There are more and more Internet services such as video on demand, voice over IP,Blog, and so on. The network quality is important for providing good services. P2P technology can decentralize the usage of bandwidth, so a server can provide services with lower bandwidth. The bandwidth is filled by P2P traffic if we don¡¦t limit the usage of P2P applications, so we need a service controller that can limit the P2P traffic to provide better quality for other applications. The traditional network systems use software solutions or hardware solutions. The software solutions offer flexibility but have low performance; The hardware solutions offer highest speed but are inflexible and expensive to modify or upgrade. there is another solution known as network processors. A network processor can be programmed and has been optimizede for packet procecssing. We need a good service classifier to classify P2P traffic, then we can limit it. The performance of a signature based service classifier is dominated by the speed of its pattern matching algorithm. In this paper, we proposed a fast ulti-pattern matching algorithm by improving WM algorithm. Serveral algorithms are implemented on IXP2400 network processor for performance evaluation, and our proposed algorithm outperforms other algorithms if its parameters are properly set.

Geologic models derived from static data alone typically fail to reproduce the production history of a reservoir, thus the importance of reconciling simulation models to the dynamic response of the reservoir. This necessity has been the motivation behind the active research work in history matching. Traditionally, history matching is performed manually by applying local and regional changes to reservoir properties. While this is still in general practice, the subjective overtone of this approach, the time and manpower requirements, and the potential loss of geologic consistency have led to the development of a variety of alternative workflows for assisted and automatic history matching. Automatic history matching requires the solution of an inverse problem by minimizing an appropriately defined misfit function. Recent advances in geostatistics have led to the building of high-resolution geologic models consisting of millions of cells. Most of these are scaled up to the submillion size for reservoir simulation purposes. History matching even the scaled up models is computationally prohibitive. The associated cost in terms of time and manpower has led to increased interest in efficient history matching techniques and in particular, to sensitivity-based algorithms because of their rapid convergence. Furthermore, of the sensitivity-based methods, streamline-based production data integration has proven to be extremely efficient computationally. In this work, we extend the history matching capability of the streamline-based technique to three-phase production while addressing in general, pertinent issues associated with history matching. We deviate from the typical approach of formulating the inverse problem in terms of derived quantities such as GOR and Watercut, or measured phase rates, but concentrate on the fundamental variables that characterize such quantities. The presented formulation is in terms of well node saturations and pressures. Production data is transformed to composite saturation quantities, the time variation of which is matched in the calibration exercise. The dependence of the transformation on pressure highlights its importance and thus a need for pressure match. To address this need, we follow a low frequency asymptotic formulation for the pressure equation. We propose a simultaneous inversion of the saturation and pressure components to account for the interdependence and thus, high non-linearity of three phase inversion. We also account for global parameters through experimental design methodology and response surface modeling. The validity of the proposed history matching technique is demonstrated through application to both synthetic and field cases.

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In robot navigation, and image content searches reliable salient features are of pivotal importance. Also in biometric human recognition, salient features are increasingly used. Regardless the application, image matching is one of the many problems in computer vision, including object recognition. This report investigates some salient features to match sub-images of different images. An underlying assumption is that sub-images, also called image objects, or objects, are possible to recognize by the salient features that can be recognized independently. Since image objects are images of 3D objects, the salient features in 2D images must be invariant to reasonably large viewing direction and distance (scale) changes. These changes are typically due to 3D rotations and translations of the 3D object with respect to the camera. Other changes that influence the matching of two 2D image objects is illumination changes, and image acquisition noise. This thesis will discuss how to find the salient features and will compare them with respect to their matching performance. Also it will explore how these features are invariant to rotation and scaling.

I develop three models that are designed to aid in the analysis of environments in which agents i) benefit from interacting with others, and ii) optimally choose their characteristics mindful of the fact that such choices will influence the quality of interaction that they can expect. Of central interest is the ways in which a concern for interaction affects the efficiency with which agents choose their characteristics. The first two models contrast with previous work in that each agents' relevant characteristics are both unobserved and endogenously determined. The first model provides an explanation for credentialism in the labour market, and demonstrates how a concern for interaction can lead to over-investment in the relevant characteristic. The second model is motivated by human capital development in the presence of peer effects, and demonstrates how a concern for interaction can exacerbate an inherent under-investment problem. The third model retains the feature of unobserved characteristics, and contrasts with previous work by embedding frictions in the process by which agents compete for partners. The model is set in a labour market and demonstrates that outcomes of interest (equilibrium matching patterns, income, inequality and welfare) are generally not monotonic in the level of frictions.

