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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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26 pages, 818 KiB  
Article
Role of Wide Bandgap Materials in Power Electronics for Smart Grids Applications
by Javier Ballestín-Fuertes, Jesús Muñoz-Cruzado-Alba, José F. Sanz-Osorio and Erika Laporta-Puyal
Electronics 2021, 10(6), 677; https://doi.org/10.3390/electronics10060677 - 13 Mar 2021
Cited by 63 | Viewed by 8136
Abstract
At present, the energy transition is leading to the replacement of large thermal power plants by distributed renewable generation and the introduction of different assets. Consequently, a massive deployment of power electronics is expected. A particular case will be the devices destined for [...] Read more.
At present, the energy transition is leading to the replacement of large thermal power plants by distributed renewable generation and the introduction of different assets. Consequently, a massive deployment of power electronics is expected. A particular case will be the devices destined for urban environments and smart grids. Indeed, such applications have some features that make wide bandgap (WBG) materials particularly relevant. This paper analyzes the most important features expected by future smart applications from which the characteristics that their power semiconductors must perform can be deduced. Following, not only the characteristics and theoretical limits of wide bandgap materials already available on the market (SiC and GaN) have been analyzed, but also those currently being researched as promising future alternatives (Ga2O3, AlN, etc.). Finally, wide bandgap materials are compared under the needs determined by the smart applications, determining the best suited to them. We conclude that, although SiC and GaN are currently the only WBG materials available on the semiconductor portfolio, they may be displaced by others such as Ga2O3 in the near future. Full article
(This article belongs to the Special Issue Electrical Characterization of Wide Bandgap Devices for Modern Power Electronics)
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13 pages, 4936 KiB  
Article
A 40-nm CMOS Piezoelectric Energy Harvesting IC for Wearable Biomedical Applications
by Chua-Chin Wang, Lean Karlo S. Tolentino, Pin-Chuan Chen, John Richard E. Hizon, Chung-Kun Yen, Cheng-Tang Pan and Ya-Hsin Hsueh
Electronics 2021, 10(6), 649; https://doi.org/10.3390/electronics10060649 - 11 Mar 2021
Cited by 6 | Viewed by 3818
Abstract
This investigation presents an energy harvesting IC (integrated circuit) for piezoelectric materials as a substitute for battery of a wearable biomedical device. It employs a voltage multiplier as first stage which uses water bucket fountain approach to boost the very low voltage generated [...] Read more.
This investigation presents an energy harvesting IC (integrated circuit) for piezoelectric materials as a substitute for battery of a wearable biomedical device. It employs a voltage multiplier as first stage which uses water bucket fountain approach to boost the very low voltage generated by the piezoelectric. The boosted voltage was further improved by the boost DC/DC converter which follows a predefined timing control directed by the digital logic for the said converter to be operated efficiently. TSMC 40-nm CMOS process was used for implementation and fabrication of the energy harvesting IC. The chip’s core has an area of 0.013 mm2. With an output of 1 V which is enough to supply the wearable biomedical devices, it exhibited the highest pump gain and accommodated the lowest piezoelectric generated voltage among recent related works. Full article
(This article belongs to the Special Issue Integrated Electronic Circuits and Systems for Unobtrusive Biomedical Sensing)
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28 pages, 2015 KiB  
Article
State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication
by Wouter Schinkel, Tom van der Sande and Henk Nijmeijer
Electronics 2021, 10(6), 651; https://doi.org/10.3390/electronics10060651 - 11 Mar 2021
Cited by 12 | Viewed by 2713
Abstract
A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to [...] Read more.
A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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22 pages, 1515 KiB  
Article
When Data Fly: An Open Data Trading System in Vehicular Ad Hoc Networks
by Markus Lücking, Felix Kretzer, Niclas Kannengießer, Michael Beigl, Ali Sunyaev and Wilhelm Stork
Electronics 2021, 10(6), 654; https://doi.org/10.3390/electronics10060654 - 11 Mar 2021
Cited by 3 | Viewed by 4617
Abstract
Communication between vehicles and their environment (i.e., vehicle-to-everything or V2X communication) in vehicular ad hoc networks (VANETs) has become of particular importance for smart cities. However, economic challenges, such as the cost incurred by data sharing (e.g., due to power consumption), hinder the [...] Read more.
Communication between vehicles and their environment (i.e., vehicle-to-everything or V2X communication) in vehicular ad hoc networks (VANETs) has become of particular importance for smart cities. However, economic challenges, such as the cost incurred by data sharing (e.g., due to power consumption), hinder the integration of data sharing in open systems into smart city applications, such as dynamic environmental zones. Moving from open data sharing to open data trading can address the economic challenges and incentivize vehicle drivers to share their data. In this context, integrating distributed ledger technology (DLT) into open systems for data trading is promising for reducing the transaction cost of payments in data trading, avoiding dependencies on third parties, and guaranteeing openness. However, because the integration of DLT conflicts with the short available communication time between fast moving objects in VANETs, it remains unclear how open data trading in VANETs using DLT should be designed to be viable. In this work, we present a system design for data trading in VANETs using DLT. We measure the required communication time for data trading between a vehicle and a roadside unit in a real scenario and estimate the associated cost. Our results show that the proposed system design is technically feasible and economically viable. Full article
(This article belongs to the Special Issue Blockchain-Based Technology for Mobile Application)
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9 pages, 3800 KiB  
Article
AlGaN Channel High Electron Mobility Transistors with Regrown Ohmic Contacts
by Idriss Abid, Jash Mehta, Yvon Cordier, Joff Derluyn, Stefan Degroote, Hideto Miyake and Farid Medjdoub
Electronics 2021, 10(6), 635; https://doi.org/10.3390/electronics10060635 - 10 Mar 2021
Cited by 32 | Viewed by 5908
Abstract
High power electronics using wide bandgap materials are maturing rapidly, and significant market growth is expected in a near future. Ultra wide bandgap materials, which have an even larger bandgap than GaN (3.4 eV), represent an attractive choice of materials to further push [...] Read more.
High power electronics using wide bandgap materials are maturing rapidly, and significant market growth is expected in a near future. Ultra wide bandgap materials, which have an even larger bandgap than GaN (3.4 eV), represent an attractive choice of materials to further push the performance limits of power devices. In this work, we report on the fabrication of AlN/AlGaN/AlN high-electron mobility transistors (HEMTs) using 50% Al-content on the AlGaN channel, which has a much wider bandgap than the commonly used GaN channel. The structure was grown by metalorganic chemical vapor deposition (MOCVD) on AlN/sapphire templates. A buffer breakdown field as high as 5.5 MV/cm was reported for short contact distances. Furthermore, transistors have been successfully fabricated on this heterostructure, with low leakage current and low on-resistance. A remarkable three-terminal breakdown voltage above 4 kV with an off-state leakage current below 1 μA/mm was achieved. A regrown ohmic contact was used to reduce the source/drain ohmic contact resistance, yielding a drain current density of about 0.1 A/mm. Full article
(This article belongs to the Special Issue Advances in Ultra-Wide Bandgap Devices)
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19 pages, 577 KiB  
Article
Efficient Design Strategy for Optimizing the Settling Time in Three-Stage Amplifiers Including Small- and Large-Signal Behavior
by Gianluca Giustolisi and Gaetano Palumbo
Electronics 2021, 10(5), 612; https://doi.org/10.3390/electronics10050612 - 6 Mar 2021
Cited by 5 | Viewed by 2576
Abstract
An analytical criterion for the optimization of the small-signal settling time in three-stage amplifiers is carried out. The criterion is based on making equal the two exponential decays of the step response. Including slew-rate effects, a useful design strategy for the design of [...] Read more.
An analytical criterion for the optimization of the small-signal settling time in three-stage amplifiers is carried out. The criterion is based on making equal the two exponential decays of the step response. Including slew-rate effects, a useful design strategy for the design of three-stage operational transconductance amplifier is provided. Extensive time-domain simulations on a transistor-level design in a 65-nm CMOS process confirm the validity of the proposed approach. Full article
(This article belongs to the Section Microelectronics)
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15 pages, 5589 KiB  
Article
A 2.53 NEF 8-bit 10 kS/s 0.5 μm CMOS Neural Recording Read-Out Circuit with High Linearity for Neuromodulation Implants
by Nishat Tarannum Tasneem and Ifana Mahbub
Electronics 2021, 10(5), 590; https://doi.org/10.3390/electronics10050590 - 3 Mar 2021
Cited by 10 | Viewed by 3191
Abstract
This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. [...] Read more.
