基于业务需求的动态服务承载网构建算法

可重构信息通信基础网络通过构建可重构服务承载网的方式为业务提供自适应承载服务。针对服务承载网动态构建中底层资源高效利用的问题,提出一种基于业务需求的动态服务承载网构建算法。算法构建了可重构服务承载网模型;基于业务需求的动态特性,定义了服务承载网构建花费;以最小化构建花费为目标,感知业务需求类型及其变化状态,并设计了相应的动态服务承载网构建算法。仿真结果表明：算法在业务性能、构建成功率、收益花费比等方面均具有良好性能。     

一种鲁棒的多态人脸识别算法

如何处理人脸识别中的多态性一直是人脸识别领域的一个难题。传统的图嵌入算法忽视了同类中多态子类间的同属关系,而且也没有恰当地处理异类间的区别信息。该文提出一种鲁棒的图嵌入人脸识别算法,该算法可以恰当地模拟同类中的多态间关系,而且能在局部流形结构与全局区别信息间实现平衡。基于多个公开数据库的人脸识别实验证明了该方法的有效性。     

Virtual network embedding based on real-time topological attributes

As a great challenge of network virtualization, virtual network embedding/mapping is increasingly important. It aims to successfully and efficiently assign the nodes and links of a virtual network (VN)...     

一种基于安全隐藏的图像分层窜改检测和内容恢复算法

提出了一种可用于数字图像的分层窜改检测和内容恢复的脆弱水印算法。图像中每个分块产生的水印信息包括认证水印和内容恢复水印,其中认证水印由奇偶校验和平均强度信息生成,恢复水印由分块DCT系数量化编码后的信息得到。根据混沌映射和最远距离安全隐藏机制,将恢复水印嵌入到映射块中,可有效避免窜改一致性问题。在接收端,采用分层窜改检测方法,减少窜改分块的漏判和误判,有效提高了窜改精度和恢复质量。实验结果表明,提出的方法与已有方法相比,无论在小面积窜改或大面积窜改情况下均可取得较好的恢复效果。     

基于非负邻域嵌入和非局部正则化的单帧图像超分辨率重建算法

单帧图像超分辨率重建是指利用一幅低分辨率图像,通过相应的算法来获取一幅高分辨率图像的技术。提出了一种基于 非负邻域嵌入和 非局部正则化 的单帧图像超分辨率重建算法,以弥补传统邻域嵌入算法的不足。在训练阶段,首先对低分辨率图像预放大2倍,以保证在放大倍数较大时,高、低分辨率图像块之间的邻域关系也能得到较好的保持;在重建阶段,使用非负邻域嵌入来有效地解决近邻数的选取问题;最后利用图像块的非局部相似性构造非局部正则项对重建结果进行修正。实验结果表明,相对于传统算法,本方法的重建结果纹理丰富、边缘清晰。     

Quadruple Difference Expansion-Based Reversible Data Hiding Method for Digital Images

ABSTRACT

Lossless data hiding is a special type of data hiding technique that guarantees not only the secret data but also the ability of cover media to be reconstructed without any distortion. A latest lossless data hiding technique is proposed by Hong Lin Jin's that is based on hiding only one data bit in the spatial domain in gray-level image. However, this method uses double difference expansion to embed bits which results in a small embedding capacity. For this purpose, we propose an improved algorithm with the potential of increasing the payload capacity and maintaining good image quality. The proposed improved algorithm is characterized by two aspects. First, the proposed improved reversible data hiding scheme is enhanced to exhibit data hiding in color palette images. Second, the embedding level is improved by using quadruple difference expansion to guarantee the embedding of 2-bit data into color images. Experiments of the proposed improved method have been conducted over several well-known test images. The results show that the proposed improved method significantly improves the embedding capacity over Hong Lin Jin's scheme by the range of 15–35% for grayscale images and 20–46% for color images while still maintaining the quality of the stego-images.     

An innovative technique for web text watermarking (AITW)

ABSTRACT

Embedding a hidden stream of bits in a cover file to prevent illegal use is called digital watermarking. The cover file could be a text, image, video, or audio. In this study, we propose invisible watermarking based on the text included in a webpage. Watermarks are based on predefined structural and syntactic rules, which are encrypted and then converted into zero-width control characters using binary model classification before embedding into a webpage. This concept means that HTML (Hyper Text Markup Language) is used as a cover file to embed the hashed and transparent zero-width watermarks. We have implemented the proposed invisible watermarking against various attacks to reach optimum robustness.     

交换超立方体网络的网络嵌入研究

本文主要研究超立方网和星型网嵌入交换超立方体网络的问题。首先,利用图形嵌入的方法,设计了超立方网到交换超立方体网络的嵌入映射,分析并证明了该嵌入映射所具有的评价性能。其次,给出了星型网到交换超立方体网络两种嵌入策略,也就是所谓的优化嵌入映射和奇偶嵌入映射,进而给出了具有更小的扩张率的星型网到另一种交换超立方体网络的嵌入方法。     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

基于直接估计梯度思想的数据降维算法

高维非线性数据的降维处理对于计算机完成高复杂度的数据源分析是非常重要的。从拓扑学角度分析,维数约简的过程是挖掘嵌入在高维数据中的低维线性或非线性的流形。该文在局部嵌入思想的流形学习算法的基础上,提出直接估计梯度值的方法,从而达到局部线性误差逼近最小化,实现高维非线性数据的维数约简,并在Swiss roll曲线上采样测试取得了良好的降维效果。