Master of Science in Mechatronics Engineering

This course covers Microcontrollers and Mechatronic Systems, focusing on hardware-software integration and system design.
Microcontroller Section: Explores microcontroller architecture, programming, memory organization, I/O configurations, and interrupt handling. Students will also study timers, ADC, and CCP modules for precise embedded system control.
Mechatronic Systems Section: Introduces system architecture, mechanical-electronic integration, and problem-solving strategies. Topics include design methodologies, tool selection, and multidisciplinary project implementation.
By combining theory with hands-on application, this course prepares students to develop, analyze, and integrate mechatronic and embedded systems for real-world applications.

This course covers Mathematics and Control Systems, providing a strong foundation for engineering problem-solving and automation.
Mathematics Section: Focuses on linear algebra, advanced matrix algebra, vector calculus, eigenvalues, and eigenvectors, essential for analyzing control and automation problems.
Control Systems Section: Covers feedback analysis, stability assessment, PID control, system reliability, and state-space analysis, including transfer functions, time-invariant state equations, controllability, and observability.
By combining mathematical precision with control theory, this course develops analytical and problem-solving skills, preparing students to design and optimize control systems in engineering applications.

This course covers Design, Material Strength, and Manufacturing Processes, providing students with essential knowledge for mechanical system development in mechatronics.
Design Section: Explores mechanical analysis, kinematic design, and component selection, focusing on bearings, rollers, turntables, linear guides, couplings, and shaft systems.
Material Strength Section: Examines 3D stress analysis, principal stresses, material deformation, Hooke’s Law, and thermal strain effects, emphasizing mechanical behavior and structural integrity.
Manufacturing Section: Introduces CNC machining, casting, sintering, joining processes, and sheet metal fabrication, highlighting process selection based on design and material properties.
This course blends theoretical principles with practical applications, preparing students for mechanical system design, analysis, and manufacturing in engineering fields.

This research seminar explores fundamental and emerging topics in mechatronics and automation, enhancing students’ ability to analyze and present state-of-the-art research.
Instructor-Guided Topics: A curated list of current research areas will be explored through papers, case studies, and discussions.
Student Research & Presentation: Students will conduct literature reviews, evaluate findings, and present structured analyses on key challenges and trends.
Guest Lectures: Faculty and industry experts may provide insights into advancements in mechatronic systems and automation.
This seminar strengthens research, analytical, and presentation skills, equipping students for advanced studies and technological innovation in mechatronics.

This course provides an in-depth study of electrical, hydraulic, and pneumatic actuators and their control systems, focusing on analysis, design, and integration in mechatronic applications.
Electrical Actuation & Drives: Covers magnetic circuits, AC/DC machines, power electronics, and gears, emphasizing motor efficiency and control strategies.
Hydraulic & Pneumatic Actuation: Explores fluid power systems, pressure control, flow regulation, and actuator dynamics, with simulation-based analysis.
Integrated Control Systems: Examines electrohydraulic and electro-pneumatic control, including servo-hydraulic and servo-pneumatic actuation for hybrid mechatronic systems.
Students gain practical skills in modeling, simulation, and optimization, preparing them for careers in automation, robotics, and mechatronic system design.

This course covers static optimization, optimal control, adaptive control, and system identification, emphasizing C++ implementation for practical applications.
Static Optimization: Introduces calculus-based optimization techniques and numerical methods.
Optimal Control: Covers LQR (continuous & discrete), LQG, and H₂/H∞ robust control for stability and performance.
Adaptive Control & System Identification: Focuses on real-time system adjustments and model estimation from experimental data.
C++ Implementation: Students develop, simulate, and optimize control algorithms using C++, applying theoretical concepts to real-world scenarios.
By the end, students will design, analyze, and implement advanced control systems for automation, robotics, and dynamic applications.

