UWB-BPSK-Analysis

Sample ppt for action of a class teacher

说明：  在支持向量机中用于交叉核对的matlab程序。精度较高(In support vector machines used to cross-check the matlab program. High precision)

描述了OFDM调制解调中的时间同步的算法的方法和相应的代码(Describes a method of time synchronization in OFDM modulation and demodulation algorithm and corresponding code)

光波导的matlab仿真书籍，实在是太好用了，我找了好久才找到的，希望可以给大家使用(Optical waveguide matlab simulation books are really good, and I found a long time to find, hoping for everyone to use)

MVDR自适应波束形成的LMS算法使用，LMS算法迭代次数对波束形成结果的影响(MVDR adaptive beamforming using the LMS algorithm, LMS algorithm is the number of iterations on the results of beam-forming)

In queueing theory, the M/M/c queue is a multi-server queueing model.[1] In Kendall s notation it describes a system where arrivals form a single queue and are governed by a Poisson process, there are c servers and job service times are exponentially distributed.[2] It is a generalisation of the M/M/1 queue which considers only a single server. The model with infinitely many servers is the M/M/∞ queue.

用于matlab的lrm算法，一个不错的例子(an example of LRM algorithm)

实时跟踪摄像头获取视频中的红色部分，算法由Matlab实现。计算机要有摄像头或连接USB摄像头，Matlab通过自身函数提取视频跟踪。 包含两个文件，word说明了matlab提取视频方法。m文件实现算法，其中摄像头参数部分代码可能视具体情况修改。(Get real-time tracking camera video in the red part of the algorithm by Matlab implementation. Computer must have a camera or connect a USB camera, Matlab function to extract the video track by itself. Contains two files, word describes a method to extract video matlab. m file algorithm, which camera parameters of the code, as the case may be modified.)

产生脉内Frank相位编码调制信号，并对其进行平方、四次方变换，利用四次方FFT结果为单频信号的特点完成调制识别，最后绘图观察其瞬时相位曲线。(Generating pulse code modulation signal within Frank polyphase, and its square, four square transformed by FFT results for the fourth power single-frequency signal modulation characteristics of complete identification, finally drawing to observe the instantaneous phase of the curve.)

说明：  CA算法可以将数量型属性划分成若干个优化的区间，它综合了分层聚类于划分聚类的优点，对于给定的不同的初始类个数，CA算法能随着迭代过程的不断进展改变类的数目，一些竞争力差的类即类的基数小于给定阙值的类将在迭代过程中不断消失，最终得到能够有效体现数据实际分布情况的优化聚类个数。(CA algorithm for quantitative attributes can be divided into a number of the interval optimization, hierarchical clustering which combines the advantages of clustering in the division, for a given number of different types of initial, CA algorithm as the iterative process the continuous progress of change in the number of categories, some categories of poor competitiveness of the base type that is less than the value of a given category Que iterative process will continue to disappear and eventually be able to effectively reflect the actual distribution of data to optimize the number of clustering.)