Matching Items (4)
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- All Subjects: Edge Detection
- Creators: Cochran, Douglas
- Creators: Ilkturk, Utku
- Creators: Marzke, Robert
Description
The behaviors of various amorphous materials are characterized at high pressures to deduce phase transitions, coordination changes, densification, and other structural or electronic alterations in the system. Alongside, improvements on high pressure techniques are presented to measure equations of state of glassy materials and probe liquids using in-situ high resolution nuclear magnetic resonance (NMR) spectroscopy. 27Al NMR is used to quantify coordination changes in CaAl2O4 glass pressure cycled to 16 GPa. The structure and coordination environments remain unchanged up to 8 GPa at which 93% of the recovered glass exists as 4-fold Al, whereas the remaining population exists as [5,6]Al. Upon densification, [5,6]Al comprise nearly 30% of observed Al, most likely through the generation of 3-coordinated oxygen. A method to determine the volumetric equation of state of amorphous solids using optical microscopy in a diamond anvil cell is also described. The method relies on two dimensional image acquisition and analysis to quantify changes in the projected image area with compression. The area analysis method is used to determine the compression of cubic crystals, yielding results in good agreement with diffraction and volumetric measurements. A NMR probe capable of reaching 3 GPa is built to understand the nature of magnetic field gradients and improve upon the resolution of high pressure studies conducted in a diamond anvil cell. Field gradients in strength up to 6 G/cm are caused largely by mismatches in the magnetic susceptibility between the sample and gasket, which is proven to shift the chemical shift distribution by use of several different metallic gaskets. Polyamorphic behavior in triphenyl phosphite is studied at pressures up to 0.7 GPa to elucidate the formation of the glacial phase at high pressures. A perceived liquid-liquid phase transition is shown to follow a positive Clapeyron slope, and closely follows the predicted glass transition line up to 0.4 GPa and temperatures below 270 K. A drastic change in morphology is indicative of a transformation from liquid I to liquid II and followed by optical microscopy.
ContributorsAmin, Samrat A (Author) / Yarger, Jeffery L (Thesis advisor) / Wolf, George (Committee member) / Marzke, Robert (Committee member) / Arizona State University (Publisher)
Created2012
Description
Modern measurement schemes for linear dynamical systems are typically designed so that different sensors can be scheduled to be used at each time step. To determine which sensors to use, various metrics have been suggested. One possible such metric is the observability of the system. Observability is a binary condition determining whether a finite number of measurements suffice to recover the initial state. However to employ observability for sensor scheduling, the binary definition needs to be expanded so that one can measure how observable a system is with a particular measurement scheme, i.e. one needs a metric of observability. Most methods utilizing an observability metric are about sensor selection and not for sensor scheduling. In this dissertation we present a new approach to utilize the observability for sensor scheduling by employing the condition number of the observability matrix as the metric and using column subset selection to create an algorithm to choose which sensors to use at each time step. To this end we use a rank revealing QR factorization algorithm to select sensors. Several numerical experiments are used to demonstrate the performance of the proposed scheme.
ContributorsIlkturk, Utku (Author) / Gelb, Anne (Thesis advisor) / Platte, Rodrigo (Thesis advisor) / Cochran, Douglas (Committee member) / Renaut, Rosemary (Committee member) / Armbruster, Dieter (Committee member) / Arizona State University (Publisher)
Created2015
Description
The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data. This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.
ContributorsReynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The recovery of edge information in the physical domain from non-uniform Fourier data is of importance in a variety of applications, particularly in the practice of magnetic resonance imaging (MRI). Edge detection can be important as a goal in and of itself in the identification of tissue boundaries such as those defining the locations of tumors. It can also be an invaluable tool in the amelioration of the negative effects of the Gibbs phenomenon on reconstructions of functions with discontinuities or images in multi-dimensions with internal edges. In this thesis we develop a novel method for recovering edges from non-uniform Fourier data by adapting the "convolutional gridding" method of function reconstruction. We analyze the behavior of the method in one dimension and then extend it to two dimensions on several examples.
ContributorsMartinez, Adam (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Platte, Rodrigo (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2013-05