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Electronics
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 3
Number of Followers: 177  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2079-9292
Published by MDPI Homepage  [258 journals]
  • Electronics, Vol. 13, Pages 824: Machine Learning Techniques for
           Cyberattack Prevention in IoT Systems: A Comparative Perspective of
           Cybersecurity and Cyberdefense in Colombia

    • Authors: Emanuel Ortiz-Ruiz, Juan Ramón Bermejo, Juan Antonio Sicilia, Javier Bermejo
      First page: 824
      Abstract: This study investigates the application of machine learning techniques for cyberattack prevention in Internet of Things (IoT) systems, focusing on the specific context of cyberattacks in Colombia. The research presents a comparative perspective on cyberattacks in Colombia, aiming to identify the most effective machine learning methods for mitigating and preventing such threats. The study evaluates the performance of logistic regression, naïve Bayes, perceptron, and k-nearest neighbors algorithms in the context of cyberattack prevention. Results reveal the strengths and weaknesses of these techniques in addressing the unique challenges posed by cyberattackers in Colombia’s IoT infrastructure. The findings provide valuable insights for enhancing cybersecurity measures in the region and contribute to the broader field of IoT security.
      Citation: Electronics
      PubDate: 2024-02-20
      DOI: 10.3390/electronics13050824
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 827: A Spiking LSTM Accelerator for Automatic
           Speech Recognition Application Based on FPGA

    • Authors: Tingting Yin, Feihong Dong, Chao Chen, Chenghao Ouyang, Zheng Wang, Yongkui Yang
      First page: 827
      Abstract: Long Short-Term Memory (LSTM) finds extensive application in sequential learning tasks, notably in speech recognition. However, existing accelerators tailored for traditional LSTM networks grapple with high power consumption, primarily due to the intensive matrix–vector multiplication operations inherent to LSTM networks. In contrast, the spiking LSTM network has been designed to avoid these multiplication operations by replacing multiplication and nonlinear functions with addition and comparison. In this paper, we present an FPGA-based accelerator specifically designed for spiking LSTM networks. Firstly, we employ a low-cost circuit in the LSTM gate to significantly reduce power consumption and hardware cost. Secondly, we propose a serial–parallel processing architecture along with hardware implementation to reduce inference latency. Thirdly, we quantize and efficiently deploy the synapses of the spiking LSTM network. The power consumption of the accelerator implemented on Artix-7 and Zynq-7000 is only about 1.1 W and 0.84 W, respectively, when performing the inference for speech recognition with the Free Spoken Digit Dataset (FSDD). Additionally, the energy consumed per inference is remarkably efficient, with values of 87 µJ and 66 µJ, respectively. In comparison with dedicated accelerators designed for traditional LSTM networks, our spiking LSTM accelerator achieves a remarkable reduction in power consumption, amounting to orders of magnitude.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050827
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 828: Joint Optimization on Trajectory, Data
           Relay, and Wireless Power Transfer in UAV-Based Environmental Monitoring
           System

    • Authors: Jaewook Lee, Haneul Ko
      First page: 828
      Abstract: In environmental monitoring systems based on the Internet of Things (IoT), sensor nodes (SNs) typically send data to the server via a wireless gateway (GW) at regular intervals. However, when SNs are located far from the GW, substantial energy is expended in transmitting data. This paper introduces a novel unmanned aerial vehicle (UAV)-based environmental monitoring system. In the proposed system, the UAV conducts patrols in the designated area, and SNs periodically transmit the collected data to the GW or the UAV. This transmission decision is made while taking into account the respective distance between both the GW and the UAV. To ensure a high-quality environmental map, characterized by a consistent collection of a satisfactory amount of up-to-date data while preventing energy depletion in the SNs and the UAV, the UAV periodically decides on three types of UAV operations. These decisions involve deciding where to move, deciding whether to relay or aggregate the data from the SNs, and deciding whether to transfer energy to the SNs. For the optimal decisions, we introduce an algorithm, called DeepUAV, using deep reinforcement learning (DRL) to make decisions in UAV operations. In DeepUAV, the controller continually learns online and enhances the UAV’s decisions through trial and error. The evaluation results indicate that DeepUAV successfully gathers a substantial amount of the current data consistently while mitigating the risk of energy depletion in SNs and the UAV.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050828
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 829: Influence of α-Stable Noise on the
           Effectiveness of Non-Negative Matrix Factorization—Simulations and
           Real Data Analysis

    • Authors: Anna Michalak, Rafał Zdunek, Radosław Zimroz, Agnieszka Wyłomańska
      First page: 829
      Abstract: Non-negative matrix factorization (NMF) has been used in various applications, including local damage detection in rotating machines. Recent studies highlight the limitations of diagnostic techniques in the presence of non-Gaussian noise. The authors examine the impact of non-Gaussianity levels on the extraction of the signal of interest (SOI). The simple additive model of the signal is proposed: SOI and non-Gaussian noise. As a model of the random component, i.e., noise, a heavy-tailed α-stable distribution with two important parameters (σ and α) was proposed. If SOI is masked by noise (controlled by σ), the influence of non-Gaussianity level (controlled by α) is more critical. We performed an empirical analysis of how these parameters affect SOI extraction effectiveness using NMF. Finally, we applied two NMF algorithms to several (both vibration and acoustic) signals from a machine with faulty bearings at different levels of non-Gaussian disturbances and the obtained results align with the simulations. The main conclusion of this study is that NMF is a very powerful tool for analyzing non-Gaussian data and can provide satisfactory results in a wide range of a non-Gaussian noise levels.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050829
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 830: CycleGAN-Based Data Augmentation for
           Subgrade Disease Detection in GPR Images with YOLOv5

    • Authors: Yang Yang, Limin Huang, Zhihou Zhang, Jian Zhang, Guangmao Zhao
      First page: 830
      Abstract: Vehicle-mounted ground-penetrating radar (GPR) technology is an effective means of detecting railway subgrade diseases. However, existing methods of GPR data interpretation largely rely on manual identification, which is not only inefficient but also highly subjective. This paper proposes a semi-supervised deep learning method to identify railway subgrade diseases. This method addresses the sample imbalance problem in the defect dataset by utilizing a data augmentation method based on a generative adversarial network model. An initial network model for disease identification is obtained by training the YOLOv5 network with a small number of existing samples. The intelligently extended samples are then labeled to achieve a balance in the disease samples. The network is trained to improve the recognition accuracy of the intelligent model using a more complete dataset. The experimental results show that the accuracy of the proposed method can reach up to 94.53%, which is 23.85% higher than that of the supervised learning model without an extended dataset. This has strong industrial application value for railway subgrade disease detection as the potential learning ability of the model can be explored to a greater extent, thereby improving the recognition accuracy of subgrade diseases.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050830
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 831: Training of Deep Joint
           Transmitter-Receiver Optimized Communication System without Auxiliary
           Tools

    • Authors: Wenhao Sun, Yuchen He, Tianfeng Yan, Zhongdong Wu, Yide Ma
      First page: 831
      Abstract: Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050831
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 832: Evaluating the Potential of Floating
           Photovoltaic Plants in Pumped Hydropower Reservoirs in Spain

    • Authors: Arsenio Barbón, Claudia Rodríguez-Fernández, Luis Bayón, Javier Aparicio-Bermejo
      First page: 832
      Abstract: The Spanish government is a strong advocate of reducing CO2 emissions and has made a clear commitment to the implementation of renewable energies. As reflected in Spain’s National Energy and Climate Plan (NECP), its objective is to double the current capacity of pumped hydropower storage (PHS) plants by 2030. Therefore, the study presented here is both current and forward-looking. This paper presents the results of the analysis of the technical potential of installing floating photovoltaic (FPV) plants at 25 PHS plants in Spain, i.e., the total capacity of Spanish hydropower plants. The study was conducted using various assessment indicators: the global horizontal irradiance ratio, electrical efficiency ratio, area required ratio, pumping area ratio, volume ratio of water pumped per day, and achievable power ratio. In summary, the following conclusions can be drawn: (i) The global horizontal irradiance ratio indicates whether a FPV plant is economically viable. From this point of view, the Aguayo PHS plant and the Tanes PHS plant are not suitable, as this ratio is very low; (ii) the compliance with the electrical efficiency ratio is flexible, and all hydropower plants meet this criterion; (iii) maximising the use of the assigned grid connection capacity is one of the goals sought by electrical companies when implementing FPV plants at existing PHS plants. The following hydropower plants are not suitable for the implementation of an FPV plant in view of the following: La Muela I, La Muela II, Aguayo, Sallente, Aldeadavila II, Moralets, Guillena, Bolarque II, Montamara, and IP; (iv) if the aim is energy storage, the following hydropower plants are not suitable for the implementation of an FPV plant: the La Muela I, La Muela II, Tajo de la Encantada, Aguayo, Sallente, Aldeadavila II, Conso, Moralets, Guillena, Bolarque II, Tanes, Montamara, Soutelo, Bao-Puente Bibey, Santiago de Jares, IP, and Urdiceto; (v) if the aim is to expand an FPV plant already installed at a PHS plant, the following hydropower plants do not meet this criterion: the La Muela I, La Muela II, Aguayo, Sallente, Aldeadavila, Moralets, Guillena, Bolarque II, Montamara, and IP. There are only eight hydropower plants that meet conditions (i), (iii) and (iv): the Villarino, Torrejon, Valparaiso, Gabriel y Galan, Guijo de Granadilla, Pintado, and Gobantes.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050832
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 833: Microwave Metamaterial Absorber with
           Radio Frequency/Direct Current Converter for Electromagnetic Harvesting
           System

    • Authors: Jerzy Mizeraczyk, Magdalena Budnarowska
      First page: 833
      Abstract: This article presents the analysis of the electromagnetic (EM) properties of a novel metamaterial (MM) array in the microwave frequency range. The background for this work is the rapid development of portable devices with low individual energy consumption for the so-called “Internet of Things” (IoT) and the demand for energy harvesting from the environment on a micro scale through harvesters capable of powering billions of small receivers globally. The main goal of this work was to check the potential of the novel MM array structure for EM energy harvesting. The proposed MM array was analyzed in the CST Studio simulation environment. This resulted in the determination of the substitute average EM parameters (absorption, reflection, and transmission) of the MM array. Then, the MM array was manufactured, and the simulation results of the MM array parameters were experimentally validated in a microwave waveguide test system. Based on this conclusion, a prototype of the microwave MM absorber, together with an RF/DC converter, was designed and manufactured for harvesting EM energy from the environment. The system’s energy efficiency was evaluated, and its potential application in energy harvesting technology was appraised. Using a microwave horn antenna, the EM energy harvesting efficiency of the prototype was evaluated. It was about 50% at a microwave frequency of about 2.6 GHz. This may make the prototype attractive as an EM energy harvester or bolometric sensor.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050833
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 834: PHIR: A Platform Solution of Data-Driven
           Health Monitoring for Industrial Robots

    • Authors: Fei Jiang, Chengyun Hu, Chongwei Liu, Rui Wang, Jianyong Zhu, Shiru Chen, Juan Zhang
      First page: 834
      Abstract: The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050834
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 835: Offline Mongolian Handwriting Recognition
           Based on Data Augmentation and Improved ECA-Net

    • Authors: Qing-Dao-Er-Ji Ren, Lele Wang, Zerui Ma, Saheya Barintag
      First page: 835
      Abstract: Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050835
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 836: Toward Greener Smart Cities: A Critical
           Review of Classic and Machine-Learning-Based Algorithms for Smart Bin
           Collection

    • Authors: Alice Gatti, Enrico Barbierato, Andrea Pozzi
      First page: 836
      Abstract: This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050836
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 837: Bridging the Cybersecurity Gap: A
           Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas
           Network Industrial Control and Automation Systems

    • Authors: Thierno Gueye, Asif Iqbal, Yanen Wang, Ray Tahir Mushtaq, Mohd Iskandar Petra
      First page: 837
      Abstract: This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050837
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 838: Design and Parameter Optimization of
           Double-Mosquito Combination Coils for Enhanced Anti-Misalignment
           Capability in Inductive Wireless Power Transfer Systems

    • Authors: Wencong Huang, Jinying Huang, Ying Hu, Yuqiao Zhu, Yufang Chang
      First page: 838
      Abstract: This paper proposes a novel double-mosquito combination (DMC) coil for inductive wireless power transfer (IPT) systems to improve their anti-misalignment capability. The DMC coil consists of a mosquito coil with single-turn spacing and a tightly wound close-wound coil. By superimposing the magnetic fields generated by both coils, a relatively uniform magnetic field distribution is achieved on the receiving coil plane. This approach addresses the challenges of significant output voltage fluctuations and reduced transmission efficiencies caused by coupling coil misalignments in conventional IPT systems. To further optimize the DMC coil, an interaction law between its parameters and the mutual inductance is established, setting the coil mutual inductance fluctuation rate as the optimization objective, and using the coil turn spacing, number of turns, and outer diameter as constraint conditions. The beetle antennae search algorithm (BAS) is employed to enhance the whale optimization algorithm (WOA), facilitating the adaptive optimization of the coil parameters. An experimental IPT system platform with a 50 mm transmission distance is developed to validate the robust anti-misalignment capability of the proposed coil. The results demonstrate that within a horizontal misalignment range of 50 mm, the system’s output voltage fluctuation rate stays below 7.4%, and the transmission efficiency remains above 83%.
      Citation: Electronics
      PubDate: 2024-02-21
      DOI: 10.3390/electronics13050838
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 839: Ultrathin Antenna-in-Package Based on
           TMV-Embedded FOWLP for 5G mm-Wave Applications

