5 papers
Optimal Sampling and Reconstruction of Graph Signals in the Fractional Fourier Domain
Xiaopeng Cheng, Zhichao Zhang, Yangfan He
Graph signal sampling and reconstruction are commonly formulated in the graph Fourier transform (GFT) domain. However, the reconstruction performance may be limited when practical…
A Novel Unified Extended Matrix for Graph Signal Processing: Theory and Application
Yunyan Zheng, Zhichao Zhang, Wei Yao
Graph signal processing has become an essential tool for analyzing data structured on irregular domains. While conventional graph shift operators (GSOs) are effective for certain t…
MPFSR-Enhanced GNNs: Spectral Graph Neural Networks Enhancement Through Learnable Multiple-Parameter Graph Fractional Fourier Transforms
Manjun Cui, Xiaopeng Cheng, Yangfan He +1
Graph neural networks (GNNs) excel in processing non-Euclidean data, but traditional spectral GNNs rely on static bases and fundamentally lack active spectral regulation. Although…
A Unified Framework for 2D Nonseparable Fractional Fourier Transform: From Geometric Completeness to Applications
Daxiang Li, Zhichao Zhang, Wei Yao
The one-dimensional (1D) fractional Fourier transform (FRFT) generalizes the Fourier transform, offering significant advantages in the time-frequency analysis of non-stationary sig…
Graph Chirp Signal and Graph Fractional Vertex-Frequency Energy Distribution
Manjun Cui, Zhichao Zhang, Wei Yao
Graph signal processing (GSP) has emerged as a powerful framework for analyzing data on irregular domains. In recent years, many classical techniques in signal processing (SP) have…