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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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20 pages, 2743 KiB  
Article
Near-Field Warping Sampling Scheme for Broad-Side Antenna Characterization
by Maria Antonia Maisto, Rocco Pierri and Raffaele Solimene
Electronics 2020, 9(6), 1047; https://doi.org/10.3390/electronics9061047 - 24 Jun 2020
Cited by 21 | Viewed by 3635
Abstract
In this paper the problem of sampling the field radiated by a planar source observed over a finite planar aperture located in the near-field is addressed. The problem is cast as the determination of the spatial measurement positions which allow us to discretize [...] Read more.
In this paper the problem of sampling the field radiated by a planar source observed over a finite planar aperture located in the near-field is addressed. The problem is cast as the determination of the spatial measurement positions which allow us to discretize the radiation problem so that the singular values of the radiation operator are well-approximated. More in detail, thanks to a suitably warping transformation of the observation variables, the kernel function of the relevant operator is approximated by a band-limited function and hence the sampling theorem applied to achieved discretization. It results in the sampling points having to be non-linearity arranged across the measurement aperture and their number can be considerably lowered as compared to more standard sampling approach. It is shown that the proposed sampling scheme works well for measurement apertures that are not too large as compared to the source’s size. As a consequence, the method appears better suited for broad-side large antenna whose radiated field is mainly concentrated in front of the antenna. A numerical analysis is included to check the theoretical findings and to study the trade-off between the field accuracy representation (over the measurement aperture) and the truncation error in the estimated far-field radiation pattern. Full article
(This article belongs to the Special Issue Photonic and Microwave Sensing Developments and Applications)
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20 pages, 2233 KiB  
Article
Exploring Malware Behavior of Webpages Using Machine Learning Technique: An Empirical Study
by Alhanoof Faiz Alwaghid and Nurul I. Sarkar
Electronics 2020, 9(6), 1033; https://doi.org/10.3390/electronics9061033 - 23 Jun 2020
Cited by 4 | Viewed by 3550
Abstract
Malware is one of the most common security threats experienced by a user when browsing webpages. A good understanding of the features of webpages (e.g., internet protocol, port, URL, Google index, and page rank) is required to analyze and mitigate the behavior of [...] Read more.
Malware is one of the most common security threats experienced by a user when browsing webpages. A good understanding of the features of webpages (e.g., internet protocol, port, URL, Google index, and page rank) is required to analyze and mitigate the behavior of malware in webpages. This main objective of this paper is to analyze the key features of webpages and to mitigate the behavior of malware in webpages. To this end, we conducted an empirical study to identify the features that are most vulnerable to malware attacks and its results are reported. To improve the feature selection accuracy, a machine learning technique called bagging is employed using the Weka program. To analyze these behaviors, phishing and botnet data were obtained from the University of California Irvine machine learning repository. We validate our research findings by applying honeypot infrastructure using the Modern Honeypot Network (MHN) setup in a Linode Server. As the data suffer from high variance in terms of the type of data in each row, bagging is chosen because it can classify binary classes, date classes, missing values, nominal classes, numeric classes, unary classes and empty classes. As a base classifier of bagging, random tree was applied because it can handle similar types of data such as bagging, but better than other classifiers because it is faster and more accurate. Random tree had 88.22% test accuracy with the lowest run time (0.2 sec) and a receiver operating characteristic curve of 0.946. Results show that all features in the botnet dataset are equally important to identify the malicious behavior, as all scored more than 97%, with the exception of TCP and UDP. The accuracy of phishing and botnet datasets is more than 89% on average in both cross validation and test analysis. Recommendations are made for the best practice that can assist in future malware identification. Full article
(This article belongs to the Section Computer Science & Engineering)
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25 pages, 33441 KiB  
Article
FlexAlign: An Accurate and Fast Algorithm for Movie Alignment in Cryo-Electron Microscopy
by David Střelák, Jiří Filipovič, Amaya Jiménez-Moreno, Jose María Carazo and Carlos Óscar Sánchez Sorzano
Electronics 2020, 9(6), 1040; https://doi.org/10.3390/electronics9061040 - 23 Jun 2020
Cited by 5 | Viewed by 5195
Abstract
Cryogenic Electron Microscopy (Cryo-EM) has been established as one of the key players in Structural Biology. It can reconstruct a 3D model of the sample at the near-atomic resolution, which led to a Method of the year award by Nature, and the Nobel [...] Read more.
Cryogenic Electron Microscopy (Cryo-EM) has been established as one of the key players in Structural Biology. It can reconstruct a 3D model of the sample at the near-atomic resolution, which led to a Method of the year award by Nature, and the Nobel Prize in 2017. With the growing number of facilities, faster microscopes, and new imaging techniques, new algorithms are needed to process the so-called movies data produced by the microscopes in real-time, while preserving a high resolution and maximum of additional information. In this article, we present a new algorithm used for movie alignment, called FlexAlign. FlexAlign is able to correctly compensate for the shift produced during the movie acquisition on-the-fly, using the current generation of hardware. The algorithm performs a global and elastic local registration of the movie frames using Cross-Correlation and B-spline interpolation for high precision. We show that our execution time is compatible with real-time correction and that we preserve the high-resolution information up to high frequency. Full article
(This article belongs to the Special Issue FPGA/GPU Acceleration of Biomedical Engineering Applications)
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15 pages, 8766 KiB  
Article
Test Solution for Heatsinks in Power Electronics Applications
by Davide Piumatti, Stefano Borlo, Matteo Vincenzo Quitadamo, Matteo Sonza Reorda, Eric Giacomo Armando and Franco Fiori
Electronics 2020, 9(6), 1020; https://doi.org/10.3390/electronics9061020 - 19 Jun 2020
Cited by 2 | Viewed by 3173
Abstract
Power electronics technology is widely used in several areas, such as in the railways, automotive, electric vehicles, and renewable energy sectors. Some of these applications are safety critical, e.g., in the automotive domain. The heat produced by power devices must be efficiently dissipated [...] Read more.
Power electronics technology is widely used in several areas, such as in the railways, automotive, electric vehicles, and renewable energy sectors. Some of these applications are safety critical, e.g., in the automotive domain. The heat produced by power devices must be efficiently dissipated to allow them to work within their operational thermal limits. Moreover, numerous ageing effects are due to thermal stress, which causes mechanical issues. Therefore, the reliability of a circuit depends on its dissipation system, even if it consists of a simple passive heatsink mounted on the power device. During the Printed Circuit Board (PCB) production, an incorrect assembly of the heatsink can cause a worse heat dissipation with a significant increase of the junction temperatures (Tj). In this paper, three possible test strategies are compared for testing the correct assembling of heatsinks. The considered strategies are used at the PCB end-manufacturing. The effectiveness of the different test methods considered is assessed on a case study corresponding to a Power Supply Unit (PSU). Full article
(This article belongs to the Section Power Electronics)
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18 pages, 1652 KiB  
Article
A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich
by Michael Wittmann, Lorenz Neuner and Markus Lienkamp
Electronics 2020, 9(6), 1021; https://doi.org/10.3390/electronics9061021 - 19 Jun 2020
Cited by 7 | Viewed by 5774
Abstract
The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems [...] Read more.
The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))
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21 pages, 1782 KiB  
Article
Priority-Based Bandwidth Management in Virtualized Software-Defined Networks
by Luca Leonardi, Lucia Lo Bello and Simone Aglianò
Electronics 2020, 9(6), 1009; https://doi.org/10.3390/electronics9061009 - 17 Jun 2020
Cited by 20 | Viewed by 3721
Abstract
In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the [...] Read more.
In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the combined use of Software-Defined Networking (SDN) and Network Virtualization (NV) is a promising solution, as such techniques allow to reduce the network management complexity. This work presents the PrioSDN Resource Manager (PrioSDN_RM), a resource management mechanism based on admission control for virtualized SDN-based networks. The proposed combination imposes bounds on the resource utilization for the virtual slices, which therefore share the network links, while maintaining isolation from each other. The presented approach exploits a priority-based runtime bandwidth distribution mechanism to dynamically react to load changes (e.g., due to alarms). The paper describes the design of the approach and provides experimental results obtained on a real testbed. Full article
(This article belongs to the Section Networks)
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19 pages, 4276 KiB  
Article
A Reconfigurable Convolutional Neural Network-Accelerated Coprocessor Based on RISC-V Instruction Set
by Ning Wu, Tao Jiang, Lei Zhang, Fang Zhou and Fen Ge
Electronics 2020, 9(6), 1005; https://doi.org/10.3390/electronics9061005 - 16 Jun 2020
Cited by 28 | Viewed by 6782
Abstract
As a typical artificial intelligence algorithm, the convolutional neural network (CNN) is widely used in the Internet of Things (IoT) system. In order to improve the computing ability of an IoT CPU, this paper designs a reconfigurable CNN-accelerated coprocessor based on the RISC-V [...] Read more.
