Courses

Generalized state equations. Time-varying linear systems, state transition matrix, zero state response and zero input response. Time-invariant linear systems. Matrix functions. Transfer function matrices. Controllability, observability and stability concepts. System implementation.

Nonlinear systems analysis. Advanced stability theory. Descriptive function analysis. Nonlinear system design. Linearization with feedback. Floating control. Adaptive control. Control of multi-input multi-output industrial systems.

Concepts and classifications in discrete-event processes. Sorting, scheduling, finite states and task modeling. Automata and tail models. System analysis with graphs and Petri-net. Applications on computing and communication networks, flexible production systems and top controllers.

Basic concepts of calculation vision. Image formation. Relation of human visual perception. Early processing: low-level vision and attribute extraction. Image and video data analysis and understanding. Mathematical fundamentals, image formation and representation, boundary detection, segmentation, feature extraction, environment and region analysis, camera geometry and calibration, stereo, motion, two-dimensional and three-dimensional representation, object and stage recognition, object and person tracking, human activity recognition and inference.

Comparison of human reasoning and model development with machine intelligence, approximate reasoning and systemic models. Linguistic modeling and information representation. Fuzzy sets and fuzzy relations. Fuzzy logic: reasoning rules, IF-THEN rule models, Fuzzy reference: Fuzzy rule base. Fuzzy system modeling and definition of processes. Process control and overhead control, fuzzy controller design and fuzzy regression applications in statistical decision making.

Principles of measurement. Sensors. Signal states. Sampling and holding circuits. Multiplexers. DAC, ADC, discrete data systems. Bus systems. Data evaluation software.

Bayesian decision theory. Basic statistics and structural pattern recognition. Feature extraction and selection. Parametric and nonparametric methods. Supervised and supervised learning. Clustering Linear separator functions. Classifiers and discriminant functions. Ensemble methods. Importance of artificial neural network based learning methods with high-level separators. Syntactic pattern description. Applications.

Algorithmic background, data structures, computational models in geometric elements. Geometric search, point-location problems, range-search problems. Convex trunk, plane problem reporting and lower boundaries, convex trunk algorithms, graham scan, Jarvis gait, QUICKHULL techniques, dynamic convex trunk, 3D convex trunk. Proximity problem, accumulation of problems, a computation prototype: element uniqueness, lower bound, close pair problem: divide and manage approach, Voronoi diagram, proximity problems solved by Voronoi diagram triangulations, planar triangulations, Delaunay triangulation, intersections, application areas, planar applications: convex polygons, intersection of line segments. 3D applications: 3D convex polyhedral intersection, intersection in half areas.

Performance evaluation methods, simulation versus analytical modeling. Performance measurement and benchmarking. Workload modeling, random variables. Common distributions. Performance modeling with stochastic processes. Markov chain models of computer systems. Steady and transient analysis, queuing models, single server and multiple server queues, open and closed queue networks. Discrete event simulation, simulation languages, random number generation and tests, model validation and validation, simulation results, confidence intervals, variance reduction techniques. Analytical and simulation studies of computer systems, case studies.

Pushing a single processor at its limits. Command set design, computer arithmetic, controller and bus design, memory systems, input-output systems, computer network interruptions and exceptions, consecutive, performance and cost analysis, computer architecture history and advanced architectural research.

Internet and Intranet structures: LAN, WAN, Bridge, Router, DNS, Proxy. Internet application software: Telnet, FTP, WWW access tools and applications. WEB application and development software and design techniques.

Algorithms and techniques used in text mining. Special preprocessing techniques used in textual datasets originating from the field of statistical natural language processing. Clustering and classification algorithms applied to textual data.

Basic concepts of computer image processing. Computer image transfer hardware. Basic image processing algorithms. Image interpretation. Object definition. Parallel image processing methods. Industrial applications.

