Bayesian and Dempster-Shafer fusion

The Kalman Filter is traditionally viewed as a prediction-correction filtering algorithm. In this work we show that it can be viewed as a Bayesian fusion algorithm and derive it using Bayesian arguments. We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation. We derive the Kalman Filter from this equation using a novel method to evaluate the Chapman-Kolmogorov prediction integral. We then use the theory to fuse data from multiple sensors. Vying with this approach is the Dempster-Shafer theory, which deals with measures of “belief”, and is based on the nonclassical idea of “mass” as opposed to probability. Although these two measures look very similar, there are some differences. We point them out through outlining the ideas of the Dempster-Shafer theory and presenting the basic Dempster-Shafer fusion equation. Finally we compare the two methods, and discuss the relative merits and demerits using an illustrative example.     

Web-based systems design: a study of contemporary practices and an explanatory framework based on “method-in-action”

This paper reports the findings of a detailed study of Web-based systems design (WBSD) practices in Ireland based on data collected over a 3-year period (2002–2005), the objectives of which were to (1) contribute towards a richer understanding of the current “real-world” context of WBSD by characterising the profile of a typical project (team size, timeframe, nature of requirements, etc.) and identifying the key challenges, constraints, and imperatives (i.e. “mediating factors”) faced by Web-based system designers, and (2) understand how those contextual parameters and mediating factors influence the activity of WBSD as regards the selection and enactment of whatever design practices are therefore engaged (i.e. the use of methods, procedures, etc.). Data was gathered through a survey which yielded 165 usable responses, and later through a series of semi-structured qualitative interviews. Using grounded theory, an explanatory conceptual framework is derived, based on an extension of the “method-in-action” model, the application of which to WBSD has not been previously investigated in depth. It is proposed that this framework of WBSD issues is valuable in a number of ways to educators, researchers, practitioners, and method engineers.     

Equivalent Conditions for Convergence of Splittings of Non-Hermitian Indefinite Matrices

We present some sufficient and necessary conditions for convergent splitting of a non-Hermitian indefinite matrix. Some sufficient conditions to determinate a matrix with a (strongly) dominant symmetric part for a class of boundary value problem are also obtained. These results are applicable to identify the convergence of iterative methods for solving large sparse systems of linear equations.     

The prospects of non-volatile phase-change RAM

Phase-change read-and-write memory (PRAM) is a promising memory that can solve the problems of conventional memory—scalability, read/write speed, and reliability. We will review the opportunities and technical challenges of PRAM. The most important challenge of PRAM is the reduction of the writing current. Various approaches to reduce the writing current will be reviewed and the prospects of PRAM are discussed.     

Feature-based classifiers for design optimization

We present a design optimization method for systems with high-dimensional parameter spaces using inductive decision trees. The essential idea is to map designs into a relatively low-dimensional feature space, and to derive a classifier to search for high-performing design alternatives within this space. Unlike learning classifier systems that were pioneered by Holland and Goldberg, classifiers defined by inductive decision trees were not originally developed for design optimization. In this paper, we explore modifications to such classifiers to make them more effective in the optimization problem. We expand the notions of feature space, generalize the tree construction heuristic beyond the original information-theoretic definitions, increase the reliance on domain expertise, and facilitate the transfer of design knowledge between related systems. There is a relatively small but rapidly growing body of work in the use of inductive trees for engineering design; the method presented herein is complementary to this research effort.