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Adaptive Sampling of Spatiotemporal Phenomena With Optimization Criteria

This work was designed to find a way to optimally (or near optimally) sample spatiotemporal phenomena based on limited sensing capability, and to create a model that can be run to estimate uncertainties, as well as to estimate covariances. The goal was to maximize (or minimize) some function of the overall uncertainty.

Posted in: Information Sciences, Briefs, TSP

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Estimating Foreign-Object- Debris Density From Photogrammetry Data

Within the first few seconds after launch of STS-124, debris traveling vertically near the vehicle was captured on two 16-mm film cameras surrounding the launch pad. One particular piece of debris caught the attention of engineers investigating the release of the flame trench fire bricks. The question to be answered was if the debris was a fire brick, and if it represented the first bricks that were ejected from the flame trench wall, or was the object one of the pieces of debris normally ejected from the vehicle during launch. If it was typical launch debris, such as SRB throat plug foam, why was it traveling vertically and parallel to the vehicle during launch, instead of following its normal trajectory, flying horizontally toward the north perimeter fence?

Posted in: Information Sciences, Briefs, TSP

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Building a 2.5D Digital Elevation Model From 2D Imagery

High-quality DEMs are generated from a collection of 2D images. When projecting imagery into a georeferenced coordinate frame, one needs to have some model of the geographical region that is being projected to. This model can sometimes be a simple geometrical curve, such as an ellipse or even a plane. However, to obtain accurate projections, one needs to have a more sophisticated model that encodes the undulations in the terrain including things like mountains, valleys, and even manmade structures. The product that is often used for this purpose is a Digital Elevation Model (DEM).

Posted in: Information Sciences, Briefs

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On a Formal Tool for Reasoning About Flight Software Cost Analysis

A report focuses on the development of flight software (FSW) cost estimates for 16 Discovery-class missions at JPL. The techniques and procedures developed enabled streamlining of the FSW analysis process, and provided instantaneous confirmation that the data and processes used for these estimates were consistent across all missions. The research provides direction as to how to build a prototype rulebased system for FSW cost estimation that would provide (1) FSW cost estimates, (2) explanation of how the estimates were arrived at, (3) mapping of costs, (4) mathematical trend charts with explanations of why the trends are what they are, (5) tables with ancillary FSW data of interest to analysts, (6) a facility for expert modification/ enhancement of the rules, and (7) a basis for conceptually convenient expansion into more complex, useful, and general rule-based systems.

Posted in: Information Sciences, Briefs, TSP

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Display Provides Pilots with Real-Time Sonic-Boom Information

The impact of sonic booms can be controlled over populated areas. Supersonic aircraft generate shock waves that move outward and extend to the ground. As a cone of pressurized air spreads across the landscape along the flight path, it creates a continuous sonic boom along the flight track. Several factors can influence sonic booms: weight, size, and shape of the aircraft; its altitude and flight path; and weather and atmospheric conditions. This technology allows pilots to control the impact of sonic booms.

Posted in: Information Sciences, Briefs, TSP

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Vectorized Rebinning Algorithm for Fast Data Down-Sampling

Applications include image processing, filter design, and anti-aliasing techniques. A vectorized rebinning (down-sampling) algorithm, applicable to Ndimensional data sets, has been developed that offers a significant reduction in computer run time when compared to conventional rebinning algorithms. For clarity, a two-dimensional version of the algorithm is discussed to illustrate some specific details of the algorithm content, and using the language of image processing, 2D data will be referred to as “images,” and each value in an image as a “pixel.” The new approach is fully vectorized, i.e., the down-sampling procedure is done as a single step over all image rows, and then as a single step over all image columns.

Posted in: Information Sciences, Briefs, TSP

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Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery

Clustering/machine learning methods are used to structure data for prioritization, mapping, and downlinking. Many current and future NASA missions are capable of collecting enormous amounts of data, of which only a small portion can be transmitted to Earth. Communications are limited due to distance, visibility constraints, and competing mission downlinks. Long missions and high-resolution, multispectral imaging devices easily produce data exceeding the available bandwidth. To address this situation, computationally efficient algorithms were developed for analyzing science imagery onboard the spacecraft. These algorithms autonomously cluster the data into classes of similar imagery, enabling selective downlink of representatives of each class, and a map classifying the terrain imaged rather than the full dataset, reducing the volume of the downlinked data. A range of approaches was examined, including k-means clustering using image features based on color, texture, temporal, and spatial arrangement.

Posted in: Information Sciences, Briefs

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