Brief historical perspective. Understanding and using Integrated Development Environment (IDE). Java basics, introduction to graphical user interfaces (GUI) for Java, using Swing Components and Java Listeners, Swing dialogue boxes, developing GUI applications, theory of Object Oriented Programming with Unified Modeling Language (UML). Object-Oriented (OO) problem solving, Object-Oriented (OO) concepts (inheritance, composition, abstract classes etc.), object relations, developing Object-Oriented (OO) applications with design patterns, Unit Testing, Project Presentations.

The main topics discussed in this course are development methodologies and design patterns. Software life cycle phases like requirements, design, implementation, testing and deployment will be discussed with methodologies like Waterfall, prototyping and Extreme Programming. Also design patterns like creational, structural and behavioral patterns will be evaluated.

This is a Non-credit course. The widening of students perspective and awareness of topic of interest to computer engineers through seminar offered by faculty, guest speakers and graduate students.

Program of research leading to M.S. degree, arranged between a student and the faculty member. Students register to this course in all semesters starting from the beginning of their second semester while the research program or write-up of the thesis is in progress.

This course covers a number of advanced topics in database management systems and modern database applications. The specific topics include advanced concurrency control techniques, query processing and optimization strategies for relational database systems, advanced indexing methods, parallel and distributed database systems, next-generation data models, data mining on large databases, data on the web, and topics in data security and privacy.

Pushing a single processor to its limits. Instruction set design and its effect on computer performance. micro-programming. Addressing techniques. Memory hierarchy. Associative, virtual and cache memory. Memory management. Interrupts, DMA and channels. Comparative study of commercial computer architecture.

This course gives essential information for operating system and computer network security basics, risk analysis, security policies, concept of trusted computers and networks. Conventional and public key cryptography. Authentication and digital signatures. Authentication protocols and applications, certification, LINUX security issues. Data link layer, network layer and application level security. Firewalls and security tools. Secure payment systems. Case studies and programming projects.

Design and develop applications by using the benefits of operating system and computer architecture. Threads and concurrent programming, TCP/UDP, RPC/IPC, System calls, remote procedure calls (RPC) and web services, xml and xml parsing, socket communication, logging.

Image formation. Early processing: low-level vision and feature extraction. Boundary detection. Region growing. Texture. Motion. Two-dimensional and three-dimensional representation. High-level vision: learning and matching.

Introduction to cognitive science. Parallel, distributed problems. Constraint satisfaction. Liopfield model. Supervised vs. unsupervised learning. Single vs. multi-layer perceptions. Static vs. dynamic network architecture. Comparison of neural approaches with parametric and non-parametric statistical methods. Neural network applications.

Image formation. Early processing: low-level vision and feature extraction. Boundary detection. Region growing. Texture. Motion. Two-dimensional and three-dimensional representation. High-level vision: learning and matching.

This course will present an introduction to the principles of operation, design, testing, and implementation of the unimodal and multimodal biometric systems. Major and emerging biometric technologies (fingerprint, face, hand, iris, hand geometry, palmprint, keystroke, handwriting, signature, gait, voice etc.) performance and issues related to the security and privacy aspects of these systems will be addressed. Students will be introduced to a variety of methods used for processing data from various biometrics (especially signature biometric modality) and to statistical methods employed to achieve acceptable recognition performance rates (i.e. false accept rate, false reject rate, equal error rate and correct classification rates).