one-hot encoding for other short character sets with obvious category characteristics such as letters, numbers, and Chinese characters overcomes the problems of inherent data loss, error and time logic disorder in the blocking event data set. The test results show that the R² score based on the stacking fusion model reaches 0.91, which is 18% higher than RF and 11% and 5.8% higher than SVR and XGBoost, respectively, and the RMSE and MAE values are 0.1707 and 0.0341, respectively. Therefore, the proposed road blocking data preprocessing method and road blocking loss prediction fusion model can be used to predict the amount of blocking event loss. Full article

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21 pages, 2275 KiB

Open AccessArticle

Fast Decision Algorithm of CU Size for HEVC Intra-Prediction Based on a Kernel Fuzzy SVM Classifier

by Shuqian He, Zhengjie Deng and Chun Shi

Electronics 2022, 11(17), 2791; https://doi.org/10.3390/electronics11172791 - 05 Sep 2022

Cited by 2 | Viewed by 1184

Abstract

High Efficiency Video Coding (HEVC) achieves a significant improvement in compression efficiency at the cost of extremely high computational complexity. Therefore, large-scale and wide deployment applications, especially mobile real-time video applications under low-latency and power-constrained conditions, are more challenging. In order to solve [...] Read more.

High Efficiency Video Coding (HEVC) achieves a significant improvement in compression efficiency at the cost of extremely high computational complexity. Therefore, large-scale and wide deployment applications, especially mobile real-time video applications under low-latency and power-constrained conditions, are more challenging. In order to solve the above problems, a fast decision method for intra-coding unit size based on a new fuzzy support vector machine classifier is proposed in this paper. The relationship between the depth levels of coding units is accurately expressed by defining the cost evaluation criteria of texture and non-texture rate-distortion cost. The fuzzy support vector machine is improved by using the information entropy measure to solve the negative impact of data noise and the outliers problem. The proposed method includes three stages: the optimal coded depth level “0” early decision, coding unit depth early skip, and optimal coding unit early terminate. In order to further improve the rate-distortion complexity optimization performance, more feature vectors are introduced, including features such as space complexity, the relationship between coding unit depths, and rate-distortion cost. The experimental results showed that, compared with the HEVC reference test model HM16.5, the proposed algorithm can reduce the encoding time of various test video sequences by more than 53.24% on average, while the Bjontegaard Delta Bit Rate (BDBR) only increases by 0.82%. In addition, the proposed algorithm is better than the existing algorithms in terms of comprehensively reducing the computational complexity and maintaining the rate-distortion performance. Full article

(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)

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17 pages, 4727 KiB

Open AccessArticle

RERB: A Dataset for Residential Area Extraction with Regularized Boundary in Remote Sensing Imagery for Mapping Application

by Songlin Liu, Li Zhang, Wei Liu, Jun Hu, Hui Gong, Xin Zhou and Danchao Gong

Electronics 2022, 11(17), 2790; https://doi.org/10.3390/electronics11172790 - 05 Sep 2022

Viewed by 1159

Abstract

Due to the high automaticity and efficiency of image-based residential area extraction, it has become one of the research hotspots in surveying, mapping, and computer vision, etc. For the application of mapping residential area, the extracted contour is required to be regular. However, [...] Read more.

Due to the high automaticity and efficiency of image-based residential area extraction, it has become one of the research hotspots in surveying, mapping, and computer vision, etc. For the application of mapping residential area, the extracted contour is required to be regular. However, the contour results of existing deep-learning-based residential area extraction methods are assigned accurately according to the actual range of residential areas in imagery, which are difficult to directly apply to mapping due to the extractions being messy and irregular. Most of the existing ground object extraction datasets based on optical satellite images mainly promote the research of semantic segmentation, thereby ignoring the requirements of mapping applications. In this paper, we introduce an optical satellite images dataset named RERB (Residential area Extraction with Regularized Boundary) to support and advance end-to-end learning of residential area mapping. The characteristic of RERB is that it embeds the prior knowledge of regularized contour in the dataset. In detail, the RERB dataset contains 13,892 high-quality satellite images with a spatial resolution of 2 m acquired from different cities in China, and the size of each image is approximately 256 × 256 pixels, which covers an area of more than 3640 square kilometers. The novel published RERB dataset encompasses four superiorities: (1) Large-scale and high-resolution; (2) well annotated and regular label contour; (3) rich background; and (4) class imbalance. Therefore, the RERB dataset is suitable for both semantic segmentation and mapping application tasks. Furthermore, to validate the effectiveness of the RERB, a novel end-to-end regularization extraction algorithm of residential areas based on contour cross-entropy constraints is designed and implemented, which can significantly improve the regularization degree of extraction for the mapping of residential areas. The comparative experimental results demonstrate the preponderance and practicability of our public dataset and can further facilitate future research. Full article

