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Search Results (611)

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Keywords = high-pass filtering

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13 pages, 4990 KiB  
Article
A Sinusoidal Current Generator IC with 0.04% THD for Bio-Impedance Spectroscopy Using a Digital ΔΣ Modulator and FIR Filter
by Soohyun Yun and Joonsung Bae
Electronics 2024, 13(22), 4450; https://doi.org/10.3390/electronics13224450 - 13 Nov 2024
Viewed by 328
Abstract
This paper presents a highly efficient, low-power, compact mixed-signal sinusoidal current generator (CG) integrated circuit (IC) designed for bioelectrical impedance spectroscopy (BIS) with low total harmonic distortion (THD). The proposed system employs a 9-bit sine wave lookup table (LUT) which is simplified to [...] Read more.
This paper presents a highly efficient, low-power, compact mixed-signal sinusoidal current generator (CG) integrated circuit (IC) designed for bioelectrical impedance spectroscopy (BIS) with low total harmonic distortion (THD). The proposed system employs a 9-bit sine wave lookup table (LUT) which is simplified to a 4-bit data stream through a third-order digital delta–sigma modulator (ΔΣM). Unlike conventional analog low-pass filters (LPF), which statically limit bandwidth, the finite impulse response (FIR) filter attenuates high-frequency noise according to the operating frequency, allowing the frequency range of the sinusoidal signal to vary. Additionally, the output of the FIR filter is applied to a 6-bit capacitive digital-to-analog converter (CDAC) with data-weighted averaging (DWA), enabling dynamic capacitor matching and seamless interfacing. The sinusoidal CG IC, fabricated using a 65 nm CMOS process, produces a 5 μA amplitude and operates over a wide frequency range of 0.6 to 20 kHz. This highly synthesizable CG achieves a THD of 0.04%, consumes 19.2 μW of power, and occupies an area of 0.0798 mm2. These attributes make the CG IC highly suitable for compact, low-power bio-impedance applications. Full article
(This article belongs to the Special Issue CMOS Integrated Circuits Design)
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Figure 1

Figure 1
<p>Error caused by the signal harmonic folding at the frequency mixing stage in the demodulator. (<b>a</b>) The overall system of BIS. (<b>b</b>) The effect of square waves on output voltage.</p>
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<p>Conventional sinusoidal current generator IC. (<b>a</b>) LUT based CG. (<b>b</b>) LUT-LPF based CG. (<b>c</b>) LUT-LPF based ΔΣM CG. (<b>d</b>) Proposed ΔΣM CG.</p>
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<p>Block diagram of overall system.</p>
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<p>Third-order ΔΣM architecture.</p>
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<p>Implementation of a 27th-order FIR filter with a coefficient of 1.</p>
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<p>Output spectrum of ΔΣM and frequency response of FIR filter.</p>
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<p>(<b>a</b>) Differential 6-bit CDAC. (<b>b</b>) Operation of CDAC.</p>
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<p>(<b>a</b>) Chip photograph. (<b>b</b>) Power breakdown of the CG IC.</p>
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<p>Comparison of the output of a 9-bit sinusoidal LUT, the output of a 4-bit third-order ΔΣM, and the output of a 6-bit FIR filter.</p>
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<p>Comparison of FIR filter output and V-I converter output.</p>
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<p>(<b>a</b>) THD and third-order HD as a function of its sinusoidal frequencies. (<b>b</b>) Analog waveform with 2 kΩ load at 20 kHz. (<b>c</b>) Power consumption as a function of clock frequency.</p>
Full article ">
22 pages, 27931 KiB  
Article
Experimental Measurements of Explosion Effects Propagating in the Real Geological Environment—Correlation with Small-Scale Model
by Daniel Papán, Emma Brozová and Zuzana Papánová
Buildings 2024, 14(11), 3603; https://doi.org/10.3390/buildings14113603 - 13 Nov 2024
Viewed by 309
Abstract
This research focuses on comparing small-scale and full-scale measurements of wave propagation from explosions by using scaling relationships to find significant correlations between the two. The study investigates how seismic waves generated by explosions behave in the geological environment. The research covers various [...] Read more.
This research focuses on comparing small-scale and full-scale measurements of wave propagation from explosions by using scaling relationships to find significant correlations between the two. The study investigates how seismic waves generated by explosions behave in the geological environment. The research covers various aspects such as the development of the model, the explosive materials used, measurement methods, evaluation techniques, and relevant software. A scientific approach based on the principle of backward Fourier transform was used to process and evaluate the data, which helps to filter the frequencies. One of the important calculations discussed is the determination of the attenuation coefficient, which helps to describe how waves attenuate as they pass through a material. The research also deals with dynamic scaling, using the dynamic exponent as a scaling factor to provide a better understanding of the behavior of waves at different scales. By comparing real in situ data with results from small-scale models, the study provides a robust framework for predicting the effects of explosions in complex geological environments. The research results show a high correlation coherence of the statistical data files of up to 4.1%. For dynamic tasks and model scaling, an important result can be pointed out, namely the approximately fourfold decrease in the exponents of the dependence on the distance from the excitation source and the amplitudes between P-waves (0.4316) and R-waves (0.1219). Conclusions are targeted at the possibility of correlating three types of results: small-scale simulations, numerical simulations, and a real full-scale experiment. Full article
(This article belongs to the Section Building Structures)
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Figure 1
<p>Equipment used for the measurements—accelerometer (<b>left</b>) and module (<b>right</b>). (<b>a</b>) Accelerometer B&amp;K type 8340; (<b>b</b>) Module PULSE LAN-XI B&amp;K type 3050-B-060.</p>
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<p>Types of firecrackers used in experimental measurements. (<b>a</b>) Dum Bum cat. F2; (<b>b</b>) Megatresk cat. F3; (<b>c</b>) Viper 1 cat. F3.</p>
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<p>V10b-el military explosive (<b>a</b>) and its vertical section (<b>b</b>). (<b>a</b>) Explosive V10b-el; (<b>b</b>) Vertical section of the explosive.</p>
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<p>Example from calibration measurement No. 1—calibration curve for the frequency range from 0 Hz to 20 Hz.</p>
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<p>Equipment required to carry out calibration measurements. (<b>a</b>) TIRAvib vibration generator; (<b>b</b>) TIRA BAA 120 amplifier; (<b>c</b>) Minirator MR-PRO sound signal generator.</p>
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<p>Two times derivative theoretical sweep signal generated in SigView software for amplitude range 0 Hz–20 Hz at sampling frequency f<sub>s</sub> = 1024 Hz.</p>
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<p>Graphical representation of Pearson correlation coefficient values for the calibration measurements.</p>
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<p>Geographical representation of Kamenná Poruba on the map of Slovakia.</p>
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<p>Orthophoto map showing the entire shooting range (<b>left</b>) and a scheme of the measurement setup showing the arrangement of accelerometers in the propagating wave path from the source of the explosion (<b>right</b>).</p>
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<p>Accelerometer locations A2 to A5. (<b>a</b>) Accelerometer A2; (<b>b</b>) Accelerometer A3; (<b>c</b>) Accelerometer A4; (<b>d</b>) Accelerometer A5.</p>
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<p>Placement of accelerometers in the direction away from the source of the explosion and in the path of the propagating waves.</p>
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<p>Image showing the explosion of a pair of V10b-el military explosives—measurement No. 10.</p>
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<p>Measurement No. 1—firecracker Dum Bum cat. F2—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 2—firecracker Dum Bum cat. F2—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 3—firecracker Megatresk cat. F3—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 4—firecracker Megatresk cat. F3—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 5—firecracker Viper 1 cat. F3—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 6—firecracker Viper 1 cat. F3—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 7—military explosive V10b-el—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 8—military explosive V10b-el—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No.9—military explosive V10b-el (+ 1 m)—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No.10—military explosive 2 × V10b-el (+ 2 m)—vibration velocity time history of accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Measurement No. 10—military explosive 2 × V10b-el (+ 2 m)—vibration velocity time history of individual accelerometers 1 to 4 with indication of maximum acceleration values.</p>
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<p>Attenuation curves from experimental measurements. (<b>a</b>) All measurements; (<b>b</b>) Viper 1 firecracker; (<b>c</b>) V10b-el military explosive; (<b>d</b>) 2 × V10b-el military explosive—maximum peaks No. 2.</p>
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<p>Analytical integration of the acceleration vibration waveform of accelerometer 1 from measurement No. 9 with maximum amplitude indicated.</p>
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<p>The comparison of our PPV results (<b>a</b>) with those published in the literature (<b>b</b>). (<b>a</b>) Peak particle velocity—measurement No. 9; (<b>b</b>) Attenuation relationship of vertical peak particle velocity [<a href="#B10-buildings-14-03603" class="html-bibr">10</a>].</p>
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<p>Three-dimensional visualization of the small-scale model that was used for the previous experimental measurements.</p>
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<p>Small-scale model from a previous experimental measurement (<b>left</b>) and the result of a firecracker explosion (<b>right</b>).</p>
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<p>Vibration velocity time history of the first four accelerometers from experimental measurements on a small-scale model.</p>
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<p>Graphical representation of the dependence of distance and time at each accelerometer for the P-wave propagation.</p>
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<p>Graphical representation of the dependence of distance and time at each accelerometer for the S-wave propagation.</p>
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<p>Graphical representation of the dependence of distance and time at each accelerometer for the surface Rayleigh wave propagation.</p>
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24 pages, 1458 KiB  
Article
Analysis and Optimization of Output Low-Pass Filter for Current-Source Single-Phase Grid-Connected PV Inverters
by Gurhan Ertasgin and David M. Whaley
Appl. Sci. 2024, 14(22), 10131; https://doi.org/10.3390/app142210131 - 5 Nov 2024
Viewed by 564
Abstract
In this study, the design of output low-pass capacitive–inductive (CL) filters is analyzed and optimized for current-source single-phase grid-connected photovoltaic (PV) inverters. Four different CL filter configurations with varying damping resistor placements are examined, evaluating performance concerning the output current’s total harmonic distortion [...] Read more.
