LPC-LPCC-Short-Time-Energy-master

说明：  功能 1、用不习惯GIT，专门写了这个小软件快捷备份数据到服务器，打包压缩加密当前文件夹到NAS盘或FTP服务器，小项目省事省心。 2、整合粘贴自动文本+-1，如粘一次是abc1，再粘一次是abc2 3、右下角图标点一下，复制年月日时分秒到剪切板 4、更多功能欢迎大家整合，制作撸码最强大的利器(function 1. Not used to git, specially wrote this small software to quickly backup data to the server, package, compress and encrypt the current folder to the NAS disk or FTP server, small projects save trouble and worry. 2. Integration paste automatic text + - 1, such as paste once is ABC1, paste again is ABC2 3. Click the icon in the lower right corner to copy the date, hour, minute and second to the clipboard 4. You are welcome to integrate more functions to make the most powerful weapon of luma)

ejemplo de codigo para proteger una presentacion en power point

a simple sample of bootstrap

说明：  1.此套程序适合没有支付宝接口和微信接口的个人用户，有企业接口的就忽略掉； 2.此套程序后台有码云第三方支付平台可以申请，但是支付后需要扣手续费，这个要注意一下； 3.如果自己有企业接口也只是想卖卡密可以使用咱们论坛的友价虚拟在线商城系统，里面可以出售卡密系统！ 运行环境： php5.3+Mysql 1.上传源码直接访问http://您的域名/install 2.按照提示安装，安装到最后别忘记点击一下升级数据库 3.安装完成！访问http://您的域名/admin就是你网站的后台(Kami sells source code)

说明：  proteus8.0d的基本使用方法，包括了从工程创建到完整的仿真图，适合小白入手(Proteus 8.0d basic usage method, including from engineering creation to complete simulation diagram, suitable for Xiaobai to start with)

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激光雷达点云数据拟合点云拟合icp算法(Point cloud fitting ICP algorithm for LIDAR point cloud data)

说明：  智慧港口建设方案，包括智能硬件和慧慧调度平台等的建设方案(Intelligent Port Construction Scheme, including Intelligent Hardware and Intelligent Dispatching Platform)

Multisim9shuzidianlu.rar, 压缩包解压时不能有密码。-Multisim9shuzidianlu.rar, compressed packets can not extract the password.

CN对图像进行像素级的分类，从而解决了语义级别的图像分割（semantic segmentation）问题。与经典的CNN在卷积层之后使用全连接层得到固定长度的特征向量进行分类（全联接层＋softmax输出）不同，FCN可以接受任意尺寸的输入图像，采用反卷积层对最后一个卷积层的feature map进行上采样, 使它恢复到输入图像相同的尺寸，从而可以对每个像素都产生了一个预测, 同时保留了原始输入图像中的空间信息, 最后在上采样的特征图上进行逐像素分类。(CN classifies images at the pixel level, thus resolving the problem of semantic segmentation at the semantic level. Unlike classical CNN, which uses full-connection layer to get fixed-length feature vectors after convolution layer for classification (full-connection layer + soft Max output), FCN can accept any size of input image, and uses deconvolution layer to sample feature map of the last convolution layer to restore it to the same size of input image, so that each pixel can be generated. At the same time, the spatial information of the original input image is retained. Finally, the pixel-by-pixel classification is carried out on the feature map sampled above.)