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Description: ： 本文以扩频理论为基础， 用 M A T L A B 对直接序列扩频通信系统进行了仿真。 系统中扩频编码采用 m序列， 整个系统 采用 Q P S K调制方式， 接收端同步捕获过程采用数字匹配滤波器的原理。在给定的仿真条件下， 对仿真程序进行了运行测试， 得 到了预期的仿真结果。
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Author: 799054429@qq.com |
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Description: MULTIDIMENSIONAL SCALING in matlab by Mark Steyvers 1999
％needs optimization toolbox
％Modified by Bruce Land
％--Data via globals to anaylsis programs
％--3D plotting with color coded groups
％--Mapping of MDS space to spike train temporal profiles as described in
％Aronov, et.al. "Neural coding of spatial phase in V1 of the Macaque" in
％press J. Neurophysiology-MULTIDIMENSIONAL SCALING in Matlab by Mar 1999% k Steyvers needs optimization toolbox% M odified by Bruce Land%-- Data via globals to ana ylsis programs%-- 3D plotting with color coded groups%-- Mapping of MDS space to spike train te mporal profiles as described in% Aronov, et.al. "Neural coding of spatial phase in V1 of t he Macaque "in press J. Neurophysiology%
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Author: 左贤君 |
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Description: Author: wei liu
Summary: simulation of binary and non-binary bch decoder
MATLAB Release: R14SP1
Required Products: Communications Toolbox
Description: simulation of binary bch decoding algorithm for bch(n, k) with t bits error correction capability.
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Author: joy |
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Description: 光学双稳特性曲线
调制作用:It=Ii*T(phi)
反馈作用:phi=phi_0+K*It
得透射率T(phi)与相移phi的反馈关系是
T(phi)=[phi-phi_0]/[K*Ii]
式中phi_0为初始相移
对于多干涉(F-P干涉)有:
T(phi)=1/[1+F*(sin(phi/2))^2]-Optical bistable characteristic curve modulation role: It = Ii* T (phi) feedback: phi = phi_0+ K* It may transmittance T (phi) and phase-shifting relationship between phi feedback T (phi) = [phi-phi_0 ]/[K* Ii] where phi_0 for the initial phase shift for the multi-interference (FP interference) are: T (phi) = 1/[1+ F* (sin (phi/2)) ^ 2]
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Author: ryo |
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Description: Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean
Gaussian random variables independently (separately) for each k MATLAB function random.
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Author: 飞龙 |
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Description: 这是LLE的原始算法，原文的参考文献是：S.T.Roweis and L.K.Saul. Nonlinear dimensionality reduction by locally linear embedding. Science,
290, 2000.-This is the original LLE algorithm, the original reference is: STRoweis and LKSaul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2000.
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Author: treat |
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Description: 以cos（2*pi*k*t/N）信号空间，k=0,1,……N-1, 取N= 4，8，16，32，64等基信号作为传输信号，通过计算机仿真正交信号的误码率。-To cos (2* pi* k* t/N) signal space, k = 0,1, ... ... N-1, take N = 4,8,16,32,64, such as the base signal as a transmission signal, through computer simulation orthogonal signal BER.
