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matlab语音信号处理工具箱 本工具箱可配合语音数据库使用，用于计算线性预测语音模型的参数，语音声调转换，语音自动解析分解，语音语速变换及更改发音的重音，音量，清晰度等。配套图书《Speech Processing and Synthesis Toolboxes》已经出版，翻译进行中 (matlab signal processing toolbox with the voice of the Toolbox database can be used for calculating linear predictive speech model parameters, voice tone conversion, automatic speech analysis decomposition, transformation and change of voice speech rate accented pronunciation, volume, clarity, etc. . Matching book " Speech Processing and Synthesis Toolboxes" has been published in translations)

代码给出高斯和瑞利等几种信道模型的仿真方法，代码规整，设定简单清晰(present an overview of a statistical channel model)

AES-128, AES-192, and AES-256 加密与解密。Matlab源程序。( AES-128, AES-192, and AES-256 encryption/decription functions.)

matlab仿真三维物点的透视投影成像 有程序的很好的(matlab simulation of three-dimensional object point perspective projection imaging are very good procedures)

雷达LFM信号匹配滤波程序，压缩带宽，列出了匹配滤波器的系统函数(Radar LFM signal matched filtering process, compression, bandwidth)

均值滤波（线性平滑滤波）和中值滤波程序·······(Average value filter (linear smoothing filter) and value filter )

This Algorithm comutes demagnetizig tensor from Newell Paper[1] between two rectangular shape elements.Main function is and there is equivallent function written in c-language, you can use it to compile mex file.This script can be used in field of micromagnetics and finite difference magnetic simulation software. 1. A.J. Newell, W. Williams, D.J. Dunlop, J. Geophys. Res. 98, 9551 (1993).

用matlab画出1931CIE标准色度图(With the the the matlab draw the 1931CIE standard chromaticity diagram)

这是一个动态演示电路电流流动的matlab程序，很有趣！(This is a dynamic demonstration circuit current flow matlab program, very interesting!)

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)