(This article belongs to the Special Issue Advanced Research and Applications of Deep Learning and Neural Network in Image Recognition)

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11 pages, 1511 KiB

Open AccessArticle

Multi-Class Positive and Unlabeled Learning for High Dimensional Data Based on Outlier Detection in a Low Dimensional Embedding Space

by Cheong Hee Park

Electronics 2022, 11(17), 2789; https://doi.org/10.3390/electronics11172789 - 05 Sep 2022

Viewed by 1404

Abstract

Positive and unlabeled (PU) learning targets a binary classifier on labeled positive data and unlabeled data containing data samples of positive and unknown negative classes, whereas multi-class positive and unlabeled (MPU) learning aims to learn a multi-class classifier assuming labeled data from multiple [...] Read more.

Positive and unlabeled (PU) learning targets a binary classifier on labeled positive data and unlabeled data containing data samples of positive and unknown negative classes, whereas multi-class positive and unlabeled (MPU) learning aims to learn a multi-class classifier assuming labeled data from multiple positive classes. In this paper, we propose a two-step approach for MPU learning on high dimensional data. In the first step, negative samples are selected from unlabeled data using an ensemble of k-nearest neighbors-based outlier detection models in a low dimensional space which is embedded by a linear discriminant function. We present an approach for binary prediction which determines whether a data sample is a negative data sample. In the second step, the linear discriminant function is optimized on the labeled positive data and negative samples selected in the first step. It alternates between updating the parameters of the linear discriminant function and selecting reliable negative samples by detecting outliers in a low-dimensional space. Experimental results using high dimensional text data demonstrate the high performance of the proposed MPU learning method. Full article

(This article belongs to the Special Issue Feature Papers in Computer Science & Engineering)

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12 pages, 1696 KiB

Open AccessArticle

Research on Adaptive Exponential Droop Control Strategy for VSC-MTDC System

by Jianying Li, Minsheng Yang, Jianqi Li, Yunchang Xiao and Jingying Wan

Electronics 2022, 11(17), 2788; https://doi.org/10.3390/electronics11172788 - 04 Sep 2022

Cited by 3 | Viewed by 1233

Abstract

To solve the problem of large DC voltage deviation caused by the power fluctuations and poor power distribution characteristics of converters in a voltage source converter multi-terminal DC (VSC-MTDC) system based on traditional droop control, this paper proposes an adaptive exponential droop control [...] Read more.

To solve the problem of large DC voltage deviation caused by the power fluctuations and poor power distribution characteristics of converters in a voltage source converter multi-terminal DC (VSC-MTDC) system based on traditional droop control, this paper proposes an adaptive exponential droop control strategy. This strategy introduces the relative power deviation factor of the converter, and replaces the traditional linear droop control curve with a nonlinear exponential curve. Under different working conditions, the converter adaptively adjusts the droop control coefficient of the converter according to the relative power deviation factor to realize stability for the DC voltage and a reasonable power distribution for the MTDC system. A simulation model of a three-terminal VSC-MTDC was established in MATLAB/Simulink, and the feasibility and effectiveness of the proposed strategy were verified. Full article

(This article belongs to the Special Issue Multimodal Data Fusion and Computational Optimization for Intelligent Perception)

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19 pages, 898 KiB

Open AccessArticle

RSU-Aided Optimal Member Replacement Scheme with Improved Mobility Prediction for Vehicular Clouds in VANETs

by Youngju Nam, Hyunseok Choi, Yongje Shin, Dick Mugerwa and Euisin Lee

Electronics 2022, 11(17), 2787; https://doi.org/10.3390/electronics11172787 - 04 Sep 2022

Viewed by 1061

Abstract

The technique of vehicular clouds is considered an attractive approach in VANETs, because it provides a requester vehicle the ability to use resources of neighborhood vehicles (called cloud member vehicles) to construct a vehicular cloud to use next-generation vehicular applications during driving. Generally, [...] Read more.

