CEN 511 - Department of Information Technologies - International ...

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1

INTERNATIONAL BURCH UNIVERSITY

FACULTY OF ENGINEERING AND
INFORMATION TECHNOLOGIES




BURCH
U N I V E R S I T Y
BURCH
U N I V E R S I T Y
A R A J E V O
S
A R A J E V O
S
I N T E R N A T I O N A L
I N T E R N A T I O N A L






THIRD CYCLE STUDY PROGRAM SPECIFICATION



















SARAJEVO

March
, 201
2



2

2.
CURRICULUM


CODE

COURSE NAME

T

P

C

ECTS

CEN 69
5

PhD Dissertation

0

0

0

30

CEN 9XX

Advanced Studies

1

0

0

0

CEN 69
6

PhD Dissertation

0

0

0

30

CEN 697

PhD Dissertation

0

0

0

30

CEN 698

Ph
D Dissertation

0

0

0

30

CEN 621

Cryptography and Network Security

3

0

3

7.5

CEN 622

Information Security

3

0

3

7.5

CEN 633

Database Systems

3

0

3

7.5

CEN 636

Chip Multiprocessors

3

0

3

7.5

CEN 652

Business Intelligence

3

0

3

7.5

CEN 657

Application of Computer Graphics

3

0

3

7.5

CEN 659

Computational Intelligence

3

0

3

7.5

CEN 664

Philosophical Founda
tions of Artificial Intelligence

3

0

3

7.5

CEN 665

Data Communications and Computer Networks

3

0

3

7.5

CEN 666

IT strategy

3

0

3

7.5

CEN 667

IT Governance

3

0

3

7.5

CEN 66
8

Network Management

3

0

3

7.5

CEN 669

Special Topics in Machine Learning

3

0

3

7.5

CEN 6
70

Special Topics in Data Mining

3

0

3

7.5

CEN 6
71

Special Topics in Pattern Recognition

3

0

3

7.5

CEN 673

Special
Topics in Bioinformatics

3

0

3

7.5

CEN 675

Industrial Networks

3

0

3

7.5

CEN 681

Special Topics in Computer Networks

3

0

3

7
.5

CEN 682

Special Topics in Computer and Network Security

3

0

3

7.5

CEN 691

Fuzzy Systems and Control

3

0

3

7.5

BUS 602

Advanced Research Methods

3

0

3

7.5

BUS 604

Qualitative Research Methods

3

0

3

7.5

BUS 618

Advanced Financial Reporting and Analy
sis

3

0

3

7.5

BUS 630

Investment Analysis and Portfolio Management

3

0

3

7.5

BUS 633

Financial Markets and Instrument

3

0

3

7.5

BUS 660

Advanced Econometrics

3

0

3

7.5

BUS 661

Quantitative Research Methods

3

0

3

7.5

BUS 663

Advanced Statistic

3

0

3

7.
5

BUS 669

Advanced Operation Research

3

0

3

7.5

BUS 685

Forecasting Techniques

3

0

3

7.5

EEE 603

Special Topics in Biomedical Signal Processing

3

0

3

7.5

EEE 604

Special Topics in Biomedical Image Processing

3

0

3

7.5

EEE 613

Advanced HDL Based System
s Design

3

0

3

7.5

EEE 631

Stochastic Signals And Systems I

3

0

3

7.5

EEE 632

Stochastic Signals And Systems II

3

0

3

7.5

EEE 633

Estimation And Detection Theory

3

0

3

7.5

EEE 634

Multiresolution Signal Processing

3

0

3

7.5

EEE 635

Selected Topics i
n Signal Processing

3

0

3

7.5

EEE 641

Special Topics in Communication Systems

3

0

3

7.5

EEE 642

Special Topics in
W
ireless
C
ommunication
S
ystems

3

0

3

7.5


3


Course Code : CEN 621

Course Title : CRYPTOGRAPHY AND NETWORK SECURITY

Level :
Graduate

Year
:


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Fundamental concepts of cryptography, block ciphers, stream ciphers, cryptographic hash
functions, differential and linear cr
yptanalysis, public

key encryption, digital signatures, key
distribution protocols, key

management, authentication systems, security protocol pitfalls, strong

password protocols, Kerberos, Internet cryptography, IPsec, SSL/TLS, e
-
mail

security, firewalls

COURSE OBJECTIVES

To understand the principles of encryption algorithms; conventional and public key cryptography.
To have a detailed knowledge about authentication, hash functions and application level security
mechanisms.

COURSE CONTENTS



Conventional En
cryption: Classical Techniques.



Conventional Encryption: Modern Techniques.



Conventional Encryption: Algorithms.



Confidentiality Using Conventional Encryption.



Public
-
Key Cryptography.



Introduction to Number Theory.



Message Authentication and Hash Functions.



Hash and Mac Algorithms.



Digital Signatures and Authentication Protocols.



Authentication Applications.



E
lectronic Mail Security.



IP Security.



Web Security.



Intruders, Viruses, and Worms.



Firewalls
.

TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentatio
ns(4
-
5 students per semester)



Description(%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination




Final Examination





10%

10%

20%

20%

40%

Learning outcomes

Demonstrate a systematic and critical

understanding of the theories, principles and practices of
computing;

Critically review the role of a “professional computing practitioner” with particular regard to an
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Language of Instruction

English

Textbook(s)

1.

3. William Stallings,
Cryptography and Network Security, Principles and Practices
,
4

Fourth Edition, Prentice Hall, 2005.

2.

Network Security Essentials: Applications and Standards by William Stallings.


5



Course Code : CEN 622

Course Title : INFORMATION SECURITY

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory
/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Information security is dedicated to keeping information safe from harm. This encompasses
computer security, but also communications security, operations security, and physical se
curity.

The technical content of the course gives a broad overview of essential concepts and methods for
providing and evaluating security in information processing systems (operating systems and
applications, networks, protocols, and so on). In addition
to its technical content, the course
touches on the importance of management and administration, the place information security
holds in overall business risk, social issues such as individual privacy, and the role of public policy.
The course will be orga
nized around a few broad themes:



Foundations: security mindset, essential concepts (policy, CIA, etc.)



Software security: vulnerabilities and protections, malware, program analysis



Practical cryptography: encryption, authentication, hashing, symmetric and
asymmetric
crypto



Networks: wired and wireless networks, protocols, attacks and countermeasures



Applications and special topics: databases, web apps, privacy and anonymity, voting, public
policy

COURSE OBJECTIVES

After completing the course, students will

be able to:

Identify and prioritize information assets

Identify and prioritize threats to information assets

Define an information security strategy and architecture

Plan for and respond to intruders in an information system

Describe legal and public rel
ations implications of security and privacy issues

Present a disaster recovery plan for recovery of information assets after an incident

The main goal of this course is to provide you with a background, foundation, and insight into the
many dimensions of i
nformation security. This knowledge will serve as basis for further deeper
study into selected areas of the field, or as an important component in your further studies and
involvement in computing as a whole. The primary objectives of the course are to hel
p you:

Understand information security’s importance in our increasingly computer
-
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Master the key concepts of information security and how they “work.”



