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. |
2005
|
Raman, B; Gutierrez-Osuna, R Concentration normalization with a model of gain control in the olfactory bulb Journal Article In: Sensors and Actuators B: Chemical, vol. 116, no. 1, pp. 36-42, 2005. @article{raman2005sensors,
title = {Concentration normalization with a model of gain control in the olfactory bulb},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2005sensors.pdf},
year = {2005},
date = {2005-04-13},
booktitle = {Proceedings of the 11th International Symposium on Olfaction and Electronic Nose},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {36-42},
abstract = {This article presents a biologically-inspired model to remove concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in the olfactory bulb, mediated by periglomerular interneurons. To simulate inputs to the olfactory bulb, sensor-array data are processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odorspecific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells, compresses the concentration information. The model is validated using experimental data from an array of temperature-modulated metaloxide sensors.},
keywords = {Chemical sensors, Neuromorphic models, Temperature modulation},
pubstate = {published},
tppubtype = {article}
}
This article presents a biologically-inspired model to remove concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in the olfactory bulb, mediated by periglomerular interneurons. To simulate inputs to the olfactory bulb, sensor-array data are processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odorspecific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells, compresses the concentration information. The model is validated using experimental data from an array of temperature-modulated metaloxide sensors. |
2004
|
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. |
2002
|
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
|
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. |
1999
|
Gutierrez-Osuna, R; Nagle, H T; Schiffman, S S Transient response analysis of an electronic nose using multi-exponential models Journal Article In: Sensors and Actuators B: Chemical, vol. 61, no. 1-3, pp. 170–182, 1999. @article{gutierrez1999transient,
title = {Transient response analysis of an electronic nose using multi-exponential models},
author = {R Gutierrez-Osuna and H T Nagle and S S Schiffman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1999transient.pdf},
year = {1999},
date = {1999-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {61},
number = {1-3},
pages = {170--182},
publisher = {Elsevier},
abstract = {The purpose of this study is to model the transient response of conductivity-based gas sensors in the context of odor recognition with an electronic nose. Commonly, only the steady-state response of the sensor is used for pattern recognition, ignoring the transient response, which conveys useful discriminatory information. The transient response is modeled as a sum of real exponential functions that represent the different decay processes that occur during sampling of the gas into the sensor chamber and adsorption of the odor compounds onto the sensing element. Four multi-exponential models are reviewed: Gardner transform, multi-exponential transient spectroscopy, Pade-Laplace and Pade-Z transforms. Validation on experimental data from an array of conducting-polymer gas sensors shows that the Pade-Laplace and Pade-Z models have better resolution capabilities than the two spectral transforms.},
keywords = {Chemical sensors, Electronic nose, Temperature modulation},
pubstate = {published},
tppubtype = {article}
}
The purpose of this study is to model the transient response of conductivity-based gas sensors in the context of odor recognition with an electronic nose. Commonly, only the steady-state response of the sensor is used for pattern recognition, ignoring the transient response, which conveys useful discriminatory information. The transient response is modeled as a sum of real exponential functions that represent the different decay processes that occur during sampling of the gas into the sensor chamber and adsorption of the odor compounds onto the sensing element. Four multi-exponential models are reviewed: Gardner transform, multi-exponential transient spectroscopy, Pade-Laplace and Pade-Z transforms. Validation on experimental data from an array of conducting-polymer gas sensors shows that the Pade-Laplace and Pade-Z models have better resolution capabilities than the two spectral transforms. |