Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive …

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it
came into sight. X-ray imaging is a common and easily accessible tool that has great …

The problem of sparsity pattern or support set recovery refers to estimating the set of
nonzero coefficients of an unknown vector beta* isin Ropf p based on a set of n noisy …

We discuss the problem of designing channel access architectures for enabling fast, low-
latency, grant-free, and uncoordinated uplink for densely packed wireless nodes …

We characterize the measurement complexity of compressed sensing of signals drawn from
a known prior distribution, even when the support of the prior is the entire space (rather than …

This paper considers the fundamental limit of compressed sensing for iid signal distributions
and iid Gaussian measurement matrices. Its main contribution is a rigorous characterization …

Consider a Bernoulli-Gaussian complex n-vector whose components are V i= X i B i, with X
i~ CN (0, P x) and binary B i mutually independent and iid across i. This random q-sparse …

In this paper, we derive information theoretic performance bounds to sensing and
reconstruction of sparse phenomena from noisy projections. We consider two settings …

We discuss the problem of designing channel access architectures for enabling fast, low-
latency, grant-free and uncoordinated uplink for densely packed wireless nodes …

Compressed sensing deals with efficient recovery of analog signals from linear encodings.
This paper presents a statistical study of compressed sensing by modeling the input signal …