c A.H.Dixon
11
CMPT 250 : Week 5 (Oct 4  8)
24
INSTRUCTION DESIGN
With an understanding of the parts of an instruction and their purpose, the design of
instructions is inuenced by several major factors:
1. Size of the instruction set.
Instructions set si
NAME:
Student Number:
5 pages
1
CMPT 250: Sample Test 1(A )
30 marks
Answer all questions on the test paper. Use the backs of the pages for rough work, if necessary. Be sure
your name and student number are on all pages. A VHDL summary sheet, which may be
SFU School of Engineering Science
ENSC 101 Midterm Examination
Please read the instructions to this midterm very carefully.
Ensure you legibly write your name and your student number on the answer sheet.
This is a closed book examination and, except for p
ENEE621: HW Assignment #6
Spring 2013
1. (Problem V.E.2 in Poor) Suppose that the state equation in the KalmanBucy model is modified as
follows:
Xn+1 = An Xn + Bn Un + n sn , n = 0, 1, 2, . . . ,
where cfw_sk ; k cfw_0, 1, 2, . . . =: Z+ is a known sequ
ENEE621: HW Assignment #4 Solution
Spring 2013
1. (Exercise IV.F.3 in Poor) Suppose that is a random parameter with prior density
exp( ), > 0
w() =
0,
0
where > 0 is known. Given = (0, ), the probability mass function of the observation Y is
given by
p (
ENEE621: HW Assignment #2 Solution
Spring 2013
1. Let L : IR+ := [0, ) cfw_ be the likelihood ratio function, where L(y) = p1 (y)/p0 (y) for
y . Assume that the likelihood ratio L(Y) admits a probability density fi (`) under hypothesis
Hi , i = 0, 1. If d
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ENEE 621: Midterm #1 Solution
Spring 2013
1. TRUE or FALSE: Explain your answer.
(a) You wrote down your name above. (1 pt)
(b) Suppose that Pj [L(Y ) (0, )] = 1 under both hypotheses Hj , j = 0, 1. If the pair (PF , PD )
= (1/4, 3/4) belongs to the set o
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ENEE 621: Midterm #2 Solution
Spring 2013
1. TRUE or FALSE: Justify your answers briefly.
(a) You can always trust your instructor. (0 pt)
Ans: Trust him/her at your own risk.
(b) Consider a Bayesian parameter estimation problem for an unknown scalar para
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ENEE621: HW Assignment #1
Spring 2013
1. (Exercise 4 from Poor) Suppose that the probability density function (PDF) of observation Y under
hypothesis H0 is given by
(
p0 (y) =
ey , y 0,
0, y < 0,
and the PDF under hypothesis H1 is
q
2
2 y2
, y 0,
e
p1 (y
ENEE621: HW Assignment #6
Spring 2013
1. (Problem V.E.2 in Poor) Suppose that the state equation in the KalmanBucy model is modified as
follows:
Xn+1 = An Xn + Bn Un + n sn , n = 0, 1, 2, . . . ,
where cfw_sk ; k cfw_0, 1, 2, . . . =: Z+ is a known sequ
ENEE621: HW Assignment #5 Solution
Spring 2013
1. (Exercise 4.10 in Levy) Let Y = [Y1 Y2 ]T be a twodimensional Gaussian random vector with zeromean and covariance matrix
1
.
Y = v
1
(a) Find the ML estimates of vM L and M L of v and given Y.
Ans: The
ENEE621: HW Assignment #3 Solution
Spring 2013
1. (Exercise III.F.26) Consider a sequence of i.i.d. Bernoulli observations Y1 , Y2 , . . ., with distribution
P [Yk = 1] = 1 P [Yk = 0] = 1/3
under hypothesis H0
P [Yk = 1] = 1 P [Yk = 0] = 2/3
under hypothe
Ashraf Jerbi
Assignment 3
CMPT 225
The graph below shows the runtime for three different sorting algorithms (C+ sort, quicksort
and othersort). The x axis represents the number of iterations and teh y axis is the time it took
to do that many iterations in
Quick sort implementation:
#include <iostream>
#include <ctime>
void Partition(int* ipA, int iSize) cfw_
/ Partitions of size 0 or 1 are already sorted
if (iSize <= 1) cfw_
return;
/ Select a pivot from the array randomly
int iPivot = ipA[rand() % iSize]
SFU School of Engineering Science
ENSC 101 Midterm Examination
Please read the instructions to this midterm very carefully.
Ensure you legibly write your name and your student number on the answer sheet.
This is a closed book examination and, except for p
Steve Whitmore and Mike Sjoerdsma, 2010
Principles of Persuasion
September 28, 2010
Principles of Persuasion
Learning Objectives
By the end of this module, you will be able to write more
persuasively by following these methods:
Applying critical thinking
Holistic Criteria for Evaluating ENSC 100/101 Research Papers
F (050%)
Entire phrases or sentences copied from another source.
D (5155%)
No consistent argument detectible. Essay contains collection of unrelated and
unsupported facts and assertions. No c
c A.H.Dixon
8.2
CMPT 250 : Week 4 (Sept 27  Oct 1)
18
Implementing an SMD in VHDL
There are a number of ways to express a behavioral description in VHDL. The following
method uses the information provided in the state machine diagram (SMD) to dene two
pr
NAME:
Student Number:
5 pages
1
CMPT 250: Sample Test 1(A )
30 marks
Answer all questions on the test paper. Use the backs of the pages for rough work, if necessary. Be sure
your name and student number are on all pages. A VHDL summary sheet, which may be
NAME:
Student Number:
4 pages
1
CMPT 250: Sample Test 1(B)
30 marks
Answer all questions on the test paper. Use the backs of the pages for rough work, if necessary. Be sure
your name and student number are on all pages. A VHDL summary sheet is provided.
C
NAME:
Student Number:
4 pages
1
CMPT 250: Sample Test 1(B)
30 marks
Answer all questions on the test paper. Use the backs of the pages for rough work, if necessary. Be sure
your name and student number are on all pages. A VHDL summary sheet is provided.
C
CMPT 225 D1  Fall 2010  Assignment 1 (2%)
Submit your solutions online by 5:00 pm, Thursday, Sept. 30, 2010.
This assignment is primarily a short implementation to start getting you up to speed in the lab environment, with ADTs, and with C+ programming.
ENEE621: HW Assignment #4
Spring 2013
1. (Exercise IV.F.3 in Poor) Suppose that is a random parameter with prior density
(
exp( ), > 0
0,
0
w() =
where > 0 is known. Given = (0, ), the probability mass function of the observation Y is
given by
p (y) = P
ENEE621: HW Assignment #2
Spring 2013
1. Let L : IR+ := [0, ) cfw_ be the likelihood ratio function, where L(y) = p1 (y)/p0 (y) for
y . Assume that the likelihood ratio L(Y) admits a probability density fi (`) under hypothesis
Hi , i = 0, 1. If denotes th
ENEE621: HW Assignment #5
Spring 2013
1. (Exercise 4.10 in Levy) Let Y = [Y1 Y2 ]T be a twodimensional Gaussian random vector with zeromean and covariance matrix
"
Y = v
1
1
#
.
(a) Find the ML estimates of vM L and M L of v and given Y.
(b) Are these