2014
|
Gosangi, R; Gutierrez-Osuna, R Active classification with arrays of tunable chemical sensors Journal Article In: Chemometrics and Intelligent Laboratory Systems, vol. 132, pp. 91-102, 2014. @article{rakesh2014cils,
title = {Active classification with arrays of tunable chemical sensors},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rakesh2014cils.pdf},
year = {2014},
date = {2014-02-01},
journal = {Chemometrics and Intelligent Laboratory Systems},
volume = {132},
pages = {91-102},
keywords = {Active sensing, Metal-oxide sensors},
pubstate = {published},
tppubtype = {article}
}
|
2013
|
Gosangi, R; Gutierrez-Osuna, R Active temperature modulation of metal-oxide sensors for quantitative analysis of gas mixtures Journal Article In: Sensors and Actuators B: Chemical, vol. 185, pp. 201-210, 2013. @article{rakeshmixturessab13,
title = {Active temperature modulation of metal-oxide sensors for quantitative analysis of gas mixtures},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rakeshmixturessab13.pdf},
year = {2013},
date = {2013-04-15},
journal = {Sensors and Actuators B: Chemical},
volume = {185},
pages = {201-210},
keywords = {Active sensing, Chemical sensors, Metal-oxide sensors},
pubstate = {published},
tppubtype = {article}
}
|
2011
|
Gosangi, R; Gutierrez-Osuna, R Quantification of Gas Mixtures with Active Recursive Estimation Conference Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011. @conference{gosangi2011quantification,
title = {Quantification of Gas Mixtures with Active Recursive Estimation},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gosangi2011quantification.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the 14th International Symposium on Olfaction and Electronic Nose},
journal = {AIP Conference Proceedings},
pages = {23-24},
abstract = {We present an active‐sensing strategy to estimate the concentrations in a gas mixture using temperature modulation of metal‐oxide (MOX) sensors. The approach is based on recursive Bayesian estimation and uses an information‐theoretic criterion to select operating temperatures on‐the‐fly. Recursive estimation has been widely used in mobile robotics, e.g., for localization purposes. Here, we employ a similar approach to estimate the concentrations of the constituents in a gas mixture. In this formulation, we represent a concentration profile as a discrete state and maintain a ‘belief’ distribution that represents the probability of each state. We employ a Bayes filter to update the belief distribution whenever new sensor measurements arrive, and a mutual‐information criterion to select the next operating temperature. This allows us to optimize the temperature program in real time, as the sensor interacts with its environment. We validate our approach on a simulated dataset generated from temperature modulated responses of a MOX sensor exposed to a mixture of three analytes. The results presented here provide a preliminary proof of concept for an agile approach to quantifying gas mixtures.},
keywords = {Active sensing, Metal-oxide sensors},
pubstate = {published},
tppubtype = {conference}
}
We present an active‐sensing strategy to estimate the concentrations in a gas mixture using temperature modulation of metal‐oxide (MOX) sensors. The approach is based on recursive Bayesian estimation and uses an information‐theoretic criterion to select operating temperatures on‐the‐fly. Recursive estimation has been widely used in mobile robotics, e.g., for localization purposes. Here, we employ a similar approach to estimate the concentrations of the constituents in a gas mixture. In this formulation, we represent a concentration profile as a discrete state and maintain a ‘belief’ distribution that represents the probability of each state. We employ a Bayes filter to update the belief distribution whenever new sensor measurements arrive, and a mutual‐information criterion to select the next operating temperature. This allows us to optimize the temperature program in real time, as the sensor interacts with its environment. We validate our approach on a simulated dataset generated from temperature modulated responses of a MOX sensor exposed to a mixture of three analytes. The results presented here provide a preliminary proof of concept for an agile approach to quantifying gas mixtures. |
Gosangi, R; Gutierrez-Osuna, R Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks Conference Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011. @conference{gosangi2011data,
title = {Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gosangi2011data.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the 14th International Symposium on Olfaction and Electronic Nose},
abstract = {We present a data‐driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico‐chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data‐driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients.},
keywords = {Active sensing, Metal-oxide sensors},
pubstate = {published},
tppubtype = {conference}
}
