Logical Circuit Design

Contribution to Programme Learning Outcomes:

A3, A4, B2, B3, C3, C5, C6
Synopsis: Introduction to networks; Informal retrieval; The client server paradigm; Legal and ethical considerations of web-based applications; Designing large-scale web sites; Dynamic page design with scripting; Object oriented scripting; Scripting language structure and syntax; Scripting events and event handlers; Objects and navigation; Applications for scripting in animation; data validation, data persistence, and user interaction; Synchronized and embedded multimedia with text, images, video and audio; Bandwidth consideration; XML: AML markup, well-formedness, valid documents, DTDs, XML objects, styling XML with CSS, XSL.
Modes of Assessment:

Two 1-hour midterm exams (15% each); Assignments (15%); Seminars (5%); Final Examination: 2-hours written exam (30%) + defended project (20%)

Textbooks and Supporting Material:

6. 
   Marty Hall and Larry Brown, Core Servlets and JavaServer Pages (ISBN 0-13-009229-0), 2/e
   
7. 
   Marty Hall and Larry Brown, Core Web Programming (ISBN 0-13-089793-0), 2/e
   
8. 
   Deepak Alur el al., Core J2EE Patterns (ISBN 0-13-142246-4), 2/e
   
9. 
   Steve Graham et al., Building Web Services with Java: Making Sense of XML, SOAP, WSDL, and UDDI (ISBN 0-672-32641-8), 2/e
   
10. 
    Ian F. Darwin, Java Cookbook (ISBN 0-596-00701-9), 2/e
    

751323, Theory of Computation
Providing Department: Computer Science, Faculty of IT

Module Coordinator(s):

Year: 3

Credit: 3 credit hours

Prerequisite: 210104 + 721211

Prerequisite for: 750421

Aims:

This module introduces the theory of computation through a set of abstract machines that serve as models for computation - finite automata, pushdown automata, and Turing machines - and examines the relationship between these automata and formal languages. Additional topics beyond the automata classes themselves include deterministic and nondeterministic machines, regular expressions, context­free grammars, undecidability, and the P = NP question.

Finite automata are a useful model for many important kinds of hardware and software. Here are the most important kinds: Software for designing and checking the behaviour of digital circuits; The “lexical analyzer” of a typical complier, that is, the compiler component that breaks the input text into logical units, such as identifiers, keywords, and punctuation; Software for scanning large bodies of text, such as collections of Web pages, to find occurrences of words, phrases, or other patterns; Software for verifying systems of all types that have a finite number of distinct states, such as communication protocols or protocols for secure exchange of information.
Teaching Methods: 38 hours Lectures (2-3 hours per week) + 10 hours Tutorials (average 1 per week)
Learning Outcomes:

A student completing this module should be able to:

1. 
   Acquire a full understanding and mentality of Automata Theory as the basis of all computer science languages design (A)
   
2. 
   Have a clear understanding of the Automata theory concepts such as RE's, DFA's, NFA's, Stack's, Turing machines, and Grammars (A, B).
   
3. 
   Design FAs, NFAs, Grammars, languages modelling, small compilers basics (B).
   
4. 
   Minimize FA's and Grammars of Context Free Languages (C).
   
5. 
   Design sample automata (B)
   

Assessment of Learning Outcomes

Learning outcome (1) and (2) are assessed by tutorials and examinations. Learning outcomes (4) is assessed by tutorials, homework, and examinations. Learning outcomes (3) and (5) are assessed by tutorials.

Contribution to Programme Learning Outcomes:

A1, A2, B1, B2, C2, C5

Synopsis: Basic concepts and definitions; Set operations; partition of a set; Equivalence relations; Properties on relation on set; Proving Equivalences about Sets; Central concepts of Automata Theory; Regular Expressions; Operations on Regular expressions; Finite Automata and Regular Expressions; Recursive definitions; Conversion from FA and regular expressions; Kleen’s Theory; Mealy Moore Machines; Conversion from Mealy to Moore and vice versa; Deterministic Finite Automata (DFA); Equivalence Classes; Minimization of DFA; Non-Deterministic Finite Automata (NDFA); Equivalence of Deterministic and Non- Deterministic Finite Automata; Finite Automata with Epsilon-Transition; Equivalence between DFA, NFA, NFA-Λ; Pumping Lemma for Regular Languages; Closure Properties of Regular Languages; Context Free languages; Context-Free Grammars; Regular Grammars; Parse Trees; Ambiguity in Grammars and Languages; Simplified Forms; Standard Forms; Chomsky Normal Forms; Greibach normal Forms; Pumping Lemma for Context-Free Languages; Closure Properties of Context-Free Languages; Minimization of CFGs; Pushdown Automata (PDA); Deterministic and Non-Deterministic (PDA); Formal definition of NPDA; Transition functions of NPDA; NPDA Execution; Accepting Strings with NPDA; Equivalence of PDAs and CFG; The Turing Machine; Programming Techniques for Turing Machines; Formal definition of TMs; TMs as acceptors; TMs as transducers; Recognizing Languages with TMs; Sorting with TMs; Programming in TMs; Multiple Tracks, Subroutines; Complexity issues and analysis.
Modes of Assessment:

Two 1-hour midterm exams (15% each); Assignments (15%); Tutorial contributions (5%); 2-hour Final exam (50%)

Textbooks and Supporting Material:

1. Daniel I. A. Cohen, "Introduction to computer theory", Second Edition, Prentice Hall, 1997.

2. Papadimitriou, Elements of the Theory of Computation, Prentice-Hall, 1998

3. John E. Hopcroft, Rajeev Motwani, Jeffrey D. Ullman, "Introduction to Automata Theory, Languages, and Computation", Second Edition, Prentice Hall, 2001

4. Peter Dehning, Jack B. Dennis, “Machines, Languages and Computation”, Second Edition, Prentice Hall, 1978

5. Harry R. Lewis, Christos H. Papadimitriou, "Elements of the theory of computation", Second Edition, Prentice Hall, 1998

Simulators:

In order to improve the pedagogy of this course, interactive animations of the various automata using available simulators are recommended.

750441, Advanced Computer Networks
Providing Department: Computer Science, Faculty of IT

Module Coordinator(s):

Year: 4

Credit: 3 credit hours

Prerequisite: 750341
Aims:

This module is the second level module of the curricula related to the computer network field. Its aim is to provide:

1. 
   an in depth coverage of some basic topics taught in the first level course (750341): Layered communication architecture, Routing algorithms, Congestion control algorithms,
   
2. 
   a broad coverage of some new advanced topics in the field of computer networks (wireless networks, mobile networks, VPN networks, Mobile IP, …)
   

Teaching Methods: 40 hours Lectures (2-3 hours per week) + 4 hours Tutorials (1 per 3 weeks) + 4 hours Lab (1 per 3 weeks)

Learning Outcomes:

Students completing this module should be able to:

1. Understand the main abstract concepts related to the layered communication architecture (A)

2. Analyze and implement some of the most advanced routing and congestion control algorithms. (B, C, D)

3. Evaluate the performances of computer networks (through mathematical modelling and simulation) (A, B, D)

4. Practice network simulators (B, C)

5. Understand basics and principles of new generation of computer networks (VPN, wireless networks, mobile networks, etc). (A)
Assessments of Learning Outcomes:

Learning outcomes (1) and (2) are assessed by examinations. Learning outcomes (3) and (4) are assessed by assignments and research.

Contribution to Programme Learning Outcomes

A3, A5, B2, C2, C4, C5, D1, D4, D5.

Synopsis: Layered communication architecture: layers, services, protocols, layer entities, service access points, protocol functions; Advanced Routing algorithms; Advanced Network Congestion Control algorithms; Quality of service; Real Time Transport Protocol; Internetworking; Performance Issues; Overview on VPN networks; Overview on Wireless Networks and Mobile Networks: LAN, PAN, Sensor Networks, Ad_hoc Networks; Mobile IP; Mobile TCP; IP Security
Modes of Assessment:

Two 1-hour midterm exams (15% each); Assignments (20%); Final Examination: 2-hours written exam (35%) + a research project (15%).

Textbooks and Supporting Material:

1- Andrew S. Tanenbaum, Computer Networks, (Fourth or Latest edition), Prentice Hall

2- William Stallings, Wireless Communications & Networks, 2nd edition, Prentice-Hall Pearson, 2005

3- Jochen Schiller, Mobile Communication, (Latest edition), Addison Wesley

4- G. Wright and W. Stevens, TCP/IP Illustrated, Volume 2, Addison-Wesley, 1996.

750461, Advanced Databases
Providing Department: Computer Science, Faculty of IT

Module Coordinator(s):

Year: 4

Credit: 3 credit hours

Prerequisite: 750361
Aims:

This module aims to give students information about system implementation techniques, introduction to DBMS implementation, data storage, representing data elements, database system architecture, the system catalog, query processing and optimization, transaction processing concepts, concurrency control techniques, database recovery techniques, database security and authorization, enhanced data models for advanced applications, temporal databases, deductive databases, database technology for decision support systems, distributed databases and client server architecture, advanced database concepts, and emerging technologies and applications.

