Interactions between robots and humans are influenced by form, function and expectations. Quantitative techniques evaluate performance of specific tasks and functions. Qualitative techniques are used to evaluate the interaction and to understand expectations and perceptions of the human side of the interaction. Students use humanoid robots to develop and evaluate interactions within a specific application context.

A second-level undergraduate course covering a topic not normally covered in the course offerings. The topic usually reflects new developments in the electrical and computer engineering field. Offering is based on student and faculty interests.

An applied physics course in electricity and magnetism, with emphasis on the technologies derived from them. An integrated lab component will provide team-based, hands-on examples and reviews of key concepts. Calculus 3 (Multivariable) may be taken concurrently; however, students should be proficient with vectors and calculus, including the chain rule and trigonometric functions. Co-requisite:  APMA 2120 or equivalent, and Prerequisite: PHYS 1425 and APMA 1110 or equivalent.

Student-led special topic courses which vary by semester.

This course is an introduction to the foundations behind modern data analysis and machine learning. The first part of the course covers selected topics from probability theory and linear algebra that are key components of modern data analysis. Next, we cover multivariate statistical techniques for dimensionality reduction, regression, and classification. Finally, we survey recent topics in machine learning. Prerequisite: CS 2130

This course introduces photovoltaics and solar energy generation and gives an overview of the subject. The course will describe the operation of photovoltaic cells and efficiency improvements, industrial processes, solar thermal power generation, thin films, and nanomaterials for photovoltaics and future technologies.  Prerequisites: ECE 2200 or PHYS 2415 and APMA 2130 or MATH 3250.

Quantum electronics, the study of light and matter interaction, has become the cornerstone in many areas of optical science and technology. This course reviews the principles of lasers then introduces the generalized nonlinear wave equations. This course will cover typical nonlinear effects and their applications in telecommunication, ultrafast laser, quantum computing/information and chemical/bio spectroscopy.  Prerequisite: ECE 3209.

This course explores the intricacies of AI hardware, including the current landscape and anticipating the necessary developments in response to AI's rapid growth and widespread integration across all computing tiers. Through this exploration, you will gain an understanding of both the existing technologies and the future challenges in AI hardware design and implementation. Prerequisites: ECE 2330 or CS 2130. 

This course explores advanced embedded systems topics such as design and validation of embedded computing systems, embedded C programming, real-time operating systems for microcontrollers, safety and security, cyber-physical systems, Internet of Things, and robotics. The course includes hands-on experience, paper presentations, and discussions.  Prerequisite: ECE 3430

Focuses on the techniques for designing and analyzing dependable computer-based systems. Topics include fault models and effects, fault avoidance techniques, hardware redundancy, error detecting and correcting codes, time redundancy, software redundancy, combinatorial reliability modeling, Markov reliability modeling, availability modeling, maintainability, safety modeling, trade-off analysis, design for testability, and the testing of redundant digital systems. Cross-listed as CS 4434. Prerequisite: ECE 3430 or CS 3330 and APMA 3100 or APMA 3110.