We present a data‐driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico‐chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data‐driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients. |
2010
|
Gosangi, R; Gutierrez-Osuna, R Energy-aware active chemical sensing Conference Proceedings of IEEE Sensors, IEEE 2010. @conference{gosangi2010sensorsc,
title = {Energy-aware active chemical sensing},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gosangi2010sensorsc.pdf},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings of IEEE Sensors},
pages = {1094--1099},
organization = {IEEE},
abstract = {We propose an adaptive sensing framework for metal-oxide (MOX) sensors that seeks to minimize energy consumption through temperature modulation. Our approach generates temperature programs by means of an active-sensing strategy combined with an objective function that penalizes power consumption. The problem is modeled as a partially observable Markov decision process (POMDP) and solved with a myopic policy that operates in real time. The policy selects sensing actions (i.e., temperature pulses) that balance misclassification costs (e.g., chemicals identified as the wrong target) and sensing costs (i.e., power consumption). We experimentally validate the method on a ternary chemical discrimination problem, and compare it against a "passive classifier." Our results show that, for a given energy budget, the active-sensing strategy selects temperatures with more discriminatory information than those of the passive classifier by penalizing pulses of higher temperature and longer durations.},
keywords = {Active sensing, Chemical sensors, Metal-oxide sensors, Temperature modulation},
pubstate = {published},
tppubtype = {conference}
}
We propose an adaptive sensing framework for metal-oxide (MOX) sensors that seeks to minimize energy consumption through temperature modulation. Our approach generates temperature programs by means of an active-sensing strategy combined with an objective function that penalizes power consumption. The problem is modeled as a partially observable Markov decision process (POMDP) and solved with a myopic policy that operates in real time. The policy selects sensing actions (i.e., temperature pulses) that balance misclassification costs (e.g., chemicals identified as the wrong target) and sensing costs (i.e., power consumption). We experimentally validate the method on a ternary chemical discrimination problem, and compare it against a "passive classifier." Our results show that, for a given energy budget, the active-sensing strategy selects temperatures with more discriminatory information than those of the passive classifier by penalizing pulses of higher temperature and longer durations. |
Gosangi, R; Gutierrez-Osuna, R Active temperature programming for metal-oxide chemoresistors Journal Article In: Sensors Journal, IEEE, vol. 10, no. 6, pp. 1075–1082, 2010. @article{gosangi2010sj,
title = {Active temperature programming for metal-oxide chemoresistors},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gosangi2010sj-1.pdf},
year = {2010},
date = {2010-01-01},
journal = {Sensors Journal, IEEE},
volume = {10},
number = {6},
pages = {1075--1082},
publisher = {IEEE},
abstract = {Modulating the operating temperature of metal-oxide (MOX) chemical sensors gives rise to gas-specific signatures that
provide a wealth of analytical information. In most cases, the operating temperature is modulated according to a standard waveform (e.g., ramp, sine wave). A few studies have approached the optimization of temperature profiles systematically, but these optimizations are performed offline and cannot adapt to changes in the environment. Here, we present an “active perception” strategy based on Partially Observable Markov Decision Processes (POMDP) that allows the temperature program to be optimized in real time, as the sensor reacts to its environment. We characterize the method on a ternary classification problem using a simulated sensor model subjected to additive Gaussian noise, and compare it against two “passive” approaches, a naïve Bayes classifier and a nearest neighbor classifier. Finally, we validate the method in real time using a Taguchi sensor exposed to three volatile compounds. Our results show that the POMDP outperforms both passive approaches and provides a strategy to balance classification performance and sensing costs.},
keywords = {Active sensing, Chemical sensors, Metal-oxide sensors, Temperature modulation},
pubstate = {published},
tppubtype = {article}
}
Modulating the operating temperature of metal-oxide (MOX) chemical sensors gives rise to gas-specific signatures that
