Information Technology and Software

PATENT PORTFOLIO
Information Technology and Software
Information Technology and Software
The development, implementation and maintenance of computer hardware and software systems to produce, store, organize, analyze, model, simulate and communicate information electronically.
ARC RIG
Reconfigurable Image Generator and Database Generation System
The system, the Reconfigurable Image Generator (RIG), consists of software and a hardware configuration, and a Synthetic Environment Database Generation System (RIG-DBGS). This innovative Image Generator (IG) uses Commercial-Off-The-Shelf (COTS) technologies and is capable of supporting virtually any display system. The DBGS software leverages high-fidelity real-world data, including aerial imagery, elevation datasets, and vector data. Through a combination of COTS tools and in-house created applications, the semi-automated system can process large amounts of data in days rather than weeks or months, a disadvantage of manual database generation. A major benefit of the RIG technology is that existing simulation users can leverage their investment in existing real-time 3D databases (such as OpenFlight) as part of the RIG system.
'Honeycombs' and Hexacopters Help Tell Story of Mars
Shaped External Occulter Software Suite
Assessment of the optical performance of a given occulter design requires Fresnel diffraction in 2-dimensions to propagate the light from the external occulter to the telescope and then further diffractive propagation through the telescope to the focal plane. This software performs these two propagations, with the addition of manufacturing, deployment, vibrational errors, holes and deformations on the external occulter, and with the addition of wavefront and amplitude errors within the telescope. The propagation approach is parametric with respect to the wavelength of light and with respect to mispointing of the occulter and telescope relative to the star. The software propagates the ideal occulter separately from calculating errors, eliminating the need for large computers to perform these calculations.
The heart of the NASA Center for Climate Simulation (NCCS) is the Discover supercomputer. In 2009, NCCS added more than 8,000 computer processors to Discover, for a total of nearly 15,000 processors.
First Stage Bootloader
The First Stage Bootloader reads software images from flash memory, utilizing redundant copies of the images, and launches the operating system. This bootloader finds a valid copy of the OS image and the ram filesystem image in flash memory. If none of the copies are fully valid, the bootloader attempts to construct a fully valid image by validating the images in small sections and piecing together validated sections from multiple copies. Periodically, throughout this process, the First Stage Bootloader restarts the watchdog timer. The First Stage Bootloader reads a boot table from a default location in flash memory. This boot table describes where to find the OS image and its supporting ram filesystem image in flash. It uses header information and a checksum to validate the table. If the table is corrupt, it reads the next copy until it finds a valid table. There can be many copies of the table in flash, and all will be read if necessary. The First Stage Bootloader reads the ram filesystem image into memory and validates its contents. Similar to the boot table, if one copy of the image is corrupt, it will read the remaining copies until it finds one with a valid header. If it doesn't find a valid copy, it will break the image down into smaller portions. For each section, it checks each copy until it finds a valid copy of the section and copies the valid section into a new copy of the image. The First Stage Bootloader reads the OS image and interprets it. If anything in the image is corrupt, it reads the remaining copies until it finds a fully valid copy. If no copy is fully valid, it will use individual valid records from multiple copies to create a fully valid image.
Tycho Crater's Peak; Credit: NASA Goddard/Arizona State University
Space Link Extension Return Channel Frames (SLE-RCF) Software Library
The Space Link Extension Return Channel Frames (SLE-RCF) software library helps to monitor the health and safety of spacecraft by enabling space agency ground support and mission control centers to develop standardized and interoperable mission control applications for space telemetry data. The software library eliminates the need for missions to implement custom data communication designs to communicate with any ground station. The two main tasks accomplished via the SLE-RCF software library are processing user requests and receiving data from ground stations and ground support assets. The software library contains three layers: -SLE (Space Link Extension) for the abstract workings of the protocol -DEL (Decoding and Encoding Layer) to decode and encode the abstract messages used by the SLE layer -TML (Transport Mapping Layer) to transfer the encoded messages via some underlying transport layer protocol, such as as the transmission control protocol (TCP) The library accepts configuration or SLE-RCF directives from the user and responds accordingly. Incoming data, both telemetry frames and status messages, are processed and the appropriate callback routines are triggered by the library.
