Codes | Name of the MSc Course | Credit | ECTS |
---|---|---|---|

Courses from theory and system areas, graduate seminar and MSc thesis |
|||

COM555 | Theory of Computation | 3 | 10 |

COM501 | Advanced Mathematics for Engineers | 3 | 10 |

COM509 | Parallel Computing | 3 | 10 |

COM524 | Scientific Research Methods | 3 | 10 |

COM539 | Advanced Data Communication and Networking | 3 | 10 |

COM540 | Advanced Computer System Architecture | 3 | 10 |

COM591 | Graduate Seminar | 0 | 20 |

COM500 | MSc Thesis | 9 | 30 |

Electives (Category 1) |
|||

COM502 | Expert Systems | 3 | 10 |

COM503 | Fuzzy Systems | 3 | 10 |

COM505 | Statistical Methods | 3 | 10 |

COM507 | Artificial Neural Networks | 3 | 10 |

COM508 | Advanced Image Processing | 3 | 10 |

COM513 | Object-Oriented Analysis and Design | 3 | 10 |

COM514 | Genetic Algorithms | 3 | 10 |

COM515 | Design and analysis of Algorithms | 3 | 10 |

COM517 | DistributedDatabase ManagementSystems | 3 | 10 |

COM519 | Softcomputing | 3 | 10 |

COM520 | Pattern Recognition | 3 | 10 |

COM521 | Advanced Numerical Methods | 3 | 10 |

COM522 | Cryptography and Network Security | 3 | 10 |

COM523 | Wireless and Mobile networks | 3 | 10 |

COM525 | Linear and Non-linear Programming | 3 | 10 |

COM527 | Modern Control Theory | 3 | 10 |

COM528 | Advanced Microprocessors | 3 | 10 |

COM530 | Simulation Modeling and Analysis | 3 | 10 |

COM534 | Advanced Microcontroller Programming | 3 | 10 |

COM535 | Machine Learning | 3 | 10 |

COM536 | Machine Vision | 3 | 10 |

COM537 | Software Architecture | 3 | 10 |

COM538 | Data Mining | 3 | 10 |

COM541 | Advanced Software Engineering | 3 | 10 |

COM542 | Autonomous Robotics | 3 | 10 |

COM556 | Semantic Web Technologies | 3 | 10 |

TThe courses from theory and system areas, graduate seminar, MSc thesis and technical elective courses (Category 1) are offered by the Computer Engineering department. The students can also take the technical elective courses offered by the other departments (Category 2). These courses require approval of the CE department for the students to be able to register.

**TECHNİCAL ELECTİVES OF CATEGORY 2**

Category 2 technical electives are the courses offered by other departments. These courses have been carefully selected and pre-approved by the CE Department.

- EE 503 – Advanced Digital Signal Processing
- EE 515 – VLSI Design
- EE 518 – Optimal and Adaptive Control
- EE 522 – Intelligent Control
- EE 523 – Robotics Systems
- MKE502 Advanced Control Methods in Mechatronics
- MKE504 Advanced Topics in Robotics

**COURSE OUTLİNES**

**COM 500 Master Thesis (NC)**

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 third semester while the research program or write-up of the thesis is in progress. The MSc thesis is the development of research skills and the ability to analyze and present research results in a systematic and clear way. The thesis is the culmination of the MSc study program in which students show that they are able to design and conduct computer engineering research at an academic level, and are able to theoretically reflect on computer engineering topics.

**COM 501 – Advanced Mathemaics for Engineers (3-0)3**

Review of Vector Algebra, Complex Numbers. Review of Ordinary Differential Equations. Variations of Parameters and Cauchy-Euler Differential Equations. System of Linear Differential Equations. Laplace Transforms and Fourier Series. Beta Gamma Functions. Bessels Equations. Partial Differential Equations and Probability.

**COM555 Theory of Computation (3-0) 3**

Introduction to theory of computation, automata theory, computability theory and complexity theory. Mathematical notions, terminology and definitions. Definition and examples of finite automata, designing finite automata and regular expressions. Turing machines, Church-Turing thesis, decidability and reducibility. Complexity theory: Classes P, NP and NP-Completeness.

