Turkish - English
M.Sc. in Computer Engineering - Course Descriptions
COMP501 Advanced Mathematics
A Review of Vector Analysis, Coordinate Systems, Fourier Series and Transform, Gamma and Beta Functions, Linear Differential Equations of the Second-Order (Series Solutions), Solution of Partial Differential Equations, Legendre Functions, Bessel Functions, Boundary Value Problems and Eigen value Representations, Introduction to integral equations.
COMP503 Computer Networks and Communication
Continuous wave modulation. Spectra of modulated signals. Pulse modulation schemes, PAM, PDM, PPM, and PCM techniques. Signal to quantization noise ratio. Companding. Differential PCM Delta Modulation. Adaptive Delta Modulation. FSK and PSK modulation and demodulation. Spectra of FSK and PSK signals. TDM & FDM, Modems. Computer Network protocols, circuit switching, message switching, packet-switching, ISO OSI reference model, TCP/IP, LAN, WAN. Interconnection topology and performance; Comparison of Distributed Systems and their applications; Benefits and implications to users of Distributed Systems.
COMP502 Advanced Computer Architecture
Advanced concepts and implementations in the dramatically changing world of computer architectures. Significant architecture classes, superscalar, multi-threaded, shared-memory, and distributed memory MIMDs, RISC architectures. Design spaces. Evolution of concepts and design issues.
COMP504 Database Management Systems
This course will give an in-depth analysis of database system management and managing organizational data. Introduction to database systems. Entity-Relational modeling; Normalization. The Relational Model; SQL query language. Query By Example and other query methods; Semantic Data Models; Object Oriented Databases.
Electives
COMP505 Network Administration and Management
This course will give network administration and management techniques for LANs, WANs as well as advanced metro optical and DWDM networks. Bandwidth management and QoS issues will be studied. Security and reliability issues will also be addressed.
COMP507 Advanced Digital Signal Processing
Linear systems analysis, discrete time signals and systems, Z transforms, Properties, Realization structures for digital filters, Recursive and non-recursive functions, stability criteria, Design of low pass, high pass and band pass filters, frequency response shaping, window functions, finite word length effects, signal processing techniques for random signals, Discrete Fourier Transform Decimation in frequency and time, Fast Fourier Transforms, 2D signal processing.
COMP509 Computer Aided Design
Automating development through Computer Aided Software Engineering (CASE). Trade-offs. Adoption. Upper CASE, Lower CASE, cross life cycle, CASE repository. Form and Generator tools, Analysis tools, code generators, project initiation and planning tools.
COMP511 Information Systems Development
The course emphasis is on the systems approach in studying and developing information and other systems. The systems development life cycle is considered. Methods of project management and human computer interface design are introduced. Various approaches to developing, building and managing MIS, end user computing and information centers IS planning strategies are also covered.
COMP513 Programming Languages
The identification of the major features of High Level Programming languages; Associated mathematical models. Formal description of language syntax and semantics.
COMP515 Advanced Operating Systems
Unix and Linux systems, solaris operating systems. Multimedia operating systems, multiple processors systems, security, operating system design.
COMP517 Numerical methods in Optimisation
Types of optimization problems, efficiency of common methods of solutions, extrema of functions of n variables, linear programming and the simplex method, nonlinear programming.
COMP519 Network Flows
Introduction to network flows, max-flow min-cut theorem, optimization problems on network flows, steiner trees, Flow problems on planar networks.
COMP529 Machine Learning
Introduction, concept learning and the genaral-to-specific ordering, decision tree learning, artificial neural networks, evaluating hypothesis, bayesian learning, genetic algorithms, learning sets of rules, analytical learning, combining inductive and analytical learning, reinforcement learning.
EE503 Digital System Design
Review of Combinational logic design using logic gates, MUX, PROM array, EPROM, PAL and PLA. Sequential logic design. Synthesizing various building blocks for system design. Added features on universal shift registers and universal counters. Arithmetic unit. Parallel binary adder/subtractor/ Carry-look ahead adder. BCD adder/subtractor. ASCII arithmetic machine. Parallel multiplier. High speed ALUs. Flag detection and registering. Design of hypothetical CPU. Introduction to top down design. ASM based digital system design. Realizing high resolution ADCs using low resolution converters. Flash modules. Modular based design of flash ADCs. Digital delay lines.
EE 515 Modelling and Control of Systems
Modelling of physical systems – differential equations – transfer function – state space models – time response from transfer function and state space models – discrete time systems representation. Control of systems – different modes of control – proportional, integral, derivative and combinations- on off control – controller tuning. Simulation of systems on digital computers – simulation using high level language programs – disadvantages – special simulation programs and environments – MATLAB programming environment – Toolboxes – Simulink for simulation – Case studies on typical simulation.
