Python and OpenCV for starter

这是一段应用于opencv2.0以上版本，调用USB摄像头的源码。

基于opencv 2.4 ，运行需要配置opencv库。具体过程可参考网络配置教程。在程序目录文件夹下，输入2张图片，分别命名为，1.jpg    2.jpg   。运行程序，显示surf特征点。用于图像匹配。

深度置信网络DBN，深度学习，神经网络，分类(deep belief network(DBN), deep learning, neural network, classification)

说明：  Python爬虫，自动登录京东网站，查询商品库存，价格，显示购物车详情等。 可以指定抢购商品，自动购买下单，然后手动去京东付款就行。(Python crawler, automatically log into Jingdong website, query commodity inventory, price, display shopping cart details, etc. You can specify the goods to be snapped up, place an order automatically, and then go to Jingdong to pay manually.)

基础的强化学习Q-learning算法，对初学者对Q-learning算法得理解比较有帮助，程序包括运行脚本，Q-learning算法脚本以及环境脚本。(Basic reinforcement learning Q-learning algorithm is helpful for beginners to understand Q-learning algorithm. Programs include running script, Q-learning algorithm script and environment script.)

使用opencv来进行视频的播放，可以播放绝大部分格式的视频(Use opencv for video playback, you can play most of the video formats)

Brayton cycle heat balance efficiency calculation program, used to calculate the efficiency of the helium Brayton cycle, there are matlab and python two language versions

说明：  我们描述了所提出的方法对超声（US）信号的压缩多路复用的直接应用。该技术利用压缩多路复用器架构进行信号压缩，并依靠频域中US信号的联合稀疏性进行信号重建。由于换能器元件具有压电特性，因此可以获得有关US信号频率支持的准确先验知识，并且可以在联合稀疏算法中使用。 我们在数值实验中验证了所提出的方法，并显示了它们在秩次缺陷情况下相对于最新方法的优越性。我们还证明，与没有已知支持的重建相比，该技术可显着提高体内颈动脉图像的图像质量。(We describe a direct application of the proposed methods for compressive multiplexing of ultrasound (US) signals. The technique exploits the compressive multiplexer architecture for signal compression and relies on joint-sparsity of US signals in the frequency domain for signal reconstruction. Due to piezo-electric properties of transducer elements, accurate prior knowledge of the frequency support of US signals is available and can be used in joint-sparse algorithms. We validate the proposed methods on numerical experiments and show their superiority against state-of-the-art approaches in rank-defective cases. We also demonstrate that the techniques lead to a significant increase of the image quality on in vivo carotid images compared to reconstruction without known support.)

现将图像进行转化为单通道，在对单通道的图像处理，统计图像中像素点的分布情况，为后续的均衡化等提供支持，也可以在图像处理中减小数据的量，从而加快运行时间或者算法的时间，从实际上来说彩色图像是3通道，灰度是单通道，但是灰度图像仍保留了大量的信息。此例子也可以为初学者做一个展示。

machine learning 方面有关于The Bayes Classifier的资料，语言是python(machine learning aspects have The Bayes Classifier information about language is python)