The Nuit Blanche Chronicles is a 1576 pages long / 52MB pdf file that features more than 900 entries related to compressive sensing from January 1, 2008 till December 20th, 2010 written on the blog. I did this because it has become cumbersome to do a search in the blog (the Google search feature does not work well anymore). The text of the abstract is not well delimited, the table of content is large and the titles of the entries are truncated, but it could be useful for those of you machiato-latte-drinking-iPad-reading yuppies. Get it here.
Today we also have a 50 MB presentation entitled: Sampling Theory and Practice: 50 Ways to Sample your Signal by Martin Vetterli.
But also three new papers:
Frequency extrapolation by nonconvex compressive sensing by Rick Chartrand, Emil Y. Sidky and Xiaochuan Pan. The abstract reads:
Tomographic imaging modalities sample subjects with a discrete, finite set of measurements, while the underlying object function is continuous. Because of this, inversion of the imaging model, even under ideal conditions, necessarily entails approximation. The error incurred by this approximation can be important when there is rapid variation in the object function or when the objects of interest are small. In this work, we investigate this issue with the Fourier transform (FT), which can be taken as the imaging model for magnetic resonance imaging (MRI) or some forms of wave imaging. Compressive sensing has been successful for inverting this data model when only a sparse set of samples are available. We apply the compressive sensing principle to a somewhat related problem of frequency extrapolation, where the object function is represented by a super-resolution grid with many more pixels than FT measurements. The image on the super-resolution grid is obtained through nonconvex minimization. The method fully utilizes the available FT samples, while controlling aliasing and ringing. The algorithm is demonstrated with continuous FT samples of the Shepp-Logan phantom with additional small, high-contrast objects.Fast orthogonal sparse approximation algorithms over local dictionaries by Boris Mailhe , Remi Gribonval , Pierre Vandergheynst , Frederic Bimbot. The abstract reads:
Video Compressed Sensing with Multihypothesis by Eric Tramel and Jim Fowler. The abstract reads:Abstract: In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries and time-frequency dictionaries, be they monoscale or multiscale. In this case, very fast implementations of Matching Pursuit are already available. LocOMP is almost as fast as Matching Pursuit while approaching the signal almost as well as the much slower Orthogonal Matching Pursuit.
The compressed-sensing recovery of video sequences driven by multihypothesis predictions is considered. Specifically, multihypothesis predictions of the current frame are used to generate a residual in the domain of the compressed-sensing random projections. This residual being typically more compressible than the original frame leads to improved reconstruction quality. To appropriately weight the hypothesis predictions, a Tikhonov regularization to an ill-posed least-squares optimization is proposed. This method is shown to outperform both recovery of the frame independently of the others as well as recovery based on single-hypothesis prediction.
and finally a job offer:
Postdocs in Signal Processing, Sensor Networks, and Compressive Sensing, Stevens Institute of Technology
* Date Posted Dec. 20, 2010
* Job Title
Postdocs in Signal Processing, Sensor Networks, and Compressive Sensing
Electrical and Computer Engineering
Stevens Institute of Technology
* Application Deadline Open until filled
* Position Start Date Available immediately
* Apply By E-mail Hongbin.Li@stevens.edu
* Job Categories Post-Doc
* Academic Fields Electrical and/or Electronics
Engineering - General
View University Jobs in New Jersey NEW JERSEY
STEVENS INSTITUTE OF TECHNOLOGY
We seek multiple outstanding post-doctoral associates to work in the following areas:
1) statistical signal processing with emphasis on space-time adaptive processing (STAP) and multi-input multi-output (MIMO) radars
2) wireless sensor networks (distributed detection/estimation/computing, consensus, etc.)
3) compressive sensing.
Appointment is for two years starting immediately, and may be renewable after the two-year period. For qualification, a PhD degree in electrical engineering, computer engineering, applied math/statistics, or a related field is required. Strong publication record in recognized journals is highly desirable.
To apply, send your CV, including a list of publications and references, to (email preferred)
Dr. Hongbin Li, Professor
Department of Electrical and Computer Engineering
Stevens Institute of Technology
Hoboken, NJ 07030, USA
Phone: (201) 216-5604; Fax: (201) 216-8246
Founded in 1870, Stevens is a premier private coeducational institution offering baccalaureate, masters and doctoral degrees in engineering, science, and management. The school is located in Hoboken, New Jersey; a historic town that is just minutes away from Manhattan, New York City.
* EEO/AA Policy
Stevens is an affirmative action/ equal opportunity employer.