Search results for "Gauss"

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…

Quality of wind speed fitting distributions for the urban area of Palermo, Italy

2011

Abstract This study investigates the wind speed characteristics recorded in the urban area of Palermo, in the south of Italy, by a monitoring network composed by four weather stations. This article has two main objectives: the first one, to describe with clarity and simplicity the numerical procedures adopted to perform a preliminary statistical analysis of wind speed data, providing at the same time, the necessary mathematical tools useful to perform this analysis also without special software. The second objective is to verify if there are more suitable probability distributions able to better represent the original data respect the traditional ones. After a preliminary statistical analys…

LQG Control Design for Balancing an Inverted Pendulum Mobile Robot

2011

Author's version of an article published in the journal: Intelligent Control and Automation. Also available from the publisher at: http://dx.doi.org/10.4236/ica.2011.22019 The objective of this paper is to design linear quadratic controllers for a system with an inverted pendulum on a mobile robot. To this goal, it has to be determined which control strategy delivers better performance with respect to pendulum’s angle and the robot’s position. The inverted pendulum represents a challenging control problem, since it continually moves toward an uncontrolled state. Simulation study has been done in MATLAB Simulink environment shows that both LQR and LQG are capable to control this system succe…

Pressure Dependence of the Band Gaps and Charge Densities in Si

1994

The empirical local and nonlocal pseudopotentials of Si which can describe the electronic energy structure over a wide energy range of more than 20 eV from the bottom of the valence band is determined for different pressures. The nonlocality of the potential is described by the Gaussian model. The predictions for the linear and quadratic pressure coefficients are consistent with the experiment. The valence charge densities of Si under high pressure are studied. The forbidden X-ray factor F(222) is very stable under pressure and changes by less than 3% under volume changes of the order of 5%.

Nonstationary response envelope probability densities of nonlinear oscillators

2006

The nonstationary random response of a class of lightly damped nonlinear oscillators subjected to Gaussian white noise is considered. An approximate analytical method for determining the response envelope statistics is presented. Within the framework of stochastic averaging, the procedure relies on the Markovian modeling of the response envelope process through the definition of an equivalent linear system with response-dependent parameters. An approximate solution of the associated Fokker-Planck equation is derived by resorting to a Galerkin scheme. Specifically, the nonstationary probability density function of the response envelope is expressed as the sum of a time-dependent Rayleigh dis…

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

2022

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SC…

A Probabilistic Classification Procedure Based on Response Time Analysis Towards a Quick Pre-Diagnosis of Student's Attention Deficit

2019

[EN] A classification methodology based on an experimental study is proposed towards a fast pre-diagnosis of attention deficit. Our sample consisted of school-aged children between 8 and 12 years from Valencia, Spain. The study was based on the response time (RT) to visual stimuli in computerized tasks. The process of answering consecutive questions usually follows an ex-Gaussian distribution of the RTs. Specifically, we seek to propose a simple automatic classification scheme of children based on the most recent evidence of the relationship between RTs and ADHD. Specifically, the prevalence percentage and reported evidence for RTs in relation to ADHD or to attention deficit symptoms were t…