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University of Michigan

1.
Brown, Peter.
*Sparse**Approximation* Accelerators with Spiking Neural-Networks.

Degree: PhD, Electrical and Computer Engineering, 2020, University of Michigan

URL: http://hdl.handle.net/2027.42/155317

► Today's mobile intelligent devices are often limited more by the energy required for data communication than for data processing. Thus, in addition to their traditional…
(more)

Subjects/Keywords: sparse approximation; spiking neural-network; image sparse coding; compressed sensing radar; Electrical Engineering; Engineering

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

APA (6^{th} Edition):

Brown, P. (2020). Sparse Approximation Accelerators with Spiking Neural-Networks. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/155317

Chicago Manual of Style (16^{th} Edition):

Brown, Peter. “Sparse Approximation Accelerators with Spiking Neural-Networks.” 2020. Doctoral Dissertation, University of Michigan. Accessed February 27, 2021. http://hdl.handle.net/2027.42/155317.

MLA Handbook (7^{th} Edition):

Brown, Peter. “Sparse Approximation Accelerators with Spiking Neural-Networks.” 2020. Web. 27 Feb 2021.

Vancouver:

Brown P. Sparse Approximation Accelerators with Spiking Neural-Networks. [Internet] [Doctoral dissertation]. University of Michigan; 2020. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2027.42/155317.

Council of Science Editors:

Brown P. Sparse Approximation Accelerators with Spiking Neural-Networks. [Doctoral Dissertation]. University of Michigan; 2020. Available from: http://hdl.handle.net/2027.42/155317

University of Southern California

2. Das, Abhimanyu. Subset selection algorithms for prediction.

Degree: PhD, Computer Science, 2011, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6197

► In this dissertation, we study the subset selection problem for prediction. It deals with choosing the “best” or “most informative” k-subset from a large set…
(more)

Subjects/Keywords: approximation algorithms; machine learning; regression; feature selection; sparse approximation; compressed sensing; submodularity

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APA (6^{th} Edition):

Das, A. (2011). Subset selection algorithms for prediction. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6197

Chicago Manual of Style (16^{th} Edition):

Das, Abhimanyu. “Subset selection algorithms for prediction.” 2011. Doctoral Dissertation, University of Southern California. Accessed February 27, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6197.

MLA Handbook (7^{th} Edition):

Das, Abhimanyu. “Subset selection algorithms for prediction.” 2011. Web. 27 Feb 2021.

Vancouver:

Das A. Subset selection algorithms for prediction. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2021 Feb 27]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6197.

Council of Science Editors:

Das A. Subset selection algorithms for prediction. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/644551/rec/6197

University of Toronto

3. Li, Matthew T.C. The Anchored Separated Representation for High Dimensional Problems.

Degree: 2015, University of Toronto

URL: http://hdl.handle.net/1807/70437

►

Although topics in science and engineering that involve dimensions beyond x-y-z appear obscure, in truth numerous examples abound. For instance, uncertainty quantification requires approximating and… (more)

Subjects/Keywords: Curse of Dimensionality; Function Decomposition; High Dimensional Problems; Sparse Approximation; 0538

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APA (6^{th} Edition):

Li, M. T. C. (2015). The Anchored Separated Representation for High Dimensional Problems. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/70437

Chicago Manual of Style (16^{th} Edition):

Li, Matthew T C. “The Anchored Separated Representation for High Dimensional Problems.” 2015. Masters Thesis, University of Toronto. Accessed February 27, 2021. http://hdl.handle.net/1807/70437.

MLA Handbook (7^{th} Edition):

Li, Matthew T C. “The Anchored Separated Representation for High Dimensional Problems.” 2015. Web. 27 Feb 2021.

Vancouver:

Li MTC. The Anchored Separated Representation for High Dimensional Problems. [Internet] [Masters thesis]. University of Toronto; 2015. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1807/70437.

Council of Science Editors:

Li MTC. The Anchored Separated Representation for High Dimensional Problems. [Masters Thesis]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/70437

Victoria University of Wellington

4. Jin, Wenyu. Spatial Multizone Soundfield Reproduction Design.

Degree: 2015, Victoria University of Wellington

URL: http://hdl.handle.net/10063/4983

► It is desirable for people sharing a physical space to access different multimedia information streams simultaneously. For a good user experience, the interference of the…
(more)

Subjects/Keywords: Spatial Audio; Multizone Soundfield Reproduction; Reverberation Equalization; Sparse Approximation; Adaptive Filtering

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APA (6^{th} Edition):

Jin, W. (2015). Spatial Multizone Soundfield Reproduction Design. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/4983

Chicago Manual of Style (16^{th} Edition):

Jin, Wenyu. “Spatial Multizone Soundfield Reproduction Design.” 2015. Doctoral Dissertation, Victoria University of Wellington. Accessed February 27, 2021. http://hdl.handle.net/10063/4983.

MLA Handbook (7^{th} Edition):

Jin, Wenyu. “Spatial Multizone Soundfield Reproduction Design.” 2015. Web. 27 Feb 2021.

Vancouver:

Jin W. Spatial Multizone Soundfield Reproduction Design. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2015. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10063/4983.

Council of Science Editors:

Jin W. Spatial Multizone Soundfield Reproduction Design. [Doctoral Dissertation]. Victoria University of Wellington; 2015. Available from: http://hdl.handle.net/10063/4983

University of Florida

5. Xu, Xie. Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2014, University of Florida

URL: https://ufdc.ufl.edu/UFE0046527

► Sampling and reconstruction of volumetric data are ubiquitous throughout biomedical imaging, scientific simulation, and visualization applications. In this dissertation, we focus on the reconstruction of…
(more)

Subjects/Keywords: Approximation; Boxes; Conceptual lattices; Datasets; Face centered cubic lattices; Interpolation; Mathematical lattices; Sampling rates; Signals; Supernova remnants; box-splines – compressed-sensing – reconstruction – sampling – sparse-approximation – sparse-representation – volumetric-data

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

APA (6^{th} Edition):

Xu, X. (2014). Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0046527

Chicago Manual of Style (16^{th} Edition):

Xu, Xie. “Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements.” 2014. Doctoral Dissertation, University of Florida. Accessed February 27, 2021. https://ufdc.ufl.edu/UFE0046527.

