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.
標(biāo)簽： bapibus DATA 1006 TYPE
上傳時(shí)間： 2014-01-17
上傳用戶：jennyzai
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.
標(biāo)簽： DATA HEADDATA BEGIN BAPIMATHEAD
上傳時(shí)間： 2015-12-17
上傳用戶：古谷仁美
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.
標(biāo)簽： bapibus DATA 1006 TYPE
上傳時(shí)間： 2015-12-17
上傳用戶：王小奇
AI Technology used in Data Retrieve and Search Engine.
標(biāo)簽： Technology Retrieve Engine Search
上傳時(shí)間： 2014-06-01
上傳用戶：爺?shù)臍赓|(zhì)
data mining concept and technic
標(biāo)簽： concept technic mining data
上傳時(shí)間： 2013-12-11
上傳用戶：ainimao
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
標(biāo)簽： points the minimize respect
上傳時(shí)間： 2014-01-02
上傳用戶：gyq
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
標(biāo)簽： preprocessing frequently selection technique
上傳時(shí)間： 2014-01-19
上傳用戶：lindor
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.
標(biāo)簽： files data distributed The
上傳時(shí)間： 2014-12-06
上傳用戶：wuyuying
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.
標(biāo)簽： performance asymptotic examines non-data
上傳時(shí)間： 2015-12-30
上傳用戶：225588
In recent years large scientific interest has been devoted to joint data decoding and parameter estimation techniques. In this paper, iterative turbo decoding joint to channel frequency and phase estimation is proposed. The phase and frequency estimator is embedded into the structure of the turbo decoder itself, taking into consideration both turbo interleaving and puncturing. Results show that the proposed technique outperforms conventional approaches both in terms of detection capabilities and implementation complexity.
標(biāo)簽： scientific parameter interest decoding
上傳時(shí)間： 2015-12-30
上傳用戶：894898248
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