Teaching Methods: 32 hours Lectures (2 per week) + 10 hours Laboratory (0-1 per week, on project assignment) + 6 hours Seminars (in last 3 weeks)
Learning Outcomes:

Students completing this module should be able to:

1. 
   Apply normalization techniques. (A, B)
   
2. 
   Understand how transactions are processed in a database. (A, B)
   
3. 
   Discuss/explain the different techniques in Concurrency Control. (A, B)
   
4. 
   Discuss/explain the concepts of Distributed Databases and Data Warehousing. (A)
   
5. 
   Discuss/explain some database security issues.(A)
   
6. 
   Tune and Optimize some Database Applications. (B, C, D)
   
7. 
   Discuss/explain the concepts of Object-Oriented database. (A, D)
   

Assessment of Learning Outcome:

Learning outcomes (1) through (5) are assessed by examinations tutorials and assignments. Learning outcomes (6) and (7) are assessed by projects design and implementation.

Contribution to Programme Learning Outcomes:

A2, A3, A4, A5, B1, B2, B3, C2, C4, C6, D2, D3, D5

Synopsis: Introduction, Concepts and Definitions; Normalize Techniques; Data Warehousing and Data Mining; Transaction Processing; Concurrency Control; Distributed Databases; Database Security; Database Tuning and Query Optimization; Object-Oriented Database; Different tools will be used in this course including Oracle.
Modes of Assessment:

Two 1-hour midterm exams (15% each); Assignments (10%); Seminar Presentation (10%); Final Examination: 2-hour written (unseen) exam (40%) + Project (10%)

Textbooks and Supporting Material:

1- El Masri, Fundamentals of Database Systems, Fifth Edition, 2006

2- Date, Database Systems, Eighth Edition, 2004

3- Patrick Valduriez M. TamerOzsu, Principles of Distributed Database Systems, 2^nd Edition, Prentice Hall, 1999.

750499, Project
Providing Department: Computer Science, Faculty of IT

Module Coordinator(s):

Year: 4

Credit: 3 credit hours

Prerequisite: 750398
General Descriptions:

The graduation project consists of a single project on which the student works over a period of 16 weeks that can be extended to 32 weeks (2 semesters). It is assumed that the student spends a nominal 192 hours (or 384 hours), the equivalent of 12 hours per week, working on this. There are three deliverables: demonstration, discussion, and a written report.

A student works under the supervision of a member of staff, the Supervisor. Most of the projects involve three students working together on the same project; apart from these, all students do different projects.

1. 
   How to choose a project
   
2. 
   Organisation for projects
   
3. 
   Demonstrations
   
4. 
   Report Standards
   
5. 
   Staff List
   

Aims:

The aims for the project work done in the fourth year are:

1. 
   To manage and execute a substantial project in a limited time.
   
2. 
   To identify and learn whatever new skills are needed to complete the project.
   
3. 
   To apply design and engineering skills in the accomplishment of a single task. In this context the skills mentioned may be in the general area of design and engineering in its broadest sense, or may be very specifically related to particular tools.
   

Teaching methods: Duration: 32 weeks (2 semesters) starts in first semester: Lectures: 6 or 7 in total, spread through the 2 semesters + Laboratories: none scheduled, 120 hours expected through semester
Learning Outcomes:

On completion of this module, a student should have

1. 
   Used the project supervisor appropriately as project consultant or customer. (D)
   
2. 
   Planned, executed and completed a significant design and, as appropriate, implementation within the time budget available. (B, C)
   
3. 
   Given a demonstration showing practical competence and demonstrating the results of the project. (C).
   
4. 
   Documented the project in a final report. (C)
   

Assessment of Learning Outcomes:

There is no examination.

Learning outcome (1) is assessed by the supervisor. Learning outcomes (3) is assessed by the project examination committee and by judging the demonstration. Learning outcome (4) is assessed by judging the report. Learning outcome (2) is assessed by all of these mechanisms.

Modes of Assessment:

Supervisor mark: 35% + Project Examination Committee mark: 65% (demonstration 20%, Report 25%, discussion 20%)

Contribution to Programme Learning Outcomes

B1, B2, B3, C1, C2, C3, D1, D2, D3, D4, D5

Syllabus
The occasional lectures are on topics of particular interest to students doing a project in their final year.

1. 
   Overview of projects and project assessment.
   
2. 
   Career advice.
   
3. 
   How to give a seminar.
   
4. 
   Writing English.
   
5. 
   How to give a demonstration.
   
6. 
   How to write a project report.
   

Reading List and Supporting Material:

1. 
   C. W. Dawson. The Essence of Computing Projects, A Student's Guide. ISBN 0-13-021972-X. Prentice Hall 2000.
   

The project list and notes for guidance in carrying out a project are available in the Graduation Project Committee.

Project Classifications:

Parallel Processing

Artificial Intelligence

Systems Programming

Distributed Systems

Information Systems

Communications

Formal Specifications

Web Programming

General Applications

Simulation

Communications

Computer Aided Design

Microprocessor Applications and Peripherals

Graphical Systems and Applications