Spectral recognition with a PCNN preprocessor

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Description

This is a report on work in progress. Spectral recognition is central to many areas of science and technology. Classical spectral recognition analysis techniques (least squares, partial least squares, etc.) are sensitive to offset and gain drifts and errors. This sensitivity can cause excessive costs for spectrometer resources and calibrations. Neural techniques relieve some of this sensitivity but non approach human competence. It is desirable to mimic human spectral analysis not only to improve the results but to minimize detector constraints and costs. The authors suggest that the first step in human analysis is peak detection. They are exploring the ... continued below

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6 p.

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Moore, K.R. & Blain, P. December 31, 1998.

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Description

This is a report on work in progress. Spectral recognition is central to many areas of science and technology. Classical spectral recognition analysis techniques (least squares, partial least squares, etc.) are sensitive to offset and gain drifts and errors. This sensitivity can cause excessive costs for spectrometer resources and calibrations. Neural techniques relieve some of this sensitivity but non approach human competence. It is desirable to mimic human spectral analysis not only to improve the results but to minimize detector constraints and costs. The authors suggest that the first step in human analysis is peak detection. They are exploring the one dimensional PCNN as a peak segmenter for spectral peak finding in the presence of noise and drifts in gain and offset. They present results of one dimensional pulse coded neural network peak detection with both simulated and actual static spectra. They also use the PCNN to form a scale and translation invariant feature vector that may be decomposed using classical techniques such as least squares. Finally, they propose using a PCNN to exploit the temporal aspects of spectral acquisition.

Physical Description

6 p.

Notes

OSTI as DE99001646

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  • VI-DYNN `98, Stockholm (Sweden), 22-26 Jun 1998

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  • Other: DE99001646
  • Report No.: LA-UR--98-2754
  • Report No.: CONF-9806189--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 319671
  • Archival Resource Key: ark:/67531/metadc676685

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  • December 31, 1998

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  • July 25, 2015, 2:20 a.m.

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  • Feb. 29, 2016, 7:37 p.m.

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Moore, K.R. & Blain, P. Spectral recognition with a PCNN preprocessor, article, December 31, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc676685/: accessed August 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.