Multivariate-prediction-model

This code draw a red rectangle aroud the face

BPSK调制，1/2码率卷积码实现软解调(BPSK modulation, 1/2 rate Convolutional Codes soft demodulation)

一种基于红外图像分层处理及动态压缩的 DDE 算法。该算法先将原始14bits 红外图像数据信息中的大动态低频背景和小动态高频细节进行分离提取，并分别对提取的细节层和背景层进行相应的灰度增强和灰度抑制处理，再调整和压缩各图层的动态范围并最终合成 8bits图 像。实验结果表明，该算法能较好地保留并突出原始红外图像中的边缘和细节信息，达到了预期设计的目标。 (put forward a DDE algorithm based on hierarchical processing and dynamic compression of infrared image． At first，this algorithm dissociates the background ( with wide dynamic and low frequency) and the detail ( with narrow dynamic and high frequency) in the data information ( 14 bits) of original infrared image，and pick up them respectively． Then，it enhances the gray of details layer and restrains the gray of background layer，and readjusts and compresses dynamic range of each layer． Finally，it composites output image ( 8 bits) ． The experimental results show that the proposed algorithm can retain detail information and edges in original infrared image and carry the purpose of design． )

LEACH is a basic protocol

用matlab的m函数实现pid控制的silulink模型，实现了m语言和simulik模型的结合(Matlab m-function to the pid control the silulink model to achieve the combination of the m language and simulik of model)

ofdm simulation using matlab

灰色预测GM（1，1）模型的matlab源代码，包括预测模型的建立(Gray prediction GM (1,1) model matlab source code, including the prediction model)

基于高斯过程的贝叶斯分类,详细介绍了高斯过程(Bayesian Classification With Gaussian Processes)

相控阵雷达 相控阵雷达的仿真程序 波束形成(Copyright (c) 2013, The MathWorks, Inc. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution * Neither the name of the The MathWorks, Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTIC)

说明：  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. Because it has saved the trained network weights, so the first use of identification procedures, you can directly run \ \ Release directory of the executable files, and then tested on the images directory to 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 cause very low recognition rate. Training sample selection principle is that as far as possible, representative, in the long training time Buzhi Yu s case, the number of training samples as much as possible. )