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You searched for subject:(Sparsity). Showing records 1 – 30 of 277 total matches.

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University of Ontario Institute of Technology

1. Hamidi, Shahrokh. Sparse signal representation based algorithms with application to ultrasonic array imaging.

Degree: 2016, University of Ontario Institute of Technology

 We address one- and two-layer ultrasonic array imaging. We use an array of transducers to inspect the internal structure of a given specimen. In the… (more)

Subjects/Keywords: Sparsity; Ultrasonic array imaging

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

APA (6th Edition):

Hamidi, S. (2016). Sparse signal representation based algorithms with application to ultrasonic array imaging. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/672

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):

Hamidi, Shahrokh. “Sparse signal representation based algorithms with application to ultrasonic array imaging.” 2016. Thesis, University of Ontario Institute of Technology. Accessed June 24, 2019. http://hdl.handle.net/10155/672.

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

MLA Handbook (7th Edition):

Hamidi, Shahrokh. “Sparse signal representation based algorithms with application to ultrasonic array imaging.” 2016. Web. 24 Jun 2019.

Vancouver:

Hamidi S. Sparse signal representation based algorithms with application to ultrasonic array imaging. [Internet] [Thesis]. University of Ontario Institute of Technology; 2016. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10155/672.

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

Council of Science Editors:

Hamidi S. Sparse signal representation based algorithms with application to ultrasonic array imaging. [Thesis]. University of Ontario Institute of Technology; 2016. Available from: http://hdl.handle.net/10155/672

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


Texas A&M University

2. Apaydin, Meltem. Phase Retrieval of Sparse Signals from Magnitude Information.

Degree: 2014, Texas A&M University

 The ability to recover the phase information of a signal of interest from a measurement process plays an important role in many practical applications. When… (more)

Subjects/Keywords: phase retrieval; compressive sensing; sparsity

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APA (6th Edition):

Apaydin, M. (2014). Phase Retrieval of Sparse Signals from Magnitude Information. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/153388

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):

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Thesis, Texas A&M University. Accessed June 24, 2019. http://hdl.handle.net/1969.1/153388.

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

MLA Handbook (7th Edition):

Apaydin, Meltem. “Phase Retrieval of Sparse Signals from Magnitude Information.” 2014. Web. 24 Jun 2019.

Vancouver:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Internet] [Thesis]. Texas A&M University; 2014. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1969.1/153388.

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

Council of Science Editors:

Apaydin M. Phase Retrieval of Sparse Signals from Magnitude Information. [Thesis]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/153388

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


Penn State University

3. Tutuk, Fatih. Sparse Linear Time Invariant System Identification Using Weighted Lasso.

Degree: MS, Electrical Engineering, 2014, Penn State University

 In this thesis, the identification of a single-input single-output (SISO) linear time invariant (LTI) dynamical system from a finite set of completely known input signals… (more)

Subjects/Keywords: System Identification; Sparsity; Lasso; Reweighting

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APA (6th Edition):

Tutuk, F. (2014). Sparse Linear Time Invariant System Identification Using Weighted Lasso. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/23710

Chicago Manual of Style (16th Edition):

Tutuk, Fatih. “Sparse Linear Time Invariant System Identification Using Weighted Lasso.” 2014. Masters Thesis, Penn State University. Accessed June 24, 2019. https://etda.libraries.psu.edu/catalog/23710.

MLA Handbook (7th Edition):

Tutuk, Fatih. “Sparse Linear Time Invariant System Identification Using Weighted Lasso.” 2014. Web. 24 Jun 2019.

Vancouver:

Tutuk F. Sparse Linear Time Invariant System Identification Using Weighted Lasso. [Internet] [Masters thesis]. Penn State University; 2014. [cited 2019 Jun 24]. Available from: https://etda.libraries.psu.edu/catalog/23710.

Council of Science Editors:

Tutuk F. Sparse Linear Time Invariant System Identification Using Weighted Lasso. [Masters Thesis]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/23710


UCLA

4. Flynn, John Joseph. Learning a simplicial structure using sparsity.

Degree: Statistics, 2014, UCLA

 We discuss an application of sparsity to manifold learning. We show that the activation patterns of an over-complete basis can be used to build a… (more)

Subjects/Keywords: Statistics; manifold learning; simplices; sparsity

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APA (6th Edition):

Flynn, J. J. (2014). Learning a simplicial structure using sparsity. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/52v7g1sp

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):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Thesis, UCLA. Accessed June 24, 2019. http://www.escholarship.org/uc/item/52v7g1sp.

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

MLA Handbook (7th Edition):

Flynn, John Joseph. “Learning a simplicial structure using sparsity.” 2014. Web. 24 Jun 2019.

