method_TM_2D

采用MVM，MUSIC，MEM，MNM四种方法进行空间谱估计(adopt MVM，MUSIC，MEM，MNM to estimate target space spectrum)

说明：  基于Labview与Matlab混合编程的应用研究实例(Examples of applied research based on Labview and Matlab)

一级倒立摆的鲁棒控制matlab仿真，利用LMI解出的控制器K的值(A Robust inverted pendulum control matlab simulation, using a value LMI solve the controller K)

Decompose image into subbands (undecimated wavelet), denoise, and recompose again. fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig) im : image Nsc: Number of scales daub_order: Order of the daubechie fucntion used (must be even). block: size of neighborhood within each undecimated subband. noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise) parent: are we including the coefficient at the central location at the next coarser scale? covariance: are we considering covariance or just variance? optim: for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0) sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise) Javier Portilla, Univ. de Granada, 3/03 Revised: 11/04

说明：  检验金融时间序列的单个自相关函数（individual ACF）。计算每一阶的t-ratio值。(Testing Individual ACF)

Code to calculate the RSS of a signal

Slycot Library for linear system utitlities

Kernel自适应滤波器算法是基于Kernels的在线自适应回归算法。非常适合非线性滤波，跟踪和回归。该工具箱包括算法，演示和性能比较工具(Kernel adaptive filtering algorithms are online and adaptive regression algorithms based on kernels. They are suitable for nonlinear filtering, prediction, tracking and nonlinear regression in general. This toolbox includes algorithms, demos, and tools to compare their performance.)

THIS PROJECT IS ON COMPUTER VISION AND TO PERFORM DIFFERENT OPERATIONS ON GIVEN IMAGE FILE.(I.E GETTING IMAGE INFORMATION AND LAPLACIAN TRANSFORMATION OF GIVEN IMAGE)

先得到一个定位初始估计值，再利用建立最大似然方程对这个定位初始估计值进行修正，从而得到更优定位结果值，方法更接近于最大似然方法。(Get the a position initial estimate, re-use to establish the maximum likelihood equations corrected for positioning the initial estimates, resulting in better positioning the resulting value is closer to the maximum likelihood method.)