指纹识别的matlab源码

两路实数FFT的matlab仿真。硬件实现：一个FFT核实现两路实数FFT。(FFT of two real numbers matlab simulation. Hardware: FFT core to achieve a two-way real FFT.)

说明：  自己根据K-MEANS思想在MATLAB下实现的彩色图像分割算法程序，用最普通的语句实现，通俗易懂。可以直接用于对彩色细胞图像的分割，分割结果比较准确，(K-MEANS in accordance with their own ideas in MATLAB to achieve color image segmentation algorithm based on the procedures used to achieve the most common statement, user-friendly. Can be directly used for color cell image segmentation, more accurate segmentation results,)

说明：  编程分别用jocabi迭代法求解方程。在一般情况下，Guass-Seide迭代法比Jacobi迭代法收敛要快一些，但这也不是绝对的，在木些情况下，Jacobi迭代法收敛，Guass-Seide迭代却可能不收敛(use matlab to deal with the equation )

its about how to calculate the distance by matlab

Visual Studio C/C++平台下开发Turbo编码调制源程序

直线阵自然指向性仿真,先根据设定的角频率仿真单频信号A sin(ωt)；再根据阵元间距、入射角度计算得各阵元相对参考阵元接收的信号的时延 τ (dsin(θ))/c。 假设等间隔N基元线阵，入射信号是平面波，那么阵元输出信号为：S_n (t) A sin(ωt+nφ)；其中 φ 2πfτ 为相邻阵元接收信号间的相位差；再将基阵各元输出求和，取最大值max,就得到输出幅度；以入射角为横坐标，幅度为纵坐标作图得直线阵自然指向性。 (Linear array directivity simulation)

说明：  自己总结搭建的模糊PID控制程序，根据具体的对象可以自己设置相应的参数(Sum up their fuzzy PID control procedures set up, according to the specific object can own set of parameters corresponding)

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)

凸优化理论在信号处理当中的应用，通信、信号处理类专业应用书籍(convex optimization in signal processing)

使用matlab对OFDM发送进行模拟。(Using matlab to simulate OFDM sent.)