The technique of vehicular clouds is considered an attractive approach in VANETs, because it provides a requester vehicle the ability to use resources of neighborhood vehicles (called cloud member vehicles) to construct a vehicular cloud to use next-generation vehicular applications during driving. Generally, member vehicles can move along different routes from the route of the requester vehicle in intersections and, as a result, leave the vehicular cloud. Then, the leaving member vehicle should be replaced by new member vehicles at intersections to reconstruct the vehicular cloud. However, identifying optimal replacement vehicles among many vehicles at intersections is a very difficult task involving minimizing the waste of resources of vehicles due to their irregular mobility. Thus, we propose an optimal member replacement scheme that finds optimal replacement vehicles through the improved mobility prediction of vehicles by borrowing the computational ability of RSUs on intersections. The proposed scheme first makes an improved mobility prediction model by combining both the trajectory prediction of vehicles using the Markov model and the location prediction of vehicles using the Gaussian distribution. Through the improved mobility prediction model, the proposed scheme then determines the leaving member vehicles and calculates their own leaving time. Next, the proposed scheme addresses the problem to find optimal replacement vehicles to minimize the waste resource and solves it through an integer linear programming. For the performance evaluation of the proposed scheme, we implement it in an NS-3 simulator, which includes the Manhattan mobility model, to reflect the mobility of vehicles on roads. Simulation results conducted in various environments verify that the proposed scheme achieves better performance than the existing scheme. Full article

(This article belongs to the Special Issue Exploring Emerging Cloud-Based Technologies and Future Directions in VANETs)

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14 pages, 6286 KiB

Open AccessArticle

Hysteresis Modeling of Piezoelectric Actuators Based on a T-S Fuzzy Model

by Liu Yang, Qingtao Wang, Yongqiang Xiao and Zhan Li

Electronics 2022, 11(17), 2786; https://doi.org/10.3390/electronics11172786 - 04 Sep 2022

Cited by 1 | Viewed by 1292

Abstract

Piezoelectric actuators (PEAs) have been widely used in aerospace, electronic communication and other high-accuracy manufacturing fields because of their high precision, low power consumption, fast response, and high resolution. However, piezoelectric actuators have very complicated hysteresis nonlinearity, which greatly affects their positioning and [...] Read more.

Piezoelectric actuators (PEAs) have been widely used in aerospace, electronic communication and other high-accuracy manufacturing fields because of their high precision, low power consumption, fast response, and high resolution. However, piezoelectric actuators have very complicated hysteresis nonlinearity, which greatly affects their positioning and control accuracy. Particularly in the field of active vibration control, the control accuracy of piezoelectric actuators is easily affected by noise points. To address the problem, this paper proposes a hyperplane probability c-regression model (HPCRM) algorithm to establish its T-S fuzzy model of hysteresis nonlinearity. Firstly, an improved fuzzy c regression clustering algorithm is proposed to identify the antecedent parameters of T-S fuzzy model. This algorithm not only divides the fuzzy space better but also effectively avoids the influence of noise points generated by the external environment during data acquisition. Secondly, a new type of hyperplane membership function is introduced to solve the problem that the traditional Gaussian membership function does not match the hyperplane clustering algorithm. Finally, the accuracy of the modeling method is confirmed by several comparative experiments. Experimental results show that the proposed method is more precise than the traditional fuzzy c-regression models (FCRM) and probability c-regression models (PCRM) under the sine signals of 5 Hz–100 Hz. Full article

(This article belongs to the Special Issue High Performance Control and Industrial Applications)

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17 pages, 4715 KiB

Open AccessArticle

Application of Improved Quasi-Affine Transformation Evolutionary Algorithm in Power System Stabilizer Optimization

by Jing Huang, Jiajing Liu, Cheng Zhang, Yu Kuang and Shaowei Weng

Electronics 2022, 11(17), 2785; https://doi.org/10.3390/electronics11172785 - 04 Sep 2022

Cited by 3 | Viewed by 1097

Abstract

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly [...] Read more.

This paper proposes a parameter coordination optimization design of a power system stabilizer (PSS) based on an improved quasi-affine transformation evolutionary (QUATRE) algorithm to suppress low-frequency oscillation and improve the dynamic stability of power systems. To begin, the simulated annealing (SA) algorithm randomly updates the globally optimal solution of each QUATRE iteration and matches the inferior solution with a certain probability to escape the local extreme point. This new algorithm is first applied to the power system. Since the damping ratio is one of the criteria with which to measure the dynamic stability of the power system, this paper sets the objective function according to the principle of maximization of the damping coefficient of the electromechanical mode, and uses SA-QUATRE to search a group of global optimal PSS parameter combinations to improve the safety factor of the system as much as possible. Finally, the method’s rationality and validity were validated by applying it to the simulation examples of the IEEE 4-machine 2-area system with different operation states. The comparison with the traditional optimization algorithm shows that the proposed method has more advantages for multi-machine PSS parameter coordination optimization, can restrain the low-frequency oscillation of the power system more effectively and can enhance the system’s stability. Full article

