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Electronics
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 3
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2079-9292
Published by MDPI Homepage  [249 journals]
  • Electronics, Vol. 12, Pages 585: Alleviating Class-Imbalance Data of
           Semiconductor Equipment Anomaly Detection Study

    • Authors: Seol, Choi, Kim, Hong
      First page: 585
      Abstract: Plasma-based semiconductor processing is highly sensitive, thus even minor changes in the procedure can have serious consequences. The monitoring and classification of these equipment anomalies can be performed using fault detection and classification (FDC). However, class imbalance in semiconductor process data poses a significant obstacle to the introduction of FDC into semiconductor equipment. Overfitting can occur in machine learning due to the diversity and imbalance of datasets for normal and abnormal. In this study, we suggest a suitable preprocessing method to address the issue of class imbalance in semiconductor process data. We compare existing oversampling models to reduce class imbalance, and then we suggest an appropriate sampling strategy. In order to improve the FC performance of plasma-based semiconductor process data, it was confirmed that the SMOTE-based model using an undersampling technique such as Tomek link is effective. SMOTE-TOMEK, which removes multiple classes and makes the boundary clear, is suitable for FDC to classify minute changes in plasma-based semiconductor equipment data.
      Citation: Electronics
      PubDate: 2023-01-24
      DOI: 10.3390/electronics12030585
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 586: Improving Performance of Hardware
           Accelerators by Optimizing Data Movement: A Bioinformatics Case Study

    • Authors: Peter Knoben, Nikolaos Alachiotis
      First page: 586
      Abstract: Modern hardware accelerator cards create an accessible platform for developers to reduce execution times for computationally expensive algorithms. The most widely used systems, however, have dedicated memory spaces, resulting in the processor having to transfer data to the accelerator-card memory space before the computation can be executed. Currently, the performance increase from using an accelerator card for data-intensive algorithms is limited by the data movement. To this end, this work aims to reduce the effect of data movement and improve overall performance by systematically caching data on the accelerator card. We designed a software-controlled split cache where data are cached on the accelerator and assessed its efficacy using a data-intensive Bioinformatics application that infers the evolutionary history of a set of organisms by constructing phylogenetic trees. Our results revealed that software-controlled data caching on a datacenter-grade FPGA accelerator card reduced the overhead of data movement by 90%. This resulted in a reduction of the total execution time between 32% and 40% for the entire application when phylogenetic trees of various sizes were constructed.
      Citation: Electronics
      PubDate: 2023-01-24
      DOI: 10.3390/electronics12030586
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 587: Innovation-Superposed Simultaneous
           Localization and Mapping of Mobile Robots Based on Limited Augmentation

    • Authors: Liu Yang, Chunhui Li, Wenlong Song, Zhan Li
      First page: 587
      Abstract: In this paper, Aaiming at the problem of simultaneous localization mapping (SLAM) for mobile robots, a limited-augmentation innovation superposition (LAIS) is proposed to solve the problems occurring in SLAM. By extending the single-time innovation superposition to multi-time innovation, the error accumulation during the movement of mobile robots is reduced and the accuracy of the algorithm is improved. At the same time, when the number of feature points observed by the sensor exceeds the threshold, the sensor range is restricted. Therefore, only the qualified feature points are added to the system state vector, which reduces the calculation amount of the algorithm and improves the running speed. Simulation results show that compared with other algorithms, LAIS has higher accuracy and higher running speed in environmental maps with a different number of landmark points.
      Citation: Electronics
      PubDate: 2023-01-24
      DOI: 10.3390/electronics12030587
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 588: Optimal Thickness of Double-Layer
           Graphene-Polymer Absorber for 5G High-Frequency Bands

    • Authors: Alessandro Giuseppe D’Aloia, Marcello D’Amore, Maria Sabrina Sarto
      First page: 588
      Abstract: A new analytical approach to optimize the thicknesses of a two-layer absorbing structure constituted by a graphene-based composite and a polymer dielectric spacer backed by a metallic layer acting as perfect electric conductor (PEC) is proposed. The lossy sheet is made by an epoxy-based vinyl ester resin filled with graphene nanoplatelets (GNPs) characterized by known frequency spectra of the complex permittivity. The optimal thicknesses are computed at the target frequencies of 26, 28, and 39 GHz in order to obtain a –10 dB bandwidth able to cover the 5G frequency bands between 23.8 and 40 GHz. The resulting absorbing structures, having a total thickness lower than 1 mm, are excited by transverse magnetic (TM) or electric (TE) polarized plane waves and the absorption performances are computed in the 5G high frequency range.
      Citation: Electronics
      PubDate: 2023-01-24
      DOI: 10.3390/electronics12030588
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 589: Software Engineering of Resistive
           Elements Electrophysical Parameters Simulation in the Process of Laser
           Trimming

    • Authors: Vladimir V. Kondrashov, Oleg S. Seredin, Vyacheslav V. Chapkin, Evgeny V. Zemlyakov, Ilya K. Topalov
      First page: 589
      Abstract: This study continues a series of papers covering the R&D of circuit simulators embedded into manufacturing equipment for the laser trimming of film and foil resistors intended to improve the final product’s performance and reduce process costs. Our paper describes the development of the ResModel laser trimming simulation software. Various types of trims and their features are presented for a circuit with a dynamic measurement DC source, and the optimal trim configurations are identified. The software can be used to estimate and display resistive element properties during the trimming such as its electrophysical parameters and their trends.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030589
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 590: Ischemic Stroke Lesion Segmentation Using
           Mutation Model and Generative Adversarial Network

    • Authors: Rawan Ghnemat, Ashwaq Khalil, Qasem Abu Abu Al-Haija
      First page: 590
      Abstract: Ischemic stroke lesion segmentation using different types of images, such as Computed Tomography Perfusion (CTP), is important for medical and Artificial intelligence fields. These images are potential resources to enhance machine learning and deep learning models. However, collecting these types of images is a considerable challenge. Therefore, new augmentation techniques are required to handle the lack of collected images presenting Ischemic strokes. In this paper, the proposed model of mutation model using a distance map is integrated into the generative adversarial network (GAN) to generate a synthetic dataset. The Euclidean distance is used to compute the average distance of each pixel with its neighbor in the right and bottom directions. Then a threshold is used to select the adjacent locations with similar intensities for the mutation process. Furthermore, semi-supervised GAN is enhanced and transformed into supervised GAN, where the segmentation and discriminator are shared the same convolution neural network to reduce the computation process. The mutation and GAN models are trained as an end-to-end model. The results show that the mutation model enhances the dice coefficient of the proposed GAN model by 2.54%. Furthermore, it slightly enhances the recall of the proposed GAN model compared to other GAN models.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030590
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 591: The Method for Identifying the Scope of
           Cyberattack Stages in Relation to Their Impact on Cyber-Sustainability
           Control over a System

    • Authors: Šarūnas Grigaliūnas, Rasa Brūzgienė, Algimantas Venčkauskas
      First page: 591
      Abstract: Industry X.0 is the new age of digitization, when information and communication systems are strongly linked to other systems and processes and are accessed remotely from anywhere at any time. The existing information systems’ security methods are ineffective because they should focus on and assess a broader range of factors in physical and digital spaces, especially because tactics of cybercrimes are always evolving and attackers are getting more inventive in searching for holes that might be exploited. To fight it, it is a need to be one step ahead of the attacker, including understanding the nature, stages and scope of the upcoming cyberattack. The objective of our research is to identify the impact of the scope of a cyberattack’s stages on the cyber resilience of an information and communication system, assessing the level of cybersecurity based on existing technical and operational measures. The research methodology includes a numerical simulation, an analytical comparison and experimental validation. The achieved results allow for the identification of up to 18 attack stages based on the aggregation of technical and organizational security metrics and detection sources. The analytical comparison proved the proposed method to be 13% more effective in identifying the stage of a cyberattack and its scope. Based on this research, the extensive scoping flexibility of the proposed method will enable additional control measures and methods that would reduce the impact of an attack on the robustness while increasing the cyber-sustainability of a system.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030591
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 592: An Artificial Neural Network for Solar
           Energy Prediction and Control Using Jaya-SMC

    • Authors: Mokhtar Jlidi, Faiçal Hamidi, Oscar Barambones, Rabeh Abbassi, Houssem Jerbi, Mohamed Aoun, Ali Karami-Mollaee
      First page: 592
      Abstract: In recent years, researchers have focused on improving the efficiency of photovoltaic systems, as they have an extremely low efficiency compared to fossil fuels. An obvious issue associated with photovoltaic systems (PVS) is the interruption of power generation caused by changes in solar radiation and temperature. As a means of improving the energy efficiency performance of such a system, it is necessary to predict the meteorological conditions that affect PV modules. As part of the proposed research, artificial neural networks (ANNs) will be used for the purpose of predicting the PV system's current and voltage by predicting the PV system's operating temperature and radiation, as well as using JAYA-SMC hybrid control in the search for the MPP and duty cycle single-ended primary-inductor converter (SEPIC) that supplies a DC motor. Data sets of size 60538 were used to predict temperature and solar radiation. The data set had been collected from the Department of Systems Engineering and Automation at the Vitoria School of Engineering of the University of the Basque Country. Analyses and numerical simulations showed that the technique was highly effective. In combination with JAYA-SMC hybrid control, the proposed method enabled an accurate estimation of maximum power and robustness with reasonable generality and accuracy (regression (R) = 0.971, mean squared error (MSE) = 0.003). Consequently, this study provides support for energy monitoring and control.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030592
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 593: Using the Uniform Theory of Diffraction
           to Analyze Radio Wave Propagation along Urban Street Canyons for
           Device-to-Device Communication

    • Authors: Elena Brugarolas-Ortiz, Ignacio Rodríguez-Rodríguez, José-Víctor Rodríguez, Leandro Juan-Llácer, Domingo Pardo-Quiles
      First page: 593
      Abstract: This paper examines the propagation of radio waves in so-called urban street canyons through formulations based on Geometrical Optics (GO) and the Uniform Theory of Diffraction (UTD). As this type of environment comprises a street flanked by tall buildings more or less equally spaced on both sides (creating a canyon-like morphology), estimating the attenuation that radio signals may experience in these scenarios is crucial to the planning of urban device-to-device (D2D) wireless communication. In this sense, the results obtained through the analysis based on GO/UTD (in the horizontal plane containing the transmitter and receiver) are validated by a comparison with experimental measurements, showing good agreement. This work demonstrates how the use of GO/UTD-based formulations can contribute to a simpler and computationally more efficient planning of D2D mobile communication systems in which the considered propagation environment can be modeled as an urban street canyon comprising rectangular and equispaced buildings.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030593
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 594: How to Use Nested Probes Coupling to
           Increase the Local NMR/MRI Resolution and Sensitivity for Specific
           Experiments

    • Authors: Mihaela Lupu, Joel Mispelter
      First page: 594
      Abstract: In this paper, we address resonant systems intended to be used with the commercial main resonator present on all NMR or MRI instruments. The purpose of this approach is to get an improvement regarding the spatial localization and signal to noise ratio provided by an additional smaller coil. Both coils are coupled to the same sample region, and thus, are inductively coupled through their common magnetic flux. The coupling strength is characterized by the so-called mutual inductance M. Two practical devices are presented. Firstly, a geometrical passive decoupled resonant system (M = 0) allows getting a sensitive received signal from the maximized nuclear macroscopic magnetization, excited by the main resonator and detected by the smaller sniffer coil. Secondly, a strongly coupled resonant system allows us to considerably locally improve the magnetic component of the RF near field to provide an efficient nuclear spin magnetization excitation and a high received signal. For both configurations, the behavior of the coils system regarding the amplitude of B1 is addressed. Finally, specific technical hints to achieve optimum energy transfer (impedance matching) are discussed, taking into account the non-ideal RF characteristics of the involved components. Examples of MRI experiments, as well as workbench evaluations and simulations support the principles exposed here.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030594
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 595: Multiple-Network-Based Control System
           Design for Unmanned Surveillance Applications

    • Authors: Taeyoung Uhm, Gideok Bae, Jongchan Kim, Hyojun Lee, Jongdeuk Lee, Joonyoung Jung, Sunghoon Cho, Kanghoon Lee, Youngho Choi
      First page: 595
      Abstract: Networks are essential components in the surveillance applications of control systems. In unmanned surveillance applications, numerous agents are employed to provide unmanned services. These agents secure large areas and communicate with a control system, checks their status and sends/receives data via multiple networks. These networks need to assign roles based on the application characteristics. In this study, we propose the design of a multiple-network-based control system for large surveillance areas. To this end, an interface for transmitting mission commands to agents needs to be developed because it can allow users to monitor and assign tasks to all agents. The proposed system is developed as a test bed connected to fixed/mobile agents using LoRa, Wi-Fi, Bluetooth, and LTE communication methods; moreover, its usability was tested in a real environment.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030595
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 596: Static Gesture Recognition Algorithm
           Based on Improved YOLOv5s

    • Authors: Shengwang Wu, Zhongmin Li, Shiji Li, Qiang Liu, Weiyu Wu
      First page: 596
      Abstract: With the government’s increasing support for the virtual reality (VR)/augmented reality (AR) industry, it has developed rapidly in recent years. Gesture recognition, as an important human-computer interaction method in VR/AR technology, is widely used in the field of virtual reality. The current static gesture recognition technology has several shortcomings, such as low recognition accuracy and low recognition speed. A static gesture recognition algorithm based on improved YOLOv5s is proposed to address these issues. The content-aware re-assembly of features (CARAFE) is used to replace the nearest neighbor up-sampling method in YOLOv5s to make full use of the semantic information in the feature map and improve the recognition accuracy of the model for gesture regions. The adaptive spatial feature fusion (ASFF) method is introduced to filter out useless information and retain useful information for efficient feature fusion. The bottleneck transformer method is initially introduced into the gesture recognition task, reducing the number of model parameters and increasing the accuracy while accelerating the inference speed. The improved algorithm achieved an mAP(mean average precision) of 96.8%, a 3.1% improvement in average accuracy compared with the original YOLOv5s algorithm; the confidence level of the actual detection results was higher than the original algorithm.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030596
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 597: Efficient Neural Network DPD Architecture
           for Hybrid Beamforming mMIMO

    • Authors: Tamara Muškatirović-Zekić, Nataša Nešković, Djuradj Budimir
      First page: 597
      Abstract: This paper presents several different Neural Network based DPD architectures for hybrid beamforming (HBF) mMIMO applications. They are formulated, tested and compared based on their ability to compensate nonlinear distortion of power amplifiers in a single user (SU) and multiuser (MU) Fully-Connected (FC) HBF mMIMO transmitters. The proof-of-concept is provided with a 64 × 64 FC HBF mMIMO system, with 2 RF chains. The complexity of DPD solution is reduced by using a single Real-Valued Time-Delay Neural Network with two hidden layers (RVTDNN2L) instead of using as many different DPD blocks as there are RF chains in the HBF mMIMO transmitter and it is shown that the proposed architecture better compensates nonlinear distortion compared to the traditional memory polynomial DPD. Two RVTDNN2L DPD architectures are developed and tested for linearization of MU FC HBF mMIMO systems, and it is also shown that the proposed RVTDNN2L DPD architecture efficiently linearizes MU FC HBF mMIMO transmitters in terms of Normalized Mean-Squared Error (NMSE) and Error Vector Magnitude (EVM).
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030597
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 598: Charge Transport Mechanism in the
           Forming-Free Memristor Based on PECVD Silicon Oxynitride

