ERA

Download the full-sized PDF of Real-Time Time-Warped Multiscale Signal Processing for Scientific VisualizationDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3J09WC79

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Real-Time Time-Warped Multiscale Signal Processing for Scientific Visualization Open Access

Descriptions

Other title
Subject/Keyword
scale space
multiscale representation
noise removal
visualization
signal reconstruction
time-warped signal processing
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Hamilton, Matthew J.
Supervisor and department
Boulanger, Pierre (Computing Science)
Examining committee member and department
Cheng, Irene (Computing Science)
Samavati, Faramarz (Computer Science, University of Calgary)
Ellison, Michael (Computing Science)
Zhang, Hong (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2013-07-03T14:38:30Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
This thesis considers the problem of visualizing simulations of phenomenon which span large ranges of spatial scales. These datasets tend to be extremely large presenting challenges both to human comprehension and high-performance computing. The main problems considered are how to effectively represent scale and how to efficiently compute and visualize multiscale representations for large, real-time datasets. Time-warped signal processing techniques are shown to be useful for formulating a localized notion of scale. In this case, we use time-warping in order to adapt the standard Fourier basis to local properties of the signal, giving the advantage of being localized in the frequency spectrum as compared with the standard linear notions of scale. Time-warping is also shown to have theoretical advantages in terms of signal reconstruction quality and random noise removal. In practice, these advantages are shown to only hold under certain conditions. It is then shown in the thesis how convolution-based reconstruction techniques can be mapped onto graphics processing units (GPUs) for high-performance implementation of a multiscale molecular visualization framework. We show how the same technique can likely be used for time-warped multiscale reconstruction.
Language
English
DOI
doi:10.7939/R3J09WC79
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2014-04-30T22:55:58.146+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 2083586
Last modified: 2015:10:12 16:38:46-06:00
Filename: Hamilton_Matthew_Fall_2013.pdf
Original checksum: 4f6b3ac2f03e2e6a8765c36daf385f06
Well formed: true
Valid: true
Page count: 116
Activity of users you follow
User Activity Date