provide a wealth of analytical information. In most cases, the operating temperature is modulated according to a standard waveform (e.g., ramp, sine wave). A few studies have approached the optimization of temperature profiles systematically, but these optimizations are performed offline and cannot adapt to changes in the environment. Here, we present an “active perception” strategy based on Partially Observable Markov Decision Processes (POMDP) that allows the temperature program to be optimized in real time, as the sensor reacts to its environment. We characterize the method on a ternary classification problem using a simulated sensor model subjected to additive Gaussian noise, and compare it against two “passive” approaches, a naïve Bayes classifier and a nearest neighbor classifier. Finally, we validate the method in real time using a Taguchi sensor exposed to three volatile compounds. Our results show that the POMDP outperforms both passive approaches and provides a strategy to balance classification performance and sensing costs. |
2004
|
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. |
Pasini, P; Powar, N; Gutierrez-Osuna, R; Daunert, S; Roda, A Use of a gas-sensor array for detecting volatile organic compounds (VOC) in chemically induced cells Journal Article In: Analytical and bioanalytical chemistry, vol. 378, no. 1, pp. 76–83, 2004. @article{pasini2004use,
title = {Use of a gas-sensor array for detecting volatile organic compounds (VOC) in chemically induced cells},
author = {P Pasini and N Powar and R Gutierrez-Osuna and S Daunert and A Roda},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/pasini2004use.pdf},
year = {2004},
date = {2004-01-01},
journal = {Analytical and bioanalytical chemistry},
volume = {378},
number = {1},
pages = {76--83},
publisher = {Springer},
abstract = {An application of gas sensors for rapid bioanalysis is presented. An array of temperature-modulated semiconductor sensors was used to characterize the headspace above a cell culture. Recombinant Saccharomyces cerevisiae yeast cells, able to respond to 17β-estradiol by producing a reporter protein, were used as a model system. Yeast cells had the DNA sequence of the human estrogen receptor stably integrated into the genome, and contained expression plasmids carrying estrogen-responsive sequences and the reporter gene lac-Z, encoding the enzyme β-galactosidase. The sensor-response profiles showed small but noticeable discrimination between cell samples induced with 17β-estradiol and non-induced cell samples. The sensor array was capable of detecting changes in the volatile organic compound composition of the headspace above the cultured cells, which can be associated with metabolic changes induced by a chemical compound. This finding suggests the possibility of using cross-selective gas-sensor arrays for analysis of drugs or bioactive molecules through their interaction with cell systems, with the advantage of providing information on their bioavailability.},
keywords = {Chemical sensors, Metal-oxide sensors, Temperature modulation},
pubstate = {published},
tppubtype = {article}
}
An application of gas sensors for rapid bioanalysis is presented. An array of temperature-modulated semiconductor sensors was used to characterize the headspace above a cell culture. Recombinant Saccharomyces cerevisiae yeast cells, able to respond to 17β-estradiol by producing a reporter protein, were used as a model system. Yeast cells had the DNA sequence of the human estrogen receptor stably integrated into the genome, and contained expression plasmids carrying estrogen-responsive sequences and the reporter gene lac-Z, encoding the enzyme β-galactosidase. The sensor-response profiles showed small but noticeable discrimination between cell samples induced with 17β-estradiol and non-induced cell samples. The sensor array was capable of detecting changes in the volatile organic compound composition of the headspace above the cultured cells, which can be associated with metabolic changes induced by a chemical compound. This finding suggests the possibility of using cross-selective gas-sensor arrays for analysis of drugs or bioactive molecules through their interaction with cell systems, with the advantage of providing information on their bioavailability. |
2003
|
Gutierrez-Osuna, R; Gutierrez-Galvez, A; Powar, N Transient response analysis for temperature-modulated chemoresistors Journal Article In: Sensors and Actuators B: Chemical, vol. 93, no. 1-3, pp. 57–66, 2003. @article{gutierrez2003transient,
title = {Transient response analysis for temperature-modulated chemoresistors},
author = {R Gutierrez-Osuna and A Gutierrez-Galvez and N Powar},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003transient.pdf},
year = {2003},
date = {2003-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {93},
number = {1-3},
pages = {57--66},
publisher = {Elsevier},
abstract = {This article presents a sensor excitation and signal processing approach that combines temperature modulation and transientanalysis to enhance the selectivity and sensitivity of metal-oxide gas sensors. A staircase waveform is applied to the sensor heater to extract transient information from multiple operating temperatures. Four different transientanalysis techniques, Pade–Z-transform, multi-exponential transient spectroscopy (METS), window time slicing (WTS) and a novel ridge regression solution, are evaluated on the basis of their ability to improve the sensitivity and selectivity of the sensor array. The techniques are validated on two experimental databases containing serial dilutions and mixtures of organic solvents. Our results indicate that processing of the thermal transients significantly improves the sensitivity of metal-oxide chemoresistors when compared to the quasi-stationary temperature-modulatedresponses.},
