RCLNetzwerk_Uebung3

说明：  这是个关于滤波器实验，有学习滤波器的可以看看(This is about the filter, the experimental study filter can see)

说明：  实现用bt法，welch法，周期图法，burg法对信号进行滤波(Achieved with the bt method, welch method, periodogram, burg method of signal filtering)

matlab绘图局部放大源码，看清所要关心的部分(matlab drawing enlarged source, see the part to be concerned about)

语音、图像等相关处理，如加躁去噪，频谱、幅度图，二值灰度图等(Voice plus noise denoising, image plus noise denoising, and image reversal, two values ​ ​ of other treatment)

本程序是关于随机信号特征值提取，有界面显示，特征值包括自相关函数，和功率谱密度(This program is about random signal characteristic value is extracted, and to have the interface shows, including the autocorrelation function characteristic value, and power spectral density )

对一幅图像加入高斯噪声和椒盐噪声后，分别进行均值滤波和中值滤波，比较两种滤波方法的优缺点(Of an image by adding Gaussian noise and salt and pepper noise, the mean filter, respectively, and median filtering, comparing the advantages and disadvantages of the two filtering methods)

说明：  滤波器函数，包括圆延迟，使用圆延迟线的FIR滤波器(circular delay，FIR filter using circular delay-line buffer)

本目录主要包括：文档和两个源代码。 其中一个源代码是识别程序的，另一个是一个矩阵类库的，书里面也使用过，所以一并附上。 因为已经保存了训练好的网络权值，所以第一次使用识别程序时，可以直接运行\Release目录下的可执行文件，然后对图片目录中的测试图片进行读入、识别。 当然读者也可以自行用训练样本训练网络，不过要特别注意训练样本的选择，否则可能造成识别率很低。训练样本选择的原则是，尽可能的有代表性，在训练时间不至于太长的情况下，训练样本数目尽量多。 (This directory includes: documentation and two source code. One of the source code is to identify the procedure, the other is a matrix class library, the book which is also used, so be attached. Have been saved because of the trained network weights, so the first use of identification procedures, he can run Release directory Under the executable file, then the test image directory read pictures, identification. Of course, readers are free to use the training samples to train the network, but pay special attention to training samples, otherwise it may result in knowledge Do not rate very low. Training sample selection principle is that as far as possible, representative, in the long training time Buzhi Yu s case, the training Training sample size as much as possible.)

矢量的合成分析 求矢量f(n,2)在指数x(n)为高指数年(x(n)>coefh的年）的平均值fh(2)、低指数年(x(n)<coefh的年）的平均值fl(2)、高指数年与气候平均的合成差dh(2)、低指数年与气候平均的合成差dl(2)、以及高低指数年的合成差dhl(2)和差的显著性tn(5)。 (Synthesis of vector demand vector f (n, 2) the index of x (n) for the high-index on (x (n)> coefh of the year) average fh (2), low index on (x (n) <coefh的年）的平均值fl(2)、高指数年与气候平均的合成差dh(2)、低指数年与气候平均的合成差dl(2)、以及高低指数年的合成差dhl(2)和差的显著性tn(5)。)

很好的粒子图像测速（PIV）Matlab代码，在Matlab R2010a 下测试通过(Good particle image velocimetry (PIV) Matlab code, tested under Matlab R2010a)