看n2實例 #Create a simulator object
set ns [new Simulator]
#Define different colors for data flows
#$ns color 1 Blue
#$ns color 2 Red
#Open the nam trace file
set nf [open out-1.nam w]
$ns namtrace-all $nf
set f0 [open out0.tr w]
set f1 [open out1.tr w]
#Define a finish procedure
proc finish {} {
global ns nf
$ns flush-trace
#Close the trace file
close $nf
#Execute nam on the trace file
exit 0
}
#Create four nodes
set n0 [$ns node]
set n1 [$ns node]
set n2 [$ns node]
set n3 [$ns node]
#Create links between the nodes
$ns duplex-link $n0 $n2 1Mb 10ms
Digital Signature Algorithm (DSA)是Schnorr和ElGamal簽名算法的變種,被美國NIST作為DSS(DigitalSignature Standard)。算法中應用了下述參數:
p:L bits長的素數。L是64的倍數,范圍是512到1024;
q:p - 1的160bits的素因子;
g:g = h^((p-1)/q) mod p,h滿足h < p - 1, h^((p-1)/q) mod p > 1;
x:x < q,x為私鑰 ;
y:y = g^x mod p ,( p, q, g, y )為公鑰;
H( x ):One-Way Hash函數。DSS中選用SHA( Secure Hash Algorithm )。
p, q, g可由一組用戶共享,但在實際應用中,使用公共模數可能會帶來一定的威脅。簽名及驗證協議如下:
1. P產生隨機數k,k < q;
2. P計算 r = ( g^k mod p ) mod q
s = ( k^(-1) (H(m) + xr)) mod q
簽名結果是( m, r, s )。
3. 驗證時計算 w = s^(-1)mod q
u1 = ( H( m ) * w ) mod q
u2 = ( r * w ) mod q
v = (( g^u1 * y^u2 ) mod p ) mod q
若v = r,則認為簽名有效。
DSA是基于整數有限域離散對數難題的,其安全性與RSA相比差不多。DSA的一個重要特點是兩個素數公開,這樣,當使用別人的p和q時,即使不知道私鑰,你也能確認它們是否是隨機產生的,還是作了手腳。RSA算法卻作不到。
計算pi
** Pascal Sebah : September 1999
**
** Subject:
**
** A very easy program to compute Pi with many digits.
** No optimisations, no tricks, just a basic program to learn how
** to compute in multiprecision.
OpenGL中的各種轉換是通過矩陣運算實現的,具體的說,就是當發出一個轉換命令時,該命令會生成一個4X4階的轉換矩陣(OpenGL中的物體坐標一律采用齊次坐標,即(x, y, z, w),故所有變換矩陣都采用4X4矩陣),當前矩陣與這個轉換矩陣相乘,從而生成新的當前矩陣。例如,對于頂點坐標v ,轉換命令通常在頂點坐標命令之前發出,若當前矩陣為C,轉換命令構成的矩陣為M,則發出轉換命令后,生成的新的當前矩陣為CM,這個矩陣再乘以頂點坐標v,從而構成新的頂點坐標CMv。上述過程說明,程序中繪制頂點前的最后一個變換命令最先作用于頂點之上。這同時也說明,OpenGL編程中,實際的變換順序與指定的順序是相反的。文檔對其進行了詳細的分析。
K9F1208U0M 的ALE、CLE分別由DSP 的A1 和A0 控制。DSP的低8位數據線直接與閃存的I/O0-I/O7 相連,實現命令、地址和數據的傳輸; DSP的通用I/O口IOA2 接R/B,監測存儲器的工作狀態,當R/ B 處于低電平時,表示有編程、擦除或隨機讀操作正在進行;操作完成后, R/ B 會自動返回高電平。DSP的W E 、R D 分別接FLASH的W E 、R E , 控制讀、寫操作。CS2接閃存的片選線CE。
The task of clustering Web sessions is to group Web sessions based on similarity and consists of maximizing the intra-
group similarity while minimizing the inter-group similarity.
The first and foremost question needed to be considered in clustering
W b sessions is how to measure the similarity between Web
sessions.However.there are many shortcomings in traditiona1
measurements.This paper introduces a new method for measuring
similarities between Web pages that takes into account not only the
URL but also the viewing time of the visited web page.Yhen we
give a new method to measure the similarity of Web sessions using
sequence alignment and the similarity of W eb page access in detail
Experiments have proved that our method is valid and e幣cient.