Topology reconstruction is of great importance for fault location in smart grids. However, with
the increasing development of grid infrastructure, the measurement perturbation increases …

In this paper, we consider a broad class of nonconvex and nonsmooth composition
optimization problems that can be used to model many applications in signal processing …

One-bit compressive sensing is concerned with the accurate recovery of an underlying
sparse signal of interest from its one-bit noisy measurements. The conventional signal …

This article addresses the parametric identification of block‐structured nonlinear systems in
a general form, characterized by the feedback interconnection of a multivariable linear …

In safety-critical applications, microcontrollers must meet stringent quality and performance
standards, including the maximum operating frequency F max. Machine learning models …

This paper addresses the problem of recursive set-membership identification for linear time
varying (LTV) systems when both input and output measurements are affected by bounded …

Given $ p $ samples, each of which may or may not be defective, group testing (GT) aims to
determine their defect status by performing tests on $ n< p $groups', where a group is …

Recovery of signals with elements defined on the nodes of a graph, from compressive
measurements is an important problem, which can arise in various domains such as sensor …

This paper aims to solve a system of linear equations with measurement noises. Different
from existing noise-free or stochastic formulations, we assume the noisy data is subject to …

In this paper, a recursive identification approach to single-input single-output linear time
varying (LTV) systems when both the output and the input measurements are corrupted by …