2023
|
Mortazavi, B. J.; Gutierrez-Osuna, R. A review of digital innovations for diet monitoring and precision nutrition Journal Article In: Journal of Diabetes Science and Technology, 2023. @article{,
title = {A review of digital innovations for diet monitoring and precision nutrition},
author = {B. J. Mortazavi and R. Gutierrez-Osuna},
url = {https://journals.sagepub.com/doi/full/10.1177/19322968211041356
https://psi.engr.tamu.edu/wp-content/uploads/2023/11/digitalInnovations2023bobak.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2021-07-14},
journal = {Journal of Diabetes Science and Technology},
keywords = {Chemical sensors, Diabetes, Wearable sensors},
pubstate = {published},
tppubtype = {article}
}
|
2022
|
Husseini, D. Al; Lin, P. T.; Sukhishvili, S. A.; Zhou, J.; Li, J.; Lin, P. T.; Gutierrez-Osuna, R.; Coté, G. L. Silane-modified mesoporous silica nanocoatings for selective mid-infrared waveguide-based gas sensing Journal Article Forthcoming In: Advanced Materials Interfaces, Forthcoming. @article{diana2022mesoporous,
title = {Silane-modified mesoporous silica nanocoatings for selective mid-infrared waveguide-based gas sensing},
author = {D. Al Husseini and P. T. Lin and S. A. Sukhishvili and J. Zhou and J. Li and P. T. Lin and R. Gutierrez-Osuna and G. L. Coté},
year = {2022},
date = {2022-09-29},
journal = {Advanced Materials Interfaces},
keywords = {Chemical sensors},
pubstate = {forthcoming},
tppubtype = {article}
}
|
Zhou, J.; Husseini, D. Al; Li, J.; Lin, Z.; Sukhishvili, S.; Cote, G. L.; Gutierrez-Osuna, R.; Lin, P. T. Mid-Infrared Serial Microring Resonator Array for Real-time Detection of Vapor Phase Volatile Organic Compounds Journal Article Forthcoming In: Analytical Chemistry, Forthcoming. @article{junchao2022ac,
title = {Mid-Infrared Serial Microring Resonator Array for Real-time Detection of Vapor Phase Volatile Organic Compounds},
author = {J. Zhou and D. Al Husseini and J. Li and Z. Lin and S. Sukhishvili and G. L. Cote and R. Gutierrez-Osuna and P. T. Lin },
year = {2022},
date = {2022-07-19},
journal = {Analytical Chemistry},
keywords = {Chemical sensors, Infrared spectroscopy},
pubstate = {forthcoming},
tppubtype = {article}
}
|
Das, A.; Mortazavi, B.; Sajjadi, S.; Chaspari, T.; Ruebush, L. E.; Deutz, N. E.; Cote, G. L.; Gutierrez-Osuna, R. Predicting the macronutrient composition of mixed meals from dietary biomarkers in blood Journal Article In: IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 6, pp. 2726-2736, 2022. @article{anurag2021aminoacids,
title = {Predicting the macronutrient composition of mixed meals from dietary biomarkers in blood},
author = {A. Das and B. Mortazavi and S. Sajjadi and T. Chaspari and L. E. Ruebush and N. E. Deutz and G. L. Cote and R. Gutierrez-Osuna},
url = {https://ieeexplore.ieee.org/document/9645322
https://psi.engr.tamu.edu/wp-content/uploads/2021/12/jbhi-aminoacids-2021-preprint.pdf},
year = {2022},
date = {2022-06-04},
urldate = {2021-12-05},
journal = {IEEE Journal of Biomedical and Health Informatics},
volume = {26},
number = {6},
pages = {2726-2736},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {article}
}
|
Paul, Sudip; Sharma, Rohit; Tathireddy, Prashant; Gutierrez-Osuna, Ricardo On-line drift-compensation for continuous monitoring with arrays of cross-sensitive chemical sensors Journal Article Forthcoming In: Sensors and Actuators B: Chemical, Forthcoming. @article{sudip-2022sab,
title = {On-line drift-compensation for continuous monitoring with arrays of cross-sensitive chemical sensors},
author = {Sudip Paul and Rohit Sharma and Prashant Tathireddy and Ricardo Gutierrez-Osuna},
year = {2022},
date = {2022-05-17},
journal = {Sensors and Actuators B: Chemical},
keywords = {Chemical sensors},
pubstate = {forthcoming},
tppubtype = {article}
}
|
Zhou, Junchao; Husseini, Diana Al; Li, Junyan; Lin, Zhihai; Sukhishvili, Svetlana; Coté, Gerard L.; Gutierrez-Osuna, Ricardo; Lin, Pao Tai Detection of Volatile Organic Compounds Using Mid-Infrared Silicon Nitride Waveguide Sensors Journal Article In: Scientific Reports, vol. 12, no. 5572 , 2022. @article{junchao-2022-scirep,
title = {Detection of Volatile Organic Compounds Using Mid-Infrared Silicon Nitride Waveguide Sensors},
author = {Junchao Zhou and Diana Al Husseini and Junyan Li and Zhihai Lin and Svetlana Sukhishvili and Gerard L. Coté and Ricardo Gutierrez-Osuna and Pao Tai Lin},
url = {https://www.nature.com/articles/s41598-022-09597-9
https://psi.engr.tamu.edu/wp-content/uploads/2022/04/s41598-022-09597-9.pdf},
year = {2022},
date = {2022-03-15},
urldate = {2022-03-15},
journal = {Scientific Reports},
volume = { 12},
number = {5572 },
keywords = {Chemical sensors},
pubstate = {published},
tppubtype = {article}
}
|
2021
|
Yang, M.; Paromita, P.; Chaspari, T.; Das, A.; Sajjadi, S.; Mortazavi, B. J.; Gutierrez-Osuna, R. A Metric Learning Approach for Personalized Meal Macronutrient Estimation from Postprandial Glucose Response Signals Proceedings Article In: Proc. IEEE/EMBS Intl. Conf. Biomedical And Health Informatics (BHI 2021)., 2021. @inproceedings{theodora2021bhi,
title = {A Metric Learning Approach for Personalized Meal Macronutrient Estimation from Postprandial Glucose Response Signals},
author = {M. Yang and P. Paromita and T. Chaspari and A. Das and S. Sajjadi and B. J. Mortazavi and R. Gutierrez-Osuna},
url = {http://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-10.pdf
https://psi.engr.tamu.edu/wp-content/uploads/2022/01/IUI21WS-HEALTHI-10.pdf},
year = {2021},
date = {2021-07-27},
urldate = {2021-07-27},
booktitle = {Proc. IEEE/EMBS Intl. Conf. Biomedical And Health Informatics (BHI 2021).},
keywords = {Chemical sensors, Continuous glucose monitors, Deep learning, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Hagve, M.; Simbo, S. Y.; Ruebush, L. E.; Engelen, M. P. K. J.; Gutierrez-Osuna, R.; Mortazavi, B. J.; Cote, G. L.; Deutz, N. E. P. Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal Journal Article In: Clinical Nutrition, 2021. @article{martin2021clinicalnutrition,
title = {Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal},
author = {M. Hagve and S. Y. Simbo and L. E. Ruebush and M. P. K. J. Engelen and R. Gutierrez-Osuna and B. J. Mortazavi and G. L. Cote and N. E. P. Deutz},
url = {https://www.sciencedirect.com/science/article/pii/S0261561421003502