In this study, the design of output low-pass capacitive–inductive (CL) filters is analyzed and optimized for current-source single-phase grid-connected photovoltaic (PV) inverters. Four different CL filter configurations with varying damping resistor placements are examined, evaluating performance concerning the output current’s total harmonic distortion (THD), the power factor (PF), and power losses. High-frequency harmonics are effectively attenuated by a second-order CL filter with the damping resistor placed parallel to the filter inductor. In addition, this filter type achieves the best performance by minimizing power loss. A systematic design methodology using filter normalization techniques allows to determine the optimum filter parameters based on the specified cut-off frequency (500 Hz), power loss (5% of rated power), and target THD (<5%). The analysis, simulations, and experiments show that under various operating conditions, this approach meets the grid connection standards (current THD < 5%, power factor between 0.8 leading and 0.95 lagging) while improving efficiency. Full article
(This article belongs to the Special Issue Advancements in Power Electronics and Control Technologies)
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Figure 1
<p>Common low-pass filter types for grid-connected inverters: The L (<b>a</b>) and LCL (<b>b</b>) filters are used to connect VSIs to the grid, while the CL (<b>c</b>) filter is designed to couple a CSI to the grid.</p>
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<p>The current-source RB-IGBT-based single-phase grid-connected PV inverter shows waveforms for every operational phase. The output filter is configured as a parallel-damped CL type.</p>
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<p>A low-pass filter with four damping variations, combining capacitors and inductors.</p>
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<p>Analysis of the normalized gain and phase margin of a CL low-pass filter under two conditions: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>D</mi> </msub> <mo>=</mo> <mspace width="3.33333pt"/> <msub> <mi>Z</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>D</mi> </msub> <mo>=</mo> <mn>2</mn> <mspace width="3.33333pt"/> <msub> <mi>Z</mi> <mn>0</mn> </msub> </mrow> </semantics></math>. FC 1 and FC 3 are first-order filters, while FC 2 and FC 4 are second-order filters. The frequency has been normalized with respect to the resonant frequency, <math display="inline"><semantics> <msub> <mi>f</mi> <mi>R</mi> </msub> </semantics></math>.</p>
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<p>Filter gain (<b>a</b>) and phase delay (<b>b</b>) at the resonant frequency, <math display="inline"><semantics> <msub> <mi>f</mi> <mi>R</mi> </msub> </semantics></math>, for each FC are presented based on the damping resistance, <math display="inline"><semantics> <msub> <mi>R</mi> <mi>D</mi> </msub> </semantics></math>. It is normalized, meaning <math display="inline"><semantics> <msub> <mi>R</mi> <mi>D</mi> </msub> </semantics></math> is expressed in relation to <math display="inline"><semantics> <msub> <mi>Z</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Plots of normalized capacitance, <math display="inline"><semantics> <msub> <mi>C</mi> <mi>n</mi> </msub> </semantics></math>, vs. inductance, <math display="inline"><semantics> <msub> <mi>L</mi> <mi>n</mi> </msub> </semantics></math>, illustrating the contours for (<b>a</b>) cutoff frequency, <math display="inline"><semantics> <msub> <mi>ω</mi> <mrow> <mi>c</mi> <mi>n</mi> </mrow> </msub> </semantics></math>, and (<b>b</b>) characteristic impedance, <math display="inline"><semantics> <msub> <mi>Z</mi> <mrow> <mn>0</mn> <mi>n</mi> </mrow> </msub> </semantics></math>, contours. The contours for <math display="inline"><semantics> <msub> <mi>ω</mi> <mrow> <mi>c</mi> <mi>n</mi> </mrow> </msub> </semantics></math> correspond to values of 2, 5, 10, and 20 pu, while those for <math display="inline"><semantics> <msub> <mi>Z</mi> <mrow> <mn>0</mn> <mi>n</mi> </mrow> </msub> </semantics></math> correspond to values of 0.5, 1, 1.5, and 2 pu.</p>
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<p>The unipolar PWM waveform’s harmonic spectrum and the gain properties of two different filter configuration sets. FC 1 and FC 3 correspond to first-order filter responses, while configurations 2 and 4 correspond to second-order responses. Each filter is characterized by a quality factor of 2 and a cutoff frequency set at 1 kHz, with the harmonic spectrum measured at a switching frequency of 4 kHz.</p>
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<p>The analysis of the filter gain for a 4 kHz unipolar PWM harmonic spectrum shows how varying the filter cutoff frequency affects (<b>a</b>) FC 1 and FC 3 and (<b>b</b>) FC 2 and FC 4. The cutoff frequencies are normalized with respect to the switching frequency, with normalized values of 0.1, 0.2, 0.5, and 1 pu. Additionally, the PWM harmonics are now presented up to only two times the switching frequency.</p>
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<p>Filter gain for configurations (<b>a</b>) FC 1 and FC 3 and (<b>b</b>) FC 2 and FC 4, on a 4 kHz unipolar PWM harmonic spectrum, demonstrating the impact of changing the filter quality factor. For every filter design, the quality factors of 1, 2, and 5 are displayed; additionally, the critically damped case, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>0.71</mn> <mspace width="3.33333pt"/> <mo>(</mo> <mn>1</mn> <mo>/</mo> <msqrt> <mn>2</mn> </msqrt> <mo>)</mo> </mrow> </semantics></math>, is displayed in (<b>b</b>). Note that 800 Hz is the cutoff frequency (0.2 pu, in relation to the <math display="inline"><semantics> <msub> <mi>f</mi> <mrow> <mi>s</mi> <mi>w</mi> </mrow> </msub> </semantics></math>).</p>
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<p>The relationship between the quality factor, filter cutoff frequency, and output current’s THD is illustrated for configurations (<b>a</b>) FC 1 and FC 3 and (<b>b</b>) FC 2 and FC 4. The quality factors, <span class="html-italic">Q</span>, of 1, 2, and 5 are depicted in the contours for all filter configurations. The critically damped case is also present in FC 2 and FC 4, which corresponds to a quality factor of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <msqrt> <mn>2</mn> </msqrt> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>According to the grid power factor limits, the real, <span class="html-italic">P</span>, reactive, <span class="html-italic">Q</span>, and apparent power, <span class="html-italic">S</span>, triangles display (<b>a</b>) 0.8 lead and (<b>b</b>) 0.95 lag.</p>
Full article ">Figure 12
<p>The real power, <span class="html-italic">P</span>, and reactive power, <span class="html-italic">Q</span>, of an undamped CL low-pass filter are displayed in the power flow diagram. The expression <math display="inline"><semantics> <mrow> <msub> <mi>Q</mi> <mi>L</mi> </msub> <mo>−</mo> <msub> <mi>Q</mi> <mi>C</mi> </msub> </mrow> </semantics></math> indicates that reactive power is either delivered or received by the grid. Additionally, the filter does not absorb real power.</p>
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<p>Normalized capacitance as a function of inductance, illustrating different cutoff frequencies and characteristic impedance values.The design region, indicated by the shaded area, limits inverter switching losses and meets specified power factor standards.</p>
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<p>THD and power loss contours of 5%, for filter configurations (<b>a</b>) 1, (<b>b</b>) 2, (<b>c</b>) 3, and (<b>d</b>) 4, at a 4 kHz switching frequency.</p>
Full article ">Figure 15