    • Authors: Andrei A. Gismatulin, Gennadiy N. Kamaev, Vladimir A. Volodin, Vladimir A. Gritsenko
      First page: 598
      Abstract: A memristor is a new generation memory that merges dynamic random access memory and flash properties. In addition, it can be used in neuromorphic electronics. The advantage of silicon oxynitride, as an active memristor layer, over other dielectrics it is compatibility with silicon technology. It is expected that SiNxOy-based memristors will combine the advantages of memristors based on nonstoichiometric silicon oxides and silicon nitrides. In the present work, the plasma-enhanced chemical vapor deposition (PECVD) method was used to fabricate a silicon oxynitride-based memristor. The memristor leakage currents determine its power consumption. To minimize the power consumption, it is required to study the charge transport mechanism in the memristor in the high-resistance state and low-resistance state. The charge transport mechanism in the PECVD silicon oxynitride-based memristor in high and low resistance states cannot be described by the Schottky effect, thermally assisted tunneling model, Frenkel effect model of Coulomb isolated trap ionization, Hill–Adachi model of overlapping Coulomb potentials, Makram–Ebeid and Lannoo model of multiphonon isolated trap ionization, Nasyrov–Gritsenko model of phonon-assisted tunneling between traps, or the Shklovskii–Efros percolation model. The charge transport in the forming-free PECVD SiO0.9N0.6-based memristor in high and low resistance states is described by the space charge limited current model. The trap parameters responsible for the charge transport in various memristor states are determined. For the high-resistance state, the trap ionization energy W is 0.35 eV, and the trap concentration Nd is 1.7 × 1019 cm−3; for the low-resistance state, the trap ionization energy W is 0.01 eV, and the trap concentration Nt is 4.6 × 1017 cm−3.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030598
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 599: Research on Dynamic Response of Cold
           Rolling Mill with Dynamic Stiffness Compensation

    • Authors: Xingdou Jia, Shen Wang, Xiaoqiang Yan, Lidong Wang, Haipeng Wang
      First page: 599
      Abstract: The vibration of the vertical system of a cold rolling mill is a major concern for factory production. In factory production, it is found that the thickness control system with dynamic stiffness compensation of the rolling mill has a significant impact on the rolling mill vibration. In this work, a mechanical hydraulic coupling vibration model of a cold rolling mill with a thickness control system with dynamic stiffness compensation was established. MATLAB/Simulink was used to simulate and analyze the vibration response characteristics of the mechanical structure of the rolling mill and mechanical hydraulic coupling system considering the dynamic stiffness compensation control mode and position control mode of the upper cylinder. We found that the control system changed the vibration response of the mechanical structure, and different control modes have different effects on the vibration response curve. When the rolling mill works in the vibration area, the thickness difference at the strip steel outlet would be larger. Considering the rolling mill vibration and strip surface quality, the excitation of the strip inlet thickness can be achieved by adjusting the dynamic stiffness compensation system and changing the rolling speed reasonably to achieve good vibration suppression and control effects.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030599
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 600: Enhancing Software-Defined Networks with
           Intelligent Controllers to Improve First Packet Processing Period

    • Authors: Ramesh Chand Meena, Surbhi Bhatia, Rutvij H. Jhaveri, Piyush Kumar Shukla, Ankit Kumar, Neeraj Varshney, Areej A. Malibari
      First page: 600
      Abstract: Software-Defined Networking (SDN) has a detailed central model that separates the data plane from the control plane. The SDN controller is in charge of monitoring network security and controlling data flow. OpenFlow-enabled routers and switches work as packet-forwarding devices in the network system. At first, OpenFlow forwarding devices like routers and switches do not know how to handle the data packets transmitted by the host. This is because they do not have any security controls, policies, or information. These packets are sent to their destination. In this situation, the OpenFlow forwarding device sends the first data packet of a host to the SDN controller, which checks the control packets for the data packet and creates flow entries in the switch flow table to act on the following categories of data packets coming from the host. These activities at the SDN controller and switch levels are time-intensive, and the first data packet from the host always takes a longer time to reach its destination. In this article, we suggest an SDN controller with instant flow entries (SDN-CIFE) to reduce the amount of time it takes for the host to transmit its first data packet. Before traffic comes from the host, our method adds the necessary flow entries to the flow table of the OpenFlow switch. The technique was made in Python and tested on a Mininet network emulator using the RYU controller. The results of the experiment show that the time it takes to process the first data packet is reduced by more than 83%.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030600
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 601: Remote Real-Time Optical Layers
           Performance Monitoring Using a Modern FPMT Technique Integrated with an
           EDFA Optical Amplifier

    • Authors: Ahmed Atef Ibrahim, Mohammed Mohammed Fouad, Azhar Ahmed Hamdi
      First page: 601
      Abstract: Fiber performance monitoring using modern online technologies in the next generation of intelligent optical networks allows for identifying the source of the degeneration and putting in protective steps to increase remote optical network stability & reliability. In this paper, the performance of the fiber performance monitoring tool (FPMT) technique was improved by integrating it with optical amplifier boards. In this regard, the improved technique detects optical layer events and all fiber soft and hard failures at the online remote rather than disrupting the data flow with a measurement accuracy for defect location of up to ~99.9%, small tolerance of up to ~1m, the longest distance to detecting optical line defects of up to ~300km, and enhanced power budget for the system with optimum insertion-loss of up to ~0.0 dB. The proposed integration method provides better results with an excellent and efficient solution at fault location measurement & detection in real-time with good financial implications of the technique. The competitiveness of the improved technique over the actual optical networks has been successfully confirmed through application to Huawei labs infrastructure nodes and displayed experimental simulation results.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030601
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 602: Aperture-Level Simultaneous Transmit and
           Receive Simplified Structure Based on Hybrid Beamforming of Switching
           Network

    • Authors: Yi, Wei, Lin, Tang, Xie, Hu
      First page: 602
      Abstract: With the increasing competition for spectrum resources, the technology of simultaneous transmit and receive (STAR) is attracting more and more attention. However, full digital aperture-level simultaneous transmit and receive (FD-ALSTAR) is difficult to implement in a large-scale array with high frequency and bandwidth due to its high hardware cost and high power consumption. Therefore, this paper combines FD-ALSTAR with hybrid beamforming technology and proposes two categories and four types of aperture-level simultaneous transmit and receive simplified structures based on hybrid beamforming to reduce the number of RF links (NRF), hardware cost, and operation power consumption. In view of the complexity of the hardware of the fully connected hybrid beamforming structure and the low amplitude and phase control accuracy of the partially connected hybrid beamforming structure, an aperture-level simultaneous transmit and receive simplified structure based on hybrid beamforming of switching network (HBF-SN-ALSTAR) is proposed, and the mathematical model is established. The simulation results show that the simplified structure proposed in this paper can effectively reduce the NRF and power consumption, increase system redundancy, and improve system reliability. In a 144 × 144 antenna array, under the condition that NRF = 16 of HBF-SN-ALSTAR, that is, 1/9 of the number of FD-ALSTAR RF links, the effective isotropic isolation (EII) of the system is only 17 dB less than that of the FD-ALSTAR. The experimental results fully prove the effectiveness of the simplified structure.
      Citation: Electronics
      PubDate: 2023-01-25
      DOI: 10.3390/electronics12030602
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 603: A Review of Distribution System State
           Estimation Methods and Their Applications in Power Systems

    • Authors: Vijaychandra, Prasad, Darapureddi, Rao, Knypiński
      First page: 603
      Abstract: This paper summarizes a review of the distribution system state estimation (DSSE) methods, techniques, and their applications in power systems. In recent years, the implementation of a distributed generation has affected the behavior of the distribution networks. In order to improve the performance of the distribution networks, it is necessary to implement state estimation methods. As transmission networks and distribution networks are not similar due to variations in line parameters, buses, and measuring instruments, transmission state estimation cannot be implemented in distribution state estimation. So, some aspects, such as accuracy, computational time, and efficiency, should be taken into account when designing distribution state estimation methods. In this paper, the traditional methods are reviewed and analyzed with data-driven techniques in order to present the advantages and disadvantages of the various methods.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030603
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 604: Functional Mapping of the Brain for
           Brain–Computer Interfacing: A Review

    • Authors: Satya P. Singh, Sachin Mishra, Sukrit Gupta, Parasuraman Padmanabhan, Lu Jia, Teo Kok Ann Colin, Yeo Tseng Tsai, Teo Kejia, Pramod Sankarapillai, Anand Mohan, Balázs Gulyás
      First page: 604
      Abstract: Brain–computer interfacing has been applied in a range of domains including rehabilitation, neuro-prosthetics, and neurofeedback. Neuroimaging techniques provide insight into the structural and functional aspects of the brain. There is a need to identify, map and understand the various structural areas of the brain together with their functionally active roles for the accurate and efficient design of a brain–computer interface. In this review, the functionally active areas of the brain are reviewed by analyzing the research available in the literature on brain–computer interfacing in conjunction with neuroimaging experiments. This review first provides an overview of various approaches of brain–computer interfacing and basic components in the BCI system and then discuss active functional areas of the brain being utilized in non-invasive brain–computer interfacing performed with hemodynamic signals and electrophysiological recording-based signals. This paper also discusses various challenges and limitations in BCI becoming accessible to a novice user, including security issues in the BCI system, effective ways to overcome those issues, and design implementations.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030604
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 605: Fast FPGA-Based Multipliers by Constant
           for Digital Signal Processing Systems

    • Authors: Olga Bureneva, Sergey Mironov
      First page: 605
      Abstract: Traditionally, the usual multipliers are used to multiply signals by a constant, but multiplication by a constant can be considered as a special operation requiring the development of specialized multipliers. Different methods are being developed to accelerate multiplications. A large list of methods implement multiplication on a group of bits. The most known one is Booth’s algorithm, which implements two-digit multiplication. We propose a modification of the algorithm for the multiplication by three digits at the same time. This solution reduces the number of partial products and accelerates the operation of the multiplier. The paper presents the results of a comparative analysis of the characteristics of Booth’s algorithm and the proposed algorithm. Additionally, a comparison with built-in FPGA multipliers is illustrated.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030605
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 606: A Control of a z-Axis Rotation-Tolerant
           Wireless Power Transfer System Using a Double DD Coil

    • Authors: Jure Domajnko, Nataša Prosen
      First page: 606
      Abstract: This paper describes the control of a wireless power transfer system using a double DD coil structure, when the transmitter and the receiver coil are rotated to one another. WPT systems using single DD coils are rotationally dependent, due to the directional magnetic field generated by the DD transmitter coil. This rotational dependance can be mitigated by using a different transmitter structure. One such possibility is the usage of the double DD coil on the transmitter and the receiver sides. A double DD coil includes two directional DD coils, which can be excited separately. The coils inside the coil structure are perpendicular to one another, which allows higher power density and additional rotation tolerance. The proposed system was tested on a low power laboratory experimental setup.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030606
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 607: An Intelligent Cluster-Based
           Communication System for Multi-Unmanned Aerial Vehicles for Searching and
           Rescuing

    • Authors: Amjad Mehmood, Zeeshan Iqbal, Arqam Ali Shah, Carsten Maple, Jaime Lloret
      First page: 607
      Abstract: It has been observed that the use of UAVs in search and rescue (SAR) operations is very advantageous. When, all of a sudden, a crisis strikes, UAV technology is incredibly helpful and works more effectively to identify the entire region of a disaster and identify victims trapped in the region. The deployment of a UAV network with a high battery lifespan and complete coverage of the disaster region is the primary emphasis of this article. For the efficient communication of UAVS, we suggested the intelligent cluster-based multi-unmanned aerial vehicle (ICBM-UAV) protocol. In order to discover victims swiftly and rescue those who are trapped in the afflicted region as soon as possible, ICBM-UAV uses the clustering technique smartly, which helps conserve drone batteries and performs some of the useful computations within the CH and hence helps to lessen workload on network congestion. Dividing the CMBM-UAV into two parts, the information gathering and the user equipment location identification, improves network life and makes the search and rescue operation more efficient and successful. After going to through vigorous result calculation, it is deduced that the proposed scheme has outperformed the existing state-of-the-art protocols such as AODV, OSLR and flocking mechanisms in terms of throughput, PDR, and coverage area probability by considering each scenario with and without the presence of obstacles. Hence, by delivering an exploitable estimate before reaching the victim, the proposed approach could drastically minimize the search and rescue time to save valuable lives.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030607
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 608: Development and Assessment of an Indoor
           Air Quality Control IoT-Based System

    • Authors: Gleiston Guerrero-Ulloa, Alex Andrango-Catota, Martín Abad-Alay, Miguel J. Hornos, Carlos Rodríguez-Domínguez
      First page: 608
      Abstract: Good health and well-being are primary goals within the list of Sustainable Development Goals (SDGs) proposed by the United Nations (UN) in 2015. New technologies, such as Internet of Things (IoT) and Cloud Computing, can aid to achieve that goal by enabling people to improve their lifestyles and have a more healthy and comfortable life. Pollution monitoring is especially important in order to avoid exposure to fine particles and to control the impact of human activity on the natural environment. Some of the sources of hazardous gas emissions can be found indoors. For instance, carbon monoxide (CO), which is considered a silent killer because it can cause death, is emitted by water heaters and heaters that rely on fossil fuels. Existing solutions for indoor pollution monitoring suffer from some drawbacks that make their implementation impossible for households with limited financial resources. This paper presents the development of IdeAir, a low-cost IoT-based air quality monitoring system that aims to reduce the disadvantages of existing systems. IdeAir was designed as a proof of concept to capture and determine the concentrations of harmful gases in indoor environments and, depending on their concentration levels, issue alarms and notifications, turn on the fan, and/or open the door. It has been developed following the Test-Driven Development Methodology for IoT-based Systems (TDDM4IoTS), which, together with the tool (based on this methodology) used for the automation of the development of IoT-based systems, has facilitated the work of the developers. Preliminary results on the functioning of IdeAir show a high level of acceptance by potential users.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030608
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 609: Long-Wavelength Luminescence of InSb
           Quantum Dots in Type II Broken-Gap Heterostructure

    • Authors: Konstantin Moiseev, Eduard Ivanov, Yana Parkhomenko
      First page: 609
      Abstract: The features of the electroluminescence spectra of narrow-gap type II InAs/InSb/InAs heterostructures containing a single layer of InSb quantum dots placed into the p-n-InAs junction were studied. The luminescent properties of the heterostructures under a forward and reverse bias in the temperature range of 77–300 K were investigated as a function of the surface density of nano-objects buried in the narrow-gap matrix. When applying the reverse bias to the heterostructures under study, the suppression of negative interband luminescence and the dominance of interface recombination transitions at the InSb/InAs type II heterojunction were observed at room temperature. The radiation, which corresponded to recombination transitions involving localized electron-hole states of the InSb quantum dots, was revealed determined and recorded at low temperatures.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030609
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 610: Design of a Low-Cost Measurement Module
           for the Acquisition of Analogue Voltage Signals

    • Authors: Sebastian Glowinski, Sebastian Pecolt, Andrzej Błażejewski, Michał Sobieraj
      First page: 610
      Abstract: The aim of this work was to design and program a low-cost universal multichannel measurement card from scratch. The constructed device has analog inputs with the possibility of using them as differential inputs. This makes it possible to measure the analog signals for most of the available sensors. Thus, universality of the device is achieved. Simultaneously, the main assumption of the project and its novelty was to develop a measurement module. It is characterized by high measurement parameters, comparable to commercial products available on the market, with a very low production cost. The usability and assumed features of the measurement module were verified and tested using a functional generator and constructed test stand. During the tests, a sampling rate of at least 250 kS/s and a resolution of at least 14 bit were used. The module enables the acquisition of analog signals with voltages in the range of ±10 V and digital signals in the transistor–transistor logic (TTL) 5 V standard with a frequency of at least 250 kS/s. In addition, our device can be controlled via a computer, and data can be downloaded via the USB interface. It has 16 input channels with the possibility of differential measurements. The proposed solution is several times cheaper than commercial solutions while maintaining comparable parameters, as shown in the conclusion of the work.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030610
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 611: D-STGCN: Dynamic Pedestrian Trajectory
           Prediction Using Spatio-Temporal Graph Convolutional Networks