**COM 524 Scientific Research Methods (3-0)3**

The course defines the understanding of science and engineering and describes the links between the interrelated technical subjects. Further, it considers the methods of scientific research and focuses on the five methods most widely used for natural sciences and engineering, giving much emphasis on experimental and field studies research methods. It includes a brief introduction to characteristics, types and scheduling of research. Research planning and design. Methodologies of research design. Measurement, data analysis. Presenting the results of research. It also stresses the important aspects of writing research proposal, presenting and report (thesis) writing. Finally it provides some information on research ethics and on controversies in research.

**COM502 – Expert Systems (3-0)3**

The evaluation of artificial intelligence systems. Decision making. Expert System (ES) characteristics. Architecture of ES. Hybrid ES. Knowledge representation in ES. Representation of knowledge by Object-attribute value triplets, Semantic networks, Frames, Logic programming, Neural networks, Production rules. Inference engine, forward and backward chaining mechanisms. Knowledge acquisition. Uncertainty, fuzzy ES. ES shells. Application of ES for solving different problems.

**COM 503 – Fuzzy Systems (3-0)3**

Fuzzy Sets. Mathematical Background of Fuzzy Systems. Representation of Fuzzy Sets. Properties of Fuzzy Sets. Fuzzy Relations and Functions. Fuzzy Arithmetic. Fuzzy Modelling. Decision Making in Fuzzy Conditions. Fuzzy Control Systems. Design Examples. Computer Simulations of Fuzzy Systems. Problems Using C++ and Matlab.

**COM 505 – Statistical Methods (3-0)3**

Descriptive Statistics. Estimation. Inferences on Population Means. Variance, Inferences on Population Proportions. Simple Regression and Correlation. Multiple Linear Regression. Analysis of Variance. Design and Analysis of Multifactor Experiments. Statistical Quality Control.

**COM 507 – Artificial Neural Networks (3-0)3**

Artificial Intelligence Computing. Expert systems. Neural networks. Biological background. Character recognition. Classification of neural networks. Supervised learning. Unsupervised learning. Neural simulation of logic gates. The Perceptron. Back propagation algorithm. Mathematical foundation for implementing Backpropagation algorithm. Hamming algorithm. Hopfield algorithm. Kohonen’s algorithm. Adaline. Delta rule. Error minimization. The XOR problem.

**COM 508 – Advanced Image Processing (3-0)3**

Image Modelling. Two-Dimensional Signal Analysis. Image Processing Techniques. Image EnhancementImage Manipulation. Image Restoration, Image Recognition. Region Extractions and Edge Detections. Problems Using C and Matlab. Laboratory Experiments.

**COM 509 – Parallel Computing (3-0)3**

This course provides a comprehensive study of scalable and parallel computer architectures for achieving a proportional increase in performance with increasing system resources. System resources are scaled by the number of processor used, the memory capacity enlarged, the access latency tolerated, the I/O bandwidth required, the performance level desired, etc. The course includes: Processors and memory Hierarchy- Bus, Cache, and Shared Memory – Pipelining and Superscalar Techniques – Instruction Pipeline Design. Message passing operations. Multiple threads and shared address spaces. Matrix algorithms in parallel programming.

**COM 513 – Object-Oriented Analysis and Design**

Object-oriented design concepts, Introducing OO concepts through typical OO programming languages. Features and problems of complex systems, evolution the object-oriented model, foundations and elements of the object-oriented model, classes and objects, relationships among classes, relationships among objects, interplay of classes and objects, approaches to identifying classes and objects, object-oriented design methodologies, methodology notation (elements of UML or any other selected notation, class and object diagrams, interaction diagrams, state transition diagrams, process and module diagrams, etc.), applications and case studies, CASE tools.

**COM 514 – Genetic Algorithms (3-0)3**

Organic evaluation. Evolutionary computation and programming. Genetic algorithms. Searching mechanisms in genetic algorithms. Genetic operators – Selection, Crossover and Mutation. Application of genetic algorithms to optimization and control. The course is tended to introduce the central aspects of genetic algorithms and their applications to difficult-to-solve optimization problems in engineering and systems design.

