MATLAB-function-Quick-Reference

说明：  讲述的直接序列扩频通信系统从信号产生，发射，信道传输，接收整个系统的程序(this is matlab for DS_SS system)

《精通matlab7.0混合编程》的例子程序，其中涉及MATLAB程序和VC++程序，其中大部分为改书所附光盘。()

我们上课用的MATLAB课件，课件不错，就是老师讲的比较差劲。(the ppt of matlab used in my class)

希望给辛苦科研的人带来一点点帮助--学习因子可以变化的PSO算法，不再是c1=c2=2，粒子进化更加灵活，下面是学习因子同步变化的pso，调用形式为[xm,fv]=LNCPSO(fitness,N,cmax,cmin,w,M,D)(People who want to work hard to bring a little bit of research to help- learning factor can vary PSO algorithm is no longer c1 = c2 = 2, particle evolution is more flexible, learning factor is below synchronous changes pso, calling in the form of [xm, fv] = LNCPSO (fitness, N, cmax, cmin, w, M, D))

说明：  matlab 的蚁群算法 (no )

crc校验产生器，输入信息位，输出加校验位的信息位(CRC checksum generator, the input information bits, the output of information bits plus parity bit)

data security in manet using key generation technique. it encodes & decodes the data to be transmitted

用MATLAB设计IIR数字带通滤波器的程序函数(Design of IIR digital with MATLAB program function bandpass filter)

The problem of image registration subsumes a number of problems and techniques in multiframe image analysis, including the computation of optic flow (general pixel-based motion), stereo correspondence, structure from motion, and feature tracking. We present a new registration algorithm based on spline representations of the displacement field which can be specialized to solve all of the above mentioned problems. In particular, we show how to compute local flow, global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of the above problems. Using a spline-based description of the flow removes the need for overlapping correlation windows, and produces an explicit measure of the correlation between adjacent flow estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D projective scene geometry. We also provide results on a number of standard motion sequences.

Implement the K nearest neighbor algorithm by your own instead of using available software. 2. Use K-fold cross validation to generate training and testing datasets. You should try different K values (3~8) to see how they affect your result. 3. Train the classifier using your training dataset, and test the classifier using your testing dataset. 4. Repeat the experiment (Step 2 and Step 3) 30 times. For each time, you need to record the training data accuracy and testing data accuracy. Finally, you can obtain the average training data accuracy and average testing data accuracy.