We study online approximations to Gaussian process models for spatially distributed systems. We apply our method to the prediction of wind fields over the ocean surface from scatterometer data. Our approach combines a sequential update of a Gaussian approximation to the posterior with a sparse representation that allows to treat problems with a large number of observations.

This paper presents practical methods for the sequential generation or simulation of a Gaussian two-dimensional random field. The specific realizations typically correspond to geospatial errors or perturbations over a horizontal plane or grid. The errors are either scalar, such as vertical errors, or multivariate, such as x, y, and z errors. These realizations enable simulation-based performanc...

Posterior Cramér Rao lower bounds (PCRLBs) [1] for sequential Bayesian estimators provide performance bounds for general nonlinear filtering problems and have been used widely for sensor management in tracking and fusion systems. However, the unconditional PCRLB [1] is an off-line bound that is obtained by taking the expectation of the Fisher information matrix (FIM) with respect to the measure...

Flexible and low-complexity variable rate coding schemes based on rate-compatible convolutional codes are presented. The codes have a wide range of code rates and are optimized for good performance on both AWGN and Rayleigh fading channels. Furthermore, the application of these codes for rate matching, combined coding and spreading in a DS-CDMA system and hybrid type-II ARQ schemes are demonstr...

Flexible and low-complexity variable rate coding schemes based on rate-compatible convolutional codes are presented. The codes have a wide range of code rates and are optimized for good performance on both AWGN and Rayleigh fading channels. Furthermore, the application of these codes for rate matching, combined coding and spreading in a DS-CDMA system and hybrid type-II ARQ schemes are demonstr...

The estimation of time-varying aerosol size distributions on the basis of differential mobility particle sizer measurements is a dynamical inverse problem with a non-linear/non-Gaussian state space model. A sequential Monte Carlo approach for determining approximations for the state estimates is proposed. The vapour pressure, which is difficult to measure accurately, is here taken as an unknown...

In this study the hardness SWCT was calculated with B3LYP,HF method and 3-21G,6-31G,6-311G basis set .Then it was investigated with the best method(B3LYP) and basis set(6-31G) to study the adsorption effects CO2 on the hardness of SWCNT with gap HOMO-LUMO in two shape: Horizontal, Vertical and Top-Center-Bridge and We also provide the effects of CO2 adsorption on the elect...

Abstract The convection–diffusion process of carbon dioxide (CO 2 ) dissolution in a saline reservoir is investigated to shed light on the effects permeability heterogeneity. Using sequential Gaussian simulation method, random fields two and three-dimension (2D 3D) structures are generated. Quantitative (average amount dissolved CO flux) qualitative (pattern velocity streamlines) measurements u...

We propose a novel strategy for training neural networks using sequential sampling-importance resampling algorithms. This global optimisation strategy allows us to learn the probability distribution of the network weights in a sequential framework. It is well suited to applications involving on-line, nonlinear, non-Gaussian or non-stationary signal processing.

Within MUCM there might occasionally arise the need to use large training set sizes, or employ observations with non-Gaussian noise characteristics or non-linear sensor models in a calibration stage. This technical report deals with Gaussian process models in these non-Gaussian, and / or large data set size cases. Treating such data within Gaussian processes is most naturally accomplished using...