scholarly journals A Blind Calibration Model for I/Q Imbalances of Wideband Zero-IF Receivers

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1868
Author(s):  
Xiaoye Peng ◽  
Zhiyu Wang ◽  
Jiongjiong Mo ◽  
Chenge Wang ◽  
Jiarui Liu ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Finite Impulse Response ◽  
Low Cost ◽  
Classification Rule ◽  
Statistical Characteristics ◽  
High Signal ◽  
Calibration Model ◽  
Frequency Dependent ◽  
Frequency Independent ◽  
Blind Calibration

Frequency-dependent I/Q imbalance and frequency-independent I/Q imbalance are the major impairments in wideband zero-IF receivers, and they both cannot be ignored. In this paper, a blind calibration model is designed for compensating these I/Q imbalances. In order to accurately estimate the imbalance parameters with low cost, a classification rule is proposed according to the frequency-domain statistical characteristics of the received signal. The calibration points in the frequency-domain are divided into two groups. Then, the amplitude imbalance and the frequency-dependent phase imbalance are derived from the group of signal points and, separately, the frequency-independent phase imbalance is calculated from the group of noise points. In the derivation of the frequency-dependent phase imbalance, a general fitting model suitable for all signal points is proposed, which does not require special calculations for either DC point or fs/2 point. Then, a finite impulse response (FIR) real-valued filter is designed to correct the impairments of received signal. The performances of the proposed calibration model are evaluated through both simulations and experiments. The simulation results show the image rejection ratio (IRR) improvement to around 35–45 dBc at high signal-to-noise ratio (SNR). Based on the mismatched data of the ADRV9009 evaluation board, the experimental results exhibit the IRR improvement of both multi-tone and wideband signals to about 30 dBc.

Sugar “Imaging” of Fruit Using a Low Cost Charge-Coupled Device Camera

2005 ◽  
Vol 13 (4) ◽  
pp. 177-186 ◽  
Author(s):  
Robert L. Long ◽  
Kerry B. Walsh ◽  
Colin V. Greensill
Keyword(s):  
Sucrose Concentration ◽  
Low Cost ◽  
Soluble Sugars ◽  
Model Performance ◽  
Ccd Camera ◽  
High Signal ◽  
Calibration Model ◽  
Signal To Noise ◽  
Charge Coupled Device ◽  
Colour Image

Sugar “imaging” of fruit has previously been reported using NIR filters and relatively expensive (high signal-to-noise) charge-coupled device (CCD) instrumentation. In a bid to use lower cost CCD instrumentation (criterion of less than AU $5,000 total component costs), the signal-to-noise constraint on calibration model performance was investigated by artificially degrading spectra from a 15-bit AtoD system. A low cost 8-bit CCD camera was then used in conjunction with a filter wheel in a transmittance configuration employing three 50 W halogen lamps. Multiple linear regregression calibrations were developed based on absorbance data of five wavelengths (830, 850, 870, 905 and 930 nm) relevant to sugar and water. Calibration models for the sucrose concentration of solutions on a cellulose matrix were poor ( R2 = 0.4) when based on individual pixel data, but acceptable ( R2 = 0.98, RMSECV = 1.1) ( n = 20, mean = 13.9% total soluble sugars (TSS), SD = 6.04) when based on an average of a 23 × 23 pixel block (i.e. 529 pixels). For a calibration based on melon tissue TSS, using spectral data averaged over groups of 529 pixels, results were poorer than expected ( R2 = 0.4, RMSEP = 1.74 ( n = 163, mean = 9.45, SD = 2.07% TSS). Predicted TSS output for all pixel blocks from an image was used to generate a false colour image. We conclude that this application requires a higher level of signal-to-noise (for example, 10-bit, > 60 dB CCD).

scholarly journals Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 645
Author(s):  
Kristen Okorn ◽  
Michael Hannigan
Keyword(s):  
Metal Oxide ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor System ◽  
Physical Factors ◽  
Time Averaging ◽  
Calibration Model ◽  
Sensor Signal ◽  
Metal Oxide Sensors ◽  
Signal Normalization

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.