    • Authors: Bogdan Ilie Sighencea, Ion Rareș Stanciu, Cătălin Daniel Căleanu
      First page: 611
      Abstract: Predicting pedestrian trajectories in urban scenarios is a challenging task that has a wide range of applications, from video surveillance to autonomous driving. The task is difficult since pedestrian behavior is affected by both their individual path’s history, their interactions with others, and with the environment. For predicting pedestrian trajectories, an attention-based interaction-aware spatio-temporal graph neural network is introduced. This paper introduces an approach based on two components: a spatial graph neural network (SGNN) for interaction-modeling and a temporal graph neural network (TGNN) for motion feature extraction. The SGNN uses an attention method to periodically collect spatial interactions between all pedestrians. The TGNN employs an attention method as well, this time to collect each pedestrian’s temporal motion pattern. Finally, in the graph’s temporal dimension characteristics, a time-extrapolator convolutional neural network (CNN) is employed to predict the trajectories. Using a lower variable size (data and model) and a better accuracy, the proposed method is compact, efficient, and better than the one represented by the social-STGCNN. Moreover, using three video surveillance datasets (ETH, UCY, and SDD), D-STGCN achieves better experimental results considering the average displacement error (ADE) and final displacement error (FDE) metrics, in addition to predicting more social trajectories.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030611
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 612: Diagnosis of Autism Spectrum Disorder
           Using Convolutional Neural Networks

    • Authors: Amna Hendr, Umar Ozgunalp, Meryem Erbilek Erbilek Kaya
      First page: 612
      Abstract: Autism spectrum disorder as a condition has posed significant early diagnosis challenges to the medical and health community for a long time. The early diagnosis of ASD is crucial for early intervention and adequate management of the condition. Several kinds of literature have shown that children with ASD have varying degrees of challenges in handwriting tasks; hence, this research has proposed the creation of a handwritten dataset of both ASD and non-ASD subjects for deep learning classification. The created dataset is based on a series of handwritten tasks given to subjects such as drawing and writing. The dataset was used to propose a deep learning automated ASD diagnosis method. Using the GoogleNet transfer learning algorithm, each handwritten task in the dataset is trained and classified for each subject. This is done because in real-life scenarios an ASD subject may not comply to performing and finishing all handwritten tasks. Using a training and testing ratio of 80:20, a total of 104 subjects’ handwritten tasks were used as input for training and classification, and it is shown that the proposed approach can correctly classify ASD with an accuracy of 90.48%, where sensitivity, specificity, and F1 score are calculated as 80%, 100%, and 100%, respectively. The results of our proposed method exhibit an impressive performance and indicate that the use of handwritten tasks has a significant potential for the early diagnosis of ASD.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030612
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 613: Data Glove with Bending Sensor and
           Inertial Sensor Based on Weighted DTW Fusion for Sign Language Recognition
           

    • Authors: Chenghong Lu, Shingo Amino, Lei Jing
      First page: 613
      Abstract: There are numerous communication barriers between people with and without hearing impairments. Writing and sign language are the most common modes of communication. However, written communication takes a long time. Furthermore, because sign language is difficult to learn, few people understand it. It is difficult to communicate between hearing-impaired people and hearing people because of these issues. In this research, we built the Sign-Glove system to recognize sign language, a device that combines a bend sensor and WonderSense (an inertial sensor node). The bending sensor was used to recognize the hand shape, and WonderSense was used to recognize the hand motion. The system collects a more comprehensive sign language feature. Following that, we built a weighted DTW fusion multi-sensor. This algorithm helps us to combine the shape and movement of the hand to recognize sign language. The weight assignment takes into account the feature contributions of the sensors to further improve the recognition rate. In addition, a set of interfaces was created to display the meaning of sign language words. The experiment chose twenty sign language words that are essential for hearing-impaired people in critical situations. The accuracy and recognition rate of the system were also assessed.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030613
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 614: Compact Microstrip Line to Rectangular
           Waveguide Transition Using Corrugated Substrate Integrated Waveguide

    • Authors: Zihao Liu, Yuan Yao, Xiaohe Cheng, Qi Li
      First page: 614
      Abstract: To meet the packaging requirements of terahertz (THz) communication systems, a microstrip line (MSL) to rectangular waveguide (RWG) transition is proposed in this paper. In the transition, the MSL is connected to the corrugated substrate integrated waveguide (CSIW) by a tapered MSL for quasi-TEM to TE10-like mode conversion on substrate, which requires no via holes or shaped dielectric, and is easy to process in THz bands. Then, the CSIW is straightly connected to the RWG transformer and converted to standard RWG, resulting in a compact structure. The working principle of the proposed transition is analyzed, and the influence of several important parameters on the S-parameters of the transition is discussed. A single transition is designed for the 325–500 GHz operation, the S11 better than −14.5 dB and S21 better than −1.03 dB have been achieved in the entire frequency band.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030614
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 615: Subthreshold Delay Variation Model
           Considering Transitional Region for Input Slew

    • Authors: Peng Cao, Weixing Xu, Yuanjie Wu, Wanyu Liu, Yu Wang
      First page: 615
      Abstract: Subthreshold design provides the promising advantage of low power consumption at the cost of performance variation and even circuit failure. An accurate and efficient statistical timing model is crucial for timing analysis and performance optimization guidance. Prior works lack the consideration of the impact of slew time or the transitional region for input slew due to process variation and efficient approaches considering the impact of load capacitance and multiple process variations in complex gates, resulting in accuracy loss. In this work, an accurate end efficient gate delay variation model is analytically derived for various input slews and load capacitances. The transitional region between fast and slow input slew is efficiently partitioned with an adaptive error tolerance method so as to characterize timing variation by linear interpolation based on that for fast and slow input slew. In order to consider the impact of load capacitance, the relation between the sensitivity of step delay and the dominant threshold voltage variation is analytically derived. For complex gates, the multiple process variations for both parallel and stacking structures are equivalently expressed by threshold voltage variation from each transistor. The proposed model has been validated under advanced TSMC (Taiwan Semiconductor Manufacturing Company) 12 nm technology at subthreshold region and achieves excellent agreement with Monte Carlo SPICE (Simulation Program with Integrated Circuit Emphasis) simulation results with the max error less than 6.49% for standard deviation of gate delay and 4.63%/6.40% for max/min delay, demonstrating over 4 times precision improvement compared with competitive analytical models.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030615
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 616: Research on Extended Target-Tracking
           Algorithms of Sea Surface Navigation Radar

    • Authors: Tian, Zhang, Fu
      First page: 616
      Abstract: To solve the problem of false tracks generated by breakdowns and clutter in point-target tracking in polar coordinates, a fusion tracking algorithm based on a converted measurement Kalman filter and random matrix expansion is proposed. The converted measurement Kalman filter (CMKF) transforms the polar coordinate data of the target at the current time into Cartesian coordinates without bias. Based on linear measurements and states, the position of the extended target and the group target was predicted and updated by using a random matrix, and its track was drawn by combining the nearest neighbors to realize the tracking of the size, shape and azimuth of the extended target. Compared with point-target tracking, the accuracy of extended multi-target tracking was increased by 45.8% based on data measured using NAVICO navigation radar aboard ships at sea. The experimental results showed that the improved method in this paper could effectively reduce the interference of clutter on target tracking and provide more information about the target motion features.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030616
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 617: Test Platform for Developing Processes of
           Autonomous Identification in RFID Systems with Proximity-Range Read/Write
           Devices

    • Authors: Bartłomiej Wilczkiewicz, Piotr Jankowski-Mihułowicz, Mariusz Węglarski
      First page: 617
      Abstract: The subject of a distributed RFID system with proximity-range read/write devices (RWD) is considered in this paper. Possible work scenarios were presented in the scope of industrial implementations and were then tested in a dedicated laboratory set. The development system is based on a high-frequency RWD integrated with a Wi-Fi microcontroller unit to create an Internet of things connected with a server (for data exchanging, user interface, etc.) via a wireless local area network. In practical applications, in order to increase the interrogation zone (IZ), there is a tendency to use one RWD with significant output power equipped with a multiplexer for managing several antennas located in the operational space. Such a solution is often economically unprofitable and even impossible to implement, especially in the case of the need to create the large IZ. Responding to market demand, the authors propose a distributed system developed on the basis of several cheap RFID reader modules and a few freely available hardware/software tools. They created the fully functional RFID platform and confirmed its usefulness in static and dynamic systems of object identification.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030617
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 618: Analysis of Individual User Data Rate in
           a TDMA-RIS-NOMA Downlink System: Beyond the Limitation of Conventional
           NOMA

    • Authors: Sourabh Tiwari, Joydeep Sengupta, Neeraj Dhanraj Bokde
      First page: 618
      Abstract: Non-orthogonal multiple access (NOMA) is playing a pivotal role in 5G technology and has the potential to be useful in future developments beyond 5G. Although the effectiveness of NOMA has largely been explored in the sum throughput maximization, the identification of individual user data rate (IDR) still remained an unexplored area. Previously, it has been shown that reconfigurable intelligent surfaces (RIS) can lead to an overall improvement in the data rate by enhancing the effective channel gain of the downlink NOMA system. When time division multiple access (TDMA) is clubbed with multiple RISs in a distributed RIS-assisted NOMA (TDMA-RIS-NOMA) downlink system, a point-to-point communication model is created between access point-to-RIS-to-user device. Due to this point-to-point communication model, optimization of the phase shifts provided by meta-atoms of each RIS is facilitated. The optimized phase shifts of meta-atoms maximize the equivalent channel gain between the access point to the user. In this scenario, the channel becomes saturated and signal-to-interference plus noise ratio (SINR) becomes a function of power coefficients only. In this study, the power coefficients are calculated to maximize the SINR of each user belonging to a NOMA cluster using a geometric progression-based power allocation method such that IDR reaches its upper bound. These observations are also verified using the recently published magic matrix-based power allocation method. There are two observations from this study: (i) the IDR is better in the case of the TDMA-RIS-NOMA downlink system than using downlink NOMA alone and (ii) irrespective of the number of meta-atoms and total cluster power, the upper bound of IDR cannot be increased beyond a certain limit for all users except the highest channel gain user. Because of the restricted upper bound for IDR, we suggest that the RIS-assisted downlink TDMA-NOMA system is more suitable for IoT applications, where minimum IDR can also suffice.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030618
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 619: A Novel Dynamic Transmission Power of
           Cluster Heads Based Clustering Scheme

    • Authors: Mengchu Nie, Pingmu Huang, Jie Zeng, Yueming Lu, Tao Zhang, Tiejun Lv
      First page: 619
      Abstract: Clustering methods are promising tools for ensuring the network scalability and maintainability of large-scale flying ad hoc networks (FANETs). However, due to the high mobility and limited energy resources of unmanned aerial vehicles (UAVs), it is difficult to maintain the network reliability and extend the network life of FANETs. In this paper, a new K-means algorithm is developed, and a dynamic transmission power of the cluster heads based clustering (DTPCH-C) scheme is proposed. The goal of this scheme is presented for FANETs to improve the reliability and lifetime of FANETs. Firstly, the optimal number of clusters is calculated and the initial UAV clusters are set up by a K-means algorithm. Then, using a weighted clustering algorithm, the adaptive node degree, the node energy and the distance from the cluster head are weighted and summed for the cluster head election. In the process of inter-cluster communication, the cluster head adjusts its transmit power in real-time through meshing and mobile prediction, thus saving the energy consumption and improving the network lifetime. The proposed DTPCH-C simultaneously optimizes the cluster number, the cluster head energy consumption, the selected cluster head, and the cluster maintenance process. The simulation results show that compared with traditional clustering methods, the proposed DTPCH-C has obvious advantages in terms of the network reliability, network life, and energy consumption.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030619
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 620: Siamese Neural Pointnet: 3D Face
           Verification under Pose Interference and Partial Occlusion

    • Authors: Qi Wang, Wei-Zhong Qian, Hang Lei, Lu Chen
      First page: 620
      Abstract: Face verification based on ordinary 2D RGB images has been widely used in daily life. However, the quality of ordinary 2D RGB images is limited by illumination, and they lack stereoscopic features, which makes it difficult to apply them in poor lighting conditions and means they are susceptible to interference from head pose and partial occlusions. Considering point clouds are not affected by illumination and can easily represent geometric information, this paper constructs a novel Siamese network for 3D face verification based on Pointnet. In order to reduce the influence of the self-generated point clouds, the chamfer distance is adopted to constrain the original point clouds and explore a new energy function to distinguish features. The experimental results with the Pandora and Curtin Faces datasets show that the accuracy of the proposed method is improved by 0.6% compared with the latest methods; in large pose interference and partial occlusion, the accuracy is improved by 4% and 5%. The results verify that our method outperforms the latest methods and can be applied to a variety of complex scenarios while maintaining real-time performance.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030620
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 621: Intelligent Pick-and-Place System Using
           MobileNet

    • Authors: Fan Hong, Donavan Wei Liang Tay, Alfred Ang
      First page: 621
      Abstract: The current development of a robotic arm solution for the manufacturing industry requires performing pick-and-place operations for work pieces varying in size, shape, and color across different stages of manufacturing processes. It aims to reduce or eliminate the human error and human intervention in order to save manpower costs and enhance safety at the workplace. Machine learning has become more and more prominent for object recognition in these pick-and-place applications with the aid of imaging devices and advances in the image processing hardware. One of the key tasks in object recognition is feature extraction and object classification based on convolutional neural network (CNN) models, which are generally computationally intensive. In this paper, an intelligent object detection and picking system based on MobileNet is developed and integrated into an educational six-axis robotic arm, which requires less computation resources. An experimental test is conducted on six-axis robotic arm called Niryo One to train the model and identify three objects with difference shapes and colors. It is shown by the confusion matrix that the MobileNet model achieves an accuracy of 91%, a dramatic improvement compared to 65% of the Niryo One’s original sequential model. The statistical study also shows the MobileNet can achieve a higher precision with more clustered spread of accuracy.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030621
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 622: A Dual Forward–Backward Algorithm
           to Solve Convex Model Predictive Control for Obstacle Avoidance in a
           Logistics Scenario

    • Authors: Daniele Ludovico, Paolo Guardiani, Alessandro Pistone, Lorenzo De Mari Casareto Dal De Mari Casareto Dal Verme, Darwin G. Caldwell, Carlo Canali
      First page: 622
      Abstract: In recent years, the logistics sector expanded significantly, leading to the birth of smart warehouses. In this context, a key role is represented by autonomous mobile robots, whose main challenge is to find collision-free paths in their working environment in real-time. Model Predictive Control Algorithms combined with global path planners, such as the A* algorithm, show great potential in providing efficient navigation for collision avoidance problems. This paper proposes a Dual Forward–Backward Algorithm to find the solution to a Model Predictive Control problem in which the task of driving a mobile robotic platform into a bi-dimensional semi-structured environment is formulated in a convex optimisation framework.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030622
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 623: Measuring Brain Activation Patterns from
           Raw Single-Channel EEG during Exergaming: A Pilot Study

