Publications associées à The Plenacoustic Function and its Sampling

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

In our recent work, the sampling and reconstruction of non-decaying signals, modeled as members of weighted-L-p spaces, were shown to be stable with an appropriate choice of the generating kernel for the shift-invariant reconstruction space. In this paper, ...

2019

Low Frequency Sound Field Reconstruction in Non-rectangular Room: A Numerical Validation

Hervé Lissek, Etienne Thierry Jean-Luc Rivet, Vu Thach Pham

Characterization of sound fields in rooms has always been a challenging task. A faithful reconstruction of sound fields in rooms generally would require an impractically high number of microphones. At low frequencies, where sound field can be modeled as a ...

2018

Sampling and Exact Reconstruction of Pulses with Variable Width

Martin Vetterli, Adam James Scholefield, Loïc Arnaud Baboulaz, Gilles Baechler

Recent sampling results enable the reconstruction of signals composed of streams of fixed-shaped pulses. These results have found applications in topics as varied as channel estimation, biomedical imaging and radio astronomy. However, in many real signals, ...

Institute of Electrical and Electronics Engineers2017

Sampling at unknown locations, with an application in surface retrieval

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Michalina Wanda Pacholska

We consider the problem of sampling at unknown locations. We prove that, in this setting, if we take arbitrarily many samples of a polynomial or real bandlimited signal, it is possible to find another function in the same class, arbitrarily far away from t ...

2017

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Matthieu Martin Jean-André Simeoni

Estimating intensity fields of stochastic phenomenons is of crucial interest in many scientific applications. Typical experimental setups involve an acquisition system, that subsequently filters and samples the probed intensity field. This equivalently def ...

2016

Shapes from Pixels

Mitra Fatemi

In today's digital world, sampling is at the heart of any signal acquisition device. Imaging devices are ubiquitous examples that capture two-dimensional visual signals and store them as the pixels of discrete images. The main concern is whether and how th ...

EPFL2016

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function u in multivariate polynomial spaces

P_\Lambda := span\{y \mapsto y^\nu | \nu \in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma := [-1,1]^d

, based on the sa ...

MATHICSE2015

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

Martin Vetterli, Jayakrishnan Unnikrishnan

Consider the task of sampling and reconstructing a bandlimited spatial field in

\Re^2

using moving sensors that take measurements along their path. It is inexpensive to increase the sampling rate along the paths of the sensors but more expensive to incre ...

Institute of Electrical and Electronics Engineers2013

On Sampling and Coding for Distributed Acoustic Sensing

Martin Vetterli, Emre Telatar, Robert Konsbruck

The issue of how to efficiently represent the data collected by a network of microphones recording spatio-temporal acoustic wave fields is addressed. Each sensor node in the network samples the sound field, quantizes the samples and transmits the encoded s ...

2012

The Plenacoustic Function and its Sampling

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Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Low Frequency Sound Field Reconstruction in Non-rectangular Room: A Numerical Validation

Sampling and Exact Reconstruction of Pulses with Variable Width

Sampling at unknown locations, with an application in surface retrieval

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Shapes from Pixels

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

On Sampling and Coding for Distributed Acoustic Sensing

Low Frequency Sound Field Reconstruction in Non-rectangular Room: A Numerical Validation

Approximation of Non-decaying Signals from Shift-Invariant Subspaces

Sampling and Exact Reconstruction of Pulses with Variable Width

Sampling at unknown locations, with an application in surface retrieval

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Attributing Aliasing Artifacts in Dirty Intensity Fields to their Parent Source

Shapes from Pixels

Sampling High-Dimensional Bandlimited Fields on Low-Dimensional Manifolds

On Sampling and Coding for Distributed Acoustic Sensing

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension