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SOLUTIONS & PRODUCTS

AMA's core strength is providing integrated solutions, combining software design, hardware design, and an innovative mindset to complex problems.

FEATURED SOLUTIONS & PRODUCTS

Scientific Computing

AMA has been instrumental in developing the CEOS Open Data Cube - an Open Source Data management platform for managing satellite data. The Committee on Earth Observation Satellites (CEOS) has long recognized a need for data processing infrastructure to support Earth science objectives in developing countries. Forest preservation initiatives, carbon measurement initiatives, water management and agricultural monitoring are just few examples of causes that can benefit greatly from remote sensing data. Currently, however, many developing nations lack the in-country expertise and computational infrastructure to utilize remote sensing data. The CEOS Open Data Cube Platform provides a flexible model to address these needs. The CEOS Data Cube Platform is a data processing platform for Earth science data, with a focus on remote-sensing data.

Exploration Visualization Environment

AMA's simulation software development team has led the development of NASA's Exploration Visualization Environment (EVE) since its inception.  EVE is a cross platform simulation, visualization, and analysis system designed to integrate engineering data with a virtual environment in support of the design and planning of aerospace missions. Through the integration of time dependent data with detailed graphical models within a full scale three-dimensional solar system or independent reference frame, an analyst can gain valuable insight into the correlation of data with simulation events by studying the data in the direct context of the mission.  EVE provides a rich set of navigation tools (both in time and space) and analysis capabilities to enable the user to analyze the data. EVE is available from NASA to support U.S. government contracts.  More information can be found at https://eve.larc.nasa.gov.

Image Processing

The super resolution algorithm was designed under NASA’s Autonomous Landing and Hazard Avoidance Technology (ALHAT) project as a way of fusing multiple low-resolution images together into a much higher resolution image to increase the landing platforms situational awareness and position accuracy. The resolution increase of the final resulting Super-Resolution image is based on the number of lower resolution images used and the Signal-to-Noise ratio (S/N) of the original images. The S/N of the resulting image is also a function of the number of images used to produce it. The resolution improvement occurs in a linear fashion based on the number of images used. Since the images must have overlapping, but not exactly coincident pixels in the scene stack, it becomes necessary to accurately co-register images. The co-registration is done by minimizing the residual differences between two images until the residual corresponds with the S/N. This process accomplishes a co-registration accuracy on the order of 1/10 a pixel. This capability significantly increased the position and attitude accuracy of the vehicle when applied to optical navigation systems.

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