    • Authors: Gianluca Amprimo, Irene Rechichi, Claudia Ferraris, Gabriella Olmo
      First page: 623
      Abstract: Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson’s Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient’s needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user’s engagement during an exergame. As a first step, healthy subjects were recruited to assess the system’s capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play (accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030623
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 624: Dual-Channel Edge-Featured Graph
           Attention Networks for Aspect-Based Sentiment Analysis

    • Authors: Junwen Lu, Lihui Shi, Guanfeng Liu, Xinrong Zhan
      First page: 624
      Abstract: The goal of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of specific aspects in a context. Recently, graph neural networks have employed dependent tree syntactic information to assess the link between aspects and contextual words; nevertheless, most of this research has neglected phrases that are insensitive to syntactic analysis and the effect between various aspects in a sentence. In this paper, we propose a dual-channel edge-featured graph attention networks model (AS-EGAT), which builds an aspect syntactic graph by enhancing the contextual syntactic dependency representation of key aspect words and the mutual affective relationship between various aspects in the context and builds a semantic graph through the self-attention mechanism. We use the edge features as a significant factor to determine the weight coefficient of the attention mechanism to efficiently mine the edge features of the graph attention networks model (GAT). As a result, the model can connect important sentiment features of related aspects when dealing with aspects that lack obvious sentiment expressions, pay close attention to important word aspects when dealing with multiple-word aspects, and extract sentiment features from sentences that are not sensitive to syntactic dependency trees by looking at semantic features. Experimental results show that our proposed AS-EGAT model is superior to the current state-of-the-art baselines. Compared with the baseline models of LAP14, REST15, REST16, MAMS, T-shirt, and Television datasets, the accuracy of our AS-EGAT model increased by 0.76%, 0.29%, 0.05%, 0.15%, 0.22%, and 0.38%, respectively. The macro-f1 score increased by 1.16%, 1.16%, 1.23%, 0.37%, 0.53%, and 1.93% respectively.
      Citation: Electronics
      PubDate: 2023-01-26
      DOI: 10.3390/electronics12030624
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 625: Harmonic Distortion Aspects in Upper Limb
           Swings during Gait in Parkinson’s Disease

    • Authors: Luca Pietrosanti, Alexandre Calado, Cristiano Maria Verrelli, Antonio Pisani, Antonio Suppa, Francesco Fattapposta, Alessandro Zampogna, Martina Patera, Viviana Rosati, Franco Giannini, Giovanni Saggio
      First page: 625
      Abstract: Parkinson’s disease (PD) is responsible for a broad spectrum of signs and symptoms, including relevant motor impairments generally rated by clinical experts. In recent years, motor measurements gathered by technology-based systems have been used more and more to provide objective data. In particular, wearable devices have been adopted to evidence differences in the gait capabilities between PD patients and healthy people. Within this frame, despite the key role that the upper limbs’ swing plays during walking, no studies have been focused on their harmonic content, to which this work is devoted. To this end, we measured, by means of IMU sensors, the walking capabilities of groups of PD patients (both de novo and under-chronic-dopaminergic-treatment patients when in an off-therapy state) and their healthy counterparts. The collected data were FFT transformed, and the frequency content was analyzed. According to the results obtained, PD determines upper limb rigidity objectively evidenced and correlated to lower harmonic contents.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030625
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 626: Novel Nth/2Nth Order Two-Band Bandpass
           Filters for Sub-6 GHz 5G Applications

    • Authors: Ceyhun Karpuz, Pınar Öztürk Özdemir, Gülfem Balasu Fırat Unuk
      First page: 626
      Abstract: In this paper, a new design method is proposed for multi-mode two-band bandpass filters based on combining a square loop resonator that has a capacitive perturbation element and short-circuited stubs. This concept is a new multi-mode two-band filter design having Nth and 2Nth order for the first and second passbands that appear in f0 and 3f0 center frequencies, respectively. It has been shown that the number of transmission modes in each passband can easily be increased by periodically cascading one unit filter cell including one square loop resonator and two short-circuited stubs. This also provides a reconfigurable filter property, namely the obtaining of two different filtering characteristics in the second passband by means of the reverse and straight placing of the cascaded unit filter cells in the horizontal axis. Two 2nd/4th order two-band bandpass filter prototypes are fabricated and tested to validate the proposed design method. The total surface areas of these fabricated prototypes are 26.0 mm × 9.0 mm and 24.4 mm × 16.8 mm, and the measured insertion losses in the first/second passband are about 0.63 dB/0.96 dB and 0.59 dB/0.83 dB. It is observed that the filter prototypes have the advantages of low insertion loss, compact size, and flexible filtering characteristics. The designed filters are suitable for operation in the 5G spectrum, which includes a range of radio frequencies sub-6 GHz.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030626
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 627: FPGA-Based Tactile Sensory Platform with
           Optical Fiber Data Link for Feedback Systems in Prosthetics

    • Authors: Guido Di Patrizio Di Patrizio Stanchieri, Moustafa Saleh, Andrea De De Marcellis, Ali Ibrahim, Marco Faccio, Maurizio Valle, Elia Palange
      First page: 627
      Abstract: In this paper, we propose and validate a tactile sensory feedback system for prosthetic applications based on an optical communication link. The optical link features a low power and wide transmission bandwidth, which makes the feedback system suitable for a large number and variety of tactile sensors. The low-power transmission is derived from the employed UWB-based optical modulation technique. A system prototype, consisting of digital transmitter and receiver boards and acquisition circuits to interface 32 piezoelectric sensors, was implemented and experimentally tested. The system functionality was demonstrated by processing and transmitting data from the piezoelectric sensor at a 100 Mbps data rate through the optical link, measuring a communication energy consumption of 50 pJ/bit. The reported experimental results validate the functionality of the proposed sensory feedback system and demonstrate its real-time operation capabilities.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030627
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 628: Antipodal Linearly Tapered Slot Antenna
           with Quasi-Hemispherical Pattern Using Subwavelength Elements

    • Authors: Rui Wang, Dashuang Liao, Feng Yang
      First page: 628
      Abstract: Antennas with quasi-hemispherical radiation patterns are preferred in many wide−area wireless communication systems which require the signals to uniformly cover a wide two−dimensional region. In this work, a simple but effective beamwidth broadening technique based on an antipodal linearly tapered slot antenna (ALTSA) is first proposed and then experimentally verified. Compared with most of the reported designs, the proposed antenna can significantly widen beamwidth and achieve a quasi-hemispherical radiation pattern without increasing the overall size and structural complexity. Only two rows of subwavelength metallic elements (eight elements in total) are simply and skillfully printed at specified positions on the dielectric substrate (relative permittivity εr = 2.94 and thickness h = 1.5 mm) of a general ALTSA whose peak gain is 11.7 dBi, approximately 200% half-power beamwidth (HPBW) enlargement can be obtained in all cut-planes containing the end-fire direction at the central frequency of 15 GHz, and the HPBW extensions in different cut-planes have good consistency. Thus, a quasi-hemispherical beam pattern can be acquired. Thanks to the simplicity of this method, the antenna size and structural complexity do not increase, resulting in the characteristics of easy fabrication and integration, being lightweight, and high reliability. This proposed method provides a good choice for wide−beam antenna design and will have a positive effect on the potential applications of wide-area wireless communication systems.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030628
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 629: Exploring LoRa and Deep Learning-Based
           Wireless Activity Recognition

    • Authors: Yang Xiao, Yunfan Chen, Mingxing Nie, Tao Zhu, Zhenyu Liu, Chao Liu
      First page: 629
      Abstract: Today’s wireless activity recognition research still needs to be practical, mainly due to the limited sensing range and weak through-wall effect of the current wireless activity recognition based on Wi-Fi, RFID (Radio Frequency Identification, RFID), etc. Although some recent research has demonstrated that LoRa can be used for long-range and wide-range wireless sensing, no pertinent studies have been conducted on LoRa-based wireless activity recognition. This paper proposes applying long-range LoRa wireless communication technology to contactless wide-range wireless activity recognition. We propose LoRa and deep learning for contactless indoor activity recognition for the first time and propose a more lightweight improved TPN (Transformation Prediction Network, TPN) backbone network. At the same time, using only two features of the LoRa signal amplitude and phase as the input of the model, the experimental results demonstrate that the effect is better than using the original signal directly. The recognition accuracy reaches 97%, which also demonstrate that the LoRa wireless communication technology can be used for wide-range activity recognition, and the recognition accuracy can meet the needs of engineering applications.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030629
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 630: Detecting Data Anomalies from Their
           Formal Specifications: A Case Study in IoT Systems

    • Authors: Benjamin Aziz
      First page: 630
      Abstract: We present in this paper a new method in detecting anomalies in datasets representing systems behaviour, which is based on comparing a dataset to the data blueprint of the system representing its normal behaviour. This method removes some of the need for applying complex machine learning algorithms that aim at detecting abnormalities in such datasets and gives a more assured outcome of the presence of abnormalities. Our method first models a system using the formal language of the π-calculus, and then applies an abstract interpretation that ultimately generates an abstract multiset representing the messages exchanged in the system model. We term this multiset as the data blueprint of the system, and it represents the normal behaviour expected. We apply this method to the case of a recent study in literature, which attempts to analyse normal and abnormal behaviour in datasets representing runs of the MQTT protocol, both under attack and no attack conditions. We show that our method is able to detect these conditions in an easier and more straightforward manner than the original case study attempts to.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030630
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 631: Development of Bidirectional Pulsed Power
           Supply and Its Effect on Copper Plating Effect of Printed Circuit Board
           Via- Filling

    • Authors: Wenguang Chen, Shoutao Wang, Zhijian Liu, Caiyi Wei, Yuanyuan Peng
      First page: 631
      Abstract: A bidirectional pulse power supply with continuously adjustable forward parameters 8 V/20 A and reverse parameters 20 V/50 A was designed using DSP (Digital Signal Processor), and the bidirectional pulse power supply was used to test copper plating on printed circuit boards with filled via holes. The effects of frequencies, pulse width ratios of forward and reverse currents, and current densities on the copper plating effect were investigated by the single variable method and were compared with DC copper plating. The experimental results showed that compared with DC power supply, the bidirectional pulse power supply had a better effect and a faster speed on via-filling copper plating, and can also reduce the use of additives, which is in line with green development. The parameters of the pulse affected the plating effect to varying degrees. In this solution system, the optimal parameters for bidirectional pulse plating are frequency 1 kHz, forward pulse current density 4 ASD (Ampere per Square Decimeter) with 50% duty cycle, and reverse pulse current density 16 ASD with 2.5% duty cycle.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030631
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 632: A Wire-Bonded Patch Antenna for
           Millimeter Wave Applications

    • Authors: Grzegorz Bogdan, Jakub Sobolewski, Paweł Bajurko, Yevhen Yashchyshyn, Jan Oklej, Dariusz Ostaszewski
      First page: 632
      Abstract: Wire bonds are one of the most common interconnects used in microelectronics; however, their application to millimeter wave monolithic microwave integrated circuits (MMICs) may severely decrease the overall system performance due to transmission loss, radiation loss, and impedance mismatch. The goal of this work was to optimize a wire-bonded patch antenna to minimize losses and maximize the gain in the frequency range from 81 to 83 GHz. Optimization was based on electromagnetic simulations of different variants of the wire bond. Results show that the optimized structure demonstrates two major advantages. Firstly, it does not require any external matching network; hence, it can be directly connected to a contact pad of an MMIC die. Secondly, the wire bond radiation effect is utilized to enhance the patch antenna gain at the broadside direction.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030632
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 633: Time Jitter Analysis of an Optical Signal
           Based on Gated On-Off Optical Sampling and Dual-Dirac Modeling

    • Authors: Tao Huang, Zhiqiang Fan, Jun Su, Qi Qiu
      First page: 633
      Abstract: A time jitter analysis method for an optical signal based on gated on-off optical sampling and dual-Dirac modeling is proposed and demonstrated experimentally. The optical signal under test is firstly sampled by an optical sampling pulse train generated through the gating on-off modulation of a Mach–Zehnder modulator (MZM). The sampled pulse is then broadened using optical true-time delay and electrical low-pass filtering to reduce its bandwidth to match the sample rate of a low-speed electrical analog-to-digital converter (ADC), which is used to quantify the sampled pulse. An eye diagram is obtained from the quantified data and used to plot a time jitter histogram. Finally, the dual-Dirac model is introduced to analyze the time jitter histogram to obtain the total jitter (TJ), including the deterministic jitter (DJ) and random jitter (RJ). In the experiment, a 19.05 ps TJ, including a 13.20 ps DJ and a 5.85 ps RJ, is measured for a 2.5 GHz optical signal using the proposed time jitter analysis method. The results agree well with those measured with a commercial real-time oscilloscope.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030633
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 634: Dispersive Optical Solitons with
           Differential Group Delay Having Multiplicative White Noise by Itô
           Calculus

    • Authors: Elsayed M. E. Zayed, Mohamed E. M. Alngar, Reham M. A. Shohib, Anjan Biswas, Yakup Yıldırım, Luminita Moraru, Simona Moldovanu, Puiu Lucian Georgescu
      First page: 634
      Abstract: The current paper recovers dispersive optical solitons in birefringent fibers that are modeled by the Schrödinger–Hirota equation with differential group delay and white noise. Itô Calculus conducts the preliminary analysis. The G′/G-expansion approach and the enhanced Kudryashov’s scheme gave way to a wide spectrum of soliton solutions with the white noise component reflected in the phase of the soliton.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030634
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 635: Onboard Centralized ISL-Building Planning
           for LEO Satellite Constellation Networks

    • Authors: Liang Qiao, Hongcheng Yan, Xiaoyi Zhou, Yong Xu, Luyuan Wang, Xin Wen
      First page: 635
      Abstract: Large-scale low earth orbit (LEO) satellite constellation projects are increasingly adopting inter-satellite links (ISLs) to enable their autonomous and collaborative operation. Due to the large number of satellite constellation network nodes and their continuous movement in orbit, the network nodes may not remain visible to each other at all times, so the ISL-building choices among satellites are diverse and vary with time. As a result, maintaining a network topology requires onboard planning and management. In this paper, we creatively propose an onboard centralized ISL-building planning scheme with the goal of autonomous topology management. A multi-antenna visibility calculation method that takes the antenna installation angle and the turntable rotation threshold into account is provided for the visibility calculation procedure. Additionally, the link-building planning process is modeled using integer linear programming (ILP); however, to tackle the computational complexity problem of ILP, a link-building planning method based on topology stability optimization is presented. The simulation results show that the proposed onboard centralized ISL-building planning scheme can operate among satellites to successfully realize network status collection, visibility calculation, link-building planning, and planning result distribution, as conducted by the dynamic primary satellite. Moreover, the inter-plane link-building planning method based on topology stability optimization improves the network topology stability on the basis of reducing the network delay.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030635
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 636: Distributed Finite-Time Control of
           Islanded Microgrid for Ancillary Services Provision

    • Authors: Sonam Shrivastava, Bidyadhar Subudhi, Jambeswar Sahu
      First page: 636
      Abstract: This paper presents a hierarchical cyber-physical multi-agent model for an AC microgrid (MG). A new distributed finite-time secondary controller is designed in the provision of ancillary services, such as voltage and frequency synchronization and active and reactive power regulation. The control algorithm developed is fully distributed and intended to restore the system voltage and frequency to their nominal value finite time. The existing distributed controllers achieve a consensus in an infinite-time horizon, whereas the proposed control provides a quick convergence to a consensus even in the face of disturbances and restores the voltage and frequency in less than 0.25 s. For accurate active and reactive power regulation, a distributed control algorithm is proposed that corrects the power mismatch among neighboring distributed generator units and eventually for the entire MG network. A Lyapunov analysis is used to establish the upper limit on the convergence time. The proposed control scheme is evaluated and compared with the previously reported asymptotic control technique based on a neighborhood tracking error using time-domain simulations under load variation and communication constraints. The controller withstands a communication link delay up to 2 s with a small deviation and settles down to the nominal values within 0.4 s. Further, the convergence analysis shows that the performance of the proposed algorithm depends exclusively on the controller parameters and communication network connectivity, not on the line and load parameters of the AC MG. The proposed controller enables the plug-and-play capability of the AC MG and effectively reduces the load change-induced disturbance.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030636
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 637: Age-Associated Changes on Gait Smoothness
           in the Third and the Fourth Age

