7站定位，基于matlab声源定位

matlab Relief特征选择，在数据挖掘中选择有用的特征

1）通过话筒，利用声卡采集一段声音 2）显示该段声音的频率分析，分析特点，并存储起来 3）尝试根据存储的声音特色，区别不同的人 4）存储不同的声音，利用声卡实现回放

资源为项目目录，解压后即可直接用Android Studio打开。另，本项目已申请软件著作权，请合理使用。

用POCS方法对图像进行超分辨率重构,matlab源码.对学习超分辨率很有帮助.强烈推荐.

空域自适应旁瓣相消的算法及其实现空域自适应旁瓣相消的算法及其实现

南京光研出版的zemax宏语言讲义，包含多个设计实例。

系统辨识大牛Ljung编写的MATLAB系统辨识使用手册，这本书详细地介绍了在MATLAB已经所属simulink环境下，系统辨识工具箱的一些使用办法，是一本非常经典的教材！Revision Historypril 1988First printingJuly 1991Second printingMay1995Third printingNovember 2000 Fourth printingRevised for Version 5.0(Release 12)pril 2001Fifth printingJuly 2002Online onlyRevised for Version 5.0.2 Release 13)June 2004Sixth printingRevised for Version 6.0.1(Release 14)March 2005Online onlyRevised for Version 6.1.1Release 14SP2)September 2005 Seventh printingRevised for Version 6.1.2(Release 14SP3)March 2006Online onlyRevised for Version 6.1.3(Release 2006a)September 2006 Online onlyRevised for Version 6.2 Release 2006b)March 2007Online onlyRevised for Version 7.0 ( Release 2007a)September 2007 Online onlyRevised for Version 7.1 (Release 2007bMarch 2008Online onlyRevised for Version 7.2(Release 2008a)October 2008Online onlyRevised for Version 7.2.1 Release 2008b)March 2009Online onlyRevised for Version 7.3(Release 2009a)September 2009 Online onlyRevised for Version 7.3.1(Release 2009b)March 2010Online onlyRevised for Version 7. 4 (Release 2010a)eptember2010 Online onlyRevised for Version 7.4.1(Release 2010b)pril 2011Online onlRevised for Version 7.4.2(Release 2011a)September 2011 Online onlyRevised for Version 7.4.3(Release 2011b)March 2012Online onlyRevised for Version 8.0( Release 2012aabout the DevelopersAbout the Developersystem Identification Toolbox software is developed in association with thefollowing leading researchers in the system identification fieldLennart Ljung. Professor Lennart Ljung is with the department ofElectrical Engineering at Linkoping University in Sweden. He is a recognizedleader in system identification and has published numerous papers and booksin this areaQinghua Zhang. Dr. Qinghua Zhang is a researcher at Institut Nationalde recherche en Informatique et en Automatique(INria) and at Institut deRecherche en Informatique et systemes Aleatoires (Irisa), both in rennesFrance. He conducts research in the areas of nonlinear system identificationfault diagnosis, and signal processing with applications in the fields of energyautomotive, and biomedical systemsPeter Lindskog. Dr. Peter Lindskog is employed by nira dynamiAB, Sweden. He conducts research in the areas of system identificationsignal processing, and automatic control with a focus on vehicle industryapplicationsAnatoli Juditsky. Professor Anatoli Juditsky is with the laboratoire JeanKuntzmann at the Universite Joseph Fourier, Grenoble, france. He conductsresearch in the areas of nonparametric statistics, system identification, andstochastic optimizationAbout the developersContentsChoosing Your System Identification ApproachLinear model structures1-2What Are Model objects?Model objects represent linear systemsAbout model data1-5Types of Model objectsDynamic System Models1-9Numeric Models1-11umeric Linear Time Invariant (LTD Models1-11Identified LTI modelsIdentified Nonlinear models1-12Nonlinear model structures1-13Recommended Model Estimation Sequence1-14Supported Models for Time- and Frequency-DomainData,,,,,,,1-16Supported Models for Time-Domain Data1-16Supported Models for Frequency-Domain Data1-17See also1-18Supported Continuous-and Discrete-Time Models1-19Model estimation commands1-21Creating Model Structures at the command Line ... 1-22about system Identification Toolbox Model Objects ... 