Time-of-Flight (TOF) cameras are active real time depth sensors. One issue of TOF sensors is measurement noise. In this paper, we present a method for providing the uncertainty associated to 3D TOF measurements based on noise modelling. Measurement uncertainty is the combination of pixel detection error and sensor noise. First, a detailed noise characterization is presented. Then, a continuous ...

Recently, Lyn et al. (Analyst, 2007, 132, 1231) compared two ways of estimating the standard uncertainty of sampling pistachio nuts for aflatoxins--a modelling method and an empirical method. Their case study used robust analysis of variance (RANOVA) to derive the uncertainty estimates, highlighting a substantial difference between the two: the estimate of sampling uncertainty derived from the ...

There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the predictions made. This article aims at ad...

in applications there occur different forms of uncertainty. the twomost important types are randomness (stochastic variability) and imprecision(fuzziness). in modelling, the dominating concept to describe uncertainty isusing stochastic models which are based on probability. however, fuzzinessis not stochastic in nature and therefore it is not considered in probabilisticmodels.since many years t...

In spite of advanced modelling capabilities, hydrologic and water quality data remain vital for scientific assessment, management, decision-making, and modelling. Although uncertainty in measured data affects all of these applications, measurement uncertainty is typically ignored in monitoring projects. To change this, we published an uncertainty estimation framework for measured discharge and ...

Existing cognitive agent programming languages that are based on the BDI model employ logical representation and reasoning for implementing the beliefs of agents. In these programming languages, the beliefs are assumed to be certain, i.e. an implemented agent can believe a proposition or not. These programming languages fail to capture the underlying uncertainty of the agent’s beliefs which is ...

Accurate modelling of information and knowledge is central to the modern command and control (C2) process. Without models and a language for describing them, it is impossible to collaborate on C2. All information which enters a C2 system will be uncertain, and hence it is important to be able to model the uncertainty in a way that makes it possible for us to understand it. Some kinds of knowled...

Scientists from different disciplines now routinely work together on complex, multi-interactive, geochemical and biochemical environmental processes and it has become necessary to define a common language that can be understood by all the relevant scientific community. This in turn will aid the transfer of expertise between scientists from different disciplines. This paper attempts to relate di...

Uncertainty, its modelling and analysis have been discussed across many literatures including statistics and operational research, knowledge management and philosophy. Adherents to Bayesian approaches have usually argued that uncertainty should either be modelled by probabilities or resolved by discussion which clarifies meaning. Others have followed Knight in distinguishing between contexts of...

Wildfire information has long been collected in Europe, with particular focus on forest fires. The European Forest Fire Information System (EFFIS) of the European Commission complements and harmonises the information collected by member countries and covers the forest fire management cycle. This latter ranges from forest fire preparedness to post-fire impact analysis. However, predicting and si...