Develop a “security mindset:” learn how to critically analyze situations of computer and
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-
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COURSE CONTENTS

1.

Introduction to Information Security

2.

Metrics for Information Security

3.

Networking and Cryptography

4.

Infor
mation Security Planning and Deployment

5.

Vulnerabilities and Protection

6.

Identity and Trust Technologies

7.

Verification and Evaluation

8.

Incident Response

9.

Human Factors

10.

Legal, Ethical, and Social Implications

TEACHING/ASSESSMENT

Description




Teaching
Methods

The primary purpose of this course is to help you understand threats to information systems and
how to

defend against them. Because the subject is so broad and complex, and is always rapidly
changing, it is

not something you can learn by instructio
n alone. My purpose as instructor is to
expose you to a variety of

important conceptual and technical aspects of the subject, helping to
6

lay a solid foundation with which you

can gain a deeper understanding by your own efforts.

I will make extensive use of

classroom discussions based around the basic text and additional
assigned

readings (or ones that you discover yourself). I will use homeworks to reinforce your
skills and
understanding,

and critical writing assignments to make you challenge and evaluate
w
hat you read. Programming assignments will give you practical experience with protocols,
vulnerabilities, and attacks.

You will be expected to participate actively in class discussions. On any given issue, you may be
asked to

summarize and critique reading

assignments from the text or articles that you have read.
You will have many

opportunities to express and defend your views in class and in your
assignments, and are expected to take

advantage of these opportunities

Description(%)

Student Assessment
Met
hods

Homework




Project





Midterm Examination




Final Examination





10%

20%

20%

40%

Learning outcomes


As a result of completing this course, students will be able to:

Describe threats to information security

Identify methods, tools and

techniques for combating these threats

Identify types of attacks and problems that occur when systems are not properly protected

Explain integral parts of overall good information security practices

Identify and discuss issues related to access control

De
scribe the need for and development of information security policies, and identify guidelines
and models for writing policies

Define risk management and explain why it is an important component of an information security
strategy and practice

Describe the
types of contingency plan and the steps involved in developing each

Identify security issues related to personnel decisions, and qualifications of security personel

Language of Instruction

English

Textbook(s)

M. Merkow and J. Breithaupt, Information Secu
rity, Pearson,2006.




7

CEN 636


Course Title : CHIP MULTIPROCESSORS

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

This advent of large
-
scale mul
ti
-
core processors, also known as Chip Multiprocessors (CMPs), will
change the way high
-
performance applications are designed, implemented, and executed. CMPs
have advantages over complex uni
-
processor systems in terms of ease of validation, power
efficien
cy, and exploiting thread level parallelism. They will not only be the central components of
future desktop machines, but they will soon be building blocks for constructing large scale parallel
and distributed, computer architectures. Recent chip multiproc
essors such as IBM's Cell and
Sun's Niagara are an important step in this direction.

COURSE OBJECTIVES

On completion of this course students should: understand the reasons for the shift from wide
-
issue superscalar to multi
-
core processors, appreciate the

challenges involved in exploiting
parallel processors and their limits. be familiar with a range of approaches to parallel programming
based on both shared
-
memory and message
-
passing models

COURSE CONTENTS



Trends in microprocessor architecture



Introduct
ion to parallel computing



Parallel algorithms



Parallel programming



Chip multiprocessor architecture and cache coherency



Transactional memory



On
-
chip interconnection networks



Manycore research issues

TEACHING/ASSESSMENT

Description




Teaching Methods

1.

Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)



Description(%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination





Final Examination





10%

10%

20%

20%

40%

Learning outcomes

Demonstrate a systematic and critical understanding of the theories, principles and practices of
computing;

Critically review the role of a “professional computing practitioner” with particula
r regard to an
understanding of legal and ethical issues;

Creatively apply contemporary theories, processes and tools in the development and evaluation of
solutions to problems and product design;

Actively participate in, reflect upon, and take responsibil
ity for, personal learning and
development, within a framework of lifelong learning and continued professional development;

Present issues and solutions in appropriate form to communicate effectively with peers and clients
from specialist and non
-
specialis
t backgrounds;

Work with minimum supervision, both individually and as a part of a team, demonstrating the
interpersonal, organisation and problem
-
solving skills supported by related attitudes necessary to
undertake employment.

Language of Instruction

Eng
lish

Textbook(s)

1.

Culler, D. E. and Singh, J. P. (1999) Parallel Computer Architecture: A
Hardware/Software approach, Morgan Kaufmann, ISBN 1
-
55860
-
343
-
3

2.

Grama, A, Anshul, G., Karypis, G and Kuman, V. (2004) Introduction to Parallel
Computing, Addison
-
Wesl
ey (2nd Edition)

8


Course Code : CEN 652

Course Title : BUSINESS INTELLIGENCE

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION


COURSE OBJECTIVES

The

overall aim of this course is to introduce students to the basic concepts and techniques of
business intelligence/ business analytics. Topics covered include business decision
-
making,
evidence
-
based management, data warehouse design and implementation, da
ta sourcing and
quality, on
-
line analytical processing (OLAP), dashboards and data mining classification,
regression and time series, case studies of business analytics practice.

COURSE CONTENTS







Teaching Methods

-
Seminar discussions introduce the
oretical material
from the recommended readings and explore the
application of theory in real world situations through
case studies

-
Laboratory sessions, which are optional, provide an
opportunity to work on the data warehousing and
data mining software sk
ills

-
Private study of the recommended readings,
assessment tasks and topic summaries each week
builds on the prior weeks



Student Assessment
Methods

Class participation


10%

Case study


20%


Research essay


30%


Project assignment (group)

30% conte
nt+10%
presentation


Learning outcomes

Professional Knowledge and Skills
:

-
Be familiar with data mining and its relationship to decision
-
making;

-
Understand the main concepts underlying data warehouse design and implementation, data
quality and retrieval

and analysis of data;

-
Be familiar with the use of business analytics in practice.

Transferable skills and other atributes:

-
Oral and written presentation: ability to express ideas clearly and precisely

-
General skills in literature search and analysis, c
ritical thinking and independent learning.

-
Team work: ability to collaborate in group projects

-
Group discussions: ability to participate in group discussions on a given subject

Language of Instruction

English

Textbook(s)

Prescribed Textbook:

-
Selected
readings (see reference list)

Useful Web Links:

-
The Data Warehousing Institute
www.tdwi.org


-
The OLAP Report
www.olapreport.com

-
Teradata University Network
http://www.teradata.com/t/page/137453/index





9

Course Code : CEN 657

Course Title : APPLICATION OF COMPUTER GRAPHICS

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Use of computer graphics in various engineering fields. Three dimensional modeling and
representation. Color, shading and lighting methods. Representation of surfaces. Graphical
databases, graphics stand
ards. Hidden surface problem, motion and animation. Texture mapping,
controlled deformations. Previous knowledge of computer graphics is required.

