Compressed sensing in the spherical near-field to far-field transformation
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Publication:2106500
DOI10.1007/978-3-031-09745-4_16zbMath1504.94030OpenAlexW4312369546MaRDI QIDQ2106500
Rudolf Mathar, Arya Bangun, Cosme Culotta-López, Dirk Heberling
Publication date: 14 December 2022
Full work available at URL: https://doi.org/10.1007/978-3-031-09745-4_16
Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Sampling theory in information and communication theory (94A20)
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Cites Work
- A mathematical introduction to compressive sensing
- A computational study of the homogeneous algorithm for large-scale convex optimization
- Distributing many points on a sphere
- On implementing a primal-dual interior-point method for conic quadratic optimization
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- PhaseMax: Convex Phase Retrieval via Basis Pursuit
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- Preface
- On the Gap Between Restricted Isometry Properties and Sparse Recovery Conditions
- Weighted Eigenfunction Estimates with Applications to Compressed Sensing
- Sensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group
- The Computational Complexity of the Restricted Isometry Property, the Nullspace Property, and Related Concepts in Compressed Sensing
- A Comparison of Popular Point Configurations on $\mathbb{S}^2$
- Phase Retrieval via Matrix Completion
- Compressed sensing
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