相位屏仿真方法.rar

此项目集成了vue，vue-cli，axios（进行了二次封装），element-ui，router

定时采集数据进SD卡和定时读取SD卡的数据示例程序（基于FATFS和STM32F407ADC）

Altium的PCB元件库,内容超多，且都是平常最有用的一些元器件，如表贴器件（FQP, QFN, SOP, TSOP, TSSOP, SON,...)，USB，无线天线，等等。 我只在Altium Winter 09（DXP 2008）中装入使用。其他如99SE，DXP200？没有测试。网友下载后可将使用结果在留言中说明。谢谢！希望大家喜欢。 以下是其中的文件列表：（200多个文件）Axial Lead Diode.PcbLibBGA_Rect.PcbLibBGA_Sq_100P.PcbLibBGA_Sq_127P.PcbLibBGA_Sq_150P.PcbLib

基于Matlab的SSIM算法实现（源码+注释，直接运行），其中包括输入3维图像运行出错的修改，还有源码中部分参数，格式不正确的修改，直接运行出结果。PS:网上一些源码说可以直接运行，实则Copy，并没有修改，深受其害，所以今天索性做了个修改完整版供大家分享。

LSD直线检测工具箱，可以通过配置，直接调用，详细的配置信息参见reco_toolbox目录下的read me.txt

这是我的毕业设计，一个基于Struts+hibernate框架做的网上书店系统，里面有源码（附数据库）、毕业论文、演讲ppt、任务书和开题报告，很全的，数据库用的是oracle，希望对大家有所帮助、、、

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

【实例简介】用FPGA实现红外循迹小车程序，用VHDL书写。能沿着黑色轨道前进

Altera FPGA/CPLD基础篇设计光盘例程

精通JSP-Web开发技术与典型应用随书光盘