Zhengxuan Wang

Modelling Immune System: Principles, Models, Analysis and Perspectives

The biological immune system is a complex adaptive system. There are lots of benefits for building the model of the immune system. For biological researchers, they can test some hypotheses about the infection process or simulate the responses of some drugs. For computer researchers, they can build distributed, robust and fault tolerant networks inspired by the functions of the immune system. This paper provides a comprehensive survey of the literatures on modelling the immune system. From the methodology perspective, the paper compares and analyzes the existing approaches and models, and also demonstrates the focusing research effort on the future immune models in the next few years.

Combining Multiple Clustering Methods Based on Core Group

As an unsupervised technique, clustering analysis has been widely applied in various fields. However, it is usually difficult to select an appropriate clustering method for an application, while no clustering method is suitable for all situations. This paper proposes a novel method to combine multiple clustering methods. First, the paper combines different agglomerative hierarchical methods in one clustering process to obtain core groups. Core group refers to the data that are always clustered together no matter what clustering method is applied. Then, it adopts other kind of clustering methods to refine the core groups and index database. In addition to conduct a series of experiments on the datasets from UCI, the paper applies the proposed method in a new research field, 3D model retrieval, to analyze and index the 3D model database.

The Transmitted Strategy of Proxy Cache Based on Segmented Video

Using proxy cache is a key technique that may help to reduce the loads of the server, network bandwidth and startup delays. Basing on the popularity of clients’ request to segment video,we extend the length for batch and patch by using dynamic cache of proxy cache for streaming media. Present transmission schemes using dynamic cache such that unicast suffix batch, unicast patch, multicast patch, multicast merge and optimal batch patch by proxy cache based on segmented video. And then quantitatively explore the impact of the choice of transmission scheme, cache allocation policy, proxy cache size, and availability of unicast versus multicast capability, on the resultant transmission cost.

Using multi-matching system based on a simplified deformable model of the human iris for iris recognition

A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wavelet transform. In the matching stage it worked in a crude manner. Driven by a simplified deformable iris model, the crude matching was refined. By means of such multi-matching system, the task of iris recognition was accomplished. This process can preserve the elastic deformation between an input iris image and a template and improve precision for iris recognition. The experimental results indicate the validity of this method.

Multi-Matching Process Based on Wavelet Transform for Iris Recognition

As a most important biometric solution for personal identification, iris recognition has received increasing attention in recent years. Based on the features of the stochastic iris textural information and local time-frequency properties of the wavelet transform, this paper proposes a new method for iris recognition using a wavelet-based multi- matching system, which includes a coarse matching and its refinement. For the low-frequency components of the wavelet multi-resolution decomposition, a statistical correlation approach is adopted for a coarse matching so that well-matched iris templates in a database can be picked out and need to be further identified. The wavelet modulus maxima are used to locate the sharp variation points, which are taken as iris feature points. A similarity measure based on the distances between the best-matched feature point pairs is taken for the refinement of the coarse matching. Our method well employs the iris morphological characteristics and the multi-matching process to improve the efficiency. The experimental results indicate the validity of the proposed method.

An auto-stopped hierarchical clustering algorithm integrating outlier detection algorithm

It is a critical problem for the clustering analysis techniques to select the appropriate value of parameters. Meanwhile, the clustering algorithms lack the effective mechanism to detect outliers while treating outliers as “noise”. By regarding outliers as valuable information, the paper proposes a novel hierarchical clustering algorithm that integrates a new outlier-mining method. The algorithm stops clustering according to the dissimilarity reflected by the detected outliers and needs only one parameter, whose appropriate value can be decided in the outlier mining process. After discussing some related topics, the paper adopts 5 real-life datasets to evaluate the performance of the clustering algorithm in outlier mining and clustering and compare it with other algorithms.

Selective Feature Combination and Automatic Shape Categorization of 3D Models

It is the key problems in 3D model retrieval to obtain good feature and classify models efficiently. Although many feature extraction methods have been proposed, none is adapted to all models. Moreover, it still relies on manual work to classify models. To solve these problems, firstly, the paper proposes a series of selective combination methods which automatically decide each feature’s appropriate weight. The experiments conduct on PSB show that the combined feature performs much better than the best single feature. Secondly, the paper proposes the iterative clustering process to obtain the shape-based 3D models classification based on the combined feature. Experiment shows that the method can classify 91% models of Princeton Shape Benchmark.

Semantic 3D Model Retrieval Based on Semantic Tree and Shape Feature

3D model retrieval emerges as an important part of multimedia information retrieval. Current researches in 3D model retrieval concentrate on the shape-based way. However, its performance isn’t satisfying because of the semantic gap. The paper explores the semantic-based 3D model retrieval method based on semantic tree and the hybrid method based on content and semantic. First, the semantic tree is adopted to illustrate models’ semantic relationship and the accurate semantic of each tree node is described with several keywords. The semantic tree of all 1814 models of the Princeton Shape Benchmark is constructed. Second, the paper realizes the semantic retrieval based on the semantic tree which facilitates user’s feedback. Third, the hybrid retrieval process integrating semantic and content is proposed. The content similarity is computed by adopting the combined shape feature. The experiments conduct on Princeton Shape Benchmark shows the effectiveness of our method.

The batch patching algorithm using dynamic cache of proxy for streaming media

The proxy cache for streaming media is the important method to economize the resources of the Internet. The cache policies influence the effect for proxy cache. In this paper, based on the clients request rate, cached the video based on segment, we proposed that the policies for batch, patch and batch patching using dynamic cache. The policies of batch and batch patching using dynamic cache based on segment enlarge the width of the batch and patching. It combines dynamic cache based on segment with the excellence of patch. Presented that allocation relation of the cache between the batch and patch using dynamic cache, and assured that batch patching using dynamic cache of proxy based on segment is optimal.

An auto-stopped hierarchical clustering algorithm for analyzing 3d model database

In the research of shape-based 3D model retrieval, the analysis and classification of 3D model database is an important topic for improving the retrieval performance. However, it encounters difficulties due to lack of valuable prior knowledge and the semantic gaps exist in 3D model retrieval. The paper proposes a new auto-stopped hierarchical clustering algorithm overcome these problems, which combines outlier detection with clustering. The Princeton Shape Benchmark along with 2 data sets from UCI is employed to evaluate the performance of the algorithm. And the new algorithm outperforms other auto-stopped algorithms and obtains better classification of 3D model database.