matlab-tracking

Codigos convolucionales Turbo

the code file that is uploded is myvad file

全部信息熵的计算，包括信息熵，条件信息熵以及信息增益。附带Andrew的信息增益教程(All information entropy calculations, including information entropy, conditional entropy and information gain. Andrew incidental information gain Tutorials)

有关经典车间调度的算法,车间作业调度问题(JSP)的遗传算法通用MATLAB源代码,(The classical job shop scheduling algorithm )

用C或者matlab编写基2 FFT快速算法，输入点数是2的幂次方，序列视作既包含实部也包含虚部。要求一个函数计可以做正变换，也可以做反变换。编写输入序列，先进行正反向变换，得到离散频谱，再将离散频谱加载到输入端进行验证，从输出端获取序列，和原来的序列进行比较，看看是否相等。序列可随意设为2的幂次项！(Written by C or matlab base 2 FFT fast algorithm, the input power of 2 points is the sequence includes both the real part as the imaginary part is also included. Require a meter to do is change the function, you can also do the inverse transform. The preparation of the input sequence, first the forward and reverse transformation, discrete spectrum, discrete spectrum and then loaded into the input validation, access to sequence from the output side, and the original sequences were compared to see if equal. Sequence can be arbitrarily set to a power of 2 items!)

说明：  本例是一个图像平滑与去噪的程序，对大家的图像预处理知识会有帮助(This example is an image smoothing and denoising procedures, we will help the image pre-knowledge)

隔离型单端反激型直直变换器的Matlab/simulink仿真例程，已检测运行无误。(Isolated single-ended flyback straight converter Matlab/Simulink simulation routines, has been detected running correctly.)

基于遗传算法的多目标优化算法（matlab）(Based on genetic algorithm for multi-objective optimization algorithm (matlab))

Three-Phase On-Line Tap Changing Regulating Transformer .

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)