Robust Calibration Algorithm Research Articles

Compact Thermal Imager (CTI) for Atmospheric Remote Sensing

The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument could be calibrated to produce scientifically useful radiance imagery of the atmosphere, clouds, and surfaces with a vertical resolution of ~460 m at limb and a horizontal resolution of ~80 m at nadir. The new detector demonstrated an excellent sensitivity to detect the weak limb radiance perturbations modulated by small-scale atmospheric gravity waves. The CTI’s high-resolution imaging was used to infer vertical cloud temperature profiles from a side-viewing geometry. For nadir imaging, the combined high-resolution and high-sensitivity capabilities allowed the CTI to better separate cloud and surface emissions, including those in the planetary boundary layer (PBL) that had small contrast against the background surface. Finally, based on the ISS’s orbit, the stable detector performance and robust calibration algorithm produced valuable diurnal observations of cloud and surface emissions with respect to solar local time during May–October 2019, when the CTI had nearly continuous operation.

Robust calibration and arbitrage-free interpolation of SSVI slices

We describe a robust calibration algorithm of a set of SSVI maturity slices (i.e., a set of 3 SSVI parameters $$\theta _t, \rho _t, \varphi _t$$ attached to each option maturity t available on the market), which grants that these slices are free of butterfly and of calendar spread arbitrage. Given such a set of consistent SSVI parameters, we show that the most natural interpolation/extrapolation of the parameters provides a full continuous volatility surface free of arbitrage. The numerical implementation is straightforward, robust and quick, yielding an effective and parsimonious solution to the smile problem, which has the potential to become a benchmark one. We thank Antoine Jacquier and Stefano De Marco for useful discussions and remarks. All remaining errors are ours.

Local modeling approaches for estimating soil properties in selected Indian soils using diffuse reflectance data over visible to near-infrared region

Robust calibration algorithms are needed for the accurate assessment of soil properties in the diffuse reflectance spectroscopy (DRS) approach. Despite several studies on different calibration algorithms, the prediction accuracy of soil properties using DRS need to be improved. Specifically, the utility of local modeling approaches for small spectral libraries is less examined compared with global modeling approaches. In this study, we compared global modeling approaches such as partial-least-squares regression (PLSR), lasso, ridge regression with several locally-weighted PLSR (PLSRLW) approaches. We also examined seven different distance measures: Euclidean distance, covariance-based distance, correlation-based distance, surface difference spectrum, information-based distance, optimized principal component Mahalanobis, and locally-linear embeddings used in the PLSRLW approach for their effectiveness in modeling soil properties using DRS. A total of 954 soil samples were collected from three different states of India: West Bengal, Odisha, and Karnataka. Five soil properties such as sand content, clay content, soil organic carbon (SOC) content, extractable iron (Fe) content and extractable zinc (Zn) content were predicted using reflectance spectra over 350–2500 nm. Root-mean-squared error (RMSE) and the coefficient of determination (R2) were used as performance statistics. Among the global modeling approaches, lasso performed better than the PLSR although it is computationally more intensive than the PLSR. In general, the correlation-based PLSRLW performed significantly better than the global approaches. Specifically, the test R2 values increased from 0.66 to 0.72 for prediction of sand content, from 0.59 to 0.70 for prediction of SOC content, and from 0.70 to 0.74 for prediction of Fe content by using the PLSRLW over PLSR. We also used depth of absorption peak of spectra at approximately 1400, 1900 and 2200 nm for mineralogical characterization of soil samples. The results suggested that the improvement in prediction accuracy of soil properties using the PLSRLW was achieved because calibration samples which had same mineralogy as the test sample were given higher weights. These results suggest that the prediction accuracy of soil properties may also be improved in small spectral libraries if an appropriate local modeling scheme is selected.

