We present a technique for building a three-dimensional description of a static scene from a dense sequence of images. These images are taken in such rapid succession that they form a solid block of data in which the temporal continuity from image to image is approximately equal to the spatial continuity in an individual image. The technique utilizes knowledge of the camera motion to form and analyze slices of this solid. These slices directly encode not only the three-dimensional positions of objects, but also such spatiotemporal events as the occlusion of one object by another. For straight-line camera motions, these slices have a simple linear structure that makes them easier to analyze. The analysis computes the threedimensional positions of object features, marks occlusion boundaries on the objects, and builds a threedimensional map of "free space. " In our article, we first describe the application of this technique to a simple camera motion, and then show how projective duality is used to extend the analysis to a wider class of camera motions and object types that include curved and moving objects. 1

A technique to perform design calculations on imprecise representations of parameters has been developed and is presented. The level of imprecision in the description of design elements is typically high in the preliminary phase of engineering design. This imprecision is represented using the fuzzy calculus. Calculations can be performed using this method, to produce (imprecise) performance parameters from imprecise (input) design parameters. The Fuzzy Weighted Average technique is used to perform these calculations. A new metric, called the γ-level measure, is introduced to determine the relative coupling between imprecise inputs and outputs. The background and theory supporting this approach are presented, along with one example. 1.

The goal is to develop an inverse model capable of simultaneously estimating the parameters appearing in an air pollution model for an instantaneous point source, by using measured gas concentration data. The approach taken was to develop the inverse model as a non-linear least squares estimation problem in which the source term is estimated using measurements of pollution concentration on the ground. The statistical basis of the least squares inverse model allows quantification of the uncertainty of the parameter estimates, which in turn allows estimation of the uncertainty of the simulation model predictions.

ABSTRACT Sensitivity analysis quantifies the dependence of system behavior on the parameters that affect the process dynamics. Classical sensitivity analysis, however, does not directly apply to discrete stochastic dynamical systems, which have recently gained popularity because of its relevance in the simulation of biological processes. In this work, sensitivity analysis for discrete stochastic processes is developed based on density function (distribution) sensitivity, using an analog of the classical sensitivity and the Fisher Information Matrix. There exist many circumstances, such as in systems with multistability, in which the stochastic effects become nontrivial and classical sensitivity analysis on the deterministic representation of a system cannot adequately capture the true system behavior. The proposed analysis is applied to a bistable chemical system—the Schlögl model, and to a synthetic genetic toggle-switch model. Comparisons between the stochastic and deterministic analyses show the significance of explicit consideration of the probabilistic nature in the sensitivity analysis for this class of processes.

ABSTRACT Sensitivity analysis quantifies the dependence of system behavior on the parameters that affect the process dynamics. Classical sensitivity analysis, however, does not directly apply to discrete stochastic dynamical systems, which have recently gained popularity because of its relevance in the simulation of biological processes. In this work, sensitivity analysis for discrete stochastic processes is developed based on density function (distribution) sensitivity, using an analog of the classical sensitivity and the Fisher Information Matrix. There exist many circumstances, such as in systems with multistability, in which the stochastic effects become nontrivial and classical sensitivity analysis on the deterministic representation of a system cannot adequately capture the true system behavior. The proposed analysis is applied to a bistable chemical system—the Schlögl model, and to a synthetic genetic toggle-switch model. Comparisons between the stochastic and deterministic analyses show the significance of explicit consideration of the probabilistic nature in the sensitivity analysis for this class of processes.

This paper presents a robust and accurate technique for an estimation of the best-t ellipse going through the given set of points. The approach is based on a least squares minimization of algebraic distances of the points with a correction of the statistical bias caused during the computation. An accurate ellipse-specic solution is guaranteed even for scattered or noisy data with outliers. Although the nal algorithm is iterative, it typically converges in a fraction of time needed for a true orthogonal tting based on Eucleidan distances of points. Keywords: ellipses, least squares, robust tting, M-estimators, statistical bias, renormalization 1

Using a frequency-doubled Nd-YAG pulsed laser and a single-intensified CCD camera, Rayleigh scattering measurements have been performed to study the cluster formation in a Mach 6 wind tunnel at NASA Langley Research Center. These studies were conducted both in the free stream and in a model flow field for various flow conditions to gain an understanding of the dependence of the Rayleigh scattering (by clusters) on the local pressures and temperatures in the facility. Using the same laser system, we have also performed simultaneous measurements of the local temperature using the rotational Raman scattering of molecular nitrogen and determined the densities of molecular oxygen and nitrogen by using the vibrational Raman scattering from these species. Quantitative results will be presented in detail with emphasis on the applicability of the Rayleigh scattering for obtaining quantitative measurements of molecular densities both in the free stream and in the model flow field. _______ Copyr...

A single-step maximum likelihood estimation procedure is developed for multidimensional scaling of dissimilarity data measured on rating scales. The procedure can fit the euclidian distance model to the data under various assumptions about category widths and under two distributional assumptions. The scoring algorithm for parameter estimation has been developed and implemented in the form of a computer program. Practical uses of the method are demonstrated with an emphasis on various advantages of the method as a statistical procedure. Key words: similarity successive categories. ratings, maximum likelihood multidimensional scaling (MDS), method

A major portion of the Location-Based Services (LBS) market deals with applications involving in-car navigation systems. The Global Positioning System (GPS) is the most popular choice for positioning in such applications. Many LBS applications involve positioning in urban areas having high rise buildings. Although GPS has good positioning accuracy in open sky conditions, it suffers from line-of-sight issues in urban canyons. This thesis formulates some new methods for aiding GPS using maps for vehicle navigation in urban canyons. GPS satellite availability in urban canyons can be improved by using a High Sensitivity GPS (HS GPS) receiver which can track weak signals. However, this introduces large errors and noise in measurements. Thus, reliability monitoring becomes necessary with such receivers in signal degraded environments. Maps and Digital Elevation Models (DEM) provide effective constraints to compute an outlier-free solution. In this research, a robust fuzzy logic-based approach is developed for road segment identification. This identified road segment is then used in a GPS computation model and is referred to as Map Aided GPS (MAGPS). The performances of

Prediction of breeding value by maxi-mization of the joint density of the ran-dom effects and of the data was studied in a conditional selection scheme pro-posed by Henderson. Dispersion parame-ters were assumed known. In this setting, a condition under which selection can be ignored is that the distribution of the selection variable must not depend on the location parameters being estimated; the conditional distribution of the selection variable given the data and the random effects also must be ancillary. Selection can be ignored when culling is based on linear or nonlinear functions of the data that do not depend on the fixed ef-fects. Contrary to results needed for un-biased estimation and prediction, selec-tion can be ignored when it is based on linear or nonlinear functions of the residuals or of the random effects. The question of what type of predictor should be used in order to maximize expected genetic progress remains open.