wavelet-transformation

Program is used to integrate a surface using data points in cylindrical coordinates. Some errors exist that compromise the solution.

自己编写的地震资料中地震褶积模型的建立，每一个部分都有标注，很清楚适用于初学者(I have written in the seismic data seismic convolution model, each part has a label, it is clear for beginners)

pid是个很实用的matlab模块，大家学习啊(pid)

Image processing Basic Programms -2 using Matlab(Image processing Basic Programms-2 using Matlab)

地震动反应谱计算与分析，该程序为精确法求解反应谱(Spectrum of ground motion calculation and analysis, the program for the precise response spectrum method)

对于给定的贝叶斯网络和数据，寻找最可能的决策。 (Find the most likely decision given a Bayesian belief network and data )

说明：  此程序包括MATLAB的程序，对大家学习Matlab非常有用，特别适合初学者，欢迎大家下载(This program includes MATLAB program to all of us to learn Matlab is very useful, especially for beginners, are welcome to download)

学数字图像处理课程做的一个大作业，车牌识别的，用matlab编程实现，并采用了自适应阈值法，最后用模板匹配的方法实现了车牌的自动识别功能(Learn digital image processing program to do a great job, license plate recognition with matlab programming, using the adaptive threshold method, and finally with the template matching method to achieve the automatic license plate recognition)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)

Implement FFT and IFFT 16 point using Matlab Simulink.