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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
ACS Applied Electronic Materials     Open Access   (Followers: 1)
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal   (Followers: 2)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 122)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
Advances in Power Electronics     Open Access   (Followers: 56)
Advancing Microelectronics     Hybrid Journal   (Followers: 2)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Archives of Electrical Engineering     Open Access   (Followers: 14)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
Batteries     Open Access   (Followers: 8)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Bioelectronics in Medicine     Hybrid Journal  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Open Access  
Circuits and Systems     Open Access   (Followers: 16)
Control Systems     Hybrid Journal   (Followers: 235)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 2)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electronic Design     Partially Free   (Followers: 125)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 125)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 114)
Electronics Letters     Open Access   (Followers: 25)
Elektronika ir Elektortechnika     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
Energy Storage     Hybrid Journal   (Followers: 2)
Energy Storage Materials     Full-text available via subscription   (Followers: 5)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 2)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Frontiers in Electronics     Open Access   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 112)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 60)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal  
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 52)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 2)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
IEEE Open Journal of Circuits and Systems     Open Access  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 90)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 19)
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 11)
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 281)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 78)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 65)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 11)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 31)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 45)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 174)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 85)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 57)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 87)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 11)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Open Access   (Followers: 76)
IET Smart Grid     Open Access   (Followers: 2)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
IETE Journal of Education     Open Access   (Followers: 3)
IETE Journal of Research     Open Access   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 9)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 10)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 12)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 23)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 16)
International Journal of Nanoscience     Hybrid Journal  
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 30)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 13)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 12)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 10)
Journal of Electronic Science and Technology     Open Access  
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access  
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 165)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 14)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 2)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 8)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 1)
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Power Electronics     Hybrid Journal   (Followers: 8)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 19)
Journal of Sensors     Open Access   (Followers: 25)
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 5)
Machine Learning with Applications     Full-text available via subscription   (Followers: 2)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
Metrology and Measurement Systems     Open Access   (Followers: 8)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
Nature Electronics     Hybrid Journal   (Followers: 3)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
npj Flexible Electronics     Open Access  
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 8)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 62)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Sensors International     Open Access   (Followers: 3)
Solid State Electronics Letters     Open Access  
Solid-State Electronics     Hybrid Journal   (Followers: 7)
Superconductivity     Full-text available via subscription   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)

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Electronics
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 3
Number of Followers: 125  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2079-9292
Published by MDPI Homepage  [84 journals]
  • Electronics, Vol. 11, Pages 1478: Numerical Solutions of a Differential
           System Considering a Pure Hybrid Fuzzy Neutral Delay Theory

    • Authors: Prasantha Bharathi Dhandapani, Jayakumar Thippan, Carlos Martin-Barreiro, Víctor Leiva, Christophe Chesneau
      First page: 1478
      Abstract: In this paper, we propose and derive a new system called pure hybrid fuzzy neutral delay differential equations. We apply the classical fourth-order Runge–Kutta method (RK-4) to solve the proposed system of ordinary differential equations. First, we define the RK-4 method for hybrid fuzzy neutral delay differential equations and then establish the efficiency of this method by utilizing it to solve a particular type of fuzzy neutral delay differential equation. We provide a numerical example to verify the theoretical results. In addition, we compare the RK-4 and Euler solutions with the exact solutions. An error analysis is conducted to assess how much deviation from exactness is found in the two numerical methods. We arrive at the same conclusion for our hybrid fuzzy neutral delay differential system since the RK-4 method outperforms the classical Euler method.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091478
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1479: TCP-LoRaD: A Loss Recovery and
           Differentiation Algorithm for Improving TCP Performance over MANETs in
           Noisy Channels

    • Authors: Nurul I. Sarkar, Ping-Huan Ho, Sonia Gul, Salahuddin Muhammad Salim Zabir
      First page: 1479
      Abstract: Mobile Ad hoc Networks (MANETs) are becoming popular technologies because they offer flexibility in setting up anytime and anywhere, and provide communication support on the go. This communication requires the use of Transmission Control Protocol (TCP) which is not originally designed for use in MANET environments; therefore, it raises serious performance issues. To overcome the deficiency of the original TCP, several modifications have been proposed and reported in the networking literature. TCP-WELCOME (Wireless Environment, Link losses, and Congestion packet loss ModEls) is one of the better TCP variants suitable for MANETs. However, it has been found that this protocol has problems with packet losses because of network congestion as it adopts the original congestion control mechanism of TCP New Reno. We also found that TCP-WELCOME does not perform well in noisy channel conditions in wireless environments. In this paper, we propose a novel loss recovery and differentiation algorithm (called TCP-LoRaD) to overcome the above-mentioned TCP problems. We validate the performance of TCP-LoRaD through an extensive simulation setup using Riverbed Modeler (formerly OPNET). Results obtained show that the proposed TCP-LoRaD offers up to 20% higher throughput and about 15% lower end-to-end delays than the TCP-WELCOME in a noisy channel under medium to high traffic loads.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091479
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1480: Augmenting Ear Accessories for Facial
           Gesture Input Using Infrared Distance Sensor Array

    • Authors: Kyosuke Futami, Kohei Oyama, Kazuya Murao
      First page: 1480
      Abstract: Simple hands-free input methods using ear accessories have been proposed to broaden the range of scenarios in which information devices can be operated without hands. Although many previous studies use canal-type earphones, few studies focused on the following two points: (1) A method applicable to ear accessories other than canal-type earphones. (2) A method enabling various ear accessories with different styles to have the same hands-free input function. To realize these two points, this study proposes a method to recognize the user’s facial gesture using an infrared distance sensor attached to the ear accessory. The proposed method detects skin movement around the ear and face, which differs for each facial expression gesture. We created a prototype system for three ear accessories for the root of the ear, earlobe, and tragus. The evaluation results for nine gestures and 10 subjects showed that the F-value of each device was 0.95 or more, and the F-value of the pattern combining multiple devices was 0.99 or more, which showed the feasibility of the proposed method. Although many ear accessories could not interact with information devices, our findings enable various ear accessories with different styles to have eye-free and hands-free input ability based on facial gestures.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091480
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1481: Constant Power Load Stabilization in DC
           Microgrids Using Continuous-Time Model Predictive Control

    • Authors: Youssef Alidrissi, Radouane Ouladsine, Abdellatif Elmouatamid, Rachid Errouissi, Mohamed Bakhouya
      First page: 1481
      Abstract: Despite its advantages over its AC counterparts, DC microgrids present a lot of challenges. One of these challenges is the instability issues caused by constant power loads (CPLs). CPLs deteriorate the system’s performance due to their incremental negative impedance characteristics. In this paper, a DC microgrid composed of a PV/battery system feeding a pure CPL was considered. A continuous-time model predictive control combined with a disturbance observer was applied to the DC–DC bidirectional converter. The purpose of the composite controller is to address the nonlinearity of the CPL and to maintain the stability of the system in a large operating region under load and PV generation variations. To show the performance of the system, several tests were performed under PV power and CPL power variations. Simulation results show good performance in terms of transient response, optimal tracking, and stability in a large operating region.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091481
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1482: Methodology for Power-Performance
           Trade-Off Management in Real-Time Embedded Applications

    • Authors: Ivan Popović, Strahinja Janković
      First page: 1482
      Abstract: An increasing number of battery-powered devices that are used outdoors or in mobile systems put emphasis on the power and energy efficiency as a form of trade-off between application performance and system power consumption. However, lack of objective metrics for the evaluation of application performance degradation poses difficulties for managing such trade-offs in real-time applications. The proposed methodology introduces metrics for modeling of application performance and the technique for its control, enabling more efficient power–performance trade-off management. The methodology allows for selective system performance degradation and fine-grained control of system behavior in the power–performance domain by extending the set of operating point parameters controllable through real-time application. The utilization and the effectiveness of the proposed methodology is evaluated in a simulated environment for different scenarios of the application execution, including system operation above the utilization bounds.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091482
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1483: Adaptive NN Control of Electro-Hydraulic
           System with Full State Constraints

    • Authors: Chenyang Jiang, Shuai Sui, Shaocheng Tong
      First page: 1483
      Abstract: This paper presents an adaptive neural network (NN) control approach for an electro-hydraulic system. The friction and internal leakage are nonlinear uncertainties, and the states in the considered electro-hydraulic system are fully constrained. In the control design, the NNs are utilized to approximate the nonlinear uncertainties. Then, by constructing barrier Lyapunov functions and based on the adaptive backstepping control design technique, a novel adaptive NN control scheme is formulated. It has been proven that the developed adaptive NN control scheme can sustain the controlled electro-hydraulic system to be stable and make the system output track the desired reference signal. Furthermore, the system states do not surpass the given bounds. The computer simulation results verify the effectiveness of the proposed controller.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091483
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1484: Design of the Buck Converter without
           Inductor Current Sensor

    • Authors: Hsiao-Hsing Chou, Wen-Hao Luo, San-Fu Wang
      First page: 1484
      Abstract: This paper proposes a novel control scheme for the buck converter without an inductor current sensor. The architecture of the proposed buck converter is simple and suitable for integration and mass production. It employs an output-voltage-measurement method to determine the switch ON time; therefore, the current sensor is not required. The design specification targets the application with a standard battery power source to generate the low voltages for low-power MCU or ASIC. The load current range aims for several hundred milliamps. The proposed control scheme is analyzed and simulated by SIMPLIS. The control scheme, theoretical analysis, circuit realization, contributions, advantages, and simulation results are presented in this paper. Furthermore, the circuit can be fabricated by a 0.35 μm CMOS process.
      Citation: Electronics
      PubDate: 2022-05-05
      DOI: 10.3390/electronics11091484
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1485: Mathematical Modelling of the Influence
           of Parasitic Capacitances of the Components of the Logarithmic
           Analogue-to-Digital Converter (LADC) with a Successive Approximation on
           Switched Capacitors for Increasing Accuracy of Conversion

    • Authors: Zynoviy Mychuda, Igor Zhuravel, Lesia Mychuda, Adam Szcześniak, Zbigniew Szcześniak, Hanna Yelisieieva
      First page: 1485
      Abstract: This paper presents an analysis of the influence of parasitic inter-electrode capacitances of the components of logarithmic analogue-to-digital converters with successive approximation with a variable logarithm base. Mathematical models of converter errors were developed and analyzed taking into account the parameters of modern components. It has been shown that to achieve satisfactory accuracy for the 16 bit LADC, the capacitance of the capacitor cell must not be less than 10 nF; for the 12 bit LADC, 1 nF is sufficient.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091485
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1486: Unsupervised and Self-Supervised Tensor
           Train for Change Detection in Multitemporal Hyperspectral Images

    • Authors: Muhammad Sohail, Haonan Wu, Zhao Chen, Guohua Liu
      First page: 1486
      Abstract: Remote sensing change detection (CD) using multitemporal hyperspectral images (HSIs) provides detailed information on spectral–spatial changes and is useful in a variety of applications such as environmental monitoring, urban planning, and disaster detection. However, the high dimensionality and low spatial resolution of HSIs do not only lead to expensive computation but also bring about inter-class homogeneity and inner-class heterogeneity. Meanwhile, labeled samples are difficult to obtain in reality as field investigation is expensive, which limits the application of supervised CD methods. In this paper, two algorithms for CD based on the tensor train (TT) decomposition are proposed and are called the unsupervised tensor train (UTT) and self-supervised tensor train (STT). TT uses a well-balanced matricization strategy to capture global correlations from tensors and can therefore effectively extract low-rank discriminative features, so the curse of the dimensionality and spectral variability of HSIs can be overcome. In addition, the two proposed methods are based on unsupervised and self-supervised learning, where no manual annotations are needed. Meanwhile, the ket-augmentation (KA) scheme is used to transform the low-order tensor into a high-order tensor while keeping the total number of entries the same. Therefore, high-order features with richer texture can be extracted without increasing computational complexity. Experimental results on four benchmark datasets show that the proposed methods outperformed their tensor counterpart, the tucker decomposition (TD), the higher-order singular value decomposition (HOSVD), and some other state-of-the-art approaches. For the Yancheng dataset, OA and KAPPA of UTT reached as high as 98.11% and 0.9536, respectively, while OA and KAPPA of STT were at 98.20% and 0.9561, respectively.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091486
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1487: Designing an Intelligent Virtual
           Educational System to Improve the Efficiency of Primary Education in
           Developing Countries

    • Authors: Vidal Alonso-Secades, Alfonso-José López-Rivero, Manuel Martín-Merino-Acera, Manuel-José Ruiz-García, Olga Arranz-García
      First page: 1487
      Abstract: Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on primary education in developing countries, showing the design of an intelligent virtual educational system to improve the efficiency of primary education through recommendations based on reliable data. The intelligent system is formed of four subsystems: data warehousing, analytical data processing, monitoring process and recommender system for educational agents. To illustrate this, the paper contains two dashboards that analyze, respectively, the digital resources usage time and an aggregate profile of teachers’ digital skills, in order to infer new activities that improve efficiency. These intelligent virtual educational systems focus the teaching–learning process on new forms of interaction on an educational future oriented to personalized teaching for the students, and new evaluation and teaching processes for each professor.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091487
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1488: Analysis and Design of an
           S/PS−Compensated WPT System with Constant Current and Constant
           Voltage Charging

    • Authors: Lin Yang, Zhi Geng, Shuai Jiang, Can Wang
      First page: 1488
      Abstract: In recent years, more and more scholars have paid attention to the research of wireless power transfer (WPT) technology, and have achieved a lot of results. In practical charging application, ensuring that the WPT system can achieve constant current and constant voltage output with zero phase angle (ZPA) operation is very important to prolong battery life and improve power transfer efficiency. This paper proposes an series/parallel series(S/PS)-compensated WPT system that can charge the battery load in constant current and constant voltage modes at two different frequency points through frequency switching. The proposed S/PS structure contains only three compensation capacitors, few compensation elements, simple structure, low economic cost, in addition, the secondary-side does not contain compensation inductor, ensuring the compactness of the secondary-side. An experimental prototype with an input voltage of 40 V is established, and the experiment proves that the model can obtain output voltage of 48 V and current of 2 A. Maximum system transmission efficiency of up to 92.48% The experimental results are consistent with the theoretical analysis results, which verifies the feasibility of the method.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091488
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1489: BSTProv: Blockchain-Based Secure and
           Trustworthy Data Provenance Sharing

