利用分块相似系数构造感知图像Hash

提出一种基于图像分块相似系数的感知稳健图像Hash.先对图像预处理,再进行重叠分块,在密钥控制下,利用高斯低通滤波器生成伪随机参考图像块,分别计算每个分块与参考图像块的相关系数得到图像特征序列.依此将相邻两个分块特征值合并以缩短Hash长度,同时对压缩后的特征序列进行重排,进一步提高图像Hash的安全性.最后对归一化特征值进行量化,并运用Huffman方法对其编码,进一步压缩Hash长度.理论分析和实验结果表明,该图像Hash方法对JPEG压缩、适度的噪声干扰、水印嵌入、图像缩放以及高斯低通滤波等常见图像处理有较好的鲁棒性,能有效区分不同图像,冲突概率低,可用于图像篡改检测.     

GSSAC:一种用于遥感影像配准的误匹配点检测方法*

遥感影像配准中,由于光照、成像角度、几何变形等因素的影响,无论采用何种配准方法,总会产生误匹配点,因此误匹配点检测也是一个非常重要的步骤。针对常用RANSAC (Random Sample Consensus)方法不稳定、无法准确检测分布不均匀匹配点的缺点,提出了分组排序采样一致性(Group Sorted Sample Consensus, GSSAC)方法来提高误匹配点检测的稳定性和精度。分组排序采样方法首先将匹配点分为若干组,在每组内计算匹配点的误差并排序,然后在每组中分别采样若干个匹配点组成估算模型参数需要的匹配点。实验结果表明,GSSAC方法可以稳定的获得高精度的检测结果。     

命名数据网络中面向移动用户的内容预取方法*

针对命名数据网络(Named Data Networking, NDN)移动性增强支持不足的问题,基于城区移动场景提出了一种基于移动预测的内容预取方法(Mobility-aware Prediction Approach for Content Prefetching, MAP-CP)及扩展方案(extended MAP-CP, eMAP-CP)。MAP-CP和eMAP-CP通过预测移动用户的下一接入点(Access Point, AP),预取在当前AP下因切换AP不能获取到的Data并缓存到所预测的下一AP相关联的NDN节点中,为切换AP后的移动用户就近服务,达到降低内容获取延迟和提高响应率的目的。eMAP-CP在MAP-CP的基础上,可预取切换AP过程中请求的Data,能够进一步提高性能。基于ndnSIM的仿真实验表明,和NDN本身相比,MAP-CP和eMAP-CP具有更低的平均内容获取延迟和更高的响应率,而且eMAP-CP更优。在移动速度为20m/s,发包速率为200pps时,MAP-CP和eMAP-CP的平均内容获取延迟可分别降低约4.0%和38.7%,响应率可分别提高约1.2%和4.9%。     

Generating strictly binary trees at random based on convex polygon triangulations

A set of maximal non-intersecting diagonals can decompose a polygon into triangles, and the edges and diagonals can be converted into the external and internal nodes of a strictly binary tree. This paper gives algorithms to generate all types of triangulations and triangulations at random. Based on that, this paper gives an algorithm to generate strictly binary trees at random. The experimental results show that the numbers of various shapes of strictly binary trees generated are nearly equal. The algorithm to generate strictly binary trees at random can be transformed to the algorithm to randomly generate binary trees.     

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.     

Depth incorporating with color improves salient object detection

Detecting salient objects in challenging images attracts increasing attention as many applications require more robust method to deal with complex images from the Internet. Prior methods produce poor saliency maps in challenging cases mainly due to the complex patterns in the background and internal color edges in the foreground. The former problem may introduce noises into saliency maps and the later forms the difficulty in determining object boundaries. Observing that depth map can supply layering information and more reliable boundary, we improve salient object detection by integrating two features: color information and depth information which are calculated from stereo images. The two features collaborate in a two-stage framework. In the object location stage, depth mainly helps to produce a noise-filtered salient patch, which indicates the location of the object. In the object boundary inference stage, boundary information is encoded in a graph using both depth and color information, and then we employ the random walk to infer more reliable boundaries and obtain the final saliency map. We also build a data set containing 100+ stereo pairs to test the effectiveness of our method. Experiments show that our depth-plus-color based method significantly improves salient object detection compared with previous color-based methods.     

Fast fractal prediction of surge over underwater vehicle

In order to realize accurate control of underwater vehicles, this paper proposed a fractal prediction model of surge. Firstly, we introduce the current state-of-the-art of fractal prediction; secondly, we complete a fractal analysis with the R_S method, calculating the fractal dimension of surge using the short-time dimension method; thirdly, we correct the vertical scaling factor of the Iterated Function System (IFS) using the fractal dimension. Then the predictive value can be calculated through anti-iteration according to the statistical significantly IFS and proposing a fractal prediction model. Finally, we complete a simulation analysis based on the actual data and a comparison to another prediction algorithm. The result shows that the fractal prediction algorithm of this article has better comprehensive performance.