As a typical artificial intelligence algorithm, the convolutional neural network (CNN) is widely used in the Internet of Things (IoT) system. In order to improve the computing ability of an IoT CPU, this paper designs a reconfigurable CNN-accelerated coprocessor based on the RISC-V instruction set. The interconnection structure of the acceleration chain designed by the predecessors is optimized, and the accelerator is connected to the RISC-V CPU core in the form of a coprocessor. The corresponding instruction of the coprocessor is designed and the instruction compiling environment is established. Through the inline assembly in the C language, the coprocessor instructions are called, coprocessor acceleration library functions are established, and common algorithms in the IoT system are implemented on the coprocessor. Finally, resource consumption evaluation and performance analysis of the coprocessor are completed on a Xilinx FPGA. The evaluation results show that the reconfigurable CNN-accelerated coprocessor only consumes 8534 LUTS, accounting for 47.6% of the total SoC system. The number of instruction cycles required to implement functions such as convolution and pooling based on the designed coprocessor instructions is better than using the standard instruction set, and the acceleration ratio of convolution is 6.27 times that of the standard instruction set. Full article
(This article belongs to the Special Issue Advanced AI Hardware Designs Based on FPGAs)
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21 pages, 7929 KiB  
Article
Regulated Charge Pumps: A Comparative Study by Means of Verilog-AMS
by Andrea Ballo, Michele Bottaro, Alfio Dario Grasso and Gaetano Palumbo
Electronics 2020, 9(6), 998; https://doi.org/10.3390/electronics9060998 - 15 Jun 2020
Cited by 29 | Viewed by 6349
Abstract
This paper proposes a comparative study of regulation schemes for charge-pump-based voltage generators using behavioral models in Verilog- Analog Mixed Signal (AMS) code. An accurate and simple model of the charge pump is first introduced. It allows reducing the simulation time of complex [...] Read more.
This paper proposes a comparative study of regulation schemes for charge-pump-based voltage generators using behavioral models in Verilog- Analog Mixed Signal (AMS) code. An accurate and simple model of the charge pump is first introduced. It allows reducing the simulation time of complex electronic systems made up by both analog and digital circuits while maintaining a good agreement with transistor-level simulations. Finally, a comprehensive comparative study of the different regulation schemes for charge pumps is reported which allows the designer to choose the most suitable topology for a given application and Charge Pump (CP) operative zone. Full article
(This article belongs to the Special Issue Low-Voltage Integrated Circuits Design and Application)
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10 pages, 1593 KiB  
Article
Progress in Violet Light-Emitting Diodes Based on ZnO/GaN Heterojunction
by Roberto Macaluso, Giuseppe Lullo, Isodiana Crupi, Daniele Sciré, Fulvio Caruso, Eric Feltin and Mauro Mosca
Electronics 2020, 9(6), 991; https://doi.org/10.3390/electronics9060991 - 13 Jun 2020
Cited by 17 | Viewed by 4130
Abstract
Progress in light-emitting diodes (LEDs) based on ZnO/GaN heterojunctions has run into several obstacles during the last twenty years. While both the energy bandgap and lattice parameter of the two semiconductors are favorable to the development of such devices, other features related to [...] Read more.
Progress in light-emitting diodes (LEDs) based on ZnO/GaN heterojunctions has run into several obstacles during the last twenty years. While both the energy bandgap and lattice parameter of the two semiconductors are favorable to the development of such devices, other features related to the electrical and structural properties of the GaN layer prevent an efficient radiative recombination. This work illustrates some advances made on ZnO/GaN-based LEDs, by using high-thickness GaN layers for the p-region of the device and an ad hoc device topology. Heterojunction LEDs consist of a quasicoalesced non-intentionally doped ZnO nanorod layer deposited by chemical bath deposition onto a metal–organic vapor-phase epitaxy -grown epitaxial layer of p-doped GaN. Circular 200 μm-sized violet-emitting LEDs with a p-n contact distance as low as 3 μm exhibit a turn-on voltage of 3 V, and an emitting optical power at 395 nm of a few microwatts. Electroluminescence spectrum investigation shows that the emissive process can be ascribed to four different recombination transitions, dominated by the electron-hole recombinations on the ZnO side. Full article
(This article belongs to the Special Issue Nitride Semiconductors Revolution: Material, Devices and Applications)
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14 pages, 2902 KiB  
Article
Automatic ECG Diagnosis Using Convolutional Neural Network
by Roberta Avanzato and Francesco Beritelli
Electronics 2020, 9(6), 951; https://doi.org/10.3390/electronics9060951 - 8 Jun 2020
Cited by 86 | Viewed by 16274
Abstract
Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution [...] Read more.
Cardiovascular disease (CVD) is the most common class of chronic and life-threatening diseases and, therefore, considered to be one of the main causes of mortality. The proposed new neural architecture based on the recent popularity of convolutional neural networks (CNN) was a solution for the development of automatic heart disease diagnosis systems using electrocardiogram (ECG) signals. More specifically, ECG signals were passed directly to a properly trained CNN network. The database consisted of more than 4000 ECG signal instances extracted from outpatient ECG examinations obtained from 47 subjects: 25 males and 22 females. The confusion matrix derived from the testing dataset indicated 99% accuracy for the “normal” class. For the “atrial premature beat” class, ECG segments were correctly classified 100% of the time. Finally, for the “premature ventricular contraction” class, ECG segments were correctly classified 96% of the time. In total, there was an average classification accuracy of 98.33%. The sensitivity (SNS) and the specificity (SPC) were, respectively, 98.33% and 98.35%. The new approach based on deep learning and, in particular, on a CNN network guaranteed excellent performance in automatic recognition and, therefore, prevention of cardiovascular diseases. Full article
(This article belongs to the Special Issue Application of Neural Networks in Biosignal Process)
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19 pages, 3752 KiB  
Article
Comparative Analysis of Rhino-Cytological Specimens with Image Analysis and Deep Learning Techniques
by Giovanni Dimauro, Vitoantonio Bevilacqua, Pio Raffaele Fina, Domenico Buongiorno, Antonio Brunetti, Sergio Latrofa, Michele Cassano and Matteo Gelardi
Electronics 2020, 9(6), 952; https://doi.org/10.3390/electronics9060952 - 8 Jun 2020
Cited by 7 | Viewed by 3320
Abstract
Cytological study of the nasal mucosa (also known as rhino-cytology) represents an important diagnostic aid that allows highlighting of the presence of some types of rhinitis through the analysis of cellular features visible under a microscope. Nowadays, the automated detection and classification of [...] Read more.
Cytological study of the nasal mucosa (also known as rhino-cytology) represents an important diagnostic aid that allows highlighting of the presence of some types of rhinitis through the analysis of cellular features visible under a microscope. Nowadays, the automated detection and classification of cells benefit from the capacity of deep learning techniques in processing digital images of the cytological preparation. Even though the results of such automatic systems need to be validated by a specialized rhino-cytologist, this technology represents a valid support that aims at increasing the accuracy of the analysis while reducing the required time and effort. The quality of the rhino-cytological preparation, which is clearly important for the microscope observation phase, is also fundamental for the automatic classification process. In fact, the slide-preparing technique turns out to be a crucial factor among the multiple ones that may modify the morphological and chromatic characteristics of the cells. This paper aims to investigate the possible differences between direct smear (SM) and cytological centrifugation (CYT) slide-preparation techniques, in order to preserve image quality during the observation and cell classification phases in rhino-cytology. Firstly, a comparative study based on image analysis techniques has been put forward. The extraction of densitometric and morphometric features has made it possible to quantify and describe the spatial distribution of the cells in the field images observed under the microscope. Statistical analysis of the distribution of these features has been used to evaluate the degree of similarity between images acquired from SM and CYT slides. The results prove an important difference in the observation process of the cells prepared with the above-mentioned techniques, with reference to cell density and spatial distribution: the analysis of CYT slides has been more difficult than of the SM ones due to the spatial distribution of the cells, which results in a lower cell density than the SM slides. As a marginal part of this study, a performance assessment of the computer-aided diagnosis (CAD) system called Rhino-cyt has also been carried out on both groups of image slide types. 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, 3984 KiB  
Article
A Fuzzy Logic-Based Control Algorithm for the Recharge/V2G of a Nine-Phase Integrated On-Board Battery Charger
by Felice De Luca, Vito Calderaro and Vincenzo Galdi
Electronics 2020, 9(6), 946; https://doi.org/10.3390/electronics9060946 - 7 Jun 2020
Cited by 17 | Viewed by 3881
Abstract
Energy demand associated with the ever-increasing penetration of electric vehicles on worldwide roads is set to rise exponentially in the coming years. The fact that more and more vehicles will be connected to the electricity network will offer greater advantages to the network [...] Read more.
