CS: Making CS Mainstream, CS Data Recovery in Wireless Sensor Networks, Routing and Signal for CS in WSNs, Very High Resolution SAR, Postdoc

One of the ways to make compressive sensing more mainstream is to enable tinkerers to play with something that implements some sorts of controlled multiplexing. Case in point, what could be done with the newly released tiny DMD based projectors or these ? and what about this DMD kit with no driver ? then there is always the more expensive DLP kits from TI ? By the way, if you are wondering how much an SLM cost, such the one used in the work of by Ivo Vellekoop and Allard Mosk ( Phase control algorithms for focusing light through turbid media ) that is about 3,000 US$ and up.

Today, we have papers and jobs mostly from Europe, enjoy!

A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks by Riccardo Masiero, Giorgio Quer, Michele Rossi and Michele Zorzi. The abstract reads:

In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using Compressive Sensing (CS) in conjunction with Principal Component Analysis (PCA). Our scheme compresses in a distributed way real world non-stationary signals, recovering them at the data collection point through the online estimation of their spatial/temporal correlation structures. The proposed technique is hereby characterized under the framework of Bayesian estimation, showing under which assumptions it is equivalent to optimal maximum a posteriori (MAP) recovery. As the main contribution of this paper, we proceed with the analysis of data collected by our indoor wireless sensor network (WSN) testbed, proving that these assumptions hold with good accuracy in the considered real world scenarios. This provides empirical evidence of the effectiveness of our approach and proves that CS is a legitimate tool for the recovery of real-world signals in WSNs.

On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks by Giorgio Quer, Riccardo Masiero, Daniele Munaretto, Michele Rossi, Joerg Widmer and Michele Zorzi. The abstract reads:

Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this work we address the data gathering problem in WSNs, where routing is used in conjunction with CS to transport random projections of the data.We analyze synthetic and real data sets and compare the results against those of random sampling. In doing so, we consider a number of popular transformations and we find that, with real data sets, none of them are able to sparsify the data while being at the same time incoherent with respect to the routing matrix. The obtained performance is thus not as good as expected and finding a suitable transformation with good sparsification and incoherence properties remains an open problem for data gathering in static WSNs.

At Fringe 2009, a meeting organized by ESA in Italy the following paper will be presented (only the abstract is available): Very High Resolution SAR Tomography via Compressive Sensing by Zhu Xiaoxiang and Bamler Richard. The abstract reads:

SAR tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. It uses stacks of several acquisitions from slightly different viewing angles (the elevation aperture) to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel.

The new class of meter-resolution spaceborne SAR systems (TerraSAR-X and COSMO-Skymed) offers a tremendous improvement in tomographic reconstruction of urban areas and man-made infrastructure. The high resolution fits well to the inherent scale of buildings (floor height, distance of windows, etc.). In order to fully exploit the potential of this class of meter-resolution data there is demand for new and improved TomoSAR inversion algorithms.

Compressive sensing (CS) is a new and attractive technique for TomoSAR. It aims at minimizing the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. It provides a good compromise between classical parametric and non-parametric spectral analysis methods for TomoSAR. Compared to parametric spectral analysis, CS is more robust to phase noise, has lower computational effort, and does not require model selection to provide the prior knowledge about the number of scatterers in a resolution cell. Compared to non-parametric spectral estimation CS overcomes the limitation of elevation resolution caused by the length of elevation aperture, i.e. CS has super-resolution properties.

In this paper the CS approach to TomoSAR is outlined. Its extension to differential (4-D) TomoSAR is introduced. Numerical simulations for realistic acquisition and noise scenarios will be presented to evaluate the potential and limits of the new technique. The first CS TomoSAR results with TerraSAR-X spotlight data (1 m resolution) over urban areas will be presented.

Finally, here a postdoc job offer that I will be put shortly on the Compressive Sensing Jobs page:
Postdoctoral Position in Wireless Communications at University of Vigo, Spain,

Description

The Signal Processing in Communications Group (GPSC, www.gts.tsc.uvigo.es/gpsc ), affiliated with the Department of Signal Theory and Communications at University of Vigo, Spain, invites applications for one postdoctoral positions in the field of wireless communications. The selected candidates will join GPSC to investigate fundamentals and algorithm design/evaluation for communication and sensor networks. Areas of particular interest include:

• Sensor networks
• Cognitive Radio
• Compressed Sensing

GPSC is formed by 6 faculty members from the University of Vigo as well as several MSc and PhD students, and participates in several research projects funded by the European Commission and the Spanish Government. Among these, the recently launched COMONSENS project (www.comonsens.org) integrates investigators from 10 different top research institutions in Spain. GPSC members also actively collaborate with the Galician R&D Center in Advanced Telecommunications (GRADIANT, www.gradiant.org ) in diverse contracts with ICT companies. Thus, the selected candidates will enjoy unique opportunities to participate in exciting research projects with both industry and academia.

Desirable background

• A Ph.D. degree in Electrical Engineering is required; PhD obtained before January 1, 2007
• Knowledge and experience in sensor networks, cognitive radio and/or compressed sensing
• Good verbal and written skills in English are required
• Strong publications in international conferences and journals in the area of communications
• Postdoctoral experience in a recognized group with expertise in the field is a plus
• Experience in the organization, management and training of technical staff/students is a plus
• Communication, computing and interpersonal skills are important
• Capacity to work both independently and within a team

Contract conditions
The initial appointment will be for one year, with annual renewal dependent on funding and performance. Expected start date is January 2010. Successful applicants will be offered a 36,000 € yearly gross salary depending on qualifications, as well as health benefits.
Applications
Interested candidates may apply to Prof. Nuria González Prelcic (nuria@gts.tsc.uvigo.es).
Applications should include electronic copies of the following:

• A detailed Curriculum Vitae. (*Please include your e-mail address and a recent picture.)
• A cover letter addressing the specified job qualifications.
• A letter of recommendation by a senior Professor/Researcher.
• A copy of the publication deemed as best representative of the candidate’s creative research.

Priority consideration will be given to applications received by November 1, 2009. Applications will be accepted until position is filled.
Contact: Nuria González Prelcic
Departamento de Teoría de la Señal y Comunicaciones
ETSET. University of Vigo
36310 Vigo. Spain
Phone: +34 986 818656, e-mail: nuria@gts.tsc.uvigo.es

Credit: Palomar Observatory, This image of the moon was taken by the Palomar Observatory at the time of the LCROSS impact.