Master Degree Curriculum in Mechatronic Engineering

The Master of Mechatronic Engineering requires completion of 32 credits, with 9 credits in the core courses, 9 credits in the specialty courses, 6 credits in the elective courses and 2 credits for the seminar. The program requires completion of a thesis of 6 credits. Admitted students with a different undergraduate degree are also required to complete a few credits of leveling courses which prepare such students for success in the Master of Mechatronic Engineering, these courses do not count toward the degree.

A minimum GPA of 14 over 20 must be maintained for graduation.

Leveling Courses (not applicable to degree)

The Master in Mechatronic Engineering assumes a B.Sc. degree in related Engineering fields. After admission any student holding any other undergraduate will be required to complete the leveling courses, determined by the advisory board of the program, that are designed to provide a back ground for the Master courses. These leveling courses are not counted for graduate credit towards the Master degree.

Students must write and defend a final thesis . The curriculum is intended to be completed in four semesters. This program is taught in English.

Core Courses: 3 courses required; 9 credits

The core curriculum is designed to provide students with a deep and broad foundation that serves throughout their career.

Specialty courses: 3 courses required; 9 credits

The purpose of this component of the curriculum is to provide each Mechatronic Engineering student the opportunity to acquire knowledge and capabilities in one or more specialized topics which extend the fundamentals developed in the core courses to more advanced or specialized areas relevant to Mechatronic Engineering practice. This also permits the student to develop a concentration or option in a more specialized technical area.

Elective courses: 2 courses required, 6 credits

Capstone

Thesis: 6 credits

The research work for the thesis is supervised by one of the department member. The thesis must be written and defended within the second calendar years after admission into the Master program. The Thesis Committee will consist of a Chair and at least two other academic referees.

Course Descriptions

Mechatronics 1

Course Content:

Analog devices, Power electronics, Digital electronics, Transducers, Time response analysis, Stability, Frequency response analysis, Belts, Chains, Cams, Mechanical systems, Microelectromechan1cal systems mems, Robotics, Applications of computers in mechatronics

References

Mechatronics 2

Course Content:

The Role of Controls in Mechatronics, The Role of Modeling in Mechatronics Design, Signals and Systems, State Space Analysis and System Properties, Response of Dynamic Systems, The Root Locus Method, Frequency Response Methods, Kalman Filters as Dynamic System State Observers, System Interfaces, Communications and Computer Networks, Fault Analysis in Mechatronic Systems, Logic System Design, Architecture, Control with Embedded Computers and Programmable Logic Controllers, Graphical System Design for Embedded Systems, Field Programmable Gate Arrays, Digital Signal Processing for Mechatronic Applications, Control System Design Via H2 Optimization, Adaptive and Nonlinear Control Design, Neural Networks and Fuzzy Systems, Advanced Control of an Electrohydraulic Axis, Design Optimization of Mechatronic Systems, Motion Control, Real Time Monitoring and Control, Micromechatronics and Microelectromechanical Motion Devices, Introduction to Computers and Logic Systems, Digital Logic Concepts and Combinational Logic Design, Application in Control and Mechatronics, Introduction to Data Acquisition, Sensors and Transducers, AD and DA Conversion, Signal Conditioning, Virtual Instrumentation Systems, Software Design and Development, Data Recording and Logging

References

Advanced Engineering Mathematics

Course Contents:

Vectors and Vector Spaces, Matrices and Systems of Linear Equations, Eigenvalues, Eigenvectors, and Diagonalization, First Order Differential Equations, Second and Higher Order Linear Differential Equations and Systems, The Laplace Transform, Series Solutions of Differential Equations, Special Functions, and Sturm–Liouville Equations, Fourier Series, Fourier Integrals and the Fourier Transform, Vector Differential Calculus, Vector Integral Calculus, Analytic Functions, Complex Integration, Laurent Series, Residues, and Contour Integration, The Laplace Inversion Integral, Conformal Mapping and Applications to Boundary Value Problems, Partial Differential Equations, Numerical Mathematics

References

Systems Identification

Course Contents:

Introduction, Databased Identification, Time invariant Systems Identification, Time varying Systems Identification, Model Validation

References

Advanced Robotic

Course Contents:

Spatial descriptions and transformations, Manipulator kinematics, Inverse manipulator kinematics, Jacobians: velocities and static forces, Manipulator dynamics, Trajectory generation, Manipulator-mechanism design, Linear control of manipulators, Nonlinear control of manipulators, Force control of manipulators Robot programming languages and systems, Off-line programming systems

References

Advanced Control

Course Contents:

