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

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

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

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

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

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

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

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

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

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

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

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

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 &