Digital_Signal_and_Image_Processing_Using_MATLAB_

数学建模清华数模讲义\model3b (Mathematical modeling Qinghua digital-analog handouts model3b)

Software radio Digital Down Converter Aliasing Nyquist FR front end

This is a matlab code and is a beneficial source code about imlut

跳频码性能分析，自相关函数的实现，据此可以分析跳频码的优劣( AHanming is the auto correction)

chebshev窗函数，包括等旁瓣级，任意主瓣宽度两个程序(chebshev window function,including side-lobe level and main-lobe width)

集成了不少常用的图像融合评价和检测的标准的matlab代码实现( English Spanish Arabic Integrates many commonly used image fusion evaluation and testing of the standard matlab code)

自己用MATLAB/Simulink搭的三相电压锁相环模块，输出幅值相位和频率(Use their own MATLAB/Simulink take the three-phase voltage phase-locked loop module, the output amplitude phase and frequency)

This is a modelling of a basic PV module. This matlab program can be used to develop different scenarios for comparison etc.

STATISTICAL SIGNAL PROCESSING Homework and their Solutions too.

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.