Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network S. N. Chaudhri a and N. S. Rajput b Indian Institute of Technology (BHU), Varanasi-221005, UP, India Keywords: Mirror Mosaicking, Gas Sensor Array, Electronic Nose, Gas Classification, Pattern Recognition, Convolutional Neural Network. Abstract: Limited dimensionality of the dataset obtained from an electronic nose (EN) is due to the number of elements in the sensor array used generally in the range of 4-8 elements only. Further, large number of sensor data can be generated by sampling the sensor responses both during the transient and steady states. The lower- dimensionality of sensor data prohibits the use of a convolutional neural network (CNN)-based pattern recognition techniques because the kernels of a CNN cannot be used on the obtained sample vectors to extract the features. In this paper, we have proposed a novel approach to enhance the data dimensionality keeping the sensor response characteristics absolutely unaltered. By leveraging the concept of mirror mosaicking technique, we have upscaled the input sample vectors into a 6×6 2-D input arrays to train the shallow CNN. Using the proposed approach, all the 16-unknown steady-state test samples classified accurately which are not used during the training. Moreover, the parameters of the classification report viz., Precision, Recall, and F1 score also obtained with a fraction value of 1.00. The proposed technique is a generic approach that can be used to classify various low-dimensional datasets obtained from various sensor arrays in various fields. 1 INTRODUCTION In the current scenario, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and modern Pattern Recognition (PR) techniques are finding their applications in almost all the research areas; for delivering better results. An electronic nose (EN) is the mimicry of the olfactory system that is a popular topic of research as a multidisciplinary area. The word multidisciplinary represents the wide area of applications of EN related to different industries. Various traditional pattern recognition approaches have been used for the classification of gases or odors, as described by (Santos et al., 2017; Fujinaka et al., 2008; Hodgins and Simmonds, 1995; Tang et al., 2010; Keller et al., 1995; Rodrguez et al., 2010; Capelli et al., 2014; Kızıl et al., 2017; Chen et al., 2013). The EN is a system that contains a gas sensor array consisting of few sensors typically 4 to 16. Moreover, data pre-processing and pattern recognition modules are the main parts of any EN system (Arshak et al., 2004). The EN system can be a https://orcid.org/0000-0002-5436-2977 b https://orcid.org/0000-0002-1650-011X made more selective for analytes under observation, using an array of sensors (Zhang et al., 2017). A gas sensor array logically has more than one sensor element to enhance the selectivity of the system. If there are fewer numbers of sensors in a gas sensor array, the resulting response dataset has a feature vector of limited size for each sample. A concept of mirror mosaicking technique is proposed in this work to broaden the applicability of deep learning pattern recognition techniques for automatic feature extraction and classification of small gas sensor array responses. Subsequently, any gas sensor array response can be analyzed using the convolutional neural network (CNN) at the sample level irrespective of the size of the gas sensor array. The feature vector of any sample is obtained from the respective gas sensor array response having the length equal to the number of the sensor elements. Each pattern recognition technique requires a specific input format or length of the feature vector. For example, various dimensional versions viz., 1-D, 2-D, and 3-D based on the type of operation of the convolution of CNN 86 Chaudhri, S. and Rajput, N. Mirror Mosaicking: A Novel Approach to Achieve High-performance Classification of Gases Leveraging Convolutional Neural Network. DOI: 10.5220/0010251500860091 In Proceedings of the 10th International Conference on Sensor Networks (SENSORNETS 2021), pages 86-91 ISBN: 978-989-758-489-3 Copyright c  2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved