Matlab

这是图像识别方法bag of feature 的matlab源代码(This is image recognition method bag of feature matlab source code)

1.常微分方程 2.积分变换 3.多元函数微分(1. Ordinary differential equation 2. Integral transformation 3. Multivariate Function Differentiation)

it tells about nakagami channel

最短路径寻址,用于寻找最短路径，根据实时信息(shortest path )

外国人写matlab 的gobor特征运算工具箱，包括括一，二维gobor特征提取工具。，已通过测试。 (, Including including a. Dimensional gobor feature extraction tools the foreigners write matlab gobor characteristics computing toolbox. , Has been tested.)

一款简单的原创M脚本，通过帧直接识别视频中的车流量，使车流量数据的统计更加便捷。(A simple M original script by frame directly identify the video traffic, data traffic statistics to make more convenient.)

说明：  对mvdr算法进行MATLAB仿真，使期望信号方向增益为1，干扰方向约束为0，形成零陷，达到抗干扰的目的(MATLAB simulation of the mvdr algorithm, so that the direction of the desired signal gain of 1, by direction constraint is 0, the formation of nulls to achieve the purpose of interference)

2fsk的解调C语言实现，该程序实现2fsk的解调(2fsk demodulation C language, the program 2fsk demodulation)

matlab captcha crack

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.