Transient response analysis for temperature-modulated chemoresistors

Gutierrez‐Osuna, Ricardo; Gutiérrez-Gálvez, A.; Powar, Nilesh

doi:10.1016/s0925-4005(03)00248-x

Cited by 100 publications

(66 citation statements)

References 26 publications

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“…Each sensor can be modeled as a group of three sensors grouped together which initiates the need to use pattern recognition and smart classification techniques [14][15][16] . A suggested neural model would associate the three previously mentioned important parameters, which would implicitly determine gas concentration level and its type.…”

Section: Resultsmentioning

confidence: 99%

Mathematical Modeling and Interpretation of PbPc Gas Sensor Array

Shilbayeh¹,

Iskandarani²

2006

American J. of Applied Sciences

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Abstract:The voltage/current characteristics and the effect of NO 2 gas on the electrical conductivity of a PbPc gas sensor array is investigated. The gas sensor is manufactured using vacuum deposition of gold electrodes on sapphire substrate with the lead-phathalocyanine vacuum sublimed on the top of the gold electrodes. Two versions of the PbPc gas sensor array are investigated. The tested types differ in the gap sizes between the deposited gold electrodes. The sensors are tested at different temperatures to account for conductivity changes as the molecular adsorption/desorption rate is affected by heat. The obtained results found to be encouraging as the sensors showed stability and sensitivity towards low concentration of applied NO 2 gas.

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Section: Resultsmentioning

confidence: 99%

Mathematical Modeling and Interpretation of PbPc Gas Sensor Array

Shilbayeh¹,

Iskandarani²

2006

American J. of Applied Sciences

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“…Specifically, we have tested five types of classifiers, namely k Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes and linear Support Vector Machines (SVMs) [14,4]. Though different approaches motivated these algorithms, all of them are simple, easily adaptable to the multiclass scenario and they have already been used in the electronic nose domain [21,5,59,18,15].…”

Section: Classifiermentioning

confidence: 99%

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

Rodríguez-Luján

Bailador

Ávila

et al. 2013

Knowledge-Based Systems

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In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Moti-vated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectro-grams from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple clas-sifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

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“…Typically for three-phase sampling systems the sum of exponentials is used since the sensors array is exposed in a stepwise fashion to the odour (15). In this case a first-oder system is appropriate to model the sensor response.…”

Section: Feature Extractionmentioning

confidence: 99%

Odour classification system for continuous monitoring applications

Trincavelli

Coradeschi

Loutfi

2009

Sensors and Actuators B: Chemical

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In this paper, we investigate the classification performance of an electronic nose system, based on tin dioxide gas sensors. In contrast to previous studies, the electronic nose is mounted on a mobile platform and samples are analyzed using only transient information in the signals. The motivation behind this work is to explore the feasibility of using electronic nose devices for odour classification in a number of future application domains which require fast and possibly real-time odour identification. To perform transient based analysis of the signals, a comparative study of different methods for feature extraction was performed. Additionally, the application of a relevance vector machine classifier is explored to further analyze the classification performance based on quality of the obtained samples. The results presented in this study can be used for the development of electronic nose devices particularly suitable for environmental monitoring applications.

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Transient response analysis for temperature-modulated chemoresistors

Cited by 100 publications

References 26 publications

Mathematical Modeling and Interpretation of PbPc Gas Sensor Array

Mathematical Modeling and Interpretation of PbPc Gas Sensor Array

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

Odour classification system for continuous monitoring applications

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