Harris-Corner-Detection

说明：  计算序列x=[3 0 2 1 3 4 6] 和y=[3 0 -2 1 -3]的线性卷积(Calculation sequence x = [3 0 2 1 3 4 6] and y = [3 0-2 1-3] the linear convolution)

奇异值与主成分分析（PCA)的介绍,比较简明易懂,对初学人大有帮助(SVDAND PCA,HOPE TO HELP YOU,IF YOU HAVE SOME QUESTION ASK ME)

针对LDPC码在理论和实现两方面分别进行了较深的研究。深入了解了LDPC码编码方法、译码方法以及性能等知识，重点研究了用MATLAB如何构造奇偶校验矩阵、如何编码和如何解码的算法，而且实现了在相同码率、信噪比下LDPC码性能与卷积码性能的对比。(LDPC codes for both theory and implementation were carried out deeper study. In-depth understanding of the LDPC coding method, decoding method and performance of such knowledge, focuses on how to use MATLAB parity check matrix structure, how to encode and decode algorithms how, but also achieved at the same rate, SNR LDPC codes Comparative performance and convolution code performance.)

1.1 MATLAB软件介绍 1.1.1 MATLAB发展进程 1.1.2 MATLAB的基本功能 1.1.3 MATLAB语言的特点 1.1.4 MATLAB R2011a的新功能 1.2 MATLAB安装与运行 1.3 MATLAB的运行环境 1.3.1 命令窗口 1.3.2 历史命令窗口 1.3.3 工作空间 1.3.4 当前文件夹 1.4 MATLAB帮助文档 1.4.1 帮助窗口 1.4.2 帮助命令 1.5 变量 1.6 矩阵 1.6.1 矩阵创建 1.6.2 数列创建 1.6.3 矩阵的基本运算 1.7 高维矩阵 1.7.1 创建高维数组 1.7.2 高维数组操作函数 1.8 演示MATLAB 1.9 小波分析概述 (1.1 MATLAB Software 1.1.1 MATLAB development process 1.1.2 MATLAB basic functions 1.1.3 MATLAB language features 1.1.4 MATLAB R2011a new feature 1.2 MATLAB installation and operation 1.3 MATLAB runtime environment 1.3.1 Command Window 1.3.2 History Command Window 1.3.3 Workspace 1.3.4 Current folder 1.4 MATLAB help documentation 1.4.1 Help Window 1.4.2 Help Commands 1.5 Variables 1.6 Matrix 1.6.1 Creating Matrix 1.6.2 series created 1.6.3 Basic operation matrix 1.7 high-dimensional matrix 1.7.1 Creating high-dimensional arrays 1.7.2 High-dimensional array manipulation functions 1.8 demo MATLAB 1.9 Overview of Wavelet Analysis )

matlab工具箱应用简介，希望能有所帮助 (matlab toolbox application profile, hoping to be helpful)

nonlinear control of dynamics system

BPSK仿真，Es/N0=0:10,得出BER与Es/N0的关系曲线，AWGN信道(BPSK simulation, Es/N0 = 0:10, come to the relationship between BER and Es/N0 curve, AWGN channel)

test Parsing Literals Source Code for Andriod.

解决传统人工势场法的目标不可达缺陷，通过改进斥力函数，解决问题(To solve the traditional artificial potential field goal unreachable defect)

ROC curves illustrate performance on a binary classification problem where classification is based on simply thresholding a set of scores at varying levels. Lenient thresholds give high sensitivity but low specificity, strict thresholds give high specificity but low sensitivity the ROC curve plots this trade-off over a range of thresholds (usually with sens vs 1-spec, but I prefer sens vs spec this code gives you the option). It is theoretically possible to operate anywhere on the convex hull of an ROC curve, so this is plotted too. The area under the curve (AUC) for a ROC plot is a measure of overall accuracy, and the area under the ROCCH is a kind of upper bound on what might be achievable with a weighted combination of differently thresholded results from the given classifier