my-ACO-for-TSP

最经典的人工神经网络教程，内容详细,适用于广大战友(Classic artificial neural network tutorial, detailed and apply to the majority of his comrades)

比较超宽带SAR图像matlab和c语言的并行算法的代码效率，很好的一个算法比较(Parallel Performance of Pure MATLAB "M-files" versus "Ccode“ as applied to formation of Wide-Bandwidth and Wide- Beamwidth SAR Imagery)

说明 在此分类的PRJ我们试图通过分配给每个签名的两个不同的公司标识由一个特别号码提取水平和垂直扫描每幅图像矩阵。每个标志图片被截断成二进制后调整大小。这是由核心例行imcount.m，而主编testpng.m运行compelte测试结果显示出来。每个标识的痕迹便可以进一步处理像一个合适的神经网络学习，推理机等熵类分类指数使用的系统是更好的有关报告，从http://cag.dat下载的解释。 demokritos.gr/publications/TR2011-1.pdf。 该例程index0.m提取每个图像矩阵的行/列聚类分析，现在可以用来替换为最新版本的“cluster.m“另一包“DEC2NARY“我们Mathworks公司提供的帐户中。 MATLAB的MATLAB的版本7.4（R2007a）(Description In this prj we attempt to classify a number of Company Logos by assigning to each 2 distinct signatures extracted by a special horizontal and vertical scan of each image matrix. Each Logo picture is truncated to binary after resizing. This is done by the core routine imcount.m, while the main prog testpng.m runs a compelte test and displays results. Traces of each Logo could then be further processed by an appropriate learning system like a Neural Net, an Inference Engine, etc. The entropic-like index used for classification is better explained in the associated report downloadable from http://cag.dat.demokritos.gr/publications/TR2011-1.pdf. The routine index0.m used for extracting the cluster analysis of each image matrix row/col can now be replaced by a most recent version "cluster.m", contained in another package "DEC2NARY" available by our Mathworks account. MATLAB release MATLAB 7.4 (R2007a) )

energy detection in cr

说明：  Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

lowpass enhancement of an image

采用matlab语言编写的数值模拟用网格的生成，网格的细化加密(Using matlab language of the numerical simulation of the grid generation, grid refinement Encryption)

bpsk modulation,demodulation,fft

说明：  该程序可以生成WVD分布，将其做成函数可以方便研究员做研究使用。(it can generate the WVD function.)

工程计算MATLAB code to calculate the reorthogonalized sine tapers input: N = the length of the time series data to be tapered p = the number of tapers requested I = the gap structure a vector of length N I(t) = 1 if there is data at time t, t=1, ..., N I(t) = 0 if there is a gap at time t output: X = N-by-p vector of the reorthogonalized sine taper(engineering MATLAB code to calculate the reorthog onalized sine tapers input : N = the length of the time series data to be tapere d p = the number of tapers I requested the gap str = ucture a vector of length N of I (t) = 1 if there is dat a at time t, t = 1, ..., I N (t) = 0 if there is a gap at time t output : X = N-by-p vector of the reorthogonalized sine t aper)