    • Authors: Yuhang Yin, Chenhui Xia, Shuli Liu, Zhimo Zhang, Chen Chen, Gang Wang, Chenqian Wang, Yafei Wu
      First page: 839
      Abstract: In this paper, a novel through mold via (TMV)-embedded fan-out wafer-level package (FOWLP) technology was demonstrated to manufacture the well-designed Antenna in Package (AiP) with ultrathin thickness (0.04 λ0). Double-sided redistribution layers (RDLs) were employed to build the patch antenna, while a TMV interposer was used to connect the front and back RDLs. By optimizing the AiP’s parameters, the patch antenna can achieve a wide impedance bandwidth of 17.8% from 24.2 to 28.5 GHz, which can cover the 5G frequency bands. Compared with previous works, the proposed AiP has significant benefits in terms of its ultralow profile, easy processing, and high gain. Hence, the TMV-embedded FOWLP should be a promising technology for fifth generation (5G) millimeter wave (mm-Wave) applications.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050839
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 840: Harnessing the Radio Frequency Power
           Level of Cellular Terminals for Weather Parameter Sensing

    • Authors: Alexandros Sakkas, Vasilis Christofilakis, Christos J. Lolis, Spyridon K. Chronopoulos, Giorgos Tatsis
      First page: 840
      Abstract: In light of recent extreme weather events, it is imperative to explore innovative methodologies for promptly and accurately measuring various meteorological parameters. The high spatial and temporal variability in precipitation often surpasses the resolution capabilities of traditional rain gauge measurements and satellite estimation algorithms. Therefore, exploring alternative methods to capture this variability is crucial. Research on the correlation between signal attenuation and precipitation could offer valuable insights into these alternative approaches. This study investigates (a) the feasibility of the classification of precipitation rate using signal power measurements in cellular terminals and (b) the impact of atmospheric humidity as well as other meteorological parameters on the signal. Specifically, signal power data were collected remotely through a specialized Android application designed for this research. During the time of analysis, the power data were processed alongside meteorological parameters obtained from the meteorological station of the Physics Department at the University of Ioannina gathered over one semester. Having in mind the radio refractivity of the air as a fascinating concept affecting the way radio waves travel through the atmosphere, the processed results revealed a correlation with signal attenuation, while a correlation between the latter and absolute humidity was also observed. Moreover, a precipitation rate classification was attained with an overall accuracy exceeding 88%.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050840
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 841: A Staged Real-Time Ground Segmentation
           Algorithm of 3D LiDAR Point Cloud

    • Authors: Weiye Deng, Xiaoping Chen, Jingwei Jiang
      First page: 841
      Abstract: Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050841
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 842: Robust Testing of AI Language Model
           Resiliency with Novel Adversarial Prompts

    • Authors: Brendan Hannon, Yulia Kumar, Dejaun Gayle, J. Jenny Li, Patricia Morreale
      First page: 842
      Abstract: In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050842
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 843: Equalizer Parameters’ Adjustment
           Based on an Oversampled Channel Model for OFDM Modulation Systems

    • Authors: Marcin Kucharczyk, Grzegorz Dziwoki, Jacek Izydorczyk, Wojciech Sułek, Adam Dustor, Wojciech Filipowski, Weronika Izydorczyk, Piotr Kłosowski, Piotr Zawadzki, Piotr Sowa, Michał Rajzer
      First page: 843
      Abstract: A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a greedy OFDM (Orthogonal Frequency Division Multiplexing) equalizer. The equalizer works flawlessly if delays between propagation paths are in the sampling grid. Otherwise, the channel impulse response loses its compressible characteristic and the number of coefficients to find increases. It is possible to get back to the simple channel model by data oversampling. The paper describes how the above idea helps the OMP (Orthogonal Matching Pursuit) algorithm estimate channel coefficients. The authors analyze the oversampling algorithm on the one hand to assess the influence of filtering function and signal resolution on the quality of the channel impulse response reconstruction. On the other hand, the abilities of the OMP algorithm are analyzed to distinguish components of the oversampled signal. Based on these analyses, we proposed modifications to the compressible channel’s impulse response reconstruction algorithm to minimize the number of transmission errors. A distinction was made between the filters used in the OMP search and channel reconstruction stages before calculating equalizer coefficients. Additionally, the results of the search stage were considered as elements within the groups.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050843
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 844: MM-NeRF: Large-Scale Scene Representation
           with Multi-Resolution Hash Grid and Multi-View Priors Features

    • Authors: Bo Dong, Kaiqiang Chen, Zhirui Wang, Menglong Yan, Jiaojiao Gu, Xian Sun
      First page: 844
      Abstract: Reconstructing large-scale scenes using Neural Radiance Fields (NeRFs) is a research hotspot in 3D computer vision. Existing MLP (multi-layer perception)-based methods often suffer from issues of underfitting and a lack of fine details in rendering large-scale scenes. Popular solutions are to divide the scene into small areas for separate modeling or to increase the layer scale of the MLP network. However, the subsequent problem is that the training cost increases. Moreover, reconstructing large scenes, unlike object-scale reconstruction, involves a geometrically considerable increase in the quantity of view data if the prior information of the scene is not effectively utilized. In this paper, we propose an innovative method named MM-NeRF, which integrates efficient hybrid features into the NeRF framework to enhance the reconstruction of large-scale scenes. We propose employing a dual-branch feature capture structure, comprising a multi-resolution 3D hash grid feature branch and a multi-view 2D prior feature branch. The 3D hash grid feature models geometric details, while the 2D prior feature supplements local texture information. Our experimental results show that such integration is sufficient to render realistic novel views with fine details, forming a more accurate geometric representation. Compared with representative methods in the field, our method significantly improves the PSNR (Peak Signal-to-Noise Ratio) by approximately 5%. This remarkable progress underscores the outstanding contribution of our method in the field of large-scene radiance field reconstruction.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050844
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 845: Pretrained Language–Knowledge Graph
           Model Benefits Both Knowledge Graph Completion and Industrial Tasks:
           Taking the Blast Furnace Ironmaking Process as an Example

    • Authors: Xiaoke Huang, Chunjie Yang
      First page: 845
      Abstract: Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050845
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 846: Efficient 2D DOA Estimation via Decoupled
           Projected Atomic Norm Minimization

    • Authors: Mingming Liu, Yangyang Dong, Chunxi Dong, Guoqing Zhao
      First page: 846
      Abstract: This paper presents an efficient two-dimensional (2D) direction of arrival (DOA) estimation method, termed as decoupled projected atomic norm minimization (D-PANM), to solve the angle-ambiguity problem. It first introduces a novel atomic metric via projecting the original atom set onto a smoothing space, based on which we formulate an equivalent semi-definite programming (SDP) problem. Then, two relatively low-complexity decoupled Toeplitz matrices can be obtained to estimate the DOAs. We further exploit the structural information hidden in the newly constructed data to avoid pair matching for the azimuth and elevation angles when the number of sensors is odd, and then propose a fast and feasible decoupled alternating projections (D-AP) algorithm, reducing computational complexity to a great extent. Numerical simulations are performed to demonstrate that the proposed algorithm is no longer restricted by angle ambiguity scenarios, but instead provides a more stable estimation performance, even when multiple signals share the same angles in both azimuth and elevation dimensions. Additionally, it greatly improves the resolution, with control of the computation load compared with the existing atomic norm minimization (ANM) algorithm.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050846
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 847: Innovative Fault Current Evaluation
           Method for Active DC Grids

    • Authors: Julian Valbuena Valbuena Godoy, Simone Negri, Francesca Oliva, Antonello Antoniazzi, Roberto Sebastiano Faranda
      First page: 847
      Abstract: DC smart grids are a promising solution for the efficient integration of renewable energy sources and loads. Still, their widespread adoption is hindered by significant challenges related to fault response, identification, and clearance. The traditional DC fault analysis method is a useful tool for straightforwardly understanding the behaviour of fault current contributions from DC converters in LVDC networks during a fault. However, when a system with multiple converters and non-negligible fault impedance need to be considered, its accuracy is severely limited due to the assumptions included in the problem solution, thus leading to the following: (a) the dependency of the results’ reliability on fault impedance values and/or other converter fault current contributions; (b) the inaccuracy of the diode current estimation; and (c) the inaccuracy of the conductor joule integral. Thus, these results’ data may be unreliable for designing protection systems for one converter or for an entire network. In order to overcome these issues, this paper proposes an innovative, simple numerical approach to DC fault current evaluation, which can be adopted when the number of converters become significant, or the network is complex. This method arises from the primary interest in solving the circuit to extract the indicators (current peak value and time, joule integral, etc.) necessary for designing circuit protections. This approach proved to grant two main advantages over traditional methods: (a) it provides accurate results, with no need to introduce any specific assumption; (b) it can be structured to manage an arbitrary number of converters; and (c) it reduces the computational processing times and resources necessary to simulate an entire DC network in comparison to other circuit solution software.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050847
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 848: Conditional Proxy Re-Encryption-Based Key
           Sharing Mechanism for Clustered Federated Learning

    • Authors: Yongjing Zhang, Zhouyang Zhang, Shan Ji, Shenqing Wang, Shitao Huang
      First page: 848
      Abstract: The need of data owners for privacy protection has given rise to collaborative learning, and data-related issues heterogeneity faced by federated learning has further given rise to clustered federated learning; whereas the traditional privacy-preserving scheme of federated learning using homomorphic encryption alone fails to fulfill the privacy protection demands of clustered federated learning. To address these issues, this research provides an effective and safeguarded answer for sharing homomorphic encryption keys among clusters in clustered federated learning grounded in conditional representative broadcast re-encryption. This method constructs a key sharing mechanism. By combining the functions of the bilinear pairwise accumulator and specific conditional proxy broadcast re-ciphering, the mechanism can verify the integrity of homomorphic encryption keys stored on cloud servers. In addition, the solution enables key management centers to grant secure and controlled access to re-encrypted homomorphic encryption keys to third parties without disclosing the sensitive information contained therein. The scheme achieves this by implementing a sophisticated access tree-based mechanism that enables the cloud server to convert forwarded ciphertexts into completely new ciphertexts customized specifically for a given group of users. By effectively utilizing conditional restrictions, the scheme achieves fine-grained access control to protect the privacy of shared content. Finally, this paper showcases the scheme’s security against selective ciphertext attacks without relying on random prediction.
      Citation: Electronics
      PubDate: 2024-02-22
      DOI: 10.3390/electronics13050848
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 849: Analysis of Magnetotherapy Device-Induced
           Fields Using Cylindrical Human Body Model

    • Authors: Mario Cvetković, Bruno Sučić
      First page: 849
      Abstract: This paper deals with the analysis of induced current density and the induced electric field in the body of a human exposed to the magnetic field of a magnetotherapy device. As the displacement currents at extremely low frequencies can be neglected, the biological tissues can thus be considered a weakly conducting medium, facilitating the use of a quasi-static eddy current approximation. The formulation is based on the surface integral equation for the unknown surface charges, whose numerical solution is obtained using the method of moments technique. A simplified model of the human body is utilized to examine various scenarios during the magnetotherapy procedure. The numerical results for the induced current density and the induced electric field are obtained using the proposed model. The analyses of various stimulating coil parameters, human body model parameters, and a displacement of the magnetotherapy coil were carried out to assess their effects on the induced current density. The results suggest that selection of the stimulating coil should be matched based on the size of the human body, but also that the position and orientation of the coil with respect to the body surface will result in different distributions of the induced fields. The results of this study could be useful for medical professionals by showing the importance of various magnetotherapy coil parameters for preparation of various treatment scenarios.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050849
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 850: An Improved Safety Belt Detection
           Algorithm for High-Altitude Work Based on YOLOv8

    • Authors: Tingyao Jiang, Zhao Li, Jian Zhao, Chaoguang An, Hao Tan, Chunliang Wang
      First page: 850
      Abstract: High-altitude work poses significant safety risks, and wearing safety belts is crucial to prevent falls and ensure worker safety. However, manual monitoring of safety belt usage is time consuming and prone to errors. In this paper, we propose an improved high-altitude safety belt detection algorithm based on the YOLOv8 model to address these challenges. Our paper introduces several improvements to enhance its performance in detecting safety belts. First, to enhance the feature extraction capability, we introduce a BiFormer attention mechanism. Moreover, we used a lightweight upsampling operator instead of the original upsampling layer to better preserve and recover detailed information without adding an excessive computational burden. Meanwhile, Slim-neck was introduced into the neck layer. Additionally, extra auxiliary training heads were incorporated into the head layer to enhance the detection capability. Lastly, to optimize the prediction of bounding box position and size, we replaced the original loss function with MPDIOU. We evaluated our algorithm using a dataset collected from high-altitude work scenarios and demonstrated its effectiveness in detecting safety belts with high accuracy. Compared to the original YOLOv8 model, the improved model achieves P (precision), R (recall), and mAP (mean average precision) values of 98%, 91.4%, and 97.3%, respectively. These values represent an improvement of 5.1%, 0.5%, and 1.2%, respectively, compared to the original model. The proposed algorithm has the potential to improve workplace safety and reduce the risk of accidents in high-altitude work environments.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050850
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 851: Moving-Target Detection for FDA-MIMO
           Radar in Partially Homogeneous Environments

    • Authors: Changshan He, Running Zhang, Bang Huang, Mingming Xu, Zhibin Wang, Lei Liu, Zheng Lu, Ye Jin
      First page: 851
      Abstract: This paper delves into the problem of moving-target detection in partially homogeneous environments (PHE) with unknown Gaussian disturbance using a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Using training data, we have derived expressions for four adaptive detectors, including the one-step and two-step generalized likelihood ratio test (GLRT), two-step Rao (TRao) test, and two-step Wald (TWald) test criteria, respectively. All the proposed detectors are characterized by the constant false-alarm rate (CFAR). The theoretical analysis and simulation results validate the effectiveness of the proposed detectors.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050851
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 852: Enhancing Frequency Regulation Support
           through Several Synthetic Inertial Approaches for WDPS

