VSC_inv_nlinear

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.)

里面大多是C语言的经典算法,适合初学者学习,研究,算是入门吧-Inside most of the classic C language algorithms, suitable for beginners to learn, research, and be regarded as entry-bar

本课程介绍DHCP协议的详细内容。 课程目标 完成本课程的学习后，您应该能够：  了解DHCP协议的工作过程-courses on the DHCP agreement in detail. Programs targeted to be completed this course of study, you should be able to :# 61548 understanding of the agreement DHCP proce

第六章实验二（1）本程序为魔方阵.C 可用来求出所指定大小的魔方-sixth chapter two experiments (1) the procedure for the Demon phalanx. C can be used to specified sizes calculated by the Cube

解析ifc小工具，非常好用适配google浏览器(Parsing IFC widgets)

flash制作，互动按钮，成套应用才能得到最佳效果-flash production, interactive buttons, packaged applications can be best results

说明：  向滑动幻灯片是一款带箭头按钮跟数字标签的jquery滑块幻灯片(A sliding slide is a jQuery slider slide with arrow buttons and digital labels.)

CMemBlockArray模板类：按指定的块大小从系统中申请内存，然后以C语言数组的方式使用。VirtualQue模板类：对循环队列的索引操作进行封装，可以将轻易地将一个数组当作循环队列进行管理。

GRNN神经网络模型 可以预测各类数据 只需要载入自己的数据即可运行(GRNN BP optimization algorithm)

分形维数的matlab代码，应应用差分盒子方法进行计算分形维数(Alternatively, one can use FracLab directly to perform many basic tasks in signal processing, including estimation, detection, denoising, modelling,)