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Sensor drift is a major problem in chemical sensors that requires addressingfor reliable and accurate detection of chemical analytes. In this paper, wedevelop a causal convolutional neural network (CNN) with a Discrete CosineTransform (DCT) layer to estimate the drift signal. In the DCT module, we applysoft-thresholding nonlinearity in the transform domain to denoise the data andobtain a sparse representation of the drift signal. The soft-threshold valuesare learned during training. Our results show that DCT layer-based CNNs areable to produce a slowly varying baseline drift signal. We train the CNN onsynthetic data and test it on real chemical sensor data. Our results show thatwe can have an accurate and smooth drift estimate even when the observed sensorsignal is very noisy.

Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors