Work

Geometric Sparsity in High Dimension

Year: 2012

Type: dissertation

Abstract: The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Kaslovsky, Daniel N. (Ph.D., Applied Mathematics)

Source: CU Scholar (University of Colorado Boulder)

Author: Daniel N. Kaslovsky

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Los Alamos National Laboratory

OpenAlex ID: W2527381096

Cites: 69

Cited by: 1

Related to: 10