Standard Calibration Approach Research Articles

Spectroflat: A generic spectrum and flat-field calibration library for spectro-polarimetric data

Flat-fielding spectro-polarimetric data with one spatial and one spectral dimension is inherently difficult as the imprint of the spectral lines needs to be separated from other wavelength-dependent instrumental effect (e.g., fringes or prefilter profiles) and wavelength-independent effects (e.g., dust and sensor response). Current approaches for spectrometers are often based on moving the grating or they depend on optical models and/or on lab calibration data. They are also limited to small spectral regions and are instrument-specific. Approaches that would be suitable for polarimeters have not been reported yet. We present an approach that allows for flat-field calibration data to be to obtained for diffraction-grating-based, long-slit spectrographs combined with temporally modulated polarimetry from high-resolution solar telescopes. This approach is based on nominal flat-fielding procedures performed during the instrument's science operations. We performed a precise and field-dependent correction of the spectrographic distortion effect (resulting in curved spectral lines, typically denoted as a "smile" effect) to ensure the orthogonality of the spectral and spatial dimensions. We identified distortions by tracking the position of multiple spectral lines within the full spectral field of view. From the raw modulated flats, we then removed the solar line imprints and derived separate flat-fields for sensor and slit dust features. Optionally, wavelength calibration and continuum correction can be included in this process. We have created generic Python libraries that can be plugged into existing Python-based data reduction pipelines or used as a standalone calibration tool. We show that for spectrographs covering many spectral lines, a correction of the smile distortion based on optical models alone is not sufficient. Our results demonstrate a suppression of fringes, sensor artifacts, and fixed-pattern imprints in demodulated data by one order of magnitude. For intensity images, the photon noise level can be closely attained after calibration. Our correction works across the full spectral range. The algorithm was tested for different wavelength regimes with emission (EUV range) or absorption (near-UV, VIS, IR range) spectra, on data acquired with ground-based (SST/TRIPPLE-SP, GREGOR/GRIS), balloon-borne (SUNRISE-III/SUSI), and space-based (SolO/SPICE) instruments. The data calibrated with our method offer robust and precise inversion results. We have extended existing spectroscopic flat-field techniques to modern instruments with large imaging sensors covering many spectral lines simultaneously, and with polarimetric capabilities, where methods described so far are not adequate. We believe that our method is applicable as a standard calibration approach for most modern high resolution large-FOV, long-slit spectrographs -- both with and without polarimetric capabilities.

A novel step-by-step procedure for the kinematic calibration of robots using a single draw-wire encoder

Robot positioning accuracy is a key factory when performing high-precision manufacturing tasks. To effectively improve the accuracy of a manipulator, often up to a value close to its repeatability, calibration plays a crucial role. In the literature, various approaches to robot calibration have been proposed, and they range considerably in the type of measurement system and identification algorithm used. Our aim was to develop a novel step-by-step kinematic calibration procedure — where the parameters are subsequently estimated one at a time — that only uses 1D distance measurement data obtained through a draw-wire encoder. To pursue this objective, we derived an analytical approach to find, for each unknown parameter, a set of calibration points where the discrepancy between the measured and predicted distances only depends on that unknown parameter. This reduces the computational burden of the identification process while potentially improving its accuracy. Simulations and experimental tests were carried out on a 6 degrees-of-freedom robot arm: the results confirmed the validity of the proposed strategy. As a result, the proposed step-by-step calibration approach represents a practical, cost-effective and computationally less demanding alternative to standard calibration approaches, making robot calibration more accessible and easier to perform.

Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default

The transformation of credit scores into probabilities of default plays an important role in credit risk estimation. The linear logistic regression has developed into a standard calibration approach in the banking sector. With the advent of machine learning techniques in the discriminatory phase of credit risk models, however, the standard calibration approach is currently under scrutiny again. In particular, the assumptions behind the linear logistic regression provide critics with a target. The previous literature has converted the calibration problem into a regression task without any loss of generality. In this paper, we draw on recent academic results in order to suggest two new single-parameter families of differentiable functions as candidates for this regression. The derivation of these two families of differentiable functions is based on the maximum entropy principle, and thus it relies on a minimum number of assumptions. We compare the performance of four calibration approaches on a realworld data set and find that one of the new single-parameter families outperforms the linear logistic regression. Further, we develop an approach to quantify that part of the general estimation error of probabilities of default which stems from the statistical dispersion of the discriminatory power.

