wxpython-in-action

说明：  手把手带你学can 让你轻松学习can编程与单片机(Hands take you to learn can so that you can easily learn can programming and microcontroller)

EMD的LabVIEW实现及其在滚动轴承故障信号分析中的应用EMD方法的研究与应用EMD信号处理方法在LabVIEW和MATLAB中的实现(EMD of LabVIEW to its Research and Application EMD signal processing method is implemented in Roller Bearing Fault Signal Analysis Application EMD method in LabVIEW and MATLAB in)

This eBook is "Random Number Generation and Monte Carlo Methods".

Qt，和 wxWidget 一样，它也是一个标准的 C++库。但是它的语法很类似于 Java 的 Swing，十分清晰，而 且 SIGNAL/SLOT 机制使得程序看起来很明白——这也是我首先选 择 Qt 的一个很重要的方面，因为我是学 Java 出身的 :) 。不过，所谓“成也萧何，败也萧 何”，这种机制虽然很清楚，但是它所带来的后果是你需要使用 Qt 的 qmake 对程序进行预 处理，才能够再使用 make 或者 nmake 进行编译。并且它的界面也不是原生风格的，尽管 Qt 使用 style 机制十分巧妙的模拟了本地界面。另外值得一提的是，Qt 不仅仅运行在桌面环境 中，Qt 已经被 Nokia 收购，它现在已经会成为 Symbian 系列的主要界面技术——Qt 是能够 运行于嵌入式平台的。(Qt, and wxWidget, it is also a standard C++ library. But its syntax is very similar to Java' s Swing, very clear, and SIGNAL/SLOT mechanism makes the program seem to understand- and this is my first choice is a very important aspect of Qt, because I was born to learn Java :). However, the so-called " as also Xiao, also lost" , although this mechanism is clear, but its consequences is that you need to use Qt' s qmake program is preprocessed, it can be reused make or nmake to compile. And its interface is not the native style, despite the use of Qt style very clever mechanism to simulate the local interface. Also worth mentioning is that, Qt only run in the desktop environment, Qt has been acquired by Nokia, it has now become the main interface technology Qt Symbian series is the ability to run on embedded platforms.)

How to work with Computer Tomography scans.

说明：  深入理解C#(中文第三版)pdf文档 带目录清晰版(C# in depth)

胡广书的《现代信号处理教程》ppt文件，相当于电子版本了，因为很全面(Hu-book " Modern Signal Processing Guide" ppt files, the equivalent of an electronic version, because it is comprehensive)

国内的经典教材，谭浩强版本的，共分为十三个章节。(Domestic classic textbook,Hemopurification version, is divided into thirteen chapters.)

说明：  A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.

一个精简典型的的建模实例，进行了建模、加载和静力分析等基本步骤(A streamlined and typical modeling example with basic steps such as modeling, loading, and static analysis)