|Basic Analysis of DC and AC circuits.
|Discrete-Time Signals and Systems
|Introduces signals and systems and the mathematical tools to design and analyze them; it lays the foundation for many, many other topics in electrical and computer engineering including circuits, control, communication and signal processing..
|Digital Logic and Computer Systems
|An overview of logic design, algorithms, computer organization and assembly language programming and computer engineering technology.
|Advanced modular logic design, design langauges, "finite" state machines and binary logic.
|Elements of microprocessor-based systems; hardware interfacing and software design for their application.
|Electrical Engineering Design 2
|Second semester of senior design.
|Intelligent Machine Design Lab
|Design, simulation, fabrication, assembly, and testing of intelligent robotic machines.
|Microcomputer Hardware and Software
|Functional behavior of microprocessors, memory, peripheral support integrated circuit hardware; microcomputer system and development software; applications.
|Elements of Machine Intelligence
|Engineering and hardware concepts pertaining to design of intelligent systems.
|Image Processing and Computer Vision
|Iage processing (image filtering, denoising), feature extraction (color, texture, wavelets), image segmentation and object recognition (face detection), video and motion analysis (optical flow, detection of moving objects), multi-view algorithms for 3D vision (stereo, structure from motion).
|Kinematics, Dynamics and Control of Robot Manipulators
|Rigid body motion, coordinate frames, forward and inverse kinematics, jacobians, quaternions, dynamics and control, planning.
|Methods in Robot Perception
|State-of-the-art techniques in sensor-based robot perception, feasibility of existing technologies with respect to new applications, approaching machine perception problems.
|Bayesian decision theory, parametric estimation and supervised learning, unsupervised learning and clustering, linear discriminant functions, neural networks, nonparametric methods, applications.
|Machine Intelligence and Synthesis
|Theory of machine intelligence applied to general problem of engineering intelligent computer systems and architecture. Applications emphasized.
|Machine Learing in Robotics
|Neural networks, locally weighted learning, stochastic optimization, Bayesian learning, Expectation-Maximization algorithm, Markov chains and hidden Markov models, Markov Decision Processes (reinforcement learning).