Course Code : CEN 511 - Home - Department of … · Web viewDistributed Object/component-based DBMS Database Interoperability including CORBA, DCOM and Java RMI Distributed document-based

INTERNATIONAL BURCH UNIVERSITYFACULTY OF ENGINEERING AND INFORMATION TECHNOLOGIES

THIRD CYCLE STUDY PROGRAM SPECIFICATION

SARAJEVO

May, 2013

1. PHD CURRICULUM OF INFORMATION TECHNOLOGY DEPARTMENT......................................................................................3

1.2 INTRODUCTION............................................................................................................................................................................. 3

1.3 MISSION...................................................................................................................................................................................... 31.3 AIMS OF THE PROGRAMME........................................................................................................................................................... 31.4 PROGRAM................................................................................................................................................................................... 31.5 LEARNING AND TEACHING............................................................................................................................................................. 3

1.5.1 Teaching/learning methods and strategies......................................................................................................................... 41.6 ASSESSMENT PROTOCOLS............................................................................................................................................................ 4

1.6.1 Assessment....................................................................................................................................................................... 41.7 LEARNING OUTCOMES.................................................................................................................................................................. 51.8 SKILLS AND OTHER ATTRIBUTES.................................................................................................................................................... 5

1.8.1 Intellectual skills................................................................................................................................................................. 51.8.2 Discipline-specific Practical skills....................................................................................................................................... 61.8.4 Transferable skills.............................................................................................................................................................. 6

1.9 METHODS FOR EVALUATING AND IMPROVING THE QUALITY AND STANDARDS OF TEACHING AND LEARNING.......................................61.10 CRITERIA FOR ADMISSION........................................................................................................................................................... 71.11 ACADEMIC ABILITY...................................................................................................................................................................... 7

1.11.1 English Language Requirement....................................................................................................................................... 71.12 SUITABILITY............................................................................................................................................................................... 7

2. CURRICULUM................................................................................................................................................................................... 8

1. PhD CURRICULUM OF INFORMATION TECHNOLOGY DEPARTMENT

1.2 Introduction

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The advances in computer technology have added new fuel to the development of almost all of the science and engineering applications. Because of its role in the improvement of civilization, this discipline became a separate engineering profession. In today’s age of information, Information Technology is one of the main branches of engineering that contribute through professional services towards more prosperous and sustainable society.

1.3 Mission

The mission of the Department of Information Technology is to educate the students to gain an understanding of the fundamentals of science and engineering so that they can develop solutions to Computer Engineering problems and enhance their skills on computer science, computer architecture, design and analysis of algorithms, software engineering communication and research skills. It is aimed to especially emphasize teamwork, independent and innovative thinking and leadership qualities.

1.3 Aims of the Programme

To facilitate the provision of a quality learning experience for each student that fosters engagement with their programme of study and promotes independent study and life-long learning;

To maintain a high quality, comprehensive and coherent computing focussed curriculum informed by research, scholarly activity and practice which enhances each participant’s career prospects;

To develop professionals with a sound understanding of computing and a critical awareness of current issues, who are able to adopt appropriate research strategies, and are informed of wider contextual issues;

To encourage the creative and appropriate application of technology to promote innovation, enterprise and employability; To promote ethical awareness and professionalism supported by a strong appreciation of industry focussed skills and prac-

tice. To promote students' self-discipline and self-assurance and the ability to learn on their own, To produce graduates for the engineering and the business communities who are observant, inquisitive and open to new

technologies for developing better solutions, To produce graduates for the engineering and business communities with integrity, determination, judgment, motivation,

ability and education to assume a leadership role to meet the demanding challenges of the society.

1.4 Program

The Information Technology PhD program is based on three years doctor of philosophy Degree Program with 180 ECTS credits. The first year of the program is dedicated to the study of advanced engineering courses of computer engineering and Information techno -logy. The Curriculum of the program includes elective courses, which give an opportunity to students to improve their academic skills according to their interests. The requirements for a PhD degree in Information Technology include the completion of minimum of 180 ECTS credits of formal course work and PhD dissertation. The PhD program of department of IT is designed to prepare students for higher-level academic positions. The topics covered in IT course work include:

the role of information technology in global society; the development of Internet business sites and electronic commerce; the role of information systems in business and government; fundamentals of computer programming, data analysis and networking; database concepts, applications and design; information systems analysis, design and implementation; Information security, information assurance and network security.

1.5 Learning and Teaching

Learning and teaching methods provide high quality learning opportunities that enable students to demonstrate achievement of the learning outcomes of the course and those of the modules which constitute their chosen route of study.

The course aims to foster the development of independent study skills and autonomy of learning and encourage a commitment to lifelong learning and continuous professional development. Teaching and learning methods increasingly promote the capacity for students to assume responsibility for their own learning and development. Progressive use of project learning, integrated assessment and product/problem based learning allow students to take on greater self-direction of their learning. Emphasis is often placed on group and team working throughout the study.

The course employs a wide range of learning opportunities and teaching methods, informed by curriculum review, pedagogic research and continuous staff development. Particular methods for each module or cohort are identified prior to delivery through the annual planning process. Innovative approaches to teaching, learning and assessment are encouraged. The course seeks to expand the application of technology in the delivery of teaching and learning support wherever appropriate.

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Scheduled sessions will include the use of lectures, seminars and research. Advantage will be taken of both technology and supportive activities to ensure that effective learning takes place. These activities will include the use of simulations, role play, case studies, projects, practical work, research, work based learning, workshops, peer group interaction, self managed teams and learner managed learning.

1.5.1 Teaching/learning methods and strategies

Lectures/classes: offer information, literature review and illustrative application and present and explore core ideas in the subject. A student will apply intellectual skills to prepare solutions to examples sheet questions which will be discussed in a small class.

Practical sessions: computational methods are taught as a series of computer-based practicals with short introductory lectures on theory. This enables a student to understand issues in application of computational methods to simulated and real prob -lems and also develop computing skills relevant to the rest of the course including the research project. Practicals, computer-based and experimental lab based, provide an opportunity for a student to consolidate the theory they have learned about in lectures and apply it to problems.

Group project: provides an opportunity to study a real computer engineering problem in depth, practice analytic and prob -lem-solving skills, and work in a team.

Individual project: involves a literature review, problem specification and experiments/analysis written up in a report. This enables a student to practice the application of techniques they have learned about to a technology problem in some depth as well as put into practice general research skills.

Expert (guest) lectures and seminars: provide a student with the opportunity to hear internal speakers and external speakers from industry. This enables a student to gain appreciation of some applications, needs and roles of computer engineers as well as career opportunities.

1.6 Assessment Protocols

The purpose of outcomes-based learning assessment is to improve the quality of learning and teaching in Information Technology department. The fundamental principles are:

Student learning is the central focus of the department‘s efforts. Each student is unique and will express learning in a unique way. Students must be able to apply their learning beyond the classroom. Students should become effective, independent, lifelong learners as a result of their educational experience.

