Model based object recognition and object localization are fundamental problems in industrial automation. We present techniques which use a scanner composed of binary light beam sensors to quickly recognize objects (as fast as 5 microseconds) and to accurately localize objects (0:025 millimeters), and we describe localization and recognition experiments and present results. Binary sensors only ...

Transportation problems of large urban conurbations inspire search for new transportation systems, that meet high environmental standards, are relatively cheap and user friendly. The latter element also includes the needs of disabled and elderly people. This article concerns a new transportation system PRT - Personal Rapid Transit. In this article the attention is focused on the analysis of the...

This paper describes a local planning, control and learning framework enabling high-speed autonomous groundvehicle traversal of rough 3D terrain replete with bumps, berms, banked-turns and even jumps. We propose an approach based on fast physical simulation and prediction, which we find offers numerous benefits: first, it takes advantage of the full expressiveness of the inherently non-linear, ...

OPTIMIZER is an on-line application that optimizes the codon usage of a gene to increase its expression level. Three methods of optimization are available: the ‘one amino acid–one codon’ method, a guided random method based on a Monte Carlo algorithm, and a new method designed to maximize the optimization with the fewest changes in the query sequence. One of the main features of OPTIMIZER is th...

Static off-line scheduling ensures predictability of worstcase behavior and high resource utilization for safetycritical applications but lacks the flexibility needed to deal with run-time fault-tolerance. We present a temporal redundancy-based recovery technique that tolerates transient task failures in statically scheduled distributed embedded systems where tasks have timing, resource, and pr...

In Autonomous Transit Networks some basic elements influence the throughput: network structure, maximum velocity, number of vehicles etc. Other parameters like station structure, dynamic routing or vehicle behavior on intersections play minor role. Yet in highly congested nets, when vehicles interfere in the traffic, some subtle decisions may influence overall system ridership. We tested the im...

This paper proposes an approach to reduce the stochastic parsing time with stochastic context-free grammars. The basic idea consists of storing a set of precomputed problems. These precomputed problems are obtained off line from a training corpus or they are computed on line from a test corpus. In this work, experiments with the UPenn Treebank are reported in order to show the performance of bo...

Many scientific, imaging, and geospatial applications produce large high-precision scalar fields sampled on a regular grid. Lossless compression of such data is commonly done using predictive coding, in which weighted combinations of previously coded samples known to both encoder and decoder are used to predict subsequent nearby samples. In hierarchical, incremental, or selective transmission, ...

Multi-dimensional transfer functions are commonly used in rectilinear volume renderings to effectively portray materials, material boundaries and even subtle variations along boundaries. However, most unstructured grid rendering algorithms only employ one-dimensional transfer functions. This paper proposes a novel pre-integrated Projected Tetrahedra (PT) rendering technique that applies bivaria...

Model based object recognition and object localiza-tion are fundamental problems in industrial automation. We present techniques which use a scanner composed of binary light beam sensors to quickly recognize objects (as fast as 5 microseconds) and to accurately localize objects (0:025 millimeters), and we describe localization and recognition experiments and present results. Binary sensors only...