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NASAs Goddard Space Flight Centers method for the extraction of Lunar data and/or planetary features is a method developed to extract Lunar features based on the combination of several image processing techniques. The technology was developed to register images from multiple sensors and extract features from images in low-contrast and uneven illumination conditions. The image processing and registration techniques can include, but is not limited to, a watershed segmentation, marked point processes, graph cut algorithms, wavelet transforms, multiple birth and death algorithms and/or the generalized Hough Transform.

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.

Currently, HSEG software is being used by Bartron Medical Imaging as a diagnostic tool to enhance medical imagery. Bartron Medical Imaging licensed the HSEG Technology from NASA Goddard adding color enhancement and developing MED-SEG, an FDA approved tool to help specialists interpret medical images. HSEG is available for licensing outside of the medical field (specifically for soft-tissue analysis).

Distributed propulsion and lightweight flexible structures on air vehicles pose a significant opportunity to improve mission performance while meeting next generation requirements including reduced fuel burn, lower emissions, and enhanced takeoff and landing performance. Flexible wing-shaping aircraft using distributed propulsion enable the ability to achieve improved aerodynamic efficiency while maintaining aeroelastic stability. Wing shaping concepts using distributed propulsion leverage the ability to introduce forces/ moments into the wing structure to affect the wing aerodynamics. This can be performed throughout the flight envelope to alter wing twist, hence local angle of attack, as the wing loading changes with air vehicle weight during cruise. Thrust-induced lift can be achieved by distributed propulsion for enhanced lift during take-off and landing. For a highly flexible wing structure, this concept could achieve a 4% improvement in lift-to-drag ratio, hence reduced fuel burn, as compared to a conventional stiff wing. This benefit is attributed to a reduction in lift-induced drag throughout the flight envelope by actively shaping the spanwise lift distribution using distributed propulsion. Vertical tail size could be reduced by utilizing differential thrust flight-propulsion control. This will result in weight reduction to achieve further fuel savings. Aeroelastic stability is addressed in the design process to meet flutter clearance requirements by proper placement of the propulsion units. This technology enables synergistic interactions between lightweight materials, propulsion, flight control, and active aeroelastic wing shaping control for reducing the environmental impact of future air vehicles.

Vehicles designed for purposes of exploration of the planets and other atmospheric bodies in the Solar System favor the use of mid-Lift/Drag blunt body geometries. Such shapes can be designed so as to yield favorable hypersonic aerothermodynamic properties for low heating and hypersonic aerodynamic properties for maneuverability and stability. The entry trajectory selected influences entry peak heating and integrated heating loads which in turn influences the design of the thermal protection system. A nominal is used to compare each shape considered. The vehicle will be subject to both launch and entry loading along with structural integrity constraints that may further influence shape design. Further, such vehicles must be sized so as to fit on existing or realizable launch vehicles, often within existing launch payload shroud constraints.

Typically, microwave power modules (MPMs) are useful only for radar and navigation purposes because they lack the linearity and efficiency required for communications. In standard configurations, conventional MPMs require both a solid-state amplifier at the front end and a microwave vacuum electronics amplifier at the back end. By contrast, Glenn's design features a wideband multi-stage distributed amplifier system. The low-power stage is a high-efficiency gallium arsenide (GaAs) pseudomorphic high-electron-mobility transistor (pHEMT)-based monolithic microwave integrated circuit (MMIC) distributed amplifier. The medium-power stage is configured to pick up and amplify the low-power signal. This stage can be either another high-efficiency GaAs pHEMT or a gallium nitride (GaN) HEMT-based MMIC distributed amplifier, depending on the need. The high-power stage, configured to pick up the signal from the second amplifier, is a high-efficiency GaN HEMT-based MMIC distributed amplifier, which supplants the traveling-wave tube amplifier found in most microwave power modules. In Glenn's novel MPM, the radar functions as a scatterometer, radiometer, and synthetic aperture imager. The high-speed communications system down-links science data acquired by Earth-observing instruments. The navigation system functions like a transponder for autonomous rendezvous and docking, and estimates the range information. Glenn's MPM gives systems the versatility to use a single power module to drive not only radar and navigation but also communications systems.

The advantage of using cardiac biometrics over existing methods is that heart signatures are more difficult to forge compared to other biometric devices. Iris scanners can be fooled by contact lenses and sunglasses, and a segment of the population does not have readable fingerprints due to age or working conditions. Previous electrocardiographic signals employed a single template and compared that template with new test templates by means of cross-correlation or linear-discriminant analysis.The benefit of this technology over competing cardiac biometric methods is that it is more reliable with a significant reduction in error rates. The benefit of this technology is that it creates a probabilistic model of the electrocardiographic features of a person instead of a single signal template of the average heartbeat. The probabilistic model described as Gaussian mixture model allows various modes of the feature distribution, in contrast to a template model that only characterizes a mean waveform. Another advantage is that the model uses both physiological and anatomical characterization of the heart, unlike other methods that mainly use only physiological characterization of the heart. By combining features from different leads, the heart of the person is better characterized in terms of anatomical orientation because each lead represents a different projection of the electrical vector of the heart. Thus, employing multiple electrocardiographic leads provides a better performance in subject verification or identification.

The Video Acuity metric is designed to provide a unique and meaningful measurement of the quality of a video system. The automated system for measuring video acuity is based on a model of human letter recognition. The Video Acuity measurement system is comprised of a camera and associated optics and sensor, processing elements including digital compression, transmission over an electronic network, and an electronic display for viewing of the display by a human viewer. The quality of a video system impacts the ability of the human viewer to perform public safety tasks, such as reading of automobile license plates, recognition of faces, and recognition of handheld weapons. The Video Acuity metric can accurately measure the effects of sampling, blur, noise, quantization, compression, geometric distortion, and other effects. This is because it does not rely on any particular theoretical model of imaging, but simply measures the performance in a task that incorporates essential aspects of human use of video, notably recognition of patterns and objects. Because the metric is structurally identical to human visual acuity, the numbers that it yields have immediate and concrete meaning. Furthermore, they can be related to the human visual acuity needed to do the task. The Video Acuity measurement system uses different sets of optotypes and uses automated letter recognition to simulate the human observer.

This innovation is an autonomic method capable of transmitting a neutralizing data signal to counteract a potentially harmful signal. This otoacoustic component of an autonomic unit can render a potentially harmful incoming signal inert. For selfmanaging systems, the technology can offer a selfdefense capability that brings new levels of automation and dependability to systems.

The Inductive Monitoring System (IMS) software provides a method of building an efficient system health monitoring software module by examining data covering the range of nominal system behavior in advance and using parameters derived from that data for the monitoring task. This software module also has the capability to adapt to the specific system being monitored by augmenting its monitoring database with initially suspect system parameter sets encountered during monitoring operations, which are later verified as nominal. While the system is offline, IMS learns nominal system behavior from archived system data sets collected from the monitored system or from accurate simulations of the system. This training phase automatically builds a model of nominal operations, and stores it in a knowledge base. The basic data structure of the IMS software algorithm is a vector of parameter values. Each vector is an ordered list of parameters collected from the monitored system by a data acquisition process. IMS then processes select data sets by formatting the data into a predefined vector format and building a knowledge base containing clusters of related value ranges for the vector parameters. In real time, IMS then monitors and displays information on the degree of deviation from nominal performance. The values collected from the monitored system for a given vector are compared to the clusters in the knowledge base. If all the values fall into or near the parameter ranges defined by one of these clusters, it is assumed to be nominal data since it matches previously observed nominal behavior. The IMS knowledge base can also be used for offline analysis of archived data.