lstm自动编码器以进行异常检测：使用LSTM自动编码器检测异常并对稀有事件进行分类-源码

这是基于最大熵的图像分割代码，针对灰度图像的

【实例简介】该车牌识别训练样本包括了所有的字符，数字0~9，和26个英文字母（由于我国车牌中英文字母没有‘I’和‘O’，所以实际是24个英文字符）。 所以共有34个文件夹。 每个文件夹中有50个图片。（即有50个不同的0，50个不同的1......） 该训练样本对车牌识别进行训练有很大帮助。

压缩包里面包括AD7616(datasheet,sch,pcb,project.)整套demo资料，可以自己制作demo测试

三菱温度PID控制实例及程序

LCD显示、LCD显示波形、AD采集、DA转换、IIC协议EERO、PS2键盘VGA钢琴、PS2键盘解码、PS2鼠标实验、SPI协议DS1302、VGA显示驱动、并行流水灯、拨码流水灯、串口收发、独立按键消抖动、蜂鸣器奏音乐、红外遥控通信、数码管动态显示、数码管静态显示、贪吃蛇、温度传感DS18B20……

GNSS惯性导航组合(第3版) Global Navigation Satellite Systems, Inertial Navigation, and Integration, Third EditionISBN 9787121278754配套MATLAB源代码Mohinder S. Grewal, California State University at FullertonAngus P. Andrews, Rockwell Science CenterChris G. Bartone, Russ College of Engineering and Technology

西安电子科技大学微机原理课程设计，吐血推荐，用8255实现开屏锁屏，设定密码，并且输入密码，然后输入的实验，会很有用的。

无人驾驶汽车系统入门 - Autoware_UsersManual_v1.1Autoware-用于城市自主驾驶的集成开源软件，支持以下功能：3D本地化3D映射路径规划路径跟随加速/制动/转向控制数据记录汽车/行人/物体检测交通信号检测交通灯识别车道检测对象跟踪传感器校准传感器融合面向云的地图连接自动化智能手机导航软件仿真虚拟现实Autoware受BSD许可证保护。请自行负责使用。为了安全使用，我们为不拥有真正自主车辆的人提供基于ROSBAG的仿真方法。如果您使用Autoware与真正的自主车辆，请在现场测试之前制定安全措施和风险评估。工尹-刁一定義亡机飞求世。Chapter1。 About This documentThis chapter describes the purpose of this document.Autoware Users ManualAutoware Developers manualChapter2。 RoS and AutowareBefore operating Autoware, RoS and Autoware are described in this chapter.Robot middleware- rosecently, the wide range potential of robotics has been focused by not only roboticsexperts but also non-robotics experts to join robot development It is believed thatthis trend leads robotics to advancement and developments to other domainsHowever, robot development is getting harder because the advancement and the complexityof robot functions have been increased Unlike Pcs and smartphones robotic developmenthas considered various hardware, OS, programming languages. Hence, the differences havemainly been obstructed for robotic developers as well as robotics experts to join robotdevelopmentTo solve the problem, the demand of making common platforms has been increased, andsome platforms have been published Within a common platform, developers can combinevarious software published by other developers, and speed up development by reusing themTherefore, it is expected that developers can more focus on fields of interestROS (Robot Operating System) is a framework for robotic software development. It wasdeveloped by Willow Garage in U.S. OSFR (Open Source Robotics Foundation) has currentlymaintained it. The open sourced ros has mainly been used by communities in U.S. andEurope as well as communities in JapanNote that ros has "os" in its name however, it is not "os" like Windows or linux. It is amiddleware that runs on uniX based OSROS FeaturesOriginal build system(Catkin)mage processing library(OpenCvv Data logging tool (ROSBAG)v Visualization tools for data and software state(rvizCoordinate transformation library (TFQt based gul development tool (RQTA""file is used to start multiple nodes atAutowareutoware is open source software based on ros. autoware is pushed on github forautonomous driving research and development. Most of autonomous drivingsystem consist of recognition, judgment, and operation. Autoware providesnecessary functions, such as 3-D map generation, localization, object recognition, andvehicle control, for autonomous drivingROS PCAutoware〔Ros)RecognitionJudgmentoperationObject DetectionLane KeepingAcceleratorLocalizationIntersectionBrakeSteeringAutowareRiderAutoware routePath PlanningVehicleVehicle control(CAN)CFigure 1 Autoware overviewwhile support systems such as driving assistance and safetydiagnosis support, use multi-core CPU3-D Map Generation and SharingLocalization(NDT: Normal Distributions Transform)object DetectionPath GenerationAutonomous Drivingtarget speed. In addition, the route includes landmarks, "way point, setintervals. The autonomous driving system operates path following by following the wayUser InterfaceA user interface called " Runtime Manager ofpath following. Furthermore, a tablet user interface, "Autoware Rider, of Autoware enablesROS PCAutoware(ROS)Runtime ManagerVIZAutoware ridertabletDisplay on vehicleOculusFigure 2 User interfacePlatform structure for autowareApplicationAutowareMiddlewareROSHost OsLinux( UbuntuCPUGPUCameraGNSSLIDARFigure 3 Platform structure for Autoware

FIR数字滤波器的设计和实现 Matlab实现原函数：x(n)=s1(n-n1)+s2(n-n2)+s3(n-n3)1将原函数x(n)加个随机噪音，表示出其图像.2用滤波因子过滤噪音函数后的图像

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