https://psi.engr.tamu.edu/wp-content/uploads/2021/07/1-s2.0-S0261561421003502-main.pdf},
year = {2021},
date = {2021-07-13},
journal = {Clinical Nutrition},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {article}
}
|
Sajjadi, S.; Mortazavi, B. J.; Das, A.; Chaspari, T.; Paromita, P.; Ruebush, L. E.; Deutz, N. E.; Gutierrez-Osuna, R. Towards the development of subject-independent inverse metabolic models Proceedings Article In: In Proc. ICASSP, 2021. @inproceedings{hooman2021icassp,
title = {Towards the development of subject-independent inverse metabolic models},
author = {S. Sajjadi and B. J. Mortazavi and A. Das and T. Chaspari and P. Paromita and L. E. Ruebush and N. E. Deutz and R. Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2021/08/ICASSP_21_SubjectIndependentIMM_CameraReady.pdf},
year = {2021},
date = {2021-06-06},
booktitle = {In Proc. ICASSP},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Das, A.; Mortazavi, B.; Chaspari, T.; Sajjadi, S.; Paromita, P.; Ruebush, L.; Deutz, N.; Gutierrez-Osuna, R. A sparse coding approach to automatic diet monitoring with continuous glucose monitors Proceedings Article In: In Proc. ICASSP, 2021. @inproceedings{anurag2021icassp,
title = {A sparse coding approach to automatic diet monitoring with continuous glucose monitors},
author = {A. Das and B. Mortazavi and T. Chaspari and S. Sajjadi and P. Paromita and L. Ruebush and N. Deutz and R. Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2021/08/ICASSP_2021_AnuragDas.pdf},
year = {2021},
date = {2021-06-06},
booktitle = {In Proc. ICASSP},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Paromita, P.; Chaspari, T.; Sajjadi, S.; Das, A.; Mortazavi, B. J.; Gutierrez-Osuna, R. Personalized Meal Classification Using Continuous Glucose Monitors Proceedings Article In: In Proc. IUI HEALTHI Workshop, 2021. @inproceedings{psyche2021healthi,
title = {Personalized Meal Classification Using Continuous Glucose Monitors},
author = {P. Paromita and T. Chaspari and S. Sajjadi and A. Das and B. J. Mortazavi and R. Gutierrez-Osuna},
year = {2021},
date = {2021-04-13},
urldate = {2021-04-13},
booktitle = {In Proc. IUI HEALTHI Workshop},
keywords = {Chemical sensors, Continuous glucose monitors, Deep learning, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Husseini, D. Al; Karanth, Y.; Zhou, J.; Willhelm, D.; Qian, X.; Gutierrez-Osuna, R.; Coté, G. L.; and P. Tai Lin,; Sukhishvili, S. A. Surface Functionalization Utilizing Mesoporous Silica Nanoparticles for Enhanced Evanescent-Field Mid-Infrared Waveguide Gas Sensing Journal Article In: Coatings, vol. 11, no. 118, pp. 1-12, 2021. @article{alhusseini2021coatings,
title = {Surface Functionalization Utilizing Mesoporous Silica Nanoparticles for Enhanced Evanescent-Field Mid-Infrared Waveguide Gas Sensing},
author = {D. Al Husseini and Y. Karanth and J. Zhou and D. Willhelm and X. Qian and R. Gutierrez-Osuna and G. L. Coté and and P. Tai Lin and S. A. Sukhishvili},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2021/01/ahhusseini2021coatings.pdf
https://www.mdpi.com/2079-6412/11/2/118},
year = {2021},
date = {2021-01-21},
journal = {Coatings},
volume = {11},
number = {118},
pages = {1-12},
keywords = {Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
2020
|
Husseini, D. Al; Zhou, J.; Willhelm, D.; Hastings, T.; Day, G. S.; Zhou, H. -C.; Coté, G. L.; Qian, X.; R. Gutierrez-Osuna, P. T. Lin; Sukhishvili, S. All-Nanoparticle Layer-by-layer Coatings for Mid-IR On-Chip Gas Sensing Journal Article In: Chemical Communications, 2020. @article{alhusseini2020chemComm,
title = {All-Nanoparticle Layer-by-layer Coatings for Mid-IR On-Chip Gas Sensing},
author = {D. Al Husseini and J. Zhou and D. Willhelm and T. Hastings and G. S. Day and H.-C. Zhou and G. L. Coté and X. Qian and R. Gutierrez-Osuna, P. T. Lin and S. Sukhishvili},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2020/10/d0cc05513a.pdf
https://pubs.rsc.org/en/content/articlelanding/2020/cc/d0cc05513a#!divAbstract},
year = {2020},
date = {2020-10-22},
journal = {Chemical Communications},
keywords = {Chemical sensors},
pubstate = {published},
tppubtype = {article}
}
|
2019
|
Huo, Z.; Mortazavi, B. J.; Chaspari, T.; Deutz, N.; Ruebush, L.; Gutierrez-Osuna, R. Predicting the meal macronutrient composition from continuous glucose monitors Proceedings Article In: Proc. IEEE Conf. on Biomedical and Health Informatics, 2019. @inproceedings{huo-2019-bhi,
title = {Predicting the meal macronutrient composition from continuous glucose monitors},
author = {Z. Huo and B. J. Mortazavi and T. Chaspari and N. Deutz and L. Ruebush and R. Gutierrez-Osuna},
year = {2019},
date = {2019-05-19},
booktitle = {Proc. IEEE Conf. on Biomedical and Health Informatics},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2018
|
Goel, N.; Chaspari, T.; Mortazavi, B. J.; Prioleau, T.; A. Sabharwal,; Gutierrez-Osuna, R. Knowledge-driven dictionaries for sparse representation of continuous glucose monitoring signals Proceedings Article In: Proc. EMBC, 2018. @inproceedings{chaspari2018cgm,
title = {Knowledge-driven dictionaries for sparse representation of continuous glucose monitoring signals},
author = {N. Goel and T. Chaspari and B. J. Mortazavi and T. Prioleau and A. Sabharwal, and R. Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/05/chaspari-2018-embc.pdf},
year = {2018},
date = {2018-07-17},
booktitle = {Proc. EMBC},
volume = {in press},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Jin, Tiening; Zhou, Juntao; Wang, Zelun; Gutierrez-Osuna, Ricardo; Ahn, Charles; Hwang, Wonjun; Park, Ken; Lin, Pao-Tai Real-time Gas Mixture Analysis Using Mid-infrared Membrane Microcavities Journal Article In: Analytical Chemistry, vol. 90, no. 7, pp. 4348-4353, 2018. @article{jin2018-ac,
title = {Real-time Gas Mixture Analysis Using Mid-infrared Membrane Microcavities},
author = {Tiening Jin and Juntao Zhou and Zelun Wang and Ricardo Gutierrez-Osuna and Charles Ahn and Wonjun Hwang and Ken Park and Pao-Tai Lin },
url = {https://www.ncbi.nlm.nih.gov/pubmed/29509404},
year = {2018},
date = {2018-03-08},
journal = {Analytical Chemistry},
volume = {90},
number = {7},
pages = {4348-4353},
keywords = {Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
Chaspari, T.; Mortazavi, B.; Prioleau, T.; Sabharwal, A.; Gutierrez-Osuna, R. Sparse representation models of continuous glucose monitoring time-series Proceedings Article In: Proc. 5th International Conference on Wearable and Implantable Body Sensor Networks, 2018. @inproceedings{theodora2018bsn,
