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The Advanced Technology Microwave Sounder (ATMS) is a passive microwave radiometer for the current generation of polar-orbiting meteorological satellites operated by NOAA. The first two ATMS instruments are manifested onboard the Suomi National Polar-orbiting Partnership (S-NPP) and NOAA-20 satellites. Several critical changes have been made to ATMS operational calibration algorithm since March 2017. The calibration processing has been revised from a Rayleigh-Jeans approximated algorithm to a full radiance algorithm in order to reduce the error introduced by the approximation over cold radiances in the higher frequency channels. In addition, based on the lessons learned from S-NPP and NOAA-20 post-launch calibration/validation tests, some major improvements have been made in the updated operational algorithm. These include reflector emission and antenna pattern corrections. Details of the radiance-based ATMS on-orbit calibration are documented in this report, and results of pre-launch calibration error budget analysis and post-launch calibration accuracy evaluation are also presented for reference.

This article presents the evaluation of using pulsed-radio frequency (RF) signals as multitone phase standards for modulated measurements at millimeter-wave (mm-Wave) bands, offering an alternative phase calibration choice for nonlinear vector network analyzers (NVNAs). Based on the NVNA measurements of two pulsed-RF signals generated by square-wave-driven mixers, a high correlation between magnitude and phase errors of unideal pulsed-RF signals is discovered, and an empirical method is developed to accurately estimate the phase errors according to magnitude-only measurements. To confirm its robustness and key preconditions for wide-range applications, theoretical support and explanation to the empirical method is revealed, in which the dominant waveform distortion of an applicable pulsed-RF signal can be decomposed into modulated perturbations with Sinc-like envelopes. The validity of the proposed empirical method, as well as its application for mm-Wave NVNA phase calibration, is verified by both experimental measurements and simulation results, where an estimation error (or phase calibration error) less than ±2° is demonstrated that is reachable in practice.

The Long chain Diol Index (LDI) is a relatively new organic geochemical proxy for sea surface temperature (SST), based on the abundance of the C30 1,15-diol relative to the summed abundance of the C28 1,13-, C30 1,13- and C30 1,15-diols. Here we substantially extend and re-evaluate the initial core top calibration by combining the original dataset with 172 data points derived from previously published studies and 262 newly generated data points. In total, we considered 595 globally distributed surface sediments with an enhanced geographical coverage compared to the original calibration. The relationship with SST is similar to that of the original calibration but with considerably increased scatter. The effects of freshwater input (e.g., river runoff) and long-chain diol contribution from Proboscia diatoms on the LDI were evaluated. Exclusion of core-tops deposited at a salinity Proboscia-derived C28 1,12-diol abundance, resulted in a substantial improvement of the relationship between LDI and annual mean SST. This implies that the LDI cannot be directly applied in regions with a strong freshwater influence or high C28 1,12-diol abundance, limiting the applicability of the LDI. The final LDI calibration (LDI = 0.0325 × SST + 0.1082; R2 = 0.88; n = 514) is not statistically different from the original calibration of Rampen et al. (2012) (https://doi.org/10.1016/j.gca.2012.01.024), although with a larger calibration error of 3 °C. This larger calibration error results from several regions where the LDI does not seem to have a strong temperature dependence with annual mean SST, posing a limitation on the application of the LDI

Coprime arrays have been widely adopted for direction-of-arrival (DOA) estimation since it can achieve an increased number of degrees of freedom (DOF). To utilize all information received by the coprime array, array interpolation methods are developed, which construct a virtual uniform linear array (ULA) with the same aperture from the non-uniform coprime array. However, the conventional non-robust DOA estimation algorithms for coprime arrays, including the interpolation based methods, suffer from degraded performance or even failed operation when some sensors are miscalibrated. In this paper, a novel maximum correntropy criterion (MCC) based virtual array interpolation algorithm for robust DOA estimation is developed to address this problem. The proposed approach treats the miscalibrated sensor observations as outliers, and by exploiting the property of MCC, the interpolated virtual array covariance matrix is reconstructed via nuclear norm minimization (NNM) with less influence of these outliers. In this manner, the robust DOA estimation is enabled by the robustly reconstructed covariance matrix. Simulation results demonstrate that the proposed algorithm can effectively the mitigate effect of the miscalibrated sensors while maintaining the enhanced DOF offered by coprime arrays.

