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Electronics
Volume 9
Issue 4
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Electronics, Volume 9, Issue 4 (April 2020) – 160 articles

Cover Story (view full-size image): We present a fully-differential implementation of the classical Sallen Key filter, which is can operate up to about 10 GHz by exploiting, unlike previously published solutions, both bipolar and MOS transistors of commercial 55-nm BiCMOS technology. The layout of the biquad filter has been implemented, and the results of post-layout simulations are reported. The biquad stage exhibits excellent SFDR (64 dB) and dynamic range (about 50 dB) due to the closed-loop operation, and good power efficiency (0.94 pW/Hz/pole) with respect to comparable active inductorless lowpass filters reported in the literature.View this paper.
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14 pages, 2704 KiB  
Article
An Analysis of Battery Degradation in the Integrated Energy Storage System with Solar Photovoltaic Generation
by Munsu Lee, Jinhyeong Park, Sun-Ik Na, Hyung Sik Choi, Byeong-Sik Bu and Jonghoon Kim
Electronics 2020, 9(4), 701; https://doi.org/10.3390/electronics9040701 - 24 Apr 2020
Cited by 25 | Viewed by 5193
Abstract
Renewable energy generation and energy storage systems are considered key technologies for reducing greenhouse gas emissions. Energy system planning and operation requires more accurate forecasts of intermittent renewable energy resources that consider the impact of battery degradation on the system caused by the [...] Read more.
Renewable energy generation and energy storage systems are considered key technologies for reducing greenhouse gas emissions. Energy system planning and operation requires more accurate forecasts of intermittent renewable energy resources that consider the impact of battery degradation on the system caused by the accumulation of charging and discharging cycles. In this study, a statistical model is presented for forecasting a day-ahead photovoltaic (PV) generation considering solar radiation and weather parameters. In addition, the technical performance of energy storage systems (ESS) should be evaluated by considering battery degradation that occurs during the charge and discharge cycles of the battery. In this study, a battery degradation model based on the data-driven method is used. Based on a suitable forecasting model, ESS scheduling is performed to charge the maximum amount of PV generation and discharge for the self-consumption of the customer load when PV generation ends. Since the battery is highly dependent on operating conditions such as depth of discharge, state of charge and temperature, two different ESS charge and discharge modes are proposed. From the simulation with the battery degradation model using parameters derived from experiments, we show that the battery is degraded along with charging cycles during testing periods. Variations in state of health are observed owing to the different characteristics of the battery according to the ESS operation modes, which are divided into the low and high SOC. Through experimental validation, it is proved that the state of charge (SOC), 0.45 is the optimal threshold that can determine the low and high SOC. Finally, the simulation results lead to the conclusion that the battery degradation in different operation modes should be taken into account to extend the end of life efficiently. Full article
(This article belongs to the Special Issue Energy Storage Technologies)
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<p>MV<sub>hour</sub> model forecast; (<b>a</b>) PV forecasting on a sunny day; (<b>b</b>) PV forecasting on a cloudy day.</p> Full article ">Figure 2
<p>Self-consumption ESS schedule in mode 1.</p> Full article ">Figure 3
<p>Self-consumption ESS schedule in mode 2.</p> Full article ">Figure 4
<p>Comparison of the day-ahead battery degradation estimations.</p> Full article ">Figure 5
<p>Relationship between the state of charge (SOC)-open circuit voltage (OCV) curve.</p> Full article ">Figure 6
<p>Slope of the linearized SOC-OCV curve.</p> Full article ">
25 pages, 2424 KiB  
Review
Quality of Life, Quality of Experience, and Security Perception in Web of Things: An Overview of Research Opportunities
by Sabina Baraković, Jasmina Baraković Husić, Dardan Maraj, Arianit Maraj, Ondrej Krejcar, Petra Maresova and Francisco Jose Melero
Electronics 2020, 9(4), 700; https://doi.org/10.3390/electronics9040700 - 24 Apr 2020
Cited by 16 | Viewed by 4765
Abstract
The Web of Things (WoT) is a technology concept that allows the integration of the Internet of Things (IoT) with the World Wide Web (WWW). It will vastly affect our lives in the near future given that it offers new services and applications [...] Read more.
The Web of Things (WoT) is a technology concept that allows the integration of the Internet of Things (IoT) with the World Wide Web (WWW). It will vastly affect our lives in the near future given that it offers new services and applications via the well-known web window. In todays’ world where one can hardly imagine everyday life without access to various online services and applications via a plethora of devices, one can notice that technology has a huge impact on our day-to-day quality of living. That is why a user’s Quality of Experience (QoE) towards used technology in general plays a crucial role in their Quality of Life (QoL). Furthermore, security perception in terms of technology is the feature that vastly affects QoE and, consequently, QoL, as the number of security and privacy threats, risks, and vulnerabilities in cyber space, i.e., the technology environment that we increasingly use, is constantly rising. In order to reach the ultimate goals—the adoption of WoT technology and improvement of our QoL—we must know how this important aspect of security is so far addressed and analyzed. Therefore, this paper gives a comprehensive and structured analysis of the existing literature in this field through a proposed framework and provides an overview of research opportunities that should be addressed and elaborated in future investigations. Full article
(This article belongs to the Special Issue Embedding Internet of Everything in New-Age Smart Environments)
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<p>Dependencies between QoL, QoE, security perception, and influence factors in WoT—a layered framework.</p> Full article ">Figure 2
<p>Phases of research methodology.</p> Full article ">Figure 3
<p>Distribution of selected papers by year.</p> Full article ">Figure 4
<p>Research steps for future.</p> Full article ">
9 pages, 2476 KiB  
Article
Novel Low-Cost Power Divider for 5.8 GHz
by Tso-Jung Chang, Krishna Pande and Heng-Tung Hsu
Electronics 2020, 9(4), 699; https://doi.org/10.3390/electronics9040699 - 24 Apr 2020
Viewed by 3142
Abstract
This paper presents a new capacitive lump-free structure for power dividers using a printed-circuit board, while maintaining size reduction and physical isolation. The conventional lumped capacitors approach has self-resonant problem and cause worse S 22 and isolation at high frequencies. To overcome such [...] Read more.
This paper presents a new capacitive lump-free structure for power dividers using a printed-circuit board, while maintaining size reduction and physical isolation. The conventional lumped capacitors approach has self-resonant problem and cause worse S 22 and isolation at high frequencies. To overcome such technical issues, the coupled-line structures were introduced in the isolation network. After optimizing the distance between output ports and position of the isolation network, tuning the characteristic impedance and electrical length of transmission lines can decide the value of the lump resistor. The first example was designed at 1 GHz, and the resistor in the isolation network was 330 ohm, having 0.2-dB insertion loss and 19% total bandwidth, while maintaining 80-degree distance between split ports and 180-degree total length, providing 21% to 67% size reduction. The second example was designed at 5.8 GHz, which was five times greater than in past research, using an RO4003C substrate while maintaining a 0.24-dB insertion loss, 17% total bandwidth, and 0.06 dB amplitude imbalance, which was only 0.01 dB more than in recent research. Such superior performance is mainly attributed to the coupled transmission lines in the isolation network featuring a capacitive lump-free isolation network. Our data indicate that amplitude imbalance, bandwidth, and miniaturization are superior to any published data. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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<p>The configuration of the proposed power divider with coupled-line section adopted in the complex isolation network.</p> Full article ">Figure 2
<p>Bisected network with open-circuit bisecting plane at the plane of symmetry for even-mode analysis.</p> Full article ">Figure 3
<p>Bisected network with short-circuit bisecting plane at the plane of symmetry for odd-mode analysis.</p> Full article ">Figure 4
<p>The complete design flow for the proposed power divider with port extension.</p> Full article ">Figure 5
<p>The layout of the synthesized power divider with definitions of physical geometries.</p> Full article ">Figure 6
<p>(<b>a</b>) The photo; (<b>b</b>) measured and simulated <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>11</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>22</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>33</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) measured and simulated <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>12</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>13</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>23</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) measured and simulated phase and amplitude imbalance of the fabricated prototype at 1 GHz.</p> Full article ">Figure 7
<p>(<b>a</b>) The photo; (<b>b</b>) measured and simulated <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>11</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>22</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>33</mn> </mrow> </msub> </mrow> </semantics></math> ; (<b>c</b>) measured and simulated <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>12</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>13</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>23</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) measured and simulated phase and amplitude imbalance of the fabricated prototype at 5.8 GHz.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) The photo; (<b>b</b>) measured and simulated <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>11</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>22</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>33</mn> </mrow> </msub> </mrow> </semantics></math> ; (<b>c</b>) measured and simulated <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>12</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>13</mn> </mrow> </msub> <mo>,</mo> <msub> <mi mathvariant="normal">S</mi> <mrow> <mn>23</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) measured and simulated phase and amplitude imbalance of the fabricated prototype at 5.8 GHz.</p> Full article ">
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 28 | Viewed by 6400
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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<p>The hybrid renewable microgrid at the University of Huelva.</p> Full article ">Figure 2
<p>(<b>a</b>,<b>b</b>) Detail of the solar PV installation of microgrid shown in <a href="#electronics-09-00698-f001" class="html-fig">Figure 1</a>; (<b>c</b>) PV panels datasheet characteristics.</p> Full article ">Figure 3
<p>(<b>a</b>) Solar PV modelling; (<b>b</b>) PV power output vs temperature and radiation (model).</p> Full article ">Figure 4
<p>Validation of the solar PV model for the three technologies and error analysis.</p> Full article ">Figure 5
<p>(<b>a</b>) Horizontal axis wind turbine of the <a href="#electronics-09-00698-f001" class="html-fig">Figure 1</a> microgrid; (<b>b</b>) wind turbine datasheet parameters.</p> Full article ">Figure 6
<p>Validation of the wind turbine model and error analysis.</p> Full article ">Figure 7
<p>Details of the alkaline electrolyzer (<b>a</b>), and datasheet characteristics (<b>b</b>).</p> Full article ">Figure 8
<p>(<b>a</b>) Alkaline electrolyzer empirical modelling scheme; (<b>b</b>) experimental test procedure (<b>c</b>) polarization curve (stack) experimental data, polynomial fitting and error analysis.</p> Full article ">Figure 9
<p>Details of the PEM fuel cell module (<b>a</b>) and datasheet characteristics (<b>b</b>).</p> Full article ">Figure 10
<p>(<b>a</b>) Fuel cell polarization curves. Experimental data vs. polynomial fitting model; (<b>b</b>) Fuel cell power curves. Experimental data vs. polynomial fitting model.</p> Full article ">Figure 11
<p>(<b>a</b>) Detail of the hydrogen compressed gas tank; (<b>b</b>) layout characteristics.</p> Full article ">Figure 12
<p>(<b>a</b>) Lead acid battery unit; (<b>b</b>) lead acid battery datasheet characteristics [<a href="#B44-electronics-09-00698" class="html-bibr">44</a>].</p> Full article ">Figure 13
<p>(<b>a</b>) Battery charge experimental data fitting results via MATLAB Curve Fitting Toolbox, frontal view and (<b>b</b>) lateral view and error analysis.</p> Full article ">Figure 14
<p>(<b>a</b>) Battery discharge experimental data fitting results via MATLAB Curve Fitting Toolbox, frontal view and (<b>b</b>) lateral view and error analysis.</p> Full article ">Figure 15
<p>(<b>a</b>) Lead acid battery model validation; (<b>b</b>) error analysis.</p> Full article ">Figure 16
<p>Simulink global model interface.</p> Full article ">Figure 17
<p>Hydrogen-priority strategy (<b>a</b>); battery priority strategy (<b>b</b>).</p> Full article ">Figure 17 Cont.
