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 …

Wireless sensor network (WSN) is an emerging and promising developing area in the
intelligent sensing field. Due to various factors like sudden sensors breakdown or saving …

An online algorithm for missing data imputation for networks with signals defined on the
edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we …

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 …

The recovery of time-varying graph signals is a fundamental problem with numerous
applications in sensor networks and forecasting in time series. Effectively capturing the …

The fractional Fourier transform (FRFT) parametrically generalizes the Fourier transform (FT)
by a transform order, representing signals in intermediate time-frequency domains. The …

In this paper, we propose a restoration method of time-varying graph signals, ie, signals on a
graph whose signal values change over time, using deep algorithm unrolling. Deep …

We introduce graph-time convolutional autoencoder (GTConvAE), a novel spatiotemporal
architecture tailored to learn unsupervised representations from multivariate time series on …

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical
problem in machine learning and signal processing with broad applications, ranging from …

The modeling of time-varying graph signals as stationary time-vertex stochastic processes
permits the inference of missing signal values by efficiently employing the correlation …