The study of sampling signals on graphs, with the goal of building an analog of sampling for
standard signals in the time and spatial domains, has attracted considerable attention …

Recently, graph theory has become a promising tool for biomedical signal analysis, wherein
the signals are transformed into a graph network and represented as either adjacency or …

Point cloud (PC)—a collection of discrete geometric samples of a 3D object's surface—is
typically large, which entails expensive subsequent operations. Thus, PC sub-sampling is of …

In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the
local and global properties of a complex graph structure within computer vision applications …

Signals typical in the real world have different modes, expressed as vectors, matrices, or
higher-order tensors. In practice, a target signal is commonly assumed to be linear in the …

Sampling is a fundamental problem in graph signal processing that selects a node subset to
collect samples, so that data in the remaining nodes can be well recovered. Existing eigen …

Learning a suitable graph is an important precursor to many graph signal processing (GSP)
pipelines, such as graph signal compression and denoising. Previous graph learning …

The multiagent rendezvous problem for single-and double-integrator agents in the presence
of unknown constant input and communication delays is investigated in this article. Existing …

We propose a denoising method of multimodal graph signals with twofold smoothness
regularization. Graph signal processing assumes that a signal has an underlying structure …

Sampling vector and tensor signals is the process of choosing sites in vectors and tensors to
place sensors in order to effectively recover the whole signals from a limited number of …