PCM编码 A律simulink

fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b) with parameters: u,b format: result = fit_ML_laplace( x,hAx ) input: x - vector, samples with laplace distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields u,b - fitted parameters CRB_b - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML

which client get the socket

three level inverter

Foundations of Fuzzy Control - A Practical Approach (2e) (C3-PPTX)

This code is a complete fingerprint core point detection. It uses orientation field for singular point detection and Zhang-suen algo. for thinning in preprocessing part

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

这是一个彩色图片转换为弧度图片转换的程序。可将灰度图片进行任意部分的存储，并存储在给定位置。(This is a process of changing color image into gray image. In the sanmetime ,it can srore any part of the gray image into the given place.)

功能是从高维数据降低到低维数据，特征选择的过程(The source code for the feature selection to reduce the high-dimensional data low-dimensional data)

Matrix inverse by Gauss-Jordan elimination

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