An orientation course to provide counsel to the students about the Department and Computer Engineering in general. An introduction to the faculty and their activities. Visit to several Computer Centers in and outside the University. Basic computer literacy: terminology, system components and operation. Internet, HTML Coding and Java, Desktop Softwares, Windows-Unix-Dos Operating Systems, Library Usage.

1. To be able to use main concepts of computer science. 
2. To be able to analyze problems and provide solutions to them.
3. To be able to explain field of applications in computer engineering.

Basic concepts of data, data structures and data types: arrays, strings, linear structures, sequential searching and sorting techniques, stacks, queues, pointers, linked lists. Various forms of m-way search and B-trees.

Learning Outcomes:

1. To be able to use and design abstract data structures. 
2. To be able to materialize array and list data types.
3. To be able to design and analyze data structures and algorithms.
4. To be able to design and materialize abstract data structures. 
5. To be able to materialize and use data structures and algorithms for search applications.

Fundamentals of computer programming: sequence, decision, repetion, syntax, compilation, debugging and maintenance, procedures, parameters, arrays, object, top-down structured design, layout and style. The emphasis is on an engineering “right-first-time” approach to solving large problems using computers. Basic concepts of algorithmics and algorithmic terminologies.

Learning Outcomes:

1. To be able to provide algorithmic solutions for basic problems.
2. To be able to use and explain main concepts of programming.
3. To be able to design and apply decision structures.
4. To be able to design and apply loop structures.
5. To be able to monitor and analyze computer software.
6. To be able to design, define and use functions.
7. To be able to use recursive approach for problem solving.
8. To be able to design, define and use class.

Sets, and Functions, The Fundamentals: Algorithms, the Integers, and Matrices, Methods of Proof, Mathematical Induction, Recursive Definitions, Recursive Algorithms, Program Correctness, Basics of Counting, The Pigeonhole Principle, Permutations and Combinations, Recurrence Relations, Divide-and-Conquer Relations, Inclusion-Exclusion, Relations and Their Properties, n-ary Relations, Representing Relations, Closures of Relations, Equivalence Relations, Partial Orderings, Graph Terminology, Representing Graphs and Graph Isomorphism, Connectivity, Euler and Hamiltonian Paths, Shortest Path Problems, Planar Graphs and Graph Coloring, Tree Traversal, Trees and Sorting, Spanning Trees, Minimum Spanning Trees, Boolean Functions and Their Representations, Minimization, Elements of Computability, Complexity Classes, P – NP – NP Completeness, Space Complexity, Number Conversion, Calculational Logic, Maximum and Minimum, Coding and Constructions.

Learning Outcomes:

1. To be able to use and compare different discrete structures.
2. To be able to analyze problems and determine appropriate solution paths.
3. To be able to show ability of abstraction. 

Ideas from object-oriented programming, methods, classes, information hiding, and inheritance; fundamental algorithms, sorting and searching; user defined classes; concept of recursion, benefits and problems; exception handling; using APIs; simple graphics programming; concept of software design.

Learning Outcomes:

1. To be able to design object-oriented problem solutions.
2. To be able to provide solutions by using object-oriented programming language. 
3. To be able to gain ability of objected-oriented programming with inheritance approach. 
4. To be able to gain ability of objected-oriented programming with abstraction approach.
5. To be able to use data structures in an object-oriented manner.

Syntax, semantics and pragmatics of programming languages. Data, storage and control. Binding of identifiers. Procedural abstraction. Definitions, sequences and concurrent processes. Types. Formal semantics. Study of key features of existing programming languages.

Learning Outcomes:

1. To able to use languages belong to different programming paradigms.
2. To able to easier to use a new programming language.
3. To be able to explain compilation and execution steps of a program.
4. To be able to implement high-level functions.

Abstract automata, especially finite state machines; push-down automata; and Turing machines. Formal languages, especially context-free languages. The relationship between automata and languages. Computability and solvability.

Learning Outcomes:

1. To be able to classify language definitions.
2. To be able to analyze problems and provide appropriate representations for them.
3. To be able to show abstraction ability.
4. To be able to identify some difficult problems in computer science. 

Introduction to Computer Architecture. Number Systems. Boolean Algebra. Logic Gates and Flip Flops. Combinational and Sequential Circuit Design. Registers, Counters. Bus Transfer. RAM, ROM units. Instruction Execution and Hardwired Control.

