Publications related to Compressed Sensing and Bayesian Experimental Design

Gaussian versus Sparse Stochastic Processes

Although our work lies in the field of random processes, this thesis was originally motivated by signal processing applications, mainly the stochastic modeling of sparse signals. We develop a mathematical study of the innovation model, under which a signal ...

EPFL2017

On the manufacturing of compliant foil bearings

Jürg Alexander Schiffmann, Karim Magdi Zakaria Abdelrahman Shalash

The fabrication of foil bearings is challenging due to the dependence on sheet metal forming to produce the compliant structure. This paper is an attempt to shed light into the foil bearing manufacturing know-how. Design of experiments techniques are used ...

2017

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

How does one compute the noise power to simulate real and complex GNSS signals?

Cyril Botteron, Jérôme Leclère

Simulation of GNSS signals is very important to test and validate algorithms. With some algorithms, such as acquisition or tracking, we do not need to simulate a realistic constellation with actual ranges, Doppler or navigation data. We can simply simulate ...

2016

Direct Temperature Map Estimation in Optical Long Baseline Interferometry

Alessio Ferrari, Ferréol Arnaud Marie Soulez

We present a method to recontruct temperature map directly from spectro-interferometric data. It uses a sparse coding method to describe each pixel as a blackbody. Results are shown on the Herbig Be HD98922. Aside from recovering the mean environment tempe ...

SPIE2016

Evaluation of the shape deviation of non rigid parts from optical measurements

Loïc Andolfatto

This paper deals with an approach to identify geometrical deviations of flexible parts from optical measurements. Each step of the approach defines a specific issue which we try to respond to. The problem of measurement uncertainties is solved using an ori ...

Springer Verlag2016

Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

Vassily Hatzimanikatis, Julien Racle, Adam Stefaniuk

Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA pol ...

Amer Inst Physics2015

A consensus algorithm for networks with process noise and quantization error

Colin Neil Jones, Ye Pu, Andrea Alessandretti, Francisco Fernandes Castro Rego

In this paper we address the problem of quantized consensus where process noise or external inputs corrupt the state of each agent at each iteration. We propose a quantized consensus algorithm with progressive quantization, where the quantization interval ...

IEEE2015

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite-dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

2015

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

MATHICSE2015

Compressed Sensing and Bayesian Experimental Design

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Gaussian versus Sparse Stochastic Processes

On the manufacturing of compliant foil bearings

Sampling and Reconstruction of Shapes with Algebraic Boundaries

How does one compute the noise power to simulate real and complex GNSS signals?

Direct Temperature Map Estimation in Optical Long Baseline Interferometry

Evaluation of the shape deviation of non rigid parts from optical measurements

Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

A consensus algorithm for networks with process noise and quantization error

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Sampling and Reconstruction of Shapes with Algebraic Boundaries

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Noise analysis of genome-scale protein synthesis using a discrete computational model of translation

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Gaussian versus Sparse Stochastic Processes

How does one compute the noise power to simulate real and complex GNSS signals?

On the manufacturing of compliant foil bearings

Direct Temperature Map Estimation in Optical Long Baseline Interferometry

Evaluation of the shape deviation of non rigid parts from optical measurements

A consensus algorithm for networks with process noise and quantization error