一种支持QoS的拓扑自适应动态组播路由算法

YAM、QoSMIC、DSDMR等一类支持QoS的动态组播路由算法允许组播成员动态地加入和离开，同时为接收方提供多个可选择的组播接入路径，以满足不同应用的QoS需求。但这些算法普遍存在控制信令开销大和结点加入时延长，可扩展性不好等问题。本文在分析这些算法的基础上，提出改进的支持QoS的动态组播路由算法，即拓扑自适应动态组播路由（Topology Adaptive Dynamic Multicast Routing，TADMR）算法。该算法避免了以往算法中大部分盲目的路径搜索，并使结点加入时延不再受限于固定的等待时钟，而与网络拓扑相自适应。性能分析和仿真结果表明，该算法具有较低的控制信令开销和结点加入时延，适用于各种网络规模和群组规模，具有良好的可扩展性。     

可拓检测的方案生成

以可拓学理论为基础，论述了可拓检测的有关定义和概念，提出了可拓检测的方案生成方法及过程，同时探讨了检测方法的优化和评价方法。     

基于IP核复用技术的SoC设计

概述了国内外IP产业的发展情况,论述了我国发展IP核复用技术SoC设计的可能性和必要性,指出我国急需发展的关键芯片及IP核种类.     

碳/碳复合材料CVI工艺中热解碳形成机理的研究

对碳碳复合材料CVI或（CVD）工艺中热碳组织的类型，沉积机理等研究情况进行了简要的综述，并对其进一步研究提出了相应观点。     

Adaptive cell division for ray tracing

In ray tracing the two most commonly used data structures are the octree and uniform cell division. The octree structure allows efficient adaptive subdivision of space, while taking care of the spatial coherence of the objects in it; however, the tree structure locating the next node in the path of a ray is complex and time consuming. The cell structure, on the other hand, can be stored in a three-dimensional array, and each cell can be efficiently accessed by specifying three indices. However, such a uniform cell division does not take care of object coherence. The proposed data structure combines the positive features of the above data structures while minimising their disadvantages. The entire object space is implicitly assumed to be a three-dimensional grid of cells. Initially, the entire object space is a single voxel which later undergoes “adaptive cell division.” But, unlike in the octree structure, where each voxel is divided exactly at the middle of each dimension, in adaptive cell division, each voxel is divided at the nearest cell boundary. The result is that each voxel contains an integral number of cells along each axis. Corresponding to the implicit cell division we maintain a three-dimensional array, with each array element containing the voxel number which is used to index into the voxel array. The voxel array is used to store information about the structure of each voxel, in particular, the objects in each voxel. While a ray moves from one voxel to another we always keep track of the cell through which the ray is currently passing. Since only arrays are involved in accessing the next voxel in the path of the ray, the operation is very efficient.     

Finite-element grid improvement by minimization of stiffness matrix trace

A new and simple method of finite-element grid improvement is presented. The objective is to improve the accuracy of the analysis. The procedure is based on a minimization of the trace of the stiffness matrix. For a broad class of problems this minimization is seen to be equivalent to minimizing the potential energy. The method is illustrated with the classical tapered bar problem examined earlier by Prager and Masur. Identical results are obtained.     

Microscopical studies of water/flour systems

Summary Wheat flour particles sprinkled on a water surface form strands which are visible in the light microscope. These strands form aggregates by means of mechanical forces which show viscoelastic behaviour. Amino acid analysis of both the protein strands and the aggregates formed shows that they consist of gluten protein. The formation of the protein strands occurs at the air/water interface and results from the action of a protein film which spreads around the flour particles.

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Mikroskopische Untersuchungen von Mehl/Wasser-Systemen

Zusammenfassung Weizenmehlpartikel, die auf Wasser gestreut werden, bilden lichtmikroskopisch sichtbare Stränge aus. Durch mechanische Beanspruchung lassen sich these Stränge zu Aggregaten zusammenfassen, die visco-elastisches Verhalten zeigen. Die Aminosäureanalyse sowohl der Proteinstrange wie auch der daraus gebildeten Aggregate zeigt, daß es sich dabei um Kleberprotein handelt. Die Proteinfäden entstehen an der Wasser-Luft-Grenzflache als Folge eines spreitenden Proteinfilms, der sich um die Mehlpartikel herum ausbreitet.

     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at