rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Active Control Strategies for Chemical Sensors and Sensor Arrays
  name:
    name part: Gosangi, Rakesh
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Gutierrez-Osuna, Ricardo
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Chemical sensors are generally used as one-dimensional devices, where one measures the sensor's response at a fixed setting, e.g., infrared absorption at a specific wavelength, or conductivity of a solid-state sensor at a specific operating temperature. In many cases, additional information can be extracted by modulating some internal property (e.g., temperature, voltage) of the sensor. However, this additional information comes at a cost (e.g., sensing times, power consumption), so offline optimization techniques (such as feature-subset selection) are commonly used to identify a subset of the most informative sensor tunings. An alternative to offline techniques is active sensing, where the sensor tunings are adapted in real-time based on the information obtained from previous measurements. Prior work in domains such as vision, robotics, and target tracking has shown that active sensing can schedule agile sensors to manage their sensing resources more efficiently than passive sensing, and also balance between sensing costs and performance. Inspired from the history of active sensing, in this dissertation, we developed active sensing algorithms that address three different computational problems in chemical sensing. First, we consider the problem of classification with a single tunable chemical sensor. We formulate the classification problem as a partially observable Markov decision process, and solve it with a myopic algorithm. At each step, the algorithm estimates the utility of each sensing configuration as the difference between expected reduction in Bayesian risk and sensing cost, and selects the configuration with maximum utility. We evaluated this approach on simulated Fabry-Perot interferometers (FPI), and experimentally validated on metal-oxide (MOX) sensors. Our results show that the active sensing method obtains better classification performance than passive sensing methods, and also is more robust to additive Gaussian noise in sensor measurements. Second,…
  note: by Rakesh Gosangi.
  note: "Major Subject: Computer Engineering"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2013 http://hdl.handle.net/1969.1/151254
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Engineering
  subject:
    topic: Active sensing
  subject:
    topic: Chemical sensors
  subject:
    topic: Tunable sensors
  subject:
    topic: Adaptive sensing
  subject:
    topic: Markov models
  subject:
    topic: Feature selection
  identifier: http://hdl.handle.net/1969.1/151254
  record info:
    record content source: TXA
    record creation date: 140312
    record change date: 20151206092543.0
    record identifier: in00003462355
    language of cataloging:
      language term: eng