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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 309)
Control Systems     Hybrid Journal   (Followers: 249)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 195)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 191)
Electronics     Open Access   (Followers: 131)
Advances in Electronics     Open Access   (Followers: 125)
Electronic Design     Partially Free   (Followers: 124)
Electronics For You     Partially Free   (Followers: 123)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 115)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 88)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 84)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 81)
IET Power Electronics     Open Access   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 67)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 63)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 62)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 58)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 53)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 52)
Advances in Power Electronics     Open Access   (Followers: 49)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 34)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 33)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 29)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 28)
Electronics Letters     Open Access   (Followers: 28)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 24)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
Journal of Sensors     Open Access   (Followers: 23)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 20)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 17)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
Circuits and Systems     Open Access   (Followers: 16)
Machine Learning with Applications     Full-text available via subscription   (Followers: 16)
Archives of Electrical Engineering     Open Access   (Followers: 15)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
International Journal of Control     Hybrid Journal   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
Superconductivity     Full-text available via subscription   (Followers: 13)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 13)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 12)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 11)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 11)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 11)
IETE Journal of Research     Open Access   (Followers: 10)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 10)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
Solid-State Electronics     Hybrid Journal   (Followers: 10)
Open Journal of Antennas and Propagation     Open Access   (Followers: 10)
Nature Electronics     Hybrid Journal   (Followers: 9)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 9)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 9)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
Batteries     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 8)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 7)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 7)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Electronic Markets     Hybrid Journal   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
Journal of Power Electronics     Hybrid Journal   (Followers: 6)
Energy Storage Materials     Full-text available via subscription   (Followers: 6)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 6)
Journal of Optoelectronics Engineering     Open Access   (Followers: 5)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Frontiers in Electronics     Open Access   (Followers: 4)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 4)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 4)
Advanced Materials Technologies     Hybrid Journal   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 4)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 3)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Advancing Microelectronics     Hybrid Journal   (Followers: 3)
IETE Journal of Education     Open Access   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Sensors International     Open Access   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 3)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 3)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
ACS Applied Electronic Materials     Open Access   (Followers: 2)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 2)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
npj Flexible Electronics     Open Access  
Elektronika ir Elektortechnika     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Open Journal of Circuits and Systems     Open Access  
Journal of Electronic Science and Technology     Open Access  
Solid State Electronics Letters     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Chinese Journal of Electronics     Open Access  
Problemy Peredachi Informatsii     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
Semiconductors and Semimetals     Full-text available via subscription  

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Electronics
Journal Prestige (SJR): 0.548
Citation Impact (citeScore): 3
Number of Followers: 131  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2079-9292
Published by MDPI Homepage  [84 journals]
  • Electronics, Vol. 11, Pages 3098: A Novel Radiation-Hardened CCDM-TSPC
           Compared with Seven Well-Known RHBD Flip-Flops in 180 nm CMOS Process

    • Authors: Shixin Wang, Lixin Wang, Yue Wang, Min Guo, Yuanzhe Li
      First page: 3098
      Abstract: Numerous radiation-hardened-by-design (RHBD) flip-flops have been developed to increase the dependability of digital chips for space applications over the past two decades. In this paper, the radiation immunity and performance of seven well-known RHBD flip-flops are discussed. A novel cross-connected dual modular redundant true single-phase clock (TSPC) D flip-flop (CCDM-TSPC) is proposed. The presented CCDM-TSPC replaces the typical master-slave D flip-flop (MS-DFF) with the fundamental TSPC structure to shorten the circuit’s propagation time. All sensitive points in the circuit are radiation-hardened by using means of cross-connection. The simulation results of the SPECTRE tool show that CCDM-TSPC is completely immune to single-event upsets (SEUs). CCDM-TSPC reduces the C-Q delay by 75% and the layout area by 85% compared with the traditional triple modular redundancy D flip-flop (TMR-DFF).
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193098
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3099: On the Fast DHT Precoding of OFDM
           Signals over Frequency-Selective Fading Channels for Wireless Applications
           

    • Authors: Kelvin Anoh, Cagri Tanriover, Moisés V. Ribeiro, Bamidele Adebisi, Chan Hwang See
      First page: 3099
      Abstract: Due to high power consumption and other problems, it is unlikely that orthogonal frequency-division multiplexing (OFDM) would be included in the uplink of the future 6G standard. High power consumption in OFDM systems is motivated by the high peak-to-average power ratio (PAPR) introduced by the inverse Fourier transform (IFFT) processing kernel in the time domain. Linear precoding of the symbols in the frequency domain using discrete Hartley transform (DHT) could be used to minimise the PAPR problem, however, at the cost of increased complexity and power consumption. In this study, we minimise the computation complexity of the DHT precoding on OFDM transceiver schemes and the consequent power consumption. We exploit the involutory properties of the processing kernels to process the DHT and IFFT as a single-processing block, thus reducing the system complexity and power consumption. These also enable a novel power-saving receiver design. We compare the results to three other precoding schemes and the standard OFDM scheme as the baseline; while improving the power consumption efficiency of a Class-A power amplifier from 4.16% to 16.56%, the bit error ratio is also enhanced by up to 5 dB when using a 12−rate error-correction coding and 7 dB with interleaving.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193099
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3100: Nonlinear Dynamic System Identification
           in the Spectral Domain Using Particle-Bernstein Polynomials

    • Authors: Michele Alessandrini, Laura Falaschetti, Giorgio Biagetti, Paolo Crippa, Claudio Turchetti
      First page: 3100
      Abstract: System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using the input/output observations of such systems with or without prior knowledge of some of the system parameters. Many valid algorithms are available in the literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model with exogenous inputs (NARMAX) and its derivatives (NARX, NARMA). Different nonlinear estimators can be used for those algorithms, such as polynomials, neural networks or wavelet networks. This paper uses a different approach, named particle-Bernstein polynomials, as an estimator for SI. Moreover, unlike the mentioned algorithms, this approach does not operate in the time domain but rather in the spectral components of the signals through the use of the discrete Karhunen–Loève transform (DKLT). Some experiments are performed to validate this approach using a publicly available dataset based on ground vibration tests recorded from a real F-16 aircraft. The experiments show better results when compared with some of the traditional algorithms, especially for large, heterogeneous datasets such as the one used. In particular, the absolute error obtained with the prosed method is 63% smaller with respect to NARX and from 42% to 62% smaller with respect to various artificial neural network-based approaches.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193100
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3101: A High-Capacity Reversible Data-Hiding
           

    • Authors: V. M. Manikandan, Kandala Sree Rama Murthy, Bhavana Siddineni, Nancy Victor, Praveen Kumar Reddy Maddikunta, Saqib Hakak
      First page: 3101
      Abstract: Reversible data hiding (RDH) is a recently emerged research domain in the field of information security domain with broad applications in medical images and meta-data handling in the cloud. The amount of data required to handle the healthcare sector has exponentially increased due to the increase in the population. Medical images and various reports such as discharge summaries and diagnosis reports are the most common data in the healthcare sector. The RDH schemes are widely explored to embed the medical reports in the medical image instead of sending them as separate files. The receiver can extract the clinical reports and recover the original medical image for further diagnosis. This manuscript proposes an approach that uses a new lossless compression-based RDH scheme that creates vacant room for data hiding. The proposed scheme uses run-length encoding and a modified Elias gamma encoding scheme on higher-order bit planes for lossless compression. The conventional Elias gamma encoding process is modified in the proposed method to embed some additional data bits during the encoding process itself. The revised approach ensures a high embedding rate and lossless recovery of medical images at the receiver side. The experimental study is conducted on both natural images and medical images. The average embedding rate from the proposed scheme for the medical images is 0.75 bits per pixel. The scheme achieved a 0 bit error rate during image recovery and data extraction. The experimental study shows that the newly introduced scheme performs better when compared with the existing RDH schemes.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193101
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3102: Improving Pneumonia Classification and
           Lesion Detection Using Spatial Attention Superposition and Multilayer
           Feature Fusion

    • Authors: Kang Li, Fengbo Zheng, Panpan Wu, Qiuyuan Wang, Gongbo Liang, Lifen Jiang
      First page: 3102
      Abstract: Pneumonia is a severe inflammation of the lung that could cause serious complications. Chest X-rays (CXRs) are commonly used to make a diagnosis of pneumonia. In this paper, we propose a deep-learning-based method with spatial attention superposition (SAS) and multilayer feature fusion (MFF) to facilitate pneumonia diagnosis based on CXRs. Specifically, an SAS module, which takes advantage of the channel and spatial attention mechanisms, was designed to identify intrinsic imaging features of pneumonia-related lesions and their locations, and an MFF module was designed to harmonize disparate features from different channels and emphasize important information. These two modules were concatenated to extract critical image features serving as the basis for pneumonia diagnosis. We further embedded the proposed modules into a baseline neural network and developed a model called SAS-MFF-YOLO to diagnose pneumonia. To validate the effectiveness of our model, extensive experiments were conducted on two CXR datasets provided by the Radiological Society of North America (RSNA) and the AI Research Institute. SAS-MFF-YOLO achieved a precision of 88.1%, a recall of 98.2% for pneumonia classification and an AP50 of 99% for lesion detection on the AI Research Institute dataset. The visualization of intermediate feature maps showed that our method could facilitate uncovering pneumonia-related lesions in CXRs. Our results demonstrated that our approach could be used to enhance the performance of the overall pneumonia detection on CXR imaging.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193102
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3103: Design and Analysis of Omnidirectional
           Receiver with Multi-Coil for Wireless Power Transmission

    • Authors: An, Yuan, Li, Cao
      First page: 3103
      Abstract: In order to solve the misalignment problem of wireless power transmission, this paper designed and analyzed the omnidirectional receiver with multi-coil. The circuit models and transmission characteristics of the wireless power transfer system (WPTS) containing multi-coil were established. Besides, an accurate mutual inductance modelling method of the circular coil under different horizontal offset and angular deflection was presented. Based on the circuit models and the mutual inductance model, the relationship between transmission characteristics and the coil parameters were studied. Simultaneously, the design method which can synthetically and quantificationally consider the radius of the coil and the number of turns was proposed. Finally, an omnidirectional receiver with multi-coil using the quantificational model and transmission characteristics in different horizontal offset and angular deflection was designed. The transmission efficiency of three-coils-receiver system was up 12.3% higher than that of one-coil-receiver system and 4% higher than that of two-coils-receiver system when horizontal offset was between 0 cm and 30 cm. Similarly, when angular deflection was between 0 and π/2, the efficiency of three-coils-receiver system was up 73.25% higher than that of one-coil-receiver system and 1.6% higher than that of two-coils-receiver system.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193103
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3104: EE-MPTCP: An Energy-Efficient Multipath
           TCP Scheduler for IoT-Based Power Grid Monitoring Systems

    • Authors: Zihang Dong, Yunming Cao, Naixue Xiong, Pingping Dong
      First page: 3104
      Abstract: The Internet-of-Things (IoT) based monitoring system has significantly promoted the intelligence and automation of power grids. The inspection robots and wireless sensors used in the monitoring system usually have multiple network interfaces to achieve high throughput and reliability transmission. The concurrent usage of these available interfaces with Multipath TCP (MPTCP) can enhance the quality of service of the communications. However, traditional MPTCP scheduling algorithms may bring about data disorder and even buffer blocking, which severely affects the transmission performance of MPTCP. And the common MPTCP improvement mechanisms for IoT lack sufficient attention to energy consumption, which is important for the battery-limited wireless sensors. With the aim to promote conservative energy without loss of throughput, this paper develops an integrated multipath scheduler for energy consumption optimization named energy-efficient MPTCP (EE-MPTCP).  EE-MPTCP first constructs a target optimization function which considers both network throughput and energy consumption. Then, based on the proposed MPTCP transmission model and existing energy efficiency model, the network throughput and energy consumption of each path can be estimated. Finally, a heuristic scheduling algorithm is proposed to find a suitable set of paths for each application. As confirmed by experiments based on Linux testbed as well as the NS3 simulation platform, the proposed scheduler can shorten the average completion time and reduce the energy consumption by up to 79.9% and 79.2%, respectively.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193104
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3105: Crowd Anomaly Detection in Video Frames
           Using Fine-Tuned AlexNet Model

    • Authors: Arfat Ahmad Khan, Muhammad Asif Nauman, Muhammad Shoaib, Rashid Jahangir, Roobaea Alroobaea, Majed Alsafyani, Ahmed Binmahfoudh, Chitapong Wechtaisong
      First page: 3105
      Abstract: This study proposed an AlexNet-based crowd anomaly detection model in the video (image frames). The proposed model was comprised of four convolution layers (CLs) and three Fully Connected layers (FC). The Rectified Linear Unit (ReLU) was used as an activation function, and weights were adjusted through the backpropagation process. The first two CLs are followed by max-pool layer and batch normalization. The CLs produced features that are utilized to detect the anomaly in the image frame. The proposed model was evaluated using two parameters—Area Under the Curve (AUC) using Receiver Operator Characteristic (ROC) curve and overall accuracy. Three benchmark datasets comprised of numerous video frames with various abnormal and normal actions were used to evaluate the performance. Experimental results revealed that the proposed model outperformed other baseline studies on all three datasets and achieved 98% AUC using the ROC curve. Moreover, the proposed model achieved 95.6%, 98%, and 97% AUC on the CUHK Avenue, UCSD Ped-1, and UCSD Ped-2 datasets, respectively.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193105
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3106: Regularized Zero-Forcing Dirty Paper
           Precoding in a High-Throughput Satellite Communication System

    • Authors: Mingchuan Yang, Xinye Shao, Guanchang Xue, Botao Liu, Yanyong Su
      First page: 3106
      Abstract: In order to maximize the available data rate and spectrum utilization efficiency, a high-throughput satellite communication system adopts the full spectrum reuse scheme, which will cause serious co-frequency interference. In this paper, a forward link model, considering the effects of free space loss, rainfall attenuation, and beam gain, is established, and the classical low-complexity of the zero-forcing precoding algorithm is improved in order to solve the serious co-frequency interference. Moreover, the regularized zero-forcing precoding algorithm considering the influence of system noise is studied, and a low complexity regularized zero-forcing dirty paper precoding algorithm is proposed, whose basic principle is to sort users based on the principle of channel maximum norm selection and practical application scenarios. Simulation results show that it can encode users sequentially, according to the channel conditions, to maximize the SINR (signal-to-interference-plus-noise ratio) and increase the throughput of the system.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193106
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3107: Bidding Strategy of Two-Layer
           Optimization Model for Electricity Market Considering Renewable Energy
           Based on Deep Reinforcement Learning

