Discrete cosine transform based causal convolutional neural network for drift compensation in chemical sensors

Diaa Badawi, Agamyrat Agambayev, Sule Ozev, A. Enis Çetin

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)8012-8016
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: Jun 6 2021Jun 11 2021

Keywords

  • Chemical sensor
  • Chemical sensor drift
  • Convolutional neural networks
  • Discrete cosine transform
  • Time series analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Discrete cosine transform based causal convolutional neural network for drift compensation in chemical sensors'. Together they form a unique fingerprint.

Cite this