Smart Environmental Monitor Based on Neural Networks and Multi-Spectral Pattern Recognition
Abstract
In Phase I of this project, Physical Optics Corporation (POC) accomplished the goal of the original proposal which was to develop and optimize a unique neural network (NN) algorithm that performs rapid spectral signal processing and identification. POC's NN algorithm was tested with extremely noisy Raman spectra from Lawrence Livermore National Laboratory and experimentally showed at least ten times better sensitivity and reliability than conventional spectral signal processing methods. POC built a portable demonstration system with POC's NN and successfully demonstrated real-time spectral signature identification operations. POC proposed, for Phase II implementation, a holographic optical neural network (HONN) system that is capable of rapid hyperspectral imaging through an acoustic-optic tunable filter (AOTF), real-time spectral feature identification, and mapping. The success of the Phase II project will make automatic and rapid hyperspectral image analysis and feature location possible. Neural networks, Holographic optical spectral feature identification, Portable smart spectrometer, Hyperspectral image processing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1993
- Accession Number
- ADA276445
Entities
People
- Taiwei Lu