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Model-based compressive sensing

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Structure Modeling of High Dimensional Data

Mahdad Hosseini Kamal

The digitization of our most common appliances has led to a literal data deluge, some- times referred to as Big Data. The ever increasing volume of data we generate, coupled with our desire to exploit it ever faster, forces us to come up with innovative da ...

EPFL2015

Sub-Nyquist artifacts and sampling moiré effects, Part 2: Spectral-domain analysis

Isaac Amidror

Sampling moiré effects occur due to aliasing (foldover) when a continuous periodic signal g(x) is sampled using a sampling frequency that does not respect the Nyquist condition. However, visible beating artifacts may also occur when g(x) is sampled using s ...

2015

A reduced basis localized orthogonal decomposition

Assyr Abdulle, Patrick Henning

In this work we combine the framework of the Reduced Basis method (RB) with the framework of the Localized Orthogonal Decomposition (LOD) in order to solve parametrized elliptic multiscale problems. The idea of the LOD is to split a high dimensional Finite ...

2015

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014

Compressive Sampling Strategies for Multichannel Signals

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...

EPFL2012

Universal and efficient compressed sensing by spread spectrum and application to realistic Fourier imaging techniques

Pierre Vandergheynst, Yves Wiaux, Rémi Gribonval, Gilles Puy

We advocate a compressed sensing strategy that consists of multiplying the signal of interest by a wide bandwidth modulation before projection onto randomly selected vectors of an orthonormal basis. Firstly, in a digital setting with random modulation, con ...

Institute of Electrical and Electronics Engineers2012

Methods and apparatus for estimating a sparse channel

Martin Vetterli, Ali Hormati, Yann Barbotin

Embodiments include a method for sending a selected number of pilots (20) to a sparse channel having a channel impulse response limited in time comprising sending the selected number of the pilots (20). The pilots (20) are equally spaced in the frequency d ...

USPTO2011

Random Sampling ADC for Sparse Spectrum Sensing

Andreas Peter Burg

Scanning large bandwidths (spectrum sensing) pushes today’s analog hardware to its limits since periodic sampling at Nyquist rate with sufficient resolution is often prohibitively complex. In this paper, we consider a scenario where the signal to be acquir ...

2011

Diffractionless Fourier Domain Microscopy

Theo Lasser, Rainer Leitgeb, Cédric Blatter, Tilman Schmoll

Several methods have been proposed and realized to increase the lateral resolution in microscopy and ultimately to beat the actual diffraction limit as stated by Abbe. Structured illumination for example enhances the lateral resolution by virtually extendi ...

2011

Spread Spectrum for Universal Compressive Sampling

Pierre Vandergheynst, Yves Wiaux, Rémi Gribonval, Gilles Puy

We propose a universal and efficient compressive sampling strategy based on the use of a spread spectrum technique. The method essentially consists in a random pre-modulation of the signal of interest followed by projections onto randomly selected vectors ...

2011