(This article belongs to the Special Issue Machine Learning in the Industrial Internet of Things)

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18 pages, 3827 KiB

Open AccessArticle

A Self-Tuned Method for Impedance-Matching of Planar-Loop Resonators in Conformable Wearables

by Sen Bing, Khengdauliu Chawang and J.-C. Chiao

Electronics 2022, 11(17), 2784; https://doi.org/10.3390/electronics11172784 - 04 Sep 2022

Cited by 5 | Viewed by 1390

Abstract

Loop structure has been used as a single resonator and in meta-materials. Variations from the loop structures such as split-ring resonators have been utilized as sensing elements in integrated devices for wearable applications or in array configurations for free-space resonance. Previously, impedance formula [...] Read more.

Loop structure has been used as a single resonator and in meta-materials. Variations from the loop structures such as split-ring resonators have been utilized as sensing elements in integrated devices for wearable applications or in array configurations for free-space resonance. Previously, impedance formula and equivalent circuit models have been developed for a single loop made of a conductor wire with a negligible wire diameter in the free space. Despite the features of being planar and small, however, the quality factors of single-loop resonators or antennas have not been sufficiently high to use them efficiently for sensing or power transfer. To investigate the limitation, we first experimentally examined the formula and equivalent circuits for a single loop made of planar metal sheets, along with finite element simulations. The loop performance factor was varied to validate the formula and equivalent circuits. Then a tuning element was utilized in the planar loop to improve resonance by providing distributed impedance-matching to the loop. The proposed tuning method was demonstrated with simulations and measurements. A new equivalent circuit model for the tuned loop resonator was established. Quality factors at resonance show significant improvement and the tuning can be done for a specific resonance order without changing the loop radius. It was also shown that the tuning method provided more robust performance for the resonator. The tuning mechanism is suitable for miniature planar device architectures in sensing applications, particularly for implants and wearables that have constraints in dimensions and form factors. The equivalent circuit model can also be applied for meta-materials in arrayed configurations. Full article

(This article belongs to the Special Issue Design, Development and Testing of Wearable Devices)

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15 pages, 8070 KiB

Open AccessArticle

LNFCOS: Efficient Object Detection through Deep Learning Based on LNblock

by Beomyeon Hwang, Sanghun Lee and Hyunho Han

Electronics 2022, 11(17), 2783; https://doi.org/10.3390/electronics11172783 - 04 Sep 2022

Cited by 5 | Viewed by 1735

Abstract

In recent deep-learning-based real-time object detection methods, the trade-off between accuracy and computational cost is an important consideration. Therefore, based on the fully convolutional one-stage detector (FCOS), which is a one-stage object detection method, we propose a light next FCOS (LNFCOS) that achieves [...] Read more.

In recent deep-learning-based real-time object detection methods, the trade-off between accuracy and computational cost is an important consideration. Therefore, based on the fully convolutional one-stage detector (FCOS), which is a one-stage object detection method, we propose a light next FCOS (LNFCOS) that achieves an optimal trade-off between computational cost and accuracy. In LNFCOS, the loss of low- and high-level information is minimized by combining the features of different scales through the proposed feature fusion module. Moreover, the light next block (LNblock) is proposed for efficient feature extraction. LNblock performs feature extraction with a low computational cost compared with standard convolutions, through sequential operation on a small amount of spatial and channel information. To define the optimal parameters of LNFCOS suggested through experiments and for a fair comparison, experiments and evaluations were conducted on the publicly available benchmark datasets MSCOCO and PASCAL VOC. Additionally, the average precision (AP) was used as an evaluation index for quantitative evaluation. LNFCOS achieved an optimal trade-off between computational cost and accuracy by achieving a detection accuracy of 79.3 AP and 37.2 AP on the MS COCO and PASCAL VOC datasets, respectively, with 36% lower computational cost than the FCOS. Full article

(This article belongs to the Collection Computer Vision and Pattern Recognition Techniques)

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20 pages, 1384 KiB

Open AccessFeature PaperArticle

A Hybrid Method for Keystroke Biometric User Identification

by Md L. Ali, Kutub Thakur and Muath A. Obaidat

Electronics 2022, 11(17), 2782; https://doi.org/10.3390/electronics11172782 - 03 Sep 2022

Cited by 15 | Viewed by 2235

Abstract

The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions [...] Read more.

