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Trusted materials using orthogonal testing. 2015 Annual report

Van Benthem, Mark V.

The purpose of this project is to prove (or disprove) that a reasonable number of simple tests can be used to provide a unique data signature for materials, changes in which could serve as a harbinger of material deviation, prompting further evaluations. The routine tests are mutually orthogonal to any currently required materials specification tests.

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TYPE Other Report YEAR 2015
OSTIDOI

An evaluation of algorithms and methods for compressing and decompressing atmospheric transmission data for use in at-sensor measurements

Proceedings of SPIE - The International Society for Optical Engineering

Van Benthem, Mark V.; Woodbury, Drew P.

In this paper, we describe the use of various methods of one-dimensional spectral compression by variable selection as well as principal component analysis (PCA) for compressing multi-dimensional sets of spectral data. We have examined methods of variable selection such as wavelength spacing, spectral derivatives, and spectral integration error. After variable selection, reduced transmission spectra must be decompressed for use. Here we examine various methods of interpolation, e.g., linear, cubic spline and piecewise cubic Hermite interpolating polynomial (PCHIP) to recover the spectra prior to estimating at-sensor radiance. Finally, we compressed multi-dimensional sets of spectral transmittance data from moderate resolution atmospheric transmission (MODTRAN) data using PCA. PCA seeks to find a set of basis spectra (vectors) that model the variance of a data matrix in a linear additive sense. Although MODTRAN data are intricate and are used in nonlinear modeling, their base spectra can be reasonably modeled using PCA yielding excellent results in terms of spectral reconstruction and estimation of at-sensor radiance. The major finding of this work is that PCA can be implemented to compress MODTRAN data with great effect, reducing file size, access time and computational burden while producing high-quality transmission spectra for a given set of input conditions.

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TYPE Conference Poster YEAR 2015
ScopusOSTI

Tensor analysis methods for activity characterization in spatiotemporal data

Haass, Michael J.; Van Benthem, Mark V.; Ochoa, Edward M.

Tensor (multiway array) factorization and decomposition offers unique advantages for activity characterization in spatio-temporal datasets because these methods are compatible with sparse matrices and maintain multiway structure that is otherwise lost in collapsing for regular matrix factorization. This report describes our research as part of the PANTHER LDRD Grand Challenge to develop a foundational basis of mathematical techniques and visualizations that enable unsophisticated users (e.g. users who are not steeped in the mathematical details of matrix algebra and mulitway computations) to discover hidden patterns in large spatiotemporal data sets.

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TYPE SAND Report YEAR 2014
OSTIDOI
Results 26–50 of 75
Results 26–50 of 75