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Cornell University

1.
Sharp, Matthew.
Pilot *Signal* Design For Estimation Of *Sparse* Channels With Application To Cooperative Systems
.

Degree: 2011, Cornell University

URL: http://hdl.handle.net/1813/33575

► In recent years, there has been a resurgence in the interest of using HF radio band (3-30 MHz) for military, government, and emergency applications. In…
(more)

Subjects/Keywords: Channel Estimation; Cooperative Communication; Sparse Signal Recovery

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APA (6^{th} Edition):

Sharp, M. (2011). Pilot Signal Design For Estimation Of Sparse Channels With Application To Cooperative Systems . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/33575

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Sharp, Matthew. “Pilot Signal Design For Estimation Of Sparse Channels With Application To Cooperative Systems .” 2011. Thesis, Cornell University. Accessed October 18, 2019. http://hdl.handle.net/1813/33575.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sharp, Matthew. “Pilot Signal Design For Estimation Of Sparse Channels With Application To Cooperative Systems .” 2011. Web. 18 Oct 2019.

Vancouver:

Sharp M. Pilot Signal Design For Estimation Of Sparse Channels With Application To Cooperative Systems . [Internet] [Thesis]. Cornell University; 2011. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1813/33575.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sharp M. Pilot Signal Design For Estimation Of Sparse Channels With Application To Cooperative Systems . [Thesis]. Cornell University; 2011. Available from: http://hdl.handle.net/1813/33575

Not specified: Masters Thesis or Doctoral Dissertation

University of Victoria

2. Pant, Jeevan Kumar. Compressive sensing using lp optimization.

Degree: Dept. of Electrical and Computer Engineering, 2012, University of Victoria

URL: http://hdl.handle.net/1828/3921

► Three problems in compressive sensing, namely, *recovery* of *sparse* signals from noise-free measurements, *recovery* of *sparse* signals from noisy measurements, and *recovery* of so called…
(more)

Subjects/Keywords: Compressive sensing; Lp Optimization; Sparse signal recovery; Nonconvex compressive sensing

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APA (6^{th} Edition):

Pant, J. K. (2012). Compressive sensing using lp optimization. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/3921

Not specified: Masters Thesis or Doctoral Dissertation

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

Pant, Jeevan Kumar. “Compressive sensing using lp optimization.” 2012. Thesis, University of Victoria. Accessed October 18, 2019. http://hdl.handle.net/1828/3921.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Pant, Jeevan Kumar. “Compressive sensing using lp optimization.” 2012. Web. 18 Oct 2019.

Vancouver:

Pant JK. Compressive sensing using lp optimization. [Internet] [Thesis]. University of Victoria; 2012. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1828/3921.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pant JK. Compressive sensing using lp optimization. [Thesis]. University of Victoria; 2012. Available from: http://hdl.handle.net/1828/3921

Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

3.
Yamada, Randy Matthew.
Identification of Interfering Signals in Software Defined Radio Applications Using *Sparse* *Signal* Reconstruction Techniques.

Degree: MS, Electrical and Computer Engineering, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/50609

► Software-defined radios have the agility and flexibility to tune performance parameters, allowing them to adapt to environmental changes, adapt to desired modes of operation, and…
(more)

Subjects/Keywords: software-defined radio; sparse signal reconstruction; interference; decimation; alias; spectrum recovery

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APA (6^{th} Edition):

Yamada, R. M. (2013). Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/50609

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

Yamada, Randy Matthew. “Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques.” 2013. Masters Thesis, Virginia Tech. Accessed October 18, 2019. http://hdl.handle.net/10919/50609.

MLA Handbook (7^{th} Edition):

Yamada, Randy Matthew. “Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques.” 2013. Web. 18 Oct 2019.

Vancouver:

Yamada RM. Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/10919/50609.

