Computational approaches in compressed sensing

• Main Author: Woolway, Matthew
• Format: Others
• Language: en
• Published: 2014
• Subjects: Algorithms.; Computer programming.; Computational complexity.; Numerical analysis > Data processing.
• Online Access: http://hdl.handle.net/10539/15334

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. === 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.