Chapter 10 Interlaboratory and intralaboratory surveys Reference methods and reference materials

Precise capacitor ratios are employed in a variety of analog and mixed signal integrated circuits. The use of identical unit capacitors to form larger capacitances can easily produce 1% accuracy, but, in many cases, 0.1% accuracy can provide important performance advantages. Unfortunately, the ultimate matching precision of the ratio is limited by a number of systematic and random error sources. We have analyzed the source and significance of the systematic error sources on actual integrated circuit layouts and isolated five key contributors. Based on this analysis, we have developed a list of generic layout rules and a layout scheme that predict matching accuracies better than 0.1% for the individual systematic error sources using capacitor sizes in the range of 20-40 /spl mu/m.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Context.Filamentary structures are an important part of star-forming interstellar clouds. The properties of filaments hold clues to their formation mechanisms and their role in the star-formation process.Aims.We compare the properties of filaments in the Orion Molecular Cloud 3 (OMC-3), as seen in mid-infrared (MIR) absorption and far-infrared (FIR) dust emission. We also wish to characterise some potential sources of systematic errors in filament studies.Methods.We calculated optical depth maps of the OMC-3 filaments based on the MIR absorption seen inSpitzerdata and FIR dust emission observed withBerscheland the ArTéMiS instrument. We then compared the filament properties extracted from the data. Potential sources of error were investigated more generally with the help of radiative transfer models.Results.The widths of the selected OMC-3 filament segments are in the range 0.03–0.1 pc, with similar average values seen in both MIR and FIR analyses. Compared to the widths, the individual parameters of the fitted Plummer functions are much more uncertain. The asymptotic power-law index has typically valuesp ~3 but with a large scatter. Modelling shows that the FIR observations can systematically overestimate the filament widths. The effect is potentially tens of per cent at column densities aboveN(H2) ~ 1022cm–2but is reduced in more intense radiation fields, such as the Orion region. Spatial variations in dust properties could cause errors of similar magnitude. In the MIR analysis, dust scattering should generally not be a significant factor, unless there are high-mass stars nearby or the dust MIR scattering efficiency is higher than in the tested dust models. Thermal MIR dust emission can be a more significant source of error, especially close to embedded sources.Conclusions.The analysis of interstellar filaments can be affected by several sources of systematic error, but mainly at high column densities and, in the case of FIR observations, in weak radiation fields. The widths of the OMC-3 filaments were consistent between the MIR and FIR analyses and did not reveal any systematic dependence on the angular resolution of the observations.

Contamination of the 5394 Å spectral region by telluric lines

peer reviewed

Measurement of the power spectral density (PSD) of a rough surface or feature involves large random and systematic errors. While random errors can be reduced by averaging together many PSDs, systematic errors can be reduced only by carefully studying and understanding the sources of these systematic errors. Using both analytical expressions and numerical simulations for the measurement of the PSD of line-edge roughness, three sources of systematic errors are evaluated: aliasing, leakage, and averaging. Exact and approximate expressions for each of these terms are derived over a range of roughness exponents, allowing a measured PSD to be corrected for its systematic biases. The smallest measurement bias is obtained when appropriate data windowing is used, and when the sampling distance is set to twice the measurement signal width. Uncorrected PSD measurements are likely to systematically bias the extracted roughness exponent to higher values.

The proposal to develop an extensive North-West European Loran-C system, replacing many existing chains of the Decca Navigator System (DNS), has led to an intensive debate on the merits of the two navigation aids, especially in the United Kingdom. The paper reviews the principal sources of random and systematic position errors in the two systems. The wide range of DNS random errors, predominantly due to skywave interference, are compared with the Loran-C random errors, and typical coverage limits of acceptable repeatable accuracy are presented. The paper also identifies the factors which control the magnitudes of Loran-C and DNS systematic effects due to land paths. It demonstrates that differences between the two systems are substantially less than are predicted by simple models. Loran-C and DNS techniques for dealing with land paths are compared and the errors experienced by Loran users are shown to be reduced by modelling and publishing ASF values.This paper is based on a presentation made by the author at a technical symposium of the Wild Goose Association.

Precise Point Positioning (PPP) is an increasingly recognized precisely the GPS/GNSS positioning technique. In order to improve the accuracy of PPP, the error sources in PPP measurements should be reduced as much as possible and the ambiguities should be correctly resolved. The correct ambiguity resolution requires a careful control of residual errors that are normally categorized into random and systematic errors. To understand effects from two categorized errors on the PPP ambiguity resolution, those two GPS datasets are simulated by generating in locations in South Korea (denoted as SUWN) and Hong Kong (PolyU). Both simulation cases are studied for each dataset; the first case is that all the satellites are affected by systematic and random errors, and the second case is that only a few satellites are affected. In the first case with random errors only, when the magnitude of random errors is increased, L1 ambiguities have a much higher chance to be incorrectly fixed. However, the size of ambiguity error is not exactly proportional to the magnitude of random error. Satellite geometry has more impacts on the L1 ambiguity resolution than the magnitude of random errors. In the first case when all the satellites have both random and systematic errors, the accuracy of fixed ambiguities is considerably affected by the systematic error. A pseudorange systematic error of 5 cm is the much more detrimental to ambiguity resolutions than carrier phase systematic error of 2 mm. In the 2 nd case when only a portion of satellites have systematic and random errors, the L1 ambiguity resolution in PPP can be still corrected. The number of allowable satellites varies from stations to stations, depending on the geometry of satellites. Through extensive simulation tests under different schemes, this paper sheds light on how the PPP ambiguity resolution (more precisely L1 ambiguity resolution) is affected by the characteristics of the residual errors in PPP observations. The numerical examples recall the PPP data analysts that how accurate the error correction models must achieve in order to get all the ambiguities resolved correctly.

