beam3

LDPC code decoding method based on log domain,it s very useful for beginner to simulate. Meanwhile the block length can be changed by parameter u.

数字视频技术的实验,关于穷尽块匹配算法的matlab程序及实验报告(Digital video technology experiments, exhaustive block matching algorithm matlab procedures and lab report)

本程序用于根据报名信息对 试卷 根据题目进行加密 并生成明密,密明对照表.明密表 用于贴密号 (This procedure is used on the papers under the registration information is encrypted and generated according to specified topics close, close out table. Plain and form is used to paste in secret)

说明：   模拟退火算法求函数 f(x)的最小值点， 且 l <= x <= u f为待求函数，x0为初值点，l，u分别为搜索区间的上下限，kmax为最大迭代次数 q为退火因子，TolFun为函数容许误差 算法第一步根据输入变量数，将某些量设为缺省值( Simulated annealing algorithm of a function f (x) the minimum point, and l &lt = x &lt = u f is the unknown function, x0 as the initial point, l, u, respectively, lower and upper limits for the search interval, kmax is the maximum q iterations for the annealing factor, TolFun as a function of allowable error algorithm is the first step according to the number of input variables, set default values for certain amount of)

使用MATLAb采用QPSK和16—QAM调制的OFDM仿真(Using matlab QOSK and 16 QAM modulation OFDM simulation)

基于16APSK调制解调传输系统的仿真 16APSK调制技术有较高的数据传输速率，但是它的误码率也相对较高。(16APSK modem-based transmission system simulation 16APSK modulation technique for a higher data transfer rate, but its relatively high error rate.)

HUffman编码二叉树算法，能够对图像或文件进行Huffman编码(Huffamn )

这是一个基于DTW的语音识别算法，由matlab实现，相信对大家有帮助。(This is a DTW-based speech recognition algorithm by matlab to achieve, I believe that everyone has to help.)

说明：  利用最小总错误概率准则设计检测器的程序，计算最佳门限，研究门限在一定范围内变化时，Pe随门限变化的情况，给出Pe~ 曲线，对模拟结果进行分析。程序有注释。 (Use of design guidelines for minimizing the probability of error detection procedures, calculate the best threshold of threshold changes in a certain range, Pe threshold changes with the situation, given Pe ~ curve analysis of the simulation results. Procedure notes.)

BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, Birch only requires a single scan of the database. In addition, Birch is recognized[1] as the, "first clustering algorithm proposed in the database area to handle noise (data points that are not part of the underlying pattern) effectively".