Vancouver:

Flynn JJ. Learning a simplicial structure using sparsity. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Jun 24]. Available from: http://www.escholarship.org/uc/item/52v7g1sp.

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

Council of Science Editors:

Flynn JJ. Learning a simplicial structure using sparsity. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/52v7g1sp

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


Texas A&M University

5. Phillipson, Kaitlyn Rose. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems.

Degree: PhD, Mathematics, 2016, Texas A&M University

 Computational algebraic geometry is the study of roots of polynomials and polynomial systems. We are familiar with the notion of degree, but there are other… (more)

Subjects/Keywords: sparsity; sums of squares; approximations

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APA (6th Edition):

Phillipson, K. R. (2016). Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157880

Chicago Manual of Style (16th Edition):

Phillipson, Kaitlyn Rose. “Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems.” 2016. Doctoral Dissertation, Texas A&M University. Accessed June 24, 2019. http://hdl.handle.net/1969.1/157880.

MLA Handbook (7th Edition):

Phillipson, Kaitlyn Rose. “Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems.” 2016. Web. 24 Jun 2019.

Vancouver:

Phillipson KR. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1969.1/157880.

Council of Science Editors:

Phillipson KR. Quantitative Aspects of Sums of Squares and Sparse Polynomial Systems. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157880


University of New Mexico

6. Potluru, Vamsi. Matrix Factorization: Nonnegativity, Sparsity and Independence.

Degree: Department of Computer Science, 2014, University of New Mexico

 Matrix factorization arises in a wide range of application domains and is useful for extracting the latent features in the dataset. Examples include recommender systems,… (more)

Subjects/Keywords: Matrix factorization; Nonnegativity; Sparsity; Independence

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APA (6th Edition):

Potluru, V. (2014). Matrix Factorization: Nonnegativity, Sparsity and Independence. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/24336

Chicago Manual of Style (16th Edition):

Potluru, Vamsi. “Matrix Factorization: Nonnegativity, Sparsity and Independence.” 2014. Doctoral Dissertation, University of New Mexico. Accessed June 24, 2019. http://hdl.handle.net/1928/24336.

MLA Handbook (7th Edition):

Potluru, Vamsi. “Matrix Factorization: Nonnegativity, Sparsity and Independence.” 2014. Web. 24 Jun 2019.

Vancouver:

Potluru V. Matrix Factorization: Nonnegativity, Sparsity and Independence. [Internet] [Doctoral dissertation]. University of New Mexico; 2014. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1928/24336.

Council of Science Editors:

Potluru V. Matrix Factorization: Nonnegativity, Sparsity and Independence. [Doctoral Dissertation]. University of New Mexico; 2014. Available from: http://hdl.handle.net/1928/24336


University of Alberta

7. Bonar, Christopher David. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data.

Degree: MS, Department of Physics, 2012, University of Alberta

 Local time-frequency analysis, also known as spectral decomposition, allows for a more detailed interpretation of time-series by providing the evolution of the frequency spectrum through… (more)

Subjects/Keywords: Group Sparsity; Inversion; Seismic; Local Time-Frequency Analysis; Sparsity; Spectral Decomposition

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APA (6th Edition):

Bonar, C. D. (2012). Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/ff365590j

Chicago Manual of Style (16th Edition):

Bonar, Christopher David. “Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data.” 2012. Masters Thesis, University of Alberta. Accessed June 24, 2019. https://era.library.ualberta.ca/files/ff365590j.

MLA Handbook (7th Edition):

Bonar, Christopher David. “Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data.” 2012. Web. 24 Jun 2019.

Vancouver:

Bonar CD. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2019 Jun 24]. Available from: https://era.library.ualberta.ca/files/ff365590j.

Council of Science Editors:

Bonar CD. Sparsity and Group Sparsity Constrained Inversion for Spectral Decomposition of Seismic Data. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/ff365590j


University of Canterbury

8. Wu, Bing. Exploiting data sparsity in parallel magnetic resonance imaging.

Degree: Electrical and Computer Engineering, 2010, University of Canterbury

 Magnetic resonance imaging (MRI) is a widely employed imaging modality that allows observation of the interior of human body. Compared to other imaging modalities such… (more)

Subjects/Keywords: Magnetic resonance imaging; image sparsity; parallel MRI

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APA (6th Edition):

Wu, B. (2010). Exploiting data sparsity in parallel magnetic resonance imaging. (Thesis). University of Canterbury. Retrieved from http://hdl.handle.net/10092/3914

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):

Wu, Bing. “Exploiting data sparsity in parallel magnetic resonance imaging.” 2010. Thesis, University of Canterbury. Accessed June 24, 2019. http://hdl.handle.net/10092/3914.

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

MLA Handbook (7th Edition):

Wu, Bing. “Exploiting data sparsity in parallel magnetic resonance imaging.” 2010. Web. 24 Jun 2019.