Aiming at the method of indoor calibration of a transmission visibility meter has not been established; a new calibration method by measuring the luminous transmittance of attenuator piece was established. Based on the high homogeneity stability lighting and the multi-spectral transmittance analysis technique, a calibration system of the transmission visibility meter by measuring the luminous transmittance of the attenuator piece was designed. The experiment was designed by measuring the luminous transmittance with the range of 0.2–99.2% of a series of calibrated attenuators and the visibility in the range of 40 m to 20 km, then the luminous transmittance errors, the meteorological optical range (MOR) errors and their uncertainty were obtained. The result showed that the luminous transmission error was within ±0.18%, which satisfied the calibration requirement for transmission visibility meter transmittance; the MOR calibration error met the accuracy requirements of the International Civil Aviation Organisation for visibility measurement; this work will have guiding significance in establishing a calibration procedure for the transmission visibility meter.

For proper cave surveying using DistoX, the device needs to be calibrated with adequate accuracy. Calibrating does not require any tools; but, tools to make calibration easier have been developed. Theoretical consideration shows that the use of certain tools enables one to introduce a type of calibration error that goes undetected by the calibration software. In this study, the existence of such errors is experimentally confirmed and their magnitude is estimated. It is demonstrated to be crucial that the DistoX is calibrated and that the calibration is valid, that is, that the device has not changed since it was last calibrated. No part of the DistoX must have moved or changed its magnetization since calibration, not even the battery. The calibration method used and the quality of the resulting calibration are important too. It is highly recommended that the DistoX be checked immediately before surveying a cave and thus avoid the possibility of using an uncalibrated, not validly calibrated, or poorly calibrated device. To complete the check, a few survey shots are measured multiple times with the device at different roll angles, and the back shot of one of the shots is measured. If the device is properly calibrated, the measurements will agree with each other within the acceptable measurement error. This is not the case for a device that is not properly calibrated., Comment: 18 pages, 3 figures, 2 tables

Objective Preoperative digital templating is a standard procedure in total hip arthroplasty. Deviations between template size and final implant size may result from inaccurate calibration, templating as well as intraoperative decisions. So far, the explicit effect of calibration errors on templating has not been addressed adequately. Materials and Methods A mathematical simulation of calibration errors up to ± 24% was applied to the templating of acetabular cups (38 to 72 mm diameter). The effect of calibration errors on template component size as deviation from optimal size was calculated. Results The relationship between calibration error and component size deviation is inverse and linear. Calibration errors have a more pronounced effect on larger component sizes. Calibration errors of 2–6% result in templating errors of up to two component sizes. Common errors of up to 12% may result in templating errors of 3–4 sizes for common implant sizes. A tabular matrix visualizes the effect. Conclusion Calibration errors play a significant role in component size selection during digital templating. Orthopedic surgeons should be aware of this effect and try to identify and address this source of error.

Background Noise-induced hearing loss (NIHL) is often characterized by the presence of an audiometric notch at 3000-6000 Hz in a behavioral audiogram. The audiometric notch is widely used to investigate NIHL in children and young adults. However, the determinants of the audiometric notch in young adults largely remain unknown. Purpose The study aimed to investigate the determinants of the audiometric notch in young adults. Research Design A cross-sectional design was adopted for the study. Study Sample A sample of 124 adults (38 males and 86 females) aged 18-35 years with normal otoscopic and tympanometric findings was recruited. Data Collection and Analysis Hearing thresholds and real-ear sound pressure levels (RESPLs) were obtained with calibrated ER-3A (Etymotic Research, Elk Grove Village, IL) andTDH-50P receivers (Telephonics, Farmingdale, NY). Distortion-product otoacoustic emissions (DPOAEs) were used to evaluate the cochlear function. The external auditory canal (EAC) length was measured using the acoustical method. Noise exposure background (NEB) was estimated using the Noise Exposure Questionnaire. The notched audiograms were identified using: Phillips, Coles, and Niskar criteria. Results The prevalence of notched audiograms was substantially higher for TDH-50P supra-aural receivers than for ER-3A insert receivers. RESPLs at 6000 and 8000 Hz were the major predictors of notched audiograms for TDH-50P receivers. These predictors explained around 45% of the variance in the notched audiograms. The notched audiograms obtained with TDH-50P receivers showed no association with NEB. Individuals with notched audiograms measured using TDH-50P did not show convincing evidence of cochlear dysfunction as assessed by DPOAEs. Individuals with notched audiograms obtained with TDH-50P receivers revealed an average of shorter EAC and a poorer hearing threshold at 6000 Hz. Conclusions The calibration error in the RESPLs at 6000 and 8000 Hz that are likely to be influenced by the shorter EAC was the major determinant of the notched audiograms when the supra-aural transducers were used to measure hearing thresholds. Therefore, the supra-aural receivers should not be used to estimate the prevalence of NIHL in children and young adults when the less restrictive notch identification criteria are used to identify NIHL. Real-ear calibration techniques that are least influenced by the standing waves in the EAC should be preferred when investigating the prevalence of and risk factors for NIHL in young adults.

