Search results for " positioning"
showing 10 items of 178 documents
Monitoring displacements of an earthen dam using GNSS and remote sensing
2014
This paper shows the results of a scientific research in which a GNSS continuous monitoring system for earth-dam deformations has been developed, then, deformations have been related with reservoir water surface and level. The experiment was conducted near Bivona (Sicily, Italy), on the Castello dam (Magazzolo Lake). On the top of the dam three control points were placed and three GNSS permanent stations were installed. The three stations continuously transmitted data to the control centre of the University of Palermo. The former has been determined using freely available satellite data (specifically Landsat 7 SLC-Off) collected during the whole study period (DOYs 101 to 348 2011). Issues r…
Physics-aware Gaussian processes in remote sensing
2018
Abstract Earth observation from satellite sensory data poses challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression has excelled in biophysical parameter estimation tasks from airborne and satellite observations. GP regression is based on solid Bayesian statistics, and generally yields efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations between the state vector and the radiance observations is available though and could be useful to improve pre…
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion
2020
Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative so…
ICA of full complex-valued fMRI data using phase information of spatial maps.
2015
Background ICA of complex-valued fMRI data is challenging because of the ambiguous and noisy nature of the phase. A typical solution is to remove noisy regions from fMRI data prior to ICA. However, it may be more optimal to carry out ICA of full complex-valued fMRI data, since any filtering or voxel-based processing may disrupt information that can be useful to ICA. New method We enable ICA of the full complex-valued fMRI data by utilizing phase information of estimated spatial maps (SMs). The SM phases are first adjusted to properly represent spatial phase changes of all voxels based on estimated time courses (TCs), and then these are used to segment the voxels into BOLD-related and unwant…
State Estimation of a Nonlinear Unmanned Aerial Vehicle Model using an Extended Kalman Filter
2008
An Extended Kalman Filter is designed in order to estimate both state variables and wind velocity vector at the same time for a non conventional unmanned aircraft. The proposed observer uses few measurements, obtained by means of either conventional simple air data sensors or a low cost GPS. To cope with the low rate of the GPS with respect to the other sensors, the EKF algorithm has been modified to allow for a dual rate measurement model. State propagation is obtained by means of an accurate six degrees of freedom nonlinear model of the aircraft dynamics. To obtain joint estimation of state and disturbance, wind velocity components are included in the set of the state variables. Both stoc…
Inhomogeneous spatio-temporal point processes on linear networks for visitors’ stops data
2022
We analyse the spatio-temporal distribution of visitors' stops by touristic attractions in Palermo (Italy) using theory of stochastic point processes living on linear networks. We first propose an inhomogeneous Poisson point process model, with a separable parametric spatio-temporal first-order intensity. We account for the spatial interaction among points on the given network, fitting a Gibbs point process model with mixed effects for the purely spatial component. This allows us to study first-order and second-order properties of the point pattern, accounting both for the spatio-temporal clustering and interaction and for the spatio-temporal scale at which they operate. Due to the strong d…
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.
Emergency Colorectal Surgery Checklist and Technical Considerations
2019
A surgical checklist is a step-by-step control procedure carried on for checking through the most delicate phases of the perioperative period, in order to increase surgical patient’s safety and avoid preventable complications. The checklist implementation within operating rooms have proved to be effective in reducing morbidity and mortality rates significantly, without being costly and lengthy. These results have been confirmed also in emergency surgery, which represents in itself a cause of higher risks for patients. Thus, the checklist use has rapidly spread out all over the world. The mechanism responsible for the improvement of surgical outcomes is not completely clear, partly due to am…
Precipitable water vapour content from ESR/SKYNET sun-sky radiometers: validation against GNSS/GPS and AERONET over three different sites in Europe
2018
The estimation of the precipitable water vapour content (W) with high temporal and spatial resolution is of great interest to both meteorological and climatological studies. Several methodologies based on remote sensing techniques have been recently developed in order to obtain accurate and frequent measurements of this atmospheric parameter. Among them, the relative low cost and easy deployment of sun–sky radiometers, or sun photometers, operating in several international networks, allowed the development of automatic estimations of W from these instruments with high temporal resolution. However, the great problem of this methodology is the estimation of the sun-photometric calibration par…
Indicators of Tourist Market Appeal and Sustainability. An Analysis of Italian Opera Houses
2007
This paper aims at pointing out how Italian Opera Houses are facing the challenge involved with tourism, after some recent institutional changes which transformed them in private foundations. Here we put in evidence the potential tourist appeal and the sustainability of a tourist approach for all of the Italian Opera Houses. This issue is addressed constructing two indicators of market appeal and sustainability. Resorting to a deep documentary research and an ad hoc questionnaire survey, we detected and measured the relevant dimensions of market appeal (physical setting, popularity outside the local area, overall tourist appeal of the hosting city, geographical allocation, proximity to othe…