Conference Research Projects

Compressive sampling of correlated signals

The recently developed theory of Compressive sensing (CS) has shown that sparse signals can be reconstructed from a much smaller number of measurements than their bandwidth suggests. In this paper we present a sampling scheme to acquire ensembles of correlated signals at a sub-Nyquist rate. The sampling architecture uses simple analog building blocks including analog […]

Journal Research Projects

Blind Deconvolution Using Convex Programming

We consider the problem of recovering two unknown vectors, and , of length from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension and the other with dimension . Although the observed convolution is nonlinear in both and , it is linear in the […]

Conference Research Projects

Compressive Multiplexing of Correlated Signals

We propose two compressive multiplexers for the efficient sampling of ensembles of correlated signals. We show that we can acquire correlated ensembles, taking advantage of their (a priori-unknown) correlation structure, at a sub-Nyquist rate using simple modulation and filtering architectures. We recast the reconstruction of the ensemble as a low-rank matrix recovery problem from generalized […]

Journal Research Projects

Compressive Acquisition and Least-Squares Reconstruction of Correlated Signals

This letter presents a framework for the compressive acquisition of correlated signals. We propose an implementable sampling architecture for the acquisition of ensembles of correlated (lying in an a priori unknown subspace) signals at a sub-Nyquist rate. The sampling architecture acquires structured compressive samples of the signals after preprocessing them with easy-toimplement components. Quantitatively, we […]

Journal Research Projects

A Convex Approach to Blind MIMO Communications

This letter considers the blind separation of convolutive mixtures in a multi-in-multi-out (MIMO) communication system. Multiple source signals are transmitted simultaneously over a shared communication medium (modeled as linear convolutive channels) to multiple receivers. We recast the joint recovery of the source signals, and the channel impulse responses as a block-rank-one matrix recovery problem, which […]

Conference Research Projects

Deep Ptych: Subsampled Fourier Ptychography Using Generative Priors

This paper proposes a novel framework to regularize the highly ill-posed and non-linear Fourier ptychography problem using generative models. We demonstrate experimentally that our proposed algorithm, Deep Ptych, outperforms the existing Fourier ptychography techniques, in terms of quality of reconstruction and robustness against noise, using far fewer samples. We further modify the proposed approach to […]

Conference Research Projects

Channel Protection Using Random Modulation

This paper shows that modulation protects a bandlimited signal against convolutive interference. A signal , bandlimited to BHz, is modulated (pointwise multiplied) with a known random sign sequence , alternating at a rate , and the resultant spread spectrum signal is convolved against an M-tap channel impulse response to yield the observed signal , where […]

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