Publications related to Divergent X-ray tomography reconstruction and optimisation

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...

2010

Impulse Radio Ultra-WideBand ranging and localization for sensor networks

Hai Zhan

In this thesis, we focus on Impulse Radio (IR) Ultra-WideBand (UWB) ranging and positioning techniques under indoor propagation environments. IR-UWB, a new carrierless communication scheme using impulses, is a candidate technology for future communication, ...

EPFL2009

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Jean-Yves Le Boudec, John Farserotu, Hai Zhan

We consider the problem of ranging with Impulse Radio (IR) Ultra-WideBand (UWB) radio under weak Line Of Sight (LOS) environments and additive Gaussian noise. We use a Bayesian approach where the prior distribution of the channel follows the IEEE 802.15.4a ...

IEEE2009

Acoustic tomography for scalar and vector fields: theory and application to temperature and wind estimation

Martin Vetterli, Luciano Sbaiz, Ivana Jovanovic

Acoustic tomography is a type of inverse problem. The idea of estimating physical quantities that influence sound propagation by measuring the parameters of propagation has proven to be successful in many practical domains, including temperature and wind e ...

2009

Inverse problems in acoustic tomography

Ivana Jovanovic

Acoustic tomography aims at recovering the unknown parameters that describe a field of interest by studying the physical characteristics of sound propagating through the considered field. The tomographic approach is appealing in that it is non-invasive and ...

EPFL2008

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Matthias Seeger, Matthias Bethge

Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full poster ...

2008

Hard X-ray phase tomography with low-brilliance sources

Christian David

We report on a method for tomographic phase contrast imaging of centimeter sized objects. As opposed to existing techniques, our approach can be used with low-brilliance, lab based x-ray sources and thus is of interest for a wide range of applications in m ...

2007

Spectral Properties and Stability of Self-Similar Wave Maps

Roland Donninger

In this thesis the Cauchy problem and in particular the question of singularity formation for co--rotational wave maps from 3+1 Minkowski space to the three--sphere

S^3

is studied. Numerics indicate that self--similar solutions of this model play a cruci ...

University of Vienna2007

Tomographic reconstruction of three-dimensional objects from hard X-ray differential phase contrast projection images

Christian David

We report on a method for tomographic phase contrast imaging of centimeter sized objects. As opposed to existing techniques, our approach can be used with low-brilliance, lab based X-ray sources and thus is of interest to a wide range of applications in me ...

2007

Use of digital holographic microscopy in tomography

Christian Depeursinge, Tristan Colomb, Etienne Cuche, Frédéric Montfort, Pierre Marquet, Jonas Kühn, Florian Charrière, Mihaela Marian

Digital Holographic Microscopy (DHM) provides three-dimensional (3D) images with a high vertical accuracy in the nanometer range and a diffracted limited transverse resolution. This paper focuses on 3 different tomographic applications based on DHM. First, ...

SPIE2006

Divergent X-ray tomography reconstruction and optimisation

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Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Impulse Radio Ultra-WideBand ranging and localization for sensor networks

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Acoustic tomography for scalar and vector fields: theory and application to temperature and wind estimation

Inverse problems in acoustic tomography

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Hard X-ray phase tomography with low-brilliance sources

Spectral Properties and Stability of Self-Similar Wave Maps

Tomographic reconstruction of three-dimensional objects from hard X-ray differential phase contrast projection images

Use of digital holographic microscopy in tomography

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Impulse Radio Ultra-WideBand ranging and localization for sensor networks

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

A Novel Bayesian Impulse Radio Ultra-WideBand Ranging Algorithm

Acoustic tomography for scalar and vector fields: theory and application to temperature and wind estimation

Inverse problems in acoustic tomography

Hard X-ray phase tomography with low-brilliance sources

Spectral Properties and Stability of Self-Similar Wave Maps

Tomographic reconstruction of three-dimensional objects from hard X-ray differential phase contrast projection images

Use of digital holographic microscopy in tomography