keywords = {Chemical sensors, Metal-oxide sensors, Temperature modulation},
pubstate = {published},
tppubtype = {article}
}
This article presents a sensor excitation and signal processing approach that combines temperature modulation and transientanalysis to enhance the selectivity and sensitivity of metal-oxide gas sensors. A staircase waveform is applied to the sensor heater to extract transient information from multiple operating temperatures. Four different transientanalysis techniques, Pade–Z-transform, multi-exponential transient spectroscopy (METS), window time slicing (WTS) and a novel ridge regression solution, are evaluated on the basis of their ability to improve the sensitivity and selectivity of the sensor array. The techniques are validated on two experimental databases containing serial dilutions and mixtures of organic solvents. Our results indicate that processing of the thermal transients significantly improves the sensitivity of metal-oxide chemoresistors when compared to the quasi-stationary temperature-modulatedresponses. |
Gutierrez-Osuna, R; Powar, N Odor mixtures and chemosensory adaptation in gas sensor arrays Journal Article In: International Journal on Artificial Intelligence Tools, vol. 12, no. 1, pp. 1–16, 2003. @article{gutierrez2003odor,
title = {Odor mixtures and chemosensory adaptation in gas sensor arrays},
author = {R Gutierrez-Osuna and N Powar},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003odor.pdf},
year = {2003},
date = {2003-01-01},
journal = {International Journal on Artificial Intelligence Tools},
volume = {12},
number = {1},
pages = {1--16},
publisher = {WORLD SCIENTIFIC PUBLISHING},
abstract = {Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.},
keywords = {Metal-oxide sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors. |
2002
|
Gutierrez-Osuna, R; Gutierrez-Galvez, A Habituation in the KIII olfactory model using gas sensor arrays Conference Proceedings of the 9th International Symposium on Olfaction and Electronic Nose, 2002. @conference{gutierrez2002habituation,
title = {Habituation in the KIII olfactory model using gas sensor arrays},
author = {R Gutierrez-Osuna and A Gutierrez-Galvez},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002habituation.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the 9th International Symposium on Olfaction and Electronic Nose},
pages = {171--173},
abstract = {Inspired by the habituation process in the olfactory system, this article presents an approach for analyzing electronic-nose data using Freeman’s KIII neurodynamics model. In order to ensure the additivity of patterns from odor mixtures, input data from a gas sensor array is first processed with a family of discriminant functions that yield an orthogonal binary representation. The process of habituation is then simulated through synaptic depression with a decay term that reduces the strength of mitral and granule connections when the KIII model is excited with a continuous stimulus. As a result, the system is able to mimic the effects of habituation when processing odor mixtures with gas sensor arrays.},
keywords = {Metal-oxide sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
Inspired by the habituation process in the olfactory system, this article presents an approach for analyzing electronic-nose data using Freeman’s KIII neurodynamics model. In order to ensure the additivity of patterns from odor mixtures, input data from a gas sensor array is first processed with a family of discriminant functions that yield an orthogonal binary representation. The process of habituation is then simulated through synaptic depression with a decay term that reduces the strength of mitral and granule connections when the KIII model is excited with a continuous stimulus. As a result, the system is able to mimic the effects of habituation when processing odor mixtures with gas sensor arrays. |
Perera-Lluna, A; Sundic, T; Pardo, A; Gutierrez-Osuna, R; Marco, S A portable electronic nose based on embedded PC technology and GNU/Linux: Hardware, software and applications Journal Article In: Sensors Journal, IEEE, vol. 2, no. 3, pp. 235–246, 2002. @article{perera2002portable,
title = {A portable electronic nose based on embedded PC technology and GNU/Linux: Hardware, software and applications},
author = {A Perera-Lluna and T Sundic and A Pardo and R Gutierrez-Osuna and S Marco},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/perera2002portable.pdf},
year = {2002},
date = {2002-01-01},
journal = {Sensors Journal, IEEE},
volume = {2},
