2016-1-IEEE-TVT-3-VehicularFogComputing

模拟轧制，斜轧；两辊斜轧；热态轧制；斜轧成型(Simulated rolling, cross rolling)

一款Bootstrap垂直手风琴折叠菜单。该Bootstrap垂直手风琴折叠菜单使用boostrap网格进行布局(A Bootstrap vertical accordion folding menu. The Bootstrap vertical accordion folding menu is laid out using boostrap grid)

说明：  该代码包括单隐含层BP和双隐含层BP。建立基于BP神经网络的预测模型，对数据进行随机排列，选取训练样本和测试样本，训练样本训练网络，测试样本进行验证(The code includes single hidden layer BP and double hidden layer BP. Establish a prediction model based on BP neural network, arrange the data randomly, select training samples and test samples, train the training network of training samples, and verify the test samples.)

说明：  Best-Buy-Stop-Sell-Stop-EA

VC界面 悬浮窗口 很经典的代码,有需要的同学可以用一用和学一学啊()

在proteus下可以实现小车的仿真，演示功能(It can realize the simulation of the car under the proteus, demonstration function)

永远碰不到的小球 Flash版，因为你的鼠标无论移向哪里，小球都会躲着你，永远不会让你的鼠标碰触到它们，真的挺有意思，基于Flash ActionScript动作脚本编写而成的Flash特效。

利用局部二值模式（LBP）特征提取方法，svm分类器分类进行图像分类的matlab程序源码(Using local binary pattern (LBP) feature extraction method, svm classifier classification image classification matlab program source code)

使用SVM分类器来预测乳腺癌病人的预后(特征选择；分类器构建)，评价模型时使用无被交叉验证，性能评价指标包括准确率，AUC，灵敏度，特异度。学会最基本的机器学习方法。可查看分发给大家的代码，以后遇到类似的问题，可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; classifier construction), and the model was used without cross-validation. Performance evaluation indicators included accuracy, AUC, sensitivity, and specificity. Learn the most basic machine learning methods. You can view the code distributed to everyone, and later encounter similar problems, similar ideas and code can be used.)

说明：  利用 GUI 界面设计滤波器设计，有点难，但具有参考价值(Use GUI interface design, filter design, hard, but the reference value)