Multibeam-Sonar-Reverberation

基于肤色的人脸检测MATLAB源代码皮肤检测的最终目标是构建一个决策规则，并以此将图像中的像素划分为肤色和非肤色两类，一般通过引入一个衡量像素点颜色与“标准肤色”的距离而实现的，该距离是由相应肤色建模的方法决定的(Face detection based on skin color skin detection MATLAB source code for the ultimate goal is to build a decision-making rules, and use of the image pixels into skin and non skin types, usually by introducing a measure of the pixel color with the " standard colors" achieved the distance, the distance of the corresponding color from the decision modeling approach)

空间面板，杜宾模型，SEM，SAM,包括例子，代码等等。(Space panel, Durbin model, SEM, SAM)

根据地层参数通过简化佐普利兹方程求取反射系数，制作合成地震记录，从而显示不同地层模型的AVO响应。(Formation parameters by simplifying Zuopu Leeds equation strike reflection coefficient, making synthetic seismogram, which show different stratigraphic model AVO response.-In accordance with formation parameter equation by simplifying Zuopu Leeds find reflection coefficient, the production of synthetic seismograms, which show different stratigraphic model AVO response.)

matlab光斑形成，代码和文章，仅供学习参考(matlab spot formation, code and articles)

code matalab of algorithme gentic using in recognition a object

A Book published in 2010 on error control coding using matlab(A practical guide to error-control coding using matlab)

全球导航系统中，软件接收机基带处理部分的捕获电路的算法仿真(FFT acquisition )

介绍了关于超宽带系统中基于OFDM信道估计的matlab仿真图形(Introduced the system based on OFDM UWB channel estimation matlab simulation graph)

独立成分分析（Independent Component Analysis, ICA）是近年来发展起来的一种有效的盲分离技术，最早是由法国学者Herault和Jutten于1986年提出。ICA方法的提出最初是用来解决“鸡尾酒会”问题，其过程可以归纳为，在源信号与传输通道参数均未知的情况下，仅根据源信号的统计特性，出现测信号恢复出源信号。ICA分析的关键在于根据一定的优化准则建立描述输出信号独立程度的优化判据，即目标函数，并设计相应的优化算法，寻求最优的分离矩阵，使得输出信号中各分量尽可能相互独立。采用ICA方法对信号进行盲分离时，可避免过多地受大能量噪声成分的影响，将部分小能量信号源识别出来。随着ICA技术的不断发展，目前ICA技术已经广泛应用于语音增强、音频识别、通信、医学信号处理、金融数据挖掘、图像消噪、数字水印、遥感等领域，并取得了一定的研究成果。(Independent Component Analysis (ICA) Component ini order, developed in recent years is a kind of effective separation technology, originally from French scholars Herault and Jutten in 1986. The proposed method is initially ICA to solve the "cocktail" problem, the process can be divided into, in the source signal transmission channels and in the case of unknown parameters according to the source of the signals, and the statistical characteristics of recovering the source signal. According to the analysis of the ICA key lies in the establishment of optimal criteria described the output signal of the independent, objective function optimization criterion, and the corresponding optimization algorithm, the optimum )

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