Time-Domain Nonlinear SSI Analysis of Foundation Sliding Using Frequency-Dependent Foundation Impedance Derived From SASSI

2008 ◽  
Author(s):  
Mansour Tabatabaie ◽  
Thomas Ballard
Keyword(s):  
Frequency Domain ◽  
Linear Systems ◽  
Time Domain ◽  
Soil Structure ◽  
Wave Radiation ◽  
Far Field ◽  
Frequency Dependent ◽  
Nonlinear Springs ◽  
Mat Foundation ◽  
The Time Domain

Dynamic soil-structure interaction (SSI) analysis of nuclear power plants is often performed in frequency domain using programs such as SASSI [1]. This enables the analyst to properly a) address the effects of wave radiation in an unbounded soil media, b) incorporate strain-compatible soil shear modulus and damping properties and c) specify input motion in the free field using the de-convolution method and/or spatially variable ground motions. For structures that exhibit nonlinearities such as potential base sliding and/or uplift, the frequency-domain procedure is not applicable as it is limited to linear systems. For such problems, it is necessary to solve the problem in the time domain using the direct integration method in programs such as ADINA [2]. The authors recently introduced a sub-structuring technique called distributed parameter foundation impedance (DPFI) model that allows the structure to be partitioned from the total SSI system and analyzed in the time domain while the foundation soil is modeled using the frequency-domain procedure [3]. This procedure has been validated for linear systems. In this paper we have expanded the DPFI model to incorporate nonlinearities at the soil/structure interface by introducing nonlinear shear and normal springs arranged in series between the DPFI and structure model. This combination of the linear far-field impedance (DPFI) plus nonlinear near-field soil springs allows the foundation sliding and/or uplift behavior be analyzed in time domain while maintaining the frequency-dependent stiffness and radiation damping nature of the far-field foundation impedance. To check the accuracy of this procedure, a typical NPP foundation mat supported at the surface of a layered soil system and subjected to harmonic forced vibration was first analyzed in the frequency domain using SASSI to calculate the target linear response and derive a linear, far-field DPFI model. The target linear solution was then used to validate two linear time-domain ADINA models: Model 1 consisting of the mat foundation+DPFI derived from the linear SASSI model and Model 2 consisting of the total SSI system (mat foundation plus a soil block). After linear alignment, the nonlinear springs were added to both ADINA models and re-analyzed in time domain. Model 2 provided the target nonlinear solution while Model 1 provided the results using the DPFI+nonlinear springs. By increasing the amplitude of the vibration load, different levels of foundation sliding were simulated. Good agreement between the results of two models in terms of the displacement response of the mat and cyclic force-displacement behavior of the springs validates the accuracy of the procedure presented herein.

Impact of Frequency Dependence of Gas Labyrinth Seal Rotordynamic Coefficients on Centrifugal Compressor Stability

2010 ◽  
Author(s):  
Giuseppe Vannini ◽  
Manish R. Thorat ◽  
Dara W. Childs ◽  
Mirko Libraschi
Keyword(s):  
Molecular Weight ◽  
Numerical Model ◽  
Control Volume ◽  
Labyrinth Seal ◽  
Labyrinth Seals ◽  
Frequency Dependent ◽  
Rotordynamic Coefficients ◽  
Frequency Independent ◽  
Rotor Surface ◽  
Independent Model

A numerical model developed by Thorat & Childs [1] has indicated that the conventional frequency independent model for labyrinth seals is invalid for rotor surface velocities reaching a significant fraction of Mach 1. A theoretical one-control-volume (1CV) model based on a leakage equation that yields a reasonably good comparison with experimental results is considered in the present analysis. The numerical model yields frequency-dependent rotordynamic coefficients for the seal. Three real centrifugal compressors are analyzed to compare stability predictions with and without frequency-dependent labyrinth seal model. Three different compressor services are selected to have a comprehensive scenario in terms of pressure and molecular weight (MW). The molecular weight is very important for Mach number calculation and consequently for the frequency dependent nature of the coefficients. A hydrogen recycle application with MW around 8, a natural gas application with MW around 18, and finally a propane application with molecular weight around 44 are selected for this comparison. Useful indications on the applicability range of frequency dependent coefficients are given.

scholarly journals Wind Turbine Noise Reduction from Seismological Data

2021 ◽  
Author(s):  
Janis Heuel ◽  
Wolfgang Friederich
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Computational Cost ◽  
Noise Signal ◽  
Noise Model ◽  
Threshold Function ◽  
High Signal ◽  
Time Data ◽  
Negative Effects ◽  
Before And After

