BoundaryCond

some idioms in english

kalman滤波跟踪系统的源代码，初学者必看(kalman filter tracking system, source code, beginners must see)

matlab实现MUSIC算法,信号样本数1000，估计自相关矩阵为八阶。(matlab to realize music algorithm)

用于声源定位的二维声源位置图创建，可以同时画出几个方向的声源(Sound Source Localization for Two-dimensional sound source location map creation, can be drawn in several directions at the same time the sound source)

说明：  脉冲信号调制解决了线性调频信号的调制特性(LFM)

Kongsberg EM 系列多波束解码说明书，可以参考里面的说明帮助实现底层解码。(Kongsberg EM Series Multibeam echo sounder EM datagram formats)

非持续性CAMA是一种冲突避免的CSMA协议，如果终端检测到信道状态空闲，则立即进行数据传输，而信道状态忙，则停止载波监听，并随机在等待一段时间后再进行载波监听。(Non-durable CAMA avoid CSMA protocol is a kind of conflict, if the end channel state idle, detected immediately for data transmission, the channel state is busy, stop the carrier sense, and random carrier sense of the reentry after waiting for a period of time. )

说明：  文档中的matlab源代码主要是针对单回路液位控制系统的PID控制实现，内有simulink原理图(Matlab source code in the document is mainly for single-loop PID control of water level control system to achieve, within the simulink diagram)

source code of simlate function of fuzzy bp neural network

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)