<p>Hydrogen-priority strategy (<b>a</b>); battery priority strategy (<b>b</b>).</p> Full article ">Figure 18
<p>Annual meteorological data on the microgrid location (Huelva, southwest of Spain). (<b>a</b>) Inclined solar radiation and its components; (<b>b</b>) ambient temperature—mean and standard deviation (σ); (<b>c</b>) wind speed (W10)—mean and standard deviation (σ); (<b>d</b>) wind statistical analysis—Weibull distribution and fitting—shape (k) and scale (<b>c</b>) factor.</p> Full article ">Figure 19
<p>(<b>a</b>) Load profile; (<b>b</b>) Simulated microgrid configuration setup.</p> Full article ">Figure 20
<p>Net power balance. (<b>a</b>) Hydrogen-priority EMS strategy results; (<b>b</b>) battery-priority EMS strategy results.</p> Full article ">
11 pages, 4425 KiB  
Article
A 2.4 GHz 2.9 mW Zigbee RF Receiver with Current-Reusing and Function-Reused Mixing Techniques
by Zhikuang Cai, Mingmin Shi, Shanwen Hu and Zixuan Wang
Electronics 2020, 9(4), 697; https://doi.org/10.3390/electronics9040697 - 24 Apr 2020
Cited by 3 | Viewed by 3501
Abstract
This study presents a low-power Zigbee receiver with a current-reusing structure and function-reused mixing techniques. To reduce the overall power consumption, a low noise amplifier (LNA) and a power amplifier (PA) share the biasing current with a voltage-controlled oscillator (VCO) in the receiving [...] Read more.
This study presents a low-power Zigbee receiver with a current-reusing structure and function-reused mixing techniques. To reduce the overall power consumption, a low noise amplifier (LNA) and a power amplifier (PA) share the biasing current with a voltage-controlled oscillator (VCO) in the receiving (RX) mode and transmitting (TX) mode, respectively. The function-reused mixer reuses the radio frequency trans-conductance (RF gm) stage to amplify the down-converted intermediate frequency (IF) signal, obtaining a free IF gain without extra power consumption. A peak detector circuit detects the receiving signal strength and auto-adjusts the biasing current to save power when a strong signal strength is detected. Meanwhile, the peak detector helps to provide a coarse gain control as part of the auto-gain-control function. As part of the IF gain range is shared by the multiple-feedback (MFB) low-pass filter, the number of programmable-gain IF amplifier stages can be reduced, which also means a decrease in power consumption. A prototype of this wireless sensor network (WSN) receiver was designed and fabricated using the TSMC 130 nm CMOS process under a supply voltage of 1 V. The entire receiver realizes a noise figure (NF) of 3.5 dB and a receiving sensitivity of −90 dBm for the 0.25 Mbps offset quadrature phase shift keying (O-QPSK) signal with a power consumption of 2.9 mW. Full article
(This article belongs to the Special Issue Nanoscale CMOS Technologies)
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<p>The system topology of the proposed recursive receiver.</p> Full article ">Figure 2
<p>Circuit schematic of the current-reusing structure of the low noise amplifier (LNA) and voltage controlled oscillator (VCO).</p> Full article ">Figure 3
<p>The circuit schematic of the proposed function-reused mixer.</p> Full article ">Figure 4
<p>Simulated loop gain versus frequency at the LO frequency of 2.4 GHz.</p> Full article ">Figure 5
<p>Simulated IIP3 curves with and without the recursive IF amplification stage.</p> Full article ">Figure 6
<p>The topology of the programmable gain amplifier.</p> Full article ">Figure 7
<p>The circuit schematic of the fixed-gain stage.</p> Full article ">Figure 8
<p>(<b>a</b>) The simulated AC response of the LPF. (<b>b</b>) The simulated AC response of the programmable gain amplifier (PGA) in different gain modes.</p> Full article ">Figure 9
<p>The microphotograph of the proposed receiver.</p> Full article ">Figure 10
<p>Measured out-of-band IIP3 and OIP3 at the LO frequency of 2.4 GHz.</p> Full article ">Figure 11
<p>Measured and simulated NF versus input frequency with a fixed IF frequency of 2 MHz.</p> Full article ">Figure 12
<p>(<b>a</b>) The testing board of the proposed receiver. (<b>b</b>) The received signal at an IF frequency of 2 MHz with 250 kbps O-QPSK measured by Agilent 89600 VSA.</p> Full article ">
24 pages, 18904 KiB  
Article
A Visitor Assistance System Based on LoRa for Nature Forest Parks
by Ana Elisa Ferreira, Fernando Molano Ortiz, Thales Teixeira de Almeida and Luís Henrique M. K. Costa
Electronics 2020, 9(4), 696; https://doi.org/10.3390/electronics9040696 - 24 Apr 2020
Cited by 6 | Viewed by 3377
Abstract
Ecotourism activities are attracting more people each day, including national forest parks. Unfortunately, the number of incidents involving visitors to natural parks grows at the same pace. Among the most prevalent risks inside forests are getting lost and the occurrence of natural disasters. [...] Read more.
Ecotourism activities are attracting more people each day, including national forest parks. Unfortunately, the number of incidents involving visitors to natural parks grows at the same pace. Among the most prevalent risks inside forests are getting lost and the occurrence of natural disasters. In this work, we propose a system for monitoring and assisting visitors of forest parks, based on a low power wide range wireless network, LoRa. The proposed visitor assisting system is composed of mobile terminals that communicate between them and with fixed infrastructure, using a protocol designed for exchanging visitor locations data. The infrastructure consists of wireless gateways distributed on the trails, the totems. User terminals, the mobile nodes, work collaboratively through a Delay and Disruption Tolerant Network (DTN), to cope with the possibility that the gateway infrastructure does not cover the whole trail. In addition to improvements and gains for minimizing risks, the proposal also brings contributions to the preservation of the environment, raising awareness of the influence of human presence in the natural environment and to the development of environmental education actions. Full article
(This article belongs to the Special Issue Application of Wireless Sensor Networks in Monitoring)
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<p>Vegetation and experiment location.</p> Full article ">Figure 2
<p>Measuring location map and prototypes. (<b>a</b>) Map of the site of experiments at PARNASO (<a href="https://www.icmbio.gov.br/parnaserradosorgaos/" target="_blank">https://www.icmbio.gov.br/parnaserradosorgaos/</a>). (<b>b</b>) The pole-mounted prototype node.</p> Full article ">Figure 3
<p>Probability Density Function (PDF) of average power measurements (W) for LoRa SF12 at a distance of 250 m.</p> Full article ">Figure 4
<p>PDF of instantaneous power measurements (W) for LoRa SF12 at a distance of 250 m.</p> Full article ">Figure 5
<p>Nakagami-m and Gamma composite PDF.</p> Full article ">Figure 6
<p>Network scheme.</p> Full article ">Figure 7
<p>Short-term static scenario.</p> Full article ">Figure 8
<p>Two mobile nodes scenario.</p> Full article ">Figure 9
<p>Message types and format.</p> Full article ">Figure 10
<p>Communication between terminals.</p> Full article ">Figure 11
<p>Communication between totem and terminal.</p> Full article ">Figure 12
<p>Communication in an emergency situation.</p> Full article ">Figure 13
<p>Route of Petrópolis—Teresópolis crossing.</p> Full article ">Figure 14
<p>Second day trail landscape.</p> Full article ">Figure 15
<p>Locations for installing totems.</p> Full article ">Figure 16
<p>Received Signal Strength Indicator (RSSI) vs. distance—50 m/min—simulated vs. practical.</p> Full article ">Figure 17
<p>RSSI vs. distance—100 m/min - simulated vs. practical.</p> Full article ">Figure 18
<p>Packet Delivery Ratio (PDR) vs. distance—50 m/min—simulated vs. practical.</p> Full article ">Figure 19
<p>PDR vs. distance—100 m/min—simulated vs. practical.</p> Full article ">Figure 20
<p>Accumulated load offered to the network as a function of distance and speed of the visitor (mobile node) in the trail.</p> Full article ">
25 pages, 40852 KiB  
Article
A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot
by Xu Lei, Bin Feng, Guiping Wang, Weiyu Liu and Yalin Yang
Electronics 2020, 9(4), 695; https://doi.org/10.3390/electronics9040695 - 24 Apr 2020
Cited by 11 | Viewed by 4601
Abstract
The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an [...] Read more.
The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments. Full article
(This article belongs to the Special Issue Modeling, Control, and Applications of Field Robotics)
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<p>Schematic diagram of simultaneous localisation and mapping (SLAM).</p> Full article ">Figure 2
<p>The process of the standard FastSLAM. (<b>a</b>) Sampling. (<b>b</b>) Measurement. (<b>c</b>) Importance weight. (<b>d</b>) Resampling.</p> Full article ">Figure 3
<p>Particle update process of FCPSO. (<b>a</b>) Information sharing between particles. (<b>b</b>) Define the virtual particle to get the global optimisation goal. (<b>c</b>) Deal with particle premature convergence using chaotic optimisation. (<b>d</b>) Particles move toward global optimisation target.</p> Full article ">Figure 4
<p>Scheme of IFastSLAM.</p> Full article ">Figure 5
<p>Simulation interface. (<b>a</b>) Simulation environment. (<b>b</b>) A screenshot of the process of simulation.</p> Full article ">Figure 6
<p>The simulation result of four SLAM algorithms. (<b>a</b>) FastSLAM. (<b>b</b>) Particle swarm optimisation (PSO)-FastSLAM. (<b>c</b>) Adaptive generic FastSLAM algorithm (AGA-FastSLAM). (<b>d</b>) IFastSLAM.</p> Full article ">Figure 6 Cont.