    • Authors: Muhammad Asad, Jose Angel Sanchez-Fernandez
      First page: 852
      Abstract: The aim of this paper is to propose an enhancement to the primary frequency control (PFC) of the San Cristobal Island hybrid wind–diesel power system (WDPS). Naturally, variable speed wind turbines (VSWT) provide negligible inertia. Therefore, various control strategies, i.e., modified synthetic inertial control, droop control and traditional inertial control, if introduced into VSWT, enable them to release hidden inertia. Based on these strategies, a WDPS has been simulated under seven different control strategies, to evaluate the power system performance for frequency regulation (FR). Furthermore, the student psychology-based algorithm (SBPA) methodology is used to optimize the WDPS control. The results show that modified synthetic inertial control is the most suitable approach to provide FR. However, further exhaustive research validates that droop control is a better alternative than modified synthetic inertial control due to the negligible system performance differences. In addition, droop control does not require a frequency derivative function in the control system. Therefore, the hybrid system is more robust. Moreover, it reduces the steady state error, which makes the power system more stable. In addition, a pitch compensation control is introduced in blade pitch angle control (BPAC) to enhance the pitch angle smoothness and to help the power system to return to normal after perturbations. Moreover, to justify the performance of hybrid WDPS, it is tested under certain real-world contingency events, i.e., loss of a wind generator, increased wind speed, fluctuating wind speed, and simultaneously fluctuating load demand and wind speed. The simulation results validate the proposed WDPS control strategy performance.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050852
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 853: Urban Rail System Modeling and Simulation
           Based on Dynamic Train Density

    • Authors: Xinyang Yu, Xin Wang, Yuxin Qin
      First page: 853
      Abstract: To further improve the simulation calculation ability of urban rail traction systems during the peak operation period and provide an accurate and reliable simulation tool for the subsequent train schedule and energy storage system design, a multi-train circuit model with a bilateral power supply was established in this paper, and a power calculation algorithm based on dynamic train density was designed. The circuit topology in the model can be dynamically adjusted according to the number of trains to improve the operation rate. Based on the spatial and electrical data of a real section of the subway, the urban rail circuit model was built on the MATLAB platform, and the actual operation data of the subway was imported for verification. The experimental results show that the multi-train model can accurately reflect the influence of voltage fluctuations on the traction system under different train running conditions, and the results fit the actual operation conditions. By comparing the influence of different train intervals on the RBE (regenerative braking energy) utilization, the results show that the optimal RBE utilization rate can be achieved by adjusting the train interval in the peak period.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050853
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 854: MeMPA: A Memory Mapped M-SIMD
           Co-Processor to Cope with the Memory Wall Issue

    • Authors: Angela Guastamacchia, Andrea Coluccio, Fabrizio Riente, Giovanna Turvani, Mariagrazia Graziano, Maurizio Zamboni, Marco Vacca
      First page: 854
      Abstract: The amazing development of transistor technology has been the main driving force behind modern electronics. Over time, this process has slowed down introducing performance bottlenecks in data-intensive applications. A main cause is the classical von Neumann architecture, which entails constant data exchanges between processing units and data memory, wasting time and power. As a possible alternative, the Beyond von Neumann approach is now rapidly spreading. Although architectures following this paradigm vary a lot in layout and functioning, they all share the same principle: bringing computing elements as near as possible to memory while inserting customized processing elements, able to elaborate more data. Thus, power and time are saved through parallel execution and usage of processing components with local memory elements, optimized for running data-intensive algorithms. Here, a new memory-mapped co-processor (MeMPA) is presented to boost systems performance. MeMPA relies on a programmable matrix of fully interconnected processing blocks, each provided with memory elements, following the Multiple-Single Instruction Multiple Data model. Specifically, MeMPA can perform up to three different instructions, each on different data blocks, concurrently. Hence, MeMPA efficiently processes data-crunching algorithms, achieving energy and time savings up to 81.2% and 68.9%, respectively, compared with a RISC-V-based system.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050854
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 855: Multi-Modal Sarcasm Detection with
           Sentiment Word Embedding

    • Authors: Hao Fu, Hao Liu, Hongling Wang, Linyan Xu, Jiali Lin, Dazhi Jiang
      First page: 855
      Abstract: Sarcasm poses a significant challenge for detection due to its unique linguistic phenomenon where the intended meaning is often opposite of the literal expression. Current sarcasm detection technology primarily utilizes multi-modal processing, but the connotative semantic information provided by the modality itself is limited. It is a challenge to mine the semantic information contained in the combination of sarcasm samples and external commonsense knowledge. Furthermore, as the essence of sarcasm detection lies in measuring emotional inconsistency, the rich semantic information may introduce excessive noise to inconsistency measurement. To mitigate these limitations, we propose a hierarchical framework in this paper. Specifically, to enrich the semantic information of each modality, our approach uses sentiment dictionaries to obtain the sentiment vectors by evaluating the words extracted from various modalities, and then combines them with each modality. Furthermore, in order to mine the joint semantic information implied in the modalities and improve measurement of emotional inconsistency, the emotional information representation obtained by fusing each modality’s data is concatenated with the sentiment vector. Then, cross-modal fusion is performed through cross-attention, and, finally, the sarcasm is recognized by fusing low-level information in the cross-modal fusion layer. Our model is evaluated on a public multi-modal sarcasm detection dataset based on Twitter, and the results demonstrate its superiority.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050855
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 856: RETRACTED: Aljohani et al. A Novel Deep
           Learning CNN for Heart Valve Disease Classification Using Valve Sound
           Detection. Electronics 2023, 12, 846

    • Authors: Randa I. Aljohani, Hanan A. Hosni Mahmoud, Alaaeldin Hafez, Magdy Bayoumi
      First page: 856
      Abstract: The Electronics Editorial Office retracts the article, “A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection” [...]
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050856
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 857: A Personalized Federated Learning Method
           Based on Clustering and Knowledge Distillation

    • Authors: Jianfei Zhang, Yongqiang Shi
      First page: 857
      Abstract: Federated learning (FL) is a distributed machine learning paradigm under privacy preservation. However, data heterogeneity among clients leads to the shared global model obtained after training, which cannot fit the distribution of each client’s dataset, and the performance of the model degrades. To address this problem, we proposed a personalized federated learning method based on clustering and knowledge distillation, called pFedCK. In this algorithm, each client has an interactive model that participates in global training and a personalized model that is only trained locally. Both of the models perform knowledge distillation with each other through the feature representation of the middle layer and the soft prediction of the model. In addition, in order to make an interaction model only obtaining the model information from the client, which has similar data distribution and avoids the interference of other heterogeneous information, the server will cluster the clients according to the similarity of the amount of parameter variation uploaded by different interaction models during every training round. By clustering clients, interaction models with similar data distributions can cooperate with each other to better fit the local dataset distribution. Thereby, the performance of personalized model can be improved by obtaining more valuable information indirectly. Finally, we conduct simulation experiments on three benchmark datasets under different data heterogeneity scenarios. Compared to the single model algorithms, the accuracy of pFedCK improved by an average of 23.4% and 23.8% over FedAvg and FedProx, respectively; compared to typical personalization algorithms, the accuracy of pFedCK improved by an average of 0.8% and 1.3%, and a maximum of 1.0% and 2.9% over FedDistill and FML.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050857
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 858: Enhanced Efficiency in Permanent Magnet
           Synchronous Motor Drive Systems with MEPA Control Based on an Improved
           Iron Loss Model

    • Authors: Fang Xu, Handong Ren, Hongwu Zhan
      First page: 858
      Abstract: This paper investigates the efficiency optimization problem in the drive system of Permanent Magnet Synchronous Motors (PMSM) based on the iron loss resistance model. Initially, an efficiency calculation method is proposed, utilizing the direct current (DC) side power based on the iron loss motor model as the input power. This approach determines the system’s input power by measuring DC side voltage and current, accounting for the impacts of temperature, magnetic saturation, and inverter nonlinearity. It avoids the need for intricate nonlinear loss modeling of the inverter, thereby simplifying the computational requirements for system efficiency. Simultaneously, the d-q axis equivalent equations based on the iron loss motor model are employed to calculate the system’s output power, enhancing the accuracy of the system efficiency calculation. An improved discrete gradient descent algorithm is presented based on this system efficiency calculation model, accelerating the search for the optimal current angle and improving its accuracy. In comparison to existing methods, this approach exhibits adaptive step size and provides more precise and higher efficiency calculations, resulting in faster search speeds. Experimental and simulation evaluations are conducted to assess the effectiveness of the proposed methods.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050858
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 859: Method for Fault Diagnosis of Track
           Circuits Based on a Time–Frequency Intelligent Network

    • Authors: Feitong Peng, Tangzhi Liu
      First page: 859
      Abstract: In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050859
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 860: Critical Review on the Sustainability of
           Electric Vehicles: Addressing Challenges without Interfering in Market
           Trends

    • Authors: Sergi Obrador Rey, Lluc Canals Casals, Levon Gevorkov, Lázaro Cremades Oliver, Lluís Trilla
      First page: 860
      Abstract: The primary focus in electrifying the transportation sector should be sustainability. This can be effectively attained through the application of the seven eco-efficiency principles, which constitute the global standard for assessing the sustainability of products. Consequently, this framework should guide the development of current electric vehicle designs. The first section of the present article assesses the alignment of the automotive industry with these sustainability requirements. Results show that even though the electric vehicle promotes the use of cleaner energy resources, it falls short of adhering to the remaining principles. The implementation of advanced models in battery management systems holds great potential to enhance lithium-ion battery systems’ overall performance, increasing the durability of the batteries and their intensity of use. While many studies focus on improving current electric equivalent models, this research delves into the potential applicability of Reduced-Order Model techniques for physics-based models within a battery management systems context to determine the different health, charge, or other estimations. This study sets the baseline for further investigations aimed at enhancing the reduced-order physics-based modeling field. A research line should be aimed at developing advanced and improved cell-state indicators, with enhanced physical insight, for various lithium-ion battery applications.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050860
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 861: Frequency Security Control Technology for
           Simulated Wind Storage Integrated Power Grid

    • Authors: Weichao Li, Shouyuan Wu, Feng Zhang, Ning Shao
      First page: 861
      Abstract: Electronic control strategies are pivotal in the evolution of power systems, which have higher requirements for power leveling and optimization, frequency safety, and frequency stability. In contrast, the core objectives of existing energy storage services are mostly limited to one function, which cannot fully meet the operational requirements of power systems. This paper presents research on a frequency security controller based on digital twin technology and aimed to enhance the safety of the system. The proposed controller can simultaneously smooth out active wind power fluctuations and optimize reactive power, participate in system frequency regulation, and improve system damping to damp low-frequency oscillations based on simulating the actual operating environment. Simulations also verify the effectiveness of the proposed controller in DIgSILENT/PowerFactory based on a two-area system. This active/reactive power-based system support service will bring new economic benefits to wind energy storage systems.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050861
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 862: Style-Guided Adversarial Teacher for
           Cross-Domain Object Detection

    • Authors: Longfei Jia, Xianlong Tian, Yuguo Hu, Mengmeng Jing, Lin Zuo, Wen Li
      First page: 862
      Abstract: The teacher–student framework is widely employed for cross-domain object detection. However, it suffers from two problems. One is that large distribution discrepancies will cause critical performance drops. The other is that the samples that deviate from the overall distributions of both domains will greatly mislead the model. To solve these problems, we propose a style-guided adversarial teacher (SGAT) method for domain adaptation. Specifically, on the domain level, we generate target-like images based on source images to effectively narrow the gaps between domains. On the sample level, we denoise samples by estimating the probability density ratio of the ‘target-style’ and target distributions, which could filter out the unrelated samples and highlight the related ones. In this way, we could guarantee reliable samples. With these reliable samples, we learn the domain-invariant features through teacher–student mutual learning and adversarial learning. Extensive experiments verify the effectiveness of our method. In particular, we achieve 52.9% mAP on Clipart1k and 42.7% on Comic2k, which are 6.4% and 5.0% higher than the compared baselines.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050862
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 863: Load Scheduling of Smart Net-Zero
           Residential Buildings Based on Pandemic Situation

    • Authors: S. M. Mahfuz Alam, Mohd. Hasan Ali
      First page: 863
      Abstract: Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers’ preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050863
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 864: Gradient-Based Optimization for Intent
           Conflict Resolution

    • Authors: Idris Cinemre, Kashif Mehmood, Katina Kralevska, Toktam Mahmoodi
      First page: 864
      Abstract: The evolving landscape of network systems necessitates automated tools for streamlined management and configuration. Intent-driven networking (IDN) has emerged as a promising solution for autonomous network management by prioritizing declaratively defined desired outcomes over traditional manual configurations without specifying the implementation details. This paradigm shift towards flexibility, agility, and simplification in network management is particularly crucial in addressing inefficiencies and high costs linked to manual management, notably in the radio access part. This paper explores the concurrent operation of multiple intents, acknowledging the potential for conflicts, and proposes an innovative reformulation of these conflicts to enhance network administration effectiveness. Following the initial detection of conflicts among intents using a gradient-based approach, our work employs the Multiple Gradient Descent Algorithm (MGDA) to minimize all loss functions assigned to each intent simultaneously. In response to the challenge posed by the absence of a closed-form representation for each key performance indicator in a dynamic environment for computing gradient descent, the Stochastic Perturbation Stochastic Approximation (SPSA) is integrated into the MGDA algorithm. The proposed method undergoes initial testing using a commonly employed toy example in the literature before being simulated for conflict scenarios within a mobile network using the ns3 network simulator.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050864
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 865: Enhancing Zero Trust Models in the
           Financial Industry through Blockchain Integration: A Proposed Framework

