Lecture-1

To calculation the BER of Qpsk overAWGN ang Rayleigh Fading channel

极值中值滤波编码，在matlab环境下的编码，采用极值中值编码。(Extreme Value Filtering coding, in the Matlab environment coding, using the maximum value coding.)

捷联惯导力学编排解算，很好的一段小程序，希望帮的到大家(SINS mechanical choreography solver)

File with compression factor of the particle swarm algorithm, the weight to improve particle swarm optimization, adaptive weighting method, the random weighting method, particle swarm optimization variable learning factor, learning factor of asynchronous change, the second-order particle swarm optimization, second-order oscillating particle swarm algorithm, chaotic particle swarm optimization, hybrid particle swarm optimization, hybrid particle swarm optimization, simulated annealing algorithm

说明：  首先，建立两个模拟信号的数学模型 有用信号 干扰信号。两个信号的中心频率、信号带宽等参数由学生自己选定，要求两个信号的频谱不重叠 分别画出三个模拟信号的时域波形和频谱图；然后，根据x_a (t)的中心频率和带宽，按照奈奎斯特采样定理选择采样频率fs，分别对信号s_a1 (t)、s_a2 (t)、x_a (t)进行时域采样，得到离散信号s_1 (n)、s_2 (n)、x(n)。利用FFT算法分析离散信号的频谱，分别画出三个离散信号的时域波形和频谱图； 接着，设计数字滤波器H(z)，要求该滤波器对干扰信号s_2 (n)的衰减大于40dB。提出滤波器的设计指标，并设计滤波器，给出滤波器的设计结果，绘制滤波器的幅频特性和相频特性曲线，验证滤波器的设计结果是否达到设计指标要求；此外，选择实现数字滤波器H(z)的结构，画出滤波器结构信号流图；然后，将合成信号x(n)输入数字滤波器H(z)，按照所选择的滤波器结构，设计计算机程序计算滤波器的输出响应y(n)，画出y(n)的时域波形和频谱图；(the synthetic signal x (n) is input into the digital filter H (z). According to the selected filter structure, a computer program is designed to calculate the output response y (n) of the filter, and the time-domain waveform and spectrum diagram of Y (n) are drawn)

一种基于正方形的摄像机参数标定程序，简单实用(camera calib )

基于dtc svm_dtc 的船舶电力推进系统仿真模型(Propulsion system simulation model based on dtc svm_dtc ship )

利用积分原理求解输入信号的均值和均方差值(solve mean value and mean square deviation )

This derivation of the normalised least mean square algorithm is based on Farhang- Boroujeny 1999, pp.172-175, and Diniz 1997, pp 150-3. To derive the NLMS algorithm we consider the standard LMS recursion, for which we select a variable step size parameter, μ(n). This parameter is selected so that the error value , e+(n), will be

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)