COURSE OBJECTIVES

Explaining the basic function of the human eye and how this impinges on resolution, quant
isation,
and colour representation for digital images; describe a number of colour spaces and their relative
merits; explain the workings of cathode ray tubes, liquid crystal displays, and laser printers.

Describing and explain the following algorithms: B
resenham's line drawing, mid
-
point line drawing,
mid
-
point circle drawing, Bezier cubic drawing, Douglas and Pucker's line chain simplification,
Cohen
-
Sutherland line clipping, scanline polygon fill, Sutherland
-
Hodgman polygon clipping, depth
sort, binary
space partition tree,
z
-
buffer, A
-
buffer, ray tracing, error diffusion.

Using matrices and homogeneous coordinates to represent and perform 2D and 3D
transformations; understand and use 3D to 2D projection, the viewing volume, and 3D clipping.

Understandi
ng Bezier curves and patches; understand sampling and super
-
sampling issues;
understand lighting techniques and how they are applied to both polygon scan conversion and ray
tracing; understand texture mapping.

Explaining how to use filters, point processin
g, and arithmetic operations in image processing and
describe a number of examples of the use of each; explain how halftoning, ordered dither, and
error diffusion work; understand and be able to explain image compression and the workings of a
number of com
pression techniques.

COURSE CONTENTS



Introduction to graphics applications



Graphics display devices and program structure



Drawing lines and simple curves



Line clipping and introduction to polygons



Polygon clipping and rasterization



Geometric transformatio
ns



Projections and viewing



Hidden surface removal



Object definition techniques



Lighting and shading



Texture mapping



Efficiency. Further steps towards visual realism

TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communi
cations with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)



Description(%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination




Final Examination





10%

10%

2
0%

20%

40%

Learning outcomes

Demonstrate a systematic and critical understanding of the theories, principles and practices of
computing;

Critically review the role of a “professional computing practitioner” with particular regard to an
understanding of le
gal and ethical issues;

Creatively apply contemporary theories, processes and tools in the development and evaluation of
solutions to problems and product design;

Actively participate in, reflect upon, and take responsibility for, personal learning and
10

dev
elopment, within a framework of lifelong learning and continued professional development;

Present issues and solutions in appropriate form to communicate effectively with peers and clients
from specialist and non
-
specialist backgrounds;

Work with minimum s
upervision, both individually and as a part of a team, demonstrating the
interpersonal, organisation and problem
-
solving skills supported by related attitudes necessary to
undertake employment.

Language of Instruction

English

Textbook(s)

1.
Computer grap
hics: principles and practice
. Addison
-
Wesley (2nd ed.) by Foley, J.D.,
van Dam, A., Feiner, S.K. & Hughes, J.F. (1990)..

2.
Digital image processing
.

Addison
-
Wesley by Gonzalez, R.C. & Woods, R.E. (1992)..

3.
Computer graphics and virtual environments:

from realism to real
-
time
.

Addison
-
Wesley by Slater, M., Steed, A. & Chrysanthou, Y. (2002).


11


Course Code : CEN 659

Course Title : COMPUTATIONAL INTELLIGENCE

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/
Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

With the advance of increasingly faster computing hardware and cheaper memory chips,
computational intelligence
, also known as a part of “
soft computation
”, a relatively new area of
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-
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湴n潬o 瑨t
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-
潢橥捴楶攠j敳楧e ⁣潮瑲潬汥o献

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-
潮 數e敲楥湣i 潮 jAq䱁B 瑯t汢潸l猠 景f 乎 慮搠 c䰠 瑯t 獯汶攠 灲慣瑩捡氠 捯c瑲潬o
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㔮R䝡楮d桡湤s
-
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r⁣潮瑲潬汥o⁤ 獩杮s

㘮Sp畲癥礠v渠n桥⁳瑡瑥
-

-
慲琠t灰汩捡瑩潮猠潦⁃潭灵瑡t楯湡氠䥮l敬汩来湣n⁩渠捯湴n潬⁥湧楮敥r楮朮

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敮杩湥敲楮朮

COURSE OBJECTIVES

Introducing concepts, models, algorithms, and tools for development of intelligent systems.
Example topics include
artificial neural networks, genetic algorithms, fuz
zy systems, swarm
intelligence, ant colony optimization, artificial life, and hybridizations of the above techniques. This
domain is called Computational Intelligence, and it is a numerical interpretation of biological
intelligence.

COURSE CONTENTS


TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)

Participation of different teaching methods
depends on the subject
.

Description(%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination




Final Examination





10%

10%

20%

20%

40%

Learning outcomes

On completion of this course, the student will hav
e:



An understanding of the fundamental Computational Intelligence
models



Implemented neural networks, genetic algorithms, fuzzy neural networks, and ant colony
optimization algorithms.



Applie
d Computational Intelligence techniques to classification, pattern recognition,
prediction, rule extraction, and optimization problems.

12


Language of Instruction

English

Textbook(s)

A. P. Engelbrecht, Computational Intelligence, John Wiley & Sons Lt
d, 2007.

13



Course Code : CEN 664

Course Title : PHILOSOPHICAL FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTIO
N

Action and agency; behaviorism; belief; computational models of mind; concepts; consciousness;
content; context; Davidson and anomalous monism; Dreyfus's criticisms; folk psychology;
functionalism; Goedel's theorem; intentionality; the Language of Though
t; mental representation;
naturalism; perception; possible worlds; practical reasoning; propositional attitudes; rationality;
reasons and causes; reference; Searle and Chinese Room; the self; thought and language; Turing
Test; Weak AI vs. Strong AI. Previo
us knowledge of artificial intelligence is required.

COURSE OBJECTIVES

The course will focus on “classical AI”, which uses concepts of knowledge representation and
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桯眠 瑯
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COURSE CONTENTS



Concepts of AI



Intelligent agents



Propositional logic



Uncertain Knowledge and Reasoning



Planning and Acting in the Real World

TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)


Description(%)

Student Assessment
Methods

Project





Midterm Examination





Final Examination





2
5
%

2
5
%

50
%

Learning outcomes



Demonstrate a systematic and critical understanding of the theories, principles and
practices of computing;



Critically review the role of a “professional computing practitioner” with particular regard

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Creatively apply contemporary theories, processes and tools in the development and
evaluation of solutions to problems and product design;



Actively participate in, reflect upon, and take responsibility for,

personal learning and
development, within a framework of lifelong learning and continued professional
development;



Present issues and solutions in appropriate form to communicate effectively with peers
and clients from specialist and non
-
specialist backgr
ounds;



Work with minimum supervision, both individually and as a part of a team,
demonstrating the interpersonal, organisation and problem
-
solving skills supported by
related attitudes necessary to undertake employment.

Language of Instruction

English

Te
xtbook(s)

1.

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (second edition).

2.

Artificial Intelligence: A Philosophical Introduction, by Jack Copeland. Blackwell. (1993).

3.

Artificial Intelligence: A New Synthesis, by Nils J. Nilsson. Morg
an Kaufmann. (1998).