    • Authors: Clement Daah, Amna Qureshi, Irfan Awan, Savas Konur
      First page: 865
      Abstract: As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model’s performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050865
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 866: Cardiac Healthcare Digital Twins
           Supported by Artificial Intelligence-Based Algorithms and Extended
           Reality—A Systematic Review

    • Authors: Zofia Rudnicka, Klaudia Proniewska, Mark Perkins, Agnieszka Pregowska
      First page: 866
      Abstract: Recently, significant efforts have been made to create Health Digital Twins (HDTs), Digital Twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has huge potential to form part of mainstream medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI)-based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this systematic review, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology, was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. In this study, 253 literature sources were taken into account. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050866
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 867: Edge-Bound Change Detection in
           Multisource Remote Sensing Images

    • Authors: Zhijuan Su, Gang Wan, Wenhua Zhang, Zhanji Wei, Yitian Wu, Jia Liu, Yutong Jia, Dianwei Cong, Lihuan Yuan
      First page: 867
      Abstract: Detecting changes in multisource heterogeneous images is a great challenge for unsupervised change detection methods. Image-translation-based methods, which transform two images to be homogeneous for comparison, have become a mainstream approach. However, most of them primarily rely on information from unchanged regions, resulting in networks that cannot fully capture the connection between two heterogeneous representations. Moreover, the lack of a priori information and sufficient training data makes the training vulnerable to the interference of changed pixels. In this paper, we propose an edge-oriented generative adversarial network (EO-GAN) for change detection that indirectly translates images using edge information, which serves as a core and stable link between heterogeneous representations. The EO-GAN is composed of an edge extraction network and a reconstructive network. During the training process, we ensure that the edges extracted from heterogeneous images are as similar as possible through supplemented data based on superpixel segmentation. Experimental results on both heterogeneous and homogeneous datasets demonstrate the effectiveness of our proposed method.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050867
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 868: Dynamic Co-Operative Energy-Efficient
           Routing Algorithm Based on Geographic Information Perception in
           Opportunistic Mobile Networks

    • Authors: Tong Wang, Jianqun Cui, Yanan Chang , Feng Huang , Yi Yang 
      First page: 868
      Abstract: Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05∼31.11%, and more messages were delivered by 14.82∼115.9%.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050868
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 869: On the Use of Near-Field Constellation
           Focusing for Physical Layer Security with Extremely Large Antenna Arrays

    • Authors: João Ferreira, João Guerreiro, Rui Dinis, Paulo Montezuma
      First page: 869
      Abstract: In the fast-changing world of wireless communications, the combination of extremely large antenna arrays (ELAAs) and energy-efficient transmission methods is envisioned for the 6G. The application of directivity in the transmitted constellation can increase physical layer security (PLS) and promote the energy efficiency of transmission. In such scenarios, large constellations can be divided into multiple binary phase shift keying (BPSK) components, with each component being individually amplified and transmitted by an antenna. In this work, we consider an ELAA acting as a transmitter and constellation decomposition at the sub-array level. We investigate the impact of considering a near-field channel model in terms of secrecy rate and mutual information. In addition to the energy efficiency of the constellation decomposition, it is demonstrated that the particularities of near-field beamforming increase the PLS, namely in terms of robustness to eavesdropping.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050869
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 870: Bad Data Repair for New Energy Stations
           in Power System Based on Multi-Model Parallel Integration Approach

    • Authors: Chenghao Li, Mingyang Liu, Ze Gao, Yi Wang, Chunsun Tian
      First page: 870
      Abstract: The accurate and reliable acquisition of measurement information is very important for the stable operation of power systems, especially the operation status information of new energy stations. With the increasing proportion of new energy stations in power systems, the quality issues of data from these stations, caused by communication congestion, interference, and network attacks, become more pronounced. In this paper, to deal with the issue of low accuracy and poor performance of bad data restoration in new energy stations, a novel deep learning approach by combining the modified long short-term memory (LSTM) neural network and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is proposed. The proposed method can be implemented in a parallel ensemble way. First, the normal data set acquired from multiple sections of new energy stations is utilized to train the modified LSTM and WGAN-GP model. Secondly, according to the data characteristics and rules captured by each model, the two models are systematically integrated and the bad data repair model pool is constructed. Subsequently, the results of model repair are screened and merged twice by the parallel integration framework to obtain the final repair result. Finally, the extensive experiments are carried out to verify the proposed method. The simulative results of energy stations in a real provincial power grid demonstrate that the proposed method can effectively repair bad data, thereby enhancing the data quality of new energy stations.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050870
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 871: Detection Techniques for DBI Environment
           in Windows

    • Authors: Seongwoo Park, Yongsu Park
      First page: 871
      Abstract: Dynamic binary instrumentation (DBI) is a technique that enables the monitoring and analysis of software, providing enhanced performance compared to other analysis tools. However, to provide the robust dynamic analysis capabilities, it commonly requires the setup of separate environments for analysis, thereby increasing the contrast with normal execution and the distinctive features that may reveal the presence of the DBI environment. Malware adapts to detect the presence of DBI environments, and it consequently leads to the expansion of the attack surface. In this paper, we provide an in-depth exploration of anti-instrumentation techniques that can be exploited by malware, with a specific focus on the Windows operating system. Leveraging the unique features of the DBI environment, we introduce and categorize DBI detection techniques. Additionally, we conduct a comprehensive analysis of the techniques through the implementation algorithms with bypassing methods for the techniques. Our experiments showcase the effectiveness of these techniques on the latest versions of several DBI frameworks. Furthermore, we address associated concerns with the aim of contributing to the development of enhanced tools to combat malicious activities exploiting DBI and propose directions for future research.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050871
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 872: Beamforming Optimization with the
           Assistance of Deep Learning in a Rate-Splitting Multiple-Access
           Simultaneous Wireless Information and Power Transfer System with a Power
           Beacon

    • Authors: Mario R. Camana, Carla E. Garcia, Insoo Koo
      First page: 872
      Abstract: This study examined the implementation of rate-splitting multiple access (RSMA) in a multiple-input single-output system using simultaneous wireless information and power transfer (SWIPT) technology. The coexistence of a base station and a power beacon was considered, aiming to transmit information and energy to two sets of users. One set comprises users who solely harvest energy, whereas the other can decode information and energy using a power-splitting (PS) structure. The main objective of this optimization was to minimize the total transmit power of the system while satisfying the rate requirements for PS users and ensuring minimum energy harvesting (EH) for both PS and EH users. The non-convex problem was addressed by dividing it into two subproblems. The first subproblem was solved using a deep learning-based scheme, combining principal component analysis and a deep neural network. The semidefinite relaxation method was used to solve the second subproblem. The proposed method offers lower computational complexity compared to traditional iterative-based approaches. The simulation results demonstrate the superior performance of the proposed scheme compared to traditional methods such as non-orthogonal multiple access and space-division multiple access. Furthermore, the ability of the proposed method to generalize was validated by assessing its effectiveness across several challenging scenarios.
      Citation: Electronics
      PubDate: 2024-02-23
      DOI: 10.3390/electronics13050872
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 873: Automation of System Security
           Vulnerabilities Detection Using Open-Source Software

    • Authors: João Pedro Seara, Carlos Serrão
      First page: 873
      Abstract: Cybersecurity failures have become increasingly detrimental to organizations worldwide, impacting their finances, operations, and reputation. This issue is worsened by the scarcity of cybersecurity professionals. Moreover, the specialization required for cybersecurity expertise is both costly and time-consuming. In light of these challenges, this study has concentrated on automating cybersecurity processes, particularly those pertaining to continuous vulnerability detection. A cybersecurity vulnerability scanner was developed, which is freely available to the community and does not necessitate any prior expertise from the operator. The effectiveness of this tool was evaluated by IT companies and systems engineers, some of whom had no background in cybersecurity. The findings indicate that the scanner proved to be efficient, precise, and easy to use. It assisted the operators in safeguarding their systems in an automated fashion, as part of their security audit strategy.
      Citation: Electronics
      PubDate: 2024-02-24
      DOI: 10.3390/electronics13050873
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 874: A Reduced Sparse Dictionary
           Reconstruction Algorithm Based on Grid Selection

    • Authors: Zhiqi Gao, Caimei Zhao, Pingping Huang, Wei Xu, Weixian Tan
      First page: 874
      Abstract: A sparse dictionary reconstruction algorithm based on grid selection is introduced to solve the grid mismatch when using the sparse recovery space time adaptive processing (SR-STAP) algorithm. First, the atom most closely related to clutter is selected from the traditional dictionary through the spectral value dimensionality reduction method. The local mesh is divided around the selected atoms to create mesh cells, and the mesh cells that are most likely to appear in the real clutter points are judged according to the local selection iteration criteria. In this way, the mesh spacing is refined, the local mesh selection is carried out step by step, and the optimal atoms in the local region are constantly adjusted and selected to narrow the search region until the iteration termination condition is met. Finally, the space-time plane is divided using a novel meshing technique that centers around the optimal atom. By removing atoms beyond the maximum range of spatial and Doppler frequencies, the simplified sparse dictionary can overcome the mesh mismatch problem. The simulation results demonstrate that the algorithm enhances the sparse recovery accuracy of clutter space-time spectrum, mitigates the mesh mismatch effect, and boosts STAP performance.
      Citation: Electronics
      PubDate: 2024-02-24
      DOI: 10.3390/electronics13050874
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 875: Hardware–Software Co-Design of an
           Audio Feature Extraction Pipeline for Machine Learning Applications

    • Authors: Jure Vreča, Ratko Pilipović, Anton Biasizzo
      First page: 875
      Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.
      Citation: Electronics
      PubDate: 2024-02-24
      DOI: 10.3390/electronics13050875
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 876: Light Field Visualization for Training
           and Education: A Review

    • Authors: Mary Guindy, Peter A. Kara
      First page: 876
      Abstract: Three-dimensional visualization technologies such as stereoscopic 3D, virtual reality, and augmented reality have already emerged in training and education; however, light field displays are yet to be introduced in such contexts. In this paper, we characterize light field visualization as a potential candidate for the future of training and education, and compare it to other state-of-the-art 3D technologies. We separately address preschool and elementary school education, middle and high school education, higher education, and specialized training, and assess the suitability of light field displays for these utilization contexts via key performance indicators. This paper exhibits various examples for education, and highlights the differences in terms of display requirements and characteristics. Additionally, our contribution analyzes the scientific-literature-related trends of the past 20 years for 3D technologies, and the past 5 years for the level of education. While the acquired data indicates that light field is still lacking in the context of education, general research on the visualization technology is steadily rising. Finally, we specify a number of future research directions that shall contribute to the emergence of light field visualization for training and education.
      Citation: Electronics
      PubDate: 2024-02-24
      DOI: 10.3390/electronics13050876
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 877: The Design and Dynamic Control of a
           Unified Power Flow Controller with a Novel Algorithm for Obtaining the
           Least Harmonic Distortion

    • Authors: Armel Asongu Nkembi, Nicola Delmonte, Paolo Cova, Minh Long Hoang
      First page: 877
      Abstract: This study investigates the control and dynamic operation of the Unified Power Flow Controller made of shunt and series converters, a Static Synchronous Compensator, and a Static Synchronous Series Compensator, respectively, connected back-to-back through a common DC-link capacitor. The model of a 48-pulse Voltage Source Converter is constructed from a three-level Neutral Point Clamped converter, which allows the total harmonic distortion to be reduced. An optimal conduction angle tracking system of the three-level inverter is designed to minimize distortion by detecting proper harmonic component elimination. Starting from the six-step modulation strategy, the dq decoupled control schemes of both compensators in open and closed loops are presented. Finally, the MATLAB-Simulink model of the power flow controller is implemented and analyzed. The results show that the controller can track the power changes and apply a suitable voltage to the power system so that the power flow can be controlled. This way, the power flow controller dynamically improves the voltage and power quality across the power network while simultaneously improving the transient stability of the system. It can eliminate all system disturbances resulting from oscillations and harmonics in voltage and current within a very short time. The procedural approach used to model and simulate the Unified Power Flow Controller, as well as the new algorithm used to obtain the harmonic number that minimizes the total harmonic distortion, can be applied to any AC power system.
      Citation: Electronics
      PubDate: 2024-02-24
      DOI: 10.3390/electronics13050877
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 878: A Quadrature Oscillator with a
           Frequency-Tuned Distributed RC Network Analysis

    • Authors: Jahyun Koo, Cheonhoo Jeon
      First page: 878
      Abstract: This paper introduces an innovative two-stage distributed RC oscillator design, enhancing the noise performance and frequency stability for compact electronic devices. This work significantly reduces the comparator noise and improves system reliability by implementing a novel approach to increase the signal transition slope, coupled with optimized resistor and capacitor configurations. The study employs a quadrature oscillator topology and a precise reference voltage generation method, effectively addressing the challenges of mismatch and noise performance. A 469.2 kHz quadrature oscillator with two-stage distributed RC is implemented with a 0.18 μm CMOS process, achieving a FoM of −160 dBc/Hz at 100 Hz with a stable −20 dB roll-off in the phase noise and an Allan deviation floor of less than 0.7 ppm.
      Citation: Electronics
      PubDate: 2024-02-25
      DOI: 10.3390/electronics13050878
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 879: Implementation of Highly Reliable
           Convolutional Neural Network with Low Overhead on Field-Programmable Gate
           Array