Vancouver:

Wu B. Exploiting data sparsity in parallel magnetic resonance imaging. [Internet] [Thesis]. University of Canterbury; 2010. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10092/3914.

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

Council of Science Editors:

Wu B. Exploiting data sparsity in parallel magnetic resonance imaging. [Thesis]. University of Canterbury; 2010. Available from: http://hdl.handle.net/10092/3914

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


University of Washington

9. Tank, Alex. Discovering Interactions in Multivariate Time Series.

Degree: PhD, 2018, University of Washington

 In large collections of multivariate time series it is of interest to determine interactions between each pair of time series. Classically, interactions between time series… (more)

Subjects/Keywords: Granger causality; sparsity; time series; Statistics; Statistics

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APA (6th Edition):

Tank, A. (2018). Discovering Interactions in Multivariate Time Series. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43162

Chicago Manual of Style (16th Edition):

Tank, Alex. “Discovering Interactions in Multivariate Time Series.” 2018. Doctoral Dissertation, University of Washington. Accessed June 24, 2019. http://hdl.handle.net/1773/43162.

MLA Handbook (7th Edition):

Tank, Alex. “Discovering Interactions in Multivariate Time Series.” 2018. Web. 24 Jun 2019.

Vancouver:

Tank A. Discovering Interactions in Multivariate Time Series. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1773/43162.

Council of Science Editors:

Tank A. Discovering Interactions in Multivariate Time Series. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/43162


Clemson University

10. Cooper, John. Sparsity Regularization in Diffuse Optical Tomography.

Degree: PhD, Mathematical Science, 2012, Clemson University

 The purpose of this dissertation is to improve image reconstruction in Diffuse Optical Tomography (DOT), a high contrast imaging modality that uses a near infrared… (more)

Subjects/Keywords: Optical; Regularization; Sparsity; Tomography; Applied Mathematics

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APA (6th Edition):

Cooper, J. (2012). Sparsity Regularization in Diffuse Optical Tomography. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/1000

Chicago Manual of Style (16th Edition):

Cooper, John. “Sparsity Regularization in Diffuse Optical Tomography.” 2012. Doctoral Dissertation, Clemson University. Accessed June 24, 2019. https://tigerprints.clemson.edu/all_dissertations/1000.

MLA Handbook (7th Edition):

Cooper, John. “Sparsity Regularization in Diffuse Optical Tomography.” 2012. Web. 24 Jun 2019.

Vancouver:

Cooper J. Sparsity Regularization in Diffuse Optical Tomography. [Internet] [Doctoral dissertation]. Clemson University; 2012. [cited 2019 Jun 24]. Available from: https://tigerprints.clemson.edu/all_dissertations/1000.

Council of Science Editors:

Cooper J. Sparsity Regularization in Diffuse Optical Tomography. [Doctoral Dissertation]. Clemson University; 2012. Available from: https://tigerprints.clemson.edu/all_dissertations/1000


Université Catholique de Louvain

11. Plumat, Jérôme. Image classification and reconstruction using Markov Random Field modeling and sparsity.

Degree: 2012, Université Catholique de Louvain

The medical imaging requires fast and accurate volume reconstruction. This may be used to evaluate, before any treatment, differences between previsions, formulated in an MRI… (more)

Subjects/Keywords: Volume reconstruction; Markov Random Field; Sparsity

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APA (6th Edition):

Plumat, J. (2012). Image classification and reconstruction using Markov Random Field modeling and sparsity. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/120111

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):

Plumat, Jérôme. “Image classification and reconstruction using Markov Random Field modeling and sparsity.” 2012. Thesis, Université Catholique de Louvain. Accessed June 24, 2019. http://hdl.handle.net/2078.1/120111.

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

MLA Handbook (7th Edition):

Plumat, Jérôme. “Image classification and reconstruction using Markov Random Field modeling and sparsity.” 2012. Web. 24 Jun 2019.

Vancouver:

Plumat J. Image classification and reconstruction using Markov Random Field modeling and sparsity. [Internet] [Thesis]. Université Catholique de Louvain; 2012. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/2078.1/120111.

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

Council of Science Editors:

Plumat J. Image classification and reconstruction using Markov Random Field modeling and sparsity. [Thesis]. Université Catholique de Louvain; 2012. Available from: http://hdl.handle.net/2078.1/120111

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


Virginia Tech

12. Lewis, Cannada Andrew. The Unreasonable Usefulness of Approximation by Linear Combination.

Degree: PhD, Chemistry, 2018, Virginia Tech

 Through the exploitation of data-sparsity  – a catch all term for savings gained from a variety of approximations – it is possible to reduce the computational… (more)

Subjects/Keywords: Electronic Structure; Data-Sparsity; Tensor; Reduced Scaling

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APA (6th Edition):

Lewis, C. A. (2018). The Unreasonable Usefulness of Approximation by Linear Combination. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83866

Chicago Manual of Style (16th Edition):

Lewis, Cannada Andrew. “The Unreasonable Usefulness of Approximation by Linear Combination.” 2018. Doctoral Dissertation, Virginia Tech. Accessed June 24, 2019. http://hdl.handle.net/10919/83866.

