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

arXiv: https://arxiv.org/abs/2011.06681 · HTML: https://mlsanity.com/paper/2011.06681/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer](https://mlsanity.com/paper/2305.17510/) (2023)
- [A Probabilistic Hadamard U-Net for MRI Bias Field Correction](https://mlsanity.com/paper/2403.05024/) (2024)
- [DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer](https://mlsanity.com/paper/2211.08577/) (2022)
- [Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets](https://mlsanity.com/paper/2303.06797/) (2023)
- [Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer](https://mlsanity.com/paper/2309.12201/) (2023)

## Abstract (arXiv)

Sensor drift is a major problem in chemical sensors that requires addressing for reliable and accurate detection of chemical analytes. In this paper, we develop a causal convolutional neural network (CNN) with a Discrete Cosine Transform (DCT) layer to estimate the drift signal. In the DCT module, we apply soft-thresholding nonlinearity in the transform domain to denoise the data and obtain a sparse representation of the drift signal. The soft-threshold values are learned during training. Our results show that DCT layer-based CNNs are able to produce a slowly varying baseline drift signal. We train the CNN on synthetic data and test it on real chemical sensor data. Our results show that we can have an accurate and smooth drift estimate even when the observed sensor signal is very noisy.