Publications associées à Matrix pencil based phase derivative estimation in digital holographic interferometry

Disentangling Thermal from Electronic Contributions in the Spectral Response of Photoexcited Perovskite Materials

Majed Chergui, Thomas Charles Henry Rossi, Lijie Wang, Malte Oppermann

Disentangling electronic and thermal effects in photoexcited perovskite materials is crucial for photovoltaic and optoelectronic applications but remains a challenge due to their intertwined nature in both the time and energy domains. In this study, we emp ...

Amer Chemical Soc2024

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Nature Portfolio2024

Functional-Basis Analysis of Non-Stationary Signals in Modern Power Grids: Theory and Implementation in Embedded Systems

Alexandra Cameron Karpilow

Situational awareness strategies are essential for the reliable and secure operation of the electric power grid which represents critical infrastructure in modern society. With the rise of converter-interfaced renewable generation and the consequent shift ...

EPFL2024

Characterization of High Harmonic Frequencies in Reactor Noise Experiments Within the CORTEX Project

Andreas Pautz, Vincent Pierre Lamirand

We present a novel technique of neutron noise detection and experimental data interpretation developed during the EU H2020 project CORTEX aiming to improve the capabilities for identification and localization of neutron noise sources. The experimental data ...

2022

The spectral bias of polynomial neural networks

Volkan Cevher, Moulik Choraria, Leello Tadesse Dadi, Grigorios Chrysos

Polynomial neural networks (PNNs) have been recently shown to be particularly effective at image generation and face recognition, where high-frequency information is critical. Previous studies have revealed that neural networks demonstrate a spectral bias ...

2022

Versatile shaping of optical frequency combs and applications

Jianqi Hu

Optical frequency combs are a series of phase-locked and equidistant laser lines in the spectral domain. In the time domain, they correspond to periodic pulse trains by the Fourier relation. The ability to shape optical combs and pulse trains is pivotal to ...

EPFL2021

Stimulus evoked causality estimation in stereo-EEG

Silvestro Micera, Fiorenzo Artoni

Objective. Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain ...

IOP PUBLISHING LTD2021

Spherical cap harmonic analysis (SCHA) for characterising the morphology of rough surface patches

Katrin Beyer, Mahmoud S. M. Shaqfa

We use spherical cap harmonic (SCH) basis functions to analyse and reconstruct the morphology of scanned genus-0 rough surface patches with open edges. We first develop a novel one-to-one conformal mapping algorithm with minimal area distortion for paramet ...

2021

Spectral cross-cumulants for multicolor super-resolved SOFI imaging

Marcel Leutenegger, Theo Lasser, Aleksandra Radenovic, Adrien Charles-François Raymond Descloux, Tomas Lukes, Kristin Stefanie Grussmayer

Super-resolution opticalfluctuation imaging provides a resolution beyond the diffraction limitby analysing stochasticfluorescencefluctuations with higher-order statistics. Usingnthorderspatio-temporal cross-cumulants the spatial resolution and the sampling ...

Nature Research2020

Slepian Guided Filtering of Graph Signals

Dimitri Nestor Alice Van De Ville, Miljan Petrovic

Joint localization of graph signals in vertex and spectral domain is achieved in Slepian vectors calculated by either maximizing energy concentration (mu) or minimizing modified embedded distance (xi) in the subgraph of interest. On the other hand, graph L ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Phase resolved characterization of nanoscale features in optical microstructures

Michail Symeonidis

In this study, we apply interferometric microscopy to study the phase, alongside the intensity, of the light field transmitted through a wide variety of samples. Additionally, we conduct those interferometric measurements at different wavelengths within th ...

EPFL2019

Sampling Continuous-time Sparse Signals: A Frequency-domain Perspective

Martin Vetterli, Benjamin Bejar Haro

We address the problem of sampling and reconstruction of sparse signals with finite rate of innovation. We derive general conditions under which perfect reconstruction is possible for sampling kernels satisfying Strang-Fix conditions. Previous results on t ...

2018

Reduced Leakage Synchrophasor Estimation: Hilbert Transform Plus Interpolated DFT

Mario Paolone, Asja Derviskadic, Guglielmo Frigo

Synchrophasor estimation is typically performed by means of spectral analysis based on the discrete Fourier transform (DFT). Traditional DFT approaches, though, suffer from several uncertainty contributions due to the stationarity assumption, spectral leak ...

2018

Low-dimensional data embedding for scalable astronomical imaging in the SKA telescope era

Vijay Kartik

Astronomy is one of the oldest sciences known to humanity. We have been studying celestial objects for millennia, and continue to peer deeper into space in our thirst for knowledge about our origins and the universe that surrounds us. Radio astronomy -- ob ...

EPFL2018

Unified Theory for Recovery of Sparse Signals in a General Transform Domain

Kyong Hwan Jin

Compressed sensing is provided a data-acquisition paradigm for sparse signals. Remarkably, it has been shown that the practical algorithms provide robust recovery from noisy linear measurements acquired at a near optimal sampling rate. In many real-world a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Spectral MAB for Unknown Graph Processes

Pascal Frossard, Laura Toni

In this work, we study graph-based multi-arms bandit (MAB) problems aimed at optimizing actions on irregular and high-dimensional graphs. More formally, we consider a decision-maker that takes sequential actions over time and observes the experienced rewar ...

IEEE Computer Society2018

Miniature hyperspectral systems

Dragos Constantin

Spectroscopy methods have been used for decades to obtain information about various materials, ranging from galaxies billions of light years from earth, to Petri dishes containing rich bacteria cultures. From spectrometers in chemical labs to spectral came ...

EPFL2017

Towards Generalized FRI Sampling with an Application to Source Resolution in Radioastronomy

Martin Vetterli, Thierry Blu, Hanjie Pan

It is a classic problem to estimate continuous-time sparse signals, like point sources in a direction-of-arrival problem, or pulses in a time-of-flight measurement. The earliest occurrence is the estimation of sinusoids in time series using Prony's method. ...

Institute of Electrical and Electronics Engineers2017

Compressive Sampling Using Annihilating Filter-Based Low-Rank Interpolation

Kyong Hwan Jin

While the recent theory of compressed sensing provides an opportunity to overcome the Nyquist limit in recovering sparse signals, a solution approach usually takes the form of an inverse problem of an unknown signal, which is crucially dependent on specifi ...

IEEE2017

Graph-based structures in data science : fundamental limits and applications to machine learning

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017