<p>THD and power loss contours of 2, 3, 4, and 5%, for filter configurations (<b>a</b>) 1, (<b>b</b>) 2, (<b>c</b>) 3 and (<b>d</b>) 4, considering a capacitance of 0.12 pu and a switching frequency of 4 kHz. <math display="inline"><semantics> <msub> <mi>P</mi> <mi>d</mi> </msub> </semantics></math> and THD for each filter can be decreased by increasing the quality factor, with FC 3 providing the smallest variance in both <span class="html-italic">Q</span> and <math display="inline"><semantics> <msub> <mi>P</mi> <mi>d</mi> </msub> </semantics></math>.</p>
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<p>Inverter circuit simulation using PSIM including PV array, DC link inductor, inverter (waveshaper and unfolding circuit), low-pass output filter, grid model, and control blocks (feedforward).</p>
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<p>CL filter flowchart for determining filter parameters.</p>
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<p>The output power of the CSI varies with (<b>a</b>) output current THD, (<b>b</b>) power factor, and (<b>c</b>) damping resistance power loss (<math display="inline"><semantics> <msub> <mi>P</mi> <mi>d</mi> </msub> </semantics></math>) as the normalized capacitance is changed for different irradiances. Here, <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>500</mn> </mrow> </semantics></math> Hz.</p>
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<p>The CSI output power is analyzed in relation to (<b>a</b>) output current THD, (<b>b</b>) PF, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>d</mi> </msub> </semantics></math>, with variations in cutoff frequency as a function of irradiance.</p>
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<p>The CSI output power is analyzed in relation to (<b>a</b>) output current THD, (<b>b</b>) power factor, and (<b>c</b>) <math display="inline"><semantics> <msub> <mi>P</mi> <mi>d</mi> </msub> </semantics></math>, by varying the quality factor across different irradiance levels. <math display="inline"><semantics> <msub> <mi>C</mi> <mi>n</mi> </msub> </semantics></math> = 0.1 pu and <math display="inline"><semantics> <msub> <mi>f</mi> <mi>c</mi> </msub> </semantics></math> = 500 Hz.</p>
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<p>The power loss due to damping resistance is analyzed for various total harmonic distortion (THD) values in relation to <math display="inline"><semantics> <msub> <mi>C</mi> <mi>n</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>f</mi> <mi>c</mi> </msub> </semantics></math>, and <span class="html-italic">Q</span>, specifically under rated output power conditions.</p>
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<p>Grid-connected inverter’s output voltage and current (<b>a</b>) simulation (<b>b</b>) measurement. The output filter configuration is FC 3. The parameters can be seen in <a href="#applsci-14-10131-t005" class="html-table">Table 5</a> and <a href="#applsci-14-10131-t006" class="html-table">Table 6</a>. The vertical and horizontal scales are 2 A, 20 V, and 5 ms per division.</p>
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<p>Measurment of the (<b>a</b>) output current with its (<b>b</b>) FFT spectrum. The output filter configuration is FC 3.</p>
Full article ">
15 pages, 3753 KiB  
Article
FPGA-Based High-Frequency Voltage Injection Sensorless Control with Novel Rotor Position Estimation Extraction for Permanent Magnet Synchronous Motor
by Indra Ferdiansyah and Tsuyoshi Hanamoto
World Electr. Veh. J. 2024, 15(11), 506; https://doi.org/10.3390/wevj15110506 - 5 Nov 2024
Viewed by 481
Abstract
This study developed a realization of sensorless control for a permanent magnet synchronous motor (PMSM) using a field-programmable gate array (FPGA). Both position and speed were estimated using a high-frequency (HF) injection scheme. Accurate estimation is essential to ensure the proper functioning of [...] Read more.
This study developed a realization of sensorless control for a permanent magnet synchronous motor (PMSM) using a field-programmable gate array (FPGA). Both position and speed were estimated using a high-frequency (HF) injection scheme. Accurate estimation is essential to ensure the proper functioning of sensorless motor control. To improve the estimation accuracy of the rotor position and reduce the motor speed ripple found in conventional methods, a new extraction strategy for estimating the rotor position and motor speed is proposed. First, signal modulation compensation was applied to expand the information of the error function in order to provide more accurate data to the tracking loop system for rotor position extraction. Second, to minimize the motor speed ripple caused by the HF injection, motor speed estimation was performed after obtaining the rotor position information using a differential equation with a low-pass filter (LPF) to avoid the direct effect of the injected signal. Verified experimentally, the results showed that the rotor position error did not exceed 10 el.deg, so these methods effectively reduce the rotor position estimation error by about 30%, along with the motor speed ripple. Therefore, better performance in sensorless PMSM control can be achieved in motor control applications. Full article
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Figure 1
<p>The correlation of rotor position between actual and estimated in a two-phase rotating reference frame.</p>
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<p>The extraction information of <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>Δ</mo> <mi>θ</mi> </mrow> </semantics></math> for tracking loop rotor position estimation: (<b>a</b>) without compensation in the modulation signal; (<b>b</b>) with compensation in the modulation signal.</p>
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<p>Rotor position estimation tracking loop systems: (<b>a</b>) conventional method; (<b>b</b>) proposed method.</p>
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<p>Motor speed estimation under 150 min<sup>−1</sup>: (<b>a</b>) conventional method; (<b>b</b>) proposed method.</p>
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<p>Vector control FPGA implementation architecture for sensorless PMSM.</p>
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<p>FPGA implementation hardware architecture for extraction of rotor position and motor speed.</p>
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<p>Experimental setup.</p>
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<p>Rotor position estimation under constant speed at 100 min<sup>−1</sup>: (<b>a</b>) steady-state performance with conventional method; (<b>b</b>) steady-state performance with proposed method.</p>
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<p>Rotor position estimation under sudden direction change from 100 min<sup>−1</sup> to −100 min<sup>−1</sup>: (<b>a</b>) conventional method; (<b>b</b>) proposed method.</p>
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<p>Rotor position estimation under sudden direction change of −100 min<sup>−1</sup> to 100 min<sup>−1</sup>: (<b>a</b>) conventional method; (<b>b</b>) proposed method.</p>
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<p>Rotor position estimation under 100 min<sup>−1</sup>: (<b>a</b>) rotor position estimation with a load of 0.3 Nm; (<b>b</b>) correlation of rotor position estimation error with varying load.</p>
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<p>Comparison of motor control step response 120 min<sup>−1</sup> to 150 min<sup>−1</sup> between sensored and sensorless systems: (<b>a</b>) sensored control; (<b>b</b>) proposed sensorless control.</p>
Full article ">
7 pages, 2057 KiB  
Article
Spectrum Broadening Due to Nonselective Linear Absorption
by Xingchu Zhang and Weilong She
Optics 2024, 5(4), 445-451; https://doi.org/10.3390/opt5040033 - 28 Oct 2024
Viewed by 398
Abstract
The position and linewidth of an emission spectrum reflect the physical properties of the luminophor. So, keeping the spectrum from distortion is very important in its measurement. However, we find that the spectrum linewidth will be broadened when the near-infrared radiation from a [...] Read more.
