testingbearing5

一个用于模拟人的听觉神经系统的工具包，由meddis开发。主要模拟从外部刺激到机电过程转换后的过程。(One for the nervous system of human hearing simulation toolkit developed by the meddis. Main simulation process from the external stimulus to the electromechanical conversion process.)

说明：   <小波分析理论与MATLAB 7实现>是“MATLAB应用技术”系列丛书之一，以最新推出的MATLAB中的小波分析工具箱Wavelet Toolbox 3.0版本为基础。全书共分为三部分，第1部分着重介绍了小波理论基础，包括小波基础知识、连续小波变换、离散小波变换、多分辨率分析与正交小波变换、小波变换和多采样滤波器组、二维小波变换与图像处理及小波包的基本原理等；第2部分重点说明了小波分析工具箱的详细使用方法，包括图形用户接口、小波通用函数、一维小波变换的MATLAB实现、二维小波变换的MATLAB实现、小波包变换的MATLAB实现、信号和图像的降噪和压缩，以及最新的信号和图像的提升小波变换等内容；第3部分主要介绍了小波工具箱的应用基础，以及小波变换在语音和生物医学信号处理中、故障诊断中、数字水印中的应用方法。 本书可作为理工科各专业的高年级本科生、研究生学习小波分析的辅助教材，也可作为研究和应用这一领域的科技工作者的参考书。 (err)

用matlab分析数值的，有拉个朗日插值、三次样条插值法、NEWTON插值、hermite插值、mewton形式的hermite插值、平方根法、gauss消去法、三角分解法、jacobi迭代法、 gauss迭代法、sor迭代法、最速下降法、共额梯度法、mewton迭代法、逆broyden迭代法。一共15段代码。 (Numerical analysis using matlab, there are a Long Day interpolation, cubic spline interpolation, NEWTON interpolation, hermite interpolation, mewton form hermite interpolation, square root method, gauss elimination, triangular decomposition, jacobi iterative method, gauss iterative method, sor iterative method, steepest descent method, a total amount of gradient method, mewton iterative method, inverse broyden iteration. A total of 15 sections of the code.)

这是用于工程测量进行大坝变形分析的软件，比较有价值，欢迎试用。(This is for engineering measurements dam deformation analysis software, valuable, welcomed the trial.)

基于辨识ARMA模型的野值剔除方法与卡尔曼滤波修正算法(ARMA model identification methods and eliminate outlier correction algorithm based on Kalman filter)

cooperative communication sources.

图像分割的方法有许多种，其中阈值分割的方法很常用，提供一种基于otsu算法的图像分割方法，很实用。(There are many kinds of method of image segmentation, threshold segmentation method is commonly used, provide a method of image segmentation based on otsu algorithm is very practical)

重要参数的提取，包括单边带、双边带、载波抑制及四倍频,使用混沌与分形分析的例程。( Extract important parameters, Including single sideband, double sideband, suppressed carrier and quadruple, Use Chaos and fractal analysis routines.)

K_average聚类分析程序，包含多个子文件，适用于模式识别和聚类分析(K_average)

The local binary pattern (LBP) is a simple yet very efficient operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, for example, by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. LBP is also very flexible: it can be easily adapted to different types of problems and used together with other image descriptors