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本程序是ldpc的C语言源代码，如果有需要可以下载学习，可以学习整个通信框架(This program is ldpc the C language source code can be downloaded if there is a need to learn, you can learn the entire communication framework)

This example shows the complete Kill, Hard Disk

基于Costas编码的跳频信号的互相关时延估计，matlab版本：2013a(Delay estimation based on cross-correlation coding Costas frequency hopping signals, matlab version: 2013a)

在matlab环境下，实现了回声状态网络的训练、测试功能(The methods described for training ESNs are described in detail)

Sound synthesis with Matlab. Simple implementation of a multi-oscillator synthesizer. Includes FM modulation, 2 LFO s, 2 Env s. Good to learn the basis of sound synthesis in Matlab.

Author: Fahd Ahmad Abbasi Summary: Matches two pictures given as arguments. MATLAB Release: R13 Required Products: Image Processing Toolbox Description: The function takes two images as argument and using edge detection checks whether they are the same or not...a cool and simple code which can be used in security systems. The level at which the two pictures should be matched can be controlled. (Author : Fahd Ahmad Abbasi Summary : Matches two pictures given as arguments. MATLAB B. Release : R13 Required Products : Image Processing Toolbox Description : The function takes two images as argument and us ing edge detection checks whether they are the s ame or not ... a cool and simple code which can be u sed in security systems. The level at which the t wo pictures should be matched can be controlled .)

实现彩色图像的中值滤波，去除噪声点，模糊图像(Color image median filtering to remove noise spots, blurred image)

地球物理正演 重磁多边形模型正演，计算园的数据(Forward gravity and magnetic geophysical polygon forward modeling, data calculation park)

摩托罗拉 hc908sr12的开发代码 codewarrior软件开发的(Motorola hc908sr12 development code CodeWarrior software development)

基于BP神经网络的遥感图像分类代码。从样本中提取崇明岛东滩十种地物的光谱特征，并训练BP网络，再利用网络进行分类(BP neural network-based remote sensing image classification code. Extracted from samples of 10 kinds of Chongming Island, Dongtan features of the spectral characteristics and to train BP network, and then use the network to classify)