The position and linewidth of an emission spectrum reflect the physical properties of the luminophor. So, keeping the spectrum from distortion is very important in its measurement. However, we find that the spectrum linewidth will be broadened when the near-infrared radiation from a sodium lamp passes through a nonselective linear absorbing filter. This counterintuitive linewidth-broadening phenomenon is obvious when the residual light power after the filter is low enough, typically lower than 2.48×104 μW. This novel linewidth-broadening effect is different from the well-known Lorentzian, Doppler, and Voigt broadening, and is likely to be more independent evidence of the discrete wavelet structure of classical plane light waves. The effect is significant in high-sensitivity spectroscopy measurements, for example streak camera spectroscopy and Raman spectroscopy experiments. In addition, this effect may also be significant for cosmological research. Full article
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<p>The experimental setup.</p>
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<p>The transmittances of six types of nonselective linear absorption filters (Fs) vs. the wavelengths. The right is a photograph of F5 and F6, respectively.</p>
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<p>Spectrum broadening due to nonselective linear absorption at 706.62 nm. (<b>a</b>) The initial spectral line without passing through the filter; (<b>b</b>–<b>d</b>) the spectrum after passing through the filters with transmittances of <math display="inline"><semantics> <mrow> <mn>0.146</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> (<b>b</b>), <math display="inline"><semantics> <mrow> <mn>0.076</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>(<b>c</b>), and <math display="inline"><semantics> <mrow> <mn>0.048</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mo> </mo> </mrow> </semantics></math>(<b>d</b>), respectively. The points and lines denote the experimental measured data and the theoretical predictions, respectively.</p>
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<p>Spectrum broadening due to nonselective linear absorption at 727.17 nm. (<b>a</b>) The initial spectral line without passing through the filter; (<b>b</b>–<b>d</b>) the spectrum after passing through the filters with transmittances of <math display="inline"><semantics> <mrow> <mn>1.228</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> (<b>b</b>), <math display="inline"><semantics> <mrow> <mn>0.262</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> (<b>c</b>), and <math display="inline"><semantics> <mrow> <mn>0.131</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> (<b>d</b>), respectively. The points and lines denote the experimental measured data and the theoretical predictions, respectively.</p>
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<p>Spectrum broadening due to nonselective linear absorption at 738.27 nm. (<b>a</b>) The initial spectral line without passing through the filter; (<b>b</b>–<b>d</b>) the spectrum after passing through the filters with transmittances of <math display="inline"><semantics> <mrow> <mn>0.146</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math> (<b>b</b>), <math display="inline"><semantics> <mrow> <mn>0.097</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mo> </mo> </mrow> </semantics></math> (<b>c</b>), and <math display="inline"><semantics> <mrow> <mn>0.057</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> <mo> </mo> </mrow> </semantics></math> (<b>d</b>), respectively. The points and lines denote the experimental measured data and the theoretical predictions, respectively.</p>
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11 pages, 2767 KiB  
Article
A 2.4 GHz High-Efficiency Rectifier Circuit for Ambient Low Electromagnetic Power Harvesting
by Jinxin Du, Ruimeng Wang and Pingyi Zheng
Sensors 2024, 24(21), 6854; https://doi.org/10.3390/s24216854 - 25 Oct 2024
Viewed by 448
Abstract
A novel 2.4 GHz high-efficiency rectifier circuit suitable for working under very-low-input electromagnetic (EM) power conditions (−20 to −10 dBm) is proposed for typical indoor power harvesting. The circuit features a SMS7630 Schottky diode in a series with a voltage booster circuit at [...] Read more.
A novel 2.4 GHz high-efficiency rectifier circuit suitable for working under very-low-input electromagnetic (EM) power conditions (−20 to −10 dBm) is proposed for typical indoor power harvesting. The circuit features a SMS7630 Schottky diode in a series with a voltage booster circuit at the front end and a direct-current (DC)-pass filter at the back end. The voltage booster circuit consists of an asymmetric coupled transmission line (CTL) and a high-impedance microstrip line (of 100 Ω instead of 50 Ω) to significantly increase the potential at the diode’s input, thereby enabling the diode to operate effectively even in very-low-power environments. The experimental measurements show that the microwave direct-current (MW-DC) conversion efficiency of the rectifier circuit reaches 31.1% at a −20 dBm input power and 62.4% at a −10 dBm input power, representing a 7.4% improvement compared to that of the state of the art. Furthermore, the rectifier circuit successfully shifts the input power level corresponding to the peak rectification efficiency from 0 dBm down to −10 dBm. This design is a promising candidate for powering low-energy wireless sensors in typical indoor environments (e.g., the home or office) with low EM energy density. Full article
(This article belongs to the Special Issue Energy Harvesting Technologies for Wireless Sensors)
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<p>Design process of the rectifier circuit: (<b>a</b>) basic design with a 50 Ω microstrip line; (<b>b</b>) intermediate design with a CTL and a 50 Ω microstrip line; (<b>c</b>) final design with a CTL and a 100 Ω microstrip line.</p>
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<p>Transmission coefficient of the DC-pass filter.</p>
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<p>Two-port model of CTL [<a href="#B17-sensors-24-06854" class="html-bibr">17</a>].</p>
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<p>CTL voltage boosting effect as a function of the CTL linewidth ratio.</p>
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<p>Potential at the input and output ports of the CTL as a function of the input power for a CTL linewidth ratio of 8.</p>
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<p>MW−DC conversion efficiency of three rectifier circuits (simulated in ADS).</p>
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<p>(<b>a</b>) Reflection coefficient and (<b>b</b>) radiation pattern (at 2.4 GHz) of the receiving antenna.</p>
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<p>Experiment setup: (<b>a</b>) for measuring the DC output <span class="html-italic">V<sub>out</sub></span>; (<b>b</b>) for measuring the microwave power <span class="html-italic">P<sub>in</sub></span> captured by the rectenna.</p>
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<p>MW−DC conversion efficiency and DC voltage output of the final rectifier circuit.</p>
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<p>Evolution of the MW-DC conversion efficiency of the final rectifier circuit as a function of different incidence angles of microwaves.</p>
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18 pages, 6152 KiB  
Article
0.5 V, Low-Power Bulk-Driven Current Differencing Transconductance Amplifier
by Montree Kumngern, Fabian Khateb and Tomasz Kulej
Sensors 2024, 24(21), 6852; https://doi.org/10.3390/s24216852 - 25 Oct 2024
Viewed by 570
Abstract
This paper presents a novel low-power low-voltage current differencing transconductance amplifier (CDTA). To achieve a low-voltage low-power CDTA, the BD-MOST (bulk-driven MOS transistor) technique operating in a subthreshold region is used. The proposed CDTA is designed in 0.18 µm CMOS technology, can operate [...] Read more.
