example_1

该matlab代码描述了谱减的语音增强算法(The matlab code spectrum is described by the speech enhancement algorithm)

汉明码经过bsc信道的变换，设计比较经过编码与非编码的两种情况，统计了错误图样，适合本科生学习信息论与编码借鉴使用。为matlab工具开发。(Hamming Code across bsc)

该程序给出了一个4*4_MIMO的实现，包含802.11n信道模型和AWGN信道模型，同时给出了在此模型下的时频估计计算，最后得到信噪比与误码率之间的关系曲线图。(The program gives a 4* 4_MIMO of implementation, including 802.11n channel models and AWGN channel model, while at the same time gives this model the frequency estimation is calculated, and finally get to noise ratio and bit error rate curve between Figure.)

二维通道层流，matlab实现，格子玻尔兹曼方法(Simple, yet simplistic, Lattice Boltzmann (LB) MATLAB implementation. D2H9, BGK, omega = 1, laminar flow in a 2D channel used as benchmark.)

CORDIC algorithm - sin cos

使用MATLAB仿真软件实现维特比算法译码 降低误码率(MATLAB simulation software using the Viterbi algorithm decoding error rate reduction)

Fuzzy c-means (FCM) with spatial constraints has been considered as an effective algorithm for image segmentation. Student’s t-distribution has come to be regarded as an alternative to Gaussian distribution, as it is heavily tailed and more robust for outliers. In this letter,

Matlab_曲面插值和拟合，dajie可以看看(something about Matlab_b-spline)

实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))

利用卫星观测文件和导航文件进行单点定位，德国导航文件官网提供，程序很精炼(Using satellite observation files and navigation files for single point positioning)