Topics related to internet network search and network page mining; fundamentals of information retrieval such as web page crawlers, interpretation, indexing structures, relevance sorting algorithms, document similarity and clustering algorithms, customized search engines, evaluation, natural language processing and data mining adapted to network web pages.

Analysis of asynchronous sequential logic circuits. Huffman criterion. Mealy-Moore status diagrams. Analysis and synthesis methods. Determination of open and hidden equivalent situations. Internal state reduction and coding. Determination of excitation and output functions. Analysis of synchronous sequential circuits. Clock pulse time and frequency limits. Minimizing the number of internal states.

In this course, Social Network Analysis will be examined using computerized computing methods. This course covers complex networks and their properties, force laws, characterization of complex networks, social networks on the internet, search and navigation in complex networks, epidemic spread, P2P networks and network economics. This course will include the most recent academic publications.

General information about control computers. Distributed control architecture. Implementation of discrete-time controllers. Non-stressed transition to automatic operation, integral anti-rise algorithms.

The concept of adaptation and theoretical/approximate models. Fundamentals and implementations of adaptive systems. Principles of real-time parameter estimation and process-model definition. Auto-tuning and gain-ranking concepts. Self-regulating random and predictive controllers. Combination of analytical and non-analytical adaptive systems. Industrial applications.

The basic structure of interactive graphics systems, the characteristics of different hardware devices. Control of imaging devices, simple package, device independence, and standard packages applications. Distributed architectural graphics, hidden line and hidden surface algorithms for the representation of surfaces.

Definition of optimization. Extreme calculation and parameter optimization. Calculation of variations. Boundary conditions. Dynamic optimization under conditions of equality and inequality. Lagrange multipliers. Pontryagin’s maximum principle. Hamilton Jacobi-Bellman equation. Numerical optimal control method and its applications.

Modeling of artificial neural networks (ANN9. Forward and feedback networks. Representation of artificial neural networks with directional graphs. The role of feedback. Learning process concepts in ANN computational structures. Stone-Weierstrass and Kolmogorov theorems. Back-propagation algorithm and its relation to the co-system system theory. Types of networks. Neurodynamic networks. System modeling and control applications.

Problem definition. Mean and covariance propagation in linear stochastic systems. Colored sight. Forming filters. Status vector observer. Kalman filters when discrete and continuous. Wiener-Hopf equation. Optimal smoothing. Kalman filter design and implementation. Estimation in nonlinear systems. Statistical linearization, Extended Kalman filter desing.

Object oriented programing, class object concepts, class structure, constructors and destructors, special, preserved and general sections, reloading operator and functions names in the class, derived classes, virtual classes, polymorphism, modeling languages, applications in JAVA programming language.

Optimization problems and their classification. Unlimited Linear and nonlinear optimization. Limited optimization. Khun-Tucker conditions, punishment functions. Linear, quadratic and nonlinear programming techniques. Integer and Geometric programming. Engineering applications.

The main wavelet function. Basic concepts of scaling functions. Wavelet transform. Time and frequency placement. Multi-resolution analysis and their relevance to filter banks. Fast wavelet transform and digital realization. Data compression, error diagnosis, process recognition and wavelet networks applications.

Mathematical model structures of linear and nonlinear dynamic systems. Discrete and continuous signal recognition methods. Parameter estimation with periodic, binary and random signals. Computer recognition algorithms. Applications on industrial systems.

An advanced introduction to Web programming principles and applications covering client-side and server-side programming. Expression of new markup languages with open standards-based Web markup language principles and practices, advanced markup language transformation challenges and technologies, common target languages for text, graphics, mathematical and multimedia content, building techniques and principles and techniques to identify challenges and technologies, advanced Web applications, Web resources Basics of search engines, using semantic web, metadata and prediction for reasoning.

Mathematical modeling of mechanical, fluid, heat, chemical and electrical systems. Model structures. Linear and nonlinear system models. Connection equations. Numerical integration methods. Computer simulation algorithms. Discrete-event based simulation. Simulation applications on industrial systems.

