data: it_header TYPE bapibus1006_head OCCURS 0 WITH HEADER LINE.
data: it_centr TYPE bapibus1006_central OCCURS 0 WITH HEADER LINE.
data: g_bp LIKE bapibus1006_head-bpartner.
data: g_cat LIKE bapibus1006_head-partn_cat VALUE 1 .
data: it_guid LIKE bus000___i OCCURS 0 WITH HEADER LINE.
data: it_custo LIKE bus000_eew OCCURS 0 WITH HEADER LINE.
data:BEGIN OF HEADdata.
INCLUDE STRUCTURE BAPIMATHEAD.
data:END OF HEADdata.
data:BEGIN OF PLANTdata.
INCLUDE STRUCTURE BAPI_MARC.
data:END OF PLANTdata.
data:BEGIN OF PLANTdataX.
INCLUDE STRUCTURE BAPI_MARCX.
data:END OF PLANTdataX.
READ TABLE XMKPF INDEX 1.
IF XMKPF-TCODE2 = MIGO_GR .
LOOP AT XMSEG.
data: it_header TYPE bapibus1006_head OCCURS 0 WITH HEADER LINE.
data: it_centr TYPE bapibus1006_central OCCURS 0 WITH HEADER LINE.
data: g_bp LIKE bapibus1006_head-bpartner.
data: g_cat LIKE bapibus1006_head-partn_cat VALUE 1 .
data: it_guid LIKE bus000___i OCCURS 0 WITH HEADER LINE.
data: it_custo LIKE bus000_eew OCCURS 0 WITH HEADER LINE.
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
Feature selection is a preprocessing technique frequently used in data mining and machine learning tasks. It can reduce dimensionality, remove irrelevant data, increase learning accuracy, and improve results comprehensibility. FCBF is a fast correlation-based filter algorithm designed for high-dimensional data and has been shown effective in removing both irrelevant features and redundant features
The data files included are .MAT or *.dat (ASCII)files. The m-files and the
data may be distributed, provided that the source is acknowledged in any
publication and the data are not sold. Since this software is being distributed
free of charge, the authors are not offering any technical support. Students who
have any questions or difficulties using this software, or require the
additional functions from the Signal Processing Toolbox should contact their
professor.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
