Signal Processing using Wavelets for Enhancing Electronic Nose Performance

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dc.contributor.advisor Dr. Edward Grant, Committee Member en_US
dc.contributor.advisor Dr. H. Troy Nagle, Committee Chair en_US
dc.contributor.advisor Dr. Charles Smith, Committee Member en_US
dc.contributor.advisor Dr. Mark White, Committee Member en_US Phaisangittisagul, Ekachai en_US 2010-04-02T18:43:36Z 2010-04-02T18:43:36Z 2007-07-19 en_US
dc.identifier.other etd-03102007-130517 en_US
dc.description.abstract In recent years, many new technologies of electronic devices that mimic the mammalian olfactory system, electronic noses (e-noses), have been developed in many research institutions and commercial organizations around the world. These devices have been used in a wide range of applications such as food and beverage quality, environmental monitoring, medical diagnosis. Over the past decade, many researchers have spent a great deal of effort improving e-nose performance and also extended the use of the e-nose devices, not only for discriminating or classifying different odor samples, but also for quantifying an ingredient of a given odor sample. This dissertation focuses on two technical areas. First, an implementation of an e-nose signal processing system is developed to improve classification performance for small portable devices with fast response times and reduced cost. Second, the signal processing system is extended to odor mixture analysis. The advances made this research are based on a modern signal processing technique, specifically wavelet analysis. Ultimately, the performance of e-nose devices is highly dependent on the quality of features from the sensors' response. Therefore, a new transient feature extraction method using wavelet decomposition to capture the transient sensor's response has been developed. The evaluation of these transient features shows promising results in terms of classification performance, number of sensors employed in the e-nose device, and simplification of the classifier. For handling different types of odor samples, a simplified multiple classifier system is developed based on an "odor type signature." Analyzing mixtures of odors is a challenge for e-nose systems. Herein a new method is developed for predicting a sensor's response to mixtures of odors. The combination of wavelet decomposition and reconstruction is adopted to implement the mixed odor sensor-response predictor. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject wavelet reconstruction en_US
dc.subject wavelet decomposition en_US
dc.subject transient-feature extraction en_US
dc.subject sensor selection en_US
dc.subject machine olfaction en_US
dc.subject odor-type signature en_US
dc.subject Odor classification en_US
dc.title Signal Processing using Wavelets for Enhancing Electronic Nose Performance en_US PhD en_US dissertation en_US Electrical Engineering en_US

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