DSpproject3

这是关于风力发电机齿轮箱的文章，希望对大家有帮助。(This is about the wind turbine gearbox article, we want to help.)

有关介绍MATLAB内容的有关教程，主要讲解了MATLAB的使用方法。(Tutorial on MATLAB content, mainly on the use of MATLAB.)

基于标准的粒子群算法编写了一个简单通用的matlab程序(matlab code of standard particle swarm optimization)

录制一段自己的语音信号，并对录制的信号进行采样；画出采样后语音信号的时域波形和频谱图；给定滤波器的性能指标，采用窗函数法和双线性变换设计滤波器，并画出滤波器的频率响应；然后用自己设计的滤波器对采集的信号进行滤波，画出滤波后信号的时域波形和频谱，并对滤波前后的信号进行对比，分析信号的变化；回放语音信号；最后，设计一个信号处理系统界面。(Section of their own voice recording signal, and recording the signal sampling draw sample after the speech signal time-domain waveform and frequency spectrum given filter performance indicators, using a window function method and bilinear transformation design of filters, and draw the frequency response filter then use the filter of their own design for the signal acquisition filtering, after filtering the signal to draw the time-domain waveform and spectrum, and filtering of signals before and after contrast, analysis of signal changes playback voice signal Finally, the design of a signal processing system interface.)

this is compression of noise in the virtual immages

通过三维模型生成的各种姿态图像与输入图像对比，找出匹配度最高的图像，即可估计出输入图像的目标姿态。(The image with the best matching degree can be found images which generated 3D model by matching with input image, and the pose is estimated.)

说明：  两种不同的假设： H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率 H0 ： xn wn () () = n=1,2,…,N 其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互 独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布 1 0 2 ( ) 20 p θ π θ π ?? ≤ ≤ = ??? 其它 1）改变输入信噪比（改变 A 或噪声方差均可），给定虚警概率，画出 输入信噪比与检测概率之间的理论曲线。(注意：理论检测曲线与样本 数有关) 2）改变样本数，用 Monte-Carlo 实验方法得出 PF＝0.001 时输入信噪比 与检测概率之间关系曲线（至少三条），并得出结论。 3）改变 M-C 实验次数，样本数不变，用 Monte-Carlo 实验方法得出 PF ＝0.001 时输入信噪比与检测概率之间关系曲线（至少三条），并得出 结论。(Draw the theoretical curve between input SNR and detection probability)

matrix converter with induction motor

radial basisi function

Salary system. Foxpro_vb6