    • Authors: Xiu Ji, Cong Li, Dexin Li, Chenglong Qi
      First page: 3107
      Abstract: In the future, the large-scale participation of renewable energy in electricity market bidding is an inevitable trend. In order to describe the Nash equilibrium effect and market power between renewable energy and traditional power generators in the tacit competition in the electricity market, a bidding strategy based on deep reinforcement learning is proposed. The strategy is divided into two layers; the inner layer is the electricity market clearing model, and the outer layer is the deep reinforcement learning optimization algorithm. Taking the equilibrium supply function as the clearing model of the electricity market, considering the green certificate trading mechanism and the carbon emission mechanism, and taking the maximization of social welfare as the objective function, the optimal bidding on the best electricity price is solved. Finally, the calculation examples of the 3-node system and the 30-node system show that compared with other algorithms, more stable convergence results can be obtained, the Nash equilibrium in game theory can be reached, social welfare can be maximized, renewable energy has more market power in the market. The market efficiency evaluation index is introduced to analyze the market efficiency of the two case systems. The final result is one of great significance and value to the reasonable electricity price declaration, the optimization of market resources, and the policy orientation of the electricity market with renewable energy.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193107
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3108: Design and Experimental Study of a
           Curved Contact Quadrupole Railgun

    • Authors: Xiangyu Du, Shaowei Liu, Jiao Guan
      First page: 3108
      Abstract: The railgun is a promising weapon, but suffers from poor contact and harsh magnetic field environment. We used the moment of inertia to measure the deformation resistance of the rail, studied the contact characteristics of the railgun by contact force, and compared the performances of different structures of the rail. The magnetic field environment in the bore and the thrust on the armature of different structure railguns were studied by FEM-BEM simulation, and the final structure of the hyperbolic augmented quadrupole railgun was determined. The new structure of the railgun possesses better deformation resistance and contact characteristics, and can provide an electromagnetic shielding area and greater thrust. The test results show that the proposed railgun exhibits less rail damage and less armature ablation after launch.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193108
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3109: Empirical Analysis of Data Streaming and
           Batch Learning Models for Network Intrusion Detection

    • Authors: Kayode S. Adewole, Taofeekat T. Salau-Ibrahim, Agbotiname Lucky Imoize, Idowu Dauda Oladipo, Muyideen AbdulRaheem, Joseph Bamidele Awotunde, Abdullateef O. Balogun, Rafiu Mope Isiaka, Taye Oladele Aro
      First page: 3109
      Abstract: Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193109
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3110: Simultaneous Obstacles Avoidance and
           Robust Autonomous Landing of a UAV on a Moving Vehicle

    • Authors: Jinglong Guo, Xin Dong, Yang Gao, Daochun Li, Zhan Tu
      First page: 3110
      Abstract: For unmanned aerial vehicles (UAVs), landing on a moving vehicle robustly is an open challenge, especially under cluttered surroundings with the presence of unknown obstacles. Those undesired environmental factors could induce collisions and thus affect flight safety significantly. Currently, there are few solutions to address such a challenge. In this paper, we propose a systematic autonomous landing scheme that enables the robust autonomous landing performance of a quadrotor UAV. The proposed scheme integrates target detection, state estimation, trajectory planning, and landing control. The position and attitude information of the target ground vehicle and the test quadrotor are estimated by the onboard vision system and GPS. In order to detect landing markers at different altitudes, a particular landing pad with an Apriltag bundle is implemented. As a typical aerial–terrestrial cooperation system, the trajectory planner of the quadrotor updates continuously to avoid obstacles via real-time sensing and re-planning. A finite state machine is used to label the current flight status and triggers the control laws correspondingly. The effectiveness of the proposed method has been validated in a high-fidelity simulator with environmental obstacles.
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193110
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3111: Advances in Explainable Artificial
           Intelligence and Edge Computing Applications

    • Authors: Corchado, Ossowski, Rodríguez-González, De la Prieta
      First page: 3111
      Abstract: Artificial Intelligence (AI) and its applications have undergone remarkable experimental development in the last decade and are now the basis for a large number of decision support systems. [...]
      Citation: Electronics
      PubDate: 2022-09-28
      DOI: 10.3390/electronics11193111
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3112: Correction: Khan et al. An Adaptive
           Enhanced Technique for Locked Target Detection and Data Transmission over
           Internet of Healthcare Things. Electronics 2022, 11, 2726

    • Authors: Muhammad Amir Khan, Jawad Khan, Nabila Sehito, Khalid Mahmood, Haider Ali, Inam Bari, Muhammad Arif, Rania M. Ghoniem
      First page: 3112
      Abstract: There was an error in the original publication [...]
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193112
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3113: Contemporary Study on Deep Neural
           Networks to Diagnose COVID-19 Using Digital Posteroanterior X-ray Images

    • Authors: Saad Akbar, Humera Tariq, Muhammad Fahad, Ghufran Ahmed, Hassan Jamil Syed
      First page: 3113
      Abstract: COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pre-trained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193113
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3114: Decreasing the Negative Impact of Time
           Delays on Electricity Due to Performance Improvement in the Rwanda
           National Grid

    • Authors: Darius Muyizere, Lawrence K. Letting, Bernard B. Munyazikwiye
      First page: 3114
      Abstract: One of the most common power problems today is communication and control delays. This can adversely affect decision interaction in grid security management. This paper focuses on communication signal delays and how to identify and address communication system failure issues in the context of grid monitoring and control, with emphasis on communication signal delay. An application to solve this problem uses a thyristor switch capacitor (TSC) and a thyristor-controlled reactor (TCR) to improve the power quality of the Rwandan National Grid (RNG) with synchronous and PV generators. It is to counteract the negative effects of time delays. To this end, the TSC and TCR architectures use two methods: the fuzzy logic controller (FLC) method and the modified predictor method (MPM). The experiment was performed using the Simulink MATLAB tool. The power quality of the system was assessed using two indicators: the voltage index and total harmonic distortion. The FLC-based performance was shown to outperform the MPM for temporary or permanent failures if the correct outcome was found. As a result, we are still unsure if TSC and TCR can continue to provide favorable results in the event of a network cyber-attack.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193114
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3115: Physical Contamination Detection in Food
           Industry Using Microwave and Machine Learning

    • Authors: Ali Darwish, Marco Ricci, Flora Zidane, Jorge A. Tobon Vasquez, Mario R. Casu, Jerome Lanteri, Claire Migliaccio, Francesca Vipiana
      First page: 3115
      Abstract: The detection of contaminants in food products after packaging by a non-invasive technique is a serious need for companies operating in the food industry. In recent years, many technologies have been investigated and developed to overcome the intrinsic drawbacks of the currently employed techniques, such as X-rays and metal detector, and to offer more appropriate solutions with respect to techniques developed in the academic domain in terms of acquisition speed, cost, and the penetration depth (infrared, hyperspectral imaging). A new method based on MW sensing is proposed to increase the degree of production quality. In this paper, we are going to present a novel approach from measurements setup to a binary classification of food products as contaminated or uncontaminated. The work focuses on combining MW sensing technology and ML tools such as MLP and SVM in a complete workflow that can operate in real time in a food production line. A very good performance accuracy that reached 99.8% is achieved using the non-linear SVM algorithm, while the accuracy of the performance of the MLP classifier reached 99.3%.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193115
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3116: Certificateless Remote Data Integrity
           Auditing with Access Control of Sensitive Information in Cloud Storage

    • Authors: Genqing Bian, Fan Zhang, Rong Li, Bilin Shao
      First page: 3116
      Abstract: With the spread of cloud storage technology, checking the integrity of data stored in the cloud effectively is increasingly becoming a concern. Following the introduction of the first remote data integrity audit schemes, different audit schemes with various characteristics have been proposed. However, most of the existing solutions have problems such as additional storage overhead and additional certificate burden. This paper proposes a certificateless remote data integrity auditing scheme which takes into account the storage burden and data privacy issues while ensuring the correctness of the data audit results. In addition, the certificateless design concept enables the scheme proposed in this paper to avoid a series of burdens brought by certificates. The scheme designed in this paper provides a data access control function whereby only users who hold a valid token generated by the data owner can access the target data from the cloud. Finally, this paper provides a detailed security proof to ensure the rationality of the results. A theoretical analysis and subsequent experimental verification show that the proposed scheme is both effective and feasible.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193116
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3117: Research on Structured Extraction Method
           for Function Points Based on Event Extraction

    • Authors: Delong Han, Xungang Gu, Chengpeng Zheng, Gang Li
      First page: 3117
      Abstract: Software size is a significant input for software cost estimation, and the implementation of software size estimation dramatically affects the results and efficiency of cost estimation. Traditionally, the software size estimation is implemented by strictly trained experts and is more labor-intensive for large software projects, which is relatively expensive and inefficient. Function Point Analysis is a widely used method for software size estimation, supported by several international standards. We propose a structured and automated function point extraction method based on event extraction in natural language processing to address the problem of complex and inefficient manual recognition for function point recognition. This approach has been validated in 10 industrial cases. Experimental results show that our method can identify more than 70% of the function points, which significantly improves the efficiency of Function Point Analysis implementation. This paper could be a guide on the application of artificial intelligence techniques to software cost estimation.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193117
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3118: Efficient FPGA Implementation of an RFIR
           Filter Using the APC–OMS Technique with WTM for High-Throughput
           Signal Processing

    • Authors: Kasarla Satish Reddy, Sowmya Madhavan, Przemysław Falkowski-Gilski, Parameshachari Bidare Divakarachari, Arun Mathiyalagan
      First page: 3118
      Abstract: Nowadays, Finite Impulse Response (FIR) filters are used to change the attributes of a signal in the time or frequency domain. Among FIR filters, a reconfigurable filter has the advantage of changing the coefficient in real-time, while performing the operation. In this paper, the Anti-Symmetric Product Coding (APC) and Odd Multiple Storage (OMS) modules are utilized to implement the reconfigurable FIR filter (RFIR–APC–OMS). Herein, the APC–OMS module is used to reduce the area of the RFIR architecture. The performance of the RFIR–APC–OMS is analyzed in terms of: area, power, delay, LUT, flip flop, slices, and frequency. RFIR–APC–OMS has reduced 3.44% of area compared to the existing RFIR architecture employing the Dynamic Reconfigurable Partial Product Generator (DRPPG) module.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193118
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3119: Intelligent Vehicle Trajectory Tracking
           Control Based on VFF-RLS Road Friction Coefficient Estimation

    • Authors: Yanxin Nie, Yiding Hua, Minglu Zhang, Xiaojun Zhang
      First page: 3119
      Abstract: This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control lies in the fast and accurate identification of road friction coefficients. Therefore, an improved strategy is designed based on traditional recursive least squares (RLS), which is utilized for accurate identification of the friction coefficient. First, the tire force and slip rate required for the estimation of the road friction coefficient by constructing the vehicle dynamics model and tire effective model are calculated. In this paper, a variable forgetting factor recursive least squares (VFF-RLS) method is proposed for the construction of the friction coefficient estimator. Second, the identified results are output to the model predictive controller (MPC) constructed in this paper as a way to improve tire slip angle constraints, to realize the trajectory tracking of the intelligent vehicle. Finally, the joint simulation test results of Carsim and Matlab/Simulink show that the trajectory tracking system based on the VFF-RLS friction coefficient estimator has outstanding tracking performance.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193119
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3120: A DDoS Vulnerability Analysis System
           against Distributed SDN Controllers in a Cloud Computing Environment

    • Authors: Sumit Badotra, Sarvesh Tanwar, Salil Bharany, Ateeq Ur Rehman, Elsayed Tag Eldin, Nivin A. Ghamry, Muhammad Shafiq
      First page: 3120
      Abstract: Software-Defined Networking (SDN) is now a well-established approach in 5G, Internet of Things (IoT) and Cloud Computing. The primary idea behind its immense popularity is the separation of its underlying intelligence from the data-carrying components like routers and switches. The intelligence of the SDN-based networks lies in the central point, popularly known as the SDN controller. It is the central control hub of the SDN-based network, which has full privileges and a global view over the entire network. Providing security to SDN controllers is one such important task. Whenever one wishes to implement SDN into their data center or network, they are required to provide the website to SDN controllers. Several attacks are becoming a hurdle in the exponential growth of SDN, and among all one such attack is a Distributed Denial of Service (DDoS) attack. In a couple of years, several new SDN controllers will be available. Among many, Open Networking Operating System (ONOS) and OpenDayLight (ODL) are two popular SDN controllers laying the foundation for many other controllers. These SDN controllers are now being used by numerous businesses, including Cisco, Juniper, IBM, Google, etc. In this paper, vulnerability analysis is carried out against DDoS attacks on the latest released versions of both ODL and ONOS SDN controllers in real-time cloud data centers. For this, we have considered distributed SDN controllers (located at different locations) on two different clouds (AWS and Azure). These controllers are connected through the Internet and work on different networks. DDoS attacks are bombarded on the distributed SDN controllers, and vulnerability is analyzed. It was observed with experimentation that, under five different scenarios (malicious traffic generated), ODL-3 node cluster controller had performed better than ONOS. In these five different scenarios, the amount of malicious traffic was incregradually increased. It also observed that, in terms of disk utilization, memory utilization, and CPU utilization, the ODL 3-node cluster was way ahead of the SDN controller.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193120
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3121: Analytical Solution for Transient
           Reactive Elements for DC-DC Converter Circuits

    • Authors: Amr Marey, Mahajan Sagar Bhaskar, Dhafer Almakhles, Hala Mostafa
      First page: 3121
      Abstract: This paper develops an analytical method for modeling the inductor currents and capacitor voltages (ICCV) of a generic DC-DC converter system. The purpose of the designed methodology is to propose a new generalized modeling technique for DC-DC converter systems that accurately models the transient behavior of those systems. The modeled converter is assumed to operate over some number of circuit stages. Each circuit stage can be separately modeled as a linear time-invariant (LTI) system that is solved through the uni-lateral Laplace transform. Furthermore, the initial conditions (ICs) of these LTI systems are related through different algebraic expressions and discrete-time difference equations that originate from the continuity of the ICCV with respect to time. These discrete-time difference equations are then solved with the uni-lateral Z-transform to determine the ICs of the ICCV at each switching period. The generalized theoretical analysis is applied to the study of the transient behavior of the buck-boost converter across various different circuit parameters. This analysis justified with laboratory experimentation of the buck-boost converter, and the transient behavior of the buck-boost converter is compared for each experimental parameter set. The experimental results and the theoretical analysis provide very similar results across the different converter parameters.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193121
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3122: Leveraging Machine Learning for
           Fault-Tolerant Air Pollutants Monitoring for a Smart City Design

    • Authors: Muneeb A. Khan, Hyun-chul Kim, Heemin Park
      First page: 3122
      Abstract: Air pollution has become a global issue due to its widespread impact on the environment, economy, civilization and human health. Owing to this, a lot of research and studies have been done to tackle this issue. However, most of the existing methodologies have several issues such as high cost, low deployment, maintenance capabilities and uni-or bi-variate concentration of air pollutants. In this paper, a hybrid CNN-LSTM model is presented to forecast multivariate air pollutant concentration for the Internet of Things (IoT) enabled smart city design. The amalgamation of CNN-LSTM acts as an encoder-decoder which improves the overall accuracy and precision. The performance of the proposed CNN-LSTM is compared with conventional and hybrid machine learning (ML) models on the basis of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The proposed model outperforms various state-of-the-art ML models by generating an average MAE, MAPE and MSE of 54.80%, 52.78% and 60.02%. Furthermore, the predicted results are cross-validated with the actual concentration of air pollutants and the proposed model achieves a high degree of prediction accuracy to real-time air pollutants concentration. Moreover, a cross-grid cooperative scheme is proposed to tackle the IoT monitoring station malfunction scenario and make the pollutant monitoring more fault resistant and robust. The proposed scheme exploits the correlation between neighbouring monitoring stations and air pollutant concentration. The model generates an average MAPE and MSE of 10.90% and 12.02%, respectively.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193122
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3123: Efficient Elliptic Curve Operators for
           Jacobian Coordinates

