Max F Reid

US Patent:

54831687, Jan 9, 1996

Filed:

Mar 1, 1993

Appl. No.:

8/024133

Inventors:

Max B. Reid - San Francisco CA

Assignee:

The United States of America as represented by the Administrator of the
National Aeronautics and Space Administration - Washington DC

International Classification:

G02B 2764

US Classification:

324525

Abstract:

The present invention relates to an optical system for creating a potential field map of a bounded two dimensional region containing a goal location and an arbitrary number of obstacles. The potential field mapping system has an imaging device and a processor. Two image writing modes are used by the imaging device, electron deposition and electron depletion. Patterns written in electron deposition mode appear black and expand. Patterns written in electron depletion mode are sharp and appear white. The generated image represents a robot's workspace. The imaging device under processor control then writes a goal location in the workspace using the electron deposition mode. The black image of the goal expands in the workspace. The processor stores the generated images, and uses them to generate a feedback pattern. The feedback pattern is written in the workspace by the imaging device in the electron deposition mode to enhance the expansion of the original goal pattern.

US Patent:

53332105, Jul 26, 1994

Filed:

Jul 2, 1992

Appl. No.:

7/908141

Inventors:

Liljana Spirkovska - Sunnyvale CA
Max B. Reid - Sunnyvale CA

Assignee:

The United States of America as represented by the Administrator of the
National Aeronautics and Space Administration - Washington DC

International Classification:

G06K 946

US Classification:

382 15

Abstract:

A method and system for performing pattern analysis with a neural network coarse-code a pattern to be analyzed so as to form a plurality of sub-patterns collectively defined by data. Each of the sub-patterns comprises sets of sub-pattern data. The neural network includes a plurality of fields, each field being associated with one of the sub-patterns so as to receive the sub-pattern data therefrom. Training and testing by the neural network then proceeds in the usual way, with one modification: the transfer function thresholds the value obtained from summing the weighted products of each field over all sub-patterns associated with each pattern being analyzed by the system.