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Volume 8
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Electronics, Volume 8, Issue 1 (January 2019) – 115 articles

Cover Story (view full-size image): Current trends in cyber–physical system design include devices with robotic hands capable of performing manipulation and grasping tasks. These devices can be used for enabling robotic communication via sign language. This paper measures how end-users feel about interpreting sign language represented by a humanoid robot as opposed to subtitles on a screen. Stemming from this dichotomy, dactylology, basic vocabulary representation, and end-user satisfaction are the main topics covered. The experiments were performed using TEO, a humanoid robot developed at the University Carlos III de Madrid, via representations in Spanish Sign Language, with a group of deaf and hearing-impaired participants. View this paper.
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21 pages, 4742 KiB  
Article
A 10 kW ZVS Integrated Boost Dual Three-Phase Bridge DC–DC Resonant Converter for a Linear Generator-Based Wave-Energy System: Design and Simulation
by Nagendrappa Harischandrappa and Ashoka K. S. Bhat
Electronics 2019, 8(1), 115; https://doi.org/10.3390/electronics8010115 - 21 Jan 2019
Cited by 4 | Viewed by 5449
Abstract
The design and performance analysis of a 10 kW three-phase DC–DC LCL-type resonant converter having a built-in boost function were carried out. This high-power converter is proposed for its application in grid-interfacing a linear generator (LG)-based wave-energy system. Fixed-frequency control is used, and [...] Read more.
The design and performance analysis of a 10 kW three-phase DC–DC LCL-type resonant converter having a built-in boost function were carried out. This high-power converter is proposed for its application in grid-interfacing a linear generator (LG)-based wave-energy system. Fixed-frequency control is used, and the converter was designed to operate with a lagging power factor. It is shown that all switches turn on with zero-voltage switching (ZVS) for wide input voltage and load variations. This results in reduced switching losses and stresses, which is very important in large-power applications. The performance of the converter was studied through PSIM simulation software. Theoretical and simulation results are presented for comparison. Power-loss break-down analysis of the designed converter was carried out and the summary of results is presented. Full article
(This article belongs to the Special Issue Advanced Power Conversion Technologies)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Structure of a permanent-magnet linear generator, (<b>b</b>) typical waveform of phase voltage of a linear-generator output (<span class="html-italic">v</span><sub>phase</sub>).</p> Full article ">Figure 2
<p>Block diagram of grid integration of a wave-energy plant [<a href="#B24-electronics-08-00115" class="html-bibr">24</a>].</p> Full article ">Figure 3
<p>Proposed DC–DC LCL-type series resonant converter [<a href="#B24-electronics-08-00115" class="html-bibr">24</a>,<a href="#B25-electronics-08-00115" class="html-bibr">25</a>].</p> Full article ">Figure 4
<p>PSIM simulation waveforms for Case 1: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 180°. <span class="html-italic">v</span><sub>AB</sub>, <span class="html-italic">v</span><sub>BC</sub>, <span class="html-italic">v</span><sub>CA</sub>, and <span class="html-italic">i</span><sub>LsA</sub>, <span class="html-italic">i</span><sub>LsB</sub>, <span class="html-italic">i</span><sub>LsC</sub> for (<b>a</b>) Module 1 and (<b>b</b>) Module 2. <span class="html-italic">v</span><sub>rect_in_ab</sub> or <span class="html-italic">v</span><sub>Lab</sub>, and <span class="html-italic">v</span><sub>CsA</sub> for (<b>c</b>) Module 1 and (<b>d</b>) Module 2.</p> Full article ">Figure 5
<p>PSIM simulation waveforms for Case 1: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 180°. <span class="html-italic">i</span><sub>Lab</sub>, <span class="html-italic">i</span><sub>LsA</sub>, and <span class="html-italic">i</span><sub>rect_in.a</sub> for (<b>a</b>) Module 1 and (<b>b</b>) Module 2. <span class="html-italic">v</span><sub>Lab</sub>, <span class="html-italic">v</span><sub>Lbc</sub>, <span class="html-italic">v</span><sub>Lca</sub>, and <span class="html-italic">i</span><sub>rect_in,a</sub> for (<b>c</b>) Module 1 and (<b>d</b>) Module 2.</p> Full article ">Figure 6
<p>PSIM simulation waveforms for Case 1: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 180°. Voltage across MOSFET (<span class="html-italic">v</span><sub>DS</sub>) and current through it (<span class="html-italic">i</span><sub>s</sub>) to show ZVS of switches <span class="html-italic">S</span><sub>1</sub>–<span class="html-italic">S</span><sub>3</sub> and switches <span class="html-italic">S</span><sub>4</sub>–<span class="html-italic">S</span><sub>6</sub> for (<b>a</b>) Module 1 and (<b>b</b>) Module 2.</p> Full article ">Figure 7
<p>PSIM simulation waveforms for Case 1: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 180°. Phase voltages (<b>a</b>) across the primary terminals (<span class="html-italic">v</span><sub>A12p</sub>, <span class="html-italic">v</span><sub>B12p</sub>, <span class="html-italic">v</span><sub>C12p</sub>), and the primary current in Phase A of the three-phase boost transformer T<sub>3</sub>; (<b>b</b>) across the secondary terminals of three-phase boost transformer T<sub>3</sub> (<span class="html-italic">v</span><sub>A12s</sub>, <span class="html-italic">v</span><sub>B12s</sub>, <span class="html-italic">v</span><sub>C12s</sub>), and output voltage of the boost rectifier before filtering (<span class="html-italic">v</span><sub>boost</sub>).</p> Full article ">Figure 8
<p>PSIM simulation waveforms for Case 2: <span class="html-italic">V</span><sub>in</sub>(max) = 270 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 84°. (<b>a</b>)–(<b>d</b>) <a href="#electronics-08-00115-f004" class="html-fig">Figure 4</a> waveforms repeated.</p> Full article ">Figure 9
<p>PSIM simulation waveforms for Case 2: <span class="html-italic">V</span><sub>in</sub>(max) = 270 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span>= 84°. (<b>a</b>)–(<b>d</b>) <a href="#electronics-08-00115-f005" class="html-fig">Figure 5</a> waveforms repeated.</p> Full article ">Figure 10
<p>PSIM simulation waveforms for Case 2: <span class="html-italic">V</span><sub>in</sub>(max) = 270 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 84°. (<b>a</b>)–(<b>b</b>) <a href="#electronics-08-00115-f006" class="html-fig">Figure 6</a> waveforms repeated.</p> Full article ">Figure 11
<p>PSIM simulation waveforms for Case 2: <span class="html-italic">V</span><sub>in</sub>(max) = 270 V, full load, <span class="html-italic">R</span><sub>L</sub> = 16 Ω, <span class="html-italic">δ</span> = 84°. (<b>a</b>)–(<b>b</b>) <a href="#electronics-08-00115-f007" class="html-fig">Figure 7</a> waveforms repeated.</p> Full article ">Figure 12
<p>PSIM simulation waveforms for Case 5: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, 20% of full load, <span class="html-italic">R</span><sub>L</sub> = 80 Ω, <span class="html-italic">δ</span> = 98°. (<b>a</b>)–(<b>d</b>) <a href="#electronics-08-00115-f004" class="html-fig">Figure 4</a> waveforms repeated.</p> Full article ">Figure 13
<p>PSIM simulation waveforms for Case 5: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, 20% of full load, <span class="html-italic">R</span><sub>L</sub> = 80 Ω, <span class="html-italic">δ</span> = 98°. (<b>a</b>)–(<b>d</b>) <a href="#electronics-08-00115-f005" class="html-fig">Figure 5</a> waveforms repeated.</p> Full article ">Figure 14
<p>PSIM simulation waveforms for Case 5: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, 20% of full load, <span class="html-italic">R</span><sub>L</sub> = 80 Ω, <span class="html-italic">δ</span> = 98°. (<b>a</b>)–(<b>b</b>) <a href="#electronics-08-00115-f006" class="html-fig">Figure 6</a> waveforms repeated.</p> Full article ">Figure 15
<p>PSIM simulation waveforms for Case 5: <span class="html-italic">V</span><sub>in</sub>(min) = 135 V, 20% of full load, <span class="html-italic">R</span><sub>L</sub> = 80 Ω, <span class="html-italic">δ</span> = 98°. (<b>a</b>)–(<b>b</b>) <a href="#electronics-08-00115-f007" class="html-fig">Figure 7</a> waveforms repeated.</p> Full article ">Figure 16
<p>Theoretical and simulation efficiencies:(<b>a</b>) <span class="html-italic">V</span><sub>in</sub>(min) = 135 V full load (10 kW), half load (5 kW), and 20% of full load; and (<b>b</b>) <span class="html-italic">V</span><sub>in</sub>(max) = 270 V full load (10 kW), and half load (5 kW).</p> Full article ">Figure 17
<p>Simulation waveforms with <span class="html-italic">V</span><sub>in</sub>(min) = 135 V for step changes in <span class="html-italic">i</span><sub>o</sub>: (<b>a</b>) waveforms of <span class="html-italic">v</span><sub>o</sub> and <span class="html-italic">i</span><sub>o</sub>, (<b>b</b>) expanded waveform of <span class="html-italic">v</span><sub>o</sub>, for full load (<span class="html-italic">R</span><sub>L</sub> = 16 Ω, δ = 180°) to half load (<span class="html-italic">R</span><sub>L</sub> = 32 Ω, δ = 107°) at <span class="html-italic">t</span> = 0.2 sec.; (<b>c</b>) waveforms of <span class="html-italic">v</span><sub>o</sub> and <span class="html-italic">i</span><sub>o</sub> (<b>d</b>) expanded waveform of <span class="html-italic">v</span><sub>o</sub>, for half load (<span class="html-italic">R</span><sub>L</sub> = 32 Ω, δ = 107°) to 20% of full load (<span class="html-italic">R</span><sub>L</sub> = 80 Ω, δ = 98°) at <span class="html-italic">t</span> = 0.25 sec. Output voltage <span class="html-italic">v</span><sub>o</sub> to be regulated is 392 V.</p> Full article ">Figure 18
<p>Simulation waveforms with <span class="html-italic">V</span><sub>in</sub>(min) = 135 V for step changes in <span class="html-italic">i</span><sub>o</sub> from 20% of full load (<span class="html-italic">R</span><sub>L</sub> = 80 Ω, δ = 98°) to half load (<span class="html-italic">R</span><sub>L</sub> = 32 Ω, δ = 107°) at <span class="html-italic">t</span> = 0.2 s, and then to full load (<span class="html-italic">R</span><sub>L</sub> = 16 Ω, δ = 180°) at <span class="html-italic">t</span> = 0.25 s: (<b>a</b>) waveforms of <span class="html-italic">v</span><sub>o</sub> and <span class="html-italic">i</span><sub>o</sub>, (<b>b</b>) expanded waveform of <span class="html-italic">v</span><sub>o</sub>.</p> Full article ">Figure 19
<p>Simulation waveforms with <span class="html-italic">V</span><sub>in</sub>(min) = 135 V for step changes in load current at <span class="html-italic">t</span> = 0.2 s: (<b>a</b>) waveforms of resonant tank currents (<span class="html-italic">i</span><sub>Lsa</sub>, <span class="html-italic">i</span><sub>Lsb</sub>, <span class="html-italic">i</span><sub>Lsc</sub>) in Phases A, B, and C, (<b>b</b>) expanded waveforms of <span class="html-italic">i</span><sub>Lsa</sub>, <span class="html-italic">i</span><sub>Lsb</sub>, <span class="html-italic">i</span><sub>Lsc</sub> for full load (<span class="html-italic">R</span><sub>L</sub> = 16 Ω, δ = 180°) to half load (<span class="html-italic">R</span><sub>L</sub> = 32 Ω, δ = 107°) for Module 1, (<b>c</b>) <span class="html-italic">i</span><sub>Lsa</sub>, <span class="html-italic">i</span><sub>Lsb</sub>, <span class="html-italic">i</span><sub>Lsc</sub>, (<b>d</b>) expanded waveforms of <span class="html-italic">i</span><sub>Lsa</sub>, <span class="html-italic">i</span><sub>Lsb</sub>, <span class="html-italic">i</span><sub>Lsc</sub> for half load (<span class="html-italic">R</span><sub>L</sub> = 32 Ω, δ = 107°) to 20% of full load (<span class="html-italic">R</span><sub>L</sub> = 80 Ω, δ = 98°) for Module 2.</p> Full article ">Figure 20
<p>Simulation waveforms of resonant tank currents (<span class="html-italic">i</span><sub>Lsa</sub>, <span class="html-italic">i</span><sub>Lsb</sub>, <span class="html-italic">i</span><sub>Lsc</sub>) in Phases A, B, and C with <span class="html-italic">V</span><sub>in</sub>(min) = 135 V for step changes in load current from 20% of full load (<span class="html-italic">R</span><sub>L</sub>= 80 Ω, δ = 98°) to half load (<span class="html-italic">R</span><sub>L</sub>= 32 Ω, δ = 107°) at <span class="html-italic">t</span> = 0.2 s, and then to full load (<span class="html-italic">R</span><sub>L</sub>= 16 Ω, δ = 180°) at <span class="html-italic">t</span> = 0.25 s. (<b>a</b>) Module 1 and (<b>b</b>) Module 2.</p> Full article ">Figure 21
<p>Simulation waveforms of switch currents (<span class="html-italic">i</span><sub>s1</sub>–<span class="html-italic">i</span><sub>s6</sub>) with <span class="html-italic">V</span><sub>in</sub>(min) = 135 V for step changes in load current: (<b>a</b>)–(<b>b</b>) from full load (<span class="html-italic">R</span><sub>L</sub>= 16 Ω, δ = 180°) to half load (<span class="html-italic">R</span><sub>L</sub>= 32 Ω, δ = 107°) at <span class="html-italic">t</span> = 0.2 s. (<b>a</b>) Module 1 and (<b>b</b>) Module 2. (<b>c</b>)–(<b>d</b>) from half load (<span class="html-italic">R</span><sub>L</sub>= 16 Ω, δ = 180°) to 20% of full load (<span class="html-italic">R</span><sub>L</sub>= 80 Ω, δ = 98°) at <span class="html-italic">t</span> = 0.25 s. (<b>c</b>) Module 1 and (<b>d</b>) Module 2.</p> Full article ">Figure A1
<p>Phasor equivalent circuit used for analyzing Module 1 of the converter shown in <a href="#electronics-08-00115-f003" class="html-fig">Figure 3</a> [<a href="#B24-electronics-08-00115" class="html-bibr">24</a>,<a href="#B25-electronics-08-00115" class="html-bibr">25</a>]<span class="html-small-caps">.</span></p> Full article ">
17 pages, 5226 KiB  
Review
Recent Progress in the Design of 4G/5G Reconfigurable Filters
by Yasir I. A. Al-Yasir, Naser Ojaroudi Parchin, Raed A. Abd-Alhameed, Ahmed M. Abdulkhaleq and James M. Noras
Electronics 2019, 8(1), 114; https://doi.org/10.3390/electronics8010114 - 20 Jan 2019
Cited by 62 | Viewed by 10441
Abstract
Currently, several microwave filter designs contend for use in wireless communications. Among various microstrip filter designs, the reconfigurable planar filter presents more advantages and better prospects for communication applications, being compact in size, light-weight and cost-effective. Tuneable microwave filters can reduce the number [...] Read more.
