CIF:Small:Theoretical Foundations for Robust Signal Processing on Spatial Networks — NSF Award to University of Delaware (DE, $287
Many complex and interconnected phenomena in the world - such as social media, sensor grids, and brain connectivity - can be modeled using graphs or networks. Unlike classical signal processing, which works with data regularly arranged in a homogeneous space (like audio or images), graph signal processing (GSP) analyze
| Award title | CIF:Small:Theoretical Foundations for Robust Signal Processing on Spatial Networks |
|---|---|
| Award ID | 2427965 |
| Awardee | University of Delaware |
| City | NEWARK |
| State | DE |
| Amount obligated | $287,125 |
| Principal investigator | Mahya Ghandehari |
| Program | Comm & Information Foundations |
| Start date | 10/01/2025 |
| Abstract | Many complex and interconnected phenomena in the world - such as social media, sensor grids, and brain connectivity - can be modeled using graphs or networks. Unlike classical signal processing, which works with data regularly arranged in a homogeneous space (like audio or images), graph signal processing (GSP) analyzes signals that lie on irregular graphs or networks. Important outcomes of such analysis include detecting patterns, detecting and reducing noise, and visualizing the network. GSP h |
| Source | NSF Awards |
$799/mo
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