Computational approaches in compressed sensing
Date
2014-09-01
Authors
Woolway, Matthew
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Abstract
This thesis aims to provide a summary on computational approaches to solving the
Compressed Sensing problem. The theoretical problem of solving systems of linear
equations has long been investigated in academic literature. A relatively new field,
Compressed Sensing is an application of such a problem. Specifically, with the ability to
change the way in which we obtain and process signals. Under the assumption of sparse
signals, Compressed Sensing is able to recover signals sampled at a rate much lower than
that of the current Shannon/Nyquist sampling rate. The primary goal of this thesis, is to
describe major algorithms currently used in the Compressed Sensing problem. This is done
as a means to provide the reader with sufficient up to date knowledge on current
approaches as well as their means of implementation, on central processing units (CPUs)
and graphical processing units (GPUs), when considering computational concerns such as
computational time, storage requirements and parallelisability.
Description
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science. Johannesburg, 2014.