MLA Handbook (7^{th} Edition):

Xu, Xie. “Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements.” 2014. Web. 27 Feb 2021.

Vancouver:

Xu X. Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements. [Internet] [Doctoral dissertation]. University of Florida; 2014. [cited 2021 Feb 27]. Available from: https://ufdc.ufl.edu/UFE0046527.

Council of Science Editors:

Xu X. Volumetric Data Reconstruction from Irregular Samples and Compressively Sensed Measurements. [Doctoral Dissertation]. University of Florida; 2014. Available from: https://ufdc.ufl.edu/UFE0046527

Iowa State University

6. Sang, Hejian. Approximate Bayesian approaches and semiparametric methods for handling missing data.

Degree: 2018, Iowa State University

URL: https://lib.dr.iastate.edu/etd/16748

► This thesis consists of four research papers focusing on estimation and inference in missing data. In the first paper (Chapter 2), an approximate Bayesian approach…
(more)

Subjects/Keywords: Bayesian approximation computing; Gaussian Mixture Models; Profile likelihood; Sparse model; Statistics and Probability

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APA (6^{th} Edition):

Sang, H. (2018). Approximate Bayesian approaches and semiparametric methods for handling missing data. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16748

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Sang, Hejian. “Approximate Bayesian approaches and semiparametric methods for handling missing data.” 2018. Thesis, Iowa State University. Accessed February 27, 2021. https://lib.dr.iastate.edu/etd/16748.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sang, Hejian. “Approximate Bayesian approaches and semiparametric methods for handling missing data.” 2018. Web. 27 Feb 2021.

Vancouver:

Sang H. Approximate Bayesian approaches and semiparametric methods for handling missing data. [Internet] [Thesis]. Iowa State University; 2018. [cited 2021 Feb 27]. Available from: https://lib.dr.iastate.edu/etd/16748.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sang H. Approximate Bayesian approaches and semiparametric methods for handling missing data. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16748

Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

7. Varnai, Peter (author). Exploiting Kronecker Structures: With applications to optimization problems arising in the field of adaptive optics.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:98f7cf6e-6ded-4f50-8df7-89944d6a0830

► We study the important mathematical problem of approximating the inverse of low Kronecker-rank matrices in this same form. A traditional alternating least squares (ALS) scheme…
(more)

Subjects/Keywords: Kronecker product; inverse approximation; low Kronecker-rank; wavefront control; adaptive optics; sparse aperture mask

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APA (6^{th} Edition):

Varnai, P. (. (2017). Exploiting Kronecker Structures: With applications to optimization problems arising in the field of adaptive optics. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:98f7cf6e-6ded-4f50-8df7-89944d6a0830

Chicago Manual of Style (16^{th} Edition):

Varnai, Peter (author). “Exploiting Kronecker Structures: With applications to optimization problems arising in the field of adaptive optics.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:98f7cf6e-6ded-4f50-8df7-89944d6a0830.

MLA Handbook (7^{th} Edition):

Varnai, Peter (author). “Exploiting Kronecker Structures: With applications to optimization problems arising in the field of adaptive optics.” 2017. Web. 27 Feb 2021.

Vancouver:

Varnai P(. Exploiting Kronecker Structures: With applications to optimization problems arising in the field of adaptive optics. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:98f7cf6e-6ded-4f50-8df7-89944d6a0830.

Council of Science Editors:

Varnai P(. Exploiting Kronecker Structures: With applications to optimization problems arising in the field of adaptive optics. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:98f7cf6e-6ded-4f50-8df7-89944d6a0830

Louisiana State University

8. Srinivasagopalan, Srivathsan. Oblivious buy-at-bulk network design algorithms.

Degree: PhD, Computer Sciences, 2011, Louisiana State University

URL: etd-04202011-092524 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3439

► Large-scale networks such as the Internet has emerged as arguably the most complex distributed communication network system. The mere size of such networks and all…
(more)

Subjects/Keywords: Spanning Tree; Network Design; Doubling-Dimension; Sparse Covers; Algorithms; Approximation; Graph Theory; Buy-at-Bulk

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APA (6^{th} Edition):

Srinivasagopalan, S. (2011). Oblivious buy-at-bulk network design algorithms. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04202011-092524 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3439

Chicago Manual of Style (16^{th} Edition):

Srinivasagopalan, Srivathsan. “Oblivious buy-at-bulk network design algorithms.” 2011. Doctoral Dissertation, Louisiana State University. Accessed February 27, 2021. etd-04202011-092524 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3439.

MLA Handbook (7^{th} Edition):

Srinivasagopalan, Srivathsan. “Oblivious buy-at-bulk network design algorithms.” 2011. Web. 27 Feb 2021.

Vancouver:

Srinivasagopalan S. Oblivious buy-at-bulk network design algorithms. [Internet] [Doctoral dissertation]. Louisiana State University; 2011. [cited 2021 Feb 27]. Available from: etd-04202011-092524 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3439.

Council of Science Editors:

Srinivasagopalan S. Oblivious buy-at-bulk network design algorithms. [Doctoral Dissertation]. Louisiana State University; 2011. Available from: etd-04202011-092524 ; https://digitalcommons.lsu.edu/gradschool_dissertations/3439

9. -5189-8939. A Study Of The Mathematics Of Deep Learning.

