8 citations · 10 across the 6 of their papers we have counts for
6 papers · 1 filter
On Sampling of Multiple Correlated Stochastic Signals
Lin Jin, Hang Sheng, Hui Feng +1
Multiple stochastic signals possess inherent statistical correlations, yet conventional sampling methods that process each channel independently result in data redundancy. To lever…
Subset Random Sampling and Reconstruction of Finite Time-Vertex Graph Signals
Hang Sheng, Qinji Shu, Hui Feng +1
Finite time-vertex graph signals (FTVGS) provide an efficient representation for capturing spatio-temporal correlations across multiple data sources on irregular structures. Althou…
Sampling Theory of Jointly Bandlimited Time-vertex Graph Signals
Hang Sheng, Hui Feng, Junhao Yu +2
Time-vertex graph signal (TVGS) models describe time-varying data with irregular structures. The bandlimitedness in the joint time-vertex Fourier spectral domain reflects smoothnes…
Subset Random Sampling of Finite Time-vertex Graph Signals
Hang Sheng, Qinji Shu, Hui Feng +1
Time-varying data with irregular structures can be described by finite time-vertex graph signals (FTVGS), which represent potential temporal and spatial relationships among multipl…
Sampling of Correlated Bandlimited Continuous Signals by Joint Time-vertex Graph Fourier Transform
Zhongyi Ni, Feng Ji, Hang Sheng +2
When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples. In this paper, we study the sampling theory of multipl…
Sampling Theory of Bandlimited Continuous-Time Graph Signals
Feng Ji, Hui Feng, Hang Sheng +1
A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such…