**COM 515 Design and Analysis of Algorithms (3-0)3**

Algorithm design techniques, algorithm analysis. Graph algorithms. Shortest paths. Maximum flow algorithm. Amortized Analysis, Algorithms for Parallel computers, Randomized algorithms, Backtracking, Graph coloring, Branch and Bound, Approximation Algorithms, NP-completeness, NP-complete problems.

**COM 517 Distributed Database Management Systems (3-0)3**

Introduction to Databases and Distributed Database Systems, Background, Distributed Database Architectures, Distributed Database Design, Horizontal Fragmentation, Vertical Fragmentation, Introduction to Query Processing, Query Processing in Distributed Databases, Query Optimization, Introduction to Transaction Management, Distributed Concurrency Control, Parallel DBMSs Issues, Distributed DBMS reliability,, Distributed Multidatabase systems

**COM519 Softcomputing (3-0)3**

Elements of Softcomputing. Fuzzy logic, fuzzy and linguistic modeling, neurocomputing, evolutionary computing, probabilistic computing, chaotic computing and machine learning. Hybrid intellectual systems. Fuzzy neural networks and their learning. Neuro-genetic systems. Fuzzy-genetic systems. Neuro-fuzzy-genetic systems. Modelling and application of Softcomputing elements for solving different engineering problems.

**COM520 Pattern Recognition (3-0)3**

An introduction to the pattern recognition. Syntactic and Decision Theoretic approach, Statistical Pattern Classification, Feature Analysis, Nonparametric decision theoretic classification. Linear and Nonlinear discriminant functions, linear separability. Training procedures. Error correction training procedures. Gradient techniques. Minimum squared error procedures. Clustering. Distance measures. Unsupervised and supervised clustering algorithms. Adaptive sample set construction, Batchelor and Wilkins algorithms, k-means algorithm. Graph-theoretical methods, spanning tree methods. Multilayer perceptron, Neural network based recognition. Radial based networks, Learning using gradient descent. Machine learning. Assignments will be given to design an Pattern Recognition System using C-language or MATLAB.

**COM 521 Advanced Numerical Methods (3-0)3**

Types of optimization problems, Nonlinear algebraic equations, sets of linear algebraic equations, eigenvalue problems, interpolation, curve fitting, ordinary differential equations, and partial differential equations, solution of partial differential equations of the parabolic, elliptic and hyperbolic type. Applications include thermodynamics, automatic control systems, kinematics, and design

**COM522 Cryptography and Network Security(3-0)3**

Cryptographic algorithms. Public key encryption, differential and linear cryptanalysis, the Advanced Encryption Standard, Cryptographic hash functions, authentication protocols, key distribution protocols, key management, security protocol pitfalls, Internet cryptography, IP sec., SSL/TLS, e-mail security, firewalls.

**COM523 Wireless and Mobile Networks (3-0)3**

Introduction to Wireless and Mobile Networks, Wireless Transmission, Wireless and Mobile Network Architectures, Mobile Routing, Mobile IP, Transport Protocols over Wireless Networks, Cellular networks, Satellite Networks, Wireless LAN, Bluetooth Technology and Applications, Broadband Wireless Access, WiMax: Architectures and topologies, Ad Hoc Networks- Routing, Device and Service Discovery, QoS in Mobile Networks, Peer-to-Peer Networks and Applications.

**COM 525 Linear and Non-linear Programming (3-0) 3**

Optimization Models Linear Programming . Feasibility and Optimality. Duality and Sensitivity . Representation of Constraints. The Simplex Method . Network Problems. Unconstrained Optimization. Newton’s Method. Methods for Unconstrained Optimization. Nonlinear Least-Squares Data Fitting. Optimality Conditions for Constrained Problems. The Lagrange Multipliers and the Lagrangian Function. Optimality Conditions for Nonlinear Constraints . Feasible-Point Methods. Sequential Quadratic Programming. Reduced-Gradient Methods. Penalty and Barrier Methods. Interior-Point Methods for Linear and Convex Programming

**COM 527 Modern Control Theory (3-0) 3**

Systems, State equations, System identification. Types of system models and relationships. Least squares fit to data, Kalman filters, Recursive system, Optimal control theory, Pontryagin’s maximum principle, Linear-quadratic optimal control. Predictive control. Linear robust control, Nonlinear robust control. Linearization, Nonlinear adaptive control. Time-invariant and time-varying systems, Adaptive control.

