TF-analysis

在加性高斯白噪声信道里，BPSK调制信号与MPSK调制信号的比较(in the awgn channel , bpsk system vs mpsk)

It is Radar System design and signal prcessing code based on standard algorithm .this is vey important for biggener those who are working on Radar signal processing area.This code contain various chapter. this is basically chapter No4

matlab code diagnostic by weibull function

MM1 code ..please check this out. This is MM1 tandem code that I used recently. May need to modify a bit to your situation. Ok have fun and enjoy!!!!

基于matlab的haimmse算法的应用程序(Based the matlab' s haimmse algorithm application procedures)

MATLAB系统与语言简介 矩阵运算与数组运算 (The MATLAB Language Introduction matrix computing array operations)

汽轮机转子系统前三阶临界转速的传递矩阵法(Turbine rotor system critical speed before the third-order transfer matrix method)

16-QAM + fading + AWGN Rayleigh Fading Ampalitude without Phase Fading

使用改进的LBP算法CLBP实现图像纹理特征的提取，并使用卡方统计方法计算类间距离并实现图像分布。本人已经实验，对15类病毒图像进行分类，不调任何参数的情况下实现67 以上的准确率。( Using an improved algorithm CLBP LBP texture feature extraction of image, and use the chi-square statistic calculated inter-class distance and achieve image distribution. I have experimented on 15 viroid image classification, and more than 67 accuracy rate without adjusting any parameters.)

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)