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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Analysis Of Sensor Signals For Online Detection Of Hydrocarbons In Liquids In The Presence Of Interferents, Karthick Sothivelr
Analysis Of Sensor Signals For Online Detection Of Hydrocarbons In Liquids In The Presence Of Interferents, Karthick Sothivelr
Dissertations (1934 -)
Current applicability of many chemical sensors is limited due to the lack of adequate selectivity to enable real-world applications. Often, the chemically sensitive element of the sensor is only partially selective to any specific target analyte, potentially giving rise to low probability of detection. Other challenges include the need to identify and quantify the target analytes in a mixture, especially in the presence of non-target interferents. In this dissertation, to enhance the selectivity of the sensor, analysis of sensor signals for detection and quantification of mixtures of hydrocarbon compounds in liquids in the presence of interferents using estimation theory and …
Novel Convergence Results In Nonlinear Filtering, Jennifer Lynn Bonniwell
Novel Convergence Results In Nonlinear Filtering, Jennifer Lynn Bonniwell
Dissertations (1934 -)
In this dissertation, the discrete-time extended Kalman filter is analyzed for its ability to attenuate finite-energy disturbances, known as the H-infinity property. Though the extended Kalman filter is designed to be a locally optimal minimum variance estimator, this dissertation proves that it has additional properties, such as H-infinity. This analysis is performed with the extended Kalman filter in direct form. Since this form reduces assumptions placed on the system in previous works on convergence and H-2 properties of the extended Kalman filter, the extended Kalman filter used as a nonlinear observer for noise-free models is revisited using the direct form …
Analysis Of Sensor Signals And Quantification Of Analytes Based On Estimation Theory, Karthick Sothivelr
Analysis Of Sensor Signals And Quantification Of Analytes Based On Estimation Theory, Karthick Sothivelr
Master's Theses (2009 -)
Compact sensor systems for on-site monitoring of groundwater for trace organic compounds are currently under development. To permit near real-time analysis of samples containing multiple analytes, the present work investigates a sensor signal processing approach based on estimation theory, specifically using Kalman Filter and Extended Kalman Filter. As a first step towards the analysis of groundwater samples containing multiple compounds, the approach presented in this work permits estimation of analyte concentration(s) in binary mixtures and single analyte samples on-line, before the sensor response reaches steady-state. Sensor signals from binary mixtures and single analyte samples of BTEX compounds (benzene, toluene, ethylbenzene, …
Automation Of Energy Demand Forecasting, Sanzad Siddique
Automation Of Energy Demand Forecasting, Sanzad Siddique
Master's Theses (2009 -)
Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning …
Physiologically-Motivated Feature Extraction Methods For Speaker Recognition, Jianglin Wang
Physiologically-Motivated Feature Extraction Methods For Speaker Recognition, Jianglin Wang
Dissertations (1934 -)
Speaker recognition has received a great deal of attention from the speech community, and significant gains in robustness and accuracy have been obtained over the past decade. However, the features used for identification are still primarily representations of overall spectral characteristics, and thus the models are primarily phonetic in nature, differentiating speakers based on overall pronunciation patterns. This creates difficulties in terms of the amount of enrollment data and complexity of the models required to cover the phonetic space, especially in tasks such as identification where enrollment and testing data may not have similar phonetic coverage. This dissertation introduces new …