Council of Science Editors:

Yamada RM. Identification of Interfering Signals in Software Defined Radio Applications Using Sparse Signal Reconstruction Techniques. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/50609

University of California – San Diego

4.
Ding, Yacong.
Channel Estimation for Massive MIMO Systems Based on *Sparse* Representation and *Sparse* *Signal* * Recovery*.

Degree: Electrical Engineering (Communication Theory and Systems), 2018, University of California – San Diego

URL: http://www.escholarship.org/uc/item/1hx7c4zk

► Massive multiple-input multiple-output (MIMO) is a promising technology for next generation communication systems, where the base station (BS) is equipped with a large number of…
(more)

Subjects/Keywords: Electrical engineering; Compressive Sensing; Dictionary Learning; Massive MIMO; Sparse Bayesian Learning; Sparse Representation; Sparse Signal Recovery

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APA (6^{th} Edition):

Ding, Y. (2018). Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/1hx7c4zk

Not specified: Masters Thesis or Doctoral Dissertation

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

Ding, Yacong. “Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery.” 2018. Thesis, University of California – San Diego. Accessed October 18, 2019. http://www.escholarship.org/uc/item/1hx7c4zk.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ding, Yacong. “Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery.” 2018. Web. 18 Oct 2019.

Vancouver:

Ding Y. Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2019 Oct 18]. Available from: http://www.escholarship.org/uc/item/1hx7c4zk.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ding Y. Channel Estimation for Massive MIMO Systems Based on Sparse Representation and Sparse Signal Recovery. [Thesis]. University of California – San Diego; 2018. Available from: http://www.escholarship.org/uc/item/1hx7c4zk

Not specified: Masters Thesis or Doctoral Dissertation

Boston University

5. Aksoylar, Cem. Discovery of low-dimensional structure in high-dimensional inference problems.

Degree: PhD, Electrical & Computer Engineering, 2017, Boston University

URL: http://hdl.handle.net/2144/20836

► Many learning and inference problems involve high-dimensional data such as images, video or genomic data, which cannot be processed efficiently using conventional methods due to…
(more)

Subjects/Keywords: Electrical engineering; Convex optimization; Learning on networks; Sparse recovery; Statistical complexity; Statistical signal processing

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

APA (6^{th} Edition):

Aksoylar, C. (2017). Discovery of low-dimensional structure in high-dimensional inference problems. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/20836

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

Aksoylar, Cem. “Discovery of low-dimensional structure in high-dimensional inference problems.” 2017. Doctoral Dissertation, Boston University. Accessed October 18, 2019. http://hdl.handle.net/2144/20836.

MLA Handbook (7^{th} Edition):

Aksoylar, Cem. “Discovery of low-dimensional structure in high-dimensional inference problems.” 2017. Web. 18 Oct 2019.

Vancouver:

Aksoylar C. Discovery of low-dimensional structure in high-dimensional inference problems. [Internet] [Doctoral dissertation]. Boston University; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/2144/20836.

Council of Science Editors:

Aksoylar C. Discovery of low-dimensional structure in high-dimensional inference problems. [Doctoral Dissertation]. Boston University; 2017. Available from: http://hdl.handle.net/2144/20836

University of California – San Diego

6.
Nalci, Alican.
Rectified *Sparse* Bayesian Learning and Effects and Limitations of Nuisance Regression in Functional MRI.

Degree: Electrical and Computer Engineering, 2019, University of California – San Diego

URL: http://www.escholarship.org/uc/item/1k7004f2

► This dissertation considers the problems of *sparse* *signal* *recovery* (SSR) and nuisance regression in functional MRI (fMRI). The first part of the dissertation introduces a…
(more)

Subjects/Keywords: Electrical engineering; Neurosciences; Bayesian Learning; Functional MRI; Nuisance; Regression; Sparse Signal Recovery

Record Details Similar Records

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

APA (6^{th} Edition):

Nalci, A. (2019). Rectified Sparse Bayesian Learning and Effects and Limitations of Nuisance Regression in Functional MRI. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/1k7004f2

Not specified: Masters Thesis or Doctoral Dissertation

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

Nalci, Alican. “Rectified Sparse Bayesian Learning and Effects and Limitations of Nuisance Regression in Functional MRI.” 2019. Thesis, University of California – San Diego. Accessed October 18, 2019. http://www.escholarship.org/uc/item/1k7004f2.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Nalci, Alican. “Rectified Sparse Bayesian Learning and Effects and Limitations of Nuisance Regression in Functional MRI.” 2019. Web. 18 Oct 2019.