    • Authors: Wesam Eid, Turki F. Al-Somani, Marius C. Silaghi
      First page: 3123
      Abstract: The speed up of group operations on elliptic curves is proposed using a new type of projective coordinate representation. These operations are the most common computations in key exchange and encryption for both current and postquantum technology. The boost this improvement brings to computational efficiency impacts not only encryption efforts but also attacks. For maintaining security, the community needs to take note of this development as it may need to operate changes in the key size of various algorithms. Our proposed projective representation can be viewed as a warp on the Jacobian projective coordinates, or as a new operation replacing the addition in a Jacobian projective representation, basically yielding a new group with the same algebra elements and homomorphic to it. Efficient algorithms are introduced for computing the expression Pk+Q where P and Q are points on the curve and k is an integer. They exploit optimized versions for particular k values. Measurements of the numbers of basic computer instructions needed for operations based on the new representation show clear improvements. The experiments are based on benchmarks selected using standard NIST elliptic curves.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193123
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3124: A Novel Heterogeneous Parallel System
           Architecture Based EtherCAT Hard Real-Time Master in High Performance
           Control System

    • Authors: Hongzhe Shi, Weiyang Lin, Chenlu Liu, Jinyong Yu
      First page: 3124
      Abstract: EtherCAT is one of the preferred real-time Ethernet technologies. However, EtherCAT is not applicable in high-end control fields due to real-time constraints. Clock synchronization and cycle time are the most representative limitations. In this paper, a novel Heterogeneous Parallel System Architecture (HPSA) with features of parallel computation and hard real-time is presented. An HPSA-based EtherCAT hard real-time master is developed to significantly improve clock synchronization and shorten cycle time. Traditional EtherCAT masters feature serial processing and run on a PC. This HPSA-based master consists of two parts: EtherCAT master stack (EMS) and EtherCAT operating system (EOS). EMS implements the parallel operation of EtherCAT to realize the shorter cycle time, and EOS brings a hard real-time environment to the HPSA-based master to improve clock synchronization. Furthermore, this HPSA-based master operates on a heterogeneous System-on-a-chip (SoC). EMS and EOS form a heterogeneous architecture inside this SoC to achieve low-latency process scheduling. Experimental results show that in our HPSA-based EtherCAT hard real-time master, the cycle time reaches the sub-50 μs range, and the synchronization error reduces to several nanoseconds. Thus, this HPSA-based master has great application value in high-performance control systems.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193124
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3125: A Novel Target Tracking Scheme Based on
           Attention Mechanism in Complex Scenes

    • Authors: Yu Wang, Zhutian Yang, Wei Yang, Jiamin Yang
      First page: 3125
      Abstract: In recent years, target tracking algorithms based on deep learning have realized significant progress, especially the Siamese neural network structure, which has a simple structure and excellent scalability. Although these methods provide excellent generalization capabilities, they fail to perform the task of learning target information discrimination smoothly due to being affected by distractors such as background clutter, occlusion, and target size. To solve this problem, in this paper we propose a newly improved Siamese network target tracking algorithm based on an attention mechanism. We introduce a channel attention module and a spatial attention module into the original network to improve the problem of insufficient semantic extraction ability of the convolutional layer of the tracking algorithm in complex environments. A channel attention mechanism enhances the feature extraction ability by using the network to learn the importance of each channel and establish the relationship between channels, while a spatial attention mechanism strengthens the feature extraction ability by establishing the importance of spatial position in locating the target or carrying out a certain degree of deformation. In this paper, the above two models are combined to improve the robustness of trackers without sacrificing tracking speed. We conduct a comprehensive experiment on the Object Tracking Benchmark dataset. The experimental results show that our algorithm outperforms other real-time trackers in both accuracy and robustness in most complex environments.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193125
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3126: Adaptive Two-Index Fusion
           Attribute-Weighted Naive Bayes

    • Authors: Xiaoliang Zhou, Donghua Wu, Zitong You, Dongyang Wu, Ning Ye, Li Zhang
      First page: 3126
      Abstract: Naive Bayes (NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independence assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to its high efficiency and easy implementation, the filter-attribute-weighted NB methods have received great attentions. However, there still exist several challenges, such as the poor representation ability for a single index and the fusion problem of two indexes. To overcome the above challenges, we propose a general framework of an adaptive two-index fusion attribute-weighted NB (ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, the intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a regulatory factor β to fuse two indexes, which can adaptively adjust the optimal ratio of any two indexes on various datasets. Furthermore, a range query method is proposed to infer the optimal interval of regulatory factor β. Finally, the weight of each attribute is calculated using the optimal value β and is integrated into an NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter-weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive regulatory factor β. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive regulatory factor β.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193126
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3127: Mixed-Variable Bayesian Optimization for
           Analog Circuit Sizing through Device Representation Learning

    • Authors: Konstantinos Touloupas, Paul Peter Sotiriadis
      First page: 3127
      Abstract: In this work, a deep representation learning method is proposed to build continuous-valued representations of individual integrated circuit (IC) devices. These representations are used to render mixed-variable analog circuit sizing problems as continuous ones and to apply a low-budget black box Bayesian optimization (BO) variant to solve them. By transforming the initial search spaces into continuous-valued ones, the BO’s Gaussian process models (GPs), which typically operate on real-valued spaces, can be used to guide the optimization search towards the global optimum. The proposed Device Representation Learning approach involves using device simulation data and training a composite model of a Variational Autoencoder (VAE) and a dense Neural Network. The latent variables of the trained VAE model serve as the representations of the integrated device and replace the discrete-valued parametrizations of particular devices. A thorough explanation of the proposed methodology’s mathematical formulation is given and example sizing applications on real-world analog circuits and integrated devices underline its efficiency.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193127
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3128: Design of a New Non-Coherent
           Cross-QAM-Based M-ary DCSK Communication System

    • Authors: Zhuwen Yang, Guofa Cai
      First page: 3128
      Abstract: In this paper, a new non-coherent cross-quadrature amplitude modulation (XQAM)-based M-ary differential chaos shift keying (XQAM-MDCSK) system is proposed. In such a system, an autocorrelator is adopted at the receiver to obtain the channel compensation value. This framework can be extended to various amplitude phase shift keying-based MDCSK systems, such as star QAM-based MDCSK (star QAM-MDCSK) and square QAM-based MDCSK (SQAM-MDCSK) systems. Moreover, the bit error rate (BER) expression of the proposed XQAM-MDCSK system is derived over a multipath Rayleigh fading channel. Results show that the proposed XQAM-MDCSK system can achieve better BER performance and a lower peak-to-average power ratio (PAPR) compared to the star QAM-MDCSK system. Furthermore, we also show that the performance of the proposed system can be close to that of a system with perfect channel state information (CSI).
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193128
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3129: Ghost-Free Multi-Exposure Image Fusion
           Technology Based on the Multi-Scale Block LBP Operator

    • Authors: Xinrong Ye, Zhengping Li, Chao Xu
      First page: 3129
      Abstract: This paper proposes a ghost-free multi-exposure image fusion technique based on the multi-scale block LBP (local binary pattern) operator. The method mainly includes two steps: first, the texture variation, brightness, and spatial consistency weight maps of the image are computed, and then these three image features are used to construct the initial weight map. Finally, the multi-resolution method is used to fuse the images to obtain the resulting image. The main advantage of this technique lies in the step of extracting the details of the source image based on the multi-scale block LBP operator, which is used to preserve the details of the brightest and darkest areas in high dynamic range scenes and preserve the texture features of the source image. Another advantage is that a new LBP operator-based motion detection method is proposed for fusing multi-exposure images in dynamic scenes containing moving objects. In addition, this paper also studies two spatially consistent weight distribution methods and compares and discusses the effects of these two methods on the results of dynamic image fusion. Through a large number of experimental comparisons, the superiority and feasibility of this method are proved.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193129
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3130: Single-Frequency Network Terrestrial
           Broadcasting with 5GNR Numerology Using Recurrent Neural Network

    • Authors: Majid Mosavat, Guido Montorsi
      First page: 3130
      Abstract: We explore the feasibility of Terrestrial Broadcasting in a Single-Frequency Network (SFN) with standard 5G New Radio (5GNR) numerology designed for uni-cast transmission. Instead of the classical OFDM symbol-by-symbol detector scheme or a more complex equalization technique, we designed a Recurrent-Neural-Network (RNN)-based detector that replaces the channel estimation and equalization blocks. The RNN is a bidirectional Long Short-Term Memory (bi-LSTM) that computes the log-likelihood ratios delivered to the LDPC decoder starting from the received symbols affected by strong intersymbol/intercarrier interference (ISI/ICI) on time-varying channels. To simplify the RNN receiver and reduce the system overhead, pilot and data signals in our proposed scheme are superimposed instead of interspersed. We describe the parameter optimization of the RNN and provide end-to-end simulation results, comparing them with those of a classical system, where the OFDM waveform is specifically designed for Terrestrial Broadcasting. We show that the system outperforms classical receivers, especially in challenging scenarios associated with large intersite distance and large mobility. We also provide evidence of the robustness of the designed RNN receiver, showing that an RNN receiver trained on a single signal-to-noise ratio and user velocity performs efficiently also in a large range of scenarios with different signal-to-noise ratios and velocities.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193130
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3131: Sinusoidal Current Signal-Based Fire
           Detection System with Automatic Address Assignment

    • Authors: Man Hee Lee, Seog Chae, Soo Young Shin
      First page: 3131
      Abstract: In this paper, a novel sinusoidal current signal-based fire detection system is proposed with automatic address assignment. The system model employs a conventional power line to embed fire information, i.e., the address, rather than using an additional communication line. At the transmitter, different frequencies of the sinusoidal current signal are combined and transmitted through a power line. At the receiver, fast Fourier transform (FFT) is applied to distinguish the frequency bins, which can represent the addresses of fire detectors. The proposed system model is implemented and the numerical results are presented in terms of measurements.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193131
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3132: A Dual Polarization 3-D Beamforming AiP

    • Authors: Oh
      First page: 3132
      Abstract: This paper describes the implementation of an antenna-in-package (AiP) with a dual polarization function, supporting a three-dimensional (3D) beamforming operation. In order to implement 3D beamforming, a Yagi-type end-fire antenna supporting each of the x and y directions and a patch-type broadsided antenna supporting the z-direction were implemented. The broadside antennas have dual polarization functions so that they can be received in any direction. Each antenna was implemented in four array structures to support beamforming operations. The broadside antenna was designed in a 2 × 2 array structure, with a patch-type antenna and two linear dual polarization functions. The single antenna operated with a gain of 6 dBi, an E-plane beam width of ±45 degrees, and an H-plane beam width of ±50 degrees and had an antenna gain of 9~11 dBi as well as a vertical/horizontal forming operation with a radiation angle of ±22 degrees The end-fire antenna unit was designed in a 1 × 4 array structure with a Yagi-type antenna. The single antenna had a gain of 4 dBi, with an antenna gain of 8 dBi in the array structure, and it was improved to 11 dBi by adding a parasitic array director. The final end-fire antenna unit had a radiation angle of ±11 degrees and a beamforming coverage of ±45 degrees The vertical and horizontal design results were secured for reception in any direction, and all the array antennas had a return loss of 10 dB or less in the entire frequency band, from 57 to 66 GHz.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193132
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3133: Efficient Dynamic Phishing Safeguard
           System Using Neural Boost Phishing Protection

    • Authors: Abdul Quadir Md, Dibyanshu Jaiswal, Jay Daftari, Sabireen Haneef, Celestine Iwendi, Sanjiv Kumar Jain
      First page: 3133
      Abstract: The instances of privacy and security have reached the point where they cannot be ignored. There has been a rise in data breaches and fraud, particularly in banks, healthcare, and government sectors. In today’s world, many organizations offer their security specialists bug report programs that help them find flaws in their applications. The breach of data on its own does not necessarily constitute a threat or attack. Cyber-attacks allow cyberpunks to gain access to machines and networks and steal financial data and esoteric information as a result of a data breach. In this context, this paper proposes an innovative approach to help users to avoid online subterfuge by implementing a Dynamic Phishing Safeguard System (DPSS) using neural boost phishing protection algorithm that focuses on phishing, fraud, and optimizes the problem of data breaches. Dynamic phishing safeguard utilizes 30 different features to predict whether or not a website is a phishing website. In addition, the neural boost phishing protection algorithm uses an Anti-Phishing Neural Algorithm (APNA) and an Anti-Phishing Boosting Algorithm (APBA) to generate output that is mapped to various other components, such as IP finder, geolocation, and location mapper, in order to pinpoint the location of vulnerable sites that the user can view, which makes the system more secure. The system also offers a website blocker, and a tracker auditor to give the user the authority to control the system. Based on the results, the anti-phishing neural algorithm achieved an accuracy level of 97.10%, while the anti-phishing boosting algorithm yielded 97.82%. According to the evaluation results, dynamic phishing safeguard systems tend to perform better than other models in terms of uniform resource locator detection and security.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193133
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3134: A Performance-Oriented Optimization
           Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for
           Multi-Objective Sustainable Supply Chain Network Design

    • Authors: Yurong Guo, Quan Shi, Chiming Guo
      First page: 3134
      Abstract: The decision-making of sustainable supply chain network (SSCN) design is a strategy capacity for configuring network facility and product flow. When optimizing conflicting economic, environmental, and social performance objectives, it is difficult to select the optimal scheme from obtained feasible decision schemes. In this article, according to the triple bottom line of sustainability, a multi-objective sustainable supply chain network optimization model is developed, and a novel performance-oriented optimization framework is proposed. This framework, referred to as performance-oriented optimization framework, integrates multi-objective meta-heuristic algorithms and entropy-weighted technique for order preference by similarity to an ideal solution (EW-TOPSIS). The optimization framework can comprehensively evaluate the performance of overall SSCN by EW-TOPSIS and guide the evolution process of algorithms. In this framework, decision-makers can obtain the feasible schemes calculated by meta-heuristics and determine the optimal one according to the performance value evaluated by EW-TOPSIS. This article combines three performance evaluation strategies with four meta-heuristic algorithms, namely, non-dominated Sorting Genetic Algorithm-II (NSGA-2), multi-objective differential evolutionary (MODE), multi-objective particle swarm optimization (MOPSO), and multi-objective gray wolr optimization (MOGWO), for verifying the effectiveness of the performance-oriented optimization framework. The results validate that the proposed framework has much better sustainability performance than the traditional optimization algorithms and evaluation methods. Furthermore, the proposed performance-oriented optimization framework can provide managers with a special optimal scheme with the best sustainability performance. Finally, some research prospects are presented such as more multi-criteria decision making methods.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193134
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3135: New Sliding Mode Control Based on
           Tracking Differentiator and RBF Neural Network

