On the mathematical formulation of the wiring problem

The wiring problem is usually transformed into a graphtheoretical analysis-synthesis problem. For a practical algorithm it is necessary that certain constraints are taken into account in the construction of the respective graph. These constraints originate from the desire to place terminals—eventually in a previously determined sequence—at the periphery of the board or the wiring layer of an integrated circuit (IC). For printed boards we can also have some demands regarding the sequence of contacts and the side on which the components have to be placed. The purpose of this paper is to show how these requirements, which are indispensible for a proper printed board layout, can be examined. Some of these requirements have not appeared in literature before. The inquiry consists of two separate tests, each eventually combined with modification steps. The details of these modifications are not a subject of this paper. In the last section the content of this paper is compared with other publications in this field.     

A linear predictive method for on-line track recognition and event analysis for cylindrical magnetic spectrometers

A fast and simple track search algorithm based on a linear predictive method and applicable to cylindrical magnetic spectrometers is described. Resulting track parameters, expressed in terms of linear predictors, can be used to do event analysis on-line. The application of the algorithm to the μ → 3e experiment using the SINDRUM detector is detailed.     

New concept for a high-power beam dump

A new concept for a dump for the ion and neutral beams used in the controlled nuclear fusion program uses thin sheets of a refractory metal such as tungsten formed into troughs having semi-circular cross sections. High-velocity water flowing circumferentially removes heat by subcooled nucleate boiling. Possible advantages are modular construction, relatively long sputtering lifetime, and a lower pressure drop than in tubular beam dumps. An example design calculation is shown for a dump estimated to be capable of absorbing an incident flux of 10 kW/cm² with reasonable safety factors.     

Dimensional properties of graphs and digital spaces

Many applications of digital image processing now deal with three- and higher-dimensional images. One way to represent n-dimensional digital images is to use the specialization graphs of subspaces of the Alexandroff topological space ⁿ (where denotes the integers with the Khalimsky line topology). In this paper the dimension of any such graph is defined in three ways, and the equivalence of the three definitions is established. Two of the definitions have a geometric basis and are closely related to the topological definition of inductive dimension; the third extends the Alexandroff dimension to graphs. Diagrams are given of graphs that are dimensionally correct discrete models of Euclidean spaces, n-dimensional spheres, a projective plane and a torus. New characterizations of n-dimensional (digital) surfaces are presented. Finally, the local structure of the space ⁿ is analyzed, and it is shown that ⁿ is an n-dimensional surface for all n1.     

A simple distributed mechanism for accounting system self-configuration in next-generation charging and billing

Modern communication systems are becoming increasingly dynamic and complex. In this article a novel mechanism for next generation charging and billing is presented that enables self-configurability for accounting systems consisting of heterogeneous components. The mechanism is required to be simple, effective, efficient, scalable and fault-tolerant. Based on simulation results it is shown that the proposed simple distributed mechanism is competitive with usual cost-based or random mechanisms under realistic assumptions and up to non-extreme workload situations as well as fulfilling the posed requirements.     

3D flow features visualization via fuzzy clustering

A key approach to visualizing a flow field is to emphasize regions with significant behavior. However, it is difficult to give concrete criteria for classifying feature regions. In this paper, we use a novel framework in which fuzzy sets are used to determine flow features: Fuzzy relationships assess structural properties of features. A fuzzy c-means-like clustering algorithm is used to evaluate the importance of each voxel. Our approach can be readily modified with new fuzzy relationships describing other features of interest to users. We use a multi-resolution approach which displays structural features in greater detail, and represents the background by coarse-grained information. Experiments on synthetic and real datasets show that our framework can highlight significant aspects of the whole flow while avoiding occlusion and clutter. Interactive performance is achieved via a GPU implementation.     

Sketch theory as a framework for knowledge management

In this paper, we give an overview of sketch theory as a knowledge management framework and discuss its strengths relative to logic, the semantic web and relational algebra. Sketch theory, for example, supports modularity among meta-data, instance data and uncertainty. It also provides a notion of constraint-preserving map. We explore Q-trees as a technique for inference with sketches and compare it to logical deduction. Ideas can be formulated in distinct ways even within a fixed formalism. We illustrate solution of this alignment problem using sketches and the notion of Morita equivalence of logical theories. Sketch theory provides rich notions of contextual view with which we compute illustrative examples. Finally, we outline a program for advancing sketch theory as a complement to other knowledge management technologies and discuss transformations between sketches and other models.     

Robust Statistical Pixel Estimation

Robust statistical methods are employed to reduce the noise in Monte Carlo ray tracing. Through the use of resampling, the sample mean distribution is determined for each pixel. Because this distribution is uni‐modal and normal for a large sample size, robust estimates converge to the true mean of the pixel values. Compared to existing methods, less additional storage is required at each pixel because the sample mean distribution can be distilled down to a compact size, and fewer computations are necessary because the robust estimation process is sampling independent and needs a small input size to compute pixel values. The robust statistical pixel estimators are not only resistant to impulse noise, but they also remove general noise from fat‐tailed distributions. A substantial speedup in rendering can therefore be achieved by reducing the number of samples required for a desired image quality. The effectiveness of the proposed approach is demonstrated for path tracing simulations.