Browsing by Subject "Microwave devices -- Calibration."
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Item Calibration methodology for a microwave non-invasive glucose sensor.(2008-06-09T15:41:02Z) McClung, Melanie J.; Jean, B. Randall.; Engineering.; Baylor University. Dept. of Electrical and Computer Engineering.Non-invasive measuring techniques for determining biological parameters are more heavily researched with the growth of the biomedical industry. One of the top areas in non-invasive research deals with diabetes. This disease affects more than 20 million people in the United States, and there is an increasing desire to find a testing process that is non-invasive, easy to use, and safe for users. Microwave technology has improved greatly during recent years and is now seen more often in conjunction with biomedical research. Microwaves are capable of taking measurements of materials inside of a closed volume without the need to come into contact with the material. This makes them ideal for measuring biological parameters, specifically glucose concentrations in the blood. This thesis expands on the development of a microwave sensor to non-invasively measure blood glucose levels and will examine the possibility of developing a calibration for a device using the microwave sensor.Item Principal component and neural network calibration of a microwave frequency composition measurement sensor.(2008-03-03T17:17:16Z) Maule, Charles Stephen.; Marks, Robert J.; Engineering.; Baylor University. Dept. of Electrical and Computer Engineering.Microwave sensors are becoming more prevalent throughout a variety of industries. While providing an effective form of measurement, microwave sensors are difficult to calibrate and provide results which can be difficult to interpret. An improved method for calibrating microwave sensors has been developed which transforms the waveform of a microwave spectrometer using principal component analysis and the results are used to train an artificial neural network to analyze a subject material. Broadband microwave spectrum calibration (BBMSC) is demonstrated using waveforms captured by a microwave spectrometer in a circular waveguide containing pulp stock slurry. This thesis provides a review of the general applications of microwave sensors, details state-of-the-art calibration methods, as well as providing an introduction to principal component analysis and neural networks. The thesis continues by presenting the BBMSC method in detail, as well as how this method is applied to a set of waveforms of pulp-stock data and concludes with a discussion of the potency of BBMSC and recommendations for the future.