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In the tasks of environmental monitoring is of great importance to havecompact and portable systems able to identify environmental contaminants thatfacilitate tasks related to waste management and environmental restoration. Inthis paper, a prototype sensor is described to identify contaminants in theenvironment. This prototype is made with an array of tin oxide SnO2 gas sensorsused to identify chemical vapors, a step of data acquisition implemented withARM (Advanced RISC Machine) low-cost platform (Arduino) and a neural networkable to identify environmental contaminants automatically. The neural networkis used to identify the composition of contaminant census. In the computersystem, the heavy computational load is presented only in the training process,once the neural network has been trained, the operation is to spread the dataacross the network with a much lighter computational load, which consistsmainly of a vector-matrix multiplication and a search table that holds theactivation function to quickly identify unknown samples.

Sistema Sensor para el Monitoreo Ambiental Basado en Redes Neuronales