A novel motion-reconstruction method for inertial sensors with constraints

Motion reconstruction for rigid bodies and rigid-body frames using data from inertial measurement units (IMUs) is a challenging task. Position and orientation determination by means of IMUs is erroneous, as deterministic and stochastic errors accumulate over time. The former of which errors can be minimized by standard calibration approaches, however, sensor calibration with respect to a common reference coordinate system to correct misalignment, has not been fully addressed yet. The latter stochastic errors are mostly reduced using sensor fusion. In this paper, we present a novel motion-reconstruction method utilizing optimization to correct measured IMU data by means of correction polynomials to minimize the deviation of motion constraints. In addition, we perform gyrometer and accelerometer calibration with an industrial manipulator to address deterministic IMU errors, especially misalignment. To evaluate the performance of the novel methods, two types of experiments, one at constant orientation and another with simultaneous translation and rotation, were conducted utilizing the manipulator. The experiments were repeated for five individual IMUs successively. Application of the calibration and optimization methods yielded an average decrease of 95% in the maximum position error compared to the results of common motion reconstruction. Moreover, the average position error over the measurement duration decreased by nearly 90%. The proposed method is applicable to velocity, position, and orientation constraints for every experiment that starts and ends at standstill.

A Bayesian approach to high-fidelity interferometric calibration – I. Mathematical formalism

ABSTRACT High-fidelity radio interferometric data calibration that minimizes spurious spectral structure in the calibrated data is essential in astrophysical applications, such as 21 cm cosmology, which rely on knowledge of the relative spectral smoothness of distinct astrophysical emission components to extract the signal of interest. Existing approaches to radio interferometric calibration have been shown to impart spurious spectral structure to the calibrated data if the sky model used to calibrate the data is incomplete. In this paper, we introduce BayesCal: a novel solution to the sky-model incompleteness problem in interferometric calibration, designed to enable high-fidelity data calibration. The BayesCal data model supplements the a priori known component of the forward model of the sky with a statistical model for the missing and uncertain flux contribution to the data, constrained by a prior on the power in the model. We demonstrate how the parameters of this model can be marginalized out analytically, reducing the dimensionality of the parameter space to be sampled from and allowing one to sample directly from the posterior probability distribution of the calibration parameters. Additionally, we show how physically motivated priors derived from theoretical and measurement-based constraints on the spectral smoothness of the instrumental gains can be used to constrain the calibration solutions. In a companion paper, we apply this algorithm to simulated observations with a HERA-like array and demonstrate that it enables up to four orders of magnitude suppression of power in spurious spectral fluctuations relative to standard calibration approaches.

Calibrating 1D hydrodynamic river models in the absence of cross-section geometry using satellite observations of water surface elevation and river width

Abstract. Hydrodynamic modeling has been increasingly used to simulate water surface elevation which is important for flood prediction and risk assessment. Scarcity and inaccessibility of in situ bathymetric information have hindered hydrodynamic model development at continental-to-global scales. Therefore, river cross-section geometry is commonly approximated by highly simplified generic shapes. Hydrodynamic river models require both bed geometry and roughness as input parameters. Simultaneous calibration of shape parameters and roughness is difficult, because often there are trade-offs between them. Instead of parameterizing cross-section geometry and hydraulic roughness separately, this study introduces a parameterization of 1D hydrodynamic models by combining cross-section geometry and roughness into one conveyance parameter. Flow area and conveyance are expressed as power laws of flow depth, and they are found to be linearly related in log–log space at reach scale. Data from a wide range of river systems show that the linearity approximation is globally applicable. Because the two are expressed as power laws of flow depth, no further assumptions about channel geometry are needed. Therefore, the hydraulic inversion approach allows for calibrating flow area and conveyance curves in the absence of direct observations of bathymetry and hydraulic roughness. The feasibility and performance of the hydraulic inversion workflow are illustrated using satellite observations of river width and water surface elevation in the Songhua river, China. Results show that this approach is able to reproduce water level dynamics with root-mean-square error values of 0.44 and 0.50 m at two gauging stations, which is comparable to that achieved using a standard calibration approach. In summary, this study puts forward an alternative method to parameterize and calibrate river models using satellite observations of river width and water surface elevation.

