BVP_tutorial

Matlab Neural network about Neural network and application

介绍了机器人的运动学的求解过程，对于初学者有很好的知道作用(Describes the process of solving robot kinematics, good for beginners to know the role of)

matlab codings for line coding

matlab应用于医学数字信号处理的外文参考文献，既有程序也有理论说明。(Digital signal processing (DSP) techniques, like Radon transformation, Projection techniques, Fourier transformation in polar form, Hankel transformation, etc., are used in Medical imaging techniques like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) during the process of imaging)

说明：  射频噪声干扰，噪声调频干扰，噪声调幅干扰模式的仿真以及SAR成像的仿真实现(several jammming modes simulation; SAR imaging)

Source separation problems in digital signal processing are those which are used to separate useful signal a mix of signals of interest, other signals and noise. The typical example of a source separation problem is the cocktail party problem, where many number of peoples are talking simultaneously in a room and a listener is trying to follow one of the discussions. The human brain can separate the voices of different people (source separation) using neurons in the auditory region. But source separation using digital signals is difficult task with the available techniques. Several approaches have been proposed for solving this issue but most of the techniques are still in development stages. Some of the successfully tested approaches are principal components analysis (PCA) and Independent components analysis (ICA). Both the approaches work well when there are no delays or echoes present

RLS波束形成代码，供大家参考学习，关于阵列信号处理的内容。(RLS beamforming code, for your reference learning, about the content of the array signal processing.)

遗传算法无功优化，小波包分析提取振动信号中的特征频率，插值与拟合的matlab实现。( Genetic algorithm based reactive power optimization, Wavelet packet analysis to extract vibration signal characteristic frequency, Interpolation and fitting matlab implementation.)

说明：  关于SAR回波仿真的模拟和RD成像算法。(Simulation of SAR echo simulation and RD imaging algorithm.)

Singularity detection for 1-D signal using wavelet maxima.