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Recovery of noisy pyroelectric-detector signals through neural-network processing

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4 Author(s)
Gonzalez, Martin G. ; Laboratorio Láser, Facultad de Ingeniería, Universidad de Buenos Aires, Paseo Colón 850 (1063), Buenos Aires, Argentina ; Peuriot, Alejandro L. ; Slezak, Veronica B. ; Santiago, Guillermo D.

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We introduce a neural-network-based filter devised to extend the dynamic range of pyroelectric detectors which otherwise would only be useful for medium-to-high energy measurements. To accomplish this task, we trained a multilayer perceptron through the back-propagation method using the theoretical signal derived from the detector equivalent electric circuit. We tested the performance of the neural-network filter both numerically and experimentally. In the former case we recovered theoretical signals corrupted with white and impulse noise and compared the results with those obtained through the use of standard filtering methods. In the latter case, we applied the filter to measure pulses from a Nd:YAG laser whose energy was below the detector noise-equivalent energy. With this processing technique in a standard PC we have been able to accurately measure laser energy values as low as one-tenth the detector’s noise-equivalent energy and at 10–20 Hz repetition rate.

Published in:

Review of Scientific Instruments  (Volume:76 ,  Issue: 5 )