297822139tap_equalizer

神经网络中的sofm算法的具体运用及程序实现，很有启发性(Sofm neural network algorithm and procedures used to achieve specific, very enlightening)

求正弦信号fft，并利用该fft计算正弦信号的功率，信噪比等(Seeking sinusoidal signal fft, fft calculated using the sinusoidal signal power, SNR, etc.)

LMS三角定位迭代加权算法 LMS三角定位迭代加权算法 LMS三角定位迭代加权算法(LMS triangulation iterative weighting algorithm LMS triangulation iterative weighting algorithm LMS triangulation iterative weighting algorithm)

wireshark 抓取pacp包进行分析 判断延迟 丢失等(wireshark packet analysis grab pacp judge delayed loss, etc.)

用simulink对风电场进行建模分析得到功率以及电流的仿真图(This example uses an initial state vector to start the simulation steady-state. When you make changes to the model (add, delete, rename blocks, etc), the initial state vector needs to be regenerated or disabled, otherwise Simulink signals an error when the simulation is started. To disable the use of initial state vector for this model, go to the Data Import/Export section of the Model Configuration Parameters tool and uncheck the Initial state option under the Load workspace section. To regenerate the initial state vector after a change to the model, follow the instructions given in the initialization file for this model. Type open( init_power_wind_dfig ) at the matlab prompt to view this file.)

说明：  图像双边去噪的matlab代码，是当今主流的效果好而且速度快的去噪方法(Image bilateral denoising matlab code, is the mainstream of today s effective and fast de-noising method)

在科学与工程计算中，最小二乘法用来拟合数据，精度很高。(Scientific and Engineering Computing, the least squares method is used to fit the data to high precision.)

主成分分析法实现了对于多维特征的优化，共两个文件，希望对大家有帮助(failed to translate)

时频工具箱，以上给了很多函数，但常用的基本就是短时傅里叶变换STFT、Wigner-Ville分布WVD、伪Wigner-Ville分布PWVD和提取瘠ridges、得到理想时频表示tfrideal。(Time-frequency toolbox over to a lot of functions, but the basic is commonly used short-time Fourier transform STFT, Wigner-Ville distribution WVD, pseudo-Wigner-Ville distribution PWVD and extraction barren ridges, when you get the ideal frequency representation tfrideal.)

This text deals exclusively with signal processing of digital data, although Chapter 1 briefly describes analog processes commonly found in medical devices. This text should be of interest to a broad spectrum of engineers, but it is written specifically for biomedical engineers (also known as bioengineers). Although the applications are different, the signal processing methodology used by biomedical engineers is identical to that used by other engineers such electrical and communications engineers. The major difference for biomedical engineers is in the level of understanding required for appropriate use of this technology. An electrical engineer may be required to expand or modify signal processing tools, while for biomedical engineers, signal processing techniques are tools to be used.