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超宽带高斯噪声的计算2，与其他的几个共用(Ultra-Wideband Gaussian noise calculation 2, and some other common)

说明：  信道仿真模型 可以方便快捷的进行相关的仿真操作(Channel simulation models can be convenient and efficient operation of the associated simulation)

FILTER DESIGN BY IMPULSE INVARIANCE

BER for BPSK in AWGN with receive diversity

变频信号的快速FFT变换，能准确的给出信号的频谱特征(Fast FFT of the signal frequency, can give accurate spectral characteristics of the signal)

数学建模中的一种评价算法--基于topsis熵权评价法和改进的灰色预测模型代码，里面的内容根据自己的情况改。(An evaluation algorithm in mathematical modeling is based on the TOPSIS entropy weight evaluation method and improved grey prediction model code, and the contents are changed according to their own situation.)

LMS及NLMS的源程序，MATLAB实现 打开既可以使用(NLMS and LMS . It isproformed by the app Matlab.)

Fractal, is filling in the form of non-integer dimension of space morphology. Fractal can be said to come from the existence of a theory of thinking.(Three-dimensional map of the program requirements. Fractal can be said to come from the existence of a theory of thinking.)

GM(1,1)模型1-4 1：GM(1,1)模拟模型，在matlab中的输入方法为gm1（x），x指要模拟的序列。 2：GM(1,1)预测模型，在matlab中的输入方法为gm2（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 3：GM(1,1)群模拟模型，在matlab中的输入方法为gm3（x），x指要模拟的序列。 4：GM(1,1)群预测模型，在matlab中的输入方法为gm4（x，K），x指要模拟的序列，K指从以后序列第一个数据算起的第k个待预测数据。 gm4对序列趋势比较好的数据预测效果较好，对上下变动的数据，特别是后4个数据趋势跟前面的数据相反的，预测效果很差。 gm2对上下变动的数据，预测效果比gm4好，但对趋势较好的数据，预测精度没有gm4高。 gm3比gm1模拟精度要高。 可以以x=[1 3 5 7 9 11 13 15]进行实验。x输入默认行向量。 所有程序在matlab6.0上调试通过。(GM (1,1) 1-4 1 : GM (1,1) simulation model Matlab in the input method for gm1 (x), x entail simulation sequence. 2 : GM (1,1) model, in the Matlab input method for gm2 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. 3 : GM (1,1)- simulation model, in the Matlab input method for gm3 (x), x entail simulation sequence. 4 : GM (1,1)- forecasting model, in the Matlab input method for gm4 (x, K), x entail simulated sequence, from the K refers to the first series after a run of data k pending forecast data. Gm4 right sequence relatively good trend data predicted better results, the next change in the data, especially after four data with the previous trend data contrary, predict poor. Gm2 next to the change in forecast resu)

create vector in 3d space. find angle & length