Energy demand associated with the ever-increasing penetration of electric vehicles on worldwide roads is set to rise exponentially in the coming years. The fact that more and more vehicles will be connected to the electricity network will offer greater advantages to the network operators, as the presence of an on-board battery of discrete capacity will be able to support a whole series of ancillary services or smart energy management. To allow this, the vehicle must be equipped with a bidirectional full power charger, which will allow not only recharging but also the supply of energy to the network, playing an active role as a distributed energy resource. To manage recharge and vehicle-to-grid (V2G) operations, the charger has to be more complex and has to require a fast and effective control structure. In this work, we present a control strategy for an integrated on-board battery charger with a nine-phase electric machine. The control scheme integrates a fuzzy logic controller within a voltage-oriented control strategy. The control has been implemented and simulated in Simulink. The results show how the voltage on the DC-bus is controlled to the reference value by the fuzzy controller and how the CC/CV charging mode of the battery is possible, using different charging/discharging current levels. This allows both three-phase fast charge and V2G operations with fast control response time, without causing relevant distortion grid-side (Total Harmonic Distortion is maintained around 3%), even in the presence of imbalances of the machine, and with very low ripple stress on the battery current/voltage. Full article
(This article belongs to the Special Issue Prospects for Integrating Electric Vehicles into Power Systems)
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26 pages, 459 KiB  
Article
Game Theoretical Analysis of a Multi-MNO MVNO Business Model in 5G Networks
by Erwin Jairo Sacoto Cabrera, Luis Guijarro and Patrick Maillé
Electronics 2020, 9(6), 933; https://doi.org/10.3390/electronics9060933 - 4 Jun 2020
Cited by 4 | Viewed by 5114
Abstract
This paper analyzes the economic feasibility of a business model for multi-Mobile Network Operators (MNOs) and Mobile Virtual Network Operators (MVNOs), which is an envisioned scenario in mobile telecommunications markets supported by 5G networks. A business model for the provision of service to [...] Read more.
This paper analyzes the economic feasibility of a business model for multi-Mobile Network Operators (MNOs) and Mobile Virtual Network Operators (MVNOs), which is an envisioned scenario in mobile telecommunications markets supported by 5G networks. A business model for the provision of service to end-users through an MVNO using the infrastructure support of two MNOs is proposed. We analyze the proposal though a model that captures both system and economic features. As regards the systems features, an MVNO provides service to final users using the infrastructure support of two MNOs. The agreement between MVNO and MNOs is such that the MVNO will split the network traffic between the two MNOs and will pay to each MNO for the traffic served through its infrastructure. As regards the economic features, the incentives are modelled through the user utilities and the operators’ profits; and game theory is used to model the strategic interaction between the users’ subscription decision and the MNO network capacities decision. We conclude that such a model is feasible from an economic point of view for all the actors. Full article
(This article belongs to the Section Networks)
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20 pages, 2309 KiB  
Article
Battery Second-Life for Dedicated and Shared Energy Storage Systems Supporting EV Charging Stations
by Giuseppe Graber, Vito Calderaro, Vincenzo Galdi and Antonio Piccolo
Electronics 2020, 9(6), 939; https://doi.org/10.3390/electronics9060939 - 4 Jun 2020
Cited by 17 | Viewed by 6059
Abstract
Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use [...] Read more.
Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use of energy storage systems (ESSs) could be an effective solution to reduce the peak power request by CSs in PAs to the grid. Moreover, II-Life battery modules are a potential approach for cutting costs and implementing sustainable solutions. We propose a method to size ESSs coupled to CSs by using II-Life battery modules. Our methodology is based on the estimation of the residual cycles and the decrease in the supplied power due to the battery aging for defining the number of EV battery packs required for an ESS use case. Then, economic evaluations are presented to compare II-Life with the equivalent I-Life storage system. Full article
(This article belongs to the Special Issue Prospects for Integrating Electric Vehicles into Power Systems)
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19 pages, 4949 KiB  
Article
A Trustworthy SIoT Aware Mechanism as an Enabler for Citizen Services in Smart Cities
by Ateeq Ur Rehman, Rizwan Ali Naqvi, Abdul Rehman, Anand Paul, Muhammad Tariq Sadiq and Dildar Hussain
Electronics 2020, 9(6), 918; https://doi.org/10.3390/electronics9060918 - 1 Jun 2020
Cited by 42 | Viewed by 4067
Abstract
In the recent era, new information technologies have a significant impact on social networks. Initial integration of information and communication technologies (ICT) into city operations has promoted information city, ease of communication and principles of smart communities. Subsequently, the idea of the Internet [...] Read more.
In the recent era, new information technologies have a significant impact on social networks. Initial integration of information and communication technologies (ICT) into city operations has promoted information city, ease of communication and principles of smart communities. Subsequently, the idea of the Internet of Things (IoT) with the specific focus of social IoT (SIoT) has contributed towards the smart cities (SC), which support the city operations with minimal human interaction. The user-generated data obtained by SIoT can be exploited to produce new useful information for creating citizen-centered smart services for SC. The aim of this research is twofold. Firstly, we used the concept of local and global trust to provide new services in SC based on popular online social networks (OSN) data used by the citizens. Secondly, the sustainability of the three different OSN is assessed. This paper investigates the social network domain with regard to the SC. Although in SC, OSN are increasing day by day, there is still an unresolved issue of trust among their users and also OSN are not much sustainable. In this research, we are analyzing the sustainability of different OSN for the SC. We employ datasets of three different social networks for our analyses. A local trust model is used to identify the central user within the local cluster while the global trust-based framework is used to identify the opinion leaders. Our analysis based on the datasets of Facebook, Twitter, and Slashdot unveil that filtration of these central-local users and opinion leaders result in the dispersion and significant reduction in a network. A novel model is being developed that outlines the relationship between local and global trust for the protection of OSN users in SC. Furthermore, the proposed mechanism uses the data posted by citizens on OSN to propose new services by mitigating the effect of untrusted users. Full article
(This article belongs to the Special Issue IoT Services, Applications, Platform, and Protocols)
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12 pages, 2029 KiB  
Article
Characterization of Novel Structures Consisting of Micron-Sized Conductive Particles That Respond to Static Magnetic Field Lines for 4G/5G (Sub-6 GHz) Reconfigurable Antennas
by Adnan Iftikhar, Jacob M. Parrow, Sajid M. Asif, Adnan Fida, Jeffery Allen, Monica Allen, Benjamin D. Braaten and Dimitris E. Anagnostou
Electronics 2020, 9(6), 903; https://doi.org/10.3390/electronics9060903 - 29 May 2020
Cited by 10 | Viewed by 3508
Abstract
Controlling Radio Frequency (RF) signals through switching technology is of interest to designers of modern wireless platforms such as Advanced Wireless services (AWS) from 2.18 GHz–2.2 GHz, mid-bands of sub-6 GHz 5G (2.5 GHz and 3.5 GHz), and 4G bands around 600 MHz/700 [...] Read more.
Controlling Radio Frequency (RF) signals through switching technology is of interest to designers of modern wireless platforms such as Advanced Wireless services (AWS) from 2.18 GHz–2.2 GHz, mid-bands of sub-6 GHz 5G (2.5 GHz and 3.5 GHz), and 4G bands around 600 MHz/700 MHz, 1.7 GHz/2.1 GHz/2.3 GHz/2.5 GHz. This is because certain layout efficiencies can be achieved if suitable components are chosen to control these signals. The objective of this paper is to present a new model of an RF switch denoted as a Magnetostatic Responsive Structure (MRS) for achieving reconfigurable operation in 4G/5G antennas. In particular, the ABCD matrices of the MRS are derived from the S-parameter values and shown to be a good model from 100 kHz to 3.5 GHz. Furthermore, an overall agreement between simulations, analytical results, and circuit model values are shown. Full article
(This article belongs to the Special Issue Reconfigurable Antennas)
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14 pages, 2616 KiB  
Article
Multistability Emergence through Fractional-Order-Derivatives in a PWL Multi-Scroll System
by José Luis Echenausía-Monroy, Guillermo Huerta-Cuellar, Rider Jaimes-Reátegui, Juan Hugo García-López, Vicente Aboites, Bahia Betzavet Cassal-Quiroga and Héctor Eduardo Gilardi-Velázquez
Electronics 2020, 9(6), 880; https://doi.org/10.3390/electronics9060880 - 26 May 2020
Cited by 20 | Viewed by 2726
Abstract
In this paper, the emergence of multistable behavior through the use of fractional-order-derivatives in a Piece-Wise Linear (PWL) multi-scroll generator is presented. Using the integration-order as a bifurcation parameter, the stability in the system is modified in such a form that produces a [...] Read more.