In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent work has shown that changing just the random seed is enough for otherwise well-tuned deep neural networks to vary in their individual predicted probabilities. In light of this, we investigate the role of model uncertainty methods in the medical domain. Using RNN ensembles and various Bayesian RNNs, we show that population-level metrics, such as AUC-PR, AUC-ROC, log-likelihood, and calibration error, do not capture model uncertainty. Meanwhile, the presence of significant variability in patient-specific predictions and optimal decisions motivates the need for capturing model uncertainty. Understanding the uncertainty for individual patients is an area with clear clinical impact, such as determining when a model decision is likely to be brittle. We further show that RNNs with only Bayesian embeddings can be a more efficient way to capture model uncertainty compared to ensembles, and we analyze how model uncertainty is impacted across individual input features and patient subgroups., Comment: Published in the ACM Conference on Health, Inference, and Learning (CHIL) 2020. Code available at https://github.com/Google-Health/records-research

In this paper, a calibration method is proposed which eliminates the zeroth order effect in lateral shearing interferometry. An analytical expression of the calibration error function is deduced, and the relationship between the phase-restoration error and calibration error is established. The analytical results show that the phase-restoration error introduced by the calibration error is proportional to the phase shifting error and zeroth order effect. The calibration method is verified using simulations and experiments. The simulation results show that the phase-restoration error is approximately proportional to the phase shift error and zeroth order effect, when the phase shifting error is less than 2° and the zeroth order effect is less than 0.2. The experimental result shows that compared with the conventional method with 9-frame interferograms, the calibration method with 5-frame interferograms achieves nearly the same restoration accuracy.

Robot arm with hot wire cutting is one of the emerging techniques used to carve foam in architectural fabrication. A literature review revealed that there is very little research information related to procedures or calibration techniques. This study aimed to present a pedagogical procedure for using the robotic arm with hot wire cutting. The procedure was tested and demonstrated through various conditions that occur during operations. First, the basic structure of a hot wire cutter was described and different feed rates and heat strengths with the cutter were calibrated to determine the appropriate setting values and effects. In addition, toolpath settings are discussed to minimize deviations in the cutting process. The procedure was evaluated based on the roles of tools, design practices and materials in the robotic fabrication. The data collected and optimal conditions established from the test were used to fabricate architectural components. Then, a test model was developed and its components were sliced into appropriate sizes, followed by physical piece-by-piece cutting. The pieces were assembled sequentially and their assemblages were compared with the expected results. The study was concluded with observations that the objectives expected for the fabrication of the test model were met using the tool. Further, the advantages and drawbacks of the process were addressed. In addition, the interactive relation of digital design and robotic fabrication, as well as their contribution to architectural design, was significantly emphasized.

Partial Discharge (PD) measurement on power transformers using Ultra-High Frequency (UHF) techniques has great advantages on IEC method including possibility of online application, localization and greater immunity against external noises. But lacking a method to calibrate or at least determine sensitivity of UHF method for power transformers is the main disadvantage of this method. Thus severity of PD detected by UHF method cannot be determined. In this paper, main parameters that affect the relation between severity of PD, defined by apparent charge of IEC method, and received signals by UHF method are investigated in detail using laboratory measurements and simulation tools. The purpose of paper is to investigate possibility of PD calibration using UHF probes by evaluation of amount of calibration error caused by each of the above parameters. Then, a method to estimate the minimum possible ratio between UHF signal and corresponding apparent charge is proposed based on the results obtained within investigation of calibration parameters.

In the present work partial least square regression (PLS) models were built for quantification of the major components of 154 Brazilian bee pollen samples. Bee pollen has nutritive and therapeutic properties that make it attractive for human health. However, studies on the nutrient and bioactive compound composition of this product are needed, as well as the verification of the presence of contaminants that are harmful to health. The conventional analysis methods are costly and time-consuming, while near infrared spectroscopy (NIR) associated to PLS regression allows a fast and non-costly quantification of the bee pollen components without samples pre-treatment. The calibration models exhibited the determination coefficients, R 2 > 0.94. The mean percent calibration error varied from 1.49 to 5.58%. For external validation, R 2 ranged from 0.89 to 0.98 among the six. The results indicated that some models are good for quantification, while others are qualified for screening calibration.

