This directory includes matlab interface of the curvelet transform
using usfft.
Basic functions
fdct_usfft.m -- forward curvelet transform
afdct_usfft.m -- adjoint curvelet transform
ifdct_usfft.m -- inverse curvelet transform
fdct_usfft_param.m -- returns the location of each curvelet in phase-space
Useful tools
fdct_usfft_dispcoef.m -- returns a matrix contains all curvelet coefficients
fdct_usfft_pos2idx.m -- for fixed scale and fixed direction, returns
the curvelet which is closest to a certain point on the image
Demos
fdct_usfft_demo_basic.m -- display the shape of a curvelet
fdct_usfft_demo_recon.m -- partial reconstruction using curvelet
fdct_usfft_demo_disp.m -- display all the curvelet coefficients of an image
fdct_usfft_demo_denoise.m -- image denoising using curvelet
UWB 功率控制 容量
Main Matlab script is in runsim.m. It generates random topologies,
optimizes, and display results.
IMPORTANT: you may need to add manually the lib path in Matlab in order to
get all the necessary functions.
Reference: Radunovic, Le Boudec, "Joint Power Control, Scheduling and Routing in UWB networks"
function [U,center,result,w,obj_fcn]= fenlei(data)
[data_n,in_n] = size(data)
m= 2 ％ Exponent for U
max_iter = 100 ％ Max. iteration
min_impro =1e-5 ％ Min. improvement
c=3
[center, U, obj_fcn] = fcm(data, c)
for i=1:max_iter
if F(U)>0.98
break
else
w_new=eye(in_n,in_n)
center1=sum(center)/c
a=center1(1)./center1
deta=center-center1(ones(c,1),:)
w=sqrt(sum(deta.^2)).*a
for j=1:in_n
w_new(j,j)=w(j)
end
data1=data*w_new
[center, U, obj_fcn] = fcm(data1, c)
center=center./w(ones(c,1),:)
obj_fcn=obj_fcn/sum(w.^2)
end
end
display(i)
result=zeros(1,data_n) U_=max(U)
for i=1:data_n
for j=1:c
if U(j,i)==U_(i)
result(i)=j continue
end
end
end
