Date of Award


Document Type


Degree Name

Master of Science


Department of Electrical and Computer Engineering

First Advisor

Juan R. Vasquez, PhD


In an effort to find a less invasive way of testing for different cell abnormalities and finding more practical tests for different cellular mutations, this project makes use of a well-known technique called cellular impedance spectroscopy coupled with stochastic estimation. Impedance spectroscopy, the measurement of the complex resistance of a biological body, is not a new technology; it has been around for many years and has been used to make electrical representations of different biological systems. The problem with this procedure is that models cannot be used for system identification. Stochastic estimation can complement a model produced by analyzing the input/output characteristics of a cell sample to account for modeling inadequacies produced by the linear modeling of electrical impedance spectroscopy alone. In this thesis, biological cell samples were submitted to a sinusoidal voltage at a different range of frequencies. The cell samples created an output which was used to model the electrical behavior of the biological system. This electrical representation was used to build a fixed-interval stochastic smoother. The stochastic smoother was then used to estimate the output measurements of different cell samples and ultimately identify a cell type based on the evaluation of the residuals produced. Results show that, given residual values, one could apply a binary logic windowing technique that would show a difference in the cell samples tested, thereby being able to identify the cell sample in question.

AFIT Designator


DTIC Accession Number