Sistema Sensor para el Monitoreo Ambiental Basado en Redes Neuronales

TL;DR

This paper presents a portable environmental sensor prototype using SnO2 gas sensors, an Arduino platform, and a neural network for automatic identification of chemical vapors, aiding waste management and environmental restoration.

Contribution

It introduces a compact sensor system integrating gas sensors, a low-cost microcontroller, and neural networks for real-time environmental contaminant identification.

Findings

Successful identification of chemical vapors using the neural network

Efficient data processing with minimal computational load after training

Prototype demonstrates potential for portable environmental monitoring

Abstract

In the tasks of environmental monitoring is of great importance to have compact and portable systems able to identify environmental contaminants that facilitate tasks related to waste management and environmental restoration. In this paper, a prototype sensor is described to identify contaminants in the environment. This prototype is made with an array of tin oxide SnO2 gas sensors used to identify chemical vapors, a step of data acquisition implemented with ARM (Advanced RISC Machine) low-cost platform (Arduino) and a neural network able to identify environmental contaminants automatically. The neural network is used to identify the composition of contaminant census. In the computer system, the heavy computational load is presented only in the training process, once the neural network has been trained, the operation is to spread the data across the network with a much lighter computational load, which consists mainly of a vector-matrix multiplication and a search table that holds the activation function to quickly identify unknown samples.