Range Measurement Errors Research Articles

Negative correlation and optimal tracking with Doppler measurements

In target tracking systems that utilize Doppler radar (or sonar), the measurements consist of range, range rate, and one or two angles, When multiple sensors are used in the proper geometry, the final accuracy is determined primarily by each sensor's range estimation accuracy. In this case it is particularly important to extract the most accurate state estimate in the range direction by processing optimally the range and range rate measurements. The usual assumption in the design of tracking filters is that the measurement errors in range and range rate are uncorrelated. However, in the case of the commonly used upsweep chirp (linear FM) waveforms, there can be a significant negative correlation between the range and range rate measurement noises. The purpose of this note is to quantify the performance improvement one can obtain when this correlation is present, as it is in certain waveforms, and accounted for.

Length Measurements of the Aorta After Endovascular Abdominal Aortic Aneurysm Repair

Background successful endovascular repair of abdominal aortic aneurysms (AAA) generally leads to a decrease in aneurysm size. Theoretically, this may lead to foreshortening of the excluded segment. If so, vertically rigid endografts may dislocate over time and cover renal or hypogastric arteries. Aim to assess length changes of the infrarenal aorta after endovascular AAA exclusion. Patients and methods forty-four consecutive patients were scheduled for the EndoVascular TechnologiesÒendograft, a vertically non-rigid prosthesis which would potentially accommodate longitudinal changes. Twenty-four patients had completed at least 6 months of follow-up. In 18/24 patients a decrease in size was established by aneurysm volume measurements at 6 months» follow-up. Helical computer tomography (CT) angiograms were processed on a workstation. Aortic lengths were measured along the central lumen line from the lower renal artery orifice to the native aortic bifurcation. The computer tomography angiogram (CTA) reconstruction thickness of 2 mm yields at least a 4-mm error for each length measurement. Results in the shrinking aneurysm group, the median length change was 0 mm (range −9 mm to +4 mm) at 6 months» follow-up (n =18) and also 0 mm (range −7 mm to +4 mm) at 12 months» follow-up ( n =10). In 16/18 patients, length changes remained within the measurement error range of 4 mm. Conclusion in this group of shrinking aneurysms after endovascular AAA repair, foreshortening of the excluded aortic segment appears not to be a clinically significant problem.

A guided-wave optical electric field sensor with improved temperature stability

This paper reports on the study of an electric field sensor based on the Y-cut LiNbO/sub 3/ waveguide which has slight temperature dependence. The errors in the electric field measurements due to temperature variations were theoretically studied and the increase in the error with a temperature change of 100 K has been proven to be extremely low, 2.5%. As a technology to achieve high temperature stability, we adopted a new method of attaching optical fibers to a waveguide and a method of suppressing the pyroelectric effect by depositing (ITO) In/sub 2/O/sub 3/-SnO/sub 3/ films onto the Z-faces. The temperature dependence of the fabricated sensor was measured and it was found that the measurement errors in a broad temperature range from -30 to 90/spl deg/C was /spl plusmn/7% or less.

Tracking Filters for Radar Systems with Correlated Measurement Noise

where the tracking system model presumes white process and measurement noise. In practice, the measurement noise may not be white. Its bandwidth may be on the order of several hertz. For example, target scintillation (or glint) causes the range and angle measurement errors to have a finite bandwidth. Another example of correlated measurement error is the radial velocity measurement error appearing as a result of radar frequency instability and target velocity fluctuations.When the measurement frequency is much smaller than the error bandwidth, the errors of successive measurements are approximately uncorrelated and can be treated as white noise. However, the measurement frequency of some modern radars is sufficiently high and the correlation cannot be ignored without tracking accuracy deterioration.A possible approach to circumvent the effect of colored noise is the target state augmentation technique.

Evaluating the burst hypothesis at a site-specific level using the lack-of-fit test.