In this paper, the emergence of multistable behavior through the use of fractional-order-derivatives in a Piece-Wise Linear (PWL) multi-scroll generator is presented. Using the integration-order as a bifurcation parameter, the stability in the system is modified in such a form that produces a basin of attraction segmentation, creating many stable states as scrolls are generated in the integer-order system. The results here presented reproduce the same phenomenon reported in systems with integer-order derivatives, where the multistable regimen is obtained through a parameter variation. The multistable behavior reported is also validated through electronic simulation. The presented results are not only applicable in engineering fields, but they also enrich the analysis and the understanding of the implications of using fractional integration orders, boosting the development of further and better studies. Full article
(This article belongs to the Special Issue Recent Advances in Modeling, Analysis and Control of Hybrid Systems and Their Applications)
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22 pages, 6025 KiB  
Review
A Survey on Through-the-Road Hybrid Electric Vehicles
by Gianfranco Rizzo, Shayesteh Naghinajad, Francesco Antonio Tiano and Matteo Marino
Electronics 2020, 9(5), 879; https://doi.org/10.3390/electronics9050879 - 25 May 2020
Cited by 19 | Viewed by 6595
Abstract
Hybrid Electric Vehicles (HEVs) can be divided into three categories according to how the two propulsion systems (the thermal and the electric ones) supply the driving torque to the vehicle. When the torque is supplied only by an electric propulsion system, while the [...] Read more.
Hybrid Electric Vehicles (HEVs) can be divided into three categories according to how the two propulsion systems (the thermal and the electric ones) supply the driving torque to the vehicle. When the torque is supplied only by an electric propulsion system, while the heat engine takes care of generating the electricity needed to operate the system, it is called a hybrid-series. Conversely, when both propulsion systems provide torque, the vehicle is identified with parallel hybrid wording. Among the parallel hybrids there is a particular configuration called Through-the-Road (TTR). In this configuration, the two propulsion systems are not mechanically connected to each other, but it is precisely the road that allows hybrid propulsion. This architecture, dating back to the early twentieth century, is still used by several manufacturers and carries with it peculiar configurations and control methods. It is also a configuration that fits well with the transformation of conventional vehicles into a hybrid. The paper presents a survey of the TTR HEV solution, evidencing applications, potentialities and limits. 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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12 pages, 5602 KiB  
Article
Design of a Low Power 10-b 8-MS/s Asynchronous SAR ADC with On-Chip Reference Voltage Generator
by Khuram Shehzad, Deeksha Verma, Danial Khan, Qurat Ul Ain, Muhammad Basim, Sung Jin Kim, YoungGun Pu, Keum Cheol Hwang, Youngoo Yang and Kang-Yoon Lee
Electronics 2020, 9(5), 872; https://doi.org/10.3390/electronics9050872 - 24 May 2020
Cited by 14 | Viewed by 7776
Abstract
This paper presents an energy-efficient low power 10-b 8-MS/s asynchronous successive approximation register (SAR) analog-to-digital (ADC) converter. An inverted common-mode charge recovery technique is proposed to reduce the switching energy and to improve the linearity of the digital-to-analog converter (DAC). The proposed switching [...] Read more.
This paper presents an energy-efficient low power 10-b 8-MS/s asynchronous successive approximation register (SAR) analog-to-digital (ADC) converter. An inverted common-mode charge recovery technique is proposed to reduce the switching energy and to improve the linearity of the digital-to-analog converter (DAC). The proposed switching technique consumes only 149 CVREF2 switching energy for the 10-bit case. A rail-to-rail dynamic latch comparator is implemented with adaptive power control for better power efficiency. Additionally, to optimize the power consumption and performance of the logic part, a modified asynchronous type SAR control logic with digitally controllable delay cells is adopted. An on-chip reference voltage generator is also designed with an ADC core for practical use. The structure is realized using 55-nm complementary metal–oxide–semiconductor (CMOS) process technology. The proposed architecture achieves an effective number of bits (ENOB) of 9.56 bits and a signal-to-noise and distortion ratio (SNDR) level of 59.3 dB with a sampling rate of 8 MS/s at measurement level. The whole architecture consumes only 572 µW power when a power supply of 1 V is applied. Full article
(This article belongs to the Special Issue Analog/Digital Mixed Circuit and RF Transceiver Design)
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17 pages, 5667 KiB  
Article
A Pipeline for Adaptive Filtering and Transformation of Noisy Left-Arm ECG to Its Surrogate Chest Signal
by Farzad Mohaddes, Rafael Luiz da Silva, Fatma Patlar Akbulut, Yilu Zhou, Akhilesh Tanneeru, Edgar Lobaton, Bongmook Lee and Veena Misra
Electronics 2020, 9(5), 866; https://doi.org/10.3390/electronics9050866 - 23 May 2020
Cited by 9 | Viewed by 4588
Abstract
The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least [...] Read more.
The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations. Full article
(This article belongs to the Section Microelectronics)
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18 pages, 4504 KiB  
Article
Continuous Gesture Recognition Based on Time Sequence Fusion Using MIMO Radar Sensor and Deep Learning
by Wentai Lei, Xinyue Jiang, Long Xu, Jiabin Luo, Mengdi Xu and Feifei Hou
Electronics 2020, 9(5), 869; https://doi.org/10.3390/electronics9050869 - 23 May 2020
Cited by 20 | Viewed by 4346
Abstract
Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated [...] Read more.
Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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32 pages, 1710 KiB  
Review
Network Management and Monitoring Solutions for Vehicular Networks: A Survey
by João A. F. F. Dias, Joel J. P. C. Rodrigues, Vasco N. G. J. Soares, João M. L. P. Caldeira, Valery Korotaev and Mario L. Proença
Electronics 2020, 9(5), 853; https://doi.org/10.3390/electronics9050853 - 21 May 2020
Cited by 9 | Viewed by 5818
Abstract
Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such [...] Read more.
Vehicular networks are emerging as a promising technology that enables reliable and low-cost solutions for intelligent transport systems (ITSs), mainly due to their enormous potential to be considered for multiple purposes and scenarios. These networks are characterized by unique and challenging features such as packet fragmentation, low node density, short contact duration, and network disruption. These features may result in the absence of a path between the source and destination nodes, which is one of the most challenging issues faced by this type of network. To overcome some of these problems, it is necessary to provide vehicular networks with sophisticated tools or methodologies to implement monitoring and management operations. However, designing efficient solutions for this type of network is not an easy task due to its particular characteristics. This paper elaborates on a comprehensive survey focusing on promising proposals to deal with monitoring and management functionalities in vehicular networks. This work aims not only to present the state of the art on monitoring and management solutions but also to analyze their benefits and drawbacks, identify open issues, and provide guidelines for further contributions. Full article
(This article belongs to the Special Issue Delay Tolerant Networks and Applications)
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21 pages, 2479 KiB  
Article
Nonlinear Voltage Control for Three-Phase DC-AC Converters in Hybrid Systems: An Application of the PI-PBC Method
by Federico M. Serra, Lucas M. Fernández, Oscar D. Montoya, Walter Gil-González and Jesus C. Hernández
Electronics 2020, 9(5), 847; https://doi.org/10.3390/electronics9050847 - 20 May 2020
Cited by 29 | Viewed by 3741
Abstract
In this paper, a proportional-integral passivity-based controller (PI-PBC) is proposed to regulate the amplitude and frequency of the three-phase output voltage in a direct-current alternating-current (DC-AC) converter with an LC filter. This converter is used to supply energy to AC loads in hybrid [...] Read more.
In this paper, a proportional-integral passivity-based controller (PI-PBC) is proposed to regulate the amplitude and frequency of the three-phase output voltage in a direct-current alternating-current (DC-AC) converter with an LC filter. This converter is used to supply energy to AC loads in hybrid renewable based systems. The proposed strategy uses the well-known proportional-integral (PI) actions and guarantees the stability of the system by means of the Lyapunov theory. The proposed controller continues to maintain the simplicity and robustness of the PI controls using the Hamiltonian representation of the system, thereby ensuring stability and producing improvements in the performance. The performance of the proposed controller was validated based on simulation and experimental results after considering parametric variations and comparing them with classical approaches. Full article
(This article belongs to the Special Issue Grid-Connected Renewable Energy Sources)
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15 pages, 4448 KiB  
Article
Assessment of Head Impacts and Muscle Activity in Soccer Using a T3 Inertial Sensor and a Portable Electromyography (EMG) System: A Preliminary Study
by Matthew T. O. Worsey, Bethany S. Jones, Andres Cervantes, Sabrina P. Chauvet, David V. Thiel and Hugo G. Espinosa
Electronics 2020, 9(5), 834; https://doi.org/10.3390/electronics9050834 - 19 May 2020
Cited by 14 | Viewed by 5086
Abstract
Heading the ball is an important skill in soccer. Head impacts are of concern because of the potential adverse health effects. Many elite players now wear GPS (that include inertial monitoring units) on the upper spine for location tracking and workload measurement. By [...] Read more.