The U37K′ proxy for past sea surface temperature (SST) is based on the unsaturation ratio of C37 alkenones. It is considered a diagenetically robust proxy, but biases have been invoked because the index can be altered by preferential degradation of the C37:3 alkenone, resulting in higher reconstructed SST. However, alkenone degradation rate constants are poorly constrained, making it difficult to evaluate the plausibility of such a bias. Therefore, we quantitatively assessed the effect of: (1) different alkenone degradation rate constants; (2) differential degradation factors between di- and tri-unsaturated C37 alkenones; (3) and initial U37K′ values on the U37K′ paleothermometer for two depositional environments (shelf and upper-slope), by means of a Reaction-Transport Model (RTM). RTM results reveal that preferential degradation of C37:3 can potentially alter the original signal of the U37K′ paleothermometer, but SST biases (ΔSST) are largely within U37K′ calibration error (ΔSST 1.5 °C are associated with marked downcore decreases in alkenone concentration. Consequently, we caution against the interpretation of U37K′ indices when extensive degradation results in very low alkenone concentrations (

Residual error in calibration coefficients corresponding to observed CMB maps is an important issue while estimating a pure CMB signal. A component separation method, if these errors in the input foreground contaminated CMB maps are not properly taken into account, may lead to bias in the cleaned CMB map and estimated CMB angular power spectrum. But the inability to exactly determine the calibration coefficients corresponding to each observed CMB map from any CMB experiment makes it very difficult to incorporate their exact and actual values in a component separation analysis. Hence the effect of any random and residual calibration error on the cleaned CMB map and its angular power spectrum of a component separation problem can only be understood by performing detailed Monte Carlo simulations. In this paper, we investigate the impact of using input foreground contaminated CMB maps with random calibration errors on posterior density of cleaned CMB map and theoretical CMB angular power spectrum over large angular scales of the sky following the Gibbs ILC method proposed by \cite{Sudevan:2018qyj}. By performing detailed Monte Carlo simulations of WMAP and Planck temperature anisotropy observations with calibration errors compatible with them we show that the best-fit map corresponding to posterior maximum is minimally biased in Gibbs ILC method by a CMB normalization bias and residual foreground bias. The bias in best-fit CMB angular power spectrum with respect to the case where no calibration error is present are $\sim 28 \mu K^2$ and $-4.7 \mu K^2$ respectively between $2 \le \ell \le 15$ and $16 \le \ell \le 32$. The calibration error induced error in best-fit power spectrum causes an overall $6\%$ increase of the net error when added in quadrature with the cosmic variance induced error., Comment: 8 pages, 4 figures

In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately, most existing confidence calibration methods are pertaining to image and document classification tasks where consideration on uncertainty is not crucial. In this paper, we propose a novel calibration method that takes data uncertainty into consideration. The proposed method achieves an outstanding expected calibration error (ECE) (0.57%) mainly owing to data uncertainty consideration, compared to a conventional temperature scaling method of which ECE value is 1.11%., Comment: Submitted to IEEE Conference on Games(CoG) 2020

Factory-calibrated continuous glucose monitoring (FC-CGM) sensors are new devices used in type 1 diabetes (T1D) therapy to measure the glucose concentration almost continuously for 10&ndash, 14 days without requiring any in vivo calibration. Understanding and modelling CGM errors is important when designing new tools for T1D therapy. Available literature CGM error models are not suitable to describe the FC-CGM sensor error, since their domain of validity is limited to 12-h time windows, i.e., the time between two consecutive in vivo calibrations. The aim of this paper is to develop a model of the error of FC-CGM sensors. The dataset used contains 79 FC-CGM traces collected by the Dexcom G6 sensor. The model is designed to dissect the error into its three main components: effect of plasma-interstitium kinetics, calibration error, and random measurement noise. The main novelties are the model extension to cover the entire sensor lifetime and the use of a new single-step identification procedure. The final error model, which combines a first-order linear dynamic model to describe plasma-interstitium kinetics, a second-order polynomial model to describe calibration error, and an autoregressive model to describe measurement noise, proved to be suitable to describe FC-CGM sensor errors, in particular improving the estimation of the physiological time-delay.

