This paper presents a novel approach to high-resolution ultrasonic environment imaging for autonomous mobile systems. It describes the fusion of sensor data from multiple wide-angled ultrasonic transducers with overlapping detection cones for creating local environment models. A fitting technique defined as tangential regression is introduced that considers sensor properties as well as physical reflection properties of ultrasound. This allows a reliable detection and localization of straight-line segments which describe the boundary of geometric objects. Experimental results show that straight-line segments extracted at different robot locations can be aggregated and merged in order to obtain a global environment map

Similarity of edge labeled graphs is considered in the sense of minimum squared distance between corresponding values. Vertex correspondences are established by isomorphisms if both graphs are of equal size and by subisomorphisms if one graph has fewer vertices than the other. Best fit isomorphisms and subisomorphisms amount to solutions of quadratic assignment problems and are computed exactly as well as approximately by minimum cost flow, linear assignment relaxations and related graph algorithms.

Autonomous airships, or aerobots, that have to traverse extensive distances or explore other bodies of the solar system require careful management of onboard energy resources. For planetary exploration, available energy has to be used for science data gathering, communications with an orbiter or directly with Earth, altitude control and hazard avoidance, close-up navigation for science site investigation, and surface sampling. Consequently, long-distance traverses should be done by relying as much as possible on external energy sources, and in particular on wind energy. In this paper, we address the problem of planning opportunistic flight paths that use know regional or global wind patterns to carry the aerobot to its destination. We assume that the aerobot is able to control its vertical displacement, while horizontal displacement is to be achieved through wind propulsion. We show how energy minimal and time minimal trajectories can be computed for aerobots traversing homogenous wind fields. We also present computational results for 2D and 3D wind fields.

Autonomous airships, or aerobots, designed to traverse large distances or explore other bodies of the solar system require careful management of onboard energy resources. For planetary exploration, available energy has to be used for science data gathering, communication with an orbiter or directly with Earth, altitude control and hazard avoidance, close-up navigation for science site investigation, and surface sampling. Consequently, long-distance traverses should be done by relying as much as possible on external energy sources, and in particular on wind energy. In this paper, we address the problem of planning opportunistic flight paths that use know wind fields to carry the aerobot to its destination. We assume that the aerobot is able to control its vertical displacement, while horizontal displacement is to be achieved through wind propulsion. We show how energy minimal and time minimal trajectories can be computed for aerobots traversing layered homogenous wind fields, using an approach that is based on the solution of sets of linear programming problems. Finally, we present illustrative computational results obtained for 2D and 3D wind fields.

This paper deals with the fusion of random variables when cross covariances are unknown. This is a vital problem in nearly every real world application since cross covariances are often impossible to obtain, but also cannot be ignored. We provide a rigorous derivation of the fusion equations which are also known as covariance intersection. This approach allows one to derive an iterative scheme for simultaneous mapping and localization. The algorithm can also be used for multi-robot explorations where highly correlated decentralized maps have to be fused to form a consistent global map. We show the mapping and localization results based on dense laser range scans.

Abstract Motivation: Primer design involves various parameters such as string-based alignment scores, melting temperature, primer length and GC content. This entails a design approach from multicriteria decision making. Values of some of the criteria are easy to compute while others require intense calculations. Results: The reference point method was found to be tractable for trading-off between deviations from ideal values of all the criteria. Some criteria computations are based on dynamic programs with value iteration whose run time can be bounded by a low-degree polynomial. For designing standard PCR primers, the scheme offers in a relative gain in computing speed of up to \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(50\ :\ 1\) \end{document}over ad-hoc computational methods. Single PCR primer pairs have been used as model systems in order to simplify the quantization of the computational acceleration factors. The program has been structured so as to facilitate the analysis of large numbers of primer pairs with minor modifications. The scheme significantly increases primer design throughput which in turn facilitates the use of oligonucleotides in a wide range of applications including: multiplex PCR and other nucleic acid-based amplification systems, as well as in zip code targeting, oligonucleotide microarrays and nucleic acid-based nanoengineering. Availability: A public version of the software DOPRIMER is accessible under http://doprimer.interactiva.de Contact: [email protected]

This paper presents a technique for segmenting spot-objects of possibly different sizes in images of biochip arrays. As these images are taken after the biochips underwent a complex chemical process, they are subject to blur effects, distortions, and local intensity shifts. Image segmentation is reduced to quantisation which in turn is reduced to function approximation. The approximation requires neither parametric assumptions nor parametric input and it iteratively optimizes an n-step histogram function by coarser step functions. Thus, a discrete "scale space" is created. The notion of so-called stable minima is used for finding good binarisation thresholds. Stability issues are combined with heuristic criteria tailored to segmenting biochip images. Finally, the complete approach is applied to a series of real biochip images.

Estimating superresolution models from low-resolution sensor data is of great interest for many applications in image processing and computer vision. However, in general the estimation of super-resolution models is difficult due to the computational complexity of existing methods. In this paper, we present an approach to estimating super-resolution world models using stochastic causal networks. The basic elements of our approach include the use of stochastic sensor models, the computation of spatial representations based on random field models, and the development of stochastic estimation procedures to compute these world models from sensor observations. The approach requires only polynomial effort for computing both single cell marginals under arbitrary observations and maximum a posteriori probability (MAP) solutions. We also present approximate methods that further decrease the computational effort for model updating using multiple observations per sensor.

Binarization is reduced to function approximation in terms of quantization which is overlaid either with a recursion scheme or with discrete scale-space filtering. Approximations are solved to optimality not requiring any parametric input except that there be two segment classes of the image.

A relation between probabilistic logic and capacities (belief functions) is formally established. the use of lower probabilities is demonstrated to enrich expressiveness for frameworks based on polytopes of probability laws. © 1995 John Wiley & Sons, Inc.

Several notions of optimality are introduced for two-dimensional geometric versioning of polygons. The storage required for a series of versions is minimized by determining references and restating versions from the references. Structural properties are presented as well as algorithms which yield optimal or near-optimal references for some of the optimality notions. Several of the algorithms are shown to be efficient.

Abstract Several decomposition types of orders and related discrete structures have been investigated so far. In this paper, we present a decomposition for orders based on partial “ignoring” of the order. Structures that are in a certain sense “prime” turn out to be decomposable. The relation to other decomposition methods is analyzed. Bounds for activity networks and reliability functions are obtained. © 1994 by John Wiley & Sons, Inc.