14

Course Code : CEN 665

Course Title : DATA COMMUNICATIONS AND COMPUTER NETWORKS

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

This course introduces the basics of data communication and networking. Students will develop
an understanding of the general principles of networking as implemented in networks connected
to the Internet. Specific attention will be given to the principles

of network architecture and
layering, multiplexing, network addressing, routing and routing protocols. Activities include setting
up a local area network, the Internet, security, network management and network performance
analysis.

COURSE OBJECTIVES

The

goal of this course is that the student will develop an understanding of the underlying
structure of networks and how they operate. At the end of this course a student should be able to:

1.
Explain basic networking concepts by studying client/server archite
cture, network
scalability, geographical scope, the Internet, intranets and extranets.

2.
Identify, describe and give examples of the networking applications used in everyday tasks
such as reading email or surfing the web.

3.
Describe layered communication, th
e process of encapsulation, and message routing in
network equipped devices using appropriate protocols.

4.
Design and build an Ethernet network by designing the subnet structure and configuring
the routers to service that network.

5.
Manage network management

and systems administration.

6.
Construct a patch cord to connect a host computer to a network.

COURSE CONTENTS

Basics of data transmission, data communication services (SMDS, X.25, FR, ISDN, ATM, BISDN),
definition uses classification and topologies of co
mputer networks, multiple access methods,
layered network structure. OSI and TCP/IP reference models, example networks, network
standardization, physical layer, types of transmission medium, X.21, ISDN and V.35, interfaces,
functions of data link layer, fr
aming, flow control, error control, HDLC, SLIP and PPP Protocols,
medium access control (MAC) sublayer, repeaters, bridges, LAN switches, routers, layer
-
3
switches and gateways, Networking and internetworking principles; Internet routing, congestion
contro
l and operation. Local area networks: Topologies, medium access under contention,
queuing principles, performance evaluation, network management, message handling systems,
www, multimedia applications, multimedia, coding, compression, security, directory s
ervices.

TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)



Description(%)

Student Assessment
Methods

Research Pro
ject





Midterm Examination




Final Examination





25%

25%

50%

Learning outcomes

Demonstrate a systematic and critical understanding of the theories

and

principles of comput
er
networks
;

Creatively apply contemporary theories, processes and
tools in the development and evaluation of
solutions to problems and
network design;

Language of Instruction

English

Textbook(s)

1. Behrouz A. Forouzan. Data Communications and Networking (4th Edition). McGraw Hill. 2007.
ISBN: 0
-
07
-
296775
-
7.

2.William S
tallings,
Data and Computer Communications
, Pearson, 2009

3.
Dr. K.V. Prasad
,
Principles of Digital Communication Systems and Computer Networks
,
Charles River Media
, 2003

4
.

Larry L. Peterson & Bruce S. Davie,

Computer Networks A Systems Approach
,
Third Ed
ition,
Morgan Kaufmann Publishers, 2003.

15

5
. Nader

F.

Mir,
Computer and Communication Networks
, Prentice Hall, 2006.


16


Course Code : CEN 666

Course Title : IT STRATEGY

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Information security is dedicated to keeping information safe from harm. This encompasses
computer security, but also communications security, operations security, and physical security.

T
he technical content of the course gives a broad overview of essential concepts and methods for
providing and evaluating security in information processing systems (operating systems and
applications, networks, protocols, and so on). In addition to its tec
hnical content, the course
touches on the importance of management and administration, the place information security
holds in overall business risk, social issues such as individual privacy, and the role of public policy.
The course will be organized arou
nd a few broad themes:



Foundations: security mindset, essential concepts (policy, CIA, etc.)



Software security: vulnerabilities and protections, malware, program analysis



Practical cryptography: encryption, authentication, hashing, symmetric and asymmetric

crypto



Networks: wired and wireless networks, protocols, attacks and countermeasures

Applications and special topics: databases, web apps, privacy and anonymity, voting, public
policy.

COURSE OBJECTIVES

The main goal of this course is to provide you with

a background, foundation, and insight into the
many dimensions of information security. This knowledge will serve as basis for further deeper
study into selected areas of the field, or as an important component in your further studies and
involvement in c
omputing as a whole. The primary objectives of the course are to help you:



Understand information security’s importance in our increasingly computer
-
摲楶敮⁷ir汤l



Master the key concepts of information security and how they “work.”



Develop a “security mind
set:” learn how to critically analyze situations of computer and
湥瑷潲欠 畳u来 晲潭 愠 獥s畲楴礠 灥r獰s捴楶攬c 楤敮瑩t祩湧y 瑨t 獡汩s湴n楳獵敳i 癩敷灯楮瑳i
慮搠dr慤e
-
潦晳f



As a part of your general education, the course will also help you learn to:



Clearly and

coherently communicate (both verbally and in writing) about complex
technical topics.

Work and interact collaboratively in groups to examine, understand and explain key aspects of
information security.


COURSE CONTENTS

Developing & Delivering on the IT V
alue Proposition

Developing IT Strategy for Business Value

Linking IT to Business Metrics, Managing Perceptions of IT

IT in the New World of Corporate Governance Reforms

Creating and Evolving a Technology Roadmap

The IT Budgeting Process

Information
Management: the Nexus of Business &IT

Strategic Experimentation with IT

Information Delivery: IT's evolving role

Digital Dashboards

Managing Electronic Communications

Developing IT Capabilities

IT Sourcing

Delivering IT Functions: A Decision Framewo
rk

Building Better IT Leaders from the Bottom Up


TEACHING/ASSESSMENT

Description



1. Interactive lectures and communications with
students


17


Teaching Methods

2. Discussions and group works

3. Presentations

4. Guest Instructors(4
-
5 guests per semeste
r)

Description(%)

Student Assessment
Methods

Project




25%

Midterm Examination



25%

Final Examination



50%


Learning outcomes

After completing the course, students will be able to:



Identify and prioritize information assets



Identify and prioritize
threats to information assets



Define an information security strategy and architecture



Plan for and respond to intruders in an information system



Describe legal and public relations implications of security and privacy issues

Present a disaster recovery pl
an for recovery of information assets after an incident.


Language of Instruction

English

Textbook(s)

James D. McKeen

and Heather Smith,
IT Strategy in Action , Pearson 2009


18


Course Code : CEN 667

Course Title : IT GOVERNANCE

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

The main objective of this course is to present IT governance which has task to disseminate
authority to the various layers in
the organizational structures within specific business, while
ensuring appropriate and prudent use of that authority. This doesn't refer simply to hierarchical
structures; experience has taught us that network structures allow for specialization, teaming,
and
building infrastructure to support those teams. Specialization allows the sum of the parts of the
organization to be greater than the whole. Governance in any form is about leadership. And IT
governance is about the way in which leadership accomplishes

the delivery of mission
-
critical
business capability using Information Technology strategy, goals, and objectives. IT governance is
concerned with the strategic alignment between the goals and objectives of the business and the
utilization of its IT resou
rces to effectively achieve the desired results. In the course will be
presented various methodologies and standards which will help to govern IT using best practices
and standards.


