The robustness of sparse network under limited attack capacity

The paper studies the robustness of the network in terms of the network structure. We define a strongly dominated relation between nodes and then we use the relation to merge the network. Based on that, we design a dominated clustering algorithm aiming at finding the critical nodes in the network. Furthermore, this merging process is lossless which means the original structure of the network is kept. In order to realize the visulization of the network, we also apply the lossy consolidation to the network based on detection of the community structures. Simulation results show that compared with six existed centrality algorithms, our algorithm performs better when the attack capacity is limited. The simulations also illustrate our algorithm does better in assortative scale-free networks.     

激发相干态下双模介观传输线的量子效应

将介观无损耗传输线中的驻波场看成作相对运动的两列同频率的行波的叠加，此电路量子化后成为两个频率相同、不同模的光子场。计算和分析了激发相干态下电压和电流梯度的量子涨落，指出了双模传输线与一般的LC电路、双模传输线与单模传输线量子涨落的异同之处。     

极化自由度对分束器出射光场的量子相干性影响的研究

我们在讨论粒子数态光场在分束器上干涉后得到的输出态的量子相干性时,考虑了入射场的极化自由度.利用campos[1]等人提出的量子分束器的SU(2)理论模型,计算得到了输出光场所处状态的表达式.进而讨论了光场在两个不同的入射空间模上极化方向对两个输出空间模上光场二阶量子干涉度的影响.     

改进的JPEG-LS编码方法在飞行试验中的应用

本文提出了一种JPEG-LS编码改进方法,编码过程遵循JPEG-LS的无损模式。提出JPEG-LS编码器,该编码器能产生比其他的JPEG-LS编码器更高速率的编码效率。理论分析和实验表明本编码方法具有更高的编码效率和更低的误码率,适合应用于飞行试验中。     

Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks

In this article, we investigate limitations of importing methods based on algorithmic information theory from monoplex networks into multidimensional networks (such as multilayer networks) that have a large number of extra dimensions (i.e., aspects). In the worst-case scenario, it has been previously shown that node-aligned multidimensional networks with non-uniform multidimensional spaces can display exponentially larger algorithmic information (or lossless compressibility) distortions with respect to their isomorphic monoplex networks, so that these distortions grow at least linearly with the number of extra dimensions. In the present article, we demonstrate that node-unaligned multidimensional networks, either with uniform or non-uniform multidimensional spaces, can also display exponentially larger algorithmic information distortions with respect to their isomorphic monoplex networks. However, unlike the node-aligned non-uniform case studied in previous work, these distortions in the node-unaligned case grow at least exponentially with the number of extra dimensions. On the other hand, for node-aligned multidimensional networks with uniform multidimensional spaces, we demonstrate that any distortion can only grow up to a logarithmic order of the number of extra dimensions. Thus, these results establish that isomorphisms between finite multidimensional networks and finite monoplex networks do not preserve algorithmic information in general and highlight that the algorithmic information of the multidimensional space itself needs to be taken into account in multidimensional network complexity analysis.     

基于提升方案的CDF(1,5)整-整可逆双正交小波变换在渐进性无损图像压缩中的应用

讨论了基于双正交小波变换提升方案在图像压缩中的应用 ,研究了CDF( 1,3) ,CDF( 1,5 )双正交小波消失矩对图像压缩的影响。实验结果表明 ,在图像压缩中应尽可能选取具有较高消失矩的双正交小波 ,用CDF( 1,5 )双正交小波提升方案实现了基于整数运算的整 整可逆双正交小波变换 ,以及快速的、渐进性的直至无损图像压缩     

A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.     

一种整数-整数小波变换

提出了一种产生整数－整数小波变换的方法。此方法可得到与信号或图像相匹配的满足最小均方误差准则的小波基，而且这种小波变换是可逆的双正交小波变换．此变换可用于无损或有损信号和图像压缩编码     

Distributed Lossless Compression Algorithm for Hyperspectral Images Based on Classification

This article addresses the problem of distributed lossless compression for hyperspectral images and proposes an effective lossless compression algorithm based on classification. First, a band selection algorithm was performed on the hyperspectral images to select those bands with considerable information. Next, the K-means algorithm was performed on those selected bands to obtain the classification map. To make full use of the spectral and spatial correlation, a multilinear regression model was introduced to construct the high-quality side information of each class within the identical block according to the classification map. Subsequently, the (n, k) linear grouping codes were employed to perform the distributed source coding for each class separately. The experimental results showed that the proposed algorithm has a competitive lossless compression performance compared with other state-of-the-art algorithms.     

一种彩色图像的小波域预测编码方法

彩色图像存在空间相关性和帧间相关性,如何有效的去除这两种相关性是提高彩色图像压缩比的关键。提出一种小波变换域的自适应预测无损压缩方法,分为三个步骤:首先对彩色图像各个分量进行可逆整数小波变换减小空间相关性,然后利用联想记忆神经网络对小波系数图像分别进行空间预测和帧间预测,进一步去除空间和帧间相关性,最后对预测残差进行算术编码。实验证明该算法是可行的和有效的,对测试图像的压缩效果优于JPEG LS标准。