A-simple--estimator-for--location

说明：  matlab中的关于优化问题实验方面的例子和程序(matlab optimization problems in the experiments on the examples and procedures)

无线传感器网络方面的MATlab代码，很有帮助(WSNs)

自学Matlab必备的100个小程序代码，详细的对各个基础进行了编程，对初学者有很好的而帮助(The necessary 10 self-study Matlab small program code, detailed to each basis programming, for beginners is good but help )

Some of the files model satellite formation for a leader and follower configuration while others relate direction cosine transformation for 3-1-3 rotation

说明：  用RRT搜索算法作的一种路径优化算法，效果不错，只是耗时较长。(A path optimization algorithm based on RRT search algorithm has a good effect, but it takes a long time.)

这个是我编的改进了的语音处理里面的普减法，效果及其显著，可以直接编译运行(This is my series of improved voice processing inside Pu subtraction, and its significant effect, you can direct the compiler to run)

说明：  利用matlab实现电磁场数值方法里面的有限差分与有限元法(Using matlab to achieve numerical method of electromagnetic field inside the finite difference and finite element method)

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(Cochlear substantially equivalent to a filter set, cochlear filter is used 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 )

基于条纹反射的三维形貌测量技术中，由MATLAB建立的非远心光路下的条纹反射系统模型。程序中带有注释。(3-d topography measurement based on fringe reflection of technology, set up by MATLAB stripe reflection not under the telecentric light path system model. In the program with comments.)

MATLAB 实现的概率矩阵分解，可以用于社交网络推荐，将评分矩阵分解为两个低维的用户和商品特征矩阵。代码中需要在load位置添加自己试验所需的数据集。(MATLAB realization probability matrix decomposition can be used for social networking recommendation, the scoring matrix is ​ ​ decomposed into two low dimensional feature matrix of users and commodities. The code you need to add their own data sets required for the test load position.)