Graph signal processing (GSP) uses network structures to analyze and manipulate
interconnected signals. These graph signals can also be time-varying. The established joint …

This paper generalizes the proportionate-type adaptive algorithm to the graph signal
processing and proposes two proportionate-type adaptive graph signal recovery algorithms …

The Cyclic prefix (CP) in traditional OFDM systems is used to combat inter-symbol
Interference (ISI) and inter-carrier Interference (ICI), which simplifies the design of the …

This brief proposes a joint graph signal recovery and topology learning algorithm using a
Variational Bayes (VB) framework in the case of non-Gaussian measurement noise. It is …

This paper develops a distributed reconstruction algorithm, that can be implemented
efficiently, for time-varying graph signals. The reconstruction problem is formulated as an …

This brief investigates the problem of Graph Signal Recovery (GSR) when the topology of
the graph is not known in advance. In this brief, the elements of the weighted adjacency …

Sampling and reconstruction on the spatially distributed networks is an innovative topic in
graph signal processing. Recently, it has been shown that k-bandlimited graph signals can …

On the Euclidean domains of classical signal processing, linking of signal samples to
underlying coordinate structures is straightforward. While graph adjacency matrices totally …

In this paper, the graph signal recovery problem is addressed by employing an aggregation
of samples in the vertex domain and the Fourier graph transform domain. The statistical …

This paper presents a new algorithm for reconstructing time-varying graph signals using
spatiotemporal feature learning. We introduce a time series analysis method to capture the …