Temporary aspects of the animation process, current techniques of computer animation, motion generation and basic animation techniques to produce motion,  (task of determining the movement of an object to the computer), research of state of the art products such as dynamic simulation automatic control systems of flexible and rigid objects in computer animation and the evolution of behaviors.

Process control computers. Dynamic models of processes. Feedback control design. Feedforward control. Multiple cycle control systems. Alternative controller structures. Industrial applications.

Sequencing and combining DNA, searching and matching in known large databases of nucleic acid and protein sequences, calculation of evolutionary trees, bioinformatics algorithms applied to  predicting protein structure and function

Introduction to discrete structures and applications. The main emphasis is on the application of graphic theory such as computer science, engineering, operations research, social science and biology. This course includes topics such as Connectivity, tree structures, coloring, Euler-Hamilton paths and circuits, shortest route and network flow and problems, matching and coverage problems.

Language and graph theory: isomorphism, existence and counting. Properties of addressable and unaddressed graphs such as connectivity and planarity, Euler and being perfect. Characterized questions of modeling practical problems: Data structures and algorithms for the discovery of graphs. Graphical algorithms and complexity analysis.

Motivation for finite state language processing, finite state identifiers, finite state receivers, regular expressions, advanced finite state operators and calculus, finite state converters, state finite automata, an overview of the theory of algorithms for semantic analysis and labeling properties, finite state automata, calculation morphology, finite state applications of the most favor theory, syntax of finite state approaches.

Basic theory of two and three dimensional graphs. Three-dimensional object modeling and manipulation. Projection. Polygonal networks model. Basic lighting. Simple Phong and Gourad models. Ray tracing. Radioactivity techniques. Shadow creation, surface coating. Three-dimensional animation, visualization techniques and applications.

Parallel computer organization and architecture. Common and distributed memory architectures. Synchronization techniques. Static and dynamic scheduling. Hardware / software interactions in parallel systems. Parallel system software and compilers. Sample parallel machines and performance evaluations.

It is aimed for graduate students to present as a seminar  by examining the latest publications on special topics in Computer and Information Systems Engineering sciences in groups or individually. Seminar topics are selected from the topics related to the thesis topic of the student. Each student has to prepare two seminar reports and a visual presentation by computer in one semester.

Calculation models. Finite state models, Turing machines, Sequential functions. Computability and irresolvability. Computational complexity. Acceleration and layering theorems. Unsolvable problems (NP-complete, PSPACE-complete, proven unsolvable). Relative problems. Advanced topics.

Hardware and software platforms and tools, MAC Protocols for Ad Hoc Wireless Networks , Routing Protocols for Ad Hoc Wireless Networks, service discovery, Bluetooth, sensor networks, sensor network architectures, introduction to ad hoc networks, topology control and routing in sensor networks, transport protocols, data dissemination and data fusion , data storage and sensor networks, sensor data processing, power management and network life, sensor network applications inquiry.

Topics of mobile and wireless networks from physical level to application, architecture of mobile and wireless networks, problems of physical level in wireless communication, characteristics of wireless connections, wireless mobile phones, wireless internet protocol and multicast, access to wireless tools, mobile routing, mobile internet protocol, transport protocols in wireless networks, wireless local area networks, bluetooth technology and application, wireless application protocols, device and service discovery.

The aim of this course is to introduce the algorithms and technologies of distributed systems. Both the basics of these systems and their practical applications will be seen. The subject of this course contains communication mechanisms and protocols, RPC, RMI, flow-oriented communication, distributed algorithms, introduction schemes, DNS, naming and localization, traditional mapping, refresh and consistency, adherence models and protocols, error tolerance, group communication, two and three stage operations, control points, control mechanisms, distributed file systems, parallel architecture systems, middle layer software frameworks and mobile systems.

