Title page for ETD etd-05282009-143016


Type of Document Master's Thesis
Author Mahadevan, Aparna
URN etd-05282009-143016
Title Separating a Gas Mixture Into Its Constituent Analytes Using Fica
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Bell, Amy E. Committee Chair
Raman, Sanjay Committee Member
Xuan, Jianhua Jason Committee Member
Keywords
  • Fingerprint-based ICA
  • Sensor Array
  • Independent Components Analysis
  • Gas Mixtures
Date of Defense 2009-05-05
Availability unrestricted
Abstract
Unlike the conventional “lock-and-key” sensor design in which one sensor is finely tuned to respond to one analyte, the sensor array approach employs multiple sensors in which one sensor responds to many analytes. Consequently, signal processing algorithms must be used to identify the analyte present from the array’s response. The analyte identification process becomes significantly more complicated when a mixture of analytes is presented to the sensor array. Conventional methods that are employed in gas mixture identification are plagued by several design issues like: complexity, scalability, and flexibility. This thesis derives and develops a novel method, fingerprint-based ICA (FICA), to extract and identify individual analytes from a sensor array’s response to a gas mixture of the analytes. FICA is a simple, flexible, and scalable signal processing system that employs independent components analysis (ICA) to extract and identify individual analytes present in a gas mixture; separation and identification of gas mixtures using ICA has not been investigated previously. FICA takes a fundamentally different approach that reflects the underlying property of gas mixtures: gas mixtures are composed of individual analyte responses. Conventional signal processing methods that identify gas mixtures have been developed and implemented in this work; this helps us understand the drawbacks in the conventional approach. FICA's performance is compared to the performance of conventional methods using metric like error rate and false positives rate. Properties like flexibility, scalability, and the data requirements for both conventional methods and FICA are examined. Results obtained in this work indicates that FICA results in lower error rates, and it's performance is better than conventional methods like multi-stage multi-stage support vector machines, and PCR. Furthermore, FICA provides the most simple, scalable, and flexible signal processing system.
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