<p>The simulation result of four SLAM algorithms. (<b>a</b>) FastSLAM. (<b>b</b>) Particle swarm optimisation (PSO)-FastSLAM. (<b>c</b>) Adaptive generic FastSLAM algorithm (AGA-FastSLAM). (<b>d</b>) IFastSLAM.</p> Full article ">Figure 7
<p>The estimation error. (<b>a</b>) Robot position error in X axis. (<b>b</b>) Robot position error in Y axis.</p> Full article ">Figure 8
<p>Simulation environment.</p> Full article ">Figure 9
<p>The mobile test platform used by the ACFR.</p> Full article ">Figure 10
<p>The Victoria Park dataset validation results. (<b>a</b>) PSO-FastSLAM. (<b>b</b>) AGA-FastSLAM. (<b>c</b>) IFastSLAM.</p> Full article ">Figure 10 Cont.
<p>The Victoria Park dataset validation results. (<b>a</b>) PSO-FastSLAM. (<b>b</b>) AGA-FastSLAM. (<b>c</b>) IFastSLAM.</p> Full article ">Figure 11
<p>Field experiment condition. (<b>a</b>) Mobile robot used in the field experiment. (<b>b</b>) Field experimental site.</p> Full article ">Figure 12
<p>Grid map by different algorithm. (<b>a</b>) PSO-FastSLAM. (<b>b</b>) AGA-FastSLAM. (<b>c</b>) IFastSLAM.</p> Full article ">Figure 13
<p>The estimated trajectory for different algorithms in the field experiment.</p> Full article ">
13 pages, 2684 KiB  
Article
A Compact Broadband Monolithic Sub-Harmonic Mixer Using Multi-Line Coupler
by Jincai Wen, Shengzhou Zhang and Lingling Sun
Electronics 2020, 9(4), 694; https://doi.org/10.3390/electronics9040694 - 24 Apr 2020
Cited by 4 | Viewed by 3818
Abstract
A compact broadband monolithic sub-harmonic mixer is presented in a 70 nm GaAs Technology for millimeter wave wireless communication application. The proposed mixer adopts a novel multi-line coupler structure; where the two-sided coupling energy of radio frequency (RF) and local oscillation (LO) signals [...] Read more.
A compact broadband monolithic sub-harmonic mixer is presented in a 70 nm GaAs Technology for millimeter wave wireless communication application. The proposed mixer adopts a novel multi-line coupler structure; where the two-sided coupling energy of radio frequency (RF) and local oscillation (LO) signals are both collected and efficiently feed to anti-parallel diode pair (APDP) topology; resulting in broadband performance and compact chip size. As a comparison in the same circuit configuration; the five-line coupler can expand the bandwidth of the existing three-line coupler by 85% and reduce the area by 39.5% when the central frequency is 127 GHz. The measured conversion gain is −16.2 dB to −19.7 dB in a wide operation frequency band of 110–170 GHz. The whole chip size is 0.47 × 0.66 mm2 including test pads. The proposed mixer exhibits good figure-of-merits for D-band down-converter applications Full article
(This article belongs to the Special Issue Millimeter-Wave Integrated Circuits and Systems for 5G Applications)
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<p>The schematic of the present anti-parallel diode pair (APDP)-based sub-harmonic mixer.</p> Full article ">Figure 2
<p>(<b>a</b>) three-line coupler (3Lines), (<b>b</b>) multi-line coupler with two-sided coupled local oscillation (LO) signal (4Lines_LO), (<b>c</b>) multi-line coupler with two-sided coupled radio frequency (RF) signal (4Lines_RF), and (<b>d</b>) multi-line coupler with simultaneous two-sided coupled LO and RF signals (5Lines).</p> Full article ">Figure 3
<p>The simulated transmission characteristics of (<b>a</b>) RF to Diode Pair and (<b>b</b>) LO to Diode Pair.</p> Full article ">Figure 3 Cont.
<p>The simulated transmission characteristics of (<b>a</b>) RF to Diode Pair and (<b>b</b>) LO to Diode Pair.</p> Full article ">Figure 4
<p>The simulated conversion gain of (<b>a</b>) different couplers and (<b>b</b>) different spacing.</p> Full article ">Figure 5
<p>The schematic of the present APDP-based sub-harmonic mixer.</p> Full article ">Figure 6
<p>Microscope photograph of the sub-harmonic mixer chip.</p> Full article ">Figure 7
<p>Measurement diagram of the sub-harmonic mixer chip.</p> Full article ">Figure 8
<p>Measured and simulated conversion gains vs. LO power.</p> Full article ">Figure 9
<p>Measured and simulated conversion gains vs. RF frequency.</p> Full article ">Figure 10
<p>Measured isolation results vs. RF frequency.</p> Full article ">Figure 11
<p>Measured and simulated conversion gains vs. intermediate frequency (IF).</p> Full article ">
17 pages, 4771 KiB  
Article
Machine Learning to Ensure Data Integrity in Power System Topological Network Database
by Adnan Anwar, Abdun Mahmood, Biplob Ray, Md Apel Mahmud and Zahir Tari
Electronics 2020, 9(4), 693; https://doi.org/10.3390/electronics9040693 - 24 Apr 2020
Cited by 17 | Viewed by 3730
Abstract
Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental [...] Read more.
Operational and planning modules of energy systems heavily depend on the information of the underlying topological and electric parameters, which are often kept in database within the operation centre. Therefore, these operational and planning modules are vulnerable to cyber anomalies due to accidental or deliberate changes in the power system database model. To validate, we have demonstrated the impact of cyber-anomalies on the database model used for operation of energy systems. To counter these cyber-anomalies, we have proposed a defence mechanism based on widely accepted classification techniques to identify the abnormal class of anomalies. In this study, we find that our proposed method based on multilayer perceptron (MLP), which is a special class of feedforward artificial neural network (ANN), outperforms other exiting techniques. The proposed method is validated using IEEE 33-bus and 24-bus reliability test system and analysed using ten different datasets to show the effectiveness of the proposed method in securing the Optimal Power Flow (OPF) module against data integrity anomalies. This paper highlights that the proposed machine learning-based anomaly detection technique successfully identifies the energy database manipulation at a high detection rate allowing only few false alarms. Full article
(This article belongs to the Special Issue Applications of IoT for Microgrids)
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<p>Functionality of energy database within energy management system database architecture.</p> Full article ">Figure 2
<p>Three bus test system [<a href="#B20-electronics-09-00693" class="html-bibr">20</a>].</p> Full article ">Figure 3
<p>IEEE 24 bus reliability test system [<a href="#B22-electronics-09-00693" class="html-bibr">22</a>].</p> Full article ">Figure 4
<p>Proposed database anomaly detection framework.</p> Full article ">Figure 5
<p>Solution architecture.</p> Full article ">Figure 6
<p>DB attack with line parameter alteration of a single line.</p> Full article ">Figure 7
<p>DB attack with line parameter alteration of multiple lines.</p> Full article ">Figure 8
<p>t-SNE of the dataset for experimental scenario 5.</p> Full article ">
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 129 | Viewed by 10291
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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<p>Architecture of a Deep Neural Network.</p> Full article ">Figure 2
<p>Swarm intelligence Concept.</p> Full article ">Figure 3
<p>Relationship between spider monkey optimization (SMO), SIA and nature-inspired algorithms (NIA).</p> Full article ">Figure 4
<p>SMO algorithm work flow diagram.</p> Full article ">Figure 5
<p>SMO–deep neural network (DNN) Hybrid Classifier for network intrusion detection.</p> Full article ">
10 pages, 1467 KiB  
Article
Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
by Xianzhen Yang, Siyuan Yan, Xiao Li, Qiang Wu and Fu Li
Electronics 2020, 9(4), 691; https://doi.org/10.3390/electronics9040691 - 24 Apr 2020
Cited by 2 | Viewed by 2846
Abstract
Dual-band RF amplifiers play increasingly important roles in next-generation mobile communication systems including 5G, and the out-of-band intermodulation products are often not negligible since they generate interference to adjacent channels. In this article, following our previous modeling of cross-modulation for amplified dual-band signals, [...] Read more.
Dual-band RF amplifiers play increasingly important roles in next-generation mobile communication systems including 5G, and the out-of-band intermodulation products are often not negligible since they generate interference to adjacent channels. In this article, following our previous modeling of cross-modulation for amplified dual-band signals, an analytical expression of out-of-band intermodulation for dual-band orthogonal frequency-division multiplexing signals is derived using the third-order intercept points I P 3 . The experimental measurement results validate the proposed analytical expression. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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<p>Output power spectrum of dual-band signals including out-of-band intermodulation.</p> Full article ">Figure 2
<p><math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>y</mi> </msub> <mrow> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with and without CM and out-of-band intermodulation.</p> Full article ">Figure 3
<p>Experiment setup.</p> Full article ">Figure 4
<p>The red line is the theoretically predicted PSD, and the blue line is the experimental result.</p> Full article ">
14 pages, 2165 KiB  
Article
Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance
by Moonyoung Kwon, Hohyun Cho, Kyungho Won, Minkyu Ahn and Sung Chan Jun
Electronics 2020, 9(4), 690; https://doi.org/10.3390/electronics9040690 - 23 Apr 2020
Cited by 19 | Viewed by 4885
Abstract
Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which [...] Read more.
Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers’ attention, and some predictors have been proposed using the alpha band’s power, as well as other spectral bands’ powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 × 10 − 7 ), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone. Full article
(This article belongs to the Special Issue The Challenges in Brain-Computer Interface (BCI) - toward Practical BCI)
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<p>Distribution of classification accuracy for 41 online MI-BCI sessions in increasing order. Sessions above the blue line (classification accuracy ≥ 70%) and sessions below the red line (classification accuracy &lt; 60%) were categorized as the high- and low-performance groups, respectively.</p> Full article ">Figure 2
<p>Eyes-open resting state: (<b>a</b>) The RPL distributions of theta, alpha, beta, and gamma powers between the high- and low-performance groups. (<b>b</b>) Regression analysis between the EO predictor with three frequency bands (theta, alpha, and beta) and online BCI classification accuracy.</p> Full article ">Figure 3
<p>Eyes-closed resting state: (<b>a</b>) The relative power level (RPL) distributions of theta, alpha, beta, and gamma powers between the high- and low-performance groups. (<b>b</b>) Regression analysis between the EC predictor with two frequency bands (alpha and beta) and online BCI classification accuracy.</p> Full article ">Figure 4
<p>(<b>a</b>) Regression analysis between our proposed resting state predictor (RSP) and online BCI classification accuracy, (<b>b</b>) Regression analysis between the PP factor (Ahn et al. [<a href="#B23-electronics-09-00690" class="html-bibr">23</a>]) and online BCI classification accuracy. Outliers (at the 90% confidence interval) are shown as blank markers.</p> Full article ">Figure 5
<p>(<b>a</b>) Regression analysis between the modified PP factor (Case 2, beta power in the denominator) and online BCI classification accuracy, and (<b>b</b>) Regression analysis between the modified PP factor (Case 3, no beta power) and online BCI classification accuracy. Outliers (90% confidence interval) are shown as blank markers.</p> Full article ">Figure 6
<p>(<b>a</b>) Comparison between percentage change in BCI classification accuracy and percentage change in prediction value. (<b>b</b>) Our proposed predictor (RSP) and (<b>c</b>) the PP factor for session variabillity. Blank markers indicate outliers.</p> Full article ">
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 5966
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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<p>A practical scenario where UAVs act as base (BSs) or relay stations (RSs).</p> Full article ">Figure 2
<p>A practical scenario where UAVs act as relay stations (RSs).</p> Full article ">Figure 3
<p>UAV-based cloud computing for IoT applications utilizing services provided by different collectors and clouds.</p> Full article ">Figure 4
<p>An example of satellite backhauling.</p> Full article ">Figure 5
<p>A block diagram view of RRA via ML.</p> Full article ">Figure 6
<p>A scenario where the UAV is used as collector–relay.</p> Full article ">Figure 7
<p>ML black box for choosing the best UAV based on traffic features.</p> Full article ">Figure 8
<p>A scenario where multiple UAVs share the same file.</p> Full article ">Figure 9
<p>RL is used in combination with SL to find the number of UAVs.</p> Full article ">Figure 10
<p>An example of area partition with subareas served by a single UAV (<b>upper-side plot</b>); the boundaries of the curves change as a function of the traffic variations, thus implying a modification of the UAV locations accordingly (<b>lower-side plot</b>).</p> Full article ">
31 pages, 14018 KiB  
Article
BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference
by Sung-Wook Park, Jun-Ho Huh and Jong-Chan Kim
Electronics 2020, 9(4), 688; https://doi.org/10.3390/electronics9040688 - 23 Apr 2020
Cited by 27 | Viewed by 6973
Abstract
In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. [...] Read more.
