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In this paper, we present a novel network‐based approach for reconstructing signed distance functions from fluid particles. The method uses a weighting kernel to transfer particles to a regular grid, which forms the input to a convolutional neural network. We propose a regression‐based regularization to reduce surface noise without penalizing high‐curvature features. The reconstruction exhibits improved spatial surface smoothness and temporal coherence compared with existing state of the art surface reconstruction methods. The method is insensitive to particle sampling density and robustly handles thin features, isolated particles, and sharp edges.
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Accelerated Graph Learning from Smooth Signals
We consider network topology identification subject to a signal smoothness prior on the nodal observations. A fast dual-based proximal gradient algorithm is developed to efficiently tackle a strongly convex, smoothness-regularized network inverse problem known to yield high-quality graph solutions. Unlike existing solvers, the novel iterations come with global convergence rate guarantees and do not require additional step-size tuning. Reproducible simulated tests demonstrate the effectiveness of the proposed method in accurately recovering random and real-world graphs, markedly faster than state-of-the-art alternatives and without incurring an extra computational burden.
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- PAR ID:
- 10301111
- Date Published:
- Journal Name:
- IEEE Signal Processing Letters
- ISSN:
- 1070-9908
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LSP.2021.3123459
