Operational considerations for pattern recognition demonstration for transition of optical processing to systems (TOPS)

1992 ◽  
Author(s):  
Charles F. Hester ◽  
Mark G. Temmen ◽  
James D. Brasher ◽  
Jason M. Kinser ◽  
J. R. DeWitt ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Optical Processing

A HYBRID ELECTRO-OPTICAL ARCHITECTURE FOR CLASSIFICATION TREES AND ASSOCIATIVE MEMORY MECHANISMS

1993 ◽  
Vol 02 (03) ◽  
pp. 373-393
Author(s):  
P. ISRAEL ◽  
C. KOUTSOUGERAS
Keyword(s):  
Pattern Recognition ◽  
Decision Trees ◽  
Intelligent Systems ◽  
Data Flow ◽  
Optical Processing ◽  
Real Time Processing ◽  
Time Processing ◽  
Processing Elements ◽  
Small Set ◽  
Associative Recall

An architecture is presented here which can be used for some important paradigms of intelligent systems. This architecture targets applications which require real time processing of stream inputs with versatile hardware which exploits parallelism. The architecture is particularly suited for pattern recognition paradigms which are based on the use of decision trees. Artificially intelligent systems based on decision trees interestingly present some common computational requirements which can be served very efficiently by a Data Flow architecture. A small set of different functions is computed repeatedly with simple result tokens passed from one computation to successive ones. Developments in optical processing have introduced elements which are particularly suited to the computational requirements of some of these systems, and therefore they can be effectively employed in this architecture. The architecture presented here is based on Data Flow design principles and is enhanced with optical processing elements. The function of the architecture is illustrated by discussing the mapping of two specific AI paradigms—a pattern classifier and an associative recall mechanism.

The Theoretical Contrast in Scanning and Conventional High Resolution Microscopes

1969 ◽  
Vol 27 ◽  
pp. 170-171
Author(s):  
E. Zeitler ◽  
M. G. R. Thomson
Keyword(s):  
Small Volume ◽  
Volume Element ◽  
Optical Processing ◽  
Image Construction ◽  
Object Interaction ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Optical Treatment ◽  
Electron Microscopes ◽  
Intensity Contrast

In the formation of an image each small volume element of the object is correlated to an areal element in the image. The structure or detail of the object is represented by changes in intensity from element to element, and this variation of intensity (contrast) is determined by the interaction of the electrons with the specimen, and by the optical processing of the information-carrying electrons. Both conventional and scanning transmission electron microscopes form images which may be considered in this way, but the mechanism of image construction is very different in the two cases. Although the electron-object interaction is the same, the optical treatment differs.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

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