In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS. Full article
(This article belongs to the Special Issue Electronic Solutions for Artificial Intelligence Healthcare)
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<p>DCGAN (Deep Convolutional Generative Adversarial Network) generator used for LSUN (Large-Scale Scene Understanding) [<a href="#B11-electronics-09-00688" class="html-bibr">11</a>].</p> Full article ">Figure 2
<p>Conditional adversarial network.</p> Full article ">Figure 3
<p>Structure and operation of InfoGAN (Information maximizing Generative Adversarial Network).</p> Full article ">Figure 4
<p>Comparison of GAWWN (Generative Adversarial What-Where Network) with GAN-INT-CLS from Reed, including the ground-truth images. GAN-INT is Learning with manifold interpolation, GAN-CLS is Matching-aware discriminator, and GAN-INT-CLS is a combination of GAN-INT and GAN-CLS [<a href="#B25-electronics-09-00688" class="html-bibr">25</a>].</p> Full article ">Figure 5
<p>Surface normal map-to-natural scene mapping example.</p> Full article ">Figure 6
<p>Artistic style transfer using cycle-consistent adversarial networks [<a href="#B28-electronics-09-00688" class="html-bibr">28</a>].</p> Full article ">Figure 7
<p>Discriminator structure of the proposed model.</p> Full article ">Figure 8
<p>VAE (Variational AutoEncoder).</p> Full article ">Figure 9
<p>Exponential linear unit (<b>left</b>), leaky rectified linear unit (<b>right</b>).</p> Full article ">Figure 10
<p>Representative images of EBGAN (Energy-Based Generative Adversarial Network) [<a href="#B41-electronics-09-00688" class="html-bibr">41</a>].</p> Full article ">Figure 11
<p>Representative images of CEGAN (Calibrating Energy-based Generative Adversarial Network) [<a href="#B42-electronics-09-00688" class="html-bibr">42</a>].</p> Full article ">Figure 12
<p>Representative images of AVB (Adversarial Variational Bayes) [<a href="#B43-electronics-09-00688" class="html-bibr">43</a>].</p> Full article ">Figure 13
<p>Representative images of VEEGAN (Variational Encoder Enhancement to Generative Adversarial Network) [<a href="#B44-electronics-09-00688" class="html-bibr">44</a>].</p> Full article ">Figure 14
<p>Representative images of <span class="html-italic">G</span> (<b>left</b>) and <span class="html-italic">VAE</span> (<b>right</b>) in the proposed model (L2 loss).</p> Full article ">Figure 15
<p>Representative images of <span class="html-italic">G</span> (<b>left</b>) and <span class="html-italic">VAE</span> (<b>right</b>) in the proposed model (L1 loss).</p> Full article ">Figure 16
<p>Results of convergence measurement for the BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space) model.</p> Full article ">Figure 17
<p>Convergence measurement results of the proposed model using L2 loss.</p> Full article ">Figure 18
<p>Convergence measurement results of the proposed model using L1 loss.</p> Full article ">Figure 19
<p>Observation results of mode collapse by epoch.</p> Full article ">Figure A1
<p>Image generation result of LSUN bedroom and CelebA datasets at 50 epoch.</p> Full article ">Figure A2
<p>Image generation result of LSUN bedroom and CelebA datasets at 100 epoch.</p> Full article ">Figure A3
<p>Image generation result of LSUN bedroom and CelebA datasets at 200 epoch.</p> Full article ">Figure A4
<p>Results of convergence measurement for the LSUN bedroom dataset.</p> Full article ">Figure A5
<p>Results of convergence measurement for the CelebA dataset.</p> Full article ">
20 pages, 3490 KiB  
Article
Cooperative Task Assignment of a Heterogeneous Multi-UAV System Using an Adaptive Genetic Algorithm
by Fang Ye, Jie Chen, Yuan Tian and Tao Jiang
Electronics 2020, 9(4), 687; https://doi.org/10.3390/electronics9040687 - 23 Apr 2020
Cited by 62 | Viewed by 5177
Abstract
The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, [...] Read more.
The cooperative multiple task assignment problem (CMTAP) is an NP-hard combinatorial optimization problem. In this paper, CMTAP is to allocate multiple heterogeneous fixed-wing UAVs to perform a suppression of enemy air defense (SEAD) mission on multiple stationary ground targets. To solve this problem, we study the adaptive genetic algorithm (AGA) under the assumptions of the heterogeneity of UAVs and task coupling constraints. Firstly, the multi-type gene chromosome encoding scheme is designed to generate feasible chromosomes that satisfy the heterogeneity of UAVs and task coupling constraints. Then, AGA introduces the Dubins car model to simulate the UAV path formation and derives the fitness value of each chromosome. In order to comply with the chromosome coding strategy of multi-type genes, we designed the corresponding crossover and mutation operators to generate feasible offspring populations. Especially, the proposed mutation operators with the state-transition scheme enhance the stochastic searching ability of the proposed algorithm. Last but not least, the proposed AGA dynamically adjusts the number of crossover and mutation populations to avoid the subjective selection of simulation parameters. The numerical simulations verify that the proposed AGA has a better optimization ability and convergence effect compared with the random search method, genetic algorithm, ant colony optimization method, and particle search optimization method. Therefore, the effectiveness of the proposed algorithm is proven. Full article
(This article belongs to the Special Issue Recent Trends in Multi-Robot Systems: From Theoretical Contributions to Practical Applications)
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<p>Examples of Dubins paths.</p> Full article ">Figure 2
<p>Chromosome with multi-type genes.</p> Full article ">Figure 3
<p>Two transformations of the original chromosome.</p> Full article ">Figure 4
<p>Trajectories of UAVs.</p> Full article ">Figure 5
<p>Crossover example.</p> Full article ">Figure 6
<p>Mutation of assigned information.</p> Full article ">Figure 7
<p>State transition of three elements.</p> Full article ">Figure 8
<p>Mutate the execution order of targets.</p> Full article ">Figure 9
<p>Mutate the assigned information of a certain task.</p> Full article ">Figure 10
<p>Adaptive setting of <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mi>c</mi> <mi>r</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>N</mi> <mrow> <mi>m</mi> <mi>u</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 11
<p>Trajectories of UAVs.</p> Full article ">Figure 12
<p>Monte Carlo results of Scenario 1.</p> Full article ">Figure 13
<p>Monte Carlo results of Scenario 2.</p> Full article ">Figure 14
<p>Monte Carlo results of Scenario 3.</p> Full article ">
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 30 | Viewed by 4796
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>An edge cloud architecture and its interaction.</p> Full article ">Figure 2
<p>Petersen graphs without and with coloring.</p> Full article ">Figure 3
<p>The process of a genetic algorithm.</p> Full article ">Figure 4
<p>Performance comparisons when the number of mobile devices is 200.</p> Full article ">Figure 5
<p>Performance comparisons when the number of mobile devices is 400.</p> Full article ">Figure 6
<p>Performance comparisons when the number of mobile devices is 600.</p> Full article ">Figure 7
<p>Performance comparisons when the number of mobile devices is 800.</p> Full article ">Figure 8
<p>Performance comparisons when the number of mobile devices is 1000.</p> Full article ">Figure 8 Cont.
<p>Performance comparisons when the number of mobile devices is 1000.</p> Full article ">
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 19 | Viewed by 4210
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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<p>Proposed surrogate model inserted on a modular optimization framework for automatic IC design.</p> Full article ">Figure 2
<p>Schematics for device characterization of (<b>a</b>) Oxide Capacitance (<math display="inline"><semantics> <mrow> <msubsup> <mi>C</mi> <mrow> <mi>o</mi> <mi>x</mi> </mrow> <mo>′</mo> </msubsup> </mrow> </semantics></math>). (<b>b</b>) Threshold Voltage (<span class="html-italic">V<sub>TH</sub></span>). (<b>c</b>) Normalization current (<span class="html-italic">I<sub>S</sub></span>). (<b>d</b>) Saturation voltage (<span class="html-italic">V<sub>DSAT</sub></span>). early voltage (<span class="html-italic">V<sub>A</sub></span>).</p> Full article ">Figure 3
<p>Sample of characterization data of a given CMOS technology in typical corner (TT) (<b>a</b>) NMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>H</mi> </mrow> </msub> </semantics></math>. (<b>b</b>) PMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>H</mi> </mrow> </msub> </semantics></math>. (<b>c</b>) NMOS: <math display="inline"><semantics> <msub> <mi>I</mi> <mi>S</mi> </msub> </semantics></math>. (<b>d</b>) PMOS: <math display="inline"><semantics> <msub> <mi>I</mi> <mi>S</mi> </msub> </semantics></math>. (<b>e</b>) NMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>S</mi> <mi>A</mi> <mi>T</mi> </mrow> </msub> </semantics></math>. (<b>f</b>) PMOS: <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>S</mi> <mi>A</mi> <mi>T</mi> </mrow> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math>. (<b>g</b>) NMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mi>A</mi> </msub> </semantics></math>. (<b>h</b>) PMOS: <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>V</mi> <mi>A</mi> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 3 Cont.
