piotr_toolbox_V3.26

目标跟踪的扩展卡尔曼滤波算法主函数的文件是：kal_demo.m 近似网格滤波的主函数文件是：bayes_demo.m 近似网格滤波划分网格的方法是：以目标上一个时刻的位置作为中心进行网格的划分，每个网格大小为1，总的区域为5*5 改进后算法的主函数文件是：trackiing_demo.m(Target tracking extended Kalman filter algorithm is the main function of the document: kal_demo.m approximate mesh filter paper is the main function: bayes_demo.m approximate mesh filter into the grid method is: in order to target the location of a moment as the central to carry out the division of the grid, each grid size of 1, with a total area of 5* 5 to improve the algorithm)

Example of key performance indicators applied on a real problem

针对20个已知类别数据，前15个训练样本和带刺的5个样本，通过该程序可识别出待测的样本属于哪一类。(Data for the 20 known types, the first 15 training samples and barbed 5 samples can be identified by the program which type of samples tested.)

说明：  编写一个函数，输入n为偶数时，调用函数求1/2+1/4+...+1/n,当输入n为奇数时，调用函数 1/1+1/3+...+1/n(利用指针函数) (Write a function, the input n is even, the function call seeking 1/2+1/4+...+ 1/n, when the input n is odd, call the function 1/1+1/3+...+ 1/n (using the pointer function))

在数学建模培训中，降落伞问题很是突出，联系此题对同学有很大的提高，从而程序也很重要。所以发此文件。(Parachute Su snow modeling program title race body)

利用改进的并行遗传退火算法求解了tsp问题，得到性能收敛图和最短路径(Using improved parallel genetic annealing algorithm the tsp problem, get map and the shortest route convergence performance)

OMNet++的一个例子，实现了简单的路由。重要的语句后面加上了注释，适合初学者，非常有用。(A example about wirless network for OMNet++.)

小波变换的MATLAB变换程序 可以对信号进行降噪声处理(xiaobobianhuan.m)

高斯白噪声信道下原信号的误码率特性，仅供参考(Gaussian white noise channel bit error rate characteristics of the original signal)

去噪处理是图像处理中较为重要的环节。中值滤波是抑制图像的噪声的一个行之有效的办法，选择适当大小的中值滤波窗口可以在最大限度的保持图像精度的基础上去除图像噪声。本文在对中值滤波去噪算法的适用性特点进行研究的基础上，进一步研究了中值滤波去噪算法，同时对其他去噪算法，如均值滤波、低通滤波的小波变换进行分析研究，并做了相应的比较。(Noise reduction is one of the most important parts of image processing. Median filtering is a proven way to remove image noise. To select the appropriate size of the window can maximize to remove image noise on the basis of maintaining the accuracy of the image. On the basis of investigating the application of the median filtering algorithm denoising characteristics, we do further study of the median filtering algorithm denoising. At the mean time we analyzed the mean filtering and wavelet transform algorithm, and compared them.)