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You searched for subject:(Sparse approximation). Showing records 1 – 30 of 38 total matches.

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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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 "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 (6th 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 (16th 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 (7th 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.

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

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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

  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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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… 

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APA (6th 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 (16th 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 (7th 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

 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… 

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APA (6th 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 (16th 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 (7th 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

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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

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… 

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

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

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APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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

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APA (6th 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 (16th 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 (7th 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

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