This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel
model termed the\(\tau _2\)-model, which utilizes the squared\(\ell _1/\ell _2\) norms for this …

Echo state networks are a recently developed type of recurrent neural network where the
internal layer is fixed with random weights, and only the output layer is trained on specific …

Image and signal processing problems of practical importance, such as incomplete data
recovery and compressed sensing, are often modeled as nonsmooth optimization problems …

For a reservoir computer composed of a single nonlinear node and delay line, we show that
after a finite period of discrete time, the distance between two reservoir outputs is bounded …

A common approach for performing sparse tensor recovery is to use an N-mode FISTA
method. However, this approach may fail in some cases by missing some values in the true …

Although largely different concepts, echo state networks and compressed sensing models
both rely on collections of random weights; as the reservoir dynamics for echo state …

The Dantzig selector (DS) is an efficient estimator designed for high-dimensional linear
regression problems, especially for the case where the number of samples n is much less …

Sažetak In most of the applications, signals acquired from different sensors are composite
and are corrupted by some noise. In the presence of noise, separation of composite signals …