Context-aware adaptive IP mobility anchoring

The novel distributed mobility management trend is a promising direction to cope with the increasing mobile data traffic and flatten network architectures. Most of the novel mobility approaches distribute the mobility anchors through the access level, as opposed to the centralized mobility anchoring model. Other recent approaches argue that mobility anchors closer to the content servers may be the solution to optimize the mobility performance. However, none of the mobility anchoring models is ideal for all scenarios, since it depends on the user, the session and the network. Hence, we propose an IP mobility approach driven by the context of the user, sessions and the network, where the mobility anchors for IP address allocation and for routing/forwarding are distributed through the network nodes, while the mobility context is managed by the mobile devices. Although each session is properly anchored in the establishment phase, the routing/forwarding is adapted over time, according to the user, the session and the network context: the proposed approach is able to signal different mobility anchors to optimize the routing path to new and ongoing sessions of the user. The outcome of the evaluation shows that the proposed approach overall reduces the data cost, the data delay, the tunneled packets and the tunnel length, when compared with other anchoring models.     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.     

具有随机采样特性的网络化系统H∞ 滤波

研究一类具有随机采样特性的网络化系统H∞滤波问题.通过将传感器的随机采样过程建模成马尔可夫链,将数据量化作用转化为模型的参数不确定性,并用二值随机变量描述丢包过程,从而用一个多随机变量的马尔可夫不确定性模型来描述滤波误差系统.应用Lyapunov稳定性理论和随机系统分析方法,导出了滤波误差系统随机稳定且具有给定H∞性能的充分条件,并给出了滤波器的设计方法.仿真结果验证了所提出方法的有效性.     

联合 LTP 和 LPQ 的人脸识别方法

提出一种联合局部三值模式（LTP）和局部相位量化（LPQ）的人脸识别方法,该方法首先对预处理后的人脸分别采用LTP算子、LPQ算子提取各自的直方图,然后,通过特征选择将两者联合为LTP＿LPQ直方图,最后通过最近邻分类器进行分类识别。在ORL和YALE标准人脸数据库上的实验表明,该方法能有效地提高人脸的识别率。     

基于DMWT和QIMM 的图像水印算法

利用多尺度分析的思想,对原始图像进行重复行信号预滤波,把标量小波变换扩展到多小波变换后选取变换出来的低频子带进行水印嵌入。为了保证水印信息的安全性,对水印进行加密置乱处理。然后结合量化索引模数调制（QIMM）的优越性能,根据水印的信息不同选择不同的子块进行嵌入并实现了水印的盲提取。实验证明,算法具有良好的视觉性和鲁棒性,并大大扩展了水印的容量。     

双序渐进式概念格合并算法

针对传统的概念格合并算法仅为单向的纵向或横向合并,提出一种基于概念内涵、外延升降序的双序渐进式合并算法。按照概念的内涵或外延的升序和降序,从两个方向(由上到下和由下到上)同时进行子概念格的纵向或横向合并。算法分析和示例结果表明,该算法不仅在结构上较好地保留了原有信息,而且在效率上也获得了显著的提高。     

一种DCT域的最优均值量化盲图像水印算法

针对常规的图像水印算法不能很好地实现水印不可感知性和鲁棒性折中的问题,给出了一种最优均值量化盲水印算法.该算法首先对水印图像进行Arnold置乱,提高了水印的安全性;然后将载体图像进行8×8分块DCT(离散余弦变换),并根据均值量化原理将置乱后的水印嵌入到局部量化噪声值最小的图像子块中,提高了水印的不可感知性;最后水印提取时可以实现盲提取.实验结果表明,该算法对加噪、剪切、JPEG压缩、滤波等多种攻击都具有很强的鲁棒性.     

Quantized output feedback stabilization of uncertain systems with input nonlinearities via sliding mode control

This paper is concerned with the quantized output feedback stabilization problem for a class of uncertain systems with nonsmooth nonlinearities in the actuator device via sliding mode control schemes. It is assumed that system signals are quantized before being transmitted through communication channels. First, a dynamical compensator is developed to estimate unmeasurable system state. Then a sliding surface, in the augmented space using the system output and the estimated state, is proposed, and an adaptive sliding mode control scheme with a static adjustment law of the quantization parameter is established. It is shown that the proposed quantized feedback control strategy is able to tackle parameter uncertainty, external disturbances, and nonsymmetric input nonlinearity simultaneously and guarantees the reachability of the sliding modes of the uncertain system. Finally, an example is given to verify the validity of the theoretical results. Copyright © 2012 John Wiley & Sons, Ltd.     

一种基于复合混沌序列的扩频水印算法*

提出了一种安全性高的DWT（discrete wavelet transform）域鲁棒性扩频水印算法。该算法用Arnold变换对水印置乱,而后基于复合混沌二值序列对水印图案信号进行加密和扩频,并给出了一种小波域中量化步长的自适应确定方法,通过奇偶量化的方法将二值水印比特批量嵌入到载体的频域系数中。复合混沌密钥的使用与小波域系数修改的隐蔽性,增强了水印算法的安全性,基于混沌序列扩频码的扩频水印技术增强了水印的鲁棒性,同时很好地兼顾了水印的不可见性,并且实现了水印信息的盲提取。