    • Authors: Lian-Shan Sun, Xue Bai, Chao Zhang, Yang Li, Yong-Bin Zhang, Wen-Qiang Guo
      First page: 1489
      Abstract: In the Big Data era, data provenance has become an important concern for enhancing the trustworthiness of key data that are rapidly generated and shared across organizations. Prevailing solutions employ authoritative centers to efficiently manage and share massive data. They are not suitable for secure and trustworthy decentralized data provenance sharing due to the inevitable dishonesty or failure of trusted centers. With the advent of the blockchain technology, embedding data provenance in immutable blocks is believed to be a promising solution. However, a provenance file, usually a directed acyclic graph, cannot be embedded in blocks as a whole because its size may exceed the limit of a block, and may include various sensitive information that can be legally accessed by different users. To this end, this paper proposed the BSTProv, a blockchain-based system for secure and trustworthy decentralized data provenance sharing. It enables secure and trustworthy provenance sharing by partitioning a large provenance graph into multiple small subgraphs and embedding the encrypted subgraphs instead of raw subgraphs or their hash values into immutable blocks of a consortium blockchain; it enables decentralized and flexible authorization by allowing each peer to define appropriate permissions for selectively sharing some sets of subgraphs to specific requesters; and it enables efficient cross-domain provenance composition and tracing by maintaining a high-level dependency structure among provenance graphs from different domains in smart contracts, and by locally storing, decrypting, and composing subgraphs obtained from the blockchain. Finally, a prototype is implemented on top of an Ethereum-based consortium blockchain and experiment results show the advantages of our approach.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091489
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1490: High-Power Electromagnetic Pulse Effect
           Prediction for Vehicles Based on Convolutional Neural Network

    • Authors: Le Cao, Shuai Hao, Yuan Zhao, Cheng Wang
      First page: 1490
      Abstract: This study presents a prediction model for high-power electromagnetic pulse (HPEMP) effects on aboveground vehicles based on convolutional neural networks (CNNs). Since a vehicle is often located aboveground and is close to the air-ground–half-space interface, the electromagnetic energy coupled into the vehicle by the ground reflected waves cannot be ignored. Consequently, the analysis of the vehicle’s HPEMP effect is a composite electromagnetic scattering problem of the half-space and the vehicles above it, which is often analyzed using different half-space numerical methods. However, traditional numerical methods are often limited by the complexity of the actual half-space models and the high computational demands of complex targets. In this study, a prediction method is proposed based on a CNN, which can analyze the electric field and energy density under different incident conditions and half-space environments. Compared with the half-space finite-difference time-domain (FDTD) method, the accuracy of the prediction results was above 98% after completing the training of the CNN network, which proves the correctness and effectiveness of the method. In summary, the CNN prediction model in this study can provide a reference for evaluating the HPEMP effect on the target over a complex half-space medium.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091490
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1491: Quality Enhancement of MPEG-H 3DA
           Binaural Rendering Using a Spectral Compensation Technique

    • Authors: Hyeongi Moon, Young-cheol Park
      First page: 1491
      Abstract: The latest MPEG standard, MPEG-H 3D Audio, employs the virtual loudspeaker rendering (VLR) technique to support virtual reality (VR) and augmented reality (AR). During the rendering, the binaural downmixing of channel signals often induces the so-called comb filter effect, an undesirable spectral artifact, due to the phase difference between the binaural filters. In this paper, we propose an efficient algorithm that can mitigate such spectral artifacts. The proposed algorithm performs spectral compensation in both the panning gain and downmix signal domains depending on the frequency range. In the low-frequency bands where a band has a wider bandwidth than the critical-frequency scale, panning gains are directly compensated. In the high-frequency bands, where a band has a narrower bandwidth than the critical-frequency scale, a signal compensation similar to the active downmix is performed. As a result, the proposed algorithm optimizes the performance and the complexity within MPEG-H 3DA framework. By implementing the algorithm on MPEG-H 3DA BR, we verify that the additional computation complexity is minor. We also show that the proposed algorithm improves the subjective quality of MPEG-H 3DA BR significantly.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091491
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1492: On the Potential of MP-QUIC as Transport
           Layer Aggregator for Multiple Cellular Networks

    • Authors: Zsolt Krämer, Felicián Németh, Attila Mihály, Sándor Molnár, István Pelle, Gergely Pongrácz, Donát Scharnitzky
      First page: 1492
      Abstract: Multipath transport protocols have the ability to simultaneously utilize the different paths and thus outperform single-path solutions in terms of achievable goodput, latency, or reliability. In this paper our goal is to examine the potential of connecting a mobile terminal to multiple mobile networks simultaneously in a dynamically changing environment. To achieve this, first we analyze a dataset obtained from an LTE drive test involving two operators. Then we study the performance of MP-QUIC, the multipath extension of QUIC, in a dynamic emulated environment generated from the collected traces. Our results show that MP-QUIC may leverage multiple available channels to provide uninterrupted connectivity, and a better overall goodput even when compared to using only the best available channel for communication. We also compare the MP-QUIC performance with MPTCP, identify challenges with the current protocol implementations to fill in the available aggregate capacity, and give insights on how the achievable throughput could be increased.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091492
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1493: A Design Methodology for Wideband
           Current-Reuse Receiver Front-Ends Aimed at Low-Power Applications

    • Authors: Arash Abbasi, Frederic Nabki
      First page: 1493
      Abstract: This work gives a design perspective on low-power and wideband RF-to-Baseband current-reuse receivers (CRR). The proposed CRR architecture design shares a single supply and biasing current among both LNTA and baseband circuits to reduce power consumption. The work discusses topology selection and a suitable design procedure of the low noise transconductance amplifier (LNTA), down-conversion passive-mixer, active-inductor (AI) and TIA circuits. Layout considerations are also discussed. The receiver was simulated in 130 nm CMOS technology and occupies an active area of 0.025 mm2. It achieves a wideband input matching of less than −10 dB from 0.8 GHz to 3.4 GHz. A conversion-gain of 39.5 dB, IIP3 of −28 dBm and a double-sideband (DSB) NF of 5.6 dB is simulated at a local-oscillator (LO) frequency of 2.4 GHz and an intermediate frequency (IF) of 10 MHz, while consuming 1.92 mA from a 1.2 V supply.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091493
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1494: Isolation and Grading of Faults in
           Battery Packs Based on Machine Learning Methods

    • Authors: Sen Yang, Boran Xu, Hanlin Peng
      First page: 1494
      Abstract: As the installed energy storage stations increase year by year, the safety of energy storage batteries has attracted the attention of industry and academia. In this work, an intelligent fault diagnosis scheme for series-connected battery packs based on wavelet characteristics of battery voltage correlations is designed. First, the cross-cell voltages of multiple cells are preprocessed using an improved recursive Pearson correlation coefficient to capture the abnormal electrical signals. Secondly, the wavelet packet decomposition is applied to the coefficient series to obtain fault-related features from wavelet sub-bands, and the most representative characteristic principal components are extracted. Finally, the artificial neural network (ANN) and multi-classification relevance vector machine (mRVM) are employed to classify and evaluate fault mode and fault degree, respectively. Physical injection of external and internal short circuits, thermal damage, and loose connection failure is carried out to collect real fault data for model training and method validation. Experimental results show that the proposed method can effectively detect and locate different faults using the extracted fault features; mRVM is better than ANN in thermal fault diagnosis, while the overall diagnosis performance of ANN is better than mRVM. The success rates of fault isolation are 82% and 81%, and the success rates of fault grading are 98% and 90%, by ANN and mRVM, respectively.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091494
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1495: Coronary Artery Disease Detection Model
           Based on Class Balancing Methods and LightGBM Algorithm

    • Authors: Shasha Zhang, Yuyu Yuan, Zhonghua Yao, Jincui Yang, Xinyan Wang, Jianwei Tian
      First page: 1495
      Abstract: Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091495
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1496: Integration and Deployment of
           Cloud-Based Assistance System in Pharaon Large Scale
           Pilots—Experiences and Lessons Learned

    • Authors: Andrej Grguric, Miran Mosmondor, Darko Huljenic
      First page: 1496
      Abstract: The EU project Pharaon aims to support older European adults by integrating digital services, tools, interoperable open platforms, and devices. One of the objectives is to validate the integrated solutions in large-scale pilots. The integration of mature solutions and existing systems is one of the preconditions for the successful realization of the different aims of the pilots. One such solution is an intelligent, privacy-aware home-care assistance system, SmartHabits. After briefly introducing the Pharaon and SmartHabits, the authors propose different Pharaon models in the Ambient/Active Assisted Living (AAL) domain, namely the Pharaon conceptual model, Pharaon reference logical architecture view, AAL ecosystem model, meta AAL ecosystem model, and Pharaon ecosystem and governance models. Building on the proposed models, the authors provide details of the holistic integration and deployment process of the SmartHabits system into the Pharaon ecosystem. Both technical and supporting integration challenges and activities are discussed. Technical activities, including syntactic and semantic integration and securing the transfer of the Pharaon sensitive data, are among the priorities. Supporting activities include achieving legal and regulatory compliance, device procurement, and use-case co-designing in COVID-19 conditions.
      Citation: Electronics
      PubDate: 2022-05-06
      DOI: 10.3390/electronics11091496
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1497: Remote Prototyping of FPGA-Based Devices
           in the IoT Concept during the COVID-19 Pandemic

    • Authors: Michał Melosik, Mariusz Naumowicz, Marek Kropidłowski, Wieslaw Marszalek
      First page: 1497
      Abstract: This paper presents a system for the remote design and testing of electronic circuits and devices with FPGAs during COVID-19 and similar lockdown periods when physical access to laboratories is not permitted. The system is based on the application of the IoT concept, in which the final device is a test board with an FPGA chip. The system allows for remote visual inspection of the board and the devices linked to it in the laboratory. The system was developed for remote learning taking place during the lockdown periods at Poznan University of Technology (PUT) in Poland. The functionality of the system is confirmed by two demonstration tasks (the use of the temperature and humidity DHT11 sensor and the design of a generator of sinusoidal waveforms) for students in the fundamentals of digital design and synthesis courses. The proposed solution allows, in part, to bypass the time-consuming simulations, and accelerate the process of prototyping digital circuits by remotely accessing the infrastructure of the microelectronics laboratory.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091497
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1498: Modeling Distributed MQTT Systems Using
           Multicommodity Flow Analysis

    • Authors: Pietro Manzoni, Vittorio Maniezzo, Marco A. Boschetti
      First page: 1498
      Abstract: The development of technologies that exploit the Internet of Things (IoT) paradigm has led to the increasingly widespread use of networks formed by different devices scattered throughout the territory. The Publish/Subscribe paradigm is one of the most used communication paradigms for applications of this type. However, adopting these systems due to their centralized structure also leads to the emergence of various problems and limitations. For example, the broker is typically the single point of failure of the system: no communication is possible if the broker is unavailable. Moreover, they may not scale well considering the massive numbers of IoT devices forecasted in the future. Finally, a network architecture with a single central broker is partially at odds with the edge-oriented approach. This work focuses on the development of an adaptive topology control approach, able to find the most efficient network configuration maximizing the number of connections and reduce the waste of resources within it, starting from the definition of the devices and the connections between them present in the system. To reach the goal, we leverage an integer linear programming mathematical formulation, providing the basis to solve and optimize the problem of network configuration in contexts where the resources available to the devices are limited.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091498
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1499: Video Super-Resolution Using Multi-Scale
           and Non-Local Feature Fusion

    • Authors: Yanghui Li, Hong Zhu, Qian Hou, Jing Wang, Wenhuan Wu
      First page: 1499
      Abstract: Video super-resolution can generate corresponding to high-resolution video frames from a plurality of low-resolution video frames which have rich details and temporally consistency. Most current methods use two-level structure to reconstruct video frames by combining optical flow network and super-resolution network, but this process does not deeply mine the effective information contained in video frames. Therefore, we propose a video super-resolution method that combines non-local features and multi-scale features to extract more in-depth effective information contained in video frames. Our method obtains long-distance effective information by calculating the similarity between any two pixels in the video frame through the non-local module, extracts the local information covered by different scale convolution cores through the multi-scale feature fusion module, and fully fuses feature information using different connection modes of convolution cores. Experiments on different data sets show that the proposed method is superior to the existing methods in quality and quantity.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091499
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1500: Recognizing Students and Detecting
           Student Engagement with Real-Time Image Processing

    • Authors: Mustafa Uğur Uçar, Ersin Özdemir
      First page: 1500
      Abstract: With COVID-19, formal education was interrupted in all countries and the importance of distance learning has increased. It is possible to teach any lesson with various communication tools but it is difficult to know how far this lesson reaches to the students. In this study, it is aimed to monitor the students in a classroom or in front of the computer with a camera in real time, recognizing their faces, their head poses, and scoring their distraction to detect student engagement based on their head poses and Eye Aspect Ratios. Distraction was determined by associating the students’ attention with looking at the teacher or the camera in the right direction. The success of the face recognition and head pose estimation was tested by using the UPNA Head Pose Database and, as a result of the conducted tests, the most successful result in face recognition was obtained with the Local Binary Patterns method with a 98.95% recognition rate. In the classification of student engagement as Engaged and Not Engaged, support vector machine gave results with 72.4% accuracy. The developed system will be used to recognize and monitor students in the classroom or in front of the computer, and to determine the course flow autonomously.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091500
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1501: 100 Gbps Dynamic Extensible Protocol
           Parser Based on an FPGA

