shuzixinhaochuli-matlab-shiyan1

OFDM经典书籍，美国作者，从ofdm的历史和原理做了详细的说明(OFDM classic books)

MATLAB初学者 MATLAB作图，介绍了一些常用的东东(MATLAB for beginners MATLAB mapping, introduced a number of commonly used Dongdong)

说明：  灰度共生矩阵方法用来提供一幅图像的灰度共生矩阵值，然后送到分类器中进行分类(gray level co-occurrence matrix，a very important method of extracting features)

该文档主要实现matlab中的msk调制解调功能，广泛运用于现代数字通信中(The document is mainly achieved in the matlab msk modulation and demodulation functions, widely used in modern digital communications)

I send the matlab code for image compression using embedded zero tree and set partitioning in hierarchial tree.Also i send matlab code for speech compression using rabiner algorithm and OFDM transceiver design.

以matlab做pitch-shift的功能展示.基於phase-vocoder技術在頻域上做pitch-shift.(In matlab to do the pitch-shift function display based on phase-vocoder techniques in the frequency domain to make pitch-shift.)

本文档是在matlab中配置vlfeat的说明，详细说明了配置步骤，并附带了一个关于图像拼接的实例，希望对有需要的朋友有帮助(This document is in the matlab configuration vlfeat instructions detailing the configuration steps, and comes with an example on the image mosaic, we want to help a friend in need)

MATLAB经典的数值计算算法设计教程MATLAB classical numerical algorithm design tutorial(MATLAB classical numerical algorithm design tutorials MATLAB classical numerical algorithm design tutorial)

卡尔曼滤波的故障诊断，才用卡尔曼滤波方法的故障诊断技术的matlab 与simulink 仿真代码(karman filter with design)

调制识别的传统算法， 零中心归一化瞬时幅度的谱密度的最大值 零中心归一化瞬时幅度绝对值得标准偏差 零中心非弱信号段瞬时相位非线性分量绝对值的标准偏差 零中心非弱信号段瞬时相位非线性分量的标准偏差 零中心归一化非弱信号段瞬时频率绝对值得标准偏差(Traditional modulation recognition algorithm, the zero center owned by an instantaneous amplitude of the spectral density of the maximum zero center owned by one of the instantaneous amplitude is definitely worth the nonlinear component of the instantaneous phase of the absolute value of standard deviation of zero-center non-weak signal segment, standard deviation of zero center weak signal segment of the standard deviation of the nonlinear component of the instantaneous phase zero center owned by the instantaneous frequency of a non-weak signal segment is definitely worth the standard deviation)