Approximation Algorithms for Model-Based Compressive Sensing

Approximation-Tolerant Model-Based Compressive Sensing

The goal of sparse recovery is to recover a k-sparse signal x ∈ R from (possibly noisy) linear measurements of the form y = Ax, where A ∈ Rm×n describes the measurement process. Standard results in compressive sensing show that it is possible to recover the signal x from m = O(k log(n/k)) measurements, and that this bound is tight. The framework of modelbased compressive sensing [BCDH10] overco...

An Energy Approximation Model Based on Restricted Isometry Property in Compressive Spectrum Sensing for Cognitive Radio

In recent decades, rapid growth in wireless communication service makes the limited spectrum resources become increasingly scarce. Cognitive radio [1] can solve this problem by dynamic spectrum access technology. Spectrum sensing is one of the key technology of cognitive radios and the quick and accurate perception of broadband spectrum hole is the main challenge. In order to improve the effici...

Nearly Linear-Time Model-Based Compressive Sensing

Compressive sensing is a method for recording a k-sparse signal x ∈ R with (possibly noisy) linear measurements of the form y = Ax, where A ∈ Rm×n describes the measurement process. Seminal results in compressive sensing show that it is possible to recover the signal x from m = O(k log n k ) measurements and that this is tight. The model-based compressive sensing framework overcomes this lower ...

A compressive sensing-based approach for Preisach hysteresis model identification

The Preisach hysteresis model has been adopted extensively in magnetic and smart materialbased systems. Fidelity of the model hinges on accurate identification of the Preisach density function. Existing work on the identification of the density function usually involves applying an input that provides sufficient excitation and measuring a large set of output data. In this paper, we propose a no...

Approximation Algorithms for Model-Based Diagnosis