ZVSPWM

合成孔径.ppt,合成孔径雷达.ppt,脉冲压缩雷达.ppt,目标分辨与模糊.ppt(Synthetic aperture. Ppt, synthetic aperture radar. Ppt, pulse compression radar. Ppt, objectives and fuzzy to tell. Ppt)

PCM通信系统脉冲编码调制的信源编码，可以运行。模拟PCM的编码的波形！(PCM communication system pulse code modulation source coding, can run. Simulation of the PCM waveform coding!)

小飞机图像匹配（在模板中查找特点图像的位置）MatLAB编程(Small plane image matching (Find features images in the template position) MatLAB Programming)

基于Matlab的带通滤波器的设计源码，可以通过此代码，模拟带通滤波器的设计。并附有仿真后的图像。(Matlab-based band-pass filter design source, through this code, analog band-pass filter design. Together with simulation, the image.)

用共轭梯度算法——FR算法求解无约束最优化问题(Conjugate gradient algorithm- FR algorithm for solving unconstrained optimization problems)

Corner is important feature points in the image corners with processing information, the amount of data is relatively small, but the large amount of information. Corner has not clearly defined, research and analysis of diagonal point algorithm and comparison is necessary

basic telecommunication, modulation demodulation.

说明：  matlab科学计算的几乎所有数学算法以及仿真。每一个m文件在我学习的时候都验证了其正确性。前面的比较简单，逐步深入。另外对部分算法本人在学习中做了优化，更简洁。(matlab scientific computing almost all mathematical algorithms and simulation. M each file in my learning at all times to verify its correctness. In front of a relatively simple step by step in depth. In addition to some algorithm I have done a study of optimization, a more concise.)

这个是我数字信号处理这门课的课程设计,文件包括用matlab编写的代码和设计报告.这个设计包括卷积演示程序、采样定理演示程序、模拟滤波器设计演示程序、设计切比雪夫I型低通滤波器、切比雪夫I型低通滤波器、双线性变换法设计巴特沃斯低通数字滤波器、用凯塞窗设计高通滤波器.(This is my digital signal processing the course of the curriculum design, preparation of documents including the use of matlab code and design report. Convolution demonstration of this design include the procedures, sampling theorem demo program, analog filter design demo program, design Chebyshev-I low-pass filter, Chebyshev type I low-pass filter, bilinear transform design Butterworth low-pass digital filter with Kaiser window design high-pass filter.)

ParInfoGain - Computes parallel information gain and gain ratio in Matlab using the Matlab Parallel Computing Toolbox or the Distributed Server (if available) Information gain is defined as: InfoGain(Class,Attribute) = H(Class) - H(Class | Attribute) Gain ratio is defined as: GainRatio(Class, Attribute) = (H(Class) - H(Class | Attribute)) / H(Attribute). ...where H is the entropy, defined as - sum(i=1 to k) pi log2 pi