Publication

Superior GRAPPA reconstruction with reduced g-factor noise using 2D CAIPIRINHA for 3D EPI

Publications associées (30)

On the reconstruction of the attenuation function of a return-stroke current from the Fourier Transform of finite-duration measurements

A correct representation of the lightning current is crucial when the electromagnetic field radiated to a point of interest has to be computed. Based on the engineering models of Transmission Line type, such representation involves the knowledge of the ret ...

2022

High-frequency broadband laser phase noise cancellation using a delay line

Timo Zwettler

Laser phase noise remains a limiting factor in many experimental settings, including metrology, time-keeping, as well as quantum optics. Hitherto this issue was addressed at low frequencies ranging from well below 1 Hz to maximally 100 kHz. However, a wide ...

2021

Transverse Noise, Decoherence, and Landau Damping in High-Energy Hadron Colliders

Sondre Vik Furuseth

High-energy hadron colliders are designed to generate particle collisions within specialized detectors. A higher number of collisions is achieved with high-quality beams of low transverse emittances, meaning a small transverse cross-section, and high inten ...

EPFL2021

Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms

David Atienza Alonso, Fabio Isidoro Tiberio Dell'Agnola, Adriana Arza Valdes, Giulio Masinelli

This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its ...

2021

Moment approach of the multi-species non-linear Coulomb collision operator adapted to particle-in-cell codes

Stephan Brunner, Laurent Villard, Emmanuel Lanti, Noé Thomas Elie Ohana, Moahan Murugappan, Claudio Gheller

In this article an approximated version of the multi-species, non-linear Coulomb collision operator is derived via the use of a truncated moment expansion of the distribution function to compute the Rosenbluth potentials. The evolution of the distribution ...

2021

Compound Admittance Matrix Estimation of Three-phase Untransposed Power Distribution Grids using Synchrophasor Measurements

Jean-Yves Le Boudec, Mario Paolone, Fabrizio Sossan, Rahul Kumar Gupta

The paper considers the problem of estimating the parameters of the compound admittance matrix of three-phase untransposed low-and medium-voltage electrical distribution networks using synchrophasor measurements from phasor measurement units (PMUs). The wo ...

2021

Differentially Private Stochastic Coordinate Descent

Rachid Guerraoui, Georgios Damaskinos

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this ve ...

AIAA2021

The Globalization of an Interaction Ritual Chain: “Clapping for Carers” During the Conflict Against COVID-19

Alexandre André Robert Rigal

Sociologists have long been interested in the theoretical possibility of a universal ritual. Despite a growing number of indicators of world society and globalization, there have not been attempts to observe and analyze the international reach of particula ...

2021

PIRACY: An Optimized Pipeline for Functional Connectivity Analysis in the Rat Brain

Rolf Gruetter, Ileana Ozana Jelescu, Ting Yin, Yujian Diao

Resting state functional MRI (rs-fMRI) is a widespread and powerful tool for investigating functional connectivity (FC) and brain disorders. However, FC analysis can be seriously affected by random and structured noise from non-neural sources, such as phys ...

2021

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Sabine Süsstrunk, Majed El Helou, Deblina Bhattacharjee, Xiaoyu Lin

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

IEEE Institute of Electrical and Electronics Engineers2021