Vancouver:

Nalci A. Rectified Sparse Bayesian Learning and Effects and Limitations of Nuisance Regression in Functional MRI. [Internet] [Thesis]. University of California – San Diego; 2019. [cited 2019 Oct 18]. Available from: http://www.escholarship.org/uc/item/1k7004f2.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nalci A. Rectified Sparse Bayesian Learning and Effects and Limitations of Nuisance Regression in Functional MRI. [Thesis]. University of California – San Diego; 2019. Available from: http://www.escholarship.org/uc/item/1k7004f2

Not specified: Masters Thesis or Doctoral Dissertation

Colorado School of Mines

7.
Babakmehr, Mohammad.
Compressive power systems : applications of compressive sensing and *sparse* *recovery* in the analysis of smart power grids.

Degree: PhD, Electrical Engineering and Computer Science, 2017, Colorado School of Mines

URL: http://hdl.handle.net/11124/170676

► During the last two decades, an intelligence revolution intensively changed the technology of electrical power networks, forming a new generation of power systems called smart…
(more)

Subjects/Keywords: Graph theory; Signal processing; Sparse recovery; Power systems; Compressive sensing; Smart grid

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

APA (6^{th} Edition):

Babakmehr, M. (2017). Compressive power systems : applications of compressive sensing and sparse recovery in the analysis of smart power grids. (Doctoral Dissertation). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/170676

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

Babakmehr, Mohammad. “Compressive power systems : applications of compressive sensing and sparse recovery in the analysis of smart power grids.” 2017. Doctoral Dissertation, Colorado School of Mines. Accessed October 18, 2019. http://hdl.handle.net/11124/170676.

MLA Handbook (7^{th} Edition):

Babakmehr, Mohammad. “Compressive power systems : applications of compressive sensing and sparse recovery in the analysis of smart power grids.” 2017. Web. 18 Oct 2019.

Vancouver:

Babakmehr M. Compressive power systems : applications of compressive sensing and sparse recovery in the analysis of smart power grids. [Internet] [Doctoral dissertation]. Colorado School of Mines; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/11124/170676.

Council of Science Editors:

Babakmehr M. Compressive power systems : applications of compressive sensing and sparse recovery in the analysis of smart power grids. [Doctoral Dissertation]. Colorado School of Mines; 2017. Available from: http://hdl.handle.net/11124/170676

Indian Institute of Science

8.
Mukund Sriram, N.
Grassmannian Fusion Frames for Block *Sparse* *Recovery* and Its Application to Burst Error Correction.

Degree: 2013, Indian Institute of Science

URL: http://etd.iisc.ernet.in/2005/3469 ; http://etd.iisc.ernet.in/abstracts/4336/G25889-Abs.pdf

► Fusion frames and block *sparse* *recovery* are of interest in *signal* processing and communication applications. In these applications it is required that the fusion frame…
(more)

Subjects/Keywords: Burst Impulse Noise; Signal Processing; Grassmannian Fusion Frames; Block Sparse Recovery; Burst Error Correction; Sparse Signal Processing; Orthogonal Frequency-Division Multiplexing (OFDM); Block Sparsity; Block Sparse Signal Recovery; Sparse Error Correction; Communication Engineering

Record Details Similar Records

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

APA (6^{th} Edition):

Mukund Sriram, N. (2013). Grassmannian Fusion Frames for Block Sparse Recovery and Its Application to Burst Error Correction. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/2005/3469 ; http://etd.iisc.ernet.in/abstracts/4336/G25889-Abs.pdf