Assessment of the IT Learning Outcomes (ITLOs) begins with the normal assessment process in the major courses that are taken by students. Each course defines course outcomes and relates the course outcomes to the ITLOs. Students also prepare portfolios that reflect their achievements and capabilities, and the evaluation of the portfolios by a faculty committee represents the final assessment of a student‘s achievement in the ITLOs.

1.6.1 Assessment

Assessment of knowledge and understanding is by: Unseen written examinations Written essay assignments Assessment of practical work Group project report write-up and team presentation Individual project report and short presentation/viva

1.7 Learning outcomes

The Doctor of Philosophy in Information Technology program will enable graduates to understand and articulate the different levels and aspects of information technology in the context of an enterprise. The Major Learning Outcomes for department of Information Technology are as follows:

Critical Thinking and Quantitative Reasoning in IT: IT graduates will be able to use critical thinking and quantitative pro-cesses to identify, analyze and solve problems, and evaluate solutions in an IT context.

Information Technology Application: IT graduates will be able to select existing and cutting-edge IT tools and proced -ures to develop modules and systems.

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Information Technology Management: IT graduates will be able to assess and determine information resource require -ments to develop solutions suitable for IT and business managers operating in a multinational and multicultural environment.

Information Technology Professional Practice: IT graduates will be able to work effectively in individual and group situ -ations, understand how groups interact, be able to assume a leadership role when required, and understand the fundamentals of professional and ethical conduct.

Information Technology Systems Theory and Practice: IT graduates will be able to understand and communicate the fundamentals of systems theory in the development of appropriate systems that function in a global environment.

On successful completion, IT department master students will be able to demonstrate:

a systematic understanding of key aspects of computing, including acquisition of coherent and detailed knowledge, at least some of which is at, or informed by, the forefront of defined aspects of a discipline

an ability to deploy accurately established techniques of research, analysis and design a wide breadth of understanding that enables them to devise and sustain arguments and solve problems using ideas and

techniques, some of which are at the forefront of computing practice, and describe and comment upon particular aspects of current research, or equivalent advanced scholarship

an appreciation of the uncertainty, ambiguity and limits of knowledge consistent application of the development methods and techniques that they have learned to review, consolidate, extend

upon, and to initiate and carry out projects to a professional level an ability to critically evaluate arguments, assumptions, abstract concepts and data, to make judgements, and to frame ap-

propriate questions to achieve a solution – or identify a range of solutions – to a problem.

1.8 Skills and other attributes

On successful completion of master level students should be able to demonstrate they: have the ability to manage their own learning, and make use of scholarly review and primary sources (for example, re -

ferred research articles and/or original materials appropriate to the discipline) can communicate information, ideas, problems and solutions to both specialist and non-specialist audiences they have the qualities and transferable skills requiring the exercise of initiative and personal responsibility, decision-mak -

ing in complex and unpredictable contexts and the learning ability needed to undertake appropriate further training of a professional or equivalent nature

1.8.1 Intellectual skills

By the end of the course a student will have developed skills in: Synthesis: integrate theory and practice, and devise appropriate theoretical models of computer engineering systems. Computational analysis: select and apply appropriate computational techniques to solve a given problem Experimental analysis: acquire, analyse and interpret synthetic and experimental data and understand the strengths and

limitation of using each type of experimental data analysis. Critical analysis: research, read, critique and discuss scientific articles, especially those that cross discipline boundaries

between engineering and other fields. Present a written argument based on reading from a variety of sources. Problem solving: apply engineering principles to solve different problems. Evaluation: interpret experimental data scientifically and demonstrate skills necessary to plan, conduct and report on a

research project

1.8.2 Discipline-specific Practical skills

By the end of the course a student will be expected to have practical skills to enable them to: select and apply appropriate computational methods to solve different engineering problems. use information technology for the collection and analysis of experimental data. undertake a research project independently and with minimal supervision/guidance. understand issues in and have gained experience in working in multi-disciplinary teams.

1.8.4 Transferable skills

By the end of the course a student will have developed a range of transferable skills including skills in: Managing their own learning and conducting independent thinking and study Problem specification and modelling Applying mathematical and computational methods to solve (engineering) problems Use of general information technology

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Managing a research project, including planning and time management Conducting an engineering-based research-based work, from hypothesis to report writing Working in a multi-disciplinary team Critical analysis

1.9 Methods for Evaluating and Improving the Quality and Standards of Teaching and Learning

Student Focus groups and the annual student survey Class room observation of Lecturers Advanced Professional Diploma in Teaching and Learning in Higher Education Membership of the Higher Education Academy External Examiners reports Accreditation Visits Curriculum Area Review Course Committees Annual and periodic review

Mechanisms for gaining student feedback on teaching quality and their learning experience Questionnaries collected for each component of the course and considered by the course director/tutors in a department meeting and acted on as appropriate. Termly individual meetings between students and the Course Director. Self-assessment progress reports completed by students at the end of each term.

Mechanisms for the review and evaluation of teaching, learning, assessment, the curriculum and outcome standards Departmental meeting in June/July at which course tutors consider current course structure, delivery arrangements, student performance in assessment, and student feedback and make recommendations for change and improvement. Also used to help spread best practice for teaching and learning techniques. Examiners reports (both internal and external) on the examinations in a particular year, commenting on pass rates, standards of learning and examination performance. Teaching evaluation questionnaires. Annual Course Director report to the Department Academic Committee with details on admissions, staffing, course changes and feedback, student performance, destination of graduated PhD students, and any difficulties encountered on the course. Student destination, whether employment or further study. An Advisory Board (from industry and clinical practice) providing occasional and valuable comments on the progress and development of the course from their respective perspectives.

Indicators of Quality and Standards Student feedback Retention and success rates for each level for each course Student Module Evaluations Annual Student Questionnaires First Destination Statistics Professional accreditation External Examiner reports

1.10 Criteria for Admission

The admissions policy for overall Scheme, in which the Computing course operates, is to admit any applicant who is capable of benefiting from and successfully completing their chosen course. Where selection criteria are devised they will be tuned to satisfy the widening participation agenda and equal opportunity policy of the University. Admissions profiles will be reviewed annually as will selection criteria and will provide a fair and objective basis for selection to oversubscribed courses. Admission with advanced standing will follow University Procedures. Applications will normally be considered in the light of a candidate’s ability to meet the following criteria:

1.11 Academic ability

1) The applicant has provided appropriate indications of proven and potential academic excellence. Appropriate indicators include two or more confidential references, academic transcripts or their equivalent, (on the application form) a statement outlining how the course will help progress the applicant’s career, and performance at interview.

2) The applicant has provided sufficient evidence, in the view of the assessor, to suggest that they have the academic ability and commitment to pursue the chosen programme to a successful conclusion within the required time limits. This includes; a sufficient level of mathematics and/or computer programming completed on the first degree or otherwise as a foundation for successful completion of the course; an understanding of how the PhD will help the applicant progress their academic career, and evidence of the ability (prior experience or potential) to work in a multi-disciplinary team.