<p>Sample of characterization data of a given CMOS technology in typical corner (TT) (<b>a</b>) NMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>H</mi> </mrow> </msub> </semantics></math>. (<b>b</b>) PMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>T</mi> <mi>H</mi> </mrow> </msub> </semantics></math>. (<b>c</b>) NMOS: <math display="inline"><semantics> <msub> <mi>I</mi> <mi>S</mi> </msub> </semantics></math>. (<b>d</b>) PMOS: <math display="inline"><semantics> <msub> <mi>I</mi> <mi>S</mi> </msub> </semantics></math>. (<b>e</b>) NMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>S</mi> <mi>A</mi> <mi>T</mi> </mrow> </msub> </semantics></math>. (<b>f</b>) PMOS: <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>S</mi> <mi>A</mi> <mi>T</mi> </mrow> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math>. (<b>g</b>) NMOS: <math display="inline"><semantics> <msub> <mi>V</mi> <mi>A</mi> </msub> </semantics></math>. (<b>h</b>) PMOS: <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>V</mi> <mi>A</mi> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Comparison of the percentage error of prediction of CMOS parameters using models based on curve-fitting and Gaussian process regression (GPR) for sizes (<b>a</b>) KL = 1, KWL = 4. (<b>b</b>) KL = 10, KWL = 50. These models were built from the characterization data.</p> Full article ">Figure 5
<p>Active-RC second order filter under design (<b>a</b>) circuit topology. (<b>b</b>) Transistor level schematic of the second order internally compensated amplifier.</p> Full article ">Figure 6
<p>Pareto front of the optimization of filter (<span class="html-italic">FC</span> = 100 KHz) using SQP and NSGA-II optimization algorithms in (<b>a</b>) TSMC 180 nm CMOS process. (<b>b</b>) IBM 130 nm CMOS process. (<b>c</b>) TSMC 65 nm CMOS process.</p> Full article ">Figure 7
<p>Values of the optimization variables from solutions in the Pareto front for the filter optimization (<span class="html-italic">FC</span> = 100 KHz) obtained with: (<b>a</b>) SQP-180 nm, (<b>b</b>) SQP-130 nm, (<b>c</b>) SQP-65 nm, (<b>d</b>) NSGA-II-180 nm, (<b>e</b>) NSGA-II-130 nm, and (<b>f</b>) NSGA-II-65 nm.</p> Full article ">Figure 8
<p>Circuit schematic of the capacitor-less low dropout (LDO) with type-A single stage error amplifier internal frequency compensation provided by <math display="inline"><semantics> <msub> <mi>C</mi> <mi>C</mi> </msub> </semantics></math>.</p> Full article ">Figure 9
<p>Small-signal macromodel of the LDO for the calculation of the power supply rejection (PSR).</p> Full article ">Figure 10
<p>Small-signal macromodel of the LDO for the calculation of the phase margin.</p> Full article ">Figure 11
<p>Pareto front of the optimization of the LDO circuit using SQP and NSGA-II optimization algorithms and (<b>a</b>) TSMC 180 nm. (<b>b</b>) IBM 130 nm. (<b>c</b>) TSMC 65 nm CMOS process.</p> Full article ">Figure 12
<p>Values of the optimization variables of the solutions in the Pareto front obtained with: (<b>a</b>) SQP-180 nm, (<b>b</b>) SQP-130 nm, (<b>c</b>) SQP-65 nm, (<b>d</b>) NSGA-II-180 nm, (<b>e</b>) NSGA-II-130 nm and (<b>f</b>) NSGA-II-65 nm</p> Full article ">Figure 13
<p>Circuit schematic of a 5-stage current starved voltage controlled oscillator (VCO). The control voltage is generated with a biasing current and a diode connected transistor.</p> Full article ">Figure 14
<p>Pareto front of the optimization of the CSVCO circuit using SQP and NSGA-II optimization algorithms and (<b>a</b>) TSMC 180 nm process. (<b>b</b>) IBM 130 nm process. (<b>c</b>) TSMC 65 nm CMOS process.</p> Full article ">Figure 15
<p>Values of the optimization variables of the solutions in the Pareto front obtained with: (<b>a</b>) SQP-180 nm, (<b>b</b>) SQP-130 nm, (<b>c</b>) SQP-65 nm, (<b>d</b>) NSGA-II-180 nm, (<b>e</b>) NSGA-II-130 nm and (<b>f</b>) NSGA-II-65 nm.</p> Full article ">Figure 15 Cont.
<p>Values of the optimization variables of the solutions in the Pareto front obtained with: (<b>a</b>) SQP-180 nm, (<b>b</b>) SQP-130 nm, (<b>c</b>) SQP-65 nm, (<b>d</b>) NSGA-II-180 nm, (<b>e</b>) NSGA-II-130 nm and (<b>f</b>) NSGA-II-65 nm.</p> Full article ">Figure 16
<p>History of the objective function through the iterative optimization process using (<b>a</b>) SQP algorithm. (<b>b</b>) NSGA-II algorithm.</p> Full article ">
12 pages, 2827 KiB  
Article
An Efficient Hardware Implementation of Residual Data Binarization in HEVC CABAC Encoder
by Dinh-Lam Tran, Xuan-Tu Tran, Duy-Hieu Bui and Cong-Kha Pham
Electronics 2020, 9(4), 684; https://doi.org/10.3390/electronics9040684 - 23 Apr 2020
Cited by 6 | Viewed by 4084
Abstract
HEVC-standardized encoders employ the CABAC (context-based adaptive binary arithmetic coding) to achieve high compression ratios and video quality that supports modern real-time high-quality video services. Binarizer is one of three main blocks in a CABAC architecture, where binary symbols (bins) are generated to [...] Read more.
HEVC-standardized encoders employ the CABAC (context-based adaptive binary arithmetic coding) to achieve high compression ratios and video quality that supports modern real-time high-quality video services. Binarizer is one of three main blocks in a CABAC architecture, where binary symbols (bins) are generated to feed the binary arithmetic encoder (BAE). The residual video data occupied an average of 75% of the CABAC’s work-load, thus its performance will significantly contribute to the overall performance of whole CABAC design. This paper proposes an efficient hardware implementation of a binarizer for CABAC that focuses on low area cost, low power consumption while still providing enough bins for high-throughput CABAC. On the average, the proposed design can process upto 3.5 residual syntax elements (SEs) per clock cycle at the maximum frequency of 500 MHz with an area cost of 9.45 Kgates (6.41 Kgates for the binarizer core) and power consumption of 0.239 mW (0.184 mW for the binarizer core) with NanGate 45 nm technology. It shows that our proposal achieved a high overhead-efficiency of 1.293 Mbins/Kgate/mW, much better than the other related high performance designs. In addition, our design also achieved a high power-efficiency of 8288 Mbins/mW; this is important factor for handheld applications. Full article
(This article belongs to the Section Computer Science & Engineering)
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<p>General hardware architecture of CABAC encoder.</p> Full article ">Figure 2
<p>General hardware architecture of a binarizer [<a href="#B4-electronics-09-00684" class="html-bibr">4</a>].</p> Full article ">Figure 3
<p>Diagonal scanning: (<b>a</b>) in large transform block, and (<b>b</b>) within 4 × 4 transform block.</p> Full article ">Figure 4
<p>Process of residual SEs generation.</p> Full article ">Figure 5
<p>Overall block diagram of residual SE binarization module.</p> Full article ">Figure 6
<p>Scanning and SE generation architecture.</p> Full article ">Figure 7
<p>Proposed hardware implementation of residual SE generation.</p> Full article ">Figure 8
<p>Truncated rice binarization hardware architecture for X or Y coordinates.</p> Full article ">Figure 9
<p>Hardware architecture of residual binarizer.</p> Full article ">Figure 10
<p>Proposed combined last X-Y significant binarization architecture.</p> Full article ">
15 pages, 2895 KiB  
Article
A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
by Gohar Ali, Fernando Moreira, Omar Alfandi, Babar Shah and Mohammed Ilyas
Electronics 2020, 9(4), 683; https://doi.org/10.3390/electronics9040683 - 23 Apr 2020
Cited by 1 | Viewed by 2918
Abstract
Real-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster [...] Read more.
Real-time flows using time division multiple access (TDMA) scheduling in cluster-based wireless sensor networks try to schedule more flows per time frame to minimize the schedule length to meet the deadline. The problem with the previously used cluster-based scheduling algorithm is that intra-cluster scheduling does not consider that the clusters may have internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not utilize their empty time-slots and thus increase schedule length. In this paper, we propose a new intra-cluster scheduling algorithm by considering that clusters may have having internal or outgoing flows. Thus, intra-cluster scheduling algorithms do not differentiate the intra-cluster time slots and utilize their empty time slots. The objective is to schedule more flows per time frame, to reduce schedule length and improve the acceptance rate of flows. Simulation results show that the acceptance rate of the proposed scheme has a higher performance than the previous scheme. Full article
(This article belongs to the Special Issue Ubiquitous Sensor Networks)
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<p>Minor frame.</p> Full article ">Figure 2
<p>Cluster-based real-time flows.</p> Full article ">Figure 3
<p>Flow diagram of proposed scheme.</p> Full article ">Figure 4
<p>Simulation results for w.r.t deadline.</p> Full article ">Figure 5
<p>Simulation results w.r.t flows.</p> Full article ">Figure 5 Cont.
<p>Simulation results w.r.t flows.</p> Full article ">Figure 6
<p>Simulation results for w.r.t Clusters.</p> Full article ">Figure 6 Cont.
<p>Simulation results for w.r.t Clusters.</p> Full article ">Figure 7
<p>Simulation results for w.r.t IntraSend time slots.</p> Full article ">Figure 7 Cont.
<p>Simulation results for w.r.t IntraSend time slots.</p> Full article ">Figure 8
<p>Simulation results for w.r.t IntraRecv time slots.</p> Full article ">Figure 9
<p>IntraSend slot utilization.</p> Full article ">Figure 10
<p>IntraRecv time slot utilization.</p> Full article ">
3 pages, 155 KiB  
Editorial
Mobile Oriented Future Internet (MOFI): Architectural Designs and Experimentations
by Seok-Joo Koh
Electronics 2020, 9(4), 682; https://doi.org/10.3390/electronics9040682 - 23 Apr 2020
Cited by 2 | Viewed by 2209
Abstract
With the explosive growth of smart phones and Internet-of-Things (IoT) services, the effective support of seamless mobility for a variety of mobile devices and users is becoming one of the key challenging issues [...] Full article
(This article belongs to the Special Issue Mobile Oriented Future Internet (MOFI): Architectural Designs and Experimentations)
27 pages, 933 KiB  
Review
On-Site and External Energy Harvesting in Underground Wireless
by Usman Raza and Abdul Salam
Electronics 2020, 9(4), 681; https://doi.org/10.3390/electronics9040681 - 22 Apr 2020
Cited by 22 | Viewed by 5714
Abstract
Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer [...] Read more.
Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer techniques are explored. Moreover, key energy harvesting technologies are presented that utilize available energy sources in the field such as vibration, solar, and wind. In this regard, the Electromagnetic (EM)- and Magnetic Induction (MI)-based approaches are explained. Furthermore, the vibration-based energy harvesting models are reviewed as well. These energy harvesting approaches lead to design of an efficient wireless underground communication system to power underground nodes for prolonged field operation in decision agriculture. Full article
(This article belongs to the Special Issue Wireless Power/Data Transfer, Energy Harvesting System Design)
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<p>The organization of the article.</p> Full article ">Figure 2
<p>Wireless Powered Communication Network (WPCN) models with different transmitter and receiver schemes: (<b>a</b>) Physically different energy transmitter and information receiver, (<b>b</b>) Energy transmitter and information receiver co-located in same physical entity, (<b>c</b>) out-of-band transmission of information and energy and (<b>d</b>) full-duplex transmission of energy and information.</p> Full article ">Figure 3
<p>Major performance improving mechanisms for WPCNs; energy transfer is denoted by green lines and information by red lines: (<b>a</b>) wireless powered cooperative communication, (<b>b</b>) wireless powered cooperative communication joint scheduling for communication and energy transfer and (<b>c</b>) energy beamforming.</p> Full article ">Figure 4
<p>(<b>a</b>) WPCN implementation using hybrid energy sources, (<b>b</b>) WPCN using cognitive radio to efficiently use frequency spectrum.</p> Full article ">Figure 5
<p>Simultaneous Wireless Information and Power Transfer (SWIPT) using static and mobile base stations where arrow heads represent the direction of information and power flow. Idle users only harvest energy from base stations. Active users transmit and receive energy and information.</p> Full article ">Figure 6
<p>SWIPT Architecture: (<b>a</b>) Time Switching, (<b>b</b>) Power Splitting.</p> Full article ">Figure 7
<p>Vibration energy harvester: (<b>a</b>) source for the vibration is on the surface with energy harvester deployed in the soil, (<b>b</b>) deployment in agriculture field.</p> Full article ">Figure 7 Cont.
<p>Vibration energy harvester: (<b>a</b>) source for the vibration is on the surface with energy harvester deployed in the soil, (<b>b</b>) deployment in agriculture field.</p> Full article ">
11 pages, 801 KiB  
Article
Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
by Chang Seok You, Jeong Seon Yeom and Bang Chul Jung
Electronics 2020, 9(4), 680; https://doi.org/10.3390/electronics9040680 - 22 Apr 2020
Cited by 5 | Viewed by 3000
Abstract
Low-power wide-area networks (LPWANs) have received extensive attention from both academia and industry, since they can efficiently provide massive connectivity to internet of things (IoT) devices in wide geographical areas with low cost and low power consumption. Recently, it was shown that macro-diversity [...] Read more.
Low-power wide-area networks (LPWANs) have received extensive attention from both academia and industry, since they can efficiently provide massive connectivity to internet of things (IoT) devices in wide geographical areas with low cost and low power consumption. Recently, it was shown that macro-diversity among multiple gateways significantly improves the performance of uplink LPWANs by coherently combining multiple received signals at gateways. We call such networks cooperative LPWANs. In this paper, the error performance of an uplink cooperative LPWAN is mathematically analyzed in terms of outage probability, bit error rate (BER), and diversity order. It is assumed that there exist multiple (two or more) gateways that have multiple antennas and are located at arbitrary positions in the LPWAN area. Each gateway exploits the optimal maximum-ratio combining (MRC) technique to decode the received signal, and then the signals after MRC are delivered to the cloud fusion center for coherent combining in the cooperative LPWAN. The main results, the closed-form expressions of outage probability and BER, were derived by utilizing the hyper-Erlang distribution. Furthermore, the macro-diversity order was mathematically derived. The mathematical analysis was validated through extensive computer simulations. It worth noting that the mathematical analysis of the error performance of cooperative LPWANs is the first theoretical result in the literature to the best of our knowledge. Full article
(This article belongs to the Special Issue Energy-Efficient B5G/6G Ultra-Dense Networks: Challenges and Solutions)
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<p>Uplink cooperative technique in a low-power wide-area network (LPWAN) with multiple antenna gateways (GWs).</p> Full article ">Figure 2
<p>Outage probability performance of the LPWAN while varying the total received SNR when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>K</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>K</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>5</mn> <mo>)</mo> <mo>(</mo> <mn>5</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>1</mn> <mo>.</mo> <mn>5</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>BER performance of LPWAN for varying the total received SNR when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>; i.e.,<math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>K</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>K</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> <mo>∈</mo> <mfenced separators="" open="{" close="}"> <mo>(</mo> <mn>1</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>5</mn> <mo>)</mo> <mo>(</mo> <mn>5</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Diversity order of LPWAN according to the number of total received antennas when <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>d</mi> <mn>3</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">
19 pages, 1715 KiB  
Article
A Collision-Free Hybrid MAC Protocol Based on Pipeline Parallel Transmission for Distributed Multi-Channel Underwater Acoustic Networks
by Jun Zhang, Zhi Hu, Yan Xiong and Gengxin Ning
Electronics 2020, 9(4), 679; https://doi.org/10.3390/electronics9040679 - 22 Apr 2020
Cited by 4 | Viewed by 3193
Abstract
The transmission rate between two nodes is usually very low in underwater acoustic networks due to the low available bandwidth of underwater acoustic channels. Therefore, increasing the transmission parallelism among network nodes is one of the most effective ways to improve the performance [...] Read more.
The transmission rate between two nodes is usually very low in underwater acoustic networks due to the low available bandwidth of underwater acoustic channels. Therefore, increasing the transmission parallelism among network nodes is one of the most effective ways to improve the performance of underwater acoustic networks. In this paper, we propose a new collision-free hybrid medium access control (MAC) protocol for distributed multi-channel underwater acoustic networks. In the proposed protocol, handshaking and data transmission are implemented as a pipeline on multiple acoustic channels. Handshaking is implemented using the time division multiple access (TDMA) technique in a dedicated control channel, which can support multiple successful handshakes in a transmission cycle and avoid collision in the cost of additional delay. Data packets are transmitted in one or multiple data channels, where an algorithm for optimizing the transmission schedule according to the inter-nodal propagation delays is proposed to achieve collision-free parallel data transmission. Replication computation technique, which is usually used in parallel computation to reduce the requirement of communication or execution time, is used in the data packet scheduling to reduce communication overhead in distributed environments. Simulation results show that the proposed protocol outperforms the slotted floor acquisition multiple access (SFAMA), reverse opportunistic packet appending (ROPA), and distributed scheduling based concurrent transmission (DSCT) protocols in throughput, packet delivery rate, and average energy consumption in the price of larger end-to-end delay introduced by TDMA based handshaking. Full article
(This article belongs to the Special Issue Underwater Communication and Networking Systems)
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<p>Serial transmission and time domain parallel transmission. (<b>a</b>) serial transmission; (<b>b</b>) time domain parallel transmission.</p> Full article ">Figure 2
<p>An example of creating a space domain parallel transmission opportunity by power control. (<b>a</b>) transmit with normal power; (<b>b</b>) transmit with reduced power.</p> Full article ">Figure 3
<p>Implementing the typical transmission process with a four-stage pipeline.</p> Full article ">Figure 4
<p>Merging the request to send (RTS), clear to send (CTS), and acknowledgement (ACK) stages into one control channel.</p> Full article ">Figure 5
<p>An example of replication computation. (<b>a</b>) common method; (<b>b</b>) replication computation.</p> Full article ">Figure 6
<p>Applying replication computation to parallel data transmission. (<b>a</b>) conventional method; (<b>b</b>) replication computation.</p> Full article ">Figure 7
<p>Performance of the slotted floor acquisition multiple access (SFAMA), reverse opportunistic packet appending (ROPA), and distributed scheduling based concurrent transmission (DSCT) protocols and that of the proposed method under different network loads. (<b>a</b>) throughput; (<b>b</b>) packet delivery rate; (<b>c</b>) average end-to-end delay; and (<b>d</b>) average energy consumption.</p> Full article ">Figure 8
<p>Influence of packet size. (<b>a</b>) durations for handshaking and data transmission under different packet sizes; (<b>b</b>) throughputs of DSCT and the proposed protocols under different packet sizes.</p> Full article ">Figure 9
<p>Performance of the SFAMA, ROPA, and DSCT protocols and that of the proposed method under different network sizes. (<b>a</b>) throughput; (<b>b</b>) packet delivery rate; (<b>c</b>) average end-to-end delay; and (<b>d</b>) average energy consumption.</p> Full article ">
18 pages, 3906 KiB  
Article
A Smart Glucose Monitoring System for Diabetic Patient
by Amine Rghioui, Jaime Lloret, Mohamed Harane and Abdelmajid Oumnad
Electronics 2020, 9(4), 678; https://doi.org/10.3390/electronics9040678 - 22 Apr 2020
Cited by 56 | Viewed by 27427
Abstract
Diabetic patients need ongoing surveillance, but this involves high costs for the government and family. The combined use of information and communication technologies (ICTs), artificial intelligence and smart devices can reduce these costs, helping the diabetic patient. This paper presents an intelligent architecture [...] Read more.
Diabetic patients need ongoing surveillance, but this involves high costs for the government and family. The combined use of information and communication technologies (ICTs), artificial intelligence and smart devices can reduce these costs, helping the diabetic patient. This paper presents an intelligent architecture for the surveillance of diabetic disease that will allow physicians to remotely monitor the health of their patients through sensors integrated into smartphones and smart portable devices. The proposed architecture includes an intelligent algorithm developed to intelligently detect whether a parameter has exceeded a threshold, which may or may not involve urgency. To verify the proper functioning of this system, we developed a small portable device capable of measuring the level of glucose in the blood for diabetics and body temperature. We designed a secure mechanism to establish a wireless connection with the smartphone. Full article
(This article belongs to the Special Issue Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications)
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<p>Proposed architecture for diabetic monitoring.</p> Full article ">Figure 2
<p>Hardware Block Diagram.</p> Full article ">Figure 3
<p>Pedometer.</p> Full article ">Figure 4
<p>Flowchart of the system.</p> Full article ">Figure 5
<p>Smart algorithm for monitoring diabetic patients.</p> Full article ">Figure 6
<p>Structure of information transmission.</p> Full article ">Figure 7
<p>Flow Chart of Programming.</p> Full article ">Figure 8
<p>The packet captured during transmission between ESP8266 and Arduino.</p> Full article ">Figure 9
<p>The I/O Graphs.</p> Full article ">Figure 10
<p>The graph of correctly and incorrectly classified instances of algorithms.</p> Full article ">Figure 11
<p>The graph for training time results for different algorithms.</p> Full article ">Figure 12
<p>Graphical representation of FP, TP, precision, recall, and F-measure of different algorithms.</p> Full article ">Figure 13
<p>Graphical representation of sensitivity, specificity, precision, and accuracy of different algorithms.</p> Full article ">Figure 14
<p>KAPPA Statistics and error rate values.</p> Full article ">
8 pages, 2593 KiB  
Article
Inexpensive Millimeter-Wave Communication Channel Using Glow Discharge Detector and Satellite Dish Antenna
by Lidor Kahana, Daniel Rozban, Moshe Gihasi, Amir Abramovich, Yitzhak Yitzhaky and Natan Kopeika
Electronics 2020, 9(4), 677; https://doi.org/10.3390/electronics9040677 - 21 Apr 2020
Cited by 4 | Viewed by 3749
Abstract
A full proof of concept for low-cost millimeter wave (MMW) communication link is demonstrated in this study. The suggested MMW channel is based on a very inexpensive commercially available off-axis dish antenna usually used for TV satellites, two Arduino Uno micro controller boards, [...] Read more.
