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You searched for `+publisher:"University of New South Wales" +contributor:("Solo, Victor, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW")`

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University of New South Wales

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

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

URL: http://handle.unsw.edu.au/1959.4/52400 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11073/SOURCE01?view=true

► 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

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://handle.unsw.edu.au/1959.4/52431 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:11104/SOURCE01?view=true

► 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

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://handle.unsw.edu.au/1959.4/57040 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:42307/SOURCE02?view=true

► 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

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://handle.unsw.edu.au/1959.4/53487 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12182/SOURCE02?view=true

► 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

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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