    • Authors: Ke Wang, Zhichuan Guo, Mangu Song, Meng Sha
      First page: 1501
      Abstract: In order to facilitate the transition between networks and the integration of heterogeneous networks, the underlying link design of the current mainstream Information-Centric Networking (ICN) still considers the characteristics of the general network and extends the customized ICN protocol on this basis. This requires that the network transmission equipment can not only distinguish general network packets but also support the identification of ICN-specific protocols. However, traditional network protocol parsers are designed for specific network application scenarios, and it is difficult to flexibly expand new protocol parsing rules for different ICN network architectures. For this reason, we propose a general dynamic extensible protocol parser deployed on FPGA, which supports the real-time update of network protocol parsing rules by configuring extended protocol descriptors. At the same time, the multi-queue protocol management mechanism is adopted to realize the grouping management and rapid parsing of the extended protocol. The results demonstrate that the method can effectively support the protocol parsing of 100 Gbps high-speed network data packets and can dynamically update the protocol parsing rules under ultra-low latency. Compared with the current commercial programmable network equipment, this solution improves the protocol update efficiency by several orders of magnitude and better supports the online updating of network equipment.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091501
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1502: Learning-Based Methods for Cyber Attacks
           Detection in IoT Systems: Methods, Analysis, and Future Prospects

    • Authors: Usman Inayat, Muhammad Fahad Zia, Sajid Mahmood, Haris M. Khalid, Mohamed Benbouzid
      First page: 1502
      Abstract: Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091502
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1503: Current Collapse Conduction Losses
           Minimization in GaN Based PMSM Drive

    • Authors: Pavel Skarolek, Ondrej Lipcak, Jiri Lettl
      First page: 1503
      Abstract: The ever-increasing demands on the efficiency and power density of power electronics converters lead to the replacement of traditional silicon-based components with new structures. One of the promising technologies represents devices based on Gallium-Nitride (GaN). Compared to silicon transistors, GaN semiconductor switches offer superior performance in high-frequency converters, since their fast switching process significantly decreases the switching losses. However, when used in hard-switched converters such as voltage-source inverters (VSI) for motor control applications, GaN transistors increase the power dissipated due to the current conduction. The loss increase is caused by the current-collapse phenomenon, which increases the dynamic drain-source resistance of the device shortly after the turn-on. This disadvantage makes it hard for GaN converters to compete with other technologies in electric drives. Therefore, this paper offers a purely software-based solution to mitigate the negative consequences of the current-collapse phenomenon. The proposed method is based on the minimum pulse length optimization of the classical 7-segment space-vector modulation (SVM) and is verified within a field-oriented control (FOC) of a three-phase permanent magnet synchronous motor (PMSM) supplied by a two-level GaN VSI. The compensation in the control algorithm utilizes an offline measured look-up table dependent on the machine input power.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091503
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1504: SSA-SL Transformer for Bearing Fault
           Diagnosis under Noisy Factory Environments

    • Authors: Seoyeong Lee, Jongpil Jeong
      First page: 1504
      Abstract: Among the smart factory studies, we describe defect detection research conducted on bearings, which are elements of mechanical facilities. Bearing research has been consistently conducted in the past; however, most of the research has been limited to using existing artificial intelligence models. In addition, previous studies assumed the factories situated in the bearing defect research were insufficient. Therefore, a recent research was conducted that applied an artificial intelligence model and the factory environment. The transformer model was selected as state-of-the-art (SOTA) and was also applied to bearing research. Then, an experiment was conducted with Gaussian noise applied to assume a factory situation. The swish-LSTM transformer (Sl transformer) framework was constructed by redesigning the internal structure of the transformer using the swish activation function and long short-term memory (LSTM). Then, the data in noise were removed and reconstructed using the singular spectrum analysis (SSA) preprocessing method. Based on the SSA-Sl transformer framework, an experiment was performed by adding Gaussian noise to the Case Western Reserve University (CWRU) dataset. In the case of no noise, the Sl transformer showed more than 95% performance, and when noise was inserted, the SSA-Sl transformer showed better performance than the comparative artificial intelligence models.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091504
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1505: Efficient Colour Image Encryption
           Algorithm Using a New Fractional-Order Memcapacitive Hyperchaotic System

    • Authors: Zain-Aldeen S. A. Rahman, Basil H. Jasim, Yasir I. A. Al-Yasir, Raed A. Abd-Alhameed
      First page: 1505
      Abstract: In comparison with integer-order chaotic systems, fractional-order chaotic systems exhibit more complex dynamics. In recent years, research into fractional chaotic systems for the utilization of image cryptosystems has become increasingly highlighted. This paper describes the development, testing, numerical analysis, and electronic realization of a fractional-order memcapacitor. Then, a new four-dimensional (4D) fractional-order memcapacitive hyperchaotic system is suggested based on this memcapacitor. Analytically and numerically, the nonlinear dynamic properties of the hyperchaotic system have been explored, where various methods, including equilibrium points, phase portraits of chaotic attractors, bifurcation diagrams, and the Lyapunov exponent, are considered to demonstrate the chaos behaviour of this new hyperchaotic system. Consequently, an encryption cryptosystem algorithm is used for colour image encryption based on the chaotic behaviour of the memcapacitive model, where every pixel value of the original image is incorporated in the secret key to strengthen the encryption algorithm pirate anti-attack robustness. For generating the keyspace of that employed cryptosystem, the initial condition values, parameters, and fractional-order derivative value(s) (q) of the memcapacitive chaotic system are utilized. The common cryptanalysis metrics are verified in detail by histogram, keyspace, key sensitivity, correlation coefficient values, entropy, time efficiency, and comparisons with other recent related fieldwork in order to demonstrate the security level of the proposed cryptosystem approach. Finally, images of various sizes were encrypted and recovered to ensure that the utilized cryptosystem approach is capable of encrypting/decrypting images of various sizes. The obtained experimental results and security metrics analyses illustrate the excellent accuracy, high security, and perfect time efficiency of the utilized cryptosystem, which is highly resistant to various forms of pirate attacks.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091505
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1506: A Novel Anti-Risk Method for Portfolio
           Trading Using Deep Reinforcement Learning

    • Authors: Han Yue, Jiapeng Liu, Dongmei Tian, Qin Zhang
      First page: 1506
      Abstract: In the past decade, the application of deep reinforcement learning (DRL) in portfolio management has attracted extensive attention. However, most classical RL algorithms do not consider the exogenous and noise of financial time series data, which may lead to treacherous trading decisions. To address this issue, we propose a novel anti-risk portfolio trading method based on deep reinforcement learning (DRL). It consists of a stacked sparse denoising autoencoder (SSDAE) network and an actor–critic based reinforcement learning (RL) agent. SSDAE will carry out off-line training first, while the decoder will used for on-line feature extraction in each state. The SSDAE network is used for the noise resistance training of financial data. The actor–critic algorithm we use is advantage actor–critic (A2C) and consists of two networks: the actor network learns and implements an investment policy, which is then evaluated by the critic network to determine the best action plan by continuously redistributing various portfolio assets, taking Sharp ratio as the optimization function. Through extensive experiments, the results show that our proposed method is effective and superior to the Dow Jones Industrial Average index (DJIA), several variants of our proposed method, and a state-of-the-art (SOTA) method.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091506
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1507: Machine Learning Models for Early
           Prediction of Sepsis on Large Healthcare Datasets

    • Authors: Javier Enrique Camacho-Cogollo, Isis Bonet, Bladimir Gil, Ernesto Iadanza
      First page: 1507
      Abstract: Sepsis is a highly lethal syndrome with heterogeneous clinical manifestation that can be hard to identify and treat. Early diagnosis and appropriate treatment are critical to reduce mortality and promote survival in suspected cases and improve the outcomes. Several screening prediction systems have been proposed for evaluating the early detection of patient deterioration, but the efficacy is still limited at individual level. The increasing amount and the versatility of healthcare data suggest implementing machine learning techniques to develop models for predicting sepsis. This work presents an experimental study of some machine-learning-based models for sepsis prediction considering vital signs, laboratory test results, and demographics using Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4), a publicly available dataset. The experimental results demonstrate an overall higher performance of machine learning models over the commonly used Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA) scoring systems at the time of sepsis onset.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091507
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1508: Delivering Extended Cellular Coverage
           and Capacity Using High-Altitude Platforms

    • Authors: Steve Chukwuebuka Arum, David Grace, Paul Daniel Mitchell
      First page: 1508
      Abstract: Interest in delivering cellular communication using a high-altitude platform (HAP) is increasing partly due to its wide coverage capability. In this paper, we formulate analytical expressions for estimating the area of a HAP beam footprint, average per-user capacity per cell, average spectral efficiency (SE) and average area spectral efficiency (ASE), which are relevant for radio network planning, especially within the context of HAP extended contiguous cellular coverage and capacity. To understand the practical implications, we propose an enhanced and validated recursive HAP antenna beam-pointing algorithm, which forms HAP cells over an extended service area while considering beam broadening and the degree of overlap between neighbouring beams. The performance of the extended contiguous cellular structure resulting from the algorithm is compared with other alternative schemes using the carrier-to-noise ratio (CNR) and carrier-to-interference-plus-noise ratio (CINR). Results show that there is a steep reduction in average ASE at the edge of coverage. The achievable coverage is limited by the minimum acceptable average ASE at the edge, among other factors. In addition, the results highlight that efficient beam management can be achieved using the enhanced and validated algorithm, which significantly improves user CNR, CINR, and coverage area compared with other benchmark schemes. A simulated annealing comparison verifies that such an algorithm is close to optimal.
      Citation: Electronics
      PubDate: 2022-05-07
      DOI: 10.3390/electronics11091508
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1509: Business Process Outcome Prediction
           Based on Deep Latent Factor Model

    • Authors: Ke Lu, Xinjian Fang, Xianwen Fang
      First page: 1509
      Abstract: Business process outcome prediction plays an essential role in business process monitoring. It continuously analyzes completed process events to predict the executing cases’ outcome. Most of the current outcome prediction focuses only on the activity information in historical logs and less on the embedded and implicit knowledge that has not been explicitly represented. To address these issues, this paper proposes a Deep Latent Factor Model Predictor (DLFM Predictor) for uncovering the implicit factors affecting system operation and predicting the final results of continuous operation cases based on log behavior characteristics and resource information. First, the event logs are analyzed from the control flow and resource perspectives to construct composite data. Then, the stack autoencoder model is trained to extract the data’s main feature components for improving the training data’s reliability. Next, we capture the implicit factors at the control and data flow levels among events and construct a deep implicit factor model to optimize the parameter settings. After that, an expansive prefix sequence construction method is proposed to realize the outcome prediction of online event streams. Finally, the proposed algorithm is implemented based on the mainstream framework of neural networks and evaluated by real logs. The results show that the algorithm performs well under several evaluation metrics.
      Citation: Electronics
      PubDate: 2022-05-08
      DOI: 10.3390/electronics11091509
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1510: Few-Shot Learning with Collateral
           Location Coding and Single-Key Global Spatial Attention for Medical Image
           Classification

    • Authors: Wenjing Shuai, Jianzhao Li
      First page: 1510
      Abstract: Humans are born with the ability to learn quickly by discerning objects from a few samples, to acquire new skills in a short period of time, and to make decisions based on limited prior experience and knowledge. The existing deep learning models for medical image classification often rely on a large number of labeled training samples, whereas the fast learning ability of deep neural networks has failed to develop. In addition, it requires a large amount of time and computing resource to retrain the model when the deep model encounters classes it has never seen before. However, for healthcare applications, enabling a model to generalize new clinical scenarios is of great importance. The existing image classification methods cannot explicitly use the location information of the pixel, making them insensitive to cues related only to the location. Besides, they also rely on local convolution and cannot properly utilize global information, which is essential for image classification. To alleviate these problems, we propose a collateral location coding to help the network explicitly exploit the location information of each pixel to make it easier for the network to recognize cues related to location only, and a single-key global spatial attention is designed to make the pixels at each location perceive the global spatial information in a low-cost way. Experimental results on three medical image benchmark datasets demonstrate that our proposed algorithm outperforms the state-of-the-art approaches in both effectiveness and generalization ability.
      Citation: Electronics
      PubDate: 2022-05-09
      DOI: 10.3390/electronics11091510
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1511: Empowering the Internet of Things Using
           Light Communication and Distributed Edge Computing

    • Authors: Abdelhamied A. Ateya, Mona Mahmoud, Adel Zaghloul, Naglaa. F. Soliman, Ammar Muthanna
      First page: 1511
      Abstract: With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient.
      Citation: Electronics
      PubDate: 2022-05-09
      DOI: 10.3390/electronics11091511
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1512: Dual Phase Lock-In Amplifier with
           Photovoltaic Modules and Quasi-Invariant Common-Mode Signal

    • Authors: Pavel Baranov, Ivan Zatonov, Bien Bui Duc
      First page: 1512
      Abstract: In measuring small voltage deviations of about 1 µV and lower, it is important to separate useful signals from noise. The measurement of small voltage deviations between the amplitudes of two AC signals in wide frequency and voltage ranges, is performed by using lock-in amplifiers with the differential input as a comparator (null-indicator). The resolution and measurement accuracy of lock-in amplifiers is largely determined by the common-mode rejection ratio in their measuring channel. This work presents a developed differential signal recovery circuit with embedded photovoltaic modules, which allows implementing the dual phase lock-in amplifier with the differential input and quasi-invariant common-mode signal. The obtained metrological parameters of the proposed dual phase analog lock-in amplifier prove its applicability in comparing two signal amplitudes of 10√2 µV to 10√2 V in the frequency range of 20 Hz to 100 kHz with a 10 nV resolution. The proposed dual phase analog lock-in amplifier was characterized by a 130 to 185 dB CMRR in the frequency range up to 100 kHz with 20 nV/√Hz white noise.
      Citation: Electronics
      PubDate: 2022-05-09
      DOI: 10.3390/electronics11091512
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1513: Feedback ARMA Models versus Bayesian
           Models towards Securing OpenFlow Controllers for SDNs