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Author: caomin |
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Description: OFDM程序，这么安排矩阵的目的是为了构造共轭对称矩阵
共轭对称矩阵的特点是 在ifft/fft的矢量上 N点的矢量
在0，N/2点必须是实数 一般选为0
1至N/2点 与 （N/2）+1至N-1点关于N/2共轭对称- BPSK simulation using a carrier cosine wave with ISI
clc
close all
clear all
figure(1)
n=160
for i=1:n
data(i)= 2*round(rand)-1
end
create modulated BPSK signal
first expand the bit stream
exdata=[]
for i=1:length(data)
for rep=1:5
exdata= [exdata data(i)]
end
end
ts=.1
t=1:ts:80.9
carrier=cos(pi*t)
multiply expanded bitstream by cosine wave with carrier frequency
this is the BPSK that is to be transmitted over the channel
bpsk=carrier.*exdata
bpsk=[bpsk(length(bpsk)-1) bpsk(length(bpsk)) bpsk]
plot(bpsk)
generating the noise
p=rand(1,800)*2*pi
p=rand*2*pi
snr=10
r=sqrt(-1*(1/snr*log(1- rand)))
no = 5*(r.* exp(j*p))
no = (r.* exp(j*p))
value of alpha
al=rand+j*rand
al=1
Spreading channel with the alpha as the variable
for k=5:5:795
for l = 1:5
al=round(rand)+j*round(rand)
rec(k+l)=bpsk(k+l)+al*bpsk(k-5+l)
end
end
rxdata=rec+ no
begin demodulation
first multiply recie
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Author: 卞敏捷 |
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Description: 遗传算法的PID调节
题目：已知 ，利用GA 寻优PID参数，其中K=1，T=2， ，二进制/实数编码，位数不限，M，Pc，Pm自选，性能指标 ，Q=100为仿真计算步长。-PID regulation of genetic algorithms Title: known, the use of PID parameters of GA optimization, in which K = 1, T = 2,, binary/real-coded, not limited to the median, M, Pc, Pm-on-demand, performance indicators, Q = 100 step for the simulation.
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Author: qiqi |
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Description: PID控制算法中，根据一节延迟传递函数的放大倍数K、延迟时间L和时间常数T，获得PID中比例环节、微分环节和积分环节的参数-PID control algorithm, according to a delay in the transfer function of magnification K, the delay time L and time constant T, to obtain the proportion of PID in the link, differential and integral link in the parameters of link
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Author: Watson |
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Description: 建立正交频分多路复用(OFDM)系统调制器的仿真模型，并编程给出具有K=10个子信道、符号区间T=100s、采用16点QAM信号时，OFDM系统调制器的输出信号。-The establishment of orthogonal frequency division multiplexing (OFDM) system, the simulation model of the modulator and the program is given with K = 10 Ge sub-channel, symbol interval T = 100s, with 16-point QAM signal, OFDM system, the modulator output signal .
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Author: 张燕燕 |
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Description: NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个：
1 确定因素，并建立网络层和控制层模型。
2 创建比较矩阵。
3 按照指标类型针对每列进行规范化。
4 求出每个比较矩阵的最大特征值和对应的特征向量。
5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。
6 将各个特征向量单位化(归一化)，组成判断矩阵。
7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。
8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。
-NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following:
A determining factor, and a network layer and control layer model.
2 create a comparison matrix.
For each of the three types of indicators in accordance with normalized columns.
4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors.
5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements.
6 will each feature vector units of (normalized), to determine the composition of matrix.
7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix.
8 of the weighted super-matrix units of (normalized), seeking the powe
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Author: chen |
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Description: In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition
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Author: sofi |
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Description: clear
num=[0,0,10]
den=[1,2,10]
p=roots(den)
[u,w]=solve( w^2=10 , 2*w*u=2 , u,w )
[y,x,t]=step(num,den)
plot(t,y)
[yss,n]=max(y)
finalvalue=dcgain(num,den)
percentovershoot=100*(yss-finalvalue)/finalvalue
timetopeak=t(n)
k=length(t)
while (y(k)>0.98*finalvalue)&(y(k)<1.02*finalvalue)
k=k-1
end
settlingtime=t(k)
p
w=vpa(w,4)
u=vpa(u,4)
yss
timetopeak
finalvalue
settlingtime
-clear num = [0,0,10] den = [1,2,10] p = roots (den) [u, w] = solve ( ' w ^ 2 = 10' , ' 2* w* u = 2 ' ,' u, w ' ) [y, x, t] = step (num, den) plot (t, y) [yss, n] = max (y) finalvalue = dcgain (num , den) percentovershoot = 100* (yss-finalvalue)/finalvalue timetopeak = t (n) k = length (t) while (y (k)> 0.98* finalvalue) & (y (k) < 1.02* finalvalue) k = k-1 end settlingtime = t (k) p w = vpa (w, 4) u = vpa (u, 4) yss timetopeak finalvalue settlingtime
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Author: 董森 |
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Description: 常见概率分布的随机数生成程序，包括均晕分布、高斯分布、对数正态分布、K分布、t分布、韦布尔分布。-Common probability distribution of the random number generation process, including both halo distribution, Gaussian distribution, lognormal distribution, K distribution, t distribution, Weibull distribution.
