MIMO-OFDM Wireless Coummunication with MATLAB代码整理，学习用。

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这是C++大学教程（C++ How To Program）第三版中文版课本和它的教师用书，就是封面是大蜜蜂的那本书，我们学校用的是第五版，但没有答案，第三版和第五版习题大同小异，可以对照题号找答案。

(Wind Atlas Analysis and Application Program)是在目前陆上风电场设计过程中应 用较为广泛的一种模型软件。 经8.3版本测试通过

STM32F0 HAL IAP 升级的嵌入式端参考代码，基于STM32CUBEIDE环境工程，以STM32F030K6T6为例，容易进行代码移植到STM32F0各系列。并另有STM32 IAP PC Software 上位机软件提供参考（https://download.csdn.net/download/hwytree/12839411）。

通过线性序列机对flash（m25p16）的PP指令时序代码进行编写和仿真，内含m25p16仿真模型文件，在sim文件夹flash_sim_lib中

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STM32F1 HAL IAP 升级的嵌入式端参考代码，基于STM32CUBEIDE环境工程，以STM32F103ZET6为例，容易进行代码移植到STM32F1各系列。并另有STM32 IAP PC Software 上位机软件提供参考（https://download.csdn.net/download/hwytree/12839411）。

用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets