TD2new

ROI area identification, iteration, MCAA, zbthresh This program is simple, everyone can learn under

MATLAB 是一款数据分析和处理功能都非常强大的科技应用软件，利用它可以灵活方便地分析处理音频信号。文章介绍了用 MATLAB 软件处理音频信号的基本流程，并以实例形式列出了几款技术实现程序。(MATLAB is a data analysis and processing functions are very powerful application software, which allows flexible and convenient way of processing the audio signal. This paper introduces the audio signal processing using MATLAB software, the basic process and several examples were listed in the technology program.)

此文件是应用主元分析法进行故障信号处理的matlab语言，有很强的实用性，包括漂移故障，短路故障，开路故障等情况。(This file is the application of principal component analysis for fault signal processing matlab language, very practical, including drift fault, short circuit fault, open circuit failure.)

用电压电流双闭环控制电机的matlab模型，自己动手建的(With voltage and current double-loop motor control matlab model built yourself)

用matlab软件实现画一个凸轮的形状，并作出它的位移，速度，加速度曲线。(Matlab software used to draw a cam shape, and make its displacement, velocity and acceleration curves.)

rs编码仿真,搭配matlab工具箱的rs解码进行验证,绝对可用!(rs coding simulation, with matlab toolbox rs decoder to verify, absolutely free!)

将一个512×512的lena.bmp图像进行DCT变换，对得到的DCT系数按8×8块进行分层（分三层），重组填充得到新的系数矩阵，结果发现原图像的能量集中在了新图像的左上角低频部分。(The lena.bmp an image of 512 × 512 is subjected to DCT transform, the DCT coefficients obtained by layering 8 × 8 block (of three), to obtain a new recombinant filling coefficient matrix, the energy of the original image is found concentrated in upper left corner of the low-frequency part of the new image.)

贝叶斯压缩感知的仿真程序，此程序使用LAPLACE先验，(This is a simple example to test the fast Laplace algorithm from the following paper: S. D. Babacan, R. Molina, A. K. Katsaggelos. bayesian Compressive Sensing using Laplace Priors)

matlab7.0应用编程，中文资料，每章一个文件(Application Programming matlab7.0, English information, each devoted to a file)

Locally-Linear Embedding (LLE)[9] was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems. LLE also begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describe the point as a linear combination of its neighbors. Finally, it uses an eigenvector-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model.