Making Do with Less: An Introduction to Compressed Sensing

A Block-Wise random sampling approach: Compressed sensing problem

The focus of this paper is to consider the compressed sensing problem. It is stated that the compressed sensing theory, under certain conditions, helps relax the Nyquist sampling theory and takes smaller samples. One of the important tasks in this theory is to carefully design measurement matrix (sampling operator). Most existing methods in the literature attempt to optimize a randomly initiali...

Accelerating Magnetic Resonance Imaging through Compressed Sensing Theory in the Direction space-k

Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI can be accelerated through several methods such as parallel imaging and compressed sensing, where a fraction of the K-space lines...

Unmanned aerial vehicle field sampling and antenna pattern reconstruction using Bayesian compressed sensing

Antenna 3D pattern measurement can be a tedious and time consuming task even for antennas with manageable sizes inside anechoic chambers. Performing onsite measurements by scanning the whole 4π [sr] solid angle around the antenna under test (AUT) is more complicated. In this paper, with the aim of minimum duration of flight, a test scenario using unmanned aerial vehicles (UAV) is proposed. A pr...

Frames for compressed sensing using coherence

We give some new results on sparse signal recovery in the presence of noise, for weighted spaces. Traditionally, were used dictionaries that have the norm equal to 1, but, for random dictionaries this condition is rarely satised. Moreover, we give better estimations then the ones given recently by Cai, Wang and Xu.

An Introduction to Compressed Sensing