A full proof of concept for low-cost millimeter wave (MMW) communication link is demonstrated in this study. The suggested MMW channel is based on a very inexpensive commercially available off-axis dish antenna usually used for TV satellites, two Arduino Uno micro controller boards, and glow discharge detectors (GDD). The GDD is a robust and inexpensive room-temperature plasma device which was found to be a sensitive MMW radiation detector. The Arduino micro controllers are used to encode a text message into serial bits and also decode it. Those serial bits were used to modulate the MMW radiation in On-Off keying. The detection of MMW radiation was performed using a simple and inexpensive GDD. The suggested MMW channel can be used as point to point backhaul wireless communication for the 5th generation of cellular communication. Full article
(This article belongs to the Collection Millimeter and Terahertz Wireless Communications)
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Figure 1
<p>Block diagram of the experimental setup of MMW projection system.</p> Full article ">Figure 2
<p>The glow discharge detectors (GDD) electrical circuit.</p> Full article ">Figure 3
<p>The dish antennas setup for the receiver (<b>a</b>) and transmission side (<b>b</b>) in the experiment. (<b>c</b>) Demonstration of the alignment process.</p> Full article ">Figure 4
<p>The voltage values of the Arduino transmitted bits in TTL levels (top, yellow signal), the raw detected signal received at the GDD detector of size 295 mV pick to pick’ (bottom, blue signal) and the reconstructed signal of the transmitted bits in the receiver side at the comparator output in TTL levels (middle, green signal).</p> Full article ">Figure 5
<p>The bit error rate performance for free space communication systems under various modulation method as a function of single to noise ration.</p> Full article ">
12 pages, 4093 KiB  
Article
Capacitive Coupling Wireless Power Transfer with Quasi-LLC Resonant Converter Using Electric Vehicles’ Windows
by KangHyun Yi
Electronics 2020, 9(4), 676; https://doi.org/10.3390/electronics9040676 - 21 Apr 2020
Cited by 20 | Viewed by 5661
Abstract
This paper proposes a new capacitive coupling wireless power transfer method for charging electric vehicles. Capacitive coupling wireless power transfer can replace conventional inductive coupling wireless power transfer because it has negligible eddy-current loss, relatively low cost and weight, and good misalignment performance. [...] Read more.
This paper proposes a new capacitive coupling wireless power transfer method for charging electric vehicles. Capacitive coupling wireless power transfer can replace conventional inductive coupling wireless power transfer because it has negligible eddy-current loss, relatively low cost and weight, and good misalignment performance. However, capacitive coupling wireless power transfer has a limitation in charging electric vehicles due to too small coupling capacitance via air with a very high frequency operation. The new capacitive wireless power transfer uses glass as a dielectric layer in a vehicle. The area and dielectric permittivity of a vehicle’s glass is large; hence, a high capacity coupling capacitor can be obtained. In addition, switching losses of a power conversion circuit are reduced by quasi-LLC resonant operation with two transformers. As a result, the proposed system can transfer large power and has high efficiency. A 1.6 kW prototype was designed to verify the operation and features of the proposed system, and it has a high efficiency of 96%. Full article
(This article belongs to the Section Power Electronics)
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Figure 1
<p>Electric vehicle charging method. (<b>a</b>) Slow wired charging. (<b>b</b>) Quick wired charging. (<b>c</b>) Wireless power transfer (WPT) charging.</p> Full article ">Figure 2
<p>Coupling capacitor implementation in a vehicle.</p> Full article ">Figure 3
<p>Proposed capacitive coupling wireless power transfer (CCWPT) charging system using the windows of an electric vehicle.</p> Full article ">Figure 4
<p>Proposed quasi-LLC resonant CCWPT power conversion circuit.</p> Full article ">Figure 5
<p>Key waveforms of the proposed CCWPT circuit.</p> Full article ">Figure 6
<p>Structure of capacitor with glass dielectric layer.</p> Full article ">Figure 7
<p>Equivalent circuit for the proposed CCWPT system.</p> Full article ">Figure 8
<p>Output voltage gain according to the transformer turn ratios. (<b>a</b>) Output voltage gain according to a turn ratio of transformer T<sub>1</sub>. (<b>b</b>) Output voltage gain according to a turn ratio of transformer T<sub>2</sub>.</p> Full article ">Figure 9
<p>Experimental set: transmitter circuit, rectifier, the resonant inductor, two transformers, the coupling capacitors using glass and copper plate.</p> Full article ">Figure 10
<p>Experimental key waveforms according to load variation: the gate-source voltage of <span class="html-italic">M</span><sub>1</sub>, the drain-source voltage of <span class="html-italic">M</span><sub>1</sub>, the resonant inductor current, the voltage across the magnetized inductor of the transformer <span class="html-italic">T</span><sub>2</sub>, the voltage of the coupling capacitor, the zero voltage switching (ZVS) operation of <span class="html-italic">M</span><sub>1.</sub></p> Full article ">Figure 11
<p>Power conversion efficiency according to output power.</p> Full article ">
24 pages, 8494 KiB  
Article
Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study
by Konstantinos Tarnaris, Ioanna Preka, Dionisis Kandris and Alex Alexandridis
Electronics 2020, 9(4), 675; https://doi.org/10.3390/electronics9040675 - 21 Apr 2020
Cited by 51 | Viewed by 6784
Abstract
The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue [...] Read more.
The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn. Full article
(This article belongs to the Section Networks)
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<p>K-coverage calculation algorithm.</p> Full article ">Figure 2
<p>Area coverage calculation algorithm.</p> Full article ">Figure 3
<p>Optimal node position with the use of the GA algorithm in case study 1.</p> Full article ">Figure 4
<p>Best and mean fitness values for each generation of the GA algorithm in case study 1.</p> Full article ">Figure 5
<p>Optimal node positions with the use of the PSO algorithm in case study 1.</p> Full article ">Figure 6
<p>Best fitness value for each iteration of the PSO algorithm in case study 1.</p> Full article ">Figure 7
<p>Optimal node positions with the use of the GA algorithm in case study 2.</p> Full article ">Figure 8
<p>Best and mean fitness values for each generation of the GA algorithm in case study 2.</p> Full article ">Figure 9
<p>Optimal node positions with the use of the PSO algorithm in case study 2.</p> Full article ">Figure 10
<p>Best fitness value for each iteration of the PSO algorithm in case study 2.</p> Full article ">Figure 11
<p>Optimal node positions with the use of the GA algorithm in case study 3.</p> Full article ">Figure 12
<p>Best and mean fitness values for each generation of the GA algorithm in case study 3.</p> Full article ">Figure 13
<p>Optimal node positions with the use of the PSO algorithm in case study 3.</p> Full article ">Figure 14
<p>Best fitness value for each iteration of the PSO algorithm in case study 3.</p> Full article ">Figure 15
<p>Optimal node positioning with the use of the GA algorithm in case study 4.</p> Full article ">Figure 16
<p>Best and mean fitness values for each generation of the GA algorithm in case study 4.</p> Full article ">Figure 17
<p>Optimal node positioning with the use of the PSO algorithm in case study 4.</p> Full article ">Figure 18
<p>Best fitness value for each iteration of the PSO algorithm in case study 4.</p> Full article ">
24 pages, 7267 KiB  
Article
Tan-Sun Transformation-Based Phase-Locked Loop in Detection of the Grid Synchronous Signals under Distorted Grid Conditions
by Guangjun Tan, Chunan Zong and Xiaofeng Sun
Electronics 2020, 9(4), 674; https://doi.org/10.3390/electronics9040674 - 20 Apr 2020
Cited by 4 | Viewed by 3099
Abstract
When three-phase voltages are polluted with unbalance, DC offsets, or higher harmonics, it is a challenge to quickly detect their parameters such as phases, frequency, and amplitudes. This paper proposes a phase-locked loop (PLL) for the three-phase non-ideal voltages based on the decoupling [...] Read more.
When three-phase voltages are polluted with unbalance, DC offsets, or higher harmonics, it is a challenge to quickly detect their parameters such as phases, frequency, and amplitudes. This paper proposes a phase-locked loop (PLL) for the three-phase non-ideal voltages based on the decoupling network composed of two submodules. One submodule is used to detect the parameters of the fundamental and direct-current voltages based on Tan-Sun transformation, and the other is used to detect the parameters of the higher-harmonic voltages based on Clarke transformation. By selecting the proper decoupling vector by mapping Hilbert space to Euclidean space, the decoupling control for each estimated parameter can be realized. The settling time of the control law can be set the same for each estimated parameter to further improve the response speed of the whole PLL system. The system order equals the number of the estimated parameters in each submodule except that a low-pass filter is required to estimate the average amplitude of the fundamental voltages, so the whole PLL structure is very simple. The simulation and experimental results are provided in the end to validate the effectiveness of the proposed PLL technique in terms of the steady and transient performance. Full article
(This article belongs to the Special Issue Grid-Connected and Isolated Renewable Energy Systems)
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Figure 1
<p>The whole phase-locked loop (PLL) structure.</p> Full article ">Figure 2
<p>The fundamental components (FC)-detection submodules (DS) structure in detection of fundamental-voltage parameters.</p> Full article ">Figure 3
<p>The FC&amp;DC-DS in detection of the fundamental-voltage and DC-offset voltage parameters.</p> Full article ">Figure 4
<p>Reconstruction structure of the unbalanced voltages containing DC offsets.</p> Full article ">Figure 5
<p>The higher-harmonic components (HC)-DS in detection of the higher-harmonic voltage parameters.</p> Full article ">Figure 6
<p>Reconstruction structure of the higher-harmonic voltages.</p> Full article ">Figure 7
<p>Simulation results when three-phase unbalanced voltages are suddenly injected with DC offsets and higher harmonics. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>. The actual and estimated parameters of (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">a</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">b</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>; (<b>e</b>) <span class="html-italic">f</span>; (<b>f</b>) <math display="inline"><semantics> <msub> <mi>θ</mi> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ca</mi> </msub> </semantics></math>.</p> Full article ">Figure 8
<p>Simulation results when <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math> has a step change from <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>130</mn> </mrow> </semantics></math>° to <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>150</mn> </mrow> </semantics></math>°. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>. The actual and estimated parameters of (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">a</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">b</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>; (<b>e</b>) <span class="html-italic">f</span>; (<b>f</b>) <math display="inline"><semantics> <msub> <mi>θ</mi> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ca</mi> </msub> </semantics></math>.</p> Full article ">Figure 9
<p>Simulation results when the grid frequency <span class="html-italic">f</span> has a step change from <math display="inline"><semantics> <mrow> <mn>50</mn> <mspace width="3.33333pt"/> <mi>Hz</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>45</mn> <mspace width="3.33333pt"/> <mi>Hz</mi> </mrow> </semantics></math>. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>. The actual and estimated parameters of (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">a</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">b</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>; (<b>e</b>) <span class="html-italic">f</span>; (<b>f</b>) <math display="inline"><semantics> <msub> <mi>θ</mi> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ca</mi> </msub> </semantics></math>.</p> Full article ">Figure 9 Cont.
