pumc_11_8

基于等均值等方差的图象矢量量化Matlab程序(Based on the mean variance of the image, such as vector quantization Matlab procedures)

中心显示格式和修正中心显示格式求解poisson方程，矩形区域，狄利克雷边界情况！(Center display format and the correction center display format for solving poisson equations, rectangular area, Dirichlet boundary conditions!)

Newton Interpolation Method Values yi of the interpolating polynomial at the points xi. Coordinates of the points of interpolation are stored in vectors x and y. Horner s method is used to evaluate a polynomial. Second output parameter a holds coeeficients of the interpolating polynomial in Newton s form.

Using MoM for analysis Radar cross section RCS

粒子群算法（PSO）优化BP神经网络，用来进行非线性的校正，效果良好(Particle swarm optimization (PSO) to optimize BP neural network, used for non-linear correction, to good effect)

采用归一化的方法对灰色图像进进行分割，分割效果较好(Using normalized method for gray image into the partition, partition better)

在matlab仿真的基础上，用maxplus2实现等波纹法的程序代码(In matlab simulation, based on the use of such corrugated maxplus2 realize law code)

matlab图形图像处理源程序 实现DWT水印的嵌入和提取 (graphic image processing matlab source code to achieve DWT watermark embedding and extraction)

一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。 (A new approach to compare models for body area networks (BAN) that accommodates multiple links and multiple subjects is presented. The absolute measure described allows comparison across a spectrum of possible characterizations ranging from single-parameter for an entire ensemble, through to per-activity, per-subject and per-link based parameterized models. The use of an explicit trade-off between error and complexity, combined in a goodness-of-fit measure, is shown to have important consequences when applied to a range of typical BAN channel data. It is shown that there are different implications in choice of model, and it’s associated complexity, for mixed-activity “everyday” data, when compared with set-activity dynamic data (e.g. walking). The deficiency of mean path loss, or even median path loss measures, as a sole characterization of the BAN channel is also highlighted.)

使用改进的LBP算法CLBP实现图像纹理特征的提取，并使用卡方统计方法计算类间距离并实现图像分布。本人已经实验，对15类病毒图像进行分类，不调任何参数的情况下实现67 以上的准确率。( Using an improved algorithm CLBP LBP texture feature extraction of image, and use the chi-square statistic calculated inter-class distance and achieve image distribution. I have experimented on 15 viroid image classification, and more than 67 accuracy rate without adjusting any parameters.)