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A specific species of the genus Geobacter exhibits useful electrical properties when processing a molecule often found in waste water. A team at ASU including Dr Cèsar Torres and Dr Sudeep Popat used that species to create a special type of solid oxide fuel cell we refer to as a

A specific species of the genus Geobacter exhibits useful electrical properties when processing a molecule often found in waste water. A team at ASU including Dr Cèsar Torres and Dr Sudeep Popat used that species to create a special type of solid oxide fuel cell we refer to as a microbial fuel cell. Identification of possible chemical processes and properties of the reactions used by the Geobacter are investigated indirectly by taking measurements using Electrochemical Impedance Spectroscopy of the electrode-electrolyte interface of the microbial fuel cell to obtain the value of the fuel cell's complex impedance at specific frequencies. Investigation of the multiple polarization processes which give rise to measured impedance values is difficult to do directly and so examination of the distribution function of relaxation times (DRT) is considered instead. The DRT is related to the measured complex impedance values using a general, non-physical equivalent circuit model. That model is originally given in terms of a Fredholm integral equation with a non-square integrable kernel which makes the inverse problem of determining the DRT given the impedance measurements an ill-posed problem. The original integral equation is rewritten in terms of new variables into an equation relating the complex impedance to the convolution of a function based upon the original integral kernel and a related but separate distribution function which we call the convolutional distribution function. This new convolutional equation is solved by reducing the convolution to a pointwise product using the Fourier transform and then solving the inverse problem by pointwise division and application of a filter function (equivalent to regularization). The inverse Fourier transform is then taken to get the convolutional distribution function. In the literature the convolutional distribution function is then examined and certain values of a specific, less general equivalent circuit model are calculated from which aspects of the original chemical processes are derived. We attempted to instead directly determine the original DRT from the calculated convolutional distribution function. This method proved to be practically less useful due to certain values determined at the time of experiment which meant the original DRT could only be recovered in a window which would not normally contain the desired information for the original DRT. This limits any attempt to extend the solution for the convolutional distribution function to the original DRT. Further research may determine a method for interpreting the convolutional distribution function without an equivalent circuit model as is done with the regularization method used to solve directly for the original DRT.
ContributorsBaker, Robert Simpson (Author) / Renaut, Rosemary (Thesis director) / Kostelich, Eric (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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The solution of the linear system of equations $Ax\approx b$ arising from the discretization of an ill-posed integral equation with a square integrable kernel is considered. The solution by means of Tikhonov regularization in which $x$ is found to as the minimizer of $J(x)=\{ \|Ax -b\|_2^2 + \lambda^2 \|L x\|_2^2\}$

The solution of the linear system of equations $Ax\approx b$ arising from the discretization of an ill-posed integral equation with a square integrable kernel is considered. The solution by means of Tikhonov regularization in which $x$ is found to as the minimizer of $J(x)=\{ \|Ax -b\|_2^2 + \lambda^2 \|L x\|_2^2\}$ introduces the unknown regularization parameter $\lambda$ which trades off the fidelity of the solution data fit and its smoothing norm, which is determined by the choice of $L$. The Generalized Discrepancy Principle (GDP) and Unbiased Predictive Risk Estimator (UPRE) are methods for finding $\lambda$ given prior conditions on the noise in the measurements $b$. Here we consider the case of $L=I$, and hence use the relationship between the singular value expansion and the singular value decomposition for square integrable kernels to prove that the GDP and UPRE estimates yield a convergent sequence for $\lambda$ with increasing problem size. Hence the estimate of $\lambda$ for a large problem may be found by down-sampling to a smaller problem, or to a set of smaller problems, and applying these estimators more efficiently on the smaller problems. In consequence the large scale problem can be solved in a single step immediately with the parameter found from the down sampled problem(s).
ContributorsHorst, Michael Jacob (Author) / Renaut, Rosemary (Thesis director) / Cochran, Douglas (Committee member) / Wang, Yang (Committee member) / Barrett, The Honors College (Contributor) / School of Music (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
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Description
Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise

Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.
ContributorsCrider, Lauren Nicole (Author) / Cochran, Douglas (Thesis director) / Renaut, Rosemary (Committee member) / Kosut, Oliver (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05