It has been hypothesized that periodontal disease progresses by means of sudden losses of periodontal attachment surface area. Obtaining reliable tests of this burst hypothesis has proven to be difficult; the signal (true model of disease progression) often gets lost in the noise. The purpose of this study was to determine how reliably we could distinguish sudden changes from linear disease progression at a site using a time series of clinical attachment levels. Specifically, the following question was investigated: If, in reality, disease progresses by means of sudden changes in clinical attachment level (bursts), and a linear model is fitted to these data, what is the likelihood of rejecting the linear model using the lack-of-fit test? This likelihood was determined as a function of the probing measurement error (range: 0.2 to 1.0 mm) and the number of clinical examinations over time. The results suggested that bursts of 2 mm or smaller cannot be reliably distinguished from linear disease progression using the lack-of-fit test, except under unusual clinical circumstances. Under typical clinical circumstances, burst sizes needed to be 3 to 5 mm in order to be reliably distinguished from linear disease progression. These results are probably overly optimistic. The ability to verify the burst hypothesis at the site level is likely to be even less than our results indicate because of various assumptions that were required. We conclude that the lack-of-fit test will reliably reject the linear model at a site-specific level only if true disease progresses in such a fashion that a handful of sudden changes leads to a tooth mortality event.

Design of a practical tracking algorithm with radar measurements

The extended Kalman filter (EKF) has been widely used as a nonlinear filtering method for radar tracking problems. However, it has been found that if cross-range measurement errors of the target position are large, the performance of the conventional EKF degrades considerably due to nonnegligible nonlinear effects. A new filtering algorithm for improving the tracking performance with radar measurements is developed based on the fact that correct evaluation of the measurement error covariance is possible in the Cartesian coordinate system. The proposed algorithm may be viewed as a modification of the EKF in which the variance of the range measurement errors is evaluated in an adaptive manner. The filter structure facilitates the incorporation of the sequential measurement processing scheme, and this makes the resulting algorithm favorable to both estimation accuracy and computational efficiency. Computer simulation results show that the proposed method offers superior performance in comparison to previous methods. Moreover, our developed algorithm provides some useful insight into the radar tracking problem.

An Algorithm for the Rational Choice of Sodium Profile during Hemodialysis

The incidence of intradialytic disequilibrium syndrome and symptomatic hypotension has increased significantly among dialysis patients over the last ten years. Profiled hemodialysis (PHD) is a new technique, based on the intradialytic modulation of dialysate sodium concentration, which aspires to reduce to previous imbalances. This paper presents a new algorithm for the determination of a rational dialysate sodium profile during PHD. A mathematical model of solute kinetics, monocompartmental for sodium and bicompartmental for urea is used. The algorithm allows the sodium profile to be elaborated a priori before each dialysis session, respecting the individual sodium mass removal and weight gain. A procedure allowing the adjustment of the method to the individual characteristics, on the basis of routine measurements performed before each session is also presented. The method was validated during seven dialysis sessions. Comparison between data measured in vivo and those predicted by the model showed standard deviations corresponding to the range of laboratory measurement errors: 1.50 mEq/L for sodium and 0.87 mmol/L for urea. In vivo implementation of PHD by our algorithm allows one to remove an amount of sodium close to that established a priori on the basis of patient's need.

Simulation of error in optical radar range measurements

We describe a computer simulation of atmospheric and target effects on the accuracy of range measurements using pulsed laser radars with p-i-n or avalanche photodiodes for direct detection. The computer simulation produces simulated images as a function of a wide variety of atmospheric, target, and sensor parameters for laser radars with range accuracies smaller than the pulse width. The simulation allows arbitrary target geometries and simulates speckle, turbulence, and near-field and far-field effects. We compare simulation results with actual range error data collected in field tests.