    • Authors: Wael Hosny Fouad Aly, Hassan Kanj, Nour Mostafa, Samer Alabed
      First page: 1513
      Abstract: In software-defined networking (SDN), the control layers are moved away from the forwarding switching layers. SDN gives more programmability and flexibility to the controllers. OpenFlow is a protocol that gives access to the forwarding plane of a network switch or router over the SDN network. OpenFlow uses a centralized control of network switches and routers in and SDN environment. Security is of major importance for SDN deployment. Transport layer security (TLS) is used to implement security for OpenFlow. This paper proposed a new technique to improve the security of the OpenFlow controller through modifying the TLS implementation. The proposed model is referred to as the secured feedback model using autoregressive moving average (ARMA) for SDN networks (SFBARMASDN). SFBARMASDN depended on computing the feedback for incoming packets based on ARMA models. Filtering techniques based on ARMA techniques were used to filter the packets and detect malicious packets that needed to be dropped. SFBARMASDN was compared to two reference models. One reference model was Bayesian-based and the other reference model was the standard OpenFlow.
      Citation: Electronics
      PubDate: 2022-05-09
      DOI: 10.3390/electronics11091513
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1514: Analysis of the Applicable Range of the
           Standard Lambertian Model to Describe the Reflection in Visible Light
           Communication

    • Authors: Xiangyang Zhang, Xiaodong Yang, Nan Zhao, Muhammad Bilal Khan
      First page: 1514
      Abstract: The existing visible light communication simulation research on reflection is mainly based on the standard Lambertian model. In recent years, some papers have mentioned that the standard Lambertian model is too simplified and approximate to meet the actual situation. To solve this problem, a variety of more complex reflection models have been proposed. However, the more complex models require more computation. To balance computation and simulation accuracy, by consulting the literature, this study found that the standard Lambertian model has a certain requirement of the incident angle range to describe reflection on a wall covered in plaster. In this paper, the inappropriate index Q of the standard Lambertian model is defined, and then the relationship between Q and the light-emitting diode position with only the first reflection considered is determined through a preliminary calculation. The calculation shows that, in an empty room with plaster walls, and when the distance is greater than 0.685 m, the standard Lambertian model can be used; when the distance is less than 0.685 m, other, more complex models need to be adopted according to the actual situation.
      Citation: Electronics
      PubDate: 2022-05-09
      DOI: 10.3390/electronics11091514
      Issue No: Vol. 11, No. 9 (2022)
       
  • Electronics, Vol. 11, Pages 1515: Research on the Finite Time Compound
           Control of Continuous Rotary Motor Electro-Hydraulic Servo System

    • Authors: Xiao-Jing Wang, Qi-Zheng Zhang, Chun-Hui Li
      First page: 1515
      Abstract: Aiming at the influence of friction, leakage, noise and other nonlinear factors on the performance of the electro-hydraulic servo system of a continuous rotary motor, a finite-time composite controller for the aforementioned servo system is proposed. First, a mathematical model of the electro-hydraulic servo system was analyzed, and the input and output angle data of the motor were collected for system identification. Subsequently, the ARMAX identification model of the continuous rotary motor system was obtained. Then, according to the observed advantages, namely faster capability of the finite-time controller (FTC) to converge the system, and ability of the finite-time observer to reduce the steady-state error of the system, the finite-time controller and finite-time state observer of a continuous rotary electro-hydraulic servo motor were respectively designed. Finally, comparison with PID control simulation shows that the compound controller could effectively improve the control accuracy and performance of the system.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101515
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1516: A Multivariate Temporal Convolutional
           Attention Network for Time-Series Forecasting

    • Authors: Renzhuo Wan, Chengde Tian, Wei Zhang, Wendi Deng, Fan Yang
      First page: 1516
      Abstract: Multivariate time-series forecasting is one of the crucial and persistent challenges in time-series forecasting tasks. As a kind of data with multivariate correlation and volatility, multivariate time series impose highly nonlinear time characteristics on the forecasting model. In this paper, a new multivariate time-series forecasting model, multivariate temporal convolutional attention network (MTCAN), based on a self-attentive mechanism is proposed. MTCAN is based on the Convolution Neural Network (CNN) model, using 1D dilated convolution as the basic unit to construct asymmetric blocks, and then, the feature extraction is performed by the self-attention mechanism to finally obtain the prediction results. The input and output lengths of this network can be determined flexibly. The validation of the method is carried out with three different multivariate time-series datasets. The reliability and accuracy of the prediction results are compared with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Long Short-Term Memory (ConvLSTM), and Temporal Convolutional Network (TCN). The prediction results show that the model proposed in this paper has significantly improved prediction accuracy and generalization.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101516
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1517: Experimental Study on FM-CSK
           Communication System for WSN

    • Authors: Darja Cirjulina, Dmitrijs Pikulins, Ruslans Babajans, Maris Zeltins, Deniss Kolosovs, Anna Litvinenko
      First page: 1517
      Abstract: The current paper presents the experimental study of the frequency-modulated chaos shift keying (FM-CSK) communication system. The proposed system has the potential to enhance the security aspects of the physical layer to meet the needs for safe data transmission in wireless sensor networks (WSN). Compared to common digital FM-DCSK, the studied analog FM-CSK communication system provides a more straightforward design. As chaos oscillators are the core elements of the FM-CSK communication system, the paper investigates the selected oscillators’ properties, including the implementation aspects—the impact of reactive element value deviations on dynamics and synchronization stability. Chaotic dynamics are evaluated with the mean-square-displacement-based 0–1 test, while correlation analysis is used to evaluate synchronization. The impact of the different chaos oscillators’ employment on FM-CSK communication system performance is examined, and the bit error ratio is used for noise immunity evaluation.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101517
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1518: Combining Event-Based Maneuver Selection
           and MPC Based Trajectory Generation in Autonomous Driving

    • Authors: Ni Dang, Tim Brüdigam, Marion Leibold, Martin Buss
      First page: 1518
      Abstract: Maneuver planning, which plays a key role in selecting desired lanes and speeds, is an essential element of autonomous driving. Generally, for a vehicle driving on a multilane road, there are several potential maneuvers in both longitudinal and lateral directions. Selecting the best maneuver from the various options represents a significant challenge. In this paper, we propose a maneuver selection algorithm and combine it with a trajectory generation algorithm, which is based on model predictive control (MPC). The maneuver selection method is a higher-level planner, which selects only one maneuver from all possible maneuvers based on the current situation and delivers it to a lower-level MPC-based trajectory tracking controller. The effectiveness of the proposed algorithm is validated by simulating an overtaking scenario on a multilane highway.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101518
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1519: Landslide Displacement Prediction Model
           Using Time Series Analysis Method and Modified LSTM Model

    • Authors: Zian Lin, Xiyan Sun, Yuanfa Ji
      First page: 1519
      Abstract: Landslides are serious and complex geological and natural disasters that threaten the safety of people’s health and wealth worldwide. To face this challenge, a landslide displacement prediction model based on time series analysis and modified long short-term memory (LSTM) model is proposed in this paper. Considering that data from different time periods have different time values, the weighted moving average (WMA) method is adopted to decompose the cumulative landslide displacement into the displacement trend and periodic displacement. To predict the displacement trend, we combined the displacement trend of landslides in the early stage with an LSTM model. Considering the repeatability and periodicity of rainfall and reservoir water level in every cycle, a long short-term memory fully connected (LSTM-FC) model was constructed by adding a fully connected layer to the traditional LSTM model to predict periodic displacement. The two predicted displacements were added to obtain the final landslide predicted displacement. In this paper, under the same conditions, we used a polynomial function algorithm to compare and predict the displacement trend with the LSTM model and used the LSTM-FC model to compare and predict the displacement trend with eight other commonly used algorithms. Two prediction results indicate that the modified prediction model is able to effectively predict landslide displacement.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101519
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1520: Intelligent Bus Scheduling Control Based
           on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

    • Authors: Jiehan Yu, Zhendong Xie, Zhiguo Dong, Haina Song, Jiayi Su, Honggang Wang, Jinchao Xiao, Xiaosong Liu, Jingfeng Yang
      First page: 1520
      Abstract: The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101520
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1521: Approximated Backscattered Wave Models
           of a Lossy Concentric Dielectric Sphere for Fruit Characterization

    • Authors: Hoang Nam Dao, Chuwong Phongcharoenpanich, Monai Krairiksh
      First page: 1521
      Abstract: Approximated models of electromagnetic waves scattered from a sphere with two different dielectric layers were developed and reported in this paper. We proposed that the dielectric properties of a concentric dielectric sphere object, for example, some types of fruit, could be estimated by this model, from some wave components of the backscattered wave. The models were suitable for lossy objects because only a single bounce of the wave was assumed. In terms of first bounce as well as total backscattered wave results, the reported values agreed well with the values calculated by a commercial software. The measurement results verified the calculated wave components. The dielectric properties determination of real fruits was performed and exhibited the potential in fruit characterization. The main advantage of these models is that they can provide the magnitude and phase information of each backscattered wave component, which makes quality monitoring of fruits to be possible.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101521
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1522: A Bus-Scheduling Method Based on
           Multi-Sensor Data Fusion and Payment Authenticity Verification

    • Authors: Wenan Gong, Ting Zeng, Haina Song, Jiayi Su, Honggang Wang, Lin Hu, Jinchao Xiao, Xiaosong Liu, Ming Li, Jingfeng Yang
      First page: 1522
      Abstract: It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101522
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1523: Efficient Discovery of Partial Periodic
           Patterns in Large Temporal Databases

    • Authors: Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Chennupati Saideep, Koji Zettsu, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy
      First page: 1523
      Abstract: Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101523
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1524: A Data-Driven Model to Forecast
           Multi-Step Ahead Time Series of Turkish Daily Electricity Load

    • Authors: Kamil Demirberk Ünlü
      First page: 1524
      Abstract: It is critical to maintain a balance between the supply and the demand for electricity because of its non-storable feature. For power-producing facilities and traders, an electrical load is a piece of fundamental and vital information to have, particularly in terms of production planning, daily operations, and unit obligations, among other things. This study offers a deep learning methodology to model and forecast multistep daily Turkish electricity loads using the data between 5 January 2015, and 26 December 2021. One major reason for the growing popularity of deep learning is the creation of new and creative deep neural network topologies and significant computational advancements. Long Short-Term Memory (LSTM), Gated Recurrent Network, and Convolutional Neural Network are trained and compared to forecast 1 day to 7 days ahead of daily electricity load. Three different performance metrics including coefficient of determination (R2), root mean squared error, and mean absolute error were used to evaluate the performance of the proposed algorithms. The forecasting results on the test set showed that the best performance is achieved by LSTM. The algorithm has an R2 of 0.94 for 1 day ahead forecast, and the metric decreases to 0.73 in 7 days ahead forecast.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101524
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1525: Distributed Deep Learning: From
           Single-Node to Multi-Node Architecture

    • Authors: Jean-Sébastien Lerat, Sidi Ahmed Mahmoudi, Saïd Mahmoudi
      First page: 1525
      Abstract: During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered quite important in the design of a time-performing multi-node architecture because DDL depends on the time required by all the nodes. The impact of computational resources (CPU and GPU) is also discussed since the GPU is known to speed up computations. Experimental results show that the local parallelism impacts the global speedup of the DDL depending on the neural model complexity and the size of the dataset. Moreover, our approach achieves a better speedup than Horovod.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101525
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1526: Ternary Neural Networks Based on on/off
           Memristors: Set-Up and Training

    • Authors: Antoni Morell, Elvis Díaz Machado, Enrique Miranda, Guillem Boquet, Jose Lopez Vicario
      First page: 1526
      Abstract: Neuromorphic systems based on hardware neural networks (HNNs) are expected to be an energy and time-efficient computing architecture for solving complex tasks. In this paper, we consider the implementation of deep neural networks (DNNs) using crossbar arrays of memristors. More specifically, we considered the case where such devices can be configured in just two states: the low-resistance state (LRS) and the high-resistance state (HRS). HNNs suffer from several non-idealities that need to be solved when mapping our software-based models. A clear example in memristor-based neural networks is conductance variability, which is inherent to resistive switching devices, so achieving good performance in an HNN largely depends on the development of reliable weight storage or, alternatively, mitigation techniques against weight uncertainty. In this manuscript, we provide guidelines for a system-level designer where we take into account several issues related to the set-up of the HNN, such as what the appropriate conductance value in the LRS is or the adaptive conversion of current outputs at one stage to input voltages for the next stage. A second contribution is the training of the system, which is performed via offline learning, and considering the hardware imperfections, which in this case are conductance fluctuations. Finally, the resulting inference system is tested in two well-known databases from MNIST, showing that is competitive in terms of classification performance against the software-based counterpart. Additional advice and insights on system tuning and expected performance are given throughout the paper.
      Citation: Electronics
      PubDate: 2022-05-10
      DOI: 10.3390/electronics11101526
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1527: Fast Study Quadric Interpolation in the
           Conformal Geometric Algebra Framework

    • Authors: Gerardo Martinez-Terán, Oswaldo Ureña-Ponce, Gerardo Soria-García, Susana Ortega-Cisneros, Eduardo Bayro-Corrochano
      First page: 1527
      Abstract: Interpolating trajectories of points and geometric entities is an important problem for kinematics. To describe these trajectories, several algorithms have been proposed using matrices, quaternions, dual-quaternions, and the Study quadric; the last one allows the embedding of motors as 8D vectors into projective space P7, where the interpolation of rotations and translations becomes a linear problem. Furthermore, conformal geometric algebra (CGA) is an effective and intuitive framework for representing and manipulating geometric entities in Euclidean spaces, and it allows the use of quaternions and dual-quaternions formulated as Motors. In this paper, a new methodology for accelerating the Study quadric Interpolation based on Conformal Geometric Algebra is presented. This methodology uses General Purpose Graphics Processing Units (GPUs) and it is applied for medical robotics, but it can also be extended to other areas such as aeronautics, robotics, and graphics processing.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101527
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1528: Quantum Brain Networks: A Perspective