**COM 528 – Advanced Microprocessor (3-0)3**

Introduction to microprocessors, 8-bit microprocessor architecture, 8085 and Z80 instruction sets, microprocessor programming examples,16-bit microprocessor architecture, 8086 instruction set, programming examples, microprocessor interfacing techniques, memory, input-output, and interrupts. Programming microcontrollers using high level languages (e.g. C)

**OM530 Simulation Modeling And Analysis (3-0)3**

Fundamental theoretical concepts of discrete simulation. A selected simulation language to be taught. Overview of analog computer simulation. Review of basic probability and statistics. Selecting input probability distribution, random number generators, output data analysis for a single system, statistical techniques for comparing alternative systems, simulation languages and GPSS.

**COM 534 Advanced Microcontroller Programming (3-0)3**

Microcontrollers versus microprocessors, microcontroller architectures, types of microcontrollers, microcontroller system development cycle, basic microcontroller programming in C, microcontroller interface programming, microcontroller interrupt handling mechanisms, using external interrupts, using timer interrupts, microcontroller C programming in real-time, advanced real-time programming for parallel and serial input-output, microcontroller busses, microcontroller system design examples using C.

**COM 535 – Machine Learning (3-0)3**

This course provides a broad introduction to machine learning. Supervised learning- generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines; Unsupervised learning- clustering, dimensionality reduction, kernel methods; Learning theory- bias/variance tradeoffs; VC theory; large margins; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing

**COM 536 – Machine Vision (3-0)3**

Image, its properties, analysis, preprocessing. Shape representation. Shape Description Techniques, Regions and Edges. Curves and surfaces. Dynamic vision. Object recognition, Image understanding. 3D vision. Geometry and radiometry of 3D vision. Mathematical morphology of machine vision. Robot Vision and Programming, Pattern Matching Techniques, Motion analysis. Problems Using C and Matlab. Laboratory Experiments.

**COM537 Software Architecture (3-0)3**

Introduction to Software Architecture, Stakeholders and Their Business Goals, Software Quality Attributes, Middleware Architectures and Technologies, Service-Oriented Architectures and Technologies, Software Architecture Process, Documenting, Semantic Web, Aspect Oriented Architectures, Model-Driven Architecture, Software Product Lines.

**COM538 Data Mining (3-0)3**

Introduction, Machine Learning and Classification, Input, Output, Preparing the data and mining it, Classification, Evaluation and Credibility, Data Preparation for Knowledge Discovery, clustering, Associations, Visualization, Summarization and Deviation Detection, Applications.

**COM539 ****Advanced Data Communication and Networking**

The course covers advanced concepts of the analysis and design of data networks and their operation; architecture, media, communication channel characteristics, routing, protocols and protocol architecture, including modeling and performance analysis. Includes network simulation.

**COM540 ****Advanced Computer System Architecture **

This course deals with the design and performance evaluation of advanced/highperformance computer systems. The emphasis is on microprocessors, chip-multiprocessors and memory hierarchy design. Data storage and low-power dissipation schemes presented. Special attention is paid to pipelining, ILP (instruction-level parallelism), DLP (data-level parallelism) and TLP (thread-level parallelism) using hardware and/or software techniques to yield high performance. Multicore systems, Multiprocessors, distributed processing. Grid and Cloud computing.

**COM541 Advanced Software Engineering (3-0)3**

The course aims to present the principals, techniques, and methods for professional and systematic software development. Domain-Specific Languages, Generative Development, System Design and Service Oriented Architecture. Unified Modeling Language (UML), CASE tools like Rational Rose and programming languages like JAVA, will be used in the context of this course. In order for students to deepen in Software engineering, several software examples will be examined during the course lectures, like operational software etc.

**COM542 Autonomous Robotics (3-0) 3**

Introduce the student to advance topics in autonomous robotics. This should be of interest to graduate students in computer engineering working in robotics or intelligent control and machine learning as applied to robotics at NEU. Different kinds of information processing techniques and control architectures will be considered. The topics include localization and mapping. sensing and control ideas, path planning, obstacle avoidance, navigation. Vehicles (or robots) motion modelling and control. The intelligent aspect of mobile robots will be the focus. Emphasis is on hands-on implementation with soccer robots as test beds. .

