NEW_APPROACH

该载波同步方案可应用于低信噪比和高动态同存的条件下 接收机设计中,通过对该方案进行MATLAB 仿真,结果表明了该方案能有效地解决了载波快速捕获与精确跟踪的 问题。(The carrier synchronization scheme can be applied with low SNR and high dynamic receiver design under the conditions of existence, through the program MATLAB simulation results show that the scheme can effectively solve the fast carrier capture and accurate tracking of the problem .)

这是一个Matlab实现的PWM型直流变换器的Simulink仿真，变换方式是Buck-Boost变换方式。示例，提供了一个从5伏到25伏电压，的升压变换。(This is a Matlab Simulink simulation of PWM DC-DC converter, and transform the way Buck-Boost transform. Example, from 5 volts to 25 volts, the boost transformation.)

改良后的下垂控制，效果还行，仅供参考.用于并网逆变器控制。(Droop control improved, and the results were OK, for reference purposes only. Used in grid inverter control.)

模型是机动模型的卡尔曼滤波的程序及其应用(Model is the motor model Kalman filter process and its application)

多元逐步回归算法,在matlab软件中的运用,简单实用 (multiple stepwise regression algorithm in Matlab software on the use of simple and practical)

此文件夹为七单元线阵的波达方向（music罚法）估计程序，包含不加平滑的估计程序和加上前向平滑的估计程序。程序中每部分都有详细的中文注释，非常容易理解，特别适合于初学者。是本人学习波达方向估计时的心得，希望对大家有帮助。(This folder linear array of seven units of DOA (music Penalty Act) estimation procedures, including without smoothing estimation procedures and smooth with the forward estimates process. Each part of the program are detailed notes in Chinese, is very easy to understand, especially for beginners. DOA estimation is when I am learning experiences, we want to help.)

matlab 电力电子 逆变器 比例谐振控制 RCD负载(matlab power electronics inverter control RCD load ratio of the resonance)

matlab simulink model of an uninterrupted power supply

可以对脑电信号进行预处理，并且可以建立脑网络(EEG signal processing)

说明：  不带像素操作的机器视觉代码-The brain transforms and discards pixels. The transformation is a smoothing and differencing filter. The output of an individual filter is ambiguous.The output of 5 filters lets us find edges and corners. A small number of filters covers the entire image. This method is fast and discards irrelevant information(Without pixel machine vision operation code-The brain transforms and discards pixels. The transformation is a smoothing and differencing filter. The output of an individual filter is ambiguous.The output of 5 filters lets us find edges and corners. A small number of filters covers the entire image. This method is fast and discards irrelevant information)