Tech Briefs

SmaggIce Version 1.8

SmaggIce version 1.8 is a set of software tools for geometrical modeling of, and generation of grids that conform to, both clean and iced airfoils. A prior version (SmaggIce 1.2) was described in “Preparing and Analyzing Iced Airfoils” (LEW-17399), NASA Tech Briefs, Vol. 28, No. 8 (August 2004), page 32. Ice shapes, especially those that include rough surfaces, pose difficulty in generating high-quality grids that are essential for predicting airflows by use of computational fluid dynamics. SmaggIce version 1.8 contains software tools needed to overcome this difficulty. For a given airfoil, it allows the user to define the flow domain, decompose the domain into blocks, generate grids, merge gridded blocks, and control the density and smoothness of each grid. Among the unique features of version 1.8 is a thin Cshaped block, called a “viscous sublayer block,” which is wrapped around an iced airfoil and its wake line and serves as a means to generate highly controlled grids near the rough ice surface. Users can modify block boundary shapes using control points of non-uniform rational B-spline (NURBS) curves. Concave ice regions can be smoothed during geometrical modeling or creation of the viscous sublayer block.

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Processing TES Level-1B Data

TES L1B Subsystem is a computer program that performs several functions for the Tropospheric Emission Spectrometer (TES). The term “L1B” (an abbreviation of “level 1B”), refers to data, specific to the TES, on radiometric calibrated spectral radiances and their corresponding noise equivalent spectral radiances (NESRs), plus ancillary geolocation, quality, and engineering data. The functions performed by TES L1B Subsystem include shear analysis, monitoring of signal levels, detection of ice build-up, and phase correction and radiometric and spectral calibration of TES target data. Also, the program computes NESRs for target spectra, writes scientific TES level-1B data to hierarchical- data-format (HDF) files for public distribution, computes brightness temperatures, and quantifies interpixel signal variability for the purpose of firstorder cloud and heterogeneous land screening by the level-2 software summarized in the immediately following article. This program uses an in-housedeveloped algorithm, called “NUSRT,” to correct instrument line-shape factors.

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Processing TES Level-2 Data

TES Level 2 Subsystem is a set of computer programs that performs functions complementary to those of the program summarized in the immediately preceding article. TES Level-2 data pertain to retrieved species (or temperature) profiles, and errors thereof. Geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also included. The subsystem processes gridded meteorological information and extracts parameters that can be interpolated to the appropriate latitude, longitude, and pressure level based on the date and time. Radiances are simulated using the aforementioned meteorological information for initial guesses, and spectroscopic-parameter tables are generated. At each step of the retrieval, a nonlinear-least-squares-solving routine is run over multiple iterations, retrieving a subset of atmospheric constituents, and error analysis is performed. Scientific TES Level-2 data products are written in a format known as Hierarchical Data Format Earth Observing System 5 (HDF-EOS 5) for public distribution.

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Improvement in Recursive Hierarchical Segmentation of Data

A Segmentation of the Landsat ETM+ Image displayed on the left is shown on the right. The new approach eliminates processing artifacts. A further modification has been made in the algorithm and implementing software reported in “Modified Recursive Hierarchical Segmentation of Data” (GSC-14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectralimage data). The output of a prior version of the software contained artifacts, including spurious segmentation-image regions bounded by processing-window edges.The modification for suppressing the artifacts, mentioned in the cited article, was addition of a subroutine that analyzes data in the vicinities of seams to find pairs of regions that tend to lie adjacent to each other on opposite sides of the seams. Within each such pair, pixels in one region that are more similar to pixels in the other region are reassigned to the other region. The present modification provides for a parameter ranging from 0 to 1 for controlling the relative priority of merges between spatially adjacent and spatially non-adjacent regions. At 1, spatially-adjacent-/ spatially-non-adjacent- region merges have equal priority. At 0, only spatially-adjacent-region merges (no spectral clustering) are allowed. Between 0 and 1, spatially-adjacent- region merges have priority over spatially-non-adjacent ones.

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Using Heaps in Recursive Hierarchical Segmentation of Data

speed has been made in the algorithm and implementing software reported in “Modified Recursive Hierarchical Segmentation of Data” (GSC-14681-1), NASA Tech Briefs, Vol. 30, No. 6 (June 2006), page 51. That software performs recursive hierarchical segmentation of data having spatial characteristics (e.g., spectral-image data). The segmentation process includes an iterative subprocess, in each iteration of which it is necessary to determine a best pair of regions to merge [merges being justified by one or more measure(s) similarity of pixels in the regions]. In the previously reported version of the algorithm and software, the choice of a best pair of regions to merge involved the use of a fully sorted list of regions. That version was computationally inefficient because a fully sorted list is not needed: what is needed is only the identity of the pair of regions characterized by the smallest measure of dissimilarity. The present modification replaces the use of a fully sorted list with the use of data heaps, which are computationally more efficient for performing the required comparisons among dissimilarity measures. The modification includes the incorporation of standard and modified functions for creating and updating data heaps.

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Tool for Statistical Analysis and Display of Landing Sites

MarsLS is a software tool for analyzing statistical dispersion of spacecraft-landing sites and displaying the results of its analyses. Originally intended for the Mars Explorer Rover (MER) mission, MarsLS is also applicable to landing sites on Earth and non-MER sites on Mars. MarsLS is a collection of interdependent MATLAB scripts that utilize the MATLAB graphical-user- interface software environment to display landing-site data (see figure) on calibrated image-maps of the Martian or other terrain. The landing- site data comprise latitude/longitude pairs generated by Monte Carlo runs of other computer programs that simulate entry, descent, and landing. Using these data, MarsLS can compute a landing-site ellipse — a standard means of depicting the area within which the spacecraft can be expected to land with a given probability. MarsLS incorporates several features for the user’s convenience, including capabilities for drawing lines and ellipses, overlaying kilometer or latitude/longitude grids, drawing and/or specifying lines and/or points, entering notes, defining and/or displaying polygons to indicate hazards or areas of interest, and evaluating hazardous and/or scientifically interesting areas. As part of such an evaluation, MarsLS can compute the probability of landing in a specified polygonal area.

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Automated Assignment of Proposals to Reviewers

A computer program automates the process of selecting unbiased peer reviewers of research proposals submitted to NASA. Heretofore, such selection has been performed by manual searching of two large databases subject to a set of assignment rules. One database lists proposals and proposers; the other database lists potential reviewers. The manual search takes an average of several weeks per proposal. In contrast, the present software can perform the selection in seconds. The program begins by selecting one entry from each database, then applying the assignment rules to this pair of entries. If and only if all the assignment rules are satisfied, the chosen reviewer is assigned to the chosen proposal. The assignment rules enforced by the program are (1) a maximum allowable number of proposals assigned to a single reviewer; (2) a maximum allowable number of reviewers assigned to a single proposal; (3) if the proposing team includes a member affiliated with an industry, then the reviewer must not be affiliated with any industry; and (4) the reviewer must not be a member of the proposing team or affiliated with the same institution as that of a member of the proposing team.

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