    • Authors: Chunyu Qu, Yongzhuang Hu, Ziqi Guo, Fangxu Han, Xiuping Wang
      First page: 3135
      Abstract: In order to solve the problem that the control system of permanent magnet synchronous motor (PMSM) is difficult to meet the high control accuracy due to the influence of non-repeated disturbances such as external disturbance, system parameter variation, and friction force during operation, a novel sliding mode control (NSMC) method based on tracking differentiator (TD) and radial basis (RBF) neural network was proposed. Firstly, a new sliding mode reaching law is proposed by adding the state variables to the traditional exponential reaching law, which can effectively reduce the chattering of the system. Then, the speediest tracking differentiator is designed to estimate the given speed signal and its differential signal, to realize the novel sliding mode variable structure algorithm. Finally, the RBF neural network is used to compensate for the uncertainty and external interference of the system; the robustness of the system is further improved by adaptive weight updating. The simulation results show that, by comparing with the traditional exponential approach law, the overshoot of 22 r/min is reduced by the control method based on the new hybrid reaching law, the speed decrease amplitude is reduced by 77.1% under load disturbance, and the speed recovery time is shortened by 0.059 s. After the optimization of the new sliding mode control method based on tracking differentiator and RBF neural network, the overshoot of 86 r/min is further reduced, the speed decrease amplitude of load disturbance is reduced again by 48.5%, and the speed recovery time is shortened again by 0.073 s.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193135
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3136: Stability and Stabilization of TS Fuzzy
           Systems via Line Integral Lyapunov Fuzzy Function

    • Authors: Imad eddine Meredef, Mohamed Yacine Hammoudi, Abir Betka, Madina Hamiane, Khalida Mimoune
      First page: 3136
      Abstract: This paper is concerned with the stability and stabilization problem of a Takagi-Sugeno fuzzy (TSF) system. Using a non-quadratic function (well-known integral Lyapunov fuzzy candidate (ILF)) and some lemmas, new sufficient conditions are established as linear matrix inequalities (LMIs), which are solved with a stochastic fractal search (SFS). The main advantage of the technique used is its small conservatives. Motivated by the mean value theorem, a state feedback controller based on a non-quadratic Lyapunov function is designed. Unlike other approaches based on poly-quadratic Lyapunov candidates, stability conditions of the closed loop are obtained in LMI regions. It is important to highlight that the time derivatives of membership functions do not appear in the used line integral Lyapunov function, which is the well-known problem of poly-quadratic Lyapunov functions. A numerical example is given to show the advantages and the utility of the integral Lyapunov fuzzy candidate, which provides a wider feasibility region than other Lyapunov functions.
      Citation: Electronics
      PubDate: 2022-09-29
      DOI: 10.3390/electronics11193136
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3137: A Threshold Voltage Model for AOS TFTs
           Considering a Wide Range of Tail-State Density and Degeneration

    • Authors: Minxi Cai, Piaorong Xu, Bei Liu, Ziqi Peng, Jianhua Cai, Jing Cao
      First page: 3137
      Abstract: There have been significant differences in principle electrical parameters between amorphous oxide semiconductor (AOS) thin-film transistors (TFTs) and silicon-based devices for their distinct conduction mechanisms. Additionally, threshold voltage is one of the key parameters in device characterization and modeling. In this work, a threshold voltage model is developed for AOS TFTs considering the various density of exponential tail states below the conduction band, including degenerate conduction. The threshold condition is defined where the density ratio of free carriers to the trapped carriers reaches a critical value depending on the distribution parameters of tail states. The resulting threshold voltage expression is fully analytical and is of clear physical meaning, with simple parameter extraction methods. Numerical and experimental verifications show that this model provides appropriate values of threshold voltage for devices with different sub-gap tail states, which could be a useful method for identifying the threshold voltage of a large variety of AOS TFTs.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193137
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3138: Framing Network Flow for Anomaly
           Detection Using Image Recognition and Federated Learning

    • Authors: Jevgenijus Toldinas, Algimantas Venčkauskas, Agnius Liutkevičius, Nerijus Morkevičius
      First page: 3138
      Abstract: The intrusion detection system (IDS) must be able to handle the increase in attack volume, increasing Internet traffic, and accelerating detection speeds. Network flow feature (NTF) records are the input of flow-based IDSs that are used to determine whether network traffic is normal or malicious in order to avoid IDS from difficult and time-consuming packet content inspection processing since only flow records are examined. To reduce computational power and training time, this paper proposes a novel pre-processing method merging a specific amount of NTF records into frames, and frame transformation into images. Federated learning (FL) enables multiple users to share the learned models while maintaining the privacy of their training data. This research suggests federated transfer learning and federated learning methods for NIDS employing deep learning for image classification and conducting tests on the BOUN DDoS dataset to address the issue of training data privacy. Our experimental results indicate that the proposed Federated transfer learning (FTL) and FL methods for training do not require data centralization and preserve participant data privacy while achieving acceptable accuracy in DDoS attack identification: FTL (92.99%) and FL (88.42%) in comparison with Traditional transfer learning (93.95%).
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193138
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3139: Optimal Selection and Integration of
           Batteries and Renewable Generators in DC Distribution Systems through a
           Mixed-Integer Convex Formulation

    • Authors: Jerson Daniel Basto-Gil, Angel David Maldonado-Cardenas, Oscar Danilo Montoya
      First page: 3139
      Abstract: The problem concerning the optimal placement and sizing of renewable energy resources and battery energy storage systems in electrical DC distribution networks is addressed in this research by proposing a new mathematical formulation. The exact mixed-integer nonlinear programming (MINLP) model is transformed into a mixed-integer convex model using McCormick envelopes regarding the product between two positive variables. Convex theory allows ensuring that the global optimum is found due to the linear equivalent structure of the solution space and the quadratic structure of the objective function when all the binary variables are defined. Numerical results in the 21-bus system demonstrate the effectiveness and robustness of the proposed solution methodology when compared to the solution reached by solving the exact MINLP model. Numerical results showed that the simultaneous allocation of batteries and renewable energy resources allows for the best improvements in the daily operating costs, i.e., about 53.29% with respect to the benchmark case of the 21-bus grid, followed by the scenario where the renewable energy resources are reallocated while considering a fixed location for the batteries, with an improvement of 43.33%. In addition, the main result is that the difference between the exact modeling and the proposed formulation regarding the final objective function was less than 3.90% for all the simulation cases, which demonstrated the effectiveness of the proposed approach for operating distributed energy resources in monopolar DC networks.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193139
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3140: An Image Fusion Method Based on Special
           Residual Network and Efficient Channel Attention

    • Authors: Yang Li, Haitao Yang, Jinyu Wang, Changgong Zhang, Zhengjun Liu, Hang Chen
      First page: 3140
      Abstract: This paper presents an image fusion network based on a special residual network and attention mechanism. Compared with the traditional fusion network, the image fusion network has the advantages of an end-to-end network and integrates the feature extraction advantages of the attention mechanism residual network. It overcomes the shortcomings of the traditional network that need complex design rules and manual operation. In this method, hierarchical feature fusion is used to achieve effective fusion. A combined loss function is designed to optimize training results and improve image fusion quality. This paper uses many qualitative and quantitative experimental analyses on different data sets. The results show that, compared with the comparison algorithm, the method in this paper has a stronger retention ability of infrared and visible light information and better indexes. 72% of eleven indexes compared with some images in the public TNO data set are optimal or sub-optimal, and 80% are optimal or suboptimal in the RoadScene data set, which is much higher than other algorithms. The overall fusion effect is more in line with human visual perception.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193140
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3141: swAFL: A library of High-Performance
           Activation Function for the Sunway Architecture

    • Authors: Xu, Li, Hou, Wang
      First page: 3141
      Abstract: The Sunway supercomputers have recently attracted considerable attention to execute neural networks. Meanwhile, activation functions help extend the applicability of neural networks to nonlinear models by introducing nonlinear factors. Despite the numerous activation function-supported AI frameworks, only PyTorch and TensorFlow were ported to the Sunway platforms. Although these libraries can meet the minimum functional requirements to deploy a neural network on the Sunway machines, there still exist some drawbacks including the limited number of usable functions and unsatisfactory performances remaining unresolved. Therefore, two activation function algorithms with different computing accuracies were developed in this study, and an efficient implementation scheme was designed using the single instruction/multiple data extension and multiply–add instructions of the platform. Finally, an efficient library-swAFL-composed of 48 function interfaces was designed and implemented on the Sunway platforms. Experimental results indicate that swAFL outperformed PyTorch and TensorFlow by 19.5 and 23 times, respectively, on average.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193141
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3142: PDF Malware Detection Based on
           Optimizable Decision Trees

    • Authors: Abu Al-Haija, Odeh, Qattous
      First page: 3142
      Abstract: Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193142
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3143: Design of Anti-Swing PID Controller for
           Bridge Crane Based on PSO and SA Algorithm

    • Authors: Hui Li, Yan-Bo Hui, Qiao Wang, Hong-Xiao Wang, Lin-Jun Wang
      First page: 3143
      Abstract: Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193143
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3144: A High Flexible Shift Transformation
           Unit Design Approach for Coarse-Grained Reconfigurable Cryptographic
           Arrays

    • Authors: Tongzhou Qu, Zibin Dai, Yanjiang Liu, Lin Chen
      First page: 3144
      Abstract: Shift transformations are the fundamental operation of cryptographic algorithms, and the arithmetic unit implementing different types of shift transformations are utilized in the coarse-grain reconfigurable cryptographic architectures (CGRCA) to meet the different cryptographic algorithms. In this paper, a reconfigurable shift transformation unit (RSTU) is proposed to meet the complicated shift requirement of CGRCA, which achieves high flexibility and a good cost–performance ratio. The mathematical properties of shift transformation are analyzed, and several theorems are introduced to design a reconfigurable shifter. Furthermore, the reconfigurable data path of the proposed unit is presented to implement the random combination of shift operations in different granularity, and configuration word and routing algorithms are proposed to generate control information for RSTU. Moreover, the control information generation module is designed to invert the configuration word into the control information, according to the routing algorithms. As a proof-of-concept, the proposed RSTU is built using the CMOS 65 nm technology. The experimental results show that RSTU supports more shift operations, increases 18.2% speed at most, and reduces 13% area occupation, compared to the existing shifters.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193144
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3145: Risk-Based Capacitor Placement in
           Distribution Networks

    • Authors: Hamid Falaghi, Maryam Ramezani, Hasan Elyasi, Mahdi Farhadi, Abouzar Estebsari
      First page: 3145
      Abstract: In this paper, the problem of sizing and placement of constant and switching capacitors in electrical distribution systems is modelled considering the load uncertainty. This model is formulated as a multicriteria mathematical problem. The risk of voltage violation is calculated, and the stability index is modelled using fuzzy logic and fuzzy equations. The instability risk is introduced as the deviation of our fuzzy-based stability index with respect to the stability margin. The capacitor placement objectives in our paper include: (i) minimizing investment and installation costs as well as loss cost; (ii) reducing the risk of voltage violation; and (iii) reducing the instability risk. The proposed mathematical model is solved using a multi-objective version of a genetic algorithm. The model is implemented on a distribution network, and the results of the experiment are discussed. The impacts of constant and switching capacitors are assessed separately and concurrently. Moreover, the impact of uncertainty on the multi-objectives is determined based on a sensitivity analysis. It is demonstrated that the more the uncertainty is, the higher the system cost, the voltage risk and the instability risk are.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193145
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3146: A Comprehensive Study on Healthcare
           Datasets Using AI Techniques

    • Authors: Mistry, Wang, Islam, Osei
      First page: 3146
      Abstract: Due to greater accessibility, healthcare databases have grown over the years. In this paper, we practice locating and associating data points or observations that pertain to similar entities across several datasets in public healthcare. Based on the methods proposed in this study, all sources are allocated using AI-based approaches to consider non-unique features and calculate similarity indices. Critical components discussed include accuracy assessment, blocking criteria, and linkage processes. Accurate measurements develop methods for manually evaluating and validating matched pairs to purify connecting parameters and boost the process efficacy. This study aims to assess and raise the standard of healthcare datasets that aid doctors’ comprehension of patients’ physical characteristics by using NARX to detect errors and machine learning models for the decision-making process. Consequently, our findings on the mortality rate of patients with COVID-19 revealed a gender bias: female 15.91% and male 22.73%. We also found a gender bias with mild symptoms such as shortness of breath: female 31.82% and male 32.87%. With congestive heart disease symptoms, the bias was as follows: female 5.07% and male 7.58%. Finally, with typical symptoms, the overall mortality rate for both males and females was 13.2%.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193146
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3147: Techno-Economic Optimization Study of
           Interconnected Heat and Power Multi-Microgrids with a Novel
           Nature-Inspired Evolutionary Method

    • Authors: Paolo Fracas, Edwin Zondervan, Meik Franke, Kyle Camarda, Stanimir Valtchev, Svilen Valtchev
      First page: 3147
      Abstract: The world is once again facing massive energy- and environmental challenges, caused by global warming. This time, the situation is complicated by the increase in energy demand after the pandemic years, and the dramatic lack of basic energy supply. The purely “green” energy is still not ready to substitute the fossil energy, but this year the fossil supplies are heavily questioned. Consequently, engineering must take flexible, adaptive, unexpected directions. For example, even the natural gas power plants are currently considered “green” by the European Union Taxonomy, joining the “green” hydrogen. Through a tight integration of highly intermittent renewable, or other distributed energy resources, the microgrid is the technology of choice to guarantee the expected impacts, making clean energy affordable. The focus of this work lies in the techno-economic optimization analysis of Combined Heat and Power (CHP) Multi-Micro Grids (MMG), a novel distribution system architecture comprising two interconnected hybrid microgrids. High computational resources are needed to investigate the CHP-MMG. To this aim, a novel nature-inspired two-layer optimization-simulation algorithm is discussed. The proposed algorithm is used to execute a techno-economic analysis and find the best settings while the energy balance is achieved at minimum operational costs and highest revenues. At a lower level, inside the algorithm, a Sequential Least Squares Programming (SLSQP) method ensures that the stochastic generation and consumption of energy deriving from CHP-MMG trial settings are balanced at each time-step. At the upper level, a novel multi-objective self-adaptive evolutionary algorithm is discussed. This upper level is searching for the best design, sizing, siting, and setting, which guarantees the highest internal rate of return (IRR) and the lowest Levelized Cost of Energy (LCOE). The Artificial Immune Evolutionary (AIE) algorithm imitates how the immune system fights harmful viruses that enter the body. The optimization method is used for sensitivity analysis of hydrogen costs in off-grid and on-grid highly perturbed contexts. It has been observed that the best CHP-MMG settings are those that promote a tight thermal and electrical energy balance between interconnected microgrids. The results demonstrate that such mechanism of energy swarm can keep the LCOE lower than 15 c€/kWh and IRR of over 55%.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193147
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3148: Semantically Annotated Cooking
           Procedures for an Intelligent Kitchen Environment

