Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays

[eng] Electronic noses (e-noses) have been utilized during the past three decades as general purpose instruments for chemical sensing. These instruments are inspired by natural olfactory systems, where fine odour discrimination is performed without the necessity for highly specialized receptors. Ins...

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Autor: Fernández Romero, Luis
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/102416
Acceso en línea:https://hdl.handle.net/2445/102416
http://hdl.handle.net/10803/395180
Access Level:acceso abierto
Palabra clave:Olfacte
Olors
Detectors
Detectors de gasos
Smell
Odors
Gas detectors
id ES_0bd1abd0d7e27faf3e6d8c67363eeecc
oai_identifier_str oai:diposit.ub.edu:2445/102416
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
title Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
spellingShingle Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
Fernández Romero, Luis
Olfacte
Olors
Detectors
Detectors de gasos
Smell
Odors
Detectors
Gas detectors
title_short Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
title_full Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
title_fullStr Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
title_full_unstemmed Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
title_sort Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arrays
dc.creator.none.fl_str_mv Fernández Romero, Luis
author Fernández Romero, Luis
author_facet Fernández Romero, Luis
author_role author
dc.contributor.none.fl_str_mv Gutiérrez Gálvez, Agustín
Marco Colás, Santiago
Universitat de Barcelona. Departament d'Electrònica
dc.subject.none.fl_str_mv Olfacte
Olors
Detectors
Detectors de gasos
Smell
Odors
Detectors
Gas detectors
topic Olfacte
Olors
Detectors
Detectors de gasos
Smell
Odors
Detectors
Gas detectors
description [eng] Electronic noses (e-noses) have been utilized during the past three decades as general purpose instruments for chemical sensing. These instruments are inspired by natural olfactory systems, where fine odour discrimination is performed without the necessity for highly specialized receptors. Instead, odour information is extracted in these systems using arrays of broadly tuned receptors organized in a convergent pathway. Such a sensing architecture allows combining the responses of the array of receptors, giving rise to particular representations of the different odour stimuli. The key advantage provided by this approach is that odour representation is more efficient and robust when the encoding is performed by the population of receptors than by any of its individual elements (hyper-acuity). A population of receptors obtains its maximum performance in encoding odour stimulus features when it balances the benefits of sensory diversity and redundancy. By sensor diversity we understand the number of different receptor types responsible for enhancing the variability of the array response to a collection of odours. Likewise, by sensor redundancy we refer to the average number of receptor replicates on a population. The role of sensor redundancy accounts for the robustness to receptor damage and noise exhibited by the odour stimuli representation. This variety of odour receptor types along with its outstanding number of receptors is characteristic of natural olfactory systems. Though, traditional electronic noses tend to exhibit a limited number of sensor units with very much correlated responses to odour stimuli. Several strategies to enhance odour representation in gas sensor arrays are based on boosting sensor diversity and redundancy. However, it has not been until recently that large arrays of cross-selective have become technologically available. In this dissertation, we have developed one of these new generation arrays to investigate the advantages odour stimuli representation through population coding in artificial olfaction. In particular, we proposed to build a chemical sensing system based on an array of metal oxide (MOX) gas sensors, and endowed with a high a degree of sensor diversity and redundancy. We proposed the use this bio-inspired sensing architecture alongside statistical pattern recognition techniques to cope with some of the unsolved problems in machine olfaction (robustness to sensor damage, feature selection, and calibration transfer). The main contributions of this work were the following: We defined functionally sensor diversity and redundancy. These definitions were based on the clustering of the array features according to their similitude when responding to an odour dataset. We compared the different manner how natural and artificial olfactory systems encode for odour information using simple sensors models. We found that natural olfactory system principally encoded odour information in terms of odour quality, whereas that artificial ones in terms of odour quantity. Also, we studied the effect of sensor noise on odour concentration encoding. We proposed to decrease the contribution of the sensor noise by means of the redundant sensor feature averaging and sensor array optimization. These strategies were effective in case of independent sensor noise, but not for removing common sources of sensor noise. Similarly, we detected the importance of sensor failure dependency on the odour discrimination capabilities of a sensor. We found that this sensor fault distribution across had to be independent of the sensor type to prevent a dramatic worsening on the array’s predictive performance. In addition to this, we proposed an update of a feature selection method including a dimensionality reduction stage so as to take into account the redundant information provided by the sensor array. Finally, we performed instrument standardization between temperature modulated sensor arrays to correct global shifts of temperature. A method to categorize the quality of the calibration transfer based on the bias-variance trade-off was presented.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/102416
http://hdl.handle.net/10803/395180
url https://hdl.handle.net/2445/102416
http://hdl.handle.net/10803/395180
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv (c) Fernández, 2016
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Fernández, 2016
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat de Barcelona
publisher.none.fl_str_mv Universitat de Barcelona
dc.source.none.fl_str_mv Tesis Doctorals - Departament - Electrònica
