Sistema Sensor para el Monitoreo Ambiental Basado en Redes Neuronales
This provides a compact, portable solution for environmental monitoring tasks like waste management, though it appears incremental in applying standard neural networks to sensor data.
The authors developed a prototype sensor system using tin oxide gas sensors and a neural network to automatically identify environmental contaminants, achieving automated identification through a computationally efficient inference process.
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.