**COM556 Semantic Web Technologies (3-0) 3**

In this course students will be introduced to the Semantic Web vision, as well as, the languages and tools useful in Semantic Web programming. They will understand how this technology revolutionizes the World Wide Web and its uses. Ontology languages (RDF, RDF-S and OWL) and technologies (explicit metadata, ontologies, logic, and inference) will be covered. In addition, students will be exposed to; ontology engineering, application scenarios, Semantic Web Query Languages, Description Logic and state of the art Semantic Web applications, such as linked data development. Student will also learn how to develop semantic applications with Java and Jena APIs** **

**COM 591 Graduate Seminar (0-2)NC**

This course are designated for all the students of M.S. program. The students taking the course are required to make presentations of their research done on the topic related to their MSc thesis..

**EE 503 – Advanced Digital Signal Processing **

Digital processing of the continuous time signals. Discrete Fourier transforms. Fast-Fourier transform. FIR and IIR filters design. Limit cycles. Adaptive filtering. Adaptive digital filters in communication. Adaptive line enhancement and equalization. Adaptive delta and differential pulse code modulations. Problems using Matlab.

**EE 515 – VLSI Design **

Practical considerations. Technology. Device modeling. Circuit simulation. Basic integrated circuit building blocks. Amplifiers. Operational amplifiers. Digital circuits. Analog systems: analog signal processing, digital-to-analog converters, analog-to-digital converters, filters. Analog signal processing circuits: modulators, multipliers, oscillators, phase-locked loops. Structured digital circuits and systems. Laboratory Experiments.

**EE 518 – Optimal and Adaptive Control **

Optimal control problems. Calculus of variations. Pontryagin’s maximum principle. Linear quadratic regulator. Riccati equation. Parametric and non parametric identifications. Optimal estimation. Kalman filters. Adaptive control. Model reference and self-tuning adaptive control.

**EE 522 – Intelligent Control **

Uncertainty models and information representation: types of uncertainties and uncertainty measures. Intelligent control methodologies: learning control, fuzzy control, neurocontrol.

**EE 523 – Robotics Systems **

Evolution of robots, elements of robotic systems, mathematics of manipulators. Homogeneous transformations, end effectors position and orientation. Kinematics of robotic systems. Manipulator dynamics. Tree-structured manipulators. Multiple manipulators. Leading robot hands. Hand gross motion control. Obstacle avoidance techniques. Collision free wrist path planning. Hand preshape analysis. Grasp planning. Contact analysis. Hand fine motion control. Manipulability and stability of robotic systems.

**MKE502 Advanced Control Methods in Mechatronics**

Loop gain. Sensitivity. Complementary sensitivity. Loop shaping design. Motion control systems. Preview control systems. Two degree of freedom control architecture. The disturbance observer. Introduction to adaptive, optimal and robust control and comparison. Uncertainty models. The parameter space approach. Stability analysis. Lyapunov-based control of mechatronic/robotic systems. Gain scheduling controller design in parameter space. Mapping of frequency domain bounds to parameter space. Case studies

**MKE503 Advanced Topics in Robotics**

Spatial velocity concept and rigid body velocity propagation, rotation matrix and Rodriguez formula, kinematical modeling of a serial manipulator using Spatial Operator Algebra, visualization of kinematical modeling of a serial manipulator by Matlab Simulink 3d animation toolbox, inverse kinematics, singularity and redundancy, kinematical constraints of cooperating manipulators, kinematical modeling of cooperating manipulators on a mobile platform, inverse kinematical modeling of cooperating manipulators on a mobile platform, visualization of kinematical modeling of cooperating manipulators on a mobile platform by Matlab Simulink 3d animation toolbox, dynamical modeling of a serial manipulator using Spatial Operator Algebra, mass matrix factorization technique, dynamical constraints of cooperating manipulators, dynamical modeling of cooperating manipulators on a mobile platform, Matlab Simulink 3d animation for dynamical modeling of cooperating manipulators on a mobile platform.