Monday, October 22, 2012

Two jobs for students and two talks.

I just came across these two jobs for students and these two interesting talks.
The talks:

@ Supelec (France) but also broadcast here.
Structure Based Bayesian Sparse Reconstruction
Speakers: Tareq Y. Al-Naffouri, KAUST, Saudi Arabia
Date: Wednesday, October 31, 2012 - 14:00 to 15:00
Location: Council room of L2S (room B4.40), Supélec, campus of Gif-sur-Yvette, Supélec.

Abstract: There has been increased interest in sparse signal reconstruction algorithms (commonly known as compressed sensing) due to their wide applicability in various fields. In this talk, we present a novel low complexity Bayesian approach to the estimation of sparse signals. The approach jointly utilizes 1) the sparsity information of the desired signal 2) the a priori statistical information about the signal and noise and 3) the inherent structure in the sensing matrix to obtain near optimal Bayesian estimates. The proposed approach is able to deal with both Gaussian and non-Gaussian priors. The approach also exhibits relatively low complexity compared to the widely used convex relaxation methods as well as greedy matching pursuit techniques. The discussion will be illuminated with several signal processing applications including channel estimation in UWB, seismic deconvolution, and estimation and cancellation of noise/distortion.

@ UBC, Stephan Wenger Talk - Visualization Of Astronomical Nebulae Via Distributed Multi-GPU Compressed Sensing Tomography, DATE: TUESDAY, OCTOBER 23, 2012 | 1:00PM - 2:30PM

Title: Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography


"The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae.Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry."

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