chap3_09_DMRAS_ordn_d

说明：  包括灰度AGV路径识别，彩色AGV路径识别，HSI彩色空间的AGV路径识别，：路径中心线的定位，Radon变换的AGV路径偏差检测(AGV path identification, including gray, color AGV path identification, HSI color space AGV path identification,: path centerline position, Radon transform of the AGV path deviation detection)

在对角元没有作用的情况下，提取三对角矩阵(get a tridiagonal matrix )

这是学习c很好的资源啊，值得大家好好看一看的，大家多努力啊(This is a good resource to learn c ah, it is worth a good look at us, and we work hard ah)

认知无线电是一种用于提高无线电通信频谱利用率的新的智能技术. 首先简述认知无线电的背景和概念,针 对认知无线电的频谱感知功能,详细地分析基于能量检测的频谱检测方法,在此基础上研究了噪声方差的不确定 性对检测性能的影响. 针对无线信号检测具有本地局域性和能量检测存在信噪比极限的特点,提出一种分布式有 限合作检测机制,推导出基于“OR”规则的有限合作机制的检测概率,并进行Monte Carlo仿真.(Cognitive radio is a radio communications spectrum used to improve the efficiency of the new smart technology. First, the background and outlined the concept of cognitive radio, spectrum sensing for cognitive radio capabilities, a detailed analysis of the spectrum based on energy detection test In this study the noise variance based on the uncertainty of the detection performance. for wireless signal detection with local detection of localized and there is signal to noise ratio limit of the energy characteristics of a distributed cooperative detection mechanism, derived out based on " OR" rules of the detection probability of the limited cooperation mechanism and conduct Monte Carlo simulation.)

Block Matching Algorithm (continuous) Motion Estimation ESTES

利用伴随矩阵进行矩阵求逆的程序，各阶矩阵皆可，不能求各有提示（不是很熟练，所以可能写的比较浅）(matrix inversion using the method of adjoint matrix)

我们对离散时间信号进行傅里叶变换和频谱分析。(We do the discrete time signal FFT and spectrum analysis )

采用粒子群算法PSO优化BP神经网络，进行风电功率预测，含实际数据和案例(Particle swarm optimization PSO BP neural network for wind power prediction, including the actual data and case)

通过matlab和C语言的联合编程实现了微波方面smith圆图的功能(Matlab and C language through the joint programming of the microwave aspects of the function chart smith)

待辨识对象参数a=[1 -1.5 0.7] b=[1 0.5] 输入采用长度L=400的白噪声序列，输出 ，输入和输出数据均含不相关随机噪声，ρ（k）=1/k。利用上述递推公式，辨识系统参数。(To identify the object parameters a = [1-1.5 0.7] B = [1 0.5] Input the length L = 400 white noise sequence, the output, the input and output data are not related with random noise, ρ (k) = 1/k. Using the recursion formula, identification system parameters.)