Improve Calibration Accuracy Research Articles

Weighted-Combining Calibration on Multiuser MIMO Systems with Implicit Feedback

Implicit feedback is an approach that utilizes uplink channel state information (CSI) for downlink transmit beamforming on multiple-input multiple-output (MIMO) systems, relying on over-the-air channel reciprocity. The implicit feedback improves throughput efficiency because overhead of CSI feedback for change of over-the-air channel responses is omitted. However, it is necessary for the implicit feedback to calibrate circuitry responses that uplink CSI includes, because actual downlink and uplink channel responses do not match due to different transmit and receive circuitry chains. This paper presents our proposed calibration scheme, weighted-combining calibration (WCC); it offers improved calibration accuracy. In WCC, an access point (AP) calculates multiple calibration coefficients from ratios of downlink and uplink CSI, and then combines coefficients with minimum mean square error (MMSE) weights. The weights are derived using a linear approximation in the high signal to noise power ratio (SNR) regime. Analytical mean square error (MSE) of calibration coefficients with WCC and calibration schemes for comparison is expressed based on the linear approximation. Computer simulations show that the analytical MSE matches simulated one if the linear approximation holds, and that WCC improves the MSE and signal to interference plus noise power ratio (SINR). Indoor experiments are performed on a multiuser MIMO system with implicit feedback based on orthogonal frequency division multiplexing (OFDM), built using measurement hardware. Experimental results verify that the channel reciprocity can be exploited on the developed multiuser MIMO-OFDM system and that WCC is also effective in indoor environments. key words: multiple-input multiple-output (MIMO), channel reciprocity, implicit feedback, calibration, minimum mean square error (MMSE)

Quantification of low drug concentration in model formulations with multivariate analysis using surface enhanced Raman chemical imaging

This paper reports the application of surface enhanced Raman chemical imaging (SER-CI) as a potentially non-destructive quantitative analytical method for the investigation of model pharmaceutical formulations containing the active pharmaceutical ingredient (API) in low concentrations (0.5–2%). The application of chemometric techniques for processing the spectra enables the determination of API distribution in products of different concentrations. In addition, the applied multivariate curve resolution can be proper method to identify unexpected contaminants in illicit drugs. The drastic Raman signal enhancement in the presence of silver nanoparticles provides significantly improved calibration accuracy and, at the same time, radically decreased image acquisition time compared to conventional Raman chemical imaging.

Optimal pose selection for calibration of planar anthropomorphic manipulators

The paper is devoted to the calibration experiment design for serial anthropomorphic manipulators with arbitrary number of links. It proposes simple rules for the selection of manipulator configurations that allow the user to essentially improve calibration accuracy and reduce identification errors. Although the main results have been obtained for the planar manipulators, they can be also useful for calibration of more complicated mechanisms. The efficiency of the proposed approach is illustrated with several examples that deal with typical planar manipulators and an anthropomorphic industrial robot.

A New Cloud Screening Algorithm for Ground-Based Direct-Beam Solar Radiation

AbstractCloud screening of direct-beam solar radiation is an essential step for in situ calibration and atmospheric properties retrieval. The internal cloud screening module of a Langley analysis program [Langley Analyzer (LA)] used by the U.S. Department of Agriculture (USDA) UV-B Monitoring and Research Program (UVMRP) is used for screening the uncalibrated direct-beam measurements and for deriving Langley offset voltages for calibration of the UV version of the Multifilter Rotating Shadowband Radiometer (UV-MFRSR). The current LA cloud screening module utilizes data from extended clear-sky periods and tends to ignore shorter periods that typify periods of broken cloudiness, and as a result, fewer values are generated for sites with higher frequencies of cloudy days (cloudy sites). A new cloud screening algorithm is presented that calculates the total optical depth (TOD) difference between a target point and pairs of points, and identifies the target as cloudy if the mean TOD difference exceeds a certain threshold. The screening is an iterative process that finishes when no new cloudy points are found. The result at a typical clear/sunny site shows that values from partly cloudy days are consistent with those from cloud-free days, when the new method is employed. The new cloud screening algorithm picks up significantly more values at cloudy sites. The larger decrease of the annual mean value of at cloudy sites than at relatively clear sites suggests the potential for improving calibration accuracy at cloudy sites. The results also show that the new cloud screening method is capable of detecting clear points in short clear windows and in transitional regions.

