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Mark A. Davenport

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Type Title
Compressive Sensing
Digital Signal Processing
An Introduction to Compressive Sensing
Analog-to-information conversion
Approximation in ℓ_p Norms
Bases and frames
Combinatorial algorithms
Complete Vector Spaces
Compressible signals
Compressive Sensing
Computing the Best Approximation
Concentration of measure for sub-Gaussian random variables
Discrete-time Systems
Eigenbases and LSI Systems
Eigenvectors of LSI Systems
Error of the Best Approximation in an Orthobasis
Fourier Representations
Fourier Transforms as Unitary Operators
Gelfand n-widths
Group testing and data stream algorithms
Hilbert Spaces in Signal Processing
Inner Product Spaces
Instance-optimal guarantees revisited
Introduction to compressive sensing
Introduction to Digital Signal Processing
Introduction to vector spaces
Inverse Systems
Inverse z-Transform
Linear Combinations of Vectors
Linear Independence
Linear Operators
Linear regression and model selection
Linear Systems
Matrices that satisfy the RIP
Matrix Representation of the Approximation Problem
Metric Spaces
New Signal Models
Noise-free signal recovery
Normalized DTFT as an Operator
Normed Vector Spaces
Null space conditions
Orthobasis Expansions
Orthogonal Bases
Parseval's and Plancherel's Theorems
Poles and Zeros
Proof of the RIP for sub-Gaussian matrices
Properties of Inner Products
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Total Modules: 24371