Currently, several microwave filter designs contend for use in wireless communications. Among various microstrip filter designs, the reconfigurable planar filter presents more advantages and better prospects for communication applications, being compact in size, light-weight and cost-effective. Tuneable microwave filters can reduce the number of switches between electronic components. This paper presents a review of recent reconfigurable microwave filter designs, specifically on current advances in tuneable filters that involve high-quality factor resonator filters to control frequency, bandwidth and selectivity. The most important materials required for this field are also highlighted and surveyed. In addition, the main references for several types of tuneable microstrip filters are reported, especially related to new design technologies. Topics surveyed include microwave and millimetre wave designs for 4G and 5G applications, which use varactors and MEMSs technologies. Full article
(This article belongs to the Special Issue Recent Technical Developments in Energy-Efficient 5G Mobile Cells)
►▼ Show Figures

Figure 1

Figure 1
<p>3D configuration of the Combline BPF filter [<a href="#B38-electronics-08-00114" class="html-bibr">38</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) RF system installation of the two BPF reconfigurable filters; (<b>b</b>) The front side of the diaphragm; (<b>c</b>) The front side of the cavity; (<b>d</b>) Side view of the DC bias circuit; (<b>e</b>) A cross-sectional view of the installed package system [<a href="#B39-electronics-08-00114" class="html-bibr">39</a>].</p> Full article ">Figure 3
<p>A photo of the reconfigurable microstrip filter [<a href="#B40-electronics-08-00114" class="html-bibr">40</a>].</p> Full article ">Figure 4
<p>The design procedure for synthesized networks [<a href="#B41-electronics-08-00114" class="html-bibr">41</a>].</p> Full article ">Figure 5
<p>A reconfigurable filter topology using: (<b>a</b>) Acoustic wave resonators and variable capacitors; (<b>b</b>) SAW/ BAW resonators and varactors [<a href="#B48-electronics-08-00114" class="html-bibr">48</a>].</p> Full article ">Figure 6
<p>π type reconfigurable SAW filter: (<b>a</b>) Circuit configuration; (<b>b</b>) a top view of the fabricated filter [<a href="#B49-electronics-08-00114" class="html-bibr">49</a>].</p> Full article ">Figure 7
<p>Reconfigurable BAW filter: (<b>a</b>) Negative capacitance circuit; (<b>b</b>) Filter layout [<a href="#B50-electronics-08-00114" class="html-bibr">50</a>].</p> Full article ">Figure 8
<p>Active reconfigurable filter: (<b>a</b>) Active inductor-based filter [<a href="#B53-electronics-08-00114" class="html-bibr">53</a>]; (<b>b</b>) Recursive filter [<a href="#B45-electronics-08-00114" class="html-bibr">45</a>].</p> Full article ">Figure 9
<p>Laboratory install of the proposed filter [<a href="#B54-electronics-08-00114" class="html-bibr">54</a>]. VNA: Vector Network Analyser; PD: Photon Detector; TBPF: Tunable Band-Pass Filter; MZM: Mach-Zehnder Modulator; OSA: Optical Spectrum Analyzer; TLS: Tunable Laser Source.</p> Full article ">Figure 10
<p>Electronically-tuned reconfigurable filters [<a href="#B60-electronics-08-00114" class="html-bibr">60</a>].</p> Full article ">Figure 11
<p>3D Layout of the tuneable microstrip filter [<a href="#B61-electronics-08-00114" class="html-bibr">61</a>].</p> Full article ">Figure 12
<p>Biasing circuit with SPICE model of varactor switch [<a href="#B61-electronics-08-00114" class="html-bibr">61</a>].</p> Full article ">Figure 13
<p>The tuneable microstrip LPF [<a href="#B62-electronics-08-00114" class="html-bibr">62</a>].</p> Full article ">Figure 14
<p>The UWB reconfigurable filter [<a href="#B63-electronics-08-00114" class="html-bibr">63</a>].</p> Full article ">Figure 15
<p>Design and layout: (<b>a</b>) Filter structure; (<b>b</b>,<b>c</b>) MEMS switch [<a href="#B64-electronics-08-00114" class="html-bibr">64</a>].</p> Full article ">Figure 16
<p>The tuneable filter [<a href="#B69-electronics-08-00114" class="html-bibr">69</a>].</p> Full article ">
15 pages, 519 KiB  
Article
The Role of Diversity on Linear Scattering Operator: The Case of Strip Scatterers Observed under the Fresnel Approximation
by Maria Antonia Maisto and Fortuna Munno
Electronics 2019, 8(1), 113; https://doi.org/10.3390/electronics8010113 - 20 Jan 2019
Cited by 2 | Viewed by 3239
Abstract
The aim of this paper is to investigate the role of multiple views and multiple frequencies in linear inverse scattering problems. The study was performed assuming the Fresnel-zone approximation on the scattering operator. Due to the crucial role played by singular values into [...] Read more.
The aim of this paper is to investigate the role of multiple views and multiple frequencies in linear inverse scattering problems. The study was performed assuming the Fresnel-zone approximation on the scattering operator. Due to the crucial role played by singular values into analysing the linear inverse scattering problems, the impact of view and frequency diversities on singular values behaviour was established. In fact, the singular values were related to the most common metrics used to quantify the achievable performances in inverse scattering problems, such as the number of degrees of freedom (NDF), the information content and the resolution. Full article
(This article belongs to the Special Issue Microwave Imaging and Its Application)
►▼ Show Figures

Figure 1

Figure 1
<p>Geometry of the problem.</p> Full article ">Figure 2
<p>Case of two views <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>=</mo> <mrow> <mo stretchy="false">{</mo> <mo>−</mo> <msub> <mi>X</mi> <mi>o</mi> </msub> <mo>,</mo> <msub> <mi>X</mi> <mi>o</mi> </msub> <mo stretchy="false">}</mo> </mrow> </mrow> </semantics></math>. The top panel shows the two frequency bands, while in the bottom panel the singular values of the relative scattering operator are plotted. For the simulation, the configuration parameters are <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>30</mn> <mi>λ</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>30</mn> <mi>λ</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>125</mn> <mi>λ</mi> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Case of three views <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>=</mo> <mrow> <mo stretchy="false">{</mo> <mo>−</mo> <msub> <mi>X</mi> <mi>o</mi> </msub> <mo>,</mo> <mn>0</mn> <mo>,</mo> <msub> <mi>X</mi> <mi>o</mi> </msub> <mo stretchy="false">}</mo> </mrow> </mrow> </semantics></math>. The top panel gives a qualitative view of the frequency bands that now overlap. The bottom panel shows the spectrum of the kernel in terms of disjoint bands.</p> Full article ">Figure 4
<p>Singular values behaviour of <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mi>i</mi> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> views (the other parameters are setted as in <a href="#electronics-08-00113-f002" class="html-fig">Figure 2</a>). The foreseen values for the <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>n</mi> </msub> </semantics></math>s on each step are <math display="inline"><semantics> <mrow> <mn>0.1789</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>0.1549</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>0.1265</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>0.0894</mn> </mrow> </semantics></math>, while for the knees are 14, 28, 43 and 57. They agree with the values indicated in figure.</p> Full article ">Figure 5
<p>Singular values behaviour of <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mi>i</mi> </msub> </semantics></math> for the case of continuous views and <math display="inline"><semantics> <mrow> <mn>2</mn> <mi>M</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math> (the other parameters are set as in <a href="#electronics-08-00113-f002" class="html-fig">Figure 2</a>). Yellow and red lines represent the square root of the eigenvalues of <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi mathvariant="script">A</mi> <mo stretchy="false">˜</mo> </mover> <mi>i</mi> <mo>†</mo> </msubsup> <msub> <mi mathvariant="script">A</mi> <mi>i</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi mathvariant="script">A</mi> <mo stretchy="false">^</mo> </mover> <mi>i</mi> <mo>†</mo> </msubsup> <msub> <mi mathvariant="script">A</mi> <mi>i</mi> </msub> </mrow> </semantics></math>, respectively.</p> Full article ">Figure 6
<p>Illustration of how to rearrange the frequency bands to obtain Equation (<a href="#FD29-electronics-08-00113" class="html-disp-formula">29</a>) with the assumption <math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mn>0</mn> </msub> <mo>&gt;</mo> <mi>a</mi> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Singular values behaviour of <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mi>f</mi> </msub> </semantics></math> for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> frequencies The configuration parameters are <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>30</mn> <msub> <mi>λ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mi>o</mi> </msub> <mo>=</mo> <mn>45</mn> <msub> <mi>λ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mi>z</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>125</mn> <msub> <mi>λ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>4</mn> <msub> <mi>λ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>m. The foreseen values for the <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>n</mi> </msub> </semantics></math> on each step are <math display="inline"><semantics> <mrow> <mn>0.2298</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>0.1442</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>0.0894</mn> </mrow> </semantics></math>, while for the knees are 10, 27 and 43. They agree with the values indicated in figure.</p> Full article ">Figure 8
<p>Singular values behaviour of <math display="inline"><semantics> <msub> <mi mathvariant="script">A</mi> <mi>f</mi> </msub> </semantics></math> for the case of continuous frequencies for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>. The other parameters are set as in <a href="#electronics-08-00113-f007" class="html-fig">Figure 7</a>. Yellow and red lines represent the square root of the eigenvalues of <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi mathvariant="script">A</mi> <mo stretchy="false">˜</mo> </mover> <mi>f</mi> <mo>†</mo> </msubsup> <msub> <mi mathvariant="script">A</mi> <mi>f</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mover accent="true"> <mi mathvariant="script">A</mi> <mo stretchy="false">^</mo> </mover> <mi>f</mi> <mo>†</mo> </msubsup> <msub> <mi mathvariant="script">A</mi> <mi>f</mi> </msub> </mrow> </semantics></math>, respectively.</p> Full article ">
14 pages, 6848 KiB  
Article
A Hybrid Current Mode Controller with Fast Response Characteristics for Super Capacitor Applications
by Seung-Min Oh, Jae-hak Ko, Hag-Wone Kim and Kwan-Yuhl Cho
Electronics 2019, 8(1), 112; https://doi.org/10.3390/electronics8010112 - 19 Jan 2019
Cited by 4 | Viewed by 5777
Abstract
A wide-bandwidth current-controller is required for the fast charging and discharging of applications containing super capacitors. To accomplish this, peak current mode is generally used due to the speed of its response characteristics. On the other hand, peak current mode control must be [...] Read more.
A wide-bandwidth current-controller is required for the fast charging and discharging of applications containing super capacitors. To accomplish this, peak current mode is generally used due to the speed of its response characteristics. On the other hand, peak current mode control must be provided with a slope compensation function to restrain sub-harmonic oscillations. However, if the controlled output voltage is varied, the slope must be changed accordingly. Nonetheless, it is not easy to change the slope for every change in output voltage. Another solution involves the slope being set at the maximum value, which results in a slow response. Therefore, in this paper, a hybrid mode controller was proposed that uses a peak current and a newly-specified valley current. Using the proposed hybrid mode control, sub-harmonic oscillation did not occur for duty cycles larger than 0.5 and response times were fast. Full article
(This article belongs to the Special Issue Advanced Power Conversion Technologies)
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<p>Previousmethod for preventing the sub-harmonic oscillation problem.</p> Full article ">Figure 2
<p>Operation principle of peak current mode control.</p> Full article ">Figure 3
<p>Waveforms with slope compensation at ideal current and sub-harmonic oscillation current.</p> Full article ">Figure 4
<p>Comparison between ideal current and slope compensation.</p> Full article ">Figure 5
<p>Comparison between sub-harmonic oscillation current and slope compensation.</p> Full article ">Figure 6
<p>Comparison between ideal current and slope compensation.</p> Full article ">Figure 7
<p>Comparison between sub-harmonic oscillation current and slope compensation.</p> Full article ">Figure 8
<p>Inductor current waveform for changing input voltage.</p> Full article ">Figure 9
<p>Inductor current waveform for changing output voltage.</p> Full article ">Figure 10
<p>Duty cycle using different slopes.</p> Full article ">Figure 11
<p>Block diagram of Valley current mode control.</p> Full article ">Figure 12
<p>Inductor current that occurs when duty is above 0.5 in Valley current mode control.</p> Full article ">Figure 13
<p>Steady-state inductor current.</p> Full article ">Figure 14
<p>Proposed hybrid current mode control block diagram.</p> Full article ">Figure 15
<p>Inductor current due to changing output voltage.</p> Full article ">Figure 16
<p>Boost converter circuit in Powersim (PSIM) simulation.</p> Full article ">Figure 17
<p>Response time for different controllers.</p> Full article ">Figure 18
<p>Proposed hybrid current mode controller inductor current response to a lowered output voltage.</p> Full article ">Figure 19
<p>Proposed hybrid current mode controller inductor current response to a rise in output voltage.</p> Full article ">Figure 20
<p>Experimental setup with boost converter.</p> Full article ">Figure 21
<p>A waveform that implements a digital controller.</p> Full article ">Figure 22
<p>Response time for different controllers. (20 V/div, 400 ms/div).</p> Full article ">Figure 23
<p>Inductor current waveform when the output voltage is lowered in the proposed hybrid current mode control. (10 V/div, 1 A/div, 50 ms/div).</p> Full article ">Figure 24
<p>Inductor current waveform when the output voltage is raised in the proposed hybrid current mode control. (10 V/div, 1 A/div, 50 ms/div).</p> Full article ">Figure 25
<p>Inductor current waveform before a drop in output voltage. (10 V/div, 1 A/div, 25 us/div).</p> Full article ">Figure 26
<p>Inductor current waveform after a drop in output voltage. (10 V/div, 1A/div, 25 us/div).</p> Full article ">Figure 27
<p>Inductor current waveform before a rise in output voltage. (10 V/div, 1A/div, 25 us/div).</p> Full article ">Figure 28
<p>Inductor current waveform after a rise in output voltage. (10 V/div, 1A/div, 25 us/div).</p> Full article ">
21 pages, 2116 KiB  
Article
Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm
by Touqeer Ahmed Jumani, Mohd Wazir Mustafa, Madihah Md Rasid, Nayyar Hussain Mirjat, Mazhar Hussain Baloch and Sani Salisu
Electronics 2019, 8(1), 111; https://doi.org/10.3390/electronics8010111 - 19 Jan 2019
Cited by 51 | Viewed by 6227
Abstract
Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection [...] Read more.
Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels. Full article
(This article belongs to the Special Issue Power Quality in Smart Grids)
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<p>A general architecture of an MG.</p> Full article ">Figure 2
<p>Grid-connected MG model with the proposed control strategy.</p> Full article ">Figure 3
<p>Proposed optimal power flow control strategy.</p> Full article ">Figure 4
<p>The life cycle of grasshoppers [<a href="#B1-electronics-08-00111" class="html-bibr">1</a>].</p> Full article ">Figure 5
<p>Proposed Methodology.</p> Full article ">Figure 6
<p>MG power regulation. (<b>a</b>) Active power. (<b>b</b>) Reactive power.</p> Full article ">Figure 7
<p>(<b>a</b>) Active power (<b>b</b>) Reactive power flow between the MG and utility grid.</p> Full article ">Figure 8
<p>%Total Harmonic Distortion (THD) in output current after (<b>a</b>) MG injection and (<b>b</b>) load change.</p> Full article ">Figure 9
<p>Convergence curve for particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA).</p> Full article ">Figure 10
<p>PSO versus GOA for (<b>a</b>) active power and (<b>b</b>) reactive power regulation.</p> Full article ">Figure 10 Cont.
<p>PSO versus GOA for (<b>a</b>) active power and (<b>b</b>) reactive power regulation.</p> Full article ">
11 pages, 384 KiB  
Editorial
Acknowledgement to Reviewers of Electronics in 2018
by Electronics Editorial Office
Electronics 2019, 8(1), 110; https://doi.org/10.3390/electronics8010110 - 19 Jan 2019
Viewed by 2530
Abstract
Rigorous peer-review is the cornerstone of high-quality academic publishing [...] Full article
18 pages, 15139 KiB  
Article
Systematic Implementation of Multi-Phase Power Supply (Three to Six) Conversion System
by Rashid Al-Ammari, Atif Iqbal, Amith Khandakar, Syed Rahman and Sanjeevikumar Padmanaban
Electronics 2019, 8(1), 109; https://doi.org/10.3390/electronics8010109 - 18 Jan 2019
Cited by 7 | Viewed by 5959
Abstract
Multiphase (more than three) power system has gained popularity due to their inherent advantages when compared to three-phase counterpart. Multiphase power supply is extensively used in AC/DC multi-pulse converters, especially supply with multiple of three-phases. AC/DC converter with multi-pulse input is a popular [...] Read more.