Improving calibration accuracy of structured light systems using plane-based residual error compensation

The performance of a structured light system (SLS) highly depends on its calibration especially in applications such as surface metrology and product quality control where high accuracy is required. Motivated by building a real-time highly accurate flatness measurement system, we propose a plane-based residual error compensation algorithm for improving the calibration accuracy of SLSs. Following the highly accurate procedure of geometric calibration using circular control points, the proposed algorithm enforces the planar constraint on the three-dimensional reconstruction of the circular control points, which are projected on a perfect plane, to further reduce the residual calibration error. Our method compensates for the largest proportion of the residual error, in most cases being the model error of the lens distortion in the system. Experimental results show that the compensation elevates the calibration accuracy to a higher level—the planarity error is reduced by 70% and this accuracy is comparable to a well-reputed industrial mechanical measuring machine.

Improved Intact Soil-Core Carbon Determination Applying Regression Shrinkage and Variable Selection Techniques to Complete Spectrum Laser-Induced Breakdown Spectroscopy (LIBS)

Laser-induced breakdown spectroscopy (LIBS) provides a potential method for rapid, in situ soil C measurement. In previous research on the application of LIBS to intact soil cores, we hypothesized that ultraviolet (UV) spectrum LIBS (200-300 nm) might not provide sufficient elemental information to reliably discriminate between soil organic C (SOC) and inorganic C (IC). In this study, using a custom complete spectrum (245-925 nm) core-scanning LIBS instrument, we analyzed 60 intact soil cores from six wheat fields. Predictive multi-response partial least squares (PLS2) models using full and reduced spectrum LIBS were compared for directly determining soil total C (TC), IC, and SOC. Two regression shrinkage and variable selection approaches, the least absolute shrinkage and selection operator (LASSO) and sparse multivariate regression with covariance estimation (MRCE), were tested for soil C predictions and the identification of wavelengths important for soil C prediction. Using complete spectrum LIBS for PLS2 modeling reduced the calibration standard error of prediction (SEP) 15 and 19% for TC and IC, respectively, compared to UV spectrum LIBS. The LASSO and MRCE approaches provided significantly improved calibration accuracy and reduced SEP 32-55% over UV spectrum PLS2 models. We conclude that (1) complete spectrum LIBS is superior to UV spectrum LIBS for predicting soil C for intact soil cores without pretreatment; (2) LASSO and MRCE approaches provide improved calibration prediction accuracy over PLS2 but require additional testing with increased soil and target analyte diversity; and (3) measurement errors associated with analyzing intact cores (e.g., sample density and surface roughness) require further study and quantification.

A new method of camera calibration based on the segmentation model

A new way of camera calibration is proposed in this article, which divides the image into three different regions based on distortion distribution, and different models are used to calibrate each region respectively, then the whole process of camera calibration can be completed. At first, because there is less image distortion in the central region, a linear model is used to complete the calibration in this area. After that, the intrinsic and extrinsic parameters got in the linear calibration are used to calibrate the annular region, in which the influence of the radial and tangential distortion is taken into consideration. Because the points outside the annular area show more unknown distortion, the former distortion mode is not suitable for use any more. Finally, to complete the calibration of this region, the fitting method of smoothing spline is used to get the function of distortion correction. In the experiment, the mean residual of back-projection and normalized calibration error (NCE) are calculated. The results show that the proposed method is quite efficient to improve calibration accuracy compared with traditional methods. It can meet the practical requirement of high precision in vision metrology engineering.

Calibration Method for Stereovision Measurement of High-Temperature Components Using Two Infrared Cameras

The infrared vision measurement method has some advantages over visible vision in the measurement of high-temperature components. Infrared imaging is based on different imaging principles, however, making it unreasonable to adopt a conventional visiblelight camera calibration method directly. The present study proposes a dual infrared-camera calibration program in which we use the infrared imaging principle to measure high-temperature components threedimensionally. We use two ceramic balls as calibration targets against the background of an external hightemperature radiation source. Multiple feature points are generated from the precise movement of these targets. In order to improve the accuracy of the calibration method, we took the following approaches: A highly accurate edge-detection algorithm is realized by using a fuzzy neural network that is self-learning, self-adaptive, and utilizes fuzzy processing. We thus achieve a low signal-to-noise ratio and low contrast in infrared images. The distance between these two ceramic targets is used as a calibration reference to further reduce the temperature effect and to improve calibration accuracy and efficiency. The calibration results we got are an average residual error of 6.4134 µm and a variance of 2.9205 µm.