3) Applicants are normally expected to have achieved an Honours Degree (or equivalent) in engineering, physical sciences, mathematics, computer science subject, or a related subject.

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1.11.1 English Language Requirement

Applicants whose first language is not English are required to provide evidence of proficiency in English. Candidates are normally expected to meet the following criteria: For IELTS an overall score of 5 For TOEFL an overall score of 450, or for the computer-based test, an overall score of 200 or equivalent score.

1.12 Suitability

1) The programme of study that the applicant wishes to pursue is well suited to the academic interests and abilities to which they have drawn attention in their application and (where appropriate) the applicant has undertaken any preliminary academic work or course which is normally considered indispensable to acceptance on the proposed programme of study.

2) The Department of IT is able to provide appropriate supervision and facilities for the candidate’s chosen programme of work

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2. CURRICULUM

First SemesterCODE COURSE NAME T P C ECTSCEN xxx Elective I 3 0 3 7.5CEN xxx Elective II 3 0 3 7.5CEN xxx Elective III 3 0 3 7.5XXX xxx Elective IV 3 0 3 7.5

Total 12 0 12 30

Second SemesterCODE COURSE NAME T P C ECTSCEN xxx Elective I 3 0 3 7.5CEN xxx Elective II 3 0 3 7.5CEN xxx Elective III 3 0 3 7.5XXX xxx Elective IV 3 0 3 7.5

Total 12 0 12 30

Third SemesterCODE COURSE NAME T P C ECTSCEN 695 PhD Dissertation 0 0 0 30CEN 9XX Advanced Studies 1 0 0 0

Total 0 0 0 30

Forth SemesterCODE COURSE NAME T P C ECTSCEN 696 PhD Dissertation 0 0 0 30CEN 9XX Advanced Studies 1 0 0 0

Total 0 0 0 30

Fifth SemesterCODE COURSE NAME T P C ECTSCEN 697 PhD Dissertation 0 0 0 30CEN 9XX Advanced Studies 1 0 0 0

Total 0 0 0 30

Sixth SemesterCODE COURSE NAME T P C ECTSCEN 698 PhD Dissertation 0 0 0 30CEN 9XX Advanced Studies 1 0 0 0

Total 0 0 0 30

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Elective CoursesCODE COURSE NAME T P C ECTSCEN 604 Combinatorics & Graph Theory 3 0 3 7.5CEN 610 Design Patterns & Frameworks 3 0 3 7.5CEN 615 Capacity Planning for Web Services 3 0 3 7.5CEN 621 Cryptography and Network Security 3 0 3 7.5CEN 622 Information Security 3 0 3 7.5CEN 624 Distributed Database Systems 3 0 3 7.5CEN 627 Algorithm Design for Parallel Computers 3 0 3 7.5CEN 628 Parallel Programming Languages-Systems 3 0 3 7.5CEN 633 Database Systems 3 0 3 7.5CEN 636 Chip Multiprocessors 3 0 3 7.5CEN 640 Operating Systems 3 0 3 7.5CEN 645 Robot Motion Control and Planning 3 0 3 7.5CEN 651 Computational Geometry 3 0 3 7.5CEN 652 Business Intelligence 3 0 3 7.5CEN 654 Aspect-Oriented Software Development 3 0 3 7.5CEN 657 Application of Computer Graphics 3 0 3 7.5CEN 659 Computational Intelligence 3 0 3 7.5CEN 660 Model-Driven Software Development 3 0 3 7.5CEN 661 Special Topics in Decision Support Systems 3 0 3 7.5CEN 662 Natural Lang. Processing 3 0 3 7.5CEN 664 Philosophical Foundations of Artificial Intelligence 3 0 3 7.5CEN 665 Data Communications and Computer Networks 3 0 3 7.5CEN 666 IT strategy 3 0 3 7.5CEN 667 IT Governance 3 0 3 7.5CEN 668 Network Management 3 0 3 7.5CEN 669 Special Topics in Machine Learning 3 0 3 7.5CEN 670 Special Topics in Data Mining 3 0 3 7.5CEN 671 Special Topics in Pattern Recognition 3 0 3 7.5CEN 673 Special Topics in Bioinformatics 3 0 3 7.5CEN 675 Industrial Networks 3 0 3 7.5CEN 681 Special Topics in Computer Networks 3 0 3 7.5CEN 682 Special Topics in Computer and Network Security 3 0 3 7.5CEN 691 Fuzzy Systems and Control 3 0 3 7.5EEE 603 Special Topics in Biomedical Signal Processing 3 0 3 7.5EEE 604 Special Topics in Biomedical Image Processing 3 0 3 7.5EEE 613 Advanced HDL Based Systems Design 3 0 3 7.5EEE 631 Stochastic Signals And Systems I 3 0 3 7.5EEE 632 Stochastic Signals And Systems II 3 0 3 7.5EEE 633 Estimation And Detection Theory 3 0 3 7.5EEE 634 Multiresolution Signal Processing 3 0 3 7.5EEE 635 Selected Topics in Signal Processing 3 0 3 7.5EEE 641 Special Topics in Communication Systems 3 0 3 7.5EEE 642 Special Topics in Wireless Communication Systems 3 0 3 7.5BUS 602 Advanced Research Methods 3 0 3 7.5BUS 604 Qualitative Research Methods 3 0 3 7.5BUS 618 Advanced Financial Reporting and Analysis 3 0 3 7.5BUS 630 Investment Analysis and Portfolio Management 3 0 3 7.5BUS 633 Financial Markets and Instrument 3 0 3 7.5BUS 660 Advanced Econometrics 3 0 3 7.5BUS 661 Quantitative Research Methods 3 0 3 7.5BUS 663 Advanced Statistic 3 0 3 7.5BUS 669 Advanced Operation Research 3 0 3 7.5BUS 685 Forecasting Techniques 3 0 3 7.5

Course Code : CEN 604 Course Title : COMBINATORICS AND GRAPH THEORY

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Level : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Fundamental concepts and methods of graph theory and its applications in various areas of

computing and the social and natural sciences. Topics include paths and searching, trees, networks, cycles, planarity, matching, and independence. Certain NP-complete graph problems and their approximation algorithms are discussed. Special topics such as graph drawing and graph colouring are covered. In addition, extremal graph theoretical problems are introduced. Previous knowledge of algorithms is required.

COURSE OBJECTIVES Understanding of fundamental definitions and properties of graphs. Ability to read and write rigorous mathematical proofs involving graphs. Recognition of the numerous applications of graph theory in computer science and engineering.

