<div class="csl-bib-body">
<div class="csl-entry">Tawfik, A. (2018). <i>Compressed sensing for graph signals</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.47380</div>
</div>
-
dc.identifier.uri
https://doi.org/10.34726/hss.2018.47380
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/4375
-
dc.description.abstract
In this thesis we are going to tackle the problem of estimating a sparse graph signal with an unknown frequency support set of known size K from a sampled noisy version. Not knowing the location of the non-zero Fourier coefficients and by taking a number M of samples larger than K will give us a compressed sensing problem. The Bayesian Approximate Message Passing (BAMP) algorithm is used to solve the compressed sensing problem and recover the original signal from few coefficients.