This paper presents a novel low-power low-voltage current differencing transconductance amplifier (CDTA). To achieve a low-voltage low-power CDTA, the BD-MOST (bulk-driven MOS transistor) technique operating in a subthreshold region is used. The proposed CDTA is designed in 0.18 µm CMOS technology, can operate with a supply voltage of 0.5 V, and consumes 1.05 μW of power. The proposed CDTA is used to realize a current-mode universal filter. The filter can realize five standard transfer functions of low-pass, band-pass, high-pass and band-stop, and all-pass from the same circuit. Neither component-matching conditions nor input signals of the inverse type are required to realize these filter functions. The current-mode filter offers low-input and high-output impedance and uses grounded capacitors. The natural frequency and quality factor of the filters can be orthogonally controlled. The proposed CDTA and its applications are simulated using SPICE to confirm the feasibility and functionality of the new circuits. Full article
(This article belongs to the Special Issue CMOS Integrated Circuits for Sensor Applications)
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<p>CDTA: (<b>a</b>) circuit symbol, (<b>b</b>) equivalent circuit.</p>
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<p>CMOS implementation of proposed 0.5 V CDTA.</p>
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<p>Proposed current-mode universal filter using CDTAs.</p>
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<p>Simulated DC characteristics I<sub>z</sub> versus I<sub>p</sub>, I<sub>n</sub> for V<sub>z</sub> = 0.</p>
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<p>Simulated DC characteristics V<sub>p</sub> against I<sub>p</sub> and V<sub>n</sub> against I<sub>n</sub>.</p>
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<p>Simulated frequency responses of current gains I<sub>z</sub>/I<sub>p</sub> and I<sub>z</sub>/I<sub>n</sub>.</p>
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<p>Simulated DC characteristics of TA when R<sub>set</sub> is varied.</p>
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<p>Frequency responses of the current gains I<sub>x</sub>/I<sub>p</sub> and I<sub>x</sub>/I<sub>n</sub> (R<sub>z</sub> = R<sub>set</sub> = 300 kΩ).</p>
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<p>Frequency dependence of the impedances of (<b>a</b>) p- and n-terminals, (<b>b</b>) z-terminal.</p>
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<p>Simulated magnitude frequency responses of LPF, HPF, BPF, and BSF.</p>
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<p>Simulated magnitude and phase frequency responses of APF.</p>
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<p>Simulated frequency responses of BPF when the resistors R<sub>set</sub> (R<sub>set</sub> = R<sub>set1</sub> = R<sub>set2</sub>) are changed.</p>
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<p>Simulated frequency responses of BPF when R<sub>2</sub> is varied.</p>
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<p>Simulated magnitude frequency response of the BPF when the parameters: (<b>a</b>) process, (<b>b</b>) supply voltage, and (<b>c</b>) temperature are varied.</p>
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<p>Simulated Monte Carlo analysis for the BPF with 1% tolerance of the capacitors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Simulated THD of the LPF, (<b>a</b>) input and output waveforms of 0.936% THD, (<b>b</b>) dependence of THD on the input signal magnitude.</p>
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<p>The equivalent output noise of the LPF.</p>
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14 pages, 5023 KiB  
Article
Experimental Calculation of Added Masses for the Accurate Construction of Airship Flight Models
by Deibi López, Diego Domínguez, Adrián Delgado, Adrián García-Gutiérrez and Jesús Gonzalo
Aerospace 2024, 11(11), 872; https://doi.org/10.3390/aerospace11110872 - 24 Oct 2024
Viewed by 467
Abstract
In recent years, interest in airships for cargo transport and stratospheric platforms has increased, necessitating accurate dynamic modeling for stability analysis, autopilot design, and mission planning, specifically through the calculation of stability derivatives, like added mass and inertia. Despite the several CFD methods [...] Read more.
In recent years, interest in airships for cargo transport and stratospheric platforms has increased, necessitating accurate dynamic modeling for stability analysis, autopilot design, and mission planning, specifically through the calculation of stability derivatives, like added mass and inertia. Despite the several CFD methods and analytical solutions available to calculate added masses, experimental validation remains essential. This study introduces a novel methodology to measure these in a wind tunnel, comparing the results with prior studies that utilized towing tanks. The approach involves designing the test model and a crank-slider mechanism to generate motion within the wind tunnel, considering load cell sensitivity, precision, frequency range, and Reynolds numbers. A revolution ellipsoid model, made from extruded polystyrene, was used to validate analytical solutions. The test model, measuring 1 m in length with an aspect ratio of 6, weighing 482 g, was moved along rails by the crank-slider system. By increasing the motion frequency, structural vibrations affecting load cell measurements were minimized. Proper signal processing, including high-pass filtering and second-order Fourier series fitting, enabled successful virtual mass calculation, showing only a 2.1% deviation from theoretical values, significantly improving on previous studies with higher relative errors. Full article
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<p>Reference system and sign convention for the angular position of the airship. For every axis, the linear velocity (<math display="inline"><semantics> <mrow> <mi>u</mi> <mo>,</mo> <mo> </mo> <mi>v</mi> <mo>,</mo> <mo> </mo> <mi>w</mi> </mrow> </semantics></math>), the angular position (<math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>,</mo> <mo> </mo> <mi>α</mi> <mo>,</mo> <mi>ψ</mi> </mrow> </semantics></math>) and the angular velocity (<math display="inline"><semantics> <mrow> <mi>P</mi> <mo>,</mo> <mo> </mo> <mi>Q</mi> <mo>,</mo> <mo> </mo> <mi>R</mi> </mrow> </semantics></math>) are defined.</p>
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<p>Parameter selection methodology for mobile device design and data collection.</p>
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<p>Analytical solution for an ellipsoid of revolution using values from <a href="#aerospace-11-00872-t001" class="html-table">Table 1</a>. <b>Left figure</b>: contribution of aerodynamic and virtual mass forces. <b>Right figure</b>: inertia force contribution to the net force of the experiment, considering all the influences.</p>
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<p>Hardware distribution: computer for motor control and data recording (1), data acquisition system (2), force sensor (3), stepper motor (4), mock-up model (5), crank-rod (6), slider or connecting rod (7), and guide rails (8).</p>
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<p>Detail of the load cell housing and the mock-up production.</p>
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<p>Mock-up configuration for lateral displacement. (<b>left</b>) figure: detail of the 3D model design. (<b>right</b>) physical implementation inside the wind tunnel.</p>
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<p>Data post-processing methodology.</p>
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<p>Spectral analysis (step 2): energy spectrum of the measured signal normalized with the total energy as function of the frequency.</p>
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<p>Signal filtering (step 4): original (blue) and filtered (red) signal.</p>
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<p>Fourier series fitting (step 6). Resulting signal (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>) after inertial removal and Fourier Series Adjustment following Equation (24). The black-dotted line represents the contribution of the virtual mass, given by the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>a</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>-term.</p>
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15 pages, 8152 KiB  
Article
Synthesis of High-Selectivity Two-Dimensional Filter Banks Using Sigmoidal Function
by Peter Apostolov
Electronics 2024, 13(21), 4146; https://doi.org/10.3390/electronics13214146 - 22 Oct 2024
Viewed by 540
Abstract
This paper explores an application of the sigmoidal function—the complementary integral Gaussian error function (erfc(.))—in two-dimensional (2D) uniform and nonuniform filter bank synthesis. The complementary integral Gaussian error function graph represents a smooth low-pass filter magnitude response. A parameter changes the function slope [...] Read more.