COURSE CONTENTS Basic definitions of graphs Trees and Eulerian graphs Matchings Connected graphs and paths Graph coloring Hamiltonian cycles Planar graphs

TEACHING/ASSESSMENTDescription

Teaching Methods

1. Interactive lectures and communications with students2. Discussions and group works3. Presentations(4-5 students per semester)

Description(%)Student Assessment Methods

HomeworkActively ParticipationProject Midterm Examination Final Examination

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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 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 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 by related attitudes necessary to undertake employment.

Language of Instruction English

Textbook(s) 1. Graph Theory, Wiley Interscience, 2001.ISBN 0-471-38925-0Introduction to Graph Theory, 2nd Edition, by Douglas B. West

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Course Code : CEN 610 Course Title : DESIGN PATTERNS AND FRAMEWORKSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION This course is an introduction to software design patterns. Each pattern represents a best practice

solution to a software problem in some context. The course will cover the rationale and benefits of object-oriented software design patterns. Several example problems will be studied to investigate the development of good design patterns. Specific patterns, such as Observer, State, Adapter, Strategy, Decorator and Abstract Factory will be discussed. Programming projects in the Java language will provide experience in the use of these patterns. In addition, distributed object frameworks, such as RMI, will be studied for their effective use of design patterns.

COURSE OBJECTIVESCOURSE CONTENTS 1. Introduction To Design Patterns

2. Introduction To Java 3. The Observer Pattern 4. The Template Method Pattern 5. Some OO Design Principles 6. Factory Patterns: Factory Method and Abstract Factory 7. The Singleton Pattern 8. The Iterator Pattern 9. The Composite Pattern

10. The Facade Pattern 11. The State and Strategy Patterns 12. Functors and the Command Pattern 13. The Adapter Pattern 14. The Proxy Pattern 15. RMI 16. The Decorator Pattern 17. Dynamic Proxies In Java 18. The Chain of Responsibility Pattern 19. Concurrency Patterns 20. The Visitor Pattern 21. AntiPatterns


Teaching Methods




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Textbook(s)1. Design Patterns - Elements Of Reusable Object-Oriented Software, Gamma, et. al.,

Addison-Wesley, 1995 2. Applied Java Patterns, Stephen Stelting and Olav Maassen, Prentice Hall, 2002 3. Java Design Patterns - A Tutorial, James W. Cooper, Addison-Wesley, 2000

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Course Code : CEN 615 Course Title : CAPACITY PLANNING FOR WEB SERVICESLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Web services rely on large-scale systems that consist of thousands of computers, networks,

software components, and users. Large-scale systems are inherently complex. The randomness associated with the way users request Web services compounds the problem of managing and planning the capacity of those services. The Web has special features that make its performance problems unique and demand novel approaches to dealing with them. This course presents a sound and practical approach to addressing these challenges using models based on probability fundamentals and the theory of queuing networks. In this way, it provides a quantitative approach to analyzing Web services, which lends, itself to the development of performance and availability predictive models for managing and planning the capacity of Web services.

COURSE OBJECTIVES Goal is to familiarize the student with the fundamental design and performance issues in Computer Networks and to prepare him/her for research in this area. After a brief introduction of basic communications and networking concepts, the course takes a more "quantitative" tone. Probability and Queuing models are used to analyze throughput and delay behavior. Mathematical models are also used to study multi-access communications, scheduling, and congestion control.

COURSE CONTENTS When Web Performance is a Problem Protocols and Interaction Models for Web Services Basic Performance Concepts Performance Issues of Web Services Planning the Capacity of Web Services. Understanding and Characterizing the Workload Benchmarks and Performance Tests System-Level Performance Models Component-Level Performance Models Web Performance Modeling Availability of Web Services Workload Forecasting Measuring Performance


Teaching Methods




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Textbook(s) 1. Capacity Planning for Web Services: metrics, models, and methods ,Daniel A. Menascé, George Mason University,

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2. Virgilio A. F. Almeida, Prentice Hall, 2001

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Course Code : CEN 621 Course Title : CRYPTOGRAPHY AND NETWORK SECURITYLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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 cryptanalysis, 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 Encryption: 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. Electronic Mail Security. IP Security. Web Security. Intruders, Viruses, and Worms. Firewalls.


Teaching Methods




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Textbook(s) 1. 3. William Stallings, Cryptography and Network Security, Principles and Practices, Fourth Edition, Prentice Hall, 2005.

2. Network Security Essentials: Applications and Standards by William Stallings.

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Course Code : CEN 622 Course Title : INFORMATION SECURITYLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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.

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 organized 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 assetsIdentify and prioritize threats to information assetsDefine an information security strategy and architecturePlan for and respond to intruders in an information systemDescribe legal and public relations implications of security and privacy issuesPresent a disaster recovery plan for recovery of information assets after an incidentThe 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 computing as a whole. The primary objectives of the course are to help you:Understand information security’s importance in our increasingly computer-driven world.Master the key concepts of information security and how they “work.”

Develop a “security mindset:” learn how to critically analyze situations of computer and network usage from a security perspective, identifying the salient issues, viewpoints, and trade-offs.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 in-formation security.

COURSE CONTENTS 1. Introduction to Information Security2. Metrics for Information Security3. Networking and Cryptography 4. Information 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 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 instruction alone. My purpose as instructor is to expose you to a variety of important conceptual and technical aspects of the subject, helping to 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

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what 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


HomeworkProject Midterm Examination Final Examination

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Learning outcomes As a result of completing this course, students will be able to: Describe threats to information securityIdentify methods, tools and techniques for combating these threatsIdentify types of attacks and problems that occur when systems are not properly protectedExplain integral parts of overall good information security practicesIdentify and discuss issues related to access controlDescribe the need for and development of information security policies, and identify guidelines and models for writing policiesDefine risk management and explain why it is an important component of an information security strategy and practiceDescribe the types of contingency plan and the steps involved in developing eachIdentify security issues related to personnel decisions, and qualifications of security personel


Textbook(s) M. Merkow and J. Breithaupt, Information Security, Pearson,2006.

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Course Code : CEN 624 Course Title : DISTRIBUTED DATABASE SYSTEMSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Database distribution architectures. Distributed query processing. Distributed query optimization.

Distributed transaction management. Distributed concurrency control. Distributed reliability protocols. Multidatabase systems. Mobile distributed database management.

COURSE OBJECTIVES The aim of this module is to build on the previous background of database systems by deepening the understanding of the theoretical and practical aspects of the database technologies, showing the need for distributed database technology to tackle deficiencies of the centralised database systems, introducing the concepts and techniques of distributed database including principles, architectures, design, implementation and major domain of application, exposing students to active research topics of the distributed database field, and the module addresses advanced issues faced in distributed database application development.