This paper presents a power-efficient complementary metal-oxide-semiconductor (CMOS) neural signal-recording read-out circuit for multichannel neuromodulation implants. The system includes a neural amplifier and a successive approximation register analog-to-digital converter (SAR-ADC) for recording and digitizing neural signal data to transmit to a remote receiver. The synthetic neural signal is generated using a LabVIEW myDAQ device and processed through a LabVIEW GUI. The read-out circuit is designed and fabricated in the standard 0.5 μμm CMOS process. The proposed amplifier uses a fully differential two-stage topology with a reconfigurable capacitive-resistive feedback network. The amplifier achieves 49.26 dB and 60.53 dB gain within the frequency bandwidth of 0.57–301 Hz and 0.27–12.9 kHz to record the local field potentials (LFPs) and the action potentials (APs), respectively. The amplifier maintains a noise–power tradeoff by reducing the noise efficiency factor (NEF) to 2.53. The capacitors are manually laid out using the common-centroid placement technique, which increases the linearity of the ADC. The SAR-ADC achieves a signal-to-noise ratio (SNR) of 45.8 dB, with a resolution of 8 bits. The ADC exhibits an effective number of bits of 7.32 at a low sampling rate of 10 ksamples/s. The total power consumption of the chip is 26.02 μμW, which makes it highly suitable for a multi-channel neural signal recording system. Full article
(This article belongs to the Section Circuit and Signal Processing)
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13 pages, 700 KiB  
Article
Analytical Study of Periodic Restricted Access Window Mechanism for Short Slots
by Elizaveta Zazhigina, Ruslan Yusupov, Evgeny Khorov and Andrey Lyakhov
Electronics 2021, 10(5), 549; https://doi.org/10.3390/electronics10050549 - 26 Feb 2021
Cited by 8 | Viewed by 1968
Abstract
The tremendous number of devices involved in the Internet of Things is bringing new challenges to wireless networking. The more devices that transmit in a wireless network, the higher the contention for the channel. The novel Wi-Fi HaLow standard introduces a new channel [...] Read more.
The tremendous number of devices involved in the Internet of Things is bringing new challenges to wireless networking. The more devices that transmit in a wireless network, the higher the contention for the channel. The novel Wi-Fi HaLow standard introduces a new channel access mechanism called the Periodic Restricted Access Window (PRAW), which aims to reduce this contention. With this mechanism, an access point can define a series of time intervals during which only a predefined group of stations can transmit data while the other stations are forbidden to access the channel. Unfortunately, the standard does not suggest how to configure the PRAW mechanism according to scenario-specific requirements and restrictions. Many Internet of Things scenarios require the fast and low energy consumption delivery of measurement data from wireless sensors while saving channel resources for other stations that transmit, for example, multimedia traffic. Therefore, this paper studies the problem of the minimization of the channel timeshare consumed by the PRAW with restrictions on the average delay and power consumption. To solve the problem and configure the PRAW optimally, a novel analytical model is developed. The key feature of the model is the consideration of the case of short PRAW slots that allow the computational complexity to be reduced and high accuracy to be achieved. These properties make the model suitable for implementation in real devices. Full article
(This article belongs to the Special Issue Wireless Network Protocols and Performance Evaluation)
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16 pages, 2662 KiB  
Review
Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules
by Matjaž Gams and Tine Kolenik
Electronics 2021, 10(4), 514; https://doi.org/10.3390/electronics10040514 - 22 Feb 2021
Cited by 16 | Viewed by 5793
Abstract
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress [...] Read more.
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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20 pages, 11932 KiB  
Article
An Efficient FPGA Implementation of Richardson-Lucy Deconvolution Algorithm for Hyperspectral Images
by Karine Avagian and Milica Orlandić
Electronics 2021, 10(4), 504; https://doi.org/10.3390/electronics10040504 - 21 Feb 2021
Cited by 4 | Viewed by 3356
Abstract
This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated [...] Read more.
This paper proposes an implementation of a Richardson-Lucy (RL) deconvolution method to reduce the spatial degradation in hyperspectral images during the image acquisition process. The degradation, modeled by convolution with a point spread function (PSF), is reduced by applying both standard and accelerated RLdeconvolution algorithms on the individual images in spectral bands. Boundary conditions are introduced to maintain a constant image size without distorting the estimated image boundaries. The RL deconvolution algorithm is implemented on a field-programmable gate array (FPGA)-based Xilinx Zynq-7020 System-on-Chip (SoC). The proposed architecture is parameterized with respect to the image size and configurable with respect to the algorithm variant, the number of iterations, and the kernel size by setting the dedicated configuration registers. A speed-up by factors of 61 and 21 are reported compared to software-only and FPGA-based state-of-the-art implementations, respectively. Full article
(This article belongs to the Special Issue Hardware Architectures for Real Time Image Processing)
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16 pages, 7001 KiB  
Article
Optimization of a 3D-Printed Permanent Magnet Coupling Using Genetic Algorithm and Taguchi Method
by Ekaterina Andriushchenko, Ants Kallaste, Anouar Belahcen, Toomas Vaimann, Anton Rassõlkin, Hamidreza Heidari and Hans Tiismus
Electronics 2021, 10(4), 494; https://doi.org/10.3390/electronics10040494 - 20 Feb 2021
Cited by 14 | Viewed by 3098
Abstract
In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster [...] Read more.
In recent decades, the genetic algorithm (GA) has been extensively used in the design optimization of electromagnetic devices. Despite the great merits possessed by the GA, its processing procedure is highly time-consuming. On the contrary, the widely applied Taguchi optimization method is faster with comparable effectiveness in certain optimization problems. This study explores the abilities of both methods within the optimization of a permanent magnet coupling, where the optimization objectives are the minimization of coupling volume and maximization of transmitted torque. The optimal geometry of the coupling and the obtained characteristics achieved by both methods are nearly identical. The magnetic torque density is enhanced by more than 20%, while the volume is reduced by 17%. Yet, the Taguchi method is found to be more time-efficient and effective within the considered optimization problem. Thanks to the additive manufacturing techniques, the initial design and the sophisticated geometry of the Taguchi optimal designs are precisely fabricated. The performances of the coupling designs are validated using an experimental setup. Full article
(This article belongs to the Special Issue Robust Design Optimization of Electrical Machines and Devices)
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20 pages, 4541 KiB  
Article
Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography
by Imayanmosha Wahlang, Arnab Kumar Maji, Goutam Saha, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz and Elzbieta Jasinska
Electronics 2021, 10(4), 495; https://doi.org/10.3390/electronics10040495 - 20 Feb 2021
Cited by 26 | Viewed by 4849
Abstract
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) [...] Read more.
This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images. Full article
(This article belongs to the Special Issue Bioelectronic Technologies and Artificial Intelligence for Medical Diagnosis and Healthcare)
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12 pages, 5423 KiB  
Article
Performance Evaluation of LoRa 920 MHz Frequency Band in a Hilly Forested Area
by Bilguunmaa Myagmardulam, Ryu Miura, Fumie Ono, Toshinori Kagawa, Lin Shan, Tadachika Nakayama, Fumihide Kojima and Baasandash Choijil
Electronics 2021, 10(4), 502; https://doi.org/10.3390/electronics10040502 - 20 Feb 2021
Cited by 14 | Viewed by 4444
Abstract
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics [...] Read more.
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics in forested areas and tree canopy openness were investigated in the Takakuma experimental forest in Kagoshima, Japan. The aim was to evaluate the performance of the LoRa 920 MHz band with spreading factor (SF12) in a forested hilly area. The received signal strength indicator (RSSI) was measured as a function of the distance between the transmitter antenna and ground station (GS). To illustrate the effect of canopy openness on radio wave propagation, sky view factor (SVF) and a forest canopy height model were considered at each location of a successfully received RSSI. A positive correlation was found between the RSSI and SVF. It was found that between the GS and transmitter antenna, if the canopy height is above 23 m, the signal diffracted and RSSI fell to −120 to −127 dBm, so the presence of the obstacle height should be considered. Further research is needed to clarify the detailed tree density between the transmitter and ground station to propose an optimal propagation model for a forested environment. Full article
(This article belongs to the Special Issue IoT Anywhere—A Low Power Sensors and Long-Range communication for IoT)
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23 pages, 8107 KiB  
Article
Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection
by Robertas Damaševičius, Algimantas Venčkauskas, Jevgenijus Toldinas and Šarūnas Grigaliūnas
Electronics 2021, 10(4), 485; https://doi.org/10.3390/electronics10040485 - 18 Feb 2021
Cited by 73 | Viewed by 8136
Abstract
The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of [...] Read more.