This paper explores an application of the sigmoidal function—the complementary integral Gaussian error function (erfc(.))—in two-dimensional (2D) uniform and nonuniform filter bank synthesis. The complementary integral Gaussian error function graph represents a smooth low-pass filter magnitude response. A parameter changes the function slope and increases the magnitude response selectivity. The theory is applied to 2D band-pass filter banks. Exact expressions for the magnitude response parameters are determined. As a result, 2D uniform and nonuniform filter banks with very high selectivity and exact shapes are obtained. Three synthesis examples of 2D filter banks with circular and fan-shaped magnitude responses are provided. The theoretical exposition is supplemented with two examples of image analysis using 2D uniform and nonuniform filter banks. A procedure to reduce the computations in image analysis is proposed. A comparison of filter synthesis between Parks–McLellan’s 2D filters and the erfc(.) demonstrates the significantly shorter calculation time of the proposed method. Full article
(This article belongs to the Section Circuit and Signal Processing)
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<p>Magnitude responses of the ideal low-pass filter and the magnitude response of the low-pass filter with the erfc(.).</p>
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<p>Magnitude response of BP filter.</p>
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<p>A 2D band-pass circular filter response in dB with the following parameters: <span class="html-italic">f<sub>t</sub></span> = 0.5, <span class="html-italic">β</span> = 385.3394, <span class="html-italic">BW</span> = 0.1, <span class="html-italic">δ</span> = −3.01 dB, and Δ<span class="html-italic">f</span> = 1/1000.</p>
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<p>Two-dimensional band-pass circular filter response in dB—top view.</p>
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<p>Two-dimensional circular filter banks: <span class="html-italic">N</span> = 11, <span class="html-italic">β</span> = 38.53393, <span class="html-italic">BW</span> = 0.1, <span class="html-italic">δ</span> = <math display="inline"><semantics> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mrow> <msqrt> <mn>2</mn> </msqrt> </mrow> </mrow> </mrow> </semantics></math>, and Δ<span class="html-italic">f</span> = 0.01.</p>
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<p>Diametral cut of 2D circular filter banks: <span class="html-italic">f<sub>x</sub></span> <math display="inline"><semantics> <mo>∈</mo> </semantics></math> [−1, 1]; <span class="html-italic">f<sub>y</sub></span> = 0.</p>
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<p>Two-dimensional fan filter banks: <span class="html-italic">N</span> = 11, <span class="html-italic">β</span> = 38.53393, <span class="html-italic">BW</span> = 0.1, <span class="html-italic">δ</span> = <math display="inline"><semantics> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mrow> <msqrt> <mn>2</mn> </msqrt> </mrow> </mrow> </mrow> </semantics></math>, and Δ<span class="html-italic">f</span> = 0.01.</p>
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<p>Two-dimensional axial fan filter banks: <span class="html-italic">N</span> = 11, <span class="html-italic">α</span> = π/11, <span class="html-italic">β</span> = 38.53393, <span class="html-italic">δ</span> = <math display="inline"><semantics> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mrow> <msqrt> <mn>2</mn> </msqrt> </mrow> </mrow> </mrow> </semantics></math>, and Δ<span class="html-italic">f</span> = 0.01.</p>
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<p>Two-dimensional axial fan filter bank: <span class="html-italic">N</span> = 1, <span class="html-italic">α</span> = <span class="html-italic">π</span>/11, <span class="html-italic">β</span> = 38.53393, <span class="html-italic">δ</span> = <math display="inline"><semantics> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mrow> <msqrt> <mn>2</mn> </msqrt> </mrow> </mrow> </mrow> </semantics></math>, <span class="html-italic">θ</span> = 0, and Δ<span class="html-italic">f</span> = 0.01.</p>
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<p>The magnitude response of the 2D nonuniform filter banks; <span class="html-italic">N</span> = 6.</p>
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<p>Diametral cut of 2D nonuniform filter banks; <span class="html-italic">N</span> = 6, <span class="html-italic">f<sub>x</sub></span> <math display="inline"><semantics> <mo>∈</mo> </semantics></math> [−1, 1], <span class="html-italic">f<sub>y</sub></span> = 0.</p>
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<p>Image analysis with 2D circular uniform filter banks; <span class="html-italic">N</span> = 11: (<b>a</b>) original image “Moon’s surface”; (<b>b</b>) low-pass filtered; (<b>c</b>–<b>k</b>) band-pass filtered with nine uniform BP filter banks; (<b>l</b>) high-pass filtered; (<b>m</b>) recovered image by summing the first two filter banks (<b>b</b>,<b>c</b>); (<b>n</b>) recovered image by summing the first three filter banks (<b>b</b>–<b>d</b>); (<b>o</b>) recovered image by summing the outputs of all filter banks (<b>b</b>–<b>l</b>).</p>
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<p>Image analysis using 2D circular nonuniform filter banks; <span class="html-italic">N</span> = 6. (<b>a</b>) original image; (<b>b</b>) low-pass filtered; (<b>c</b>–<b>f</b>) band-pass filtered with four nonuniform BP filter banks; (<b>g</b>) high-pass filtered; (<b>h</b>) recovered image by summing the outputs of all filter banks (<b>b</b>–<b>g</b>).</p>
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<p>Total computational time of 2D band pass circular filter synthesis with erfc(.).</p>
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<p>Total computational time of 2D band pass circular filter synthesis with Remez algorithm.</p>
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13 pages, 4137 KiB  
Article
Research on Through-Flame Imaging Using Mid-Wave Infrared Camera Based on Flame Filter
by Fengxun Zheng, Guodong Sun, Yanpeng Suo, Hao Ma and Tengxiao Feng
Sensors 2024, 24(20), 6696; https://doi.org/10.3390/s24206696 - 18 Oct 2024
Viewed by 540
Abstract
High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to [...] Read more.
High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target’s outline. Importantly, as the temperature of the target increases, the clarity of the target’s contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines. Full article
(This article belongs to the Section Sensing and Imaging)
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<p>(<b>a</b>) Infrared emission spectrum of a Bunsen burner flame [<a href="#B23-sensors-24-06696" class="html-bibr">23</a>]; (<b>b</b>) transmittance of CO<sub>2</sub>, CO, and H<sub>2</sub>O gasses.</p>
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<p>A schematic diagram of MWIR through-flame imaging.</p>
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<p>The transmittance curve of the flame filter.</p>
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<p>The FG390 camera (<b>a</b>) and its infrared image with temperature measurement (<b>b</b>).</p>
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<p>The experimental composition of MWIR through-flame imaging.</p>
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<p>A 3D view of the alcohol lamp flame.</p>
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<p>The visible-light images (<b>a</b>–<b>f</b>) and infrared images (<b>g</b>–<b>l</b>) at different target temperatures of 20 °C to 300 °C.</p>
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<p>Comparison of wide-band infrared image (<b>a</b>) and through-flame image (<b>b</b>).</p>
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<p>Six 3D views at different temperature levels, illustrating the variations in the raw data (digital number value) across the 80 × 80 pixel area of the alcohol lamp’s outer flame and the target.</p>
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<p>Curve of target–flame ratio (TFR) versus target temperature.</p>
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<p>A visible-light image (<b>a</b>) and an infrared image (<b>b</b>) of pipelines in a tube furnace captured by the through-flame imager.</p>
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25 pages, 6348 KiB  
Article
1-V Mixed-Mode Universal Filter Using Differential Difference Current Conveyor Transconductance Amplifiers
by Montree Kumngern, Fabian Khateb and Tomasz Kulej
Appl. Sci. 2024, 14(20), 9422; https://doi.org/10.3390/app14209422 - 16 Oct 2024
Viewed by 597
Abstract
This paper presents a mixed-mode universal filter using differential difference current conveyor transconductance amplifiers (DDCCTA). Despite using a minimum number of MOS differential pairs, the proposed DDCCTA is a multiple-input, multiple-output device, that was achieved using the multiple-input bulk-driven MOS transistor (MIBD-MOST) technique, [...] Read more.
This paper presents a mixed-mode universal filter using differential difference current conveyor transconductance amplifiers (DDCCTA). Despite using a minimum number of MOS differential pairs, the proposed DDCCTA is a multiple-input, multiple-output device, that was achieved using the multiple-input bulk-driven MOS transistor (MIBD-MOST) technique, multiple-output current followers and transconductance gains. A subthreshold technique is used to achieve minimum power consumption of the DDCCTA. Thanks to the multiple-input and multiple-output of DDCCTA, the mixed-mode universal filter based on the proposed element can realize five standard filter responses, i.e., low-pass, high-pass, band-pass, band-stop, and all-pass responses, of four modes, i.e., voltage-mode, current-mode, transadmittance-mode, and transimpedance-mode, thus providing 194 filter responses from a single circuit. The natural frequency and quality factor of the filter response can be controlled electronically and orthogonally. The proposed DDCCTA and mixed-mode universal filter are simulated and designed using 0.18 μm CMOS technology to confirm the functionality of the new circuit. The mixed-mode universal filter uses ±0.5 V of supply voltage and consumes 0.374 mW of power when operating at a natural frequency of 10 kHz. Full article
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<p>Proposed DDCCTA: (<b>a</b>) CMOS structure, (<b>b</b>) its electrical symbol.</p>
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<p>MIBD-MOST: (<b>a</b>) symbol, (<b>b</b>) implementation of MIBD-MOST, (<b>c</b>) implementation of large resistance RMOS.</p>
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<p>Proposed mixed-mode universal filter using DDCCTAs.</p>
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<p>DC transfer characteristics: (<b>a</b>) V<sub>x</sub> against V<sub>y+</sub> and its error, (<b>b</b>) V<sub>x</sub> against V<sub>y−</sub> and its error.</p>
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<p>Simulated transconductance characteristic with different setting currents: (<b>a</b>) DC characteristic, (<b>b</b>) AC characteristic.</p>
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<p>The parasitic impedances at x-, z- and o-terminals of the DDCCTA.</p>
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<p>The simulated magnitude and phase frequency responses of VM for: (<b>a</b>) LPF, HPF, BPF, BSP, (<b>b</b>) APF.</p>
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<p>The simulated magnitude and phase frequency responses of CM for: (<b>a</b>) LPF, HPF, BPF, BSP, (<b>b</b>) APF.</p>
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<p>The simulated magnitude and phase frequency responses of TAM for: (<b>a</b>) LPF, HPF, BPF, BSP, (<b>b</b>) APF.</p>
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<p>The simulated magnitude and phase frequency responses of TIM for: (<b>a</b>) LPF, HPF, BPF, BSP, (<b>b</b>) APF.</p>
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<p>The simulated frequency response of BPF for: (<b>a</b>) the natural frequency was varied by I<sub>set</sub> (I<sub>set</sub> = I<sub>set1</sub> = I<sub>set2</sub>), (<b>b</b>) the quality factor was varied by I<sub>set3</sub>.</p>
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<p>The simulated frequency response of the BPF when the process was varied: (<b>a</b>) VM, (<b>b</b>) CM.</p>
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<p>The simulated frequency response of the BPF when the supply voltage was varied: (<b>a</b>) VM, (<b>b</b>) CM.</p>
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<p>The simulated frequency response of the BPF when the temperature was varied: (<b>a</b>) VM, (<b>b</b>) CM.</p>
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<p>Simulated Monte-Carlo analysis for the BPF with 15% tolerance of the capacitors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>: (<b>a</b>) histogram for VM, (<b>b</b>) histogram for CM.</p>
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<p>Simulated THD results of LPF: (<b>a</b>) THD with different voltage input amplitude of VM, (<b>b</b>) THD with different current input amplitude of CM, (<b>c</b>) input and output waveforms of 0.983% THD of VM, (<b>d</b>) input and output waveforms of 1.05% THD of CM.</p>
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<p>The equivalent output noises of LPF: (<b>a</b>) VM, (<b>b</b>) CM.</p>
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13 pages, 4219 KiB  
Article
Dynamic Sliding Mode Control of Spherical Bubble for Cavitation Suppression
by Ali Karami-Mollaee and Oscar Barambones
Axioms 2024, 13(10), 706; https://doi.org/10.3390/axioms13100706 - 13 Oct 2024
Viewed by 552
Abstract
Cavitation is a disadvantageous phenomenon that occurs when fluid pressure drops below its vapor pressure. Under these conditions, bubbles form in the fluid. When these bubbles flow into a high-pressure area or tube, they erupt, causing harm to mechanical parts such as centrifugal [...] Read more.
Cavitation is a disadvantageous phenomenon that occurs when fluid pressure drops below its vapor pressure. Under these conditions, bubbles form in the fluid. When these bubbles flow into a high-pressure area or tube, they erupt, causing harm to mechanical parts such as centrifugal pumps. The difference in pressure in a fluid is the result of varying temperatures. One way to eliminate cavitation is to reduce the radius of the bubbles to zero before they reach high-pressure areas, using a robust approach. In this paper, sliding mode control is used for this purpose due to its invariance property. To force the radius of the bubbles toward zero and prevent chattering, a new dynamic sliding mode control approach is used. In dynamic sliding mode control, chattering is removed by passing the input control through a low-pass filter, such as an integrator. A general model of the spherical bubble is used, transferred to the state space, and then a state proportional-integral feedback is applied to obtain a linear system with a new input control signal. A comparison is also made with traditional sliding mode control using state feedback, providing a trusted comparison. Full article
(This article belongs to the Special Issue New Perspectives in Control Theory)
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<p>DSMC proposed approach structure.</p>
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<p>The radius of the bubble in DSMC for Air.</p>
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<p>The ultrasonic wave in DSMC for Air.</p>
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<p>The sliding surface in DSMC for Air.</p>
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<p>The radius of the bubble in DSMC for Argon.</p>
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<p>The ultrasonic wave in DSMC for Argon.</p>
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<p>The sliding surface in DSMC for Argon.</p>
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<p>TSMC proposed approach structure.</p>
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<p>The radius of the bubble in TSMC for Air.</p>
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<p>The ultrasonic wave in TSMC for Air.</p>
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<p>The sliding surface in TSMC for Air.</p>
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17 pages, 7887 KiB  
Article
Integrated Precision High-Frequency Signal Conditioner for Variable Impedance Sensors
by Miodrag Brkić, Jelena Radić, Kalman Babković and Mirjana Damnjanović
Sensors 2024, 24(20), 6501; https://doi.org/10.3390/s24206501 - 10 Oct 2024
Viewed by 594
Abstract
In this paper, a signal conditioner intended for use in variable impedance sensors is presented. First, an inductive linear displacement sensor design is described, and the signal conditioner discrete realization is presented. Second, based on this system’s requirements, the integrated conditioner is proposed. [...] Read more.
