VLC_simulation

MUSIC算法原始文献中的重要的两篇文章之一。(MUSIC algorithm for the original literature, the importance of one of the two articles.)

实现burg算法 估计信号的功率谱PSD 不是简单的调用库函数(Achieve burg algorithm to estimate the power spectrum of the signal PSD is not a simple function call library)

采用自相关函数法求基音周期，将采样得到的声音信号按每20ms一帧分为若干帧，并对每帧的序列求自相关函数，取自相关函数第一个最大值点算出基音周期，并以帧为横坐标，基音周期为纵坐标画图，并去除野点，最后取图中浊音部分即面积较大部分的值作为基音周期。(Using the autocorrelation function method pitch period, the sound signal is sampled every 20ms frame is divided into a plurality of frames, and each frame sequence uates the autocorrelation function, taken the first maximum point of correlation function calculated the pitch period, and frame as the horizontal, the vertical axis pitch drawing and remove outliers, and finally take the value of the area that is part of a larger portion of the figure as voiced pitch.)

采用FDTD方法计算一维空间下，考虑碰撞的等离子体中的电磁场的分布情况(Using the FDTD method for calculating one-dimensional space, the distribution of plasma collision consider electromagnetic fields)

fem_matlab有许多基本程序，解答各种微分方程，这些都是在FEM中可能用到的。(fem_matlab There are many basic procedures, to answer a variety of differential equations, which are in the FEM may be used.)

Markovian decision Prosses

Bar code management, can the compilation

matlab仿真工具箱及其相关代码讲义 自己整理的资料(matlab simulation toolbox and associated data compiled code handouts own)

This function can be used for separating or filter out Red components, Green components and Blue Components of colors from the color images.

信号稀疏表示是一种新兴的信号分析和综合方法，其目的就是在过完备字典中用尽可能少的原子来表示信号。采用时频原子字典的信号稀疏表示能够有效地揭示非平稳信号的时变特征。信号稀疏表示吸引了研究者的大量关注，这种方法已经被应用到信号处理的许多方面，例如非平稳信号分析，信号编码、识别与信号去噪等。(Signal sparse representation is a new method of signal analysis and synthesis, and its purpose is in over-complete dictionary of atoms using as little as possible to represent the signal. When using frequency atom dictionary signal sparse representation can effectively reveal the time-varying characteristics of non-stationary signals. Signal sparse representation attracted a lot of attention of researchers, this method has been applied to many aspects of signal processing, such as non-stationary signal analysis, signal encoding, recognition and signal denoising.)