Search results for "Gaussia"

Comparison of leaf surface roughness analysis methods by sensitivity to noise analysis

2015

International audience; Surface roughness is of great interest in agricultural spraying because it is used to characterise leaf surface wettability to predict the behaviour of droplets on a leaf surface. In recent years, the use of texture analysis to estimate surface roughness has emerged. In this paper we propose to estimate leaf surface roughness by using an optimisation of the Generalized Fourier Descriptors method. This approach is then compared with two other standard methods in the literature, one based on grey level intensity variation and the other on wavelet decomposition. Since roughness has many definitions and each method is calculated differently, we propose a new approach to …

Deep Gaussian Processes for Geophysical Parameter Retrieval

2018

This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, provides an efficient solution that scales well to large datasets, and improves prediction accuracy over standard full and sparse GP models. We give empirical evidence of performance for estimation of surface dew point temperature from infrared sounding data.

Closed Form Approximation of Swap Exposures

2013

This paper provides closed form lower and upper bounds for the price of European swaption on cross currency basis swap with the presence of dynamic basis spreads. Cross currency basis spreads are treated as integrals of spot spreads, approach familiar from interest rate models. The spot spread is modelled by two-factor mean reverting Gaussian model that is equivalent to two-factor Hull-White model introduced by [Hull and White(1994)]. This model allows closed form approximations and relatively well fitting and simple calibration to the spread term structure.

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

2019

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among…

Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015

[EN] Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were…

Analog Multiple Description Joint Source-Channel Coding Based on Lattice Scaling

2015

Joint source-channel coding schemes based on analog mappings for point-to-point channels have recently gained attention for their simplicity and low delay. In this paper, these schemes are extended either to scenarios with or without side information at the decoders to transmit multiple descriptions of a Gaussian source over independent parallel channels. They are based on a lattice scaling approach together with bandwidth reduction analog mappings adapted for this multiple description scenario. The rationale behind lattice scaling is to improve performance through bandwidth expansion. Another important contribution of this paper is the proof of the separation theorem for the communication …

Functional Brain Segmentation Using Inter-Subject Correlation in fMRI

2016

The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily‐life situations. A new exploratory data‐analysis approach, functional segmentation inter‐subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is h…

Fractional viscoelastic behaviour under stochastic temperature process

2018

Abstract This paper deals with the mechanical behaviour of a linear viscoelastic material modelled by a fractional Maxwell model and subject to a Gaussian stochastic temperature process. Two methods are introduced to evaluate the response in terms of strain of a material under a deterministic stress and subjected to a varying temperature. In the first approach the response is determined making the material parameters change at each time step, due to the temperature variation. The second method, takes advantage of the Time–Temperature Superposition Principle to lighten the calculations. In this regard, a stochastic characterisation for the Time–Temperature Superposition Principle method is p…

Intelligent Sampling for Vegetation Nitrogen Mapping Based on Hybrid Machine Learning Algorithms

2021

Upcoming satellite imaging spectroscopy missions will deliver spatiotemporal explicit data streams to be exploited for mapping vegetation properties, such as nitrogen (N) content. Within retrieval workflows for real-time mapping over agricultural regions, such crop-specific information products need to be derived precisely and rapidly. To allow fast processing, intelligent sampling schemes for training databases should be incorporated to establish efficient machine learning (ML) models. In this study, we implemented active learning (AL) heuristics using kernel ridge regression (KRR) to minimize and optimize a training database for variational heteroscedastic Gaussian processes regression (V…

Noisy dynamics in long and short Josephson junctions

The study of nonlinear dynamics in long Josephson junctions and the features of a particular kind of junction realized using a graphene layer, are the main topics of this research work. The superconducting state of a Josephson junction is a metastable state, and the switching to the resistive state is directly related to characteristic macroscopic quantities, such as the current the voltage across the junction, and the magnetic field through it. Noise sources can affect the mean lifetime of this superconducting metastable state, so that noise induced effects on the transient dynamics of these systems should be taken into account. The long Josephson junctions are investigated in the sine-Gor…