The security of information is among the greatest challenges facing organizations and institutions. Cybercrime has risen in frequency and magnitude in recent years, with new ways to steal, change and destroy information or disable information systems appearing every day. Among the types of penetration into the information systems where confidential information is processed is malware. An attacker injects malware into a computer system, after which he has full or partial access to critical information in the information system. This paper proposes an ensemble classification-based methodology for malware detection. The first-stage classification is performed by a stacked ensemble of dense (fully connected) and convolutional neural networks (CNN), while the final stage classification is performed by a meta-learner. For a meta-learner, we explore and compare 14 classifiers. For a baseline comparison, 13 machine learning methods are used: K-Nearest Neighbors, Linear Support Vector Machine (SVM), Radial basis function (RBF) SVM, Random Forest, AdaBoost, Decision Tree, ExtraTrees, Linear Discriminant Analysis, Logistic, Neural Net, Passive Classifier, Ridge Classifier and Stochastic Gradient Descent classifier. We present the results of experiments performed on the Classification of Malware with PE headers (ClaMP) dataset. The best performance is achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner. Full article
(This article belongs to the Special Issue High Accuracy Detection of Mobile Malware Using Machine Learning)
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11 pages, 2607 KiB  
Article
Dynamic Analysis of the Switched-Inductor Buck-Boost Converter Based on the Memristor
by Yan Yang, Dongdong Li and Dongqing Wang
Electronics 2021, 10(4), 452; https://doi.org/10.3390/electronics10040452 - 11 Feb 2021
Cited by 18 | Viewed by 3454
Abstract
The direct current (DC)–DC converter presents abundant nonlinear phenomena, such as periodic bifurcation and chaotic motion, under certain conditions. For a switched-inductor buck-boost (SIBB) converter with the memristive load, this paper constructs its state equation model under two operating statuses, investigates its chaotic [...] Read more.
The direct current (DC)–DC converter presents abundant nonlinear phenomena, such as periodic bifurcation and chaotic motion, under certain conditions. For a switched-inductor buck-boost (SIBB) converter with the memristive load, this paper constructs its state equation model under two operating statuses, investigates its chaotic dynamic characteristics, and draws and analyzes the bifurcation diagrams of the inductive current and phase portraits, under some parameter changing by the MATLAB simulation based on the state equation. Then, by applying certain minor perturbations to parameters, the chaotic phenomenon suppression method is explored by controlling peak current in continuous current mode (CCM) to keep the converter run normally. Finally, the power simulation (PSIM) verifies that the waveforms and the phase portraits controlling the corresponding parameters are consistent with those of the MATLAB simulation. Full article
(This article belongs to the Special Issue Recent Advances in Chaotic Systems and Their Security Applications)
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26 pages, 1484 KiB  
Article
Time Efficient Unmanned Aircraft Systems Deployment in Disaster Scenarios Using Clustering Methods and a Set Cover Approach
by Donald Mahoro Ntwari, Daniel Gutierrez-Reina, Sergio Luis Toral Marín and Hissam Tawfik
Electronics 2021, 10(4), 422; https://doi.org/10.3390/electronics10040422 - 9 Feb 2021
Cited by 2 | Viewed by 2213
Abstract
Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network [...] Read more.
Unmanned aircraft, which are more commonly known as drones, are nowadays extensively used in an ever increasing set of applications. In a wider system, the aircraft are usually associated to additional elements such as ground-based controllers. Furthermore, when these components form a network of elements that can communicate, the system is said to form an Unmanned Aircraft System (UAS). This system is particularly effective when the aircraft within are organized into swarms with sets of objectives to accomplish. The extensive use of swarms into UASs is more and more exploited nowadays due to the decreasing cost of those aircraft. In the present work we are interested in a particular application of UASs, namely their deployment in disaster scenarios for communications services provision to targets on the ground. These ground targets, however, are not part of the UASs and should not be confused with ground-based controllers. The present work does not only focus on coverage for ground targets but also on a guaranteed minimum number of covers for each target, which is called the redundancy requirement. The research work also ensures that the deployed UAS forms a unique connected component so that a steady stream of communication is kept with the targets to cover. Research work similar to the present perform the initial deployment of their aircraft in a different manner, either randomly, based on a predetermined grid formation, or using other elaborated methods. This work proposes a new solution based on the use of clustering algorithms, combined to a design of the problem formulated as a set cover optimization model. The clustering phase is used to discretize the search space and ease the optimization phase by locating regions of interest, and then a further procedure is applied, only when needed, to reconnect scattered connected components and guarantee connectivity in the networks. This way of doing it has achieved a deployment of UASs with maximum coverage for all targets, a guaranteed minimum number of covers for each of them, and results in a competitive computation time. The latter also allowed for more scalability by extending the tests to very large input instances. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications to Autonomous Vehicular Networks)
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13 pages, 800 KiB  
Article
A Machine Learning Approach for Anomaly Detection in Industrial Control Systems Based on Measurement Data
by Sohrab Mokhtari, Alireza Abbaspour, Kang K. Yen and Arman Sargolzaei
Electronics 2021, 10(4), 407; https://doi.org/10.3390/electronics10040407 - 8 Feb 2021
Cited by 111 | Viewed by 13172
Abstract
Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed [...] Read more.
Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed a novel solution to this problem based on measurement data in the supervisory control and data acquisition (SCADA) system. The proposed approach is called measurement intrusion detection system (MIDS), which enables the system to detect any abnormal activity in the system even if the attacker tries to conceal it in the system’s control layer. A supervised machine learning model is generated to classify normal and abnormal activities in an ICS to evaluate the MIDS performance. A hardware-in-the-loop (HIL) testbed is developed to simulate the power generation units and exploit the attack dataset. In the proposed approach, we applied several machine learning models on the dataset, which show remarkable performances in detecting the dataset’s anomalies, especially stealthy attacks. The results show that the random forest is performing better than other classifier algorithms in detecting anomalies based on measured data in the testbed. Full article
(This article belongs to the Special Issue Security of Cyber-Physical Systems)
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28 pages, 11195 KiB  
Article
Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles
by Patiphon Narksri, Eijiro Takeuchi, Yoshiki Ninomiya and Kazuya Takeda
Electronics 2021, 10(4), 411; https://doi.org/10.3390/electronics10040411 - 8 Feb 2021
Cited by 8 | Viewed by 3050
Abstract
A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped [...] Read more.
A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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12 pages, 700 KiB  
Article
Transparent Control Flow Transfer between CPU and Accelerators for HPC
by Daniel Granhão and João Canas Ferreira
Electronics 2021, 10(4), 406; https://doi.org/10.3390/electronics10040406 - 7 Feb 2021
Cited by 1 | Viewed by 2133
Abstract
Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can [...] Read more.
Heterogeneous platforms with FPGAs have started to be employed in the High-Performance Computing (HPC) field to improve performance and overall efficiency. These platforms allow the use of specialized hardware to accelerate software applications, but require the software to be adapted in what can be a prolonged and complex process. The main goal of this work is to describe and evaluate mechanisms that can transparently transfer the control flow between CPU and FPGA within the scope of HPC. Combining such a mechanism with transparent software profiling and accelerator configuration could lead to an automatic way of accelerating regular applications. In this work, a mechanism based on the ptrace system call is proposed, and its performance on the Intel Xeon+FPGA platform is evaluated. The feasibility of the proposed approach is demonstrated by a working prototype that performs the transparent control flow transfer of any function call to a matching hardware accelerator. This approach is more general than shared library interposition at the cost of a small time overhead in each accelerator use (about 1.3 ms in the prototype implementation). Full article
(This article belongs to the Special Issue Recent Advances in Field-Programmable Logic and Applications)
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26 pages, 1016 KiB  
Article
Identity and Access Management Resilience against Intentional Risk for Blockchain-Based IOT Platforms
by Alberto Partida, Regino Criado and Miguel Romance
Electronics 2021, 10(4), 378; https://doi.org/10.3390/electronics10040378 - 4 Feb 2021
Cited by 13 | Viewed by 6762
Abstract
Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain [...] Read more.
Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain technology transfers data and value in an immutable and decentralised fashion. Security, composed of both non-intentional and intentional risk management, is a fundamental design requirement for both IoT and blockchain. We study how blockchain answers some of the IoT security requirements with a focus on intentional risk. The review of a sample of security incidents impacting public blockchains confirm that identity and access management (IAM) is a key security requirement to build resilience against intentional risk. This fact is also applicable to IoT solutions built on a blockchain. We compare the two IoT platforms based on public permissionless distributed ledgers with the highest market capitalisation: IOTA, run on an alternative to a blockchain, which is a directed acyclic graph (DAG); and IoTeX, its contender, built on a blockchain. Our objective is to discover how we can create IAM resilience against intentional risk in these IoT platforms. For that, we turn to complex network theory: a tool to describe and compare systems with many participants. We conclude that IoTeX and possibly IOTA transaction networks are scale-free. As both platforms are vulnerable to attacks, they require resilience against intentional risk. In the case of IoTeX, DIoTA provides a resilient IAM solution. Furthermore, we suggest that resilience against intentional risk requires an IAM concept that transcends a single blockchain. Only with the interplay of edge and global ledgers can we obtain data integrity in a multi-vendor and multi-purpose IoT network. Full article
(This article belongs to the Special Issue IoT Security and Privacy through the Blockchain)
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16 pages, 12094 KiB  
Article
Development and Realization of an Experimental Bench Test for Synchronized Small Angle Light Scattering and Biaxial Traction Analysis of Tissues
by Emanuele Vignali, Emanuele Gasparotti, Luigi Landini and Simona Celi
Electronics 2021, 10(4), 386; https://doi.org/10.3390/electronics10040386 - 4 Feb 2021
Cited by 8 | Viewed by 2493
Abstract
Insights into the mechanical and microstructural status of biological soft tissues are fundamental in analyzing diseases. Biaxial traction is the gold standard approach for mechanical characterization. The state of the art methods for microstructural assessment have different advantages and drawbacks. Small angle light [...] Read more.
Insights into the mechanical and microstructural status of biological soft tissues are fundamental in analyzing diseases. Biaxial traction is the gold standard approach for mechanical characterization. The state of the art methods for microstructural assessment have different advantages and drawbacks. Small angle light scattering (SALS) represents a valuable low energy technique for soft tissue assessment. The objective of the current work was to develop a bench test integrating mechanical and microstructural characterization capabilities for tissue specimens. The setup’s principle is based on the integration of biaxial traction and SALS analysis. A dedicated control application was developed with the objective of managing the test procedure. The different components of the setup are described and discussed, both in terms of hardware and software. The realization of the system and the corresponding performances are then presented. Full article
(This article belongs to the Special Issue Digital Twin Technology: New Frontiers for Personalized Healthcare)
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31 pages, 57911 KiB  
Article
Comparative Study of Optimal Multivariable LQR and MPC Controllers for Unmanned Combat Air Systems in Trajectory Tracking
by Alvaro Ortiz, Sergio Garcia-Nieto and Raul Simarro
Electronics 2021, 10(3), 331; https://doi.org/10.3390/electronics10030331 - 1 Feb 2021
Cited by 10 | Viewed by 3870
Abstract
Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying [...] Read more.
Guidance, navigation, and control system design is, undoubtedly, one of the most relevant issues in any type of unmanned aerial vehicle, especially in the case of military missions. This task needs to be performed in the most efficient way possible, which involves trying to satisfy a set of requirements that are sometimes in opposition. The purpose of this article was to compare two different control strategies in conjunction with a path-planning and guidance system with the objective of completing military missions in the most satisfactory way. For this purpose, a novel dynamic trajectory-planning algorithm is employed, which can obtain an appropriate trajectory by analyzing the environment as a discrete 3D adaptive mesh and performs a softening process a posteriori. Moreover, two multivariable control techniques are proposed, i.e., the linear quadratic regulator and the model predictive control, which were designed to offer optimal responses in terms of stability and robustness. Full article
(This article belongs to the Special Issue Autonomous Navigation Systems: Design, Control and Applications)
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16 pages, 7646 KiB  
Article
An Advanced CNN-LSTM Model for Cryptocurrency Forecasting
by Ioannis E. Livieris, Niki Kiriakidou, Stavros Stavroyiannis and Panagiotis Pintelas
Electronics 2021, 10(3), 287; https://doi.org/10.3390/electronics10030287 - 26 Jan 2021
Cited by 88 | Viewed by 15071
Abstract
Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing [...] Read more.
Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks. Full article
(This article belongs to the Special Issue Regularization Techniques for Machine Learning and Their Applications)
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20 pages, 11766 KiB  
Article
An Enhanced Multicell-to-Multicell Battery Equalizer Based on Bipolar-Resonant LC Converter
by Xuan Luo, Longyun Kang, Chusheng Lu, Jinqing Linghu, Hongye Lin and Bihua Hu
Electronics 2021, 10(3), 293; https://doi.org/10.3390/electronics10030293 - 26 Jan 2021
Cited by 29 | Viewed by 3576
Abstract
In a battery management system (BMS), battery equalizer is used to achieve voltage consistency between series connected battery cells. Recently, serious inconsistency has been founded to exist in retired batteries, and traditional equalizers are slow or inefficient to handle the situation. The multicell-to-multicell [...] Read more.
In a battery management system (BMS), battery equalizer is used to achieve voltage consistency between series connected battery cells. Recently, serious inconsistency has been founded to exist in retired batteries, and traditional equalizers are slow or inefficient to handle the situation. The multicell-to-multicell (MC2MC) topology, which can directly transfer energy from consecutive strong cells to consecutive weak cells, is promising to solve the problem, but its performance is limited by the existing converter. Therefore, this paper proposes an enhanced MC2MC equalizer based on a novel bipolar-resonant LC converter (BRLCC), which supports flexible and efficient operation modes with stable balancing power, can greatly improve the balancing speed without much sacrificing the efficiency. Mathematical analysis and comparison with typical equalizers are provided to illustrate its high balancing speed and good efficiency. An experimental prototype for 8 cells is built, and the balancing powers under different operation modes are from 1.426 W to 12.559 W with balancing efficiencies from 84.84% to 91.68%. Full article
(This article belongs to the Special Issue Challenges of Battery Management System)
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24 pages, 8233 KiB  
Article
FPGA Implementation for CNN-Based Optical Remote Sensing Object Detection
by Ning Zhang, Xin Wei, He Chen and Wenchao Liu
Electronics 2021, 10(3), 282; https://doi.org/10.3390/electronics10030282 - 25 Jan 2021
Cited by 53 | Viewed by 7483
Abstract
In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and have shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these methods to observe objects on the ground. Due [...] Read more.
In recent years, convolutional neural network (CNN)-based methods have been widely used for optical remote sensing object detection and have shown excellent performance. Some aerospace systems, such as satellites or aircrafts, need to adopt these methods to observe objects on the ground. Due to the limited budget of the logical resources and power consumption in these systems, an embedded device is a good choice to implement the CNN-based methods. However, it is still a challenge to strike a balance between performance and power consumption. In this paper, we propose an efficient hardware-implementation method for optical remote sensing object detection. Firstly, we optimize the CNN-based model for hardware implementation, which establishes a foundation for efficiently mapping the network on a field-programmable gate array (FPGA). In addition, we propose a hardware architecture for the CNN-based remote sensing object detection model. In this architecture, a general processing engine (PE) is proposed to implement multiple types of convolutions in the network using the uniform module. An efficient data storage and access scheme is also proposed, and it achieves low-latency calculations and a high memory bandwidth utilization rate. Finally, we deployed the improved YOLOv2 network on a Xilinx ZYNQ xc7z035 FPGA to evaluate the performance of our design. The experimental results show that the performance of our implementation on an FPGA is only 0.18% lower than that on a graphics processing unit (GPU) in mean average precision (mAP). Under a 200 MHz working frequency, our design achieves a throughput of 111.5 giga-operations per second (GOP/s) with a 5.96 W on-chip power consumption. Comparison with the related works demonstrates that the proposed design has obvious advantages in terms of energy efficiency and that it is suitable for deployment on embedded devices. Full article
(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))
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18 pages, 8929 KiB  
Article
Wind Turbine Operation Curves Modelling Techniques
by Davide Astolfi
Electronics 2021, 10(3), 269; https://doi.org/10.3390/electronics10030269 - 23 Jan 2021
Cited by 20 | Viewed by 3757
Abstract
Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing [...] Read more.
Wind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity. Full article
(This article belongs to the Special Issue Wind Turbine Power Systems)
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9 pages, 2451 KiB  
Article
Solar Energy Conversion and Storage Using a Photocatalytic Fuel Cell Combined with a Supercapacitor
by Tatiana Santos Andrade, Vassilios Dracopoulos and Panagiotis Lianos
Electronics 2021, 10(3), 273; https://doi.org/10.3390/electronics10030273 - 23 Jan 2021
Cited by 7 | Viewed by 3338
Abstract
This work studies the production of electricity by a photocatalytic fuel cell and its storage in a supercapacitor. We propose a simple construction, where a third electrode bearing activated carbon is added to the device to form a supercapacitor electrode in combination with [...] Read more.