Degree: PhD, Applied Mathematics & Statistics, 2020, Johns Hopkins University

URL: http://jhir.library.jhu.edu/handle/1774.2/63535

► "Deep Learning"/"Deep Neural Nets" is a technological marvel that is now increasingly deployed at the cutting-edge of artificial intelligence tasks. This ongoing revolution can be…
(more)

Subjects/Keywords: Deep Learning Neural Networks Stochastic Optimization Function Approximation Sparse Coding PAC-Bayes

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APA (6^{th} Edition):

-5189-8939. (2020). A Study Of The Mathematics Of Deep Learning. (Doctoral Dissertation). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/63535

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Chicago Manual of Style (16^{th} Edition):

-5189-8939. “A Study Of The Mathematics Of Deep Learning.” 2020. Doctoral Dissertation, Johns Hopkins University. Accessed February 27, 2021. http://jhir.library.jhu.edu/handle/1774.2/63535.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

MLA Handbook (7^{th} Edition):

-5189-8939. “A Study Of The Mathematics Of Deep Learning.” 2020. Web. 27 Feb 2021.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Vancouver:

-5189-8939. A Study Of The Mathematics Of Deep Learning. [Internet] [Doctoral dissertation]. Johns Hopkins University; 2020. [cited 2021 Feb 27]. Available from: http://jhir.library.jhu.edu/handle/1774.2/63535.

Author name may be incomplete

Council of Science Editors:

-5189-8939. A Study Of The Mathematics Of Deep Learning. [Doctoral Dissertation]. Johns Hopkins University; 2020. Available from: http://jhir.library.jhu.edu/handle/1774.2/63535

Author name may be incomplete

UCLA

10.
Ren, Fengbo.
A Scalable VLSI Architecture for Real-Time and Energy-Ecient *Sparse* *Approximation* in Compressive Sensing Systems.

Degree: Electrical Engineering, 2014, UCLA

URL: http://www.escholarship.org/uc/item/73p6w2zv

► Digital electronic industry today relies on Nyquist sampling theorem, which requires to double the size (sampling rate) of the signal representation on the Fourier basis…
(more)

Subjects/Keywords: Electrical engineering; Computer engineering; Compressive Sensing; Energy-Efficient Design; Integrated Circuit; Sparse Approximation; VLSI Architecture; Wireless Health

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APA (6^{th} Edition):

Ren, F. (2014). A Scalable VLSI Architecture for Real-Time and Energy-Ecient Sparse Approximation in Compressive Sensing Systems. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/73p6w2zv

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Ren, Fengbo. “A Scalable VLSI Architecture for Real-Time and Energy-Ecient Sparse Approximation in Compressive Sensing Systems.” 2014. Thesis, UCLA. Accessed February 27, 2021. http://www.escholarship.org/uc/item/73p6w2zv.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ren, Fengbo. “A Scalable VLSI Architecture for Real-Time and Energy-Ecient Sparse Approximation in Compressive Sensing Systems.” 2014. Web. 27 Feb 2021.

Vancouver:

Ren F. A Scalable VLSI Architecture for Real-Time and Energy-Ecient Sparse Approximation in Compressive Sensing Systems. [Internet] [Thesis]. UCLA; 2014. [cited 2021 Feb 27]. Available from: http://www.escholarship.org/uc/item/73p6w2zv.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ren F. A Scalable VLSI Architecture for Real-Time and Energy-Ecient Sparse Approximation in Compressive Sensing Systems. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/73p6w2zv

Not specified: Masters Thesis or Doctoral Dissertation

University of Colorado

11.
Peng, Ji.
Uncertainty Quantification via *Sparse* Polynomial Chaos Expansion.

Degree: PhD, Mechanical Engineering, 2015, University of Colorado

URL: https://scholar.colorado.edu/mcen_gradetds/112

► Uncertainty quantification (UQ) is an emerging research area that aims to develop methods for accurate predictions of quantities of interest (QoI's) from complex engineering…
(more)

Subjects/Keywords: Basis design; Compressive sampling; Polynomial chaos expansion; Sparse approximation; Uncertainty quantification; Applied Mathematics; Mechanical Engineering; Statistics and Probability

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

APA (6^{th} Edition):

Peng, J. (2015). Uncertainty Quantification via Sparse Polynomial Chaos Expansion. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/mcen_gradetds/112

Chicago Manual of Style (16^{th} Edition):

Peng, Ji. “Uncertainty Quantification via Sparse Polynomial Chaos Expansion.” 2015. Doctoral Dissertation, University of Colorado. Accessed February 27, 2021. https://scholar.colorado.edu/mcen_gradetds/112.

MLA Handbook (7^{th} Edition):

Peng, Ji. “Uncertainty Quantification via Sparse Polynomial Chaos Expansion.” 2015. Web. 27 Feb 2021.

Vancouver:

Peng J. Uncertainty Quantification via Sparse Polynomial Chaos Expansion. [Internet] [Doctoral dissertation]. University of Colorado; 2015. [cited 2021 Feb 27]. Available from: https://scholar.colorado.edu/mcen_gradetds/112.

Council of Science Editors:

Peng J. Uncertainty Quantification via Sparse Polynomial Chaos Expansion. [Doctoral Dissertation]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/mcen_gradetds/112

University of Minnesota

12.
Razaviyayn, Meisam.
Successive convex *approximation*: analysis and applications.

Degree: PhD, Electrical Engineering, 2014, University of Minnesota

URL: http://hdl.handle.net/11299/163884

► The block coordinate descent (BCD) method is widely used for minimizing a continuous function f of several block variables. At each iteration of this method,…
(more)

Subjects/Keywords: Beamformer Design; Convex Optimization; Heterogeneous Networks; Sparse Dictionary Leaning; Successive Convex Approximation; Successive Upper-bound Minimization

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

APA (6^{th} Edition):

Razaviyayn, M. (2014). Successive convex approximation: analysis and applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/163884

Chicago Manual of Style (16^{th} Edition):

Razaviyayn, Meisam. “Successive convex approximation: analysis and applications.” 2014. Doctoral Dissertation, University of Minnesota. Accessed February 27, 2021. http://hdl.handle.net/11299/163884.

