Doped ZnO Nanostructured Sensor in Electronic Nose for Detection of Ammonia, Hydrogen and Liquefied Petroleum Gas

Das, Samir Chandra; Tudu, Bipan; Bhattacharyya, Nabarun; Bandyopadhyay, Rajib; Pramanik, Panchanan

doi:10.1007/978-3-642-34216-5_47

Cited by 3 publications

(2 citation statements)

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“…fingerprints in order to discriminate between various gases in the air [6,7,8,9]. Nevertheless, EN system performance is prone to several issues, for instance, the gas sensor properties often change with time, which is known as the drift problem [10], this problem can occur if the gas sensors are exposed to reactive gases for a long period.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

An interactive software tool for gas identification

Djelouat

Ali

Amira

et al. 2018

Journal of Natural Gas Science and Engineering

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This paper presents the design of an interactive graphical user interface (GUI) to monitor and quantify a developed electronic nose (EN) platform for gas identification. To this end, an EN system has been implemented using a multi-sensing embedded platform comprised of a data acquisition unit, an RFID module and a signal processing unit. The gas data are collected using two different types of gas sensors, namely, seven commercial Figaro sensors and in-house fabricated 4 × 4 tin-oxide gas array sensor. The collected gas data are processed for identification by means of dimensionality reduction algorithms and classification techniques where the software implementation and the quantification of these algorithms have been carried out. Subsequently, the GUI was designed to enable several operations. The GUI allows the user to visualize the sensors responses for any selected gas at any point of the acquisition process as well as visualizing the data distribution. Beside, it provides an easy approach to evaluate the EN system performance in terms of data identification and execution time by computing the classification accuracy using a 10-fold cross validation technique. Furthermore, the GUI, which is freely distributed, grants the users the privilege to upload other types of data to enable different pattern recognition applications.

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Section: Accepted Manuscriptmentioning

confidence: 99%

An interactive software tool for gas identification

Djelouat

Ali

Amira

et al. 2018

Journal of Natural Gas Science and Engineering

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“…Using EN for gas identification based on fingerprints obtained from gas sensors responses has been presented in [10,11]. The problem with such EN systems is that the exposure to reactive gases for long period of time can result in a change of the gas sensor properties, which is known as the drift problem [12] and non selectivity of the sensors [13] which relates with the reactivity of a chemical sensor to so called interference gases which are different from the nominal gas towards which the sensor is targeted.…”

Section: Introductionmentioning

confidence: 99%

Compressive sensing based electronic nose platform

Djelouat

Ali

Amira

et al. 2017

Digital Signal Processing

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Electronic nose (EN) systems play a significant role for gas monitoring and identification in gas plants. Using an EN system which consists of an array of sensors provides a high performance. Nevertheless, this performance is bottlenecked by the high system complexity incorporated with the high number of sensors. In this paper a new EN system is proposed using data sets collected from an inhouse fabricated 4×4 tin-oxide gas array sensor. The system exploits the theory of compressive sensing (CS) and distributed compressive sensing (DCS) to reduce the storage capacity and power consumption. The obtained results have shown that compressing the transmitted data to 20 % of its original size will preserve the information by achieving a high reconstruction quality. Moreover, exploiting DCS will maintain the same reconstruction quality for just 15 % of the original size. This high quality of reconstruction is explored for classification using several classifiers such as decision tree (DT), K-nearest neighbour (KNN) and extended nearest neighbour (ENN) along with linear discrimination analysis (LDA) as feature reduction technique. CS-based reconstructed data has achieved a 95% classification accuracy. Furthermore, DCS-based reconstructed data achieved a 98.33% classification accuracy which is the same as using original data without compression.

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