Learning Outcomes:

1. To be able to analyze and design combinational circuits.
2. To be able to design arithmetic and logic processing units.
3. To be able to understand fundamentals of memory units and programmable logic circuits and use them for a circuit design.  
4. To be able to design finite state machines and sequential logic circuits.

Surveys and applications of numerical techniques related to matrix inversion, systems of linear equations and optimization, finite difference expressions, interpolation and approximation, numerical differentiation and integration. The problems of speed, accuracy and applicability of the topics are examined with related algorithms. The applications of these numerical methods and subjects on computers using efficient programming techniques and with necessary programming languages.

Learning Outcomes:

1. To be able to explain effects of  finite representation of real numbers for a given algorithm.
2. To be able to find numerical errors in calculations and compare numerical errors of different algorithms for the same problem.
3. To be able to solve numerical problems that include derivative, integral, interpolation and/or optimization.
4. To be able to apply recursive solutions to numerical problems.
5. To be able to implement linear/non-linear systems for the given problem.
6. To be able to apply appropriate numerical algorithm for given linear/non-linear system.

The role of algoritms in computing, Growth of functions, recurrences, probabilistic analysis and randomized algorithms, dynamic programming, greedy algorithms, advanced data structures, graph algorithms, NP-Completeness.

Learning Outcomes:

1. To be able to provide solutions for problems with an algorithmic approach.
2. To be able to identify algorithm design options for a given problem.
3. To be able to analyse complexity of applications.

Elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, law of large numbers, central limit theorem, parameter estimation, confidence intervals, and hypothesis testing.

Learning Outcomes:

1.To be able to explain and apply the concepts of probability and random variables.
2.To be able to describe and use common probability distributions and their properties.
3.To be able to employ descriptive statistics.
4.To be able to estimate distribution parameters.
5.To be able to calculate confidence intervals.
6.To be able to conduct hypothesis testing.
7.To be able to utilize computation as a tool to explore concepts in probability and statistics and to do quantitative analysis.

Basic computer organization and design. Instruction fetch, decode and execution. CPU organization. Hardwired and microprogrammed control organization. Arithmetic algorithms and arithmetic processor design. Input-Output organization. Memory organization, virtual memories, caches, and their management. Machine language and assembly language. Instruction formats and addressing modes. Survey of computer architectures: Von Neumann, Parallel and RISC. Pipelining and other advanced techniques for performance improvements. Introduction to parallel computing, interconnection networks, and multiprocessors.

Learning Outcomes:

1.To be able to design general purpose and dedicated microprocessors.
2.To be able to realize and test designed microprocessor.
3.To be able to develop programs in Assembly language.

Overview of computer networks: Network architecture and the ISO-OSI model. Circuit switching, packet switching. Network topology: Connectivity analysis, delay analysis and backbone analysis. Physical layer: Transmission and multiplexing, terminal handling, errors. Data link layer and link protocols. Network layer: Routing and congestion, satellite and packet radio networks, local networks. Transmission and session layer, presentation layer, application layer. Ethernet, token ring networks, protocols, discussion of some networks and their properties, network planning and management.

Learning Outcomes:

1.To be able to analyse systems using fundamental concepts of communication.
2.To be able to analyse layered network architecture and Internet protocols.
3.To be able to realize the designed networks and protocols.

Relevance of information management in the context of computer engineering; introduction to database systems and the relational model; normal forms and their benefits; building databases, underlying methodology, database languages; issues associated with information retrieval; SQL, its use and power; information systems in the context of networks, intranets, extranets; special systems and applications; particular issues, access, security, and integrity; relevant legal and ethical issues.

Learning Outcomes:

1. To be able  to design a relational database.
2.To be able to create a relational database.
3.To be able to learn relational algebra and SQL.
4.To be able to learn how to apply normalization to databases.
5.To be able to learn database concurrency methods.
6.To be able to learn methods to provide consistency and privacy in databases.
7.To be able to learn project development and presentation capability by teamwork.

Software engineering, role of software engineers; evaluation of software and principles thereof, software lifecycle models; notions of requirements, specification, design implementation; main techniques; importance of maintenance; quality concerns at all stages of the software development process; important benefits of and good practice in software re-use; verification and validation; the use of metrics; structure of teams; human computer interface as a software engineering activity.

Learning Outcomes:

1. To be able to identify requirements for a given project topic.
2. To be able to find difference between functional and non-functional requirements in a project.
3. To be able to make software design with an object-oriented approach.
4. To be able to write project definition.
5. To be able to use and apply software process model.

