Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be
inferred from limited measurements of scattered electric fields. Parsimonious models provide …

Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once
compressively acquired, many signals need to be processed using advanced techniques …

This tutorial text gives a unifying perspective on machine learning by covering both
probabilistic and deterministic approaches-which are based on optimization techniques …

This paper investigates the problem of estimating the frequency components of a mixture of
s complex sinusoids from a random subset of n regularly spaced samples. Unlike previous …

This comprehensive and engaging textbook introduces the basic principles and techniques
of signal processing, from the fundamental ideas of signals and systems theory to real-world …

Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

Motivated by recent work on atomic norms in inverse problems, we propose a new approach
to line spectral estimation that provides theoretical guarantees for the mean-squared-error …

Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory,
techniques and algorithms used for the analysis and processing of non-stationary signals …

Sparsity of representations of signals has been shown to be a key concept of fundamental
importance in fields such as blind source separation, compression, sampling and signal …

Wideband analog signals push contemporary analog-to-digital conversion (ADC) systems to
their performance limits. In many applications, however, sampling at the Nyquist rate is …