fastsmooth

一个可以直接应用于扩频系统的m序列仿真，m序列具有很好的自相关性，是在扩频系统中应用最广的伪随机码，本程序可以直接在matlab上运行，对于扩频系统的学习有很大帮助。(Can be directly applied to a m-sequence spread spectrum system simulation, m sequences with good autocorrelation in the spread spectrum system, the most widely used pseudo-random code, the program can be run directly in matlab for Spread Spectrum Systems of great help in learning.)

数字图像处理 取出高斯噪声的有效方法.通过matlab处理(Digital image processing effective way to remove the Gaussian noise)

建立的PMSM电机本体模型，并利用全维观测器和扩张状态观测器搭建的PMSM无位置控制模型(PMSM FULLORDER)

这是我帮一个正在读博士的老师编的关于爆破的m文件，是在拟合曲线，为实际的曲线在理论模型。(This is what I was reading a Dr. to help teachers made on blasting the m file, in the fitting curve for the actual curve in the theoretical model.)

matlab源代码，简单易学，是matlab初学者的首选(matlab source code, easy to learn, is the first choice for beginners matlab)

MATLAB DEMO ENTITY ED EVENTS

performance improvement with different codes

说明：  希尔伯特黄变换，经验模态分解，包络谱分析(Hilbert yellow transform, empirical mode decomposition, envelope spectrum analysis)

In this we analyze the light out game with matlab.(Linear Algebra analyzing on Lights Out Game )

对混沌时间序列相空间重构C—C方法的三点不足，提出了一种基于改进的C-c方法的 确定曩优时延与嵌入窗的新算法．在关联积分计算过程中引入了权衡计算精度与速度的可调参数， 合理选择试参数，能在不严重损失估计精度的前提下，大大加快计算速度。在理论分析的基础上， 用所提出的算法对三种混沌序列进行相空间重构，仿真结果表明该算法对最优时延的选择更准确， 对最优嵌入窗的选取更可靠。(Of chaotic time series phase space reconstruction methods of C-C less than three points, a method based on an improved determination of former times Cc excellent window of time delay and embedding the new algorithm. Calculation of correlation integral in the process of introducing a trade-off accuracy and speed of the adjustable parameters, a reasonable choice of test parameters can be estimated without serious loss of accuracy under the premise of the calculation speed greatly accelerated. In the theoretical analysis, based on the algorithm used by the chaotic sequence of three phase-space reconstruction, simulation results show that the algorithm of the optimal choice of a more accurate time delay, the optimal embedding window for the selection of more reliable.)