Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

We consider the problem of recovering random graph signals from nonlinear
measurements. For this setting, closed-form Bayesian estimators are usually intractable and …

In this article, we consider the problem of recovering random graph signals with complex
values. For general Bayesian estimation of complex-valued vectors, it is known that the …

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal
properties that can be exploited for effective graph signal reconstruction from limited …

Graph Signal Processing (GSP) leverages pair-wise relationship between nodes of a graph
to formulate operators on signals defined over the nodes. Most existing graph signal …

While frequency-domain algorithms have been demonstrated to be powerful for
conventional nonlinear signal processing, there is still not much progress in literature …

Conventional authentication techniques based on cryptography and computational
hardness are facing growing challenges for deployment in resource-constrained Internet-of …

This brief investigates distributed and adaptive estimation of streaming data based nonlinear
graph filters. To begin with, a new distributed polynomial filter for nonlinear graphs is …

This paper studies the problem of jointly estimating multiple network processes driven by a
common unknown input, thus effectively generalizing the classical blind multi-channel …

To address the difficulty and inflexibility associated with choosing kernel parameters for
single kernel adaptive filters (KAFs), this article proposes a multikernel adaptive filter for …