Spatial Calibration of a Dual PTZ‐Fixed Camera System for Tracking Moving Objects in Video

A dual camera setup is proposed, consisting of a fixed (stationary) camera and a pan‐tilt‐zoom (PTZ) camera, employed in an automatic video surveillance system. The PTZ camera is zoomed in on a selected point in the fixed camera view and it may automatically track a moving object. For this purpose, two camera spatial calibration procedures are proposed. The PTZ camera is calibrated in relation to the fixed camera image, using interpolated look-up tables for pan and tilt values. For the calibration of the fixed camera, an extension of the Tsai algorithm is proposed, based only on measurements of distances between calibration points. This procedure reduces the time needed to obtain the calibration set and improves calibration accuracy. An algorithm for calculating PTZ values required for tracking of a moving object with the PTZ camera is also presented. The performance of the proposed algorithms is evaluated using the measured data.

Nonlinearity of FY-2D infrared detector in thermal window channels and its correction method

PDF HTML阅读 XML下载 导出引用 引用提醒 FY-2D红外探测器窗区通道非线性特性及其定标订正方案 DOI: 作者: 作者单位: 国家气象局国家卫星气象中心,国家气象局国家卫星气象中心,国家气象局国家卫星气象中心,国家气象局国家卫星气象中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家杰出青年科学基金 Nonlinearity of FY-2D infrared detector in thermal window channels and its correction method Author: Affiliation: National Satellite Meteorological Center,National Satellite Meteorological Center,National Satellite Meteorological Center,National Satellite Meteorological Center Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:利用实验室定标与基于高光谱探测器IASI的交叉匹配样本，分析了FY-2D红外探测器在窗区通道的非线性特征，结果显示，VISSR在信号两端都存在非线性特征，但对低信号端的影响最为显著.与实验室测量结果比较，线性定标在低温端会带来大于10 K的亮温偏差.为了同时保证定标结果的质量及其稳定性，采用分段定标方案，高温端为线性定标而中低温端为非线性.从一致性以及台风个例分析等方面对定标方案进行了验证，分析结果表明，分段定标方案稳定，对低温端的定标精度改进显著，与线性结果比较台风监测亮温精度提升大于7K. Abstract:FY-2 Visible and Infrared Spin Scan Radiometer had obviously nonlinear characteristics at low signal. It brought significant biases when using linear calibration in low temperature targets monitoring especially for typhoon targets, which seriously influences FY-2 quantitative application. Nonlinearity of FY-2D infrared detector in window channels was investigated using laboratory calibration measurements and collocation samples based on high-spectral sensor IASI. It was found that nonlinear performance existed both at high and low signal, but impacts at low signal were the most significant. Comparing with laboratory measurements, linear calibration could cause more than 10 K deviations at low temperature. In order to guarantee the quality of calibration results and its stability, nonlinear calibration scheme was applied only in low temperature area. Nonlinear calibration scheme was investigated from its consistency, and also typhoon application. The results indicated nonlinear calibration was stable, and significantly improved calibration accuracy at low temperature, especially in typhoon monitoring which was advanced at least by 7K. 参考文献 相似文献 引证文献

Helping students to improve their academic performance: A pilot study of a workbook with self-monitoring exercises

Background: There is increasing interest in developing student self-regulated learning skills, especially self-monitoring, to improve academic performance.Aims: A pilot study to investigate the impact of self-monitoring exercises on calibration accuracy and academic performance in undergraduate medical students on a Biomedical Science (BMS) module.Method: A randomised trial of 51 second-year students comparing a structured workbook with and without self-monitoring exercises.Results: Participants significantly improved calibration accuracy after completing the intervention, as well as increased self-efficacy and greater satisfaction with performance. The intervention group significantly improved their BMS exam score compared with the control group.Conclusion: A relatively simple intervention seems to have the potential to improve self-monitoring skills and academic performance. Further research is recommended to identify if the development of self-monitoring skills by a similar intervention leads to long-term improvement in academic performance, if low-performing students can significantly benefit from a similar intervention and if there is transfer of improved self-monitoring skills from one context to another.

