Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for +publisher:"University of New South Wales" +contributor:("Solo, Victor, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW"). Showing records 1 – 4 of 4 total matches.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of New South Wales

1. Marjanovic, Goran. lq sparse signal estimation with applications.

Degree: Electrical Engineering & Telecommunications, 2012, University of New South Wales

 The use of sparsity has emerged in the last fifteen years as an important tool for solving many problems in the areas of signal processing… (more)

Subjects/Keywords: Inverse problems; Sparse; Non convex; Matrix completion; Inverse covariance; Linear regression; Penalized problem

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Marjanovic, G. (2012). lq sparse signal estimation with applications. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Marjanovic, Goran. “lq sparse signal estimation with applications.” 2012. Doctoral Dissertation, University of New South Wales. Accessed April 15, 2021. http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true.

MLA Handbook (7th Edition):

Marjanovic, Goran. “lq sparse signal estimation with applications.” 2012. Web. 15 Apr 2021.

Vancouver:

Marjanovic G. lq sparse signal estimation with applications. [Internet] [Doctoral dissertation]. University of New South Wales; 2012. [cited 2021 Apr 15]. Available from: http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true.

Council of Science Editors:

Marjanovic G. lq sparse signal estimation with applications. [Doctoral Dissertation]. University of New South Wales; 2012. Available from: http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true


University of New South Wales

2. Seneviratne, Seneviratne. l0 Sparse signal processing and model selection with applications.

Degree: Electrical Engineering & Telecommunications, 2012, University of New South Wales

 Sparse signal processing has far-reaching applications including compressed sensing, media compression/denoising/deblurring, microarray analysis and medical imaging. The main reason for its popularity is that many… (more)

Subjects/Keywords: l0 Norm; Sparse Signal Processing; Model Selection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Seneviratne, S. (2012). l0 Sparse signal processing and model selection with applications. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/52431 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11104/SOURCE01?view=true

Chicago Manual of Style (16th Edition):

Seneviratne, Seneviratne. “l0 Sparse signal processing and model selection with applications.” 2012. Doctoral Dissertation, University of New South Wales. Accessed April 15, 2021. http://handle.unsw.edu.au/1959.4/52431 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11104/SOURCE01?view=true.

MLA Handbook (7th Edition):

Seneviratne, Seneviratne. “l0 Sparse signal processing and model selection with applications.” 2012. Web. 15 Apr 2021.

Vancouver:

Seneviratne S. l0 Sparse signal processing and model selection with applications. [Internet] [Doctoral dissertation]. University of New South Wales; 2012. [cited 2021 Apr 15]. Available from: http://handle.unsw.edu.au/1959.4/52431 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11104/SOURCE01?view=true.

Council of Science Editors:

Seneviratne S. l0 Sparse signal processing and model selection with applications. [Doctoral Dissertation]. University of New South Wales; 2012. Available from: http://handle.unsw.edu.au/1959.4/52431 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11104/SOURCE01?view=true


University of New South Wales

3. Piggott, Marc. Stochastic Algorithms in Riemannian Manifolds and Adaptive Networks.

Degree: Electrical Engineering & Telecommunications, 2016, University of New South Wales

 The combination of adaptive network algorithms and stochastic geometric dynamics has the potential to make a large impact in distributed control and signal processing applications.… (more)

Subjects/Keywords: convergence; strong mixing; correlation; distributed learning; stochastic averaging; Lie groups; distributed learning; LMS; convergence; strong mixing

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Piggott, M. (2016). Stochastic Algorithms in Riemannian Manifolds and Adaptive Networks. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/57040 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42307/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Piggott, Marc. “Stochastic Algorithms in Riemannian Manifolds and Adaptive Networks.” 2016. Doctoral Dissertation, University of New South Wales. Accessed April 15, 2021. http://handle.unsw.edu.au/1959.4/57040 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42307/SOURCE02?view=true.

MLA Handbook (7th Edition):

Piggott, Marc. “Stochastic Algorithms in Riemannian Manifolds and Adaptive Networks.” 2016. Web. 15 Apr 2021.

Vancouver:

Piggott M. Stochastic Algorithms in Riemannian Manifolds and Adaptive Networks. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2021 Apr 15]. Available from: http://handle.unsw.edu.au/1959.4/57040 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42307/SOURCE02?view=true.

Council of Science Editors:

Piggott M. Stochastic Algorithms in Riemannian Manifolds and Adaptive Networks. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/57040 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42307/SOURCE02?view=true


University of New South Wales

4. Cassidy, Benjamin. Statistical signal processing methods for imaging brain activity.

Degree: Electrical Engineering & Telecommunications, 2014, University of New South Wales

 Functional neuroimaging involves the study of cognitive scientific questions by measuring and modelling brain activity, using techniques such as Functional Magnetic Resonance Imaging (fMRI) and… (more)

Subjects/Keywords: Magnetoencephalography; Statistical signal processing; Brain imaging; FMRI

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cassidy, B. (2014). Statistical signal processing methods for imaging brain activity. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53487 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12182/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Cassidy, Benjamin. “Statistical signal processing methods for imaging brain activity.” 2014. Doctoral Dissertation, University of New South Wales. Accessed April 15, 2021. http://handle.unsw.edu.au/1959.4/53487 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12182/SOURCE02?view=true.

MLA Handbook (7th Edition):

Cassidy, Benjamin. “Statistical signal processing methods for imaging brain activity.” 2014. Web. 15 Apr 2021.

Vancouver:

Cassidy B. Statistical signal processing methods for imaging brain activity. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2021 Apr 15]. Available from: http://handle.unsw.edu.au/1959.4/53487 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12182/SOURCE02?view=true.

Council of Science Editors:

Cassidy B. Statistical signal processing methods for imaging brain activity. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53487 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12182/SOURCE02?view=true

.