CS: Near-Optimal Bayesian Localization via Incoherence and Sparsity, Lp/RIP/Pessimism, Two jobs and a call for paper

Everybody has seen the Youtube video, but for those interested in using it as an example in a paper, the avi video is on the Coast Guards' site. Maybe it could be used as a benchmark of some kind. In the meantime, we have one paper, two job announcements for two postdocs, an internship and a CfP.

Volkan Cevher just sent me this freshly accepted and very interesting paper entitled Near-Optimal Bayesian Localization via Incoherence and Sparsity by himself, Petros Boufounos, Richard Baraniuk, Anna Gilbert, Martin Strauss. The abstract reads:

Source localization using a network of sensors is a classical problem with applications in tracking, habitat monitoring, etc. A solution to this estimation problem must satisfy a number of competing resource constraints, such as estimation accuracy, communication and energy costs, signal sampling requirements and computational complexity. This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal solution. By exploiting the spatial sparsity of the posterior density, we demonstrate that the optimal solution can be computed using fast sparse approximation algorithms. We show that exploiting the signal sparsity can reduce the sensing and computational cost on the sensors, as well as the communication bandwidth. We further illustrate that the sparsity of the source locations can be exploited to decentralize the computation of the source locations and reduce the sensor communications even further. We also discuss how recent results in 1-bit compressive sensing can impact the sensor communications by transmitting only the timing information relevant to the problem. Finally, we develop a computationally efficient algorithm for bearing estimation using a network of sensors with provable guarantees.

I'll have to dwell into it further, but the mixing of sensor network and 1-bit compressive sensing makes it a unique and promising approach.

Remi Gribonval released the slides of his lecture at the Cambridge Workshop on Sparsity and Large-Scale inverse problems, on Lp minimisation and the role of restricted isometry constants. The presentation is entitled: Some stories about Lp-minimization, the Restricted Isometry Property, and excessive pessimism.. I should come back to this later and will probably integrate it in the Big Picture Section.

Remi also made me aware of this first announcement as featured in the Compressive Sensing Jobs listing:

January 16th, 2009: Two Post-Docs at INRIA Rennes, France (IRISA). The postdocs will work on theoretical, algorithmic and practical aspects of sparse representations of large-dimensional data, with a particular emphasis on acoustic fields, for various applications such as compressed sensing, source separation and localization, and signal classification. Previous experience in sparse representations (time-frequency and time-scale transforms, pursuit algorithms, support vector machines and related approaches) is desirable, as well as a strong taste for the mathematical aspects of signal processing. For additional technical information, please contact :Rémi GRIBONVAL - SMALL/ECHANGE project leader - METISS Project-Team - INRIA-Bretagne Atlantique - Email: remi.gribonval@inria.fr - phone: +33 2 99 84 25 06. The positions, funded for at least 2 years (up to three years), will be renewed on a yearly basis depending on scientific progress and achievement. The gross minimum salary will be 28287 € annually (~ 1923 € net per month) and will be adjusted according to experience. The usual funding support of any French institution (medical insurance, etc.) will be provided. More information can be found here

and then I found this one on the internets:

January 16th, 2009: Intern at Microsoft Research Asia. Beijing. MRA is recruiting interns for Stars of Tomorrow Internship Program.. For more information see here.
Position: Full-time Intern
Group: Visual Computing Group
Quantity: 1
Work Location: Beijing. The interns will be working with the VC manager, professor Yi Ma, on applications of sparse representation and compressive sensing to problems in computer vision and pattern recognition (e.g., face recognition, image/video segmentation and analysis). The interns will be required to conduct programming, simulation, and experiment for various research projects, or if capable, conduct mathematical analysis of algorithms and solve new problems. Qualified applicants please fill in the application form and send it together with a full resume in both English and Chinese (PDF/Word/Txt/Html format) to: MSRAih@microsoft.com, and please note you are applying for MSRA VC Group. Know more about Stars of Tomorrow Internship Program, please visit http://www.msra.cn/ur/intern.aspx. If any question, please email to msrastar@microsoft.com.

Finally, here is a Call for Paper in the IEEE Signal Processing Magazine (SPM) Convex Optimization for Signal Processing (SPM 2009)

• Submission Deadline: May 5, 2009
• Notification Due: Dec 1, 2009
• Final Version Due: Jan 15, 2010

link: http://apollo.ee.columbia.edu/spm/?i=cfp/May10

Call For Papers

In recent years, we have witnessed technical breakthroughs in a wide variety of topics where the key to success is the use of convex optimization. In fact, convex optimization has now emerged as a major signal processing technique and has made significant impact on numerous problems previously considered intractable. Today, innovative applications of convex optimization in signal processing range from those in adaptive filtering, detection and estimation, sensor array processing, MIMO communications, sensor networks, sampling theory, and more recently, image processing, speech processing and cognitive radios - and the scope of its applications is still expanding. Considering the foundational nature and potential impact of convex optimization in signal processing, there appears to be a clear need for a special issue that introduces convex optimization to the broad signal processing community, gives insights into how convex optimization can make a difference, and showcases some notable successes. This special issue aims to solicit papers that provide tutorials of convex optimization techniques (including available software) and various successful signal processing applications. Also welcome are tutorial papers that deal with emerging, meaningful applications; or that give friendly overviews of certain theoretically advanced convex optimization techniques relevant to signal processing.

To enhance readability and appeal for a broad signal processing audience, prospective authors are encouraged to use an intuitive approach in their presentation; e.g., by using simple instructive examples, considering special cases that show insights into the ideas, and using illustrations to the extent possible.

Examples of topics that will be addressed in this special issue include, but are not limited to:

* Adaptive filtering
* Beamforming
* Convex optimization fundamentals including relaxation techniques and software toolboxes
* Image processing applications, including denoising, MRI image processing, phase unwrapping
* Compressed sensing
* Detection and estimation
* MIMO communications
* Sensor networks
* Cognitive radios
* Speech applications

Submission Procedure:

Prospective authors should submit their white papers through the web submission system at www.ee.columbia.edu/spm. The white paper should be no more than 6 pages in the IEEE double-space one-column 11-point format.

Schedule (all deadlines are firm no exceptions)

• White paper due: May 5, 2009
• Invitation notification: June 1, 2009
• Manuscript submission: September 1, 2009
• Notification of acceptance: December 1, 2009
• Final manuscript decision: January 15, 2010
• Publication date: May, 2010

Guest Editors:

• Yonina Eldar, Technion, Israel Institute of Technology, yonina@ee.technion.ac.il
• Zhi-Quan Luo, University of Minnesota, luozq@umn.edu
• Wing-Kin (Ken) Ma, The Chinese University of Hong Kong, wkma@ee.cuhk.edu.hk
• Daniel Palomar, Hong Kong University of Science and Technology, palomar@ust.hk
• Nikos Sidiropoulos, Technical University of Crete, nikos@telecom.tuc.gr