Integrated Sensing and Acting with Tunable Chemical Sensors
Grant
Overview
Affiliation
Other
View All
Overview
abstract
The objective of this research is to develop probabilistic methods for active sensing with tunable chemosensors. The approach is formulated as a partially observable Markov decision process, whereby the system takes sequential sensing actions to identify a chemical target. The investigators will develop algorithms to adapt sensor tunings on-the-fly for two types of scenario: target discrimination and background rejection. These algorithms will be validated on two tunable sensor technologies (metal-oxide chemoresistors and infrared interferometers) combined with tunable preconcentrators.Intellectual merit. Active sensing has received minimal attention in chemosensors, where most work has focused on off-line optimization. Likewise, chemometrics techniques assume that a full spectrum of the chemical is available. In contrast, the proposed work argues that only the most informative wavelengths/features are needed, and that these should be selected dynamically. This research will lead to agile systems that can adapt sensing parameters on-the-fly in response to their environments. The work will also provide a better understanding of active sensing across a range of scenarios (e.g. static vs. dynamic stimuli) and sensor characteristics (e.g. specific vs. cross-selective). Broader impacts. For a number of applications, from quality control to environmental monitoring, there is a need to rapidly detect, identify and quantify volatile compounds. The proposed methodology generalizes to a large class of chemical detectors, and may enable the development of power-aware networks of chemical sensors. The project will provide research opportunities for undergraduate students; since 2001, the PI has supervised 45 senior-design projects involving over 140 students.