We review compressive sensing and its extension to classification and joint signal recovery. We present an overview of compressed sensing, followed by some simulation results on perfect reconstruction for sparse signals. We review previous work on compressed signal classification and discuss relations between the two earlier papers. Finally, we discuss joint signal reconstruction for compressed...

The speed and accuracy of spectrum sensing techniques are essential factors in the performance of cognitive radio networks. The limitations imposed by computational complexity and a shortened monitoring time impede the success of spectrum sensing operation performed by cognitive radio nodes. Compressive sensing techniques are viewed as novel approaches to solve scalability problems in some sign...

Compressive sensing (CS) has been widely used for the data gathering in wireless sensor networks for the purpose of reducing the communication overhead recent years. In this paper, we first show that with simple modification, 1-bit compressive sensing can also been used for the data gathering in wireless sensor networks to further reduce the communication overhead. We also propose a novel blind...

Blind compressive sensing (CS) is considered for reconstruction of hyperspectral data imaged by a coded aperture camera. The measurements are manifested as a superposition of the coded wavelengthdependent data, with the ambient three-dimensional hyperspectral datacube mapped to a two-dimensional measurement. The hyperspectral datacube is recovered using a Bayesian implementation of blind CS. Se...

Compressive sensing investigates the recovery of a signal that can be sparsely represented in an orthonormal basis or overcomplete dictionary given a small number of linear combinations of the signal. We present a novel matching pursuit algorithm that uses the measurements to probabilistically select a subset of bases that is likely to contain the true bases constituting the signal. The algorit...