提出了一種CPM信號Laurent分解和最小均方誤差檢測相結(jié)合的低復(fù)雜度接收機(jī),在降低運(yùn)算量的同時(shí),保證了低信噪比情況下接近于最大似然ML、最優(yōu)檢測器的接收機(jī)性能。理論推導(dǎo)和仿真結(jié)果均驗(yàn)證了該算法的有效性。
上傳時(shí)間： 2013-11-15
上傳用戶：徐孺
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
標(biāo)簽： instantaneous algorithm Bayesian Gaussian
上傳時(shí)間： 2013-12-19
上傳用戶：jjj0202
Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble. Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
標(biāo)簽： Description Randomness Attribute Filtered
上傳時(shí)間： 2015-04-10
上傳用戶：ynzfm
WML（Wireless Markup Language - 無線標(biāo)記語言）這種描述語言同我們常聽說的HTML語言同出一家，都屬于X ML語言這一大家族。HTML語言寫出的內(nèi)容，我們可以在我們的PC機(jī)上用IE或是Netscape等瀏覽器進(jìn)行閱讀，而 WML語言寫出的文件則是專門用來在手機(jī)等的一些無線終端顯示屏上顯示，供人們閱讀的，并且同樣也可以向使用者提供人機(jī)交互界面，接受使用者輸入的查詢等信息，然后向使用者返回他所想要獲得的最終信息。
標(biāo)簽： Language Wireless Markup HTML
上傳時(shí)間： 2013-12-05
上傳用戶：csgcd001
IDCT-M is a medium speed 1D IDCT core -- it can accept a continous stream of 12-bit input words at a rate of -- 1 bit/ck cycle, operating at 50MHz speed, it can process MP@ML MPEG video -- the core is 100％ synthesizable
標(biāo)簽： continous IDCT-M accept medium
上傳時(shí)間： 2015-07-07
上傳用戶：1583060504
供初學(xué)空時(shí)編碼（vblast接收）的matlab仿真程序，是關(guān)于vblast接收中ML算法的簡單的仿真
標(biāo)簽： vblast matlab 空時(shí)編碼 接收
上傳時(shí)間： 2015-09-04
上傳用戶：diets
該程序模擬UNIX中save與resume函數(shù),并介紹在VC中如何使用匯編進(jìn)行機(jī)器級的操作. 主函數(shù)很簡單首先引入兩個(gè)外部函數(shù),extern "C"表示按傳統(tǒng)C命名習(xí)慣.函數(shù)save將程序指針保存在(*s)中并返回0,為什么有 if(save(&sp)){...} if后的語句看起來永遠(yuǎn)都不會(huì)被執(zhí)行,但是運(yùn)行結(jié)果表明它被執(zhí)行了.這個(gè)問題同UNIX中處理機(jī)調(diào)度函數(shù)(switch)的那個(gè)if語句(第一句)一樣. 程序執(zhí)行完save(&sp)后得到因?yàn)闂l件為假而執(zhí)行else語句,卻在判斷之前將程序指針保存在sp中了. else語句中的resume(&sp),該函數(shù)很狡猾將堆棧中的返回地址改變了,改到了sp所指出,即將程序指針改到了執(zhí)行條件判斷前.resume返回1,條件滿足,執(zhí)行if語句. save函數(shù)堆棧: eip ebp+8 s ebp+4 ebp ebp+0 resume函數(shù)堆棧與save的相同. 新建一個(gè)win32的工程,將unixc.cpp和unix.obj加入過程即可. unix.obj是用masm6.11生成的:ml /c /coff unix.asm,生成coff格式的obj而不是omf格式.
標(biāo)簽： save resume extern 函數(shù)
上傳時(shí)間： 2015-09-10
上傳用戶：變形金剛
ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN
標(biāo)簽： the provides computer download
上傳時(shí)間： 2015-11-29
上傳用戶：ywqaxiwang
Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.
標(biāo)簽： Hidden_Markov_model_for_automatic speech_recognition implements left-right
上傳時(shí)間： 2016-01-23
上傳用戶：569342831
一種常用空分復(fù)用的MIMO系統(tǒng)，v-blast系統(tǒng)的各種檢測算法：ML，MMSE,ZF,以及采用迫零的連續(xù)干擾消除檢測算法
上傳時(shí)間： 2013-12-13
上傳用戶：源弋弋
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