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Optimal Spectral Feature Detection for Spectrum Sensing at Very Low SNR

Related publications (45)

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Super Resolution Phase Retrieval for Sparse Signals

Martin Vetterli, Juri Ranieri, Gilles Baechler, Amina Chebira, Miranda Krekovic

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Validation of probabilistic classifiers

Michel Bierlaire, Timothy Michael Hillel

Non-parametric probabilistic classification models are increasingly being investigated as an alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...

2018

The detection of two-component mixture alternatives

Daria Rukina

In this thesis, we deal with one of the facets of the statistical detection problem. We study a particular type of alternative, the mixture model. We consider testing where the null hypothesis corresponds to the absence of a signal, represented by some kno ...

EPFL2018

Spatial fringe analysis techniques for multiple phase and phase derivative estimation

Rishikesh Dilip Kulkarni

Analysis of fringe patterns for the accurate estimation of phase and phase derivatives is of crucial importance in optical interferometry as these quantities provide important information on the physical parameters under study. A wide range of applications ...

EPFL2016

Method and apparatus for low complexity spectral analysis of bio-signals

David Atienza Alonso, Andreas Peter Burg, Srinivasan Murali, Georgios Karakonstantis, Aviinaash Sankaranarayanan

A method and device for reducing the computational complexity of a processing algorithm, of a discrete signal, in particular of the spectral estimation and analysis of bio-signals, with minimum or no quality loss, which comprises steps of (a) choosing a do ...

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

Reverberant Audio Source Separation via Sparse and Low-Rank Modeling

Pierre Vandergheynst

The performance of audio source separation from underde- termined convolutive mixture assuming known mixing filters can be significantly improved by using an analysis sparse prior optimized by a reweighting l1 scheme and a wideband data- fidelity term, as ...

Institute of Electrical and Electronics Engineers2014

Distributed spectrum sensing in the presence of selfish users

Ali H. Sayed

We study the problem of decentralized spectrum sensing in the presence of selfish secondary users. We employ diffusion strategies to guide the estimation process and a reputation mechanism to encourage secondary users to participate in the sharing of infor ...

IEEE2013

Recursive Compressed Sensing

Martin Vetterli, Nikolaos Freris, Orhan Öçal

We introduce a recursive algorithm for performing compressed sensing on streaming data. The approach consists of a) recursive encoding, where we sample the input stream via overlapping windowing and make use of the previous measurement in obtaining the nex ...

Submitted to IEEE Transactions on Information Theory2013

Blind Phase Calibration in Sparse Recovery

Rémi Gribonval, Gilles Puy

We consider a blind calibration problem in a compressed sensing measurement system in which each sensor introduces an unknown phase shift to be determined. We show that this problem can be approached similarly to the problem of phase retrieval from quadrat ...

2013