A hybrid recurrent neural network model for yeast production monitoring and control in a wine base medium

A dynamic model based on a recurrent neural network was established to follow the growth of yeast in a wine-base medium. It leads to the estimation and prediction of the yeast concentration in batch cultures, based on the on-line measurement of the volume of CO 2 released and the initial yeast concentration. The mean error of the predicted value of the final yeast concentration is lower than 5%. A hybrid model combining this model with a measurement model (based on linear correlations reflecting the reaction scheme) also leads to the estimation and prediction of the sugar and ethanol concentrations in the culture medium with respective mean errors of 1.6 and 1 g l −1. Moreover, this model was used in an open-loop control strategy in order to achieve a final concentration of yeast by setting the culture temperature. Adjusting culture temperature during growth was necessary for only 4% of the cultures, in order to remain within the range of measurement error (3×10 6 cells ml −1) of yeast concentration. The performance of the model and of the control algorithm used could be assessed by controlling six successive cultures.

Comment: Systematic errors in ground referenced geometric height monitoring

Manolakis and Lefas see (IEE Proc.-F, vol.140, no.2, p.138-44, 1993) mentioned that the errors in range measurements are due to inner mode delay differences, the effects of bottom and top antenna switching and other factors, such as varying range delay and bias correction quantisation factors, along with transponder time delay. This error is treated by the authors as fixed bias. In general, it is not uniform throughout. The end behaviour is influenced by many factors and therefore cannot be adequately modelled by deterministic variables, and hence this error is to be treated as a random time function.

Analytical partitioning of total cross sections for electron scattering on noble gases

Empirical analytical formulae are proposed, aiming at the description of partial cross sections for electron scattering on noble gases. Selected measurements of total, excitation and ionization cross sections for these gases have been fitted by these analytical formulae in the 20-5000 eV energy range. The elastic cross sections, obtained as the difference, compare quite well with the elastic experimental data for all gases but for helium. The difference elastic cross sections were fitted by a cross section formula derived from electron scattering on a single or double Yukawa potential in the first Born approximation. Our analysis shows a good consistency between partial and total cross sections for the four heavier gases. For helium, we suggest the possibility of large errors in the high energy part of total, elastic and excitation cross sections. The analytical expressions given in this paper allow to reproduce the total and partial cross sections within the measurement errors in a large energy range from a few tens of eV, up to several keV.

Stability and Uniformity of Oil Droplets in Preparation of O/W Emulsion Agar Gel.

We investigated the stability of a monodispersed o/w emulsion and the uniformity of oil droplet distribution in an agar-based emulsion gel during successive stages of gel preparation. At each stage of the emulsion-sol preparation, almost no differences in the droplet diameter and number in excess of the range of measurement error were found. These results directly indicate that the monodispersity of the emulsion was maintained before and after heating of the agar sol. To evaluate oil droplet uniformity in the emulsion gel, sections of 5 mm in height were cut from the top, middle and bottom of the emulsion gel. No differences were found in the number of oil droplets and the coefficient of variation of droplet sizes in top, middle and bottom sections of the gels with oil volume fractions ranging from 0.01 to 0.27 in any of the emulsion gels.

Drug interactions of zonisamide with phenytoin and sodium valproate: serum concentrations and protein binding

The influence of co-medication with zonisamide (ZNS) on the serum concentration and protein binding of phenytoin (PHT) and sodium valproate (VPA) was studied in 21 pediatric patients. No significant correlation between the daily ZNS dose, and total serum concentrations, free concentrations or free fractions (FF) of PHT or VPA was observed. The patient study showed that changes in the FF of PHT and VPA were correlated more closely with the serum protein and bilirubin levels than changes in the ZNS dosage. An in vitro study revealed that the addition of ZNS caused decreases in the FF of PHT and VPA. However, these decreases were within the range of measurement error and were negligible. In conclusion, no significant effect of ZNS on the serum concentration or protein binding of PHT or VPA was demonstrated.