We use a bootstrap simulation framework to evaluate the relative importance of different sources of random and systematic error when estimating diet or food consumption of cetaceans, using a data set on harbour porpoise diet in Scottish (UK) waters from 1992–2003 (N=180) as a model. We also evaluate the consequences of applying explicit weightings to individual samples and/or sub-sets (‘strata’) of samples. In terms of the precision of estimates of diet composition, sampling error was the most important source of error, to the extent that overall 95% confidence limits changed only very slightly when sub-sampling error and regression errors were taken into account. On the other hand, for estimates of total food consumption by the porpoise population in Scottish waters, uncertainties about population size and energetic requirements were more important than uncertainty about diet composition. In relation to the accuracy of estimates of diet composition, the study also highlighted the importance of selecting regressions appropriate to prey in the study area (as opposed to ones constructed for the same prey species in another area) and demonstrated that applying equal weighting to individual samples or sample strata can substantially alter the resulting picture of diet. Therefore, the rationale for applying such weightings needs to be carefully considered.

Solid tissue phantom are the preferred tool for the development, validation, testing and calibration of photon migration instrument. Accuracy, or trueness, of the optical properties of reference phantoms is of the utmost importance as they will be used as the conventional true value against which instrument errors will be evaluated. A detailed quantitative analysis of the uncertainty of time-resolved transmittance characterization of solid optical tissue phantom is presented. Random error sources taken into account are Poisson noise of the photon counting process, additive dark count noise and instrument response function stability. Systematic error sources taken into account are: phantom thickness uncertainty, refractive index uncertainty, time correlated single photon counting system time base calibration uncertainty. Correction procedures for these systematic errors are presented whenever a correction is possible.

Oral and cervical cancers are a growing global health problem that disproportionately impacts women and men living in the developing world. The high death rate in developing countries is largely due to the fact that these countries do not have the appropriate medical infrastructure and resources to support the organized screening and diagnostic programs that are available in the developed world. Diffuse reflectance spectroscopy (DRS) with a fiber-optic probe can noninvasively quantify the optical properties of epithelial tissues and has shown the potential as a cost-effective, easy-to-use, and sensitive tool for diagnosis of early precancerous changes in the cervix and oral cavity. However, current fiber-optic DRS systems have not been designed to be robust and reliable for use in developing countries. They are subject to various sources of systematic or random errors, arising from the uncontrolled probe-tissue interface and lack of real-time calibration, use bulky and expensive optical components, and require extensive training. This chapter describes a portable DRS device that is specifically designed for detection of oral and cervical cancers in resource-poor settings. The device uses an innovative smart fiber-optic probe to eliminate operator bias, state-of-the-art photonics components to reduce size and power consumption, and automated software to reduce the need of operator training. The size and cost of the smart fiber-optic DRS system may be further reduced by incorporating a smartphone based spectrometer.

Human movement analysis using stereophotogrammetryPart 2: Instrumental errors

TRMM/CERES‐VIRS Single Satellite Footprint (SSF) data and AERONET Sun/sky radiometer observations from 1998 have been combined to validate SSF aerosol optical thickness (τ) retrievals over ocean along with a quantitative evaluation of the effects of potential subpixel cloud contamination and surface wind on the satellite τ retrievals. Potential subpixel cloud contamination is verified in Visible/Infrared Scanner (VIRS) SSF aerosol retrievals and constitutes a major source of systematic and random errors of the retrieval algorithm as determined from comparisons with AERONET observations. A positive correlation between the surface wind speed (which determines the roughness of the ocean surface) and the SSF τ has been observed for large surface wind speed. The validation results imply this correlation represents the real relationship between the surface wind and the wind‐driven aerosols rather than the disturbing effect of the surface reflectance associated with the rough ocean surface. After the potential subpixel cloud contamination is minimized and the effects of large surface wind are removed in the τ match‐ups, the positive biases in the SSF τ (compared to AERONET τ) for mean conditions have been reduced from 0.05 to 0.02 in VIRS channel 1 (0.63 μm) and 0.05 to 0.03 in channel 2 (1.61 μm). Random errors have also been reduced from 0.09 to 0.06 at 0.63 μm, and from 0.06 to 0.05 at 1.61 μm. The validation results support the application of the SSF aerosol data in radiation and climate studies as well as supply useful guidance for the adjustment and improvement of the aerosol retrieval algorithm.

Evaluation of the Flow-Dialysis Technique for Analysis of Protein-Ligand Interactions: An Experimental and a Monte Carlo Study

Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error. We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE.

Differential absorption lidar and Raman lidar have been applied to the range-resolved measurements of water vapor density for more than 20 years. During this period, there have been considerable advances in laser and lidar technology, as well as in the understanding of the factors required to optimize both lidar techniques for water vapor measurements. Results have been obtained using both lidar techniques that have led to improved understanding of water vapor distributions in the atmosphere. This paper reviews the theory of the measurements, including the sources of systematic and random error; the progress in lidar technology and techniques during that period, including a brief look at some of the lidar systems in development or proposed; and the steps being taken to improve such lidar systems.