Published October 16, 2000 | Version v1
Report Open

A systematic profile/feature-based intelligence for spectral sensors

Description

Argonne National Laboratory (ANL) has been creating a special-purpose software-engineering tool to support research and development of spectrum-output-type [chemical] sensors. The modular software system is called SAGE, the Sensor Algorithm Generation Environment and includes general-purpose signal conditioning algorithms (GP/SAGE) as well as intelligent classifiers, pattern recognizes, response accelerators, and sensitivity analyzers. GP/SAGE is an implementation of an approach for delivering a level of encapsulated intelligence to a wide range of sensors and instruments. It capitalizes on the genene classification and analysis needed to process most profile-type data. The GP/SAGE native data format is a generalized one-dimensional vector, signature, or spectrum. GP/SAGE modules form a computer-aided software engineering (CASE) workbench where users can experiment with various conditioning, filtering, and pattern recognition stages, then automatically generate final algorithm source code for data acquisition and analysis systems. SAGE was designed to free the [chemical] sensor developer from the signal processing allowing them to focus on understanding and improving the basic sensing mechanisms. The SAGE system's strength is its creative application of advanced neural computing techniques to response-vector and response-surface data, affording new insight and perspectives with regard to phenomena being studied for sensor development

Availability note (English)

Available from INIS in electronic form; Also available from OSTI as DE00766345; PURL: https://www.osti.gov/servlets/purl/766345-H78xcD/webviewable/

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Additional details

Publishing Information

Imprint Pagination
12 p.
Report number
ANL/ES/CP--103151

Conference

Title
SPIE International Symposium on Environmental and Industrial Sensing
Dates
5-8 Nov 2000
Place
Boston, MA (United States)

INIS

Country of Publication
United States
Country of Input or Organization
United States
INIS RN
32025783
Subject category
S99: GENERAL AND MISCELLANEOUS;
Resource subtype / Literary indicator
Conference
Descriptors DEI
DATA PROCESSING; IMAGE PROCESSING; PATTERN RECOGNITION; S CODES; SENSITIVITY; SIGNAL CONDITIONING
Descriptors DEC
COMPUTER CODES; PROCESSING

Optional Information

Contract/Grant/Project number
W-31109-ENG-38
Funding organization
US Department of Energy (United States)