Active sensing

Through the years we have worked on several aspects of machine olfaction, ranging from instrumentation for solid-state and optical sensors to neuromorphic models of the olfactory pathway. Our current focus is on the development of active sensing strategies for tunable chemical sensors, that is, sensors whose selectivity towards different chemical species can be fine-tuned programmatically; this includes metal-oxide chemical sensors under temperature modulation and Fabry-Perot infrared interferometers.

The figure illustrates the process of active classification with an array of four metal-oxide sensors, ten temperatures per sensor, and a discrimination problem with six chemicals. At time t = 0, no information is available except that classes are a priori equiprobable: p(ωi) = 1/6. On the basis of this information, the active-classification algorithm decides to take the first sensing action (a1, measure sensor S2 at temperature T4), which leads to observation o1 and an updated posterior probability, p(ωi|o1, a1). After four sensing actions, evidence accumulated in the posterior p(ωi|o1, . . ., o4, a1, . . ., a4) and the cost of additional measurements are sufficient for the algorithm to assign the unknown sample to class ω3. In this toy example, an accurate classification is reached via only 10% of all sensor configurations.

We are also developing active-sensing strategies for Fabry-Perot Interferometers (FPI), devices that can act as tunable infrared (IR) spectrometers. IR spectroscopy provides a wealth of information to help estimate the identity and concentrations of chemicals, and allows us to analyse complex chemical mixtures without the need for physically separating them (i.e., via chromatography).

The video below illustrates the process of recognizing a chemical out of eight possible targets. The active sensing algorithm can identify the target by selecting only a few measurements, as opposed to having to capture the entire spectrum.

2017

Karkamkar, P; Gutierrez-Osuna, R

Optical Computation of Chemometrics Projections using a Digital Micromirror Device Inproceedings

Proc. International Symposium on Olfaction and Electronic Nose (ISOEN), 2017.

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Wang, Z; Gutierrez-Osuna, R

Mixture quantification in the presence of unknown interferences Inproceedings

Proc. International Symposium on Olfaction and Electronic Nose (ISOEN), 2017.

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2016

Huang, J; Gutierrez-Osuna, R

Active wavelength selection for mixture identification with tunable mid-infrared detectors Journal Article

Analytica Chimica Acta, in press , 2016.

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2015

Huang, J; Gutierrez-Osuna, R

Detection of weak chemicals in strong backgrounds with a tunable infrared sensor Inproceedings

International Symposium on Olfaction and Electronic Nose, 2015.

BibTeX

Huang, J; Gutierrez-Osuna, R

Active wavelength selection for mixture analysis with tunable infrared detectors Journal Article

Sensors and Actuators B: Chemical, 208 , pp. 245–257, 2015.

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2014

Gosangi, R; Gutierrez-Osuna, R

Active classification with arrays of tunable chemical sensors Journal Article

Chemometrics and Intelligent Laboratory Systems, 132 , pp. 91-102, 2014.

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2013

Gosangi, R; Gutierrez-Osuna, R

Active temperature modulation of metal-oxide sensors for quantitative analysis of gas mixtures Journal Article

Sensors and Actuators B: Chemical, 185 , pp. 201-210, 2013.

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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.

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2012

Gosangi, R; Gutierrez-Osuna, R

Active Decomposition and Sensing in Networks of Distributed Chemical Sensors Technical Report

2012.

Abstract | Links | BibTeX

Huang, J; Gutierrez-Osuna, R

Active Analysis of Chemical Mixtures with Multi-modal Sparse Non-negative Least Sqares Technical Report

2012.

Abstract | Links | BibTeX

Huang, J; Gosangi, R; Gutierrez-Osuna, R

Active Concentration-Independent Chemical Identification with a Tunable Infrared Sensor Journal Article

Sensors Journal, IEEE, 2012.

Abstract | Links | BibTeX

2011

Gutierrez-Osuna, R; Gosangi, R; Hierlemann, A

Invited: Advances in Active and Adaptive Chemical Sensing Conference

Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011.

Abstract | Links | BibTeX

Gosangi, R; Gutierrez-Osuna, R

Quantification of Gas Mixtures with Active Recursive Estimation Conference

Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011.

Abstract | Links | BibTeX

Huang, J; Gosangi, R; Gutierrez-Osuna, R

Active Sensing with Fabry-Perot Infrared Interferometers Conference

Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011.

Abstract | Links | BibTeX

Gosangi, R; Gutierrez-Osuna, R

Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks Conference

Proceedings of the 14th International Symposium on Olfaction and Electronic Nose, 2011.

Abstract | Links | BibTeX

2010

Gosangi, R; Gutierrez-Osuna, R

Energy-aware active chemical sensing Conference

Proceedings of IEEE Sensors, IEEE 2010.

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Gutierrez-Osuna, R; Hierlemann, A

Adaptive Microsensor Systems Journal Article

Annual Review of Analytical Chemistry, 3 , pp. 255–276, 2010.

Abstract | Links | BibTeX

Gosangi, R; Gutierrez-Osuna, R

Active temperature programming for metal-oxide chemoresistors Journal Article

Sensors Journal, IEEE, 10 (6), pp. 1075–1082, 2010.

Abstract | Links | BibTeX

2009

Gosangi, R; Gutierrez-Osuna, R

Active chemical sensing with partially observable Markov decision processes Conference

Proceedings of 13th International Symposium on Olfaction and Electronic Noses, 2009.

Abstract | Links | BibTeX

2008

Hierlemann, A; Gutierrez-Osuna, R

Higher-order chemical sensing Journal Article

Chemical reviews, 108 (2), pp. 563, 2008.

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