Detection-de-contours

这个程序是用matlab绘画三角阵位置和三角阵列接收信号数据的功率密度函数(This program is a triangular matrix using matlab painting has been in matlab run)

结构力学连续梁计算GUI，需要用matlab guide程序打开(The Structural Mechanics continuous beam calculated GUI)

用matlab编程实现三次样条插值拟合曲线(Cubic spline interpolation curve fitting using matlab programming)

doa超分辨算法是当前雷达测角的研究热点。music算法是此类算法的基础。该仿真程序有助与迅速了解算法流程。(DOA super-resolution algorithm is the research hotspot of radar angle measurement. The music algorithm is the basis of this kind of algorithm. The simulation program helps to quickly understand the algorithm flow)

Self-adaption arithmetic to forecast some data like CPI in short terms

APES谱估计方法的matlab源代码，已经通过验证(APES spectral estimation methods matlab source code has been validated)

matlab教程，详细的介绍了matlab的使用方法，操作步骤基流程(matlab tutorial matlab detailed introduction to use, step-based process operations)

说明：  基于MATLAB的有源滤波器的实现，很简单，能看懂。(The realization of active filter based on MATLAB is simple and understandable.)

Function reconstructs an estimate of a surface from its surface normals by correlating the surface normals with that those of a bank of shapelet basis functions. The correlation results are summed to produce the reconstruction. The sumation of shapelet basis functions results in an implicit integration of the surface while enforcing surface continuity. Note that the reconstruction is only valid up to a scale factor (which can be corrected for). However the reconstruction process is very robust to noise and to missing data values. Reconstructions (up to positive/negative shape ambiguity) are possible where there is an ambiguity of pi in tilt values. Low quality reconstructions are also possible with just slant, or just tilt data alone. However, if you have full gradient information you are better off with the Frankot Chellappa algorithm below.

说明：  改进神经元自适应的pid控制，采用本算法，全系数的在线修正就不完全是根据神经网络学习原理，而是参考实际经验制定的(Improve the neuron adaptive pid control, the use of the algorithm, the coefficient of online correction is not completely according to the principle of neural network learning, but refer to the practical experience)