Process systems are characterized by nonlinearity, uncertainty, large scales, and also the
need of pursuing both safety and economic optimality in operations. As a result they are …

This article provides an overview of the current landscape of signal processing (SP) on
directed graphs (digraphs). Directionality is inherent to many real-world (information …

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 …

Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …

The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

The goal of this paper is to establish the fundamental tools to analyze signals defined over a
topological space, ie a set of points along with a set of neighborhood relations. This setup …

Contemporary power grids are being challenged by rapid and sizeable voltage fluctuations
that are caused by large-scale deployment of renewable generators, electric vehicles, and …

Robust screening of materials on the basis of structure–property–activity relationships to
discover active photocatalysts is a highly sought out aspect of photocatalysis research …

Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical
in many recent graph spectral signal restoration schemes, including image denoising …