MLA Handbook (7th Edition):

Lewis, Cannada Andrew. “The Unreasonable Usefulness of Approximation by Linear Combination.” 2018. Web. 24 Jun 2019.

Vancouver:

Lewis CA. The Unreasonable Usefulness of Approximation by Linear Combination. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10919/83866.

Council of Science Editors:

Lewis CA. The Unreasonable Usefulness of Approximation by Linear Combination. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83866


University of British Columbia

13. Lebed, Evgeniy. Sparse signal recovery in a transform domain .

Degree: 2008, University of British Columbia

 The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last thirty years many transforms such… (more)

Subjects/Keywords: Wavelets; Transforms; Sparsity

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APA (6th Edition):

Lebed, E. (2008). Sparse signal recovery in a transform domain . (Thesis). University of British Columbia. Retrieved from http://hdl.handle.net/2429/4171

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):

Lebed, Evgeniy. “Sparse signal recovery in a transform domain .” 2008. Thesis, University of British Columbia. Accessed June 24, 2019. http://hdl.handle.net/2429/4171.

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

MLA Handbook (7th Edition):

Lebed, Evgeniy. “Sparse signal recovery in a transform domain .” 2008. Web. 24 Jun 2019.

Vancouver:

Lebed E. Sparse signal recovery in a transform domain . [Internet] [Thesis]. University of British Columbia; 2008. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/2429/4171.

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

Council of Science Editors:

Lebed E. Sparse signal recovery in a transform domain . [Thesis]. University of British Columbia; 2008. Available from: http://hdl.handle.net/2429/4171

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


Princeton University

14. Li, Chenchuan. Inference in Regressions with Many Controls .

Degree: PhD, 2016, Princeton University

 In this thesis, we consider inference on a scalar coefficient of interest in a linear regression model with many potential control variables. Without any constraint… (more)

Subjects/Keywords: Bound; Causal; Control; Inference; Regressions; Sparsity

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APA (6th Edition):

Li, C. (2016). Inference in Regressions with Many Controls . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01w3763925c

Chicago Manual of Style (16th Edition):

Li, Chenchuan. “Inference in Regressions with Many Controls .” 2016. Doctoral Dissertation, Princeton University. Accessed June 24, 2019. http://arks.princeton.edu/ark:/88435/dsp01w3763925c.

MLA Handbook (7th Edition):

Li, Chenchuan. “Inference in Regressions with Many Controls .” 2016. Web. 24 Jun 2019.

Vancouver:

Li C. Inference in Regressions with Many Controls . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Jun 24]. Available from: http://arks.princeton.edu/ark:/88435/dsp01w3763925c.

Council of Science Editors:

Li C. Inference in Regressions with Many Controls . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp01w3763925c


University of Waterloo

15. Yang, Shenghao. Split Cuts From Sparse Disjunctions.

Degree: 2019, University of Waterloo

 Cutting planes are one of the major techniques used in solving Mixed-Integer Linear Programming (MIP) models. Various types of cuts have long been exploited by… (more)

Subjects/Keywords: mixed-integer programming; split cuts; sparsity; decomposition

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APA (6th Edition):

Yang, S. (2019). Split Cuts From Sparse Disjunctions. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14363

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):

Yang, Shenghao. “Split Cuts From Sparse Disjunctions.” 2019. Thesis, University of Waterloo. Accessed June 24, 2019. http://hdl.handle.net/10012/14363.

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

MLA Handbook (7th Edition):

Yang, Shenghao. “Split Cuts From Sparse Disjunctions.” 2019. Web. 24 Jun 2019.

Vancouver:

Yang S. Split Cuts From Sparse Disjunctions. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10012/14363.

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

Council of Science Editors:

Yang S. Split Cuts From Sparse Disjunctions. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14363

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

16. Orchard, Peter Raymond. Sparse inverse covariance estimation in Gaussian graphical models.

Degree: PhD, 2014, University of Edinburgh

 One of the fundamental tasks in science is to find explainable relationships between observed phenomena. Recent work has addressed this problem by attempting to learn… (more)

Subjects/Keywords: sparsity; Gaussian; latent variables; copula; GWishart; HMC

Page 1 Page 2 Page 3 Page 4 Page 5 Page 6 Page 7 Sample image

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APA (6th Edition):

Orchard, P. R. (2014). Sparse inverse covariance estimation in Gaussian graphical models. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/9955

Chicago Manual of Style (16th Edition):

Orchard, Peter Raymond. “Sparse inverse covariance estimation in Gaussian graphical models.” 2014. Doctoral Dissertation, University of Edinburgh. Accessed June 24, 2019. http://hdl.handle.net/1842/9955.