This work studies the production of electricity by a photocatalytic fuel cell and its storage in a supercapacitor. We propose a simple construction, where a third electrode bearing activated carbon is added to the device to form a supercapacitor electrode in combination with the supporting electrolyte of the cell. The photocatalytic fuel cell is based on a CdS-sensitized mesoporous TiO2 photoanode and an air cathode bearing only nanoparticulate carbon as an oxygen reduction electrocatalyst. Full article
(This article belongs to the Special Issue Recent Advances in Nanoelectronics for Energy Conversion, Storage, and Saving)
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19 pages, 8688 KiB  
Article
Research on the Coordinated Control of Regenerative Braking System and ABS in Hybrid Electric Vehicle Based on Composite Structure Motor
by Qiwei Xu, Chuan Zhou, Hong Huang and Xuefeng Zhang
Electronics 2021, 10(3), 223; https://doi.org/10.3390/electronics10030223 - 20 Jan 2021
Cited by 16 | Viewed by 4239
Abstract
An antilock braking system (ABS) can ensure that the wheels are not locked during the braking process which is an important system to ensure the safety of braking. Regenerative braking is also a crucial system for hybrid vehicles and helps to improve the [...] Read more.
An antilock braking system (ABS) can ensure that the wheels are not locked during the braking process which is an important system to ensure the safety of braking. Regenerative braking is also a crucial system for hybrid vehicles and helps to improve the cruising range of the car. As such, the coordinated control of a braking system and an ABS is an important research direction. This paper researches the coordinated control of the regenerative braking system and the ABS in the hybrid vehicle based on the composite structure motor (CSM-HEV). Firstly, two new braking modes which are engine-motor coordinated braking (EMCB) and dual-motor braking (DMB) are proposed and the coordinated control model of regenerative braking and ABS is established. Then, for the purpose of optimal operating efficiency and guaranteeing the vehicle brake slip rate, a braking force distribution strategy based on predictive control algorithm is proposed. Finally, the Simulink model is established to simulate the control strategy. Results show that the slip rate can well track the target and ensure the efficient operation of the system. Compared with the normal braking mode, the braking energy recovery rate of EMCB is similar, but it can reduce the fuel loss of the engine during the braking process by 30.1%, DMB can improve the braking energy recovery efficiency by 16.78%, and the response time to track target slip is increased by 12 ms. Full article
(This article belongs to the Special Issue Emerging Trends in Control-Oriented Modeling of Hybrid Electric Vehicles in a Connected Framework)
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18 pages, 3416 KiB  
Article
NDF of Scattered Fields for Strip Geometries
by Ehsan Akbari Sekehravani, Giovanni Leone and Rocco Pierri
Electronics 2021, 10(2), 202; https://doi.org/10.3390/electronics10020202 - 17 Jan 2021
Cited by 13 | Viewed by 5759
Abstract
Solving inverse scattering problems by numerical methods requires investigating the number of independent pieces of information that can be reconstructed stably. To this end, we address the evaluation of the Number of Degrees of Freedom (NDF) of far-zone scattered fields for some strip [...] Read more.
Solving inverse scattering problems by numerical methods requires investigating the number of independent pieces of information that can be reconstructed stably. To this end, we address the evaluation of the Number of Degrees of Freedom (NDF) of far-zone scattered fields for some strip geometries under the first-order Born approximation. The analysis is performed by employing the Singular Value Decomposition (SVD) of the scattering operator in the two-dimensional scalar geometry of one or more strips illuminated by a TM polarized plane wave. It is known that investigating the scattering scene at different incident plane waves (multi-view configuration) enhances the NDF. Therefore we mean to examine the minimum number of incident plane waves providing the NDF of the scattered fields both by theoretical estimations and numerical verifications. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Microwave and Wireless Communications)
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14 pages, 3227 KiB  
Article
Device and Circuit Exploration of Multi-Nanosheet Transistor for Sub-3 nm Technology Node
by Yoongeun Seon, Jeesoo Chang, Changhyun Yoo and Jongwook Jeon
Electronics 2021, 10(2), 180; https://doi.org/10.3390/electronics10020180 - 15 Jan 2021
Cited by 24 | Viewed by 7643
Abstract
A multi-nanosheet field-effect transistor (mNS-FET) device was developed to maximize gate controllability while making the channel in the form of a sheet. The mNS-FET has superior gate controllability for the stacked channels; consequently, it can significantly reduce the short-channel effect (SCE); however, punch-through [...] Read more.
A multi-nanosheet field-effect transistor (mNS-FET) device was developed to maximize gate controllability while making the channel in the form of a sheet. The mNS-FET has superior gate controllability for the stacked channels; consequently, it can significantly reduce the short-channel effect (SCE); however, punch-through inevitably occurs in the bottom channel portion that is not surrounded by gates, resulting in a large leakage current. Moreover, as the size of the semiconductor device decreases to several nanometers, the influence of the parasitic resistance and parasitic capacitance increases. Therefore, it is essential to apply design–technology co-optimization, which analyzes not only the characteristics from the perspective of the device but also the performance from the circuit perspective. In this study, we used Technology Computer Aided Design (TCAD) simulation to analyze the characteristics of the device and directly fabricated a model that describes the current–voltage and gate capacitance characteristics of the device by using Berkeley short-channel insulated-gate field-effect transistor–common multi-gate (BSIM–CMG) parameters. Through this model, we completed the Simulation Program with Integrated Circuit Emphasis (SPICE) simulation for circuit analysis and analyzed it from the viewpoint of devices and circuits. When comparing the characteristics according to the presence or absence of bottom oxide by conducting the above research method, it was confirmed that subthreshold slope (SS) and drain-induced barrier lowering (DIBL) are improved, and power and performance in circuit characteristics are increased. Full article
(This article belongs to the Special Issue New CMOS Devices and Their Applications)
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29 pages, 2701 KiB  
Article
Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling
by Muhammad Wasimuddin, Khaled Elleithy, Abdelshakour Abuzneid, Miad Faezipour and Omar Abuzaghleh
Electronics 2021, 10(2), 170; https://doi.org/10.3390/electronics10020170 - 14 Jan 2021
Cited by 30 | Viewed by 5710
Abstract
Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and [...] Read more.
Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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16 pages, 8431 KiB  
Article
A Design Method of Compensation Circuit for High-Power Dynamic Capacitive Power Transfer System Considering Coupler Voltage Distribution for Railway Applications
by Jianying Liang, Donghua Wu and Jin Yu
Electronics 2021, 10(2), 153; https://doi.org/10.3390/electronics10020153 - 12 Jan 2021
Cited by 10 | Viewed by 3494
Abstract
Capacitive power transfer (CPT) is a promising method to solve the problems caused by the traditional Pantograph-catenary contact power supply for railway applications. In contrast, the CPT system suffers a broken risk because of the small coupling capacitor. This paper has analyzed the [...] Read more.
Capacitive power transfer (CPT) is a promising method to solve the problems caused by the traditional Pantograph-catenary contact power supply for railway applications. In contrast, the CPT system suffers a broken risk because of the small coupling capacitor. This paper has analyzed the CPT coupler’s voltage distributions for dynamic CPT systems when high power is required in real railway applications. The triangle relationship among the coupler voltages is derived. The circuit of the CPT system to accolated the coupler voltage is analyzed. Then, the compensation parameters are given. With the adopted LCLC-CL topology, the design process is presented by considering the coupler voltages. An experimental setup is conducted to validate the proposed design method. The experimental results show that the system can achieve 3 kW output power with 92.46% DC-DC efficiency and the voltage distribution aggress well with the designed values. Full article
(This article belongs to the Special Issue Wireless Power Transfer and Its Applications)
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21 pages, 7753 KiB  
Article
Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability
by Taewon Ahn, Yongki Lee and Kihong Park
Electronics 2021, 10(2), 144; https://doi.org/10.3390/electronics10020144 - 11 Jan 2021
Cited by 27 | Viewed by 5570
Abstract
This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and [...] Read more.
This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems. Full article
(This article belongs to the Section Systems & Control Engineering)
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11 pages, 3220 KiB  
Article
Compact Continuous Time Common-Mode Feedback Circuit for Low-Power, Area-Constrained Neural Recording Amplifiers
by Joon Young Kwak and Sung-Yun Park
Electronics 2021, 10(2), 145; https://doi.org/10.3390/electronics10020145 - 11 Jan 2021
Cited by 6 | Viewed by 7087
Abstract
A continuous-time common-mode feedback (CMFB) circuit for low-power, area-constrained neural recording amplifiers is proposed. The proposed CMFB circuit is compact; it can be realized by simply replacing passive components with transistors in a low-noise folded cascode operational transconductance amplifier (FC-OTA) that is one [...] Read more.
