The developments in structural health monitoring techniques have led to the invention of
various sensors that can be effective damage indicator. Due to environmental or …

Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …

This lecture note presents a new method to capture and represent compressible signals at a
rate significantly below the Nyquist rate. This method, called compressive sensing, employs …

Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to
their performance limits. In many applications, however, sampling at the Nyquist rate is …

We propose simple and extremely efficient methods for solving the basis pursuit problem
\min{‖u‖_1:Au=f,u∈R^n\}, which is used in compressed sensing. Our methods are based …

We present a framework for solving the large-scale \ell_1-regularized convex minimization
problem: \min‖x‖_1+μf(x). Our approach is based on two powerful algorithmic ideas …

The new theory of compressive sensing enables direct analog-to-information conversion of
compressible signals at sub-Nyquist acquisition rates. We develop new theory, algorithms …

A Fixed-Point Continuation Method for l1-Regularized Minimization with Applications to
Compressed Sensing Page 1 CAAM Technical Report TR07-07 A Fixed-Point Continuation …

We propose a new approach for nonadaptive dimensionality reduction of manifold-modeled
data, demonstrating that a small number of random linear projections can preserve key …

This paper demonstrates that convolution with random waveform followed by random time-
domain subsampling is a universally efficient compressive sensing strategy. We show that …