MLA Handbook (7th Edition):

Orchard, Peter Raymond. “Sparse inverse covariance estimation in Gaussian graphical models.” 2014. Web. 24 Jun 2019.

Vancouver:

Orchard PR. Sparse inverse covariance estimation in Gaussian graphical models. [Internet] [Doctoral dissertation]. University of Edinburgh; 2014. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1842/9955.

Council of Science Editors:

Orchard PR. Sparse inverse covariance estimation in Gaussian graphical models. [Doctoral Dissertation]. University of Edinburgh; 2014. Available from: http://hdl.handle.net/1842/9955


Colorado School of Mines

17. Andrade de Almeida, Lucas José. Seismic data interpolation using sparsity constrained inversion.

Degree: MS(M.S.), Geophysics, 2017, Colorado School of Mines

 Missing data reconstruction is an ongoing challenge in seismic processing for incomplete and irregular acquisition. The problem of missing data negatively affects several important processing… (more)

Subjects/Keywords: Interpolation; Sparsity; Analysis; Synthesis; Seismic processing

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APA (6th Edition):

Andrade de Almeida, L. J. (2017). Seismic data interpolation using sparsity constrained inversion. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/171138

Chicago Manual of Style (16th Edition):

Andrade de Almeida, Lucas José. “Seismic data interpolation using sparsity constrained inversion.” 2017. Masters Thesis, Colorado School of Mines. Accessed June 24, 2019. http://hdl.handle.net/11124/171138.

MLA Handbook (7th Edition):

Andrade de Almeida, Lucas José. “Seismic data interpolation using sparsity constrained inversion.” 2017. Web. 24 Jun 2019.

Vancouver:

Andrade de Almeida LJ. Seismic data interpolation using sparsity constrained inversion. [Internet] [Masters thesis]. Colorado School of Mines; 2017. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/11124/171138.

Council of Science Editors:

Andrade de Almeida LJ. Seismic data interpolation using sparsity constrained inversion. [Masters Thesis]. Colorado School of Mines; 2017. Available from: http://hdl.handle.net/11124/171138


Georgia Tech

18. 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

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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 June 24, 2019. http://hdl.handle.net/1853/59782.

MLA Handbook (7th Edition):

Fair, Kaitlin Lindsay. “A biologically plausible sparse approximation solver on neuromorphic hardware.” 2017. Web. 24 Jun 2019.

Vancouver:

Fair KL. A biologically plausible sparse approximation solver on neuromorphic hardware. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Jun 24]. 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


University of Minnesota

19. Farahmand, Shahrokh. Distributed and robust tracking by exploiting set-membership and sarsity.

Degree: 2011, University of Minnesota

 Target tracking research and development are of major importance and continuously expanding interest to a gamut of traditional and emerging applications, which include radar- and… (more)

Subjects/Keywords: Distributed; Robust,; Sparsity,; Trget tracking; Electrical Engineering

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APA (6th Edition):

Farahmand, S. (2011). Distributed and robust tracking by exploiting set-membership and sarsity. (Thesis). University of Minnesota. Retrieved from http://purl.umn.edu/113021

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):

Farahmand, Shahrokh. “Distributed and robust tracking by exploiting set-membership and sarsity.” 2011. Thesis, University of Minnesota. Accessed June 24, 2019. http://purl.umn.edu/113021.

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

MLA Handbook (7th Edition):

Farahmand, Shahrokh. “Distributed and robust tracking by exploiting set-membership and sarsity.” 2011. Web. 24 Jun 2019.

Vancouver:

Farahmand S. Distributed and robust tracking by exploiting set-membership and sarsity. [Internet] [Thesis]. University of Minnesota; 2011. [cited 2019 Jun 24]. Available from: http://purl.umn.edu/113021.

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

Council of Science Editors:

Farahmand S. Distributed and robust tracking by exploiting set-membership and sarsity. [Thesis]. University of Minnesota; 2011. Available from: http://purl.umn.edu/113021

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


University of Minnesota

20. Bazerque, Juan Andres. Leveraging sparsity for genetic and wireless cognitive networks.

Degree: PhD, Electrical Engineering, 2013, University of Minnesota

 Sparse graphical models can capture uncertainty of interconnected systems while promoting parsimony and simplicity - two attributes that can be utilized to identify the topology… (more)

Subjects/Keywords: Cognitive radios; Genetics; Kernels; Networks; Sparsity; Tensors

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APA (6th Edition):

Bazerque, J. A. (2013). Leveraging sparsity for genetic and wireless cognitive networks. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/157612

Chicago Manual of Style (16th Edition):

Bazerque, Juan Andres. “Leveraging sparsity for genetic and wireless cognitive networks.” 2013. Doctoral Dissertation, University of Minnesota. Accessed June 24, 2019. http://purl.umn.edu/157612.