COURSE OBJECTIVES

The main objective of this course is to present IT gov
ernance which has task to disseminate
authority to the various layers in the organizational structures within specific business, while
ensuring appropriate and prudent use of that authority. This doesn't refer simply to hierarchical
structures; experience
has taught us that network structures allow for specialization, teaming, and
building infrastructure to support those teams. Specialization allows the sum of the parts of the
organization to be greater than the whole. Governance in any form is about leader
ship. And IT
governance is about the way in which leadership accomplishes the delivery of mission
-
critical
business capability using Information Technology strategy, goals, and objectives. IT governance is
concerned with the strategic alignment between the

goals and objectives of the business and the
utilization of its IT resources to effectively achieve the desired results. In the course will be
presented various methodologies and standards which will help to govern IT using best practices
and standards.


COURSE CONTENTS


TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)



Description(%)

Student Assessment
Methods

Proj
ect




40%

Final Examination




60%




Learning outcomes


After completing the course, students will be able to:



Identify and prioritize information assets



Identify and prioritize threats to information assets



Define an inf
ormation security strategy and architecture



Plan for and respond to intruders in an information system



Describe legal and public relations implications of security and privacy issues

Present a disaster recovery plan for recovery of information assets after

an incident.

Language of Instruction

English

Textbook(s)

1. International IT Governance
: Alan Calder & Steve Watkins, Koganb Page, 206

2.
Business Continuity Planning Methodology
, Akhtar Syed, Afsar Syed, Sentryx 2004.

3.
The Disaster Recovery Handbook
,

Michael Wallace and Lawrence Webber, Amacom, 2004.

4.

Disaster Recovery Planning
, John William ToigoPrentice Hall, 2003.

5.
Application Security in the ISO 27001 Environment
, Vinnod Avasudavan et al. IT
19

Governance Publishing 2008.

6.
Text of standards: IS
O 27001, 27002, 27003, 2700,
20000
-
1, 20000
-
2,

ISO / IEC

7.
Business Continuity BS 25999
-
1 and BS 25999
-
2
, British Standardisation Institute.


20


Course Code : CEN 668

Course Title : NETWORK MANAGEMENT

Level :
Graduate

Year :


Semester :

ECTS Credits : 7
.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Review of the principle of
Network Management Architectures & Applications, Simple Network
Management Protocols, Network Management Functions


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瑷潲欠k慮慧敭敮琠
c畮捴楯湳c
-

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p祳瑥y猠慮搠A灰汩捡l楯湳

COURSE OBJECTIVES

The main objective of this course is to present



SNMP network management concepts




SNMP management informati
on




standard MIB’s



SNMPv1 protocol and Practical issues



introduction to RMON, SNMPv2 and SNMPv3.

COURSE CONTENTS



Network Management Architectures & Applications



Network Management Architectures & Applications



Simple Network Management Protocol
-

SNM
P v1



Network Management Functions
-

Fault



Simple Network Management Protocol
-

SNMP v2



Network Management Functions
-

Security



Simple Network Management Protocol
-

SNMP v3



Simple Network Management Protocol
-

SNMP v3



Network Management Functions
-

Ac
counting & Performance



Remote Network Monitoring RMON 1



Remote Network Monitoring RMON 2



Management Tools, Systems and Applications


TEACHING/ASSESSMENT

Description


Teaching Methods

1. Interactive lectures and communications with
students

2. Discu
ssions and group works

3. Presentations


Description(%)

Student Assessment
Methods

Project





20%

Midterm Examination


30%

Final Examination




50%



Learning outcomes




Network Management Standards and Models. Network Management Protocols.
Abstract Sy
ntax



Notation One (ASN.1). Simple Network Management Protocol (SNMP). SNMPv2 and
SNMPv3.



Structure of Management Information (SMI). Management Information Base (MIB).
Remote



Monitoring (RMON). RMON 1 and 2. Network Management tools


Language of Instruc
tion

English

Textbook(s)

1. William Stallings, “SNMP, SNMPv2, SNMPv3 and RMON 1 and 2”, Third Edition, Addison
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-

s)
䍨慰瑥t


4
-


2. “Network Management


Principles and Practice” by Mani Subramanian, AddisonWesley Pub
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楯測′ 〰.




21


Course Code :
CEN 669

Course Title : SPECIAL TOPICS IN
MACHINE LEARNING


Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Machine learn
ing techniques and statistical pattern recognition, supervised learning
(generative/discriminative learning, parametric/non
-
parametric learning, neural networks, support
vector machines); unsupervised learning (clustering, dimensionality reduction, kernel
methods);
learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and
adaptive control, applications areas (robotic control, data mining, autonomous navigation,
bioinformatics, speech recognition, and text and web data p
rocessing).

COURSE OBJECTIVES

Present the key algorithms and theory that form the core of machine learning. Draw on concepts
and results from many fields, including statistics, artifical intelligence, philosophy, information
theory, biology, cognitive sci
ence, computational complexity, and control theory.

COURSE CONTENTS



Concept Learning



Bayesian Learning,



Computational Learning Theory



Machine learning techniques and statistical pattern recognition



supervised learning (generative/discriminative learning,
parametric/non
-
parametric
learning, neural networks)



supervised learning (support vector machines)



unsupervised learning (clustering, dimensionality reduction, kernel methods)



learning theory (bias/variance tradeoffs; VC theory; large margins)



Reinforcemen
t learning and adaptive control



Applications areas (robotic control, data mining, autonomous navigation, bioinformatics,
speech recognition, and text and web data processing).



Evaluation Hypotheses



Decision Tree Learning

TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Research, project and presentations


Description(%)

Student Assessment
Methods

Research Paper Presentation



Project





Final Examination





25%

25%

50%

Learning outcomes



Demonstrate a systematic and critical understanding of the theories, principles and practices of
computing;



Creatively apply contemporary theories, processes and tools in the development and
evaluation of solutions to proble
ms in machine learning;



Actively participate in, reflect upon, and take responsibility for, personal learning and
development, within a framework of lifelong learning and continued professional development;



Present issues and solutions in appropriate form
to communicate effectively with peers and
clients from specialist and non
-
specialist backgrounds;



Work with minimum supervision, both individually and as a part of a team, demonstrating the
interpersonal, organisation and problem
-
solving skills supported b
y related attitudes necessary
to undertake employment.

Language of Instruction

English

Textbook(s)



T. Hastie,R. Tibshirani, J. Friedman,

The Elements of Statistical Learning
, Second
Edition, Springer, 2008.



Mitchell T.,
Machine Learning
, McGraw Hill, 1
997.



Du and Swamy,

Neural Networks in a Softcomputing Framework,

Springer
-
Verlag
22

London Limited, 2006.



Sebe, Cohen, Garg and Huang,
Machine Learning in Computer

Vision,
Springer, 2005.



Chow and Cho,
Neural Networks and Computing
, Imperial College Press, 20
07.