title = {Sparse representation models of continuous glucose monitoring time-series},
author = {T. Chaspari and B. Mortazavi and T. Prioleau and A. Sabharwal and R. Gutierrez-Osuna },
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/05/chaspari-2018-bsn.pdf},
year = {2018},
date = {2018-03-04},
booktitle = { Proc. 5th International Conference on Wearable and Implantable Body Sensor Networks},
keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2017
|
Wang, Z; Gutierrez-Osuna, R Mixture quantification in the presence of unknown interferences Proceedings Article In: Proc. International Symposium on Olfaction and Electronic Nose (ISOEN), 2017. @inproceedings{wang2017isoen,
title = {Mixture quantification in the presence of unknown interferences},
author = {Z Wang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/wang2017isoen.pdf},
year = {2017},
date = {2017-03-15},
booktitle = {Proc. International Symposium on Olfaction and Electronic Nose (ISOEN)},
journal = {Proc. International Symposium on Olfaction and Electronic Nose (ISOEN)},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2016
|
Huang, J; Gutierrez-Osuna, R Active wavelength selection for mixture identification with tunable mid-infrared detectors Journal Article In: Analytica Chimica Acta, vol. in press, 2016. @article{huang2016aca,
title = {Active wavelength selection for mixture identification with tunable mid-infrared detectors},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2016aca.pdf},
year = {2016},
date = {2016-08-08},
journal = {Analytica Chimica Acta},
volume = {in press},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
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
|
Li, J; Gutierrez-Osuna, R; Hodges, R D; Luckey, G; Crowell, J; Schiffman, S S; Nagle, H T Odor Assessment of Automobile Interior Components Using Ion Mobility Spectrometry Proceedings Article In: IEEE Sensors Conference, 2015. @inproceedings{li2015sensors,
title = {Odor Assessment of Automobile Interior Components Using Ion Mobility Spectrometry},
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/li2015sensors.pdf},
year = {2015},
date = {2015-11-01},
booktitle = {IEEE Sensors Conference},
keywords = {Chemical sensors, Infrared spectroscopy, Olfaction},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Huang, J; Gutierrez-Osuna, R Detection of weak chemicals in strong backgrounds with a tunable infrared sensor Proceedings Article In: International Symposium on Olfaction and Electronic Nose, 2015. @inproceedings{huang2015isoen,
title = {Detection of weak chemicals in strong backgrounds with a tunable infrared sensor},
author = {J Huang and R Gutierrez-Osuna},
year = {2015},
date = {2015-06-28},
booktitle = {International Symposium on Olfaction and Electronic Nose},
keywords = {Active sensing, Chemical sensors},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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}
}
|
Huang, J; Gutierrez-Osuna, R Active wavelength selection for mixture analysis with tunable infrared detectors Journal Article In: Sensors and Actuators B: Chemical, vol. 208, pp. 245–257, 2015. @article{huang2014sab,
title = {Active wavelength selection for mixture analysis with tunable infrared detectors},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2014sab.pdf},
year = {2015},
date = {2015-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {208},
pages = {245–257},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
|
2014
|
Li, J; Hodges, R D; Gutierrez-Osuna, R; Luckey, G; Crowell, J; Schiffman, S S; Nagle, H T Odor Assessment of Automobile Cabin Air by Machine Olfaction Proceedings Article In: Proc. IEEE Sensors Conference, 2014. @inproceedings{li2004sensorsconf,
title = {Odor Assessment of Automobile Cabin Air by Machine Olfaction},
author = {J Li and R D Hodges and R Gutierrez-Osuna 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/li2004sensorsconf.pdf},
year = {2014},
date = {2014-11-02},
booktitle = {Proc. IEEE Sensors Conference},
keywords = {Chemical sensors, Electronic nose, Olfaction},
pubstate = {published},
tppubtype = {inproceedings}
}
|
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}
}
|
Huang, J; Gutierrez-Osuna, R Active analysis of chemical mixtures with multi-modal sparse non-negative least squares Conference 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013. @conference{jinicassp2013,
title = {Active analysis of chemical mixtures with multi-modal sparse non-negative least squares},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/jinicassp2013.pdf},
year = {2013},
date = {2013-02-28},
booktitle = {38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {conference}
}
|
2012
|
Huang, J; Gutierrez-Osuna, R Active Analysis of Chemical Mixtures with Multi-modal Sparse Non-negative Least Sqares Technical Report 2012. @techreport{huang2012techreport,
title = {Active Analysis of Chemical Mixtures with Multi-modal Sparse Non-negative Least Sqares},
author = {J Huang and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2012techreport.pdf},
year = {2012},
date = {2012-12-05},
abstract = {New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature-selection approaches, in which the subset is elected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple near-optimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MM-NNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {techreport}
}
New sensor technologies such as Fabry-Pérot interferometers (FPI) offer low-cost and portable alternatives to traditional infrared absorption spectroscopy for chemical analysis. However, with FPIs the absorption spectrum has to be measured one wavelength at a time. In this work, we propose an active-sensing framework to select a subset of wavelengths that best separates the specific components of a chemical mixture. Compared to passive feature-selection approaches, in which the subset is elected offline, active sensing selects the next feature on-the-fly based on previous measurements so as to reduce uncertainty. We propose a novel multi-modal non-negative least squares method (MM-NNLS) to solve the underlying linear system, which has multiple near-optimal solutions. We tested the framework on mixture problems of up to 10 components from a library of 100 chemicals. MM-NNLS can solve complex mixtures using only a small number of measurements, and outperforms passive approaches in terms of sensing efficiency and stability |