number = {3},
pages = {235--246},
publisher = {IEEE},
abstract = {This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented.},
keywords = {Electronic nose, Metal-oxide sensors},
pubstate = {published},
tppubtype = {article}
}
This paper describes a portable electronic nose based on embedded PC technology. The instrument combines a small footprint with the versatility offered by embedded technology in terms of software development and digital communications services. A summary of the proposed hardware and software solutions is provided with an emphasis on data processing. Data evaluation procedures available in the instrument include automatic feature selection by means of SFFS, feature extraction with linear discriminant analysis (LDA) and principal component analysis (PCA), multi-component analysis with partial least squares (PLS) and classification through k-NN and Gaussian mixture models. In terms of instrumentation, the instrument makes use of temperature modulation to improve the selectivity of commercial metal oxide gas sensors. Field applications of the instrument, including experimental results, are also presented. |
Gutierrez-Osuna, R; Gutierrez-Galvez, A; Powar, N Transient Response Analysis for Temperature Modulated Chemoresistors Conference Proceedings of the 9th International Meeting on Chemical Sensors, 2002. @conference{gutierrez2002transient,
title = {Transient Response Analysis for Temperature Modulated Chemoresistors},
author = {R Gutierrez-Osuna and A Gutierrez-Galvez and N Powar},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2002transient.pdf},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the 9th International Meeting on Chemical Sensors},
keywords = {Chemical sensors, Metal-oxide sensors, Temperature modulation},
pubstate = {published},
tppubtype = {conference}
}
|
2001
|
Perera-Lluna, A; Gutierrez-Osuna, R; Marco, S ipNOSE: A portable volatile analyzer based on embedded technology for intensive computation and time dependent signal processing Conference Proceedings of the 8th International Symposium on Olfaction and the Electronic Nose, 2001. @conference{perera2001volatile,
title = {ipNOSE: A portable volatile analyzer based on embedded technology for intensive computation and time dependent signal processing},
author = {A Perera-Lluna and R Gutierrez-Osuna and S Marco},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/perera2001volatile.pdf},
year = {2001},
date = {2001-03-25},
booktitle = {Proceedings of the 8th International Symposium on Olfaction and the Electronic Nose},
abstract = {Most electronic noses need a computer and special software in order to analyze data from sensors. In the case of portable electronic noses, most of them are operated by microcontrollers with limited data storage (usually feature vectors) and simple signal processing capabilities. Here we suggest the integration of a small form factor computer inside the electronic nose. This concept allows us to easily perform temperature modulation over metal oxide sensors, remote connectivity under TCP/IP networking, large data storage and complex signal processing.},
keywords = {Electronic nose, Metal-oxide sensors},
pubstate = {published},
tppubtype = {conference}
}
Most electronic noses need a computer and special software in order to analyze data from sensors. In the case of portable electronic noses, most of them are operated by microcontrollers with limited data storage (usually feature vectors) and simple signal processing capabilities. Here we suggest the integration of a small form factor computer inside the electronic nose. This concept allows us to easily perform temperature modulation over metal oxide sensors, remote connectivity under TCP/IP networking, large data storage and complex signal processing. |
Gutierrez-Osuna, R; Powar, N; Sun, P Chemosensory adaptation in an electronic nose Conference Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering Conference, IEEE 2001. @conference{gutierrez2001chemosensory,
title = {Chemosensory adaptation in an electronic nose},
author = {R Gutierrez-Osuna and N Powar and P Sun},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2001chemosensory.pdf},
year = {2001},
date = {2001-01-01},
booktitle = {Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering Conference},
pages = {223--229},
organization = {IEEE},
abstract = {This article presents a computational mechanism inspired by the process of chemosensory adaptation in the mammalian olfactory system. The algorithm operates on multiple subsets of the sensory space, generating a family of discriminant functions for different volatile compounds. A set of selectivity coefficients is associated to each discriminant function on the basis of its behavior in the presence of mixtures. These coefficients are employed to form a weighted average of the discriminant functions and establish a feedback signal that reduces the contribution of certain sensory inputs, inhibiting the overall selectivity of the system to previously detected analytes. The algorithm is validated on a database of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.},