Effects of atmospheric conditions on wind profiles and aeolian sand transport with an example from white sands national monument

AbstractQuantifying aeolian sand transport rates relies upon the computation of the near‐surface shear velocity (u*) determined from velocity profiles of the wind. While it has been recognized that various conditions, such as saltation, surface roughness, surface slope and atmospheric conditions, have an effect on the velocity profile, it is commonly assumed that measurements made above the surface will be representative of the near‐surface shear velocity. Airflow and temperature data collected over a flat substrate at White Sands National Monument in New Mexico, however, show the significant effects that atmospheric conditions have on velocity profiles. During the day, when solar insolation is heating the surface, atmospheric conditions in the lowest several metres become unstable, resulting in enhanced convection and vertical mixing so that the velocity gradient changes little with height. As a result, the shear stress in this region of vertical mixing lessens, while the near‐surface shear stress is increased because the higher wind speeds are now nearer the surface. At night, the near‐surface atmospheric conditions are stable, thereby reducing convection and vertical mixing, resulting in stratified airflow and increased shear velocity away from the surface. Unless this atmospheric effect is accounted for, estimates of sediment transport rates may be in error by as much as a factor of 15 times when wind speeds are near threshold velocity. At wind speeds approaching 10 ms1, at 5m above the surface, this error in computing sediment transport is reduced to a factor of only two to three times, and may be within the range of measurement error.

Validation of Stratospheric Aerosol and Gas Experiments I and II satellite aerosol optical depth measurements using surface radiometer data

The stratospheric aerosol measurement II, stratospheric aerosol and gas experiment (SAGE) I, and SAGE II series of solar occultation satellite instruments were designed for the study of stratospheric aerosols and gases and have been extensively validated in the stratosphere. They are also capable, under cloud‐free conditions, of measuring the extinction due to aerosols in the troposphere. Such tropospheric extinction measurements have yet to be validated by appropriate lidar and in situ techniques. In this paper published atmospheric aerosol optical depth measurements, made from high‐altitude observatories during volcanically quiet periods, have been compared with optical depths calculated from local SAGE I and SAGE II extinction profiles. Surface measurements from three such observatories have been used, one located in Hawaii and two within the continental United States. Data have been intercompared on a seasonal basis at wavelengths between 0.5 and 1.0 μm and found to agree within the range of measurement errors and expected atmospheric variation. The mean rms difference between the optical depths for corresponding satellite and surface measured data sets is 29%, and the mean ratio of the optical depths is 1.09.

Power and Accuracy of the Schmidt and Hunter Meta-Analytic Procedures

Many Monte Carlo studies have evaluated the Schmidt and Hunter (S&H) (1977) meta-analytic procedures for combining independent correlation coefficients and their 75% rule for testing the homogeneity of variance. This study is different in using simulated data that are more typical of those data sets included in meta-analyses (Cormwell, 1989) such that sample size, effect size, degree of range restriction, measurement error, and number of correlations were simultaneously manipulated in evaluating the S&H procedures' reliability, Type I and II error, bias, and standard error. Results supported other studies which revealed that estimates of mean rho tend to be accurate even in the presence of small sample sizes, few correlations, and substantial measurement error. The power to detect variance among the rhos, however, is low in most situations; thus, the S&H procedures do not find variance among rhos when it exists (Type II error) and also have a high Type I error rate under all conditions simulated. These results are summarized in terms of the reliability of the S&H procedure in estimating mean rho and the variance of rhos.

Monte Carlo Comparisons of Three Tests for Homogeneity of Independent Correlations

A comparison of the power and Type I error rates of three tests of homogeneity for independent product-moment correlation coefficients (X;, Q, and LRS) were evaluated using Monte Carlo methods. Conditions simulated include data having varying sample sizes, reliabilities, and range restriction, with central and noncentral conditions, and having normal and nonnormal distributions. The LRS test almost always had a Type I error rate higher than the nominal alpha. Correspondingly, it was always more powerful than the other two tests given the same condition. The X2 and Q tests performed comparably regarding their Type I and Type II error rates. However, for all three tests power decreases substantially with the introduction of range restriction or measurement error when fewer than 18 correlations are being tested. Average Type II error rate for "perfect" data was less than .01 for both a = .05 and .01. The presence of range restriction and unreliability increased the average Type II error rate to approximately .56 for a = .05 and to .76 for a = .01 with no effect on Type I error rate. The Q test is recommended based on its performance in this paper and findings presented by Alexander, Scozzaro, and Borodkin (1989). A general computer program was used to generate these results which can be employed to estimate power for specific situations.

