Sparse polynomial chaos expansions (PCE) are a popular surrogate modelling method that
takes advantage of the properties of PCE, the sparsity-of-effects principle, and powerful …

Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

In this paper, we study the problem of user activity detection and large-scale fading
coefficient estimation in a random access wireless uplink with a massive MIMO base station …

Alzheimer's disease (AD), the most common form of dementia, shares many aspects of
abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based …

We give a basic introduction to Gaussian Process regression models. We focus on
understanding the role of the stochastic process and how it is used to define a distribution …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

Several problems arising in electromagnetics can be directly formulated or suitably recast for
an effective solution within the compressive sensing (CS) framework. This has motivated a …

We describe the maximum-likelihood parameter estimation problem and how the
Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the …

The data of interest are assumed to be represented as N-dimensional real vectors, and
these vectors are compressible in some linear basis B, implying that the signal can be …

The early identification of brain anatomy deviating from the normal pattern of growth and
atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes …