7 papers · 1 filter
Spectral Graph Uncertainty Principles via the Graph Fractional Fourier Transform
Yu Zhang, Bing-Zhao Li
This paper develops a graph fractional uncertainty principle in the graph fractional Fourier transform (GFRFT) domain. We introduce localization operators in the vertex domain and…
Graph Fractional Fourier Transform: A Unified and Efficient Sampling Theory
Yu Zhang, Jia-Yin Peng, Bing-Zhao Li
The graph Fourier transform (GFT) is a fundamental tool in graph signal processing and has recently been extended to the graph fractional Fourier transform (GFRFT). Existing sampli…
Two-Channel Filter Banks on Joint Time-Vertex Graphs with Oversampled Graph Laplacian Matrix
Yu Zhang, Bing-Zhao Li
To address the limitations of conventional critically sampled graph filter banks in joint time-vertex signal processing, which require decomposing the joint graph into bipartite su…
Sampling of Graph Signals Based on Joint Time-Vertex Fractional Fourier Transform
Yu Zhang, Bing-Zhao Li
With the growing demand for non-Euclidean data analysis, graph signal processing (GSP) has gained significant attention for its capability to handle complex time-varying data. This…
Graph Linear Canonical Transform: Definition, Vertex-Frequency Analysis and Filter Design
Jian Yi Chen, Bing Zhao Li
This paper proposes a graph linear canonical transform (GLCT) by decomposing the linear canonical parameter matrix into fractional Fourier transform, scale transform, and chirp mod…
Discrete Linear Canonical Transform on Graphs: Uncertainty Principle and Sampling
Yu Zhang, Bing-Zhao Li
With an increasing influx of classical signal processing methodologies into the field of graph signal processing, approaches grounded in discrete linear canonical transform have fo…