Heading the ball is an important skill in soccer. Head impacts are of concern because of the potential adverse health effects. Many elite players now wear GPS (that include inertial monitoring units) on the upper spine for location tracking and workload measurement. By measuring the maximum acceleration of the head and the upper spine, we calculated the acceleration ratio as an attenuation index for participants (n = 8) of different skill levels during a front heading activity. This would allow for in-field estimates of head impacts to be made and concussive events detected. For novice participants, the ratio was as high as 8.3 (mean value 5.0 ± 1.8), whereas, for experienced players, the mean ratio was 3.2 ± 1.5. Elite players stiffen the neck muscles to increase the ball velocity and so the torso acts as a support structure. Electromyography (EMG) signals that were recorded from the neck and shoulder before and after a training intervention showed a major increase in mean average muscle activity (146%, p = 3.39 × 10−6). This was accompanied by a major decrease in acceleration ratio (34.41%, p = 0.008). The average head-ball impact velocity was 1.95 ± 0.53 m/s determined while using optical motion capture. For this low velocity, the impact force was 102 ± 19 N, 13% of the published concussive force. The voluntary action of neck muscles decreases isolated head movements during heading. Coaches and trainers may use this evidence in their development of junior players. Full article
(This article belongs to the Special Issue Wearable Electronics for Assessing Human Motor (dis)Abilities)
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30 pages, 409 KiB  
Review
Covid-19: Open-Data Resources for Monitoring, Modeling, and Forecasting the Epidemic
by Teodoro Alamo, Daniel G. Reina, Martina Mammarella and Alberto Abella
Electronics 2020, 9(5), 827; https://doi.org/10.3390/electronics9050827 - 17 May 2020
Cited by 77 | Viewed by 19201
Abstract
We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data [...] Read more.
We provide an insight into the open-data resources pertinent to the study of the spread of the Covid-19 pandemic and its control. We identify the variables required to analyze fundamental aspects like seasonal behavior, regional mortality rates, and effectiveness of government measures. Open-data resources, along with data-driven methodologies, provide many opportunities to improve the response of the different administrations to the virus. We describe the present limitations and difficulties encountered in most of the open-data resources. To facilitate the access to the main open-data portals and resources, we identify the most relevant institutions, on a global scale, providing Covid-19 information and/or auxiliary variables (demographics, mobility, etc.). We also describe several open resources to access Covid-19 datasets at a country-wide level (i.e., China, Italy, Spain, France, Germany, US, etc.). To facilitate the rapid response to the study of the seasonal behavior of Covid-19, we enumerate the main open resources in terms of weather and climate variables. We also assess the reusability of some representative open-data sources. Full article
(This article belongs to the Section Artificial Intelligence)
17 pages, 4497 KiB  
Article
Closing the Wearable Gap—Part VI: Human Gait Recognition Using Deep Learning Methodologies
by Samaneh Davarzani, David Saucier, Preston Peranich, Will Carroll, Alana Turner, Erin Parker, Carver Middleton, Phuoc Nguyen, Preston Robertson, Brian Smith, John Ball, Reuben Burch, Harish Chander, Adam Knight, Raj Prabhu and Tony Luczak
Electronics 2020, 9(5), 796; https://doi.org/10.3390/electronics9050796 - 12 May 2020
Cited by 23 | Viewed by 4546
Abstract
A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well [...] Read more.
A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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13 pages, 7898 KiB  
Article
Experimental Implementation of a Low-Cost, Fully-Analog Self-Jamming Canceller for UHF RFID Devices
by Massimiliano Rossi, Riccardo Maria Liberati, Marco Frasca and John Richardson
Electronics 2020, 9(5), 786; https://doi.org/10.3390/electronics9050786 - 11 May 2020
Cited by 4 | Viewed by 4018
Abstract
It is quite common for transceivers to operate with the RF receiver and transmitter working on different time slots. Typical applications are radars and transceivers in the field of communications. Generally, the receiver is turned off when the transmitter broadcasts and vice versa. [...] Read more.
It is quite common for transceivers to operate with the RF receiver and transmitter working on different time slots. Typical applications are radars and transceivers in the field of communications. Generally, the receiver is turned off when the transmitter broadcasts and vice versa. This is done in order to prevent the transmitter from blinding the receiver or causing the RF low noise amplification (LNA) stage to saturate. When keeping a receiver active, some leakage of RF energy is inevitable, and therefore shielding is applied to mitigate spurious signals. However, there are many applications wherein the receiver cannot be turned off. To address these applications, we investigate the design and performance of a fully-analog self-jamming canceller able to operate in UHF (Ultra High Frequency) RFID devices. While the traditional cost to design and build this type of topology can be quite high, our proposal is based on a low-cost physical approach. In addition to using common SMT (Surface Mount Technology) devices, we leveraged a new piece of modular technology offered by X-Microwave which allows designers to easily produce RF solutions with a broad portfolio of modular system drop-in blocks. A prototype was realized and the measured results are in close agreement with theoretical simulations. Significant damping of the leaked signal in the receiving channel was realized. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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9 pages, 2873 KiB  
Article
Design and Analysis of fT-Doubler-Based RF Amplifiers in SiGe HBT Technology
by Md Arifur R. Sarker and Ickhyun Song
Electronics 2020, 9(5), 772; https://doi.org/10.3390/electronics9050772 - 8 May 2020
Cited by 2 | Viewed by 3873
Abstract
For performance-driven systems such as space-based applications, it is important to maximize the gain of radio-frequency amplifiers (RFAs) with a certain tolerance against radiation, temperature effects, and small form factor. In this work, we present a K-band, compact high-gain RFA using an f [...] Read more.
For performance-driven systems such as space-based applications, it is important to maximize the gain of radio-frequency amplifiers (RFAs) with a certain tolerance against radiation, temperature effects, and small form factor. In this work, we present a K-band, compact high-gain RFA using an fT-doubler topology in a silicon-germanium (SiGe) heterojunction bipolar transistors (HBTs) technology platform. The through-silicon vias (TSVs), typically used for small-size chip packaging purposes, have been effectively utilized as an adjustable matching element for input impedance, reducing the overall area of the chip. The proposed RFA, fabricated in a modest 0.35 µm SiGe technology, achieves a gain of 14.1 dB at 20 GHz center frequency, and a noise figure (NF) of 11.2 dB at the same frequency, with a power consumption of 3.3 mW. The proposed design methodology can be used for achieving high gain, avoiding a complex multi-stage amplifier design approach. Full article
(This article belongs to the Special Issue Extreme-Environment Electronics: Challenges and Solutions)
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15 pages, 1821 KiB  
Article
Hand Movement Activity-Based Character Input System on a Virtual Keyboard
by Md Abdur Rahim and Jungpil Shin
Electronics 2020, 9(5), 774; https://doi.org/10.3390/electronics9050774 - 8 May 2020
Cited by 13 | Viewed by 4363
Abstract
Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way [...] Read more.
Nowadays, gesture-based technology is revolutionizing the world and lifestyles, and the users are comfortable and care about their needs, for example, in communication, information security, the convenience of day-to-day operations and so forth. In this case, hand movement information provides an alternative way for users to interact with people, machines or robots. Therefore, this paper presents a character input system using a virtual keyboard based on the analysis of hand movements. We analyzed the signals of the accelerometer, gyroscope, and electromyography (EMG) for movement activity. We explored potential features of removing noise from input signals through the wavelet denoising technique. The envelope spectrum is used for the analysis of the accelerometer and gyroscope and cepstrum for the EMG signal. Furthermore, the support vector machine (SVM) is used to train and detect the signal to perform character input. In order to validate the proposed model, signal information is obtained from predefined gestures, that is, “double-tap”, “hold-fist”, “wave-left”, “wave-right” and “spread-finger” of different respondents for different input actions such as “input a character”, “change character”, “delete a character”, “line break”, “space character”. The experimental results show the superiority of hand gesture recognition and accuracy of character input compared to state-of-the-art systems. Full article
(This article belongs to the Special Issue Human Computer Interaction and Its Future)
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15 pages, 7022 KiB  
Article
A Quadratic Fractional Map without Equilibria: Bifurcation, 0–1 Test, Complexity, Entropy, and Control
by Adel Ouannas, Amina-Aicha Khennaoui, Shaher Momani, Giuseppe Grassi, Viet-Thanh Pham, Reyad El-Khazali and Duy Vo Hoang
Electronics 2020, 9(5), 748; https://doi.org/10.3390/electronics9050748 - 1 May 2020
Cited by 27 | Viewed by 2710
Abstract
Fractional calculus in discrete-time systems is a recent research topic. The fractional maps introduced in the literature often display chaotic attractors belonging to the class of “self-excited attractors”. The field of fractional map with “hidden attractors” is completely unexplored. Based on these considerations, [...] Read more.