COMP506 Expert Systems
Expert systems provide a software methodology for capturing and applying the knowledge needed to solve highly specialized problems. An expert system is essentially a set of individual rules that analyzes the input from the user and either suggests a course of action for solving the problem at hand, provides advice, or presents a diagnosis. This course presents the underlying theory of expert systems and the issues involved in building practical applications. This course mainly uses CLIPS as the implementation language. General topics covered in this course are knowledge representation, inference methods, reasoning under uncertainty, and inexact reasoning.
COMP508 Object Oriented Programming and Analysis
The aim of the course is to give the students an understanding of the basic features of the object-oriented approach. Classes and objects. Membership. Methods and properties. Abstract methods and classes. Virtual methods. Properties of a class and properties of an object. Constructors and destructors. Examples in C++ and Java programming languages. Object oriented analysis.
COMP510 Advanced Computer Graphics
Introduction and overview. Display devices, files and data structures; Interactive input - devices and techniques; Graphics software, Standards, GKS; Fundamental algorithms for 2-D graphics output and attributes; Viewing, windowing and clipping; 2-D transformations - matrix representation and control; 3-D concepts and representations; 3-D transformations and projections, viewing and clipping.
COMP512 Knowledge Engineering
Basic concepts and techniques, knowledge representation, drawing inferences, tools and languages for expert systems, expert systems development, knowledge acquisition, expert system applications.
COMP514 Application Software
Categories of software. Characterization of commercial application software (package software). Integrated environments. Broad overview of the Windows Operating System. Understanding of various Windows applications and the relationship between them. Linking and Embedding. General overview and advanced features of word processing (MS Word). General overview and advanced features of spreadsheets (Ms Excel). Database management systems and comparison with spreadsheets. Graphics software (MS PowerPoint). Desktop publishing. Communications software (Cheyenne BitWare). Overview of other major general purpose application areas.
COMP516 Advanced Artificial Intelligence
Problem solving and search techniques, game playing, knowledge representation, expert systems and rule chaining, machine translation, machine learning, neural networks.
COMP526 Digital Image Processing
Characteristics of image signals, representation in time and frequency, spatial and temporal domains, digitizer, 2D, Z transforms, realization of digital image processing system 2D FFT, Discrete Cosine Transform, Spatial filtering techniques, pattern recognition, Edge detection and enhancement, contrast enhancement, histogram equalization, median filtering, image data compression.
COMP532 Operations Research
Introduction to OR, formulating mathematical methods, Properties of convex polyhedrons, The linear programming model, Primal simplex method, revised simplex method, Duality theorem and sensitivity analysis, Assignment and Transportation problems, Game theory, Introduction to nonlinear programming modules, Methods of line search, Convex Programming, Kuhn-Tucker conditions, The method of feasible directions, Introduction to integer programming models, Branch-and-Bound method.
COMP534 Algorithmic Graph Theory
Basic concepts, algorıthmic techniques, shortest paths, trees and acyclic digraphs, connectivity and routing, graph coloring algorithms, covers, dominationt sets, matching, factors, parallel algorithms, computational complexity.
EE504 Microprocessors and Interfacing
Overview of architecture, organization and instruction set of 8086 family processors. Minimum mode and maximum mode configurations. Pin outs and functional details of pins for minimum and maximum mode. Buffered 8086 system. I/O port design for 8-bit wide data, 16-bit data and 32-bit data for 8-bit and 16-bit addresses. PROM and Ram memory interfacing. Interfacing keyboard and displays. Interfacing ADCs and DACs. DMA controller interface. Interfacing interrupt mechanism. Peripheral interface devices. Interfacing co-processors. Serial communication with USART and RS232C. Designing microcomputers for specific applications.
EE 524 System Identification and Adaptive Control
Determination of dynamic and static models from input output data – Stochastic modeling and system identification – determination of transfer function from step impulse and frequency response – off-line and on-line methods of parameter estimation – least squares method – regression methods – and other methods – comparison of different methods – Case studies using MATLAB/ Digital computer programs. Adaptive Control – need for adaptation – variable parameter systems – self-tuning and self-adaptive controllers- simple adaptive control schemes.
EE 528 Artificial Neural Networks
Neural network concepts: What is a neural network? Biological neuron, artificial neuron, topologies. Learning in neural networks Types of learning and learning rules: Error correction learning, Hebbian learning, Competitive learning, Boltzmann learning. Application tasks: Functional approximation, classification, association, application examples. Feedforward networks: Perceptron, multi-layer perceptron, radial basis function network, self-organizing feature map.Feedback networks: Hopfield, Boltzman machine, real-time recurrent network.
Date : 2008-11-21
