代码搜索：Estimation
找到约 3,786 项符合「Estimation」的源代码
代码结果 3,786
www.eeworm.com/read/249550/12488365
pdf channel estimation techniques based on pilot arrangement in ofdm systems.pdf
www.eeworm.com/read/238040/13910504
pdf alpha channel estimation in high resolution images and image sequences.pdf
www.eeworm.com/read/355606/10254416
m ls_channel_extimation.m
randn('state',sum(100*clock));
%%%channel model
tau= [0 0.5e-006 1.0000e-006];
fd=0.4;
pdb=[0 -5 -10];
ts=0.5e-006;
chan = rayleighchan(ts,fd,tau,pdb);
OFDM_carrier_nubmber=64;
SNR=0:10:20;
h
www.eeworm.com/read/291161/8439083
m hosademo.m
function hosademo
%HOSADEMO Demonstrates some of the capabilities of the HOSA Toolbox
% hosademo
%
% HOSADEMO presents a menu of demos.
% The HOSA Toolbox offers several routines for
%
www.eeworm.com/read/290613/8471852
m hosademo.m
function hosademo
%HOSADEMO Demonstrates some of the capabilities of the HOSA Toolbox
% hosademo
%
% HOSADEMO presents a menu of demos.
% The HOSA Toolbox offers several routines for
%
www.eeworm.com/read/289837/8523607
m hosademo.m
function hosademo
%HOSADEMO Demonstrates some of the capabilities of the HOSA Toolbox
% hosademo
%
% HOSADEMO presents a menu of demos.
% The HOSA Toolbox offers several routines for
%
www.eeworm.com/read/430527/8740550
m hosademo.m
function hosademo
%HOSADEMO Demonstrates some of the capabilities of the HOSA Toolbox
% hosademo
%
% HOSADEMO presents a menu of demos.
% The HOSA Toolbox offers several routines for
%
www.eeworm.com/read/386050/8767491
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e
www.eeworm.com/read/428849/8834935
m contents.m
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li
www.eeworm.com/read/428849/8834962
m~ contents.m~
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li