digital-signal-process-on-MATLAB

matlab重要的学习资料 初学者的好帮手 程序代码非常重要 自适应滤波器设计参考文献(matlab important information for beginners to learn a good helper program code is very important reference adaptive filter design)

各种数字信号处理matlab源程序代码，包括滤波、平滑等等(digital signal processing )

时频分析工具箱,需要的人可以试试,还不错(time-frequency analysis toolkit, the people can try out, quite good)

buck-boost open loop

chapter 3 of the fdtd method for electromagnetics with matlab simulations

rrt算法与一定概率偏向目标点的rrt算法，并对路径进行处理(RRT algorithm and a certain probability bias to the target point of the RRT algorithm, and the path to deal with)

粒子追踪测速迭代松弛算法的实现，粒子图像分割及匹配均为自行编制的子例程(Particle tracking velocimetry iterative relaxation algorithm, particle image segmentation and matching are themselves prepared subroutine)

ACA_TSP是用蚁群算法（ant colony algorithm）解决旅行商问题（travelling salesman problem）。文件打开后运行ACA_TSP.m文件即可得到结果，代码中有详细注释，方便修改。运行示例已经保存为图片附在压缩包中。(ACA_TSP is to solve the traveling salesman problem (travelling salesman problem) ACO (ant colony algorithm). Run ACA_TSP.m file to get the file to open the result, the code has detailed notes, easy to modify. Run the sample has been saved as a picture attached to the compressed package.)

这是无线通信中两径模型的源代码，比较了不同增益对接收功率的影响，能直接运行。(This is a two-path model in wireless communications source code, compare the effects of different gain on the received power can be run directly.)

Smooths a matrix (with/without NaN s) via recursive moving average method and eliminates data gaps. (MOVING_AVERAGE(X,F) smooths the vector data X with a boxcar window of size 2F+1, i.e., by means of averaging each element with the F elements at his right and the F elements at his left. The extreme elements are also averaged but with less data, obviously. Leaving the edges intact. The method is really fast. MOVING_AVERAGE2(X,M,N) smooths the matrix X with a boxcar window of size (2M+1)x(2N+1), i.e., by means of averaging each element with its surrounding elements that fits in the mentioned box centered on it. This one is also really fast. The elements at the edges are averaged too, but the corners are left intact. NANMOVING_AVERAGE(X,F) or NANMOVING_AVERAGE(X,F,1) accept NaN s elements in the vector X the latter interpolates also those NaN s elements surrounded by numeric elements. NANMOVING_AVERAGE2(X,M,N) or NANMOVING_AVERAGE2(X,M,N,1) accept elements NaN s in the matrix X the latter interpolates also those NaN elements surrounded by numeric elements.)