This paper is devoted to the study and implementation of real-time techniques for the estimation of time-varying, contingently correlated quantities, and relevant uncertainty. An estimation algorithm based on a metrological customization of the Kalman filtering technique is presented, starting from a Bayesian approach. Moreover, a fuzzy-logic routine for real-time treatment of possible outliers is incorporated in the overall software procedure. The system applicability is demonstrated by results of simulations performed on dimensional measurement models.

We consider an epidemic model where the spread of the epidemic can be described by a discrete-time Galton-Watson branching process. Between times n and n + 1, any infected individual is detected with unknown probability π and the numbers of these detected individuals are the only observations we have. Detected individuals produce a reduced number of offspring in the time interval of detection, and no offspring at all thereafter. If only the generation sizes of a Galton-Watson process are observed, it is known that one can only estimate the first two moments of the offspring distribution consistently on the explosion set of the process (and, apart from some lattice parameters, no parameters that are not determined by those moments). Somewhat surprisingly, in our context, where we observe a binomially distributed subset of each generation, we are able to estimate three functions of the parameters consistently. In concrete situations, this often enables us to estimate π consistently, as well as the mean number of offspring. We apply the estimators to data for a real epidemic of classical swine fever.

In this paper an algorithm for real-time estimation of the position and orientation of a moving object using a video camera is presented. The algorithm is based on the extended Kalman filter which iteratively computes the object pose from the position measured in the image plane of a set of feature points of the object. A new technique is proposed for the selection of the optimal feature points based on the representation of the object geometry by means of a Binary Space Partitioning (BSP) tree. At each sample time, a visit algorithm of the tree allows pre-selecting all the feature points of the object that are visible from the camera in the pose predicted by the Kalman filter. A further selection is performed to find the optimal set of visible points to be used for image feature extraction. Experimental results are presented which confirm the feasibility and effectiveness of the proposed technique.