A comment, Radar course results and some workshop talks in Compressive Sensing

Unlike what I said originally comparing it to some other work, Imaging via Three-dimensional Compressive Sampling (3DCS) by Xianbiao Shu and Narendra Ahuja portends to deal with the third dimension of videos (time) with TV regularization. Very interesting.

Greg Charvat sent me the following:

Hi Igor,

A number of your readers from nuit-blanche were attending our 1-week build a radar course at MIT this past week (http://web.mit.edu/professional/short-programs/courses/radar_systems.html ).In fact, one of the guys who (along with his team) heard about this course from your blog acquired this SAR image of MIT's Kresge auditorium:  http://mrvacuumtube.blogspot.com/2011/08/update-on-mit-prof-ed-1-week-radar_06.html 

Other results are as follows:http://mrvacuumtube.blogspot.com/2011/08/update-on-mit-prof-ed-1-week-radar_06.htmlhttp://mrvacuumtube.blogspot.com/2011/08/update-on-mit-prof-ed-1-week-radar.html 

And what it looks like to distribute 30 radar kits:http://mrvacuumtube.blogspot.com/2011/08/update-on-mit-prof-ed-1-week-radar.html 

Thank you again for posting.  Thanks to your help we have spread the knowledge of developing SAR radar sensors to another 30 people this past week. 

Cheers, 

Greg

Thanks Greg, let's get more people in the business of getting large amount of data through strange means :)

In other news, Dick Lipton mentions the Ann Arbor Coding, Complexity and Sparsity meeting of last week in Give Me A Lever. Some of the talks are already on the workshop website. Here is a list of the ones I found this morning:

• Anna Gilbert Title: A Survey of Sparse Approximation (pdf)
• Brett HemenwayTitle: Error Correction (pdf)
• Shachar Lovett  Title: Pseudorandomness - tutorial
• Andrew McGregor Title: Data Streams Tutorial
• Mike Saks, Rutgers UniversityTitle: Approximating the longest increasing subsequence in polylogarithmic time
• David Woodruff Title: (1+eps)-Approximate Sparse Recovery

The Texas A&M meeting on Greedy Algorithms in Banach spaces and Compressed Sensing has all its talks/slides up:

Fernando Albiac A charachterization of 1-greedy bases. Talk

Gagik Amirkhanyan Convergence of greedy approximation in L_p(0,1) and C(0,1) with respect to Walsh system.

Stephen Dilworth Nigel Kalton's work on greedy algorithms in Banach spaces. Talk1 Talk2

Kevin Ford New explicit constructions of RIP matrices. Talk

Smbat Gogyan On greedy algorithm in L1(0,1). Talk

Eugenio Hernández Democracy functions and optimal embeddings for approximation spaces. Talk

Anna Kamont Rearrangements with supporting trees, isomorphisms and combinatorics of coloured dyadic intervals.

Boris Kashin Compressed Sensing and Approximation of the Partial Sum of the Dirichlet Series.

Tatiana Kulikova Zero sets of odd polynomials and the quantitative sharpenning of a theorem by Birch.

Denka Kutzarova Branch greedy algorithms.

Entao Liu Super greedy type algorithms and their applications.

Eugene Livshitz On the optimality of Orthogonal Greedy Algorithm for dictionaries with small coherence. Talk

Maria De Natividade Democracy functions for wavelet bases in Lorentz spaces. Talk

Krzysztof Smela Survey and remarks on greedy algorithms.

Vladimir Temlyakov Lebesgue-type inequalities for greedy approximation with respect to bases. Talk1 Talk2 Talk3

Rachel Ward Near-equivalence of the Restricted Isometry Property and the Johnson-Lindenstrauss Lemma. Talk

Przemysław Wojtaszczyk What I know and do not know about compressed sensing?Talk1 Talk2 Talk3

Andras Zsak One-greedy bases.

A big thank you to all the organizers.