Android studio 串口通信

C# ZedGraph实时多条曲线数据更新实例 具体 可以参考https://www.cnblogs.com/JiYF/p/11182658.html这里去查看效果 主要代码已说明，看懂就不要下载了，没有积分博客留言，给出邮箱，我给你发。

标定算法的仿真程序，包括Tasi和张正友两种方法的仿真，可以实现相机镜头的畸变校正

马尔科夫决策过程的matlab编程实现

仓储物资管理系统【SQLServer数据库+课程设计文档】1.需求分析 1.1系统目标与要求 1.2系统分析 1.3业务流程图 2.系统逻辑方案 2.1数据流程图（DFD）2.2数据字典（简称DD） 3系统总体结构设计 3.1软件系统总体结构设计 3.2数据存储的总体设计 3.3系统功能模块设计 4数据库详细设计 4.1数据库设计 4.2数据库的完整性和安全性 5.数据库操作（SQL Server2000语句完成） 5.1创建仓储物资管理系统 5.2创建各类别索引信息表 5.3建立存储过程向表中插入、修改、删除数据 5.3.1插入数据的存储过程 5.4建

也是刚学的matlab，学习GUI编程，还是直接看程序，写代码比较好。看书吧，程序敲下来都有不通的。自己结合别人代码稍微改了下，训练和测试库都带了，界面简单明了，可以运行。为了像自己一样的matlab小白，我还写了一个运行指导。简单但是对做识别整个流程很清晰的。

U盘防火墙 源代码解析 ，可以自动查出自动运行病毒，欢迎大家下载，并交流

主要分为整数倍频偏估计，小数倍频偏估计还有残余频偏的估计

SPWM 电压滞环 无差拍 周期性干扰补偿控制 仅供自己备份用

1、FM17550开发资料，复旦微的芯片，用于非接NFC开发2、NFC读卡器芯片3、包括Demo及芯片手册

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