1-22When to Construct a Model Structure Independently ofEstimation1-23Commands for Constructing Model Structures1-24Model Properties1-25See als1-27Modeling Multiple-Output Systems ......... 1-28About Modeling multiple-Output Systems1-28Modeling Multiple Outputs Directly1-29Modeling multiple outputs as a Combination ofSingle-Output Models.......1-29Improving Multiple-Output Estimation Results byWeighing Outputs During Estimation ....... 1-30Identified linear Time-Invariant models1-32IDLTI Models1-32Configuration of the Structure of Measured and Noise oRepresentation of the Measured and noise Components foVarious model Types1-33Components ....1-35Imposing Constraints on the Values of ModeParameters1-37Estimation of Linear models1-8Data Import and Processing2「Supported Data ...2-3Ways to Obtain Identification DataWays to Prepare Data for System Identification ... 2-6Requirements on Data SamplingRepresenting Data in MATLAB Workspace·····Time-Domain Data Representation2-9Time-Series Data Representation2-10ContentsFrequency-Domain Data Representation ....... 2-11Importing Data into the Gui2-17Types of Data You Can import into the GUi2-17Importing time-Domain Data into the GUI2-18Importing Frequency-Domain Data into the GUI2-22Importing Data Objects into the GUI ......... 2-30Specifying the data sampling interval2-34Specifying estimation and validation Data2-35Preping data Using Quick StartCreating Data Sets from a Subset of Signal Channelo2-362-37Creating multiexperiment Data Sets in the gUi2-39Managing data in the gui ............. 2-46Representing Time- and Frequency-Domain Data Usingiddata object2-55iddata constructor2-55iddata Properties.........2-58Creating Multiexperiment Data at the Command Line .. 2-61Select Data Channels, I/O Data and Experiments in iddataObjects2-63Increasing Number of Channels or Data Points of iddataObjects2-67Managing iddata Objects2-69Representing Frequency-Response Data Using idfrdObiec2-76idfrd Constructor2-76idfrd Properties2-77Select I/o Channels and Data in idfrd Objects ..... 2-79Adding Input or Output Channels in idfrd Objects2-80Managing idfrd Objects2-83Operations That Create idfrd Objects2-83Analyzing Data quality2-85Is your data ready for modeling?2-85Plotting Data in the guI Versus at the command line2-86How to plot data in the gui2-86How to plot data at the command line2-92How to Analyze Data Using the advice Command2-94Selecting Subsets of Data2-96IXWhy Select Subsets of Data?2-96Extract Subsets of Data Using the GUI2-97Extract Subsets of data at the Command Line2-99Handling Missing Data and outliers2-100Handling missing data2-100Handling outliers2-101Extract and Model Specific Data Segments2-102See also2-103Handling offsets and Trends in Data2-104When to detrend data2-104Alternatives for Detrending Data in GUi or at theCommand-Line2-105Next Steps After detrending2-107How to Detrend Data Using the Gui2-108How to detrend data at the Command line2-109Detrending Steady-State Dat109cending transient Dat2-109See also2-110Resampling Data2-111What Is resampling?...,,.,,,,,,,,,,,.2-111Resampling data without Aliasing Effects2-112See also2-116Resampling data Using the GUi.,,,,2-117Resampling Data at the Command line2-118Filtering Data2-120Supported Filters2-120Choosing to Prefilter Your Data2-120See also2-121How to Filter Data Using the gui2-122Filtering Time-Domain Data in the GuI........ 2-122Content

多图像超分辨率的实现主要就是将具有相似而又不同却又互相补充信息的配准影像融到一起，得到非均匀采样的较高分辨率数据，复原需要亚像素精度的运动矢量场，然而它们之间的运动模型估计精确与否直接影响到重建的效果，因此影像配准和运动模型的估计精度是高分辨率图像重建的关键。由于实际中不同时刻获得的影像数据间存在较大的变形、缩放、旋转和平移，因此必须对其进行配准，在此基础上进行运动模型估计。然后通过频率域或空间域的重建处理，生成均匀采样的超分辨率数据

介绍EM算法，EM算法的基本思路，EM算法源代码

【实例简介】抽象代数即近世代数。 　　代数〔Algebra〕是数学的其中一门分支，当中可大致分为初等代数学和抽象代数学两部分。 　　初等代数学是指19世纪上半叶以前发展的方程理论，主要研究某一方程〔组〕是否可解，如何求出方程所有的根〔包括近似根〕，以及方程的根有何性质等问题。