Classification and structure of operating systems. Storage media, memory management and dynamic storage strategies. Scheduling algorithms. I/O and interrupt structures. Protection and security. Queueing and network control models. System software: Linkers, loaders, assemblers, translators and programming environments. Case studies of existing operating systems and implementation of operating system modules.

Learning Outcomes:

1. To be able to learn principles of operating systems. 
2. To be able to analyze and identify corresponding problems for the platforms including operating systems.
3. To able to compare different solution methods for problems of operating system and select the most appropriate one.

This course is designed to introduce the engineering students to economic and management concepts. Topics will include economic concepts such as; cash flow, interest rates, rate of return, demand supply relations, product pricing, taxes, inflation, and related subjects; and management analysis such as management layers, network analysis, project management via CPM/PERT networks, optimization concepts, linear programming, and decision analysis. The course also includes use of related software.

Learning Outcomes:

1.To be able to recognize the personal and technical properties required for a project manager.
2.To be able to identify the fundamental steps of project management and risk factors.
3.To be able to describe the outlines of preparation, planning, application, inspection and completion phases of a project.
4.To be able to tell the fundamental economic concepts required for a project manager.
5.To be able to demonstrate computer-aided project planning and management with examples using MS Project software.

Probability spaces, random variables, distribution and density functions, random vectors, sequences of random variables, convergence notions, the central limit theorem, the law of large numbers, stochastic processes, stationary notions, Poisson processes, Gaussian processes, transformations of stochastic processes, ergodicity, second order random processes, representation theorems, Markov processes, homogeneous Markov models and applications.

An introduction to Shannon’s information theory and elementary binary coding schemes with and without noise. The concept of information, entropy, simple sources, Markov sources, continuous sources, information channels, average error, ambiguity, transformation, capacity, noiseless coding, Kraft-McMillan theorem, Shannon-Fano and Huffmann coding schemes, error-correcting codes, linear codes, cyclic codes. Data Compression.

Specification and verification techniques for real-time systems with many interacting components. formal design of real-time systems using (a) programming languages with unambiguous semantics of time – related behavior and (b) scheduling algorithms. Real-time operating systems, concepts of programming languages for real-time systems. Synchronous programming languages for reactive systems and their mathematical background. Software development for real-time systems.

Elements of microprocessors and microcomputers, software and hardware for microprocessors; microcontrollers; embedded system design with microcontrollers, memory interface, analog-digital input/output interfaces and interrupt interface of typical microprocessors/controllers; programming with assembly and high level languages; real-time working, real-time operating systems; design of single and general purpose microprocessor/controllers using FPGAs; system control, analysis of feedback control systems, controller design; data acquisition, fundamentals of digital signal processing.

Propositional logic: syntax, semantics, decision procedures; first-order logic: syntax, semantics, definability, formal system, completeness, undecidability, incompleteness; second-order logic; advanced topics: many-valued logic, modal logic, temporal logic, fuzzy logic.

Fuzzy set theory, fuzzy relations, fuzzy rule base, approximate reasoning, fuzzy control, fuzzy logic system design.

During this course, we will be creating and maintaining a web server, creating HTML pages and learn how to program effectively in PHP. We will create a web site of your choosing as Project.

This course covers the fundamentals of programming mobile devices, how to access and use mobile device hardware, and the interaction between mobile apps and web services. Topics include the design and implementation of user interfaces on the Android platform, cross-platform mobile application development, and programming with sensor such as the camera and the GPS.

This course provides a hands-on comprehensive study of Cloud concepts, the history and fundamentals of cloud computing across various cloud service models including IaaS, SaaS, PaaS, including requirements, constraints, architecture, principals, areas of implementation, advantages/disadvantages and comparison to hybrid and local architectures. PaaS topics covers a range of Cloud platforms such as Google App Engine, Amazon Web Services(AWS), Microsoft Azure and others. Topics include the design, development and implementation of cloud based mobile applications, web applications, databases and systems as well as migrating existing software applications to the cloud, working on hybrid solutions. Course offers wide range of case studies of globally recognized technology startups, products and brands which are built on cloud platforms and analyze and review them in terms of technology, operations, finance, branding and business plan aspects.

This course covers the basic image processing techniques and approaches for image content analysis.. The course content includes memory representation of images, basic image processing techniques, keypoint extraction and description, image matching and fundamentals of camera geometry.

The summer practice allows students to experience practical applications in companies. It also allows students to know general structure of the host company. With this experience students involve ongoing real-life projects.

