Title :
Neuro-ASIC for low cost supervision of water pollution
Author_Institution :
SIBET GmbH, Hannover, Germany
Abstract :
The design of a neural ASIC is presented that implements a low-cost system for the supervision of water quality in urban canalization or rivers. A trainable multilayer perceptron estimates the parameter COD (chemical oxygen demand) which is generally used to estimate water quality. The system detects correlations in the signals of low-cost sensors. It constitutes a significant cost reduction in the supervision of water environment pollution. The paper focuses on the electronic implementation
Keywords :
application specific integrated circuits; chemical analysis; multilayer perceptrons; neural chips; parameter estimation; water pollution measurement; canal; chemical oxygen demand; low cost supervision; multilayer perceptron; neuro-ASIC; rivers; water environment pollution; water pollution; water quality; Chemical analysis; Chemical sensors; Costs; Laboratories; Microorganisms; Neural networks; Pollution measurement; Sensor systems; Testing; Water pollution;
Conference_Titel :
Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, 1996. Proceedings., International Workshop on
Conference_Location :
Venice
Print_ISBN :
0-8186-7456-3
DOI :
10.1109/NICRSP.1996.542751