Gosangi, R; Gutierrez-Osuna, R Active Decomposition and Sensing in Networks of Distributed Chemical Sensors Technical Report 2012. @techreport{gosangi2012techreport,
title = {Active Decomposition and Sensing in Networks of Distributed Chemical Sensors},
author = {R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gosangi2012techreport.pdf},
year = {2012},
date = {2012-12-05},
abstract = {Active sensing enables a sensor to optimize its tunings on-the-fly based on information obtained from previous measurements. When applied to networks of distributed sensors, however, active sensing becomes computationally impractical due to the combinatorial number of sensing configurations. To address this problem, we present an active decomposition and sensing (ADS) method that combines the advantages of classifier decomposition with those of active sensing. Namely, we use class posteriors to decompose the problem across the sensors in the network. Each sensor then applies active sensing to select the next tuning to solve its specific subproblem. As a result, the method scales linearly (rather than combinatorially) with the number of sensors. We validate ADS on a database of infrared absorption spectroscopy containing 50 chemicals. Our results show that active decomposition improves classification performance and reduces sensing costs when compared to using active sensing only at the node level.},
keywords = {Active sensing, Chemical sensors},
pubstate = {published},
tppubtype = {techreport}
}
Active sensing enables a sensor to optimize its tunings on-the-fly based on information obtained from previous measurements. When applied to networks of distributed sensors, however, active sensing becomes computationally impractical due to the combinatorial number of sensing configurations. To address this problem, we present an active decomposition and sensing (ADS) method that combines the advantages of classifier decomposition with those of active sensing. Namely, we use class posteriors to decompose the problem across the sensors in the network. Each sensor then applies active sensing to select the next tuning to solve its specific subproblem. As a result, the method scales linearly (rather than combinatorially) with the number of sensors. We validate ADS on a database of infrared absorption spectroscopy containing 50 chemicals. Our results show that active decomposition improves classification performance and reduces sensing costs when compared to using active sensing only at the node level. |
Huang, J; Gosangi, R; Gutierrez-Osuna, R Active Concentration-Independent Chemical Identification with a Tunable Infrared Sensor Journal Article In: Sensors Journal, IEEE, 2012. @article{huang2012sj,
title = {Active Concentration-Independent Chemical Identification with a Tunable Infrared Sensor},
author = {J Huang and R Gosangi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2012sj.pdf},
year = {2012},
date = {2012-09-03},
journal = {Sensors Journal, IEEE},
abstract = {This paper presents an active sensing framework for concentration-independent identification of volatile chemicals using a tunable infrared interferometer. The framework operates in real time to generate a sequence of absorption lines that can best discriminate among a given set of chemicals. The active-sensing algorithm was previously developed to optimize temperature programs for metal-oxide chemosensors. Here, we adapt it to tune a non-dispersive infrared spectroscope based on a Fabry-Perot interferometer (FPI). We also extend this framework to allow the identification of chemical samples irrespective of their concentrations. Namely, we use non-negative matrix factorization (NMF) to create concentration-independent absorption profiles of different chemicals, and then employ linear least squares to fit sensor observations to the response profiles. We tested the framework on a simulated classification problem with 27 chemicals and compared against a passive sensing approach; active sensing consistently outperformed passive sensing in terms of classification performance for various sensing budgets and at various levels of sensor noise. We also validated the approach experimentally using a commercial FPI sensor and a database of eight household chemicals. Our results show the method is able to predict the sample identity irrespective of concentration.},
keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},
pubstate = {published},
tppubtype = {article}
}
This paper presents an active sensing framework for concentration-independent identification of volatile chemicals using a tunable infrared interferometer. The framework operates in real time to generate a sequence of absorption lines that can best discriminate among a given set of chemicals. The active-sensing algorithm was previously developed to optimize temperature programs for metal-oxide chemosensors. Here, we adapt it to tune a non-dispersive infrared spectroscope based on a Fabry-Perot interferometer (FPI). We also extend this framework to allow the identification of chemical samples irrespective of their concentrations. Namely, we use non-negative matrix factorization (NMF) to create concentration-independent absorption profiles of different chemicals, and then employ linear least squares to fit sensor observations to the response profiles. We tested the framework on a simulated classification problem with 27 chemicals and compared against a passive sensing approach; active sensing consistently outperformed passive sensing in terms of classification performance for various sensing budgets and at various levels of sensor noise. We also validated the approach experimentally using a commercial FPI sensor and a database of eight household chemicals. Our results show the method is able to predict the sample identity irrespective of concentration. |
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. |
Gutierrez-Osuna, R; Hierlemann, A Adaptive Microsensor Systems Journal Article In: Annual Review of Analytical Chemistry, vol. 3, pp. 255–276, 2010. @article{gutierrez2010arac,
title = {Adaptive Microsensor Systems},
