Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

We consider the recovery of a (real-or complex-valued) signal from magnitude-only
measurements, known as phase retrieval. We formulate phase retrieval as a convex …

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of
information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater …

We introduce a novel deep-learning inspired formulation of the\textit {phase retrieval
problem}, which asks to recover a signal $ y_0\in\R^ n $ from $ m $ quadratic observations …

Phase retrieval algorithms have become an important component in many modern
computational imaging systems. For instance, in the context of ptychography and speckle …

Phase retrieval, ie the problem of recovering a function from the squared magnitude of its
Fourier transform, arises in many applications, such as X-ray crystallography, diffraction …

High-throughput computational imaging requires efficient processing algorithms to retrieve
multi-dimensional and multi-scale information. In computational phase imaging, phase …

We investigate the implementation of a new stochastic Kuramoto-Vicsek-type model for
global optimization of nonconvex functions on the sphere. This model belongs to the class of …

The frozen-wave-based longitudinal orbital angular momentum multiplexing (LOAMM)
system developed in [IEEE Photonics J. 10, 7900416 (2018) 10.1109/JPHOT …

We advance both the theory and practice of robust $\ell_p $-quasinorm regression for $ p\in
(0, 1] $ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the …