Talks and Poster Presentations (with Proceedings-Entry):
N. Görtz, S. C. Birgmeier:
"Sparse Measurement Matrices for Compressed-Sensing Recovery by Bayesian Approximate Message Passing";
Talk: 24th International ITG Workshop on Smart Antennas (WSA 2020),
Hamburg, Deutschland;
02-18-2020
- 02-20-2020; in: "24th International ITG Workshop on Smart Antennas",
VDE,
(2020),
ISBN: 978-3-8007-5200-3;
1
- 6.
English abstract:
Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing recovery algorithm. Simulations show that such sparse matrices, which allow for large savings in storage and computation time, can achieve a recovery performance that is as good as the benchmark given by random Gaussian matrices.
German abstract:
Sparse measurement matrices with very few randomly selected +1/-1 non-zero elements are designed for use with Bayesian Approximate Message Passing as a compressed sensing recovery algorithm. Simulations show that such sparse matrices, which allow for large savings in storage and computation time, can achieve a recovery performance that is as good as the benchmark given by random Gaussian matrices.
Keywords:
Signal Processing, Compressed Sensing, Approximate Message Passing
Created from the Publication Database of the Vienna University of Technology.