A continuous-time common-mode feedback (CMFB) circuit for low-power, area-constrained neural recording amplifiers is proposed. The proposed CMFB circuit is compact; it can be realized by simply replacing passive components with transistors in a low-noise folded cascode operational transconductance amplifier (FC-OTA) that is one of the most widely adopted OTAs for neural recording amplifiers. The proposed CMFB also consumes no additional power, i.e., no separate CMFB amplifier is required, thus, it fits well to low-power, area-constrained multichannel neural recording amplifiers. The proposed CMFB is analyzed in the implementation of a fully differential AC-coupled neural recording amplifier and compared with that of an identical neural recording amplifier using a conventional differential difference amplifier-based CMFB in 0.18 μm CMOS technology post-layout simulations. The AC-coupled neural recording amplifier with the proposed CMFB occupies ~37% less area and consumes ~11% smaller power, providing 2.67× larger output common mode (CM) range without CM bandwidth sacrifice in the comparison. Full article
(This article belongs to the Special Issue Energy Efficient Circuit Design Techniques for Low Power Systems)
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23 pages, 22731 KiB  
Article
AC Current Ripple Harmonic Pollution in Three-Phase Four-Leg Active Front-End AC/DC Converter for On-Board EV Chargers
by Aleksandr Viatkin, Riccardo Mandrioli, Manel Hammami, Mattia Ricco and Gabriele Grandi
Electronics 2021, 10(2), 116; https://doi.org/10.3390/electronics10020116 - 7 Jan 2021
Cited by 6 | Viewed by 3629
Abstract
Three-phase four-leg voltage-source converters have been considered for some recent projects in smart grids and in the automotive industry, projects such as on-board electric vehicles (EVs) chargers, thanks to their built-in ability to handle unbalanced AC currents through the 4th wire (neutral). Although [...] Read more.
Three-phase four-leg voltage-source converters have been considered for some recent projects in smart grids and in the automotive industry, projects such as on-board electric vehicles (EVs) chargers, thanks to their built-in ability to handle unbalanced AC currents through the 4th wire (neutral). Although conventional carrier-based modulations (CBMs) and space vector modulations (SVMs) have been commonly applied and extensively studied for three-phase four-leg voltage-source converters, very little has been reported concerning their pollution impact on AC grid in terms of switching ripple currents. This paper introduces a thorough analytical derivation of peak-to-peak and RMS values of the AC current ripple under balanced and unbalanced working conditions, in the case of three-phase four-leg converters with uncoupled AC-link inductors. The proposed mathematical approach covers both phase and neutral currents. All analytical findings have been applied to two industry recognized CBM methods, namely sinusoidal pulse-width modulation (PWM) and centered PWM (equivalent to SVM). The derived equations are effective, simple, and ready-to-use for accurate AC current ripple calculations. At the same time, the proposed equations and diagrams can be successfully adopted to design the conversion system basing on the grid codes in terms of current ripple (or total harmonic distortion (THD)/total demand distortion (TDD)) restrictions, enabling the sizing of AC-link inductors and the determination of the proper switching frequency for the given operating conditions. The analytical developments have been thoroughly verified by numerical simulations in MATLAB/Simulink and by extensive experimental tests. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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15 pages, 7254 KiB  
Article
SmartFit: Smartphone Application for Garment Fit Detection
by Kamrul H. Foysal, Hyo Jung Chang, Francine Bruess and Jo Woon Chong
Electronics 2021, 10(1), 97; https://doi.org/10.3390/electronics10010097 - 5 Jan 2021
Cited by 16 | Viewed by 5848
Abstract
The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution [...] Read more.
The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market. Full article
(This article belongs to the Special Issue Smart Bioelectronics and Wearable Systems)
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20 pages, 3923 KiB  
Article
Carbon Nanotube Field Effect Transistor (CNTFET) and Resistive Random Access Memory (RRAM) Based Ternary Combinational Logic Circuits
by Furqan Zahoor, Fawnizu Azmadi Hussin, Farooq Ahmad Khanday, Mohamad Radzi Ahmad, Illani Mohd Nawi, Chia Yee Ooi and Fakhrul Zaman Rokhani
Electronics 2021, 10(1), 79; https://doi.org/10.3390/electronics10010079 - 4 Jan 2021
Cited by 55 | Viewed by 8794
Abstract
The capability of multiple valued logic (MVL) circuits to achieve higher storage density when compared to that of existing binary circuits is highly impressive. Recently, MVL circuits have attracted significant attention for the design of digital systems. Carbon nanotube field effect transistors (CNTFETs) [...] Read more.
The capability of multiple valued logic (MVL) circuits to achieve higher storage density when compared to that of existing binary circuits is highly impressive. Recently, MVL circuits have attracted significant attention for the design of digital systems. Carbon nanotube field effect transistors (CNTFETs) have shown great promise for design of MVL based circuits, due to the fact that the scalable threshold voltage of CNTFETs can be utilized easily for the multiple voltage designs. In addition, resistive random access memory (RRAM) is also a feasible option for the design of MVL circuits, owing to its multilevel cell capability that enables the storage of multiple resistance states within a single cell. In this manuscript, a design approach for ternary combinational logic circuits while using CNTFETs and RRAM is presented. The designs of ternary half adder, ternary half subtractor, ternary full adder, and ternary full subtractor are evaluated while using Synopsis HSPICE simulation software with standard 32 nm CNTFET technology under different operating conditions, including different supply voltages, output load variation, and different operating temperatures. Finally, the proposed designs are compared with the state-of-the-art ternary designs. Based on the obtained simulation results, the proposed designs show a significant reduction in the transistor count, decreased cell area, and lower power consumption. In addition, due to the participation of RRAM, the proposed designs have advantages in terms of non-volatility. Full article
(This article belongs to the Special Issue RRAM Devices: Materials, Designs, and Properties)
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19 pages, 972 KiB  
Review
A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning
by Jianbin Xiong, Dezheng Yu, Shuangyin Liu, Lei Shu, Xiaochan Wang and Zhaoke Liu
Electronics 2021, 10(1), 81; https://doi.org/10.3390/electronics10010081 - 4 Jan 2021
Cited by 82 | Viewed by 8644
Abstract
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development [...] Read more.
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed. Full article
(This article belongs to the Collection Electronics for Agriculture)
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31 pages, 6703 KiB  
Article
A Hybrid Prognostics Deep Learning Model for Remaining Useful Life Prediction
by Zhiyuan Xie, Shichang Du, Jun Lv, Yafei Deng and Shiyao Jia
Electronics 2021, 10(1), 39; https://doi.org/10.3390/electronics10010039 - 29 Dec 2020
Cited by 24 | Viewed by 4584
Abstract
Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, [...] Read more.
Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment. Full article
(This article belongs to the Special Issue Applications and Methodologies of Artificial Intelligence in Big Data Analysis)
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21 pages, 1803 KiB  
Article
A Procedure for Tracing Supply Chains for Perishable Food Based on Blockchain, Machine Learning and Fuzzy Logic
by Zeinab Shahbazi and Yung-Cheol Byun
Electronics 2021, 10(1), 41; https://doi.org/10.3390/electronics10010041 - 29 Dec 2020
Cited by 60 | Viewed by 8442
Abstract
One of the essential points of food manufacturing in the industry and shelf life of the products is to improve the food traceability system. In recent years, the food traceability mechanism has become one of the emerging blockchain applications in order to improve [...] Read more.
One of the essential points of food manufacturing in the industry and shelf life of the products is to improve the food traceability system. In recent years, the food traceability mechanism has become one of the emerging blockchain applications in order to improve the anti-counterfeiting area’s quality. Many food manufacturing systems have a low level of readability, scalability, and data accuracy. Similarly, this process is complicated in the supply chain and needs a lot of time for processing. The blockchain system creates a new ontology in the traceability system supply chain to deal with these issues. In this paper, a blockchain machine learning-based food traceability system (BMLFTS) is proposed in order to combine the new extension in blockchain, Machine Learning technology (ML), and fuzzy logic traceability system that is based on the shelf life management system for manipulating perishable food. The blockchain technology in the proposed system has been developed in order to address light-weight, evaporation, warehouse transactions, or shipping time. The blockchain data flow is designed to show the extension of ML at the level of food traceability. Finally, reliable and accurate data are used in a supply chain to improve shelf life. Full article
(This article belongs to the Section Computer Science & Engineering)
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9 pages, 2763 KiB  
Article
LTPS TFTs with an Amorphous Silicon Buffer Layer and Source/Drain Extension
by Hye In Kim, Jung Min Sung, Hyung Uk Cho, Yong Jo Kim, Young Gwan Park and Woo Young Choi
Electronics 2021, 10(1), 29; https://doi.org/10.3390/electronics10010029 - 28 Dec 2020
Cited by 8 | Viewed by 6834
Abstract
A low leakage poly-Si thin film transistor (TFT) is proposed featuring hydrogenated amorphous silicon (a-Si:H) buffer layer and source/drain extension (SDE) by using technology computer aided design (TCAD) simulation. This architecture reduces off-current effectively by suppressing two leakage current generation mechanisms with little [...] Read more.