Multiphase (more than three) power system has gained popularity due to their inherent advantages when compared to three-phase counterpart. Multiphase power supply is extensively used in AC/DC multi-pulse converters, especially supply with multiple of three-phases. AC/DC converter with multi-pulse input is a popular solution to reduce the ripple in the DC output. Single-phase and three-phase transformers and phase transformation from single to multiphase are employed in variable speed drives application to feed the multi-cell H-Bridge converters and multi-pulse AC-DC converters. Six-phase system is extensively discussed in the literature for numerous applications ranging from variable speed drives to multiphase wind energy generation system. This paper shows the systematic phase transformation technique from three-phase to six-phase (both symmetrical and asymmetrical) for both understanding and teaching purposes. Such an approach could help students understand a promising advanced concept in their undergraduate courses. When phase difference between the two consecutive phases of six phases has a phase difference of 60°, it is called a symmetrical six-phase system; while an asymmetrical or quasi, six-phase has two set of three-phase with a phase shift of 30° between the two sets. Simulation and experimental results are also presented. Full article
(This article belongs to the Section Power Electronics)
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<p>(<b>a</b>) Three-Phase Phasor Diagram; (<b>b</b>) symmetrical Six-phase Phasor Diagram; (<b>c</b>) asymmetrical Six-phase Phasor Diagram.</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Three-Phase Phasor Diagram; (<b>b</b>) symmetrical Six-phase Phasor Diagram; (<b>c</b>) asymmetrical Six-phase Phasor Diagram.</p> Full article ">Figure 2
<p>Symmetrical six-phase output.</p> Full article ">Figure 3
<p>Asymmetrical or quasi six-phase output.</p> Full article ">Figure 4
<p>Symmetrical six-phase output with primary star; (<b>a</b>) connection diagram; (<b>b</b>) symbolic representation.</p> Full article ">Figure 5
<p>Six-phase output with primary delta; (<b>a</b>) connection diagram; (<b>b</b>) symbolic representation.</p> Full article ">Figure 6
<p>Output voltage phasors (<b>a</b>) Representation 1 (<b>b</b>) Representation 2.</p> Full article ">Figure 7
<p>Quasi six-phase output with primary delta; (<b>a</b>) connection diagram; (<b>b</b>) symbolic representation.</p> Full article ">Figure 8
<p>Obtaining equal magnitudes two sets of three-phase outputs with 30° phase shifts.</p> Full article ">Figure 9
<p>Obtaining equal magnitudes two sets of three-phase outputs with 30° phase shifts.</p> Full article ">Figure 10
<p>Equivalent circuit of single-phase transformer for fundamental frequency.</p> Full article ">Figure 11
<p>Equivalent circuit of single-phase transformer.</p> Full article ">Figure 12
<p>(<b>a</b>) Phase voltage [V] and phase current [A] at the primary side (with PWM inverter); (<b>b</b>) phase Voltage [V] and Phase current [A] at the secondary side (with PWM inverter); (<b>c</b>) phase voltage and phase current at the primary side (with sine ac voltage source); and (<b>d</b>) phase Voltage and Phase current at the secondary side (with sine ac voltage source).</p> Full article ">Figure 12 Cont.
<p>(<b>a</b>) Phase voltage [V] and phase current [A] at the primary side (with PWM inverter); (<b>b</b>) phase Voltage [V] and Phase current [A] at the secondary side (with PWM inverter); (<b>c</b>) phase voltage and phase current at the primary side (with sine ac voltage source); and (<b>d</b>) phase Voltage and Phase current at the secondary side (with sine ac voltage source).</p> Full article ">Figure 13
<p>(<b>a</b>) Six–phase balanced phase voltage [V] and phase current [A] at the primary side (with PWM inverter); (<b>b</b>) mechanical speed [rad/sec] and electromagnetic torque [N-m] of the six–phase motor (with PWM inverter); (<b>c</b>) six–phase balanced phase voltage [V] and phase current [A] at the secondary side (with sine ac voltage source); and (<b>d</b>) mechanical speed [rad/sec] and electromagnetic torque [N-m] of the six – phase motor (with sine ac voltage source).</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>) Six–phase balanced phase voltage [V] and phase current [A] at the primary side (with PWM inverter); (<b>b</b>) mechanical speed [rad/sec] and electromagnetic torque [N-m] of the six–phase motor (with PWM inverter); (<b>c</b>) six–phase balanced phase voltage [V] and phase current [A] at the secondary side (with sine ac voltage source); and (<b>d</b>) mechanical speed [rad/sec] and electromagnetic torque [N-m] of the six – phase motor (with sine ac voltage source).</p> Full article ">Figure 14
<p>Experimental set-up.</p> Full article ">Figure 15
<p>Presence of 3rd harmonic current in the (<b>a</b>) star-connected primary windings and (<b>b</b>) delta-connected primary windings.</p> Full article ">Figure 16
<p>(<b>a</b>) Symmetrical six-phase output with star-connected primary; (<b>b</b>) quasi six-phase output with primary in star; (<b>c</b>) symmetrical six-phase output with Delta-connected primary; (<b>d</b>) quasi six-phase output with delta-connected primary.</p> Full article ">Figure 16 Cont.
<p>(<b>a</b>) Symmetrical six-phase output with star-connected primary; (<b>b</b>) quasi six-phase output with primary in star; (<b>c</b>) symmetrical six-phase output with Delta-connected primary; (<b>d</b>) quasi six-phase output with delta-connected primary.</p> Full article ">Figure 17
<p>Six-phase motor.</p> Full article ">Figure 18
<p>(<b>a</b>) Symmetrical Six-phase motor under no-load condition; (<b>b</b>) symmetrical motor under full-load condition.</p> Full article ">
12 pages, 5598 KiB  
Article
A Variation-Aware Design Methodology for Distributed Arithmetic
by Yue Lu, Shengyu Duan, Basel Halak and Tom Kazmierski
Electronics 2019, 8(1), 108; https://doi.org/10.3390/electronics8010108 - 18 Jan 2019
Cited by 2 | Viewed by 4155
Abstract
Distributed arithmetic (DA) brings area and power benefits to digital designs relevant to the Internet-of-Things. Therefore, new error resilient techniques for DA computation are urgently required to improve robustness against the process, voltage, and temperature (PVT) variations. This paper proposes a new in-situ [...] Read more.
Distributed arithmetic (DA) brings area and power benefits to digital designs relevant to the Internet-of-Things. Therefore, new error resilient techniques for DA computation are urgently required to improve robustness against the process, voltage, and temperature (PVT) variations. This paper proposes a new in-situ timing error prevention technique to mitigate the impact of variations in DA circuits by providing a guardband for significant (most significant bit) computations. This guardband is initially achieved by modifying the sign extension block and carefully gate-sizing. Therefore, least significant bit (LSB) computation can correspond to the critical path, and timing error can be tolerated at the cost of acceptable accuracy loss. Our approach is demonstrated on a 16-tap finite impulse respons (FIR) filter using the 65 nm CMOS process and the simulation results show that this design can still maintain high-accuracy performance without worst case timing margin, and achieve up to 32 % power savings by voltage scaling when the worst case margin is considered with only 9 % area overhead. Full article
(This article belongs to the Special Issue VLSI Architecture Design for Digital Signal Processing)
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<p>Conventional most significant bit (MSB)-first distributed arithmetic ROM and accumulator (RAC) structure.</p> Full article ">Figure 2
<p>Distribution of path delay in each clock cycle.</p> Full article ">Figure 3
<p>The state change of full adder in the accumulator circuit for an 8-bit distributed arithmetic (DA) computation.</p> Full article ">Figure 4
<p>DA circuit with the modified dynamic sign extension block.</p> Full article ">Figure 5
<p>Distribution of path delay in each clock cycle with the proposed dynamic sign extension block.</p> Full article ">Figure 6
<p>An example of the proposed synthesis approach.</p> Full article ">Figure 7
<p>Distribution of path delay in each clock cycle with synthesis optimization, when (<b>a</b>) <span class="html-italic">m</span> = 4 (<b>b</b>) <span class="html-italic">m</span> = 6.</p> Full article ">Figure 8
<p>Frequency response of conventional design versus proposed design in the worst case (slow-slow (SS) process corner and 125 <math display="inline"><semantics> <msup> <mrow/> <mo>∘</mo> </msup> </semantics></math>C temperature).</p> Full article ">Figure 9
<p>Power dissipation and error probability versus supply voltage with different process corners and temperature.</p> Full article ">Figure 10
<p>The error rate comparisons between the gate-sized conventional and proposed design with 0.9 V supply voltage using 1000 test samples.</p> Full article ">
16 pages, 8113 KiB  
Article
Longitudinal Attitude Control Decoupling Algorithm Based on the Fuzzy Sliding Mode of a Coaxial-Rotor UAV
by Kewei Li, Yiran Wei, Chao Wang and Hongbin Deng
Electronics 2019, 8(1), 107; https://doi.org/10.3390/electronics8010107 - 18 Jan 2019
Cited by 13 | Viewed by 5755
Abstract
A longitudinal attitude decoupling algorithm based on the fuzzy sliding mode control for a small coaxial rotor unmanned aerial vehicle (UAV) is presented in this paper. The attitude system of a small coaxial rotor UAV is characterized by nonlinearity, strong coupling and uncertainty, [...] Read more.
A longitudinal attitude decoupling algorithm based on the fuzzy sliding mode control for a small coaxial rotor unmanned aerial vehicle (UAV) is presented in this paper. The attitude system of a small coaxial rotor UAV is characterized by nonlinearity, strong coupling and uncertainty, which causes difficulties pertaining to its flight control. According to its six-degree-of-freedom model and structural characteristics, the dynamic model was established, and a longitudinal attitude decoupling algorithm was proposed. A fuzzy sliding mode control was used to design the controller to adapt to the underactuated system. Compared with the uncoupled fuzzy sliding mode control, simulation results indicated that the proposed method could improve the stability of the system, presented with a better adapting ability, and could effectively suppress the modeling error and external interference of the coaxial rotor aircraft attitude system. The proposed method also has the advantages of high accuracy, good stability, and the ease of implementation. Full article
(This article belongs to the Special Issue Autonomous Control of Unmanned Aerial Vehicles)
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<p>(<b>a</b>) The structure design and (<b>b</b>) force analysis of the coaxial-rotor Rotorcraft.</p> Full article ">Figure 2
<p>The structure of the controller.</p> Full article ">Figure 3
<p>The input membership function.</p> Full article ">Figure 4
<p>The output membership function.</p> Full article ">Figure 5
<p>Position tracking with the decoupling algorithm of the <span class="html-italic">x</span>-axis.</p> Full article ">Figure 6
<p>Position tracking without the decoupling algorithm of the <span class="html-italic">x</span>-axis.</p> Full article ">Figure 7
<p>Position tracking with the decoupling algorithm of the <span class="html-italic">z</span>-axis.</p> Full article ">Figure 8
<p>Position tracking without the decoupling algorithm of the <span class="html-italic">z</span>-axis.</p> Full article ">Figure 9
<p>Angle tracking with the decoupling algorithm.</p> Full article ">Figure 10
<p>Angle tracking without the decoupling algorithm.</p> Full article ">Figure 11
<p>Speed tracking with the decoupling algorithm.</p> Full article ">Figure 12
<p>Speed tracking with the decoupling algorithm.</p> Full article ">Figure 13
<p>Position tracking with the decoupling algorithm.</p> Full article ">Figure 14
<p>Control input <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>1</mn> </msub> </mrow> </semantics></math> with the decoupling algorithm.</p> Full article ">Figure 15
<p>Control input <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mn>2</mn> </msub> </mrow> </semantics></math> with the decoupling algorithm.</p> Full article ">
17 pages, 2196 KiB  
Article
Drift-Diffusion Simulation of High-Speed Optoelectronic Devices
by Ivan Pisarenko and Eugeny Ryndin
Electronics 2019, 8(1), 106; https://doi.org/10.3390/electronics8010106 - 18 Jan 2019
Cited by 7 | Viewed by 4601
Abstract
In this paper, we address the problem of research and development of the advanced optoelectronic devices designed for on-chip optical interconnections in integrated circuits. The development of the models, techniques, and applied software for the numerical simulation of carrier transport and accumulation in [...] Read more.
In this paper, we address the problem of research and development of the advanced optoelectronic devices designed for on-chip optical interconnections in integrated circuits. The development of the models, techniques, and applied software for the numerical simulation of carrier transport and accumulation in high-speed AIIIBV (A and B refer to group III and V semiconductors, respectively) optoelectronic devices is the purpose of the paper. We propose the model based on the standard drift-diffusion equations, rate equation for photons in an injection laser, and complex analytical models of carrier mobility, generation, and recombination. To solve the basic equations of the model, we developed the explicit and implicit techniques of drift-diffusion numerical simulation and applied software. These aids are suitable for the stationary and time-domain simulation of injection lasers and photodetectors with various electrophysical, constructive, and technological parameters at different control actions. We applied the model for the simulation of the lasers with functionally integrated amplitude and frequency modulators and uni-travelling-carrier photodetectors. According to the results of non-stationary simulation, it is reasonable to optimize the parameters of the lasers-modulators and develop new construction methods aimed at the improvement of photodetectors’ response time. Full article
(This article belongs to the Section Microelectronics)
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<p>The cross-section of a resonant-cavity-enhanced p-i-n photodetector: 1, photon flux; 2, half-reflecting mirror; 3, ohmic contacts; 4, totally-reflecting mirror; <math display="inline"><semantics> <mi>L</mi> </semantics></math> is the length of the resonant cavity; <math display="inline"><semantics> <mi>H</mi> </semantics></math> is the height of the absorption region.</p> Full article ">Figure 2
<p>The cross-section of the laser-modulator (<b>a</b>): 1, silicon substrate of IC; 2, GaAs<sub>x</sub>P<sub>1−x</sub> gradient buffer layer; 3, p-GaAs layer; 4, 5, n-GaAs layers; 6, 7, highly doped p<sup>+</sup> and n<sup>+</sup> regions; 8, nanoheterostructure of the integrated amplitude (<b>b</b>) or frequency (<b>c</b>) modulator; 9, power contacts; 10, control contacts [<a href="#B31-electronics-08-00106" class="html-bibr">31</a>,<a href="#B32-electronics-08-00106" class="html-bibr">32</a>,<a href="#B33-electronics-08-00106" class="html-bibr">33</a>,<a href="#B34-electronics-08-00106" class="html-bibr">34</a>]. Energy band diagram of the amplitude modulator (<b>b</b>) contains quantum wells in conduction (layers 11, 12) and valence (layers 12, 13) bands. The spatial overlap of quantum wells is located in layer 12. Layers 11, 13 are the regions of quantum well’s space division. Energy band diagram of the frequency modulator (<b>c</b>) includes two quantum wells with different depths in valence band (14 and 16) and one quantum well in conduction band (15).</p> Full article ">Figure 3
<p>The results of time-domain drift-diffusion simulation of the lasers with functionally integrated amplitude (<b>a</b>,<b>b</b>) and frequency (<b>c</b>,<b>d</b>) modulators: carrier densities in the central cross-section of the active region (<b>a</b>) and linear density of photons (<b>b</b>); electron (<b>c</b>) and peak photon (<b>d</b>) densities in the regions, which generate optical radiation with <span class="html-italic">λ</span><sub>1</sub> and <span class="html-italic">λ</span><sub>2</sub> wavelengths (electron density corresponds to the central cross-section of the active region).</p> Full article ">Figure 4
<p>The spatial distributions of photon density in the resonant cavity of the p-i-n RCE photodetector, as shown in <a href="#electronics-08-00106-f001" class="html-fig">Figure 1</a>, at the instants of 0.02 (<b>a</b>), 0.04 (<b>b</b>), and 0.1 (<b>c</b>) ps; the time dependence of optical power absorbed in the resonant cavity of the p-i-n photodetector (<b>d</b>).</p> Full article ">Figure 5
<p>The results of the drift-diffusion numerical simulation of the InP/In<sub>0.57</sub>Ga<sub>0.47</sub>As uni-travelling-carrier (UTC) photodiode: the energy band diagram and quasi-Fermi levels (<b>a</b>), the spatial distributions of electric field intensity (<b>b</b>) and carrier densities (<b>c</b>); the dependence of current density on time in the case of illumination by 5-ps rectangular laser pulse (<b>d</b>).</p> Full article ">
18 pages, 4563 KiB  
Article
Energy-Efficient Gabor Kernels in Neural Networks with Genetic Algorithm Training Method
by Fanjie Meng, Xinqing Wang, Faming Shao, Dong Wang and Xia Hua
Electronics 2019, 8(1), 105; https://doi.org/10.3390/electronics8010105 - 18 Jan 2019
Cited by 22 | Viewed by 6410
Abstract
Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has [...] Read more.