COURSE CONTENTS Data Fragmentation, Replication, and allocation techniques for DDBMS Methods for designing and implementing DDBMS e.g. designing a distributed relational

database cluster, federated, parallel databases and client server architecture Advanced Concepts in DDBMS distributed transaction management, concurrency and recovery in DDBMS Distributed Deadlock Management and replication Servers Distributed Query Processing and Optimisation Distributed Object/component-based DBMS Database Interoperability including CORBA, DCOM and Java RMI Distributed document-based systems, XML Workflow management, Emerging related database technologies Parallel Databases Mobile database Web Databases


Teaching Methods




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Textbook(s) 1. Tamer Ozsu, M. and Valduriez, P. Principles of Distributed Database Systems, (2nd Edition) Prentice Hall International Inc. 1999 ISBN 0-13-607938-5 (Main text)

2. Oefali, R., Harkey Dan and Edwards, J. The essential Distributed Objects-Survival

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guide. John Wiley & Sons, Inc. 1996 ISBN 0-471-12993-3 (recommended reading)

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Course Code : CEN 627 Course Title : ALGORITHM DESIGN FOR PARALLEL COMPUTERSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Taxonomy of parallel architectures, Interconnection networks, Basic communication operations,

Performance of parallel systems: speedup, efficiency, cost, granularity and data mapping, sources of parallel overhead, Scalability of parallel systems: overhead function, isoefficiency, cost optimality, degree of concurrency, Matrix partitioning schemes, Dense matrix transposition, matrix-vector multiplication, matrix-matrix multiplication, solution of linear system of equations, Solution of sparse linear system of equations: iterative methods, load-balancing and communication minimization, direct methods, scheduling problem

COURSE OBJECTIVES To understand interconnecting networks, performance of parallel system, scalability of parallel systems

COURSE CONTENTS Taxonomy of parallel architectures. Dynamic interconnection networks. Static interconnection networks: types, performance evaluation, network embedding. Static interconnection networks: routing mechanism, communication costs. Basic communication operations: all-to-all broadcast, reduction, prefix sums. Basic communication operations: all-to-all personalized communication, circular shift. Scalability of parallel systems: overhead function, isoefficiency, cost optimality, degree

of concurrency. Matrix algorithms: matrix partitioning schemes. Dense matrix algorithms: matrix transposition, matrix-vector multiplication. Dense matrix algorithms: matrix-matrix multiplication, solution of linear system of

equations. Solution of sparse linear system of equations: iterative methods, load-balancing and

communication minimization. Solution of sparse linear system of equations: direct methods, scheduling problem.


Teaching Methods




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Textbook(s) Inroduction to Parallel Computing. A. Grama, A. Gupta, and G. Karypis, V. Kumar

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Course Code : CEN 628 Course Title : PARALLEL PROGRAMMING LANGUAGES-SYSTEMSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Parallel programming models, languages and environments. Fundamental concepts: memory

hierarchy, communication, locality, latency, synchronization, load balancing. Parallel programming models: data parallel, shared address space, message passing, data-driven, object oriented, functional. Parallel programming languages and runtime systems: data parallel languages, message passing libraries and language constructs, data-driven object based languages, shared memory programming, multithreading.

COURSE OBJECTIVES Describe generic issues (as discussed in the syllabus), which must be addressed by any parallel programming system. Explain, given a description of a previously unseen parallel application, where specific instances of the generic issues will arise. Explain, in considerable detail, the ways in which the generic issues are addressed by the MPI and Pthreads programming models and their supporting infrastructure. Apply their practical experience with MPI and Pthreads to write clean, adaptable and scalable parallel programs for simple applications. Compare the approaches proposed by a range of more speculative programming models. Review and critically evaluate literature describing new parallel programming models.

COURSE CONTENTS Concurrent programming concepts; overview of course Techniques for parallelizing programs Synchronization, atomic actions, await statements Pthreads library and MPD language Formal semantics; avoiding interference; properties Critical sections: spin locks Critical sections: efficient locks; fair solutions Parallel programming; bag of tasks paradigm Semaphores: basic concepts and uses Semaphores: the method of passing the baton Semaphores: scheduling; use in Pthreads Parallel scientific computing; grid computations Barrier synchronization Monitors: basic concepts Monitors: synchronization techniques Multiprocessor implementations Message passing: basic concepts and examples Message passing: clients and servers Message passing: interacting peers Message passing in Java, MPD, and MPI Remote operations: RPC and rendezvous Examples of RPC and rendezvous Java RMI; implementation of RMI Programming distributed systems Distributed parallel programming Heartbeat and pipeline algorithms


Teaching Methods




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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 legal and ethical issues;

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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 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.


Textbook(s) 1. G.R. Andrews, Foundations of Multithreaded, Parallel and Distributed Programming2. B. Wilkinson, M.Allen, Parallel Programming, Techniques and Applications'

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Course Code : CEN 633 Course Title : ADVANCED DATABASE SYSTEMSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Serializability theory. Locking, timestamp-ordering, optimistic schedulers. Multiversion and

distributed concurrency control. Distributed atomic commitment protocols. Multidatabase systems. Active database systems. Real-time database systems. Object-oriented database systems

COURSE OBJECTIVES At the end of the course students should be able to gain an awareness of the basic issues in object–oriented data models, learn about the Web–DBMS integration technology and XML for internet database applications, familiarize with the data–warehousing and data mining techniques and other advanced topics, apply the knowledge acquired to solve simple problems

COURSE CONTENTS The Extended Entity Relationship Model and Object Model Object–Oriented Databases Overview of object–oriented concepts Object structure and type constructors\ OODBMS architecture and storage issues Transactions and concurrency control Object Relational and Extended Relational Databases Architectures for parallel databases Distributed database concepts Data fragmentation Replication and allocation techniques for distributed database design Query processing in distributed databases Concurrency control and recovery in distributed databases An overview of client–server architecture Databases on the Web and Semi–Structured Data Enhanced Data Models for Advanced Applications


Teaching Methods




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Textbook(s) 1. Elmasri and Navathe, Fundamentals of Database Systems2. Ramakrishnan and Gehrke, Database Management Systems

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Course Code : CEN 636 Course Title : CHIP MULTIPROCESSORSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION This advent of large-scale multi-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 efficiency, 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 multiprocessors 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 Introduction to parallel computing Parallel algorithms Parallel programming Chip multiprocessor architecture and cache coherency Transactional memory On-chip interconnection networks Manycore research issues


Teaching Methods




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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-Wesley (2nd Edition)

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Course Code : CEN 640 Course Title : ADVANCED OPERATING SYSTEMSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION

COURSE OBJECTIVES Learning the role of operating systems. Learning the theory underlying how operating systems are implementer and the implications of resulting design choices. Developing practical skills needed to understand and modify operating system code, feel competent to do so, and understand why it matters. Acquiring sufficient knowledge to be able to solve problems & know how to learn additional relevant info when needed.

COURSE CONTENTS Introduction Processes and Threads Synchronization (mostly deadlock prevention) Memory Management Input/Output File Systems Multimedia


Teaching Methods




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Textbook(s) Jain, "The Art of Computer Systems Performance Analysis", 1991, Wiley. ovet and Cesati, "Understanding the Linux Kernel", 3rd edition, 2005, O'Reilly

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Course Code : CEN 645 Course Title : ROBOT MOTION CONTROL AND PLANNINGLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Basic concepts of motion planning, representations of state and movement, potential functions,

roadmaps, cell decompositions, robot dynamics, basic control, constrained motion, hybrid planning and control, logical reasoning methods for planning.