MLA Handbook (7^{th} Edition):

Razaviyayn, Meisam. “Successive convex approximation: analysis and applications.” 2014. Web. 27 Feb 2021.

Vancouver:

Razaviyayn M. Successive convex approximation: analysis and applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/11299/163884.

Council of Science Editors:

Razaviyayn M. Successive convex approximation: analysis and applications. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/163884

University of Melbourne

13. Qadar, Muhammad Ali. Adaptive canonical correlation analysis methods for effective fMRI data analysis.

Degree: 2018, University of Melbourne

URL: http://hdl.handle.net/11343/225001

► Functional magnetic resonance imaging (fMRI) is a powerful non-invasive technique that enables monitoring blood oxygenation level dependent (BOLD) contrasts as a proxy for neuronal activity.…
(more)

Subjects/Keywords: canonical correlation analysis; functional magnetic resonance imaging; sparse decomposition; basis expansion; regularization; 2DCCA; rank-1 approximation; group analysis

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

APA (6^{th} Edition):

Qadar, M. A. (2018). Adaptive canonical correlation analysis methods for effective fMRI data analysis. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/225001

Chicago Manual of Style (16^{th} Edition):

Qadar, Muhammad Ali. “Adaptive canonical correlation analysis methods for effective fMRI data analysis.” 2018. Doctoral Dissertation, University of Melbourne. Accessed February 27, 2021. http://hdl.handle.net/11343/225001.

MLA Handbook (7^{th} Edition):

Qadar, Muhammad Ali. “Adaptive canonical correlation analysis methods for effective fMRI data analysis.” 2018. Web. 27 Feb 2021.

Vancouver:

Qadar MA. Adaptive canonical correlation analysis methods for effective fMRI data analysis. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/11343/225001.

Council of Science Editors:

Qadar MA. Adaptive canonical correlation analysis methods for effective fMRI data analysis. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/225001

14.
Fair, Kaitlin Lindsay.
A biologically plausible *sparse* *approximation* solver on neuromorphic hardware.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

URL: http://hdl.handle.net/1853/59782

► We develop a novel design methodology to map the biologically plausible Locally Competitive Algorithm (LCA) to the brain-inspired TrueNorth chip to solve for the *sparse*…
(more)

Subjects/Keywords: Neuromorphic; Bio-inspired; TrueNorth; Sparsity; Sparse approximation

…node dynamics of an LCA system. . . . . . . . . . . . . . . 16
2.9
A *sparse* *approximation*… …from an
overcomplete dictionary.
The *sparse* *approximation* problem can be solved using the… …*sparse* *approximation*
of a signal, where the signal is described as a linear combination of a… …dictionary size.
*Sparse* *approximation* solvers implemented on low-power hardware offer opportunities… …in real-world embedded systems. An efficient *sparse* *approximation* solver implemented on…

Record Details Similar Records

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

APA (6^{th} Edition):

Fair, K. L. (2017). A biologically plausible sparse approximation solver on neuromorphic hardware. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59782

Chicago Manual of Style (16^{th} Edition):

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Doctoral Dissertation, Georgia Tech. Accessed February 27, 2021. http://hdl.handle.net/1853/59782.

MLA Handbook (7^{th} Edition):

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Web. 27 Feb 2021.

Vancouver:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1853/59782.

Council of Science Editors:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59782

15. Jo, Jason. Structured low complexity data mining.

Degree: PhD, Mathematics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/31510

► Due to the rapidly increasing dimensionality of modern datasets many classical *approximation* algorithms have run into severe computational bottlenecks. This has often been referred to…
(more)

Subjects/Keywords: Greedy sparse approximation; Weighted matrix completion

…*sparse* *approximation* of dense Power Law Signals using m = 128 measurements. Best viewed in… …enjoy structured *sparse* *approximation* guarantees, these methods all suffer from the fact that… …complexity and improvement in *approximation* accuracy.
3
Chapter 2
Structured *Sparse* Solutions to… …best s-*sparse* *approximation* [1] no such method
has been developed for the weighted… …x5B;12] is presented to solve the weighted *sparse* *approximation* problem. We emphasize…

Record Details Similar Records

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

APA (6^{th} Edition):

Jo, J. (2015). Structured low complexity data mining. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31510

Chicago Manual of Style (16^{th} Edition):

Jo, Jason. “Structured low complexity data mining.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed February 27, 2021. http://hdl.handle.net/2152/31510.

MLA Handbook (7^{th} Edition):

Jo, Jason. “Structured low complexity data mining.” 2015. Web. 27 Feb 2021.

Vancouver:

Jo J. Structured low complexity data mining. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2152/31510.

Council of Science Editors:

Jo J. Structured low complexity data mining. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31510

NSYSU

16.
Chang, Feng-cheng.
Novel Gaussian Integer *Sparse* Code Multiple Access.

Degree: Master, Communications Engineering, 2016, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0722116-110404

► *Sparse* code multiple access (SCMA) is proposed for 5th generation mobile networks. Because SCMA has good spectrum efficiency. Each user is assigned a SCMA codebook,…
(more)

Subjects/Keywords: Peak to Average Power Ratio; Gaussian Approximation of Interference; Sparse Gaussian Integer Perfect Sequence; Message Passing Algorithm; Codebook Design; SCMA; Multiple User Detector

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

APA (6^{th} Edition):

Chang, F. (2016). Novel Gaussian Integer Sparse Code Multiple Access. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0722116-110404

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Chang, Feng-cheng. “Novel Gaussian Integer Sparse Code Multiple Access.” 2016. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0722116-110404.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Chang, Feng-cheng. “Novel Gaussian Integer Sparse Code Multiple Access.” 2016. Web. 27 Feb 2021.

Vancouver:

Chang F. Novel Gaussian Integer Sparse Code Multiple Access. [Internet] [Thesis]. NSYSU; 2016. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0722116-110404.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chang F. Novel Gaussian Integer Sparse Code Multiple Access. [Thesis]. NSYSU; 2016. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0722116-110404

Not specified: Masters Thesis or Doctoral Dissertation

17. Ribeiro, Flávio Protásio. Arrays de microfones para medida de campos acústicos.

Degree: PhD, Sistemas Eletrônicos, 2012, University of São Paulo

URL: http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26032012-115753/ ;

►

Imageamento acústico é um problema computacionalmente caro e mal-condicionado, que envolve estimar distribuições de fontes com grandes arranjos de microfones. O método clássico para imageamento… (more)

Subjects/Keywords: Acoustic imaging; Aproximação de Kronecker; Array processing; Array processing; Fast transform; Imagens acústicas; Kronecker approximation; Mínimos quadrados regularizados; Reconstrução esparsa; Regularized least squares; Sparse reconstruction; Transformadas rápidas

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

APA (6^{th} Edition):

Ribeiro, F. P. (2012). Arrays de microfones para medida de campos acústicos. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26032012-115753/ ;

Chicago Manual of Style (16^{th} Edition):

Ribeiro, Flávio Protásio. “Arrays de microfones para medida de campos acústicos.” 2012. Doctoral Dissertation, University of São Paulo. Accessed February 27, 2021. http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26032012-115753/ ;.

MLA Handbook (7^{th} Edition):

Ribeiro, Flávio Protásio. “Arrays de microfones para medida de campos acústicos.” 2012. Web. 27 Feb 2021.

Vancouver:

Ribeiro FP. Arrays de microfones para medida de campos acústicos. [Internet] [Doctoral dissertation]. University of São Paulo; 2012. [cited 2021 Feb 27]. Available from: http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26032012-115753/ ;.

Council of Science Editors:

Ribeiro FP. Arrays de microfones para medida de campos acústicos. [Doctoral Dissertation]. University of São Paulo; 2012. Available from: http://www.teses.usp.br/teses/disponiveis/3/3142/tde-26032012-115753/ ;

Brno University of Technology

18.
Hrbáček, Radek.
Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci: Exploitng *sparse* signal representations in capturing and recovery of nuclear magnetic resonance data.

Degree: 2019, Brno University of Technology

URL: http://hdl.handle.net/11012/26517

► This thesis deals with the nuclear magnetic resonance field, especially spectroscopy and spectroscopy imaging, *sparse* signal representation and low-rank *approximation* approaches. Spectroscopy imaging methods are…
(more)

Subjects/Keywords: nukleární magnetická rezonance; spektroskopie; spektroskopické zobrazování; řídká reprezentace signálů; komprimované snímání; aproximace s nízkou hodností; nuclear magnetic resonance; spectroscopy; spectroscopy imaging; sparse signal representation; compressed sensing; low-rank approximation

Record Details Similar Records

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APA (6^{th} Edition):

Hrbáček, R. (2019). Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci: Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/26517

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Hrbáček, Radek. “Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci: Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data.” 2019. Thesis, Brno University of Technology. Accessed February 27, 2021. http://hdl.handle.net/11012/26517.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hrbáček, Radek. “Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci: Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data.” 2019. Web. 27 Feb 2021.

Vancouver:

Hrbáček R. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci: Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/11012/26517.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hrbáček R. Využití řídké reprezentace signálu při snímání a rekonstrukci v nukleární magnetické rezonanci: Exploitng sparse signal representations in capturing and recovery of nuclear magnetic resonance data. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/26517

Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

19. Shapero, Samuel Andre. Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions.

Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech

URL: http://hdl.handle.net/1853/51719

► *Sparse* *approximation* is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous…
(more)

Subjects/Keywords: Regularized least-squares; Sparse approximation; Analog circuits; FPAA; Neural network; Hopfield network; Locally competitive algorithm (LCA); Least squares; Bayesian statistical decision theory; Analog computers

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

APA (6^{th} Edition):

Shapero, S. A. (2013). Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51719

Chicago Manual of Style (16^{th} Edition):

Shapero, Samuel Andre. “Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions.” 2013. Doctoral Dissertation, Georgia Tech. Accessed February 27, 2021. http://hdl.handle.net/1853/51719.

MLA Handbook (7^{th} Edition):

Shapero, Samuel Andre. “Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions.” 2013. Web. 27 Feb 2021.

Vancouver:

Shapero SA. Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1853/51719.

Council of Science Editors:

Shapero SA. Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/51719

University of Edinburgh

20.
Yaghoobi Vaighan, Mehrdad.
Adaptive *sparse* coding and dictionary selection.

Degree: PhD, 2010, University of Edinburgh

URL: http://hdl.handle.net/1842/4070

► The *sparse* coding is *approximation*/representation of signals with the minimum number of coefficients using an overcomplete set of elementary functions. This kind of approximations/ representations…
(more)

Subjects/Keywords: 621.382; sparse approximation; dictionary learning; inverse problems; dictionary design; gradient based optimization

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APA (6^{th} Edition):

Yaghoobi Vaighan, M. (2010). Adaptive sparse coding and dictionary selection. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/4070

Chicago Manual of Style (16^{th} Edition):

Yaghoobi Vaighan, Mehrdad. “Adaptive sparse coding and dictionary selection.” 2010. Doctoral Dissertation, University of Edinburgh. Accessed February 27, 2021. http://hdl.handle.net/1842/4070.

MLA Handbook (7^{th} Edition):

Yaghoobi Vaighan, Mehrdad. “Adaptive sparse coding and dictionary selection.” 2010. Web. 27 Feb 2021.

Vancouver:

Yaghoobi Vaighan M. Adaptive sparse coding and dictionary selection. [Internet] [Doctoral dissertation]. University of Edinburgh; 2010. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1842/4070.

