Nonflat surface level pyramid: a high connectivity multidimensional interconnection network

Parallel machines are extensively used to increase computational speed in solving different scientific problems. Various topologies with different properties have been proposed so far and each one is suitable for specific applications. Pyramid interconnection networks have potentially powerful architecture for many applications such as image processing, visualization, and data mining. The major advantage of pyramids which is important for image processing systems is hierarchical abstracting and transferring the data toward the apex node, just like the human being vision system, which reach to an object from an image. There are rapidly growing applications in which the multidimensional datasets should be processed simultaneously. For such a system, we need a symmetric and expandable interconnection network to process data from different directions and forward them toward the apex. In this paper, a new type of pyramid interconnection network called Non-Flat Surface Level (NFSL) pyramid is proposed. NFSL pyramid interconnection networks constructed by L-level A-lateral-base pyramids that are named basic-pyramids. So, the apex node is surrounded by the level-one surfaces of NFSL that are the first nearest level of nodes to apex in the basic pyramids. Two topologies which are called NFSL-T and NFSL-Q originated from Trilateral-base and Quadrilateral-base basic-pyramids are studied to exemplify the proposed structure. To evaluate the proposed architecture, the most important properties of the networks are determined and compared with those of the standard pyramid networks and its variants.     

Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances

This paper analyzes the robustness of globally exponential stability of time-varying delayed neural networks (NNs) subjected to random disturbances. Given a globally exponentially stable neural network, and in the presence of noise, we quantify how much noise intensity that the delayed neural network can remain to be globally exponentially stable. We characterize the upper bounds of the noise intensity for the delayed NNs to sustain globally exponential stability. The upper bounds of parameter uncertainty intensity are characterized by using transcendental equation. A numerical example is provided to illustrate the theoretical result.     

Dynamic watermarking scheme for quantum images based on Hadamard transform

In this paper, a novel watermarking scheme for quantum images based on Hadamard transform is proposed. In the new scheme, a unitary transform controlled by a classical binary key is implemented on quantum image. Then, we utilize a dynamic vector, instead of a fixed parameter as in other previous schemes, to control the embedding process. The dynamic embedding vector is decided by both the carrier quantum image and the watermark image, which is only known by the authorized owner. The proposed scheme is analyzed from visual quality, computational complexity, and payload capacity. Analysis and results show that the proposed scheme has better visual quality under a higher embedding capacity and lower complexity compared with other schemes proposed recently.     

Relative image similarity learning with contextual information for Internet cross-media retrieval

With the fast explosive rate of the amount of image data on the Internet, how to efficiently utilize them in the cross-media scenario becomes an urgent problem. Images are usually accompanied with contextual textual information. These two heterogeneous modalities are mutually reinforcing to make the Internet content more informative. In most cases, visual information can be regarded as an enhanced content of the textual document. To make image-to-image similarity being more consistent with document-to-document similarity, this paper proposes a method to learn image similarities according to the relations of the accompanied textual documents. More specifically, instead of using the static quantitative relations, rank-based learning procedure by employing structural SVM is adopted in this paper, and the ranking structure is established by comparing the relative relations of textual information. The learning results are in more accordance with the human’s recognition. The proposed method in this paper can be used not only for the image-to-image retrieval, but also for cross-modality multimedia, where a query expansion framework is proposed to get more satisfactory results. Extensive experimental evaluations on large scale Internet dataset validate the performance of the proposed methods.     

Phase morphology and impact toughness of impact polypropylene copolymer

Factors influencing the impact toughness of two impact polypropylene copolymers (IPC) with almost the same ethylene content, molecular weight and molecular weight distribution were studied by temperature gradient extraction fractionation (TGEF), scanning electron microscopy (SEM), nuclear magnetic resonance (NMR) and differential scanning calorimetry (DSC). The results indicate that poor interfacial adhesion between the disperse phase and the continuous matrix, larger dimensions and non-uniform distribution of disperse phases are main reasons for the low impact toughness of IPC B that possesses of a low content of ethylene-propylene segmented copolymer with long crystallizable PE and PP sequences as a compatibilizer between the disperse phase and the matrix.     

注射螺杆螺棱结构对计量段物料输送性能的研究

运用有限元分析软件POLYFLOW,根据聚碳酸酯物料注塑成型的实际工作状态,对PC物料在矩形断面和锯齿形断面流道中的流动进行了三维等温非牛顿流场模拟计算,在得到速度场的基础上对计算结果进行了后处理,分析了不同螺杆构型的输送能力,分布混合能力以及物料在不同螺杆构型中的流动状况.     

从废旧聚苯乙烯泡沫塑料中回收苯乙烯

本文采用有机溶剂甲苯作为消泡剂，用热裂解法从废旧聚苯乙烯泡沫塑料中回收苯乙烯，苯乙烯收率可达８０％以上。     

高柔韧性聚氨酯丙烯酸酯的合成及应用

选用不同结构的多元醇作骨架，合成一系列高性能的聚氨酯丙烯酸树脂，并通过对聚氨酯丙烯酸树脂性能的对比研究，发现以聚丁二烯二元醇为骨架的聚氨酯丙烯酸酯树脂具有极佳的柔韧性和优异的低温耐曲折性。     

结合方向纹理熵的Haar-like特征在线boosting跟踪算法

针对传统基于Haar-like特征的on-line boosting跟踪算法（HBT）需要产生大规模随机特征、占用大量计算资源和存储空间的缺点,提出结合方向纹理熵的Haar-like特征在线boosting跟踪算法（HBTT）。HBTT算法利用灰度共生矩阵的熵获得目标纹理的方向信息,在此基础上有针对性地产生具有方向纹理信息的Haar-like特征,从而可有效避免无效随机特征的产生,减小特征池容量;更进一步,可根据目标纹理的复杂程度自动调整特征数量,使得算法更灵活。在跟踪过程中,在线学习模块可以使错误率较高的特征被结合了目标纹理方向信息的Haar-like特征所替换。与HBT算法比较,HBTT算法的跟踪误差降低了10%以上;在相同特征池容量下,置信度提高了2%以上。实验结果表明,该算法不仅具有较高的鲁棒性,而且在跟踪效率和性能上都有所提高。