A low leakage poly-Si thin film transistor (TFT) is proposed featuring hydrogenated amorphous silicon (a-Si:H) buffer layer and source/drain extension (SDE) by using technology computer aided design (TCAD) simulation. This architecture reduces off-current effectively by suppressing two leakage current generation mechanisms with little on-current loss. The amorphous silicon buffer layer having large bandgap energy (Eg) suppresses both thermal generation and minimum leakage current, which leads to higher on/off current ratio. In addition, the formation of lightly doped region near the drain alleviates the field-enhanced generation in the off-state by reducing electric field. TCAD simulation results show that the proposed TFT shows more than three orders of magnitude lower off-current than low-temperature polycrystalline silicon (LTPS) TFTs, while maintaining on-current. Full article
(This article belongs to the Special Issue Reliability Assessment and Modeling of Optical and Semiconductor Devices)
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26 pages, 2892 KiB  
Article
A Survey of Candidate Waveforms for beyond 5G Systems
by Filipe Conceição, Marco Gomes, Vitor Silva, Rui Dinis, Adão Silva and Daniel Castanheira
Electronics 2021, 10(1), 21; https://doi.org/10.3390/electronics10010021 - 25 Dec 2020
Cited by 24 | Viewed by 4191
Abstract
The 5G and beyond future wireless networks aim to support a large variety of services with increasing demand in terms of data rate and throughput while providing a higher degree of reliability, keeping the overall system complexity affordable. One of the key aspects [...] Read more.
The 5G and beyond future wireless networks aim to support a large variety of services with increasing demand in terms of data rate and throughput while providing a higher degree of reliability, keeping the overall system complexity affordable. One of the key aspects regarding the physical layer architecture of such systems is the definition of the waveform to be used in the air interface. Such waveforms must be studied and compared in order to choose the most suitable and capable of providing the 5G and beyond services requirements, with flexible resource allocation in time and frequency domains, while providing high spectral and power efficiencies. In this paper, several beyond 5G waveforms candidates are presented, along with their transceiver architectures. Additionally, the associated advantages and disadvantages regarding the use of these transmission techniques are discussed. They are compared in a similar downlink transmission scenario where three main key performance indicators (KPIs) are evaluated. They are the peak-to-average power ratio, the overall system spectral efficiency (wherein the out of band emissions are measured, along with the spectral confinement of the power spectral density of the transmitted signals) and the bit error rate performance. Additionally, other KPIs are discussed. Full article
(This article belongs to the Special Issue Advanced Communication Techniques for 5G and Internet of Things)
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12 pages, 6124 KiB  
Article
Ultra-Wideband MIMO Array for Penetrating Lunar Regolith Structures on the Chang’e-5 Lander
by Wei Lu, Yuxi Li, Yicai Ji, Chuanjun Tang, Bin Zhou and Guangyou Fang
Electronics 2021, 10(1), 8; https://doi.org/10.3390/electronics10010008 - 23 Dec 2020
Cited by 7 | Viewed by 2538
Abstract
The Chang’e-5 lunar exploration mission of China is equipped with a Lunar Regolith Penetrating Radar (LRPR) for measuring the thickness and structures of the lunar regolith in the landing area. Since the LRPR is stationary, an ultra-wideband multiple-input multiple-output (MIMO) array is designed [...] Read more.
The Chang’e-5 lunar exploration mission of China is equipped with a Lunar Regolith Penetrating Radar (LRPR) for measuring the thickness and structures of the lunar regolith in the landing area. Since the LRPR is stationary, an ultra-wideband multiple-input multiple-output (MIMO) array is designed as a replacement for conventional mobile subsurface probing systems. The MIMO array, with 12 antenna elements and a switch matrix, operates in the frequency band from 1.0 to 4.75 GHz. In this work, the design and layout of the antenna elements were optimized with respect to the lander. To this end, the antenna elements were designed as miniaturized Vivaldi antennas with quarter elliptical slots (i.e., quarter elliptical slotted antenna, or QESA). QESAs are significantly small while being able to mitigate the impact of the lander on antenna electrical performances. QESAs also have a wide operating bandwidth, flat gain, and excellent time domain characteristics. In addition, a high-temperature resistant ultra-light radome with high transmissivity is designed to protect the external antenna array. After calibration, the MIMO array is used to detect targets embedded in volcanic ash. The detection depth reaches 2.5 m, and the detection effect is good. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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19 pages, 644 KiB  
Article
A Public Platform for Virtual IoT-Based Monitoring and Tracking of COVID-19
by Younchan Jung and Ronnel Agulto
Electronics 2021, 10(1), 12; https://doi.org/10.3390/electronics10010012 - 23 Dec 2020
Cited by 18 | Viewed by 4341
Abstract
The world is developing an app that alerts my smartphone when a COVID-19 (COrona VIrus Disease 19) confirmed case comes near me. However, regardless of what will be put to practical use first, the COVID-19 tracking system should satisfy the issues of legalization [...] Read more.
The world is developing an app that alerts my smartphone when a COVID-19 (COrona VIrus Disease 19) confirmed case comes near me. However, regardless of what will be put to practical use first, the COVID-19 tracking system should satisfy the issues of legalization of location tracking and scalability as a public platform used by the world. Additional problems need solutions related to real-time authentication for information gathering, blind naming and privacy of tracked persons, and quality of service on the Query/Reply procedure. This paper proposes the Software-Defined Networking Controller-centric global public platform to monitor and track information for the COVID-19 relevant people and provide real-time information disclosure services to world-wide Centers for Disease Control and Prevention (CDCs) and regular users. The CDC manages a list of people who needs to be monitored related to the COVID-19 and forcibly installs COVID-19 virtual Internet of Things (vIoT) nodes in the form of applications on their smartphones. In addition to these nodes, the vIoT support nodes also engage as information providers to improve the quality of information services. The design of our platform aims to ensure confidentiality and authentication services giving individually different secret keys. In addition, our platform meets system scalability and reduces Query/Reply latency, where the platform accommodates a large number of world-wide CDCs and persons in control per CDC. Full article
(This article belongs to the Special Issue Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications)
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23 pages, 1281 KiB  
Article
CNN2Gate: An Implementation of Convolutional Neural Networks Inference on FPGAs with Automated Design Space Exploration
by Alireza Ghaffari and Yvon Savaria
Electronics 2020, 9(12), 2200; https://doi.org/10.3390/electronics9122200 - 21 Dec 2020
Cited by 24 | Viewed by 4286
Abstract
Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement [...] Read more.
Convolutional Neural Networks (CNNs) have a major impact on our society, because of the numerous services they provide. These services include, but are not limited to image classification, video analysis, and speech recognition. Recently, the number of researches that utilize FPGAs to implement CNNs are increasing rapidly. This is due to the lower power consumption and easy reconfigurability that are offered by these platforms. Because of the research efforts put into topics, such as architecture, synthesis, and optimization, some new challenges are arising for integrating suitable hardware solutions to high-level machine learning software libraries. This paper introduces an integrated framework (CNN2Gate), which supports compilation of a CNN model for an FPGA target. CNN2Gate is capable of parsing CNN models from several popular high-level machine learning libraries, such as Keras, Pytorch, Caffe2, etc. CNN2Gate extracts computation flow of layers, in addition to weights and biases, and applies a “given” fixed-point quantization. Furthermore, it writes this information in the proper format for the FPGA vendor’s OpenCL synthesis tools that are then used to build and run the project on FPGA. CNN2Gate performs design-space exploration and fits the design on different FPGAs with limited logic resources automatically. This paper reports results of automatic synthesis and design-space exploration of AlexNet and VGG-16 on various Intel FPGA platforms. Full article
(This article belongs to the Section Artificial Intelligence Circuits and Systems (AICAS))
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10 pages, 3058 KiB  
Article
Electrical Performance and Stability Improvements of High-Mobility Indium–Gallium–Tin Oxide Thin-Film Transistors Using an Oxidized Aluminum Capping Layer of Optimal Thickness
by Hyun-Seok Cha, Hwan-Seok Jeong, Seong-Hyun Hwang, Dong-Ho Lee and Hyuck-In Kwon
Electronics 2020, 9(12), 2196; https://doi.org/10.3390/electronics9122196 - 20 Dec 2020
Cited by 12 | Viewed by 3675
Abstract
We examined the effects of aluminum (Al) capping layer thickness on the electrical performance and stability of high-mobility indium–gallium–tin oxide (IGTO) thin-film transistors (TFTs). The Al capping layers with thicknesses (tAls) of 3, 5, and 8 nm were deposited, respectively, [...] Read more.
