Rui just sent me the following:

Hi Igor,

Not sure if this is the appropriate way to do it, but I wanted to (shamelessly) point you (and the readers of nuit blanche) to our recent work on adaptive compressive sensing for structured sparsity:

In this work we investigate the problem of estimating the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the signal strength required (and sufficient) to accurately estimate such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.

Hope this is interesting to you and your readers! All the best,

Rui

--

Dept. of Mathematics and Computer Science, TU/e

http://www.win.tue.nl/~rmcastro

No shame to be had Rui, it's good and interesting work !

Here is the paper: Adaptive Compressed Sensing for Estimation of Structured Sparse Sets by Rui M. Castro, Ervin Tánczos

This paper investigates the problem of estimating the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately estimating such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.

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