ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points.
Ordinary usage:
[R, T] = icp(model,data)
INPUT:
model - matrix with model points,
data - matrix with data points,
OUTPUT:
R - rotation matrix and
T - translation vector accordingly
so
newdata = R*data + T .
newdata are transformed data points to fit model
see help icp for more information
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
％ A 2D homogeneous convection-diffusion case (u=exp(-ex*deta*x-ex*deta*y) with a square with
％ all Dirichlet boundary, note that reaction coefficient is not zero
％ by indirect BKM
A 2D homogeneous Helmholtz case (u=sin(x)cos(y) with a square) with
％ two Dirichlet edges (x=1,y=1) and two Neumann edges (x=0,y=0)
％ by indirect symmetric BKM
Please read them carefully before you write the package to their specific functions (at least 20 characters). As far as possible not to let the station for the time you have spent on that amendment. Have to extract the password to the password
