2016
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Li, J; Gutierrez-Osuna, R; Hodges, R D; Luckey, G; Crowell, J; Schiffman, S S; Nagle, H T Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components Journal Article In: IEEE Sensors Journal, vol. in press, 2016. @article{li2016sj,
title = {Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components},
author = {J Li and R Gutierrez-Osuna and R D Hodges and G Luckey and J Crowell and S S Schiffman and H T Nagle},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/li2016sj-1.pdf},
year = {2016},
date = {2016-05-01},
journal = {IEEE Sensors Journal},
volume = {in press},
keywords = {Chemical sensors, Electronic nose, Machine olfaction},
pubstate = {published},
tppubtype = {article}
}
|
2015
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Nagle, H T; Gutierrez-Osuna, R; Suslick, K S; Persaud, K; Covington, J; Hodges, R D; Luckey, G; Crowell, J; Schiffman, S S Augmenting human odor assessments of cabin air quality of automobiles by instrumental measurements Proceedings Article In: International Symposium on Olfaction and Electronic Nose, 2015. @inproceedings{nagle2015isoen,
title = {Augmenting human odor assessments of cabin air quality of automobiles by instrumental measurements},
author = {H T Nagle and R Gutierrez-Osuna and K S Suslick and K Persaud and J Covington and R D Hodges and G Luckey and J Crowell and S S Schiffman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/nagle2015isoen.pdf},
year = {2015},
date = {2015-06-28},
booktitle = {International Symposium on Olfaction and Electronic Nose},
keywords = {Chemical sensors, Machine olfaction},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2004
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Raman, B; Gutierrez-Galvez, A; Perera-Lluna, A; Gutierrez-Osuna, R Sensor-based machine olfaction with a neurodynamics model of the olfactory bulb Conference Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE 2004. @conference{raman2004sensor,
title = {Sensor-based machine olfaction with a neurodynamics model of the olfactory bulb},
author = {B Raman and A Gutierrez-Galvez and A Perera-Lluna and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2004sensor.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages = {319--324},
organization = {IEEE},
abstract = {We propose a biologically inspired model of olfactory processing for chemosensor arrays. The model captures three functions in the early olfactory pathway: chemotopic convergence of receptor neurons onto the olfactory bulb, center on-off surround lateral interactions, and adaptation to sustained stimuli. The projection of ORNs onto glomerular units is simulated with a self-organizing model of chemotopic convergence, which leads to odor specific spatial patterning. This information serves as an input to a network of mitral cells with center on-off surround lateral inhibition, which enhances the initial contrast among odors and decouples odor identity from intensity. Finally, slow adaptation of mitral cells adds a temporal dimension to the spatial patterns that further enhances odor discrimination. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors.},
keywords = {Machine olfaction, Metal-oxide sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
We propose a biologically inspired model of olfactory processing for chemosensor arrays. The model captures three functions in the early olfactory pathway: chemotopic convergence of receptor neurons onto the olfactory bulb, center on-off surround lateral interactions, and adaptation to sustained stimuli. The projection of ORNs onto glomerular units is simulated with a self-organizing model of chemotopic convergence, which leads to odor specific spatial patterning. This information serves as an input to a network of mitral cells with center on-off surround lateral inhibition, which enhances the initial contrast among odors and decouples odor identity from intensity. Finally, slow adaptation of mitral cells adds a temporal dimension to the spatial patterns that further enhances odor discrimination. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. |
2002
|
Nagle, H T; Gutierrez-Osuna, R; Kermani, B; Schiffman, S S Environmental monitoring Book Chapter In: Handbook of Machine Olfaction, pp. 419–444, Wiley Online Library, 2002. @inbook{nagle2003environmental,
title = {Environmental monitoring},
author = {H T Nagle and R Gutierrez-Osuna and B Kermani and S S Schiffman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/nagle2003environmental.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Handbook of Machine Olfaction},
pages = {419--444},
publisher = {Wiley Online Library},
abstract = {In this chapter, we review some of the previous proof-of-principle work done in this field. Examples of water, land and air monitoring experiments are examined. Four case studies are then presented. The first three demonstrate the ability of the e-nose to classify odors from animal confinement facilities (odor source determination, odorant threshold detection, and odor abatement evaluation). The fourth case study demonstrates that the e-nose can differentiate between five types of fungi that commonly lower indoor air quality in office buildings and industrial plants. Finally, we conclude that environmental monitoring is a promising application area for electronic nose technology.},
keywords = {Machine olfaction},
pubstate = {published},
tppubtype = {inbook}
}
In this chapter, we review some of the previous proof-of-principle work done in this field. Examples of water, land and air monitoring experiments are examined. Four case studies are then presented. The first three demonstrate the ability of the e-nose to classify odors from animal confinement facilities (odor source determination, odorant threshold detection, and odor abatement evaluation). The fourth case study demonstrates that the e-nose can differentiate between five types of fungi that commonly lower indoor air quality in office buildings and industrial plants. Finally, we conclude that environmental monitoring is a promising application area for electronic nose technology. |
Gutierrez-Osuna, R; Sun, P A biologically-plausible computational architecture for sensor-based machine olfaction Conference Proceedings of the ninth International Symposium on Olfaction and Electronic Nose, 2002. @conference{gutierrez2002biologically,