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Author: 袁浩 |
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Description: 模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始，对当前解重复“产生新解→计算目标函数差→接受或舍弃”的迭代，并逐步衰减t值，算法终止时的当前解即为所得近似最优解，这是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。退火过程由冷却进度表(Cooling
Schedule)控制，包括控制参数的初值t及其衰减因子Δt、每个t值时的迭代次数L和停止条件S。 -Simulated annealing algorithm derived from the theory of solid annealing, the solid heat to full high and let it slowly cooling, heating, the temperature rise inside the solid particles with the shape into disorder, which can be increased gradually while slowly cooling particles increasingly ordered, the temperature has reached equilibrium in each state, and finally reached the ground state at room temperature, which can be reduced to minimum. According to Metropolis criterion, particles tend to equilibrium at a temperature T, the probability e-ΔE/(kT), where E is the temperature T, internal energy, ΔE change its volume, k the Boltzmann constant. Simulated annealing with a solid portfolio optimization problem, the internal energy E is modeled as the objective function value f, temperature T evolved into control parameter t, which are solutions of combinatorial optimization problems of the simulated annealing algorithm: the initial solution from the initial value of t i and the control
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Author: leansmall |
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Description: FBG反射谱，要用的人会非常需要，需要的人可以看看，很不错的-FBG id1=fopen( lambdaCMT.txt , wt )
fid2=fopen( rhoCMT.txt , wt )
f1=inline( -i.*delta.*y1-k.*y2 , t , lambda , wk , delta , k , y1 , y2 )
f2=inline( -k.*y1+i.*delta.*y2 , t , lambda , wk , delta , k , y1 , y2 )
lambda=1547
while lambda<1552
t0=-501426
h=2.0057e+003
n=500
y1=1
y2=0
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Author: Xiaojun |
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Description: target tracking
The CV and CA models can be used to model the
distance between front and host vehicles, but we don’t
know when a specific model should be used. The
interacting multiple model (IMM) estimator [1] is an
algorithm which can be used to handle such case. In
IMM algorithm, at time k the previous estimates from the
multiple models are mixed based on the mixing
probabilities to generate different mixed initial conditions
for different filters.
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Author: jailin |
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Description: Chapter 18 README FILE
Prepared by: William H. Tranter
Department of Electrical and Computer Engineering
Virginia Tech - Mail Code 0350
Blacksburg, VA 24061
email: btranter@vt.edu
Revision Dates: June 20, 2004
Note: This readme file contains changes and or corrections to programs contained in Chapter 18 of the textbook
W. H. Tranter, K. S. Shanmugan, T. S. Rappaport, and K. L. Kosbar, Principles of Communications System Simulation with Wireless Applications, Prentice Hall PTR, 2004 (ISBN 0-13-494790-8).
Files: c18_cdmaex1.m and c18_cdmaK.m
Explanation: The two files c18_cdmaex1.m and c18_cdmaK.m are identical except for the file names. The name c18_cdmaex1.m was used in the text and the name c18_cdmaK.m was used in Appendix B. Sorry for the confusion.(CDMA communication system modeling, simulation, a full set of complete system modeling and simulation)
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Author: lklx520
|
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Description: 等效介质理论 K-T模型算法（球形，针行，硬币状孔隙）(The K-T model algorithm of the equivalent medium theory)
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Author: sousoutu |
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