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You searched for subject:(Sparse signal recovery). Showing records 1 – 17 of 17 total matches.

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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

 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 (6th 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 (16th 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 (7th 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

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

 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 (6th Edition):

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

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):

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

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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 sparse signals and parameter perturbations from parameterized signal models.

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

 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… 

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

Degree: 2015, University of Pennsylvania

 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 (6th Edition):

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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

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APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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… 

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

APA (6th 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 (16th 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 (7th 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

[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

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

APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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… 

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

APA (6th 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 (16th 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 (7th 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

.