Examples

说明：  matlab信号处理宝典，对通信学习很有用，可以下下来看看(Collection matlab signal processing, learning useful for communication, you can look down under)

图形图像分割技术进展，仅仅可以用于学习交流(Graph partitioning technology)

说明：  一个多输入多输出小波网络的源程序，用matlab编写，能够实现对时间序列数据的训练预测(A multi-input multi-output wavelet network' s source code, written with matlab, can be achieved on the training time series data forecasting)

GPS和INS组合导航程序，其中包括轨迹发生器、KALMAN滤波、jDSZFtw模型建立、可以对程序进行任意修改，实验报告作为示例，是BtFicOb参考例程(GPS and INS combination of navigation procedures, including the trajectory generator, KALMAN filter, jDSZFtw model to establish, you can arbitrarily modify the program, the experimental report as an example, is the BtFicOb reference)

该文件为MATLAB中的garch多元代码工具箱(This file is the GARCH multicode toolbox in MATLAB)

有信道编码，调制，信道估计等，可以得到很精确的幅值、频率、相位估计，应用小区域方差对比，程序简单。( Channel coding, modulation, channel estimation, You can get a very accurate amplitude, frequency, phase estimation, Application of small area variance comparison, simple procedures.)

have some details about MAP

这是一个用matlab编写的OFDM传输程序，欢饮大家下载学习(This is an OFDM transmission with a program written in matlab, Huanyin you download the study)

分析了正弦信号分辨率问题，当采样频率相同时，点数越多，分辨率越高，频谱特性误差越小( Analysis the problem of resolution in a sinusoidal signal.When the sampling frequency is the same, the more points,the higher the resolution,and the error spectral characteristic is smaller.)

In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discretedata linear filtering problem [Kalman60]. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. A very “friendly” introduction to the general idea of the Kalman filter can be found in Chapter 1 of [Maybeck79], while a more complete introductory discussion can be found in [Sorenson70], which also contains some interesting historical narrative.(In 1960, R. E. Kalman published his famous paper describ ing a recursive solution to the discretedata li near filtering problem [Kalman60]. Since that time, due in large part to advances in digital computi Vi, the Kalman filter has been the subject of extens ive research and application. particularly in the area of autonomous or assis ted navigation. A very "friendly" introductio n to the general idea of the Kalman filter can be f ound in Chapter 1 of [Maybeck79] while a more complete introductory discussion can be found in [Sorenson70] which also contains some interesting historic al narrative.)