KS_RS_SPXY

说明：  在matlab下用神经网络来识别数字1到9(matlab number recognition)

英语译成中文(简体) 所提供的功能解决在量子信息领域的共同问题。给定一个任意多粒子的状态，是真正的多方纠缠？如果被检测为纠缠状态，功能也为它提供了一个所谓的纠缠的见证。 另外一个文档，压缩文件中包含的检测和真正的多方纠缠量化功能。为此目的，乙Jungnitsch，吨Moroder和O. Gühne，物理的思想。牧师快报。 106，190502（2011）（或http://arxiv.org/abs/1010.6049）被应用。 更确切地说，提供下列文件： * fdecwit.m 一个函数来检测任何一个国家真正的多方层面多方纠缠。如果国家被检测为纠缠，它也提供了一个证人。 * fpptwit.m 此功能还可以检测真正的多方纠缠，并提供了证人。然而，所涉及的最小化是那么复杂。因此，虽然它检测到的国家较少，这个功能是一个可行的颗粒较多。 * entmon.m 评估纠缠单调在Phys主办。牧师快报。 106，190502（2011年）的多方状态。 * pt.m 辅助函数来计算一个Hermitian算子部分转置。(The provided functions tackle a common problem in the field of quantum information. Given an arbitrary multiparticle state, is it genuinely multipartite entangled ? If the state is detected as entangled, the functions also provide a so-called entanglement witness for it. Besides a documentation, the zip-file contains functions for the detection and quantification of genuine multipartite entanglement. For this purposes, the ideas of B. Jungnitsch, T. Moroder and O. Gühne, Phys. Rev. Lett. 106, 190502 (2011) (or http://arxiv.org/abs/1010.6049) are applied. More precisely, the following files are provided: * fdecwit.m A function to detect genuine multipartite entanglement of a multipartite state of any dimension. In case the state is detected as entangled, it also provides a witness. * fpptwit.m This function also detects genuine multipartite entanglement and provides a witness. However, the involved minimization is less complex. Therefore, although it detects fewer states, this f)

matlab isolated word recognition database

关于MATLAB的电力电子仿真，自己收藏吧，值得收藏(About the MATLAB simulation of power electronics, his collection of it, worthy of collection)

说明：  滤波的常用方法,数字图像处理的常用滤波算法，试过，可以用！(Common method of filtering, digital filtering algorithm commonly used in image processing, tried, you can use!)

混合高斯建模读取图片序列，前景背景比较(Gaussian mixture modeling reading the image sequence, foreground and background)

特征提取是模式识别中最重要的一个部分，这里提供的matlab代码计算一些基本的话音特征，而且，提供了一个简单的基于直方图的特征分类方法的实现。基本的特征包括：特征统计、Energy Entropy Standard Deviation (std)、均值信息熵、过零率检测、频谱衰减等等六个特征(Pattern recognition feature extraction is the most important part, here' s matlab code to compute some basic voice features, but also provides a simple classification method based on histogram characteristics of the implementation. Basic features include: Features and Statistics, Energy Entropy Standard Deviation (std), mean entropy, the zero-rate detection, attenuation, and so the six characteristics of the spectrum)

Start with the runlocalization track.m which is the entrance function to your lab. This function reads two les determined by simoutle and maple input arguments which contain information about sensor readings and the map of the environment respectively, runs a loop for all the sensor readings and calls the ekf localize.m to perform one iteration of EKF localization on the readings and plots the estimation(red)/ground truth(green) and odometry(blue) information.(This lab consists of two parts: 1. A preparatory case study with a standard Kalman lter where you learn more about the behavior of the Kalman lter. Very little extra code is needed. 2. The main lab 1 problem in which you need to complete an implementation of an Extended Kalman lter based robot localization.)

说明：  用matlab程序编写，计算分形图像的计盒维数。(Programming using matlab to calculate the fractal box dimension image.)

THE FASTEST WAY TO SORT N NUMBERS sortN library