Showing papers on "Bounding overwatch published in 1992"

Unifying the landmark developments in optimal bounding ellipsoid identification

Abstract: : A quite general class of Optimal Bounding Ellipsoid (OBE) algorithms including all methods published to date, can be unified into a single framework called the Unified OBE (UOBE) algorithm. UOBE is based on generalized weighted recursive least squares in which very broad classes of 'forgetting factors' and data weights may be employed. Different instances of UOBE are distinguished by their weighting policies and the criteria used to determine their optimal values. A study of existing OBE algorithms, with a particular interest in the tradeoff between algorithm performance interpretability and convergence properties, is presented. Results suggest that an interpretable, converging UOBE algorithm will be found. In this context, a new UOBE technique, the set membership stochastic approximation (SM-SA) algorithm is introduced. SM-SA possesses interpretable optimization measures and known conditions under which its estimator will converge.

A linear-time simple bounding volume algorithm

Abstract: Publisher Summary Three-dimensional bounding volume is a ubiquitous tool of accelerating ray tracing and visibility testing. The two most common types of bounding volume are box and sphere. That is because the intersections between rays and boxes or rays and spheres involve very simple computations. The smallest bounding volume has received considerable attention in the computational geometry community. The O(N) algorithm for the smallest bounding sphere and the O(N log N) algorithm for the three-dimensional Voronoi diagram exist, and they are both optimal. Simple and fast approximation algorithms for smallest bounding volumes are highly desirable for practitioners. This chapter presents a simple algorithm to compute near-optimal bounding volumes enclosing N points in O(N) time. It further illustrates the worst case for the naive bounding volume algorithm and a situation in which the simple bounding-sphere algorithm fails without principal component analysis.

Bounding signal probabilities for testability measurement using conditional syndromes

Abstract: An algorithm for bounding the random pattern testability of individual faults in a circuit is proposed. Auxiliary gates for bounding the testability are constructed, converting the problem into one of determining the signal probability at the output of the auxiliary gate. The results presented are in terms of lower bounds of the testabilities of faults. The bounds generated by the algorithm can be used by designers to identify pseudorandom pattern resistant faults, to enable them to modify the circuit structure to make the faults easy to detect, and, hence, to increase the fault coverage. >

Lower and upper bounds for incremental algorithms

Abstract: An incremental algorithm (also called a dynamic update algorithm) updates the answer to some problem after an incremental change is made in the input. We examine methods for bounding the performance of such algorithms. First, quite general but relatively weak bounds are considered, along with a careful examination of the conditions under which they hold. Next, a more powerful proof method, the Incremental Relative Lower Bound is presented, along with its application to a number of important problems. We then examine an alternative approach, $\delta$-analysis, which had been proposed previously, apply it to several new problems and show how it can be extended. For the specific problem of updating the transitive closure of an acyclic digraph, we present the first known incremental algorithm that is efficient in the $\delta$-analysis sense. Finally, we critique the existing approaches to incremental lower bounds, examining their strengths and weaknesses in light of the general goal of lower bounding incremental algorithms, and considering under what conditions which method(s) of analysis are most appropriate and useful.

Limited Complexity and Parallel Implementation of Polytope Updating

Abstract: The methods introduced in this paper aim at helping the practical applications of polytope based parameter and state bounding, and for this purpose parallel computing and limited complexity procedures are introduced to update convex polytopes. As opposed to the case of covariance based recursive statistical estimation where parallel computation can only result moderate improvement for lower dimensions of parametervectors, polytope updating can be parallelized more efficiently. The increase in the speed of the procedure can be made proportional to the number of applied processors. This result means that using sufficent number of processors polytope based parameter bounding can be made even faster than statistical estimation. Another development of this paper is the introduction of some schemes to limit the complexity of the updated polytopes. Combining limited complexity calculations with parallel processing helps the way towards on-line applications of polytope-based parameter and state bounding in adaptive control and signal processing.

Stealth terrain navigation for multi-vehicle path planning

Abstract: In this paper, we propose a method for solving visibility-based terrain path planning problems for groups of vehicles using data parallel machines. The discussion focuses on path planning for two groups of vehicles so that they move in a bounding overwatch manner. Furthermore, the planned paths for the vehicles themselves are subject to intervisibility constraints, configuration constraints, and different terrain traversabilities due to variations in terrain type and slope. A spatial-temporal sampling approach is adopted to discretize the solution space and facilitate fast computation on a data parallel machine. One of the key computations in the planning is the region-to-region visibility analysis, which is computationally expensive but essential to the choice of subgoals to carry out reconnaissance activities. A parallel algorithm for this analysis is developed. By reducing the communication complexity, our algorithm achieves much faster running time than traditional methods. The algorithms are implemented on a Connection Machine CM-2, and the experimental results show that the planning system effectively generates good paths.

A Comparison of Set Estimation Methods for Output Error Models with Bounded Errors

Abstract: The computation of bounds on parameters, rather than point estimates and covariances, is considered for discrete time output error models which are linear in the parameters. The additive noise is assumed to be unknown but bounded in an l ? norm by a given constant. In the case for given input-output data, the set of all admissible parameters consistent with the given model equation and the noise bound, is a convex polytope. In this paper we attempt to estimate optimally by means of minimizing the parameter uncertainty intervals. Therefore, we compare ellipsoidal bounding with orthotopic bounding (linear programming) and a simulation example is given to illustrate the results.

Optimal bounding error algorithms with adaptive tracking capability

Abstract: A class of set membership (SM)-based techniques which are of particular interest in applications requiring real-time identification is considered. The optimal bounding ellipsoid (OBE) algorithms are interpreted as a blending of the classical least square error minimization approach with knowledge of bounds on model errors arising from SM considerations. Various adaptive strategies are discussed. It is shown that the adaptive OBE algorithms are more effective in tracking time-varying systems than least squares techniques. >

Automatic selection of bounding abstractions

Abstract: Since the complexity of model-basedreasoningincreasesdrastically with the model size, automated modelinghasbecomean active researcharea. However, unlike humanengineers,few modelingprograms introduceapproximationsthat are customizedto the questionat hand. In this paper, we focus on a single aspectof automatedmodel management:shifting model accuracy. We describea domain-independent theoryof query-directedmodel simplification thatuses bounding abstractionsto guaranteethe accuracyof the simplifications introduced. We have tested our theory by implementingthe SUPprogramwhich evaluates inequality relationsin an approximatemodel without sacrificingaccuracy.Thesetechniquesare basedon our previouslyreportedwork on model sensitivity analyszs (MSA) and fitting approximations. SUP uses MSA, the subtaskof predictinghow a changein modelswill affect the resulting predictedbehavior,to determineif onemodel is aboundingabstractionof another.Whenever two modelsare relatedby fitting approximations, the MSA computationreducesso the simpleproblem of computing the sign of partial derivatives in a single model,a task which is easilyperformedby Mathemat-