The recognition of 3-D objects from their silhouettes demands a shape representation which is invariant to minor changes in viewpoint and articulation. This invariance can be achieved by parsing a silhouette into parts and relationships that are stable across similar object views. Medial descriptions, such as skeletons and shock graphs, attempt to decompose a shape into parts, but suffer from instabilities that lead to similar shapes being represented by dissimilar part sets. We propose a novel shape parsing approach based on identifying and regularizing the ligature structure of a given medial axis. The result of this process is a bone graph, a new medial shape abstraction that captures a more intuitive notion of an object’s parts than a skeleton or a shock graph, and offers improved stability and within-class deformation invariance over the shock graph. The bone graph, unlike the shock graph, has attributed edges that specify how and where two medial parts meet. We propose a novel shape matching framework that exploits this relational information by formulating the problem as an inexact directed acyclic graph matching, and extending a leading bipartite graph-based matching framework introduced for matching shock graphs. In addition to accommodating the relational information, our new framework is better able to enforce hierarchical and sibling constraints between nodes, resulting in a more general and more powerful matching framework. We evaluate our matching framework with respect to a competing shock graph matching framework, and show that for the task of view-based object categorization, our matching framework applied to bone graphs outperforms the competing framework. Moreover, our matching framework applied to shock graphs also outperforms the competing shock graph matching algorithm, demonstrating the generality and improved performance of our matching algorithm.

No description available.

The recognition of 3-D objects from their silhouettes demands a shape representation which is invariant to minor changes in viewpoint and articulation. This invariance can be achieved by parsing a silhouette into parts and relationships that are stable across similar object views. Medial descriptions, such as skeletons and shock graphs, attempt to decompose a shape into parts, but suffer from instabilities that lead to similar shapes being represented by dissimilar part sets. We propose a novel shape parsing approach based on identifying and regularizing the ligature structure of a given medial axis. The result of this process is a bone graph, a new medial shape abstraction that captures a more intuitive notion of an object’s parts than a skeleton or a shock graph, and offers improved stability and within-class deformation invariance over the shock graph. The bone graph, unlike the shock graph, has attributed edges that specify how and where two medial parts meet. We propose a novel shape matching framework that exploits this relational information by formulating the problem as an inexact directed acyclic graph matching, and extending a leading bipartite graph-based matching framework introduced for matching shock graphs. In addition to accommodating the relational information, our new framework is better able to enforce hierarchical and sibling constraints between nodes, resulting in a more general and more powerful matching framework. We evaluate our matching framework with respect to a competing shock graph matching framework, and show that for the task of view-based object categorization, our matching framework applied to bone graphs outperforms the competing framework. Moreover, our matching framework applied to shock graphs also outperforms the competing shock graph matching algorithm, demonstrating the generality and improved performance of our matching algorithm.

Direct georeferencing of aerial imagery has the potential to meet escalating demand for image data sets of increasingly higher temporal and spatial resolution. However, variability in terms of spatial accuracy within the resulting images may severely limit the use of this technology with regard to operations involving other data sets. Spatial misalignment between data sets can be corrected manually; however, an automated solution is preferable given the volume of data involved. This research has developed and tested an automated custom solution to the spatial misalignment between directly georeference aerial thermal imagery and vector data representing building outlines. The procedure uses geometric matches between image features and vector objects to relate pixel locations to geographic coordinates. The results suggest that the concept is valid and capable of significantly improving the spatial accuracy of directly georeferencing aerial imagery.

The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhibit non-linear temporal fluctuations and thus a high degree of redundancy. The initial part of this thesis considers various dynamic time warping techniques used for normalizing the temporal differences between speech patterns. Redundancy removal methods are also considered, and their effect on the recognition accuracy is assessed. Although the use of dynamic time warping algorithms provide considerable improvement in the accuracy of isolated word recognition schemes, the performance is ultimately limited by their poor ability to discriminate between acoustically similar words. Methods for enhancing the identification rate among acoustically similar words, by using common pattern features for similar sounding regions, are investigated. Pattern matching based, speaker independent systems, can only operate with a high recognition rate, by using multiple reference patterns for each of the words included in the vocabulary. These patterns are obtained from the utterances of a group of speakers. The use of multiple reference patterns, not only leads to a large increase in the memory requirements of the recognizer, but also an increase in the computational load. A recognition system is proposed in this thesis, which overcomes these difficulties by (i) employing vector quantization techniques to reduce the storage of reference patterns, and (ii) eliminating the need for dynamic time warping which reduces the computational complexity of the system. Finally, a method of identifying the acoustic structure of an utterance in terms of voiced, unvoiced, and silence segments by using fuzzy set theory is proposed. The acoustic structure is then employed to enhance the recognition accuracy of a conventional isolated word recognizer.