Time of flight estimation method using beamforming for acoustic tomography

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Characterization of Non-Stationary Signals in Electric Grids: a Functional Dictionary Approach

Mario Paolone, Asja Derviskadic, Guglielmo Frigo, Alexandra Cameron Karpilow

With the expanding role of converter-interfaced distributed energy resources, modern power grids are evolving towards low-inertia networks that are increasingly vulnerable to extreme dynamics. Consequently, advanced signal processing techniques are needed ...

IEEE Institute of Electrical and Electronics Engineers2021

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

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

Electrical Signal Modeling in Cochlear Implants. Study of Temperature and Humidity Effects

Maria-Alexandra Paun

The present paper discusses the climatic effects of humidity and temperature on cochlear implant functioning and the quality of the electrical sound signal. MATLAB Simulink simulations were prepared, offering insights into signal behavior under such climat ...

MDPI2021

Adding Indoor Capacity without Fiber Backhaul: An mmWave Bridge Prototype

Andreas Peter Burg, Ruben Merz, Adrian Schumacher

Today, a large portion of mobile data traffic is consumed behind the shielding walls of buildings or in the Faraday cage of trains. This renders cellular network coverage from outdoor cell sites difficult. Indoor small cells and distributed antennas along ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Robert West, Alberto Garcia Duran

Time series with missing data are signals encountered in important settings for machine learning. Some of the most successful prior approaches for modeling such time series are based on recurrent neural networks that transform the input and previous state ...

IEEE2021

Mechanotaxis directs Pseudomonas aeruginosa twitching motility

Alexandre Louis André Persat, Lorenzo Anton-Louis Talà, Xavier Jean-Yves Pierrat, Jose Negrete Jr, Zaïnebe Dhirani, Marco Julian Kühn

The opportunistic pathogen Pseudomonas aeruginosa explores surfaces using twitching motility powered by retractile extracellu-lar filaments called type IV pili (T4P). Single cells twitch by sequen-tial T4P extension, attachment, and retraction. How single ...

National Academy of Sciences2021

A Signal Peak Separation Index for axisymmetric B-tensor encoding

Jean-Philippe Thiran, Gabriel Girard, Marco Pizzolato, Jonathan Rafael Patino Lopez, Gaëtan Olivier D Rensonnet

Diffusion-weighted MRI (DW-MRI) has recently seen a rising interest in planar, spherical and general B-tensor encodings. Some of these sequences have aided traditional linear encoding in the estimation of white matter microstructural features, generally by ...

Springer2021

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

EPFL2021