Engineering topics
Publications and source records attributed to Mjolsness, E..
We introduce a general type of optimization algorithm that infers data models relating two or more disjunct feature spaces, representing different aspects of a set of objects.
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We introduce a general type of optimization algorithm which infers data models relating two different but intertwined types of information about each of a set of objects.
The Autonomous Sciencecraft Constellation-Techsat 21 mission is a NASA New Millennium Program mission selected for flight in 2004. ASC will autonomously perform science analyses of X-band radar data, while in Earth orbit. Additional information is contained in the original extended abstract.
For the first part of our investigation, we will use a new artificial intelligent (AI) software code designed to: (1) recognize craters down to two pixel resolution in size and (2) calculate crater statistics on all available Viking imagery. Additional information is contained in the original extended abstract.
This paper discusses the use of a crater detection algorithm for Mars crater imagery.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
We introduce a general type of optimization algorithm which infers data models relating two different but intertwined types of information about each of a set of objects.
Explore the source record for details and available documents.
In this paper we introduce a general framework for an image based autonomous rock detection process for Martian terrain. A rock detection algorithm, based on this framework, is described and demonstrated on examples of real Mars Rover data. An attempt is made to produce a system that is independent of parameters to ease on-board implementation for real time in-situ operation.
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data.
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To maximize the return on future planetary missions, it will be critical that rovers have the capability to analyze information onboard and select and return data that is most likely to yield valuable scientific discoveries. Additional information is contained in the original extended abstract.
The science return from future robotic exploration of the Martian surface can be enhanced by performing routine processing using on board computers.
We describe a general approach to automatic model generation in the description of dynamic regulatory networks.