Lyapunov-exponents

说明：  一款使用纯CSS3制作的鼠标滑过图片遮罩层动画特效(A special animation effect using a pure CSS3 mouse to slide over the image mask layer)

说明：  本程序为主成分分析PCA方法实现数据的降维(Principal component analysis)

经典等温线（等高线）绘制算法，以Matlab编写。(Classic isotherms (contour lines) rendering algorithm in order to prepare Matlab.)

VB仿Windows可显示行号的记事本程序,有需要的下。(VB notepad application in imitation of a Windows to display line Numbers, in need.)

vc小计算器,实现加减乘除功能.-vc small calculators, and the arithmetic function to achieve.

说明：  用Matlab实现钢琴演奏卡农，运行matlabpiano.m即可(MATLAB Playing Canon)

说明：  数组数据类型的相关指令和知识点以及相关的面试题(Relevant instructions and knowledge points of array data types and related interview questions)

采用低功耗 MCU 处理器， 及低功耗 G SENSOR 加速度传感器， 成熟的计步算法， 计步精度达 95%。(With low power MCU processor and low power G SENSOR acceleration sensor, mature step counting algorithm achieves a step counting accuracy of 95%.)

西安交大最新关于小波的论文，对初学者帮助非常大，请大家多多支持-Xi"an Jiaotong University on the latest wavelet paper for beginners great help, we can support

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.)