Importance Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to automate IED detection have been limited by small samples and have not demonstrated expert-level performance. There is a need for a validated automated method to detect IEDs with expert-level reliability. Objective To develop and validate a computer algorithm with the ability to identify IEDs as reliably as experts and classify an EEG recording as containing IEDs vs no IEDs. Design, Setting, and Participants A total of 9571 scalp EEG records with and without IEDs were used to train a deep neural network (SpikeNet) to perform IED detection. Independent training and testing data sets were generated from 13 262 IED candidates, independently annotated by 8 fellowship-trained clinical neurophysiologists, and 8520 EEG records containing no IEDs based on clinical EEG reports. Using the estimated spike probability, a classifier designating the whole EEG recording as positive or negative was also built. Main Outcomes and Measures SpikeNet accuracy, sensitivity, and specificity compared with fellowship-trained neurophysiology experts for identifying IEDs and classifying EEGs as positive or negative or negative for IEDs. Statistical performance was assessed via calibration error and area under the receiver operating characteristic curve (AUC). All performance statistics were estimated using 10-fold cross-validation. Results SpikeNet surpassed both expert interpretation and an industry standard commercial IED detector, based on calibration error (SpikeNet, 0.041; 95% CI, 0.033-0.049; vs industry standard, 0.066; 95% CI, 0.060-0.078; vs experts, mean, 0.183; range, 0.081-0.364) and binary classification performance based on AUC (SpikeNet, 0.980; 95% CI, 0.977-0.984; vs industry standard, 0.882; 95% CI, 0.872-0.893). Whole EEG classification had a mean calibration error of 0.126 (range, 0.109-0.1444) vs experts (mean, 0.197; range, 0.099-0.372) and AUC of 0.847 (95% CI, 0.830-0.865). Conclusions and Relevance In this study, SpikeNet automatically detected IEDs and classified whole EEGs as IED-positive or IED-negative. This may be the first time an algorithm has been shown to exceed expert performance for IED detection in a representative sample of EEGs and may thus be a valuable tool for expedited review of EEGs.

Objetivo: Fue determinar la viabilidad de la metodología de espectroscopía del infrarrojo cercano para estimar la madurez de la caña de azúcar (Saccharum spp.). Diseño/metodología/aproximación: El manejado de los parámetros para evaluar la madurez fueron los sólidos solubles totales (ºBrix) y Pol (%). Este trabajo se realizó en la Estación Experimental Agroindustrial Obispo Colombres en Tucumán, Argentina, donde se colectaron 1265 muestras de jugos de caña de azúcar en laboratorio, donde se examinaron los sólidos solubles totales (ºBrix) y Pol (%) con los métodos habituales y para la obtención de los espectros de las muestras de jugo se utilizó el espectrofotómetro de infrarrojo cercano FOSS NIR Systems modelo 6500 para líquidos. Y el modelo de predicción para ºBrix y Pol (%) en jugos, se generó a partir del tratamiento matemático SNV y Detrend y arreglos 1,4,4,1 y 2,4,4,1 (derivada, GAP y suavizados, respectivamente). Resultados: Los modelos de predicción generados para ºBrix y Pol (%), tienen valores de error estándar de calibración (SEC) de 0.126 y 0.296; error estándar de predicción (SEP) de 0.181 y 0.327; y coeficiente de correlación de la calibración (R2) de 0.997 y 0.991, respectivamente. Limitaciones/implicaciones: El estudio se realizó en la Estación Experimental Agroindustrial Obispo Colombres en Tucumán, Argentina, por motivos de logística entre Instituciones Públicas y Privadas en México. Hallazgos/Conclusiones: Estos resultados indican que los modelos desarrollados para ºBrix y Pol (%) se pueden utilizar como una alternativa más económica a los procedimientos convencionales en la determinación de la madurez ya que mejora la velocidad en las determinaciones, no emplea reactivos químicos y requiere de menor mano de obra.

Coprime array with M + N sensors can achieve an increased degrees-of-freedom (DOF) of O ( M N ) for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method.

