Separating a Gas Mixture Into Its Constituent Analytes Using Fica

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2009

Author dc:creator

• Mahadevan, Aparna

Chair dc:contributor.committeechair

• Bell, Amy E.

Committee members dc:contributor.committeemember

• Raman, Sanjay
• Xuan, Jianhua Jason

• Fingerprint-based ICA
• Sensor Array
• Independent Components Analysis
• Gas Mixtures

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-05282009-143016

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/33312