Talks and Poster Presentations (with Proceedings-Entry):
R. R. Müller, A. Bereyhi, C. Mecklenbräuker:
"Oversampled Adaptive Sensing with Random Projections";
Talk: Information Theory and Applications Workshop (ITA 2018),
San Diego (CA), USA (invited);
- 02-16-2018; in: "Information Theory and Applications Workshop (ITA 2018)",
We develop a Bayesian framework for sensing which adapts the sensing time and/or basis functions to the instantaneous sensing quality measured in terms of the expected posterior mean-squared error. For sparse Gaussian sources a significant reduction in average sensing time and/or mean-squared error is achieved in comparison to non-adaptive sensing. For compression ratio 3, a sparse 10% Gaussian source and equal average sensing times, the proposed method gains about 2 dB over the performance bound of optimum compressive sensing, about 3 dB over non-adaptive 3-fold oversampled orthogonal sensing and about 6 to 7 dB to LASSO-based recovery schemes while enjoying polynomial time complexity.
We utilize that in the presence of Gaussian noise the mean-squared error conditioned on the current observation is proportional to the derivative of the conditional mean estimate with respect to this observation.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Created from the Publication Database of the Vienna University of Technology.