author = {R Gutierrez-Osuna and A Hierlemann},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2010arac.pdf},
year = {2010},
date = {2010-01-01},
journal = {Annual Review of Analytical Chemistry},
volume = {3},
pages = {255--276},
publisher = {Annual Reviews},
abstract = {We provide a broad review of approaches for developing chemosensor systems whose operating parameters can adapt in response to environmental changes or application needs. Adaptation may take place at the instrumentation level (e.g., tunable sensors) and at the data-analysis level (e.g., adaptive classifiers). We discuss several strategies that provide tunability at the device level: modulation of internal sensing parameters, such as frequencies and operation voltages; variation of external parameters, such as exposure times and catalysts; and development of compact microanalysis systems with multiple tuning options. At the data-analysis level, we consider adaptive filters for change, interference, and drift rejection; pattern classifiers that can adapt to changes in the statistical properties of training data; and active-sensing techniques that can tune sensing parameters in real time. We conclude with a discussion of future opportunities for adaptive sensing in wireless distributed sensor systems.},
keywords = {Active sensing, Chemical sensors},
pubstate = {published},
tppubtype = {article}
}
We provide a broad review of approaches for developing chemosensor systems whose operating parameters can adapt in response to environmental changes or application needs. Adaptation may take place at the instrumentation level (e.g., tunable sensors) and at the data-analysis level (e.g., adaptive classifiers). We discuss several strategies that provide tunability at the device level: modulation of internal sensing parameters, such as frequencies and operation voltages; variation of external parameters, such as exposure times and catalysts; and development of compact microanalysis systems with multiple tuning options. At the data-analysis level, we consider adaptive filters for change, interference, and drift rejection; pattern classifiers that can adapt to changes in the statistical properties of training data; and active-sensing techniques that can tune sensing parameters in real time. We conclude with a discussion of future opportunities for adaptive sensing in wireless distributed sensor systems. |
2008
|
Hierlemann, A; Gutierrez-Osuna, R Higher-order chemical sensing Journal Article In: Chemical reviews, vol. 108, no. 2, pp. 563, 2008. @article{hierlemann2008higher,
title = {Higher-order chemical sensing},
author = {A Hierlemann and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/hierlemann2008higher.pdf},
year = {2008},
date = {2008-01-01},
journal = {Chemical reviews},
volume = {108},
number = {2},
pages = {563},
keywords = {Active sensing, Chemical sensors},
pubstate = {published},
tppubtype = {article}
}
|
2007
|
Raman, B; Kotseroglou, T; Clark, L; Lebl, M; Gutierrez-Osuna, R Neuromorphic processing for optical microbead arrays: dimensionality reduction and contrast enhancement Journal Article In: Sensors Journal, IEEE, vol. 7, no. 4, pp. 506–514, 2007. @article{raman2007neuromorphic,
title = {Neuromorphic processing for optical microbead arrays: dimensionality reduction and contrast enhancement},
author = {B Raman and T Kotseroglou and L Clark and M Lebl and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2007neuromorphic.pdf},
year = {2007},
date = {2007-01-01},
journal = {Sensors Journal, IEEE},
volume = {7},
number = {4},
pages = {506--514},
publisher = {IEEE},
abstract = {This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on-off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response. |
2006
|
Raman, B; Sun, P A; Gutierrez-Galvez, A; Gutierrez-Osuna, R Processing of chemical sensor arrays with a biologically inspired model of olfactory coding Journal Article In: Neural Networks, IEEE Transactions on, vol. 17, no. 4, pp. 1015–1024, 2006. @article{raman2006processing,
title = {Processing of chemical sensor arrays with a biologically inspired model of olfactory coding},
author = {B Raman and P A Sun and A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006processing.pdf},
year = {2006},
date = {2006-01-01},
journal = {Neural Networks, IEEE Transactions on},
volume = {17},
number = {4},
pages = {1015--1024},
publisher = {IEEE},
abstract = {This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology. |
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, 2006. @article{raman2006concentration,
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/raman2006concentration.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {36--42},
publisher = {Elsevier},
abstract = {This article presents a biologically inspired model capable of removing concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in theolfactorybulb, which is mediated by periglomerular interneurons. To simulate inputs to the olfactorybulb, signals from a chemosensor array are first processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odor-specific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells in the olfactorybulb, is used to compress concentration information. The model is validated using experimental data from an array of temperature-modulated metal-oxide chemoresistors.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This article presents a biologically inspired model capable of removing concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in theolfactorybulb, which is mediated by periglomerular interneurons. To simulate inputs to the olfactorybulb, signals from a chemosensor array are first processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odor-specific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells in the olfactorybulb, is used to compress concentration information. The model is validated using experimental data from an array of temperature-modulated metal-oxide chemoresistors. |
Raman, B; Yamanaka, T; Gutierrez-Osuna, R Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb Journal Article In: Sensors and Actuators B: Chemical, vol. 119, no. 2, pp. 547–555, 2006. @article{raman2006contrast,
title = {Contrast enhancement of gas sensor array patterns with a neurodynamics model of the olfactory bulb},