    • Authors: Massimiliano Pau, Giuseppina Bernardelli, Bruno Leban, Micaela Porta, Valeria Putzu, Daniela Viale, Gesuina Asoni, Daniela Riccio, Serena Cerfoglio, Manuela Galli, Veronica Cimolin
      First page: 637
      Abstract: Although gait disorders represent a highly prevalent condition in older adults, the alterations associated with physiologic aging are often not easily differentiable from those originated by concurrent neurologic or orthopedic conditions. Thus, the detailed quantitative assessment of gait patterns represents a crucial issue. In this context, the study of trunk accelerations may represent an effective proxy of locomotion skills in terms of symmetry. This can be carried out by calculating the Harmonic Ratio (HR), a parameter obtained through the processing of trunk accelerations in the frequency domain. In this study, trunk accelerations during level walking of 449 healthy older adults (of age > 65) who were stratified into three groups (Group 1: 65–74 years, n = 175; Group 2: 75–85 years, n = 227; Group 3: >85 years, n = 47) were acquired by means of a miniaturized Inertial Measurement Unit located in the low back and processed to obtain spatio-temporal parameters of gait and HR, in antero-posterior (AP), medio-lateral (ML) and vertical (V) directions. The results show that Group 3 exhibited a 16% reduction in gait speed and a 10% reduction in stride length when compared with Group 1 (p < 0.001 in both cases). Regarding the cadence, Group 3 was characterized by a 5% reduction with respect to Groups 1 and 2 (p < 0.001 in both cases). The analysis of HR revealed a general trend of linear decrease with age in the three groups. In particular, Group 3 was characterized by HR values significantly lower (−17%) than those of Group 1 in all three directions and significantly lower than Group 2 in ML and V directions (−10%). Taken together, such results suggest that HR may represent a valid measure to quantitatively characterize the progressive deterioration of locomotor abilities associated with aging, which seems to occur until the late stages of life.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030637
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 638: Drive System Inverter Modeling Using
           Symbolic Regression

    • Authors: Matko Glučina, Nikola Anđelić, Ivan Lorencin, Sandi Baressi Baressi Šegota
      First page: 638
      Abstract: For accurate and efficient control performance of electrical drives, precise values of phase voltages are required. In order to achieve control of the electric drive, the development of mathematical models of the system and its parts is often approached. Data-driven modeling using artificial intelligence can often be unprofitable due to the large amount of computing resources required. To overcome this problem, the idea is to investigate if a genetic programming–symbolic regressor (GPSR) algorithm could be used to obtain simple symbolic expressions which could estimate the mean phase voltages (black-box inverter model) and duty cycles (black-box compensation scheme) with high accuracy using a publicly available dataset. To obtain the best symbolic expressions using GPSR, a random hyperparameter search method and 5-fold cross-validation were developed. The best symbolic expressions were chosen based on their estimation performance, which was measured using the coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE). The best symbolic expressions for the estimation of mean phase voltages achieved R2, MAE, and RMSE values of 0.999, 2.5, and 2.8, respectively. The best symbolic expressions for the estimation of duty cycles achieved R2, MAE, and RMSE values of 0.9999, 0.0027, and 0.003, respectively. The originality of this work lies in the application of the GPSR algorithm, which, based on a mathematical equation it generates, can estimate the value of mean phase voltages and duty cycles in a three-phase inverter. Using the obtained model, it is possible to estimate the given aforementioned values. Such high-performing estimation represents an opportunity to replace expensive online equipment with a cheaper, more precise, and faster approach, such as a GPSR-based model. The presented procedure shows that the symbolic expression for the accurate estimation of mean phase voltages and duty cycles can be obtained using the GPSR algorithm.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030638
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 639: Towards Deploying DNN Models on Edge for
           Predictive Maintenance Applications

    • Authors: Rick Pandey, Sebastian Uziel, Tino Hutschenreuther, Silvia Krug
      First page: 639
      Abstract: Almost all rotating machinery in the industry has bearings as their key building block and most of these machines run 24x7. This makes bearing health prediction an active research area for predictive maintenance solutions. Many state of the art Deep Neural Network (DNN) models have been proposed to solve this. However, most of these high performance models are computationally expensive and have high memory requirements. This limits their use to very specific industrial applications with powerful hardwares deployed close the the machinery. In order to bring DNN-based solutions to a potential use in the industry, we need to deploy these models on Microcontroller Units (MCUs) which are cost effective and energy efficient. However, this step is typically neglected in literature as it poses new challenges. The primary concern when inferencing the DNN models on MCUs is the on chip memory of the MCU that has to fit the model, the data and additional code to run the system. Almost all the state of the art models fail this litmus test since they feature too many parameters. In this paper, we show the challenges related to the deployment, review possible solutions and evaluate one of them showing how the deployment can be realized and what steps are needed. The focus is on the steps required for the actual deployment rather than finding the optimal solution. This paper is among the first to show the deployment on MCUs for a predictive maintenance use case. We first analyze the gap between State Of The Art benchmark DNN models for bearing defect classification and the memory constraint of two MCU variants. Additionally, we review options to reduce the model size such as pruning and quantization. Afterwards, we evaluate a solution to deploy the DNN models by pruning them in order to fit them into microcontrollers. Our results show that most models under test can be reduced to fit MCU memory for a maximum loss of 3% in average accuracy of the pruned models in comparison to the original models. Based on the results, we also discuss which methods are promising and which combination of model and feature work best for the given classification problem.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030639
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 640: Neural Adaptive Impedance Control for
           Force Tracking in Uncertain Environment

    • Authors: Hao An, Chao Ye, Zikang Yin, Weiyang Lin
      First page: 640
      Abstract: Torque-based impedance control, a kind of classical active compliant control, is widely required in human–robot interaction, medical rehabilitation, and other fields. Adaptive impedance control effectively tracks the force when the robot comes in contact with an unknown environment. Conventional adaptive impedance control (AIC) introduces the force tracking error of the last moment to adjust the controller parameters online, which is an indirect method. In this paper, joint friction in the robot system is first identified and compensated for to enable the excellent performance of torque-based impedance control. Second, neural networks are inserted into the torque-based impedance controller, and a neural adaptive impedance control (NAIC) scheme with directly online optimized parameters is proposed. In addition, NAIC can be deployed directly without the need for data collection and training. Simulation studies and real-world experiments with a six link rotary robot manipulator demonstrate the excellent performance of NAIC.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030640
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 641: E-Government Development—A Key
           Factor in Government Administration Effectiveness in the European Union

    • Authors: Nicoleta Mihaela Doran, Silvia Puiu, Roxana Maria Bădîrcea, Marilen Gabriel Pirtea, Marius Dalian Doran, George Ciobanu, Lavinia Daniela Mihit
      First page: 641
      Abstract: The phenomenon of digitisation of the public sector is an irreversible process that affects both the way public institutions are organised and the communication relationships between people and institutions. The COVID-19 pandemic represented a challenge and a strong impetus in accelerating the digitisation process of public administration at the global level such that it is currently difficult to make a clear distinction between governance and e-governance. The purpose of this research is to investigate the impact of the intensification of the digitisation process of public services in increasing the efficiency of governments at the level of the member states of the European Union, based on a cluster analysis. A robust least squares regression method was used to estimate the effects of the three dimensions of the e-government development index (EGDI) on government effectiveness. The results of the analysis highlighted the fact that the skills of the population in using online services determine the increase in a double percentage of government efficiency in intensively digitised states compared to states where the digitisation of public services is less developed. The development of the telecommunications infrastructure also has a significant positive impact on the efficiency of the government. However, online services offered by public authorities have proven to negatively influence government efficiency in both clusters.
      Citation: Electronics
      PubDate: 2023-01-27
      DOI: 10.3390/electronics12030641
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 642: Design of A Smart Tourism Management
           System through Multisource Data Visualization-Based Knowledge Discovery

    • Authors: Zhicong Qin, Younghwan Pan
      First page: 642
      Abstract: Nowadays, tourism management is a universal concern in the world. It is important for generating tourism characteristics for travelers, so as to digitally facilitate tourism business scheduling. Currently, there is still a lack of technologies that are competent in managing tourism business affairs. Therefore, in this paper a smart tourism management system is designed through multisource data visualization-based knowledge discovery. Firstly, this work presents the total architecture of a tourism management system with respect to three modules: data collection, data visualization, and knowledge discovery. Then, multisource business data are processed with the use of visualization techniques so as to output statistical analysis results for different individuals. On this basis, characterized knowledge can be found from previous visualization results and demonstrated for travelers or administrators. In addition, a case study on real data is conducted to test running performance of the proposed tourism management system. The main body of public service tourism is the government or other social organizations that do not regard profit as the main purpose; public service tourism a general term for products and services with obvious public nature. The testing results show that user preferences can be mined and corresponding travelling plans can be suggested via multisource data visualization-based knowledge discovery means.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030642
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 643: A Meta-Model to Predict and Detect
           Malicious Activities in 6G-Structured Wireless Communication Networks

    • Authors: Haider W. Oleiwi, Doaa N. Mhawi, Hamed Al-Raweshidy
      First page: 643
      Abstract: The rapid leap in wireless communication systems incorporated a plethora of new features and challenges that accompany the era of 6G and beyond being investigated and developed. Recently, machine learning techniques were widely deployed in many fields, especially wireless communications. It was used to improve network traffic performance regarding resource management, frequency spectrum optimization, latency, and security. The studies of modern wireless communications and anticipated features of ultra-densified ubiquitous wireless networks exposed a risky vulnerability and showed a necessity for developing a trustworthy intrusion detection system (IDS) with certain efficiency/standards that have not yet been achieved by current systems. IDSs lack acceptable immunity against repetitive, updatable, and intelligent attacks on wireless communication networks, significantly concerning the modern infrastructure of 6G communications, resulting in low accuracies/detection rates and high false-alarm/false-negative rates. For this objective principle, IDS system complexity was reduced by applying a unique meta-machine learning model for anomaly detection networks was developed in this paper. The five main stages of the proposed meta-model are as follows: the accumulated datasets (NSL KDD, UNSW NB15, CIC IDS17, and SCE CIC IDS18) comprise the initial stage. The second stage is preprocessing and feature selection, where preprocessing involves replacing missing values and eliminating duplicate values, leading to dimensionality minimization. The best-affected subset feature from datasets is selected using feature selection (i.e., Chi-Square). The third step is represented by the meta-model. In the training dataset, many classifiers are utilized (i.e., random forest, AdaBoosting, GradientBoost, XGBoost, CATBoost, and LightGBM). All the classifiers undergo the meta-model classifier (i.e., decision tree as the voting technique classifier) to select the best-predicted result. Finally, the classification and evaluation stage involves the experimental results of testing the meta-model on different datasets using binary-class and multi-class forms for classification. The results proved the proposed work’s high efficiency and outperformance compared to existing IDSs.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030643
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 644: Towards Posture and Gait Evaluation
           through Wearable-Based Biofeedback Technologies

    • Authors: Paola Cesari, Matteo Cristani, Florenc Demrozi, Francesco Pascucci, Pietro Maria Picotti, Graziano Pravadelli, Claudio Tomazzoli, Cristian Turetta, Tewabe Chekole Workneh, Luca Zenti
      First page: 644
      Abstract: In medicine and sport science, postural evaluation is an essential part of gait and posture correction. There are various instruments for quantifying the postural system’s efficiency and determining postural stability which are considered state-of-the-art. However, such systems present many limitations related to accessibility, economic cost, size, intrusiveness, usability, and time-consuming set-up. To mitigate these limitations, this project aims to verify how wearable devices can be assembled and employed to provide feedback to human subjects for gait and posture improvement, which could be applied for sports performance or motor impairment rehabilitation (from neurodegenerative diseases, aging, or injuries). The project is divided into three parts: the first part provides experimental protocols for studying action anticipation and related processes involved in controlling posture and gait based on state-of-the-art instrumentation. The second part provides a biofeedback strategy for these measures concerning the design of a low-cost wearable system. Finally, the third provides algorithmic processing of the biofeedback to customize the feedback based on performance conditions, including individual variability. Here, we provide a detailed experimental design that distinguishes significant postural indicators through a conjunct architecture that integrates state-of-the-art postural and gait control instrumentation and a data collection and analysis framework based on low-cost devices and freely accessible machine learning techniques. Preliminary results on 12 subjects showed that the proposed methodology accurately recognized the phases of the defined motor tasks (i.e., rotate, in position, APAs, drop, and recover) with overall F1-scores of 89.6% and 92.4%, respectively, concerning subject-independent and subject-dependent testing setups.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030644
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 645: An Improved SVM with Earth Mover’s
           Distance Regularization and Its Application in Pattern Recognition

    • Authors: Rui Feng, Haitao Dong, Xuri Li, Zhaochuang Gu, Runyang Tian, Houde Li
      First page: 645
      Abstract: A support vector machine (SVM) aims to achieve an optimal hyperplane with a maximum interclass margin and has been widely utilized in pattern recognition. Traditionally, a SVM mainly considers the separability of boundary points (i.e., support vectors), while the underlying data structure information is commonly ignored. In this paper, an improved support vector machine with earth mover’s distance (EMD-SVM) is proposed. It can be regarded as an improved generalization of the standard SVM, and can automatically learn the distribution between the classes. To validate its performance, we discuss the necessity of the structural information of EMD-SVM in the linear and nonlinear cases, respectively. Experimental validation was designed and conducted in different application fields, which have shown its superior and robust performance.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030645
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 646: A Kind of Optoelectronic Memristor Model
           and Its Applications in Multi-Valued Logic

    • Authors: Jiayang Wang, Yuzhe Lin, Chenhao Hu, Shiqi Zhou, Shenyu Gu, Mengjie Yang, Guojin Ma, Yunfeng Yan
      First page: 646
      Abstract: Memristors have been proved effective in intelligent computing systems owing to the advantages of non-volatility, nanometer size, low power consumption, compatibility with traditional CMOS technology, and rapid resistance transformation. In recent years, considerable work has been devoted to the question of how to design and optimize memristor models with different structures and physical mechanisms. Despite the fact that the optoelectronic effect inevitably makes the modelling process more complex and challenging, relatively few research works are dedicated to optoelectronic memristor modelling. Based on this, this paper develops an optoelectronic memristor model (containing mathematical model and circuit model). Moreover, the composite memristor circuit (series- and parallel-connected configuration) with a rotation mechanism is discussed. Further, a multi-valued logic circuit is designed, which is capable of performing multiple logic functions from 0–1, verifying the validity and effectiveness of the established memristor model, as well as opening up a new path for the circuit implementation of fuzzy logic.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030646
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 647: A Survey on Resource Management for 6G
           Heterogeneous Networks: Current Research, Future Trends, and Challenges

