This course is concerned with the fundamentals of broadband communication networks including network architecture, Switch fabrics, design methodology; traffic management, connection admission control (CAC), usage parameter control (UPC), flow and congestion control; capacity and buffer allocation, service scheduling, performance measures, performance modeling and queueing analysis

Evolution of computer security. Types of security threats, hardware threats, software threats, physical threats, cryptanalysis. The theory of secure message passing. Methods of encryption, private networks, Data Encryption Standard, Public Key Cryptosystems. Secrecy and Privacy in a network environment, long haul networks, local area networks. Protocols for computer network security.

This course is concerned with the architecture, protocol, and software aspects of mobile systems. The topics to be discussed include mobile communication and computing systems, supporting ad hoc networks, mobile network and transport layers, wireless application protocol, support for mobility, service advertisement and discovery in mobile systems, and emerging issues such as environment-aware software and low-power protocols and software.

This is an introductory course on algorithms at the graduate level. It assumes familiarity with basic data structures such as lists, queues, trees and graphs, and emphasizes creativity in the design of algorithms, and rigorous analysis. Correctness (soundness and completeness) and efficiency (with respect to average-, best- and worst-case time and space) properties are considered in the context of algorithms for classes of problems such as optimization and decision problems. The course also gives insights into when a problem may be intractable, and how we may deal with intractability.

Fundamentals of software requirement analysis, software development as an engineering activity, basic process models, software specifications, modularity, cohesion, coupling, encapsulation, information hiding, principles of object-oriented design, software project management, quality assurance and control. Principles of Software Architecture: Fundamental software architecture styles, synchronous & as synchronous communication of software components. Languages for software design specification: UML (class diagrams, sequence diagrams, collaboration diagrams, state diagrams). Overview of verification and validation techniques. Maintenance, evolution and reengineering, configuration management. Software metrics, quality assurance, fundamental cost and effort prediction models. Trends in software engineering (e.g., model-driven development, agile approaches).

Introduces students to systematic testing of software systems. Software verification, reviews, metrics, quality assurance, and prediction of software reliability and availability. Students are expected to have programming experience with reading and writing code for large projects.

The course consists of two related parts. The first part deals with the engineering of reliable software. It introduces basic software reliability concepts, describes relevant models and discusses processes for engineering of reliable software, including schemes and patterns for the design of reliable and fault tolerant software. The second part addresses development of secure software. It presents key software security concept, techniques and models, overviews major software security vulnerabilities and their exploitation, and considers processes for development of secure software.

This course covers data models, file systems, database system architectures, query languages, integrity and security and database design. Students attending this course should have at least a 4A level Electrical Engineering or Computer Engineering background

Conventional approaches for tackling complex systems are usually implemented under the assumption of a good understanding of the process dynamics/functionalities and its operating environment. These techniques fail, however, to provide satisfactory results when applied to ill-defined processes (for which analytical and experimental modeling may not be easily obtained) that may operate in unpredictable and possibly noisy environment. Recent developments in the area of intelligent systems and soft computing have presented powerful alternatives for dealing with the behavior of this class of systems. This course outlines fundamentals of soft computing based design approaches using such tools as approximate reasoning, fuzzy inferencing, neural networks, evolutionary algorithms, and neuro-fuzzy systems. Fundamentals and advances on these procedures are outlined along with their potential applications to various real world applications in virtually most fields of engineering including pattern recognition, system planning, classification, power generation, intelligent transportation, systems and control, intelligent mechatronics, optimization, communication, robotics and manufacturing, to name a few.

Building large-scale and complex software systems from available parts with the goal of increasing return on investment, decreasing time to market, and assuring quality and reliability. The course covers the basic software component concepts, overview of advanced topics on software components and component-based software engineering from research and practice