    • Authors: Eduardo R. Miranda, José D. Martín-Guerrero, Satvik Venkatesh, Carlos Hernani-Morales, Lucas Lamata, Enrique Solano
      First page: 1528
      Abstract: We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101528
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1529: Anomaly Detection in Multi-Host
           Environment Based on Federated Hypersphere Classifier

    • Authors: Junhyung Kwon, Byeonggil Jung, Hyungil Lee, Sangkyun Lee
      First page: 1529
      Abstract: Detecting anomalous inputs is essential in many mission-critical systems in various domains, particularly cybersecurity. In particular, deep neural network-based anomaly detection methods have been successful for anomaly detection tasks with the recent advancements in deep learning technology. Nevertheless, the existing methods have considered somewhat idealized problems where it is enough to learn a single detector based on a single dataset. In this paper, we consider a more practical problem where multiple hosts in an organization collect their input data, while data sharing among the hosts is prohibitive due to security reasons, and only a few of them have experienced abnormal inputs. Furthermore, the data distribution of the hosts can be skewed; for example, a particular type of input can be observed by a limited subset of hosts. We propose the federated hypersphere classifier (FHC), which is a new anomaly detection method based on an improved hypersphere classifier suited for running in the federated learning framework to perform anomaly detection in such an environment. Our experiments with image and network intrusion detection datasets show that our method outperforms the state-of-the-art anomaly detection methods trained in a host-wise fashion by learning a consensus model as if we have accessed the input data from all hosts but without communicating such data.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101529
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1530: Federated Learning with Dynamic Model
           Exchange

    • Authors: Hannes Hilberger, Sten Hanke, Markus Bödenler
      First page: 1530
      Abstract: Large amounts of data are needed to train accurate robust machine learning models, but the acquisition of these data is complicated due to strict regulations. While many business sectors often have unused data silos, researchers face the problem of not being able to obtain a large amount of real-world data. This is especially true in the healthcare sector, since transferring these data is often associated with bureaucratic overhead because of, for example, increased security requirements and privacy laws. Federated Learning should circumvent this problem and allow training to take place directly on the data owner’s side without sending them to a central location such as a server. Currently, there exist several frameworks for this purpose such as TensorFlow Federated, Flower, or PySyft/PyGrid. These frameworks define models for both the server and client since the coordination of the training is performed by a server. Here, we present a practical method that contains a dynamic exchange of the model, so that the model is not statically stored in source code. During this process, the model architecture and training configuration are defined by the researchers and sent to the server, which passes the settings to the clients. In addition, the model is transformed by the data owner to incorporate Differential Privacy. To trace a comparison between central learning and the impact of Differential Privacy, performance and security evaluation experiments were conducted. It was found that Federated Learning can achieve results on par with centralised learning and that the use of Differential Privacy can improve the robustness of the model against Membership Inference Attacks in an honest-but-curious setting.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101530
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1531: A Novel Two-Stage Deep Learning
           Structure for Network Flow Anomaly Detection

    • Authors: Ming-Tsung Kao, Dian-Ye Sung, Shang-Juh Kao, Fu-Min Chang
      First page: 1531
      Abstract: Unknown cyber-attacks have appeared constantly. Several anomaly detection techniques based on semi-supervised learning have been proposed to detect these unknown cyber-attacks. Among them, the Denoising Auto-Encoder (DAE) scheme performs better than others in accuracy but is not good enough in precision. This paper proposes a novel two-stage deep learning structure for network flow anomaly detection by combining the models of Gate Recurrent Unit (GRU) and DAE. By using supervised anomaly detection with a selection mechanism to assist semi-supervised anomaly detection, the precision and accuracy of the anomaly detection system are improved. In the proposed structure, we first use the GRU model to analyze the network flow and then take the outcome from the Softmax function as a confidence score. When the score is more than or equal to the predefined confidence threshold, the GRU model outputs the flow as a positive result, no matter the flow is classified as normal or abnormal. When the score is less than the confidence threshold, GRU model outputs the flow as a negative result and passes the flow to DAE model for flow classification. DAE then determines a reconstruction error threshold by learning the pattern of normal flows. Accordingly, the flow is normal or abnormal depending on whether it is under or over the reconstruction error threshold. A comparative experiment is performed using NSL-KDD dataset as benchmark. The results revealed that the precision using the proposed scheme is 0.83% better than DAE. The accuracy using the proposed approach is 90.21%, which is better than Random Forest, Naïve Bayes, One-Dimensional Convolutional Neural Network, two-stage Auto-Encoder, etc. In addition, the proposed approach is also applied to the environment of software defined network (SDN). By adopting our approach in SDN environment, the precision and F-measure are significantly improved.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101531
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1532: Evaluation of GaN HEMTs in H3TRB
           Reliability Testing

    • Authors: Jose A. Rodriguez, Tsz Tsoi, David Graves, Stephen B. Bayne
      First page: 1532
      Abstract: Gallium Nitride (GaN) power devices can offer better switching performance and higher efficiency than Silicon Carbide (SiC) and Silicon (Si) devices in power electronics applications. GaN has extensively been incorporated in electric vehicle charging stations and power supplies, subjected to harsh environmental conditions. Many reliability studies evaluate GaN power devices through thermal stresses during current conduction or pulsing, with a few focusing on high blocking voltage and high humidity. This paper compares GaN-on-Si High-Electron-Mobility Transistors (HEMT) device characteristics under a High Humidity, High Temperature, Reverse Bias (H3TRB) Test. Twenty-one devices from three manufacturers were subjected to 85 °C and 85% relative humidity while blocking 80% of their voltage rating. Devices from two manufacturers utilize a cascade configuration with a silicon metal-oxide-semiconductor field-effect transistor (MOSFET), while the devices from the third manufacturer are lateral p-GaN HEMTs. Through characterization, three sample devices have exhibited degraded blocking voltage capability. The results of the H3TRB test and potential causes of the failure mode are discussed.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101532
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1533: Toward Smart Communication Components:
           Recent Advances in Human and AI Speaker Interaction

    • Authors: Hyejoo Kim, Sewoong Hwang, Jonghyuk Kim, Zoonky Lee
      First page: 1533
      Abstract: This study aims to investigate how humans and artificial intelligence (AI) speakers interact and to examine the interactions based on three types of communication failures: system, semantic, and effectiveness. We divided service failures using AI speaker user data provided by the top telecommunication service providers in South Korea and investigated the means to increase the continuity of product use for each type. We proved the occurrence of failure due to system error (H1) and negative results on sustainable use of the AI speaker due to not understanding the meaning (H2). It was observed that the number of users increases as the effectiveness failure rate increases. For single-person households constituted by persons in their 30s and 70s or older, the continued use of AI speakers was significant. We found that it alleviated loneliness and that human-machine interaction using AI speaker could reach a high level through a high degree of meaning transfer. We also expect AI speakers to play a positive role in single-person households, especially in cases of the elderly, which has become a tough challenge in the recent times.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101533
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1534: Transforming Future Cities: Smart City

    • Authors: Dhananjay Singh, Antonio J. Jara
      First page: 1534
      Abstract: The primitive elements of city transformation include the integration of urban infrastructure and artificial intelligence and cutting edge IoT technologies [...]
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101534
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1535: A Triple Relation Network for Joint
           Entity and Relation Extraction

    • Authors: Zixiang Wang, Liqun Yang, Jian Yang, Tongliang Li, Longtao He, Zhoujun Li
      First page: 1535
      Abstract: Recent methods of extracting relational triples mainly focus on the overlapping problem and achieve considerable performance. Most previous approaches extract triples solely conditioned on context words, but ignore the potential relations among the extracted entities, which will cause incompleteness in succeeding Knowledge Graphs’ (KGs) construction. Since relevant triples give a clue for establishing implicit connections among entities, we propose a Triple Relation Network (Trn) to jointly extract triples, especially handling extracting implicit triples. Specifically, we design an attention-based entity pair encoding module to identify all normal entity pairs directly. To construct implicit connections among these extracted entities in triples, we utilize our triple reasoning module to calculate relevance between two triples. Then, we select the top-K relevant triple pairs and transform them into implicit entity pairs to predict the corresponding implicit relations. We utilize a bipartite matching objective to match normal triples and implicit triples with the corresponding labels. Extensive experiments demonstrate the effectiveness of the proposed method on two public benchmarks, and our proposed model significantly outperforms previous strong baselines.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101535
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1536: Real Time Power Control in a High
           Voltage Power Supply for Dielectric Barrier Discharge Reactors:
           Implementation Strategy and Load Thermal Analysis

    • Authors: Gabriele Neretti, Arturo Popoli, Silvia Giuditta Scaltriti, Andrea Cristofolini
      First page: 1536
      Abstract: Atmospheric-pressure plasma treatments for industrial and biomedical applications are often performed using Dielectric Barrier Discharge reactors. Dedicated power supplies are needed to provide the high voltage frequency waveforms to operate these nonlinear and time-dependent loads. Moreover, there is a growing technical need for reliable and reproducible treatments, which require the discharge parameters to be actively controlled. In this work, we illustrate a low-cost power supply topology based on a push–pull converter. We perform experimental measurements on two different reactor topologies (surface and volumetric), showing that open loop operation of the power supply leads to a temperature and average power increase over time. The temperature increases by ΔTvol~120 °C and ΔTsup~70 °C, while the power increases by ΔPvol~78% and ΔPsup~60% for the volumetric (40 s) and superficial reactors (120 s), respectively. We discuss how these changes are often unwanted in practical applications. A simplified circuital model of the power supply–reactor system is used to infer the physical relation between the observed reactor thermal behavior and its electrical characteristics. We then show a control strategy for the power supply voltage to ensure constant average power operation of the device based on real-time power measurements on the high voltage side of the power supply and an empirical expression relating the delivered power to the power supply output voltage. These are performed with an Arduino Due microcontroller unit, also used to control the power supply. In a controlled operation the measured power stays within 5% of the reference value for both configurations, reducing the temperature increments to ΔTvol~80 °C and ΔTsup~44 °C, respectively. The obtained results show that the proposed novel control strategy is capable of following the transient temperature behavior, achieving a constant average power operation and subsequently limiting the reactor thermal stress.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101536
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1537: Integrated Electro-Thermal Model for
           Li-Ion Battery Packs

    • Authors: Simone Barcellona, Silvia Colnago, Paolo Montrasio, Luigi Piegari
      First page: 1537
      Abstract: Lithium-ion battery is considered one of the most attractive energy storage systems for electric vehicles. However, one of its main drawbacks is the sensitivity to temperature. In a battery pack composed of lithium-ion batteries, during the charge/discharge operations, the temperature gradually increases, especially in the batteries positioned in the central part of the battery pack. This leads the central batteries to age faster and exposes them to the risk of a thermal runaway. In order to mitigate these problems, thermal management systems are needed. However, for the implementation of the control, it is important to know the temperature distribution inside the whole pack. In this paper, an integrated electro-thermal model capable of estimating the thermal behavior of each battery cell, composing the battery pack, only knowing the total current and ambient temperature, is proposed and analyzed. The proposed model was tuned and validated by means of experimental results. The circuital approach used in this model gives good results with a low degree of complexity.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101537
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1538: Single-Ridge Waveguide Compact and
           Wideband Hybrid Couplers for X/Ku-Band Applications

    • Authors: Guendalina Simoncini, Riccardo Rossi, Federico Alimenti, Roberto Vincenti Gatti
      First page: 1538
      Abstract: Hybrid couplers are important devices that combine or divide signals in various microwave applications. Wideband performance, low losses and small size are key features in most modern radar and communication systems. This paper presents a new geometry for single-ridge, air-filled waveguide quadrature hybrid couplers at the X/Ku band on a single layer using multiple pairs of slots cut on a common ridge coupling section. Bandwidth can be progressively extended by increasing the number of slot pairs. Two designs characterized by compact size and state-of-the-art performance are proposed, leading to a fractional bandwidth up to 46.88% and a maximum dimension of 1.18 wavelengths. A tolerance analysis is presented to highlight the design robustness and reliability.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101538
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1539: Use of 3D Printing for Horn Antenna
           Manufacturing

    • Authors: Jana Olivová, Miroslav Popela, Marie Richterová, Eduard Štefl
      First page: 1539
      Abstract: This article describes the manufacturing of a horn antenna using a 3D commercial printer. The horn antenna was chosen for its simplicity and practical versatility. The standardised horn antenna is one of the most widely used antennas in microwave technology. A standardised horn antenna can be connected to standardised waveguides. The horn antenna has been selected so that this antenna can be fabricated by 3D printing and thus obtain the equivalent of a standardised horn antenna. This 3D horn antenna can then be excited by a standardised waveguide. The 3Dprinted horn antenna with metallic layers has very good impedance characteristics, standing wave ratio and radiation patterns that are close to those of a standardised horn antenna. The 3D-based horn antenna is suitable for applications where low antenna weight is required, such as aerospace and satellite technologies. The article also describes a manufacturing procedure for a horn antenna (E-sector horn antenna) that is plated with galvanic layers of silver and gold. The design of the plated horn antenna in the Matlab application using the Antenna Toolbox extension is also described, including 3D printing procedures, post-processing procedures (plating) and practical testing of its functionality. The measured results are compared to simulations of the standardised horn antenna and then analysed.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101539
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1540: Modulating the Filamentary-Based
           Resistive Switching Properties of HfO2 Memristive Devices by Adding Al2O3
           Layers

