Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network With Hadamard Transform and Additive Layers
Real-Time Low-Cost Drift Compensation for Chemical Sensors Using a Deep Neural Network With Hadamard Transform and Additive Layers
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DOI：
10.1109/jsen.2021.3084220
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发表时间：
2021-08
影响因子：
4.3
通讯作者：
Diaa Badawi;Agamyrat Agambayev;S. Ozev;Ieee A. Enis Cetin Fellow
中科院分区：
文献类型：
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作者：
Diaa Badawi;Agamyrat Agambayev;S. Ozev;Ieee A. Enis Cetin Fellow
在本文中，我们提出了一种计算高效的深度学习框架来解决化学传感器的灵敏度漂移补偿问题。该框架通过具有基于无乘法哈达玛变换层的深度神经网络来估计来自传感器测量的基础漂移信号。此外，我们提出了一种可以在低成本处理器上实时高效实现的加性神经网络。时间加性神经网络结构对于每个“卷积”操作仅执行一次乘法。常规网络和加法网络都可以具有基于 Hadamard 变换的层，这些层在特征图上实现正交变换，并在变换域中执行软阈值操作以消除噪声。我们还研究了离散余弦变换 (DCT) 的使用，并将其与哈达玛变换进行比较。我们提供的实验结果表明 Hadamard 变换优于 DCT。
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.