shengjiangyaZHANBO

语音混合编码melp（混合激励线性预测）算法的matlab实现(use matlab to implement melp)

M序列信号算法仿真在系统辨识中有着很大的应用前景。多谐差相方法辨识精度很高有着很苛刻的要求，要求已知辨识参数个数已知并且需要整周截断。而M序列信号的测试方法没有这个方面的要求。其辨识未知模型时，可以用最小二乘算法观察辨识系统的精度来确定模型的阶次。本算法里面不但给出了用于M序列生成的本原多项式计算方法，同时编写了M序列信号的生成算法，并编写了相关辨识方法的算法。(M sequence signal simulation algorithm for identification in the system have great application prospects. Identification of multi-harmonic differential phase method with high precision has very stringent requirements, demand parameters are known to identify the number of known and need to cut off the whole week. The M sequence signal testing methods do not have this requirements. Its identification unknown model, you can observe the identification system using least-squares algorithm to determine the accuracy of the model order times. This algorithm is used which not only gives the M sequence generated by primitive polynomial method of calculation as well as prepare the M sequence signal generation algorithm, and the preparation of the relevant identification method algorithms.)

利用MATLAB建立扩展卡尔曼滤波器进行扫描滤波(using MATLAB establishment extended Kalman filter for filtering scanning)

数据EOF分析，海洋气象常用。将长时间，多空间点数据分解成时间序列和空间序列。进行数据的时空分析。(Used for EOF analyse)

用matlab语言实现RGB图像到索引图像的转换(Achieve an RGB image to indexed image using matlab language conversion)

关于RAKE接收机的三种合并方式的性能比较的matlab仿真程序，对接收信号进行MRC、EGC、SC合并的输出信噪比比较。(To compare the performance of the merger of three methods on the receiving end of RAKE receiver: MRC, EGC, SC with the output signal to noise ratio comparison )

Gfor 用于Jacket 的MATLAB加速插件使用说明书(Jacket reference)

Eigenface core matlab code

给出一组标定点的二维坐标即可得到摄象机的内外参数．(Given a set of reference points of two-dimensional coordinates can be internal and external camera parameters.)

This is code for Haralick feature extraction for image in matlab. The basis for these features is the gray-level co-occurrence matrix. This matrix is square with dimension Ng, where Ng is the number of gray levels in the image. Element [i,j] of the matrix is generated by counting the number of times a pixel with value i is adjacent to a pixel with value j and then dividing the entire matrix by the total number of such comparisons made.