An optical-fiber sensor for use in water systems utilizing digital signal processing techniques and artificial neural network pattern recognition

An optical-fiber sensor is reported which is capable of detecting ethanol in water. A single optical-fiber sensor was incorporated into a 1-km length of 62.5-/spl mu/m core diameter polymer-clad silica optical fiber. In order to maximize sensitivity, a U-bend configuration was used for the sensor where the cladding was removed and the core exposed directly to the fluid under test. The sensor was interrogated using optical time domain reflectrometry, as it is intended to extend this work to multiple sensors on a single fiber. In this investigation, the sensor was exposed to air, water, and alcohol. The signal processing technique has been designed to optimize the neural network adopted in the existing sensor system. In this investigation, a discrete Fourier transform, using a fast Fourier transform algorithm, is chosen and its application leads to an improvement in efficiency of the neural network i.e., minimizing the computing resources. Using the Stuttgart neural network simulator, a feed-forward three-layer neural network was constructed with the number of input nodes corresponding to the number of points required to represent the sensor frequency domain response.