author = {B Raman and T Yamanaka and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2006contrast.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {119},
number = {2},
pages = {547--555},
publisher = {Elsevier},
abstract = {We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gassensorarrays. The model captures two functions in the early olfactorypathway: chemotopic convergence of sensory neurons onto the olfactorybulb, and center on–off surround lateral interactions. Sensor features are first topologically projected onto a two-dimensional lattice according to their selectivity profile, leading to odor-specific spatial patterning. The resulting patterns serve as inputs to a network of mitral cells with center on–off surround lateral inhibition, which enhances the initialcontrast among odors and decouples odor identity from intensity. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. Our results indicate that the model is able to improve the separability between odor patterns that is available at the inputs.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
We propose a biologically inspired signal processing model capable of enhancing the discrimination of multivariate patterns from gassensorarrays. The model captures two functions in the early olfactorypathway: chemotopic convergence of sensory neurons onto the olfactorybulb, and center on–off surround lateral interactions. Sensor features are first topologically projected onto a two-dimensional lattice according to their selectivity profile, leading to odor-specific spatial patterning. The resulting patterns serve as inputs to a network of mitral cells with center on–off surround lateral inhibition, which enhances the initialcontrast among odors and decouples odor identity from intensity. The model is validated using experimental data from an array of temperature-modulated metal-oxide sensors. Our results indicate that the model is able to improve the separability between odor patterns that is available at the inputs. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Contrast enhancement and background suppression of chemosensor array patterns with the KIII model Journal Article In: International journal of intelligent systems, vol. 21, no. 9, pp. 937–953, 2006. @article{gutierrez2006contrast,
title = {Contrast enhancement and background suppression of chemosensor array patterns with the KIII model},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2006contrast.pdf},
year = {2006},
date = {2006-01-01},
journal = {International journal of intelligent systems},
volume = {21},
number = {9},
pages = {937--953},
publisher = {Wiley Online Library},
abstract = {Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning Journal Article In: Sensors and Actuators B: Chemical, vol. 116, no. 1, pp. 29–35, 2006. @article{gutierrez2006increasing,
title = {Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2006increasing.pdf},
year = {2006},
date = {2006-01-01},
journal = {Sensors and Actuators B: Chemical},
volume = {116},
number = {1},
pages = {29--35},
publisher = {Elsevier},
abstract = {The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a newHebbian/anti-Hebbianlearning rule to increase the separability of sensor-arraypatterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase patternseparability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a newHebbian/anti-Hebbianlearning rule to increase the separability of sensor-arraypatterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase patternseparability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration. |
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. |
Raman, B; Gutierrez-Osuna, R Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction Conference Proceedings of IEEE International Joint Conference on Neural Networks, IEEE 2005. @conference{raman2005ijcnn,
title = {Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction},
author = {B Raman and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/raman2005ijcnn.pdf},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of IEEE International Joint Conference on Neural Networks},
pages = {131--136},
organization = {IEEE},
abstract = {We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-Hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-Hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array. |
Gutierrez-Galvez, A; Gutierrez-Osuna, R Contrast enhancement of sensor-array patterns through hebbian/antihebbian learning Conference Proceedings of the 11th International Symposium on Olfaction and Electronic Nose, 2005. @conference{gutierrez2005contrast,
title = {Contrast enhancement of sensor-array patterns through hebbian/antihebbian learning},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2005isoen.pdf},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of the 11th International Symposium on Olfaction and Electronic Nose},
abstract = {The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a new Hebbian/anti-Hebbian learning rule to increase the contrast of sensor-array patterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The system is characterized on synthetic data showing its ability to increase the separation between patterns and its robustness against noise. Experimental data from an array of temperature-modulated metal-oxide sensors is used to validate the contrast enhancement ability of the system.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a new Hebbian/anti-Hebbian learning rule to increase the contrast of sensor-array patterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The system is characterized on synthetic data showing its ability to increase the separation between patterns and its robustness against noise. Experimental data from an array of temperature-modulated metal-oxide sensors is used to validate the contrast enhancement ability of the system. |
2004
|
Gutierrez-Galvez, A; Gutierrez-Osuna, R; Raman, B Pattern recognition for chemosensor arrays with the KIII model Conference Proceedings of the 2004 Symposium on Intentional Dynamic Systems, 2004. @conference{gutierrez2004pattern,
title = {Pattern recognition for chemosensor arrays with the KIII model},
author = {A Gutierrez-Galvez and R Gutierrez-Osuna and B Raman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2004pattern.pdf},
year = {2004},
date = {2004-04-24},
booktitle = {Proceedings of the 2004 Symposium on Intentional Dynamic Systems},
