| Paper ID | ASPS-6.3 |
| Paper Title |
DISCRETE COSINE TRANSFORM BASED CAUSAL CONVOLUTIONAL NEURAL NETWORK FOR DRIFT COMPENSATION IN CHEMICAL SENSORS |
| Authors |
Diaa Badawi, University of Illinois at Chicago, United States; Agamyrat Agambayev, Sule Ozev, Arizona State University, United States; Ahmet Enis Cetin, University of Illinois at Chicago, United States |
| Session | ASPS-6: Sensing & Sensor Processing |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Signal Processing Systems [DIS-EMSA] |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
