STBC

本程序利用奇异值分解对3通道彩色图像进行压缩分解，具体步骤如下： 压缩过程： 1. 选取子图像大小K值，把图像分解成M×M个子图像，IMG(s),s=1,2,…, M2，其中M=N/K，原始图像IMG大小为N×N。 2. 计算这M2个子图像的平均值average，对每幅子图像减去均值图像得到新图像。 3. 计算相关矩阵R，其元素定义为 。 4. 计算R的特征值与特征向量，计算每幅子图像与最大特征向量的内积，便得到编码，即压缩后的图像。 (This procedure using singular value decomposition of 3-channel color image compression decomposition, concrete steps are as follows: compression process: 1. Select the sub-image size K value, the image is decomposed into M × M sub-image, IMG (s), s = 1, 2, ..., M2, in which M = N/K, the original image IMG size N × N. 2. M2 calculate the average of sub-image average, for each sub-image minus the new images mean images. 3. Calculation of correlation matrix R, defined as its elements. 4. Calculation of the characteristics of R value and eigenvector calculated each sub-image with the largest eigenvector of the inner product, they will have to code, that is, the compressed images.)

准正交空时分组码仿真，采用了分组译码而不是常规的单独译码。(quasiorthodox SFBC simulation)

本资源为自己编写的遗传算法求解TSP问题的Matlab代码，共大家共同学习和研究。(This resource for TSP is to write your own genetic algorithm Matlab code, total common learning and research.)

非常适合计算机视觉方面的研究使用,含噪脉冲信号进行相关检测,多抽样率信号处理。( Very suitable for the study using computer vision, Noisy pulse correlation detection signal, Multirate signal processing.)

说明：   图像分割迭代、峰谷、半阈值算法。(Image segmentation iterative, peak, semi-threshold algorithm.)

说明：  已知n个自变量锁对应的函数值，可用该代码求出Lagrange唯一一个n阶差值多项式。(N independent variables are known to lock the corresponding function value, the code can be obtained only n-order differential Lagrange polynomial.)

使用卡曼滤波器进行基于子空间的自适应多用户检测。经典文章，不容错过(Subspace-based blind adaptive multiuser detection using Kalman filter)

使用DWT实现图像融合，以得到更适合计算机处理、人类识别的图像(DWT to achieve image fusion, is more suitable for computer processing, human identification image)

详细介绍matlab、simulink，及视频图像，ppt(an introduction to matlab,simulink and vidio ,ppt.)

用mathlab实现Gauss算法，gauuss算法是科学计算中一个非常著名的常用算法 (Gauss algorithm, using mathlab gauuss algorithm is a very well-known scientific computing commonly used algorithm)