The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most a ordable devices currently available. We recorded EEG signals at Fp1 using the two di erent devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies., Spanish Department of Transportation, Madrid, Spain (Grant No. SPIP2014-1426 to L.L.D.S.), A.C. is funded by a Spanish Ministry of Economy and Competitiveness grant (PSI2016-80558-R to A.C.), S.R. is funded by an Andalusian Government Excellence Research grant (P11-TIC-7983), L.J.F. is funded by a Spanish Ministry of Economy and Competitiveness grant (PSI2014-53427-P) and a Fundación Séneca grant (19267/PI/14), L.L.D.S. is currently supported by the Ramón y Cajal fellowship program (RYC-2015-17483), C.D.-P. is currently supported by the CEIMAR program (CEIMAR2018-2), C.D.-P. and L.L.D.S. are supported by a Santander Bank—CEMIX UGR-MADOC grant (Project PINS 2018-15)

To improve the accuracy of calibration of C-arm, and overcome the space limitation in surgery, we proposed a new calibrator for online calibration of C-arm. After the image rectification by a polynomial fitting-based global correction method, the C-arm was assumed as an ideal pinhole model. The relationships between two kinds of spatial calibration errors and the distribution of fiducial points were studied: the performance of FRE (Fiducial Registration Error) and TRE (Target Registration Error) were not consistent, but both were best at the 12 marked points, the TRE decreased with the increase of the uniformity of calibration points distribution, and with the decrease of the distance between the target point and the center of calibration points. A calibrator with 12 fiducial points conical helically distributed, which could be placed on the knee, was an attractive option. A total of 10 experiments on C-arm calibration accuracy were conducted and the mean value of mapping error was 0.41 mm. We designed an ACL reconstruction navigation system and carried out specimen experiments on 4 pairs of dry femur and tibia. The mean accuracy of navigation system was 0.85 mm, which is important to the tunnel positioning for ACL reconstruction.

An approach for null broadening beamforming is proposed based on adaptive variable diagonal loading (VDL) and combined with the covariance matrix taper (CMT) approach, aiming at improving the robustness of adaptive antenna null broadening beamforming when array calibration error exists. Hence, it is named VDL-CMT. In this novel approach, the signal-to-noise ratio in the tapered sample covariance matrix is estimated and the VDL factor can be obtained adaptively. Then, the covariance matrix of the CMT approach is loaded with the obtained VDL factor. According to simulation results, in the case of array calibration error, robustness of the VDL-CMT is significantly improved and its performance is better than that of the existing adaptive antenna null broadening beamforming approaches.

The traditional antenna calibration for time division duplex multiple input multiple output (TDD-MIMO) systems assume that free-space channel keeps constant during calibration, which is unreasonable under the high-speed rail and other time-varying channel scenarios for time variability can cause calibration error. This paper analyzes the performance of traditional antenna calibration methods, and then proposes an antenna calibration method based on Wiener channel prediction for time-varying TDD-MIMO system. Through theoretical derivation, we get the capacity formula of the TDD-MIMO system using traditional antenna calibration and antenna calibration based on channel prediction. The theoretical analysis and simulation results both indicate that the performance degradation of antenna calibration can be caused by time-varying channel and the prediction algorithm proposed in the paper can well compensate for the performance loss and significantly improve the antenna calibration performance for time-varying TDD-MIMO system.

6 Tablas.-- 3 Figuras, The analytical calibration of an instrumental method is very important, being considered as a key point in method validation. There are different validation guidelines; showing that analytical calibration process variety prevails in terms of nomenclature, methodology employed and acceptance criteria. Very common mistakes in the analytical calibration process are the use of correlation and/or determination coefficients as a test for linearity, the negligence in the heteroscedasticity of the experimental data and selection of appropriate weighting factor, misunderstanding about the regression through the origin and using zero-point calibration. Once the calibration function is established, their linearity can be confirmed by using different procedures such as graphical plots, statistical significance tests and numerical parameters. In particular, deviation from back-calculated concentrations expressed in the form of percentage of relative error (%RE) can be considered very useful for unambiguous linearity evaluation. Some case studies were included to explain the linearity assessment from a practical viewpoint., The author would like to thanks to Dr. Dámaso Hornero and Dr. María Roca for the critical reviews and helpful discussions about the manuscript.

Uncalibrated instrument transformers present at the inputs of phasor measurement units (PMUs) can significantly degrade their outputs. This also causes problems in downstream applications that use PMU data. This paper presents a method for calibrating voltage transformers online using synchrophasor measurements. The proposed approach aims to find the optimal locations where good quality measurements must be added in order to bring the calibration error of all the measurements below a predefined threshold. The IEEE 118-bus system, the IEEE 300-bus system, and a 2383-bus Polish system have been used as the test systems for this analysis. The advantage of the proposed approach is its effectiveness and robustness.