MLA Handbook (7th Edition):

Bazerque, Juan Andres. “Leveraging sparsity for genetic and wireless cognitive networks.” 2013. Web. 24 Jun 2019.

Vancouver:

Bazerque JA. Leveraging sparsity for genetic and wireless cognitive networks. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2019 Jun 24]. Available from: http://purl.umn.edu/157612.

Council of Science Editors:

Bazerque JA. Leveraging sparsity for genetic and wireless cognitive networks. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/157612


Cornell University

21. Niculae, Vlad. Learning Deep Models with Linguistically-Inspired Structure .

Degree: 2018, Cornell University

 Many applied machine learning tasks involve structured representations. This is particularly the case in natural language processing (NLP), where the discrete, compositional nature of words… (more)

Subjects/Keywords: Computer science; ML; NLP; SparseMAP; sparsity; structure

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APA (6th Edition):

Niculae, V. (2018). Learning Deep Models with Linguistically-Inspired Structure . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59540

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):

Niculae, Vlad. “Learning Deep Models with Linguistically-Inspired Structure .” 2018. Thesis, Cornell University. Accessed June 24, 2019. http://hdl.handle.net/1813/59540.

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

MLA Handbook (7th Edition):

Niculae, Vlad. “Learning Deep Models with Linguistically-Inspired Structure .” 2018. Web. 24 Jun 2019.

Vancouver:

Niculae V. Learning Deep Models with Linguistically-Inspired Structure . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/1813/59540.

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

Council of Science Editors:

Niculae V. Learning Deep Models with Linguistically-Inspired Structure . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59540

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


Duquesne University

22. Sano, Teresa. Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising.

Degree: MS, Computational Mathematics, 2009, Duquesne University

 Digital image denoising is a widely know problem in image processing. In this work, we focus on removing additive white Gaussian noise from a given… (more)

Subjects/Keywords: image processing; denoising; sparsity; geometric features

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APA (6th Edition):

Sano, T. (2009). Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising. (Masters Thesis). Duquesne University. Retrieved from https://dsc.duq.edu/etd/1145

Chicago Manual of Style (16th Edition):

Sano, Teresa. “Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising.” 2009. Masters Thesis, Duquesne University. Accessed June 24, 2019. https://dsc.duq.edu/etd/1145.

MLA Handbook (7th Edition):

Sano, Teresa. “Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising.” 2009. Web. 24 Jun 2019.

Vancouver:

Sano T. Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising. [Internet] [Masters thesis]. Duquesne University; 2009. [cited 2019 Jun 24]. Available from: https://dsc.duq.edu/etd/1145.

Council of Science Editors:

Sano T. Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising. [Masters Thesis]. Duquesne University; 2009. Available from: https://dsc.duq.edu/etd/1145


Princeton University

23. Bastian, Caleb Deen. Analysis of Multivariate High-Dimensional Complex Systems and Applications .

Degree: PhD, 2016, Princeton University

 Complex systems are those having interactions among system states, where states are each considered as input or output variables. These interactions can produce diverse phenomena,… (more)

Subjects/Keywords: Complex System; EMT; HDMR; MIMO; Sparsity

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APA (6th Edition):

Bastian, C. D. (2016). Analysis of Multivariate High-Dimensional Complex Systems and Applications . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp018k71nk579

Chicago Manual of Style (16th Edition):

Bastian, Caleb Deen. “Analysis of Multivariate High-Dimensional Complex Systems and Applications .” 2016. Doctoral Dissertation, Princeton University. Accessed June 24, 2019. http://arks.princeton.edu/ark:/88435/dsp018k71nk579.

MLA Handbook (7th Edition):

Bastian, Caleb Deen. “Analysis of Multivariate High-Dimensional Complex Systems and Applications .” 2016. Web. 24 Jun 2019.

Vancouver:

Bastian CD. Analysis of Multivariate High-Dimensional Complex Systems and Applications . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Jun 24]. Available from: http://arks.princeton.edu/ark:/88435/dsp018k71nk579.

Council of Science Editors:

Bastian CD. Analysis of Multivariate High-Dimensional Complex Systems and Applications . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp018k71nk579


Penn State University

24. Mo, Xuan. adaptive sparse representations for video anomaly detection.

Degree: PhD, Electrical Engineering, 2014, Penn State University

 Video surveillance systems are widely used in the transportation domain to identify unusual patterns such as traffic violations, accidents, unsafe driver behavior, street crime, and… (more)

Subjects/Keywords: video anomaly detection; sparsity model; kernel function; multi-object; low rank sparsity prior; outlier rejection

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APA (6th Edition):

Mo, X. (2014). adaptive sparse representations for video anomaly detection. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/22603

Chicago Manual of Style (16th Edition):

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Doctoral Dissertation, Penn State University. Accessed June 24, 2019. https://etda.libraries.psu.edu/catalog/22603.

MLA Handbook (7th Edition):

Mo, Xuan. “adaptive sparse representations for video anomaly detection.” 2014. Web. 24 Jun 2019.

Vancouver:

Mo X. adaptive sparse representations for video anomaly detection. [Internet] [Doctoral dissertation]. Penn State University; 2014. [cited 2019 Jun 24]. Available from: https://etda.libraries.psu.edu/catalog/22603.

Council of Science Editors:

Mo X. adaptive sparse representations for video anomaly detection. [Doctoral Dissertation]. Penn State University; 2014. Available from: https://etda.libraries.psu.edu/catalog/22603

25. Auría Rasclosa, Anna. Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging.

Degree: 2017, EPFL

 Many natural images have low intrinsic dimension (a.k.a. sparse), meaning that they can be represented with very few coefficients when expressed in an adequate domain.… (more)

Subjects/Keywords: inverse problems; compressed sensing; sparsity; structured sparsity; convex optimization; optical interferometry; diffusion MRI; spherical deconvolution; HARDI; microstructure imaging.

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APA (6th Edition):

Auría Rasclosa, A. (2017). Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/227089

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):

Auría Rasclosa, Anna. “Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging.” 2017. Thesis, EPFL. Accessed June 24, 2019. http://infoscience.epfl.ch/record/227089.

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

MLA Handbook (7th Edition):

Auría Rasclosa, Anna. “Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging.” 2017. Web. 24 Jun 2019.

Vancouver:

Auría Rasclosa A. Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Jun 24]. Available from: http://infoscience.epfl.ch/record/227089.

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

Council of Science Editors:

Auría Rasclosa A. Sparse inverse problems for Fourier imaging: applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/227089

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


Washington University in St. Louis

26. Tang, Gongguo. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures.

Degree: PhD, Electrical and Systems Engineering, 2011, Washington University in St. Louis

 The last decade witnessed the burgeoning development in the reconstruction of signals by exploiting their low-dimensional structures, particularly, the sparsity, the block-sparsity, the low-rankness, and… (more)

Subjects/Keywords: Electrical Engineering; Statistics; Mathematics; block-sparsity recovery; compressive sensing; fixed point theory; low-rank matrix recovery; semidefinite programming; sparsity recovery

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APA (6th Edition):

Tang, G. (2011). Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. (Doctoral Dissertation). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/etd/647

Chicago Manual of Style (16th Edition):

Tang, Gongguo. “Computable Performance Analysis of Recovering Signals with Low-dimensional Structures.” 2011. Doctoral Dissertation, Washington University in St. Louis. Accessed June 24, 2019. https://openscholarship.wustl.edu/etd/647.

MLA Handbook (7th Edition):

Tang, Gongguo. “Computable Performance Analysis of Recovering Signals with Low-dimensional Structures.” 2011. Web. 24 Jun 2019.

Vancouver:

Tang G. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. [Internet] [Doctoral dissertation]. Washington University in St. Louis; 2011. [cited 2019 Jun 24]. Available from: https://openscholarship.wustl.edu/etd/647.

Council of Science Editors:

Tang G. Computable Performance Analysis of Recovering Signals with Low-dimensional Structures. [Doctoral Dissertation]. Washington University in St. Louis; 2011. Available from: https://openscholarship.wustl.edu/etd/647

27. Vinyes, Marina. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.

Degree: Docteur es, Signal, Image, Automatique, 2018, Université Paris-Est

En apprentissage automatique on a pour but d'apprendre un modèle, à partir de données, qui soit capable de faire des prédictions sur des nouvelles données… (more)

Subjects/Keywords: Normes atomiques; Optimisation convexe; Parcimonie matricielle; Rang faible; Parcimonie; Atomic norms; Convex optimisation; Matrix sparsity; Low rank; Sparsity

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APA (6th Edition):

Vinyes, M. (2018). Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2018PESC1130

Chicago Manual of Style (16th Edition):

Vinyes, Marina. “Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.” 2018. Doctoral Dissertation, Université Paris-Est. Accessed June 24, 2019. http://www.theses.fr/2018PESC1130.

MLA Handbook (7th Edition):

Vinyes, Marina. “Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques.” 2018. Web. 24 Jun 2019.