    • Authors: Kalishettyhalli Mahadevaiah, Perez, Lisker, Schubert, Perez-Bosch Quesada, Wenger, Mai
      First page: 1540
      Abstract: The resistive switching properties of HfO2 based 1T-1R memristive devices are electrically modified by adding ultra-thin layers of Al2O3 into the memristive device. Three different types of memristive stacks are fabricated in the 130 nm CMOS technology of IHP. The switching properties of the memristive devices are discussed with respect to forming voltages, low resistance state and high resistance state characteristics and their variabilities. The experimental I–V characteristics of set and reset operations are evaluated by using the quantum point contact model. The properties of the conduction filament in the on and off states of the memristive devices are discussed with respect to the model parameters obtained from the QPC fit.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101540
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1541: Sensor-System-Based Network with
           Low-Power Communication Using Multi-Hop Routing Protocol Integrated with a
           Data Transmission Model

    • Authors: Midasala, Janapati, Srinivasu, Ramachandran, Mousavi, Gandomi
      First page: 1541
      Abstract: Wireless sensor networks (WSNs) comprise several cooperating sensor nodes capable of sensing, computing, and transmitting sensed signals to a central server. This research proposes a sensor system-based network with low power communication using swarm intelligence integrated with multi-hop communication (SIMHC). This routing protocol selects the optimal route based on link distance, transmission power, and residual energy to optimize the network lifetime and node energy efficiency. Moreover, adaptive clustering-based locative data transmission (ACLDT) is applied for optimizing data transmission. The proposed approach combines clustering with data transfer via location-based routing and low-power communication in two phases to calculate the ideal cluster heads (CHs). First, a CH seeks the next hop from the nearest CH. Then, a path to the base station is formed by developing CH chains. The results reveal that the proposed sensor system based on data transmission and low-power consumption achieved a network lifetime of 96%, an average delay of 53 ms, a coverage rate (CR) of 83%, a throughput of 97%, and energy efficiency of 95%.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101541
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1542: Optimization of Spatial Resolution and
           Image Reconstruction Parameters for the Small-Animal Metis™ PET/CT
           System

    • Authors: Zhao, Liu, Li, Song, Zhang, Chen
      First page: 1542
      Abstract: Purpose: The aim of this study was to investigate the optimization of the spatial resolution and image reconstruction parameters related to image quality in an iterative reconstruction algorithm for the small-animal Metis™ PET/CT system. Methods: We used a homemade Derenzo phantom to evaluate the image quality using visual assessment, the signal-to-noise ratio, the contrast, the coefficient of variation, and the contrast-to-noise ratio of the 0.8 mm hot rods of eight slices in the center of the phantom PET images. A healthy mouse study was performed to analyze the influence of the optimal reconstruction parameters and the Gaussian post-filter FWHM. Results: In the phantom study, the image quality was the best when the phantom was placed at the end, keeping the central axis parallel to the X-axis of the system, and selecting between 30 and 40 iterations, a 0.314 mm reconstructed voxel size, and a 1.57 mm Gaussian post-filter FWHM. The optimization of the spatial resolution could reach 0.6 mm. In the animal study, it was suitable to choose a voxel size of 0.472 mm, between 30 and 40 iterations, and a 2.36 mm Gaussian post-filter FWHM. Conclusions: Our results indicate that the optimal imaging conditions and reconstruction parameters are very necessary to obtain high-resolution images and quantitative accuracy, especially for the high-precision recognition of tiny lesions.
      Citation: Electronics
      PubDate: 2022-05-11
      DOI: 10.3390/electronics11101542
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1543: Speed Estimation Method of Linear Motor
           Extended Kalman Filter Based on Attenuation Memory

    • Authors: Zheng Li, Lucheng Zhang, Jinsong Wang, Weisong Sun, Pengju Wang, Xiaoqiang Guo, Hexu Sun
      First page: 1543
      Abstract: In allusion to the phenomenon that the extended Kalman filter is easy to diverge in the mover position estimation of permanent magnet synchronous linear motor, a linear motor extended Kalman filter speed estimation method based on attenuation memory is designed. By setting the attenuation factor, α, the extended Kalman filter is introduced to increase the weight of the latest speed data and restrain the divergence of the filter, so as to achieve a better speed tracking effect. In the simulation experiment of the sensorless control of a linear motor, the AMEKF algorithm can significantly improve the speed estimation accuracy of standard EKF, and the speed estimation error is reduced by 0.75%. At the same time, it still maintains a good speed tracking effect and good dynamic performance under variable speed and different load conditions.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101543
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1544: Estimation of the Output Characteristic
           of a Photovoltaic Generator under Power Curtailment and Considering
           Converter Losses

    • Authors: Donghui Ye, Jose Miguel Riquelme-Dominguez, Sergio Martinez
      First page: 1544
      Abstract: Power curtailment methods contribute to the frequency stability of power systems with a high share of photovoltaic generation. This paper focuses on an online strategy that estimates the output characteristic of a photovoltaic generator while operating in power curtailment mode by using voltage and current measurements and the nonlinear least-squares curve-fitting. In contrast to previously reported methods, this work introduces an improvement consisting of the estimation of the power losses of the electronic converter that connects the photovoltaic panel with the grid. Thus, the impact of a mismatch between the dc-side and the ac-side is reduced. The procedure is tested at different power levels and with fluctuations of irradiance and temperature. The results show that the converter efficiency, expressed as an exponential function with two quadratic equations, is associated with irradiance and temperature. It is also found that the two-stage converter efficiency is mainly affected by the irradiance level when the photovoltaic system operates on the left side of the power-voltage characteristic. In contrast, the temperature level influences the converter efficiency significantly when working on the right side of the maximum power point. The estimated efficiency curves can improve the accuracy of the power curtailment method and can be used for designing the two-stage converter. The effectiveness of the proposed power curtailment method has been tested in a two-stage photovoltaic system through MATLAB/Simulink.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101544
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1545: Bio-Inspired Hybrid Optimization
           Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive
           Review

    • Authors: Rajiv Yadav, Indu Sreedevi, Daya Gupta
      First page: 1545
      Abstract: Researchers are facing significant challenges to develop robust energy-efficient clustering and routing protocols for Wireless Sensor Networks (WSNs) in different areas such as military, agriculture, education, industry, environmental monitoring, etc. WSNs have made an everlasting imprint on everyone’s lives. The bulk of existing routing protocols has focused on cluster head election while disregarding other important aspects of routing including cluster formation, data aggregation, and security, among others. Although cluster-based routing has made a significant contribution to tackling this issue, the cluster head (CH) selection procedure may still be improved by integrating critical characteristics. Nature-inspired algorithms are gaining traction as a viable solution for addressing important challenges in WSNs, such as sensor lifespan and transmission distance. Despite this, the sensor node batteries cannot be changed when installed in a remote or unsupervised area due to their wireless nature. As a result, numerous researches are being done to lengthen the life of a node span. The bulk of existing node clustering techniques suffers from non-uniform cluster head distribution, an imbalanced load difficulty within clusters, concerning left-out nodes, coverage area, and placement according to a recent study. Metaheuristic algorithms (DE, GA, PSO, ACO, SFO, and GWO) have the advantages of being simple, versatile, and derivation-free, as well as effectively utilizing the network’s energy resource by grouping nodes into clusters to increase the lifespan of the entire network. In this paper, we explore recently used hybridization techniques (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) for bio-inspired algorithms to improve the energy efficiency of WSNs. This paper also discusses how critical issues can be addressed by speeding up the implementation process, how more efficient data can be transferred, as well as how energy consumption can be reduced by using bio-inspired hybrid optimization algorithms.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101545
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1546: Receiver Analog Front-End Cascading
           Transimpedance Amplifier and Continuous-Time Linear Equalizer for Signals
           of 5 to 30 Gb/s

    • Authors: Pragada Venkata Satya Challayya Naidu, Chih-Wen Lu
      First page: 1546
      Abstract: A 5–30 Gb/s receiver analog front-end (AFE) cascading transimpedance amplifier (TIA) and continuous-time linear equalizer (CTLE) were implemented using a Taiwan Semiconductor 180 nm process. The system comprises a two-stage differential input pair CTLE, TIA, and a differential termination resistor Rm. A source-degenerated transconductance stage was adopted in the CTLE, and source follower and shunt feedback resistor stages were adopted in the TIA. The proposed CTLE could achieve high frequencies by altering the tail current with fixed degenerate capacitance CS and resistance RS. The proposed AFE achieved high bandwidth, and the use of a feedback resistor Rf and inductor Lf improved its high-frequency performance. Simulation results revealed that the CTLE can compensate for 16 dB of channel loss at a 3 GHz Nyquist frequency and can open closed eyes in a 6 Gb/s non-return-to-zero signal with a bit error rate of 0.16 × 10−12 for a 231 − 1 pseudorandom binary sequence input. The AFE could compensate for 12 dB of channel loss at a 15 GHz Nyquist frequency and can open closed eyes in a 30 Gb/s PAM4 signal from a pseudorandom binary sequence input; it consumed 27 mW of power at 1.8 V.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101546
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1547: Infrared Small-Target Detection Using
           Multiscale Local Average Gray Difference Measure

    • Authors: Feng Xie, Minzhou Dong, Xiaotian Wang, Jie Yan
      First page: 1547
      Abstract: In infrared (IR) guidance and target tracking systems, dim target intensity and complex background clutter are some of the typical challenges, especially for the accurate detection of small objects. In this article, we propose a novel IR target detection method based on new local contrast measures. First, the local average gray difference measure (LAGDM) is presented to accentuate the difference between a small object and its local background. Then, an LAGDM map is generated to effectively enhance targets and suppress background clutter. Finally, we use an adaptive segmentation method to separate the object from the background. Experimental results on multiple sequences show that the proposed small-target detection method can effectively improve the signal-to-clutter ratio (SCR) of the image, and it exhibits robust performance against cloudy sky, sea sky, and mountain forest backgrounds.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101547
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1548: Efficient Ring-Topology Decentralized
           Federated Learning with Deep Generative Models for Medical Data in
           eHealthcare Systems

    • Authors: Zhao Wang, Yifan Hu, Shiyang Yan, Zhihao Wang, Ruijie Hou, Chao Wu
      First page: 1548
      Abstract: By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101548
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1549: Investigation of the Relationship
           between Vibration Signals Due to Oil Impurity and Cavitation Bubbles in
           Hydraulic Pumps

    • Authors: Özgür Yılmaz, Murat Aksoy, Zehan Kesilmiş
      First page: 1549
      Abstract: Although hydraulic pumps are frequently used in daily life, improper use due to oil analysis or oil contamination is ignored. There is no instantaneous inspection; instead, the oil is changed periodically at certain times, whether it is contaminated or not. Hydraulic systems operate based on Pascal’s law, which states that the fluid will distribute the pressure equally to every point in a closed area. The fluid oil taken from an oil reservoir is moved into the pump by engine power. During this movement, as it passes through different pressure areas and different sections, undesirable events such as viscosity change and gas formation occur in the hydraulic oil. These formations collide with the outer walls and cause cavitation with respect to unwanted oil impurities. This cavitation causes unwanted vibration signals to occur in the normal working order of the system. As a result of cavitation, the particles that affect the lubricity and fluidity of the oil in the oil are mixed into the liquid and circulate freely. At the connection points, the blockage caused by the liquid in the pump cylinder block or the valve plate and the collisions of particles is effective. As a result, it creates vibrations of different frequencies. The frequency and amplitudes of these vibrations differ according to the degree of oil contamination. A method has been developed to find the degree of contamination of the oil circulating in the pump by looking at the amplitude and frequency of these vibrations measured from the motor body. There exist standards about the pollution of hydraulic fluid. With these standards, the maximum number of particles allowed for a given pollution level is defined. This topic is discussed in the conclusion to this study. This method has also been proven experimentally. Error and vibration analysis studies on pumps using a different approach are available in the literature. In these studies, pressure variation, total energy transmission, or artificial intelligence models were used to detect anomalies in the pump. In this study, the impurity rate of the oil was set at five different levels and the operating regime of the pump at each level was investigated experimentally. Rayleigh–Plesset and Zwart–Gerber–Belamri models, which are the most common cavitation models, were used to explain the bubble formation in the moving oil and the relationship of these bubbles with vibration. Frequency components were examined by the Discrete Fast Fourier Analysis method, where the operation of the pump was affected by the increase in oil impurity.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101549
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1550: Collaborative Reliable Event Transport
           Based on Mobile-Assisted Sensing in Urban Digital Twin

    • Authors: Taehun Yang, Soochang Park, Sang-Ha Kim
      First page: 1550
      Abstract: For urban digital twin, this paper comes up with a novel urban data acquisition scheme, denoted by collaborative reliable event transport (cRET), that conducts micro-scale sensing resolution in urban environments. cRET relies on battery-powered sensors with Bluetooth low-energy (BLE) modules and the smart mobile devices that people carry around urban places. However, the traditional data acquisition schemes with mobile assistance suffer from the poor communication channel quality of BLE. So, it is tough to achieve enough reliability of event observation. Hence, cRET utilizes overhearing-based collaboration among sensors to improve the data delivery ratio. It also could support reliable transmission over mobile devices despite high-speed moving. A proof-of-concept demonstrates that the reliability is improved by the overhearing and collaboration among sensors against low-channel conditions and a high moving speed of mobile devices, i.e., 30 km/h and more.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101550
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1551: Deep Learning-Based Multiple Co-Channel
           Sources Localization Using Bernoulli Heatmap

    • Authors: Meiyan Lin, Yonghui Huang, Baozhu Li, Zhen Huang, Zihan Zhang, Wenjie Zhao
      First page: 1551
      Abstract: Multiple sources localization (MSL) has received considerable attention in scenarios of commercial, industrial, and defense areas. In this paper, a novel deep learning-based approach with observations of received signal strength (RSS) is proposed for the localization of multiple co-channel sources. The proposed method, named MSLocNet, formulates the MSL problem as a Bernoulli heatmap regression problem, solved by a fully convolutional network (FCN). The proposed MSLocNet enables simultaneous localization of variable numbers of sources, and exhibits better localization performance. Simulations, under complex environments with shadow fading, are conducted to validate the improved localization accuracy of the proposed method over other benchmark schemes. Moreover, experiments are carried out in a real environment to verify the feasibility of the proposed method.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101551
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1552: Torque Ripple Suppression Method of
           Switched Reluctance Motor Based on an Improved Torque Distribution
           Function

    • Authors: Xiao Ling, Chenhao Zhou, Lianqiao Yang, Jianhua Zhang
      First page: 1552
      Abstract: Currently, torque ripple is a crucial factor hindering the application of the switched reluctance motor (SRM). Hence, it is of crucial importance to suppress this undesirable torque ripple. This paper proposes a new torque ripple suppression method of SRM based on the improved torque distribution function. Firstly, the electromagnetic characteristic model of a 8/6-pole four-phase SRM is established, and the cerebellar model articulation controller (CMAC) is used to complete the learning of each model. Then, the improved torque distribution function is planned based on the torque model to give the reference torque of each phase, and the inverse torque model is used to realize the mapping of the reference torque to the reference flux linkage. Finally, the duty of each phase voltage PWM wave modulation is output based on the PID control theory. The proposed accurate model-based planning scheme is implemented on the simulation platform, and the results shows that the maximum torque fluctuation of the output results is reduced to within 3%, and the average error is reduced to within 1%, which is much lower than the error of 15% under the traditional direct torque control method.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101552
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1553: Optimization of Fuzzy Controller for
           Predictive Current Control of Induction Machine

    • Authors: Toni Varga, Tin Benšić, Marinko Barukčić, Vedrana Jerković Štil
      First page: 1553
      Abstract: An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization parameter adjustment is needed. The choice of controller parameters is explained, along with the methodology behind the criterion for objective function value calculation. The optimized controller is implemented as an outer-loop speed controller for Predictive Current Control (PCC) of an induction machine. The performance of the proposed control method is compared with that of several other model predictive control methods. The results show a 55% decrease in speed tracking error and 74% decrease in torque overshoot.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101553
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1554: TMRN-GLU: A Transformer-Based Automatic
           Classification Recognition Network Improved by Gate Linear Unit

    • Authors: Yujun Zheng, Yongtao Ma, Chenglong Tian
      First page: 1554
      Abstract: Automaticmodulation recognition (AMR) has been a long-standing hot topic among scholars, and it has obvious performance advantages over traditional algorithms. However, CNN and RNN, which are commonly used in serial classification tasks, suffer from the problems of not being able to make good use of global information and slow running speed due to serial operations, respectively. In this paper, to solve the above problems, a Transformer-based automatic classification recognition network improved by Gate Linear Unit (TMRN-GLU) is proposed, which combines the advantages of CNN with a high efficiency of parallel operations and RNN with a sufficient extraction of global information of the temporal signal context. Relevant experiments on the RML2016.10b public dataset show that the proposed algorithm not only has a significant advantage in the number of parameters compared with the existing algorithms, but also has improved recognition accuracy under various signal-to-noise ratios.In particular, the accuracy of the proposed algorithm improves significantly compared with other algorithms under low signal-to-noise ratio conditions. The accuracy is improved by at least 9% at low signal-to-noise ratio (6 dB) and about 3% at high signal-to-noise ratio (>2 dB).
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101554
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1555: An IoT System for Real-Time Monitoring
           of DC Motor Overload

    • Authors: Milutin Radonjić, Žarko Zečević, Božo Krstajić
      First page: 1555
      Abstract: The excavators are heavy machines widely used in the civil engineering and surface mining industry. Recent studies show that 95% of contractors face the problem of finding skilled operators. Unskilled operators not only worsen productivity but also very often cause machine failures through unprofessional handling. Motivated by these studies and guided by the mining company’s requirements, we present a prototype of an IoT system for monitoring DC motor overload on the EKG-15 excavator. The IoT system consists of a microprocessor device mounted inside the excavator and an external cloud platform that can be accessed via the Internet. The proposed solution detects and warns the operator when the DC motor overload occurs, thus reducing the probability of its damage. In addition, overload data is sent to the cloud platform for later research, analysis and processing. The main benefit of the proposed solution is that it can be applied to existing industry machinery, thus reducing the maintenance cost and increasing productivity. After several months of use of the proposed system in real working conditions, it has been shown that the overload occurrence and its duration time are approximately reduced by 60% and 80%, respectively.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101555
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1556: Optimal Query Expansion Based on Hybrid
           Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

    • Authors: Ram Kumar, Kuldeep Narayan Tripathi, Subhash Chander Sharma
      First page: 1556
      Abstract: The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.
      Citation: Electronics
      PubDate: 2022-05-12
      DOI: 10.3390/electronics11101556
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1557: An Optimized Algorithm for Dangerous
           Driving Behavior Identification Based on Unbalanced Data

    • Authors: Shengxue Zhu, Chongyi Li, Kexin Fang, Yichuan Peng, Yuming Jiang, Yajie Zou
      First page: 1557
      Abstract: It is of great significance to identify dangerous driving behavior by extracting vehicle trajectory through video monitoring to ensure highway traffic safety. At present, there is no suitable method to identify dangerous driving vehicles accurately based on trajectory data. This paper aims to develop a detection algorithm for identifying dangerous driving behavior based on the road scene, which is mainly composed of imbalanced dangerous driver detection and labeling, extraction of driving behavior characteristics and the establishment of a recognition model about dangerous driving behavior. Firstly, this paper defines the risk index of the vehicle related to five types of dangerous driving behavior: dangerous following, lateral deviation, frequent acceleration and deceleration, frequent lane change, and forced insertion. Then, a variety of methods, including K-means clustering, local factor anomaly algorithm, isolation forest and OneClassSVM, are used to carry out anomaly detection on the risk indicators of drivers, and the optimal method is proposed to identify dangerous drivers. Then, the speed and acceleration of each vehicle are Fourier transformed to obtain the characteristics of the driver’s driving behavior. Finally, considering the imbalanced characteristic of the analyzed dataset with a very small proportion of dangerous drivers, this paper compares a variety of imbalanced classification algorithms to optimize the recognition performance of dangerous driving behavior. The results show that the OneClassSVM detection algorithm can be effectively applied to the identification of dangerous driving behavior. The improved Xgboost algorithm performs best for the extremely imbalanced data of dangerous drivers.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101557
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1558: Improved LS-SVM Method for Flight Data
           Fitting of Civil Aircraft Flying at High Plateau

    • Authors: Nongtian Chen, Youchao Sun, Zongpeng Wang, Chong Peng
      First page: 1558
      Abstract: High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101558
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1559: Traffic Sign Based Point Cloud Data
           Registration with Roadside LiDARs in Complex Traffic Environments

    • Authors: Zheyuan Zhang, Jianying Zheng, Yanyun Tao, Yang Xiao, Shumei Yu, Sultan Asiri, Jiacheng Li, Tieshan Li
      First page: 1559
      Abstract: The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101559
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1560: A New mm-Wave Antenna Array with
           Wideband Characteristics for Next Generation Communication Systems

    • Authors: Mehr E Munir, Abdullah G. Al Harbi, Saad Hassan Kiani, Mohamed Marey, Naser Ojaroudi Parchin, Jehanzeb Khan, Hala Mostafa, Javed Iqbal, Muhammad Abbas Khan, Chan Hwang See, Raed A. Abd-Alhameed
      First page: 1560
      Abstract: This paper presents a planar multi-circular loop antenna with a wide impedance bandwidth for next generation mm-wave systems. The proposed antenna comprises three circular rings with a partial ground plane with a square slot. The resonating structure is designed on a 0.254 mm thin RO5880 substrate with a relative permittivity of 2.3. The single element of the proposed design showed a resonance response from 26.5 to 41 GHz, with a peak gain of 4 dBi and radiation efficiency of 96%. The proposed multicircular ring antenna element is transformed into a four-element array system. The array size is kept at 18.25 × 12.5 × 0.254 mm3 with a peak gain of 11 dBi. The antenna array is fabricated and measured using the in-house facility. The simulated and measured results are well agreed upon and are found to be suitable for mm-wave communication systems.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101560
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1561: Defect Detection for Metal Base of
           TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm

    • Authors: Jiayi Liu, Xingfei Zhu, Xingyu Zhou, Shanhua Qian, Jinghu Yu
      First page: 1561
      Abstract: Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOLO-V5. Finally, the K-means++ clustering algorithm was applied to reduce the sensitivity to the initial clustering center, making the positioning more accurate and reducing the network loss. The proposed YOLO-SO model was compared with other object detection algorithms such as YOLO-V3, YOLO-V4, and Faster R-CNN. Experimental results demonstrated that the YOLO-SO model reaches 84.0% mAP, 5.5% higher than the original YOLO-V5 algorithm. Moreover, the YOLO-SO model had clear advantages in terms of the smallest weight size and detection speed of 25 FPS. These advantages make the YOLO-SO model more suitable for the real-time detection of metal TO-base appearance defects.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101561
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1562: Preoperative Virtual Reality Surgical
           Rehearsal of Renal Access during Percutaneous Nephrolithotomy: A Pilot
           Study

    • Authors: Ben Sainsbury, Olivia Wilz, Jing Ren, Mark Green, Martin Fergie, Carlos Rossa
      First page: 1562
      Abstract: Percutaneous Nephrolithotomy (PCNL) is a procedure used to treat kidney stones. In PCNL, a needle punctures the kidney through an incision in a patient’s back and thin tools are threaded through the incision to gain access to kidney stones for removal. Despite being one of the main endoscopic procedures for managing kidney stones, PCNL remains a difficult procedure to learn with a long and steep learning curve. Virtual reality simulation with haptic feedback is emerging as a new method for PCNL training. It offers benefits for both novices and experienced surgeons. In the first case, novices can practice and gain kidney access in a variety of simulation scenarios without offering any risk to patients. In the second case, surgeons can use the simulator for preoperative surgical rehearsal. This paper proposes the first preliminary study of PCNL surgical rehearsal using the Marion Surgical PCNL simulator. Preoperative CT scans of a patient scheduled to undergo PCNL are used in the simulator to create a 3D model of the renal system. An experienced surgeon then planned and practiced the procedure in the simulator before performing the surgery in the operating room. This is the first study involving survival rehearsal using a combination of VR and haptic feedback in PCNL before surgery. Preliminary results confirm that surgical rehearsal using a combination of virtual reality and haptic feedback strongly affects decision making during the procedure.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101562
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1563: Advances in Machine Condition Monitoring
           and Fault Diagnosis

    • Authors: Wenxian Yang, Radoslaw Zimroz, Mayorkinos Papaelias
      First page: 1563
      Abstract: In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...]
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101563
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1564: Inter-continental Data Centre Power Load
           Balancing for Renewable Energy Maximisation

    • Authors: Rasoul Rahmani, Irene Moser, Antonio L. Cricenti
      First page: 1564
      Abstract: The ever increasing popularity of Cloud and similar services pushes the demand for data centres, which have a high power consumption. In an attempt to increase the sustainability of the power generation, data centres have been fed by microgrids which include renewable generation—so-called `green data centres’. However, the peak load of data centres often does not coincide with solar generation, because demand mostly peaks in the evening. Shifting power to data centres incurs transmission losses; shifting the data transmission has no such drawback. We demonstrate the effectivity of computational load shifting between data centres located in different time zones using a case study that balances demands between three data centres on three continents. This study contributes a method that exploits the opportunities provided by the varied timing of peak solar generation across the globe, transferring computation load to data centres that have sufficient renewable energy whenever possible. Our study shows that balancing computation loads between three green data centres on three continents can improve the use of renewables by up to 22%. Assuming the grid energy does not include renewables, this amounts to a 13% reduction in CO2 emissions.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101564
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1565: Detection of Image Steganography Using
           Deep Learning and Ensemble Classifiers

    • Authors: Mikołaj Płachta, Marek Krzemień, Krzysztof Szczypiorski, Artur Janicki
      First page: 1565
      Abstract: In this article, the problem of detecting JPEG images, which have been steganographically manipulated, is discussed. The performance of employing various shallow and deep learning algorithms in image steganography detection is analyzed. The data, images from the BOSS database, were used with information hidden using three popular steganographic algorithms: JPEG universal wavelet relative distortion (J-Uniward), nsF5, and uniform embedding revisited distortion (UERD) at two density levels. Various feature spaces were verified, with the discrete cosine transform residuals (DCTR) and the Gabor filter residuals (GFR) yielding best results. Almost perfect detection was achieved for the nsF5 algorithm at 0.4 bpnzac density (99.9% accuracy), while the detection of J-Uniward at 0.1 bpnzac density turned out to be hardly possible (max. 56.3% accuracy). The ensemble classifiers turned out to be an encouraging alternative to deep learning-based detection methods.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101565
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1566: Cross-Layer Optimization Spatial
           Multi-Channel Directional Neighbor Discovery with Random Reply in mmWave
           FANET

    • Authors: Yifei Song, Liang Zeng, Zeyu Liu, Zhe Song, Jie Zeng, Jianping An
      First page: 1566
      Abstract: MmWave FANETs play an increasingly important role in the development of UAVs technology. Fast neighbor discovery is a key bottleneck in mmWave FANETs. In this paper, we propose a two-way neighbor discovery algorithm based on a spatial multi-channel through cross-layer optimization. Firstly, we give two boundary conditions of the physical (PHY) layer and media access control (MAC) layer for successful link establishment of mmWave neighbor discovery and give the optimal pairing of antenna beamwidth in different stages and scenarios using cross-layer optimization. Then, a mmWave neighbor discovery algorithm based on a spatial multi-channel is proposed, which greatly reduces the convergence time by increasing the discovery probability of nodes in the network. Finally, a random reply algorithm is proposed based on dynamic reserved time slots. By adjusting the probability of reply and the number of reserved time slots, the neighbor discovery time can be further reduced when the number of nodes is larger. Simulations show that as the network scale is 100 to 500 nodes, the convergence time is 10 times higher than that of the single channel algorithm.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101566
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1567: Impact Analysis of Emerging Semantic
           Communication Systems on Network Performance