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Understanding the role of sensor diversity and redundancy to encode for chemical information in gas sensor arraysFernández Romero, LuisOlfacteOlorsDetectorsDetectors de gasosSmellOdorsDetectorsGas detectors[eng] Electronic noses (e-noses) have been utilized during the past three decades as general purpose instruments for chemical sensing. These instruments are inspired by natural olfactory systems, where fine odour discrimination is performed without the necessity for highly specialized receptors. Instead, odour information is extracted in these systems using arrays of broadly tuned receptors organized in a convergent pathway. Such a sensing architecture allows combining the responses of the array of receptors, giving rise to particular representations of the different odour stimuli. The key advantage provided by this approach is that odour representation is more efficient and robust when the encoding is performed by the population of receptors than by any of its individual elements (hyper-acuity). A population of receptors obtains its maximum performance in encoding odour stimulus features when it balances the benefits of sensory diversity and redundancy. By sensor diversity we understand the number of different receptor types responsible for enhancing the variability of the array response to a collection of odours. Likewise, by sensor redundancy we refer to the average number of receptor replicates on a population. The role of sensor redundancy accounts for the robustness to receptor damage and noise exhibited by the odour stimuli representation. This variety of odour receptor types along with its outstanding number of receptors is characteristic of natural olfactory systems. Though, traditional electronic noses tend to exhibit a limited number of sensor units with very much correlated responses to odour stimuli. Several strategies to enhance odour representation in gas sensor arrays are based on boosting sensor diversity and redundancy. However, it has not been until recently that large arrays of cross-selective have become technologically available. In this dissertation, we have developed one of these new generation arrays to investigate the advantages odour stimuli representation through population coding in artificial olfaction. In particular, we proposed to build a chemical sensing system based on an array of metal oxide (MOX) gas sensors, and endowed with a high a degree of sensor diversity and redundancy. We proposed the use this bio-inspired sensing architecture alongside statistical pattern recognition techniques to cope with some of the unsolved problems in machine olfaction (robustness to sensor damage, feature selection, and calibration transfer). The main contributions of this work were the following: We defined functionally sensor diversity and redundancy. These definitions were based on the clustering of the array features according to their similitude when responding to an odour dataset. We compared the different manner how natural and artificial olfactory systems encode for odour information using simple sensors models. We found that natural olfactory system principally encoded odour information in terms of odour quality, whereas that artificial ones in terms of odour quantity. Also, we studied the effect of sensor noise on odour concentration encoding. We proposed to decrease the contribution of the sensor noise by means of the redundant sensor feature averaging and sensor array optimization. These strategies were effective in case of independent sensor noise, but not for removing common sources of sensor noise. Similarly, we detected the importance of sensor failure dependency on the odour discrimination capabilities of a sensor. We found that this sensor fault distribution across had to be independent of the sensor type to prevent a dramatic worsening on the array’s predictive performance. In addition to this, we proposed an update of a feature selection method including a dimensionality reduction stage so as to take into account the redundant information provided by the sensor array. Finally, we performed instrument standardization between temperature modulated sensor arrays to correct global shifts of temperature. A method to categorize the quality of the calibration transfer based on the bias-variance trade-off was presented.[spa] La nariz electrónica (e-nose) ha sido utilizada durante las últimas tres décadas como instrumento de propósito general para la detección química. Este instrumento está inspirado en los sistemas olfativos naturales, donde la discriminación de olores se realiza eficientemente sin la necesidad de receptores altamente especializados. La ventaja clave proporcionada por esta aproximación es que la representación de los olores es más eficiente y robusta cuando la codificación del olor es llevada a cabo por una población de receptores, pues esta supera la calidad de cualquiera realizada por sus elementos individuales. Una población de receptores obtiene su máximo rendimiento en la codificación de las características de un estímulo odorífero cuando se equilibran los beneficios de la diversidad y redundancia sensoriales. Lamentablemente, las narices electrónicas tradicionales tienden a exhibir un número limitado de sensores con respuestas muy correlacionas ante diferentes conjuntos de estímulos odoríferos. Sin embargo, no ha sido hasta hace relativamente poco que la creación grandes matrices de sensores con selectividades cruzadas ha sido tecnológicamente posibles. El objetivo de esta tesis es el desarrollo de una de estas matrices de nueva generación para investigar las ventajas de la representación de los estímulos odoríferos través de una codificación poblacional soportada por la diversidad y redundancia sensoriales. En particular, hemos construido un sistema de detección química basado en una matriz de sensores de gas de óxido metálico (MOX), y dotada de un alto grado de diversidad y redundancia sensoriales. Hemos utilizado esta arquitectura de detección química bioinspirada junto técnicas de reconocimiento de patrones estadísticas para hacer frente a algunos de los problemas sin resolver en olfacción artificial, a saber, la robustez al fallo de sensores, la selección de características, y la transferencia de calibración entre diferentes narices electrónicas.Universitat de BarcelonaGutiérrez Gálvez, AgustínMarco Colás, SantiagoUniversitat de Barcelona. Departament d'Electrònica2016info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/102416http://hdl.handle.net/10803/395180Tesis Doctorals - Departament - Electrònicareponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglés(c) Fernández, 2016info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1024162026-05-27T06:46:51Z
score 15,228081

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