Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network With Hadamard Transform and Additive Layers

In this paper, we propose a computationally efficient deep learning framework to address the issue of sensitivity drift compensation for chemical sensors. The framework estimates the underlying drift signal from sensor measurements by means of a deep neural network with a multiplication-free Hadamard transform based layer. In addition, we propose an additive neural network which can be efficiently implemented in real-time on low-cost processors. The temporal additive neural network structure performs only one multiplication per "convolution" operation. Both the regular network and the additive network can have Hadamard transform based layers that implement orthogonal transforms over feature maps and perform soft-thresholding operations in the transform domain to eliminate noise. We also investigate the use of the Discrete Cosine Transform (DCT) and compare it with the Hadamard transform. We present experimental results demonstrating that the Hadamard transform outperforms the DCT.