dataset

傅里叶变换后频谱图 B：陷波函数作用后的频谱图 C：经逆变换的滤波后图像，效果不是很理想 Ifft2：为验证自己的快速傅里叶变换程序的正确性，用matlab自带逆变换程序将频谱逆变换后图形，与原图相符，说明快速傅里叶变换程序正确。(This is a FFT )

This report will provide some insight on the new incompressible CFD solver that will be available in version 980 of LS-DYNA.

Fixed-point base-2 logarithm (DW_log2) // Computes the base-2 logarithm of a fixed point value in the // range [1,2).

本文针对SLAM算法中数据关联过程，提出了一种简单易行的改进方法，将欧氏距离与马氏距离结合用于数据关联。算法不必计算地图所有特征与所有量测之间的马氏距离，而是首先利用相对简单的欧氏距离计算缩小了待关联特征的搜寻范围。利用人工合成数据的仿真结果表明，改进后的数据关联方法可以大幅减少系统计算量，提高关联效率，且不会造成错误关联的增加。(This article SLAM algorithm for data association process, a simple method to Euclidean distance combined with the Mahalanobis distance for data association. Algorithms do not have to calculate all the characteristics of the map with all measurements between the Mahalanobis distance, but the first to use a relatively simple calculation of Euclidean distance to be associated characteristics of narrowing the search. The use of synthetic data simulation results show that the improved methods of data association can significantly reduce the system to calculate the volume and improve the efficiency of association, and will not lead to errors associated with an increase.)

ns下的dd协议代码 可以进行wsn路由协议的仿真(the code of directed diffusion)

数字图像隐藏技术的仿真，针对一个图片现将其转换为灰白图像，然后将要隐藏的图片加入样本中，加入椒盐噪声攻击，最后将图片还原。(Digital image information hiding technology simulation, for a picture now converted to gray image, will then be hidden image is added to the sample, adding salt and pepper noise attack, finally will restore the picture.)

to get the theoretical and simulation plots for 4 16 64 QAM plots for AWGN channel

Dijkstra算法，在通信过程非常重要的一个算法，这是它的MATLAB仿真程序。(Dijkstra algorithm, the communication process is very important algorithm, which is its MATLAB simulation program.)

说明：  用6阶CIC实现，加噪声仿真。序内加详尽注释。(6 bands CIC realized, plus noise simulation. Sequence with a detailed note.)

这是一个用k均值聚类的matlab 程序 程序对高维数据利用k均值进行了聚类(This is a k-means clustering matlab program procedures for high-dimensional data for the use of k-means clustering)