12 citations · 22 across the 14 of their papers we have counts for
15 papers · 1 filter
Complex Graph Laplacian Regularizer for Inferencing Grid States
Chinthaka Dinesh, Junfei Wang, Gene Cheung +1
In order to maintain stable grid operations, system monitoring and control processes require the computation of grid states (e.g. voltage magnitude and angles) at high granularity.…
Efficient Directed Graph Sampling via Gershgorin Disc Alignment
Yuejiang Li, Hong Vicky Zhao, Gene Cheung
Graph sampling is the problem of choosing a node subset via sampling matrix to collect samples $\mathbf{y} = \mathbf{H} \mathbf{x} \in \mathbb…
Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems
Fen Wang, Gene Cheung, Taihao Li +2
Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations. In…
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
Chinthaka Dinesh, Gene Cheung, Ivan Bajic
3D point cloud (PC) -- a collection of discrete geometric samples of a physical object's surface -- is typically large in size, which entails expensive subsequent operations like v…
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Saghar Bagheri, Gene Cheung, Antonio Ortega +1
Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising. Previous graph learnin…
Sampling Signals on Graphs: From Theory to Applications
Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega +1
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…