    • Authors: Hayder Faeq Alhashimi, MHD Nour Hindia, Kaharudin Dimyati, Effariza Binti Hanafi, Nurhizam Safie, Faizan Qamar, Khairul Azrin, Quang Ngoc Nguyen
      First page: 647
      Abstract: The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. Heterogeneous networks (HetNets) have received a lot of attention in recent years due to their potential as a novel structure for evolutionary networks. When compared to homogeneous networks, HetNets provide more potential for spatial spectrum reuse and higher quality of service (QoS). However, effective resource management (RM) solutions are essential to prevent interference and accomplish spectrum sharing due to mutual interference. This paper presents a comprehensive review of resource management in 6G HetNets. The study aims to give crucial background on HetNets to aid in the creation of more effective methods in this field of study. First, a detailed examination of recent work is presented in resource management aspects such as power allocation, user association, mode selection, and spectrum allocation. Second, we identify the most severe challenges associated with the current resource management methods and propose suitable solutions. Finally, several open issues and emerging areas of research are highlighted.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030647
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 648: The Inflection Point of Single Event
           Transient in SiGe HBT at a Cryogenic Temperature

    • Authors: Pan, Guo, Lu, Zhang, Liu
      First page: 648
      Abstract: Basing our findings on our previous pulsed laser testing results, we have experimentally demonstrated that there is an inflection point of a single event transient (SET) in the silicon-germanium heterojunction bipolar transistors (SiGe HBTs) with a decreasing temperature from +20 ℃ to -180 ℃. Additionally, the changes in the parasitic resistivity of the carrier collection pathway due to incomplete ionization could play a key role. In this paper, we found that the incident-heavy ion’s parameters could also have an important impact on the SET inflection point by introducing the ion track structures generated by Geant4 simulation to the TCAD transient simulation. Heavy ion with a low linear energy transfer (LET) will not trigger the ion shunt effect of SiGe HBT and the inflection point will not occur until -200 ℃. For high LET ions’ incidence, the high-density electron-hole pairs (EHPs) could significantly affect the parasitic resistivity on the pathway and lead to an earlier inflection point. The present results and methods could provide a new reference for the effective evaluation of single-event effects in bipolar transistors and circuits at cryogenic temperatures and provide new evidence of the SiGe technology’s potential for applications in extreme cryogenic environments.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030648
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 649: A Hybrid Edge-Cloud System for Networking
           Service Components Optimization Using the Internet of Things

    • Authors: Souvik Pal, N. Z. Jhanjhi, Azmi Shawkat Abdulbaqi, D. Akila, Abdulaleem Ali Almazroi, Faisal S. Alsubaei
      First page: 649
      Abstract: The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030649
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 650: Technical Study of Deep Learning in Cloud
           Computing for Accurate Workload Prediction

    • Authors: Zaakki Ahamed, Maher Khemakhem, Fathy Eassa, Fawaz Alsolami, Abdullah S. Al-Malaise Al-Ghamdi
      First page: 650
      Abstract: Proactive resource management in Cloud Services not only maximizes cost effectiveness but also enables issues such as Service Level Agreement (SLA) violations and the provisioning of resources to be overcome. Workload prediction using Deep Learning (DL) is a popular method of inferring complicated multidimensional data of cloud environments to meet this requirement. The overall quality of the model depends on the quality of the data as much as the architecture. Therefore, the data sourced to train the model must be of good quality. However, existing works in this domain have either used a singular data source or have not taken into account the importance of uniformity for unbiased and accurate analysis. This results in the efficacy of DL models suffering. In this paper, we provide a technical analysis of using DL models such as Recurrent Neural Networks (RNN), Multilayer Perception (MLP), Long Short-Term Memory (LSTM), and, Convolutional Neural Networks (CNN) to exploit the time series characteristics of real-world workloads from the Parallel Workloads Archive of the Standard Workload Format (SWF) with the aim of conducting an unbiased analysis. The robustness of these models is evaluated using the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error metrics. The findings of these highlight that the LSTM model exhibits the best performance compared to the other models. Additionally, to the best of our knowledge, insights of DL in workload prediction of cloud computing environments is insufficient in the literature. To address these challenges, we provide a comprehensive background on resource management and load prediction using DL. Then, we break down the models, error metrics, and data sources across different bodies of work.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030650
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 651: Segmentation of Nucleus and Cytoplasm
           from H&E-Stained Follicular Lymphoma

    • Authors: Pranshu Saxena, Anjali Goyal, Mariyam Aysha Bivi, Sanjay Kumar Singh, Mamoon Rashid
      First page: 651
      Abstract: This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030651
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 652: Machine Learning Techniques for
           Non-Terrestrial Networks

    • Authors: Romeo Giuliano, Eros Innocenti
      First page: 652
      Abstract: Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements and integration in the 5G 3GPP standards, NTNs have been gaining importance in the last years and will provide further applications and services. 3GPP standardization is integrating low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs) and unmanned aerial systems (UASs) as non-terrestrial elements (NTEs) in the NTNs within the terrestrial 5G standard. Considering the NTE characteristics (e.g., traffic congestion, processing capacity, oscillation, altitude, pitch), it is difficult to dynamically set the optimal connection based also on the required service to properly steer the antenna beam or to schedule the UE. To this aim, machine learning (ML) can be helpful. In this paper, we present novel services supported by the NTNs and their architectures for the integration in the terrestrial 5G 3GPP standards. Then, ML techniques are proposed for managing NTN connectivity as well as to improve service performance.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030652
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 653: A Magnetic Field Containment Method for
           an IPT System with Multiple Transmitting Coils Based on Reflective
           Properties

    • Authors: Xu Yang, Junfeng Yang, Jing Fan, Bao Wang, Dingzhen Li
      First page: 653
      Abstract: Inductive power transfer (IPT) systems with multiple transmitting coils are mainly used in specific scenarios, such as IPT sharing platforms and dynamic wireless charging of electric vehicles, etc. However, it faces problems of electromagnetic field leakage and low efficiency. A new magnetic field containment method based on reflective properties is proposed to solve the above shortcomings. Firstly, the reflective properties and performance figures of the IPT system with a unified passive compensation network are described and derived. Then, an S−LCL topology appropriate for the time−varying coupling IPT system is presented, where the IPT system’s transmitter consists of multiple coils that are compatible with one or more moving receivers and is powered by an inverter. Then, magnetic field focusing, power transfer and overall efficiency are analyzed and simulated. Finally, an experimental prototype is built to validate the feasibility of the proposed system. The experimental results show that the proposed method can increase the power transfer of the coupled transmitting coil and reduce the magnetic field leakage of the standby transmitting coils without complex shielding measures, switch, position detection and communication circuits.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030653
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 654: Stacked Siamese Generative Adversarial
           Nets: A Novel Way to Enlarge Image Dataset

    • Authors: Shanlin Liu, Ren Han, Rami Yared
      First page: 654
      Abstract: Deep neural networks often need to be trained with a large number of samples in a dataset. When the training samples in a dataset are not enough, the performance of the model will degrade. The Generative Adversarial Network (GAN) is considered to be effective at generating samples, and thus, at expanding the datasets. Consequently, in this paper, we proposed a novel method, called the Stacked Siamese Generative Adversarial Network (SSGAN), for generating large-scale images with high quality. The SSGAN is made of a Color Mean Segmentation Encoder (CMS-Encoder) and several Siamese Generative Adversarial Networks (SGAN). The CMS-Encoder extracts features from images using a clustering-based method. Therefore, the CMS-Encoder does not need to be trained and its output has a high interpretability of human visuals. The proposed Siamese Generative Adversarial Network (SGAN) controls the category of generated samples while guaranteeing diversity by introducing a supervisor to the WGAN. The SSGAN progressively learns features in the feature pyramid. We compare the Fréchet Inception Distance (FID) of generated samples of the SSGAN with previous works on four datasets. The result shows that our method outperforms the previous works. In addition, we trained the SSGAN on the CelebA dataset, which consists of cropped images with a size of 128 × 128. The good visual effect further proves the outstanding performance of our method in generating large-scale images.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030654
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 655: Research Based on Improved CNN-SVM Fault
           Diagnosis of V2G Charging Pile

    • Authors: Yuyi Yang, Wu Zhu
      First page: 655
      Abstract: With the increasing number of electric vehicles, V2G (vehicle to grid) charging piles which can realize the two-way flow of vehicle and electricity have been put into the market on a large scale, and the fault maintenance of charging piles has gradually become a problem. Aiming at the problems that convolutional neural networks (CNN) are easy to overfit and the low localization accuracy in fault diagnosis of V2G charging piles, an improved fault classification model based on convolutional neural networks (CNN-SVM) is proposed. Firstly, the hardware adaptation optimization is carried out for the CNN structure, the wavelet packet transformation is used to extract the fault current signal feature information into the CNN, and the CNN-SVM model is constructed by SVM (Support Vector Machine) instead of the SoftMax classifier in the CNN. The PSO (particle swarm algorithm) is used to optimize the parameters of the SVM model to obtain the optimal model. Finally, the superiority of the proposed method is verified by multi-working cases. The experimental results show that the fault classification accuracy of the CNN-SVM model is far higher than that of the traditional deep learning network and has practical significance for fault diagnosis of the switch module of the charging pile.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030655
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 656: Multidimensional Domain Knowledge
           Framework for Poet Profiling

    • Authors: Ai Zhou, Yijia Zhang, Mingyu Lu
      First page: 656
      Abstract:
      Authors hip profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text’s stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030656
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 657: A Novel Fusion Approach Consisting of GAN
           and State-of-Charge Estimator for Synthetic Battery Operation Data
           Generation

    • Authors: Kei Long Wong, Ka Seng Chou, Rita Tse, Su-Kit Tang, Giovanni Pau
      First page: 657
      Abstract: The recent success of machine learning has accelerated the development of data-driven lithium-ion battery state estimation and prediction. The lack of accessible battery operation data is one of the primary concerns with the data-driven approach. However, research on battery operation data augmentation is rare. When coping with data sparsity, one popular approach is to augment the dataset by producing synthetic data. In this paper, we propose a novel fusion method for synthetic battery operation data generation. It combines a generative, adversarial, network-based generation module and a state-of-charge estimator. The generation module generates battery operation features, namely the voltage, current, and temperature. The features are then fed into the state-of-charge estimator, which calculates the relevant state of charge. The results of the evaluation reveal that our method can produce synthetic data with distributions similar to the actual dataset and performs well in downstream tasks.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030657
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 658: Differential Privacy-Enabled Multi-Party
           Learning with Dynamic Privacy Budget Allocating Strategy

    • Authors: Ke Pan, Kaiyuan Feng
      First page: 658
      Abstract: As one of the promising paradigms of decentralized machine learning, multi-party learning has attracted increasing attention, owing to its capability of preventing the privacy of participants from being directly exposed to adversaries. Multi-party learning enables participants to train their model locally without uploading private data to a server. However, recent studies have shown that adversaries may launch a series of attacks on learning models and extract private information about participants by analyzing the shared parameters. Moreover, existing privacy-preserving multi-party learning approaches consume higher total privacy budgets, which poses a considerable challenge to the compromise between privacy guarantees and model utility. To address this issue, this paper explores an adaptive differentially private multi-party learning framework, which incorporates zero-concentrated differential privacy technique into multi-party learning to get rid of privacy threats, and offers sharper quantitative results. We further design a dynamic privacy budget allocating strategy to alleviate the high accumulation of total privacy budgets and provide better privacy guarantees, without compromising the model’s utility. We inject more noise into model parameters in the early stages of model training and gradually reduce the volume of noise as the direction of gradient descent becomes more accurate. Theoretical analysis and extensive experiments on benchmark datasets validated that our approach could effectively improve the model’s performance with less privacy loss.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030658
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 659: Flexible Agent Architecture: Mixing
           Reactive and Deliberative Behaviors in SPADE

    • Authors: Javier Palanca, Jaime A. Rincón, Carlos Carrascosa, Vicente Julián, Andrés Terrasa
      First page: 659
      Abstract: Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030659
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 660: Lightweight Video Super-Resolution for
           Compressed Video

    • Authors: Ilhwan Kwon, Jun Li, Mukesh Prasad
      First page: 660
      Abstract: Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030660
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 661: A Multi-Objective Approach to Robust
           Control of Air Handling Units for Optimized Energy Performance

    • Authors: Mubashir Wani, Faizal Hafiz, Akshya Swain, Abhisek Ukil
      First page: 661
      Abstract: This paper presents a robust control framework with meta-heuristic intelligence to optimize the energy performance of air handling units (AHUs) and to maximize the thermal comfort of occupants by judiciously selecting the temperature set points of two controllers (i.e., the H∞ controller and the boiler controller). The selection of these set points is formulated as a multi-objective optimization problem, where the goal is to balance energy consumption with thermal comfort. Furthermore, the uncertainty weights of the H∞ controller are estimated to minimize oscillations in the outflow air temperature of the AHU plant. The performance of the proposed framework is investigated by considering the real-time weather data of Auckland, New Zealand. The results of the simulation show that the proposed robust control framework could significantly reduce oscillations in the outflow air temperature compared with the conventional case, where the temperature set points are selected empirically. Moreover, annual energy savings of of 49.13% are achieved without compromising the thermal comfort.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030661
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 662: Digital Service Platform and Innovation
           in Healthcare: Measuring Users’ Satisfaction and Implications

    • Authors: Fotis Kitsios, Stavros Stefanakakis, Maria Kamariotou, Lambros Dermentzoglou
      First page: 662
      Abstract: When it comes to scheduling health consultations, e-appointment systems are helpful for patients. Non-attendance is a common obstacle that many medical practitioners must endure when it comes to the management of appointments in healthcare facilities and outpatient health settings. Prior surveys have found that many users are open to use such mechanisms and that patients would be likely to schedule an online appointment with their doctor if such a system was made accessible. Few studies have sought to determine how well e-appointment systems work, how well they are received by their users, and whether or not they increase the number of appointments booked. The purpose of this research was to collect information that would help executives of a state hospital in Thessaloniki, Greece, to improve their electronic appointment system by measuring the level of satisfaction their patients have with it. The results show that the level of service provided by the electronic appointment system is not satisfactory. The quality of the website is another significant factor that does not contribute to the level of satisfaction experienced by patients.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030662
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 663: Modeling and Simulation of Si Grating
           Photodetector Fabricated Using MACE Method for NIR Spectrum

    • Authors: Akhmadi Surawijaya, Zefanya Chandra, Muhammad Amin Sulthoni, Irman Idris, Trio Adiono
      First page: 663
      Abstract: In this research, we modeled a silicon-based photodetector for the NIR-IR spectrum using a grating structure fabricated using the metal-assisted chemical etching method. A nanostructure fabricated by using this method is free of defects such as unwanted sidewall metal depositions. The device is simulated using Lumerical finite difference time domain (FDTD) for optical characteristics and Lumerical CHARGE for electrical characteristics. First, we optimized the grating structure duty cycle parameter for maximum optical power absorption using the particle swarm optimization algorithm provided in Lumerical FDTD, and then used the optimized parameter for our simulations. From Lumerical FDTD simulations, we found that the Cr masker metal used in the fabrication process acts as a resonant cavity and a potential candidate for internal photo emission (IPE) effects. By using Lumerical CHARGE, we performed electrical simulation and by adding the IPE calculation we found that at 850 nm wavelength the Si grating photodetector device exhibited 19 mA/W responsivity and detectivity of 2.62 × 106 Jones for −1 volt operating voltage.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030663
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 664: Toward Building Smart Contract-Based
           Higher Education Systems Using Zero-Knowledge Ethereum Virtual Machine

    • Authors: Dénes László Fekete, Attila Kiss
      First page: 664
      Abstract: The issuing and verification of higher education certificates, including all higher education documents, still functions in a costly and inappropriately bureaucratic manner. Blockchain technology provides a more secure and consistent way to revolutionize the widely used generalized mechanisms and system concepts. In this paper, the most necessary requirements are examined regarding a blockchain-based higher education system, based on the most well-known research papers. Moreover, the opportunities of working on an education system by maintaining a decentralized structure organization are recommended as well. This paper recommends the most suitable blockchain scaling solution for the architecture of an education system which uses the most state-of-the-art EVM (Ethereum virtual machine) compatible approach to implement the higher education system with all the predefined requirements. It is proven that the explained smart contract-based higher education system, which uses zkEVM (zero-knowledge Ethereum virtual machine), consists of all necessary functionalities and satisfies all predefined requirements. In fact, the recommended system, by using a modular blockchain structure, implements all the functionality and capability of the examined related works in one system, namely GDPR (General Data Protection Regulation), which is compatible and more secure.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030664
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 665: A Novel Classification Model of Date
           Fruit Dataset Using Deep Transfer Learning

    • Authors: Amjad Alsirhani, Muhammad Hameed Siddiqi, Ayman Mohamed Mostafa, Mohamed Ezz, Alshimaa Abdelraof Mahmoud
      First page: 665
      Abstract: Date fruits are the most common fruit in the Middle East and North Africa. There are a wide variety of dates with different types, colors, shapes, tastes, and nutritional values. Classifying, identifying, and recognizing dates would play a crucial role in the agriculture, commercial, food, and health sectors. Nevertheless, there is no or limited work to collect a reliable dataset for many classes. In this paper, we collected the dataset of date fruits by picturing dates from primary environments: farms and shops (e.g., online or local markets). The combined dataset is unique due to the multiplicity of items. To our knowledge, no dataset contains the same number of classes from natural environments. The collected dataset has 27 classes with 3228 images. The experimental results presented are based on five stages. The first stage applied traditional machine learning algorithms for measuring the accuracy of features based on pixel intensity and color distribution. The second stage applied a deep transfer learning (TL) model to select the best model accuracy of date classification. In the third stage, the feature extraction part of the model was fine-tuned by applying different retrained points to select the best retraining point. In the fourth stage, the fully connected layer of the model was fine-tuned to achieve the best classification configurations of the model. In the fifth stage, regularization was applied to the classification layer of the best-selected model from the fourth stage, where the validation accuracy reached 97.21% and the best test accuracy was 95.21%.
      Citation: Electronics
      PubDate: 2023-01-28
      DOI: 10.3390/electronics12030665
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 666: Comparison of Deep Learning Models for
           Automatic Detection of Sarcasm Context on the MUStARD Dataset

    • Authors: Alexandru-Costin Băroiu, Ștefan Trăușan-Matu
      First page: 666
      Abstract: Sentiment analysis is a major area of natural language processing (NLP) research, and its sub-area of sarcasm detection has received growing interest in the past decade. Many approaches have been proposed, from basic machine learning to multi-modal deep learning solutions, and progress has been made. Context has proven to be instrumental for sarcasm and many techniques that use context to identify sarcasm have emerged. However, no NLP research has focused on sarcasm-context detection as the main topic. Therefore, this paper proposes an approach for the automatic detection of sarcasm context, aiming to develop models that can correctly identify the contexts in which sarcasm may occur or is appropriate. Using an established dataset, MUStARD, multiple models are trained and benchmarked to find the best performer for sarcasm-context detection. This performer is proven to be an attention-based long short-term memory architecture that achieves an F1 score of 60.1. Furthermore, we tested the performance of this model on the SARC dataset and compared it with other results reported in the literature to better assess the effectiveness of this approach. Future directions of study are opened, with the prospect of developing a conversational agent that could identify and even respond to sarcasm.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030666
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 667: Detection and Classification of Printed
           Circuit Boards Using YOLO Algorithm

    • Authors: Matko Glučina, Nikola Anđelić, Ivan Lorencin, Zlatan Car
      First page: 667
      Abstract: Printed circuit boards (PCBs) are an indispensable part of every electronic device used today. With its computing power, it performs tasks in much smaller dimensions, but the process of making and sorting PCBs can be a challenge in PCB factories. One of the main challenges in factories that use robotic manipulators for “pick and place” tasks are object orientation because the robotic manipulator can misread the orientation of the object and thereby grasp it incorrectly, and for this reason, object segmentation is the ideal solution for the given problem. In this research, the performance, memory size, and prediction of the YOLO version 5 (YOLOv5) semantic segmentation algorithm are tested for the needs of detection, classification, and segmentation of PCB microcontrollers. YOLOv5 was trained on 13 classes of PCB images from a publicly available dataset that was modified and consists of 1300 images. The training was performed using different structures of YOLOv5 neural networks, while nano, small, medium, and large neural networks were used to select the optimal network for the given challenge. Additionally, the total dataset was cross validated using 5-fold cross validation and evaluated using mean average precision, precision, recall, and F1-score classification metrics. The results showed that large, computationally demanding neural networks are not required for the given challenge, as demonstrated by the YOLOv5 small model with the obtained mAP, precision, recall, and F1-score in the amounts of 0.994, 0.996, 0.995, and 0.996, respectively. Based on the obtained evaluation metrics and prediction results, the obtained model can be implemented in factories for PCB sorting applications.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030667
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 668: FPGA/AI-Powered Architecture for Anomaly
           Network Intrusion Detection Systems

    • Authors: Cuong Pham-Quoc, Tran Hoang Quoc Bao, Tran Ngoc Thinh
      First page: 668
      Abstract: This paper proposes an architecture to develop machine learning/deep learning models for anomaly network intrusion detection systems on reconfigurable computing platforms. We build two models to validate the framework: Anomaly Detection Autoencoder (ADA) and Artificial Neural Classification (ANC) in the NetFPGA-sume platform. Three published data sets NSL-KDD, UNSW-NB15, and CIC-IDS2017 are used to test the deployed models’ throughput, latency, and accuracy. Experimental results with the NetFPGA-SUME show that the ADA model uses 20.97% LUTs, 15.16% FFs, 19.42% BRAM, and 6.81% DSP while the ANC model requires 21.39% LUTs, 15.19% FFS, 14.59% BRAM, and 3.67% DSP. ADA and ANC achieve a bandwidth of up to 28.7 Gbps and 34.74 Gbps, respectively. In terms of throughput, ADA can process at up to 18.7 Gops, while ADA can offer 10 Gops with different datasets. With the NSL-KDD dataset, the ADA model achieves 90.87% accuracy and a false negative rate of 4.86%. The ANC model with UNSW-NB15 and CIC-IDS2017 obtains accuracy of 87.49% and 98.22%, respectively, with the false negative rates achieving 2.0% and 6.2%, respectively.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030668
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 669: Base Station MIMO Antenna in 1 × 6
           Array Configurations with Reflector Design for Sub-6 GHz 5G Applications

    • Authors: Mohd. Wasim, Shelej Khera, Praveen Kumar Malik, Samudrala Vara Kumari, Sudipta Das, Walid El-Shafai, Moustafa H. Aly
      First page: 669
      Abstract: In this article, a base station array antenna in 1 × 6 configuration is proposed for sub-6GHz 5G applications. Analyses have been performed on two orthogonally arranged dipole strips, a balun with various feeding schemes, and a reflector with different side walls. At the balanced feed position, aluminum is used to connect the feeding balun and the dipole through a hole. A single crossed antenna element of size 66 × 66 × 78 mm3 is fabricated using an FR-4 substrate with a dielectric constant of 4.4, 1.6 mm thickness, and an operating frequency band from 3.2 to 5.22 GHz. The radiating element provides a stable and high gain of 11–18 dB using reflectors with sidewalls. The proposed element is simulated, and its electrical downward tilt is investigated for a 1 × 6 array arrangement with dimensions of 642 mm × 112 mm × 90 mm. Various radiation performance parameters are measured, such as gain, FBR (>26 dB), HPBW, and XPD (>11.5 dB) at 60° in the H-plane. A reflection coefficient of less than −15 dB and port-to-port isolation of greater than 27 dB are achieved. Simulation and measurement of radiation patterns are performed for the operating frequencies of 3.2, 4.2, and 5.2 GHz.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030669
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 670: A Low-Loss Impedance Transformer-Less
           Fish-Tail-Shaped MS-to-WG Transition for K-/Ka-/Q-/U-Band Applications

    • Authors: Atul Varshney, Vipul Sharma, Chittaranjan Nayak, Amit Kumar Goyal, Yehia Massoud
      First page: 670
      Abstract: This paper presents a low-loss, high-transmission, broadside-coupled, transverse, reciprocal, two-port, and nature-inspired Ka-band transition design to move the electromagnetic energy of a rectangular waveguide (RWG) to the microstrip (MS) line. The proposed transition is simple in structure, with an excellent insertion loss, S12/S21, (IL) near −0.40 dB and return loss, S11/S22, of <−21 dB, while the VSWR value is very close to one. Thus, this transition is an outstanding candidate for MIC/MMIC-based millimeter wave, military, and RADAR applications, as well as in wireless and satellite communications as a compatible connector. This transition also provides a bandwidth of 21.50 GHz (23.52–45.0 GHz) for the abovementioned microwave applications, at a <−10 dB return loss (RL). The proposed transition model also exhibits a −15 dB absolute bandwidth of 27.06–23.44 GHz, with an insertion loss < −0.60 dB. Due to a return loss of <−15 dB over an ultra-wide bandwidth, the proposed transition is not only a good candidate for full Ka-band (26–40 GHz) applications but also covers applications for K-band from 23.74 GHz to 26.0 GHz, Q-band applications from 33.0 to 45.0 GHz, and U-band applications from 40.0 GHz to 45 GHz, with approximately 97% power transmission between the transmission lines and only 3% power reflections. The impedance matching at the designed frequency between the RWG and MS line is achieved by flaring one end of the MS line inside the RWG in a fishtail shape, without the need for a quarter-wave/tapered/exponential/Binomial, or multi-section Chebyshev transformer. The main goal of this research was to design a multi-section impedance-transformer-free, simple, and easy-to-fabricate MS line, to share electromagnetic (EM) energy between an MS line and RWG in 30 GHz satellite applications and 30 GHz high-frequency applications, for interconnects screen printed on an organic substrate for flexible, wearable, textile conformal antennas. This work also presents an exact RLC electrical equivalence model of the MS line (fishtail) to RWG transition at 30 GHz. The novelty of this work is that the proposed transition can be used for four microwave bands of electromagnetic energy transmission, with extremely low reflection, and with a compact, simple-design MS line, and simple RWG.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030670
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 671: Two-Dimensional Positioning with Machine
           Learning in Virtual and Real Environments

    • Authors: Dávid Kóczi, József Németh, József Sárosi
      First page: 671
      Abstract: In this paper, a ball-on-plate control system driven only by a neural network agent is presented. Apart from reinforcement learning, no other control solution or support was applied. The implemented device, driven by two servo motors, learned by itself through thousands of iterations how to keep the ball in the center of the resistive sensor. We compared the real-world performance of agents trained in both a real-world and in a virtual environment. We also examined the efficacy of a virtually pre-trained agent fine-tuned in the real environment. The obtained results were evaluated and compared to see which approach makes a good basis for the implementation of a control task implemented purely with a neural network.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030671
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 672: A Power Transformer Fault Diagnosis
           Method Based on Improved Sand Cat Swarm Optimization Algorithm and
           Bidirectional Gated Recurrent Unit

    • Authors: Wanjie Lu, Chun Shi, Hua Fu, Yaosong Xu
      First page: 672
      Abstract: The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO is further proposed to optimize the BiGRU parameters to build an optimal diagnosis model based on BiGRU. For the ISCSO, four improvement methods are proposed. The traditional sand cat swarm algorithm is improved using logistic chaotic mapping, the water wave dynamic factor, adaptive weighting, and the golden sine strategy. Then, benchmarking functions are used to test the optimization performance of ISCSO and the four algorithms, and the results show that ISCSO has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on L-Isomap and ISCSO-BiGRU is obtained. Using the model for fault diagnosis, the example simulation results show that using L-ISOMP to filter and downscale the model inputs can better improve model performance. The results are compared with the SCSO-BiGRU, WOA-BiGRU, GWO-BiGRU, and PSO-BiGRU fault diagnosis models. The results show that the fault diagnosis rate of ISCSO-BiGRU is 94.8%, which is 11.69%, 10.39%, 7.14%, and 5.9% higher than that of PSO-BiGRU, GWO-BiGRU, WOA-BiGRU, and SCSO-BiGRU, respectively, and validate that the proposed method can effectively improve the fault diagnosis performance of transformers.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030672
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 673: Table Structure Recognition Method Based
           on Lightweight Network and Channel Attention

    • Authors: Tao Zhang, Yi Sui, Shunyao Wu, Fengjing Shao, Rencheng Sun
      First page: 673
      Abstract: The table recognition model rows and columns aggregated network (RCANet) uses a semantic segmentation approach to recognize table structure, and achieves better performance in table row and column segmentation. However, this model uses ResNet18 as the backbone network, and the model has 11.35 million parameters and a volume of 45.5 M, which is inconvenient to deploy to lightweight servers or mobile terminals. Therefore, from the perspective of model compression, this paper proposes the lightweight rows and columns attention aggregated network (LRCAANet), which uses the lightweight network ShuffleNetv2 to replace the original RCANet backbone network ResNet18 to simplify the model size. Considering that the lightweight network reduces the number of feature channels, it has a certain impact on the performance of the model. In order to strengthen the learning between feature channels, the rows attention aggregated (RAA) module and the columns attention aggregated (CAA) module are proposed. The RAA module and the CAA module add the squeeze and excitation (SE) module to the original row and column aggregated modules, respectively. Adding the SE module means the model can learn the correlation between channels and improve the prediction effect of the lightweight model. The experimental results show that our method greatly reduces the model parameters and model volume while ensuring low-performance loss. In the end, the average F1 score of our model is only 1.77% lower than the original model, the parameters are only 0.17 million, and the volume is only 0.8 M. Compared with the original model, the parameter amount and volume are reduced by more than 95%.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030673
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 674: Pruning Multi-Scale Multi-Branch Network
           for Small-Sample Hyperspectral Image Classification

    • Authors: Yu Bai, Meng Xu, Lili Zhang, Yuxuan Liu
      First page: 674
      Abstract: In recent years, the use of deep learning models has developed rapidly in the field of hyperspectral image (HSI) classification. However, most network models cannot make full use of the rich spatial-spectral features in hyperspectral images, being disadvantaged by their complex models and low classification accuracy for small-sample data. To address these problems, we present a lightweight multi-scale multi-branch hybrid convolutional network for small-sample classification. The network contains two new modules, a pruning multi-scale multi-branch block (PMSMBB) and a 3D-PMSMBB, each of which contains a multi-branch part and a pruning part. Each branch of the multi-branch part contains a convolutional kernel of different scales. In the training phase, the multi-branch part can extract rich feature information through different perceptual fields using the asymmetric convolution feature, which can effectively improve the classification accuracy of the model. To make the model lighter, pruning is introduced in the master branch of each multi-branch module, and the pruning part can remove the insignificant parameters without affecting the learning of the multi-branch part, achieving a light weight model. In the testing phase, the multi-branch part and the pruning part are jointly transformed into one convolution, without adding any extra parameters to the network. The study method was tested on three datasets: Indian Pines (IP), Pavia University (PU), and Salinas (SA). Compared with other advanced classification models, this pruning multi-scale multi-branch hybrid convolutional network (PMSMBN) had significant advantages in HSI small-sample classification. For instance, in the SA dataset with multiple crops, only 1% of the samples were selected for training, and the proposed method achieved an overall accuracy of 99.70%.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030674
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 675: Hierarchical Queue Management Priority
           and Balancing Based Method under the Interaction Prediction Principle

