基于simulink的uwb仿真 uwb.mdl: UWB model - open this to Begin. uwb_lib.mdl: Library blocks for UWB model. uwb_init.m: Initialization called before model is loaded. uwb_settings: Sets up structure containing system parameters ( uwb in workspace). uwb_imr.m: Initializes UWB channel impulse response. uwb_sv_*.m: Four M-files used to generate channel impulse responses (MAT files).
上傳時間： 2013-12-22
上傳用戶：gengxiaochao
Windows Programming Bible. The Beginner s best book to Begin windows programming.
標簽： Programming programming Beginner Windows
上傳時間： 2014-10-10
上傳用戶：cuibaigao
Procedure TSPSA: Begin init-of-T { T為初始溫度} S={1，……，n} {S為初始值} termination=false while termination=false Begin for i=1 to L do Begin generate(S′form S) { 從當前回路S產(chǎn)生新回路S′} Δt:=f(S′))-f(S) {f(S)為路徑總長} IF(Δt<0) OR (EXP(-Δt/T)>Random-of-[0,1]) S=S′ IF the-halt-condition-is-TRUE THEN termination=true End T_lower End End
標簽： Procedure init-of-T TSPSA Begin
上傳時間： 2013-12-20
上傳用戶：shinesyh
When I use opencv, I use this very useful paper to Begin the study. This is all I searched from the internet informations.
上傳時間： 2014-01-12
上傳用戶：lht618
source code of Begin linux programming
標簽： programming source Begin linux
上傳時間： 2015-10-03
上傳用戶：gaojiao1999
function Binary_Search(L,a,b,x) Begin if a>b then return(-1) else Begin m:=(a+b) div 2 if x=L[m] then return(m) else if x>L[m] then
標簽： Begin Binary_Search function return
上傳時間： 2015-12-17
上傳用戶：tb_6877751
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.
標簽： DATA HEADDATA Begin BAPIMATHEAD
上傳時間： 2015-12-17
上傳用戶：古谷仁美
PADI 網(wǎng)上學習 Potential divers will have another way to Begin independent study with the PADI Open Water Diver Course Online
標簽： PADI independent Potential another
上傳時間： 2015-12-27
上傳用戶：zhichenglu
procedureTForm1.Button1Click(Sender:TObject) var S:String Begin S:=idhttp1.Get(PostURL.text+ ?username= +EdUserName.Text+ &password= +EdPassWord.Text+ &password2= +EdPassWord.Text+ &email= + EdEMail.Text+ &gendernew=1&year=&month=&day=&locationnew =&site=&oicq=&icq=&yahoo=&msn=&bio=&styleidnew =&tppnew=0&pppnew=0&timeformatnew=12&cstatus=&dateformatnew =yyyy-mm-dd&showemail=1&newsletter=1&timeoffsetnew=8&avatar =&sig=®submit=注冊 ) //一句代碼 ifPos( 非常感謝您的注冊 ,s)>1then //判斷部分 ShowMessage( 注冊成功! ) else ifPos( 已經(jīng)被注冊了 ,S)>1then ShowMessage( 該用戶名或E-Mail已被注冊! ) else ShowMessage( 已關(guān)閉注冊,注冊失敗! ) end
標簽： procedureTForm PostURL TObject Button
上傳時間： 2014-08-24
上傳用戶：努力努力再努力
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
蟲蟲下載站版權(quán)所有 京ICP備2021023401號-1