Controlling calibration errors in gravitational-wave detectors by precise location of calibration forces

We present results of finite element analysis simulations which could lead to more accurate calibration of interferometric gravitational wave detectors. Calibration and actuation forces applied to the interferometer test masses cause elastic deformation, inducing errors in the calibration. These errors increase with actuation frequency, and can be greater than 50% at frequencies above a few kilohertz. We show that they can be reduced significantly by optimizing the position at which the forces are applied. The Advanced LIGO [1] photon calibrators use a two-beam configuration to reduce the impact of local deformations of the test mass surface. The position of the beams over the test mass can be chosen such both the local and the bulk induced elastic deformation are minimized. Our finite element modeling indicates that with two beams positioned within ±1 mm of their optimal locations, calibration errors due to test mass elastic deformation can be kept below 1% for frequencies up to 3.5 kHz. We thus show that precise control of the location of calibration forces could considerably improve calibration accuracy, especially at high frequencies.

Quantitative laser-induced breakdown spectroscopy of potassium for in-situ geochronology on Mars

Laser-induced breakdown spectroscopy is explored for the development of an in-situ K–Ar geochronology instrument for Mars. Potassium concentrations in standard basaltic glasses and equivalent rock samples in their natural form are quantified using the potassium doublet at 766.49 and 769.90nm. Measurement precision varies from 0.5 to 5.5 (% RSD) over the 3.63% to 0.025% potassium by weight for the standard samples, and little additional precision is achieved above 20 laser shots at 5 locations. For the glass standards, the quantification limits are 920 and 66ppm for non-weighted and weighted calibration methods, respectively. For the basaltic rocks, the quantification limits are 2650 and 328ppm for the non-weighted and weighted calibration methods, respectively. The heterogeneity of the rock samples leads to larger variations in potassium signal; however, normalizing the potassium peak by base area at 25 locations on the rock improved calibration accuracy. Including only errors in LIBS measurements, estimated age errors for the glasses range from approximately ±30Ma for 3000Ma samples to±2Ma for 100Ma samples. For the basaltic rocks, the age errors are approximately ±120Ma for 3000Ma samples and ±8Ma for 100Ma samples.

An Efficient Camera Calibration Technique Offering Robustness and Accuracy Over a Wide Range of Lens Distortion

In the field of machine vision, camera calibration refers to the experimental determination of a set of parameters that describe the image formation process for a given analytical model of the machine vision system. Researchers working with low-cost digital cameras and off-the-shelf lenses generally favor camera calibration techniques that do not rely on specialized optical equipment, modifications to the hardware, or an a priori knowledge of the vision system. Most of the commonly used calibration techniques are based on the observation of a single 3-D target or multiple planar (2-D) targets with a large number of control points. This paper presents a novel calibration technique that offers improved accuracy, robustness, and efficiency over a wide range of lens distortion. This technique operates by minimizing the error between the reconstructed image points and their experimentally determined counterparts in "distortion free" space. This facilitates the incorporation of the exact lens distortion model. In addition, expressing spatial orientation in terms of unit quaternions greatly enhances the proposed calibration solution by formulating a minimally redundant system of equations that is free of singularities. Extensive performance benchmarking consisting of both computer simulation and experiments confirmed higher accuracy in calibration regardless of the amount of lens distortion present in the optics of the camera. This paper also experimentally confirmed that a comprehensive lens distortion model including higher order radial and tangential distortion terms improves calibration accuracy.

