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STRUCTURE

用戶(hù)定義的值類(lèi)型。與類(lèi)相似，結(jié)構(gòu)可以包含構(gòu)造函數(shù)、常數(shù)、字段、方法、屬性、索引器、運(yùn)算符和嵌套類(lèi)型。

  • This routine calls the glpk library to solve a LP/MIP problem. A typical LP problem has following s

    This routine calls the glpk library to solve a LP/MIP problem. A typical LP problem has following STRUCTURE: [min|max] C x s.t. Ax ["="|"<="|">="] b {x <= UB} {x >= LB} The calling syntax is: [XMIN,FMIN,STATUS,EXTRA]=glpkmex(SENSE,C,A,B,CTYPE,LB,UB,... VARTYPE,PARAM,LPSOLVER,SAVE)

    標(biāo)簽： problem following routine library

    上傳時(shí)間： 2014-12-01

    上傳用戶(hù)：changeboy

  • In 揚(yáng)erformance of multi-carrier DS CDMA Systems?we apply a multi-carrier signaling technique to a di

    In 揚(yáng)erformance of multi-carrier DS CDMA Systems?we apply a multi-carrier signaling technique to a direct-sequence CDMA system, where a data sequence multiplied by a spreading sequence modulates multiple carriers, rather than a single carrier. The receiver provides a correlator for each carrier, and the outputs of the correlators are combined with a maximal-ratio combiner. This type of signaling has the desirable properties of exhibiting a narrowband interference suppression effect, along with robustness to fading, without requiring the use of either an explicit RAKE STRUCTURE or an interference suppression filter.

    標(biāo)簽： multi-carrier erformance signaling technique

    上傳時(shí)間： 2017-07-31

    上傳用戶(hù)：宋桃子

  • sba, a C/C++ package for generic sparse bundle adjustment is almost invariably used as the last step

    sba, a C/C++ package for generic sparse bundle adjustment is almost invariably used as the last step of every feature-based multiple view reconstruction vision algorithm to obtain optimal 3D STRUCTURE and motion (i.e. camera matrix) parameter estimates. Provided with initial estimates, BA simultaneously refines motion and STRUCTURE by minimizing the reprojection error between the observed and predicted image points.

    標(biāo)簽： adjustment invariably package generic

    上傳時(shí)間： 2013-12-18

    上傳用戶(hù)：xsnjzljj

  • to study the example of creation of tables, formative DB, and filling with their information. Using

    to study the example of creation of tables, formative DB, and filling with their information. Using this example to create DB, satisfying a logical STRUCTURE, to developed in previous laboratory work and to fill with its information. To study a command SQL SELECT.

    標(biāo)簽： information formative creation example

    上傳時(shí)間： 2017-09-15

    上傳用戶(hù)：dengzb84

  • I implement Dijkstra s Single Source Shortest Path, say SSP, algorithm for directed graphs using a s

    I implement Dijkstra s Single Source Shortest Path, say SSP, algorithm for directed graphs using a simple data STRUCTURE, say simple scheme, Fibonacci heaps, say F-heap scheme, and Pairing heaps, say P-heap scheme, and measure the relative performance of the three implementations.

    標(biāo)簽： implement algorithm Dijkstra Shortest

    上傳時(shí)間： 2014-01-01

    上傳用戶(hù)：BIBI

  • Data STRUCTURE and algorithms in C++

    C++ 的經(jīng)典算法書(shū), pdf版, 十分清晰, Goodrich等人著.

    標(biāo)簽： C++

    上傳時(shí)間： 2015-06-03

    上傳用戶(hù)：m009988

  • data STRUCTURE in C快速排序

    設(shè)初始序列為a1,a2,……,an，以序列中的某個(gè)元素ai為基準(zhǔn)(軸)，經(jīng)調(diào)整后，使得ai左邊的元素均小于ai，右邊的均大于等于ai，而后對(duì)這兩個(gè)子區(qū)再分別使用快速排序。 [具體要求] (1) 需要用一維數(shù)組a來(lái)存儲(chǔ)等待排序的序列； (2) 設(shè)置兩個(gè)工作指針i和j； (3) 每次快速排序都以排序區(qū)域的首元素為基準(zhǔn)(軸)； (4) 程序用遞歸函數(shù)來(lái)實(shí)現(xiàn)。

    標(biāo)簽： c++

    上傳時(shí)間： 2015-12-28

    上傳用戶(hù)：markhu53

  • read_segy_file_legacy

    read_segy_file_legacy Function reads a SEG-Y file and outputs a seismic STRUCTURE Last updated: April 6, 2007: fix bug in reading of user-specified header

    標(biāo)簽： read_segy_file_legacy

    上傳時(shí)間： 2019-04-04

    上傳用戶(hù)：wc12

  • =read_segy_file

    Function reads a SEG-Y file and outputs a seismic STRUCTURE; the function

    標(biāo)簽： read_segy_file

    上傳時(shí)間： 2019-04-04

    上傳用戶(hù)：wc12

  • Web Data Mining

    The rapid growth of the Web in the past two decades has made it the larg- est publicly accessible data source in the world. Web mining aims to dis- cover useful information or knowledge from Web hyperlinks, page con- tents, and usage logs. Based on the primary kinds of data used in the mining process, Web mining tasks can be categorized into three main types: Web STRUCTURE mining, Web content mining and Web usage mining.

    標(biāo)簽： Mining Data Web

    上傳時(shí)間： 2020-05-26

    上傳用戶(hù)：shancjb

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