This is a decommissioned version of ERA which is running to enable completion of migration processes. All new collections and items and all edits to existing items should go to our new ERA instance at https://ualberta.scholaris.ca - Please contact us at erahelp@ualberta.ca for assistance!
Search
Skip to Search Results- 3Compressed sensing
- 1DOA estimation
- 1Multidimensional signal processing
- 1Permutation
- 1Random matrix
- 1Restricted isometry property
-
The Strong Restricted Isometry Property of Sub-Gaussian Matrices and the Erasure Robustness Property of Gaussian Random Frames
DownloadSpring 2016
In this thesis we will study the robustness property of sub-gaussian random matrices. We first show that the nearly isometry property will still hold with high probability if we erase a certain portion of rows from a sub-gaussian matrix, and we will estimate the erasure ratio with a given small...
-
Parameter Estimation in Low-Rank Models from Small Sample Size and Undersampled Data: DOA and Spectrum Estimation
DownloadSpring 2015
In estimation theory, a set of parameters are estimated from a finite number of measurements (samples). In general, the quality of estimation degrades as the number of samples is reduced. In this thesis, the problem of parameter estimation in low-rank models from a small number of samples is...
-
Parallel Sampling and Reconstruction with Permutation in Multidimensional Compressed Sensing
DownloadFall 2013
The advent of compressed sensing provides a new way to sample and compress signals. In this thesis, a parallel compressed sensing architecture is proposed, which samples a two-dimensional reshaped multidimensional signal column by column using the same sensing matrix. Compared to architectures...