Robust Calibration of Radio Interferometers in Non-Gaussian Environment

The development of new phased array systems in radio astronomy, as the low frequency array (LOFAR) and the square kilometre array (SKA), formed of a large number of small and flexible elementary antennas, has led to significant challenges. Among them, model calibration is a crucial step in order to provide accurate and thus meaningful images and requires the estimation of all the perturbation effects introduced along the signal propagation path, for a specific source direction and antenna position. Usually, it is common to perform model calibration using the a priori knowledge regarding a small number of known strong calibrator sources but under the assumption of Gaussianity of the noise. Nevertheless, observations in the context of radio astronomy are known to be affected by the presence of outliers which are due to several causes, e.g., weak non-calibrator sources or man made radio frequency interferences. Consequently, the classical Gaussian noise assumption is violated leading to severe degradation in performances. In order to take into account the outlier effects, we assume that the noise follows a spherically invariant random distribution. Based on this modeling, a robust calibration algorithm is presented in this paper. More precisely, this new scheme is based on the design of an iterative relaxed concentrated maximum likelihood estimation procedure which allows to obtain closed-form expressions for the unknown parameters with a reasonable computational cost. This is of importance as the number of estimated parameters depends on the number of antenna elements, which is large for the new generation of radio interferometers. Numerical simulations reveal that the proposed algorithm outperforms the state-of-the-art calibration techniques.

Digital Offset Cancellation for Long Time-Constant Subthreshold OTA-C Integrators

Systems using integrators with very long time constants can place a severe constraint on allowable amplifier offset. Postfabrication cancellation of these offsets is a requirement in these applications. This brief describes the design, simulation, and measurement of a subthreshold operational transconductance amplifier (OTA) with digital calibration and its robust calibration algorithm. Statistical simulations show the scheme's ability to reduce the output offset by a factor of 40. Measurements on many prototype OTAs validate the technique's ability to bring the standard deviation of integrated output offset to less than 10 mV in most cases, or about 30 $\mu \mbox{V}$ input-referred offsets. A secant-based algorithm is proposed that finds the optimum digital calibration code for each OTA in a small number of search steps that minimizes the integrated output offset. This algorithm is robust to nonmonotonic tuning characteristics, allowing the additional circuitry to be minimally sized. For low-frequency applications below 5 Hz, the scheme has nearly no net impact on die area in processes allowing circuitry below the integration capacitor.

Distributed radio interferometric calibration

Increasing data volumes delivered by a new generation of radio interferometers require computationally efficient and robust calibration algorithms. In this paper, we propose distributed calibration as a way of improving both computational cost as well as robustness in calibration. We exploit the data parallelism across frequency that is inherent in radio astronomical observations that are recorded as multiple channels at different frequencies. Moreover, we also exploit the smoothness of the variation of calibration parameters across frequency. Data parallelism enables us to distribute the computing load across a network of compute agents. Smoothness in frequency enables us reformulate calibration as a consensus optimization problem. With this formulation, we enable flow of information between compute agents calibrating data at different frequencies, without actually passing the data, and thereby improving robustness. We present simulation results to show the feasibility as well as the advantages of distributed calibration as opposed to conventional calibration.

Electromagnetic tracking for catheter reconstruction in ultrasound-guided high-dose-rate brachytherapy of the prostate.

The accurate delivery of high-dose-rate brachytherapy is dependent on the correct identification of the position and shape of the treatment catheters. In many brachytherapy clinics, transrectal ultrasound (TRUS) imaging is used to identify the catheters. However, manual catheter identification on TRUS images can be time consuming, subjective, and operator dependent because of calcifications and distal shadowing artifacts. We report the use of electromagnetic (EM) tracking technology to map the position and shape of catheters inserted in a tissue-mimicking phantom. The accuracy of the EM system was comprehensively quantified using a three-axis robotic system. In addition, EM tracks acquired from catheters in a phantom were compared with catheter positions determined from TRUS and CT images to compare EM system performance to standard clinical imaging modalities. The tracking experiments were performed in a controlled laboratory environment and also in a typical brachytherapy operating room to test for potential EM distortions. The robotic validation of the EM system yielded a mean accuracy of <0.5mm for a clinically acceptable field of view in a nondistorting environment. The EM-tracked catheter representations were found to have an accuracy of <1mm when compared with TRUS- and CT-identified positions, both in the laboratory environment and in the brachytherapy operating room. The achievable accuracy depends to a large extent on the calibration of the TRUS probe, geometry of the tracked devices relative to the EM field generator, and locations of surrounding clinical equipment. To address the issue of variable accuracy, a robust calibration algorithm has been developed and integrated into the workflow. The proposed mapping technique was also found to improve the workflow efficiency of catheter identification. The high baseline accuracy of the EM system, the consistent agreement between EM-tracked, TRUS- and CT-identified catheters, and the improved workflow efficiency illustrate the potential value of using EM tracking for catheter mapping in high-dose-rate brachytherapy.

