University of Texas System

Jordan Wolf

George C Kramer

Richard B Voigt

A de-noising algorithm is executed dynamically as data is received to generate and update a set of candidate solutions. Each candidate solution is a representation of the data using one or more line segments, and each line segment is fitted to the data within the time period that the segment spans. During each iteration of the algorithm, one candidate solution is identified as a best solution, and properties of the best solution are utilized to dynamically compute properties of the data. To limit the number of active candidate solutions and the corresponding processing power required to update and evaluate them, candidate solutions that fall too far behind the best candidate solution are eliminated from consideration. The de-noising algorithm finds particular utility in the context of a load cell signal that is representative of a weight of an intravenous fluid container.

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Santa Fe, TX, United States of America

Jordan Wolf