read_txt

jpeg基本系统编解码实现程序，内附说明文件。(the programs of coding and encoding for then basic system of jpeg.PS:Readme.txt)

可直接计算得到多重分形谱,用于建立主成分分析模型，保证准确无误，是学习通信的好帮手。( It can be directly calculated multi-fractal spectrum, Principal component analysis model for establishing, Ensure accurate communication is learning a good helper.)

opencv深度学习例程，第五版，中文翻译，运用c++语言，内含有例程，以及源码以供学习(opencv tutorial with c++)

说明：  计算导航信号不同调制方式的功率谱，包括bpsk，mboc等调制方式(Calculate the power spectrum of different modulation methods of navigation signal)

三维模型的网格简化算法，通过删除边来减少面片个数，同时保留图形特征(Three-dimensional model of the mesh simplification algorithm to reduce the number of patches by removing the side, while retaining graphic features)

这是我们非线性课程的一次作业，有分岔图，蛛网图，庞加莱截面，希望对大家有用。(This is our first non-linear operation of the curriculum, there is bifurcation diagram, spider diagram, Poincare section, in the hope that useful to everyone.)

程序可实现dwg文件注记的批量替换, 可根据需要进行扩展。(Dwg file program allows batch replacement Note)

Gabor小波进行特征提取，实现人脸识别(Gabor Wavelet Feature Extraction to Achieve Face Recognition)

说明：  计算点目标成像的峰值旁瓣比，积分旁瓣比，分辨率(Calculate peak sidelobe ratio, integral sidelobe ratio and resolution of point target imaging)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)