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Mark A. Davenport
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Content by Mark A. Davenport
Other authors' collections containing modules by Mark A. Davenport
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Digital Signal Processing
(col11172)
Author:
Mark A. Davenport
Institution:
Rice University
Summary:
This course provides an overview of discrete-time signal processing from a vector space perspective. Topics will include sampling, filter design, multirate signal processing and filterbanks, Fourier and wavelet analysis, subspace methods, and a variety of topics relating to inverse problems and "least-squares signal processing''.
Subject:
Mathematics and Statistics,
Science and Technology
Language:
English
Popularity:
85.92%
Revised:
2011-12-16
Revisions:
4
An Introduction to Compressive Sensing
(col11133)
Authors:
Richard Baraniuk
,
Mark A. Davenport
,
Marco F. Duarte
,
Chinmay Hegde
Subject:
Mathematics and Statistics,
Science and Technology
Language:
English
Popularity:
83.58%
Revised:
2011-04-02
Revisions:
5
Sub-Gaussian random variables
(m37185)
Author:
Mark A. Davenport
Keywords:
Strictly sub-Gaussian distributions
,
Sub-Gaussian distributions
Summary:
In this module we introduce the sub-Gaussian and strictly sub-Gaussian distributions. We provide some simple examples and illustrate some of the key properties of sub-Gaussian random variables.
Subject:
Mathematics and Statistics
Language:
English
Popularity:
80.81%
Revised:
2011-04-10
Revisions:
6
Introduction to compressive sensing
(m37172)
Authors:
Mark A. Davenport
,
Marco F. Duarte
,
Chinmay Hegde
,
Richard Baraniuk
Keywords:
Compressibility
,
Compressive sensing
,
Nonlinear approximation
,
Sensing
,
Signal acquisition
,
Sparse recovery
,
Sparsity
,
Transform coding
Summary:
Introduction to compressive sensing. This course introduces the basic concepts in compressive sensing. We overview the concepts of sparsity, compressibility, and transform coding. We then review applications of sparsity in several signal processing problems such as sparse recovery, model selection, data coding, and error correction. We overview the key results ... sensor networks.
[Expand Summary]
Introduction to compressive sensing. This course introduces the basic concepts in compressive sensing. We overview the concepts of sparsity, compressibility, and transform coding. We then review applications of sparsity in several signal processing problems such as sparse recovery, model selection, data coding, and error correction. We overview the key results in these fields, focusing primarily on both theory and algorithms for sparse recovery. We also discuss applications of compressive sensing in communications, biosensing, medical imaging, and sensor networks.
[Collapse Summary]
Subject:
Mathematics and Statistics
Language:
English
Popularity:
71.71%
Revised:
2011-04-10
Revisions:
7
z-Transform Examples
(m34823)
Author:
Mark A. Davenport
Subject:
Science and Technology
Language:
English
Popularity:
70.11%
Revised:
2010-07-19
Revisions:
2
Compressive Sensing
(col10458)
Authors:
Mark A. Davenport
,
Richard Baraniuk
,
Ronald DeVore
Institution:
Rice University
Language:
English
Popularity:
66.27%
Revised:
2007-09-21
Revisions:
New
Stability, Causality, and the z-Transform
(m34818)
Author:
Mark A. Davenport
Subject:
Science and Technology
Language:
English
Popularity:
65.78%
Revised:
2010-07-19
Revisions:
2
Gelfand n-widths
(m15133)
Authors:
Mark A. Davenport
,
Ronald DeVore
,
Chris Rozell
,
Michael Wakin
Language:
English
Popularity:
62.26%
Revised:
2007-09-21
Revisions:
New
Analog-to-information conversion
(m37375)
Authors:
Mark A. Davenport
,
Jason Laska
Keywords:
Analog to digital converter (ADC)
,
Multiband signal
,
Random demodulator
Summary:
In this module we describe the random demodulator and how it can be used in the application of the theory of compressive sensing to the problem of acquiring a high-bandwidth continuous-time signal.
Subject:
Mathematics and Statistics
Language:
English
Popularity:
57.99%
Revised:
2011-04-15
Revisions:
4
Introduction to Digital Signal Processing
(m33588)
Author:
Mark A. Davenport
Keywords:
Digital Signal Processing
,
Vector Spaces
Subject:
Mathematics and Statistics
Language:
English
Popularity:
55.20%
Revised:
2010-07-16
Revisions:
2
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