    • Authors: George Kondylakis, George Galanakis, Nikolaos Partarakis, Xenophon Zabulis
      First page: 3148
      Abstract: Food preparation is one of the essential tasks in daily life and involves a large number of physical interactions between hands, utensils, ingredients, etc. The fundamental unit in the food preparation activity is the concept of a recipe. The recipe describes the cooking process—the way to make a dish in a sequential order of cooking steps. Frequently, following these steps can be an extremely complicated process, which requires coordination, monitoring and execution of multiple tasks simultaneously. This work introduces a cooking assistance system powered by Computer Vision techniques that provide the user with guidance in the accomplishment of a cooking activity in terms of a recipe and its correct execution. The system can provide the user with guidance for carrying out a recipe through the appropriate messages, which appear in a panel specifically designed for the user. Throughout the process, the system can validate the correctness of each step by (a) detection and motion estimation of the ingredients and utensils in the scene and (b) spatial arrangement of them in terms of where each one is located to another. The system was first evaluated on individual algorithmic steps and on the end-to-end execution of two recipes with promising results.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193148
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3149: Framework for Predicting and Modeling
           Stock Market Prices Based on Deep Learning Algorithms

    • Authors: Theyazn H. H. Aldhyani, Ali Alzahrani
      First page: 3149
      Abstract: The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193149
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3150: Design of Irregularly Distributed
           Antenna Array Towards Smart 6G Networks

    • Authors: Kunye Wang, Zheng Ma, Yao Zhao, Yunkai Deng, Yunhua Luo, Mang He, Haitao Xu
      First page: 3150
      Abstract: The integration of sensing, computing, storage, and communications is one of the main research directions of current network development. To achieve the integration, an innovative design in the antenna array is essential. This paper presents a novel design of an irregularly distributed antenna array for smart 6G networks. Firstly, to solve the problem of fast amplitude and phase distribution of conformal arrays, the fast electromagnetic code of the multilevel fast multipole (MLFMA) based on volume surface integral equations (VSIE) is used to simulate the radiation characteristics of the irregularly distributed antenna arrays. Secondly, another stochastic global optimization algorithm, Simulated Annealing (SA), has been widely used to solve multiscale nonlinear problems. Finally, the performance of the proposed antenna array is given by simulation results and tests, to prove the effectiveness and correctness.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193150
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3151: Internet of Things-Based Intelligent
           Attendance System: Framework, Practice Implementation, and Application

    • Authors: Van Dung Nguyen, Huynh Van Khoa, Tam Nguyen Kieu, Eui-Nam Huh
      First page: 3151
      Abstract: Tracking coronavirus patients and determining their close contacts (as part of COVID-19 mapping) have been huge challenges. In universities, in particular, there are many students and large gatherings who are at a higher risk of obtaining COVID-19. Many smart attendance management systems have been proposed that are based on RFID and fingerprint sensor modules, facial recognition, etc. However, these techniques operate with specific requirements, such as GPUs and large memories/datasets, or by combining recognizance and thermal cameras. To solve these issues and reduce costs, we designed an Internet of Things (IoT)-based intelligent attendance management system. In this paper, we compare the advantages/disadvantages of existing smart attendance management systems. We designed an IoT-based intelligent attendance management system based on the cloud, a web server, Google API, a non-contact body temperature sensor, and the Raspberry Pi 4 module (4G). We conducted a survey at a university and summarized the satisfaction levels of using our system.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193151
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3152: Bee Sound Detector: An Easy-to-Install,
           Low-Power, Low-Cost Beehive Conditions Monitoring System

    • Authors: Dimitrios I. Kiromitis, Christos V. Bellos, Konstantinos A. Stefanou, Georgios S. Stergios, Thomas Katsantas, Sotirios Kontogiannis
      First page: 3152
      Abstract: One of the most significant agricultural tasks in beekeeping involves continually observing the conditions inside and outside the beehive. This is mainly performed for the early detection of some harmful events. There have been many studies on how to detect and prevent such occurrences by performing periodic interventions or, when the frequency of such actions is hard to enforce, by using sensory systems that record the temperature, humidity, and weight of the beehive. Nevertheless, such methods are inaccurate, and their delivered outcomes usually diverge from the actual event or false trigger and introduce more effort and damage. In this paper, the authors propose a new low-cost, low-power system called Bee Sound Detector (BeeSD). BeeSD is a low-cost, embedded solution for beehive quality control. It incorporates the sensors mentioned above as well as real-time sound monitoring. With the combination of temperature, humidity, and sound sensors, the BeeSD can spot Colony Collapse Disorder events due to famine and extreme weather events, queen loss, and swarming. Furthermore, as a system, the BeeSD uses cloud logging and an appropriate mobile phone application to push notifications of extreme measurements to the farmers. Based on achieved performance indicators, the authors present their BeeSD IoT device and system operation, focusing on its advantages of low-cost, low-power, and easy-to-install characteristics.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193152
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3153: G-Band Broad Bandwidth Extended
           Interaction Klystron with Traveling-Wave Output Structure

    • Authors: Xiaotao Xu, Hengliang Li, Xuesong Yuan, Qingyun Chen, Yifan Zu, Hailong Li, Yong Yin, Yang Yan
      First page: 3153
      Abstract: In this paper, we investigate a large-sized beam tunnel, G-band extended interaction klystron (EIK) with a traveling wave output structure for the development of broad bandwidth EIKs. The high-quality factor F was introduced to estimate the bandwidth characteristics of the cluster cavities, and the optimal cluster cavity structure parameters were obtained based on this factor. The simulation mode of the device was designed by the 3D particle-in-cell (PIC) commercial simulation software. Four cluster cavities with a staggered distribution of frequencies were employed to expand the bunching bandwidth, and two traveling wave modes, 2π−π/10 and 2π−2π/10, were used as the operating modes in the output structure, effectively increasing the output bandwidth. The simulation findings show that the maximum output power is 170 W, the corresponding gain is 37.5 dB, and the 3-dB bandwidth is up to 1.25 GHz. The three-hole coupling structure with a large-sized beam tunnel provides convenience for the fabrication of devices in the G-band, and our study shows a potential method for the realization of a G-band broadband EIK.
      Citation: Electronics
      PubDate: 2022-09-30
      DOI: 10.3390/electronics11193153
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3154: Ensemble Self-Paced Learning Based on
           Adaptive Mixture Weighting

    • Authors: Liwen Liu, Zhong Wang, Jianbin Bai, Xiangfeng Yang, Yunchuan Yang, Jianbo Zhou
      First page: 3154
      Abstract: Self-paced learning (SPL) is a learning mechanism inspired by human and animal learning processes that gives variable weights to samples, gradually introducing simple to more complicated samples into the learning set as the “age” parameter increases. To regulate the learning process, a self-paced weighting regularization term with an “age” parameter is introduced to the learning function. Several self-paced weighting methods have been proposed, and different regularization terms might result in varied learning performance. However, on the one hand, it is difficult to select a suitable weighting method for SPL. On the other hand, it is challenging to determine the “age” parameter, and it is easy for SPL to obtain poor results as the “age” of the model increases. To solve the aforementioned difficulties, an ensemble SPL approach with an adaptive mixture weighting mechanism is proposed in this study. First, as the “age” parameter increases, a set of base classifiers is collected to produce a new data set, which is used to learn the second-level classifier. Then, the ensemble model is used to generate the final output to avoid the selection of the optimal “age” parameter. An adaptive mixture weighting method is designed to reduce the dependence of parameters on human experience. The previous methods find it difficult to determine the “age” parameters or self-paced parameters. In this paper, these parameters can be adjusted adaptively during the learning process. In comparison with the previous SPL techniques, the proposed method achieves the best results in 27 of the 32 datasets in the experiments with the adaptive parameters. The statistical tests are carried out to show that the proposed method is superior to other state-of-the-art algorithms.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193154
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3155: A No-Reference Quality Assessment Method
           for Screen Content Images Based on Human Visual Perception Characteristics
           

    • Authors: Yuxin Hong, Caihong Wang, Xiuhua Jiang
      First page: 3155
      Abstract: The widespread application of screen content images (SCIs) has met the needs of remote display and online working. It is a topic that is challenging and worthwhile discussing in research on quality assessment for SCIs. However, existing methods focus on extracting artificial features to predict image quality, which are subjective and incomplete, or lack good interpretability. To overcome these problems, we propose an effective quality assessment method for SCIs based on human visual perceptual characteristics. The proposed method simulates the multi-channel working mechanism of the human visual system (HVS) through pyramid decomposition and the information extraction process of brains with the help of dictionary learning and sparse coding. The input SCIs are first decomposed at multiple scales, and then dictionary learning and sparse coding are applied to the images at each scale. Furthermore, the sparse representation results are analyzed from multiple perspectives. First, a pooling scheme about generalized Gaussian distribution and log-normal distribution is designed to describe the sparse coefficients with and without zero values, respectively. Then the sparse coefficients are used to characterize the energy characteristics. Additionally, the probability of each atom is calculated to describe the statistical property of SCIs. Since the above process only deals with brightness, color-related features are also added to make the model more general and robust. Experimental results on three public SCI databases show that the proposed method can achieve better performance than existing methods.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193155
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3156: Chaos-Based Cryptography: Text
           Encryption Using Image Algorithms

    • Authors: Marcin Lawnik, Lazaros Moysis, Christos Volos
      First page: 3156
      Abstract: Currently, chaotic cryptography is mainly concentrated on image encryption. Once-popular stream-encryption algorithms, e.g., textual data, are now rarely considered. This article studies how chaotic image-encryption algorithms can be used to encrypt text. The proposed approach to this problem consists of two stages: in the first stage, the text message is intended for encryption into an image; in the second step, the selected image-encryption algorithm is used. An example illustrates the efficiency of this method. In addition, the article presents measures used in image-encryption analysis that confirm the security of the obtained cipher-image, such as entropy (value close to 8), correlation of adjacent pixels (values close to 0), or measures related to differential cryptanalysis. The proposed method offers a new look at text encryption using chaos, by applying image-encryption algorithms already known from the literature.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193156
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3157: Sparse 3D Point Cloud Parallel
           Multi-Scale Feature Extraction and Dense Reconstruction with Multi-Headed
           Attentional Upsampling

    • Authors: Meng Wu, Hailong Jiao, Junxiang Nan
      First page: 3157
      Abstract: Three-dimensional (3D) point clouds have a wide range of applications in the field of 3D vision. The quality of the acquired point cloud data considerably impacts the subsequent work of point cloud processing. Due to the sparsity and irregularity of point cloud data, processing point cloud data has always been challenging. However, existing deep learning-based point cloud dense reconstruction methods suffer from excessive smoothing of reconstruction results and too many outliers. The reason for this is that it is not possible to extract features for local and global features at different scales and provide different levels of attention to different regions in order to obtain long-distance dependence for dense reconstruction. In this paper, we use a parallel multi-scale feature extraction module based on graph convolution and an upsampling method with an added multi-head attention mechanism to process sparse and irregular point cloud data to obtain extended point clouds. Specifically, a point cloud training patch with 256 points is inputted. The PMS module uses three residual connections in the multi-scale feature extraction stage. Each PMS module consists of three parallel DenseGCN modules with different size convolution kernels and different averaging pooling sizes. The local and global feature information of the augmented receptive field is extracted efficiently. The scale information is obtained by averaging the different pooled augmented receptive fields. The scale information was obtained using the different average pooled augmented receptive fields. The upsampling stage uses an upsampling rate of r=4, The self-attentive features with a different focus on different point cloud data regions obtained by fusing different weights make the feature representation more diverse. This operation avoids the bias of one attention, and each focuses on extracting valuable fine-grained feature information. Finally, the coordinate reconstruction module obtains 1024 dense point cloud data. Experiments show that the proposed method demonstrates good evaluation metrics and performance and is able to obtain better visual quality. The problems of over-smoothing and excessive outliers are effectively mitigated, and the obtained sparse point cloud is more dense.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193157
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3158: Offset Compensation in Resistive Stretch
           Sensors Using Low-Frequency Feedback Topology

    • Authors: Jakub Drzazga, Bogusław Cyganek
      First page: 3158
      Abstract: Respiration monitoring systems play an important role in healthcare and fitness. For this purpose, resistive stretch sensors are frequently used, which are cheap and simple in operation. However, they are not free from drawbacks. Varying offset due to patient movement, low signal amplitude, as well as susceptibility to interference, can all pose serious challenges. In this paper, a novel signal conditioning circuit for a resistive respiration sensor is proposed that alleviates some of the above problems. Namely, the proposed low-frequency feedback topology improves the dynamic range by offset compensation, sustaining a high signal amplification. Further advantages of the new configuration are the phase shift of 0.5 degrees in the band of interest and higher gain for the respiration signal than for the offset. The topology was proved to correctly represent signal amplitude changes, as well as to be able to sample human respiration in the home environment. However, the circuit shows some nonlinear behavior around resistance discontinuity points–settling time after body position change of the patient, which can be as long as 40 s. The circuit was tested both in bench tests and in the prototype of a respiratory polygraphy device during actual sleep apnea examinations. The results indicate that resistive stretch sensors, along with low-frequency feedback topology, are a promising development path for future respiration monitoring devices.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193158
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3159: Very Simple System for Walking-Speed
           Measurement in Geriatric Patients

    • Authors: Graziella Scandurra, Giorgio Basile, Carmine Ciofi
      First page: 3159
      Abstract: Walking speed in geriatric patients is an important index for inferring the patient’s state of health and estimating the success rate of some surgical procedures. Although different solutions for monitoring the gait of a subject exist in scientific literature and on the market, there is a need for a system that is very simple, especially to wear, considering that elderly subjects often have movement difficulties. For this reason, we investigated the possibility of using a standard miniaturized wireless microphone, that can be easily attached to patients’ clothes by means of a clip, as the sole sensing device to be worn by the test subject. A transceiver, a sound card and a PC complete the system, which turns out to be quite simple to be set up and use, thanks to a proper graphic user interface that controls its entire operation. The system essentially tracks the position of the test subject over time by measuring the propagation times of repeated sound pulses from the speaker to the microphone. To avoid hearing discomfort, the frequency of the pulses is chosen at the higher end of the audio spectrum, so that they are essentially undetectable by adults. The measurement range is in excess of 6 m, that is sufficient for the standard 4 m walking-speed test. Tests performed in a laboratory environment have confirmed the effectiveness of the approach we propose.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193159
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3160: Global Adaptive Control for Uncertain
           Nonlinear Systems under Non-Lipschitz Condition with Quantized States

    • Authors: Caihao Sun, Zongcheng Liu, Yong Chen, Yang Zhou, Qingyang Qu
      First page: 3160
      Abstract: A novel adaptive control method is designed for the uncertain nonlinear systems with quantized states under the condition that system nonlinearities do not satisfy the Lipschitz condition. In this paper, the global control for an uncertain nonlinear system with quantized states in the case that the system nonlinearities do not satisfy the Lipschitz condition is first achieved. Separation theorem is used to model the uncertainties appropriately and then an adaptive term is designed to help attenuate the effects of system uncertainties. Furthermore, the global boundedness of all signals of the closed-loop system is proved based on the Lyapunov stability theorem. Finally, simulation results are given to demonstrate the effectiveness of the proposed methods.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193160
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3161: Secure State Estimation of
           Cyber-Physical System under Cyber Attacks: Q-Learning vs. SARSA