Fractional calculus in discrete-time systems is a recent research topic. The fractional maps introduced in the literature often display chaotic attractors belonging to the class of “self-excited attractors”. The field of fractional map with “hidden attractors” is completely unexplored. Based on these considerations, this paper presents the first example of fractional map without equilibria showing a number of hidden attractors for different values of the fractional order. The presence of the chaotic hidden attractors is validated via the computation of bifurcation diagrams, maximum Lyapunov exponent, 0–1 test, phase diagrams, complexity, and entropy. Finally, an active controller with the aim for stabilizing the proposed fractional map is successfully designed. Full article
(This article belongs to the Special Issue Recent Advances in Modeling, Analysis and Control of Hybrid Systems and Their Applications)
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9 pages, 1423 KiB  
Article
Understanding of Polarization-Induced Threshold Voltage Shift in Ferroelectric-Gated Field Effect Transistor for Neuromorphic Applications
by Seungjun Moon, Jaemin Shin and Changhwan Shin
Electronics 2020, 9(5), 704; https://doi.org/10.3390/electronics9050704 - 25 Apr 2020
Cited by 10 | Viewed by 5814
Abstract
A ferroelectric-gated fin-shaped field effect transistor (Fe-FinFET) is fabricated by connecting a Pb(Zr0.2Ti0.8)O3-based ferroelectric capacitor into the gate electrode of FinFET. The ferroelectric capacitor shows coercive voltages of approximately −1.5 V and 2.25 V. The polarization-induced threshold [...] Read more.
A ferroelectric-gated fin-shaped field effect transistor (Fe-FinFET) is fabricated by connecting a Pb(Zr0.2Ti0.8)O3-based ferroelectric capacitor into the gate electrode of FinFET. The ferroelectric capacitor shows coercive voltages of approximately −1.5 V and 2.25 V. The polarization-induced threshold voltage shift in the Fe-FinFET is investigated by regulating the gate voltage sweep range. When the maximum positive gate to source voltage is varied from 4 V to 2 V with a fixed starting negative gate to source voltage, the threshold voltage during the backward sweep is increased from approximately −0.60 V to 1.04 V. In the case of starting negative gate to source voltage variation from −4 V to −0.5 V with a fixed maximum positive gate to source voltage of 4 V, the threshold voltage during the forward sweep is decreased from 1.66 V to 0.87 V. Those results can be elucidated with polarization domain states. Lastly, it is observed that the threshold voltage is mostly increased/decreased when the positive/negative gate voltage sweep range is smaller/larger than the positive/negative coercive voltage, respectively. Full article
(This article belongs to the Special Issue Steep-Switching Devices)
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18 pages, 1790 KiB  
Article
SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection
by Neelu Khare, Preethi Devan, Chiranji Lal Chowdhary, Sweta Bhattacharya, Geeta Singh, Saurabh Singh and Byungun Yoon
Electronics 2020, 9(4), 692; https://doi.org/10.3390/electronics9040692 - 24 Apr 2020
Cited by 123 | Viewed by 9798
Abstract
The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence [...] Read more.
The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches. Full article
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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27 pages, 8213 KiB  
Article
Hydrogen vs. Battery in the Long-term Operation. A Comparative Between Energy Management Strategies for Hybrid Renewable Microgrids
by Andrea Monforti Ferrario, Francisco José Vivas, Francisca Segura Manzano, José Manuel Andújar, Enrico Bocci and Luigi Martirano
Electronics 2020, 9(4), 698; https://doi.org/10.3390/electronics9040698 - 24 Apr 2020
Cited by 26 | Viewed by 5982
Abstract
The growth of the world’s energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces [...] Read more.
The growth of the world’s energy demand over recent decades in relation to energy intensity and demography is clear. At the same time, the use of renewable energy sources is pursued to address decarbonization targets, but the stochasticity of renewable energy systems produces an increasing need for management systems to supply such energy volume while guaranteeing, at the same time, the security and reliability of the microgrids. Locally distributed energy storage systems (ESS) may provide the capacity to temporarily decouple production and demand. In this sense, the most implemented ESS in local energy districts are small–medium-scale electrochemical batteries. However, hydrogen systems are viable for storing larger energy quantities thanks to its intrinsic high mass-energy density. To match generation, demand and storage, energy management systems (EMSs) become crucial. This paper compares two strategies for an energy management system based on hydrogen-priority vs. battery-priority for the operation of a hybrid renewable microgrid. The overall performance of the two mentioned strategies is compared in the long-term operation via a set of evaluation parameters defined by the unmet load, storage efficiency, operating hours and cumulative energy. The results show that the hydrogen-priority strategy allows the microgrid to be led towards island operation because it saves a higher amount of energy, while the battery-priority strategy reduces the energy efficiency in the storage round trip. The main contribution of this work lies in the demonstration that conventional EMS for microgrids’ operation based on battery-priority strategy should turn into hydrogen-priority to keep the reliability and independence of the microgrid in the long-term operation. Full article
(This article belongs to the Special Issue Intelligent Modelling and Control in Renewable Energy Systems)
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25 pages, 1269 KiB  
Article
Gaussian-Process-Based Surrogate for Optimization-Aided and Process-Variations-Aware Analog Circuit Design
by Adriana C. Sanabria-Borbón, Sergio Soto-Aguilar, Johan J. Estrada-López, Douglas Allaire and Edgar Sánchez-Sinencio
Electronics 2020, 9(4), 685; https://doi.org/10.3390/electronics9040685 - 23 Apr 2020
Cited by 15 | Viewed by 3974
Abstract
Optimization algorithms have been successfully applied to the automatic design of analog integrated circuits. However, many of the existing solutions rely on expensive circuit simulations or use fully customized surrogate models for each particular circuit and technology. Therefore, the development of an easily [...] Read more.
Optimization algorithms have been successfully applied to the automatic design of analog integrated circuits. However, many of the existing solutions rely on expensive circuit simulations or use fully customized surrogate models for each particular circuit and technology. Therefore, the development of an easily adaptable low-cost and efficient tool that guarantees resiliency to variations of the resulting design, remains an open research area. In this work, we propose a computationally low-cost surrogate model for multi-objective optimization-based automated analog integrated circuit (IC) design. The surrogate has three main components: a set of Gaussian process regression models of the technology’s parameters, a physics-based model of the MOSFET device, and a set of equations of the performance metrics of the circuit under design. The surrogate model is inserted into two different state-of-the-art optimization algorithms to prove its flexibility. The efficacy of our surrogate is demonstrated through simulation validation across process corners in three different CMOS technologies, using three representative circuit building-blocks that are commonly encountered in mainstream analog/RF ICs. The proposed surrogate is 69 X to 470 X faster at evaluation compared with circuit simulations. Full article
(This article belongs to the Section Circuit and Signal Processing)
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13 pages, 2408 KiB  
Article
A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments
by JongBeom Lim and DaeWon Lee
Electronics 2020, 9(4), 686; https://doi.org/10.3390/electronics9040686 - 23 Apr 2020
Cited by 28 | Viewed by 4521
Abstract
As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload [...] Read more.
As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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20 pages, 1792 KiB  
Review
On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
by Vahid Kouhdaragh, Francesco Verde, Giacinto Gelli and Jamshid Abouei
Electronics 2020, 9(4), 689; https://doi.org/10.3390/electronics9040689 - 23 Apr 2020
Cited by 41 | Viewed by 5716
Abstract
A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively [...] Read more.
A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies. Full article
(This article belongs to the Section Networks)
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12 pages, 1363 KiB  
Article
Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies
by Solomia Fedushko, Taras Ustyianovych and Michal Gregus
Electronics 2020, 9(4), 668; https://doi.org/10.3390/electronics9040668 - 20 Apr 2020
Cited by 56 | Viewed by 4886
Abstract
An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big [...] Read more.
An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure KPIs and metrics. The methods used in the study include machine learning models, data preprocessing, missing data imputation, SRE (site reliability engineering) indicators computation, quantitative research, and a qualitative study of data project demands. A requirements analysis for the implementation of an Operational Intelligence solution with Machine learning capabilities has been conducted and represented in the study. A model based on machine learning algorithms for transaction status code and output predictions, in order to execute system load testing, risks identification and, to avoid downtimes, is developed. Metrics and indicators for determining infrastructure load are given in the paper to obtain Operational intelligence and Site reliability insights. It turned out that data mining among the set of Operational Big Data simplifies the task of getting an understanding of what is happening with requests within the data acquisition pipeline and helps identify errors before a user faces them. Transaction tracing in a distributed environment has been enhanced using machine learning and mathematical modelling. Additionally, a step-by-step algorithm for applying the application monitoring solution in a data-based project, especially when it is dealing with Big Data is described and proposed within the study. Full article
(This article belongs to the Special Issue Electronization of Businesses - Systems Engineering and Analytics)
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11 pages, 4169 KiB  
Article
Application of Terahertz Spectroscopy to Rubber Products: Evaluation of Vulcanization and Silica Macro Dispersion
by Yasuyuki Hirakawa, Yuki Yasumoto, Toyohiko Gondo, Ryota Sone, Toshiaki Morichika, Takakazu Minato and Masahiro Hojo
Electronics 2020, 9(4), 669; https://doi.org/10.3390/electronics9040669 - 20 Apr 2020
Cited by 4 | Viewed by 2662
Abstract
Industrial applications of terahertz (THz) technology are becoming more widespread. In particular, novel evaluation methods for essential rubber products are being developed. THz absorbance spectra of various rubber polymers and reagents enable visualization of filler dispersions and vulcanization reactions. Here, improved visualization of [...] Read more.