Ground Station
Signal Combiner for Wideband Communication
Through low-loss signal combination, Glenn is leading the way to optimize radio transmission remotely during self-checking routines. Glenn's signal combiner offers a simple method to minimize signal loss significantly when combining two signals. Using conventional combiners in bit-error-rate testing results in a loss of 3 to 4 dB per band, and with a directional coupler the secondary signal experiences losses of 10 dB or more. Moreover, during signal measurements, the additional components must be placed and later removed to prevent any impact to the measurement, making for a cumbersome process. Glenn's solution is to combine the primary and secondary signals in the frequency domain through the use of a frequency division diplexer/multiplexer in combination with a wideband ADC. The multiplexer selects one or more bands in the frequency domain, and the ADC performs a non-linear conversion to digital domain by folding out-of-band signals in with the primary signal. NASA makes use of subsampling a given band within the ADC bandwidth to fold it into another band of interest, effectively frequency-shifting them to a common frequency bandwidth. Glenn's breakthrough method has two significant advantages over the conventional use of a power combiner or directional coupler in bit-error-rate testing: 1) it combines signal and noise (secondary signal) with very low loss, and 2) it enables the selection of the desired signal-to-noise ratio with no need for the later cumbersome removal of components. This streamlined process allows for invaluable in-situ or installed measurement. Glenn's novel technology has great potential for satellite, telecommunications, and wireless industries, especially with respect to equipment testing, measurement, calibration, and check-out.
Hubble's View of Comet Siding Spring; Credit: NASA, ESA, and J.-Y. Li (Planetary Science Institute)
Automata Learning in Generation of Scenario-Based Requirements in System Development
In addition, the higher the level of abstraction that developers can work from, as is afforded through the use of scenarios to describe system behavior, the less likely that a mismatch will occur between requirements and implementation and the more likely that the system can be validated. Working from a higher level of abstraction also provides that errors in the system are more easily caught, since developers can more easily see the big picture of the system. This technology is a technique for fully tractable code generation from requirements, which has an application in other areas such as generation and verification of scripts and procedures, generation and verification of policies for autonomic systems, and may have future applications in the areas of security and software safety. The approach accepts requirements expressed as a set of scenarios and converts them to a process based description. The more complete the set of scenarios, the better the quality of the process based description that is generated. The proposed technology using automata learning to generate possible additional scenarios can be useful in completing the description of the requirements.
Front Image
Interactive Diagnostic Modeling Evaluator
The i-DME is a computer-user interactive procedure for repairing the system model through its abstract representation, diagnostic matrix (D-matrix) and then translating the changes back to the system model. The system model is a schematic representation of faults, tests, and their relationship in terms of nodes and arcs. D-matrix is derived from the system models propagation paths as the relationships between faults and tests. When the relation exists between fault and test, it is represented as 1 in the D-matrix. To repair the D-matrix and wrapper/test logic by playing back a sequence of nominal and failure scenarios (given), the user sets the performance criteria and accepts/declines the proposed repairs. During D-matrix repair, the interactive procedure includes conditions ranging from modifying 0s and 1s in the matrix, adding/removing the rows (failure sources) columns (tests), or modifying test/wrapper logic used to determine test results. The translation of changes to the system model is done via a process which maps each portion of the D-matrix model to the corresponding locations in the system model. Since the mapping back to the system model is non-unique, more than one candidate system model repair can be suggested. In addition to supporting the modification, it provides a trace for each modification such that a rational basis for each decision can be verified.
Robotic Refueling Mission 3 (RRM3)
Goddard's Reconfigurable Laser Ranger (GRLR)
NASA Goddard Space Flight Center has developed a low cost, modular, and flexible space flight laser range finder consisting of optics, electronics, and interfaces for satellite servicing missions (i.e. Restore-L) using customized optics. Built upon previous NASA technologies, the system also consists of a high dynamic range receiver and adjustable laser for a wide range of measurements (i.e. multiples of km to sub-meter).
Technology Example
Computational Visual Servo
The innovation improves upon the performance of passive automatic enhancement of digital images. Specifically, the image enhancement process is improved in terms of resulting contrast, lightness, and sharpness over the prior art of automatic processing methods. The innovation brings the technique of active measurement and control to bear upon the basic problem of enhancing the digital image by defining absolute measures of visual contrast, lightness, and sharpness. This is accomplished by automatically applying the type and degree of enhancement needed based on automated image analysis. The foundation of the processing scheme is the flow of digital images through a feedback loop whose stages include visual measurement computation and servo-controlled enhancement effect. The cycle is repeated until the servo achieves acceptable scores for the visual measures or reaches a decision that it has enhanced as much as is possible or advantageous. The servo-control will bypass images that it determines need no enhancement. The system determines experimentally how much absolute degrees of sharpening can be applied before encountering detrimental sharpening artifacts. The latter decisions are stop decisions that are controlled by further contrast or light enhancement, producing unacceptable levels of saturation, signal clipping, and sharpness. The invention was developed to provide completely new capabilities for exceeding pilot visual performance by clarifying turbid, low-light level, and extremely hazy images automatically for pilot view on heads-up or heads-down display during critical flight maneuvers.
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