    • Authors: Xin Chen, Yudong Xie, Liangzhou Huo, Kai Chen, Changhao Gao, Zhiqiang Xiang, Hanying Yang, Xiaofeng Wang, Yifan Ge, Ying Zhang
      First page: 879
      Abstract: Due to the advantages of parallel architecture and low power consumption, a field-programmable gate array (FPGA) is typically utilized as the hardware for convolutional neural network (CNN) accelerators. However, SRAM-based FPGA devices are extremely susceptible to single-event upsets (SEUs) induced by space radiation. In this paper, a fault tolerance analysis and fault injection experiments are applied to a CNN accelerator, and the overall results show that SEUs occurring in a control unit (CTRL) lead to the highest system error rate, which is over 70%. After that, a hybrid hardening strategy consisting of a finite state machine error-correcting circuit (FSM-ECC) and a triple modular redundancy automatic hardening technique (TMR-AHT) is proposed in this paper to achieve a tradeoff between radiation reliability and design overhead. Moreover, the proposed methodology has very small workload and good migration ability. Finally, by full exploiting the fault tolerance property of CNNs, a highly reliable CNN accelerator with the proposed hybrid hardening strategy is implemented with Xilinx Zynq-7035. When BER is 2 × 10−6, the proposed hybrid hardening strategy reduces the whole system error rate by 78.95% with the overhead of an extra 20.7% of look-up tables (LUTs) and 20.9% of flip-flops (FFs).
      Citation: Electronics
      PubDate: 2024-02-25
      DOI: 10.3390/electronics13050879
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 880: Efficient Vision Transformer YOLOv5 for
           Accurate and Fast Traffic Sign Detection

    • Authors: Guang Zeng, Zhizhou Wu, Lipeng Xu, Yunyi Liang
      First page: 880
      Abstract: Accurate and fast detection of traffic sign information is vital for autonomous driving systems. However, the YOLOv5 algorithm faces challenges with low accuracy and slow detection when it is used for traffic sign detection. To address these shortcomings, this paper introduces an accurate and fast traffic sign detection algorithm–YOLOv5-Efficient Vision TransFormer(EfficientViT)). The algorithm focuses on improving both the accuracy and speed of the model by replacing the CSPDarknet backbone of the YOLOv5(s) model with the EfficientViT network. Additionally, the algorithm incorporates the Convolutional Block Attention Module(CBAM) attention mechanism to enhance feature layer information extraction and boost the accuracy of the detection algorithm. To mitigate the adverse effects of low-quality labels on gradient generation and enhance the competitiveness of high-quality anchor frames, a superior gradient gain allocation strategy is employed. Furthermore, the strategy introduces the Wise-IoU (WIoU), a dynamic non-monotonic focusing mechanism for bounding box loss, to further enhance the accuracy and speed of the object detection algorithm. The algorithm’s effectiveness is validated through experiments conducted on the 3L-TT100K traffic sign dataset, showcasing a mean average precision (mAP) of 94.1% in traffic sign detection. This mAP surpasses the performance of the YOLOv5(s) algorithm by 4.76% and outperforms the baseline algorithm. Additionally, the algorithm achieves a detection speed of 62.50 frames per second, which is much better than the baseline algorithm.
      Citation: Electronics
      PubDate: 2024-02-25
      DOI: 10.3390/electronics13050880
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 881: Neural Network-Driven Reliability
           Analysis in Safety Evaluation of LiDAR-Based Automated Vehicles:
           Considering Highway Vertical Alignments and Adverse Weather Conditions

    • Authors: Mingmao Cai, Chengyang Mao, Wen Zhou, Bin Yu
      First page: 881
      Abstract: Complex road environments threaten the safe operation of automated vehicles. Among these, adverse weather conditions and road geometries have particularly significant impacts. This study investigates LiDAR-based automated vehicles (LAVs) driving safety on vertical curved roads in adverse weather. A key methodology involves constructing a failure function that incorporates both the available sight distance (ASD) and the required stopping sight distance (RSD). This function is analyzed using a combined approach of neural networks and Monte Carlo simulations to quantitatively evaluate and generalize the reliability of LAVs under various conditions. The results reveal that variations in weather conditions and vertical curve radii significantly impact the ASD of LAVs, while the influence of speed is relatively minor. Notably, dense fog and rainfall can substantially reduce LAVs’ ASD on vertical curves. Furthermore, the vehicle automation level and speed have a significant impact on driving safety, emphasizing the need for road and operational domain design tailored to LAVs under adverse weather conditions and vertical curve radii.
      Citation: Electronics
      PubDate: 2024-02-25
      DOI: 10.3390/electronics13050881
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 882: Improvement in Laptop Heat Dissipation
           with Taguchi Method

    • Authors: Yeong-Hwa Chang, Chia-Hao Chu, Hung-Wei Lin
      First page: 882
      Abstract: This paper aims to investigate the feasibility of using system power consumption as a factor to improve laptop heat dissipation. The problems due to the CPU overheating are addressed. Based on the Taguchi method, the laptop fan parameters can be optimized with firmware adjustments only. In the Taguchi analysis, the fan speed, system power, and debounce time are considered as control factors, while the Cinebench point is utilized to evaluate the CPU performance. Experimental results demonstrate that the proposed heat dissipation scheme effectively reduces the idle time of a laptop fan. The improvement in heat dissipation can reduce CPU performance degradation because of overheating. According to the best combination of control factors, there is approximately a 5% increase in CPU performance despite a 0.35% increment in power consumption. This paper highlights the effectiveness of optimizing laptop fan parameters through firmware adjustments to improve heat dissipation and mitigate CPU overheating issues. Moreover, the study highlights the delicate balance between power consumption and performance gains. While there may be a slight increase in power consumption associated with the optimized heat dissipation scheme, the observed improvements in CPU performance outweigh this incremental power usage.
      Citation: Electronics
      PubDate: 2024-02-25
      DOI: 10.3390/electronics13050882
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 883: Efficient V2V Communications by
           Clustering-Based Collaborative Caching

    • Authors: Hiroki Tokunaga, Suhua Tang
      First page: 883
      Abstract: Vehicle-to-vehicle (V2V) communication plays an important role in enabling autonomous driving. However, when multiple vehicles request the same content, like road conditions, delivering it individually by V2V communication can significantly increase traffic volume, potentially causing congestion in the wireless channel. To address this issue, Content-Centric Network (CCN) technology is applied to V2V communication, which improves communication efficiency by exploiting content cached at vehicles. However, previous methods faced the following challenges: (i) vehicles could not use content stored in nearby vehicles outside the communication path, and (ii) redundant caching of the same content occurred at nearby vehicles. To tackle these challenges, this paper proposes a collaborative caching method in which vehicles are grouped into clusters and each cluster has a designated head responsible for managing caches across all vehicles within the cluster. In this way, this method enables vehicles to use the content cached at adjacent vehicles that are not directly on a communication path. In addition, it eliminates redundant caches, allowing a more diverse range of content storage. Extensive simulation results demonstrate that the proposed approach effectively reduces content delivery latency by 33% compared to the method using clusters without cooperative caching and by 19% compared to the ECV+ method.
      Citation: Electronics
      PubDate: 2024-02-25
      DOI: 10.3390/electronics13050883
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 884: A Multi-Rate Simulation Strategy Based on
           the Modified Time-Domain Simulation Method and Multi-Area Data Exchange
           Method of Power Systems

    • Authors: Ruotian Yao, Qi Chen, Hao Bai, Chengxi Liu, Tong Liu, Yongjian Luo, Weichen Yang
      First page: 884
      Abstract: Accurate modeling for power-electronic devices requires power systems to be simulated with considerably small step sizes (typically several microseconds), which causes unnecessary computational burden and reduces efficiency, especially for large-scale power systems. To achieve a balance between simulation precision and efficiency, this paper introduces an innovative multi-rate interface strategy based on the modified time-domain simulation (TDS) method and multi-area data exchange method. The modified TDS method transforms the initialization process into exchange of electric data among different subsystems, while the multi-area data exchange method is able to ensure numerical stability and simulation universality during the multi-rate simulation. The proposed strategy provides a robust interface that allows different subsystems to be engaged in simulations with different step sizes while exchanging data. To validate this strategy, simulations on an integrated system of IEEE 14-bus and 33-bus systems is conducted. In addition, the strategy is further applied to a real-world scenario of the subsystem in the Guangxi Power Grid in China. Analysis of the results indicates that the proposed multi-rate fast simulation strategy can significantly boost simulation efficiency while maintaining accuracy, which marks a notable improvement compared with the traditional single step size simulation.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050884
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 885: S2S-Sim: A Benchmark Dataset for Ship
           Cooperative 3D Object Detection

    • Authors: Wenbin Yang, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie, Junxi Chen
      First page: 885
      Abstract: The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050885
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 886: Fault Recovery of Distribution Network
           with Distributed Generation Based on Pigeon-Inspired Optimization
           Algorithm

    • Authors: Mingyang Liu, Jiahui Wu, Qiang Zhang, Hongjuan Zheng
      First page: 886
      Abstract: In this paper, a fault recovery strategy for a distribution network based on a pigeon-inspired optimization (PIO) algorithm is proposed to improve the recoverability of the network considering the increased proportion of distributed energy resources. First, an improved Kruskal algorithm-based island partitioning scheme is proposed considering the electrical distance and important load level during the island partitioning process. Secondly, a mathematical model of fault recovery is established with the objectives of reducing active power losses and minimizing the number of switching actions. The conventional PIO algorithm is improved using chaos, reverse strategy, and Cauchy perturbation strategy, and the improved pigeon-inspired optimization (IPIO) algorithm is applied to solve the problem of fault recovery of the distribution network. Finally, simulation analysis is carried out to verify the effectiveness of the proposed PIO algorithm considering a network restauration problem after fault. The results show that compared with traditional algorithms, the proposed PIO algorithm has stronger global search capability, effectively improving the node voltage after restauration and reducing circuit loss.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050886
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 887: Domain Adaptive Channel Pruning

    • Authors: Ge Yang, Chao Zhang, Ling Gao, Yufei Guo, Jinyang Guo
      First page: 887
      Abstract: Domain adaptation is an effective approach to improve the generalization ability of deep learning methods, which makes a deep model more stable and robust. However, these methods often suffer from a deployment problem when deep models are deployed on different types of edge devices. In this work, we propose a new channel pruning method called Domain Adaptive Channel Pruning (DACP), which is specifically designed for the unsupervised domain adaptation task, where there is considerable data distribution mismatch between the source and the target domains. We prune the channels and adjust the weights in a layer-by-layer fashion. In contrast to the existing layer-by-layer channel pruning approaches that only consider how to reconstruct the features from the next layer, our approach aims to minimize both classification error and domain distribution mismatch. Furthermore, we propose a simple but effective approach to utilize the unlabeled data in the target domain. Our comprehensive experiments on two benchmark datasets demonstrate that our newly proposed DACP method outperforms the existing channel pruning approaches under the unsupervised domain adaptation setting.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050887
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 888: Cyber5Gym: An Integrated Framework for 5G
           Cybersecurity Training

    • Authors: Muhammad Ali Hamza, Usama Ejaz, Hyun-chul Kim
      First page: 888
      Abstract: The rapid evolution of 5G technology, while offering substantial benefits, concurrently presents complex cybersecurity challenges. Current cybersecurity systems often fall short in addressing challenges such as the lack of realism of the 5G network, the limited scope of attack scenarios, the absence of countermeasures, the lack of reproducible, and open-sourced cybersecurity training environments. Addressing these challenges necessitates innovative cybersecurity training systems, referred to as “cyber ranges”. In response to filling these gaps, we propose the Cyber5Gym, an integrated cyber range that enhances the automation of virtualized cybersecurity training in 5G networks with cloud-based deployment. Our framework leverages open-source tools (i) Open5GS and UERANSIM for realistic emulation of 5G networks, (ii) Docker for efficient virtualization of the training infrastructure, (iii) 5Greply for emulating attack scenarios, and (iv) Shell scripts for automating complex training operations. This integration facilitates a dynamic learning environment where cybersecurity professionals can engage in real-time attack and countermeasure exercises, thus significantly improving their readiness against 5G-specific cyber threats. We evaluated it by deploying our framework on Naver Cloud with 20 trainees, each accessing an emulated 5G network and managing 100 user equipments (UEs), emulating three distinct attack scenarios (SMC-Reply, DoS, and DDoS attacks), and exercising countermeasures, to demonstrate the cybersecurity training. We assessed the effectiveness of our framework through specific metrics such as successfully establishing the 5G network for all trainees, accurate execution of attack scenarios, and their countermeasure implementation via centralized control of the master using automated shell scripts. The open-source foundation of our framework ensures replicability and adaptability, addressing a critical gap in current cybersecurity training methodologies and contributing significantly to the resilience and security of 5G infrastructures.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050888
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 889: An Optimized Device Structure with a
           Highly Stable Process Using Ferroelectric Memory in 3D NAND Flash Memory
           Applications

    • Authors: Seonjun Choi, Myounggon Kang, Hong-sik Jung, Yuri Kim, Yun-heub Song
      First page: 889
      Abstract: In this paper, we propose an optimized device structure with a highly stable process that addresses threshold voltage shift issues in the String-Select-Line (SSL) and Ground-Select-Line (GSL) gates using ferroelectric memory in 3D NAND flash memory applications. The proposed device utilizes nickel (Ni) instead of tungsten (W) for the GSL and SSL gates, enabling optimized polarization properties during the annealing process and leveraging the disparity in thermal expansion coefficients. Notably, the difference in thermal expansion coefficient from tungsten (W), employed in other Word Line (WL) gates, allows effective control over polarization properties. To validate the proposed structure, we fabricated and measured a Metal–Ferroelectric–Insulator–Silicon (MFIS) capacitor utilizing Hafnium–Zirconium Oxide (HZO) material. The measurement results indicate that a change in the upper metal layer results in a more than fivefold increase in the variance of polarization characteristics between the WL gates (responsible for the memory function) and the SSL and GSL gates dedicated to channel control. In addition, process simulation was conducted using the same device structure, confirming the application of tensile stress to the HZO thin film in the case of a W electrode and compressive stress in the case of a Ni electrode. Furthermore, applying this controlled polarization characteristic parameter to the 3D NAND flash memory structure revealed a reduction in the threshold voltage shift of the control gate from a previous change of 2.6 V or more to 0.05 V, facilitating stable control.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050889
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 890: A Systematic Review of the Design of
           Serious Games for Innovative Learning: Augmented Reality, Virtual Reality,
           or Mixed Reality'