    • Authors: Zengwang Jin, Menglu Ma, Shuting Zhang, Yanyan Hu, Yanning Zhang, Changyin Sun
      First page: 3161
      Abstract: This paper proposes a reinforcement learning (RL) algorithm for the security problem of state estimation of cyber-physical system (CPS) under denial-of-service (DoS) attacks. The security of CPS will inevitably decline when faced with malicious cyber attacks. In order to analyze the impact of cyber attacks on CPS performance, a Kalman filter, as an adaptive state estimation technology, is combined with an RL method to evaluate the issue of system security, where estimation performance is adopted as an evaluation criterion. Then, the transition of estimation error covariance under a DoS attack is described as a Markov decision process, and the RL algorithm could be applied to resolve the optimal countermeasures. Meanwhile, the interactive combat between defender and attacker could be regarded as a two-player zero-sum game, where the Nash equilibrium policy exists but needs to be solved. Considering the energy constraints, the action selection of both sides will be restricted by setting certain cost functions. The proposed RL approach is designed from three different perspectives, including the defender, the attacker and the interactive game of two opposite sides. In addition, the framework of Q-learning and state–action–reward–state–action (SARSA) methods are investigated separately in this paper to analyze the influence of different RL algorithms. The results show that both algorithms obtain the corresponding optimal policy and the Nash equilibrium policy of the zero-sum interactive game. Through comparative analysis of two algorithms, it is verified that the differences between Q-Learning and SARSA could be applied effectively into the secure state estimation in CPS.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193161
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3162: Criminal Behavior Identification Using
           Social Media Forensics

    • Authors: Noorulain Ashraf, Danish Mahmood, Muath A. Obaidat, Ghufran Ahmed, Adnan Akhunzada
      First page: 3162
      Abstract: Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193162
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3163: Person Entity Alignment Method Based on
           Multimodal Information Aggregation

    • Authors: Huansha Wang, Ruiyang Huang, Jianpeng Zhang
      First page: 3163
      Abstract: Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge graphs and knowledge complementation. Because of the lack of semantic connection between the visual modality face attribute of the person entity and the text modality attribute and relationship information, it is difficult to model the visual and text modality into the same semantic space, and, as a result, that the traditional multimodal entity alignment method cannot be applied. In view of the scarcity of multimodal person relation graphs datasets and the difficulty of the multimodal semantic modeling of person entities, this paper analyzes and crawls open-source semi-structured data from different sources to build a multimodal person entity alignment dataset and focuses on using the facial and semantic information of multimodal person entities to improve the similarity of entity structural features which are modeled using the graph convolution layer and the dynamic graph attention layer to calculate the similarity. Through verification on the self-made multimodal person entity alignment dataset, the method proposed in this paper is compared with other entity alignment models which have a similar structure. Compared with AliNet, the probability that the first item in the candidate pre-aligned entity set is correct is increased by 12.4% and average ranking of correctly aligned entities in the candidate pre-aligned entity set decreased by 32.8, which proves the positive effect of integrating multimodal facial information, applying dynamic graph attention and a layer-wise gated network to improve the alignment effect of person entities.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193163
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3164: Blockchain-Enabled Decentralized Secure
           Big Data of Remote Sensing

    • Authors: Abdul Razzaq, Syed Agha Hassnain Mohsan, Shahbaz Ahmed Khan Ghayyur, Mohammed H. Alsharif, Hend Khalid Alkahtani, Faten Khalid Karim, Samih M. Mostafa
      First page: 3164
      Abstract: Blockchain technology has emerged as a promising candidate for space exploration and sustainable energy systems. This transformative technology offers secure and decentralized strategies to process and manipulate space resources. Remote sensing provides viable potential with the coexistence of open data from various sources, such as short-range sensors on unmanned aerial vehicles (UAVs) or Internet-of-Things (IoT) tags and far-range sensors incorporated on satellites. Open data resources have most recently emerged as attractive connecting parties where owners have shown consent to share data. However, most data owners are anonymous and untrustworthy, which makes shared data likely insecure and unreliable. At present, there are several tools that distribute open data, serving as an intermediate party to link users with data owners. However, these platforms are operated by central authorities who develop guidelines for data ownership, integrity, and access, consequently restricting both users and data owners. Therefore, the need and feasibility of a decentralized system arise for data sharing and retrieving without involving these intermediate limiting parties. This study proposes a blockchain-based system without any central authority to share and retrieve data. Our proposed system features (i) data sharing, (ii) maintaining the historical data, and (iii) retrieving and evaluation of data along with enhanced security. We have also discussed the use of blockchain algorithms based on smart contracts to track space transactions and communications in a secure, verifiable, and transparent manner. We tested the suggested framework in the Windows environment by writing smart contracts prototype on an Ethereum TESTNET blockchain. The results of the study showed that the suggested strategy is efficient, practicable, and free of common security attacks and vulnerabilities.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193164
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3165: An Autonomous Vehicle Stability Control
           Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network

    • Authors: Turki Alsuwian, Mian Hamza Usman, Arslan Ahmed Amin
      First page: 3165
      Abstract: Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193165
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3166: Research on Student Performance
           Prediction Based on Stacking Fusion Model

    • Authors: Yu, Liu
      First page: 3166
      Abstract: Online learning is gradually becoming popular with the continuous development of Internet technology and the rapid development of educational informatization. It plays a key role in predicting students’ course performance based on their online learning behavior. It can optimize the effects of teaching and improve teaching strategies. Student performance prediction models that are built with a single algorithm currently have limited prediction accuracy. Meanwhile, model fusion improvement technology can combine many algorithms into a single model, thereby enhancing the overall effect of the model and providing better performance. In this paper, a stacking fusion model based on RF-CART–XGBoost–LightGBM is proposed. The first layer of the model uses a decision tree (CART), random forest, XGBoost and LightGBM as the base models. The second layer uses the LightGBM model. We used the Kalboard360 student achievement dataset, and features related to online learning behavior were selected as the model’s input for model training. Finally, we employed five-fold cross-validation to assess the model’s performance. In comparison with the four single models, the two fusion models based on the four single models both show significantly better performance. The prediction accuracies of the bagging fusion model and stacking fusion model are 83% and 84%, respectively. This proves that the proposed stacking fusion model has better performance, which helps to improve the accuracy of the performance prediction model further. It also provides an effective basis for optimizing the effects of teaching.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193166
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3167: Spatial and Temporal Normalization for
           Multi-Variate Time Series Prediction Using Machine Learning Algorithms

    • Authors: Alimasi Mongo Providence, Chaoyu Yang, Tshinkobo Bukasa Orphe, Anesu Mabaire, George K. Agordzo
      First page: 3167
      Abstract: Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance.
      Citation: Electronics
      PubDate: 2022-10-01
      DOI: 10.3390/electronics11193167
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3168: A Deep Learning-Based Approach for the
           Diagnosis of Acute Lymphoblastic Leukemia

    • Authors: Adnan Saeed, Shifa Shoukat, Khurram Shehzad, Ijaz Ahmad, Ala’ Abdulmajid Eshmawi, Ali H. Amin, Elsayed Tag-Eldin
      First page: 3168
      Abstract: Leukemia is a deadly disease caused by the overproduction of immature white blood cells (WBS) in the bone marrow. If leukemia is detected at the initial stages, the chances of recovery are better. Typically, morphological analysis for the identification of acute lymphoblastic leukemia (ALL) is performed manually on blood cells by skilled medical personnel, which has several disadvantages, including a lack of medical personnel, sluggish analysis, and prediction that is dependent on the medical personnel’s expertise. Therefore, we proposed the Multi-Attention EfficientNetV2S and EfficientNetB3 state-of-the-art deep learning architectures using transfer learning-based fine-tuning approach to distinguish the normal and blast cells from microscopic blood smear images that both are pretrained on large-scale ImageNet database. We simply modified the last block of both models and added additional layers to both models. After including this Multi-Attention Mechanism, it not only reduces the model’s complexities but also generalizes its network quite well. By using the proposed technique, the accuracy has improved and the overall loss is also minimized. Our Multi-Attention EfficientNetV2S and EfficientNetB3 models achieved 99.73% and 99.25% accuracy, respectively. We have further compared the proposed model’s performance to other individual and ensemble models. Upon comparison, the proposed model outclassed the existing literature and other benchmark models, thus proving its efficiency. 
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193168
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3169: A Review of Open-Circuit Switch Fault
           Diagnostic Methods for Neutral Point Clamped Inverter

    • Authors: Muhammed Ramees Mullali Kunnontakath Puthiyapurayil, Mohamed Nadir Nasirudeen, Yashkumar A. Saywan, Md Waseem Ahmad, Hasmat Malik
      First page: 3169
      Abstract: Due to numerous advantages, a neutral point clamped (NPC) inverter is a preferred choice for high-power applications and renewable technology. The reliability of the NPC inverter is a major concerning factor during the assessment of system performance as power semiconductor switches are vulnerable to abnormal conditions. Open-circuit (OC) switch faults are not as dangerous as short circuit (SC) faults but eventually have enough potential to cause cascaded failure to other components in the system and thus need to be supervised carefully. The OC faults result in a distortion of voltage and current signals in the NPC converter. Based on these signals, over the past few years, many efforts have been made to identify and localize the OC switch fault to the switch level in the NPC topology. In this paper, a review of different OC switch fault diagnostic methods is provided. Starting from the NPC inverter operation under healthy and faulty conditions, the various possible and unavailable switching states along with the deviation in pole voltage under different switch fault conditions is discussed. Then, based on the approach used for system-based fault detection, the OC fault detection methods are classified. The various OC methods are further discussed on the basis of signal, i.e., current, voltage or a combination of both signals used as a signature for fault detection. Emphasis is given to the principle involved, diagnostic variables utilized, the implementation approach and the diagnostic time required. Finally, the approaches are tabulated so as to provide a quick reference for NPC fault diagnostics.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193169
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3170: Isogency Hosmer–Lemeshow Logistic
           Regression-Based Secured Information Sharing for Pharma Supply Chain

    • Authors: Anitha P, Srimathi Chandrasekaran
      First page: 3170
      Abstract: Counterfeit drugs are forgery-tagged medicines that are considered to be drugs without vigorous active pharmaceutical ingredients (API). India, being the world’s largest producer of drugs, faces a crucial issue of counterfeits. Moreover, counterfeits identify their path into the pharmaceutical supply chain (PSC) effortlessly owing to the dearth of security and traceability in the prevailing system. This is because the software applications currently in use stockpile the information about drugs on centralized servers and are accessed by manufacturers, distributors and retailers via the internet. The security of such systems is found to be weak. To address these issues, in this work, a novel method called Supersingular Isogeny and Hosmer–Lemeshow Logistic Regression-based (SI-HLLR) secured information sharing for the pharmaceutical supply chain is proposed. The SI-HLLR method is split into two sections, block validation and authentication. First, with the pharmaceutical sales data provided as input, the supersingular isogeny Diffie–Hellman key exchange model is applied for block validation and then is implemented using a blockchain. Next, with the validated blocks, the authentication mechanism is performed using Hosmer–Lemeshow logistic regression-based authentication that in turn eliminates the counterfeit drugs from the pharmaceutical supply chain. The hyperledger fabric blockchain solution using SI-HLLR leads to improved security ensuring data integrity and better authentication accuracy in the proposed method.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193170
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3171: A Hybrid Asynchronous
           Brain–Computer Interface Based on SSVEP and Eye-Tracking for
           Threatening Pedestrian Identification in Driving

    • Authors: Jianxiang Sun, Yadong Liu
      First page: 3171
      Abstract: A brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has achieved remarkable performance in the field of automatic driving. Prolonged SSVEP stimuli can cause driver fatigue and reduce the efficiency of interaction. In this paper, a multi-modal hybrid asynchronous BCI system combining eye-tracking and EEG signals is proposed for dynamic threatening pedestrian identification in driving. Stimuli arrows of different frequencies and directions are randomly superimposed on pedestrian targets. Subjects scan the stimuli according to the direction of arrows until the threatening pedestrian is selected. The thresholds determined by offline experiments are used to distinguish between working and idle states of the asynchronous online experiments. Subjects need to judge and select potentially threatening pedestrians in online experiments according to their own subjective experience. The three proposed decisions filter out the results with low confidence and effectively improve the selection accuracy of hybrid BCI. The experimental results of six subjects show that the proposed hybrid asynchronous BCI system achieves better performance compared with a single SSVEP-BCI, with an average selection time of 1.33 s, an average selection accuracy of 95.83%, and an average information transfer rate (ITR) of 67.50 bits/min. These results indicate that our hybrid asynchronous BCI has great application potential in dynamic threatening pedestrian identification in driving.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193171
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3172: Behavior Analysis Using Enhanced Fuzzy
           Clustering and Deep Learning

    • Authors: Arwa A. Altameem, Alaaeldin M. Hafez
      First page: 3172
      Abstract: Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers’ previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193172
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3173: A State of Art Review on Methodologies
           of Occupancy Estimating in Buildings from 2011 to 2021

    • Authors: Liang Zhao, Yuxin Li, Ruobing Liang, Peng Wang
      First page: 3173
      Abstract: Occupancy information is important to building facility managers in terms of building energy efficiency, indoor environmental quality, comfort conditions, and safety management of buildings. When combing the distribution characteristics of the literature, it is found that the field of estimating occupancy counts is a very active area. Researchers from various countries have undertaken extensive explorations to obtain more research results. In this survey, the commonly used occupancy measurement systems and algorithms are described. Through the analysis and research of different occupancy measurement systems and algorithms, their advantages, disadvantages, and limitations are summarized, so that researchers can use them selectively. As for how to choose the method of estimating occupancy counts, suggestions are given in terms of the range of people, accuracy, cost, and privacy. There are still many pressing issues relating to high-density crowd occupancy counting, complex environmental impact, and system robustness. According to the current research progress and technology development trend, the possible future research directions are pointed out. The innovation of this review is the quantitative analysis of the selection of occupancy measurement systems for different ranges of people, and the occupancy counting accuracy situation of different measurement systems and algorithms. It provides more informed opinions on the selection of practical applications. It can be used by other researchers as a starting point for their research and/or project work.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193173
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3174: Convolution Network Enlightened
           Transformer for Regional Crop Disease Classification

    • Authors: Yawei Wang, Yifei Chen, Dongfeng Wang
      First page: 3174
      Abstract: The overarching goal of smart farming is to propose pioneering solutions for future sustainability of humankind. It is important to recognize the image captured for monitoring the growth of plants and preventing diseases and pests. Currently, the task of automatic recognition of crop diseases is to research crop diseases based on deep learning, but the existing classifiers have problems regarding, for example, accurate identification of similar disease categories. Tomato is selected as the crop of this article, and the corresponding tomato disease is the main research point. The vision transformer (VIT) method has achieved good results on image tasks. Aiming at image recognition, tomato plant images serve as this article’s data source, and their structure is improved based on global ViT and local CNN (convolutional neural network) networks, which are built to diagnose disease images. Therefore, the features of plant images can be precisely and efficiently extracted, which is more convenient than traditional artificial recognition. The proposed architecture’s efficiency was evaluated by three image sets from three tomato-growing areas and acquired by drone and camera. The results show that this article method garners an average counting accuracy of 96.30%. It provides scientific support and a reference for the decision-making process of precision agriculture.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193174
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3175: Analysis of Optimal Operation of
           Charging Stations Based on Dynamic Target Tracking of Electric Vehicles

