边缘检测算法比较以及统计细胞数目，基于细胞图片

Tecplot360脚本指南,讲述tecplot高级应用方面的，想细致研究tecplot可以看下

MCP3421使用STM32实现电压采集，可以选择多种模式。

JAVA指纹识别（uaru4000B指纹仪）采集与 验证功能放在tomcat下就可以实现access数据库

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

一个基于Python的百度文库爬虫，主要实现文库pdf文件的下载。

【实例简介】全国天气的模板界面，可用于后台界面首页，高端大气上档次

蓝牙（Bluetooth）是一种短距离无线通信技术，利用“蓝牙”技术，能够有效地简化掌上电脑、笔记本电脑和移动电话手机等移动通信终端设备之间的通信，也能够成功地简化以上这些设备与Internet之间的通信，从而使这些现代通信设备与因特网之间的数据传输变得更加迅速高效，为无线通信拓宽道路。说得通俗一点，就是蓝牙技术使得现代一些轻易携带的移动通信设备和电脑设备，不必借助电缆就能联网，并且能够实现无线上因特网，其实际应用范围还可以拓展到各种家电产品、消费电子产品和汽车等信息家电，组成一个巨大的无线通信网络。

镜像延拓法消除EMD中端点效应，可更好地帮助学习理解EMD

CoDeSys 是一种功能强大的PLC软件编程工具，它支持IEC11313-3标准IL 、ST、 FBD 、LD、 CFC、 SFC 六种PLC编程语言，用户可以在同一项目中选择不同的语言编辑子程序，功能模块等。　　CoDeSys是可编程逻辑控制PLC的完整开发环境（CoDeSys是Controlled Developement System的缩写），在PLC程序员编程时,CoDeSys为强大的IEC语言提供了一个简单的方法，系统的编辑器和调试器的功能是建立在高级编程语言的基础上（如Visual C++）。　　CoDeSys软件还可以编辑显示器界面，具有很多的控制模块，可以放置图片等强大的

这是我的毕业设计，用到的语言有：jsp+java+struts+json+jquery+mysql数据库这个项目有数据库，导入可以运行！这个项目有前台和后台，前台系统能实现购物车，订单，留言板，等模块！后台模块能实现用户管理，商品添加和删除，留言板管理，超级链接管理等功能！基本功能还是不错的！