    • Authors: Lap-Kei Lee, Xiaodong Wei, Kwok Tai Chui, Simon K. S. Cheung, Fu Lee Wang, Yin-Chun Fung, Angel Lu, Yan Keung Hui, Tianyong Hao, Leong Hou U, Nga-In Wu
      First page: 890
      Abstract: The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050890
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 891: Intelligent High-Awareness and
           Channel-Efficient Adaptive Beaconing Based on Density and Distribution for
           Vehicular Networks

    • Authors: Mohammed Alhameed, Imad Mahgoub, Elnaz Limouchi
      First page: 891
      Abstract: In vehicle ad hoc networks (VANETs), a beacon is a periodic message sent to nearby vehicles containing essential details like the sender’s vehicle ID, location, speed, and direction. Maintaining the freshness of this information without causing network congestion requires adaptive beaconing to adjust to changes in mobility and network density. Our research, based on extensive simulation experiments, identifies specific parameter sets optimal for adapting beaconing rates to different scenarios. From this analysis, we introduce a novel scheme called high-awareness and channel-efficient adaptive beaconing (HACEAB), employing fuzzy logic to adapt to various environments and conditions. Initially, the protocol gauges network density using an adaptive threshold function, followed by estimating the node spatial distribution through the quadrat statistic method to discern uniform distribution or clustering. Utilizing these data, the protocol adjusts beaconing rates via appropriate input parameters for the fuzzy logic system. Remarkably, HACEAB represents the first beaconing scheme capable of simultaneously adjusting to changes in network density and spatial distribution. Furthermore, the protocol enhances performance by adapting transmission power to fluctuations in node density and distribution. NS-3 simulations validate the efficacy of these improvements.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050891
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 892: Feed Error Prediction and Compensation of
           CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural
           Network

    • Authors: Wenkang Fang, Yingping Qian, Zhongquan Yu, Dongqiao Zhang
      First page: 892
      Abstract: Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050892
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 893: A Memristor Neural Network Based on
           Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

    • Authors: Valeri Mladenov, Stoyan Kirilov
      First page: 893
      Abstract: Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are potentially applicable in artificial and spiking neural networks, memory arrays, and many other devices and circuits for artificial intelligence. In this paper, a complete electronic realization of an analog circuit model of the modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in the LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an important step towards the forthcoming design of complex memristor-based neural networks for artificial intelligence, for implementation in very high-density integrated circuits and chips.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050893
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 894: Discrete Artificial Fish Swarm
           Algorithm-Based One-Off Optimization Method for Multiple Co-Existing
           Application Layer Multicast Routing Trees

    • Authors: Ying Li, Ning Wang, Wei Zhang, Qing Liu, Feng Liu
      First page: 894
      Abstract: As an effective multicast application mechanism, the application layer multicast (ALM) determines the path of data transmission through a routing tree. In practical applications, multiple multicast sessions often occur simultaneously; however, few studies have considered this situation. A feasible solution is to sequentially optimize each co-existing ALM routing tree. However, this approach can lead to node congestion, and, even if the node out-degree reservation strategy is adopted, an optimal solution may not be obtained. In this study, to solve the problem of routing tree construction for multiple co-existing application layer multicast sessions, an optimization model that minimizes the overall delay and instability is constructed, and a one-off optimization method based on the discrete artificial fish swarm algorithm (DAFSA) is proposed. First, Steiner node sets corresponding to the multicast sessions are selected. Then, the routing trees for each multicast session are obtained through the improved spanning tree algorithm based on the complete graph composed of Steiner node sets. The experimental results show that the proposed method can simultaneously obtain multiple co-existing ALM routing trees with a low total delay and low instability. Even if the input is a single multicast session, it can lead to ALM routing trees with a lower delay and less instability than other algorithms, and the introduction of a penalty function can effectively avoid the problem of excessive replication and forwarding loads on some end-hosts. In addition, the proposed algorithm is insensitive to parameter changes and exhibits good stability and convergence properties for networks of different sizes.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050894
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 895: A Novel Adaptive Indoor Positioning Using
           Mobile Devices with Wireless Local Area Networks

    • Authors: Yung-Fa Huang, Yi-Hsiang Hsu, Jen-Yung Lin, Ching-Mu Chen
      First page: 895
      Abstract: In this paper, mobile devices were used to estimate the received signal strength indicator (RSSI) of wireless channels with three wireless access points (APs). Using the RSSI, the path loss exponent (PLE) was adapted to calculate the estimated distance among the test points (TPs) and the APs, through the root mean square error (RMSE). Moreover, in this paper, the proposed adaptive PLE (APLE) of the TPs was obtained by minimizing the positioning errors of the PLEs. The training samples of RSSI were measured by TPs for 6 days, and different surge processing methods were used to obtain APLE and to improve the positioning accuracy. The surge signals of RSSI were reduced by the cumulated distribution function (CDF), hybrid Kalman filter (KF), and threshold filtering methods, integrating training samples and APLE. The experimental results show that with the proposed APLE, the position accuracy can be improved by 50% compared to the free space model for six TPs. Finally, dynamic real-time indoor positioning was performed and measured for the performance evaluation of the proposed APLE models. The experimental results show that, the minimum dynamic real-time positioning error can be improved to 0.88 m in a straight-line case with the hybrid method. Moreover, the average positioning error of dynamic real-time indoor positioning can be reduced to 1.15 m using the four methods with the proposed APLE.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050895
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 896: Parallel Implementation of Lightweight
           Secure Hash Algorithm on CPU and GPU Environments

    • Authors: Hojin Choi, SeongJun Choi, SeogChung Seo
      First page: 896
      Abstract: Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050896
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 897: Research on the Application of Extended
           Reality in the Construction and Management of Landscape Engineering

    • Authors: Siyu Liu, Xuefeng Zhao, Xiaolin Meng, Weiyu Ji, Liang Liu, Wangbing Li, Yibing Tao, Yunfei Peng, Qiantai Yang
      First page: 897
      Abstract: Landscape engineering plays a crucial role in urban construction and the development of ecological civilization in China. It actively designs and modifies natural elements, such as water and mountains, acting as the primary living infrastructure. This field continually receives great recognition and praise. Recent academic research has prioritized the use of extended reality (XR) technology to create a real-time interactive visual environment to tackle the issues presented by the dynamic nature of landscape engineering. This paper utilizes the PRISMA method to filter out 68 research documents related to XR in landscape engineering construction and management for bibliometric analysis. A comprehensive review is conducted on the precise and efficient utilization of XR to solve various issues in the field of landscape engineering. Using Cite Space 6.2.R6 (a visual bibliometric software) to visualize knowledge structures and research topics, the analysis includes temporal and spatial examination, application scenario analysis, and technological hierarchy analysis. The paper summarizes the current challenges that XR still faces in the landscape engineering field and envisions extensible application scenarios for XR, providing a reference roadmap for the implementation of XR in landscape engineering.
      Citation: Electronics
      PubDate: 2024-02-26
      DOI: 10.3390/electronics13050897
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 898: Cascaded Searching Reinforcement Learning
           Agent for Proposal-Free Weakly-Supervised Phrase Comprehension

    • Authors: Yaodong Wang, Lili Yue, Maoqing Li
      First page: 898
      Abstract: Phrase comprehension (PC) aims to locate a specific object in an image according to a given linguistic query. The existing PC methods work in either a fully supervised or proposal-based weakly supervised manner, which rely explicitly or implicitly on expensive region annotations. In order to completely remove the dependence on the supervised region information, this paper proposes to address PC in a proposal-free weakly supervised training paradigm. To this end, we developed a novel cascaded searching reinforcement learning agent (CSRLA). Concretely, we first leveraged a visual language pre-trained model to generate a visual–textual cross-modal attention heatmap. Accordingly, a coarse salient initial region of the referential target was located. Then, we formulated the visual object grounding as a Markov decision process (MDP) in a reinforcement learning framework, where an agent was trained to iteratively search for the target’s complete region from the salient local region. Additionally, we developed a novel confidence discrimination reward function (ConDis_R) to constrain the model to search for a complete and exclusive object region. The experimental results on three benchmark datasets of Refcoco, Refcoco+, and Refcocog demonstrated the effectiveness of our proposed method.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050898
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 899: Comparing Metaheuristic Search Techniques
           in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection
           Methods

    • Authors: Alireza Zeinalpour, Charles P. McElroy
      First page: 899
      Abstract: Distributed Denial of Service (DDoS) attacks have increased in frequency and sophistication over the last ten years. Part of the challenge of defending against such attacks requires the analysis of very large volumes of data. Metaheuristic algorithms can assist in selecting relevant features from the network traffic data for use in DDoS detection models. By efficiently exploring different combinations of features, these methods can identify subsets that are informative for distinguishing between normal and attack traffic. However, identifying an optimized solution in this area is an open research question. Tuning the parameters of metaheuristic search techniques in the optimization process is critical. In this study, a switching approximation is used in a variety of metaheuristic search techniques. This approximation is used to find the best solution for the analysis of the network traffic features in either lower or upper values between 0 and 1. We compare the fine-tuning of this parameter against standard approaches and find that it is not substantially better than the BestFirst algorithm (a standard default approach for feature selection). This study contributes to the literature by testing and eliminating various fine-tuning strategies for the metaheuristic approach.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050899
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 900: Adaptive Dynamic Boundary Sliding Mode
           Control for Robotic Manipulators under Varying Disturbances

    • Authors: Zhendong Song, Danyang Bao, Wenbin Wang, Wei Zhao
      First page: 900
      Abstract: This paper introduces an Adaptive Dynamic Bounded Sliding Mode Control (ADBSMC) method that incorporates a disturbance observer to enhance the response characteristics of the robot manipulator while eliminating the reliance on a priori knowledge. The proposed method utilizes nonlinear sliding mode manifolds and fast-terminal-type convergence laws to address errors and parameter uncertainties inherent in the nonlinear system models. The adaptive law is designed to cover all boundary conditions based on the model’s state. It can dynamically determine upper and lower bounds without requiring prior knowledge. Consequently, the ADBSMC control method amalgamates the benefits of adaptive law and fast terminal sliding mode, leading to significant enhancements in control performance compared with traditional sliding mode control (SMC), exhibiting robustness against uncertain disturbances. To mitigate external disturbances, a system-adapted disturbance observer is devised, facilitating real-time monitoring and compensation for system disturbances. The stability of ADBSMC is demonstrated through the Lyapunov method. Simulation and experimental results validate the effectiveness and superiority of the ADBSMC control scheme, showcasing its potential for practical applications.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050900
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 901: Response Time Reduction of DC–DC
           Converter in Voltage Mode with Application of GaN Transistors and Digital
           Control

    • Authors: Kaspars Kroičs, Kristiāns Gaspersons, Ahmad Elkhateb
      First page: 901
      Abstract: This paper discusses the potential to decrease the response time of a DC–DC converter through the substitution of Si transistors with GaN transistors and the implementation of digital control techniques. This paper introduces an improved methodology for designing digital voltage controllers by analyzing discretization delays and subsequently implementing a modified analog controller design method. The theoretical analysis was verified using an experimental prototype of a 100 W 48 V to 12 V GaN-based DC–DC converter. A digital controller that allows a 50 kHz bandwidth to be achieved based on an STM32G4 microcontroller was developed, and the design of the controller is discussed in detail. The converter was operated with a 500 kHz switching frequency using a 6 µH inductor and a 20 µF ceramic capacitor output filter. Although the digital control introduced a 1.2 µs delay, a converter response time equal to 40 µs was achieved. Simulation models were created and their results were verified via comparisons with experimental results obtained with an AP310 frequency response analyzer.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050901
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 902: Control Performance Requirements for
           Automated Driving Systems

    • Authors: Trevor Vidano, Francis Assadian
      First page: 902
      Abstract: This research investigates the development of risk-based performance requirements for the control of an automated driving system (ADS). The proposed method begins by determining the target level of safety for the virtual driver of an ADS. The underlying assumptions are informed by existing data. Next, geometric models of the road and vehicle are used to derive deterministic performance levels of the virtual driver. To integrate the risk and performance requirements seamlessly, we propose new definitions for errors associated with the planner, pose, and control modules. These definitions facilitate the derivation of stochastic performance requirements for each module, thus ensuring an overall target level of safety. Notably, these definitions enable real-time controller performance monitoring, thus potentially enabling fault detection linked to the system’s overall safety target. At a high level, this approach argues that the requirements for the virtual driver’s modules should be designed simultaneously. To illustrate this approach, this technique is applied to a research project available in the literature that developed an automated steering system for an articulated bus. This example shows that the method generates achievable performance requirements that are verifiable through experimental testing and highlights the importance in validating the underlying assumptions for effective risk management.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050902
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 903: Multi-Channel Hypergraph Collaborative
           Filtering with Attribute Inference

    • Authors: Yutong Jiang, Yuhan Gao, Yaoqi Sun, Shuai Wang, Chenggang Yan
      First page: 903
      Abstract: In the field of collaborative filtering, attribute information is often integrated to improve recommendations. However, challenges remain unaddressed. Firstly, existing data modeling methods often fall short of appropriately handling attribute information. Secondly, attribute data are often sparse and can potentially impact recommendation performance due to the challenge of incomplete correspondence between the attribute information and the recommendations. To tackle these challenges, we propose a hypergraph collaborative filtering with attribute inference (HCFA) framework, which segregates attribute and user behavior information into distinct channels and leverages hypergraphs to capture high-order correlations among vertices, offering a more natural approach to modeling. Furthermore, we introduce behavior-based attribute confidence (BAC) for assessing the reliability of inferred attributes concerning the corresponding behaviors and update the most credible portions to enhance recommendation quality. Extensive experiments conducted on three public benchmarks demonstrate the superiority of our model. It consistently outperforms other state-of-the-art approaches, with ablation experiments further confirming the effectiveness of our proposed method.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050903
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 904: UHD Database Focus on Smart Cities and
           Smart Transport