General structure of visual languages. Event based programming. Principles and techniques of user interface design. Visual software packages and applications.

Selected research topics. Software development and maintenance techniques in real-time and critical secure applications. Object oriented analysis and design techniques. Automatic production of preconditions. Multimedia applications. Troubleshooting techniques. Software measurement.

This course covers studies on data mining algorithms, models and applications in detail. Data preprocessing, relationship rules mining, sequential pattern mining, classification, decision tree learning, Bayes Classification, regression, artificial neural networks, clustering and evaluation methods are important topics covered in the course. In order to become experienced, students are required to use different data mining software tools and make programming studies.

Computer system modeling and performance evaluation. Tail network models and analysis methods. Multi-processor and distributed system control algorithms: Synchronization, Mutual exclusion protocols, resource management, deadlock, scheduling, load balancing. Security models, security problems in distributed systems.

Algorithms and protocols for secure network communications, encryption-related algorithms (DES, Diffie-Hellman, RSA), encryption-related hash functions, authentication, key management, secure networks, certification, trust management and secure electronic commerce, Kerberos, Internet encryption , IPsec, SSL / TLS, e-mail security, firewalls.

Theory and mechanism of programming language. Data flow analysis. General compiler organization. Lexic browser, syntax separator, symbol table, internal program structure. Code generators. Real-time memory management.

Parallel and systolic computing, interconnection network design, memory organization, cache and data bus design, processor technologies, linear arrays and grids, sequential and numerical problems,  data forwarding, communication parallel architectures and basic concepts for FFT, and mapping these algorithms to different architectural structures

Topics of high-speed networks, ATM, HIPPI, fiber channels, optical and wireless networks, protocol design, routing and flow control for high-speed networks, management and security problems.

Fundamentals of parallel architecture, communication operations, scalability of parallel systems: overhead function, cost optimization, degree of synchronization. Performance of parallel systems: acceleration, efficiency, cost, sources of parallel load. Solution of distributed linear system equations: cyclic methods, load balancing and communication reduction, direct methods, timing problem.

Representation of physical world and its properties and interpretation on world models. Complex modelling. Instability. Quantitative and qualitative interpretation techniques. Use of knowledge bases. Interpretation on space, geometry and time. Program design, planning and error detection. Expert Systems and Applications.

Introduction to information storage and information extraction (IR). IR vs DBMS. User perspective, search models, evaluation of IR systems. Formal IR models. Important types of information extraction systems are based on the measurements and methods used to evaluate the systems. Data structures and techniques such as reverse files, signature files, information filtering, clustering and cluster-based information extraction, text and multimedia systems. IR and Internet, browsing strategies, search engines, web robots and intelligent agents.

Distributed DBMS architecture. Client-server, distributed, and internet-based DB design. Distributed question optimization and processing. Distributed process management (synchronization, control and recovery). Definition and problems of federal / multiple bases. Database machines (concepts, achievements and defeats). Parallel databases.

Effective data representation and manipulation methods. Performance analysis of these methods. Priority queues, balanced search trees, multidimensional search structures, amortized complexity and its application to data structure design. Robust data structures and second memory data structures.

Robot dynamics, basic control and motion planning concepts, state and motion representations, potential functions, road maps, cell decomposition restricted motion, logical reasoning methods for hybrid planning and control planning.

Multimedia systems. Digital, audio and video formats and their integration, compression, processing and transmission. Multimedia software systems and applications.

Paradigms and machine learning problems. Introduction to induction, sample and hypothesis fields, inductive bias, sample complexity, computational complexity. Induction Theory and methodology. A general paradigm formulation for Support Vector Machines (SVMs), inductive inference. Splines (eg, MARS: Adaptive multivariate regression Splines), Controlled attribute-based induction approaches. Valiant learning framework. Degree-based learning. Genetic algorithms, genetic-based machine learning, classification systems. Learning decision trees. Description-based learning. Discovery systems.