Novel Intelligent signal processing approaches for performance enhancement of gas sensor nodes suitable for near real time resource constrained scenarios

Abstract

Artificial neural networks (ANNs) have been used for classification, quantification, and drift corrections. While applying ANNs and traditional pattern recognition techniques, additional statistical algorithms are necessary for data pre-processing and compensating for the drift. Due to such multistage off-line statistical procedures, these methods are not suitable for real-time applications. Hence, we have developed an end to-end hybrid convolutional neural network (CNN) architecture called a drift tolerant robust classifier (DTRC), suitable for real-time real-field applications. It can automatically extract salient multidimensional features from the drifted raw sensor array responses capable of efficiently classifying the gases/odors. While classifying with such extracted features, DTRC also curtails the drift impacts and outperforms the various state-of-the-art peers. The DTRC comprises three blocks performing one-, two-, and three-dimensional (1D, 2D, and 3D) operations. DTRC does not use any additional statistical algorithm to compensate for the drift, making it compatible with real-time applications. A publicly available benchmark drifted-dataset has been used to prove the efficacy of DTRC. Moreover, real-time applications requiring high accuracy to detect and estimate hazardous gases/odors are too challenging to implement with traditional approaches. Therefore, researchers have started to use CNNs for developing efficient e Noses. However, the generalization has not been discussed so far to apply CNNs for gas classification independent of types of gas sensor array responses. Recently, authors have applied CNNs to classify the gases/odors using only dynamic responses without discussing the same for static responses. The popular 2D-CNN performs better when operating on 2D input data of optimal size with suitable kernels. Hence, we have proposed a novel approach by utilizing 2D-CNN for gas classification using steady-state responses of the gas sensor array. newline