Decentralized tuple space is an ecrective paradigm for distributed collaborative applications since its loose decoupling, availability and °exibility in system's organization. However, there are very few research results on its replication and consistency. This paper presents a decentralized tuple space model to enable tuple update by creating loose update links among decentralized Tuple space nodes. Besides, tuples are grouped with tuple types, the unit of being applied a replication policy, which con-trols the distribution of replicas. The model is simulated by Desmo-J. The experiments show that applying dicrer-entiate replication policies used for dicrerent purposes is absolutely necessary, because no single policy is suitable under all use patterns.

A new framework of region-based dynamic image fusion is proposed. First, the technique of target detection is applied to dynamic images (image sequences) to segment images into different targets and background regions. Then different fusion rules are employed in different regions so that the target information is preserved as much as possible. In addition, steerable non-separable wavelet frame transform is used in the process of multi-resolution analysis, so the system achieves favorable characters of orientation and invariant shift. Compared with other image fusion methods, experimental results showed that the proposed method has better capabilities of target recognition and preserves clear background information.

针对存在配准偏差的双平台无源融合跟踪系统，提出了基于扩维Unscented 卡尔曼滤波的配准跟踪一体化方法，在跟踪算法中，采用模糊调度方法调节“当前”统计模型参数，引入渐消因子，能够在状态发生突变时，迅速调整系统参数，提高了系统的抗机动目标自适应能力。仿真结果表明，这种跟踪算法能够较好地解决双平台无源融合跟踪系统中的配准偏差问题。

Due to the widely application of 3D models, the techniques of content-based 3D shape retrieval become necessary. In this paper, a modified Principal Component Analysis (PCA) method for model normalization is introduced at first, and each model is projected in 6 different viewpoints. Secondly, a new adjacent angle distance Fouriers (AADF) descriptor is presented, which captures more precise contour feature of black-white images. Finally, based on modified PCA method, a novel composite 3D shape descriptor MADE is proposed by concatenating AADF, Tchebichef and D-buffer descriptors. Experimental results on the criterion of 3D model database PSB show that the proposed descriptor MADE has gained the best retrieval effectiveness compared with three single descriptors and two composite descriptors LFD and DESIRE.

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user’s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.