Council of Science Editors:

Yaghoobi Vaighan M. Adaptive sparse coding and dictionary selection. [Doctoral Dissertation]. University of Edinburgh; 2010. Available from: http://hdl.handle.net/1842/4070

University of South Carolina

21.
Savu, Daniel.
*Sparse**Approximation* In Banach Spaces.

Degree: PhD, Mathematics, 2009, University of South Carolina

URL: https://scholarcommons.sc.edu/etd/90

► The *sparse* *approximation* problems ask for complete recovery of functions in a given space that are supported by few of the elements of a…
(more)

Subjects/Keywords: Mathematics; Physical Sciences and Mathematics; Approximation; Banach; Compressive; Greedy; Lebesgue-type; Sparse

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APA (6^{th} Edition):

Savu, D. (2009). Sparse Approximation In Banach Spaces. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/90

Chicago Manual of Style (16^{th} Edition):

Savu, Daniel. “Sparse Approximation In Banach Spaces.” 2009. Doctoral Dissertation, University of South Carolina. Accessed February 27, 2021. https://scholarcommons.sc.edu/etd/90.

MLA Handbook (7^{th} Edition):

Savu, Daniel. “Sparse Approximation In Banach Spaces.” 2009. Web. 27 Feb 2021.

Vancouver:

Savu D. Sparse Approximation In Banach Spaces. [Internet] [Doctoral dissertation]. University of South Carolina; 2009. [cited 2021 Feb 27]. Available from: https://scholarcommons.sc.edu/etd/90.

Council of Science Editors:

Savu D. Sparse Approximation In Banach Spaces. [Doctoral Dissertation]. University of South Carolina; 2009. Available from: https://scholarcommons.sc.edu/etd/90

University of South Carolina

22.
Zheltov, Pavel.
Additive Lebesgue-Type Inequalities for Greedy * Approximation*.

Degree: PhD, Mathematics, 2010, University of South Carolina

URL: https://scholarcommons.sc.edu/etd/430

► In the *approximation* theory we are commonly interested in finding a best possible approximant to a function (also thought of as a signal or…
(more)

Subjects/Keywords: Mathematics; Physical Sciences and Mathematics; algorithms; approximation; compressed; greedy; sensing; sparse

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APA (6^{th} Edition):

Zheltov, P. (2010). Additive Lebesgue-Type Inequalities for Greedy Approximation. (Doctoral Dissertation). University of South Carolina. Retrieved from https://scholarcommons.sc.edu/etd/430

Chicago Manual of Style (16^{th} Edition):

Zheltov, Pavel. “Additive Lebesgue-Type Inequalities for Greedy Approximation.” 2010. Doctoral Dissertation, University of South Carolina. Accessed February 27, 2021. https://scholarcommons.sc.edu/etd/430.

MLA Handbook (7^{th} Edition):

Zheltov, Pavel. “Additive Lebesgue-Type Inequalities for Greedy Approximation.” 2010. Web. 27 Feb 2021.

Vancouver:

Zheltov P. Additive Lebesgue-Type Inequalities for Greedy Approximation. [Internet] [Doctoral dissertation]. University of South Carolina; 2010. [cited 2021 Feb 27]. Available from: https://scholarcommons.sc.edu/etd/430.

Council of Science Editors:

Zheltov P. Additive Lebesgue-Type Inequalities for Greedy Approximation. [Doctoral Dissertation]. University of South Carolina; 2010. Available from: https://scholarcommons.sc.edu/etd/430

The Ohio State University

23.
Zheng, Zizhan.
* Sparse* Deployment of Large Scale Wireless Networks for
Mobile Targets.

Degree: PhD, Computer Science and Engineering, 2010, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1275444923

► Deploying wireless networks at large scale is challenging. Despitevarious effort made in the design of coverage schemes and deploymentalgorithms with <i>static</i> targets in mind,…
(more)

Subjects/Keywords: Computer Science; Wireless networks; sensor networks; coverage; sparse coverage; approximation algorithms

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

APA (6^{th} Edition):

Zheng, Z. (2010). Sparse Deployment of Large Scale Wireless Networks for Mobile Targets. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1275444923

Chicago Manual of Style (16^{th} Edition):

Zheng, Zizhan. “Sparse Deployment of Large Scale Wireless Networks for Mobile Targets.” 2010. Doctoral Dissertation, The Ohio State University. Accessed February 27, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275444923.

MLA Handbook (7^{th} Edition):

Zheng, Zizhan. “Sparse Deployment of Large Scale Wireless Networks for Mobile Targets.” 2010. Web. 27 Feb 2021.

Vancouver:

Zheng Z. Sparse Deployment of Large Scale Wireless Networks for Mobile Targets. [Internet] [Doctoral dissertation]. The Ohio State University; 2010. [cited 2021 Feb 27]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1275444923.

Council of Science Editors:

Zheng Z. Sparse Deployment of Large Scale Wireless Networks for Mobile Targets. [Doctoral Dissertation]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1275444923

24. Hu, K. (author). Compressive Sensing for Near-field Source Localization.

Degree: 2014, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:f0e70c18-52d1-4c9b-a611-dbd534a0190c

►

Near-field source localization is an important aspect in many diverse areas such as acoustics, seismology, to list a few. The planar wave assumption frequently used… (more)

Subjects/Keywords: Near-field source localization; compressive sensing; Fresnel approximation; correlation; sparse modeling; joint sparse recovery; EIV model

…the Fresnel
*approximation*).
Chapter 4: *Sparse* recovery techniques for near-field source… …*sparse* regression with or without Fresnel *approximation* for
a single snapshot incurs the same… …*sparse* recovery . . .
.
.
.
.
5
5
5
6
7
3 Signal Model
3.1 Spherical wavefront model… …3.2 Fresnel *approximation* . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
11
12
4… …*Sparse* recovery techniques for near-field source localization:
based model
4.1 Grid-based model…

Record Details Similar Records

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

APA (6^{th} Edition):

Hu, K. (. (2014). Compressive Sensing for Near-field Source Localization. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f0e70c18-52d1-4c9b-a611-dbd534a0190c

Chicago Manual of Style (16^{th} Edition):

Hu, K (author). “Compressive Sensing for Near-field Source Localization.” 2014. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:f0e70c18-52d1-4c9b-a611-dbd534a0190c.

