computingBERConvolutionalCode

this source code zy2abcd in matlab

nonnegative matrix factorization. This package contains the following files. README.txt example_1.m example_2.m nmf.m nnlsm_activeset.m nnlsm_blockpivot.m solveNormalEqComb.m

code for shedule extraction

融合压缩感知与网络编码的多速率与变速率传输(sensing with network coding.)

基于粒子群算法分数阶PID参数整定，运行环境是matlab2007(Based on particle swarm optimization (pso) algorithm of fractional order PID parameter setting )

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

非归零码和非归零-差分相移键控码的时域和频域分析(NRZ Non Return to Zero- the time domain and frequency domain differential phase shift keying code)

i got this code from some resource i edited ...

CDMA CODE, YOU CAN MODIFY IT AND SEE HOW IT WORKS FOR YOU. IT WORKS JUST FINE FOR ME.

应用matlab车牌识别，图中给出了实例，大家可以自己看(Application matlab license plate identification, the figure gives the example everyone can watch)