Lead Assays with Smartphone Detection Using a Monolithic Rod with 4-(2-Pyridylazo) Resorcinol.

A monolithic rod of polyurethane foam–[4-(2-pyridylazo) resorcinol] (PUF–PAR) as a simple chemical sensor for lead assays with smartphone detection and image processing was developed. With readily available simple apparatus such as a plastic cup and a stirrer rod, the monolithic PUF rod was synthesized in a glass tube. The monolithic PUF–PAR rod could be directly loaded by standard/sample solution without sample preparation. A one-shot image in G/B value from a profile plot in ImageJ for a sample with triplicate results via a single standard calibration approach was obtained. A linear single standard calibration was: [G/B value] = −0.038[µg Pb2+] + 2.827, R2 = 0.95 for 10–30 µg Pb2+ with a limit of quantitation (LOQ) of 33 µg L−1. The precision was lower than 15% RSD. The proposed method was tested by an assay for Pb2+ contents in drinking water samples from Bangkok. The results obtained by the proposed method agree with those of ICP-OES and with 100–120% recovery, demonstrating that the method is useful for screening on-site water monitoring.

A normalized signal calibration with a long-term reference improves the robustness of RPLC-MRM/MS lipidomics in plasma.

Improving the reliability of quantification in lipidomic analyses is crucial for its successful application in the discovery of new biomarkers or in clinical practice. In this study, we propose a workflow to improve the accuracy and precision of lipidomic results issued by the laboratory. Lipid species from 11 classes were analyzed by a targeted RPLC-MRM/MS method. The peak areas of species were used to estimate concentrations by an internal standard calibration approach (IS-calibration) and by an alternative normalization signal calibration schema (NS-calibration). The latter uses a long-term reference plasma material as a matrix-matched external calibrator whose accuracy was compared to the NIST SRM-1950 mean consensus values reported by the Interlaboratory Lipidomics Comparison Exercise. The bias of lipid concentrations showed a good accuracy for 69 of 89 quantified lipids. The quantitation of species by the NS-calibration schema improved the within- and between-batch reproducibility in quality control samples, in comparison to the usual IS-calibration approach. Moreover, the NS-calibration workflow improved the robustness of the lipidomics measurements reducing the between-batch variability (relative standard deviation <10% for 95% of lipid species) in real conditions tested throughout the analysis of 120 plasma samples. In addition, we provide a free access web tool to obtain the concentration of lipid species by the two previously mentioned quantitative approaches, providing an easy follow-up of quality control tasks related to lipidomics.

A combined data mining approach for on-line prediction of key soil quality indicators by Vis-NIR spectroscopy

