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Publications associées (603)

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Noise Analysis For Environmental Loading Effect On Gps Position Time Series

Jean-Philippe Lucien Montillet, Feng Zhou

This paper focusses on the impact of environment mass loading on GPS time-series of position changes and noise characteristic. We make use of position time series of 206 GPS station ranging from 2001 to 2013 globally distributed. Firstly, we investigate th ...

Institute of Rock Structure and Mechanics, AS CR2017

Review of current GPS methodologies for producing accurate time series and their error sources

Jean-Philippe Lucien Montillet

The Global Positioning System (GPS) is an important tool to observe and model geodynamic processes such as plate tectonics and post-glacial rebound. In the last three decades, GPS has seen tremendous advances in the precision of the measurements, which all ...

Elsevier2017

Learning time varying graphs

Pascal Frossard, Andreas Loukas, Vasilis Kalofolias

We consider the problem of inferring the hidden structure of high-dimensional dynamic systems from the perspective of graph learning. In particular, we aim at capturing the dynamic relationships between nodes by a sequence of graphs. Our approach is motiva ...

Ieee2017

A leap of faith in the realm of the possible.

Caroline Dionne

The architectural projects and building operations conducted over the past three decades by Patrick Bouchain and his collaborators challenge a series of assumptions regarding architecture’s authorship, its purpose and usage. Examined in light of Paul Ricoe ...

2016

Simultaneous estimation of phase derivative and phase using parallel Kalman filter implementation

Pramod Rastogi, Rishikesh Dilip Kulkarni

This paper proposes a technique for the simultaneous estimation of interference phase derivative and phase from a complex interferogram recorded in an optical interferometric setup. The complex interferogram is represented as a spatially varying autoregres ...

2016

Robust Online Time Series Prediction with Recurrent Neural Networks

Karl Aberer, Tian Guo

Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare. However the real data is often complicated with anomalies and change points, which ...

Ieee2016

Convolutional Neural Networks on Graphs and Stationarity

The Fourier spectrum is often not sufficient to discriminate complex signals because it does not take into account higher-order moments. Wide Sense Stationarity is for sure not sufficient to characterize the signal as it considers only moments of order 2. ...

2016

Infrequent Rebalancing, Return Autocorrelation, and Seasonality

Vincent Jean Bogousslavsky

A model of infrequent rebalancing can explain specific predictability patterns in the time series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday r ...

Wiley-Blackwell2016

On the sensitivity of buildings to climate

Parag Rastogi

Building simulation requires a large number of uncertain inputs and parameters. These include quantities that may be known with reasonable confidence, like the thermal properties of materials and building dimensions, but also inputs whose correct values ca ...

EPFL2016

Redundant Hash Addressing for Large-Scale Query by Example Spoken Query Detection

Hervé Bourlard, Afsaneh Asaei, Dhananjay Ram

State of the art query by example spoken term detection (QbE-STD) systems rely on representation of speech in terms of sequences of class-conditional posterior probabilities estimated by deep neural network (DNN). The posteriors are often used for pattern ...

Idiap2016