基于分数阶傅里叶变换的参数估计

归一化多通道LMS自适应盲辨识算法

流程图绘制VISIO模板流程图绘程图VISIO模板设计

【实例简介】宾馆管理信息系统 系统简介： 宾馆在正常的运营中需要对客房资源、顾客信息、结算信息进行管理，利用宾馆管理信息系统及时了解各个环节中信息的变更，有利于提高管理效率。系统开发的总体任务是实现宾馆各种信息的系统化、规范化和自动化。 限制条件： 有关客房标准的制定、标准信息的输入，包括标准编号、标准名称、房间面积、床位数量、住房单价、是否有空调、电视机、电话以及单独卫生间等。 客房标准信息的修改、查询等。 客房基本信息的输入，包括客房编号、客房类型、客房位置、客房单价、备注等。 客房基本信息的查询、修改，包括客房编号、客房类型、客房位置、客房单价、备注等。 剩余客房信息的查询等。 订房信息的输入，包括客房编号、客房种类、客房位置、客房单价、顾客姓名、顾客身份证号码、入住日期、折扣、备注信息等。 订房信息的修改和查询，包括客房编号、客房种类、客房位置、客房单价、顾客姓名、顾客身份证号码、入住日期、折扣、备注信息等。 结算信息的输入，包括客房编号、客房种类、位置、客房单价、顾客姓名、顾客身份证号码、入住日期、折扣、结算日期、备注信息等。 结算信息的修改和查询，包括客房编号、客房种类、客房位置、客房单价、顾客姓名、顾客身份证号码、入住日期、折扣、结日期、备注信息等。

该程序需要在主页面先下载wall_delay程序，该程序在墙后建立了长为2米，宽为0.6米的矩形，利用后投影算法以及快速时延估计进行成像。并且计算最佳带宽，最佳中心频率，阵列天线数量，陈列天线间隔。使得成像质量很好。缺点在于：计算量偏大。

变尺度随机共振的源代码，包括调用的龙哥库塔方法函数代码，百分百管用

描述姿态解算三种方法 四元数 欧拉角法 方向余弦法 并结合卡尔曼滤波解算出姿态角

甘地大学电子专业Ray Ranjan Varghese设计的FPGA实现FFT，采用的是单精度的浮点，采用IEEE745格式的浮点+ROM RAM的方式成功实现FFT，含有设计报告和设计源代码，并有测试文件，真的很不错。

稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57

空时编码，包含空时分组码，空时网格码，分层空时码的的matlab程序，包含编码和译码详细代码。

该例程实现读写SD卡文件的功能。移植了FatFS文件系统。