代码搜索:Generates

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m fig3_9.m

% % This file generates the graph shown in Figure 3-9 % % Copyright (c) 1999 by P.Bretchko and R.Ludwig % "RF Circuit Design: Theory and Practice" % close all; % close all opened graphs
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m ss_mlsrs.m

function [seq]=ss_mlsrs(connections); % [seq]=ss_mlsrs(connections) % SS_MLSRS generates the maximal length shift-register sequence when the % shift-register connections are given as input to
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m bdt_f531.m

% MATLAB script that generates the probability of error versus the signal-to-noise ratio. initial_snr=0; final_snr=12; snr_step=0.75; tolerance=eps; % tolerance used for the integration
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cpp prg4_2.cpp

// File: prg4_2.cpp // the program generates 12 random integers in the range // 100 to 999. add each number value to vector vSmall if // value < 400, to vector vMedium if 400
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m bdt_f531.m

% MATLAB script that generates the probability of error versus the signal-to-noise ratio. initial_snr=0; final_snr=12; snr_step=0.75; tolerance=eps; % tolerance used for the integration
www.eeworm.com/read/213940/15121999

m ss_mlsrs.m

function [seq]=ss_mlsrs(connections); % [seq]=ss_mlsrs(connections) % SS_MLSRS generates the maximal length shift-register sequence when the % shift-register connections are given as input to
www.eeworm.com/read/213715/15127496

c msp430x22x4_tb_11.c

//****************************************************************************** // MSP430F22x4 Demo - Timer_B, PWM TB1-2, Up Mode, 32kHz ACLK // // Description: This program generates two PWM ou
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m gsamp.m

function x = gsamp(mu, covar, nsamp) %GSAMP Sample from a Gaussian distribution. % % Description % % X = GSAMP(MU, COVAR, NSAMP) generates a sample of size NSAMP from a % D-dimensional Gaussian distri
www.eeworm.com/read/210161/15205724

m ss_mlsrs.m

function [seq]=ss_mlsrs(connections); % [seq]=ss_mlsrs(connections) % SS_MLSRS generates the maximal length shift-register sequence when the % shift-register connections are given as input to
www.eeworm.com/read/210157/15205820

m bdt_f531.m

% MATLAB script that generates the probability of error versus the signal-to-noise ratio. initial_snr=0; final_snr=12; snr_step=0.75; tolerance=eps; % tolerance used for the integration