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The application of signal processing and feature extraction to reflectometry traces aiming at sensor interrogation is not sufficiently explored. In this work, traces produced by an optical time-domain reflectometer in experiments using a long-period grating in different external media are analyzed using signal processing techniques inspired by audio processing. The objective is to demonstrate that, using this type of analysis, it is possible to correctly identify the external medium through the characteristics of the reflectometry trace. Results show that the features extracted from the traces were able to produce good classifiers, one of them achieving 100% correct classification for the data set presently considered. This technology could be applied in scenarios where it is necessary to distinguish among a given set of gases or liquids nondestructively.

Salinity is a key variable in understanding several issues, from public health to food security. Measuring salinity in situ is traditionally done using conductivimetric methods, and can be challenging due to extreme temperatures, corrosive environment, and oxidation. Fiber-based methods and other alternatives proposed to date have brought a number of advantages, but present low sensor strength, complex or expensive setups, cross-influence of temperature, lack of portability, or prohibitively long response times. This work presents a simple, compact salinity sensing system that, associated with a modern interrogation technique, is capable of achieving good accuracy even in remote sensing and low salt concentrations. The sensor is a long-period grating fabricated using the point-by-point electric arc method. The interrogator is based on optical time-domain reflectometry aided by signal processing techniques inspired by audio processing. Experimental data show that the system is capable of estimating salinity in the range from 0 g/L to 80 g/L within 0.49 g/L on average, with the sensor 4 km away from the light source.