title = {A biologically-plausible computational architecture for sensor-based machine olfaction},
author = {R Gutierrez-Osuna and P Sun},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002biologically.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the ninth International Symposium on Olfaction and Electronic Nose},
pages = {57--59},
abstract = {This article presents a computational architecture for sensor-based machine olfaction based on biologically plausible models of the early stages in the olfactory pathway. We derive a concentration-response model that maps conventional sensor-array patterns into activation patterns for a population of olfactory receptor neurons (ORNs). A chemotopic convergence model is employed to generate spatial activation patterns at the glomerular layer that are consistent with neurobiology. These glomerular images serve as inputs to an implementation of Freeman’s KIII neurodynamics model.},
keywords = {Machine olfaction, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
This article presents a computational architecture for sensor-based machine olfaction based on biologically plausible models of the early stages in the olfactory pathway. We derive a concentration-response model that maps conventional sensor-array patterns into activation patterns for a population of olfactory receptor neurons (ORNs). A chemotopic convergence model is employed to generate spatial activation patterns at the glomerular layer that are consistent with neurobiology. These glomerular images serve as inputs to an implementation of Freeman’s KIII neurodynamics model. |
Gutierrez-Osuna, R Pattern analysis for machine olfaction: a review Journal Article In: Sensors Journal, IEEE, vol. 2, no. 3, pp. 189–202, 2002. @article{gutierrezosuna2002sj,
title = {Pattern analysis for machine olfaction: a review},
author = {R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrezosuna2002sj.pdf},
year = {2002},
date = {2002-01-01},
journal = {Sensors Journal, IEEE},
volume = {2},
number = {3},
pages = {189--202},
publisher = {IEEE},
abstract = {Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute for the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics have been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.},
keywords = {Machine olfaction},
pubstate = {published},
tppubtype = {article}
}
Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute for the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics have been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction. |
Gutierrez-Osuna, R; Nagle, H T; Kermani, B; Schiffman, S S Signal conditioning and preprocessing Book Chapter In: Handbook of Machine Olfaction, pp. 105–132, Wiley Online Library, 2002. @inbook{gutierrez2002signal,
title = {Signal conditioning and preprocessing},
author = {R Gutierrez-Osuna and H T Nagle and B Kermani and S S Schiffman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002signal.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Handbook of Machine Olfaction},
pages = {105--132},
publisher = {Wiley Online Library},
abstract = {The topics covered in this chapter establish the connection between gas sensors and pattern recognition, the two fundamental modules of an odor-sensing instrument that are covered in Chapters 6 and 8, respectively. A number of electronic circuits are involved in integrating pattern analysis algorithms with the underlying chemical transduction mechanisms, as shown in Figure 1. First, the response of the odor sensors (e.g. a resistance change) needs to be measured and converted into an electrical signal (e.g. a voltage). This operation is performed by means of interface circuits. Second, the electrical signal undergoes analog conditioning (e.g. filtering) to enhance its information content. Third, the analog signal is sampled, digitized and stored in computer memory (not covered in this chapter due to space constraints). Finally, the sampled signal is digitally pre-processed (e.g. autoscaling) in order to make it suitable for pattern analysis. This chapter is organized in three basic parts: interface circuits, signal conditioning and preprocessing. Section 2 presents the fundamental interface circuits for the three primary odor sensor types: resistive, piezoelectric and field-effect. Section 3 reviews the primary functions performed by analog signal conditioning circuits. Section 4 covers data pre-processing, the first stage of digital signal processing. The issue of sensor and instrumentation noise, one of the most important factors determining electronic-nose performance, is also reviewed in section 5. The chapter concludes with a review of current instrumentation trends aimed at increasing the selectivity of odor sensor systems.},
keywords = {Machine olfaction},
pubstate = {published},
tppubtype = {inbook}
}
The topics covered in this chapter establish the connection between gas sensors and pattern recognition, the two fundamental modules of an odor-sensing instrument that are covered in Chapters 6 and 8, respectively. A number of electronic circuits are involved in integrating pattern analysis algorithms with the underlying chemical transduction mechanisms, as shown in Figure 1. First, the response of the odor sensors (e.g. a resistance change) needs to be measured and converted into an electrical signal (e.g. a voltage). This operation is performed by means of interface circuits. Second, the electrical signal undergoes analog conditioning (e.g. filtering) to enhance its information content. Third, the analog signal is sampled, digitized and stored in computer memory (not covered in this chapter due to space constraints). Finally, the sampled signal is digitally pre-processed (e.g. autoscaling) in order to make it suitable for pattern analysis. This chapter is organized in three basic parts: interface circuits, signal conditioning and preprocessing. Section 2 presents the fundamental interface circuits for the three primary odor sensor types: resistive, piezoelectric and field-effect. Section 3 reviews the primary functions performed by analog signal conditioning circuits. Section 4 covers data pre-processing, the first stage of digital signal processing. The issue of sensor and instrumentation noise, one of the most important factors determining electronic-nose performance, is also reviewed in section 5. The chapter concludes with a review of current instrumentation trends aimed at increasing the selectivity of odor sensor systems. |