    • Authors: Kun Huang, Jingtao Zhao, Xiaohan Sun, Wei Li, Shu Zheng
      First page: 3175
      Abstract: In view of the large impact of traditional charging stations on the power grid and the investment in the construction of charging stations for electric vehicle infrastructure services, this paper considers the configuration of optical storage equipment in charging stations from a practical point of view and proposes an economic operation strategy for charging stations to meet the economically optimal requirements of different scenarios. First, we analyze the behavioral characteristics of multiple types of electric vehicles, consider the influence of charging queues, and establish a daily load model of charging stations by taking into account the daily monitoring load and nighttime lighting load of charging stations. Then, considering the electric vehicle (EV) charging demand, photovoltaic (PV) output and energy storage charging and discharging power, the daily economic optimal operation problem based on the dynamic target tracking of charging stations is established; the objective is to maximize the daily operating revenue of charging stations under the condition of satisfying the EV charging demand and PV consumption. Secondly, the objective function is linearized, and the economic operation model is transformed into a mixed integer linear programming model for solving, and the simulation is verified under different scenarios. The results show that the economic optimal operation strategy can adapt to the economic operation requirements of charging stations in different scenarios and maximize the charging station revenue.
      Citation: Electronics
      PubDate: 2022-10-02
      DOI: 10.3390/electronics11193175
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3176: MobileNetV2 Combined with Fast Spectral
           Kurtosis Analysis for Bearing Fault Diagnosis

    • Authors: Tian Xue, Huaiguang Wang, Dinghai Wu
      First page: 3176
      Abstract: Bearings are an important component in mechanical equipment, and their health detection and fault diagnosis are of great significance. In order to meet the speed and recognition accuracy requirements of bearing fault diagnosis, this paper uses the lightweight MobileNetV2 network combined with fast spectral kurtosis to diagnose bearing faults. On the basis of the original MobileNetV2 network, a progressive classifier is used to compress the feature information layer by layer with the network structure to achieve high-precision and rapid identification and classification. A cross-local connection structure is added to the network to increase the extracted feature information to improve accuracy. At the same time, the original fault signal of the bearing is a one-dimensional vibration signal, and the signal contains a large number of non-Gaussian noise and accidental shock defects. In order to extract fault features more efficiently, this paper uses the fast spectral kurtosis algorithm to process the signal, extract the center frequency of the original signal, and calculate the spectral kurtosis value. The kurtosis map generated by signal preprocessing is used as the input of the MobileNetV2 network for fault classification. In order to verify the effectiveness and generality of the proposed method, this paper uses the XJTU-SY bearing fault dataset and the CWRU bearing dataset to conduct experiments. Through data preprocessing methods, such as data expansion for different fault types in the original dataset, input data that meet the experimental requirements are generated and fault diagnosis experiments are carried out. At the same time, through the comparison with other typical classification networks, the paper proves that the proposed method has significant advantages in terms of accuracy, model size, training speed, etc., and, finally, proves the effectiveness and generality of the proposed network model in the field of fault diagnosis.
      Citation: Electronics
      PubDate: 2022-10-03
      DOI: 10.3390/electronics11193176
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3177: Feature Selection Techniques for Big
           Data Analytics

    • Authors: Waleed Albattah, Rehan Ullah Khan, Mohammed F. Alsharekh, Samer F. Khasawneh
      First page: 3177
      Abstract: Big data applications have tremendously increased due to technological developments. However, processing such a large amount of data is challenging for machine learning algorithms and computing resources. This study aims to analyze a large amount of data with classical machine learning. The influence of different random sampling techniques on the model performance is investigated by combining the feature selection techniques and machine learning classifiers. The experiments used two feature selection techniques: random subset and random projection. Two machine learning classifiers were also used: Naïve Bayes and Bayesian Network. This study aims to maximize the model performance by reducing the data dimensionality. In the experiments, 400 runs were performed by reducing the data dimensionality of a video dataset that was more than 40 GB. The results show that the overall performance fluctuates between 70% accuracy to 74% for using sampled and non-sample (all the data), a slight difference in performance compared to the non-sampled dataset. With the overall view of the results, the best performance among all combinations of experiments is recorded for combination 3, where the random subset technique and the Bayesian network classifier were used. Except for the round where 10% of the dataset was used, combination 1 has the best performance among all combinations.
      Citation: Electronics
      PubDate: 2022-10-03
      DOI: 10.3390/electronics11193177
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3178: Experimental Study on Positronium
           Detection under Millimeter Waves Generated from Plasma Wakefield
           Acceleration

    • Authors: Sun-Hong Min, Chawon Park, Kyo Chul Lee, Yong Jin Lee, Matlabjon Sattorov, Seonmyeong Kim, Dongpyo Hong, Gun-Sik Park
      First page: 3178
      Abstract: Positronium (Ps) is an unstable system created by the temporary combination of electrons and negative electrons, and Ps generation technology under resonance conditions at millimeter waves is emerging as a new research topic. In general, Ps can be observed when an unstable separate state remains after electron and positron pair annihilation, as in positron emission tomography (PET). However, in this study, a plasma wakefield accelerator based on vacuum electronics devices (VEDs) was designed in the ponderomotive force generating electrons and positrons simultaneously using annular relativistic electron beams. It can induce Cherenkov radiation from beam–wave interaction by using dielectric materials. According to the size of dielectric materials, the frequency of oscillation is approximately 203 GHz at the range of millimeter waves. At this time, the output power is about 109 watts-levels. Meanwhile, modes of millimeter waves polarized by a three-stepped axicon lens are used to apply the photoconversion technology. Thus, it is possible to confirm light emission in the form of a light-converted Bessel beam.
      Citation: Electronics
      PubDate: 2022-10-03
      DOI: 10.3390/electronics11193178
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3179: Discovery of Business Process Models
           from Incomplete Logs

    • Authors: Lili Wang, Xianwen Fang, Chifeng Shao
      First page: 3179
      Abstract: The completeness of event logs and long-distance dependencies are two major challenges for process mining. Until now, most process mining methods have not been able to discover long-distance dependency and assume that the directly-follows relationship in the log is complete. However, due to the existence of high concurrency and the cycle, it is difficult to guarantee that the real-life log is complete regarding the directly-follows relationship. Therefore, process mining needs to be able to deal with incompleteness. In this paper, we propose a method for discovering process models including sequential, exclusive, concurrent, and cyclic structures from incomplete event logs. The method analyzes the co-occurrence class of the log and the model and then uses the technology of combining the behavior profile and co-occurrence class to obtain the communication behavior profile of the co-occurrence class. Furthermore, a method of constructing a substructure from the event log using the co-occurrence class is presented. Finally, the whole process model is built by combining those substructures. The experimental results show that the proposed method can discover process models with complex structures involving cycles from incomplete event logs and also can deal with long-distance dependency in the event log. Meanwhile, the discovered process model has a good degree of consistency with the original model.
      Citation: Electronics
      PubDate: 2022-10-03
      DOI: 10.3390/electronics11193179
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3180: Multiwalled Carbon Nanotubes Polylactide
           Composites for Electrical Engineering—Fabrication and Electrical
           Properties

    • Authors: Lukasz Pietrzak, Grzegorz Raniszewski, Lukasz Szymanski
      First page: 3180
      Abstract: In the article, both the processes of manufacturing highly homogeneous polylactide resin/multiwalled carbon nanotubes (CNTs) and their properties are described. Regarding the application of carbon nanotubes polymer composites, one of the most important problems to solve is obtaining good dispersion of the filler in the polymer matrix. Preparation of polylactide/multiwall CNTs composites by quick polymer solidification and freezing the state of the dispersion of the nanotubes in the polymer solution is described. The method we used employs an increase in viscosity (carried out rapidly) of the sonicated polymer solution containing the CNTs by spray deposition. Good dispersion of the nanotubes is confirmed by electron microscopy. The obtained nanocomposites exhibit a low percolation threshold for electrical conductivity (above 0.25% by weight). The described method leads to obtaining an electrical conductive surface on virtually any material and reduces the small amount of an expensive filler (CNTs) needed to achieve good electrical conductivity. Furthermore, the carbon nanotubes used in the fabrication process of the composites were obtained using the liquid-source chemical-vapor deposition (LSCVD) synthesis method.
      Citation: Electronics
      PubDate: 2022-10-03
      DOI: 10.3390/electronics11193180
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3181: Emerging Trends in Deep Learning for
           Credit Scoring: A Review

    • Authors: Yoichi Hayashi
      First page: 3181
      Abstract: This systematic review aims to provide deep insights on emerging trends in, and the potential of, advanced deep learning techniques, such as machine learning algorithms being partially replaced by deep learning (DL) algorithms for credit scoring owing to the higher accuracy of the latter. This review also seeks to explain the reasons that deep belief networks (DBNs) can achieve higher accuracy than shallower networks, discusses the potential classification capabilities of DL-based classifiers, and bridges DL and explainable credit scoring. The theoretical characteristics of DBNs are also presented along with the reasons for their higher accuracy compared to that of shallower networks. Studies published between 2019 and 2022 were analysed to review and compare the most recent DL techniques that have been found to achieve higher accuracies than ensemble classifiers, their hybrids, rule extraction methods, and rule-based classifiers. The models reviewed in this study were evaluated and compared according to their accuracy and area under the receiver operating characteristic curve for the Australian, German (categorical), German (numerical), Japanese, and Taiwanese datasets, which are commonly used in the credit scoring community. This review paper also explains how tabular datasets are converted into images for the application of a two-dimensional convolutional neural network (CNN) and how “black box” models using local and global rule extraction and rule-based methods are applied in credit scoring. Finally, a new insight on the design of DL-based classifiers for credit scoring datasets is provided, along with a discussion on promising future research directions.
      Citation: Electronics
      PubDate: 2022-10-03
      DOI: 10.3390/electronics11193181
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3182: Applications of Multi-Agent Systems in
           Unmanned Surface Vessels

    • Authors: Lada Males, Dean Sumic, Marko Rosic
      First page: 3182
      Abstract: The comprehensive and safe application of unmanned surface vessels is certainly one of the biggest challenges currently facing maritime science. Such vessels can be implemented within a wide range of autonomy levels that goes from remote-controlled vessels to fully autonomous vessels in which intelligent vessel systems completely perform all necessary operations. One of the ways to achieve autonomous vessel systems is to implement multi-agent systems that take over all functions performed by the crew in classical manned crew vessels. A vessel is a complex system that conceptually can be considered as a set of interconnected subsystems. Theoretically, the functions of these subsystems could be performed using appropriate multi-agent systems. In this paper we analyzed 24 relevant papers. A review of the current state of implementation of multi-agent systems for performing the functions of unmanned surface vessels is presented.
      Citation: Electronics
      PubDate: 2022-10-04
      DOI: 10.3390/electronics11193182
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3183: Eye Aspect Ratio for Real-Time
           Drowsiness Detection to Improve Driver Safety

    • Authors: Christine Dewi, Rung-Ching Chen, Chun-Wei Chang, Shih-Hung Wu, Xiaoyi Jiang, Hui Yu
      First page: 3183
      Abstract: Drowsiness is a major risk factor for road safety, contributing to serious injury, death, and economic loss on the road. Driving performance decreases because of increased drowsiness. In several different applications, such as facial movement analysis and driver safety, blink detection is an essential requirement that is used. The extremely rapid blink rate, on the other hand, makes automatic blink detection an extremely challenging task. This research paper presents a technique for identifying eye blinks in a video series recorded by a car dashboard camera in real time. The suggested technique determines the facial landmark positions for each video frame and then extracts the vertical distance between the eyelids from the facial landmark positions. The algorithm that has been proposed estimates the facial landmark positions, extracts a single scalar quantity by making use of Eye Aspect Ratio (EAR), and identifies the eye closeness in each frame. In the end, blinks are recognized by employing the modified EAR threshold value in conjunction with a pattern of EAR values in a relatively short period of time. Experimental evidence indicates that the greater the EAR threshold, the worse the AUC’s accuracy and performance. Further, 0.18 was determined to be the optimum EAR threshold in our research.
      Citation: Electronics
      PubDate: 2022-10-04
      DOI: 10.3390/electronics11193183
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3184: A Physical-Layer Watermarking Scheme
           Based on 5G NR

    • Authors: Xu Xie, Wan Chen, Zhengguang Xu
      First page: 3184
      Abstract: Based on the existing 5G NR system, a physical-layer watermarking scheme is proposed to enhance the physical-layer security in 5G communication systems. A new scheme for watermark generation is proposed to improve the robustness of the authentication. The watermark signal is embedded in the phase of the demodulation reference signal (DMRS), and the influence of the watermark on the demodulation reference signal is reduced by designing the encoder of the watermark. Simulation results show that the watermarking scheme proposed in this paper has good anti-noise and anti-frequency-offset performance, and has good feasibility both in the Gaussian channel and Rayleigh fading channel.
      Citation: Electronics
      PubDate: 2022-10-04
      DOI: 10.3390/electronics11193184
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3185: The Effect of Data Augmentation Methods
           on Pedestrian Object Detection

    • Authors: Liu, Su, Wei
      First page: 3185
      Abstract: Night landscapes are a key area of monitoring and security as information in pictures caught on camera is not comprehensive. Data augmentation gives these limited datasets the most value. Considering night driving and dangerous events, it is important to achieve the better detection of people at night. This paper studies the impact of different data augmentation methods on target detection. For the image data collected at night under limited conditions, three different types of enhancement methods are used to verify whether they can promote pedestrian detection. This paper mainly explores supervised and unsupervised data augmentation methods with certain improvements, including multi-sample augmentation, unsupervised Generative Adversarial Network (GAN) augmentation and single-sample augmentation. It is concluded that the dataset obtained by the heterogeneous multi-sample augmentation method can optimize the target detection model, which can allow the mean average precision (mAP) of a night image to reach 0.76, and the improved Residual Convolutional GAN network, the unsupervised training model, can generate new samples with the same style, thus greatly expanding the dataset, so that the mean average precision reaches 0.854, and the single-sample enhancement of the deillumination can greatly improve the image clarity, helping improve the precision value by 0.116.
      Citation: Electronics
      PubDate: 2022-10-04
      DOI: 10.3390/electronics11193185
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3186: Link-Aware Frame Selection for Efficient
           License Plate Recognition in Dynamic Edge Networks

    • Authors: Jiaxin Liu, Rong Cong, Xiong Wang, Yaxin Zhou
      First page: 3186
      Abstract: With the booming development of Internet of Things (IoT) and computer vision technology, running vision-based applications on IoT devices becomes an overwhelming tide. In vision-based applications, the Automatic License Plate Recognition (ALPR) is one of the fundamental services for smart-city applications such as traffic control, auto-drive and safety monitoring. However, existing works about ALPR usually assume that IoT devices have sufficient power to transmit the whole captured stream to edge servers via stable network links. Considering the limited resources of IoT devices and high-dynamic wireless links, this assumption is not suitable for realizing an efficient ALPR service on low-power IoT devices in real wireless edge networks. In this paper, we propose a link-aware frame selection scheme for ALPR service in dynamic edge networks aiming to reduce the transmission energy consumption of IoT devices. Specifically, we tend to select a few key frames instead of the whole stream and transmit them under good links. We propose a two-layer recognition frame selection algorithm to optimize the frame selection by exploiting both the video content variation and real-time link quality. The extensive results show that, by carefully selecting the offloaded frames to edge servers, our algorithm can significantly reduce the energy consumption of devices by 46.71% and achieve 97.95% recognition accuracy in the high-dynamic wireless link of the edge network.
      Citation: Electronics
      PubDate: 2022-10-04
      DOI: 10.3390/electronics11193186
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3187: Optimum of Grain Loss Sensors by
           Analyzing Effects of Grain Collision Attitude on Signal Characteristics