This course covers Non-Traditional Machining and Computer-Aided Design (CAD), providing a deep understanding of advanced manufacturing techniques and digital design tools.
Non-Traditional Machining: Focuses on USM, AJM, ECM, CHM, EDM, PAM, and LBM, analyzing their working principles, process characteristics, and effects on material properties.
Computer-Aided Design (CAD): Covers virtual modeling, simulations, and manufacturing planning using 2D/3D design tools and software like EUCLID, AUTOCAD, CATIA, Pro-Engineer, and ANSYS.
Students gain hands-on expertise in machining and CAD-based design, preparing them for applications in manufacturing and product development.

This seminar explores robotics and bio-mechatronics, focusing on advancements and emerging trends. The lecturer provides a curated list of research topics examined through papers, case studies, and discussions.
Students conduct literature reviews, critically analyze topics, and present findings on key developments and future directions. Guest speakers from academia and industry may share insights into state-of-the-art technologies and applications in mechatronics and robotics.
This course enhances research, analytical, and presentation skills, equipping students with a deeper understanding of cutting-edge innovations in mechatronics and automation.

This seminar explores Micro-Robotics and MEMS/NEMS, focusing on recent advancements in design, fabrication, and applications. The lecturer presents selected research topics, while students conduct literature reviews, analyze findings, and deliver structured presentations.
Discussions cover micro-actuators, sensors, and integrated systems in robotics and automation. Guest speakers from academia and industry may share insights on emerging technologies and real-world applications.
This course enhances research, analytical, and presentation skills, preparing students for advanced studies or careers in micro-robotics and MEMS/NEMS technologies.

This course provides a comprehensive study of robotic systems, covering mechanisms, kinematics, dynamics, control, and programming.
Robot Mechanisms & Actuation: Covers end-effectors, actuators, drives, and sensors essential for robotic movement.
Kinematics & Dynamics: Explores forward/inverse kinematics, Jacobian analysis, acceleration, inertia, and trajectory planning for robot manipulators.
Control & Programming: Focuses on task-oriented control, force compliance, robot work cell design, and programming for automation.
Advanced Topics: Includes robot vision, teleoperation, haptics, closed-loop kinematic chains, parallel robots, non-holonomic systems, and legged robots.
This course equips students with theoretical and practical expertise in robotic design, simulation, and real-world applications.

This course introduces statistical machine learning, covering key concepts and algorithmic foundations.
Core Topics: Includes dimensionality reduction, overfitting, ensemble learning, and evaluation techniques.
Algorithms: Covers clustering (K-Means), classification (SVM, Decision Trees, Neural Networks), and regression (Linear & Logistic Regression).
Implementation: Students will perform theoretical derivations, computations, and algorithm development from scratch.
Final Project: A hands-on project where students apply learned techniques to a real-world problem.
This course provides a strong foundation in machine learning model design, optimization, and evaluation for practical applications.

This course covers sensors, actuators, and Programmable Logic Controllers (PLCs), essential for automation and control systems.
Sensors: Focuses on position, velocity, and force measurement, including LVDTs, encoders, RTDs, thermocouples, capacitive, piezoelectric, and pressure sensors.
Actuators: Explores electric actuators, power electronics, hydraulic and pneumatic actuators for motion control.
PLCs: Covers I/O modules, latching, timers, counters, data registers, and digital/analog operations.
Students gain practical knowledge in sensor integration, actuator control, and PLC programming, preparing them for industrial automation and mechatronic applications.

This course covers sensors, actuators, and Programmable Logic Controllers (PLCs), essential for automation and control systems.
Sensors: Focuses on position, velocity, and force measurement, including LVDTs, encoders, RTDs, thermocouples, capacitive, piezoelectric, and pressure sensors.
Actuators: Explores electric actuators, power electronics, hydraulic and pneumatic actuators for motion control.
PLCs: Covers I/O modules, latching, timers, counters, data registers, and digital/analog operations.
Students gain practical knowledge in sensor integration, actuator control, and PLC programming, preparing them for industrial automation and mechatronic applications.

