Compact and Embedded Electronic Nose for Volatile and Non-Volatile Odor Classification for Robot Applications

Abdelkhalek, Moataz; Alfayad, Samer; Benouezdou, Fethi; Fayek, Magda B.; Chassagne, Luc

doi:10.1109/access.2019.2928875

Cited by 16 publications

(16 citation statements)

References 49 publications

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“…Compared with the sensor array designed with a single heat source in a previous report [31], the design in this work is better in respect of easy determining positions of the sensors and distances between them. The so called radar plot is effective for classification of different gases using an array of sensors [45]. In this study, the five sensors are fabricated in the same process steps with the same SnO2/Pt sensing material.…”

Section: Resultsmentioning

confidence: 99%

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

Duy

Thai

Ngoc

et al. 2022

Sensors and Actuators B: Chemical

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Section: Resultsmentioning

confidence: 99%

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

Duy

Thai

Ngoc

et al. 2022

Sensors and Actuators B: Chemical

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“…However, in comparison to this effort, only 91.36% classification was achieved. According to Qiu et al (2015) and Sun et al (2018), the suggested E-nose in Abdelkhalek et al (2019) has advantages in terms of using fewer sensors, processing speed, compact size and a low-cost CPU based on an Atmel microcontroller.…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, our proposed E-nose has greater advantages in this regard, as it has a 98.44% success rate in classifying the three gas samples. This result was obtained using characteristics extracted from a transient response that lasted only 10 s, as opposed to the response used in Abdelkhalek et al (2019), which lasted between 30 and 60 s. Even if additional classification issues may require more than three sensors, the created E-reduced nose's execution time, affordability, portability and reconfigurability remain key benefits. Furthermore, the Raspberry Pi includes a number of characteristics that make it much easier for it to complete complex tasks, such as powerful machine learning techniques for gas identification challenges.…”

Section: Figure 15 Response Curves Of the E-nosementioning

confidence: 99%

The design and validation of a fast and low-cost multi-purpose electronic nose for rapid gas identification

Rouabeh

Gomri

Masmoudi

2022

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Purpose The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation. Design/methodology/approach The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used. Findings Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s. Originality/value Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

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“…Embedded e-nose systems are mounted on robots to recognize odors, with the ultimate goal of bringing the robot closer to human behavior and enabling the robot to perceive the odor of food. The e-nose device studied by Abdelkhalek et al [110] is able to distinguish between a variety of juices such as apple, orange, and pineapple, as well as between rotten and good eggs, grenade perfume and butane gas.…”

Section: Mobile E-nose Systemsmentioning

confidence: 99%

Development of compact electronic noses: a review

Liu

Meng

Lilienthal

et al. 2021

Meas. Sci. Technol.

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An electronic nose (e-nose) is a measuring instrument that mimics human olfaction and outputs ‘fingerprint’ information of mixed gases or odors. Generally speaking, an e-nose is mainly composed of two parts: a gas sensing system (gas sensor arrays, gas transmission paths) and an information processing system (microprocessor and related hardware, pattern recognition algorithms). It has been more than 30 years since the e-nose concept was introduced in the 1980s. Since then, e-noses have evolved from being large in size, expensive, and power-hungry instruments to portable, low cost devices with low power consumption. This paper reviews the development of compact e-nose design and calculation over the last few decades, and discusses possible future trends. Regarding the compact e-nose design, which is related to its size and weight, this paper mainly summarizes the development of sensor array design, hardware circuit design, gas path (i.e. the path through which the mixed gases to be measured flow inside the e-nose system) and sampling design, as well as portable design. For the compact e-nose calculation, which is directly related to its rapidity of detection, this review focuses on the development of on-chip calculation and wireless computing. The future trends of compact e-noses include the integration with the internet of things, wearable e-noses, and mobile e-nose systems.

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Compact and Embedded Electronic Nose for Volatile and Non-Volatile Odor Classification for Robot Applications

Cited by 16 publications

References 49 publications

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification

The design and validation of a fast and low-cost multi-purpose electronic nose for rapid gas identification

Development of compact electronic noses: a review

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