Design of High Speed Multichannel Data Gathering System Based on FPGA基于FPGA的高速多通道數(shù)據(jù)采集系統(tǒng)的設(shè)計(jì)
標(biāo)簽： FPGA Multichannel Gathering Design
上傳時(shí)間： 2016-10-10
上傳用戶：chenbhdt
企業(yè)營銷管理系統(tǒng)程序使用說明(VB+SQL2000)將目錄data下的db_Csell_Data.MDF和db_Csell_Log.LDF文件拷貝到SQL Server 2000的“MSSQL”->“data”目錄下. 如果沒有安裝Sql Server,請先安裝Sql Server，安裝SQL SERVER 2000數(shù)據(jù)庫時(shí)，在身份安全驗(yàn)證機(jī)制選項(xiàng)中 必須要選擇“Windows和Sql server混合安全驗(yàn)證機(jī)制。SA的密碼設(shè)置為空。如果您已經(jīng)安裝好了Sql Server 2000，
標(biāo)簽： 2000 db_Csell_Data db_Csell_Log SQL
上傳時(shí)間： 2014-01-05
上傳用戶：qq521
data插件一個(gè)js 直接在頁面文件中調(diào)用即可使用
上傳時(shí)間： 2016-10-12
上傳用戶：時(shí)代電子小智
data sheet of LMD18200T
標(biāo)簽： 18200T 18200 sheet data
上傳時(shí)間： 2016-10-13
上傳用戶：zjf3110
3d medical brain data
標(biāo)簽： medical brain data 3d
上傳時(shí)間： 2014-12-08
上傳用戶：shus521
// -*- Mode: Verilog -*- // Filename : wb_master.v // Description : Wishbone Master Behavorial // Author : Winefred Washington // Created On : 2002 12 24 // Last Modified By: . // Last Modified On: . // Update Count : 0 // Status : Unknown, Use with caution! // Description Specification // General Description: 8, 16, 32-bit WISHBONE Master // Supported cycles: MASTER, READ/WRITE // MASTER, BLOCK READ/WRITE // MASTER, RMW // Data port, size: 8, 16, 32-bit // Data port, granularity 8-bit // Data port, Max. operand size 32-bit // Data transfer ordering: little endian // Data transfer sequencing: undefined
標(biāo)簽： Description Behavorial wb_master Filename
上傳時(shí)間： 2014-07-11
上傳用戶：zhanditian
data preprocessing for text classification.
標(biāo)簽： classification preprocessing data text
上傳時(shí)間： 2016-10-16
上傳用戶：zhangliming420
CRC碼產(chǎn)生器與校驗(yàn)器程序 Features ： Executes in one clock cycle per data word Any polynomial from 4 to 32 bits Any data width from 1 to 256 bits Any initialization value Synchronous or asynchronous reset
標(biāo)簽： polynomial Features Executes clock
上傳時(shí)間： 2013-12-18
上傳用戶：Ants
Flex JSP Exchanging data
標(biāo)簽： Exchanging Flex data JSP
上傳時(shí)間： 2016-10-21
上傳用戶：hzy5825468
Recent advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique’s potential.
標(biāo)簽： experimental generation advances enormous
上傳時(shí)間： 2016-10-23
上傳用戶：wkchong
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