Introduction to Control Systems, Mathematical Modeling of Control Systems, Mathematical Modeling of Mechanical Systems and Electrical Systems, Mathematical Modeling of Fluid Systems and Thermal Systems, Transient and Steady-State Response Analyses, Control Systems Analysis and Design by the Root-Locus Method, Control Systems Analysis and Design by the Frequency-Response Method, PID Controllers and Modified PID Controllers, Control Systems Analysis in State Space, Control Systems Design in State Space

References

Artificial Intelligence

Course Contents:

The Intelligent Computer, Semantic Nets and Description Matching, Generate and Test, Means-Ends Analysis and Problem Reduction, Nets and Basic Search, Nets and Optimal Search, Trees and Adversarial Search, Rules and Rule Chaining, Rules, Substrates, and Cognitive Modeling, Frames and Inheritance, Frames and Commonsense, Numeric Constraints and Propagation, Symbolic Constraints and Propagation, Logic and Resolution Proof, Backtracking and Truth Maintenance, Planning Using If-Add-Delete Operators, Learning by Analyzing Differences, Learning by Explaining Experience, Learning by Correcting Mistakes, Learning by Recording Cases, Learning by Managing Multiple Models, Learning by Building Identification Trees, Learning by Training Neural Nets, Learning by Training Perceptron, Learning by Training Approximation Nets, Learning by Simulating Evolution, Recognizing Objects, Describing Images, Expressing Language Constraints, Responding to Questions and Commands

References

Advanced Hydraulic and Pneumatic

Course Contents:

The Hydraulic Power Unit, Hydraulic Accumulators, Fluid Power Lines, Hydraulic Valves and their Function, Hydraulic Cylinders Intensifies and Motors, Heat Exchangers for Hydraulic Systems, Synchronizing the Movement of Fluid Power Rams, Dual Pressure Hydraulic Systems, Air Cylinders and their Design, PowerOperated Holding Devices, Pneumatic Safety Circuits, Remote Control Pneumatic Systems, Combination of Fluids in a Single System, HighPressure Hydraulic Systems, Rotary Actuators, Air Motors, Safety Controls for Hydraulic Circuits, Sequencing of Hydraulic Cylindrical Motion, Packings and Seals, Air Filters Lubricators and Regulators, Pneumatic Controls, Pneumatic Logic Controls, Fluidics, Hydraulic Servo Control Systems, Hydrostatic Transmissions

References

Neural Networks

Course content:

Complexity and Challenges in Understanding Biological Ecological and Natural Systems, Fundamentals of Neural Networks and Models for Linear Data Analysis, Neural Networks for Nonlinear Pattern Recognition, Learning of Nonlinear Patterns by Neural Networks, Implementation of Neural Network Models for Extracting Reliable Patterns from Data, Data Exploration Dimensionality Reduction and Feature Extraction, Assessment of Uncertainty of Neural Network Models Using Bayesian Statistics, Discovering Unknown Clusters in Data with SelfOrganizing Maps, Neural Networks for TimeSeries Forecasting

References

Industrial automation

Course content:

Automation and Manufacturing, Important Concepts, Components and Hardware, Machine Systems, Process Systems and Automated Machinery, Software, Occupations and Trades, Industrial and Factory Business Systems, Machine and System Design, Applications

References

Simulation and Modeling in Biomechatronics

Course content:

Introduction, State of the Art, Signal Processing Methods for SSVEPBased BCIs, SSVEPBased BCI for Lower Limb Rehabilitation, A Hybrid BCI for Gaming, EMGDriven Physiological Model for Upper Limb, Exoskeleton Control Based on Neural Interface, Muscle Force Estimation Model for Gait Rehabilitation, Neuromuscular Model for Gait Rehabilitation, Conclusions and Future Prospects

References

Finite Element Methods

Course content:

The standard discrete system and origins of the finite element method, Plane stress, Generalization of the finite element concepts Galerkinweighted residual and variational approaches, Mapped elements and numerical integration infinite and singularity elements, Problems in linear elasticity, Field problems heat conduction electric and magnetic potential and fluid flow, Automatic mesh generation, The patch test reduced integration and nonconforming elements, Mixed formulation and constraints complete field methods, Incompressible problems mixed methods and other procedures of solution, Multidomain mixed approximations domain decomposition and frame methods, Errors recovery processes and error estimates, Adaptive finite element refinement, Pointbased and partition of unity approximations Extended finite element methods, The time dimension semidiscretization of field and dynamic problems and analytical solution procedures, The time dimension discrete approximation in time, Coupled systems

References

System Modeling and Computer Simulation

Course content:

Programming in C, Scripts, Software Engineering, Debugging and Testing, Objectoriented Software Development, Algorithms and data structures, Libraries, Randomness and Statistics, Information Retrieval Publishing and Presentations

References

Machine Vision

Course content:

Vision the Challenge, LowLevel Vision, IntermediateLevel Vision, 3D Vision and Motion, Toward RealTime Pattern Recognition Systems

References