journal = {Proc. 2004 Symposium on Intentional Dynamic Systems},
pages = {24--26},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {conference}
}
|
Rodriguez-Mendez, M L; Arrieta, A; Parra, V; Bernal, A; Vegas, A; Villanueva, S; Gutierrez-Osuna, R; de Saja, J A Fusion of three sensory modalities for the multimodal characterization of red wines Journal Article In: Sensors Journal, IEEE, vol. 4, no. 3, pp. 348–354, 2004. @article{rodrÃguez2004fusion,
title = {Fusion of three sensory modalities for the multimodal characterization of red wines},
author = {M L Rodriguez-Mendez and A Arrieta and V Parra and A Bernal and A Vegas and S Villanueva and R Gutierrez-Osuna and J A de Saja},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rodrÃguez2004fusion.pdf},
year = {2004},
date = {2004-01-01},
journal = {Sensors Journal, IEEE},
volume = {4},
number = {3},
pages = {348--354},
publisher = {IEEE},
abstract = {This work represents the first attempt to develop a sensory system, specifically designed for the characterization of wines, which combines three sensory modalities: an array of gas sensors, an array of electrochemical liquid sensors, and an optical system to measure color by means of CIElab coordinates. This new analytical tool, that has been called "electronic panel," includes not only sensors, but also hardware (injection system and electronics) and the software necessary for fusing information from the three modules. Each of the three sensory modalities (volatiles, liquids, and color) has been designed, tested, and optimized separately. The discrimination capabilities of the system have been evaluated on a database consisting of six red Spanish wines prepared using the same variety of grape (tempranillo) but differing in their geographic origins and aging stages. Sensor signals from each module have been combined and analyzed using pattern recognition techniques. The results of this work show that the discrimination capabilities of the system are significantly improved when signals from each module are combined to form a multimodal feature vector.},
keywords = {Chemical sensors, Electronic nose},
pubstate = {published},
tppubtype = {article}
}
This work represents the first attempt to develop a sensory system, specifically designed for the characterization of wines, which combines three sensory modalities: an array of gas sensors, an array of electrochemical liquid sensors, and an optical system to measure color by means of CIElab coordinates. This new analytical tool, that has been called "electronic panel," includes not only sensors, but also hardware (injection system and electronics) and the software necessary for fusing information from the three modules. Each of the three sensory modalities (volatiles, liquids, and color) has been designed, tested, and optimized separately. The discrimination capabilities of the system have been evaluated on a database consisting of six red Spanish wines prepared using the same variety of grape (tempranillo) but differing in their geographic origins and aging stages. Sensor signals from each module have been combined and analyzed using pattern recognition techniques. The results of this work show that the discrimination capabilities of the system are significantly improved when signals from each module are combined to form a multimodal feature vector. |
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. |
Gutierrez-Osuna, R; Raman, B Cancellation of chemical backgrounds with generalized Fisher's linear discriminants Conference Proceedings of IEEE Sensors, IEEE 2004. @conference{gutierrez2004cancellation,
title = {Cancellation of chemical backgrounds with generalized Fisher's linear discriminants},
author = {R Gutierrez-Osuna and B Raman},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2004cancellation.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings of IEEE Sensors},
pages = {1381--1384},
organization = {IEEE},
abstract = {This article presents a signal-processing technique capable of canceling the effect of background chemicals from the multivariate response of a sensor array. We propose a generalization of the Fishers eigenvalue solution that minimizes the discrimination between undesirable chemicals and a neutral reference. The proposed technique is a generalization of an earlier model that was limited to the removal of single volatiles. A reformulation of class memberships allows the new model to cancel the effect of both single and mixture backgrounds. The model is validated on experimental data from an array of temperature-modulated metal-oxide sensors exposed to binary and ternary mixtures.},
keywords = {Chemical sensors},
pubstate = {published},
tppubtype = {conference}
}
This article presents a signal-processing technique capable of canceling the effect of background chemicals from the multivariate response of a sensor array. We propose a generalization of the Fishers eigenvalue solution that minimizes the discrimination between undesirable chemicals and a neutral reference. The proposed technique is a generalization of an earlier model that was limited to the removal of single volatiles. A reformulation of class memberships allows the new model to cancel the effect of both single and mixture backgrounds. The model is validated on experimental data from an array of temperature-modulated metal-oxide sensors exposed to binary and ternary mixtures. |
Wang, M; Perera-Lluna, A; Gutierrez-Osuna, R Principal Discriminants Analysis for small-sample-size problems: application to chemical sensing Conference Proceedings of IEEE Sensors, IEEE 2004. @conference{wang2004principal,
title = {Principal Discriminants Analysis for small-sample-size problems: application to chemical sensing},
author = {M Wang and A Perera-Lluna and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/wang2004principal.pdf},
year = {2004},
date = {2004-01-01},
booktitle = {Proceedings of IEEE Sensors},
pages = {591-594},
organization = {IEEE},