Quantitation of measurement error with Optimal Segments: basis for adaptive time course smoothing

We introduce a novel technique for estimating measurement error in time courses and other continuous curves. This error estimate is used to reconstruct the original (error-free) curve. The measurement error of the data is initially assumed, and the data are smoothed with "Optimal Segments" such that the smooth curve misses the data points by an average amount consistent with the assumed measurement error. Thus the differences between the smooth curve and the data points (the residuals) are tentatively assumed to represent the measurement error. This assumption is checked by testing the residuals for randomness. If the residuals are nonrandom, it is concluded that they do not resemble measurement error, and a new measurement error is assumed. This process continues reiteratively until a satisfactory (i.e., random) group of residuals is obtained. In this case the corresponding smooth curve is taken to represent the original curve. Monte Carlo simulations of selected typical situations demonstrated that this new method ("OOPSEG") estimates measurement error accurately and consistently in 30- and 15-point time courses (r = 0.91 and 0.78, respectively). Moreover, smooth curves calculated by OOPSEG were shown to accurately recreate (predict) original, error-free curves for a wide range of measurement errors (2-20%). We suggest that the ability to calculate measurement error and reconstruct the error-free shape of data curves has wide applicability in data analysis and experimental design.

Performance of a Reservoir Nasal Cannula (Oxymizer) During Sleep in Hypoxemic Patients With COPD

To determine whether a reservoir nasal cannula (RNC) (Oxymizer) provides an arterial hemoglobin oxygen saturation as measured by pulse oximetry (SpO2) equivalent to that provided by the standard nasal cannula (SNC) during sleep in hypoxemic patients with COPD while reducing oxygen flow requirement and cost. The study took place in a sleep laboratory for three nights, with the first night for acclimatization to the new sleeping environment. In a repeated-measures design, on the second and third nights, subjects used the SNC for one night and the RNC on another night. The order in which they received the two devices was counterbalanced. The subjects were patients with COPD who had a stable PaO2 of 55 mm Hg or less or had a value of 56 to 59 mm Hg with evidence of cor pulmonale or polycythemia (or both) and an FEV1/FVC of less than 70 percent. A pulse oximeter was used to measure SpO2. An arterial blood gas measurement was taken on each night while the patients with COPD were receiving oxygen therapy via the assigned device. An EEG machine was used to record measurements of electro-oculography, chin electromyography (EMG), anterior tibialis EMG and EEG. There was a statistically significant difference between mean SpO2 during sleep (RNC, 91 percent; SNC, 93 percent; F = 7.89; p = 0.01). Nocturnal SpO2 was less than 90 percent for 24.2 percent of the time with the RNC and for 17.5 percent of the time with the SNC (F = 5.41; p = 0.03), but there was no significant difference in the amount of time that SpO2 was less than 85 percent. Compared to the SNC, in 4 of 26 patients with COPD, the RNC performed better; in 12 patients with COPD, the RNC performed the same, and in 10 patients with COPD the RNC performed worse during sleep. Sleep parameters were not significantly different between the two devices. The difference of 2 percent in mean SpO2 is within the range of SpO2 measurement error. Therefore, the two devices are equally effective when the sample is considered as a whole. Nighttime oximetry is necessary prior to prescription, since nighttime efficacy of the RNC cannot be predicted on the basis of daytime pulse oximetry.

Conducting Meta-Analysis Using the Proc Means Procedure in Sas

The methodology for using the PROC MEANS procedure in SAS to perform the data computations called for by the Hunter and Schmidt approach to meta-analysis is presented. Using SAS for meta-analytic data analysis offers several potential advantages including ease of use, flexibility, and widespread availability. Specific procedures are presented for calculating summary statistics and then correcting these summary statistics for sampling error and other statistical artifacts such as range restriction and measurement error. One particularly attractive feature of this approach is the ease with which potential moderator variables can be analyzed simply by adding a few additional statements to the SAS program.