COURSE OBJECTIVES Consideration of sensing modalities and uncertainty in planning and control algorithms. Development of representations and motion strategies capable of incorporating feedback signals. Motion subject to constraints, arising from kinematics, dynamics, and nonholonomic systems. Addressing the characteristics of dynamic environments. Developing control and planning algorithms for hybrid systems. Understanding the complexity of algorithmic problems in control and motion planning. Encouraging the application of planning algorithms in novel application areas.

COURSE CONTENTS Kinematics Jacobian Dynamics Manipulator Control Mobot: Mobile Robot Robot Sensing & Sensors Motion Planning Mapping


Teaching Methods




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Textbook(s) Robotics --- Control, Sensing, Vision and Intelligence, K. S. Fu, R. C. Gonzalez, C. S. G. Lee, McGraw-Hill, 1987, ISBN 0-07-022625-3

Introduction to Autonomous Mobile Robots, Roland Siegwart, Illah R. Nourbakhsh, The MIT Press, 2004, ISBN 0-262-19502-X.

Introduction to AI Robotics, Robin R. Murphy, The MIT Press, 2000

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Course Code : CEN 651 Course Title : COMPUTATIONAL GEOMETRYLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Algorithmic background, data structures, geometric preliminaries, models of computation.

Geometric searching, point-location, problems, range-searching problems. Convex hulls, problem statement and lower bounds, convex hull algorithms in the plane, graham's scan, Jarvis's march, QUICKHULL techniques, dynamic convex hull, convex hull in 3D. Proximity problem, a collection of problems, a computational prototype: element uniqueness, lower bounds, the closets-pair problem: a divide-and-conquer approach, the Voronoi diagram, proximity problems solved by the Voronoi diagram triangulation, planar triangulations, Delaunay triangulation, intersections, application areas, planar applications: intersection of convex polygons, star-shaped polygons; intersection of line segments. 3D applications: intersection of 3D convex polyhedra; intersection of half-spaces.

COURSE OBJECTIVES Learning the basic data structures used to represent geometric objects. Learning the varieties of algorithms used for geometric computations. Learning how to design computational solutions to geometric problems. Learning how to code efficient programs for doing geometric computations.

COURSE CONTENTS Polygons (Representation, Area, etc) Polygon Triangulation Polygon Partitioning Convex Hulls (2D) 3D Polyhedrons 3D Convex Hulls Voronoi Diagrams Delaunay Triangulation Intersection (Segment, Polygon, etc) Point Inclusion (2D & 3D) Extreme Points Planar Point Location Robot Motion Planning (shortest paths) Numerous Applications


Teaching Methods




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Textbook(s) M. de Berg, M. van Kreveld, Mark Overmars & Otfried Schwarzkopf, "Computational Geometry: Algorithms and Applications," Second Edition, Springer-Verlag, 2000. ISBN: 3-

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540-65620-0. Computational Geometry (An Introduction), by Preparata and Shamos, Springer-Verlag,

1985. Computational Geometry In C (Second Edition), by O'Rourke, Cambridge University Press,

1998.

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Course Code : CEN 652 Course Title : BUSINESS INTELLIGENCELevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTIONCOURSE 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, data 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 theoretical 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 skills -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% content+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, critical thinking and independent learning.-Team work: ability to collaborate in group projects-Group discussions: ability to participate in group discussions on a given subject


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

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http://www.teradata.com/t/page/137453/index

http://www.olapreport.com/

http://www.tdwi.org/

Course Code : CEN 654 Course Title : ASPECT-ORIENTED SOFTWARE DEVELOPMENTLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Aspect-oriented software development (AOSD) is an advanced technology for separation of

concerns, which provides explicit concepts to modularize concerns that tend to be more systemic, crosscut a broader set of modules and as such cannot be easily specified in single modules. This course will provide an in-depth analysis of the basic concepts of AOSD and teach the state -of-the-art AOSD techniques. The important topics in this course are following: separations of concerns; software evolution problems; component-oriented software development; examples of crosscutting aspects; aspect-oriented programming using Aspect-J, Composition Filters, Hyper J, Cosmos and Demeter; aspect-oriented modeling; aspects at the requirements and architecture design level; reflection and delegation techniques; design space modeling, composition anomalies.

COURSE OBJECTIVES Using the UML and design patterns to model medium-sized software systems. Reading and writing precise invariants and pre/post-conditions of software systems using OCL. Forming informed opinions about advanced topics including pattern specifications, model-driven software development (MDSD), and aspect-oriented software development (AOSD)

COURSE CONTENTS An introduction to literature review and analysis. Advanced UML modeling which includes modeling techniques using OCL, profiles,

templates, etc. Software engineering fundamentals including some core topics. An advanced topics component that addresses one or more of the following topics: Metamodeling Pattern specifications Software engineering for security Aspect-oriented software development

Model-driven software development, software factories, generative programming, etc.


Teaching Methods




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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 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 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 by related attitudes necessary to undertake employment.


Textbook(s) MDA Explained. The Model Driven Architecture: Practice and Promise, by Anneke Kleppe, Jos Warmer, Wim Bast, Addison Wesley, ISBN: 032119442X

Object Constraint Language, The: Getting Your Models Ready for MDA, by Jos Warmer et al., second edition, Addison Wesley, ISBN: 0321179366

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Course Code : CEN 657 Course Title : APPLICATION OF COMPUTER GRAPHICSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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 standards. 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, quantisation, 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: Bresenham'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. Understanding 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 processing, 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 compression 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 transformations Projections and viewing Hidden surface removal Object definition techniques Lighting and shading Texture mapping Efficiency. Further steps towards visual realism


Teaching Methods




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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 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 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 by related attitudes necessary to

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undertake employment.


Textbook(s) 1. Computer graphics: 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).