Near‐Infrared Reflectance Spectroscopy Prediction of Soil Properties: Effects of Sample Cups and Preparation

Most methods for soil analysis are based on wet chemistry. Near infrared reflectance spectroscopy (NIRS) is a cost‐effective and environmentally sound alternative technique. This study evaluated the effect of sample fineness (0.2, 0.5, 1, and 2 mm) and sample cups (transport versus spinning) on the accuracy of NIRS predictions of soil texture, cation‐exchange capacity, pH, total C and N, organic C, and potentially mineralizable N (Nmin) using 150 air‐dried samples collected from a 15‐ha site dominated by Humaquept, Endoaquept, and Dystrochrept soils. The best spectral pretreatment was determined for each property. Principal component analysis (PCA) was used to select samples in calibration and validation sets. Calibration equations were developed using the modified partial least square regression. The accuracy of NIRS prediction was evaluated using three statistics for the prediction set: coefficient of determination (R2), ratio of performance deviation (RPD), and ratio error range (RER). Across the factorial designed treatments, successful calibrations were observed for clay, sand, and Nmin (R2 ≥ 0.90, RPD ≥ 3, RER ≥ 15). Prediction accuracy of pH was poor (0.51 ≤ R2 ≤ 0.74, 1.39 ≤ RPD ≤ 1.92, 6.13 ≤ RER ≤ 8.33), while it was intermediate for remaining properties. Sample fineness of 2 mm appeared to be sufficient since finenesses of 0.2, 0.5, or 1.0 mm did not improve calibration accuracy. These findings at small scale should not be extrapolated and further investigations are required to validate them at a larger scale.

Improved ancilla preparation in spectral shearing interferometry for accurate ultrafast pulse characterization

We report a version of spectral phase interferometry for direct electric field reconstruction (SPIDER), in which spectral filters are used to produce the quasi-monochromatic fields required for upconversion. The advantages of this approach include improved calibration accuracy, robustness for strongly chirped input pulses, simplicity, and compactness. We verify the technique experimentally by measuring the spectral chirp of a grating compressor using a spatially encoded arrangement (SEA-)SPIDER.

Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance

Accurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of disaggregated traffic data. A methodology for the joint calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic-sensing technologies is presented. The calibration problem has been formulated as a stochastic optimization framework. Two different algorithms are used for solving the calibration problem: a gradient approximation-based path search method and a random search metaheuristic. The methodology is first tested by using a small synthetic study network to illustrate its effectiveness and obtain insight into its operation. The methodology is further applied to a real traffic network in Lower Westchester County, New York, to demonstrate its scalability. The estimation results are tested by using a calibrated microscopic traffic simulator. The results are compared with the base case of calibration by the use of only the conventional point sensor data. The results indicate that use of AVI data significantly improves calibration accuracy.

Role of Aerosol Absorption in Satellite Sensor Calibration

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The importance of aerosol absorption in satellite sensor vicarious calibration and/or satellite measurement of physical parameters is reiterated in a sensitivity study performed using a radiative transfer model, with field-measured data as input. Broadband shortwave surface fluxes need to be measured according to new protocols, described herein, to infer atmospheric absorption to improve calibration accuracy to within the <formula formulatype="inline"><tex>$\pm$</tex> </formula>2% stated goal of next-generation sensors such as Visible/Infrared Imager/Radiometer Suite. </para>

13.3: Improvement for DICOM GSDF Calibration for Medical Displays

The Gray Scale Standard Display Function (GSDF) for calibration of medical displays is a useful model, but as such not sufficient to achieve highest possible image quality. We will discuss a method to improve calibration accuracy over the complete display surface.

Implementation, study and calibration of a modified ASM2d for the simulation of SBR processes

An enhanced process model for SBRs has been developed. Though the basic mechanism largely draws on the Activated Sludge Model n. 2d, its new features are the splitting of the nitrification stage in a two-step process, according to the well known Nitrosomonas-Nitrobacter oxidation sequence, and an improved XPAO dynamics, involved in the anaerobic/aerobic phosphorus removal process. The model was implemented through the DLL technique allowing complied C++ modules to be linked to an ordinary Simulink block diagram. The static sensitivity study revealed that if the parameter vector is partitioned into subsets of biologically related parameters and calibrated separately, the calibration procedure does not present particularly difficult aspects. Trajectory sensitivity showed also to which extent data collection could be optimised in order to improve calibration accuracy. The study of the shape of the error functional generated by parameters couples allows a much more effective calibration strategy.