MLA Handbook (7^{th} Edition):

Hu, K (author). “Compressive Sensing for Near-field Source Localization.” 2014. Web. 27 Feb 2021.

Vancouver:

Hu K(. Compressive Sensing for Near-field Source Localization. [Internet] [Masters thesis]. Delft University of Technology; 2014. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:f0e70c18-52d1-4c9b-a611-dbd534a0190c.

Council of Science Editors:

Hu K(. Compressive Sensing for Near-field Source Localization. [Masters Thesis]. Delft University of Technology; 2014. Available from: http://resolver.tudelft.nl/uuid:f0e70c18-52d1-4c9b-a611-dbd534a0190c

25. Spencer, Timothy Scott. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.

Degree: PhD, Mathematics, 2017, Georgia Tech

URL: http://hdl.handle.net/1853/58730

► The first part of this dissertation explores the application of dominating operators in harmonic analysis by *sparse* operators. We present preliminary results on dominating certain…
(more)

Subjects/Keywords: Weighted inequalities; Sparse operators; Sparse approximation; 1-bit sensing; Restricted isometry property; Quasi-isometry; Noisy embeddings

…application of dominating operators in harmonic analysis by *sparse* operators. In the second chapter… …we introduce
*sparse* operators. Presented therein are preliminary results on dominating… …certain
operators by *sparse* operators, and we also prove several analogous results for
other… …operators. We make use of the *sparse* domination
introduced in Chapter 2 to derive weighted… …domination by *sparse* operators, but continues the
study of fractional integral operators. There is…

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

APA (6^{th} Edition):

Spencer, T. S. (2017). Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58730

Chicago Manual of Style (16^{th} Edition):

Spencer, Timothy Scott. “Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.” 2017. Doctoral Dissertation, Georgia Tech. Accessed February 27, 2021. http://hdl.handle.net/1853/58730.

MLA Handbook (7^{th} Edition):

Spencer, Timothy Scott. “Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.” 2017. Web. 27 Feb 2021.

Vancouver:

Spencer TS. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1853/58730.

Council of Science Editors:

Spencer TS. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58730

University of Michigan

26.
Maleh, Ray.
Efficient *Sparse* *Approximation* Methods for Medical Imaging.

Degree: PhD, Applied and Interdisciplinary Mathematics, 2009, University of Michigan

URL: http://hdl.handle.net/2027.42/64764

► For thousands of years, doctors had to face the daunting task of diagnosing and treating all sorts of medical ailments without the ability to view…
(more)

Subjects/Keywords: Medical Imaging; Sparse Approximation; Compressive Sensing; Parallel Approximation; Matching Pursuit; Parallel Excitation; Engineering; Health Sciences; Science

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

APA (6^{th} Edition):

Maleh, R. (2009). Efficient Sparse Approximation Methods for Medical Imaging. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/64764

Chicago Manual of Style (16^{th} Edition):

Maleh, Ray. “Efficient Sparse Approximation Methods for Medical Imaging.” 2009. Doctoral Dissertation, University of Michigan. Accessed February 27, 2021. http://hdl.handle.net/2027.42/64764.

MLA Handbook (7^{th} Edition):

Maleh, Ray. “Efficient Sparse Approximation Methods for Medical Imaging.” 2009. Web. 27 Feb 2021.

Vancouver:

Maleh R. Efficient Sparse Approximation Methods for Medical Imaging. [Internet] [Doctoral dissertation]. University of Michigan; 2009. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2027.42/64764.

Council of Science Editors:

Maleh R. Efficient Sparse Approximation Methods for Medical Imaging. [Doctoral Dissertation]. University of Michigan; 2009. Available from: http://hdl.handle.net/2027.42/64764

27. Mortada, Hassan. Separation of parameterized and delayed sources : application to spectroscopic and multispectral data : Séparation de sources paramétriques et retardées : application aux données spectroscopiques et multispectrales.

Degree: Docteur es, Traitement du signal et des images, 2018, Université de Strasbourg

URL: http://www.theses.fr/2018STRAD051

►

Ce travail est motivé par la spectroscopie de photoélectrons et l'étude de la cinématique des galaxies où les données correspondent respectivement à une séquence temporelle… (more)

Subjects/Keywords: Séparation de source retardées; Mélange anéchoique; Approximation parcimonieuse; Décomposition de spectres; Images multispectrales; B-splines; Delayed source separation; Anechoic mixing; Sparse approximation; Spectra decomposition; Multispectral images; B-splines; 006.42; 519.5; 621.36

Record Details Similar Records

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

APA (6^{th} Edition):

Mortada, H. (2018). Separation of parameterized and delayed sources : application to spectroscopic and multispectral data : Séparation de sources paramétriques et retardées : application aux données spectroscopiques et multispectrales. (Doctoral Dissertation). Université de Strasbourg. Retrieved from http://www.theses.fr/2018STRAD051

Chicago Manual of Style (16^{th} Edition):

Mortada, Hassan. “Separation of parameterized and delayed sources : application to spectroscopic and multispectral data : Séparation de sources paramétriques et retardées : application aux données spectroscopiques et multispectrales.” 2018. Doctoral Dissertation, Université de Strasbourg. Accessed February 27, 2021. http://www.theses.fr/2018STRAD051.

MLA Handbook (7^{th} Edition):

Mortada, Hassan. “Separation of parameterized and delayed sources : application to spectroscopic and multispectral data : Séparation de sources paramétriques et retardées : application aux données spectroscopiques et multispectrales.” 2018. Web. 27 Feb 2021.