23



Course Code : CEN 670

Course Title : SPECIAL TOPICS IN DATA MINING


Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

Overview of Data Mining Cl
assification, regression, time series. Measuring predictive
performance. Data preparation, data reduction. Mathematical solutions, statistical methods,
distance solutions, decision trees, decision rules.

COURSE OBJECTIVES

Introducing students to the basi
c concepts and techniques of Data Mining. Developing skills of
using recent data mining software for solving practical problems. Gaining experience of doing
independent study and research.

COURSE CONTENTS



Introduction to Data Mining Principles



Data Wareh
ousing, Data Mining, and OLAP



Data Preprocessing and Dimension Reduction in Data Mining



Regression Modelling



Naïve Bayes Estimation and Bayesian Networks



Classification and Prediction



Cluster Analysis



Mining Stream, Time
-
Series, and Sequence Data



Mining Ob
ject, Spatial, Multimedia, Text, and Web Data



Emerging Trends and Applications of Data Mining



Data Mining Trends and Knowledge Discovery



Data Mining Tasks, Techniques, and Applications


TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive
lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)


Description(%)

Student Assessment
Methods

Project





Research Paper Presentation




Final Examination





25%

25%

50%

Lea
rning outcomes



Demonstrate a systematic and critical understanding of the theories, principles and practices of
computing;



Critically review the role of a “professional computing practitioner” with particular regard to an
畮摥r獴慮摩湧 敧慬⁡湤⁥ 桩捡h

楳獵敳i



Creatively apply contemporary theories, processes and tools in the development and
evaluation of solutions to problems and product design;



Actively participate in, reflect upon, and take responsibility for, personal learning and
development, withi
n a framework of lifelong learning and continued professional development;



Present issues and solutions in appropriate form to communicate effectively with peers and
clients from specialist and non
-
specialist backgrounds;



Work with minimum supervision, bot
h individually and as a part of a team, demonstrating the
interpersonal, organisation and problem
-
solving skills supported by related attitudes necessary
to undertake employment.

Language of Instruction

English

Textbook(s)



Ian H. Witten, Eibe Frank, Mark

A. Hall, Data Mining, Practical Machine Learning Tools and
Techniques, Morgan Kaufmann Publishers, Elsevier Inc., Third Edition, 2011.



S. Sumathi, S.N. Sivanandam, Introduction to Data Mining and its Applications, Springer
-
Verlag Berlin Heidelberg 2006.



Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan
Kaufmann, Elsevier Inc., Second Ed., 2006.

24



D. T. Larose, Data Mining Methods and Models, John Wiley & Sons, Inc., 2006.



T. M. Mitchell, Machine Learning, McGraw Hill, 1997.



T.
Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction, Springer
-
Verlag, Second Ed., 2008.


25



Course Code :
CEN 671

Course Title : SPECIAL TOPICS IN
PATTERN RECOGNITION

Level :
PhD

Year :


Se
mester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

This class deals with the fundamentals of characterizing and recognizing patterns and features of
interest in digital data. We dis
cuss the basic tools and theory for understanding problems with
applications to pattern recognition. We also cover decision theory, statistical classification,
maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and
clu
stering. Additional topics on new pattern recognition algorithms and techniques from active
research are also talked about in the class.

COURSE OBJECTIVES


COURSE CONTENTS



Introduction to Pattern Recognition, Feature Detection, Classification



Review of
Probability Theory, Conditional Probability and Bayes Rule



Random Vectors, Expectation, Correlation, Covariance



Review of Linear Algebra, Linear Transformations



Decision Theory, ROC Curves, Likelihood Ratio Test



Linear and Quadratic Discriminants, Fish
er Discriminant



Sufficient Statistics, Coping with Missing or Noisy Features



Template
-
based Recognition, Feature Extraction



Eigenvector and Multilinear Analysis



Training Methods, Maximum Likelihood and Bayesian Parameter Estimation



Linear Discriminant
/Perceptron Learning, Optimization by Gradient Descent



Support Vector Machines



K
-
Nearest
-
Neighbor Classification



Non
-
parametric Classification, Density Estimation, Parzen Estimation



Unsupervised Learning, Clustering, Vector Quantization, K
-
means



Mixtu
re Modeling, Expectation
-
Maximization



Hidden Markov Models, Viterbi Algorithm, Baum
-
Welch Algorithm



Linear Dynamical Systems, Kalman Filtering



Bayesian Networks



Decision Trees, Multi
-
layer Perceptrons



Reinforcement Learning with Human Interaction



Gen
etic Algorithms



Combination of Multiple Classifiers "Committee Machines"

TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Research, project and presentation
s


Description(%)

Student Assessment
Methods

Research





Project




Final Examination





25%

25%

50%

Learning outcomes

Demonstrate a systematic and critical understanding of the theories, principles and practices of
computing;

Critically r
eview the role of a “professional computing practitioner” with particular regard to an
畮摥r獴慮摩湧 敧慬⁡湤⁥ 桩捡h⁩獳略猻

䍲C慴楶敬礠a灰汹⁣潮l敭灯r慲礠瑨敯r楥猬⁰r潣o獳敳⁡湤⁴ 潬猠楮⁴桥⁤ 癥汯vm敮琠t湤⁥癡汵慴楯渠潦o
獯汵瑩s湳⁴漠or潢汥l猠慮搠dro
摵c琠t敳楧e;

A捴楶敬礠灡r瑩捩灡瑥⁩測 r敦汥捴⁵灯測n慮搠d慫a⁲敳e潮獩扩汩瑹⁦trⰠI敲s潮慬敡r湩湧⁡ 搠
摥癥汯vm敮琬⁷楴桩t⁡ 晲慭敷潲欠潦o汩晥汯湧敡r湩湧⁡ 搠捯湴楮略搠灲潦o獳楯湡氠摥癥汯vm敮琻

mr敳e湴n楳獵敳⁡湤⁳ 汵瑩潮猠楮⁡灰r潰r楡瑥⁦ rm⁴ ⁣潭m畮楣
慴a⁥ 晥捴楶敬礠睩瑨t灥敲猠慮搠d汩敮瑳l
26

from specialist and non
-
specialist backgrounds;

Work with minimum supervision, both individually and as a part of a team, demonstrating the
interpersonal, organisation and problem
-
solving skills supported by related a
ttitudes necessary to
undertake employment.

Language of Instruction

English

Textbook(s)

1.

S. Theodoridis, K. Koutroumbas,
Pattern Recognition & MATLAB Intro
, Elsevier, 2010.

2.
R. O. Duda, P. E. Hart and D. Stork,
Pattern Classification
, 2nd. Edition,
John Wiley & Sons,
2002.

3.
K C. Bishop,
Pattern Recognition and Machine Learning
, Springer 2006.


4.
L. I. Kuncheva,
Combining Pattern Classifiers, Methods and Algorithms
, John Wiley &
Sons, Inc., 2004.

5.

S. Theodoridis, A. Pikrakis, K. Koutroumbas, D.
Cavouras,

Introduction to Pattern
Recognition A MATLAB Approach,
Academic Press, Elsevier Inc. 2010.