This course introduces Object-Oriented Programming (OOP) principles and their application in Java development.
Core Concepts: Covers objects, classes, encapsulation, and abstraction as fundamental OOP principles.
Advanced Topics: Explores inheritance, polymorphism, and generic programming for scalable software design.
Java Implementation: Focuses on class structures, object interactions, and modular programming.
Students develop strong coding skills in Java, enabling them to design and implement efficient, reusable, and maintainable software systems.

Enhanced Course Description
This course explores autonomous and mobile robots, focusing on locomotion, control, perception, and navigation.
Locomotion & Kinematics: Covers degrees of mobility, non-holonomic constraints, and wheeled robot dynamics.
Control & Planning: Examines trajectory generation, navigation algorithms, and control architecture such as subsumption, potential fields, and reinforcement learning.
Perception & Mapping: Introduces sensor models, SLAM, relative and absolute localization, and knowledge representation.
Multi-Robot Systems: Focuses on cooperation, coordination, and autonomy in robotic fleets.
Students gain practical expertise in designing, programming, and controlling autonomous robotic systems.

This course focuses on robotic system design and machine vision, integrating sensors, actuators, and control systems.
Robotic System Development: Covers kinematics, dynamics, and embedded control for robotic structures.
Machine Vision: Introduces image processing, vision-based control, and simulation techniques.
System Integration: Students design, implement, and integrate robotics and vision systems for real-world applications.
By the end, students gain practical expertise in robotic automation and vision-based navigation.

This course provides hands-on training in robot programming, focusing on motion control, task automation, and system integration.
Motion Programming: Covers J, L, and C moves, velocity control, and workspace zoning.
Routine & I/O Control: Includes creating routines, calling functions, and handling digital inputs/outputs.
System Operations: Focuses on modifying positions, saving configurations, and implementing restart methods.
Students develop practical skills in industrial robot programming, enabling them to optimize automation processes and robotic workflows.

This course covers nonlinear and adaptive control systems, focusing on analysis, stability, and controller design.
Nonlinear Control: Includes system linearization, phase-plane analysis, and Lyapunov stability.
Adaptive Control: Covers real-time parameter estimation, self-tuning regulators, and model reference control.
Stability & Implementation: Explores adaptive observers, gain scheduling, and stability of adaptive systems.
Students develop skills in designing and analyzing nonlinear and adaptive controllers for dynamic systems and automation.

This course covers digital control theory and system identification, focusing on design, analysis, and optimization.
Digital Control: Explores discrete transfer functions, state-space representation, stability analysis, and PID-based control in the discrete domain, with applications in mobile robotics.
System Identification: Covers measurement techniques, statistical modeling, parametric/non-parametric methods (ARX, OE), gradient-based optimization, and recursive estimation.
Students gain practical skills in designing digital controllers and identifying system models, preparing them for real-world control applications.

This course explores optimal and robust control techniques for system stability and performance enhancement.
Optimal Control: Covers LQR, LQG, Kalman filters, loop transfer recovery, and H₂ optimal control for efficient state feedback and estimation.
Robust Control: Focuses on H-infinity control, small-gain theorem, uncertainty modeling (LFT), and Riccati-based H∞ synthesis to ensure system stability under uncertainties.
Students develop expertise in designing optimal and robust controllers, equipping them for advanced control applications in engineering systems.

This course explores embedded systems development with real-time constraints, covering both theoretical foundations and practical implementation.
Real-Time Systems: Covers RTOS, task management, synchronization, and scheduling algorithms.
Performance & Optimization: Focuses on real-time communication, low-power design, and system performance analysis.
Hardware Interfacing: Includes external device integration and device driver development.
Students gain hands-on experience in designing, optimizing, and implementing real-time embedded systems for various applications.

This course covers microprocessor interfacing techniques, focusing on data movement, communication, and peripheral integration.
Memory & Bus Systems: Explores multiplexed pins, RAM-ROM, DRAM, and programmable interfaces.
Peripheral Interfacing: Covers A/D and D/A converters, SPI, I2C, serial ports, USB, and network communication.
System Integration: Focuses on keyboard, display, and programmable interval timer interfacing.
Students gain practical skills in microprocessor-based system design and peripheral communication for embedded applications.