Industrial applications of terahertz (THz) technology are becoming more widespread. In particular, novel evaluation methods for essential rubber products are being developed. THz absorbance spectra of various rubber polymers and reagents enable visualization of filler dispersions and vulcanization reactions. Here, improved visualization of the vulcanization reaction in thick rubber samples is discussed. Silica macro-dispersion is also analyzed because it is a general filler in automobile tires and has been difficult to evaluate with conventional techniques. Full article
(This article belongs to the Special Issue Terahertz Technology and Its Applications)
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16 pages, 1249 KiB  
Article
A Low Complexity Joint Encryption-Modulation Method for IoT Sensor Transceivers
by Dai Long Hoang, Thi Hong Tran and Yasuhiko Nakashima
Electronics 2020, 9(4), 663; https://doi.org/10.3390/electronics9040663 - 19 Apr 2020
Viewed by 3184
Abstract
Physical layer encryption (PLE) is a new research trend for securing data in communication systems. However, conventional procedures in works on PLE are of high complexity and degrade the packet error rate (PER) performance of the system. They are therefore not yet suitable [...] Read more.
Physical layer encryption (PLE) is a new research trend for securing data in communication systems. However, conventional procedures in works on PLE are of high complexity and degrade the packet error rate (PER) performance of the system. They are therefore not yet suitable for IoT sensors’ transceiver, which has limited power and computational resource. In this paper, we propose a low complexity PLE method named as joint encryption-modulation (JEM) for small transceivers such as IoT sensors. In our JEM method, data is encrypted after modulation to preserve high security. Our JEM method does not make change the constellation of the modulation after encryption; therefore, the encryption does not degrade PER performance of the system as the conventional works do. Furthermore, the encryption is performed by XOR gates and multiplexers only. It is, therefore, low complexity. Our experiment results show that the JEM method improves about 3 dB of PER performance as compared with that of conventional works. JEM method can support multiple modulation types such as BPSK, QPSK, 16-256 QAM within a small hardware cost. Compared with conventional works, JEM’s hardware resource is reduced by 87.5% in terms of FPGA synthesis and 86.5% in terms of the ASIC circuit. ASIC static power consumption of JEM is reduced by 80.6%. Full article
(This article belongs to the Section Circuit and Signal Processing)
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16 pages, 3771 KiB  
Article
Energy-Efficient Ternary Multipliers Using CNT Transistors
by Sepehr Tabrizchi, Atiyeh Panahi, Fazel Sharifi, Hamid Mahmoodi and Abdel-Hameed A. Badawy
Electronics 2020, 9(4), 643; https://doi.org/10.3390/electronics9040643 - 14 Apr 2020
Cited by 23 | Viewed by 3560
Abstract
In recent decades, power consumption has become an essential factor in attracting the attention of integrated circuit (IC) designers. Multiple-valued logic (MVL) and approximate computing are some techniques that could be applied to integrated circuits to make power-efficient systems. By utilizing MVL-based circuits [...] Read more.
In recent decades, power consumption has become an essential factor in attracting the attention of integrated circuit (IC) designers. Multiple-valued logic (MVL) and approximate computing are some techniques that could be applied to integrated circuits to make power-efficient systems. By utilizing MVL-based circuits instead of binary logic, the information conveyed by digital signals increases, and this reduces the required interconnections and power consumption. On the other hand, approximate computing is a class of arithmetic computing used in systems where the accuracy of the computation can be traded-off for lower energy consumption. In this paper, we propose novel designs for exact and inexact ternary multipliers based on carbon-nanotube field-effect transistors (CNFETs). The unique characteristics of CNFETs make them a desirable alternative to MOSFETs. The simulations are conducted using Synopsys HSPICE. The proposed design is compared against existing ternary multipliers. The results show that the proposed exact multiplier reduces the energy consumption by up to 6 times, while the best inexact design improves energy efficiency by up to 35 time compared to the latest state-of-the-art methods. Using the imprecise multipliers for image processing provides evidence that these proposed designs are a low-power system with an acceptable error. Full article
(This article belongs to the Section Microelectronics)
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15 pages, 3092 KiB  
Article
High-Capacity Data Hiding for ABTC-EQ Based Compressed Image
by Cheonshik Kim, Ching-Nung Yang and Lu Leng
Electronics 2020, 9(4), 644; https://doi.org/10.3390/electronics9040644 - 14 Apr 2020
Cited by 30 | Viewed by 3214
Abstract
We present a new data hiding method based on Adaptive BTC Edge Quantization (ABTC-EQ) using an optimal pixel adjustment process (OPAP) to optimize two quantization levels. The reason we choose ABTC-EQ as a cover media is that it is superior to AMBTC in [...] Read more.
We present a new data hiding method based on Adaptive BTC Edge Quantization (ABTC-EQ) using an optimal pixel adjustment process (OPAP) to optimize two quantization levels. The reason we choose ABTC-EQ as a cover media is that it is superior to AMBTC in maintaining a high-quality image after encoding is executed. ABTC-EQ is represented by a form of t r i o ( Q 1 , Q 2 , [ Q 3 ] , BM) where Q is quantization levels ( Q 1 ≤ Q 2 ≤ Q 3 ) , and BM is a bitmap). The number of quantization levels are two or three, depending on whether the cover image has an edge or not. Before embedding secret bits in every block, we categorize every block into smooth block or complex block by a threshold. In case a block size is 4x4, the sixteen secret bits are replaced by a bitmap of the smooth block for embedding a message directly. On the other hand, OPAP method conceals 1 bit into LSB and 2LSB respectively, and maintains the quality of an image as a way of minimizing the errors which occur in the embedding procedure. The sufficient experimental results demonsrate that the performance of our proposed scheme is satisfactory in terms of the embedding capacity and quality of an image. Full article
(This article belongs to the Special Issue Theory and Applications in Digital Signal Processing)
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18 pages, 8493 KiB  
Article
Influence of PWM Methods on Semiconductor Losses and Thermal Cycling of 15-kVA Three-Phase SiC Inverter for Aircraft Applications
by Bernardo Cougo, Lenin M. F. Morais, Gilles Segond, Raphael Riva and Hoan Tran Duc
Electronics 2020, 9(4), 620; https://doi.org/10.3390/electronics9040620 - 7 Apr 2020
Cited by 22 | Viewed by 5350
Abstract
This paper presents the influence of different pulse width modulation (PWM) methods on losses and thermal stresses in SiC power modules used in a three-phase inverter. The variation of PWM methods directly impacts instantaneous losses on these semiconductors, consequently resulting in junction temperature [...] Read more.
This paper presents the influence of different pulse width modulation (PWM) methods on losses and thermal stresses in SiC power modules used in a three-phase inverter. The variation of PWM methods directly impacts instantaneous losses on these semiconductors, consequently resulting in junction temperature swing at the fundamental frequency of the converter’s output current. This thermal cycling can significantly reduce the lifetime of these components. In order to determine semiconductor losses, one needs to characterize SiC devices to calculate the instantaneous power. The characterization methodology of the devices, the calculation of instantaneous power and temperature of SiC dies, and the influence of the different PWM methods are presented. A 15-kVA inverter is built in order to obtain experimental results to confirm the characterization and loss calculation, and we show the best PWM methods to increase efficiency and reliability of the three-phase inverter for specific aircraft applications. Full article
(This article belongs to the Section Semiconductor Devices)
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18 pages, 6896 KiB  
Article
IoT-enabled Microgrid for Intelligent Energy-aware Buildings: A Novel Hierarchical Self-consumption Scheme with Renewables
by Yanpeng Wu, Ying Wu, Josep M. Guerrero, Juan C. Vasquez, Emilio José Palacios-García and Yajuan Guan
Electronics 2020, 9(4), 550; https://doi.org/10.3390/electronics9040550 - 25 Mar 2020
Cited by 23 | Viewed by 5379
Abstract
This paper presents a novel hierarchical Internet of Things (IoT)-based scheme for Microgrid-Enabled Intelligent Buildings to achieve energy digitalization and automation with a renewable energy self-consumption strategy. Firstly, a hierarchical structure of Microgrid-Enabled Intelligent Buildings is designed to establish a two-dimensional fusion layered [...] Read more.