Deep-learning convolutional neural networks (CNNs) have proven to be successful in various cognitive applications with a multilayer structure. The high computational energy and time requirements hinder the practical application of CNNs; hence, the realization of a highly energy-efficient and fast-learning neural network has aroused interest. In this work, we address the computing-resource-saving problem by developing a deep model, termed the Gabor convolutional neural network (Gabor CNN), which incorporates highly expression-efficient Gabor kernels into CNNs. In order to effectively imitate the structural characteristics of traditional weight kernels, we improve upon the traditional Gabor filters, having stronger frequency and orientation representations. In addition, we propose a procedure to train Gabor CNNs, termed the fast training method (FTM). In FTM, we design a new training method based on the multipopulation genetic algorithm (MPGA) and evaluation structure to optimize improved Gabor kernels, but train the rest of the Gabor CNN parameters with back-propagation. The training of improved Gabor kernels with MPGA is much more energy-efficient with less samples and iterations. Simple tasks, like character recognition on the Mixed National Institute of Standards and Technology database (MNIST), traffic sign recognition on the German Traffic Sign Recognition Benchmark (GTSRB), and face detection on the Olivetti Research Laboratory database (ORL), are implemented using LeNet architecture. The experimental result of the Gabor CNN and MPGA training method shows a 17–19% reduction in computational energy and time and an 18–21% reduction in storage requirements with a less than 1% accuracy decrease. We eliminated a significant fraction of the computation-hungry components in the training process by incorporating highly expression-efficient Gabor kernels into CNNs. Full article
(This article belongs to the Special Issue Convolutional Neural Network Design and Hardware Implementation for Real-Time Vision Applications)
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<p>Convolutional kernels of each level by visualizing a pretrained convolutional neural network (CNN) model.</p> Full article ">Figure 2
<p>Part of traditional Gabor filters with different parameters: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">σ</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">θ</mi> <mo>=</mo> <mn>0</mn> <mo>–</mo> <mn>360</mn> <mo>°</mo> </mrow> </semantics></math> (from left to right), <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mtext> </mtext> <mn>4</mn> <mo>,</mo> <mtext> </mtext> <mn>10</mn> <mo>,</mo> <mtext> </mtext> <mn>20</mn> </mrow> </semantics></math> (from top to bottom), and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ψ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">σ</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">θ</mi> <mo>=</mo> <mn>0</mn> <mo>–</mo> <mn>360</mn> <mo>°</mo> </mrow> </semantics></math> (from left to right), <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mtext> </mtext> <mn>4</mn> <mo>,</mo> <mtext> </mtext> <mn>10</mn> <mo>,</mo> <mtext> </mtext> <mn>20</mn> </mrow> </semantics></math> (from top to bottom), and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ψ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mrow> <mi mathvariant="sans-serif">σ</mi> </mrow> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">θ</mi> <mo>=</mo> <mn>0</mn> <mo>~</mo> <mn>360</mn> <mo>°</mo> </mrow> </semantics></math> (from left to right), <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">λ</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mtext> </mtext> <mn>4</mn> <mo>,</mo> <mtext> </mtext> <mn>10</mn> <mo>,</mo> <mtext> </mtext> <mn>20</mn> </mrow> </semantics></math> (from top to bottom), and <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">ψ</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>A standard architecture of a deep-learning Convolutional Neural Network (CNN).</p> Full article ">Figure 4
<p>Improved two-dimensional (2D) Gabor filters with various shapes: (<b>a</b>) Conventional Gabor filters with oriented grating, (<b>b</b>) circular Gabor filters, and (<b>c</b>) more complicated Gabor filters.</p> Full article ">Figure 5
<p>Update of convolutional layer and weight matrix in the fully connected layer in a Gabor CNN.</p> Full article ">Figure 6
<p>Flow chart of multipopulation genetic algorithm (MPGA) optimization for Gabor convolutional kernels. Individual <math display="inline"><semantics> <mrow> <msub> <mrow> <mrow> <mo>{</mo> <mrow> <msub> <mi mathvariant="sans-serif">σ</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">λ</mi> </mrow> </mrow> <mn>1</mn> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">σ</mi> </mrow> </mrow> <mn>2</mn> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">λ</mi> </mrow> </mrow> <mn>2</mn> </msub> <mo>,</mo> <mtext> </mtext> <mo>⋯</mo> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">σ</mi> </mrow> </mrow> <mi>k</mi> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">λ</mi> </mrow> </mrow> <mi>k</mi> </msub> </mrow> <mo>}</mo> </mrow> </mrow> <mi>n</mi> </msub> </mrow> </semantics></math> represents a combination of Gabor kernels in the convolutional layer. The individual <math display="inline"><semantics> <mrow> <msub> <mrow> <mrow> <mo>{</mo> <mrow> <msub> <mi mathvariant="sans-serif">σ</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">λ</mi> </mrow> </mrow> <mn>1</mn> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">σ</mi> </mrow> </mrow> <mn>2</mn> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">λ</mi> </mrow> </mrow> <mn>2</mn> </msub> <mo>,</mo> <mtext> </mtext> <mo>⋯</mo> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">σ</mi> </mrow> </mrow> <mi>k</mi> </msub> <mo>,</mo> <msub> <mrow> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">λ</mi> </mrow> </mrow> <mi>k</mi> </msub> </mrow> <mo>}</mo> </mrow> </mrow> <mi>m</mi> </msub> </mrow> </semantics></math> is the optional individual selected from the previous generation.</p> Full article ">Figure 7
<p>(<b>a</b>) Visualization of part of the conventional trained kernels and (<b>b</b>) optimized Gabor kernels of the first convolutional layer (with θ = 0°, 30°, 60°, 90°, 120°, and 150°).</p> Full article ">Figure 8
<p>Fast training method for Gabor CNNs.</p> Full article ">Figure 9
<p>(<b>a</b>) Samples’ mean square error and (<b>b</b>) overall classification accuracy obtained from each structure.</p> Full article ">Figure 10
<p>Energy consumption distribution: (<b>a</b>) conventional CNN and (<b>b</b>) Gabor CNN on MINIST.</p> Full article ">Figure 10 Cont.
<p>Energy consumption distribution: (<b>a</b>) conventional CNN and (<b>b</b>) Gabor CNN on MINIST.</p> Full article ">Figure 11
<p>The normalized training time after 200 epochs of the two structures in each dataset.</p> Full article ">Figure 12
<p>The normalized storage requirement reduction of the two structures in each dataset.</p> Full article ">Figure 13
<p>(<b>a</b>) Training time reduction and (<b>b</b>) accuracy with different numbers of iterations and different sampling rates in MNIST.</p> Full article ">
19 pages, 8892 KiB  
Article
Research on an Auto-Optimized Capacitor Voltage Balancing Control Strategy of MMC SM for Renewable Energy HVDC Transmission System
by Jianfei Zhao, Changjiu Kong, Tingzhang Liu and Ruihua Li
Electronics 2019, 8(1), 104; https://doi.org/10.3390/electronics8010104 - 18 Jan 2019
Cited by 2 | Viewed by 3766
Abstract
The Modular Multilevel Converter (MMC) is one of the most attractive converter topologies in the High Voltage Direct Current (HVDC) transmission field. The latest widely used sorting method has a low algorithm complexity. It can effectively balance the sub-module (SM) capacitor voltages, but [...] Read more.
The Modular Multilevel Converter (MMC) is one of the most attractive converter topologies in the High Voltage Direct Current (HVDC) transmission field. The latest widely used sorting method has a low algorithm complexity. It can effectively balance the sub-module (SM) capacitor voltages, but it would cause relatively high switching frequency and power loss. Aiming at the problem that the sub-module (SM) capacitor voltage sorting algorithm has a large switching loss due to the high switching frequency of the device, this paper proposes an auto-optimized capacitor voltage balancing control strategy. Firstly, the topology and operation principle of MMC are analyzed. Secondly, a SM capacitor voltage control method based on the dynamic deviation threshold is proposed. Considering the switch switching state of the SM and the difference between the voltages of each SM, the algorithm can obtain the dynamic deviation valve using the closed-loop control. The method can avoid the unnecessary repeated switching of the Insulated Gate Bipolar Transistor (IGBT) under the premise of ensuring that the capacitance voltages of the SMs are basically the same, which effectively result in reducing the switching frequency of the MMC SM and reducing the switching loss, thereby improving the operating efficiency of the system. Finally aiming at the proposed control strategy, the simulation and experimental verification are carried out which shows that the proposed algorithm can better control the system voltage deviation, reduce the switching loss of the system and improve the stability of the system. Full article
(This article belongs to the Special Issue Renewable Electric Energy Systems)
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<p>Schematic diagram of the three-phase Modular Multilevel Converter (MMC) structure.</p> Full article ">Figure 2
<p>Flowchart of the sorting algorithm.</p> Full article ">Figure 3
<p>Flowchart of the proposed sub-module (SM) capacitor voltage balancing algorithm.</p> Full article ">Figure 4
<p>Dynamic <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>U</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics></math> closed loop controller.</p> Full article ">Figure 5
<p>The waveforms of the SM capacitor voltages of a single phase by the sorting algorithm (SM 1 to SM 20).</p> Full article ">Figure 6
<p>The waveforms of the SM capacitor voltages (SM 1 to SM 20) of a single phase without voltage balancing control.</p> Full article ">Figure 7
<p>The waveforms of the SM capacitor voltages by the auto-optimized capacitor voltage balancing algorithm (SM 1 to SM 20). (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>u</mi> <msub> <mi>c</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> is 800 V, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>u</mi> <msub> <mi>c</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> is 500 V, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>u</mi> <msub> <mi>c</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> is 200 V, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>u</mi> <msub> <mi>c</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> is 50 V.</p> Full article ">Figure 8
<p>SM capacitor voltages and trigger pulses without voltage balancing control. (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 9
<p>SM capacitor voltage balancing results using sorting algorithm. (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 10
<p>SM capacitor voltage balancing results using the proposed auto-optimized algorithm (Capacitance target deviation is 5.0 V). (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 11
<p>SM capacitor voltage balancing results using the proposed auto-optimized algorithm (Capacitance target deviation is 3.0 V). (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 12
<p>SM capacitor voltage balancing results using the proposed auto-optimized algorithm (Capacitance target deviation is 0.5 V). (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 13
<p>Experimental setup of a five-level single phase MMC.</p> Full article ">Figure 14
<p>Experimental device topology.</p> Full article ">Figure 15
<p>Output voltage results of the upper and lower bridge arms with no load.</p> Full article ">Figure 16
<p>Output voltage results of the single phase MMC with no load.</p> Full article ">Figure 17
<p>Output voltage and current results of the single phase MMC with a load.</p> Full article ">Figure 18
<p>SM capacitor voltages and trigger pulses without voltage balancing control. (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 19
<p>SM capacitor voltage balancing results using sorting algorithm. (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 20
<p>SM capacitor voltage balancing results using the proposed auto-optimized algorithm (Capacitance target deviation is 5.0 V). (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 21
<p>SM capacitor voltage balancing results using the proposed auto-optimized algorithm (Capacitance target deviation is 3.0 V). (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">Figure 22
<p>SM capacitor voltage balancing results using the proposed auto-optimized algorithm (Capacitance target deviation is 0.5 V). (<b>a</b>) SM capacitor voltages of the upper arm. (<b>b</b>) Trigger pulse of the SMs.</p> Full article ">
16 pages, 1914 KiB  
Article
A Simple Indoor Localization Methodology for Fast Building Classification Models Based on Fingerprints
by David Sánchez-Rodríguez, Itziar Alonso-González, Carlos Ley-Bosch and Miguel A. Quintana-Suárez
Electronics 2019, 8(1), 103; https://doi.org/10.3390/electronics8010103 - 17 Jan 2019
Cited by 5 | Viewed by 3305
Abstract
Indoor localization has received tremendous attention in the last two decades due to location-aware services being highly demanded. Wireless networks have been suggested to solve this problem in many research works, and efficient algorithms have been developed with precise location and high accuracy. [...] Read more.
Indoor localization has received tremendous attention in the last two decades due to location-aware services being highly demanded. Wireless networks have been suggested to solve this problem in many research works, and efficient algorithms have been developed with precise location and high accuracy. Nevertheless, those approaches often have high computational and high energy consumption. Hence, in temporary environments, such as emergency situations, where a fast deployment of an indoor localization system is required, those methods are not appropriate. In this manuscript, a methodology for fast building of an indoor localization system is proposed. For that purpose, a reduction of the data dimensionality is achieved by applying data fusion and feature transformation, which allow us to reduce the computational cost of the classifier training phase. In order to validate the methodology, three different datasets were used: two of them are public datasets based mainly on Received Signal Strength (RSS) from different Wi-Fi access point, and the third is a set of RSS values gathered from the LED lamps in a Visible Light Communication (VLC) network. The simulation results show that the proposed methodology considerably amends the overall computational performance and provides an acceptable location estimation error. Full article
(This article belongs to the Section Computer Science & Engineering)
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<p>The proposed methodology for fast building classification models of indoor localization.</p> Full article ">Figure 2
<p>Test environment at the University of Mannheim.</p> Full article ">Figure 3
<p>Test environment at the University of Yuan-Ze.</p> Full article ">Figure 4
<p>VLC simulation scenario at 0.75 m from the floor.</p> Full article ">Figure 5
<p>CDF for the Mannheim dataset.</p> Full article ">Figure 6
<p>CDF for the Yuan Ze dataset.</p> Full article ">Figure 7
<p>CDF for the VLC dataset.</p> Full article ">
21 pages, 11778 KiB  
Article
Rapid Prototyping of a Hybrid PV–Wind Generation System Implemented in a Real-Time Digital Simulation Platform and Arduino
by Víctor Pagola, Rafael Peña, Juan Segundo and Adalberto Ospino
Electronics 2019, 8(1), 102; https://doi.org/10.3390/electronics8010102 - 17 Jan 2019
Cited by 16 | Viewed by 4792
Abstract
The growing penetration of generation systems based on renewable energy in electric power systems is undeniable. These generation systems have many benefits, but also many challenges from the technical point of view. One of the biggest problems in the case of solar photovoltaic [...] Read more.
The growing penetration of generation systems based on renewable energy in electric power systems is undeniable. These generation systems have many benefits, but also many challenges from the technical point of view. One of the biggest problems in the case of solar photovoltaic (PV) and wind energy is the intermittency of the raw material, thus hybrid generation systems that contain both sources are being used to complement electric power generation. To analyze the problems of this type of hybrid generation systems, it is necessary to develop models and test systems that allows to study their dynamic behavior. Reported in this paper is the implementation of a full hybrid PV–wind generation system model in a real-time digital simulation platform, and the development of the electronic converter controls. These controllers were implemented in digital devices (Arduino Due) and connected to the simulation platform to test their performance in real-time. In addition, the procedure followed for the development and implementation of the controllers is presented. The proposed test system can be used in renewable energy integration studies and the development of new control strategies. Full article
(This article belongs to the Special Issue Grid Connected Photovoltaic Systems)
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<p>Hybrid photovoltaic (PV)–wind generation system configuration.</p> Full article ">Figure 2
<p>Scheme of a PV cell.</p> Full article ">Figure 3
<p>Equivalent circuits of a permanent magnet synchronous generator (PMSG), (<b>a</b>) <span class="html-italic">q</span>-axis, (<b>b</b>) <span class="html-italic">d</span>-axis, and (<b>c</b>) <span class="html-italic">0</span>-axis.</p> Full article ">Figure 4
<p>The buck converter and the maximum power point tracking (MPPT) control scheme.</p> Full article ">Figure 5
<p>Bode plot of the low-pass filter.</p> Full article ">Figure 6
<p>Flow chart of the incremental conductance method.</p> Full article ">Figure 7
<p>Boost converter and the average current control scheme.</p> Full article ">Figure 8
<p>Bode plot for the <span class="html-italic">K<sub>iL</sub></span> controller.</p> Full article ">Figure 9
<p>Bode plot for the <span class="html-italic">K<sub>Vo</sub></span> controller.</p> Full article ">Figure 10
<p>DC-link voltage control loop.</p> Full article ">Figure 11
<p>Reactive power control loop.</p> Full article ">Figure 12
<p>Schematic diagram of, (<b>a</b>) the Giga-Transceiver Analogue Output (<span class="html-italic">GTAO</span>), and (<b>b</b>) Input (<span class="html-italic">GTAI</span>) cards in RSCAD.</p> Full article ">Figure 13
<p>Schematic diagram of an Arduino Due card [<a href="#B53-electronics-08-00102" class="html-bibr">53</a>].</p> Full article ">Figure 14
<p>Real-time digital simulator (RTDS) station and Arduino Due card connection scheme.</p> Full article ">Figure 15
<p>RTDS signal adjustment from the Arduino Due card.</p> Full article ">Figure 16
<p>Methodology for the Arduino Due connection with the RTDS.</p> Full article ">Figure 17
<p>Variations in the weather conditions, (<b>a</b>) wind speed, and (<b>b</b>) solar irradiation.</p> Full article ">Figure 18
<p>Voltage tracking in terminals of the PV array by the <span class="html-italic">K<sub>v</sub></span> controller, (<b>a</b>) reference signal, and (<b>b</b>) percent error.</p> Full article ">Figure 19
<p>Behavior of the electrical variables of the PV array, (<b>a</b>) voltage, and (<b>b</b>) current.</p> Full article ">Figure 20
<p>Voltage tracking at the output of the DC–DC boost converter, (<b>a</b>) reference signal, and (<b>b</b>) percent error.</p> Full article ">Figure 21
<p>Electrical variables of the PMSG, (<b>a</b>) phase voltage and current, and (<b>b</b>) rectified voltage.</p> Full article ">Figure 22
<p>Performance of the controls for the three-phase inverter, (<b>a</b>) voltage, and (<b>b</b>) reactive power.</p> Full article ">Figure 23
<p>Active and reactive power generated by the hybrid PV–wind system transferred through the transformer.</p> Full article ">
8 pages, 1862 KiB  
Article
An X-Band 40 W Power Amplifier GaN MMIC Design by Using Equivalent Output Impedance Model
by Ruitao Chen, Ruchun Li, Shouli Zhou, Shi Chen, Jianhua Huang and Zhiyu Wang
Electronics 2019, 8(1), 99; https://doi.org/10.3390/electronics8010099 - 16 Jan 2019
Cited by 11 | Viewed by 6435
Abstract
This paper presents an X-band 40 W power amplifier with high efficiency based on 0.25 μm GaN HEMT (High Electron Mobility Transistor) on SiC process. An equivalent RC (Resistance Capacitance) model is presented to provide accurate large-signal output impedances of GaN HEMTs with [...] Read more.
