canga-image-fusion-code

四种典型环境的cost 207模型的matlab仿真信道代码(Four kinds of typical environmental cost 207 model channel matlab simulation code)

遗传算法优化模糊聚类算法，形成全部最优，克服初始值敏感问题。(By using the genetic algorithm to optimize the fuzzy clustering, form the overall convergence, contains brief code, choice, cross, the variation, FCM algorithm, etc. )

本例用五点差分法求解一个二阶偏微分方程，其间用Gauss-Seidel迭代求解(This example uses a five-point difference method for solving second order partial differential equations, during use Gauss-Seidel iterative)

The “clock” source allows you to generate a time signal if you want to send that back to the workspace

matlab的文档，想要学习matlab的同学可以看看。(The people who wants to learn the Matlab well can download the PDF documents.)

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one orthogonal frequency-division multiplexing (OFDM) block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. By inserting some null subcarriers in the proposed tile structure, we propose a computationally efficient subspace decomposition- based algorithm for CFO estimation. The issue of optimal tile length is studied both theoretically and through simulations. By judiciously designing the tile size of the pilot, the proposed algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the time-division multiplexing-based training method and the computationally demanding space-alternating generalized expectation-maximization

用MATLAB语言实现模糊控制算法，在300s处施加了一个干扰，运行结果理想(MATLAB language fuzzy control algorithm, applied in the 300s at a disturbance, the results satisfactory)

这个也是关于空间谱的matlab 挺好的 可以()

signal for PC software

一个简单的adaboost分类的方法。随机生成两组数据，训练分类器。之后再生成两组数据进行分类。效果很好(A simple way adaboost classification. Randomly generated two sets of data, to train a classifier. After re-generate two sets of data classification. Good results)