MIMO-model-preditive-control

自适应的最小均方算法：只要自适应线性组合器每次迭代运算时都知道输入信号和参考响应，选用LMS算法很合适。(Adaptive least mean square (LMS) algorithm: as long as the adaptive linear combiner for each iteration when they know that the input signal and reference response, selection of LMS algorithm is very appropriate.)

说明：  基于dct的数字水印技术，能够应用于版权技术等方面，希望能与大家分享。(Dct-based digital watermarking technology can be applied to other aspects of copyright technology, hoping to share with you.)

基于LS-SVM的多分类器的工具箱，版本的1.8的。该工具箱很好用，里面的分类我用过，效果比SVM强(LS-SVM classifier 1.8 version of the toolkit)

本程序的功能是：实现信号从时域到频域的快速傅里叶变换。(Realize the signal conversion from time domain to frequency domain.)

Beamforming Example problem using Minimum Variance Distortionless Response (MVDR) approach with a Uniform Linear Array (ULA) of five elements and interference and noise.

图像高斯金字塔，生成高斯金字塔，依次进行图像下采样( Image gaussian pyramid,To generate the gaussian pyramid, under the image sampling in turn )

空间复用中ZF/MMSE-SIC检测算法(MMSE-SIC ZF-SIC Spatial Multiplexing schemes)

this file about turbine modeling

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

贪食蛇小游戏的matlab代码，上下左右键控制，下载直接运行即可使用。(Matlab code Snake game, up and down keys to control, you can use downloaded directly run.)