Shape from perspective trihedral angle constraint

Abstract: This paper defines and investigates a fundamental problem of determining the position and orientation of a 3 0 object using $ingle perspective i m g e view. The technique is based on the interpretation of trihedral angle constraid informution. A new closed form solution io the problem is proposed. The method also provides a general analytic technique for dealing with a class of problem of shape from inverse perspective projection by using "Angle to Angle Correspondence Information ". Simulation experiments show that our method is enective and robust for real application. t This research ispariially supported by ONR NooO14-91-J1306

Software quality and reliability prediction using Dempster -Shafer theory

As software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Most accurate prediction models require extensive testing effort, implying increased cost and slowing down the development life cycle. We developed two novel statistical models based on Dempster-Shafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. The models are flexible enough to incorporate information generated throughout the development life-cycle to improve the prediction accuracy.;Our first contribution is an original algorithm for building Dempster-Shafer Belief Networks using prediction logic. This model has been applied to software quality prediction. We demonstrated that the prediction accuracy of Dempster-Shafer Belief Networks is higher than that achieved by logistic regression, discriminant analysis, random forests, as well as the algorithms in two machine learning software packages, See5 and WEKA. The difference in the performance of the Dempster-Shafer Belief Networks over the other methods is statistically significant.;Our second contribution is also based on a practical extension of Dempster-Shafer theory. The major limitation of the Dempsters rule and other known rules of evidence combination is the inability to handle information coming from correlated sources. Motivated by inherently high correlations between early life-cycle predictors of software reliability, we extended Murphy\u27s rule of combination to account for these correlations. When used as a part of the methodology that fuses various software reliability prediction systems, this rule provided more accurate predictions than previously reported methods. In addition, we proposed an algorithm, which defines the upper and lower bounds of the belief function of the combination results. To demonstrate its generality, we successfully applied it in the design of the Online Safety Monitor, which fuses multiple correlated time varying estimations of convergence of neural network learning in an intelligent flight control system

A simplified multi-zone model for determining the placement of bio-defense sensors in large buildings

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 110-112).The anthrax mailings of 2001 increased public and government awareness to the threat of bio-terrorism. Particularly vulnerable to a bio-terrorist event are large indoor facilities such as convention centers, office buildings, transportation centers, and sports arenas with their high population densities and limited physical security. Under heightened threat levels deploying bio-aerosol sensors inside these facilities provides added protection to the occupants. The challenge is determining the number and placement of sensors needed to guarantee the detection of a release inside a particular building. The methodology proposed here aims to simplify the analysis of contamination transport within buildings and provide first-order sensing requirements for dose dependant sensors in large facilities. A reduced-order model is developed that allows buildings to be subdivided into larger sections while maintaining a higher degree of accuracy than building analysis models with the same level of granularity. The problem is formulated as a network model with the nodes representing possible sensor locations and the path lengths equal to the reduction in dose as a contaminant travels between sensor locations. Techniques borrowed from network theory are then used to determine the minimum cost set of sensors that provides full building coverage. The reduced-order model estimates sensing requirements in hours or days for problems that would take months to analyze with fine grained multi-zone models and that are too large to be considered with computational fluid dynamics. Models of an office building, a convention center, and an airport terminal are constructed and their underlying network graph is employed to understand how the structure of the indoor environment affects the placement of sensors.(cont.) Additionally, the equations derived to formulate the network model are used to quantify the optimal tradeoff between sensor sensitivity and cost as a function of building parameters. Future efforts will continue on this path, focusing on how easily discernible building properties such as size, HVAC layout, and air exchange rates can be used to predict the sensing requirements in large indoor spaces.by Scott B. Van Broekhoven.S.M