The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for new data, resulting in faster classification of new data compared to the generative models. In an attempt to build an efficient model for keystroke biometric user identification, this study proposes a hybrid POHMM/SVM method taking advantage of both generative and discriminative models. The partially observable hidden Markov model (POHMM) is an extension of the hidden Markov model (HMM), which has shown promising performance in user verification and handling missing or infrequent data. On the other hand, the support vector machine (SVM) has been a widely used discriminative model in keystroke biometric systems for the last decade and achieved a higher accuracy rate for large data sets. In the proposed model, features are extracted using the POHMM model, and a one-class support vector machine is used as the anomaly detector. For user identification, the study examines POHMM parameters using five different discriminative classifiers: support vector machines, k-nearest neighbor, random forest, multilayer perceptron (MLP) neural network, and logistic regression. The best accuracy of 91.3% (mean 0.868, SD 0.132) is achieved by the proposed hybrid POHMM/SVM approach among all generative and discriminative models. Full article

(This article belongs to the Special Issue Digital Trustworthiness: Cybersecurity, Privacy and Resilience)

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10 pages, 1767 KiB

Open AccessFeature PaperArticle

A Second Order 1.8–1.9 GHz Tunable Active Band-Pass Filter with Improved Noise Performances

by Davide Colaiuda, Alfiero Leoni, Giuseppe Ferri and Vincenzo Stornelli

Electronics 2022, 11(17), 2781; https://doi.org/10.3390/electronics11172781 - 03 Sep 2022

Cited by 2 | Viewed by 1701

Abstract

In this paper, a novel active tunable band-pass filter with improved noise performances is presented. This filter is based on a negative resistance circuit (or active capacitance), where the gain obtained with a transistor is used to compensate for inductor losses. Moreover, the [...] Read more.

In this paper, a novel active tunable band-pass filter with improved noise performances is presented. This filter is based on a negative resistance circuit (or active capacitance), where the gain obtained with a transistor is used to compensate for inductor losses. Moreover, the capacitance of the resonator is obtained through a voltage-controlled reverse-biased varactor, which allows for frequency tuning. Despite the active component, the proposed filter also has good noise performance. Measurements show a tunability range from 1.816 GHz to 1.886 GHz, with a bandwidth of 38 MHz. The insertion loss maximum value is 0.4 dB, while the noise figure value has a minimum value of 2.5 dB at the center frequency within the tunability range. Full article

(This article belongs to the Special Issue Feature Papers in Circuit and Signal Processing)

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9 pages, 4577 KiB

Open AccessEditor’s ChoiceArticle

High-Performance and Robust Binarized Neural Network Accelerator Based on Modified Content-Addressable Memory

by Sureum Choi, Youngjun Jeon and Yeongkyo Seo

Electronics 2022, 11(17), 2780; https://doi.org/10.3390/electronics11172780 - 03 Sep 2022

Viewed by 1448

Abstract

The binarized neural network (BNN) is one of the most promising candidates for low-cost convolutional neural networks (CNNs). This is because of its significant reduction in memory and computational costs, and reasonable classification accuracy. Content-addressable memory (CAM) can perform binarized convolution operations efficiently [...] Read more.

The binarized neural network (BNN) is one of the most promising candidates for low-cost convolutional neural networks (CNNs). This is because of its significant reduction in memory and computational costs, and reasonable classification accuracy. Content-addressable memory (CAM) can perform binarized convolution operations efficiently since the bitwise comparison in CAM matches well with the binarized multiply operation in a BNN. However, a significant design issue in CAM-based BNN accelerators is that the operational reliability is severely degraded by process variations during match-line (ML) sensing operations. In this paper, we proposed a novel ML sensing scheme to reduce the hardware error probability. Most errors occur when the difference between the number of matches in the evaluation ML and the reference ML is small; thus, the proposed hardware identified cases that are vulnerable to process variations using dual references. The proposed dual-reference sensing structure has >49% less ML sensing errors than that of the conventional design, leading to a >1.0% accuracy improvement for Fashion MNIST image classification. In addition, owing to the parallel convolution operation of the CAM-based BNN accelerator, the proposed hardware achieved >34% processing-time improvement compared with that of the digital logic implementation. Full article

(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))

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12 pages, 4219 KiB

Open AccessArticle

Conception and Experimental Validation of a Standalone Photovoltaic System Using the SUPC5 Multilevel Inverter

by Hind El Ouardi, Ayoub El Gadari, Youssef Ounejjar and Kamal Al-Haddad

Electronics 2022, 11(17), 2779; https://doi.org/10.3390/electronics11172779 - 03 Sep 2022

Cited by 3 | Viewed by 1095

Abstract

In this work, an advanced pulse width modulation (PWM) technique was developed to provide the auto-balancing of the capacitors voltages of the five-level split-packed U-Cells (SPUC5) single-phase inverter, and then, the latter was applied to a photovoltaic (PV) system in standalone mode to [...] Read more.

