Chapter 9 Random Processes
ENCS6161 - Probability and Stochastic Processes
Concordia University
Denition of a Random Process
Assume the we have a random experiment with outcomes w belonging to the sample set S . To each w S , we assign a time function X (
Armin Tavakoli Naeini (StudenID:260414299) Exercise 1:Time 1. (a) Since the time received by process P from the server is t=10 : 54 : 23 : 674 and tround-trip=24 ms, we can find the time that P should be set to (t p) from this formula: t p = t + tround-tr
The Case for a Single-Chip Multiprocessor
Kunle Olukotun, Basem A. Nayfeh, Lance Hammond, Ken Wilson, and Kunyung Chang
Computer Systems Laboratory Stanford University Stanford, CA 94305-4070 http:/www-hydra.stanford.edu
Abstract
Advances in IC processing
Introduction to OCL
Bernhard Beckert
U NIVERSITT KOBLENZ -L ANDAU
p.1
OCL
Object Constraint Language
Part of the UML standard.
p.2
OCL
Object Constraint Language
Part of the UML standard. Formal Specication Language. Precise semantics.
p.2
OCL
Object
Eoin Woods, 2005; http:/www.eoinwoods.info
OCL Quick Reference
Eoin Woods, Zuhlke Engineering, July 2005.
This document provides a quick reference summary of the Object Constraint Language, as I understand it to be as of the UML 1.5 standard. It should n
LECTURE NOTES Course 6.041-6.431 M.I.T. FALL 2000
Introduction to Probability
Dimitri P. Bertsekas and John N. Tsitsiklis
Professors of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, Massachusetts
These notes
Chapter 3, 4 Random Variables
ENCS6161 - Probability and Stochastic Processes
Concordia University
The Notion of a Random Variable
A random variable X is a function that assigns a real number X ( ) to each outcome in the sample space of a random experimen
Chapter 12 Introduction to Queueing Theory
ENCS6161 - Probability and Stochastic Processes
Concordia University
Elements of a Queueing System
A queueing system is dened by a/b/m/k , where a: type of arrival process. a = M Poisson Process b: service time d
Chapter 11 Markov Chains
ENCS6161 - Probability and Stochastic Processes
Concordia University
Markov Processes
A Random Process is a Markov Process if the future of the process given the present is independent of the past, i.e., if t1 < t2 < < tk < tk+1 ,
Chapter 10 Analysis and Processing of Random Signals
ENCS6161 - Probability and Stochastic Processes
Concordia University
Power Spectral Density
For WSS r.p. X (t), the power spectral density (PSD)
+
SX (f )
F [RX ( )] =
+ SX (f )ej 2f df
RX ( )ej 2f d
So
ELEC 462-6831
Assignment 1
Due Time: Tuesday Feb. 12th
Instructions:
There are 5 questions to be answered. Questions 1 and 2 are related to optimal receiver
design. Questions 3 and 4 are related to the Inter-Symbol Interference cancelation.
Question 5 is