Compressed Sensing on NPR's All Tech Considered and more.

Jordan Ellenberg mentioned his interview on NPR's All Tech Considered. For those of you not in the US, NPR stands for National Public Radio and is considered a very serious radio station that provides thoughtful shows. The Big Picture is featured on the blog of All Tech Considered senior producer Art Silverman. Woohoo.

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Laura Balzano's site illustrating different audio examples can be found here. The page has been added to the big picture in compressive sensing. I have had a similar experience recently, i.e. I tried to explain compressive sensing quickly to a friend. The group testing story was a very nice and efficient way of approaching the subject. My friend got it almost instantaneously.

On a different note, The LinkedIn group on Compressive Sensing has 399 members, who is going to be the 500th ?

Here is a recent discussion that occurred in the group. For info, the latest news of that group features the RSS feed on the blog.

Amit Ashok let me know that a non-paywall version of Compressive light field imaging is available from his site. Thanks Amit.

This one just appeared on arxiv:



Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing by Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten. The abstract reads:

In this paper, we address the theoretical limitations in reconstructing sparse signals (in a known complete basis) using compressed sensing framework. We also divide the CS to non-blind and blind cases. Then, we compute the Bayesian Cramer-Rao bound for estimating the sparse coefficients while the measurement matrix elements are independent zero mean random variables. Simulation results show a large gap between the lower bound and the performance of the practical algorithms when the number of measurements are low.

My question is: why was this article rejected ?