Not specified: Masters Thesis or Doctoral Dissertation

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

Mukund Sriram, N. “Grassmannian Fusion Frames for Block Sparse Recovery and Its Application to Burst Error Correction.” 2013. Thesis, Indian Institute of Science. Accessed October 18, 2019. http://etd.iisc.ernet.in/2005/3469 ; http://etd.iisc.ernet.in/abstracts/4336/G25889-Abs.pdf.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mukund Sriram, N. “Grassmannian Fusion Frames for Block Sparse Recovery and Its Application to Burst Error Correction.” 2013. Web. 18 Oct 2019.

Vancouver:

Mukund Sriram N. Grassmannian Fusion Frames for Block Sparse Recovery and Its Application to Burst Error Correction. [Internet] [Thesis]. Indian Institute of Science; 2013. [cited 2019 Oct 18]. Available from: http://etd.iisc.ernet.in/2005/3469 ; http://etd.iisc.ernet.in/abstracts/4336/G25889-Abs.pdf.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mukund Sriram N. Grassmannian Fusion Frames for Block Sparse Recovery and Its Application to Burst Error Correction. [Thesis]. Indian Institute of Science; 2013. Available from: http://etd.iisc.ernet.in/2005/3469 ; http://etd.iisc.ernet.in/abstracts/4336/G25889-Abs.pdf

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

9. Emad, Amin. New group testing paradigms: from practice to theory.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/88135

► We propose a novel group testing framework, termed semi-quantitative group testing, motivated by a class of problems arising in genome screening experiments in addition to…
(more)

Subjects/Keywords: Integer Compressed Sensing; Group Testing; Sparse Signal Recovery; Semi-quantitative Group Testing; Code Construction; Decoding Algorithm; Message Passing

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APA (6^{th} Edition):

Emad, A. (2015). New group testing paradigms: from practice to theory. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88135

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

Emad, Amin. “New group testing paradigms: from practice to theory.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 18, 2019. http://hdl.handle.net/2142/88135.

MLA Handbook (7^{th} Edition):

Emad, Amin. “New group testing paradigms: from practice to theory.” 2015. Web. 18 Oct 2019.

Vancouver:

Emad A. New group testing paradigms: from practice to theory. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/2142/88135.

Council of Science Editors:

Emad A. New group testing paradigms: from practice to theory. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/88135

Colorado State University

10. Krakow, Lucas W. Spanning sensor resource management.

Degree: PhD, Electrical and Computer Engineering, 2019, Colorado State University

URL: http://hdl.handle.net/10217/193188

► This paper presents multiple applications of sensor resource management. The general focus entails two chapters on adaptive estimation of time-varying *sparse* signals and three chapters…
(more)

Subjects/Keywords: Decision control; Q-value approximations; Unmanned aerial vehicles; Partially observable Markov decision process; Autonomous control; Sparse signal recovery

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

APA (6^{th} Edition):

Krakow, L. W. (2019). Spanning sensor resource management. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/193188

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

Krakow, Lucas W. “Spanning sensor resource management.” 2019. Doctoral Dissertation, Colorado State University. Accessed October 18, 2019. http://hdl.handle.net/10217/193188.

MLA Handbook (7^{th} Edition):

Krakow, Lucas W. “Spanning sensor resource management.” 2019. Web. 18 Oct 2019.

Vancouver:

Krakow LW. Spanning sensor resource management. [Internet] [Doctoral dissertation]. Colorado State University; 2019. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/10217/193188.