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Course Code : CEN 659 Course Title : COMPUTATIONAL INTELLIGENCELevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION With the advance of increasingly faster computing hardware and cheaper memory chips, compu-

tational intelligence, also known as a part of “soft computation”, a relatively new area of research, is becoming more and more important in many engineering and non-engineering disciplines in -cluding control engineering. In this information-rich world, the plant to be controlled is becoming more and more complex and control objective is given in a more and more “high-level” fashion – not just the “zero steady state error”, “smaller overshoot” or the like requirements. The perfor -mance is usually multiobjective. There is another concern about the prior knowledge about the plant and about how to better control the complicated system. In practice, we know that, usually, there do exist some rules or site knowledge from the site-operators about the system and the con -trol. However, these rules, usually linguistic, may contain certain fuzziness. Therefore, new com-putational tools are needed to effectively design the controller to achieve the multi-objective per-formance indices with significant uncertainties, nonlinearities, and fuzziness in the description of the model of the plant to be controlled. Computational Intelligence is a collection of the possible computational tools to solve the above problems in control engineering. This course will equip the student with the essential knowledge and useful resources to solve some of the systems control problems not easily solved using previously learned conventional control methods. Specifically, this course will focus on the following1. Using NN/FL to model the complex static/dynamic systems;2. Using NN/FL as a tool to construct the complex nonlinear controller to better control the com-plex dynamic systems;3. Using EC as a tool to perform the multi-objective design of controllers.4. Gain hands-on experience on MATLAB toolboxes for NN and FL to solve practical control de-sign problems.5. Gain hands-on experience on MO EC for controller design.6. Survey on the state-of-art applications of Computational Intelligence in control engineering.7. Get familiar with the Internet resources on computational intelligent related to control engineer -ing.

COURSE OBJECTIVES Introducing concepts, models, algorithms, and tools for development of intelligent systems. Example topics include artificial neural networks, genetic algorithms, fuzzy 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 CONTENTSTEACHING/ASSESSMENT

Description

Teaching Methods


Participation of different teaching methods depends on the subject.



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Learning outcomes On completion of this course, the student will have: An understanding of the fundamental Computational Intelligence models Implemented neural networks, genetic algorithms, fuzzy neural networks, and ant

colony optimization algorithms. Applied Computational Intelligence techniques to classification, pattern recognition,

prediction, rule extraction, and optimization problems.


Textbook(s) A. P. Engelbrecht, Computational Intelligence, John Wiley & Sons Ltd, 2007.

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http://www.heatonresearch.com/book/programming-neural-networks-java-2.html









Course Code : CEN 660 Course Title : MODEL-DRIVEN SOFTWARE DEVELOPMENTLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION Software evolution problems, motivation for Model-Driven Software Development (MDSD),

domain modeling, meta-modeling, model-driven architecture (MDA), model-driven engineering methods, model-to-text transformations, model-to-model transformations, domain specific languages, software factories, MDSD tools, Architecture-Driven Modernization (ADM), adaption strategies for setting up a model-driven approach, obstacles of MDSD.

COURSE OBJECTIVES The purpose of this course is to conduct in-depth study of object-oriented analysis and design of software systems based on the standard design language UML. Primary topics of study include the use-case driven approach for software analysis, system design and detailed design. In particular, emphasis will be made on how to strengthen major design qualities such as robustness, changeability, interoperability, and reliability via UML based concepts, processes, methods and techniques. If time allows, a complete case study will be discussed.

COURSE CONTENTS Course introduction. Overview to Software Design. Evolution of software design. Overview of MDSD UML – Static models UML – Dynamic models Software analysis and design in the USDP Design patterns. MDSD – Key components Meta-modeling and Domain Architectures Validating analysis and design models. Other approaches to software design


Teaching Methods




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Textbook(s) 1. Markus Völter and Thomas Stahl, “Model-Driven Software Development: Technology, Engineering, Management”, Wiley 2005, ISBN ISBN: 978-0-470-02570-3.

2. Grady Booch, James Rumbaugh, and Ivar Jacobson. The Unified Modeling Language User Guide, second edition, Addison-Wesley, 2005.

3. Scott W. Ambler. The Elements of UML 2.0 Style, Cambridge University Press, 2005.

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Course Code : CEN 661 Course Title : SPECIAL TOPICS IN DECISION SUPPORT SYSTEMSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION This subject considers research process and different approaches used in studying decision

support systems. It aims to equip research students with the skills to guide them through the key steps in developing their DSS research strategies and research proposals.

COURSE OBJECTIVES This subject considers research process and different approaches used in studying decision support systems. It aims to equip research students with the skills to guide them through the key steps in developing their DSS research strategies and research proposals.


Description

Teaching Methods



Class participation 10%Paper critique 20% Literature review 30%Research proposal (group) 40%

Learning outcomes Professional Knowledge and Skills:

-Understanding of the nature and process of research in DSS-Knowledge of different approaches and methods used in DSS research-Developed fundamental research skills in DSSTransferable skills and other atributes:-Critical thinking and problem solving-Oral and written communication-Teamwork and leadership-Social and ethical perspectives


Textbook(s) Prescribed Textbook:-Selected readings (TBA)

Useful Web Links:-IFIP WG8.3 http://www.ifip-dss.org/-Data Resources http://dssresources.com-Teradata University Network http://www.teradata.com

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http://www.teradata.com/

http://dssresources.com/

http://www.ifip-dss.org/

Course Code : CEN 664 Course Title : PHILOSOPHICAL FOUNDATIONS OF ARTIFICIAL INTELLIGENCELevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION 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 Thought; 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. Previous knowledge of artificial intelligence is required.

COURSE OBJECTIVES The course will focus on “classical AI”, which uses concepts of knowledge representation and logic to solve problems of an essentially deterministic nature. Thus, students will learn how to develop intelligent agents that operate in a fairly static, predictable environment.

COURSE CONTENTS Concepts of AI Intelligent agents Propositional logic Uncertain Knowledge and Reasoning Planning and Acting in the Real World


Teaching Methods



Project Midterm Examination Final Examination

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Textbook(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. Morgan Kaufmann. (1998).

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Course Code : CEN 665 Course Title : DATA COMMUNICATIONS AND COMPUTER NETWORKSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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 architecture, 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, the 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 computer 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, 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. 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 services.


Teaching Methods



Research Project Midterm Examination Final Examination

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Learning outcomes Demonstrate a systematic and critical understanding of the theories and principles of computer networks;Creatively apply contemporary theories, processes and tools in the development and evaluation of solutions to problems and network design;


Textbook(s) 1. Behrouz A. Forouzan. Data Communications and Networking (4th Edition). McGraw Hill. 2007. ISBN: 0-07-296775-7.2.William Stallings, Data and Computer Communications, Pearson, 20093. Dr. K.V. Prasad, Principles of Digital Communication Systems and Computer Networks, Charles River Media, 20034. Larry L. Peterson & Bruce S. Davie, Computer Networks A Systems Approach, Third Edition, Morgan Kaufmann Publishers, 2003.5. Nader F. Mir, Computer and Communication Networks, Prentice Hall, 2006.

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Course Code : CEN 666 Course Title : IT STRATEGYLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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.

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 organized 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 countermeasuresApplications 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 computing as a whole. The primary objectives of the course are to help you:

Understand information security’s importance in our increasingly computer-driven world. Master the key concepts of information security and how they “work.” Develop a “security mindset:” learn how to critically analyze situations of computer and

network usage from a security perspective, identifying the salient issues, viewpoints, and trade-offs.

