star pdf file

Exploration Rover (MER) expeditions. Topics covered include: mission overview, Mer Human Centered Computers, FIDO
2001 observations and MerBoard prototypes.
Author
Display Devices; Human-Computer Interface; Applications Programs (Computers); Systems Engineering; Information
Transfer; Information Dissemination
20030025348 NASA Goddard Space Flight Center, Greenbelt, MD, USA
Architecture-Adaptive Computing Environment: A Tool for Teaching Parallel Programming
Dorband, John E.; Aburdene, Maurice F.; January 2002; 6 pp.; In English; Frontiers in Education 2002, 6-9 Nov. 2002,
Boston, MA, USA; Copyright; Avail: CASI; A02, Hardcopy
Recently, networked and cluster computation have become very popular. This paper is an introduction to a new C based
parallel language for architecture-adaptive programming, aCe C. The primary purpose of aCe (Architecture-adaptive
Computing Environment) is to encourage programmers to implement applications on parallel architectures by providing them
the assurance that future architectures will be able to run their applications with a minimum of modification. A secondary
purpose is to encourage computer architects to develop new types of architectures by providing an easily implemented
software development environment and a library of test applications. This new language should be an ideal tool to teach
parallel programming. In this paper, we will focus on some fundamental features of aCe C.
Author
Architecture (Computers); C (Programming Language); Parallel Programming
20030025676 NASA Goddard Space Flight Center, Greenbelt, MD, USA
Recursive Hierarchical Image Segmentation by Region Growing and Constrained Spectral Clustering
Tilton, James C.; December 2002; 1 pp.; In English; European Union Satellite Centre (EUSC), 4-7 Dec. 2002, Rome, Italy;
No Copyright; Avail: Other Sources; Abstract Only
This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation
(referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces
a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical
stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized
than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO
region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering)
constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained
spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG’s
computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of
HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid
processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and
for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with
classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.
Author
Applications Programs (Computers); Imaging Techniques; Algorithms
20030028932 NASA Goddard Space Flight Center, Greenbelt, MD, USA
The Goddard VLBI SINEX Files
Ma, Chopo; Petrov, Leonid; [2002]; 1 pp.; In English; IERS Workshop on Combination Research and Global Geophysical
Fluids, 18-21 Nov. 2002, Munich, Germany; No Copyright; Avail: Other Sources; Abstract Only
The Goddard VLBI group generates SINEX files for individual 24-hr sessions and multiyear TRF solutions. Each 24-hr
file includes the reduced normal matrix for the station positions, EOP, and EOP rates. A global Terrestrial Reference Frame
(TRF) SINEX file has the reduced normal matrix for station positions and velocities but no EOP parameters. Both types of
normal matrix are free from any datum constraints and are designed for combination solutions that will later apply the
necessary datum constraints. The SINEX files also contain blocks that provide the constraints and covariance of well-defined
24-hr or global TRF solutions. These blocks are not yet part of the SINEX 2.0 specification. For the TRF solutions the Goddard
VLBI group also generates the corresponding EOP time series. SINEX files and EOP time series are distributed through the
101