This paper presents a novel hierarchical Internet of Things (IoT)-based scheme for Microgrid-Enabled Intelligent Buildings to achieve energy digitalization and automation with a renewable energy self-consumption strategy. Firstly, a hierarchical structure of Microgrid-Enabled Intelligent Buildings is designed to establish a two-dimensional fusion layered architecture for the microgrid to interact with the composite loads of buildings. The building blocks and functions of each layer are defined specifically. Secondly, to achieve transparent information fusion and interactive cooperation between the supply-side and demand-side, a state transition mechanism driven by a combination of time and events is proposed to activate the real-time and mutual response of generation and loads dynamically. Thirdly, based on the above hierarchical fusion structure and data-driven state transition mechanism, a power balance control algorithm driven by a self-consumption strategy is further proposed to achieve the autonomous balance of supply and demand. Finally, the IoT Microgrid Laboratory at Aalborg University is introduced to show how to implement this novel hierarchical IoT-based scheme in a Microgrid-Enabled Intelligent Building, and the power consensus control method based on the state transition mechanism is verified to achieve a renewable energy self-consumption strategy. Full article
(This article belongs to the Special Issue Energy Management Based on Internet of Things)
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26 pages, 3201 KiB  
Article
A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
by Pablo Merino, Gabriel Mujica, Jaime Señor and Jorge Portilla
Electronics 2020, 9(3), 538; https://doi.org/10.3390/electronics9030538 - 24 Mar 2020
Cited by 12 | Viewed by 5008
Abstract
The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with [...] Read more.
The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario. Full article
(This article belongs to the Special Issue Recent Advances in Embedded Computing, Intelligence and Applications)
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18 pages, 2936 KiB  
Article
A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks
by Imtiaz Ullah and Qusay H. Mahmoud
Electronics 2020, 9(3), 530; https://doi.org/10.3390/electronics9030530 - 23 Mar 2020
Cited by 75 | Viewed by 6062
Abstract
The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to [...] Read more.
The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifies the category or subcategory of detected malicious activity. When the network flow classified as an anomaly by the level-1 model, then the level-1 model forwards the stream to the level-2 model for further investigation to find the category or subcategory of the detected anomaly. Our proposed model constructed on flow-based features of the IoT network. Flow-based detection methodologies only inspect packet headers to classify the network traffic. Flow-based features extracted from the IoT Botnet dataset and various machine learning algorithms were investigated and tested via different cross-fold validation tests to select the best algorithm. The decision tree classifier yielded the highest predictive results for level-1, and the random forest classifier produced the highest predictive results for level-2. Our proposed model Accuracy, Precision, Recall, and F score for level-1 were measured as 99.99% and 99.90% for level-2. A two-level anomalous activity detection system for IoT networks we proposed will provide a robust framework for the development of malicious activity detection system for IoT networks. It would be of interest to researchers in academia and industry. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Intelligent Intrusion Detection Systems)
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29 pages, 11127 KiB  
Article
LoRaWAN Network for Fire Monitoring in Rural Environments
by Sandra Sendra, Laura García, Jaime Lloret, Ignacio Bosch and Roberto Vega-Rodríguez
Electronics 2020, 9(3), 531; https://doi.org/10.3390/electronics9030531 - 23 Mar 2020
Cited by 55 | Viewed by 9874
Abstract
The number of forest fires that occurred in recent years in different parts of the world is causing increased concern in the population, as the consequences of these fires expand beyond the destruction of the ecosystem. However, with the proliferation of the Internet [...] Read more.
The number of forest fires that occurred in recent years in different parts of the world is causing increased concern in the population, as the consequences of these fires expand beyond the destruction of the ecosystem. However, with the proliferation of the Internet of Things (IoT) industry, solutions for early fire detection should be developed. The assessment of the fire risk of an area and the communication of this fact to the population could reduce the number of fires originated by accident or due to the carelessness of the users. This paper presents a low-cost network based on Long Range (LoRa) technology to autonomously evaluate the level of fire risk and the presence of a forest fire in rural areas. The system is comprised of several LoRa nodes with sensors to measure the temperature, relative humidity, wind speed and CO2 of the environment. The data from the nodes is stored and processed in a The Things Network (TTN) server that sends the data to a website for the graphic visualization of the collected data. The system is tested in a real environment and, the results show that it is possible to cover a circular area of a radius of 4 km with a single gateway. Full article
(This article belongs to the Special Issue Intelligent and Cooperation Communication and Networking Technologies for IoT)
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10 pages, 5232 KiB  
Article
Effect of Mg Doping on the Electrical Performance of a Sol-Gel-Processed SnO2 Thin-Film Transistor
by Won-Yong Lee, Hyunjae Lee, Seunghyun Ha, Changmin Lee, Jin-Hyuk Bae, In-Man Kang, Kwangeun Kim and Jaewon Jang
Electronics 2020, 9(3), 523; https://doi.org/10.3390/electronics9030523 - 22 Mar 2020
Cited by 16 | Viewed by 4902
Abstract
Sol-gel-processed Mg-doped SnO2 thin-film transistors (TFTs) were successfully fabricated. The effect of Mg concentration on the structural, chemical, and optical properties of thin films and the corresponding TFT devices was investigated. The results indicated that an optimal Mg concentration yielded an improved [...] Read more.
Sol-gel-processed Mg-doped SnO2 thin-film transistors (TFTs) were successfully fabricated. The effect of Mg concentration on the structural, chemical, and optical properties of thin films and the corresponding TFT devices was investigated. The results indicated that an optimal Mg concentration yielded an improved negative bias stability and increased optical band gap, resulting in transparent devices. Furthermore, the optimal device performance was obtained with 0.5 wt% Mg. The fabricated 0.5 wt% Mg-doped SnO2 TFT was characterized by a field effect mobility, a subthreshold swing, and Ion/Ioff ratio of 4.23 cm2/Vs, 1.37 V/decade, and ~1 × 107, respectively. The added Mg suppressed oxygen-vacancy formation, thereby improving the bias stability. This work may pave the way for the development of alkaline-earth-metal-doped SnO2-based thin-film devices. Full article
(This article belongs to the Special Issue Applications of Thin Films in Microelectronics)
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15 pages, 1207 KiB  
Article
Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions
by Anuroop Gaddam, Tim Wilkin, Maia Angelova and Jyotheesh Gaddam
Electronics 2020, 9(3), 511; https://doi.org/10.3390/electronics9030511 - 20 Mar 2020
Cited by 115 | Viewed by 10421
Abstract
The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is [...] Read more.
The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed. Full article
(This article belongs to the Special Issue Smart Sensor Networks)
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21 pages, 787 KiB  
Article
A Data Verification System for CCTV Surveillance Cameras Using Blockchain Technology in Smart Cities
by Prince Waqas Khan, Yung-Cheol Byun and Namje Park
Electronics 2020, 9(3), 484; https://doi.org/10.3390/electronics9030484 - 15 Mar 2020
Cited by 102 | Viewed by 16747
Abstract
The video created by a surveillance cameras plays a crucial role in crime prevention and examinations in smart cities. The closed-circuit television camera (CCTV) is essential for a range of public uses in a smart city; combined with Internet of Things (IoT) technologies [...] Read more.
The video created by a surveillance cameras plays a crucial role in crime prevention and examinations in smart cities. The closed-circuit television camera (CCTV) is essential for a range of public uses in a smart city; combined with Internet of Things (IoT) technologies they can turn into smart sensors that help to ensure safety and security. However, the authenticity of the camera itself raises issues of building up integrity and suitability of data. In this paper, we present a blockchain-based system to guarantee the trustworthiness of the stored recordings, allowing authorities to validate whether or not a video has been altered. It helps to discriminate fake videos from original ones and to make sure that surveillance cameras are authentic. Since the distributed ledger of the blockchain records the metadata of the CCTV video as well, it is obstructing the chance of forgery of the data. This immutable ledger diminishes the risk of copyright encroachment for law enforcement agencies and clients users by securing possession and identity. Full article
(This article belongs to the Special Issue Intelligent Closed-Circuit Television and Applications)
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29 pages, 10918 KiB  
Article
Sentiment Analysis Based on Deep Learning: A Comparative Study
by Nhan Cach Dang, María N. Moreno-García and Fernando De la Prieta
Electronics 2020, 9(3), 483; https://doi.org/10.3390/electronics9030483 - 14 Mar 2020
Cited by 393 | Viewed by 61706
Abstract
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the [...] Read more.
The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features. Full article
(This article belongs to the Section Artificial Intelligence)
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