At the light of the fundamentals of Compressive Sensing (CS) presented in Chapter 3, it is
clear that the phase-shift-based ToF imaging systems described in Chapter 2 offer an …

The theory of compressed sensing (CS) asserts that an unknown signal x ϵ Rp can be
accurately recovered from an underdetermined set of n linear measurements with n≪ p …

Boolean matrix factorization and Boolean matrix completion from noisy observations are
desirable unsupervised data-analysis methods due to their interpretability, but hard to …

In this work, we explore the problem of multi-user linearly-separable distributed computation,
where servers help compute the desired functions (jobs) of users, and where each desired …

This letter considers the design of measurement matrices with low complexity, easy
hardware implementation and good sensing performance for practical compressed sensing …

In practical electrocardiogram (ECG) monitoring, there are some challenges in reducing the
data burden and energy costs. Therefore, compressed sensing (CS) which can conduct …

Compressed sensing is an emerging theory of signal processing and it has wide
applications in many frontier fields. The construction of the measurement matrices is still a …

In this paper, we study the problem of compressed sensing using binary measurement
matrices and ℓ 1-norm minimization (basis pursuit) as the recovery algorithm. We derive new …

For an mxn binary matrix with d nonzero elements per column, it is interesting to identify the
minimal column degree d that corresponds to the best recovery performance. Consider this …

Compressed sensing (or compressive sensing) refers to the recovery of high-dimensional
but low-complexity objects from a limited number of measurements. Two canonical …