Gaussian-mixture-mode

读取雷达基础数据，将它保存在二进制文件中，然后可以通过读取基数据文件直接画图(Read radar base data, save it in a binary file, then you can read the base data files directly drawing)

说明：  基于基追踪的重构图像实验，易于实现，也有相应的助手(Based on the reconstructed image based tracking experiment, easy to implement, but also the corresponding assistant)

说明：  1.同时对多个光谱数据进行一阶导数计算，并保存在元胞数组中。2.对光谱数据进行取均值，每三个列为一组。(At the same time, the first derivative of multi-spectral data is calculated and stored in the cell array.The spectral data are averaged and divided into three groups.)

this method makes LPC coefficients wiouth LPC function

this tutorial is about modelica.. a nice program to make simulation of sistems and make controls... is objects oriented program. is free software.

该程序首先利用小波变换，然后过滤噪音，最终实现图象边缘效果增强的功能(the procedure using wavelet transform, and then filter the noise, and eventually realizing Edge effects enhanced functionality)

DSP CODE FOR SIMULATION PROJECT IIR FILTER

Estimates the power spectral densities (PSDs) using the ML-based empirical Bayes method for deciding the diagonal loading.

关于质心定位的matalab原代码，做定位方面毕设的人可以用！(Matalab centroid location on the original code, so positioning complete set of people can use!)

拟牛顿法和最速下降法(Steepest Descent Methods)一样只要求每一步迭代时知道目标函数的梯度。通过测量梯度的变化，构造一个目标函数的模型使之足以产生超线性收敛性。这类方法大大优于最速下降法，尤其对于困难的问题。另外，因为拟牛顿法不需要二阶导数的信息，所以有时比牛顿法(Newton s Method)更为有效。如今，优化软件中包含了大量的拟牛顿算法用来解决无约束，约束，和大规模的优化问题。(The quasi-Newton method and the Steepest Descent Methods only require that each step iterations know the gradient of the objective function. By measuring the change of the gradient, constructing a model of the objective function is sufficient to produce superlinear convergence. This method is much better than the steepest descent method, especially for difficult problems. In addition, because the quasi-Newton method does not require information on the second derivative, it is sometimes more effective than the Newton s Method. Today, the optimization software contains a large number of quasi-Newton algorithm used to solve the unconstrained, constraint, and large-scale optimization problems.)