We examined the effects of aluminum (Al) capping layer thickness on the electrical performance and stability of high-mobility indium–gallium–tin oxide (IGTO) thin-film transistors (TFTs). The Al capping layers with thicknesses (tAls) of 3, 5, and 8 nm were deposited, respectively, on top of the IGTO thin film by electron beam evaporation, and the IGTO TFTs without and with Al capping layers were subjected to thermal annealing at 200 °C for 1 h in ambient air. Among the IGTO TFTs without and with Al capping layers, the TFT with a 3 nm thick Al capping layer exhibited excellent electrical performance (field-effect mobility: 26.4 cm2/V s, subthreshold swing: 0.20 V/dec, and threshold voltage: −1.7 V) and higher electrical stability under positive and negative bias illumination stresses than other TFTs. To elucidate the physical mechanism responsible for the observed phenomenon, we compared the O1s spectra of the IGTO thin films without and with Al capping layers using X-ray photoelectron spectroscopy analyses. From the characterization results, it was observed that the weakly bonded oxygen-related components decreased from 25.0 to 10.0%, whereas the oxygen-deficient portion was maintained at 24.4% after the formation of the 3 nm thick Al capping layer. In contrast, a significant increase in the oxygen-deficient portion was observed after the formation of the Al capping layers having tAl values greater than 3 nm. These results imply that the thicker Al capping layer has a stronger gathering power for the oxygen species, and that 3 nm is the optimum thickness of the Al capping layer, which can selectively remove the weakly bonded oxygen species acting as subgap tail states within the IGTO. The results of this study thus demonstrate that the formation of an Al capping layer with the optimal thickness is a practical and useful method to enhance the electrical performance and stability of high-mobility IGTO TFTs. Full article
(This article belongs to the Special Issue Applications of Thin Films in Microelectronics)
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15 pages, 1352 KiB  
Article
A Compact and Robust Technique for the Modeling and Parameter Extraction of Carbon Nanotube Field Effect Transistors
by Laura Falaschetti, Davide Mencarelli, Nicola Pelagalli, Paolo Crippa, Giorgio Biagetti, Claudio Turchetti, George Deligeorgis and Luca Pierantoni
Electronics 2020, 9(12), 2199; https://doi.org/10.3390/electronics9122199 - 20 Dec 2020
Cited by 4 | Viewed by 3598
Abstract
Carbon nanotubes field-effect transistors (CNTFETs) have been recently studied with great interest due to the intriguing properties of the material that, in turn, lead to remarkable properties of the charge transport of the device channel. Downstream of the full-wave simulations, the construction of [...] Read more.
Carbon nanotubes field-effect transistors (CNTFETs) have been recently studied with great interest due to the intriguing properties of the material that, in turn, lead to remarkable properties of the charge transport of the device channel. Downstream of the full-wave simulations, the construction of equivalent device models becomes the basic step for the advanced design of high-performance CNTFET-based nanoelectronics circuits and systems. In this contribution, we introduce a strategy for deriving a compact model for a CNTFET that is based on the full-wave simulation of the 3D geometry by using the finite element method, followed by the derivation of a compact circuit model and extraction of equivalent parameters. We show examples of CNTFET simulations and extract from them the fitting parameters of the model. The aim is to achieve a fully functional description in Verilog-A language and create a model library for the SPICE-like simulator environment, in order to be used by IC designers. Full article
(This article belongs to the Section Microelectronics)
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17 pages, 1664 KiB  
Article
Wavelet Transform Analysis of Heart Rate to Assess Recovery Time for Long Distance Runners
by Grzegorz Redlarski, Janusz Siebert, Marek Krawczuk, Arkadiusz Zak, Ludmila Danilowicz-Szymanowicz, Lukasz Dolinski, Piotr Gutknecht, Bartosz Trzeciak, Wojciech Ratkowski and Aleksander Palkowski
Electronics 2020, 9(12), 2189; https://doi.org/10.3390/electronics9122189 - 18 Dec 2020
Cited by 1 | Viewed by 2913
Abstract
The diagnostics of the condition of athletes has become a field of special scientific interest and activity. The aim of this study was to verify the effect of a long (100 km) run on a group of runners, as well as to assess [...] Read more.
The diagnostics of the condition of athletes has become a field of special scientific interest and activity. The aim of this study was to verify the effect of a long (100 km) run on a group of runners, as well as to assess the recovery time that is required for them to return to the pre-run state. The heart rate (HR) data presented were collected the day before the extreme physical effort, on the same day as, but after, the physical effort, as well as 24 and 48 h after. The Wavelet Transform (WT) and the Wavelet-based Fractal Analysis (WBFA) were implemented in the analysis. A tool was constructed that, based on quantitative data, enables one to confirm the completion of the recovery process that is related to the extreme physical effort. Indirectly, a tool was constructed that enables one to confirm the completion of the recovery process. The obtained information proves that the return to the resting state of the body after a significant physical effort can be observed after two days entirely through the analysis of the HR. Certain practical measures were used to differentiate between two substantially different states of the human body, i.e., pre- and post-effort states were constructed. The obtained results allow for us to state that WBFA appears to be a useful and robust tool in the determination of hidden features of stochastic signals, such as HR time signals. The proposed method allows one to differentiate between particular days of measurements with a mean probability of 92.2%. Full article
(This article belongs to the Special Issue New Challenges in Biomedical Signal Processing: Computational Theory and Applications)
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13 pages, 8656 KiB  
Article
Event-Focused Digital Control to Keep High Efficiency in a Wide Power Range in a SiC-Based Synchronous DC/DC Boost Converter
by María R. Rogina, Alberto Rodríguez, Aitor Vázquez, Diego G. Lamar and Marta M. Hernando
Electronics 2020, 9(12), 2154; https://doi.org/10.3390/electronics9122154 - 16 Dec 2020
Cited by 4 | Viewed by 2068
Abstract
This paper is focused on the design of a control approach, based on the detection of events and changing between two different conduction modes, to reach high efficiency over the entire power range, especially at medium and low power levels. Although the proposed [...] Read more.
This paper is focused on the design of a control approach, based on the detection of events and changing between two different conduction modes, to reach high efficiency over the entire power range, especially at medium and low power levels. Although the proposed control strategy can be generalized for different topologies and specifications, in this paper, the strategy is validated in a SiC-based synchronous boost DC/DC converter rated for 400 V to 800 V and 10 kW. Evaluation of the power losses and current waveforms of the converter for different conduction modes and loads predicts suitable performance of quasi-square wave mode with zero voltage switching (QSW-ZVS) conduction mode for low and medium power and of continuous conduction Mode with hard switching (CCM-HS) for high power. Consequently, this paper proposes a control strategy, taking advantage of digital control, that allows automatic adjustment of the conduction mode to optimize the performance for different power ranges. Full article
(This article belongs to the Special Issue Innovative Technologies in Power Converters)
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38 pages, 2460 KiB  
Review
Visible Light Communications for Industrial Applications—Challenges and Potentials
by Yousef Almadani, David Plets, Sander Bastiaens, Wout Joseph, Muhammad Ijaz, Zabih Ghassemlooy and Sujan Rajbhandari
Electronics 2020, 9(12), 2157; https://doi.org/10.3390/electronics9122157 - 16 Dec 2020
Cited by 63 | Viewed by 10073
Abstract
Visible Light Communication (VLC) is a short-range optical wireless communication technology that has been gaining attention due to its potential to offload heavy data traffic from the congested radio wireless spectrum. At the same time, wireless communications are becoming crucial to smart manufacturing [...] Read more.
Visible Light Communication (VLC) is a short-range optical wireless communication technology that has been gaining attention due to its potential to offload heavy data traffic from the congested radio wireless spectrum. At the same time, wireless communications are becoming crucial to smart manufacturing within the scope of Industry 4.0. Industry 4.0 is a developing trend of high-speed data exchange in automation for manufacturing technologies and is referred to as the fourth industrial revolution. This trend requires fast, reliable, low-latency, and cost-effective data transmissions with fast synchronizations to ensure smooth operations for various processes. VLC is capable of providing reliable, low-latency, and secure connections that do not penetrate walls and is immune to electromagnetic interference. As such, this paper aims to show the potential of VLC for industrial wireless applications by examining the latest research work in VLC systems. This work also highlights and classifies challenges that might arise with the applicability of VLC and visible light positioning (VLP) systems in these settings. Given the previous work performed in these areas, and the major ongoing experimental projects looking into the use of VLC systems for industrial applications, the use of VLC and VLP systems for industrial applications shows promising potential. Full article
(This article belongs to the Special Issue New Challenges in Wireless and Free Space Optical Communications)
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