In this work, an advanced pulse width modulation (PWM) technique was developed to provide the auto-balancing of the capacitors voltages of the five-level split-packed U-Cells (SPUC5) single-phase inverter, and then, the latter was applied to a photovoltaic (PV) system in standalone mode to evaluate its performance in this kind of application. The SPUC5 inverter makes use of only five switches (four active bidirectional switches and one four quadrant switch), one DC source and two capacitors to generate five levels of output voltage and a current with a quasi-sinusoidal waveform which reduces the total harmonic distortion (THD) without the need to add filters or sensors, and also reduces its cost compared to the other multilevel inverters. In the proposed system; the incremental conductance (INC) algorithm is combined with a DC/DC boost converter to reach the maximum power (MP) of the PV array by tracking the MP point (MPP). The offered concept has been constructed and then simulated in the MATLAB/Simulink environment to evaluate its efficiency. According to the results, the self-balancing of the capacitors voltages has been achieved. A comparative study was performed with the traditional PWM technique. The proposed PV system has been validated by experimental results. Full article

(This article belongs to the Special Issue Multilevel Power Conversion: Analysis, Control Strategies and Applications)

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11 pages, 360 KiB

Open AccessArticle

Edge-Based Heuristics for Optimizing Shortcut-Augmented Topologies for HPC Interconnects

by Kazi Ahmed Asif Fuad, Kai Zeng and Lizhong Chen

Electronics 2022, 11(17), 2778; https://doi.org/10.3390/electronics11172778 - 03 Sep 2022

Viewed by 949

Abstract

Interconnection network topology is critical for the overall performance of HPC systems. While many regular and irregular topologies have been proposed in the past, recent work has shown the promise of shortcut-augmented topologies that offer multi-fold reduction in network diameter and hop count [...] Read more.

Interconnection network topology is critical for the overall performance of HPC systems. While many regular and irregular topologies have been proposed in the past, recent work has shown the promise of shortcut-augmented topologies that offer multi-fold reduction in network diameter and hop count over conventional topologies. However, the large number of possible shortcuts creates an enormous design space for this new type of topology, and existing approaches are extremely slow and do not find shortcuts that are globally optimal. In this paper, we propose an efficient heuristic approach, called EdgeCut, which generates high-quality shortcut-augmented topologies. EdgeCut can identify more globally useful shortcuts by making its considerations from the perspective of edges instead of vertices. An additional implementation is proposed that approximates the costly all-pair shortest paths calculation, thereby further speeding up the scheme. Quantitative comparisons over prior work show that the proposed approach achieves a

1982 \times

reduction in search time while generating better or equivalent topologies in 94.9% of the evaluated cases. Full article

(This article belongs to the Special Issue New Trends for High-Performance Computing)

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21 pages, 3141 KiB

Open AccessReview

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

by Soomaiya Hamid, Narmeen Zakaria Bawany, Ali Hassan Sodhro, Abdullah Lakhan and Saleem Ahmed

Electronics 2022, 11(17), 2777; https://doi.org/10.3390/electronics11172777 - 03 Sep 2022

Cited by 14 | Viewed by 2820

Abstract

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public [...] Read more.

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19. Full article

(This article belongs to the Special Issue Security Issues for IoT Healthcare Applications: Recent Trends and Practices)

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19 pages, 4324 KiB

Open AccessCommunication

Performance of a Pd-Zn Cathode Electrode in a H₂ Fueled Single PEM Fuel Cell

by Georgios Bampos and Symeon Bebelis

Electronics 2022, 11(17), 2776; https://doi.org/10.3390/electronics11172776 - 03 Sep 2022

Cited by 2 | Viewed by 1357

Abstract

A 21.7 wt.% Pd—7.3 wt.% Zn/C electrocatalyst prepared via the wet impregnation (w.i.) method was deposited onto commercial carbon cloth (E-TEK) and tested towards its electrocatalytic performance as a cathode electrode material for oxygen reduction reaction (ORR) in a H₂ fueled single [...] Read more.

