Data-Driven Compressed Sensing for Massive Wireless Access

Compressed Sensing & Wireless Communications

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.

Inexact Gradient Projection and Fast Data Driven Compressed Sensing

We study convergence of the iterative projected gradient (IPG) algorithm for arbitrary (possibly nonconvex) sets and when both the gradient and projection oracles are computed approximately. We consider different notions of approximation of which we show that the Progressive Fixed Precision (PFP) and the (1 + ε)-optimal oracles can achieve the same accuracy as for the exact IPG algorithm. We sh...

Electron tomography image reconstruction using data-driven adaptive compressed sensing.

Electron tomography (ET) is an increasingly important technique for the study of the three-dimensional morphologies of nanostructures. ET involves the acquisition of a set of two-dimensional projection images, followed by the reconstruction into a volumetric image by solving an inverse problem. However, due to limitations in the acquisition process, this inverse problem is ill-posed (i.e., a un...

Compressed Suffix Arrays for Massive Data

We present a fast space-efficient algorithm for constructing compressed suffix arrays (CSA). The algorithm requires O(n logn) time in the worst case, and only O(n) bits of extra space in addition to the CSA. As the basic step, we describe an algorithm for merging two CSAs. We show that the construction algorithm can be parallelized in a symmetric multiprocessor system, and discuss the possibili...