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Monitoring by Use of Clusters of Sensor-Data Vectors

Incoming data vectors are compared with clustered vectors representative of normal operation. The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the “health” of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal (“healthy”) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term “inductive” reflects the use of a process reminiscent of traditional mathematical induction to “learn” about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions.

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Processing Satellite Imagery To Detect Waste Tire Piles

Less time is needed for searching for previously unidentified piles. A methodology for processing commercially available satellite spectral imagery has been developed to enable identification and mapping of waste tire piles in California. The California Integrated Waste Management Board initiated the project and provided funding for the method’s development. The methodology includes the use of a combination of previously commercially available image-processing and georeferencing software used to develop a model that specifically distinguishes between tire piles and other objects. The methodology reduces the time that must be spent to initially survey a region for tire sites, thereby increasing inspectors’ and managers’ time available for remediation of the sites. Remediation is needed because millions of used tires are discarded every year, waste tire piles pose fire hazards, and mosquitoes often breed in water trapped in tires. It should be possible to adapt the methodology to regions outside California by modifying some of the algorithms implemented in the software to account for geographic differences in spectral characteristics associated with terrain and climate.

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PPC750 Performance Monitor

The PPC750 Performance Monitor (Perfmon) is a computer program that helps the user to assess the performance characteristics of application programs running under the Wind River VxWorks real-time operating system on a PPC750 computer. Perfmon generates a userfriendly interface and collects performance data by use of performance registers provided by the PPC750 architecture. It processes and presents run-time statistics on a per-task basis over a repeating time interval (typically, several seconds or minutes) specified by the user.

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Application-Program-Installer Builder

A computer program builds application programming interfaces (APIs) and related software components for installing and uninstalling application programs in any of a variety of computers and operating systems that support the Java programming language in its binary form. This program is partly similar in function to commercial (e.g., InstallShield) software. This program is intended to enable satisfaction of a quasi-industry-standard set of requirements for a set of APIs that would enable such installation and uninstallation and that would avoid the pitfalls that are commonly encountered during installation of software. The requirements include the following:

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Using Visual Odometry to Estimate Position and Attitude

A computer program in the guidance system of a mobile robot generates estimates of the position and attitude of the robot, using features of the terrain on which the robot is moving, by processing digitized images acquired by a stereoscopic pair of electronic cameras mounted rigidly on the robot. Developed for use in localizing the Mars Exploration Rover (MER) vehicles on Martian terrain, the program can also be used for similar purposes on terrestrial robots moving in sufficiently visually textured environments: examples include low-flying robotic aircraft and wheeled robots moving on rocky terrain or inside buildings.

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Design and Data Management System

The Design and Data Management System (DDMS) was developed to automate the NASA Engineering Order (EO) and Engineering Change Request (ECR) processes at the Propulsion Test Facilities at Stennis Space Center for efficient and effective Configuration Management (CM). Prior to the development of DDMS, the CM system was a manual, paper-based system that required an EO or ECR submitter to walk the changes through the acceptance process to obtain necessary approval signatures. This approval process could take up to two weeks, and was subject to a variety of human errors. The process also requires that the CM office make copies and distribute them to the Configuration Control Board members for review prior to meetings. At any point, there was a potential for an error or loss of the change records, meaning the configuration of record was not accurate.

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Simple, Script-Based Science Processing Archive

The Simple, Scalable, Script-based Science Processing (S4P) Archive (S4PA) is a disk-based archival system for remote-sensing data. It is based on the data-driven framework of S4P and is used for data transfer, data preprocessing, metadata generation, data archive, and data distribution. New data are automatically detected by the system.

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