A 21.7 wt.% Pd—7.3 wt.% Zn/C electrocatalyst prepared via the wet impregnation (w.i.) method was deposited onto commercial carbon cloth (E-TEK) and tested towards its electrocatalytic performance as a cathode electrode material for oxygen reduction reaction (ORR) in a H₂ fueled single proton-exchange membrane fuel cell (PEMFC). A commercial PtRu electrode (E-TEK) was used as PEM anode for hydrogen oxidation reaction (HOR). The performance of the aforementioned PEMFC was compared with that of the same PEMFC with two different Pt-based cathodes, which were prepared by deposition onto commercial carbon cloth (E-TEK) of 29 wt.% Pt/C synthesized via w.i. and of commercial 29 wt.% Pt/C (TKK). The metal loading of the tested cathode electrodes was 0.5 mg_met cm⁻². Comparison was based both on polarization curves and on electrochemical impedance spectroscopy (EIS) measurements at varying cell potential. In terms of power density, the lowest and highest performance was exhibited by the PEMFC with the 21.7 wt.% Pd—7.3 wt.% Zn/C cathode and the PEMFC with the commercial 29 wt.% Pt/C (TKK) cathode electrode, respectively. This behavior was in accordance with the results of EIS measurements, which showed that the PEMFC with the 21.7 wt.% Pd—7.3 wt.% Zn/C cathode exhibited the highest polarization resistance. Full article

(This article belongs to the Special Issue Recent Advances in Nanoelectronics for Energy Conversion, Storage, and Saving)

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20 pages, 2639 KiB

Open AccessArticle

IO-Link Wireless Sensitivity Testing Methods in Reverberation Chambers

by Christoph Cammin, Dmytro Krush, Dirk Krueger and Gerd Scholl

Electronics 2022, 11(17), 2775; https://doi.org/10.3390/electronics11172775 - 03 Sep 2022

Viewed by 1382

Abstract

Communication reliability is a challenging requirement, which implies the need for over-the-air (OTA) testing. Reverberation chambers (RCs) are widely used for OTA tests in various fields. Due to their properties, such as inherent radio channel emulation or the arbitrary orientation of the equipment [...] Read more.

Communication reliability is a challenging requirement, which implies the need for over-the-air (OTA) testing. Reverberation chambers (RCs) are widely used for OTA tests in various fields. Due to their properties, such as inherent radio channel emulation or the arbitrary orientation of the equipment under test (EUT) in the test volume, they can be used as advantageous test environments for wireless products in the field of industrial manufacturing automation, such as for the IO-Link Wireless (IOLW) standard. In this paper, the different OTA sensitivity test procedures total isotropic sensitivity (TIS), average fading sensitivity (AFS) and mean channel packet error (MCPE) method, which is based on the fundamental channel model of the wireless standard, are described and evaluated in various variants. A core aspect of the proposal is the impact of the possible use of frequency hopping of the wireless equipment under test. The respective advantages and disadvantages are shown. Overall, TIS proves to be a suitable alternative for IOLW OTA sensitivity testing. Full article

(This article belongs to the Special Issue EMC Analysis in Wireless Communication)

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12 pages, 647 KiB

Open AccessArticle

Design of a Power Regulated Circuit with Multiple LDOs for SoC Applications

by Danial Khan, Muhammad Basim, Qurat ul Ain, Syed Adil Ali Shah, Khuram Shehzad, Deeksha Verma and Kang-Yoon Lee

Electronics 2022, 11(17), 2774; https://doi.org/10.3390/electronics11172774 - 03 Sep 2022

Cited by 2 | Viewed by 1899

Abstract

In this paper, a power regulated circuit (PRC) is proposed for system-on-a-chip (SoC) applications. The proposed PRC is composed of a limiter, a bandgap reference (BGR), three low-dropout regulators (LDOs), and a bias generator. A high output voltage of an active rectifier is [...] Read more.