This paper presents an X-band 40 W power amplifier with high efficiency based on 0.25 μm GaN HEMT (High Electron Mobility Transistor) on SiC process. An equivalent RC (Resistance Capacitance) model is presented to provide accurate large-signal output impedances of GaN HEMTs with arbitrary dimensions. By introducing the band-pass filter topology, broadband impedance matching networks are achieved based on the RC model, and the power amplifier MMIC (Monolithic Microwave Integrated Circuit) with enhanced bandwidth is realized. The measurement results show that this power amplifier at 28 V operation voltage achieved over 40 W output power, 44.7% power-added efficiency and 22 dB power gain from 8 GHz to 12 GHz. The total chip size is 3.20 mm × 3.45 mm. Full article
(This article belongs to the Section Microelectronics)
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<p>RC model of active devices’ large-signal output impedance.</p> Full article ">Figure 2
<p>The R<sub>p</sub> and C<sub>p</sub> parameters versus frequency.</p> Full article ">Figure 3
<p>Comparison of 8–12 GHz optimal efficiency point impedance with measured, RC model, and GaN HEMT PDK model.</p> Full article ">Figure 4
<p>The circuit topology of the power amplifier.</p> Full article ">Figure 5
<p>Output matching network.</p> Full article ">Figure 6
<p>Inter-stage matching network.</p> Full article ">Figure 7
<p>Photograph of X-band 40W power amplifier.</p> Full article ">Figure 8
<p>(<b>a</b>) Measured output power, gain, and PAE versus frequency at 24 dBm input power; (<b>b</b>) Measured S-parameter versus frequency; (<b>c</b>) Measured output power, gain, and PAE versus input power at 8.8 GHz frequency; (<b>d</b>) Measured output power, gain, and PAE versus input power at 10.4 GHz frequency.</p> Full article ">
16 pages, 7272 KiB  
Article
Fully Integrated Low-Ripple Switched-Capacitor DC–DC Converter with Parallel Low-Dropout Regulator
by Jeong-Yun Lee, Gwang-Sub Kim, Kwang-Il Oh and Donghyun Baek
Electronics 2019, 8(1), 98; https://doi.org/10.3390/electronics8010098 - 16 Jan 2019
Cited by 11 | Viewed by 6545
Abstract
In this paper, we propose a fully integrated switched-capacitor DC–DC converter with low ripple and fast transient response for portable low-power electronic devices. The proposed converter reduces the output ripple by filtering the control ripple via combining a low-dropout regulator with a main [...] Read more.
In this paper, we propose a fully integrated switched-capacitor DC–DC converter with low ripple and fast transient response for portable low-power electronic devices. The proposed converter reduces the output ripple by filtering the control ripple via combining a low-dropout regulator with a main switched-capacitor DC–DC converter with a four-bit digital capacitance modulation control. In addition, the four-phase interleaved technique applied to the main converter reduces the switching ripple. The proposed converter provides an output voltage ranging from 1.2 to 1.5 V from a 3.3 V supply. Its peak efficiency reaches 73% with ripple voltages below 55 mV over the entire output power range. The transient response time for a load current variation from 100 μA to 50 mA is measured to be 800 ns. Importantly, the converter chip, which is fabricated using 0.13 μm complementary metal–oxide–semiconductor (CMOS) technology, has a size of 2.04 mm2. We believe that our approach can contribute to advancements in power sources for applications such as wearable electronics and the Internet of Things. Full article
(This article belongs to the Special Issue Signal Processing and Analysis of Electrical Circuit)
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<p>Types of sensor modules: (<b>a</b>) Sensor module with multichip architecture and (<b>b</b>) fully integrated sensor module (PMIC, power management IC; PMU, power management unit; and LDO, linear low dropout regulator).</p> Full article ">Figure 2
<p>Configuration of the step-down switching DC–DC converters: (<b>a</b>) Inductor-based converter and (<b>b</b>) capacitor-based converter.</p> Full article ">Figure 3
<p>Block diagram of the switched-capacitor (SC) DC–DC converter using one-boundary hysteresis feedback and its output ripple voltage.</p> Full article ">Figure 4
<p>Ripple mitigation techniques.</p> Full article ">Figure 5
<p>Operation of a step-down SC DC–DC converter. (<b>a</b>) Block diagram of a 2:1 step-down SC DC–DC converter; (<b>b</b>) operation during phase 1; (<b>c</b>) operation during phase 2; and (<b>d</b>) the output voltage ripple.</p> Full article ">Figure 6
<p>Simplified model of a 2:1 step-down switched-capacitor (SC) DC–DC converter.</p> Full article ">Figure 7
<p>Block diagram of s four-phase interleaved SC DC–DC converter.</p> Full article ">Figure 8
<p>Operation of an interleaved switched-capacitor (SC) DC–DC converter: (<b>a</b>) Without interleaving and (<b>b</b>) upon applying interleaving.</p> Full article ">Figure 9
<p>Methods for output voltage regulation: (<b>a</b>) Pulse–frequency modulation; (<b>b</b>) pulse–width modulation; and (<b>c</b>) capacitance modulation.</p> Full article ">Figure 10
<p>Binary-weighted switched-capacitor (SC) DC–DC converter cells for DCpM.</p> Full article ">Figure 11
<p>Model of the proposed switched-capacitor (SC) DC–DC converter using a four-bit DCpM.</p> Full article ">Figure 12
<p>Block diagram of the proposed switched-capacitor (SC) DC–DC converter.</p> Full article ">Figure 13
<p>Schematic of (<b>a</b>) the main switched-capacitor (SC) DC–DC converter and (<b>b</b>) the auxiliary SC DC–DC converter.</p> Full article ">Figure 14
<p>Block diagram of the proposed LDR-assisted SC DC–DC converter and its output ripple voltage.</p> Full article ">Figure 15
<p>(<b>a</b>) The simplified model of the proposed switched-capacitor (SC) DC–DC converter and (<b>b</b>) output current versus dropout voltage of the LDR pass transistor.</p> Full article ">Figure 16
<p>Microphotograph of the proposed switched-capacitor (SC) converter.</p> Full article ">Figure 17
<p>Measured output voltage and current waveforms: (<b>a</b>) the ripple voltage at a low output current with and without the low dropout regulator (LDR) in operation and (<b>b</b>) the load transient responses to a sudden current variation.</p> Full article ">Figure 18
<p>(<b>a</b>) Measured efficiency and (<b>b</b>) voltage ripple according to output current.</p> Full article ">Figure 19
<p>Loss contributions from DCpM, LDR, switching, and conduction losses versus output current for <span class="html-italic">V</span><sub>L</sub> = 1.2 V: (<b>a</b>) the loss contributions and (<b>b</b>) the ratio of loss contribution.</p> Full article ">
20 pages, 960 KiB  
Article
Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
by Syed Khandker, Joaquín Torres-Sospedra and Tapani Ristaniemi
Electronics 2019, 8(1), 97; https://doi.org/10.3390/electronics8010097 - 16 Jan 2019
Cited by 11 | Viewed by 4920
Abstract
Radio Frequency (RF) fingerprinting is widely applied for indoor positioning due to the existing Wi-Fi infrastructure present in most indoor spaces (home, work, leisure, among others) and the widespread usage of smartphones everywhere. It corresponds to a simple idea, the signal signature in [...] Read more.
Radio Frequency (RF) fingerprinting is widely applied for indoor positioning due to the existing Wi-Fi infrastructure present in most indoor spaces (home, work, leisure, among others) and the widespread usage of smartphones everywhere. It corresponds to a simple idea, the signal signature in a location tends to be stable over the time. Therefore, with the signals received from multiple APs, a unique fingerprint can be created. However, the Wi-Fi signal is affected by many factors which degrade the positioning error range to around a few meters. This paper introduces a collaborative method based on device-to-device (D2D) communication to improve the positioning accuracy using only fingerprinting and the direct communication to nearby devices. The results presented in this paper show that the positioning error can be reduced around 44% by considering D2D communication in the operational stage without adding new infrastructure for fingerprinting or complex resource-consuming filters. Moreover, the presence of very large errors is significantly reduced when the collaborative positioning based on D2D is available. Full article
(This article belongs to the Special Issue Green Communications in Smart City)
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Full article ">Figure 1
<p>Diagram of matching-based RF fingerprint positioning system with examples of the radio map, operational fingerprint and estimated position.</p> Full article ">Figure 2
<p>Basic architecture of D2D communication.</p> Full article ">Figure 3
<p>Two-way TOA diagram.</p> Full article ">Figure 4
<p>TOA estimation based on the received signal energy.</p> Full article ">Figure 5
<p>D2D communication assisted RF fingerprint positioning method.</p> Full article ">Figure 6
<p>Flowchart of the proposed method.</p> Full article ">Figure 7
<p>Visual representation of the reference fingerprints location. Fingerprints are colored according to the elevation (floor level).</p> Full article ">Figure 8
<p>CDF of the positioning errors provided by the proposed method based on D2D communications (motion and stationary scenario for the secondary devices) and traditional method based on k-NN.</p> Full article ">Figure 9
<p>Effect of physical distance among the devices in the proposed method shown as the CDF of the positioning errors.</p> Full article ">Figure 10
<p>Positioning accuracy based on other devices’ moving direction.</p> Full article ">Figure 11
<p>Effect of the increase on the amount of measurements in the proposed method shown as the CDF of the positioning errors.</p> Full article ">Figure 12
<p>Processing time vs. performance (Instead of meter, positioning error has been expressed in centimeter unit to make the figure more sensible where two scale -time and performance- come closer to each other.).</p> Full article ">Figure 13
<p>The accuracy limit in the proposed method shown as the CDF of the positioning errors.</p> Full article ">Figure 14
<p>Floor detection rate (%) of the traditional fingerprinting and D2D-based proposed methods.</p> Full article ">
20 pages, 11210 KiB  
Article
Joint Power Allocation for Coordinated Multi-Point Diversity Transmission in Rayleigh Fading Channels
by Min Lee and Seong-Keun Oh
Electronics 2019, 8(1), 101; https://doi.org/10.3390/electronics8010101 - 16 Jan 2019
Viewed by 2716
Abstract
We consider the problem of joint power allocation (JPA) in a coordinated multi-point (CoMP) joint diversity transmission (JDT) network with a total coordination point power (TCPP) constraint, aimed at maximizing the ergodic cooperative capacity (ECC) in Rayleigh fading channels. In this paper, we [...] Read more.
We consider the problem of joint power allocation (JPA) in a coordinated multi-point (CoMP) joint diversity transmission (JDT) network with a total coordination point power (TCPP) constraint, aimed at maximizing the ergodic cooperative capacity (ECC) in Rayleigh fading channels. In this paper, we first extend the JPA problem in the coordinated two-point (Co2P) JDT networkto the case of a non-unity TCPP constraint. Furthermore, we introduce more accurate log-quadratic approximated (LQA) expressions to obtain the coordinated transmission point (CTP) powers. Next, we extend our study to a coordinated three-point (Co3P) JDT network. Given the mean branch gain-to-noise ratios, we first obtain a log-linear approximated (LLA) expression for obtaining the optimum power of the third CTP (i.e., the worst quality-providing CTP). After obtaining the third-CTP power, we obtain the CTP powers of two better quality-providing CTPs by invoking the LLA CTP power expressions for Co2P JDT power allocation, under the remaining power given by the TCPP minus the third-CTP power. The numerical results demonstrate that the LQA and LLA CTP power expressions for Co2P JDT and the LLA CTP power expressions for Co3P JDT are very efficient in terms of the simplicity for JPA and CoMP set selection, as well as the resulting ECC. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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<p>CoMP JDT networks: (<b>a</b>) Co2P case (<b>b</b>) Co3P case.</p> Full article ">Figure 2
<p><math display="inline"><semantics> <msubsup> <mi>P</mi> <mn>2</mn> <mo>*</mo> </msubsup> </semantics></math> versus <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics></math>[dB], with <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>1</mn> </msub> </semantics></math>[dB] as a parameter: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 2 W, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 4 W, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 8 W.</p> Full article ">Figure 3
<p><math display="inline"><semantics> <msubsup> <mi>P</mi> <mn>3</mn> <mo>*</mo> </msubsup> </semantics></math> versus <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>3</mn> </msub> </semantics></math>[dB], with <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>1</mn> </msub> </semantics></math>[dB] and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics></math>[dB] as parameters: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 2 W, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 4 W, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 8 W.</p> Full article ">Figure 4
<p><math display="inline"><semantics> <msubsup> <mi>P</mi> <mn>3</mn> <mo>*</mo> </msubsup> </semantics></math> versus <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>3</mn> </msub> </semantics></math> (=<math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics></math>)[dB], with <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>1</mn> </msub> </semantics></math>[dB] as a parameter: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 2 W, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 4 W, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 8 W.</p> Full article ">Figure 5
<p>ECCs of the Co2P JDT network as a function of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics></math>[dB], with <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>1</mn> </msub> </semantics></math>[dB] as a parameter: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 2 W, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 4 W, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 8 W.</p> Full article ">Figure 6
<p>ECCs of the Co3P JDT network as a function of <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>3</mn> </msub> </semantics></math>[dB], with <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>1</mn> </msub> </semantics></math>[dB] and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>2</mn> </msub> </semantics></math>[dB] as parameters: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 2 W, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 4 W, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 8 W.</p> Full article ">Figure 7
<p>ECCs versus <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>γ</mi> </mrow> </semantics></math>[dB] (<math display="inline"><semantics> <msub> <mover accent="true"> <mi>γ</mi> <mo stretchy="false">¯</mo> </mover> <mn>1</mn> </msub> </semantics></math> = 5 dB): (<b>a</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 2 W, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 4 W, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>S</mi> </msub> </semantics></math> = 8 W.</p> Full article ">
18 pages, 967 KiB  
Article
Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods
by Mohamed Abdel-Nasser, Antonio Moreno and Domenec Puig
Electronics 2019, 8(1), 100; https://doi.org/10.3390/electronics8010100 - 16 Jan 2019
Cited by 53 | Viewed by 8977
Abstract
Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these [...] Read more.
Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature. Full article
(This article belongs to the Section Bioelectronics)
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Graphical abstract
Full article ">Figure 1
<p>Samples of two dynamic thermogram sequences for two cases, where each sequence comprises 20 infrared images. The infrared images of the left breast acquired at different time steps (<math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>t</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>t</mi> <mn>19</mn> </msub> <mo>,</mo> <msub> <mi>t</mi> <mn>20</mn> </msub> </mrow> </semantics></math>) for a healthy case (<b>top</b>) and a cancer patient (<b>bottom</b>).</p> Full article ">Figure 2
<p>The training and testing phases of the proposed method.</p> Full article ">Figure 3
<p>Lacunarity analysis of vascular networks extracted from infrared images, (<b>a</b>) the input infrared image, and (<b>b</b>) the extracted vascular network.</p> Full article ">Figure 4
<p>The malignancy score <math display="inline"><semantics> <msub> <mi>η</mi> <mrow> <mi>M</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </semantics></math> of two normal cases (<b>left column</b>) and two cancer patients (<b>right column</b>).</p> Full article ">
16 pages, 2159 KiB  
Article
Certificate Based Security Mechanisms in Vehicular Ad-Hoc Networks based on IEC 61850 and IEEE WAVE Standards
by Shaik Mullapathi Farooq, S. M. Suhail Hussain, Siddavaram Kiran and Taha Selim Ustun
Electronics 2019, 8(1), 96; https://doi.org/10.3390/electronics8010096 - 15 Jan 2019
Cited by 35 | Viewed by 5941
Abstract
When equipped with an on-board wireless kit, electric vehicles (EVs) can communicate with nearby entities, e.g., road side units (RSUs), via a vehicle ad-hoc network (VANET). More observability enables smart charging algorithms where charging stations (CSs) are allocated to EVs based on their [...] Read more.
When equipped with an on-board wireless kit, electric vehicles (EVs) can communicate with nearby entities, e.g., road side units (RSUs), via a vehicle ad-hoc network (VANET). More observability enables smart charging algorithms where charging stations (CSs) are allocated to EVs based on their current state of charge, destination, and urgency to charge. IEEE 1609 WAVE standard regulates VANETs, while IEC 61850 is emerging as the smart grid communication standard. In order to integrate these two domains of energy management, past research has focused on harmonizing these two standards for a full smart city solution. However, this solution requires very sensitive data to be transmitted, such as ownership of EV, owners’ personal details, and driving history. Therefore, data security in these networks is of prime concern and needs to be addressed. In this paper, different security mechanisms defined by the IEEE 1609 WAVE standard are applied for both vehicle-to-infrastructure (V2I) and vehicle-to-grid (V2G) communication. The former relates to EV–RSU, while the latter covers EV–CS communication. The implicit and explicit certificate mechanism processes proposed in IEEE 1609 WAVE for authentication are studied in great detail. Furthermore, a performance evaluation for these mechanisms is presented in terms of total time lapse for authentication, considering both the computational time and communication time delays. These results are very important in understanding the extra latency introduced by security mechanisms. Considering that VANETs may be volatile and may disappear as EVs drive away, overall timing performance becomes vital for operation. Reported results show the magnitude of this impact and compare different security mechanisms. These can be utilized to further develop VANET security approaches based on available time and the required security level. Full article
(This article belongs to the Special Issue Smart, Connected and Efficient Transportation Systems)
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Full article ">Figure 1
<p>WAVE protocol stack and different standards.</p> Full article ">Figure 2
<p>Conceptual architectural model for the vehicle-to-grid (V2G) and vehicle-to-infrastructure (V2I) systems.</p> Full article ">Figure 3
<p>Certificate acquisition (<b>1</b>), authentication with certificates (<b>2</b>), and eavesdropping (<b>3</b>).</p> Full article ">Figure 4
<p>Certificate signing of electric vehicle (EV) by certificate authority (CA).</p> Full article ">Figure 5
<p>Certificate verification of EV by road side unit (RSU).</p> Full article ">Figure 6
<p>Digital certificate hierarchy for verification.</p> Full article ">Figure 7
<p>Message exchanges for explicit certificate-based authentication process.</p> Full article ">Figure 8
<p>Implicit certificate mechanism using elliptic curve cryptography (ECC).</p> Full article ">Figure 9
<p>Timing performance with OpenSSL implementation and Riverbed Simulations.</p> Full article ">
19 pages, 2394 KiB  
Article
Design of Voltage Mode Electronically Tunable First Order All Pass Filter in ±0.7 V 16 nm CNFET Technology
by Muhammad Masud, Abu A’ain, Iqbal Khan and Nasir Husin
Electronics 2019, 8(1), 95; https://doi.org/10.3390/electronics8010095 - 15 Jan 2019
Cited by 16 | Viewed by 4749
Abstract
A novel voltage mode first order active only tuneable all pass filter (AOTAPF) circuit configuration is presented. The AOTAPF has been designed using ±0.7 V, 16 nm carbon nanotube field effect transistor (CNFET) Technology. The circuit uses CNFET based varactor and unity gain [...] Read more.
A novel voltage mode first order active only tuneable all pass filter (AOTAPF) circuit configuration is presented. The AOTAPF has been designed using ±0.7 V, 16 nm carbon nanotube field effect transistor (CNFET) Technology. The circuit uses CNFET based varactor and unity gain inverting amplifier (UGIA). The presented AOTAPF is realized with three N-type CNFETs and without any external passive components. It is to be noted that the realized circuit uses only two CNFETs between its supply-rails and thus, suitable for low-voltage operation. The electronic tunability is achieved by varying the voltage controlled capacitance of the employed CNFET varactor. By altering the varactor tuning voltage, a wide tunable range of pole frequency between 34.2 GHz to 56.9 GHz is achieved. The proposed circuit does not need any matching constraint and is suitable for multi-GHz frequency applications. The presented AOTAPF performance is substantiated with HSPICE simulation program for 16 nm technology-node, using the well-known Stanford CNFET model. AOTAPF simulation results verify the theory for a wide frequency-range. Full article
(This article belongs to the Special Issue Signal Processing and Analysis of Electrical Circuit)
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<p>Carbon nanotube field effect transistor (CNFET) (<b>a</b>) Schematic; (<b>b</b>) Top-View.</p> Full article ">Figure 2
<p>CNFET gate to channel capacitance.</p> Full article ">Figure 3
<p>CNFET based UGIA: (<b>a</b>) Transistor-level realization; (<b>b</b>) Symbol; (<b>c</b>) Parasitic model.</p> Full article ">Figure 4
<p>Effect of variation of <math display="inline"><semantics> <msub> <mi>N</mi> <mi>T</mi> </msub> </semantics></math> on unity gain inverting amplifier (UGIA): (<b>a</b>) <math display="inline"><semantics> <msub> <mi>r</mi> <mi>o</mi> </msub> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <msub> <mi>C</mi> <mi>i</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>C</mi> <mi>o</mi> </msub> </semantics></math>; (<b>c</b>) Power dissipation; (<b>d</b>) <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </semantics></math> dB bandwidth.</p> Full article ">Figure 5
<p>The UGIA: (<b>a</b>) Transient-response; (<b>b</b>) Frequency-response of Voltage-gain (<math display="inline"><semantics> <msub> <mi>V</mi> <mi>o</mi> </msub> </semantics></math>/<math display="inline"><semantics> <msub> <mi>V</mi> <mi>i</mi> </msub> </semantics></math>); (<b>c</b>) Frequency-response of Output-impedance.</p> Full article ">Figure 6
<p>The UGIA: (<b>a</b>) THD Vs input voltage at 50 GHz; (<b>b</b>) Monte Carlo simulations for <math display="inline"><semantics> <msub> <mi>V</mi> <mi>o</mi> </msub> </semantics></math> in time domain; (<b>c</b>) Monte Carlo simulations for voltage gain in frequency domain.</p> Full article ">Figure 7
<p>First order APF: (<b>a</b>) Basic scheme; (<b>b</b>) Equivalent circuit; (<b>c</b>) CNFET based implementation.</p> Full article ">Figure 8
<p>CNFET based varactor: (<b>a</b>) Transistor-level realization; (<b>b</b>) Symbol.</p> Full article ">Figure 9
<p>CV characteristics of varactor with different <math display="inline"><semantics> <msub> <mi>N</mi> <mi>T</mi> </msub> </semantics></math>.</p> Full article ">Figure 10
<p>Transient-response of AOTAPF at pole-<math display="inline"><semantics> <msub> <mi>f</mi> <mi>o</mi> </msub> </semantics></math> = 49.26 GHz and <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> </mrow> </semantics></math>0.32 V.</p> Full article ">Figure 11
<p>Ideal and simulated frequency-response of AOTAPF at <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </semantics></math> = −0.32 V: (<b>a</b>) Voltage gain; (<b>b</b>) Phase.</p> Full article ">Figure 12
<p>Frequency-response of input and output noise of AOTAPF at <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> </mrow> </semantics></math>0.32 V.</p> Full article ">Figure 13
<p>Monte Carlo simulations of AOTAPF for: (<b>a</b>) Time domain; (<b>b</b>) Voltage-gain (<math display="inline"><semantics> <msub> <mi>V</mi> <mi>o</mi> </msub> </semantics></math>/<math display="inline"><semantics> <msub> <mi>V</mi> <mi>i</mi> </msub> </semantics></math>); (<b>c</b>) Phase.</p> Full article ">Figure 14
<p>Frequency-response of AOTAPF at different values of <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </semantics></math>: (<b>a</b>) Voltage gain; (<b>b</b>) Phase.</p> Full article ">Figure 15
<p>Transient-response of AOTAPF at different values of <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </semantics></math>: (<b>a</b>) −0.30 V; (<b>b</b>) −0.33 V; (<b>c</b>) −0.50 V.</p> Full article ">
20 pages, 1376 KiB  
Article
AnScalable Matrix Computing Unit Architecture for FPGA, and SCUMO User Design Interface
by Asgar Abbaszadeh, Taras Iakymchuk, Manuel Bataller-Mompeán, Jose V. Francés-Villora and Alfredo Rosado-Muñoz
Electronics 2019, 8(1), 94; https://doi.org/10.3390/electronics8010094 - 15 Jan 2019
Cited by 8 | Viewed by 5760
Abstract
High dimensional matrix algebra is essential in numerous signal processing and machine learning algorithms. This work describes a scalable square matrix-computing unit designed on the basis of circulant matrices. It optimizes data flow for the computation of any sequence of matrix operations removing [...] Read more.
High dimensional matrix algebra is essential in numerous signal processing and machine learning algorithms. This work describes a scalable square matrix-computing unit designed on the basis of circulant matrices. It optimizes data flow for the computation of any sequence of matrix operations removing the need for data movement for intermediate results, together with the individual matrix operations’ performance in direct or transposed form (the transpose matrix operation only requires a data addressing modification). The allowed matrix operations are: matrix-by-matrix addition, subtraction, dot product and multiplication, matrix-by-vector multiplication, and matrix by scalar multiplication. The proposed architecture is fully scalable with the maximum matrix dimension limited by the available resources. In addition, a design environment is also developed, permitting assistance, through a friendly interface, from the customization of the hardware computing unit to the generation of the final synthesizable IP core. For N × N matrices, the architecture requires N ALU-RAM blocks and performs O ( N 2 ) , requiring N 2 + 7 and N + 7 clock cycles for matrix-matrix and matrix-vector operations, respectively. For the tested Virtex7 FPGA device, the computation for 500 × 500 matrices allows a maximum clock frequency of 346 MHz, achieving an overall performance of 173 GOPS. This architecture shows higher performance than other state-of-the-art matrix computing units. Full article
(This article belongs to the Special Issue Hardware and Architecture)
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Figure 1
<p>Physical placement of <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>×</mo> <mn>3</mn> </mrow> </semantics></math> matrix elements in memory columns in normal (top) and circulant (bottom) forms. Yellow and red marked memory locations represent the first column and the first row of a matrix. Grey blocks are labeled, from left to right, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>A</mi> <msub> <mi>M</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>A</mi> <msub> <mi>M</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>A</mi> <msub> <mi>M</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, since they correspond to memory blocks in hardware.</p> Full article ">Figure 2
<p>Hardware computation structure for matrix arithmetic operations. ALU-RAM blocks (shown in columns) are chained for iterated computational the FSM controls the data flow where one matrix operand is stored in RAM blocks and the second operand, either matrix, vector or scalar, is externally fed from a memory (G-mem) or any other external hardware.</p> Full article ">Figure 3
<p>Single ALU-RAM column interconnection structure. The RAM block is divided logically into two equal banks, one storing the current operand and the other saving the result of operation. The circulant address generator provides read and write addresses for both banks and modifies the address provided by the FSM in case of required direct or transposed matrix elements.</p> Full article ">Figure 4
<p>MATLAB GUI interface for the SCUMO tool.</p> Full article ">Figure 5
<p>Operation selection option in the software design tool.</p> Full article ">Figure 6
<p>Bit length option in the software design tool.</p> Full article ">Figure 7
<p>Report analysis option in the software design tool.</p> Full article ">Figure 8
<p>Console window reports option in the software design tool.</p> Full article ">Figure 9
<p>MATLAB GUI interface of the IP core generation option.</p> Full article ">
13 pages, 422 KiB  
Article
A Novel Differential Fault Analysis on the Key Schedule of SIMON Family
by Jinbao Zhang, Ning Wu, Fang Zhou, Muhammad Rehan Yahya and Jianhua Li
Electronics 2019, 8(1), 93; https://doi.org/10.3390/electronics8010093 - 15 Jan 2019
Cited by 6 | Viewed by 2842
Abstract
As a family of lightweight block ciphers, SIMON has attracted lots of research attention since its publication in 2013. Recent works show that SIMON is vulnerable to differential fault analysis (DFA) and existing DFAs on SIMON assume the location of induced faults are [...] Read more.
As a family of lightweight block ciphers, SIMON has attracted lots of research attention since its publication in 2013. Recent works show that SIMON is vulnerable to differential fault analysis (DFA) and existing DFAs on SIMON assume the location of induced faults are on the cipher states. In this paper, a novel DFA on SIMON is proposed where the key schedule is selected as the location of induced faults. Firstly, we assume a random one-bit fault is induced in the fourth round key KT−4 to the last. Then, by utilizing the key schedule propagation properties of SIMON, we determine the exact position of induced fault and demonstrate that the proposed DFA can retrieve 4 bits of the last round key KT−1 on average using one-bit fault. Till now this is the largest number of bits that can be cracked as compared to DFAs based on random bit fault model. Furthermore, by reusing the induced fault, we prove that 2 bits of the penultimate round key KT−2 could be retrieved. To the best of our knowledge, the proposed attack is the first one which extracts a key from SIMON based upon DFA on the key schedule. Finally, correctness and validity of our proposed attack is verified through detailed simulation and analysis. Full article
(This article belongs to the Section Circuit and Signal Processing)
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<p>Fault propagation when the <span class="html-italic">j</span>th bit <span class="html-italic">K<sup>T−4</sup></span> is randomly corrupted.</p> Full article ">
29 pages, 15316 KiB  
Article
Novel Dead-Time Compensation Strategy for Wide Current Range in a Three-Phase Inverter
by Jeong-Woo Lim, Hanyoung Bu and Younghoon Cho
Electronics 2019, 8(1), 92; https://doi.org/10.3390/electronics8010092 - 15 Jan 2019
Cited by 8 | Viewed by 7943
Abstract
This paper proposes a novel three-phase voltage source inverter dead-time compensation strategy for accurate compensation in wide current regions of the inverter. In particular, an analysis of the output voltage distortion of the inverter, which appears as parasitic components of the switches, was [...] Read more.
