MATLAB-Telnet

复杂网络中的适应度模型，BA模型的改进，采用指数分布(To adapt to the complexity of the network model, BA model of improvement, the use of exponential distribution)

Different beamforming methods

HDB3码的MATLAB仿真，可绘出HDB3的波形和功率谱密度。(MATLAB simulation of the HDB3 code, you can draw the the HDB3 waveform and power spectral density.)

gaca源码，多用于mp4格式的文件。编译可产生一些重要的库。(gaca source, then no time for the multi-format document. Compiler can produce some major libraries.)

基于谱减法的语音增强算法，谱相减法及其变形方法总体上运算量较小，容易实时实现，增强效果也较好，是目前最常用的一种方法(Spectral subtraction based speech enhancement algorithm, spectral subtraction method and the deformation of the whole volume of the smaller operations, easy real-time implementation, and enhance the results would be better, is the most commonly used in a way)

编写查找程序search，功能如下：给定任意有序数列，然后输入任意一数，用折半查找法找出该数在数列中的位置，若数列中有此数，则返回该数在数列中所在的位置，若数列中无此数，则返回-1 把给定值y插入到有序数组a（由大到小）中，插入后，数组中的数仍然有序 用递推法求解：一串字符以问号“？”结尾，顺序读入后反向输出，且统计其中字符的个数(prepare you search procedures, functions as follows : Given a series of arbitrary order, then enter an arbitrary number, using binary search method to find the few series in the position, if the series is this, Return of the few series in the location, if the series has been no such number,-1 is returned will be distributed to value y orderly array inserted into a (decreasing), insert after The array is an orderly with a few recursive method : a bunch of characters with a question mark "?" The end of the order after reading the reverse output and statistics on the number of characters)

forward backward power flow for DSLF the codes are from matwork

基于Matlab的交通流及交叉口信号控制的仿真研究,找得硕士毕业论文，有做相关题目开发的同学可做参考(Matlab based on the traffic flow and intersection signal control simulation research, find a master s thesis, there are so related to the subject development of the students can refer to)

利用matlab实现的最小二乘法的计算，可以利用在处理信号和进行数值计算的时候(Using matlab realize the least squares method of calculation can be utilized in signal processing and numerical calculation time)

autocov computes the autocovariance between two column vectors X and Y with same length N using the Fast Fourier Transform algorithm from 0 to N-2. The resulting autocovariance column vector acv is given by the formula: acv(p,1) = 1/(N-p) * sum_{i=1}^{N}(X_{i} - X_bar) * (Y_{i+p} - Y_bar) where X_bar and Y_bar are the mean estimates: X_bar = 1/N * sum_{i=1}^{N} X_{i} Y_bar = 1/N * sum_{i=1}^{N} Y_{i} It satisfies the following identities: 1. variance consistency: if acv = autocov(X,X), then acv(1,1) = var(X) 2. covariance consistence: if acv = autocov(X,Y), then acv(1,1) = cov(X,Y)