Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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    Fast Evaluation of Demagnetizing Field in Three Dimensional Micromagnetics using Multipole Approximation
    (2000) Tan, X.; Baras, John S.; Krishnaprasad, Perinkulam S.; Baras, John S.; Krishnaprasad, Perinkulam S.; ISR; CDCSS
    Computational micromagnetics in three dimensions is of increasing interest with the development of magnetostrictive sensors andactuators. In solving the Landau-Lifshitz-Gilbert (LLG) equation, the governing equation of magnetic dynamics for ferromagnetic materials, we need to evaluate the effective field. The effective field consists of several terms, among which the demagnetizing field is of long-range nature.

    Evaluating the demagnetizing field directly requires work of O(N^2) for a grid of N cells and thus it is the bottleneck in computational micromagnetics. A fast hierarchical algorithm using multipole approximation is developed to evaluate the demagnetizing field. We first construct a mesh hierarchy and divide the grid into boxes of different levels. The lowest level box is the whole grid while the highest level boxes are just cells. The approximate field contribution from the cells contained in a box is characterized by the box attributes, which are obtained via multipole approximation. The algorithm computes field contributions from remote cells using attributes of appropriate boxes containing those cells, and it computes contributions from adjacent cells directly. Numerical results have shown that the algorithm requires work of O(NlogN) and at the same time it achieves high accuracy. It makes micromagnetic simulation in three dimensions feasible.

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    A Hierarchical Structure For Finite Horizon Dynamic Programming Problems
    (2000) Zhang, Chang; Baras, John S.; Baras, John S.; ISR; CSHCN
    In dynamic programming (Markov decision) problems, hierarchicalstructure (aggregation) is usually used to simplify computation. Most research on aggregation ofMarkov decision problems is limited to the infinite horizon case, which has good tracking ability. However, in reallife, finite horizon stochastic shortest path problems are oftenencountered.

    In this paper, we propose a hierarchical structure to solve finite horizon stochastic shortest pathproblems in parallel. In general, the approach reducesthe time complexity of the original problem to a logarithm level, which hassignificant practical meaning.

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    Exact, Recursive, Inference of Event Space Probability Law for Discrete Random Sets with Applications
    (1991) Sidiropoulos, N.; Baras, John S.; Berenstein, Carlos A.; ISR
    In this paper we extend Choquet's result to obtain a recursive procedure for the computation of the underlying event-space probability law for Discrete Random Sets, based on Choquet's capacity functional. This is an important result, because it paves the way for the solution of statistical inference problems for Discrete Random Sets. As an example, we consider the Discrete Boolean Random Set with Radial Convex Primary Grains model, compute its capacity functional, and use our procedure to obtain a recursive solution to the problem of M-ary MAP hypothesis testing for the given model. The same procedure can be applied to the problem of ML model fitting. Various important probability functionals are computed in the process of obtaining the above results.