This paper proposes a novel three-phase voltage source inverter dead-time compensation strategy for accurate compensation in wide current regions of the inverter. In particular, an analysis of the output voltage distortion of the inverter, which appears as parasitic components of the switches, was conducted for proper voltage compensation in the low current region, and an on-line compensation voltage controller was proposed. Additionally, a new trapezoidal compensation voltage implementation method using the current phase was proposed to simplify realizing the trapezoidal shape of the three-phase compensation voltages. Finally, when the proposed dead-time compensation strategy was applied, the maximum phase voltage magnitude in the linear modulation voltage regions was defined to achieve smooth operation even at high modulation index. Simulations and experiments were conducted to verify the performance of the proposed dead-time compensation scheme. Full article
(This article belongs to the Special Issue Renewable Electric Energy Systems)
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Figure 1

Figure 1
<p>Three-phase VSI and dead-time switching patterns of a-phase leg: (<b>a</b>) Typical three-phase VSI configuration; (<b>b</b>) switching patterns and current flow direction of the one phase leg during the dead-time.</p> Full article ">Figure 2
<p>Effects of the dead-time in <a href="#electronics-08-00092-f001" class="html-fig">Figure 1</a>a (<math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mrow> <mi>s</mi> <mi>w</mi> </mrow> </msub> </mrow> </semantics></math> = 20 kHz, <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mi>d</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> = 100 V, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mi>s</mi> </msub> </mrow> </semantics></math> = 0.01 mH, <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>s</mi> </msub> </mrow> </semantics></math> = 10 Ω); (<b>a</b>) an a-phase pole voltage reference as space vector PWM (SVPWM); (<b>b</b>) comparing dead-time effects with equal voltage reference with (<b>a</b>).</p> Full article ">Figure 3
<p>Stationary reference frame <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> </semantics></math> axis and synchronous reference frame <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>−</mo> <mi>q</mi> </mrow> </semantics></math> axis with <math display="inline"><semantics> <mi>δ</mi> </semantics></math>.</p> Full article ">Figure 4
<p>Dead-time compensation voltage waveforms; (<b>a</b>) a-phase current; (<b>b</b>) average compensation pole voltage (ACPV) of a-phase; (<b>c</b>) the average compensation phase voltage of a-phase; (<b>d</b>) dead-time compensation voltages on stationary reference frame <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> </semantics></math> axis; (<b>e</b>) dead-time compensation voltages on the synchronous reference frame <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>−</mo> <mi>q</mi> </mrow> </semantics></math> axis.</p> Full article ">Figure 5
<p>Charging and discharging process of the output capacitors <math display="inline"><semantics> <mrow> <mrow> <mo>(</mo> <mrow> <msub> <mi>i</mi> <mi>p</mi> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>; (<b>a</b>) the upper switch is turning on; (<b>b</b>) the upper switch is turning off.</p> Full article ">Figure 6
<p>Comparing <math display="inline"><semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi>o</mi> <mi>f</mi> <mi>f</mi> </mrow> <mrow> <mi>e</mi> <mi>q</mi> </mrow> </msubsup> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <msubsup> <mi>T</mi> <mrow> <mi>o</mi> <mi>f</mi> <mi>f</mi> </mrow> <mrow> <mi>s</mi> <mi>i</mi> <mi>m</mi> </mrow> </msubsup> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Simulation results of <a href="#electronics-08-00092-f005" class="html-fig">Figure 5</a>; (<b>a</b>) gate voltage <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <msub> <mi>Q</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) lower output capacitor voltage <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) inverter pole voltage <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>p</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 7 Cont.
<p>Simulation results of <a href="#electronics-08-00092-f005" class="html-fig">Figure 5</a>; (<b>a</b>) gate voltage <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <msub> <mi>Q</mi> <mn>1</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) lower output capacitor voltage <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <msub> <mi>C</mi> <mn>2</mn> </msub> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) inverter pole voltage <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>p</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Variation of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>o</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> according to the current <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>p</mi> </msub> </mrow> </semantics></math> magnitude (<math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>n</mi> </mrow> </msub> </mrow> </semantics></math> = 23.5 Ω <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>o</mi> <mi>f</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> = 23.5 Ω); (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>p</mi> </msub> </mrow> </semantics></math> = 170 mA; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>p</mi> </msub> </mrow> </semantics></math> = 250 mA; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>p</mi> </msub> </mrow> </semantics></math> = 330 mA; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>p</mi> </msub> </mrow> </semantics></math> = 600 mA.</p> Full article ">Figure 9
<p>Detail of the dashed box in <a href="#electronics-08-00092-f005" class="html-fig">Figure 5</a>c.</p> Full article ">Figure 10
<p>Proposed TCV implementation strategy; (<b>a</b>) triangle waveform function <math display="inline"><semantics> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> and sinusoidal waveform function <math display="inline"><semantics> <mrow> <mi>g</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with peak <math display="inline"><semantics> <mi>k</mi> </semantics></math>; (<b>b</b>) TCV shapes comparison.</p> Full article ">Figure 11
<p>TCV with slope of the width <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>.</p> Full article ">Figure 12
<p>Proposed on-line TCV controller scheme.</p> Full article ">Figure 13
<p>Analysis of DTCV error characteristics on d-axis; (<b>a</b>) error voltage waveform of <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>d</mi> </msub> </mrow> </semantics></math>; (<b>b</b>) fast Fourier transform (FFT) result of <a href="#electronics-08-00092-f013" class="html-fig">Figure 13</a>a; (<b>c</b>) error voltage waveform of slopes of DTCV; (<b>d</b>) FFT result of <a href="#electronics-08-00092-f013" class="html-fig">Figure 13</a>c.</p> Full article ">Figure 14
<p>Six output voltage vectors of typical three-phase VSI.</p> Full article ">Figure 15
<p>The current phase <math display="inline"><semantics> <mi>ψ</mi> </semantics></math> with a-phase voltage reference <math display="inline"><semantics> <mrow> <msubsup> <mi>v</mi> <mrow> <mi>a</mi> <mi>s</mi> </mrow> <mo>∗</mo> </msubsup> </mrow> </semantics></math> and a-phase current <math display="inline"><semantics> <mrow> <msub> <mi>i</mi> <mi>a</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 16
<p><math display="inline"><semantics> <mrow> <msubsup> <mi>v</mi> <mi>α</mi> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>v</mi> <mi>β</mi> <mi>i</mi> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi>v</mi> <mi>α</mi> <mi>r</mi> </msubsup> <mo>,</mo> <msubsup> <mi>v</mi> <mi>β</mi> <mi>r</mi> </msubsup> </mrow> </semantics></math> waveforms on the stationary reference frame according to the phase <math display="inline"><semantics> <mi>ψ</mi> </semantics></math>; (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mrow> <mn>30</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mrow> <mn>90</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 17
<p>Distorted voltage regions and voltage vectors according to the current phase <math display="inline"><semantics> <mi>ψ</mi> </semantics></math>; (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msup> <mn>0</mn> <mo>∘</mo> </msup> <mo>&lt;</mo> <mi>ψ</mi> <mo>&lt;</mo> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> <mo>&lt;</mo> <mi>ψ</mi> <mo>≤</mo> <msup> <mrow> <mn>90</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 17 Cont.
<p>Distorted voltage regions and voltage vectors according to the current phase <math display="inline"><semantics> <mi>ψ</mi> </semantics></math>; (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msup> <mn>0</mn> <mo>∘</mo> </msup> <mo>&lt;</mo> <mi>ψ</mi> <mo>&lt;</mo> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>ψ</mi> <mo>=</mo> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msup> <mrow> <mn>60</mn> </mrow> <mo>∘</mo> </msup> <mo>&lt;</mo> <mi>ψ</mi> <mo>≤</mo> <msup> <mrow> <mn>90</mn> </mrow> <mo>∘</mo> </msup> </mrow> </semantics></math>.</p> Full article ">Figure 18
<p>The control block diagram of three-phase VSI with proposed DTCS.</p> Full article ">Figure 19
<p>The specific block diagram of <a href="#electronics-08-00092-f018" class="html-fig">Figure 18</a>a.</p> Full article ">Figure 20
<p>Simulation results of the proposed DTCS; (<b>a</b>) three-phase currents; (<b>b</b>) TCV of the a-phase; (<b>c</b>) <span class="html-italic">d</span>-<span class="html-italic">q</span> axis currents on the synchronous reference frame; (<b>d</b>) DTCV on the synchronous reference frame <span class="html-italic">d</span>-<span class="html-italic">q</span> axis; (<b>e</b>) positions.</p> Full article ">Figure 20 Cont.
<p>Simulation results of the proposed DTCS; (<b>a</b>) three-phase currents; (<b>b</b>) TCV of the a-phase; (<b>c</b>) <span class="html-italic">d</span>-<span class="html-italic">q</span> axis currents on the synchronous reference frame; (<b>d</b>) DTCV on the synchronous reference frame <span class="html-italic">d</span>-<span class="html-italic">q</span> axis; (<b>e</b>) positions.</p> Full article ">Figure 21
<p>Simulation results without DTCS; (<b>a</b>) three-phase currents; (<b>b</b>) <span class="html-italic">d</span>-<span class="html-italic">q</span> axis currents on the synchronous reference frame.</p> Full article ">Figure 22
<p>Experiment setting; (<b>a</b>) the three-phase VSI; (<b>b</b>) DC-power supply for the DC-link voltage source.</p> Full article ">Figure 23
<p>Three-phase currents waveforms and a-phase dead-time compensation pole voltage waveform without any DTCS.</p> Full article ">Figure 24
<p>Three-phase currents waveforms and a-phase dead-time compensation pole voltage waveform when conventional DTCS considering only <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>d</mi> </msub> </mrow> </semantics></math> was applied.</p> Full article ">Figure 25
<p>Three-phase currents waveforms and a-phase dead-time compensation pole voltage waveform when the proposed DTCS was applied.</p> Full article ">Figure 26
<p>Three-phase currents waveforms and a-phase dead-time compensation pole voltage waveform when DTCS, considering only the proper voltage level, was applied.</p> Full article ">Figure 27
<p>Stationary reference frame <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> </semantics></math> axis and on the x-y plot current waveforms with <a href="#electronics-08-00092-f023" class="html-fig">Figure 23</a>.</p> Full article ">Figure 28
<p>Stationary reference frame <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> </semantics></math> axis and on the x-y plot current waveforms with <a href="#electronics-08-00092-f024" class="html-fig">Figure 24</a>.</p> Full article ">Figure 29
<p>Stationary reference frame <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> </semantics></math> axis and on the x-y plot current waveforms with <a href="#electronics-08-00092-f025" class="html-fig">Figure 25</a>.</p> Full article ">Figure 30
<p>Stationary reference frame <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>−</mo> <mi>β</mi> </mrow> </semantics></math> axis and on the x-y plot current waveforms with <a href="#electronics-08-00092-f026" class="html-fig">Figure 26</a>.</p> Full article ">Figure 31
<p>Comparing the a-phase current THD; (<b>a</b>) 10 Hz; (<b>b</b>) 30 Hz; (<b>c</b>) 50 Hz.</p> Full article ">Figure 32
<p>Changes of current THD according to frequency change of proposed DTCS.</p> Full article ">
16 pages, 3978 KiB  
Article
Assessment of Dual Frequency GNSS Observations from a Xiaomi Mi 8 Android Smartphone and Positioning Performance Analysis
by Umberto Robustelli, Valerio Baiocchi and Giovanni Pugliano
Electronics 2019, 8(1), 91; https://doi.org/10.3390/electronics8010091 - 15 Jan 2019
Cited by 161 | Viewed by 16817
Abstract
On May 2018 the world’s first dual-frequency Global Navigation Satellite System (GNSS) smartphone produced by Xiaomi equipped with a Broadcom BCM47755 chip was launched. It is able to receive L1/E1/ and L5/E5 signals from GPS, Galileo, Beidou, and GLONASS (GLObal NAvigation Satellite System) [...] Read more.
On May 2018 the world’s first dual-frequency Global Navigation Satellite System (GNSS) smartphone produced by Xiaomi equipped with a Broadcom BCM47755 chip was launched. It is able to receive L1/E1/ and L5/E5 signals from GPS, Galileo, Beidou, and GLONASS (GLObal NAvigation Satellite System) satellites. The main aim of this work is to achieve the phone’s position by using multi-constellation, dual frequency pseudorange and carrier phase raw data collected from the smartphone. Furthermore, the availability of dual frequency raw data allows to assess the multipath performance of the device. The smartphone’s performance is compared with that of a geodetic receiver. The experiments were conducted in two different scenarios to test the smartphone under different multipath conditions. Smartphone measurements showed a lower C/N0 and higher multipath compared with those of the geodetic receiver. This produced negative effects on single-point positioning as showed by high root mean square error (RMS). The best positioning accuracy for single point was obtained with the E5 measurements with a DRMS (horizontal root mean square error) of 4.57 m. For E1/L1 frequency, the 2DRMS was 5.36 m. However, the Xiaomi Mi 8, thanks to the absence of the duty cycle, provided carrier phase measurements used for a static single frequency relative positioning with an achieved 2DRMS of 1.02 and 1.95 m in low and high multipath sites, respectively. Full article
(This article belongs to the Special Issue Green Communications in Smart City)
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<p>The location where the two sets of experimental data were collected, and the equipment used in this study: (<b>a</b>) Portici site, (<b>b</b>) Centro Direzionale Napoli (CDN) site.</p> Full article ">Figure 2
<p>Skyplots of Portici and CDN sites’ smartphone data. The beginning of the observational arc is represented by a circle. The GPS and Galileo satellites are shown in blue and red respectively.</p> Full article ">Figure 3
<p>Mean of C/N0 values on L1 frequency comparison between smartphone and geodetic receiver. Blue bars represent smartphone, red represent geodetic receiver: top rows depict mean of C/N0 for GPS and Galileo satellites in Portici site; bottom rows depict mean of C/N0 for GPS and Galileo satellites in CDN site.</p> Full article ">Figure 4
<p>Mean of C/N0 values on L1/E1 L5/E5a frequency comparison. Purple bars represent L1/E1 frequency, yellow L5/E5a: top rows depict GPS and Galileo satellites in Portici site; bottom rows depict GPS and Galileo satellites in CDN site.</p> Full article ">Figure 5
<p>L1/E1 code multipath error for CDN site (represented in grey) and Portici site (represented in blue) versus processed time expressed in epochs. (<b>a</b>) L1 code multipath for all GPS satellites tracked by smartphone; (<b>b</b>) E1 code multipath error for all Galileo satellites tracked by smartphone; (<b>c</b>) L1code multipath error for GPS satellites tracked by geodetic receiver.</p> Full article ">Figure 6
<p>Scatter plot of L1/E1 single-point position error. Grey markers represent errors when the solution has been obtained by using GPS only measurements collected by smartphone, blue circles represent error for smartphone multi-constellation approach, red circles represent error obtained by using geodetic receiver: (<b>a</b>) Portici site; (<b>b</b>) CDN site.</p> Full article ">Figure 7
<p>Carrier-based static positioning solution components performances for Portici (<b>left</b> column) and CDN (<b>right</b> column) sites.</p> Full article ">Figure 8
<p>Code multipath comparison between L1/E1 (represented in grey) and L5/E5a (represented in blue) measurements from all the visible GPS (<b>upper</b>) and Galileo satellites (<b>lower</b>) collected by smartphone in the Portici site versus epoch.</p> Full article ">Figure 9
<p>Scatter plot error comparison of single-point positioning between E1 and E5a signals collected by smartphone in the Portici site. Grey markers represent errors when the solution was obtained using Galileo E1 measurements, blue circles represent error for E5a measurements.</p> Full article ">
15 pages, 8250 KiB  
Article
Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors
by Arturo Mejia-Barron, J. Jesus de Santiago-Perez, David Granados-Lieberman, Juan P. Amezquita-Sanchez and Martin Valtierra-Rodriguez
Electronics 2019, 8(1), 90; https://doi.org/10.3390/electronics8010090 - 15 Jan 2019
Cited by 11 | Viewed by 3695
Abstract
The induction motor (IM) is one of the most important elements in industry. Although IMs are robust machines, they are susceptible to faults, where the stator winding short-circuit fault is one of the most common ones. In this work, the Shannon entropy (SE) [...] Read more.
