Adjustment of ocean color sensor calibration through multi-band statistics

The band-by-band vicarious calibration of on-orbit satellite ocean color instruments, such as SeaWiFS and MODIS, using ground-based measurements has significant residual uncertainties. This paper applies spectral shape and population statistics to tune the calibration of the blue bands against each other to allow examination of the interband calibration and potentially provide an analysis of calibration trends. This adjustment does not require simultaneous matches of ground and satellite observations. The method demonstrates the spectral stability of the SeaWiFS calibration and identifies a drift in the MODIS instrument onboard Aqua that falls within its current calibration uncertainties.

Atmospheric chlorofluoromethanes as tracers of ocean circulation and mixing: measurement and calibration techniques and studies in the Greenland and Norwegian seas

EXTRACT (SEE PDF FOR FULL ABSTRACT): An analytical system was designed and constructed for the rapid and accurate shipboard measurement of anthropogenic chlorofluoromethanes in seawater and in air, using electron capture gas chrometography. The distribution of these compounds in the marine atmosphere and the water column in the Greenland and Norwegian seas were studied during February and March, 1982. The compounds, dissolved in the ocean from the atmosphere, can be used as tracers of subsurface ocean circulation and mixing processes.

Multivariate climate reconstruction for the last 14,000 years in southernmost South America

Comparison between past changes in pollen assemblages and stable isotope ratios (deuterium and carbon) analyzed in the same peat core from Tierra del Fuego at latitude 55°S permitted identification of the relative contribution of precipitation versus temperature responsible for the respective change. Major steps in the sequence of paleoenvironmental changes, such as at 12700, 9000, 5000, and 4000 years ago are apparently related only to increase in precipitation, reflecting the latitudinal location and intensity of the westerly storm tracks. On the other hand, high paleoenvironmental variability, which is characteristic for the late-glacial and the latest Holocene, is related to temperature variability, which affects the relative moisture content. Comparison with other paleoenvironmental records suggests that the late-glacial temperature variability is probably related to variability in the extent of Antarctic sea-ice, which in turn appears to be related to the intensity of Atlantic deep-water circulation. Temperature variability during the latest Holocene, on the other hand, is probably related to the dynamics of the El Niño/Southern Oscillation.

A multivariate statistical study with a factor analysis of recent planktonic foraminiferal distribution in the Coromandel Coast of India

A study of planktonic foraminiferal assemblages from 19 stations in the neritic and oceanic regions off the Coromandel Coast, Bay of Bengal has been made using a multivariate statistical method termed as factor analysis. On the basis of abundance, 17 foraminiferal species, species were clustered into 5 groups with row normalisation and varimax rotation for Q-mode factor analysis. The 19 stations were also grouped into 5 groups with only 2 groups statistically significant using column normalisation and varimax rotation for R-mode analysis. This assemblage grouping method is suitable because groups of species/stations can explain the maximum amount of variation in them in relation to prevailing environmental conditions in the area of study.

APPROXIMATE GAUSS-MARKOV REALISATION OF MULTIVARIATE STOCHASTIC PROCESSES.

Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks to determine a finite-dimensional linear time-invariant system which, when driven by white noise, will produce an output whose spectral density is approximately PHI ( omega ), and an approximate spectral factor of PHI ( omega ). The author employs the Anderson-Faurre theory in his analysis.

MULTIVARIATE COMPARISON OF THE FORELIMB BETWEEN STUMP-TAILED AND RHESUS MACAQUES

Rhesus macaques and stump-tailed macaques are sympatric in western Yunnan (China), coexisting or occupying habitats that show little difference. This paper tests hypotheses based on theoretical expectation from the differing biomechanical demands of terrestrial and arboreal quadrupedalism in stump-tailed macaques and rhesus macaques, respectively. Individuals of these two macaque taxa were markedly separated by the first two principal components and discriminant analyses based on 18 variables of the upper limb. The rhesus macaques appear to be more adapted for arboreal quadruped habits because of elongation of the clavicle and forearm, a larger humeral head and greater midshaft sagittal diameters of the radius and ulna.

Auto-calibration of a camera system using Image Alignment

Calibration of a camera system is a necessary step in any stereo metric process. It correlates all cameras to a common coordinate system by measuring the intrinsic and extrinsic parameters of each camera. Currently, manual calibration of a camera system is the only way to achieve calibration in civil engineering operations that require stereo metric processes (photogrammetry, videogrammetry, vision based asset tracking, etc). This type of calibration however is time-consuming and labor-intensive. Furthermore, in civil engineering operations, camera systems are exposed to open, busy sites. In these conditions, the position of presumably stationary cameras can easily be changed due to external factors such as wind, vibrations or due to an unintentional push/touch from personnel on site. In such cases manual calibration must be repeated. In order to address this issue, several self-calibration algorithms have been proposed. These algorithms use Projective Geometry, Absolute Conic and Kruppa Equations and variations of these to produce processes that achieve calibration. However, most of these methods do not consider all constraints of a camera system such as camera intrinsic constraints, scene constraints, camera motion or varying camera intrinsic properties. This paper presents a novel method that takes all constraints into consideration to auto-calibrate cameras using an image alignment algorithm originally meant for vision based tracking. In this method, image frames are taken from cameras. These frames are used to calculate the fundamental matrix that gives epipolar constraints. Intrinsic and extrinsic properties of cameras are acquired from this calculation. Test results are presented in this paper with recommendations for further improvement.

Calibration of the spring constant of cantilevers of arbitrary shape using the phase signal in an atomic force microscope.

The measurement of cantilever parameters is an essential part of performing a calibrated measurement with an atomic force microscope (AFM). The thermal motion method is a widely used technique for calibrating the spring constant of an AFM cantilever, which can be applied to non-rectangular cantilevers. Given the trend towards high frequency scanning, calibration of non-rectangular cantilevers is of increasing importance. This paper presents two results relevant to cantilever calibration via the thermal motion method. We demonstrate the possibility of using the AFM's phase signal to acquire the thermal motion. This avoids the challenges associated with connecting the raw photodiode signal to a separate spectrum analyser. We also describe how numerical calculations may be used to calculate the parameters needed in a thermal motion calibration of a non-rectangular cantilever. Only accurate knowledge of the relative size of the in-plane dimensions of the cantilever is needed in this computation. We use this pair of results in the calibration of a variety of rectangular and non-rectangular cantilevers. We observe an average difference between the Sader and thermal motion values of cantilever stiffness of 10%.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

Dynamic Covariance Models for Multivariate Financial Time Series

The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covariances. Over-fitting and local optima are avoided by following a Bayesian approach instead of computing point estimates. Changes in market conditions are captured by assuming a diffusion process in parameter values, and finally computationally efficient and scalable inference is performed using particle filters. Experiments with financial data show excellent performance of the proposed method with respect to current standard models.

Stable multivariate rational interpolation for parameter-dependent aerospace models

A multivariate, robust, rational interpolation method for propagating uncertainties in several dimensions is presented. The algorithm for selecting numerator and denominator polynomial orders is based on recent work that uses a singular value decomposition approach. In this paper we extend this algorithm to higher dimensions and demonstrate its efficacy in terms of convergence and accuracy, both as a method for response suface generation and interpolation. To obtain stable approximants for continuous functions, we use an L2 error norm indicator to rank optimal numerator and denominator solutions. For discontinous functions, a second criterion setting an upper limit on the approximant value is employed. Analytical examples demonstrate that, for the same stencil, rational methods can yield more rapid convergence compared to pseudospectral or collocation approaches for certain problems. © 2012 AIAA.