lorenz

基于STM32的双向dc的软件算法，电流电压双闭环系统(Bidirectional DC based on STM32)

本文件为django的基础知识，包含django的入门技巧及学习路线，django入门与实践(This document is the basic knowledge of django, including Django introductory skills and learning routes, Django introductory and practical)

c++提示扩展工具代码-c prompt expansion tool code

一个关于RBF的神经网络算法，对学习RBF网络的同志有很大帮助和引导。-one of the RBF neural network algorithm, to learn RBF network comrades great help and guidance.

说明：  十天学会易语言图解教程,收藏使用，禁止商业化传播。(Ten days to learn easy language illustrated course, collection and use, prohibit commercial communication.)

在频率域进行高频增强和高频提升滤波，使用巴特沃斯滤波器(High frequency enhancement and high frequency lifting filter in frequency domain, using Butterworth filter)

matlab三水源新安江模型主程序源代码以及脚本程序(Matlab three water source Xin anjiang model main program source code and script procedures)

说明：  一本十分不错的关于光学图像的书籍，很适合新手(A very good book on optical images, very suitable for beginners)

Rotating shafts experience a an elliptical motion called whirl. It is important to decompose this motion into a forward and backward whil orbits. The current function makes use of two sensors to generate a bi-directional spectrogram. The method can be extended to any time-frequency distribution ％ ％ compute the forward/backward Campbell/specgtrogram ％ ％ INPUT: ％ y (n x 2) each column is measured from a different sensor ％ /////// ％ __ ％ |s1| y(:,1) ％ |__| ％ __ ％ / ________|/ ％ | | | s2 |/ y(:,2) ％ \____/ --------|/ ％ ％ Fs Sampling frequnecy ％ ％ OUTPUT: ％ B spectrogram/Campbel diagram ％ x x-axis coordinate vector (time or Speed) ％ y y-axis coordinate vector (frequency [Hz]) -an experience a elliptica l motion called whirl. It is important to decomp Rose this motion into a forward and backward whil orbits. The current function makes use of two se nsors to generate a bi-directional spectrogra m. The method can be extended to any time-freque ncy di

说明：  动态压缩感知（DSC）是压缩感知领域中一个重要的研究分支，它是近几年新兴起的一种信号处理与分析方法，与传统的压缩感知理论不同，DSC研究的对象是稀疏时变信号，并且已在视频信号处理和动态核磁共振成像等方面显示出了强大的应用潜力。本节正是在此基础上，提出了一种用于多普勒频率跟踪估计的DSC方法。首先，通过前一跟踪时刻所得到的先验DOA稀疏信息，获得当前跟踪时刻信号向量中各位置非零元素的分布概率，继而建立起动态DOA的稀疏概率模型。然后，采用加权l_1范数最小化方法重构出当前跟踪时刻的信号向量，从而确定非零元素的位置，获得DOA的实时估计值，最终实现运动目标的动态DOA跟踪。(L1 norm deals with DOA estimation)