Vancouver:

Vinyes M. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. [Internet] [Doctoral dissertation]. Université Paris-Est; 2018. [cited 2019 Jun 24]. Available from: http://www.theses.fr/2018PESC1130.

Council of Science Editors:

Vinyes M. Convex matrix sparsity for demixing with an application to graphical model structure estimation : Parcimonie matricielle convexe pour les problèmes de démixage avec une application à l'apprentissage de structure de modèles graphiques. [Doctoral Dissertation]. Université Paris-Est; 2018. Available from: http://www.theses.fr/2018PESC1130


University of Ontario Institute of Technology

28. Azimipanah, Aras. Compressive sensing based non-destructive testing using ultrasonic arrays.

Degree: 2013, University of Ontario Institute of Technology

 In this thesis, we apply compressive sensing approach and the notion of sparse signal recovery to the non-destructive testing application, using ultrasonic arrays. In many… (more)

Subjects/Keywords: Compressive sensing; Sparsity; Non-destructive testing; Array signal processing; Ultrasound imaging

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APA (6th Edition):

Azimipanah, A. (2013). Compressive sensing based non-destructive testing using ultrasonic arrays. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/351

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):

Azimipanah, Aras. “Compressive sensing based non-destructive testing using ultrasonic arrays.” 2013. Thesis, University of Ontario Institute of Technology. Accessed June 24, 2019. http://hdl.handle.net/10155/351.

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

MLA Handbook (7th Edition):

Azimipanah, Aras. “Compressive sensing based non-destructive testing using ultrasonic arrays.” 2013. Web. 24 Jun 2019.

Vancouver:

Azimipanah A. Compressive sensing based non-destructive testing using ultrasonic arrays. [Internet] [Thesis]. University of Ontario Institute of Technology; 2013. [cited 2019 Jun 24]. Available from: http://hdl.handle.net/10155/351.

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

Council of Science Editors:

Azimipanah A. Compressive sensing based non-destructive testing using ultrasonic arrays. [Thesis]. University of Ontario Institute of Technology; 2013. Available from: http://hdl.handle.net/10155/351

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


University of Colorado

29. Kaslovsky, Daniel N. Geometric Sparsity in High Dimension.

Degree: PhD, Mathematics, 2012, University of Colorado

  While typically complex and high-dimensional, modern data sets often have a concise underlying structure. This thesis explores the sparsity inherent in the geometric structure… (more)

Subjects/Keywords: Geometry; High-dimensional data; Noise; Sparsity; Applied Mathematics

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APA (6th Edition):

Kaslovsky, D. N. (2012). Geometric Sparsity in High Dimension. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/math_gradetds/15

Chicago Manual of Style (16th Edition):

Kaslovsky, Daniel N. “Geometric Sparsity in High Dimension.” 2012. Doctoral Dissertation, University of Colorado. Accessed June 24, 2019. http://scholar.colorado.edu/math_gradetds/15.

MLA Handbook (7th Edition):

Kaslovsky, Daniel N. “Geometric Sparsity in High Dimension.” 2012. Web. 24 Jun 2019.

Vancouver:

Kaslovsky DN. Geometric Sparsity in High Dimension. [Internet] [Doctoral dissertation]. University of Colorado; 2012. [cited 2019 Jun 24]. Available from: http://scholar.colorado.edu/math_gradetds/15.

Council of Science Editors:

Kaslovsky DN. Geometric Sparsity in High Dimension. [Doctoral Dissertation]. University of Colorado; 2012. Available from: http://scholar.colorado.edu/math_gradetds/15


Syracuse University

30. Liu, Sijia. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.

Degree: PhD, Electrical Engineering and Computer Science, 2016, Syracuse University

  Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate… (more)

Subjects/Keywords: convex optimization; distributed estimation; resource management; sparsity; wireless sensor networks; Engineering

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APA (6th Edition):

Liu, S. (2016). Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. (Doctoral Dissertation). Syracuse University. Retrieved from https://surface.syr.edu/etd/441

Chicago Manual of Style (16th Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Doctoral Dissertation, Syracuse University. Accessed June 24, 2019. https://surface.syr.edu/etd/441.

MLA Handbook (7th Edition):

Liu, Sijia. “Resource Management for Distributed Estimation via Sparsity-Promoting Regularization.” 2016. Web. 24 Jun 2019.

Vancouver:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Internet] [Doctoral dissertation]. Syracuse University; 2016. [cited 2019 Jun 24]. Available from: https://surface.syr.edu/etd/441.

Council of Science Editors:

Liu S. Resource Management for Distributed Estimation via Sparsity-Promoting Regularization. [Doctoral Dissertation]. Syracuse University; 2016. Available from: https://surface.syr.edu/etd/441

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