In this paper, a power regulated circuit (PRC) is proposed for system-on-a-chip (SoC) applications. The proposed PRC is composed of a limiter, a bandgap reference (BGR), three low-dropout regulators (LDOs), and a bias generator. A high output voltage of an active rectifier is given to the limiter, which limits it to a desired supply voltage for circuits in PRC. The curvature-compensated BGR robust to process, voltage and temperature (PVT) variations are designed to provide a stable reference voltage for three LDOs. The three LDOs are implemented to generate regulated output dc voltages. The proposed PRC is designed and fabricated in 130 nm bipolar-CMOS-DMOS (BCD) technology with die area of 1.9 mm × 0.860 mm, including pads. The measurement results show that the limiter limits the input voltage of (6 V to 20 V) to 5.3 V. The BGR produces a stable reference voltage of 1.24 V with a power supply rejection ratio (PSRR) of −58.6 dB and −51.9 dB at 10 Hz and 1 kHz, respectively. The LDO_5V, LDO_3V, and LDO_1.5V generate regulated output dc voltages of 5 V, 3 V, and 1.5 V, respectively, with dc load regulations of 0.43 mV/mA, 0.70 mV/mA, and 0.28 mV/mA while delivering load currents of 300 mA, 100 mA, and 100 mA, respectively. Full article

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20 pages, 2731 KiB

Open AccessArticle

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

by Mohammad Badrul Alam Miah, Suryanti Awang, Md Mustafizur Rahman, A. S. M. Sanwar Hosen and In-Ho Ra

Electronics 2022, 11(17), 2773; https://doi.org/10.3390/electronics11172773 - 02 Sep 2022

Cited by 1 | Viewed by 1228

Abstract

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of [...] Read more.

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11%. Full article

(This article belongs to the Section Computer Science & Engineering)

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17 pages, 622 KiB

Open AccessArticle

A Study on Secret Key Rate in Wideband Rice Channel

by Simone Del Prete, Franco Fuschini and Marina Barbiroli

Electronics 2022, 11(17), 2772; https://doi.org/10.3390/electronics11172772 - 02 Sep 2022

Viewed by 1166

Abstract

Standard cryptography is expected to poorly fit IoT applications and services, as IoT devices can hardly cope with the computational complexity often required to run encryption algorithms. In this framework, physical layer security is often claimed as an effective solution to enforce secrecy [...] Read more.

Standard cryptography is expected to poorly fit IoT applications and services, as IoT devices can hardly cope with the computational complexity often required to run encryption algorithms. In this framework, physical layer security is often claimed as an effective solution to enforce secrecy in IoT systems. It relies on wireless channel characteristics to provide a mechanism for secure communications, with or even without cryptography. Among the different possibilities, an interesting solution aims at exploiting the random-like nature of the wireless channel to let the legitimate users agree on a secret key, simultaneously limiting the eavesdropping threat thanks to the spatial decorrelation properties of the wireless channel. The actual reliability of the channel-based key generation process depends on several parameters, as the actual correlation between the channel samples gathered by the users and the noise always affecting the wireless communications. The sensitivity of the key generation process can be expressed by the secrecy key rate, which represents the maximum number of secret bits that can be achieved from each channel observation. In this work, the secrecy key rate value is computed by means of simulations carried out under different working conditions in order to investigate the impact of major channel parameters on the SKR values. In contrast to previous works, the secrecy key rate is computed under a line-of-sight wireless channel and considering different correlation levels between the legitimate users and the eavesdropper. Full article

(This article belongs to the Special Issue Security and Privacy for Modern Wireless Communication Systems)

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18 pages, 5698 KiB

Open AccessArticle

Features of Electron Runaway in a Gas Diode with a Blade Cathode

by Nikolay M. Zubarev, Olga V. Zubareva and Michael I. Yalandin

Electronics 2022, 11(17), 2771; https://doi.org/10.3390/electronics11172771 - 02 Sep 2022

Cited by 3 | Viewed by 1057

Abstract

Conditions for electron runaway in a gas diode with a blade cathode providing a strongly inhomogeneous distribution of the electric field in the interelectrode gap have been studied theoretically. It has been demonstrated that the character of electron runaway differs qualitatively for cathodes [...] Read more.

Conditions for electron runaway in a gas diode with a blade cathode providing a strongly inhomogeneous distribution of the electric field in the interelectrode gap have been studied theoretically. It has been demonstrated that the character of electron runaway differs qualitatively for cathodes with a different rounding radius of the edges. In the case of a relatively large edge radius (tens of microns or more), the conditions for the transition of electrons to the runaway mode are local in nature: they are determined by the field distribution in the immediate vicinity of the cathode where the electrons originate from. Here, the relative contribution of the braking force acting on electrons in a dense gas reaches a maximum. This behavior is generally similar to the behavior of electrons in a uniform field. For a cathode with a highly sharpened edge, the relative contribution of the braking force is maximum in the near-anode region. As a consequence, the runaway condition acquires a nonlocal character: it is determined by the electron dynamics in the entire interelectrode gap. Full article

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