    • Authors: Lukas Sevcik, Miroslav Uhrina, Jaroslav Frnda
      First page: 904
      Abstract: “Smart city” refers to a modern solution to organizing a city’s services, using cloud technologies to collect and evaluate large amounts of data, including data from camera systems. Smart city management covers several areas that can be implemented separately, but only their combination can realize the overall desired smart city function. One of the core areas of smart city automation is smart city transport. Transportation is a crucial system in any city, and this is why it needs to be monitored. The primary objective of this publication is to generate top-notch 4K UHD video sequences that are solely dedicated to showcasing smart cities and their transportation systems. The resulting comprehensive database will be made accessible to all professionals in the field, who can utilize it for extensive research purposes. Additionally, all the reference video sequences will be transcoded into various quality settings by altering critical parameters like the resolution, compression standard, and bit rate. The ultimate aim is to determine the best combination of video parameters and their respective settings based on the measured values. This in-depth evaluation will ensure that each video sequence is of the highest quality and provides an unparalleled experience for the service providers offering the service. The video sequences captured will be analyzed for quality assessments in smart cities or smart transport technologies. The database will also include objective and subjective ratings, along with information about the dynamics determined by spatial and temporal information. This will enable a comparison of the subjective evaluation of a selected sample of our respondents with the work of other researchers, who may evaluate it with a different sample of evaluators. The assumption of our future research is to predict the subjective quality based on the type of sequence determined by its dynamicity.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050904
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 905: Variability Management in Self-Adaptive
           Systems through Deep Learning: A Dynamic Software Product Line Approach

    • Authors: Oscar Aguayo, Samuel Sepúlveda, Raúl Mazo
      First page: 905
      Abstract: Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050905
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 906: Method of Multi-Energy Complementary
           System Participating in Auxiliary Frequency Regulation of Power Systems

    • Authors: Dawei Zhang, Gang Chen, Guo Guo, Yongcan Wang, Feipeng Lv, Yuhong Wang, Shilin Gao
      First page: 906
      Abstract: This research investigates a grid with two areas interconnected by a high-voltage direct-current (DC) link. One of the areas, called the sending-end region, has intermittent renewable generation and frequency stability issues. To address the lack of frequency-regulation (FR) resources in the sending-end region of the interconnected grid, the participation of hydroelectricity–photovoltaics and pumped storage complementary systems (HPPCSs) in auxiliary frequency-regulation (AFR) services is studied in the context of the construction of the electricity market. Firstly, the HPPCS participating in AFR services considering DC modulation is modeled by combining the operational characteristics of the actual power station. Taking the purchase cost of auxiliary service as the objective function, the optimum allocation of FR scheduling demand is achieved by the proposed method. The simulations confirm that the proposed method of HPPCS participation in the AFR service of the sending-end grid can effectively maintain the frequency stability of the regional interconnected grid while ensuring optimal economic efficiency. The proposed method provides the optimal scheduling solution for multiple energy resources participating in the AFR service of the grid.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050906
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 907: Research on the Car Searching System in
           the Multi-Storey Garage with the RSSI Indoor Locating Based on Neural
           Network

    • Authors: Jihui Ma, Lijie Wang, Xianwen Zhu, Ziyi Li, Xinyu Lu
      First page: 907
      Abstract: To solve the problem of reverse car searching in intelligent multi-story garages or parking lots, the reverse car searching method based on the intelligent garage of the PC client and mobile client APP was studied, and the interface design and function development of the system’s PC and mobile client APP were carried out. YOLOv5 network and LPRNet network were used for license plate location and recognition to realize parking and entry detection. The indoor pedestrian location method based on RSSI fingerprint signal fusion BPNet network and KNN algorithm was studied, and the location accuracy within 2.5 m was found to be 100%. The research on the A* algorithm based on spatial accessibility was conducted to realize the reverse car search function. The research results indicate that the guidance of the vehicle finding path can be completed while the number of invalid search nodes for the example maps was reduced by more than 55.0%, and the operating efficiency of the algorithm increased to 28.5%.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050907
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 908: n77 Radio Frequency Power Amplifier
           Module for 5G New-Radio High-Power User Equipment Mobile Handset
           Applications

    • Authors: Ji-Seon Paek
      First page: 908
      Abstract: This paper presents a highly efficient 5G New-Radio (NR) RF power amplifier module (PAM). The n77 PAM consists of a high-voltage differential-topology 2 μm GaAs HBT power amplifier, a CMOS controller, a silicon-on-insulator (SOI) switch, an integrated passive device (IPD) bandpass filter, a low-noise amplifier (LNA), and a bi-directional coupler. This PAM generates a saturation output power of 32.7 dBm including the loss of the SOI switch and output filter. The designed n77 PAM is tested with a commercial envelope tracker IC (ET-IC). The designed PAM with an ET-IC achieves an ACLR of −37 dBc at a 27 dBm output power with a DFT-s-OFDM QPSK 100 MHz NR signal and saves a dc power consumption of 950 mW compared to the APT mode. For the CP-OFDM 256QAM with the most stringent EVM requirements, it achieves an EVM of 1.22% at 23 dBm and saves 640 mW compared to the APT mode.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050908
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 909: SC-IZ: A Low-Cost Biologically Plausible
           Izhikevich Neuron for Large-Scale Neuromorphic Systems Using Stochastic
           Computing

    • Authors: Wei Liu, Shanlin Xiao, Bo Li, Zhiyi Yu
      First page: 909
      Abstract: Neurons are crucial components of neural networks, but implementing biologically accurate neuron models in hardware is challenging due to their nonlinearity and time variance. This paper introduces the SC-IZ neuron model, a low-cost digital implementation of the Izhikevich neuron model designed for large-scale neuromorphic systems using stochastic computing (SC). Simulation results show that SC-IZ can reproduce the behaviors of the original Izhikevich neuron. The model is synthesized and implemented on an FPGA. Comparative analysis shows improved hardware efficiency; reduced resource utilization, which is a 56.25% reduction in slices, 57.61% reduction in Look-Up Table (LUT) usage, and a 58.80% reduction in Flip-Flop (FF) utilization; and a higher operating frequency compared to state-of-the-art Izhikevich implementation.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050909
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 910: An Orientation-Aware Attention Network
           for Person Re-Identification

    • Authors: Dongshu Xu, Jun Chen, Xiaoyu Chai
      First page: 910
      Abstract: Humans always identify persons through their characteristics, salient attributes, and these attributes’ locations on the body. Most person re-identification methods focus on global and local features corresponding to the former two discriminations, cropping person images into horizontal strips to obtain coarse locations of body parts. However, discriminative clues corresponding to location differences cannot be discovered, so persons with similar appearances are often confused because of their alike components. To address the above problem, we introduce pixel-wise relative positions for the invariance of their orientations in viewpoint changes. To cope with the scale change of relative position, we combine relative positions with self-attention modules that perform on multi-level features. Moreover, in the data augmentation stage, mirrored images are given new labels due to the conversion of the relative position along a horizontal orientation and change in visual chirality. Extensive experiments on four challenging benchmarks demonstrate that the proposed approach shows its superiority and effectiveness in discovering discriminating features.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050910
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 911: Research on the Evaluation and Prediction
           of V2I Channel Quality Levels in Urban Environments

    • Authors: Shengli Pang, Zekang Li, Ziru Yao, Honggang Wang, Weichen Long, Ruoyu Pan
      First page: 911
      Abstract: The present manuscript introduces a method for evaluating and forecasting the quality of vehicle-to-infrastructure (V2I) communication channels in urban settings. This method precisely classifies and predicts channel quality levels in V2I scenarios based on long-range (LoRa) technology. This approach aims to accurately classify and predict channel quality levels in V2I scenarios. The concept of channel quality scoring was first introduced, offering a more precise description of channel quality compared to traditional packet reception rate (PRR) assessments. In the channel quality assessment model based on the gated recurrent unit (GRU) algorithm, the current channel quality score of the vehicular terminal and the spatial channel parameters (SCP) of its location are utilized as inputs to achieve the classification of channel quality levels with an accuracy of 97.5%. Regarding prediction, the focus lies in forecasting the channel quality score, combined with the calculation of SCP for the vehicle’s following temporal location, thereby achieving predictions of channel quality levels from spatial and temporal perspectives. The prediction model employs the Variational Mode Decomposition-Backoff-Bidirectional Long Short-Term Memory (VMD-BO-BiLSTM) algorithm, which, while maintaining an acceptable training time, exhibits higher accuracy than other prediction algorithms, with an R2 value reaching 0.9945. This model contributes to assessing and predicting channel quality in V2I scenarios and holds significant implications for subsequent channel resource allocation.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050911
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 912: AFL++: A Vulnerability Discovery and
           Reproduction Framework

    • Authors: Guofeng He, Yichen Xin, Xiuchuan Cheng, Guangqiang Yin
      First page: 912
      Abstract: Directed greybox fuzzing can mainly be used for vulnerability mining and vulnerability replication. However, there are still some issues with existing directional fuzzing tools. One is that after providing problematic changes or patches, it is not possible to quickly target and discover the problem. Secondly, it is difficult to break through the magic byte path, making it difficult to mine deep vulnerabilities. This article proposes a new vulnerability mining and repair framework: American Fuzz Lop Plus (AFL++). Firstly, we utilize alias analysis to enhance inter-procedural control flow graphs and redefine the distance calculation formula to obtain more accurate distances. Secondly, the Newton interpolation method is used for the energy initialization of each seed to prevent test cases from being filtered out due to low energy. A heuristic energy scheduling algorithm is proposed to judiciously schedule the energy of seeds. During the path exploration phase, by adjusting the seed energy, shorter-distance seeds quickly reach the target; with increasing time, seeds tend to explore deeper paths. We then represent the symbolic distance by the number of instructions passed to reach the target and investigate the shortest path search strategy to achieve path pruning, alleviating the problem of path explosion. Finally, based on the above methods, we implement the AFL++ prototype system, integrating directed greybox fuzzing with symbolic execution technology for vulnerability discovery. By interleaving directed symbolic execution and directed greybox fuzzing, the efficiency of vulnerability discovery and reproduction is effectively enhanced.
      Citation: Electronics
      PubDate: 2024-02-27
      DOI: 10.3390/electronics13050912
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 913: Control of a Path Following Cable Trench
           Caterpillar Robot Based on a Self-Coupling PD Algorithm

    • Authors: Zhiwei Jia, Wen Fang, Chenhao Sun, Ling Li
      First page: 913
      Abstract: Underground cable trench inspection robots work in narrow, variable friction coefficient, and complex road environments. The running trajectory easily deviates from the desired path and leads to a collision, or even the destruction of the robot or cable. Addressing this problem, a path-following control method for the dual-tracked chassis robot based on a self-coupling PID (SCPID) control algorithm was developed. The caterpillar robot dynamics were modelled and both the unknown dynamics and external bounded disturbances were defined as sum disturbances, thus mapping the nonlinear system into a linearly disturbed system, then the self-coupling PD (SCPD) controller was designed. The system proved to be a robust stability control system and only one parameter, the velocity factor, needed to be tuned to achieve parameter calibration. Meanwhile, to solve the problem that the error-based speed factor is not universal and to improve the adaptive ability of the SCPD controller, an iterative method was used for adaptive tuning. The simulation results showed that the SCPID can achieve better control. The field test results showed that the SCPD’s maximum offset angle was 56.7% and 10.3% smaller than incremental PID and sliding mode control (SMC), respectively. The inspection time of the SCPD was 20% faster than other methods in the same environment.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050913
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 914: Design of UWB Electrically Small Antenna
           Based on Distributed Passive Network Loading

    • Authors: Zhe Chen, Xianqi Lin, Yuchen Luan, Xinjie Hao, Xiaoming Yan, Guo Liu
      First page: 914
      Abstract: In this paper, an ultra-wideband electrically small antenna based on distributed passive network loading is proposed. Based on the Vivaldi antenna theory, magnetic dipole antenna theory, and distributed loading theory, the electrically small antenna achieves the purpose of being wideband using a three-dimensional design of a planar Vivaldi antenna structure under limited space constraints. At the same time, the magnetic dipole antenna is introduced to effectively expand the low-frequency bandwidth of the electrically small antenna without increasing the aperture size. Finally, through the distributed passive network loading, the wideband-conjugated matching of the electrically small antenna is achieved without increasing the size of the electrically small antenna. The −6 dB bandwidth of the electrically small antenna is 0.2 GHz–3 GHz, and the overall size is 0.06 λ0 × 0.05 λ0 × 0.12 λ0, where λ0 is the wavelength of the lowest frequency of the antenna. One sample of the proposed UWB electrically small antenna is fabricated and tested. Good agreement between simulation results and measurement results are obtained. The design method of UWB electrically small antenna proposed in this paper can be applied to the base station antenna, low-frequency detection, microwave sensing, and microwave measurement.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050914
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 915: An Aero-Engine Classification Method
           

