Digital-speech-processing-procedures

计算用于双目视觉的基本矩阵，以此进行三维重构和坐标点测量(calculate F)

说明：  描述分形编码和解码原理的非常好的一份电子文档，对实验结果也进行了详细的分析(Describe the principles of fractal coding and decoding an electronic document very well the experimental results carried out a detailed analysis of)

数据频域分解法，基于结构的环境白噪声激励响应数据，识别结构的频率和振型(The frequency domain decomposition method, based on the structure of the environmental white noise stimulus-response data, identify the structure of the frequencies and mode shapes)

系统辨识导论课程学习资料，教你学习辨识基础知识(Introduction to System Identification curriculum learning materials to teach you the basics of learning to identify)

自己编写的一个用MATLAB测量信噪比的程序。利用自己发明的一套相关算法，计算得到的信噪比精度很高。(A SNR Calculation Programme)

该程序可以构造一个不同行重列重下的QC-LDPC码，并做性能仿真(constructs qc-lpdc codes given size of sub-mnatrix(m), girth(g1), row (k) and column(j) weights)

广义预测控制GPC在matlab中的仿真与应用(Generalized Predictive Control GPC in the matlab simulation and application)

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近场源多参数估计2D-MUSIC算法（角度和距离估计）仿真分析(Simulation and analysis of 2d-music algorithm for multi parameter estimation of near field sources)

在计算机上产生一组实验数据，首先产生一段零均值白噪声数据u（n）,令功率为 ，让u(n)通过一个三阶FIR： 得到y(n). .y(n)上加三个实正弦信号f1’=0.1,f2’=0.25,f3’=0.26调整 和正弦信号幅度信噪比大致为10dB,50dB,50dB. (1) 令N=256,描绘xn波形； (2)得出真实功率谱密度 . (3) 利用此实验数据Pisarenko谐波分解法估计该实验数据的正弦频率及幅度。 (On the computer to generate a set of experimental data, first generate a zero mean white noise data u (n), so that power, let u (n) through a third-order FIR: get y (n).. Y (n) added three real sinusoidal signal f1 ' = 0.1, f2' = 0.25, f3 ' = 0.26 and sine signal amplitude adjustment signal to noise ratio is approximately 10dB, 50dB, 50dB. (1) that N = 256, depicted xn waveform (2) the true power spectral density obtained. (3) using this experimental data, Pisarenko harmonic decomposition method to estimate the experimental data of the sinusoidal frequency and amplitude.)