    • Authors: Oleksandr Lemeshko, Oleksandra Yeremenko, Larysa Titarenko, Alexander Barkalov
      First page: 675
      Abstract: This work is devoted to improving a two-level hierarchical queue management method based on priority and balancing under the interaction prediction principle. The lower level of calculations was connected with the problem optimization solution and was responsible for two tasks. Firstly, the packet flow aggregation and distribution among the macro-queues and sub-queues organized on the router interface must solve the congestion management problem. Secondly, the resource allocation problem solution was related to the balanced allocation of interface bandwidth among the sub-queues, which were weighted relative to their priorities under the traffic-engineering queues. The method’s lower-level functions were recommended to be placed on a set of processors of a routing device responsible for servicing the packets of individual macro-queues. At the same time, the processor coordinator could perform the functions of the upper-level calculations, providing interface bandwidth allocation among the macro-queues. The numerical research results of the proposed two-level hierarchical queue management method confirmed its effectiveness in ensuring high scalability. Balanced, priority-based packet flow distribution and interface bandwidth allocation among the macro-queues and sub-queues were implemented. In addition, the time was reduced for solving tasks related to queue management. The method demonstrated high convergence of the coordination procedure and the quality of the centralized calculations. The proposed approach can be used in various embedded systems.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030675
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 676: An Approach for Classification of
           Alzheimer’s Disease Using Deep Neural Network and Brain Magnetic
           Resonance Imaging (MRI)

    • Authors: Ruhul Amin Hazarika, Arnab Kumar Maji, Debdatta Kandar, Elzbieta Jasinska, Petr Krejci, Zbigniew Leonowicz, Michal Jasinski
      First page: 676
      Abstract: Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1×1,3×3, and 5×5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030676
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 677: Lightweight-BIoV: Blockchain Distributed
           Ledger Technology (BDLT) for Internet of Vehicles (IoVs)

    • Authors: Asif Ali Laghari, Abdullah Ayub Khan, Reem Alkanhel, Hela Elmannai, Sami Bourouis
      First page: 677
      Abstract: The vast enhancement in the development of the Internet of Vehicles (IoV) is due to the impact of the distributed emerging technology and topology of the industrial IoV. It has created a new paradigm, such as the security-related resource constraints of Industry 5.0. A new revolution and dimension in the IoV popup raise various critical challenges in the existing information preservation, especially in node transactions and communication, transmission, trust and privacy, and security-protection-related problems, which have been analyzed. These aspects pose serious problems for the industry to provide vehicular-related data integrity, availability, information exchange reliability, provenance, and trustworthiness for the overall activities and service delivery prospects against the increasing number of multiple transactions. In addition, there has been a lot of research interest that intersects with blockchain and Internet of Vehicles association. In this regard, the inadequate performance of the Internet of Vehicles and connected nodes and the high resource requirements of the consortium blockchain ledger have not yet been tackled with a complete solution. The introduction of the NuCypher Re-encryption infrastructure, hashing tree and allocation, and blockchain proof-of-work require more computational power as well. This paper contributes in two different folds. First, it proposes a blockchain sawtooth-enabled modular architecture for protected, secure, and trusted execution, service delivery, and acknowledgment with immutable ledger storage and security and peer-to-peer (P2P) network on-chain and off-chain inter-communication for vehicular activities. Secondly, we design and create a smart contract-enabled data structure in order to provide smooth industrial node streamlined transactions and broadcast content. Substantially, we develop and deploy a hyperledger sawtooth-aware customized consensus for multiple proof-of-work investigations. For validation purposes, we simulate the exchange of information and related details between connected devices on the IoV. The simulation results show that the proposed architecture of BIoV reduces the cost of computational power down to 37.21% and the robust node generation and exchange up to 56.33%. Therefore, only 41.93% and 47.31% of the Internet of Vehicles-related resources and network constraints are kept and used, respectively.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030677
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 678: Design of Synaptic Driving Circuit for
           TFT eFlash-Based Processing-In-Memory Hardware Using Hybrid Bonding

    • Authors: Younghee Kim, Hongzhou Jin, Dohoon Kim, Panbong Ha, Min-Kyu Park, Joon Hwang, Jongho Lee, Jeong-Min Woo, Jiyeon Choi, Changhyuk Lee, Joon Young Kwak, Hyunwoo Son
      First page: 678
      Abstract: This paper presents a synaptic driving circuit design for processing in-memory (PIM) hardware with a thin-film transistor (TFT) embedded flash (eFlash) for a binary/ternary-weight neural network (NN). An eFlash-based synaptic cell capable of programming negative weight values to store binary/ternary weight values (i.e., ±1, 0) and synaptic driving circuits for erase, program, and read operations of synaptic arrays have been proposed. The proposed synaptic driving circuits improve the calculation accuracy of PIM operation by precisely programming the sensing current of the eFlash synaptic cell to the target current (50 nA ± 0.5 nA) using a pulse train. In addition, during PIM operation, the pulse-width modulation (PWM) conversion circuit converts 8-bit input data into one continuous PWM pulse to minimize non-linearity in the synaptic sensing current integration step of the neuron circuit. The prototype chip, including the proposed synaptic driving circuit, PWM conversion circuit, neuron circuit, and digital blocks, is designed and laid out as the accelerator for binary/ternary weighted NN with a size of 324 × 80 × 10 using a 0.35 μm CMOS process. Hybrid bonding technology using bump bonding and wire bonding is used to package the designed CMOS accelerator die and TFT eFlash-based synapse array dies into a single chip package.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030678
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 679: A Fault-Tolerant Bidirectional Converter
           for Battery Energy Storage Systems in DC Microgrids

    • Authors: Mohammad Saeed Mahdavi, Mohammad Saleh Karimzadeh, Tohid Rahimi, Gevork Babamalek Gharehpetian
      First page: 679
      Abstract: Battery energy storage systems (BESSs) can control the power balance in DC microgrids through power injection or absorption. A BESS uses a bidirectional DC–DC converter to control the power flow to/from the grid. On the other hand, any fault occurrence in the power switches of the bidirectional converter may disturb the power balance and stability of the DC microgrid and, thus, the safe operation of the battery bank. This paper presents a fault-tolerant topology along with a fault diagnosis algorithm for a bidirectional DC–DC converter in a BESS. The proposed scheme can detect open circuit faults (OCFs) and reconfigure the topology to guarantee the safe and continuous operation of the system while it is connected to the DC microgrid. The proposed method can be extended to multi-phase structures of interleaved bidirectional DC–DC converters using only two power switches and n TRIACs to support the OCF occurrence on 2 × n switches of n legs. The proposed fault diagnosis algorithm detects OCFs only by observing the current of the inductors and does not require any sensor. Hence, the cost, weight, volume and complexity of the system is considerably reduced. Experimental results show that the reconfiguration of the converter, along with its fast fault detection, leads to fewer switches overloading and less DC voltage deviation.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030679
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 680: Quality-Driven Dual-Branch Feature
           Integration Network for Video Salient Object Detection

    • Authors: Xiaofei Zhou, Hanxiao Gao, Longxuan Yu, Defu Yang, Jiyong Zhang
      First page: 680
      Abstract: Video salient object detection has attracted growing interest in recent years. However, some existing video saliency models often suffer from the inappropriate utilization of spatial and temporal cues and the insufficient aggregation of different level features, leading to remarkable performance degradation. Therefore, we propose a quality-driven dual-branch feature integration network majoring in the adaptive fusion of multi-modal cues and sufficient aggregation of multi-level spatiotemporal features. Firstly, we employ the quality-driven multi-modal feature fusion (QMFF) module to combine the spatial and temporal features. Particularly, the quality scores estimated from each level’s spatial and temporal cues are not only used to weigh the two modal features but also to adaptively integrate the coarse spatial and temporal saliency predictions into the guidance map, which further enhances the two modal features. Secondly, we deploy the dual-branch-based multi-level feature aggregation (DMFA) module to integrate multi-level spatiotemporal features, where the two branches including the progressive decoder branch and the direct concatenation branch sufficiently explore the cooperation of multi-level spatiotemporal features. In particular, in order to provide an adaptive fusion for the outputs of the two branches, we design the dual-branch fusion (DF) unit, where the channel weight of each output can be learned jointly from the two outputs. The experiments conducted on four video datasets clearly demonstrate the effectiveness and superiority of our model against the state-of-the-art video saliency models.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030680
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 681: Hybrid Backstepping-Super Twisting
           Algorithm for Robust Speed Control of a Three-Phase Induction Motor

    • Authors: Sadia Ali, Alvaro Prado, Mahmood Pervaiz
      First page: 681
      Abstract: This paper proposes a Hybrid Backstepping Super Twisting Algorithm for robust speed control of a three-phase Induction Motor in the presence of load torque uncertainties. First of all, a three-phase squirrel cage Induction Motor is modeled in MATLAB/Simulink. This is then followed by the design of different non-linear controllers, such as sliding mode control (SMC), super twisting SMC, and backstepping control. Furthermore, a novel controller is designed by the synergy of two methods, such as backstepping and super twisting SMC (Back-STC), to obtain the benefits of both techniques and, thereby, improve robustness. The sigmoid function is used with an exact differentiator to minimize the high-speed discontinuities present in the input channel. The efficacy of this novel design and its performance were evidenced in comparison with other methods, carried out by simulations in MATLAB/Simulink. Regression parameters, such as ISE (Integral Square error), IAE (Integral Absolute error) and ITAE (Integral Time Absolute error), were calculated in three different modes of operation: SSM (Start-Stop Mode), NOM (Normal Operation Mode) and DRM (Disturbance Rejection Mode). In the end, the numerical values of the regression parameters were quantitatively analyzed to draw conclusions regarding the tracking performance and robustness of the implemented non-linear control techniques.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030681
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 682: Attention Mechanism Trained with Small
           Datasets for Biomedical Image Segmentation

    • Authors: Weihao Weng, Xin Zhu, Lei Jing, Mianxiong Dong
      First page: 682
      Abstract: The understanding of long-range pixel–pixel dependencies plays a vital role in image segmentation. The use of a CNN plus an attention mechanism still has room for improvement, since existing transformer-based architectures require many thousands of annotated training samples to model long-range spatial dependencies. This paper presents a smooth attention branch (SAB), a novel architecture that simplifies the understanding of long-range pixel–pixel dependencies for biomedical image segmentation in small datasets. The SAB is essentially a modified attention operation that implements a subnetwork via reshaped feature maps instead of directly calculating a softmax value over the attention score for each input. The SAB fuses multilayer attentive feature maps to learn visual attention in multilevel features. We also introduce position blurring and inner cropping specifically for small-scale datasets to prevent overfitting. Furthermore, we redesign the skip pathway for the reduction of the semantic gap between every captured feature of the contracting and expansive path. We evaluate the architecture of U-Net with the SAB (SAB-Net) by comparing it with the original U-Net and widely used transformer-based models across multiple biomedical image segmentation tasks related to the Brain MRI, Heart MRI, Liver CT, Spleen CT, and Colonoscopy datasets. Our training set was made of random 100 images of the original training set, since our goal was to adopt attention mechanisms for biomedical image segmentation tasks with small-scale labeled data. An ablation study conducted on the brain MRI test set demonstrated that every proposed method achieved an improvement in biomedical image segmentation. Integrating the proposed methods helped the resulting models consistently achieve outstanding performance on the above five biomedical segmentation tasks. In particular, the proposed method with U-Net improved its segmentation performance over that of the original U-Net by 13.76% on the Brain MRI dataset. We proposed several novel methods to address the need for modeling long-range pixel–pixel dependencies in small-scale biomedical image segmentation. The experimental results illustrated that each method could improve the medical image segmentation accuracy to various degrees. Moreover, SAB-Net, which integrated all proposed methods, consistently achieved outstanding performance on the five biomedical segmentation tasks.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030682
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 683: Development of Cloud Autonomous System
           for Enhancing the Performance of Robots’ Path

    • Authors: Kaushlendra Sharma, Rajesh Doriya, Sameer Shastri, Turki Aljrees, Kamred Udham Singh, Saroj Kumar Pandey, Teekam Singh, Jitendra Kumar Samriya, Ankit Kumar
      First page: 683
      Abstract: With the development of computer technology and artificial intelligence (AI), service robots are widely used in our daily life. At the same time, the manufacturing cost of the robots is too expensive for almost all small companies. The greatest technical limitations are the design of the service robot and the resource sharing of the robot groups. Path planning for robots is one of the issues playing an important role in every application of service robots. Path optimization, fast computation, and minimum computation time are required in all applications. This paper aims to propose the Google Cloud Computing Platform and Amazon Web Service (AWS) platforms for robot path planning. The aim is to identify the effect and impact of using a cloud computing platform for service robots. The cloud approach shifts the computation load from robots to the cloud server. Three different path-planning algorithms were considered to find the path for robots using the Google Cloud Computing Platform, while with AWS, three different types of environments, namely dense, moderate, and sparse, were selected to run the path-planning algorithms for robots. The paper presents the comparison and analysis of the results carried out for robot path planning using cloud services with that of the traditional approach. The proposed approach of using a cloud platform performs better in this case. The time factor is crucially diagnosed and presented in the paper. The major advantage derived from this experiment is that as the size of the environment increases, the respective relative delay decreases. This proves that increasing the scale of work can be beneficial by using cloud platforms. The result obtained using the proposed methodology proves that using cloud platforms improves the efficiency of path planning. The result reveals that using the cloud computing platform for service robots can change the entire perspective of using service robots in the future. The main advantage is that with the increase in the scale of services, the system remains stable, while the traditional system starts deteriorating in terms of performance.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030683
      Issue No: Vol. 12, No. 3 (2023)
       
  • Electronics, Vol. 12, Pages 684: Deep Feature Meta-Learners Ensemble
           Models for COVID-19 CT Scan Classification

    • Authors: Jibin B. Thomas, Shihabudheen KV, Sheik Mohammed Sulthan, Adel Al-Jumaily
      First page: 684
      Abstract: The infectious nature of the COVID-19 virus demands rapid detection to quarantine the infected to isolate the spread or provide the necessary treatment if required. Analysis of COVID-19-infected chest Computed Tomography Scans (CT scans) have been shown to be successful in detecting the disease, making them essential in radiology assessment and screening of infected patients. Single-model Deep CNN models have been used to extract complex information pertaining to the CT scan images, allowing for in-depth analysis and thereby aiding in the diagnosis of the infection by automatically classifying the chest CT scan images as infected or non-infected. The feature maps obtained from the final convolution layer of the Deep CNN models contain complex and positional encoding of the images’ features. The ensemble modeling of these Deep CNN models has been proved to improve the classification performance, when compared to a single model, by lowering the generalization error, as the ensemble can meta-learn from a broader set of independent features. This paper presents Deep Ensemble Learning models to synergize Deep CNN models by combining these feature maps to create deep feature vectors or deep feature maps that are then trained on meta shallow and deep learners to improve the classification. This paper also proposes a novel Attentive Ensemble Model that utilizes an attention mechanism to focus on significant feature embeddings while learning the Ensemble feature vector. The proposed Attentive Ensemble model provided better generalization, outperforming Deep CNN models and conventional Ensemble learning techniques, as well as Shallow and Deep meta-learning Ensemble CNNs models. Radiologists can use the presented automatic Ensemble classification models to assist identify infected chest CT scans and save lives.
      Citation: Electronics
      PubDate: 2023-01-29
      DOI: 10.3390/electronics12030684
      Issue No: Vol. 12, No. 3 (2023)
       
 
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