代码搜索：matlab RFID
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www.eeworm.com/read/305828/13760115
pl sinoutgensynth.pl
############################################################################################
##
## DSP Builder (Version 2.1.3 Build 31)
## Quartus II development tool and MATLAB/Simulink Interface
www.eeworm.com/read/305828/13760122
v tb_sinout.v
//////////////////////////////////////////////////////////////////////////////////////////////
//
// DSP Builder (Version 2.1.3 Build 31)
// Quartus II development tool and MATLAB/Simulink Interfa
www.eeworm.com/read/305828/13760132
vhd tb_sinout.vhd
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--
-- DSP Builder (Version 2.1.3 Build 31)
-- Quartus II development tool and MATLAB/Simulink Interface
www.eeworm.com/read/305635/13764108
m exa110701_pwelch.m
%-------------------------------------------------------------------------
% exa1100701_pwelch.m, for example 11.7.1 and fig. 11.5.1(d)
% to estimate the PSD of x(n) by Welch average;
%------------
www.eeworm.com/read/305635/13764147
m exa090804_svd.m
%-----------------------------------------------------------------------
% exa090804_svd.m, for example 9.8.4
% to test svd.m;
% 在MATLAB6.1 和 MATLAB5.3下运行该程序，所得到的 U,V矩阵稍有不同，
% 但奇异值是一样的
%---------
www.eeworm.com/read/305568/13766206
frm vbex7_14.frm
VERSION 5.00
Begin VB.Form Form1
Caption = "VB Link to MATLAB"
ClientHeight = 3195
ClientLeft = 60
ClientTop = 345
ClientWidth = 5115
Link
www.eeworm.com/read/305390/13772267
m gseid.m
function X=gseid(A,B,P,delta, max1)
% Input - A is an N x N nonsingular matrix
% - B is an N x 1 matrix
% - P is an N x 1 matrix; the initial guess
% - delta is t
www.eeworm.com/read/305390/13772286
m approot.m
function R = approot (f,X,epsilon)
% Input - f is object function
% - X is the vector of abscissas
% - epsilon is the tolerance
% Output - R is the vector of approximate
www.eeworm.com/read/305390/13772287
m jacobi.m
function X=jacobi(A,B,P,delta, max1)
% Input - A is an N x N nonsingular matrix
% - B is an N x 1 matrix
% - P is an N x 1 matrix; the initial guess
% - delta is
www.eeworm.com/read/305390/13772296
m lspoly.m
function C = lspoly(X,Y,M)
%Input - X is the 1xn abscissa vector
% - Y is the 1xn ordinate vector
% - M is the degree of the least-squares polynomial
% Output - C is the c