The induction motor (IM) is one of the most important elements in industry. Although IMs are robust machines, they are susceptible to faults, where the stator winding short-circuit fault is one of the most common ones. In this work, the Shannon entropy (SE) index and a fuzzy logic (FL) system are proposed to diagnose short-circuit faults, considering both different severity levels and different load conditions. In the proposed methodology, a filtering stage based on brick-wall band-pass filters is firstly carried out. After this stage, the SE index is computed to quantify the fault severity and a FL system is applied to diagnose the IM condition in an automatic way. Unlike other works that propose some types of space transformations, the proposal is only based on a filtering stage and a time domain index, requiring low computational resources. The obtained results demonstrate the effectiveness of the proposal, i.e., the SE index quantifies the fault severity, regardless of the mechanical load, and the proposed FL system achieves a positive classification rate of 98%. Full article
(This article belongs to the Special Issue Signal Processing and Analysis of Electrical Circuit)
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<p>General diagram of a fuzzy logic (FL) system.</p> Full article ">Figure 2
<p>Proposed methodology.</p> Full article ">Figure 3
<p>Filter design: (<b>a</b>) Frequencies of interest, (<b>b</b>) brick-wall low-pass filters and (<b>c</b>) brick-wall band-pass filters.</p> Full article ">Figure 4
<p>Experimental setup.</p> Full article ">Figure 5
<p>Current signals for (<b>a</b>) 0 SCTs, (<b>b</b>) 10 SCTs, (<b>c</b>) 20 SCTs, (<b>d</b>) 30 SCTs and (<b>e</b>) 40 SCTs at different loads (0.00, 2.04, 4.09 and 6.13 Nm).</p> Full article ">Figure 6
<p>Results for the Shannon entropy (SE), root mean square (RMS), and energy indices at (<b>a</b>) 0.00 Nm, (<b>b</b>) 2.04 Nm, (<b>c</b>) 4.09 Nm and (<b>d</b>) 6.13 Nm (left side for <span class="html-italic">f</span><sub>L</sub> and right side for <span class="html-italic">f</span><sub>R</sub>).</p> Full article ">Figure 7
<p>SE values for (<b>a</b>) <span class="html-italic">f</span><sub>L</sub>, (<b>b</b>) <span class="html-italic">f</span><sub>R</sub>, and (<b>c</b>) SE<sub>A</sub> at both different loads and different fault severities.</p> Full article ">Figure 8
<p>Gaussian distribution functions for (<b>a</b>) SE<sub>L</sub> and (<b>b</b>) SE<sub>R</sub> at both different loads and different fault severities.</p> Full article ">Figure 9
<p>Membership functions for (<b>a</b>) SE<sub>L</sub> and (<b>b</b>) SE<sub>R</sub> and (<b>c</b>) FL outputs.</p> Full article ">
9 pages, 2024 KiB  
Article
Predicting the Influence of Rain on LIDAR in ADAS
by Christopher Goodin, Daniel Carruth, Matthew Doude and Christopher Hudson
Electronics 2019, 8(1), 89; https://doi.org/10.3390/electronics8010089 - 15 Jan 2019
Cited by 138 | Viewed by 14180
Abstract
While it is well known that rain may influence the performance of automotive LIDAR sensors commonly used in ADAS applications, there is a lack of quantitative analysis of this effect. In particular, there is very little published work on physically-based simulation of the [...] Read more.
While it is well known that rain may influence the performance of automotive LIDAR sensors commonly used in ADAS applications, there is a lack of quantitative analysis of this effect. In particular, there is very little published work on physically-based simulation of the influence of rain on terrestrial LIDAR performance. Additionally, there have been few quantitative studies on how rain-rate influences ADAS performance. In this work, we develop a mathematical model for the performance degradation of LIDAR as a function of rain-rate and incorporate this model into a simulation of an obstacle-detection system to show how it can be used to quantitatively predict the influence of rain on ADAS that use LIDAR. Full article
(This article belongs to the Special Issue Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS))
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<p>Procedure for modifying LIDAR distances and intensities based on rain rate.</p> Full article ">Figure 2
<p>(<b>a</b>) The ODOA test scenario in clear weather, rendered by MAVS (<b>b</b>) When raining (R = 17 mm/h).</p> Full article ">Figure 3
<p>(<b>a</b>) Top down view of the LIDAR point cloud in the range test in clear conditions (<b>b</b>) When raining (R = 17 mm/h).</p> Full article ">Figure 4
<p>Decrease in max range for a simulated LIDAR as a function of rain rate.</p> Full article ">Figure 5
<p>Number of points in a single scan of the LIDAR as a function of rain rate.</p> Full article ">Figure 6
<p>Obstacle detection range as a function of rain rate.</p> Full article ">Figure 7
<p>LIDAR range decrease with increasing rain rate from 0 mm/h on the <b>far left</b>, 9 mm/h in the <b>middle</b>, and 17 mm/h on the <b>right</b>.</p> Full article ">
18 pages, 4743 KiB  
Article
Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation
by Merzak Ferroukhi, Abdeldjalil Ouahabi, Mokhtar Attari, Yassine Habchi and Abdelmalik Taleb-Ahmed
Electronics 2019, 8(1), 88; https://doi.org/10.3390/electronics8010088 - 14 Jan 2019
Cited by 44 | Viewed by 5609
Abstract
The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) [...] Read more.
The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. The 2nd-generation wavelets seeks to be positioned and confronted by the image and video coding methods currently used. It is in this context that we suggest a method combining bandelets and the SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage of capturing the geometrical complexity of the image structure. The second reason is the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements indicate that in some cases (for low bit rates) the performance of the proposed coding competes with the well-established ones (H.264 or MPEG4 AVC and H.265 or MPEG4 HEVC) and opens up new application prospects in the field of medical imaging. Full article
(This article belongs to the Section Computer Science & Engineering)
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<p>Simplified video coding scheme.</p> Full article ">Figure 2
<p>Typical sequence with I, B and P frames. P frame can only refer to the previous I or P frames, while a B frame can refer to previous or subsequent I or P frames.</p> Full article ">Figure 3
<p>Example of graphical steps of the bandelet transformation algorithm illustrated on a brain magnetic resonance image (MRI) (T<sub>2</sub>-weighted image acquired at 3 T).</p> Full article ">Figure 4
<p>Example of quadtree decomposition.</p> Full article ">Figure 5
<p>Histograms of bandelet vs. wavelet coefficients. Bandelet transform contains only the most significant coefficients and, therefore, those which carry information.</p> Full article ">Figure 6
<p>Medical video used for assessment. (<b>a</b>) Coronary angiography-X-ray; (<b>b</b>) abdomen/pelvis-computed tomography (CT); (<b>c</b>) heart-axial MRI; (<b>d</b>) bladder-MRI.</p> Full article ">Figure 7
<p>Diagram of the visual information fidelity (VIF) metric.</p> Full article ">Figure 8
<p>Recovered frames using: (<b>b</b>) Bandelet (Le Gall 5/3)-SPIHT and (<b>c</b>) Bandelet (CDF9/7)-SPIHT at 0.2 Mbps.</p> Full article ">Figure 9
<p>Performance evaluation of bandelet-SPIHT versus (wavelet) SPIHT in terms of objective metrics (peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) and VIF).</p> Full article ">Figure 10
<p>Comparative subjective quality assessments medical video using bandelet (CDF9/7)-SPIHT (top row), and wavelet (CDF9/7)-SPIHT (Bottom row) at 0.5 Mbps. (<b>a</b>) Coronary angiography-X-ray; (<b>b</b>) abdomen/pelvis-computed tomography (CT); (<b>c</b>) heart-axial MRI; (<b>d</b>) bladder-MRI.</p> Full article ">Figure 11
<p>Performance analysis in terms of PSNR (dB) between standard coding methods and the proposed method.</p> Full article ">
14 pages, 1567 KiB  
Article
Step-Down Partial Power DC-DC Converters for Two-Stage Photovoltaic String Inverters
by Jaime Wladimir Zapata, Samir Kouro, Gonzalo Carrasco and Thierry A. Meynard
Electronics 2019, 8(1), 87; https://doi.org/10.3390/electronics8010087 - 12 Jan 2019
Cited by 21 | Viewed by 6206
Abstract
Photovoltaic (PV) systems composed by two energy conversion stages are attractive from an operation point of view. This is because the maximum power point tracking (MPPT) range is extended, due to the voltage decoupling between the PV system and the dc-link. Nevertheless, the [...] Read more.
Photovoltaic (PV) systems composed by two energy conversion stages are attractive from an operation point of view. This is because the maximum power point tracking (MPPT) range is extended, due to the voltage decoupling between the PV system and the dc-link. Nevertheless, the additional dc-dc conversion stage increases the volume, cost and power converter losses. Therefore, central inverters based on a single-stage converter, have been a mainstream solution to interface large-scale PV arrays composed of several strings connected in parallel made by the series connections of PV modules. The concept of partial power converters (PPC), previously reported as a voltage step-up stage, has not addressed in depth for all types of PV applications. In this work, a PPC performing voltage step-down operation is proposed and analyzed. This concept is interesting from the industry point of view, since with the new isolation standards of PV modules are reaching 1500 V, increasing both the size of the string and dc-link voltage for single-stage inverters. Since grid connection remains typically at 690 V, larger strings impose more demanding operation for single-stage central inverters (required to operate at lower modulation indexes and demand higher blocking voltage devices), making the proposed step-down PPC an attractive solution. Theoretical analysis and an experimental test-bench was built in order to validate the PPC concept, the control performance and the improvement of the conversion efficiency. The experimental results corroborate the benefits of using a PPC, in terms of increasing the system efficiency by reducing the processed power of the converter, while not affecting the system performance. Full article
(This article belongs to the Special Issue Advanced Power Conversion Technologies)
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<p>Diagram of the power flow in a PV system: (<b>a</b>) With a full power converter (FPC). (<b>b</b>) With a partial power converter (PPC).</p> Full article ">Figure 2
<p>Step-down converter. (<b>a</b>) Partial power connection at dc-link side; (<b>b</b>) Partial power connection at PV side.</p> Full article ">Figure 3
<p>Dc-stage efficiency <math display="inline"><semantics> <msub> <mi>η</mi> <mrow> <mi>d</mi> <msub> <mi>c</mi> <mi>s</mi> </msub> </mrow> </msub> </semantics></math> in a PPC in terms of the partial power ratio <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>p</mi> <mi>r</mi> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 4
<p>Volume of the dc–dc converter. (<b>a</b>) Working with a full power converter; (<b>b</b>) Working with a partial power converter.</p> Full article ">Figure 5
<p>PV voltage variation in terms of the PV current variation under different solar irradiation.</p> Full article ">Figure 6
<p>Full-bridge isolated dc–dc topology for the proposed: (<b>a</b>) Step-down PPC connected at dc-link side; (<b>b</b>) Step-down PPC connected at PV-side.</p> Full article ">Figure 7
<p>Operation of the step-down PPC in terms of the transformer turns ratio <math display="inline"><semantics> <msub> <mi>n</mi> <mi>T</mi> </msub> </semantics></math> and the voltage gain <math display="inline"><semantics> <msub> <mi>G</mi> <mi>v</mi> </msub> </semantics></math>. (<b>a</b>) Step-down PPC dc-link side connected; (<b>b</b>) Step-down PPC PV-side connected.</p> Full article ">Figure 8
<p>Experimental test-bench of the step-down PPC connected at dc-link side, using a full-bridge topology for string inverter applications.</p> Full article ">Figure 9
<p>Experimental results under constant solar irradiation. (<b>a</b>) Voltage waveforms at the PV side, dc-link side and converter voltage; (<b>b</b>) Input and output current waveforms.</p> Full article ">Figure 10
<p>Experimental results under a solar irradiation reduction. (<b>a</b>) Voltage and current waveforms; (<b>b</b>) Current waveforms in the step-down Full-bridge based PPC.</p> Full article ">Figure 11
<p>Experimental efficiency curves for: (<b>a</b>) Step-down Full-bridge based PPC, for string inverter; (<b>b</b>) Isolated dc–dc full-bridge converter.</p> Full article ">Figure 12
<p>Experimental result of the partial power ratio for the step-down Full-bridge based PPC under the variation of the voltage gain.</p> Full article ">
18 pages, 6404 KiB  
Article
Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding
by Xiaorui Song, Lingda Wu, Hongxing Hao and Wanpeng Xu
Electronics 2019, 8(1), 86; https://doi.org/10.3390/electronics8010086 - 12 Jan 2019
Cited by 14 | Viewed by 5230
Abstract
Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise [...] Read more.
Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising. Full article
(This article belongs to the Section Computer Science & Engineering)
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<p>The process of the proposed denoising approach. HSI: hyperspectral image. <span class="html-italic">M</span> is the number of samples in a single scan, <span class="html-italic">N</span> is the scan number in the image, <span class="html-italic">L</span> is the band number, and <span class="html-italic">k</span> is the number of atoms in the trained spectral dictionary. The non-zero elements in the matrix are represented by the color blocks. The white blocks represent the zero elements.</p> Full article ">Figure 2
<p>Denoising results for band 60 of Washington DC Mall data with Gaussian i.i.d. noise: (<b>a</b>) Clean image; (<b>b</b>) zoomed area of clean image in red box of (<b>a</b>); (<b>c</b>) noisy image; (<b>d</b>) BM3D; (<b>e</b>) BM4D; (<b>f</b>) PCA + BM4D; (<b>g</b>) KSVD; (<b>h</b>) LRMR; (<b>i</b>) NAILRMA; (<b>j</b>) BPFA; and (<b>k</b>) HyDeSpDLS.</p> Full article ">Figure 3
<p>Denoising results for band 60 of Washington DC Mall data with Gaussian non-i.i.d. noise: <b>(a</b>) Clean image; (<b>b</b>) zoomed area of clean image in red box of (<b>a</b>); (<b>c</b>) noisy image; (<b>d</b>) BM3D; (<b>e</b>) BM4D; (<b>f</b>) PCA + BM4D; (<b>g</b>) KSVD; (<b>h</b>) LRMR; (<b>i</b>) NAILRMA; (<b>j</b>) BPFA; and (<b>k</b>) HyDeSpDLS.</p> Full article ">Figure 4
<p>Denoising results for band 60 of Washington DC Mall data with Poissonian noise: (<b>a</b>) Clean image; (<b>b</b>) zoomed area of clean image in red box of (<b>a</b>); (<b>c</b>) noisy image; (<b>d</b>) BM3D; (<b>e</b>) BM4D; (<b>f</b>) PCA + BM4D; (<b>g</b>) KSVD; (<b>h</b>) LRMR; (<b>i</b>) NAILRMA; (<b>j</b>) BPFA; and (<b>k</b>) HyDeSpDLS.</p> Full article ">Figure 4 Cont.
<p>Denoising results for band 60 of Washington DC Mall data with Poissonian noise: (<b>a</b>) Clean image; (<b>b</b>) zoomed area of clean image in red box of (<b>a</b>); (<b>c</b>) noisy image; (<b>d</b>) BM3D; (<b>e</b>) BM4D; (<b>f</b>) PCA + BM4D; (<b>g</b>) KSVD; (<b>h</b>) LRMR; (<b>i</b>) NAILRMA; (<b>j</b>) BPFA; and (<b>k</b>) HyDeSpDLS.</p> Full article ">Figure 5
<p>Denoised spectral signature results of Washington DC Mall data with Gaussian i.i.d. noise: (<b>a</b>) Noisy; (<b>b</b>) BM3D; (<b>c</b>) BM4D; (<b>d</b>) PCA + BM4D; (<b>e</b>) KSVD; (<b>f</b>) LRMR; (<b>g</b>) NAILRMA; (<b>h</b>) BPFA; and (<b>i</b>) HyDeSpDLS.</p> Full article ">Figure 6
<p>Denoised spectral signature results of Washington DC Mall data with Gaussian non-i.i.d. noise: (<b>a</b>) Noisy; (<b>b</b>) BM3D; (<b>c</b>) BM4D; (<b>d</b>) PCA + BM4D; (<b>e</b>) KSVD; (<b>f</b>) LRMR; (<b>g</b>) NAILRMA; (<b>h</b>) BPFA; and (<b>i</b>) HyDeSpDLS.</p> Full article ">Figure 7
<p>Denoised spectral signature results of Washington DC Mall data with Poissonian noise: (<b>a</b>) Noisy; (<b>b</b>) BM3D; (<b>c</b>) BM4D; (<b>d</b>) PCA + BM4D; (<b>e</b>) KSVD; (<b>f</b>) LRMR; (<b>g</b>) NAILRMA; (<b>h</b>) BPFA; and (<b>i</b>) HyDeSpDLS.</p> Full article ">Figure 8
<p>Denoising results for band 61 of the Indian Pine data: (<b>a</b>) Original image; (<b>b</b>) BM3D; (<b>c</b>) BM4D; (<b>d</b>) PCA + BM4D; (<b>e</b>) KSVD; (<b>f</b>) LRMR; (<b>g</b>) NAILRMA; (<b>h</b>) BPFA; and (<b>i</b>) HyDeSpDLS.</p> Full article ">Figure 9
<p>Denoising results for band 110 of the Indian Pine data: (<b>a</b>) Original image; (<b>b</b>) BM3D; (<b>c</b>) BM4D; (<b>d</b>) PCA + BM4D; (<b>e</b>) KSVD; (<b>f</b>) LRMR; (<b>g</b>) NAILRMA; (<b>h</b>) BPFA; and (<b>i</b>) HyDeSpDLS.</p> Full article ">
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