Learning Outcomes:

1. To be able to apply the learnings of the higher education in the real life problems.
2. To make students conscious of their mission and responsibilities of their profession.
3. To determine their working domain by allowing students to examine real life experience.
4. To be capable of proposing solutions to the problems with or without a team.
5. To improve their vocal and written communication capabilities and to understand the importance of foreign language knowledge.
6. To present obtained knowledge with a formal report.

Critical examination of ethical problems associated with computer engineering; discussion of these problems conducted within the framework of classical philosophical ethical theories; legal and quasi-legal (i.e., policy and regulative) issues; topics addressed include the process of ethical decision-making, privacy and confidentiality, computer crime, professional codes and responsibilities, professional practice, system security, impact of computers on society.

Learning Outcomes:

1.To be able to understand ethical issues in information technology.
2.The internalization of general ethical issues.
3.To be able to understand basics of privacy.
4.To be able to understand basics of property rights.

Writing technical reports, project management and planning, system analysis, requirement analysis, engineering ethics, data design, software design, presenting a project

Learning Outcomes:

1.To be able to identify problem.
2.To be able to analyze the problem, to define the scope, constraint, requirements, to be able to design a solution.
3.To be able to design a solution that is innovative, sustainable and beneficial to the environment and society.
4.To be able to learn and use new methods and technologies.
5.To be able to gain teamwork skills.
6.To be able to present written and oral presentation skills of the project.
7.To be able to describe the concepts about entrepreneurship and innovation.

Invited speakers, change management, coding documentation, coding standards, testing, reliability, security, protyping, user manuals, group demos

Learning Outcomes:

1.To be able to identify problem.
2.To be able to analyze the problem, define the scope, constraint, requirements, and design a solution.
3.To be able to design a solution that is innovative, sustainable and beneficial to the environment and society.
4.To be able to learn and use new methods and technologies.
5.To be able to gain teamwork skills.
6.To be able to present written and oral presentation skills of the project.

This course covers all aspects of TCP/IP and network socket programming. Starting with a review of IP and TCP, including its services and IPv6, we will then learn about socket programming and time permitting, we will be exploring new trends in networking and programming.

LAN/WAN analysis and design; LAN standards, internetworking, recent technologies, LAN design procedures; WAN design, network services, WAN design procedures; LAN management, SNMP (simple network management protocol); design tools, network simulators.

Nature of embedded systems, particular problems, special issues; role in computer engineering; embedded microcontrollers, embedded software; real time systems, problems of timing and scheduling; testing and performance issues, reliability; low power computing, energy sources, leakage; design methodologies, software tool support for development of such systems; problems of maintenance and upgrade; networked embedded systems; FPGA design issues.

1. To demonstrate the ability to model and design embedded systems.
2. To show ability to realize and verify systems.
3. To show ability to use embedded software development techniques.

Concepts of open source, shareware, freeware; issues of quality, conditions of use, availability; issues of software reuse; program libraries, software components; creation of additional libraries and other components; application program interfaces; use of separate compilations; use of software libraries and other software components; problems of building large systems; assessment of software including interfaces such as metrics and measures; criteria; simple principles of interface design; multimedia issues; special problems associated with color, sound, video and multimedia; advanced issues in object-oriented programming, modularity, storage management issues, parallelism; client server computing, different kinds of servers, the role of middleware; overview of the software support needed for client services and server services; illustrations of the use of object oriented techniques applied to the building of certain commonly used software tools; applets and servlets; simple design patterns; nature of the software life cycle and its different phases; concept of process; differences across various developments and the reasons for the differences.

This course explores advanced application development techniques in a large enterprise-wide setting. Topics include agile software development, multi-tier applications, server side programming, transaction processing, web programming, application installation and deployment issues.

Logical / physical arhitecture design and server components, performance measurement, advanced caching strategies, horizontal scaling with MySQL and handling peak load.

This course topics include types of enterprise application integration, their design and implementation issues as well as their qualites such as security, reliability and fault tolerance.

This course will cover 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.

This course investigates principles of distributed database systems including design and architecture, security, integrity, query processing and optimization, transaction management, concurrency control, and fault tolerance. The course blends theory with practice in that each student will use distributed database concepts to develop a JDBC application and to implement a JDBC driver onto Web-based distributed databases.

This course covers the topics of software quality management, quality management paradigms (CMMI, ISO, IEEE), methods used in quality management, enhancement of quality process, management of quality cost, error, preventive error avoidance.

