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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.

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.

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 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.

This invention is a system and associated method that is a two step process. It provides a contaminant treatment pouch, referred to as a urine cell or contaminant cell that converts urine or another liquid containing contaminants into a fortified drink, engineered to meet human hydration, electrolyte and caloric requirements. It uses a variant of forward osmosis (FO) to draw water from a urine container into the concentrated fortified drink as part of a recycling stage. An activated carbon pretreatment removes most organic molecules. Salinity of the initial liquid mix (urine plus other) is synergistically used to enhance the precipitation of organic molecules so that activated carbon can remove most of the organics. A functional osmotic bag is then used to remove inorganic contaminants. If a contaminant is processed for which the saline content is different than optimal for precipitating organic molecules, the saline content of the liquid should be adjusted toward the optimal value for that contaminant.

NASA Ames has developed an Autonomous Situational Awareness Platform system for a UAS (ASAP-U), a traffic management system to incorporate Unmanned Aerial Systems (UASs) into the National Airspace System. The Autonomous Situational Awareness Platform (ASAP) is a system that combines existing navigation technology (both aviation and maritime) with new procedures to safely integrate Unmanned Aerial Systems (UASs) with other airspace vehicles. It uses a module called ASAP-U, which includes a transmitter, receivers, and various links to other UAS systems. The module collects global positioning system GPS coordinates and time from a satellite antenna, and this data is fed to the UAS's flight management system for navigation. The ASAP-U module autonomously and continuously sends UAS information via a radio frequency (RF) antenna using Self-Organized Time Division Multiple Access (SOTDMA) to prevent signal overlap. It also receives ASAP data from other aircraft. In case of transmission overload, priority is given to closer aircraft. Additionally, the module can receive weather data, navigational aid data, terrain data, and updates to the UAS flight plan. The collected data is relayed to the flight management system, which includes various databases and a navigation computer to calculate necessary flight plan modifications based on regulations, right-of-way rules, terrain, and geofencing. Conflicts are checked against databases, and if none are found, the flight plan is implemented. If conflicts arise, modifications can be made. The ASAP-U module continuously receives and transmits data, including UAS data and data from other aircraft, to detect conflicts with other aircraft, terrain, weather, and geofencing. Based on this information, the flight management system determines the need for course adjustments and the flight control system executes them for a safe flight route.