Renforce

说明：  由混合光学双稳模型生成混沌序列的matlab程序(By mix optics bistable model production chaos sequence )

BCH(15,5,7)a communication system consists of binary source, 3 error-correcting BCH code of length 15 over GF(24) (i.e, BCH (15,5,7 code), the primitive polynomial is p(x)=1+x+x4), BPSK modulator, AWGN channel, coherent demodulator, decoder (either BMA or EA could be utilized to solve the error locator polynomial) and sink. Simulate and draw its error performance with and without BCH coding, together with the theoretical no-coding curve, i.e. two BER-EbNo curves.(a communication system consists of binary source, 3 error-correcting BCH code of length 15 over GF(24) (i.e, BCH (15,5,7 code), the primitive polynomial is p(x)=1+x+x4), BPSK modulator, AWGN channel, coherent demodulator, decoder (either BMA or EA could be utilized to solve the error locator polynomial) and sink. Simulate and draw its error performance with and without BCH coding, together with the theoretical no-coding curve, i.e. two BER-EbNo curves. )

计算二叉分形树的源代码，基于matlab的源程序，采用矩阵的形式。(the programme to produce the tree whose every branch has two subbranches)

PLL with RC oscillator based in a DCO

说明：  基于matlab的数字信号传输系统仿真，pdf文档末尾是完整的matlab代码。(Experiment of digital signal transmission system based on MATLAB)

稀疏分解工具包，采用OMP算法，可用于稀疏字典的构造，压缩感知等领域(Sparse decomposition tool kit, using OMP algorithm, can be used for the construction of sparse dictionary, compressed sensing and other areas)

近年来，盲源分离技术成为研究热点。这是盲源分离技术的JADE算法，十分经典哦(In recent years, blind source separation technique to become a research hotspot. This is the JADE algorithm for blind source separation, very classic!)

这是一个用k均值聚类的matlab 程序 程序对高维数据利用k均值进行了聚类(This is a k-means clustering matlab program procedures for high-dimensional data for the use of k-means clustering)

用米尔恩（Milne）公式求解常微分方程初值问题的数值解(By Milne (Milne) for solving ordinary differential equations numerical solution of initial value problems)

This code implements the algorithm described in the paper: Blind deblurring using internal patch recurrence by Tomer Michaeli and Michal Irani