Council of Science Editors:

Krakow LW. Spanning sensor resource management. [Doctoral Dissertation]. Colorado State University; 2019. Available from: http://hdl.handle.net/10217/193188

11.
Johnson, Erik C.
* Recovery* of

Degree: MS, 1200, 2013, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/44187

► Estimating unknown signals from parameterized measurement models is a common problem that arises in diverse areas such as statistics, imaging, machine learning, and *signal* processing.…
(more)

Subjects/Keywords: Sparse Recovery; Compressed Sensing; Sparse Signal; Parameterized Model; Dictionary Perturbation

…assumption of *sparse* *recovery*
algorithms is that the *signal* dictionary A is exactly known. For… …knowledge of the parameterized *signal* model in the
*sparse* *recovery* problem, it would be desirable… …approaches for *sparse* *recovery* have several major limitations when
applied to parameterized *signal*… …*sparse* *signal*, one can form a *sparse* *recovery* problem by defining
A(ω) ∈ C(M ×N… …replaced by the true *signal* frequencies.
Figure 1.2: *Sparse* *recovery* of a length-32 *signal*…

Record Details Similar Records

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

APA (6^{th} Edition):

Johnson, E. C. (2013). Recovery of sparse signals and parameter perturbations from parameterized signal models. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44187

Not specified: Masters Thesis or Doctoral Dissertation

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

Johnson, Erik C. “Recovery of sparse signals and parameter perturbations from parameterized signal models.” 2013. Thesis, University of Illinois – Urbana-Champaign. Accessed October 18, 2019. http://hdl.handle.net/2142/44187.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Johnson, Erik C. “Recovery of sparse signals and parameter perturbations from parameterized signal models.” 2013. Web. 18 Oct 2019.

Vancouver:

Johnson EC. Recovery of sparse signals and parameter perturbations from parameterized signal models. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2013. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/2142/44187.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Johnson EC. Recovery of sparse signals and parameter perturbations from parameterized signal models. [Thesis]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44187

Not specified: Masters Thesis or Doctoral Dissertation

12. Zhang, Anru. High-dimensional Statistical Inference: from Vector to Matrix.

Degree: 2015, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/1172

► Statistical inference for *sparse* signals or low-rank matrices in high-dimensional settings is of significant interest in a range of contemporary applications. It has attracted significant…
(more)

Subjects/Keywords: Constrained l_1 minimization; Constrained nuclear norm minimization; Genomic data integration; Low-rank matrix recovery; Optimal rate of convergence; Sparse signal recovery; Applied Mathematics; Statistics and Probability

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

APA (6^{th} Edition):

Zhang, A. (2015). High-dimensional Statistical Inference: from Vector to Matrix. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/1172

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhang, Anru. “High-dimensional Statistical Inference: from Vector to Matrix.” 2015. Thesis, University of Pennsylvania. Accessed October 18, 2019. https://repository.upenn.edu/edissertations/1172.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhang, Anru. “High-dimensional Statistical Inference: from Vector to Matrix.” 2015. Web. 18 Oct 2019.

Vancouver:

Zhang A. High-dimensional Statistical Inference: from Vector to Matrix. [Internet] [Thesis]. University of Pennsylvania; 2015. [cited 2019 Oct 18]. Available from: https://repository.upenn.edu/edissertations/1172.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang A. High-dimensional Statistical Inference: from Vector to Matrix. [Thesis]. University of Pennsylvania; 2015. Available from: https://repository.upenn.edu/edissertations/1172

Not specified: Masters Thesis or Doctoral Dissertation

13. MA CHANGZHENG. Multiple input multiple output radar three dimensional imaging technique.

Degree: 2014, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/118237

Subjects/Keywords: MIMO Radar; 3D Imaging; ISAR; Sparse Signal Recovery; L1 L0 Norms Homotopy; BiStatic Radar

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APA (6^{th} Edition):

CHANGZHENG, M. (2014). Multiple input multiple output radar three dimensional imaging technique. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/118237

Not specified: Masters Thesis or Doctoral Dissertation

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

CHANGZHENG, MA. “Multiple input multiple output radar three dimensional imaging technique.” 2014. Thesis, National University of Singapore. Accessed October 18, 2019. http://scholarbank.nus.edu.sg/handle/10635/118237.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

CHANGZHENG, MA. “Multiple input multiple output radar three dimensional imaging technique.” 2014. Web. 18 Oct 2019.

