MLP-Uing-GA

对图像进行逐点计算，并且取最大值和最小值的差值作为该点新的灰度值。 在区别边缘点和非边缘点时，首先确定一个阈值T，若图像中像素点的新 值大于该门限值，则可以初步确定为边缘点，小于T的新值被判断为非边缘点.(Image point by point basis, and taking the maximum and minimum points of difference as the new gray value. Edge in the difference between point and non-edge point, first determine a threshold T, if the image of the new pixel value is greater than the threshold, you can initially identified as the edge point, the new value is less than T have been judged as non-edge point.)

说明：  SOA高速动态响应模型,基于SOA的载流子速率方程描述(High-speed dynamic response model of SOA, SOA-based carrier rate equation description of)

50欧姆,100欧姆板厚1.0MM8层板阻抗设计-金百泽阻抗控制结构确认函(1mm8layout100ohm50ohm Impedance matching )

基于端点检测的谱减法语音增强，语音的去噪算法仿真程序，可以进一步改进(Endpoint detection of the spectral subtraction based speech enhancement)

Matlab Coce for PCA 3

matlab code for minimum energy point at particular vdd suply this is contour code.

1、 降噪步骤： （1） 一维信号的小波分解。选择一个小波并确定分解的层次，然后进行分解运算。 （2） 小波分解高频系数的阈值量化。对各个分解尺度下得高频系数选择阈值进行软阈值量化处理。 （3） 一维小波重构。根据小波分解的最底层低频系数和各高频系数进行一维小波重构。 matlab里面有关于去噪的函数，你可以找一下~~ 这说的只是基本原理，希望有所帮助(1, noise reduction steps: (1) one-dimensional signal wavelet decomposition. Select a wavelet decomposition level and determining, and then divide the operation. (2) high coefficient wavelet thresholding. For each decomposition scales have high coefficient selection threshold soft threshold quantization. (3) one-dimensional wavelet reconstruction. According to the lowest level of the low frequency wavelet coefficients and high frequency coefficients of each one-dimensional wavelet reconstruction. There are about denoising matlab function, you can find some ~ ~ This says just the basic principle, we hope to help)

非常好的资料，很难找到哦，不要错过了啊，错过就没有机会了(VERY GOOD DOCUMENT IF YOU DON T BELIEVE ME, YOU CAN LOOK INSIGHT)

卡尔曼滤波器相关的程序，对目标的速度均方根误差等进行了仿真(Kalman filter procedure, the speed of the target such as root mean square error simulation)

PYTHON programming: AN INTRODUCTION TO COMPUTER SCIENCE by JOHN ZELLE