Publications related to Uncertain Sampling with Certain Priors

Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

Jean-Philippe Thiran, Yves Wiaux, Rafael Eduardo Carrillo Rangel, Adrien Georges Jean Besson, Olivier Bernard

The theory of compressed sensing (CS) leverages upon structure of signals in order to reduce the number of samples needed to reconstruct a signal, compared to the Nyquist rate. Although CS approaches have been proposed for ultrasound (US) imaging with prom ...

Ieee2016

Shapes from pixels

Martin Vetterli, Arash Amini, Loïc Arnaud Baboulaz, Mitra Fatemi

Continuous-domain visual signals are usually captured as discrete (digital) images. This operation is not invertible in general, in the sense that the continuous-domain signal cannot be exactly reconstructed based on the discrete image, unless it satisfies ...

Institute of Electrical and Electronics Engineers2016

PCA using graph total variation

Pierre Vandergheynst, Nathanaël Perraudin, Benjamin Ricaud, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received sig- nificant attention of the signal processing and machine learning com- munity in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome th ...

Ieee2016

Riemannian Optimization For High-Dimensional Tensor Completion

Michael Maximilian Steinlechner

Tensor completion aims to reconstruct a high-dimensional data set where the vast majority of entries is missing. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem rest ...

Siam Publications2016

Implementation of Low-Power 6–8 b 30–90 GS/s Time-Interleaved ADCs With Optimized Input Bandwidth in 32 nm CMOS

Yusuf Leblebici, Lukas Kull, Thomas Toifl

A model for voltage-based time-interleaved sampling is introduced with two implementations of highly interleaved analog-to-digital converters (ADCs) for 100 Gb/s communication systems. The model is suitable for ADCs where the analog input bandwidth is of c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Learning Data Triage: Linear Decoding Works for Compressive MRI

Volkan Cevher, Yen-Huan Li

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for ...

Ieee2016

Generalized Poisson Summation Formula for Tempered Distributions

Michaël Unser, Quy Ha Nguyen

The Poisson summation formula (PSF), which relates the sampling of an analog signal with the periodization of its Fourier transform, plays a key role in the classical sampling theory. In its current forms, the formula is only applicable to a limited class ...

IEEE2015

Tutorial: Sub-Nyquist artifacts and sampling moiré effects

Isaac Amidror

Sampling moiré effects are well known in signal processing. They occur when a continuous periodic signal g(x) is sampled using a sampling frequency fs that does not respect the Nyquist condition, and the signal frequency f folds-over and gives a new, false ...

The Royal Society Publishing2015

Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Zafer Dogan

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the ...

EPFL2015

Uncertain Sampling with Certain Priors

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Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

Shapes from pixels

PCA using graph total variation

Riemannian Optimization For High-Dimensional Tensor Completion

Implementation of Low-Power 6–8 b 30–90 GS/s Time-Interleaved ADCs With Optimized Input Bandwidth in 32 nm CMOS

Learning Data Triage: Linear Decoding Works for Compressive MRI

Generalized Poisson Summation Formula for Tempered Distributions

Tutorial: Sub-Nyquist artifacts and sampling moiré effects

Random sampling of bandlimited signals on graphs

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

PCA using graph total variation

Compressed delay-and-sum beamforming for ultrafast ultrasound imaging

Shapes from pixels

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Random sampling of bandlimited signals on graphs

Implementation of Low-Power 6–8 b 30–90 GS/s Time-Interleaved ADCs With Optimized Input Bandwidth in 32 nm CMOS

Generalized Poisson Summation Formula for Tempered Distributions

Riemannian Optimization For High-Dimensional Tensor Completion

Learning Data Triage: Linear Decoding Works for Compressive MRI

Tutorial: Sub-Nyquist artifacts and sampling moiré effects