<p>Over the last years, installations of wind turbines (WTs) increased worldwide. Owing to<br>negative effects on humans, WTs are often installed in areas with low population density.<br>Because of low anthropogenic noise, these areas are also well suited for sites of<br>seismological stations. As a consequence, WTs are often installed in the same areas as<br>seismological stations. By comparing the noise in recorded data before and after<br>installation of WTs, seismologists noticed a substantial worsening of station quality leading<br>to conflicts between the operators of WTs and earthquake services.</p><p>In this study, we compare different techniques to reduce or eliminate the disturbing signal<br>from WTs at seismological stations. For this purpose, we selected a seismological station<br>that shows a significant correlation between the power spectral density and the hourly<br>windspeed measurements. Usually, spectral filtering is used to suppress noise in seismic<br>data processing. However, this approach is not effective when noise and signal have<br>overlapping frequency bands which is the case for WT noise. As a first method, we applied<br>the continuous wavelet transform (CWT) on our data to obtain a time-scale representation.<br>From this representation, we estimated a noise threshold function (Langston & Mousavi,<br>2019) either from noise before the theoretical P-arrival (pre-noise) or using a noise signal<br>from the past with similar ground velocity conditions at the surrounding WTs. Therefore, we<br>installed low cost seismometers at the surrounding WTs to find similar signals at each WT.<br>From these similar signals, we obtain a noise model at the seismological station, which is<br>used to estimate the threshold function. As a second method, we used a denoising<br>autoencoder (DAE) that learns mapping functions to distinguish between noise and signal<br>(Zhu et al., 2019).</p><p>In our tests, the threshold function performs well when the event is visible in the raw or<br>spectral filtered data, but it fails when WT noise dominates and the event is hidden. In<br>these cases, the DAE removes the WT noise from the data. However, the DAE must be<br>trained with typical noise samples and high signal-to-noise ratio events to distinguish<br>between signal and interfering noise. Using the threshold function and pre-noise can be<br>applied immediately on real-time data and has a low computational cost. Using a noise<br>model from our prerecorded database at the seismological station does not improve the<br>result and it is more time consuming to find similar ground velocity conditions at the<br>surrounding WTs.</p>

DEVELOPEMENT AN ALGORITM FOR RECOGNIZING TEXT DATA IN DIGITAL GRAPHIC IMAGES

2021 ◽  
pp. 75-78
Author(s):  
Н.Н. Беляев ◽  
О.А. Бебенина ◽  
В.Е. Бородкина
Keyword(s):  
Frequency Domain ◽  
Recognition Algorithm ◽  
Statistical Characteristics ◽  
Discrete Cosine Transformation ◽  
Text Data ◽  
Jpeg Images ◽  
Statistical Classifiers ◽  
Distribution Of Values ◽  
Data Content ◽  
Graphic Images

Предложен алгоритм распознавания, реализующий процедуры: обучения выбранных классификаторов и распознавания текстовых данных, учитывающие статистические характеристики распределения коэффициентов частотной области цифровых графических изображениях формата JPEG. The article presents an approach to development an algorithm for recognizing text data within JPEG format digital graphic images. Considered a hypothesis about influence text data content in JPEG digital graphic images on the distribution of values of the discrete cosine transformation coefficients in the frequency domain JPEG images of the format. Statistical classifiers models that provide a solution to the problem of recognition of text data in JPEG images based on analysis of its frequency domain have been determined. A recognition algorithm is proposed that implements the following procedures: training of selected classifiers and recognition of text data, taking into account the statistical characteristics of the distribution of frequency domain coefficients in JPEG format images.

Internally Modulated Chirped Pulse Based Direct Detection Type φ-OTDR System with High Signal-to-Noise Ratio and Low Cost

2019 ◽  
Vol 46 (8) ◽  
pp. 0806003
Author(s):  
李鲁川 Luchuan Li ◽  
卢斌 Bin Lu ◽  
王校 Xiao Wang ◽  
梁嘉靖 Jiajing Liang ◽  
郑汉荣 Hanrong Zheng ◽  
...  
Keyword(s):  
Direct Detection ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
High Signal ◽  
Signal To Noise ◽  
Chirped Pulse ◽  
Noise Ratio

scholarly journals Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring

2019 ◽  
Vol 12 (2) ◽  
pp. 903-920 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Sriniwasa P. N. Kumar ◽  
Naomi Zimmerman ◽  
...  
Keyword(s):  
Electrochemical Sensors ◽  
Low Cost ◽  
Air Pollutant ◽  
Calibration Model ◽  
Linear Regression Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Calibration Models ◽  
New Models

Abstract. Assessing the intracity spatial distribution and temporal variability in air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and hybrid random forest–linear regression models. Using data collected by almost 70 RAMP monitors over periods ranging up to 18 months, we recommend the use of limited quadratic regression calibration models for CO, neural network models for NO, and hybrid models for NO2 and O3 for any low-cost monitor using electrochemical sensors similar to those of the RAMP. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. Generalized models also transfer better when the RAMP is deployed to other locations. For long-term deployments, it is recommended that model performance be re-evaluated and new models developed periodically, due to the noticeable change in performance over periods of a year or more. This makes generalized calibration models even more useful since only a subset of deployed monitors are needed to build these new models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

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