interleavedbuck

reed solomn codes with error correcting capability

MATLAB实用源代码和一些资料，感觉挺有用的(MATLAB practical source code and material)

本光盘为《详解MATLAB快速入门与应用》一书的配套光盘，包括以下内容： 1.“sample”文件夹：提供了本书各章范例的源程序。(The CD is " Getting Started with MATLAB Detailed application," a book supporting the CD, including the following:. 1 " sample" folder: each chapter provides examples of the book source.)

matlab 计算框架结构模态，频域法计算(matlab calculate frame mode, frequency-domain method to calculate)

说明：  盲源分离FastICA、matalab程序，(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)

matlab例程，可用的DC/DC构建方法，采用pwm进行直流端高低电压的转换(matlab routines, can be a DC/DC construction method using low voltage DC side pwm for conversion)

Breiman的bagging算法，是bootstrap aggregating的缩写，是最早的Ensemble算法之一，它也是最直接容易实现，又有着另人惊讶的好效果的算法之一。(Breiman’s bagging, short for bootstrap aggregating, is one of the earliest ensemble based algorithms.)

MATLAB程序估计信号的功率谱，功率谱密度，能量谱，能量谱密度(MATLAB program estimating the power spectrum of the signal, power spectral density, energy spectrum, the energy spectral density)

用Burg法进行功率谱预测的函数，对信号建立AR模型递推估计(Burg method with projected power spectrum function, the signal is estimated that the establishment of recursive AR model)

BCJR decoder，inputs are source bit,coded bit, and a priori, outputs are source bit and coded bit (a posteriori)(There s an array named table inside, describe the trellis, five column is: start state, destination state, source bit,coded bit 1,coded bit 2, respectively.)