Thomas Trigano

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We consider a density estimation problem arising in nuclear physics. Gamma photons are impinging on a semiconductor detector, producing pulses of current. The integral of this pulse is equal to the total amount of charge created by the photon in the detector, which is linearly related to the photon energy. Because the inter-arrival of photons can be shorter(More)
We observe a large number of signals, all of them with identical, although unknown, shape, but with a different random shift. The objective is to estimate the individual time shifts and their distribution. Such an objective appears in several biological applications like neuroscience or ECG signal processing, in which the estimation of the distribution of(More)
Atrial fibrillation (AF) is a common heart disorder. One of the most prominent hypothesis about its initiation and maintenance considers multiple uncoordinated activation foci inside the atrium. However, the implicit assumption behind all the signal processing techniques used for AF, such as dominant frequency and organization analysis, is the existence of(More)
We consider a nonlinear inversion problem occurring in gamma spectrometry. In that framework, photon energies are converted to electrical pulses which are susceptible to overlap, creating clusters of pulses, referred to as pileup. This phenomenon introduces a distortion that can be a nuisance for the correct identification of the radionuclides. In that(More)
We consider the counting rate estimation of an unknown radioactive source, which emits photons at times modeled by an homogeneous Poisson process. A spectrometer converts the energy of incoming photons into electrical pulses, whose number provides a rough estimate of the intensity of the Poisson process. When the activity of the source is high, a physical(More)
Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the(More)
We address the problem of curve alignment with a semiparametric framework, that is without any knowledge of the shape. This study stems from a biological issue, in which we are interested in the estimation of the average heart cycle signal, but wish to estimate it without any knowledge of the pulse shape, which may differ from one patient to another. We(More)
The aim of nuclear spectroscopy is to provide as many information as possible regarding the activity and the content of an unknown radioactive source. Due to some random perturbations called pileup phenomenon, electrical pulses recorded by the spectrometric apparatus may overlap. Recent developments in compressive sensing and sparse signal reconstruction(More)
One of the main objectives of nuclear spectroscopy is the estimation of the counting rate of unknown radioactive sources. Recently, we proposed an algorithm based on a sparse reconstruction of the time signal in order to estimate precisely this counting rate, under the assumption that it remained constant over time. Computable bounds were obtained to(More)