Adaptive filters are at the core of many signal processing applications, ranging from acoustic
noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to …

Dynamic mode decomposition has emerged as a leading technique to identify
spatiotemporal coherent structures from high-dimensional data, benefiting from a strong …

In this letter, the RLS adaptive algorithm is considered in the system identification setting.
The RLS algorithm is regularized using a general convex function of the system impulse …

Identifying a nonlinear dynamic systems' governing equation is crucial for many engineering
applications, and yet a challenging task. In this study, the system's dynamics are …

Aluminum electrolysis cells are characterized by harsh environments where several
measurements have to be done manually. Due to the operational costs related to manual …

This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based
on an alternating optimization strategy with shrinkage. The proposed scheme employs a two …

In this work, we developed a nonlinear System Identification (SID) method that we called
Entropic Regression. Our method adopts an information-theoretic measure for the data …

A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC)
algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm …

Compressed sensing is a concept bearing far-reaching implications to signal acquisition
and recovery which yet continues to penetrate various engineering and scientific domains …

This letter proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm
to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum …