Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse respo...
Classical sampling records the signal level at pre-determined time instances, usually uniformly spaced. An alternative implicit sampling model is to record the timing of pre-deter...
We examine the application of current research in sparse signal recovery to the problem of channel estimation. Specifically, using an Orthogonal Frequency Division Multiplexed (O...
The emerging theory of compressed sensing (CS) provides a universal signal detection approach for sparse signals at sub-Nyquist sampling rates. A small number of random projection...
In a wide variety of imaging applications (especially medical imaging), we obtain a partial set or subset of the Fourier transform of an image. From these Fourier measurements, we...