A CMOS Single-Chip Gas Recognition Circuit for Metal Oxide Gas Sensor Arrays

Ng, Kwan Ting; Boussaid, Farid; Bermak, Amine

doi:10.1109/tcsi.2011.2143090

Cited by 85 publications

(45 citation statements)

References 32 publications

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“…The 2-D spike rank-order scheme is suggested with the inhouse fabricated 4 x 4 gas sensors array with the argument that sensor drift behavior is mainly driven by the catalyst chosen during the post-treatment schemes of the sensor array fabrication [18]. This scheme can only be applied in the sensor array where we find similar analogy.…”

Section: Performance Evaluationmentioning

confidence: 98%

“…This approach requires accurate knowledge of concentration to find these regression coefficients. The concentration independent approach [17]- [18] is used to simplify the training process of an electronic nose for field applications because estimation of concentration is not a trivial task for these applications. This approach uses the average of a sensitivity logarithm, across all the sensors in the array, as a controlled variable in the regression analysis to extract the regression coefficient (a i (k)).…”

Section: Rank-order Based Classifiersmentioning

confidence: 99%

“…The 1-D spike rankorder [17] classifier stores the reference temporal sequence of all the sensors in memory for each gas during the gas learning phase or training phase and a new test spike sequence is compared with them for its classification. The 2-D spike rankorder classifier [18] suggests grouping of the sensors which exhibit the same drift behavior. In this work, it is shown that groups of sensors, sharing the same metal catalyst drift in the same direction and, hence, a 2-D signature matrix is created for each gas by keeping the temporal sequence of sensors within one group in one row.…”

Section: Rank-order Based Classifiersmentioning

confidence: 99%

“…Firstly, these schemes rely on unique mapping between the spike sequence and the target gas, and they lack any analytical solution when shuffled spike sequences appear for each gas during the gas learning phase. Secondly, the grouping of the sensors is specific to the sensor array used in the particular work [18]- [19] and cannot be generalized to any other sensor array. This presents a major challenge to achieve a robust performance with the rank-order based classifiers while sustaining a hardware friendly implementation.…”

Section: Rank-order Based Classifiersmentioning

confidence: 99%

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Probabilistic Rank Score Coding: A Robust Rank-Order Based Classifier for Electronic Nose Applications

2015

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Motivated by the recent experimental findings about odor identification with the unique spiking patterns of neurons in the biological olfactory system, rank-order based classifiers have been proposed for gas identification in electronic nose applications. These classifiers rely on one to one mapping between the target gas and temporal sequence of spiking sensors in an electronic nose. However, shuffled spike sequences, due to low repeatability of the response patterns from the sensors, limit the performance of these classifiers. We propose a robust probabilistic rank score coding scheme that tabulates the probability of each spiking sensor at each rank, or temporal position, by exploiting all the spike sequences of the sensors for each target gas, and we analyze a new test vector with this tabular information for its identification. A new quantification metric is proposed in order to estimate the confidence of the classifier's output. Overall, our coding scheme provides an analytical solution which spots the most probable gas for any new test spike sequence with the quantitative feedback. In order to evaluate the robustness of our coding scheme, we target the identification of commonly found health-endangering indoor gases, such as C6H6, CH2O, CO, N O2, and SO2, as a case study. Data of these gases is acquired using our in-house fabricated gas sensor array under different operating conditions, as well as an array of commercially available gas sensors under uniform operational settings. An accuracy rate of 100% has been achieved in both cases with our probabilistic rank score coding scheme.Index Terms-Probabilistic rank score coding, rank-order based classifiers, shuffled spike sequences, electronic nose, gas identification.

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Section: Performance Evaluationmentioning

confidence: 98%

Section: Rank-order Based Classifiersmentioning

confidence: 99%

Section: Rank-order Based Classifiersmentioning

confidence: 99%

Section: Rank-order Based Classifiersmentioning

confidence: 99%

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Probabilistic Rank Score Coding: A Robust Rank-Order Based Classifier for Electronic Nose Applications

2015

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“…PCA is a statistical and unsupervised approach used for feature extraction and data compression [26] [27]. The purpose of PCA is to project the feature from highdimensional to a new low-dimensional space where the derived axes known as principal component are having decreasing order of importance.…”

Section: A Principal Component Analysis (Pca)mentioning

confidence: 99%

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

et al. 2016

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Abstract-Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA) based feature reduction algorithms have been analyzed using data obtained from two different types of gas sensors i.e. seven commercial Figaro sensors and in-house fabricated 4x4 tin-oxide gas array sensor . A decision tree (DT) based classifier is used to examine the performance of both PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system on chip (SoC) platform. It has been found that with the 4x4 array sensor, two discriminant function (DF) of LDA provides 3.3% better classification than five PCA components, while for the seven Figaro sensors two principal components (PC) and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz.

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Field Effect Transistor Technologies for Biological and Chemical Sensors

Salaün

Pichon

2020

Smart Sensors for Environmental and Medical Applications

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A CMOS Single-Chip Gas Recognition Circuit for Metal Oxide Gas Sensor Arrays

Cited by 85 publications

References 32 publications

Probabilistic Rank Score Coding: A Robust Rank-Order Based Classifier for Electronic Nose Applications

Probabilistic Rank Score Coding: A Robust Rank-Order Based Classifier for Electronic Nose Applications

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Field Effect Transistor Technologies for Biological and Chemical Sensors

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