    • Authors: Shuhan Du, Wei Han, Zhengyang Shi, Yurong Liao, Zhaoming Li
      First page: 915
      Abstract: Aiming at the classification identification problem of aero-engines, this paper adopts a telemetry Fourier transform infrared spectrometer to collect aero-engine hot jet infrared spectrum data and proposes an aero-engine classification identification method based on spectral feature vectors. First, aero-engine hot jet infrared spectrum data are acquired and measured; meanwhile, the spectral feature vectors based on CO2 are constructed. Subsequently, the feature vectors are combined with the seven mainstream classification algorithms to complete the training and prediction of the classification model. In the experiment, two Fourier transform infrared spectrometers, EM27 developed by Bruker and a self-developed telemetry FT-IR spectrometer, were used to telemeter the hot jet of three aero-engines to obtain infrared spectral data. The training data set and test data set were randomly divided in a ratio of 3:1. The model training of the training data set and the label prediction of the test data set were carried out by combining spectral feature vectors and classification algorithms. The classification evaluation indicators were accuracy, precision, recall, confusion matrix, and F1-score. The classification recognition accuracy of the algorithm was 98%. This paper has considerable significance for the fault diagnosis of aero-engines and classification recognition of aircrafts.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050915
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 916: Two-Level Excitation Current Driver to
           Reduce the Driving Power of an Electromagnetic Contactor

    • Authors: Tae-Hwan Park, Rae-Young Kim, Sang-Kil Lim
      First page: 916
      Abstract: As the capacity of the electrical system increases, so does the capacity of the electromagnetic contactor (MC). This increases the burden on the MC drive, which consumes unnecessary power in the system. MC is characterized by different initial starting-operating currents and holding currents to maintain contact. However, the operating voltage is constant regardless of the operating state. The initial starting current is considerably larger than that required to maintain contact. However, once the electromagnetic contactor is in the closed state, the current to maintain the contact is relatively small compared to the initial starting operating currents. Therefore, this study proposes two types of two-level excitation-current type MC drives that can reduce the drive power by employing features that have different conditions depending on the operating state of the MC. The overall drive power is reduced by applying different excitation currents based on the operating state. The controller and system proposed in this study were simulated using Powersim 9.1 (PSIM), and the feasibility was verified by manufacturing an analog-type driver using LM2576 and a digital-type driver using an MCU. The simulation and experimental results provide significant data for verifying the high performance and reliability of the proposed controller and system.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050916
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 917: Industrial Control Systems Security
           Validation Based on MITRE Adversarial Tactics, Techniques, and Common
           Knowledge Framework

    • Authors: Divine S. Afenu, Mohammed Asiri, Neetesh Saxena
      First page: 917
      Abstract: Industrial Control Systems (ICSs) have become the cornerstone of critical sectors like energy, transportation, and manufacturing. However, the burgeoning interconnectivity of ICSs has also introduced heightened risks from cyber threats. The urgency for robust ICS security validation has never been more pronounced. This paper provides an in-depth exploration of using the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) framework to validate ICS security. Although originally conceived for enterprise Information Technology (IT), the MITRE ATT&CK framework’s adaptability makes it uniquely suited to address ICS-specific security challenges, offering a methodological approach to identifying vulnerabilities and bolstering defence mechanisms. By zeroing in on two pivotal attack scenarios within ICSs and harnessing a suite of security tools, this research identifies potential weak points and proposes solutions to rectify them. Delving into Indicators of Compromise (IOCs), investigating suitable tools, and capturing indicators, this study serves as a critical resource for organisations aiming to fortify their ICS security. Through this lens, we offer tangible recommendations and insights, pushing the envelope in the domain of ICS security validation.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050917
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 918: Implementation of a Lossless Moving
           Target Defense Mechanism

    • Authors: Mariusz Żal, Marek Michalski, Piotr Zwierzykowski
      First page: 918
      Abstract: The contemporary world, dominated by information technologt (IT), necessitates sophisticated protection mechanisms against attacks that pose significant threats to individuals, companies, and governments alike. The unpredictability of human behavior, coupled with the scattered development of applications and devices, complicates supply chain maintenance, making it impossible to develop a system entirely immune to cyberattacks. Effective execution of many attack types hinges on prior network reconnaissance. Thus, hindering effective reconnaissance serves as a countermeasure to attacks. This paper introduces a solution within the moving target defense (MTD) strategies, focusing on the mutation of Internet protocol (IP) addresses in both edge and core network switches. The idea of complicating reconnaissance by continually changing IP addresses has been suggested in numerous studies. Nonetheless, previously proposed solutions have adversely impacted the quality of service (QoS) levels. Implementing these mechanisms could interrupt Transmission Control Protocol (TCP) connections and result in data losses. The IP address mutation algorithms presented in this study were designed to be fully transparent to transport layer protocols, thereby preserving the QoS for users without degradation. In this study, we leveraged the benefits of software-defined networking (SDN) and the Programming-Protocol-Ondependent Packet Processors (P4) language, which specifies packet processing methodologies in the data plane. Employing both SDN and P4 enables a dynamic customization of network device functionalities to meet network users’ specific requirements, a feat unachievable with conventional computer networks. This approach not only enhances the adaptability of network configurations but also significantly increases the efficiency and effectiveness of network management and operation.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050918
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 919: An Effective Ensemble Learning-Based
           Real-Time Intrusion Detection Scheme for an In-Vehicle Network

    • Authors: Easa Alalwany, Imad Mahgoub
      First page: 919
      Abstract: The emergence of connected and autonomous vehicles has led to complex network architectures for electronic control unit (ECU) communication. The controller area network (CAN) enables the transmission of data inside vehicle networks. However, although it has low latency and enjoys data broadcast capability, it is vulnerable to attacks on security. The lack of effectiveness of conventional security mechanisms in addressing these vulnerabilities poses a danger to vehicle safety. This study presents an intrusion detection system (IDS) that accurately detects and classifies CAN bus attacks in real-time using ensemble techniques and the Kappa Architecture. The Kappa Architecture enables real-time attack detection, while ensemble learning combines multiple machine learning classifiers to enhance the accuracy of attack detection. The scheme utilizes ensemble methods with Kappa Architecture’s real-time data analysis to detect common CAN bus attacks. This study entails the development and evaluation of supervised models, which are further enhanced using ensemble techniques. The accuracy, precision, recall, and F1 score are used to measure the scheme’s effectiveness. The stacking ensemble technique outperformed individual supervised models and other ensembles with accuracy, precision, recall, and F1 of 0.985, 0.987, and 0.985, respectively.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050919
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 920: ATCNet: A Novel Approach for Predicting
           Highway Visibility Using Attention-Enhanced Transformer–Capsule
           Networks

    • Authors: Wen Li, Xuekun Yang, Guowu Yuan, Dan Xu
      First page: 920
      Abstract: Meteorological disasters on highways can significantly reduce road traffic efficiency. Low visibility caused by dense fog is a severe meteorological disaster that greatly increases the incidence of traffic accidents on highways. Accurately predicting highway visibility and taking timely countermeasures can mitigate the impact of meteorological disasters and enhance traffic safety. This paper introduces the ATCNet model for highway visibility prediction. In ATCNet, we integrate Transformer, Capsule Networks (CapsNet), and self-attention mechanisms to leverage their respective complementary strengths. The Transformer component effectively captures the temporal characteristics of the data, while the Capsule Network efficiently decodes the spatial correlations and hierarchical structures among multidimensional meteorological elements. The self-attention mechanism, serving as the final decision-refining step, ensures that all key temporal and spatial hierarchical information is fully considered, significantly enhancing the accuracy and reliability of the predictions. This integrated approach is crucial in understanding highway visibility prediction tasks influenced by temporal variations and spatial complexities. Additionally, this study provides a self-collected publicly available dataset, WD13VIS, for meteorological research related to highway traffic in high-altitude mountain areas. This study evaluates the model’s performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE). Experimental results show that our ATCNet reduces the MSE and MAE by 1.21% and 3.7% on the WD13VIS dataset compared to the latest time series prediction model architecture. On the comparative dataset WDVigoVis, our ATCNet reduces the MSE and MAE by 2.05% and 5.4%, respectively. Our model’s predictions are accurate and effective, and our model shows significant progress compared to competing models, demonstrating strong universality. This model has been integrated into practical systems and has achieved positive results.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050920
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 921: A Low-Power Analog Integrated Euclidean
           Distance Radial Basis Function Classifier

    • Authors: Vassilis Alimisis, Christos Dimas, Paul P. Sotiriadis
      First page: 921
      Abstract: This study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scalability in mind, effectively accommodating variations in the classification parameters. The proposed classifier’s operational principles are meticulously detailed, tailored for low-power, low-voltage, and fully tunable implementations, specifically targeting biomedical applications. This design methodology materialized within a 90 nm CMOS process, utilizing the Cadence IC Suite for the comprehensive management of both the schematic and layout design aspects. During the verification phase, post-layout simulation results were meticulously cross-referenced with software-based classifier implementations. Also, a comparison study with related analog classifiers is provided. Through the simulation results and comparative study, the design architecture’s accuracy and sensitivity were effectively validated and confirmed.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050921
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 922: Spatial Transformation Accelerator with
           Parallel Data Access Scheme for Sample Reconstruction

    • Authors: Rihards Novickis, Edgars Lielāmurs, Daniels Jānis Justs, Andrejs Cvetkovs, Kaspars Ozols
      First page: 922
      Abstract: Spatial image transformation is a commonly used component in many image processing pipelines. It enables the correction of optical distortions, image registration onto a common reference plane, electronic image stabilisation, digital zoom, video mosaicking, etc. With the growing tendency to embed image processing in low-power devices, attaining an efficient transformation solution becomes increasingly decisive. Furthermore, interpolation is the key operation in achieving the high quality of the transformed data from the original data. Fortunately, different implementations have already seen several efficiency improvements in recent years. However, interpolation relies on sampling a set of neighbouring points from memory, which has yet to be addressed efficiently for smaller computational platforms with limited memory resources. In this work, we derive a generic mathematical model and circuit design principles for the spatial transformation accelerator design for N-dimensional data. Furthermore, we present an efficient simultaneous access scheme for high-quality signal reconstruction. Finally, the introduced ideas are verified in field programmable gate arrays using one-dimensional and two-dimensional data transformation use cases. The presented solution is able to transform images with sizes ranging from 256 × 256 to 8192 × 8192 and achieves a transfer rate of 275 frames per second with 512 × 512 images.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050922
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 923: Multi-Scale Feature Fusion Attention
           Network for Building Extraction in Remote Sensing Images

    • Authors: Jia Liu, Hang Gu, Zuhe Li, Hongyang Chen, Hao Chen
      First page: 923
      Abstract: The efficient semantic segmentation of buildings in high spatial resolution remote sensing images is a technical prerequisite for land resource management, high-precision mapping, construction planning and other applications. Current building extraction methods based on deep learning can obtain high-level abstract features of images. However, the extraction of some occluded buildings is inaccurate, and as the network deepens, small-volume buildings are lost and edges are blurred. Therefore, we introduce a multi-resolution attention combination network, which employs a multiscale channel and spatial attention module (MCAM) to adaptively capture key features and eliminate irrelevant information, which improves the accuracy of building extraction. In addition, we present a layered residual connectivity module (LRCM) to enhance the expression of information at different scales through multi-level feature fusion, significantly improving the understanding of context and the capturing of fine edge details. Extensive experiments were conducted on the WHU aerial image dataset and the Massachusetts building dataset. Compared with state-of-the-art semantic segmentation methods, this network achieves better building extraction results in remote sensing images, proving the effectiveness of the method.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050923
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 924: The Cascade of High-Voltage Pulsed
           Current Sources

    • Authors: Weigang Dong, Lei Chen, Jian Qiu, Haozheng Shi, Hui Zhao, Kefu Liu
      First page: 924
      Abstract: Currently, pulsed adders are used as pulsed voltage sources maturely. However, their use as pulsed current sources is significantly limited due to circuit impedance and the characteristics of power devices. This paper presents a simple yet effective design for a pulsed current source, incorporating a solid-state Marx pulsed adder as the primary power source and an inductor for energy storage. In the pulsed current source, a Marx pulsed adder produces high voltage to charge the inductor. Then, the stored inductance energy is converted to generate current pulses on the load; the amplitude of the pulsed current is unaffected by the load impedance within a certain range. The pulsed current source can be designed as a standard module, and several modules can form a cascade system for producing current pulses with higher voltage. Finally, a pulsed current source was developed, which can produce adjustable current pulses with high voltage. The design principles, control methods and the effects of the distribution parameters are described. The feasibility of the cascade pulsed power system was validated in experiments. Nine modules were connected to generate pulses of current 10 A on a 15 kΩ resistor.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050924
      Issue No: Vol. 13, No. 5 (2024)
       
  • Electronics, Vol. 13, Pages 925: High-Order Wideband Band-Pass
           Miniaturized Frequency-Selective Surface with Enhanced Equivalent
           Inductance

    • Authors: Jianjie Zhu, Qian Wang, Ming Jin
      First page: 925
      Abstract: To extend the wideband performance of high-order band-pass filtering applications, optimized designs with knitted structures based on traditional miniaturized frequency-selective surfaces (FSSs) are proposed in this paper. The presented miniaturized FSSs consist of multiple metallic capacitive layers, knitted inductive layers, and substrates. In contrast to the conventional high-order miniaturized FSSs composed of metallic frames, patches, and substrates, the optimized miniaturized FSSs replace the original metallic wire frames with knitted structures. Both proposed modified miniaturized FSSs achieve a flat pass-band from 5.5 GHz to 10.3 GHz with a 3 dB bandwidth of 71.6% under vertical incidence. The unit cells have dimensions of 0.16 λ0 × 0.16 λ0 × 0.284 λ0 and 0.16 λ0 × 0.16 λ0 × 0.279 λ0, respectively, where λ0 is the free space wavelength at 7.9 GHz, which is the center frequency of the operating band. Numerical simulations and measurements demonstrate that the proposed modified miniaturized FSSs exhibit excellent wideband performance with clean transition bands around the pass-band during oblique incidence and are suitable for applications such as radomes, where wideband filtering is essential for covering multi-band functions of radar or communication instruments.
      Citation: Electronics
      PubDate: 2024-02-28
      DOI: 10.3390/electronics13050925
      Issue No: Vol. 13, No. 5 (2024)
       
 
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