Vancouver:

CHANGZHENG M. Multiple input multiple output radar three dimensional imaging technique. [Internet] [Thesis]. National University of Singapore; 2014. [cited 2019 Oct 18]. Available from: http://scholarbank.nus.edu.sg/handle/10635/118237.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

CHANGZHENG M. Multiple input multiple output radar three dimensional imaging technique. [Thesis]. National University of Singapore; 2014. Available from: http://scholarbank.nus.edu.sg/handle/10635/118237

Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

14. Asif, Muhammad Salman. Primal dual pursuit: a homotopy based algorithm for the Dantzig selector.

Degree: MS, Electrical and Computer Engineering, 2008, Georgia Tech

URL: http://hdl.handle.net/1853/24693

► Consider the following system model y = Ax + e, where x is n-dimensional *sparse* *signal*, y is the measurement vector in a much lower…
(more)

Subjects/Keywords: Statistical estimation; Random matrices; Convex optimization; Compressed sensing; Sparse signal recovery; Linear programming; LASSO; Model selection; L1 minimization; Dantzig shrinkability; Mathematical optimization; Homotopy theory; Signal processing

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

APA (6^{th} Edition):

Asif, M. S. (2008). Primal dual pursuit: a homotopy based algorithm for the Dantzig selector. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/24693

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

Asif, Muhammad Salman. “Primal dual pursuit: a homotopy based algorithm for the Dantzig selector.” 2008. Masters Thesis, Georgia Tech. Accessed October 18, 2019. http://hdl.handle.net/1853/24693.

MLA Handbook (7^{th} Edition):

Asif, Muhammad Salman. “Primal dual pursuit: a homotopy based algorithm for the Dantzig selector.” 2008. Web. 18 Oct 2019.

Vancouver:

Asif MS. Primal dual pursuit: a homotopy based algorithm for the Dantzig selector. [Internet] [Masters thesis]. Georgia Tech; 2008. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1853/24693.

Council of Science Editors:

Asif MS. Primal dual pursuit: a homotopy based algorithm for the Dantzig selector. [Masters Thesis]. Georgia Tech; 2008. Available from: http://hdl.handle.net/1853/24693

15.
Balavoine, Aurele.
Mathematical analysis of a dynamical system for *sparse* * recovery*.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/51882

► This thesis presents the mathematical analysis of a continuous-times system for *sparse* *signal* *recovery*. *Sparse* *recovery* arises in Compressed Sensing (CS), where signals of large…
(more)

Subjects/Keywords: Sparse recovery; Neural network; L1-minimization; Nonsmooth optimization; Compressed sensing; Tracking; ISTA; LCA; Sparse matrices; Signal processing Digital techniques; Mathematical optimization

…*signal*. The ℓ1 minimization program is the most famous optimization program for *sparse* *recovery*… …the LCA
to standard digital approaches in Chapter 4.
1.1.4 *Sparse* *signal* *recovery*
Ideally… …matrix Φ can recover every *sparse* *signal*,
while stable *recovery* means that the error scales… …findings in later chapters.
2.1 *Sparse* *signal* *recovery*
Significant efforts have been put into… …13
Figure 5
LCA solution for *sparse* *recovery* . . . . . . . . . . . . . . . . . . . . . 28…

Record Details Similar Records

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

APA (6^{th} Edition):

Balavoine, A. (2014). Mathematical analysis of a dynamical system for sparse recovery. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51882

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

Balavoine, Aurele. “Mathematical analysis of a dynamical system for sparse recovery.” 2014. Doctoral Dissertation, Georgia Tech. Accessed October 18, 2019. http://hdl.handle.net/1853/51882.

MLA Handbook (7^{th} Edition):

Balavoine, Aurele. “Mathematical analysis of a dynamical system for sparse recovery.” 2014. Web. 18 Oct 2019.

