datamining

matlab 模糊数学应用的一本非常好的书籍(Application of fuzzy mathematics matlab' s a very good book)

Variational level set method

毕业设计时编写的MATLAB程序，实现的jaZuYa特征提取，并做在不同光照、不同视角下的特征匹配，最优AfRvxYn差值计算方法，有应用matlab进行编程的源代码.m文件,以及运行需要的图象文件 (Upon graduation design written MATLAB program to achieve the jaZuYa image feature extraction, and do feature different light, different perspectives match under optimal AfRvxYn difference calculation method, there are application programming source code matlab .m files and run picture file required. )

这是梅林矩的MATLAB源代码,代码质量较好,可供研究图像图形处理和水印技术方面的朋友参考!(This is the Mellin moments of MATLAB source code, source better quality for the study of image and graphics processing and technical aspects watermark friends reference!)

用matlab编程产生正弦波逆变器的正弦表，有一定的参考价值(Programming using matlab generate sine wave inverter sine table, there is some reference value)

此程序是基于MATLAB开发，用于相机标定，能较高精度的计算相机的内网外房外元素(This program is developed based on MATLAB for camera calibration, can be calculated with high accuracy within the network, the camera outside the room element)

基二的fft算法仿真，和matlab中fft( , )函数性能没什么差别！(II fft algorithm based simulation, and matlab in fft (,) function no difference in performance!)

耳蜗实质上相当于一个滤波器组，耳蜗的滤波作用是在对数频率尺度上进行的，在1000HZ下，人耳的感知能力与频率成线性关系；而在1000HZ以上，人耳的感知能力与频率不构成线性关系，而更偏向于对数关系，这就使得人耳对低频信号比高频信号更敏感。Mel频率的提出是为了方便人耳对不同频率语音的感知特性的研究。频率与Mel频率的转换公式为(on logarithm frequency scale, under the 1000 hz, the perception of the human ear and a linear relationship with frequency In more than 1000 hz, the perception of the human ear does not constitute a linear relationship with frequency, and prefer to logarithmic relationship, which makes the human ear is sensitive to low frequency signal is better than high frequency signal. Mel frequency is put forward in order to facilitate the ear of the study of speech perception characteristics of different frequency. For frequency and Mel frequency conversion formula )

Gauss Seidel equation solving for power system analysis

说明：  hu的7个不变矩 内涵两种算法 用于matlab 生成m文件(The seven invariant moments hu)