<p>Simulation results when the grid frequency <span class="html-italic">f</span> has a step change from <math display="inline"><semantics> <mrow> <mn>50</mn> <mspace width="3.33333pt"/> <mi>Hz</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>45</mn> <mspace width="3.33333pt"/> <mi>Hz</mi> </mrow> </semantics></math>. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>. The actual and estimated parameters of (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">a</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>E</mi> <mrow> <mi mathvariant="normal">b</mi> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>; (<b>e</b>) <span class="html-italic">f</span>; (<b>f</b>) <math display="inline"><semantics> <msub> <mi>θ</mi> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>g</b>) <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ca</mi> </msub> </semantics></math>.</p> Full article ">Figure 10
<p>Photo of digital experimental platform.</p> Full article ">Figure 11
<p>Experimental steady results when three-phase unbalanced voltages contain DC offsets and higher harmonics. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 11 Cont.
<p>Experimental steady results when three-phase unbalanced voltages contain DC offsets and higher harmonics. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 12
<p>Experimental results when three-phase unbalanced voltages are suddenly injected with DC offsets and higher harmonics. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 12 Cont.
<p>Experimental results when three-phase unbalanced voltages are suddenly injected with DC offsets and higher harmonics. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 13
<p>Experimental results when <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math> has a step change from <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>130</mn> </mrow> </semantics></math>° to <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>150</mn> </mrow> </semantics></math>°. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 13 Cont.
<p>Experimental results when <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>ba</mi> </msub> </semantics></math> has a step change from <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>130</mn> </mrow> </semantics></math>° to <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>150</mn> </mrow> </semantics></math>°. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 14
<p>Experimental results when the grid frequency <span class="html-italic">f</span> has a step change from <math display="inline"><semantics> <mrow> <mn>50</mn> <mspace width="3.33333pt"/> <mi>Hz</mi> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>45</mn> <mspace width="3.33333pt"/> <mi>Hz</mi> </mrow> </semantics></math>. The waveforms of (<b>a</b>) <math display="inline"><semantics> <msub> <mi mathvariant="bold-italic">e</mi> <mi>abc</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mover accent="true"> <mi>f</mi> <mo stretchy="false">^</mo> </mover> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ba</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>φ</mi> <mo stretchy="false">^</mo> </mover> <mi>ca</mi> </msub> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mi>abc</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>θ</mi> <mo stretchy="false">^</mo> </mover> <mi mathvariant="normal">a</mi> </msub> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi mathvariant="bold-italic">e</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>abc</mi> <mn>0</mn> </mrow> </msub> </semantics></math>; (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>5</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>7</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>11</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>E</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mn>13</mn> <mi mathvariant="normal">m</mi> </mrow> </msub> </semantics></math>.</p> Full article ">
14 pages, 1034 KiB  
Article
Expedited Globalized Antenna Optimization by Principal Components and Variable-Fidelity EM Simulations: Application to Microstrip Antenna Design
by Jon Atli Tomasson, Anna Pietrenko-Dabrowska and Slawomir Koziel
Electronics 2020, 9(4), 673; https://doi.org/10.3390/electronics9040673 - 20 Apr 2020
Cited by 23 | Viewed by 2748
Abstract
Parameter optimization, also referred to as design closure, is imperative in the development of modern antennas. Theoretical considerations along with rough dimension adjustment through supervised parameter sweeping can only yield initial designs that need to be further tuned to boost the antenna performance. [...] Read more.
Parameter optimization, also referred to as design closure, is imperative in the development of modern antennas. Theoretical considerations along with rough dimension adjustment through supervised parameter sweeping can only yield initial designs that need to be further tuned to boost the antenna performance. The major challenges include handling of multi-dimensional parameter spaces while accounting for several objectives and constraints. Due to complexity of modern antenna topologies, parameter interactions are often involved, leading to multiple local optima as well as difficulties in identifying decent initial designs that can be improved using local procedures. In such cases, global search is required, which is an expensive endeavor, especially if full-wave electromagnetic (EM) analysis is employed for antenna evaluation. This paper proposes a novel technique accommodating the search space exploration using local kriging surrogates and local improvement by means of trust-region gradient search. Computational efficiency of the process is achieved by constructing the metamodels over appropriately defined affine subspaces and incorporation of coarse-mesh EM simulations at the exploratory stages of the optimization process. The resulting framework enables nearly global search capabilities at the costs comparable to conventional gradient-based local optimization. This is demonstrated using two antenna examples and comparative studies involving multiple-start local tuning. Full article
(This article belongs to the Section Microwave and Wireless Communications)
►▼ Show Figures

Figure 1

Figure 1
<p>Exemplary reflection responses of (<b>a</b>) wideband monopole and (<b>b</b>) uniplanar triple-band dipole antenna at random designs: high-fidelity model (— blue), (finer) low-fidelity model (- - - black), (coarser) low-fidelity model (⋅⋅⋅⋅ green). Note that the finer version of the low-fidelity model retains good correlation with the high-fidelity one, but the correlation is not as good for the coarser version. Thus, the coarser low-fidelity model is insufficient for local search purposes (e.g., within space mapping or feature-based optimization frameworks), yet it might be still applicable for global design space exploration.</p> Full article ">Figure 2
<p>Basic steps of the proposed quasi-global optimization procedure (illustrated for a three-dimensional case): (<b>a</b>) parameter space X, initial sampling, and the best design <b><span class="html-italic">x</span></b><sup>(0)</sup> over the sample set (Steps 1 and 2), (<b>b</b>) first iteration: subspace S(0) spanned by the first two principal directions (Steps 4 and 5), (<b>c</b>) sampling of S(0) ∩ X (Step 6), construction of the surrogate (Step 7), subsequently optimized to find <span class="html-italic">x<sub>tmp</sub></span> (Step 8), and the follow-up gradient-based refinement yielding the next design <b><span class="html-italic">x</span></b><sup>(1)</sup> (Step 9).</p> Full article ">Figure 3
<p>Flowchart of the proposed globalized design optimization procedure (Algorithm 1).</p> Full article ">Figure 4
<p>Verification case studies: (<b>a</b>) ultra-wideband monopole [<a href="#B39-electronics-09-00673" class="html-bibr">39</a>] (ground plane shown using light-shade gray), and (<b>b</b>) triple-band uniplanar dipole [<a href="#B41-electronics-09-00673" class="html-bibr">41</a>].</p> Full article ">Figure 5
<p>Ultra-wideband monopole antenna: reflection responses at <b><span class="html-italic">x</span></b><sup>(0)</sup> (- - -) and at the optimized design (—) for the selected run of the proposed algorithm. The horizontal line indicates design specifications, i.e., –10 dB acceptance level for antenna reflection within 3.1 GHz to 10.6 GHz frequency range. The bottom plot illustrates realized gain at the optimum design.</p> Full article ">Figure 6
<p>Triple-band uniplanar dipole antenna: reflection responses at <b><span class="html-italic">x</span></b><sup>(0)</sup> (- - -) and at the optimized design (—) for the selected run of the proposed algorithm. The vertical lines indicate the target operating frequencies.</p> Full article ">
17 pages, 4916 KiB  
Article
Practical Time-Release Blockchain
by Sang-Wuk Chae, Jae-Ik Kim and Yongsu Park
Electronics 2020, 9(4), 672; https://doi.org/10.3390/electronics9040672 - 20 Apr 2020
Cited by 7 | Viewed by 3580
Abstract
Time-release cryptography is a special encryption technique that allows a message to be hidden for some time. The previous schemes have shortcomings in that the encryptor should predict the decryptor’s computing power precisely or the trusted agent should be always available. In this [...] Read more.
Time-release cryptography is a special encryption technique that allows a message to be hidden for some time. The previous schemes have shortcomings in that the encryptor should predict the decryptor’s computing power precisely or the trusted agent should be always available. In this paper, we propose a new, practical time-release blockchain, and find the key to decrypt the content after a certain time. In order to verify the effectiveness of the blockchain system automatically, which uses the proof-of-work (PoW) and the consensus algorithm in the the proposed technique, we have implemented a prototype version of our blockchain system using Python. The proposed method has the following advantages. First, the decryption time is automatically adjusted, even if the miner’s computing power changes over time. Second, unlike previous time-lock puzzle schemes, our algorithm does not require additional computation work for solving the puzzle. Third, our scheme does not need any trusted agents (third parties). Fourth, the proposed method uses standard cryptographic algorithms. Full article
(This article belongs to the Special Issue Data Security)
►▼ Show Figures

Figure 1

Figure 1
<p>An example for the chained blocks structure.</p> Full article ">Figure 2
<p>An example for the forked chain structure.</p> Full article ">Figure 3
<p>Algorithm to generate the public key.</p> Full article ">Figure 4
<p>Block structure of Bitcoin blockchain (<b>left</b>) and that of our scheme (<b>right</b>).</p> Full article ">Figure 5
<p>Chained blocks structure of the time-release blockchain.</p> Full article ">Figure 6
<p>An example of the chained blocks structure.</p> Full article ">Figure 7
<p>Proof-of-work of the time-release blockchain.</p> Full article ">Figure 8
<p>Ciphertext that is encrypted with <span class="html-italic">k</span> public keys.</p> Full article ">Figure 9
<p>Class definition of the header of the block.</p> Full article ">Figure 10
<p>Implementation of creating a genesis block.</p> Full article ">Figure 11
<p>Implementation of proof-of-work.</p> Full article ">Figure 12
<p>Implementation of difficulty adjustment.</p> Full article ">Figure 13
<p>The overall procedure in e-voting using blockchains [<a href="#B17-electronics-09-00672" class="html-bibr">17</a>].</p> Full article ">Figure 14
<p>Proposed e-voting procedure that is based on the time-release blockchain [<a href="#B17-electronics-09-00672" class="html-bibr">17</a>].</p> Full article ">Figure 15
<p>Brute-force attack vs. mining works.</p> Full article ">
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