In this paper, a signal conditioner intended for use in variable impedance sensors is presented. First, an inductive linear displacement sensor design is described, and the signal conditioner discrete realization is presented. Second, based on this system’s requirements, the integrated conditioner is proposed. The conditioner comprises an amplifier, a tunable band-pass filter, and a precision high-frequency AC-DC converter. It is designed in a low-cost AMS 0.35 µm CMOS process. The presented conditioner measures the sensor’s impedance magnitude by using a simplified variation of the sensor voltage and current vector measurement. It can be used for the real-time measurement of fast sensors, having small output impedance. The post-layout simulation results show that the integrated conditioner has an inductance measurement range from 10 nH to 550 nH with a nonlinearity of 1.2%. The operating frequency in this case was 8 MHz, but the circuit can be easily adjusted to different operating frequencies (due to the tunable filter). The designed IC area is 500 × 330 μm2, and the total power consumption is 93.8 mW. Full article
(This article belongs to the Special Issue CMOS Integrated Circuits for Sensor Applications)
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<p>(<b>a</b>) Sensor element for the detection of x-displacement. (<b>b</b>) Measurement setup with the discrete signal conditioner.</p>
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<p>Conditioner block diagram.</p>
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<p>Schematic of the fully differential folded amplifier.</p>
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<p>Schematic of the CMFB circuit.</p>
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<p>Schematic of the differential Tow–Thomas biquad filter in MOSFET-C topology.</p>
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<p>Schematic of the filter tuning circuit.</p>
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<p>Schematic of the proposed Gm voltage current converter (conveyor).</p>
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<p>Schematic of the CMOS AB current rectifier.</p>
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<p>Comparison of the conditioner discrete realization measurement to the impedance analyzer measurement.</p>
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<p>The integrated conditioner layout.</p>
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<p>Post-layout simulation results: amplitude and phase characteristics of the designed operational amplifier in a closed-loop (gain of 2) configuration.</p>
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<p>Post-layout simulation results: (<b>a</b>) LPF and BPF amplitude characteristics, (<b>b</b>) control of the BPF center frequency by the external resistor, and (<b>c</b>) BPF amplitude characteristics at the temperature change.</p>
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<p>Post-layout simulation results: (<b>a</b>) LPF and BPF amplitude characteristics, (<b>b</b>) control of the BPF center frequency by the external resistor, and (<b>c</b>) BPF amplitude characteristics at the temperature change.</p>
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<p>Post-layout simulation results: V<sub>od</sub> output signal of the CMOS AC-DC converter.</p>
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<p>Post-layout simulation results: transfer function of the proposed AC-DC converter.</p>
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<p>Post-layout simulation results: dependence of the integrated conditioner output voltage on the input impedance.</p>
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<p>Post-layout simulation results: the integrated conditioner input-referred noise.</p>
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25 pages, 9761 KiB  
Article
Robust Indoor Positioning with Smartphone by Utilizing Encoded Chirp Acoustic Signal
by Bingbing Cheng, Ying Huang and Chuanyi Zou
Sensors 2024, 24(19), 6332; https://doi.org/10.3390/s24196332 - 30 Sep 2024
Viewed by 670
Abstract
Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low [...] Read more.
Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods. Full article
(This article belongs to the Section Navigation and Positioning)
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<p>Acoustic positioning system for a smartphone.</p>
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<p>Hardware frame diagram (<b>a</b>) Acoustic node hardware architecture; (<b>b</b>) Scheduler hardware architecture.</p>
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<p>Frequency division multiplexing-chirp spread spectrum (FDM-CSS).</p>
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<p>Flowchart of TDOA-based positioning.</p>
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<p>The schematic diagram of FIR-MF detector.</p>
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<p>MF result within LOS condition and weak multipath.</p>
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<p>Spectral subtraction flowchart.</p>
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<p>Coarse detection result in different conditions (<b>a</b>) Coarse detection result within LOS condition; (<b>b</b>) Coarse detection result within NLOS condition.</p>
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<p>Experiment in different conditions (<b>a</b>) Experiment within LOS condition; (<b>b</b>) Experiment within NLOS condition.</p>
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<p>Multiple threshold extracting results in different conditions (<b>a</b>) Multiple threshold extracting results within LOS condition (red asterisk represents <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>T</mi> <mi>q</mi> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mfenced> </mrow> </semantics></math>); (<b>b</b>) Multiple threshold extracting results within NLOS condition (red asterisk represents <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>T</mi> <mi>q</mi> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </mfenced> </mrow> </semantics></math>).</p>
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<p>Normalized results in different conditions (<b>a</b>) Normalized results within LOS condition; (<b>b</b>) Normalized results within NLOS condition.</p>
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<p>Experiment scene 2 diagram.</p>
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<p>Threshold estimation experiment (corridor scene).</p>
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<p>Group experiments under line-of-sight conditions and non-line-of-sight conditions: (<b>a</b>) acoustic nodes are within LOS condition; (<b>b</b>) acoustic node 1 is under LOS condition, and acoustic node 3 is under NLOS condition.</p>
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<p>Ranging error in different ER.</p>
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<p>Distribution of acoustic nodes and test points in experiment scene 1.</p>
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<p>Average measurement error of RDOA in region 1 of scenario 1: (<b>a</b>) RDOA measurement average error in region 1 (using prosed method); (<b>b</b>) RDOA measurement average error in region 1 (using MF-max method).</p>
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<p>Average measurement error of RDOA in region 1 of scene 1: (<b>a</b>) RDOA measurement average error in region 1 (using MF-0.2 method); (<b>b</b>) RDOA measurement average error in region 1 (using MF-0.3 method).</p>
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<p>CDF of the RDOA measurement errors in region 2 of scene 1: (<b>a</b>) CDF of the RDOA measurement errors in region 2 (using proposed method); (<b>b</b>) CDF of the RDOA measurement errors in region 2 (using MF-max method).</p>
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<p>CDF of the RDOA measurement errors in region 2 of scene 1: (<b>a</b>) CDF of the RDOA measurement errors in region 2 (using MF-0.2 method); (<b>b</b>) CDF of the RDOA measurement errors in region 2 (using MF-0.3 method).</p>
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<p>Distribution of acoustic nodes and test points in experiment scene 2.</p>
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15 pages, 4946 KiB  
Article
Noise Analysis and Suppression Methods for the Front-End Readout Circuit of a Microelectromechanical Systems Gyroscope
by Chunhua He, Yingyu Xu, Xiaoman Wang, Heng Wu, Lianglun Cheng, Guizhen Yan and Qinwen Huang
Sensors 2024, 24(19), 6283; https://doi.org/10.3390/s24196283 - 28 Sep 2024
Viewed by 3097
Abstract
Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit [...] Read more.
Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. First, the noise analysis of the front-end readout circuit is carried out with theoretical derivation to clarify the main noise contributors. To suppress the output noise, an improved readout circuit based on the T-resistor networks is proposed, and the corresponding noise equation is derived in detail. In addition, the noise analysis of the critical circuits of the detection and control system, such as the inverting amplifiers, the first-order low-pass filters, and the first-order high-pass filters, is carried out, and the noise suppression strategy with the optimization of the resistances and is proposed. Taking the inverting amplifier as an example, the theoretical derivation is verified by measuring and comparing the output noises of different resistance schemes. In addition, the output noises of the gyroscope before and after circuit optimization are measured. Experimental results demonstrate that the output noise with the circuit optimization is reduced from 60 μV/Hz1/2 to 30 μV/Hz1/2 and the bias instability is reduced from 3.8 deg/h to 1.38 deg/h. In addition, the ARW is significantly improved from 0.035 deg/h1/2 to 0.018 deg/h1/2, which indicates that the proposed noise analysis and suppression methods are effective and feasible. Full article
(This article belongs to the Special Issue Smart Sensors and Integration Technology for MEMS Devices)
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<p>Schematic of the front-end readout circuit with feed-forward coupling compensation control.</p>
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<p>Noise model for a front-end readout circuit with feed-forward coupling compensation control.</p>
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<p>Schematic of an improved front-end readout circuit based on T-resistor networks.</p>
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<p>Noise analysis for an improved front-end readout circuit based on T-resistor networks.</p>
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<p>(<b>a</b>) Schematic of the inverting amplifier; (<b>b</b>) noise analysis for the inverting amplifier.</p>
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<p>(<b>a</b>) Noise analysis for the first-order low-pass filter; (<b>b</b>) noise analysis for the first-order high-pass filter.</p>
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<p>The output noise signal is measured by LabVIEW software when <span class="html-italic">R<sub>i</sub></span>, <span class="html-italic">R<sub>f</sub></span>, and <span class="html-italic">R<sub>c</sub></span> are adjusted.</p>
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<p>The detection and control system for an MEMS gyroscope.</p>
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<p>The output noise spectrums of the gyroscope before and after circuit optimization.</p>
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<p>Allan variance curves of the gyroscope before and after circuit optimization.</p>
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