    • Authors: Jun Li, Zhenwei Liang, Fangyu Zhu, Chuanchao Liu
      First page: 3187
      Abstract: Grain loss rate is an important indicator to evaluate the performance of combine harvesters. It is indicted that signal voltage amplitude and signal frequency are the key factors for designing a grain loss sensor. In this work, the high-speed photography and signal high-speed acquisition technique were utilized to capture grain collision attitude and the corresponding collision signal characteristics and the effect of grain moisture content and collision angle on signal voltage amplitude and signal frequency was studied in detail, which lays a good foundation for optimizing grain loss signal processing circuit parameters. Then, monitoring resolution of the grain loss sensor was improved by adding constrained damping layer to the sensitive plate under the instruction of experimental modal analysis. At last, a field experiment was carried out; the field experiment results indicate that the monitoring performance improved.
      Citation: Electronics
      PubDate: 2022-10-04
      DOI: 10.3390/electronics11193187
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3188: The Effects of Total Ionizing Dose on
           the SEU Cross-Section of SOI SRAMs

    • Authors: Peixiong Zhao, Bo Li, Hainan Liu, Jinhu Yang, Yang Jiao, Qiyu Chen, Youmei Sun, Jie Liu
      First page: 3188
      Abstract: The total ionizing dose (TID) effects on single-event upset (SEU) hardness are investigated for two silicon-on-insulator (SOI) static random access memories (SRAMs) with different layout structures in this paper. The contrary changing trends of TID on SEU sensitivity for 6T and 7T SOI SRAMs are observed in our experiment. After 800 krad(Si) irradiation, the SEU cross-sections of 6T SRAMs increases by 15%, while 7T SRAMs decreases by 60%. Experimental results show that the SEU cross-sections are not only affected by TID irradiation, but also strongly correlate with the layout structure of the memory cells. Theoretical analysis shows that the decrease of SEU cross-section of 7T SRAM is caused by a raised OFF-state equivalent resistance of the delay transistor N5 after TID exposure, which is because the radiation-induced charges are trapped in the shallow trench, and isolation oxide (STI) and buried oxide (BOX) enhance the carrier scattering rate of delay transistor N5.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193188
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3189: Research on Surface Defect Detection of
           Camera Module Lens Based on YOLOv5s-Small-Target

    • Authors: Gang He, Jianyun Zhou, Hu Yang, Yuan Ning, Huatao Zou
      First page: 3189
      Abstract: For the problem of low resolution of camera module lens surface defect image, small target and blurred defect details leading to low detection accuracy, a camera module lens surface defect detection algorithm YOLOv5s-Defect based on improved YOLOv5s is proposed. Firstly, to solve the problems arising from the anchor frame generated by the network through K-means clustering, the dynamic anchor frame structure DAFS is introduced in the input stage. Secondly, the SPP-D (Spatial Pyramid Pooling-Defect) improved from the SPP module is proposed. The SPP-D module is used to enhance the reuse rate of feature information in order to reduce the loss of feature information due to the maximum pooling of SPP modules. Then, the convolutional attention module is introduced to the network model of YOLOv5s, which is used to enhance the defective region features and suppress the background region features, thus improving the detection accuracy of small targets. Finally, the post-processing method of non-extreme value suppression is improved, and the improved method DIoU-NMS improves the detection accuracy of small targets in complex backgrounds. The experimental results show that the mean average precision mAP@0.5 of the YOLOv5s-Small-Target algorithm is 99.6%, 8.1% higher than that of the original YOLOv5s algorithm, the detection speed FPS is 80 f/s, and the model size is 18.7M. Compared with the traditional camera module lens surface defect detection methods, YOLOv5s-Small-Target can detect the type and location of lens surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals, meeting the demand for real-time and accuracy of camera module lens surface defect detection.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193189
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3190: Throughput Optimized Reversible Cellular
           Automata Based Security Algorithm

    • Authors: Surendra Kumar Nanda, Suneeta Mohanty, Prasant Kumar Pattnaik, Mangal Sain
      First page: 3190
      Abstract: Reversible cellular automation is a highly parallel system and produces enhanced throughput in its cryptographic applications. The throughput optimized security algorithm based on reversible cellular automata produces a better result in high-performance systems with many cores of CPU or GPU. We designed a throughput optimized block encryption technique using reversible cellular automata and compared its performance with other cellular automata-based algorithms. We tested its performance in both 8 core and 64 core CPU systems and the results showed an enhancement in throughput. This encryption system produced plaintext blocks that are immune to other blocks during cryptanalysis because of segmentation and the use of the different random numbers as seeds. It was built with 128 bits block encryption, but it was easily scalable to a higher block size without changing the algorithm. Each block of encryption used a cipher block chaining mode and was hence more secure and effective.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193190
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3191: Power Efficiency Characterization with
           Various Gate Oxide Thicknesses in Class DE Amplifiers for HIFU
           Applications

    • Authors: Hyun-Sik Choi, Thinh Ngo, Yushi Zhou
      First page: 3191
      Abstract: Skin and cancer cell treatments using high-intensity focused ultrasound (HIFU) have garnered considerable attention as a technology with fewer side effects. Hence, various schemes have been developed to operate ultrasound transducers with high efficiencies. Class DE power amplifiers operate in zero-voltage switching (ZVS) and zero-derivative switching (ZDS); therefore, high-efficiency operation is possible. However, during the CMOS process, a difference in efficiency arises depending on the gate oxide process, which has not yet been analyzed. In high-power devices, a thick oxide layer is primarily used to prevent breakdown. However, this can lead to a decrease in power efficiency. This study analyzes the overall power consumption for each oxide layer thickness during the AMS H35 CMOS process and compares its efficiency. The results confirm that an output power of approximately 1.8 W and a power efficiency of 94% can be obtained with just a relatively thin gate oxide thickness of approximately 10 nm. Furthermore, an additional power efficiency of approximately 3% can be obtained by reducing only the gate oxide thickness.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193191
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3192: Optimized Torque Performance of a
           7-Phase Outer-Rotor Surface-Mounted Permanent Magnet Synchronous Machine
           for In-Wheel E-Motorcycle Application

    • Authors: Ghorbani, Moradian, Benbouzid
      First page: 3192
      Abstract: Four outer rotor surface-mounted permanent magnet synchronous machines (SMPSM), supplied by a seven-phase drive system, are proposed in this study, considering different q (number of stator slot per phase per pole ratio) to achieve a satisfying value of electromagnetic torque and Back-Electromotive Force (Back-EMF) with lower torque pulsation. Accordingly, the proposed configurations are investigated, and results are comparatively reported. Thus based on the results, the best-performing configuration, the candidate model, which presents the lowest torque pulsation with a desirable value of Tavg and Back-EMF is selected. In order to demonstrate the advantages of this candidate model, an optimization analysis is performed using 2D Finite Element Analysis (FEA). The resultant values of the variables are applied, designing three optimized models. Performance results of the optimized models demonstrate that TCog reduced noticeably and TRipple declined below 5%. The Artemis Drive-Cycles analysis results are also included for the best-optimized model, considering E-Motorcycle requirements and properties for urban, rural, and motorway driving conditions. Accordingly, in terms of In-Wheel application of the optimized machine, high torque/power density along with high values of PF and efficient performance are provided for E-Motorcycle application.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193192
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3193: Adaptive BIST for Concurrent On-Line
           Testing on Combinational Circuits

    • Authors: Vasileios Chioktour, Athanasios Kakarountas
      First page: 3193
      Abstract: Safety-critical systems embedding concurrent on-line testing techniques are vulnerable to design issues causing the degradation of totally self-checking (TSC) property, which is proved to be fatal for further operations (e.g., space electronics, medical devices). In addition to the exploration of the degradation of TSC property over time, a concurrent on-line testing architecture is offered that adjusts the input activity, addressing the absence of input values or the low frequency of their appearance (e.g., during sleep mode). During concurrent on-line testing, the inputs of the circuit under test (CUT) are, at the same time, its test vectors. This architecture tolerates possible degradation of the terms that contribute to the calculation of the totally self-checking goal (TSCG(t)) . An adaptive built-in self-test (BIST) unit is proposed that dynamically applies test vector subsets when permitted, based on the frequency of appearance of the input values. The clustering of the inputs is based on the k-means algorithm and, in combination with the ordering of the test vectors to minimize the subsets, results in partitioning the test procedure in a significantly shorter time. The comparison to other solutions used for concurrent on-line testing showed that the proposed adaptive BIST has significant advantages. It can cope with rare occurrences, or even no occurrence, of input values by enabling the BIST mechanism appropriately. The results showed that it may increase the TSCG(t) up to almost 90% when applied during a low-power mode and present better concurrent test latency (CTL) when assumptions regarding the availability of all input values and the probability of occurrence are not realistic.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193193
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3194: Integrated Framework to Assess the
           Extent of the Pandemic Impact on the Size and Structure of the E-Commerce
           Retail Sales Sector and Forecast Retail Trade E-Commerce

    • Authors: Cristiana Tudor
      First page: 3194
      Abstract: With customers’ increasing reliance on e-commerce and multimedia content after the outbreak of COVID-19, it has become crucial for companies to digitize their business methods and models. Consequently, COVID-19 has highlighted the prominence of e-commerce and new business models while disrupting conventional business activities. Hence, assessing and forecasting e-commerce growth is currently paramount for e-market planners, market players, and policymakers alike. This study sources data for the global e-commerce market leader, the US, and proposes an integrated framework that encompasses automated algorithms able to estimate six statistical and machine-learning univariate methods in order to accomplish two main tasks: (i) to produce accurate forecasts for e-commerce retail sales (e-sale) and the share of e-commerce in total retail sales (e-share); and (ii) to assess in quantitative terms the pandemic impact on the size and structure of the e-commerce retail sales sector. The results confirm that COVID-19 has significantly impacted the trend and structure of the US retail sales sector, producing cumulative excess (or abnormal) retail e-sales of $227.820 billion and a cumulative additional e-share of 10.61 percent. Additionally, estimations indicate a continuation of the increasing trend, with point estimates of $378.691 billion for US e-commerce retail sales that are projected to account for 16.72 percent of total US retail sales by the end of 2025. Nonetheless, the current findings also document that the growth of e-commerce is not a consequence of the COVID-19 crisis, but that the pandemic has accelerated the evolution of the e-commerce sector by at least five years. Overall, the study concludes that the shift towards e-commerce is permanent and, thus, governments (especially in developing countries) should prioritize policies aimed at harnessing e-commerce for sustainable development. Furthermore, in light of the research findings, digital transformation should constitute a top management priority for retail businesses.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193194
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3195: Multilevel Features-Guided Network for
           Few-Shot Segmentation

    • Authors: Chenjing Xin, Xinfu Li, Yunfeng Yuan
      First page: 3195
      Abstract: The purpose of few-shot semantic segmentation is to segment unseen classes with only a few labeled samples. However, most methods ignore the guidance of low-level features for segmentation, leading to unsatisfactory results. Therefore, we propose a multilevel features-guided network using convolutional neural network techniques, which fully utilizes features from each level. It includes two novel designs: (1) a similarity-guided feature reinforcement module (SRM), which uses features from different levels, it enables sufficient guidance from the support set to the query set, thus avoiding the situation that some feature information is ignored in deep network computation, (2) a method that bridges query features at each level to the decoder to guide the segmentation, making full use of local and edge information to improve model performance. We experiment on PASCAL-5i and COCO-20i datasets to demonstrate the effectiveness of the model, the results in 1-shot setting and 5-shot setting on PASCAL-5i are 64.7% and 68.0%, which are 3.9% and 6.1% higher than the baseline model, respectively, and the results on the COCO-20i are also improved.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193195
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3196: A Novel Tightly Coupled Information
           System for Research Data Management

    • Authors: Kennedy Senagi, Henri E. Z. Tonnang
      First page: 3196
      Abstract: Most research projects are data driven. However, many organizations lack proper information systems (IS) for managing data, that is, planning, collecting, analyzing, storing, archiving, and sharing for use and re-use. Many research institutions have disparate and fragmented data that make it difficult to uphold the FAIR (findable, accessible, interoperable, and reusable) data management principles. At the same time, there is minimal practice of open and reproducible science. To solve these challenges, we designed and implemented an IS architecture for research data management. Through it, we have a centralized platform for research data management. The IS has several software components that are configured and unified to communicate and share data. The software components are, namely, common ontology, data management plan, data collectors, and the data warehouse. Results show that the IS components have gained global traction, 56.3% of the total web hits came from news users, and 259 projects had metadata (and 17 of those also had data resources). Moreover, the IS aligned the institution’s scientific data resources to universal standards such as the FAIR principles of data management and at the same time showcased open data, open science, and reproducible science. Ultimately, the architecture can be adopted by other organizations to manage research data.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193196
      Issue No: Vol. 11, No. 19 (2022)
       
  • Electronics, Vol. 11, Pages 3197: Integrating Teachers’ TPACK Levels
           and Students’ Learning Motivation, Technology Innovativeness, and
           Optimism in an IoT Acceptance Model

    • Authors: Mohammed Amin Almaiah, Raghad Alfaisal, Said A. Salloum, Shaha Al-Otaibi, Rima Shishakly, Abdalwali Lutfi, Mahmaod Alrawad, Ahmed Al Mulhem, Ali Bani Awad, Rana Saeed Al-Maroof
      First page: 3197
      Abstract: The growing use of the Internet of Things (IoT) around the world has encouraged researchers to investigate how and why the IoT is implemented in colleges and universities. Previous studies have focused on individual attitudes rather than the integration of attitudes from two different perspectives. Furthermore, other studies have investigated the use of the IoT in non-educational settings, ignoring the effect of the IoT related to the technology acceptance model (TAM) and technological pedagogical content knowledge (TPACK) model. The present work aims to address this research gap by determining the main factors that influence acceptance of the IoT, leading to increased awareness in collaborative learning, where technology forms the core tool in enhancing the use of the IoT. A questionnaire was used to collect data from teachers and students from colleges and universities in Oman and the United Arab Emirates (UAE). The data were analyzed through the structural equation modeling (SEM) method. The findings indicated that there are two levels of positive effects on the intention to use IoT. The first level is technology features, which are represented by technology optimism and technology innovation; these factors are crucial to using the IoT. The second level is learning motivation, which has a close relationship with teachers’ knowledge, and content pedagogy, which has a significant effect on the familiarity with IoT tools and applications. TAM constructs have a positive and direct impact on the intention to use IoT. The practical and managerial implications show that teachers, educators, and students can obtain benefits from these results to help IoT features to suit users’ needs.
      Citation: Electronics
      PubDate: 2022-10-05
      DOI: 10.3390/electronics11193197
      Issue No: Vol. 11, No. 19 (2022)
       
 
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