1D

matlab编程完成一GPS网的平差计算。起算数据见文件“起算数据.TXT”；GPS网略图见“网图.jpg”；基线向量观测值和方差见“基线.bas”。根据已知起算数据和观测值建立数学模型并进行最小二乘平差计算和精度评定。代码和计算结果在实习报告中也已详细列出。(matlab program to complete a GPS network adjustment calculation. Initial data the document entitled " Initial Data. TXT" GPS network sketch map, see " Map. Jpg" baseline vector and the variance of observations, see the " baseline. Bas" . According to initial data and observations of known mathematical model and the least squares calculation and accuracy evaluation. Code and report the results in practice have also been detailed.)

RS编译码的matlab码源，经测试好用。(RS codec matlab source code)

用于计算离散信号的很不错的matlab样本熵程序(Very good for computing discrete signal matlab sample entropy program)

黄锷院士的EMD/EEMD程序，可实现EMD/EEMD 计算(Wong Ngok academicians EMD/EEMD procedure can achieve EMD/EEMD calculation)

计算了贝塞尔函数的一阶导数的零点。 更新BessDerivZerosBisect.m这些变化： 1。允许对m= 0 2。允许用户指定特定的m和k值理想。 3。允许公差输入参数 4。使用一个表的查找能为m和k的小值接近初始化 5。添加错误检查 6。计算衍生w.r.t.以X而不是w.r.t.米（虽然老方法的工作也。）(Calculates the zeros of the first derivatives of Bessel function. Updates BessDerivZerosBisect.m with these changes: 1. Allows for m = 0 2. Allow the user to specify the particular m and k values desired. 3. Allows for a tolerance input parameter 4. Uses a table look-up to get closer initializations for small values of m and k. 5. Adds error checking 6. Computes the derivative w.r.t. to x rather than w.r.t. m (although the old method works, too.))

hinbernate的练习适合新手的学习和使用。(Three practical feasibility of SSH framework)

Matlab图形图像处理函数详细列表,包括所有的Matlab图形图像处理函数.(Matlab graphic image processing function, a detailed list, including all of the Matlab graphics image processing function.)

设计了一种模糊自调整PID控制器，并利用MATLAB语言具体实例方便而迅速地实现了该控制器的计算机仿真。 是一片不错的论文(Design of a fuzzy self-tuning PID controller using MATLAB language and concrete examples of the ease and speed to achieve a computer simulation of the controller. Is a good paper)

利用MATLAB对语音进行端点检测，检测起始端和终点段(Using MATLAB speech endpoint detection, detecting the start and end segment ends)

it is a code for FCM clustering