Robust Calibration Algorithm for Multiplicative Modeling Errors Against Location Deviations of Auxiliary Sources

It is well known that the azimuth deviations of the auxiliary sources severely degrade the performance of classical subspace-based calibration methods that assume the direction-of-arrivals of calibration sources are perfectly measured. Therefore, aiming at the effects of source location deviations, the estimation variance of the multiplicative modeling errors for the subspace-based calibration method is first derived by applying matrix eigen-perturbation theory and first-order perturbation analysis approach. The theoretical analysis is undertaken under the assumption that the azimuth deviations are small enough for the first-order perturbation analysis to be valid. In addition, to mitigate the effects of the location errors, a structured total least squares optimization model is established using first-order Taylor series expansion method. Then, the corresponding numerical algorithm is presented to provide a robust estimate for multiplicative modeling errors. The exact Cramer---Rao bound expressions for the unknowns are also deduced in the presence of the azimuth deviations. Simulation results confirm the effectiveness of the theoretical analysis and demonstrate the desirable behavior of the robust calibration algorithm in comparison with the subspace-based calibration methods.

Interest Rate Model Selection and Implementation for the Credit Risk Engines

This paper discusses the interest rate model selection and implementation process for the Monte Carlo credit risk engines. Special attention is paid to the real world model calibration and simulation problems, including development of the robust calibration algorithms for the illiquid interest rate curves and handling of the negative interest rates implied by the forward FX rates for many EM currency pairs.

Raman Spectroscopy: An Emerging Tool for Clinical Diagnostics

B applications of lasers and laser spectroscopy are changing the face of medicine as it is currently practiced. Spectroscopy is a promising means of extracting biochemical and morphological information from tissue that is relevant to disease progression and diagnosis. In particular, Raman spectroscopy is a powerful tool for non-invasive and real time diagnosis due to its exquisite molecular specificity and lack of sample preparation requirements. Raman spectroscopy, which measures the molecular vibrations of a sample, is currently being used to study atherosclerosis, measure blood analytes, and detect dysplasia and cancer in various tissues including the breast, cervix, prostate, and skin. In this talk, we present our results on quantitative biological spectroscopy for non-invasive blood analyte detection. Our work in this area is primarily motivated by the necessity for accurate and frequent measurement of blood glucose levels, which is most commonly achieved by withdrawal of blood. Given the inconvenience and invasiveness of this procedure, a non-invasive method would greatly benefit the increasing number of diabetics. Our laboratory has successfully demonstrated the ability to measure glucose, urea and other blood analytes in serum, whole blood and individual human volunteers. In addition, we present our results for turbidity correction and suppression of tissue autofluorescence in biological Raman spectroscopy. We show that correction for these non-analyte specific variances provides a clinically accurate and robust calibration algorithm that can be used for prospective prediction in human population. Finally, we discuss our plans for miniaturization of the device for point of care and commercial applications. Raman Spectroscopy: An Emerging Tool for Clinical Diagnostics

Performance of a high resolution cavity beam position monitor system

It has been estimated that an RF cavity Beam Position Monitor (BPM) could provide a position measurement resolution of less than 1 nm. We have developed a high resolution cavity BPM and associated electronics. A triplet comprised of these BPMs was installed in the extraction line of the Accelerator Test Facility (ATF) at the High Energy Accelerator Research Organization (KEK) for testing with its ultra-low emittance beam. The three BPMs were each rigidly mounted inside an alignment frame on six variable-length struts which could be used to move the BPMs in position and angle. We have developed novel methods for extracting the position and tilt information from the BPM signals including a robust calibration algorithm which is immune to beam jitter. To date, we have demonstrated a position resolution of 15.6 nm and a tilt resolution of 2.1 μ rad over a dynamic range of approximately ± 20 μ m .