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Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Related publications (50)

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Autocalibrated Signal Reconstruction From Linear Measurements Using Adaptive Gamp

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Aurélien Bourquard

In this paper, we reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the message-passing algorithm called adapt ...

Ieee2013

Autocalibrated Signal Reconstruction from Linear Measurements Using Adaptive GAMP

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Aurélien Bourquard

IEEE2013

Generation of Nyquist sinc pulses using intensity modulators

Luc Thévenaz, Marcelo Alfonso Soto Hernandez, Mohammad Amin Shoaie, Mohammad Mehdi Alem Karladani

Optical sinc-shaped Nyquist pulses are produced based on the generation of an ideal frequency comb using cascaded intensity modulators. Nyquist pulses with 9.8-ps temporal width, 82-fs jitter and more than 40 dB SNR are achieved. ...

2013

Approximate Sparsity Pattern Recovery: Information-Theoretic Lower Bounds

Michael Christoph Gastpar

Recovery of the sparsity pattern (or support) of an unknown sparse vector from a small number of noisy linear mea- surements is an important problem in compressed sensing. In this paper, the high-dimensional setting is considered. It is shown that if the m ...

Institute of Electrical and Electronics Engineers2013

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

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

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

Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Signal Processing in Space and Time

Francisco Pereira Correia Pinto

Sound waves propagate through space and time by transference of energy between the particles in the medium, which vibrate according to the oscillation patterns of the waves. These vibrations can be captured by a microphone and translated into a digital sig ...

EPFL2010

Model-based compressive sensing

Volkan Cevher

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K < N elements from an N-dimensional basis. Instead of taking periodic samples, we measure inner ...

Institute of Electrical and Electronics Engineers2010