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Publications associées (36)

Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots

Alcherio Martinoli, Chiara Ercolani, Faezeh Rahbar

The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly refe ...

MDPI2023

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

2022

Mapping urban underground potential in Dakar, Senegal: From the analytic hierarchy process to self-organizing maps

Michael Robert Doyle

This article presents a mapping method that seeks to provide urban planning with a diagnostic overview of the underground resources of an urban area. Resource potentials (for buildable space, groundwater or geomaterial extraction and geothermal energy) ten ...

2020

A new algorithm for high-dimensional uncertainty quantification based on dimension-adaptive sparse grid approximation and reduced basis methods

Alfio Quarteroni, Peng Chen

In this work we develop an adaptive and reduced computational algorithm based on dimension-adaptive sparse grid approximation and reduced basis methods for solving highdimensional uncertainty quantification (UQ) problems. In order to tackle the computation ...

Elsevier2015

An overview of robust compressive sensing of sparse signals in impulsive noise

Alonso Ramirez Manzanares, Rafael Eduardo Carrillo Rangel

While compressive sensing (CS) has traditionally relied on L2 as an error norm, a broad spectrum of applications has emerged where robust estimators are required. Among those, applications where the sampling process is performed in the presence of impulsiv ...

2015

Principal Flows

Victor Panaretos, Tung Huy Pham, Zhigang Yao

We revisit the problem of extending the notion of principal component analysis (PCA) to multivariate datasets that satisfy nonlinear constraints, therefore lying on Riemannian manifolds. Our aim is to determine curves on the manifold that retain their cano ...

American Statistical Association2014

Classification of urban multi-angular image sequences by aligning their manifolds

Devis Tuia, Maxime Pierre Trolliet

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modificati ...

Ieee2013

Statistics on Manifolds applied to Shape Theory

Matthieu Martin Jean-André Simeoni

In this report, we use a variety of tools from differential geometry to propose a nonlinear extension of the principal components analysis (PCA) into manifolds setting. This extension, that we shall call principal geodesics analysis (PGA), attempts to find ...

2013

A programmable triangular neighborhood function for a Kohonen self-organizing map implemented on chip

Rafal Tomasz Dlugosz

An efficient transistor level implementation of a flexible, programmable Triangular Function (TF) that can be used as a Triangular Neighborhood Function (TNF) in ultra-low power, self-organizing maps (SOMs) realized as Application-Specific Integrated Circu ...

2012

Integrating Posterior Features and Self-Organizing Maps for Isolated Word Recognition without Dynamic Programming

Serena Soldo

In recent works, the use of phone class-conditional posterior probabilities (posterior features) directly as features provided successful results in template-based ASR systems. Moreover, it has been shown that these features tend to be sparse and orthogona ...

Idiap2012

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