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Full-Text Articles in Engineering
Quantitative Detection Of Complex Mixtures Using A Single Chemical Sensor: Analysis Of Response Transients Using Multi-Stage Estimation, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco
Quantitative Detection Of Complex Mixtures Using A Single Chemical Sensor: Analysis Of Response Transients Using Multi-Stage Estimation, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco
Electrical and Computer Engineering Faculty Research and Publications
Most chemical sensors are only partially selective to any specific target analyte(s), making identification and quantification of analyte mixtures challenging, a problem often addressed using arrays of partially selective sensors. This work presents and experimentally verifies a signal-processing technique based on estimation theory for online identification and quantification of multiple analytes using only the response data collected from a single polymer-coated sensor device. The demonstrated technique, based on multiple stages of exponentially weighted recursive least-squares estimation (EW-RLSE), first determines which of the analytes included in the sensor response model are absent from the mixture being analyzed; these are then eliminated …
Obtaining Chemical Selectivity From A Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme With Multiparameter Measurement To Quantify Mixture Components And Interferents, Karthick Sothivelr, Florian Bender, Fabien Josse Phd, Edwin E. Yaz, Antonio J. Ricco
Obtaining Chemical Selectivity From A Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme With Multiparameter Measurement To Quantify Mixture Components And Interferents, Karthick Sothivelr, Florian Bender, Fabien Josse Phd, Edwin E. Yaz, Antonio J. Ricco
Electrical and Computer Engineering Faculty Research and Publications
A new approach is reported to detect and quantify the members of a group of small-aromatic-molecule target analytes: benzene, toluene, ethylbenzene, and xylenes (BTEX), dissolved in water, in the presence of interferents, using only the data collected from a single polymer-coated SH-SAW (shear horizontal surface acoustic wave) device and a two-stage adaptive estimation scheme. This technique is composed of exponentially weighted recursive least-squares estimation (EW-RLSE) and a bank of Kalman filters (BKFs) and does not require any prior knowledge of the initial concentration range of the target analytes. The proposed approach utilizes the transient sensor response to sorption and/or desorption …
Detection And Quantification Of Multi-Analyte Mixtures Using A Single Sensor And Multi-Stage Data-Weighted Rlse, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco
Detection And Quantification Of Multi-Analyte Mixtures Using A Single Sensor And Multi-Stage Data-Weighted Rlse, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco
Electrical and Computer Engineering Faculty Research and Publications
This work reports the development and experimental verification of a sensor signal processing technique for online identification and quantification of aqueous mixtures of benzene, toluene, ethylbenzene, xylenes (BTEX) and 1, 2, 4-trimethylbenzene (TMB) at ppb concentrations using time-dependent frequency responses from a single polymer-coated shear-horizontal surface acoustic wave sensor. Signal processing based on multi-stage exponentially weighted recursive leastsquares estimation (EW-RLSE) is utilized for estimating the concentrations of the analytes in the mixture that are most likely to have produced a given sensor response. The initial stages of EW-RLSE are used to eliminate analyte(s) that are erroneously identified as present in …
Sensor-Based Estimation Of Btex Concentrations In Water Samples Using Recursive Least Squares And Kalman Filter Techniques, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco
Sensor-Based Estimation Of Btex Concentrations In Water Samples Using Recursive Least Squares And Kalman Filter Techniques, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco
Electrical and Computer Engineering Faculty Research and Publications
This work investigates sensor signal processing approaches that can be used with a sensor system for direct on-site monitoring of groundwater, enabling detection and quantification of BTEX (benzene, toluene, ethylbenzene and xylene) compounds at μg/L (ppb) concentrations in the presence of interferents commonly found in groundwater. A model for the sensor response to water samples containing multiple analytes was first formulated based on experimental results. The first signal processing approach utilizes only RLSE (recursive least squares estimation) whereas the second, a two-step processing technique, utilizes both RLSE and bank of Kalman filters for the estimation process. The estimation techniques were …
Online Drift Compensation For Chemical Sensors Using Estimation Theory, Thomas H. Wenzel, Arnold Kweku Mensah-Brown, Fabien Josse, Edwin E. Yaz
Online Drift Compensation For Chemical Sensors Using Estimation Theory, Thomas H. Wenzel, Arnold Kweku Mensah-Brown, Fabien Josse, Edwin E. Yaz
Electrical and Computer Engineering Faculty Research and Publications
Sensor drift from slowly changing environmental conditions and other instabilities can greatly degrade a chemical sensor's performance, resulting in poor identification and analyte quantification. In the present work, estimation theory (i.e., various forms of the Kalman filter) is used for online compensation of baseline drift in the response of chemical sensors. Two different cases, which depend on the knowledge of the characteristics of the sensor system, are studied. First, an unknown input is considered, which represents the practical case of analyte detection and quantification. Then, the more general case, in which the sensor parameters and the input are both unknown, …