Low-cost Artificial Intelligence Enhanced Hardware Design for Data Augmentation

Abstract

This paper presents a novel low-cost hardware implementation of data augmentation using artificial neural networks for a low-power, low-cost Water Quality Indexing application. Multilayer Perceptron (MLP) feedforward network with backpropagation learning has been designed to predict the data of DO and EC using pH and ORP as the input vector. This reduces the requirement for costly sensor electrodes, decreasing the design's cost. The design has been implemented on both ASIC and Embedded platforms. The Augmentation ANN predicts DO and EC with a 98% accuracy rate and achieves a 92% reduction in cost. The results have been presented and compared with standard WQI device.