mimo-ofdm信道估计的不同算法比较,可以直接运行MIMO_OFDM_channel_estimation

matlab下的小波消噪源程序，能较好的对非平稳信号进行滤波消噪(matlab source code under the wavelet denoising can be better to filter the non-stationary signal denoising)

典型梁元的考虑几何非线性效应的牛顿法迭代程序，使用者可以在此基础上添加自己的有限元程序(Consider typical beam element geometry of the Newton method of nonlinear effects of iterative process, the user can add their own based on the finite element program)

这是基于相位相关法的图像配准拼接 MATLAB源程序(This is based on phase correlation image registration mosaic MATLAB source code)

仿真4-PAM载波调制信号在AWGN信道下的误码率和误比特率性能，并与理论值相比较。假设符号周期为1s，载波频率为10Hz，每个符号周期内采样100个点。 (Simulation 4-PAM modulation signal in AWGN channel bit error rate and bit error rate performance, and compared with the theoretical value. Assuming symbol period for 1s, the carrier frequency is 10Hz, each symbol period sampling 100 points.)

erdos reyni random graph matlab code

This paper is the result of a literature study carried out by the authors. It is a review of the dierent attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with dierent representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with dierent standard examples using combination of crossover and mutation operators in relation with path representation. Keywords: Travelling Salesman Problem Genetic Algorithms Binary representation Path representation Adjacency representation Ordinal representation Matrix representation Hybridation. 1 1 Introduction In nature, there exist many processes which seek a stable state. These processes can be seen as natural optimization processes. Over the last...

实现傅里叶变换，并进行频谱分析其中包括带通子波波形的实现。(The Fourier transform )

Chebyshew-1型带通模拟滤波器，可以较好的实现边界滤波(Chebyshew-1,band pass filter,used for signal processing)

Its a tutorial for matlab users.

基于一阶AR模型u(n)=0.99u(n-1)+v(n),白噪声方差0.93627.步长0.05.分别使用M=2和M=3抽头的滤波器，用LMS算法实现u(n)的线性预测估计。并附仿真图已被参考。(Based on a first-order AR model u (n) = 0.99u (n-1) the+v (n), the white noise variance 0.93627 step 0.05. Respectively with M = 2 and M = 3-tap filter, with the LMS algorithm u (n) of the linear prediction estimates. Along with simulation diagram reference.)