Successful modelling of visible and near-infrared (vis-NIR) spectra for on-line prediction of key soil quality indicators is crucial for accurate variable rate applications of farm input resources. The aim of this paper is to optimize modelling of on-line collected spectra for the prediction of soil pH, organic carbon (OC), extractable phosphorous (P) and potassium (K) by means of spiking, combined with clustering and/or extra-weighting. A mobile fiber-type vis-NIR spectrophotometer (CompactSpec from Tec5 Technology, Germany), with spectral range of 305−1700 nm was calibrated using 100 samples collected from five different fields, which were merged with 28 samples collected from a target field. The resulting dataset was subjected to spectral pretreatments followed by k-means clustering and 95 % confidence ellipsoid, resulting in three optimal datasets. Partial least squares regression (PLSR) analyses were carried out on the calibration set (75 % of samples) for four calibration strategies: (i) non-clustered and non-weighted (NCNW), (ii) clustered and non-weighted (CNW), (iii) non-clustered but extra-weighted (NCW), and (iv) clustered and extra-weighted (CW). Results showed that the quality of on-line predictions was the best after clustering combined with extra-weighting. Modelling based with CW significantly improved model prediction accuracy to be very good for pH (ratio of prediction deviation (RPD) = 2.32) and P (RPD = 2.05), and good for OC (RPD = 1.90) and K (RPD = 1.80), whereas results of NCNW (standard calibration approach) were the poorest to be fair for P (RPD = 1.74) and OC (RPD = 1.50), and poor for K (RPD = 1.1) and pH (RPD = 1.39). It can be concluded that optimal sample selection with k-mean clustering when combined with extra-weighting will result in accurate PLSR calibration models for on-line prediction of soil pH, P, OC and K using a multi-field diverse dataset.

Toward Fail-Safe Speaker Recognition: Trial-Based Calibration With a Reject Option

The output scores of most of the speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration step is usually performed on the scores to convert them into proper likelihood ratios, which have a clear probabilistic interpretation. The standard calibration approach transforms the system scores using a linear function trained using data selected to closely match the evaluation conditions. This selection, though, is not feasible when the evaluation conditions are unknown. In previous work, we proposed a calibration approach for this scenario called trial-based calibration (TBC). TBC trains a separate calibration model for each test trial using data that is dynamically selected from a candidate training set to match the conditions of the trial. In this work, we extend the TBC method, proposing: 1) a new similarity metric for selecting training data that result in significant gains over the one proposed in the original work; 2) a new option that enables the system to reject a trial when not enough matched data are available for training the calibration model; and 3) the use of regularization to improve the robustness of the calibration models trained for each trial. We test the proposed algorithms on a development set composed of several conditions and on the Federal Bureau of Investigation multi-condition speaker recognition dataset, and we demonstrate that the proposed approach reduces calibration loss to values close to 0 for most of the conditions when matched calibration data are available for selection, and that it can reject most of the trials for which relevant calibration data are unavailable.

A new reliable, transposable and cost-effective assay for absolute quantification of total plasmatic bevacizumab by LC-MS/MS in human plasma comparing two internal standard calibration approaches.

The quantification of monoclonal antibodies (mAbs) such as bevacizumab, a recombinant humanized immunoglobulin G1 (hIgG1), in biological fluids, is an essential prerequisite to any pharmacokinetic preclinical and clinical study. To date, reference techniques used to quantify mAbs rely on enzyme-linked immunosorbent assay (ELISA) lacking specificity. Furthermore, the commercially available ELISA kit to quantify bevacizumab in human plasma only assesses the free fraction of the drug. However, the conditions of storage and analysis of plasma samples could alter the physiological equilibrium between the free, bound and partially bound forms of bevacizumab and this could result in over- or underestimation of drug concentration. We developed a new assay for absolute quantification of total fraction of bevacizumab by liquid chromatography tandem mass spectrometry (LC-MS/MS) basing identification and quantification of bevacizumab on two specific peptides. In this report we compare our assay with two internal standard (IS) calibration approaches: one using a different human mAb (Trastuzumab) and the other using a stable isotope labeled specific peptide. After enrichment by affinity chromatography on protein A and concentration by ultrafiltration, human plasma samples were proteolyzed by trypsin. Linearity was established from 12.5 to 500μg/mL with an interday accuracy ranging from 101.7 to 110.6% and precision from 7.0% to 9.9%. This study demonstrates the importance of the choice of the IS in quantifying bevacizumab in human plasma and highlights the difficulty of reaching a reliable proteolysis with a sufficient recovery. We developed a reliable and cost-effective LC-MS/MS method to quantify total plasmatic fraction of bevacizumab in human plasma. Through our development we proposed a generic methodology easily transposable to quantify all IgG1 subclass very useful for clinical pharmacokinetics studies.