keywords = {Chemical sensors, Electronic nose, Metal-oxide sensors, Neuromorphic models, Temperature modulation},
pubstate = {published},
tppubtype = {conference}
}
This article presents a computational mechanism inspired by the process of chemosensory adaptation in the mammalian olfactory system. The algorithm operates on multiple subsets of the sensory space, generating a family of discriminant functions for different volatile compounds. A set of selectivity coefficients is associated to each discriminant function on the basis of its behavior in the presence of mixtures. These coefficients are employed to form a weighted average of the discriminant functions and establish a feedback signal that reduces the contribution of certain sensory inputs, inhibiting the overall selectivity of the system to previously detected analytes. The algorithm is validated on a database of organic solvents using an array of temperature-modulated metal-oxide chemoresistors. |
Gutierrez-Osuna, R; Korah, S; Perera, A Multi-frequency temperature modulation for metal-oxide gas sensors Conference Proceedings of the 8th International Symposium on Olfaction and the Electronic Nose, 2001. @conference{gutierrez2001multi,
title = {Multi-frequency temperature modulation for metal-oxide gas sensors},
author = {R Gutierrez-Osuna and S Korah and A Perera},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2001multi.pdf},
year = {2001},
date = {2001-01-01},
booktitle = {Proceedings of the 8th International Symposium on Olfaction and the Electronic Nose},
pages = {212--218},
abstract = {This article presents a multi-frequency approach to temperature modulation for commercial metal-oxide gas sensors. The heating element is excited with a sinusoidal waveform at different frequencies ranging from 0.125 to 4 Hz, as well as DC. Experimental results on five compounds yield 100% classification rate on temperature-modulated responses, in comparison with 50-60% for DC-heated responses.},
keywords = {Chemical sensors, Metal-oxide sensors, Temperature modulation},
pubstate = {published},
tppubtype = {conference}
}
This article presents a multi-frequency approach to temperature modulation for commercial metal-oxide gas sensors. The heating element is excited with a sinusoidal waveform at different frequencies ranging from 0.125 to 4 Hz, as well as DC. Experimental results on five compounds yield 100% classification rate on temperature-modulated responses, in comparison with 50-60% for DC-heated responses. |
2000
|
Gutierrez-Osuna, R Drift reduction for metal-oxide sensor arrays using canonical correlation regression and partial least squares Conference Proceedings of the 7th International Symposium On Olfaction & Electronic Nose, 2000. @conference{gutierrez2000drift,
title = {Drift reduction for metal-oxide sensor arrays using canonical correlation regression and partial least squares},
author = {R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2000drift.pdf},
year = {2000},
date = {2000-01-01},
booktitle = {Proceedings of the 7th International Symposium On Olfaction & Electronic Nose},
journal = {Electronic Noses and Olfaction 2000},
abstract = {The transient response of metal-oxide sensors exposed to mild odours can be oftentimes highly correlated with the behaviour of the array during the preceding wash and reference cycles. Since wash/reference gases are virtually constant overtime, variations in their transient response can be used to estimate the amount of sensor drift present in each experiment. We perform canonical correlation analysis and partial least squares to find a subset of “latent variables” that summarize the linear dependencies between odour and wash/reference responses. Ordinary least squares regression is then used to subtract these “latent variables” from the odour response. Experimental results on an odour database of four cooking spices, collected on a 10-sensor array over a period of three months, show significant improvements in predictive accuracy.},
keywords = {Metal-oxide sensors},
pubstate = {published},
tppubtype = {conference}
}
The transient response of metal-oxide sensors exposed to mild odours can be oftentimes highly correlated with the behaviour of the array during the preceding wash and reference cycles. Since wash/reference gases are virtually constant overtime, variations in their transient response can be used to estimate the amount of sensor drift present in each experiment. We perform canonical correlation analysis and partial least squares to find a subset of “latent variables” that summarize the linear dependencies between odour and wash/reference responses. Ordinary least squares regression is then used to subtract these “latent variables” from the odour response. Experimental results on an odour database of four cooking spices, collected on a 10-sensor array over a period of three months, show significant improvements in predictive accuracy. |