Publications associées à Downsampling (signal processing)

Realization and Control of Bulk and Surface Modes in 3D Nanomagnonic Networks by Additive Manufacturing of Ferromagnets

Dirk Grundler, Mohammad Hamdi, Mingran Xu, Huixin Guo, Axel Johan Marie Deenen

The high-density integration in information technology fuels the research on functional 3D nanodevices. Particularly ferromagnets promise multifunctional 3D devices for nonvolatile data storage, high-speed data processing, and non-charge-based logic operat ...

Wiley2023

Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing

Dimitri Nestor Alice Van De Ville, Maude Schneider, Daniela Maria Zöller, Anjali Bagunu Tarun

Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clinical trajectories observed in early psychopathology. Current diagnostic approaches merge clinical manifestations that co-occur across subjects and could sign ...

eLIFE SCIENCES PUBL LTD2021

Parallel Analog Computing Based on a 2×2 Multiple-Input Multiple-Output Metasurface Processor With Asymmetric Response

Romain Christophe Rémy Fleury, Ali Momeni, Amirhossein Babaee

We present a polarization-insensitive metasurface processor to perform spatial asymmetric filtering of an incident beam, thereby allowing for real-time parallel analog processing. To enable massive parallel processing, we introduce a multiple-input multipl ...

American Physical Society (APS)2021

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

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

A Graph Signal Processing Framework for the Classification of Temporal Brain Data

Ntorina Thanou, Sarah Itani

Graph Signal Processing (GSP) addresses the analysis of data living on an irregular domain which can be modeled with a graph. This capability is of great interest for the study of brain connectomes. In this case, data lying on the nodes of the graph are co ...

IEEE2020

Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction

Jean-Philippe Thiran, Tobias Kober, Tom Hilbert, Reto Meuli

Purpose MP2RAGE T-1-weighted imaging has been shown to be beneficial for various applications, mainly because of its good grey-white matter contrast, its B-1-robustness and ability to derive T(1)maps. Even using parallel imaging, the method requires long a ...

WILEY2020

Graph Signal Processing for Machine Learning: A Review and New Perspectives

Pascal Frossard, Ntorina Thanou, Michael Bronstein, Laura Toni, Xiaowen Dong

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains, such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

AlpsNMR: an R package for signal processing of fully untargeted NMR-based metabolomics

José Luis Abelleira Fernández, Sofia Moco

aSummary: Nuclear magnetic resonance (NMR)-based metabolomics is widely used to obtain metabolic fingerprints of biological systems. While targeted workflows require previous knowledge of metabolites, prior to statistical analysis, untargeted approaches re ...

OXFORD UNIV PRESS2020

Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Raphaël Pierre Liégeois, Miljan Petrovic

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying ...

2020