Vancouver:

Mortada H. Separation of parameterized and delayed sources : application to spectroscopic and multispectral data : Séparation de sources paramétriques et retardées : application aux données spectroscopiques et multispectrales. [Internet] [Doctoral dissertation]. Université de Strasbourg; 2018. [cited 2021 Feb 27]. Available from: http://www.theses.fr/2018STRAD051.

Council of Science Editors:

Mortada H. Separation of parameterized and delayed sources : application to spectroscopic and multispectral data : Séparation de sources paramétriques et retardées : application aux données spectroscopiques et multispectrales. [Doctoral Dissertation]. Université de Strasbourg; 2018. Available from: http://www.theses.fr/2018STRAD051

28.
Prater, Ashley.
Discrete *Sparse* Fourier Hermite Approximations in High Dimensions.

Degree: PhD, Mathematics, 2012, Syracuse University

URL: https://surface.syr.edu/mat_etd/70

► In this dissertation, the discrete *sparse* Fourier Hermite *approximation* of a function in a specified Hilbert space of arbitrary dimension is defined, and theoretical…
(more)

Subjects/Keywords: Fourier Hermite Series; Generalized Fourier Series; Hyperbolic Cross Sparse Index; Multiscale Quadrature; Pseudospectral Approximation; Spectral Approximation; Mathematics

…this Fourier Hermite basis, a *sparse*, discrete *approximation* is described,
and bounds for the… …that the spectral *approximation* is
a good *approximation* of f .
CHAPTER 2. *SPARSE* FOURIER… …FN f ||2
||f ||2
ω ≤ N
κs as desired.
2.3
Hyperbolic Cross *Sparse*-Grid *Approximation*… …coefficients cn are the same as in Definition 2.9.
The *sparse* grid *approximation* competes well with… …53
4.4
Discrete *Sparse* Approximations…

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

APA (6^{th} Edition):

Prater, A. (2012). Discrete Sparse Fourier Hermite Approximations in High Dimensions. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/mat_etd/70

Chicago Manual of Style (16^{th} Edition):

Prater, Ashley. “Discrete Sparse Fourier Hermite Approximations in High Dimensions.” 2012. Doctoral Dissertation, Syracuse University. Accessed February 27, 2021. https://surface.syr.edu/mat_etd/70.

MLA Handbook (7^{th} Edition):

Prater, Ashley. “Discrete Sparse Fourier Hermite Approximations in High Dimensions.” 2012. Web. 27 Feb 2021.

Vancouver:

Prater A. Discrete Sparse Fourier Hermite Approximations in High Dimensions. [Internet] [Doctoral dissertation]. Syracuse University; 2012. [cited 2021 Feb 27]. Available from: https://surface.syr.edu/mat_etd/70.

Council of Science Editors:

Prater A. Discrete Sparse Fourier Hermite Approximations in High Dimensions. [Doctoral Dissertation]. Syracuse University; 2012. Available from: https://surface.syr.edu/mat_etd/70

29.
LI JIA.
Wavelet *Approximation* and Image Restoration.

Degree: 2013, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/49127

Subjects/Keywords: Sparse approximation; wavelet tight frame; image restoration; split Bregman algorithm; alternating minimization algorithm; quasi-projection operator

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

APA (6^{th} Edition):

JIA, L. (2013). Wavelet Approximation and Image Restoration. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/49127

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

JIA, LI. “Wavelet Approximation and Image Restoration.” 2013. Thesis, National University of Singapore. Accessed February 27, 2021. http://scholarbank.nus.edu.sg/handle/10635/49127.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

JIA, LI. “Wavelet Approximation and Image Restoration.” 2013. Web. 27 Feb 2021.

Vancouver:

JIA L. Wavelet Approximation and Image Restoration. [Internet] [Thesis]. National University of Singapore; 2013. [cited 2021 Feb 27]. Available from: http://scholarbank.nus.edu.sg/handle/10635/49127.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

JIA L. Wavelet Approximation and Image Restoration. [Thesis]. National University of Singapore; 2013. Available from: http://scholarbank.nus.edu.sg/handle/10635/49127

Not specified: Masters Thesis or Doctoral Dissertation

University of Florida

30.
Sakhaee, Elham.
Joint Linear Inverse Problems with *Sparse* Solutions: Theory and Applications.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2017, University of Florida

URL: https://ufdc.ufl.edu/UFE0051036

Subjects/Keywords: compressed-sensing; computed-tomography; image-reconstruction; inverse-problems; l1-regularized-problems; medical-imaging; sparse-approximation

Record Details Similar Records

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

APA (6^{th} Edition):

Sakhaee, E. (2017). Joint Linear Inverse Problems with Sparse Solutions: Theory and Applications. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0051036

Chicago Manual of Style (16^{th} Edition):

Sakhaee, Elham. “Joint Linear Inverse Problems with Sparse Solutions: Theory and Applications.” 2017. Doctoral Dissertation, University of Florida. Accessed February 27, 2021. https://ufdc.ufl.edu/UFE0051036.

MLA Handbook (7^{th} Edition):

Sakhaee, Elham. “Joint Linear Inverse Problems with Sparse Solutions: Theory and Applications.” 2017. Web. 27 Feb 2021.

Vancouver:

Sakhaee E. Joint Linear Inverse Problems with Sparse Solutions: Theory and Applications. [Internet] [Doctoral dissertation]. University of Florida; 2017. [cited 2021 Feb 27]. Available from: https://ufdc.ufl.edu/UFE0051036.

Council of Science Editors:

Sakhaee E. Joint Linear Inverse Problems with Sparse Solutions: Theory and Applications. [Doctoral Dissertation]. University of Florida; 2017. Available from: https://ufdc.ufl.edu/UFE0051036