OFDM based on the massive pilot channel estimation algorithm simulation, including the LS estimation algorithm ANALYSIS of LSE
標(biāo)簽： estimation simulation algorithm including
上傳時間： 2017-03-28
上傳用戶：wpwpwlxwlx
it is the matlab implementation of CDMA2009 for the AWGN noise source crack for all the matter in the ANALYSIS
標(biāo)簽： the implementation for matlab
上傳時間： 2017-03-29
上傳用戶：han_zh
Advanced ASIC Chip Synthesis Using Synopsys Design Compiler. This second edition of this book describes the advanced concepts and techniques used towards ASIC chip synthesis, physical synthesis, formal verification and static timing ANALYSIS, using the Synopsys suite of tools.
標(biāo)簽： Synthesis Advanced Synopsys Compiler
上傳時間： 2017-04-04
上傳用戶：lanwei
1.an fpga implementation of the image space reconstruction algorithm for hyperspectral imaging ANALYSIS 2. fpga implemention of a median filter 3. fpga implementation of digital filters 4.hardware acceleration of edge detection algorithm on fpgas 5.implementation and evaluation of image processing algorithms on reconfigurable architecture using C-based hardware descriptive languages 6. implementing 2D median filter in fpgas 7.視頻圖像處理與分析的網(wǎng)絡(luò)資源
標(biāo)簽： implementation reconstruction hyperspectral algorithm
上傳時間： 2014-01-10
上傳用戶：894898248
網(wǎng)頁采集系統(tǒng) ================= 安裝配置 ------- 1 程序我就不說了 2 配置文件 applicationContext.xml 里面有詳細(xì)的注釋 3 已經(jīng)包含了多個論壇博客的參數(shù)，如CSDN論壇、博客園、新浪博客、百度Hi、ccidnet等的解析參數(shù) 需要的類庫 --------- 1 Spring 2.5 2 common-logging 1.1 3 paoding-ANALYSIS 2.0.4-beta 4 commons-dbcp-1.2.2.jar 5 mysql-connector-java-5.1.7-bin.jar 6 commons-pool-1.4.jar 7 Lucene.2.4.0.jar 8 Lucene-highlighter-2.4.0.jar 9 java2000.jar 演示例子 ------- 本地：在com.laozizhu.search.demo目錄下面 B/S: http://search.laozizhu.com
標(biāo)簽： applicationContext xml 頁 采集系統(tǒng)
上傳時間： 2017-04-07
上傳用戶：牧羊人8920
統(tǒng)計模式識別工具箱（Statistical Pattern Recognition Toolbox）包含： 1，ANALYSIS of linear discriminant function 2，F(xiàn)eature extraction: Linear Discriminant ANALYSIS 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines
標(biāo)簽： Statistical Recognition Pattern Toolbox
上傳時間： 2014-01-03
上傳用戶：璇珠官人
The philosophy of the book is to present various pattern recognition tasks in a unified way, including image ANALYSIS, speech processing, and communication applications. Despite their differences, these areas do share common features and their study can only benefit from a unified approach.
標(biāo)簽： recognition philosophy pattern present
上傳時間： 2017-05-05
上傳用戶：plsee
In the last decade the processing of polygonal meshes has emerged as an active and very productive research area. This can basically be attributed to two developments: Modern geometry acquisition devices, like laser scanners and MRT, easily produce raw polygonal meshes of ever growing complexity Downstream applications like ANALYSIS tools (medical imaging), computer aided manufacturing, or numerical simulations all require high quality polygonal meshes as input. The need to bridge the gap between raw triangle soup data and high-quality polygon meshes has driven the research on ecient data structures and algorithms that directly operate on polygonal meshes rather than on a (most often not feasible) intermediate CAD representation.
標(biāo)簽： processing productive the polygonal
上傳時間： 2017-06-03
上傳用戶：TF2015
The most straightforward approximation is the standard Gaussian approximation, where the MAI is approximated by a Gaussian random variable. This approximation is simple, however it is not accurate in general. In situations where the number of users is not large, the Gaussian approximation is not appropriate. In-depth ANALYSIS of must be applied. The Holtzman?s improved Gaussian approximation provides a better approximation to the MAI term. The approximation conditions the interference term on the operation condition of each user.
標(biāo)簽： approximation straightforward the Gaussian
上傳時間： 2017-06-03
上傳用戶：dyctj
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding ANALYSIS fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
標(biāo)簽： foundations The consists sections
上傳時間： 2017-06-22
上傳用戶：lps11188
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