Robustdesign

说明：  用于CT中，去除金属伪影可靠算法（matlab平台）(Reliable algorithm for removing metal artifacts in CT (matlab platform))

说明：  小波神经网络的时间序列预测代码小波神经网络是结合 小波变换理论与人工神经网络的思想而构造的一种新的 神经网络模型，它结合了小波变换良好的时频局域化性质及神经网络的自学习功能，(Time series prediction code of wavelet neural networ Time series prediction using wavelet process neural network For example, Ding and Zhong used a wavelet process neural network to solve time series prediction problems)

手机版推箱子游戏，运行于展讯平台，测试通过。-Sokoban Games Mobile, running on Spreadtrum platform for the test.

地震解释处理matlab工具箱，由权威的CREWES小组发行，直接解压后复制到matlab的toolbox下就可使用(seismic interpretation Matlab toolbox, the authority issued CREWES group, unpacked directly copied to the Matlab toolbox can be used under)

基于LabWindows/CVI温度采样软件的设计(Based on LabWindows/CVI temperature sampling software design )

* 程序名称: NET-OA网络办公系统 版本：3.0 2003.08.29 * * 版权所有: Net-OA.com * * 程序设计: 江南行客-procedures* Name : NET- OA Network Office System Version : 3.0 2003.08.29** Copyright : Net-OA.com** Design : Jiangnan Customers

自己编的一个生物电信号处理平台，包括相干平均、滤波和傅里叶分析等方法。(Its own series of a biological signal processing platform, including the coherent average, filtering and Fourier analysis methods.)

说明：  abaqus等几何分析插件，基于非均匀有理样条函数(Isogeometric analysis plug-in for abaqus based on non-uniform rational spline functions)

自适应共轭梯度的算法研究简单案例，了解matlab语言的使用(Accelerated adaptive Perry conjugate gradient algorithms)

说明：  本书将向您介绍各种有监督和无监督的深度学习算法，例如多层感知器，线性回归和其他更高级的深度卷积和递归神经网络。您还将了解图像处理，手写识别，对象识别等更多信息。(This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.)