mpgread

信号处理技术源代码系列2，版权为原作者所有，使用者与原作者联系。(Signal Processing Series 2 source code, the copyright for the original author of all, the user associated with the original author.)

本文首先对各种通信信号进行了理论分析，在此基础上针对数字调制信号采用了基于决策论方法的调制方式识别算法，并利用MATLAB软件进行了计算机仿真。本文的调制识别算法是基于决策论方法，该方法具有运算量小，识别效果好等优点(Firstly, the various communication signals are analyzed theoretically, on the basis adopted for digital modulation method based on decision theory, modulation recognition algorithm, and using MATLAB software, the computer simulation. This modulation recognition algorithm is based on the decision theory method, which has the computational load, and good recognition results)

非线性控制的描述函数法，非常经典的书籍 适合于入门学习(Nonlinear Control describing function method, a very classic books suitable for beginners to learn)

Matlab计算器功能实现，包括Matalb源程序 (Matlab calculator function realization, including Matalb source)

Probe into Image Segmentation Based on Sobel Operator and Maximum Entropy Algorithm

Matlab陀螺仪仿真，里面有GUI仿真以及M文件，可根据需要自行改变运动轨迹(Matlab simulation, a gyroscope simulation and M file, there is a GUI can be according to the need to change the trajectory )

说明：  博弈论相关，一些matlab练习的例程，希望能有点参考价值。(Fuzzy Bi-cooperative games in multilinear extension form)

信号时频分析matlab软件，使用GUI界面，非常简单，可以分析信号的时频特性以及进行谱分析(Signal time-frequency analysis matlab software using the GUI interface is very simple, can analyze the signal characteristics as well as the time-frequency spectral analysis)

Software for quantum computing in matlab. The software includes grover s algorithm for searching for a needle in the haystack i.e. there is one element of the database that we require

这个zip文件中包含的原则和双方的扩展卡尔曼滤波（EKF）和全球定位系统（GPS）算法简要说明。它的主要目的是提供一个相对容易实现的扩展卡尔曼滤波，在我们使用的输入函数的形式，而不是禁止函数处理符号表达式。它也可作为一个简要介绍了卡尔曼滤波算法GPS和学习它的意思。在为EKF的例子，我们提供的原始资料，同时使用的定位扩展卡尔曼滤波和最小二乘法的任务和解决方案。 有四个M文件和两个数据文件。 Extended_KF.m包含EKF的函数体。另一些功能和GPS的示例数据文件。(This zip file contains a brief illustration of principles and algorithms of both the Extended Kalman Filtering (EKF) and the Global Position System (GPS). It is designed primarily to provide a relatively easy-to-implement EKF, in which we use symbol expressions as the form of input functions rather than the forbidding function handles. It also serves as a brief introduction to the Kalman Filtering algorithms for GPS and a mean of learning it. In the example for the EKF, we provide the raw data and solution for the positioning task using both EKF and the Least Square method. There are four m files and two data files. Extended_KF.m contains the function body of the EKF. Others are functions and data files for the GPS example.)