Introduction to the programming techniques to effectively utilize modern multicore computers. Identifying the parallelism, naming shared data, synchronizing threads, the latency and bandwidth associated with communication, analyzing & improving parallel performance, parallel programming tools, miscallenous lab works & exercises.

An evolutionary approach to the multicore architectures, integration of multicore architectures with operating systems, OS kernel design for multiprocessors and multithreding, OS support for threads, User level threads, Kernel level threads, An example: Solaris threads, Threads and libraries, Hardware support for multithreading in a uniprocessor and in a multiprocessor.

Finite state machine design and analysis; high-level hardware description languages, VHDL, automated synthesis in design; digital integrated circuit design and advanced design principles; electrical properties of digital circuits, synchronous and asynchronous circuits, computer arithmetic and interfacing to external circuitry, digital system testing and design for testability; implementation of embedded computing systems in terms of Application Specific Integrated Circuits; Design for reuse.

Design and implementation of software for programmable embedded systems; software tools such as compilers, schedulers, code generators, and system-level design tools; data-flow and control models of computation and software synthesis for uniprocessor and multiprocessor architectures; synchronous/reactive languages and their mathematical properties; implementation of signal processing, communication and control algorithms using variety of technologies such as digital signal processors, microcontrollers, FPGAs, ASICs and real-time operating systems; real-time kernel design; software implemented fault-tolerance techniques.

Declarative programming; problem solving; knowledge representation; reasoning; acting logically; uncertainty; learning; communicating.

Artificial neural networks; evolutionary computation; fuzzy systems.

An Introduction to the machine learning with examples in different application areas. Bayesian decision theory. Supervised learning techniques. Model selection. Dimensionality reduction. Clustering. Support vector machines. Graphical models. Introduction to neural networks. Reinforcement learning.

Secrecy, integrity, authentication, history of the cryptography, the fundamental concepts of information theory, secrecy systems, monoalphabetical substitution, polyalphabetical substitution, transposition, block ciphers, Data Encryption Standard (DES), Advanced Encryption Standard (AES), key management, combining block ciphers, Symmetrical cryptosystem security architectures, Symmetrical cryptosystem design & verification. Factorization based (RSA), ECC based asymmetrical cryptosystems, Random number generators, prime number generators and primality testers, hashing algorithms (MD5-SHA0-SHA1-SHA2), PKI and related models, standards: IEEE P1363, Fips 186.2, – 140.2, x9.62, x9.63, PKCS#6, x509v3.

Link Encryption, End-to-End Encryption, Network and Internet Based Attacks, Firewalls, Email Security Issues, Web Based Security Issues, Disaster Recovery, C2 Functionality Stand-alone vs Network Operation, Hackers and Crackers, Network Crackings.

Introduction to security of information and communication technologies, fundamental security functions and related cryptographic tools, network security and related problems, protection mechanisms and tool will be evaluated. Then current hot topic security problems will be analyzed for “cloud computing”, “web applications and services”, “wireless networks”, etc. and security management of information systems will be discussed. Students will have a term project for the course about an up-to-date problem of these topics, which they are interested in and will prepare a written report about it then, present their work.

Turning theoretical ideas into solution sets in computer science. Integration of mathematical approaches with general problem solving techniques and computer science applications. Topics will be from Algorithms, Complexity Theory, Game Theory, Probability Theory, Graph Theory, Automata Theory, Algebra, and Cryptography.

Evolution strategies, evolutionary programming, genetic algorithms, genetic programming, overview of selected evolutionary computation techniques.

Mechatronics; architectures; control; AI components; computer vision; mapping; planning; navigation; communication; co-operation; robot ethics; applications.

Data mining in general, data warehousing, data preparation and data mining primitives, concept description, mining association rules in large databases, classification and prediction, cluster analysis, web mining, applications in data mining.

In addition to an introductory treatment of business fundamentals in information technology, course topics will include paradigms, strategies and methods in goal setting, team formation, intellectual property, customer projects and business management for information technologies.

This course covers rendering techniques and programming interfaces for Computer Graphics. The course topics include basic mathematical concepts in Computer Graphics (two and three dimensional transformations), lighting models, methods for basic scene modeling and visibility analysis. Application of these Computer Graphics techniques on modern hardware using OpenGL is also covered.

This course covers advanced rendering techniques for Computer Graphics. The course topics include advanced lighting models, acceleration and volume rendering techniques. Acceleration of these Computer Graphics techniques on modern hardware is also covered.