6.

Menahem Friedman, Abraham Kandel,
Introduction to Pattern Recognition, Statistical,
Structural, Neural and Fuzzy Logic Approaches,

World Scientific Pub
lishing Company, 1999.

7.

S. K. Pal, A. Pal,
Pattern Recognition, From Classical to Modern Approaches
, World
Scientific Publishing Company, 2001.

8.

A. R. Webb ,
Statistical Pattern Recognition
, Second Edition, John Wiley & Sons, Ltd., 2002.




27



Course
Code : CEN 673

Course Title :
SELECTED
TOPICS IN BIOINFORMATICS

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

The course is designed to introduce t
he
advanced

concepts, methods, and tools used in
Bioinformatics. Topics include (but not limited to) bioinformatics databases, sequence and
structure alignment, protein structure prediction, protein folding, protein
-
protein interaction, Monte
Carlo simulat
ion, and molecular dynamics. Emphasis will be put on the understanding and
utilization of these concepts and algorithms. The objective is to help the students to reach rapidly
the frontier of bioinformatics and be able to use the bioinformatics tools to so
lve the problems on
their own research.

COURSE OBJECTIVES


COURSE CONTENTS

This course consists of
eighteen

lectures that are listed below. They are given short outlines
below.

1.

Molecular evolution

2.

Gene finding.

3.

Sequence comparison methods.

4.

Amino acid re
sidue conservation

5.

Function prediction from protein sequence

6.

Protein structure comparison.

7.

Protein structure classifications.

8.

Comparative modeling.

9.

Protein structure prediction.

10.

From protein structure to function.

11.

From structure
-
based genome annotation to
understanding genes and proteins

12.

Global approaches for studying protein
-
protein interactions.

13.

Predicting the structure of protein
-
biomolecular interactions.

14.

Experimental use of DNA arrays.

15.

Mining gene expression data.

16.

Proteomics

17.

Data
management

of biologic
al information.

18.

Internet technologies for bioinformatics.


TEACHING/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)



Descri
ption(%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination




Final Examination





10%

10%

20%

20%

40%

Learning outcomes

Demonstrate a systematic and critical understanding of the theories, princip
les and practices of
computing;

Critically review the role of a “professional computing practitioner” with particular regard to an
畮摥r獴慮摩湧 敧慬⁡湤⁥ 桩捡h⁩獳略猻

䍲C慴楶敬礠a灰汹⁣潮l敭灯r慲礠瑨敯r楥猬⁰r潣o獳敳⁡湤⁴ 潬猠楮⁴桥⁤ 癥汯vm敮琠t湤

敶e汵慴楯渠潦o
獯汵瑩s湳⁴漠or潢汥l猠慮搠dr潤畣琠t敳楧e;

A捴楶敬礠灡r瑩捩灡瑥⁩測 r敦汥捴⁵灯測n慮搠d慫a⁲敳e潮獩扩汩瑹⁦trⰠI敲s潮慬敡r湩湧⁡ 搠
摥癥汯vm敮琬⁷楴桩t⁡ 晲慭敷潲欠潦o汩晥汯湧敡r湩湧⁡ 搠捯湴楮略搠灲潦o獳楯湡氠摥癥汯vm敮琻

mr敳e湴n楳獵敳⁡
湤⁳ 汵瑩潮猠楮⁡灰r潰r楡瑥⁦ rm⁴ ⁣潭m畮楣慴攠敦晥捴楶敬礠睩瑨t灥敲猠慮搠d汩敮瑳l
晲潭⁳灥捩慬楳琠慮c n
-
獰s捩慬楳i⁢ 捫杲潵湤猻

28

Work with minimum supervision, both individually and as a part of a team, demonstrating the
interpersonal, organisation and p
roblem
-
solving skills supported by related attitudes necessary to
undertake employment.

Language of Instruction

English

Textbook(s)

C.

Orengo
, D. Jones, J. Thornton, Bioinformatics: genes, proteins and computers, BIOS Scientific
Publishers Limited, 2003


29


Course Code :

CEN 675

Course Title :
CEN 675 INDUSTRIAL NETWORKS

Level :
Graduate

Year :


Semester :

ECTS Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

The course provides basic knowle
dge of industrial networks in computer engineering, such as
WorldFIP, PROFIBUS, P
-
NET, LON, Foundation Fieldbus, CAN. These networks are both relevant
to new technical applications and for understanding industrial network systems

COURSE OBJECTIVES


COURS
E CONTENTS

Layered Structure of the Industrial Communication System, Topologies of Industrial Networks,
Traffic Types for the Industrial Environment (Soft
-

& Hard
-
Real Time Response Requirements),
Operational Requirements (Reliability, Interoperability Int
erworkability, Interconnectability,
Interchangeability), Fieldbuses
-

Structure of the Reduced OSI
-
RM, Network Management,
Design, Analysis and Evaluation of MAC
-
layer Protocols: CSMA/CD, CSMA/CR, Token Bus Virtual
Token, Polling, Hybrid Protocols, Protoco
l Structure and Services for the Applications and User
Layers (MMS, Function Blocks, etc.), Structure of Standard Integrated Industrial Networks
(WorldFIP, PROFIBUS, P
-
NET, LON, Foundation Fieldbus, CAN etc.), Wireless Industrial
Networks, Use of Advanced
Microcontrollers for the Implementation of Nodes for Industrial
Network, Advanced Network Simulation Tools, Typical Distributed Industrial Applications.

TEACHING/ASSESSMENT

Description



Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)


Description(%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination




Final Examination





10%

10%

20%

20%

40%

Le
arning outcomes

Demonstrate a systematic and critical understanding of the theories, principles and practices of
computing;

Critically review the role of a “professional computing practitioner” with particular regard to an
畮摥r獴慮摩湧 敧慬⁡湤⁥ 桩捡
l⁩獳略猻

䍲C慴楶敬礠a灰汹⁣潮l敭灯r慲礠瑨敯r楥猬⁰r潣o獳敳⁡湤⁴ 潬猠楮⁴桥⁤ 癥汯vm敮琠t湤⁥癡汵慴楯渠潦o
獯汵瑩s湳⁴漠or潢汥l猠慮搠dr潤畣琠t敳楧e;

A捴楶敬礠灡r瑩捩灡瑥⁩測 r敦汥捴⁵灯測n慮搠d慫a⁲敳e潮獩扩汩瑹⁦trⰠI敲s潮慬敡r湩湧⁡ 搠
摥癥汯vm敮琬⁷楴t
楮⁡⁦ 慭敷潲欠潦o汩晥汯湧敡r湩湧⁡ 搠捯湴楮略搠灲潦o獳楯湡氠摥癥汯vm敮琻

mr敳e湴n楳獵敳⁡湤⁳ 汵瑩潮猠楮⁡灰r潰r楡瑥⁦ rm⁴ ⁣潭m畮楣慴攠敦晥捴楶敬礠睩瑨t灥敲猠慮搠d汩敮瑳l
晲潭⁳灥捩慬楳琠慮c n
-
獰s捩慬楳i⁢ 捫杲潵湤猻

t潲欠睩瑨楮業畭⁳異敲癩v楯測 扯
瑨t楮摩癩摵慬汹⁡a搠d猠愠灡r琠t映f⁴ 慭ⰠI敭潮獴s慴楮朠g桥
楮瑥r灥r獯s慬Ⱐar条湩獡瑩n渠n湤⁰ 潢汥l
-
獯汶楮朠獫楬汳⁳異l潲瑥t⁢礠r敬慴敤⁡ 瑩瑵t敳散e獳慲礠瑯
畮摥r瑡步⁥ 灬潹p敮琮

