fast_sc1

matlab code or seismic segy reading

“袋子模型”特征描述演示，有注释，以及人脸库(bag of the words demo)

MATLAB教学代码，包括37个短代码M文件，文字包含执行基本线性代数计算MATLAB命令文件等。(The MATLAB Teaching Codes consist of 37 short, text files containing MATLAB commands for performing basic linear algebra computations.)

spwm_xiazai。欢迎下载，共同学习，共同进步(spwm_xiazai. Welcome to download, a common learning and common progress)

常用MATLAB 函数逼近算法代码,用于对常用算法函数的测试用例使用(matlab function )

雷达信号处理中脉冲压缩算法的matlab例子程序()

说明：  此段代码的功能是对宽带阵列信号的输出结果进行仿真(This is the source code of simulation for the array output when wideband signals are received.)

用于分类的主判别变量算法(principal discriminant variate algorithm) ( principal discriminant variate algorithm for two classes, x1 and x2. x1 is the training samples in the 1st class with each column being the spectrum characterizing the corresponding sample. x2 is the training samples in the 2nd class with each column being the spectrum characterizing the corresponding sample. lamda is the penalty weight defining the balance between FLDA and PCA. dvnum is the number of principal discriminant variates to be computed. A defaul value for dvnum is 2. dv is the principal discriminant variates. scores is the matrix with each row being the scores of the samples on the corresponding principal discriminant variate. xmean is the mean spectra of the sample, which was used for mean-centering the data in training and prediction. )

使用matlab产生线性调频信号，并进行脉冲压缩处理。(Use matlab to produce linear FM signal, and pulse compression processing.)

Recent work by Petricoin and Liotta and co-workers (Petricoin et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002 Feb 16 359(9306):572-7. PMID: 11867112) has generated a lot of excitement and controversy. This example shows some ways that MATLAB can be used to read, visualize, pre-process (base-line correction, resample) and classify the data. The data can be downloaded from http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp (Recent work by Petricoin and Liotta and co-workers (Petricoin et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002 Feb 16 359 (9306) :572-7. PMID: 11867112) has generated a lot of excitement and controversy. This example shows some ways that MATLAB can be used to read, visualize, pre-process (base-line correction, resample) and classify the data. The data can be downloaded from http://home.ccr.cancer. gov/ncifdaproteomics/ppatterns.asp)