abstract = {Two dimensionality reduction techniques are widely used to analyze data from chemical sensor arrays: Fisher's linear discriminants analysis (LDA) and principal components analysis (PCA). LDA finds the directions of maximum discrimination in classification problems, but has a tendency to overfit when the ratio of training samples to dimensionality is low, as is commonly the case in chemical sensor array problems. PCA is more robust to overfitting but, being a variance model, fails to capture discriminatory information in low-variance sensors. In this article we propose a hybrid model, termed principal discriminants analysis (PDA), which incorporates both LDA and PCA criteria by means of a regularization parameter. The model is characterized on a synthetic dataset and validated with experimental data from an array of 15 metal-oxide sensors exposed to five varieties of roasted coffee beans. Our results show that PDA provides higher predictive accuracy than LDA or PCA alone. In addition, the model is able to find a trade-off between discriminant- and variance-based projections according to where information is located in the distribution of the data.},
keywords = {Chemical sensors},
pubstate = {published},
tppubtype = {conference}
}
Two dimensionality reduction techniques are widely used to analyze data from chemical sensor arrays: Fisher's linear discriminants analysis (LDA) and principal components analysis (PCA). LDA finds the directions of maximum discrimination in classification problems, but has a tendency to overfit when the ratio of training samples to dimensionality is low, as is commonly the case in chemical sensor array problems. PCA is more robust to overfitting but, being a variance model, fails to capture discriminatory information in low-variance sensors. In this article we propose a hybrid model, termed principal discriminants analysis (PDA), which incorporates both LDA and PCA criteria by means of a regularization parameter. The model is characterized on a synthetic dataset and validated with experimental data from an array of 15 metal-oxide sensors exposed to five varieties of roasted coffee beans. Our results show that PDA provides higher predictive accuracy than LDA or PCA alone. In addition, the model is able to find a trade-off between discriminant- and variance-based projections according to where information is located in the distribution of the data. |
2003
|
Gutierrez-Osuna, R; Gutierrez-Galvez, A Habituation in the KIII olfactory model with chemical sensor arrays Journal Article In: Neural Networks, IEEE Transactions on, vol. 14, no. 6, pp. 1565–1568, 2003. @article{gutierrez2003habituation,
title = {Habituation in the KIII olfactory model with chemical sensor arrays},
author = {R Gutierrez-Osuna and A Gutierrez-Galvez},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2003habituation.pdf},
year = {2003},
date = {2003-01-01},
journal = {Neural Networks, IEEE Transactions on},
volume = {14},
number = {6},
pages = {1565--1568},
publisher = {IEEE},
abstract = {This paper presents a novel combination of chemical sensors and the KIII model for simulating mixture perception with a habituation process triggered by local activity. Stimuli are generated by partitioning feature space with labeled lines. Pattern completion is demonstrated through coherent oscillations across granule populations using experimental odor mixtures.},
keywords = {Chemical sensors, Neuromorphic models},
pubstate = {published},
tppubtype = {article}
}
This paper presents a novel combination of chemical sensors and the KIII model for simulating mixture perception with a habituation process triggered by local activity. Stimuli are generated by partitioning feature space with labeled lines. Pattern completion is demonstrated through coherent oscillations across granule populations using experimental odor mixtures. |
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
|
Rodríguez-Méndez, M L; Arrieta, A; Parra, V; Vegas, A; Villanueva, S; Gutierrez-Osuna, R; de Saja, J A Sensor system for the complete organoleptic characterization (smell, taste and color) of wines Conference Proceedings of the 9th International Symposium on Olfaction and Electronic Nose, 2002. @conference{mendez01organoleptic,
title = {Sensor system for the complete organoleptic characterization (smell, taste and color) of wines},
author = {M L Rodríguez-Méndez and A Arrieta and V Parra and A Vegas and S Villanueva and R Gutierrez-Osuna and J A de Saja},
year = {2002},
date = {2002-01-01},
booktitle = {Proceedings of the 9th International Symposium on Olfaction and Electronic Nose},
keywords = {Chemical sensors},
pubstate = {published},
tppubtype = {conference}
}
|
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; 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. |
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. |
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. |
Gutierrez-Osuna, R; Nagle, H T A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors Journal Article In: Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 29, no. 5, pp. 626–632, 1999. @article{gutierrez1999method,
title = {A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors},
author = {R Gutierrez-Osuna and H T Nagle},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1999method.pdf},
year = {1999},
date = {1999-01-01},
journal = {Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on},
volume = {29},
number = {5},
pages = {626--632},
publisher = {IEEE},
abstract = {The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique, In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odour classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odour databases with different complexities are used to score the data-preprocessing techniques. The performance measure used is the cross-validation estimate of the classification rate of a K nearest neighbor voting rule operating on Fisher's linear discriminant projection subspace.},
keywords = {Chemical sensors},
pubstate = {published},
tppubtype = {article}
}
The performance of a pattern recognition system is dependent on, among other things, an appropriate data-preprocessing technique, In this paper, we describe a method to evaluate the performance of a variety of these techniques for the problem of odour classification using an array of gas sensors, also referred to as an electronic nose. Four experimental odour databases with different complexities are used to score the data-preprocessing techniques. The performance measure used is the cross-validation estimate of the classification rate of a K nearest neighbor voting rule operating on Fisher's linear discriminant projection subspace. |