This course introduces MEMS/NEMS fabrication technologies and sensor transduction mechanisms used in micro/nano systems.
Fabrication Techniques: Covers microscale and nanoscale manufacturing processes.
Transduction Mechanisms: Explores piezoelectric, pyroelectric, thermoelectric, thermionic, and piezoresistive principles.
Sensor Applications: Focuses on infrared, radiation, motion, acceleration, flow, pressure, and force sensors.
Data Analysis: Introduces experimental data processing techniques for MEMS/NEMS applications.
Students develop a strong foundation in microsystem design, fabrication, and sensor integration for advanced engineering applications.

This course explores teleoperation and haptic systems, focusing on interaction modeling, control, and stability.
Haptics Fundamentals: Covers human-machine interaction, sensors, actuators, and interface design.
Modeling & Control: Examines event-based haptics, force control, impedance control, and adaptive motion/force control.
Teleoperation Systems: Focuses on bilateral teleoperation, stability, transparency, and time-delay compensation.
Advanced Applications: Explores collaborative control and virtual environment integration.
Students gain hands-on experience in designing and controlling haptic and teleoperated systems for robotics and automation.

This course explores multi-robot systems and bioinspired robotics, focusing on autonomy, cooperation, and intelligent control.
Multi-Robot Systems: Covers task decomposition, resource management, deadlocks, and collaborative localization.
Navigation & Interaction: Examines multi-robot navigation and human-robot interaction strategies.
Bioinspired Robotics: Explores swarm intelligence, self-organization, ant colony behavior, and soft robotics for biomimetic applications.
Students gain expertise in designing and implementing autonomous, cooperative robotic systems for real-world applications.

This course explores traditional and biomimetic robots, focusing on bio-inspired design, multi-robot systems (MRS), and intelligent control.
Bio-Inspired Robotics: Covers actuators, sensors, materials, and biologically inspired control algorithms.
Multi-Robot Systems (MRS): Examines homogeneous/heterogeneous architectures, planning, behavior-based control, and machine learning integration.
Coordination & Communication: Focuses on inter-robot communication, auction-based task negotiation, and human-robot interaction.
Autonomy & Cooperation: Includes task decomposition, resource management, localization, and multi-robot navigation.
Students develop skills in designing, programming, and coordinating intelligent robotic systems for advanced automation.

This course covers fundamental concepts in artificial intelligence (AI), focusing on search, reasoning, planning, and learning.
Problem Solving & Reasoning: Explores search algorithms, knowledge representation, and logical reasoning.
Probabilistic Models: Covers quantifying uncertainty, probabilistic reasoning, and learning from data.
Machine Learning & AI Planning: Introduces reinforcement learning, probabilistic models, and decision-making strategies.
Students develop AI problem-solving skills, preparing them for applications in automation, robotics, and intelligent systems.

This course introduces deep learning techniques and their applications in AI-driven tasks.
Core Machine Learning Concepts: Covers datasets, evaluation, overfitting, and regularization.
Neural Networks: Explores linear/logistic regression, shallow neural networks, and deep learning architectures.
Implementation: Includes algorithm development and hands-on use of machine learning libraries.
Applications: Focuses on image classification, speech recognition, and natural language processing.
Project & Research: Concludes with student-led projects and conference-style paper presentations.
Students gain practical expertise in deep learning model development and real-world AI applications.

This course explores intelligent systems and evolutionary algorithms, focusing on nature-inspired computational methods.
Neural Networks: Covers supervised/unsupervised learning, feedforward/backpropagation, Hopfield networks, associative memories, LVQ, and RBF networks.
Evolutionary Algorithms: Introduces genetic algorithms, optimization techniques, and self-adaptive learning.
Fuzzy Logic & Hybrid Systems: Explores fuzzy controllers, neuro-fuzzy networks, and fuzzy ARMAP.
Swarm Intelligence: Examines ant colony optimization, particle swarm intelligence, and collective behavior modeling.
Students develop skills in AI-driven problem-solving and optimization, preparing them for applications in robotics, automation, and intelligent systems.