    • Authors: Harim Lee, Hyeongtae Ahn, Young Deok Park
      First page: 1567
      Abstract: With the paradigm shift from Shannon’s legacy, semantic communication (SC) is emerging as one of the promising next-generation communication technologies. The new paradigm in communication technology allows the meaning of transmitted messages to be successfully delivered to a receiver. Hence, the semantic communication focuses on the successful delivery of transmitted messages such as human language communication. In order to realize such new communication, both transmitter and receiver should share the same background knowledge with each other. Recently, several researchers have developed task-specific SC systems by exploiting astonishing achievements in deep learning, which can allow the same knowledge to be shared between them. However, since such SC systems are specialized to handle specific applications, not all users can be serviced by the SC systems. Therefore, a network will face a coexistence of an SC system and a traditional communication (TC) system. In this paper, we investigate how introducing emerging SC systems affects the performance of the TC system from a network perspective. For analysis, we consider the signal-to-noise ratio (SNR) differently for the user served by an SC system and the user served by a TC system. Then, by using two different SNR equations, we formulate a max-min fairness problem in the coexistence of SC and TC systems. Via extensive numerical results, we compare the network performance of TC and SC users with and without SC systems, and then confirm that SC systems are indeed a promising next-generation communication alternative.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101567
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1568: Remote Laboratory Offered as
           Hardware-as-a-Service Infrastructure

    • Authors: Wojciech Domski
      First page: 1568
      Abstract: This paper presents a solution for remote classes where hardware is offered as a service. The infrastructure was based on Raspberry Pi mini computers to which a set of different developments boards were connected. The proposed software architecture allows students to connect to remote resources and interact with them. Moreover, the services monitoring status of remote resources were introduced to facilitate software development and the learning process. Furthermore, live video feedback is available to visually monitor operation of the resources. Finally, a debugging server was deployed allowing us to establish a remote debugging session between a user’s PC and the dev board on the server premises. The solution offers a comprehensive remote service including user management. Safety risks of the Internet-exposed infrastructure and safety precautions were discussed. The presented RemoteLab system allows students of WUST to gain knowledge, practise and realize exercises in scope of academic courses such as robot controllers and advanced robot control. Thanks to advances in remote education and utilized tools, the RemoteLab was designed and deployed, allowing stationary classes to be substituted with remote ones, while maintaining a high level of class knowledge transfer. Up to the present, the system has been utilized by over 100 students who could realize exercises and prepare for classes thanks to 24 h system availability.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101568
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1569: Fuzzy Luenberger Observer Design for
           Nonlinear Flexible Joint Robot Manipulator

    • Authors: Houssem Jerbi, Izzat Al-Darraji, Georgios Tsaramirsis, Mourad Kchaou, Rabeh Abbassi, Obaid AlShammari
      First page: 1569
      Abstract: The process of controlling a Flexible Joint Robot Manipulator (FJRM) requires additional sensors for measuring the state variables of flexible joints. Therefore, taking the elasticity into account adds a lot of complexity as all the additional sensors must be taken into account during the control process. This paper proposes a nonlinear observer that controls FJRM, without requiring equipment sensors for measuring the states. The nonlinear state equations are derived in detail for the FJRM where nonlinearity, of order three, is considered. The Takagi–Sugeno Fuzzy Model (T-SFM) technique is applied to linearize the FJRM system. The Luenberger observer is designed to estimate the unmeasured states using error correction. The developed Luenberger observer showed its ability to control the FJRM by utilizing only the measured signal of the velocity of the motor. Stability analysis is implemented to improve the ability of the designed observer to stabilize the FJRM system. The developed observer is tested by simulation to evaluate the ability of the observer to estimate the unknown states. The results showed that the proposed control algorithm estimated the motor angle, gear angle, link angle, angular velocity of gear, and angular velocity of link with zero steady errors.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101569
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1570: Cinematographic Shot Classification with
           Deep Ensemble Learning

    • Authors: Bartolomeo Vacchetti, Tania Cerquitelli
      First page: 1570
      Abstract: Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and extreme close up. The cinematographic shot classification is a complex task, so we combined state-of-the-art techniques to deal with it. Specifically, we finetuned three separated VGG-16 models and combined their predictions in order to obtain better performances by exploiting the stacking learning technique. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy. Our method was able to achieve 77% accuracy without relying on data augmentation techniques. We also evaluated our approach in terms of f1 score, precision, and recall and we showed confusion matrices to show that most of our misclassified samples belonged to a neighboring class.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101570
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1571: Machine-Learning-Based Multi-Corner
           Timing Prediction for Faster Timing Closure

    • Authors: Zhenyu Zhao, Shuzheng Zhang, Guoqiang Liu, Chaochao Feng, Tianhao Yang, Ao Han, Lei Wang
      First page: 1571
      Abstract: For the purpose of fixing timing violations, static timing analysis (STA) of full-corners is repeatedly executed, which is time-consuming. Given a timing path, timing results at some corners (“dominant corners”) are utilized to predict timing at other corners (“non-dominant corners”), which can greatly shorten the runtime of STA. However, the huge number of combinations of the dominant corners and the wide difference in prediction accuracy make it difficult to apply multi-corner timing prediction to chip industrial design. In this paper, we propose a dominant corner selection strategy to quickly determine the dominant corner combination with high prediction accuracy, along with which a new multi-corner timing prediction process is established to speed up STA. Experimental results show that our method can not only effectively accelerate STA, but also ensure the high prediction accuracy of the prediction timing. On the public ITC’99 benchmark, the prediction accuracy of the dominant corner combination selected by the proposed method is up to 98.2%, which is an improvement of 15% compared to the state-of-the-art method. For industrial application, we apply our method by using timing results on only 2 dominant corners to predict the other 12 non-dominant corners, which accelerates the runtime of the timing closure process by more than 2×.
      Citation: Electronics
      PubDate: 2022-05-13
      DOI: 10.3390/electronics11101571
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1572: Global Maximum Power Point Tracking of
           Photovoltaic Module Arrays Based on Improved Artificial Bee Colony
           Algorithm

    • Authors: Kuei-Hsiang Chao, Jia-Yan Li
      First page: 1572
      Abstract: In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though the P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on the PVMA, the I-ABC algorithm could get rid of stuck on tracking the local maximum power point (LMPP), but quickly and stably track the global maximum power point (GMPP), thereby improving the power generation efficiency. This proposed I-ABC algorithm could search for the higher power point of a PVMA by a small bee colony, determine the next tracking direction through the perturb and observe (P&O) method, and keep tracking until the GMPP is obtained. This method could prevent tracking the GMPP for too long due to applying a small bee colony. First, in this study, the photovoltaic modules produced by Sunworld Co., Ltd. were used and were configured as a PVMA with four series and three parallel connections under different numbers of shaded modules and different shading ratios, so that corresponding P-V output characteristic curves with multi-peak values were generated. Then, the GMPP was tracked by the proposed MPPT method. The simulation and experimental results showed that the proposed method performed better both in dynamic response and steady-state performance than the traditional artificial bee colony (ABC) algorithm. According to the experimental results, it showed that the tracking accuracy for the GMPP based on the proposed MPPT with 100 iterations under 5 different shading ratios was about 100%; on the other hand, that of the traditional ABC algorithm was 70%, and that of the P&O method was lower at about 30%.
      Citation: Electronics
      PubDate: 2022-05-14
      DOI: 10.3390/electronics11101572
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1573: A Semi-Unsupervised Segmentation
           Methodology Based on Texture Recognition for Radiomics: A Preliminary
           Study on Brain Tumours

    • Authors: Massimo Donelli, Giuseppe Espa, Paola Feraco
      First page: 1573
      Abstract: Because of the intrinsic anatomic complexity of the brain structures, brain tumors have a high mortality and disability rate, and an early diagnosis is mandatory to contain damages. The commonly used biopsy is the diagnostic gold standard method, but it is invasive and, due to intratumoral heterogeneity, biopsies may lead to an incorrect result. Moreover, some tumors cannot be resectable if located in critical eloquent areas. On the other hand, medical imaging procedures can evaluate the entire tumor in a non-invasive and reproducible way. Radiomics is an emerging diagnosis technique based on quantitative medical image analyses, which makes use of data provided by non-invasive diagnosis techniques such as X-ray, computer-tomography (CT), magnetic resonance (MR), and proton emission tomography (PET). Radiomics techniques require the comprehensive analysis of huge numbers of medical images to extract a large and useful number of phenotypic features (usually called radiomics biomarkers). The goal is to explore and obtain the associations between features of tumors, diagnosis and patients’ prognoses to choose the best treatments and maximize the patient’s survival rate. Current radiomics techniques are not standardized in term of segmentation, feature extraction, and selection, moreover, the decision on suitable therapies still requires the supervision of an expert doctor. In this paper, we propose a semi-automatic methodology aimed to help the identification and segmentation of malignant tissues by using the combination of binary texture recognition, growing area algorithm, and machine learning techniques. In particular, the proposed method not only helps to better identify pathologic tissues but also permits to analyze in a fast way the huge amount of data, in Dicom format, provided by non-invasive diagnostic techniques. A preliminary experimental assessment has been conducted, considering a real MRI database of brain tumors. The method has been compared with the segmentation software’s tools “slicer 3D”. The obtained results are quite promising and demonstrate the potentialities of the proposed semi-unsupervised segmentation methodology.
      Citation: Electronics
      PubDate: 2022-05-14
      DOI: 10.3390/electronics11101573
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1574: Global Sensitivity Analysis of Economic
           Model Predictive Longitudinal Motion Control of a Battery Electric Vehicle
           

    • Authors: Matthias Braband, Matthias Scherer, Holger Voos
      First page: 1574
      Abstract: Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of driving strategy. Many research works have noted the potential of model predictive control (MPC) methods to reduce energy consumption. However, this results in a complex control system with many parameters that affect the energy efficiency. Thus, an important question remains: how do these partially uncertain (system or controller) parameters influence the energy efficiency' In this article, a global variance-based sensitivity analysis method is used to answer this question. Therefore, a detailed powertrain model controlled by a longitudinal nonlinear MPC (NMPC) is developed and parameterized. Afterwards, a qualitative Morris screening is performed on this model, in order to reduce the parameter set. Subsequently, the remaining parameters are quantified using Generalized Sobol Indices, in order to take the time dependence of physical processes into account. This analysis reveals that the variations in vehicle mass, battery temperature, rolling resistance and auxiliary consumers have the greatest influence on the energy consumption. In contrast, the parameters of the NMPC only account for a maximum of 5% of the output variance.
      Citation: Electronics
      PubDate: 2022-05-14
      DOI: 10.3390/electronics11101574
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1575: Developing a Cyber Incident Exercises
           Model to Educate Security Teams

    • Authors: Basil Alothman, Aldanah Alhajraf, Reem Alajmi, Rawan Al Farraj, Nourah Alshareef, Murad Khan
      First page: 1575
      Abstract: Since cyber attacks are increasing and evolving rapidly, the need to enhance cyber-security defense is crucial. A cyber incident exercise model is a learning technique to provide knowledge about cyber security to enhance a security team’s incident response. In this research work, we proposed a cyber incident model to handle real-time security attacks in various scenarios. The proposed model consisted of three teams: (1) the black team, (2) the red team, and (3) the blue team. The black team was a group of instructors responsible for setting up the environment. They had to educate the red and blue teams about cyber security and train them on facing cyber attacks. Once the training period was completed, the members were divided into two teams to conduct a cyber-security competition in a cyber game scenario. Each of the two teams performed a different task. The red team was the offensive team that was responsible for launching cyber-security attacks. The blue team was the defensive team that was responsible for countering attacks and minimizing the damage caused by attackers; they had to conduct both cyber-security configuration and incident handling. During the scenario, the black team was responsible for guiding and monitoring both the red and the blue teams, ensuring the rules were applied throughout the competition. At the end of the competition, the members of each team changed with each other to make sure every team member was using the knowledge they gained from the training period and every participant was evaluated impartially. Finally, we showed the security team’s offensive and defensive skills via the red team and the blue team, respectively.
      Citation: Electronics
      PubDate: 2022-05-14
      DOI: 10.3390/electronics11101575
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1576: Inter-Turn Short Circuit Fault Diagnosis
           of PMSM

    • Authors: Xinglong Chen, Peng Qin, Yongyi Chen, Jianjian Zhao, Wenhao Li, Yao Mao, Tao Zhao
      First page: 1576
      Abstract: Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faulty position and severity of the inter-turn short-circuit (ITSC) fault, which is one of the most destructive and frequent faults in PMSM. Compared with the existing research results gained by particle swarm optimization algorithms, in this paper, the methods using other optimization algorithms incorporating genetic algorithm, whale optimization algorithm and stochastic parallel gradient descent algorithm (SPGD) can acquire more stable and precise results. In particular, the method based on SPGD can obtain the most desirable performance among the methods mentioned above; that is, the relative error of short-circuit turns ratio is approximately as low as 0.03%. In addition, in the case of asymmetric input three-phase voltage and with the adverse impact of high-order harmonics at different load moments, the fault diagnosis method based on SPGD still maintains relatively satisfactory properties. Finally, the verification on the actual PMSM platform demonstrates that the SPGD can still diagnose the faulty severity.
      Citation: Electronics
      PubDate: 2022-05-14
      DOI: 10.3390/electronics11101576
      Issue No: Vol. 11, No. 10 (2022)
       
  • Electronics, Vol. 11, Pages 1577: The Joint Phantom Track Deception and
           TDOA/FDOA Localization Using UAV Swarm without Prior Knowledge of
           Radars’ Precise Locations

    • Authors: Yubing Wang, Weijia Wang, Xudong Zhang, Lirong Wu, Hang Yin
      First page: 1577
      Abstract: This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars’ precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars’ positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results.
      Citation: Electronics
      PubDate: 2022-05-14
      DOI: 10.3390/electronics11101577
      Issue No: Vol. 11, No. 10 (2022)
       
 
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