Vancouver:

Balavoine A. Mathematical analysis of a dynamical system for sparse recovery. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1853/51882.

Council of Science Editors:

Balavoine A. Mathematical analysis of a dynamical system for sparse recovery. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51882

Pontifical Catholic University of Rio de Janeiro

16. YUNEISY ESTHELA GARCIA GUZMAN. [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES.

Degree: 2018, Pontifical Catholic University of Rio de Janeiro

URL: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608

►

[pt] A estimação de direção de chegada (DoA) é uma importante área de processamento de arranjos de sensores que é encontrada em uma ampla gama… (more)

Subjects/Keywords: [pt] ESTIMACAO DE DIRECAO DE CHEGADA - DOA; [en] DIRECTION-OF-ARRIVAL ESTIMATION - DOA; [pt] PROCESSAMENTO DE ARRANJOS DE SINAIS; [en] SENSOR ARRAY SIGNAL PROCESSING; [pt] COMPRESSED SENSING - CS; [en] COMPRESSED SENSING - CS; [pt] RECUPERACAO ESPARSA; [en] SPARSE RECOVERY; [pt] ITERATIVE HARD THRESHOLDING - IHT - ALGORITHM; [en] ITERATIVE HARD THRESHOLDING - IHT - ALGORITHM

Record Details Similar Records

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

APA (6^{th} Edition):

GUZMAN, Y. E. G. (2018). [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES. (Thesis). Pontifical Catholic University of Rio de Janeiro. Retrieved from http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608

Not specified: Masters Thesis or Doctoral Dissertation

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

GUZMAN, YUNEISY ESTHELA GARCIA. “[en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES.” 2018. Thesis, Pontifical Catholic University of Rio de Janeiro. Accessed October 18, 2019. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

GUZMAN, YUNEISY ESTHELA GARCIA. “[en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES.” 2018. Web. 18 Oct 2019.

Vancouver:

GUZMAN YEG. [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES. [Internet] [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. [cited 2019 Oct 18]. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

GUZMAN YEG. [en] DIRECTION FINDING TECHNIQUES BASED ON COMPRESSIVE SENSING AND MULTIPLE CANDIDATES. [Thesis]. Pontifical Catholic University of Rio de Janeiro; 2018. Available from: http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=35608

Not specified: Masters Thesis or Doctoral Dissertation

17.
Nguyen, Ha.
* Signal* representations: from images to irregular-domain signals.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/50575

► Efficient representations of high-dimensional data such as images, that can essentially describe the data with a few parameters, play a vital role in many problems…
(more)

Subjects/Keywords: signal representation; sparse representation; directional wavelet; contourlet transform; inverse rendering; computational relighting; albedo recovery; matrix factorization; graph signal processing; graph wavelet transform; graph multiresolution; multiresolution mesh processing; geometry compression

…4.4.1 Graph Downsampling . . . . . . . . . . . . . . . . .
4.4.2 *Signal* Compression… …general system that describes a *signal* representation followed by an approximation… …A piece-wise constant *signal* on the Minnesota unweighted
graph… …Reconstructions of the original *signal* from 30% of total
wavelet coefficients using coloring-based and… …*signal* on the Minnesota graph. . . . . . . . . . . . . . . .
Original 3D triangle mesh and its…

Record Details Similar Records

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

APA (6^{th} Edition):

Nguyen, H. (2014). Signal representations: from images to irregular-domain signals. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50575

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

Nguyen, Ha. “Signal representations: from images to irregular-domain signals.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed October 18, 2019. http://hdl.handle.net/2142/50575.

MLA Handbook (7^{th} Edition):

Nguyen, Ha. “Signal representations: from images to irregular-domain signals.” 2014. Web. 18 Oct 2019.

Vancouver:

Nguyen H. Signal representations: from images to irregular-domain signals. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/2142/50575.

Council of Science Editors:

Nguyen H. Signal representations: from images to irregular-domain signals. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50575