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 techni -

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

COURSE CONTENTS Developing & Delivering on the IT Value PropositionDeveloping IT Strategy for Business ValueLinking IT to Business Metrics, Managing Perceptions of ITIT in the New World of Corporate Governance ReformsCreating and Evolving a Technology RoadmapThe IT Budgeting ProcessInformation Management: the Nexus of Business &ITStrategic Experimentation with ITInformation Delivery: IT's evolving roleDigital DashboardsManaging Electronic CommunicationsDeveloping IT CapabilitiesIT SourcingDelivering IT Functions: A Decision FrameworkBuilding Better IT Leaders from the Bottom Up


Teaching Methods

1. Interactive lectures and communications with students2. Discussions and group works3. Presentations4. Guest Instructors(4-5 guests per semester)

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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 plan for recovery of information assets after an incident.


Textbook(s) James D. McKeen and Heather Smith, IT Strategy in Action , Pearson 2009

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Course Code : CEN 667 Course Title : IT GOVERNANCELevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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 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 OBJECTIVES 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 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.


Description

Teaching Methods



Project 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 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 plan for recovery of information assets after an incident.


Textbook(s) 1. International IT Governance: Alan Calder & Steve Watkins, Koganb Page, 2062. 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 Governance Publishing 2008.6. Text of standards: ISO 27001, 27002, 27003, 2700, 20000-1, 20000-2, ISO / IEC7. Business Continuity BS 25999-1 and BS 25999-2, British Standardisation Institute.

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Course Code : CEN 668 Course Title : NETWORK MANAGEMENTLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : 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 – Security, Network Management Functions - Accounting & Performance, Remote Network Monitoring RMON, Management Tools, Systems and Applications

COURSE OBJECTIVES The main objective of this course is to present SNMP network management concepts SNMP management information 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 - SNMP 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 - Accounting & Performance Remote Network Monitoring RMON 1 Remote Network Monitoring RMON 2 Management Tools, Systems and Applications


Teaching Methods1. Interactive lectures and communications with

students2. Discussions and group works3. Presentations


Project 20%Midterm Examination 30%Final Examination 50%

Learning outcomes Network Management Standards and Models. Network Management Protocols. Ab-stract Syntax

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

Structure of Management Information (SMI). Management Information Base (MIB). Re-mote

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


Textbook(s) 1. William Stallings, “SNMP, SNMPv2, SNMPv3 and RMON 1 and 2”, Third Edition, Addison Wesley, 1999. (Unit - V) (Chapter – 4-7)2. “Network Management – Principles and Practice” by Mani Subramanian, AddisonWesley Pub Co, First Edition, 2000.

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Course Code : CEN 669 Course Title : SPECIAL TOPICS IN MACHINE LEARNING


Instructor : COURSE DESCRIPTION Machine learning 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 processing).

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 science, 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) Reinforcement 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 Methods

1. Interactive lectures and communications with students2. Discussions and group works3. Research, project and presentations


Research Paper PresentationProject Final Examination

25%25%50%


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





Textbook(s) T. Hastie,R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Second Edition, Springer, 2008.

Mitchell T., Machine Learning, McGraw Hill, 1997. Du and Swamy, Neural Networks in a Softcomputing Framework, Springer-Verlag

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, 2007.

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Course Code : CEN 670 Course Title : SPECIAL TOPICS IN DATA MINING


Instructor : COURSE DESCRIPTION Overview of Data Mining Classification, 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 basic 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 Warehousing, 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 Object, 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 Methods



Project Research Paper Presentation Final Examination

25%25%50%








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.

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.

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Course Code : CEN 671 Course Title : SPECIAL TOPICS IN PATTERN RECOGNITIONLevel : PhD Year : Semester : ECTS Credits : 7.5Status : 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 discuss 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 clustering. Additional topics on new pattern recognition algorithms and techniques from active research are also talked about in the class.

COURSE OBJECTIVESCOURSE 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, Fisher 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 Mixture 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 Genetic Algorithms Combination of Multiple Classifiers "Committee Machines"


Teaching Methods

1. Interactive lectures and communications with students2. Discussions and group works3. Research, project and presentations


Research Project Final Examination

25%25%50%



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,

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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 Publishing 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.

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Course Code : CEN 673 Course Title : SELECTED TOPICS IN BIOINFORMATICSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION The course is designed to introduce the 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 simulation, 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 solve the problems on their own research.

COURSE OBJECTIVESCOURSE CONTENTS This course consists of eighteen lectures that are listed below. They are given short outlines

below. 1. Molecular evolution2. Gene finding.3. Sequence comparison methods.4. Amino acid residue conservation5. Function prediction from protein sequence6. 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 proteins12. 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. Proteomics17. Data management of biological information.18. Internet technologies for bioinformatics.


Teaching Methods




10%10%20%20%40%



Textbook(s) C. Orengo, D. Jones, J. Thornton, Bioinformatics: genes, proteins and computers, BIOS Scientific

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Publishers Limited, 2003

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Course Code : CEN 675 Course Title : CEN 675 INDUSTRIAL NETWORKS Level : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

Instructor : COURSE DESCRIPTION The course provides basic knowledge 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 OBJECTIVESCOURSE 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 Interworkability, 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, Protocol 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 Methods




10%10%20%20%40%



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&ots=qdtUGpSrcs&sig=_bcBtZ3votcDfsL9xAwMZ3mKnvI&hl=en&ei=QlCSSuj1NaW8mgO-7tm9DQ&sa=X&oi=book_result&ct=result&resnum=7#v=onepage&q=&f=false

Course Code : CEN 681 Course Title : SPECIAL TOPICS IN COMPUTER NETWORKSLevel : Graduate Year : Semester : ECTS Credits : 7.5Status : Compulsory/Elective Hours/Week : 3 Total Hours : 45

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



Instructor : COURSE DESCRIPTION This course introduces the new technologies and trends in computer networking. Students will

develop an understanding of the new trends of computer 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, describe 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 trends 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. 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 services.


Teaching Methods



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


Textbook(s) 1.Dr. K.V. Prasad, Principles of Digital Communication Systems and Computer Networks, Charles River Media, 20032. Nader F. Mir, Computer and Communication Networks, Prentice Hall, 2006.3. Computer networks related articles.

Course Code :CEN 691 COURSE TITLE : FUZZY SYSTEMS AND CONTROLLevel : Graduate 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

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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 Fuzzy 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 Properties of Fuzzy Systems I-II Design of Fuzzy Systems From Input-Output Data Non adaptive Fuzzy Control Adaptive Fuzzy Control


Teaching Methods Lecturing, problem solving, submissions by students, class discussions

Description (%)Student Assessment Methods


10%10%20%20%40%

Learning outcomes Evaluate basic theories, processes and outcomes of computing; Apply bioinformatics 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 critical understanding of the well-established principles of bioinformat-ics, and of the way in which those principles have developed as technology has pro-gressed

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


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.

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Documents

Course Code : CEN 511 - Home - Department of … · Web viewDistributed Object/component-based DBMS Database Interoperability including CORBA, DCOM and Java RMI Distributed document-based