Language of Instruction

English

Textbook(s)

Bjorn Axelsson,Geoffrey Easton
, Industrial Networks, Routledge, 1990, ISBN
-
13: 9780415025799


Additional References



http://www.managingautomation.com/maonline/channel/IndustrialNetworks/



http://books.google.com/books?id=RAgOAAAAQAAJ&dq=industrial+networks&printsec
=frontcover&source=bl&ot
s=qdtUGpSrcs&sig=_bcBtZ3votcDfsL9xAwMZ3mKnvI&hl=en
&ei=QlCSSuj1NaW8mgO
-
7tm9DQ&sa=X&oi=book_result&ct=result&resnum=7#v=onepage&q=&f=false




30

Course Code : CEN 681

Course Title :
SPECIAL TOPICS IN COMPUTER NETWORKS

Level :
Graduate

Year :


Semester :

ECT
S Credits : 7.5

Status :

Compulsory/Elective

Hours/Week :

3

Total Hours :
45

Instructor :

COURSE DESCRIPTION

This course introduces the
new technologies and trends

in computer
networking. Students will
develop an understanding of the
new trends

of
comp
uter
networking as implemented in networks
connected to the Internet. Specific attention will be given to the
advanced

network architecture
and layering, multiplexing, network addressing, routing and routing protocols. Activities include
setting up a
high
speed
local area network, the Internet, security, network management and
network performance analysis.

COURSE OBJECTIVES

The goal of this course is that the student will develop an understanding of the underlying
structure of
new technologies in computer

networks and how they operate. At the end of this
course a student should be able to:

1.
Explain basic networking concepts by studying client/server architecture, network scalability,
geographical scope, the Internet, intranets and extranets.

2.
Identify, des
cribe and give examples of the
new computer
networking applications
.


3.
Describe layered communication, the process of encapsulation, and message routing in network
equipped devices using appropriate protocols.

4.
Design and build a

gigabit

Ethernet network by

designing the subnet structure and configuring
the routers to service that network.

5.
Manage network management and systems administration.

6.
Construct a patch cord to connect a host computer to a network.

COURSE CONTENTS

Basics of
new technologies and tr
ends in
computer networks, multiple access methods, layered
network structure. OSI and TCP/IP reference models,
new
example networks, network
standardization, physical layer, types of transmission medium, functions of data link layer,
framing, flow control
, error control, HDLC, SLIP and PPP Protocols, medium access control
(MAC) sublayer, repeaters, bridges, LAN switches, routers, layer
-
3 switches and gateways,
Networking and internetworking principles; Internet routing, congestion control and operation.
Lo
cal area networks: Topologies, medium access under contention, queuing principles,
performance evaluation, network management, message handling systems, www, multimedia
applications, multimedia, coding, compression, security, directory services.

TEACHING
/ASSESSMENT

Description




Teaching Methods

1. Interactive lectures and communications with
students

2. Discussions and group works

3. Presentations(4
-
5 students per semester)



Description(%)

Student Assessment
Methods

Research Project





Midterm Ex
amination




Final Examination





25%

25%

50%

Learning outcomes

Demonstrate a systematic and critical understanding of the theories, principles and practices of
computer networking
;

Critically review the
new technologies related to computer n
etworks
;

Creatively apply contemporary theories, processes and tools in the development and evaluation of
solutions to problems and
computer network design;

Language of Instruction

English

Textbook(s)

1.
Dr. K.V. Prasad
,
Principles of Digital Communicatio
n Systems and Computer Networks
,
Charles River Media
, 2003

2
. Nader

F.

Mir,
Computer and Communication Networks
, Prentice Hall, 2006.

3. Computer networks related articles.




31



Course Code :
CEN 691

COURSE TITLE : FUZZY SYSTEMS AND CONTROL

Level :
Graduat
e

Year :

Semester :

ECTS Credits :
7,5

Status :

Elective

Hours/Week :

3

Total Hours :
45

Course Coordinator :

COURSE DESCRIPTION

Introduction. Fuzzy sets and basic operations on fuzzy sets. Linguistic variables and fuzzy if
-
then
rules. Fuzzy rule base

and fuzzy inference engine. Fuzzifiers and defuzzifiers. Design of fuzzy
systems from input
-
output data. Nonadaptive fuzzy control. Adaptive fuzzy control.

COURSE OBJECTIVES



To comprehend what is meant by fuzziness.



You will develop an understanding of
fuzzy theory. 3
-

Learn how to use the fuzzy
systems approach to solving engineering problems in control, signal processing and
communications.

COURSE CONTENTS



Introduction



Fuzzy Sets and Basic Operations on Fuzzy Sets



Further Operations on Fuzzy Sets



Fuzz
y Relations and the Extension Principle



Linguistic Variables and Fuzzy If
-
Then Rules



Fuzzy Logic and Approximate Reasoning



Fuzzy Rule Base and Fuzzy Inference Engine



Fuzzifiers and Defuzzifiers



Midterm



Fuzzy Systems as Nonlinear Mappings



Approximation Prop
erties of Fuzzy Systems I
-
II



Design of Fuzzy Systems From Input
-
Output Data



Non adaptive Fuzzy Control



Adaptive Fuzzy Control

TEACHING/ASSESSMENT

Description

Teaching Methods

Lecturing, problem solving, submissions by students, class discussions


Descr
iption (%)

Student Assessment
Methods

Homework




Actively Participation

Project





Midterm Examination




Final Examination





10%

10%

20%

20%

40%

Learning outcomes



Evaluate basic theories, processes and outcomes of computing;



Apply bioinf
ormatics and biological techniques and relevant tools to the specification,
analysis, design, implementation and testing of a simple computing product. For
example identification of genes involved in specific biological process in the cell.



Knowledge and c
ritical understanding of the well
-
established principles of
bioinformatics, and of the way in which those principles have developed as technology
has progressed



Knowledge of all of the main development methods relevant to the field of computing,
and abilit
y to evaluate critically the appropriateness of different approaches to solving
problems in the field of genetics and genetic engineering.

Language of Instruction

English

Textbook(s)



A Course in Fuzzy Systems and Control, Li
-
Xin Wang, 1997, Prentice Hall
.



Fuzzy Control and Fuzzy Systems, Witold Pedrycz, 1989, Research Studies Press Ltd.,
John Wiley & Sons Inc.