The Circular Gap Couette Flow Modeling

The simulation of Couette flow inside the circular gap between coaxial cylinders is observed due to the difficulty of the experimental implementation of the equal plane one, especially for rarefied gas conditions and high Knudsen numbers. Geometry configuration of the experimental facility have been defined in order to minimize speed profile deviation inside the gap from linear shape, existing in case of laminar flow and preventing appearance of Taylor – Gërtler vortexes with chosen flow parameters. The previous stage, that was conducted using the hot-wire anemometer in atmosphere pressure conditions has shown the efficiency of the facility and sufficient agreement of calibration data, received using considered calibration method and data of standard calibration approach when the model wind tunnel DISA is applied.

Parameters Recovery via Calibration in the Heston Model: A Comprehensive Review

Numerous publications take a perfect recovery of the actual parameters during a calibration of stochastic volatility models, such as the Heston model and other continuous option pricing models, for granted. However, we show that this is a misleading assumption and that a high accuracy in capturing the true model parameters is not guaranteed for standard calibration approaches, as they are commonly utilized in the financial industry. Having conducted several thousand model calibrations within an artificial testing environment, we are able to identify three factors that influence the resulting calibrated parameters strongly. These are the number of calibration instruments (i.e., option prices or implied Black-Scholes volatilities) available for the calibration, the error measure selected for the calibration, as well as the choice of initial values (particularly their distance to the known true model parameters). Using synthetic, yet generic, data instead of market observed data, we are able to avoid model risk and contribute purely to the literature on calibration risk. We quantify the impact of these factors on the calibration performance by measuring the calibration error (i.e., the distance between the calibrated and known true model parameters). Based on our numerical results, we are able to derive conclusions which can be useful for academics and practitioners alike. We further specify a calibration setup which does not assure, but significantly increases, the probability of a perfect or near-perfect model calibration.

EVALUATION OF THE QUALITY OF ACTION CAMERAS WITH WIDE-ANGLE LENSES IN UAV PHOTOGRAMMETRY

Abstract. The application of light-weight cameras in UAV photogrammetry is required due to restrictions in payload. In general, consumer cameras with normal lens type are applied to a UAV system. The availability of action cameras, like the GoPro Hero4 Black, including a wide-angle lens (fish-eye lens) offers new perspectives in UAV projects. With these investigations, different calibration procedures for fish-eye lenses are evaluated in order to quantify their accuracy potential in UAV photogrammetry. Herewith the GoPro Hero4 is evaluated using different acquisition modes. It is investigated to which extent the standard calibration approaches in OpenCV or Agisoft PhotoScan/Lens can be applied to the evaluation processes in UAV photogrammetry. Therefore different calibration setups and processing procedures are assessed and discussed. Additionally a pre-correction of the initial distortion by GoPro Studio and its application to the photogrammetric purposes will be evaluated. An experimental setup with a set of control points and a prospective flight scenario is chosen to evaluate the processing results using Agisoft PhotoScan. Herewith it is analysed to which extent a pre-calibration and pre-correction of a GoPro Hero4 will reinforce the reliability and accuracy of a flight scenario.

EVALUATION OF THE QUALITY OF ACTION CAMERAS WITH WIDE-ANGLE LENSES IN UAV PHOTOGRAMMETRY

The application of light-weight cameras in UAV photogrammetry is required due to restrictions in payload. In general, consumer cameras with normal lens type are applied to a UAV system. The availability of action cameras, like the GoPro Hero4 Black, including a wide-angle lens (fish-eye lens) offers new perspectives in UAV projects. With these investigations, different calibration procedures for fish-eye lenses are evaluated in order to quantify their accuracy potential in UAV photogrammetry. Herewith the GoPro Hero4 is evaluated using different acquisition modes. It is investigated to which extent the standard calibration approaches in OpenCV or Agisoft PhotoScan/Lens can be applied to the evaluation processes in UAV photogrammetry. Therefore different calibration setups and processing procedures are assessed and discussed. Additionally a pre-correction of the initial distortion by GoPro Studio and its application to the photogrammetric purposes will be evaluated. An experimental setup with a set of control points and a prospective flight scenario is chosen to evaluate the processing results using Agisoft PhotoScan. Herewith it is analysed to which extent a pre-calibration and pre-correction of a GoPro Hero4 will reinforce the reliability and accuracy of a flight scenario.

Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials.

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood function and an ad-hoc planner to cope with the large uncertainty resulting from the unknown task and decoder. The method has been evaluated in closed-loop online experiments with 8 users using a previously proposed BCI protocol for reaching tasks over a grid. The results show that it is possible to have a usable BCI control from the beginning of the experiment without any prior calibration. Furthermore, comparisons with simulations and previous results obtained using standard calibration hint that both the quality of recorded signals and the performance of the system were comparable to those obtained with a standard calibration approach.

Spectrophotometric method for determination of aluminium content in water and beverage samples employing flow-batch sequential injection system

A sensitive, precise and reliable flow-batch method for the determination of aluminium (Al) was developed using a sequential injection-monosegmented flow system incorporating a mixing chamber unit. Eriochrome cyanine R (ECR) was used as a chromogenic reagent in the presence of N,N-dodecyltrimethylammonium bromide (DTAB). The Al-ECR complex at pH 6 gave a maximum absorption at 584nm. In-line single standard calibration and a standard addition procedure were developed employing the monosegmented flow technique. Under the optimum conditions, a linear calibration graph in the range of 0.0075–0.625mgL−1 Al was obtained with limits of detection and quantitation of 0.0020 and 0.0070mgL−1, respectively. Relative standard deviations were 0.8 and 1.3% for 0.010 and 0.025mgL−1 Al (n=11), respectively. A sample throughput of 24h−1 using an in-line standard calibration approach and 6h−1 using four standard addition levels was achieved. The developed system was successfully applied to water samples and beverage samples. The results agreed well with those obtained from the ICP-AES method. Good recoveries between 85 and 104% were obtained.

Speciation and quantification of organotin compounds in sediment and drinking water by isotope dilution liquid chromatography-inductively coupled plasma-mass spectrometry

A rapid, simple isotope dilution method for detecting organotins in sediment samples.

Calibrating optical tweezers with Bayesian inference

We present a new method for calibrating an optical-tweezer setup that does not depend on input parameters and is less affected by systematic errors like drift of the setup. It is based on an inference approach that uses Bayesian probability to infer the diffusion coefficient and the potential felt by a bead trapped in an optical or magnetic trap. It exploits a much larger amount of the information stored in the recorded bead trajectory than standard calibration approaches. We demonstrate that this method outperforms the equipartition method and the power-spectrum method in input information required (bead radius and trajectory length) and in output accuracy.

Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis

The lack of a robust framework for quantifying the parametric and predictive uncertainty of conceptual rainfall‐runoff (CRR) models remains a key challenge in hydrology. The Bayesian total error analysis (BATEA) methodology provides a comprehensive framework to hypothesize, infer, and evaluate probability models describing input, output, and model structural error. This paper assesses the ability of BATEA and standard calibration approaches (standard least squares (SLS) and weighted least squares (WLS)) to address two key requirements of uncertainty assessment: (1) reliable quantification of predictive uncertainty and (2) reliable estimation of parameter uncertainty. The case study presents a challenging calibration of the lumped GR4J model to a catchment with ephemeral responses and large rainfall gradients. Postcalibration diagnostics, including checks of predictive distributions using quantile‐quantile analysis, suggest that while still far from perfect, BATEA satisfied its assumed probability models better than SLS and WLS. In addition, WLS/SLS parameter estimates were highly dependent on the selected rain gauge and calibration period. This will obscure potential relationships between CRR parameters and catchment attributes and prevent the development of meaningful regional relationships. Conversely, BATEA provided consistent, albeit more uncertain, parameter estimates and thus overcomes one of the obstacles to parameter regionalization. However, significant departures from the calibration assumptions remained even in BATEA, e.g., systematic overestimation of predictive uncertainty, especially in validation. This is likely due to the inferred rainfall errors compensating for simplified treatment of model structural error.