Global Arc

Comprehensive laboratory-based course in electronic system design and analysis. Covers formal methods for the design and analysis of moderately complex real-world electronic systems. Course is centered around a semester-long design project involving a computer-controlled vehicle designed and constructed by teams of two students. Integrates microprocessors, communications, and control. Prerequisites: ECE 201 and 203.

The course will cover topics related to electronic system design through the various layers of abstraction from devices to ICs. The emphasis will be on understanding fundamental system-design tradeoffs, related to the speed, precision, power with intuitive design methods, quantitative performance measures, and practical circuit limitations. The understanding of these fundamental concepts will prepare students for a wide range of advanced topics from circuits and systems for communication to emerging areas of sensing and biomedical electronics.

Intro to fundamentals and operations of semiconductor devices and sensors and micro/nano fabrication technologies used to make them. Devices include field-effect transistors, photodetectors and solar cells, light-emitting diodes and lasers. Applications include: computing and microchips, optical transmission of info (the internet backbone), displays and renewable energy. Students will fabricate their own devises in a clean room and test via microprobes. Special emphasis placed on the interplay between the material properties, fabrication capabilities, device performance and ultimate system performance. Prerequisites:MAT103-104 and PHY103-104.

Fundamentals of quantum mechanics and statistical mechanics needed for understanding the principles of operation of modern solid state and optoelectronic devices and quantum computers. Topics covered include Schrödinger Equation, Operator and Matrix Methods, Quantum Statistics and Distribution Functions, and Approximation Methods, with examples from solid state and materials physics and quantum electronics. Prerequisites: (PHY 103 or PHY 105) and (PHY 104 or PHY 106) or EGR 151 and EGR 153. MAT 201 and MAT 202, or EGR 152 and EGR 154.

Robotic systems are quickly becoming more capable and adaptable, entering new domains from transportation to healthcare. To operate in dynamic environments, interact with other agents, and accomplish complex tasks, these systems require sophisticated decision-making. This course delves into the core concepts and techniques underpinning modern autonomous robots, including planning under uncertainty, active perception, learning-based control, and multiagent decision-making. Lectures cover the theoretical foundations and the practical component introduces the Robot Operating System (ROS) framework through hands-on assignments with mobile robots.

This course provides the students with a broad and solid background in electromagnetism, including both statics and dynamics, as described by Maxwell's equations. Fundamental concepts of diffraction theory, Fourier optics, polarization of light, and geometrical optics will be discussed. Emphasis is on engineering principles, and applications will be discussed throughout. A laboratory component will acquaint students with modern simulation tools for modeling optical phenomena. Examples include cavities, waveguides, antennas, fiber optic communications, and imaging. Prerequisite: PHY 103 and PHY 104 or equivalent.

Machine learning for predictive data analytics; information-based learning; similarity-based learning; probability-based learning; error-based learning; deep learning; evaluation.

Communication systems have become a ubiquitous part of modern life. This course introduces students to the fundamental of digital communication and wireless systems. Topics include concepts from information, compression, channel, modulation, radio propagation to principles of wireless cellular, and WiFi systems. At the end of the semester, students are expected to gain a deep understanding of the basis of wireless communication systems and the connection between theoretical concepts and real-world systems.

Introduces distributed algorithms to optimize networked systems in electronic, mechanical, or biochemical substrates and other methodologies of optimization, both structures and numerical algorithms, for a variety of engineering applications. Applications will be selectively drawn from the following: computer networking, Internet protocols, communication systems, signal processing, circuit design, controlled dynamic systems, computational geometry, and financial engineering. Two 90-minute lectures. Prerequisite: MAT 202. No previous exposure to optimization theory, algorithms, or any specific application areas is required.

This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing. Prerequisite: Linear algebra at the level of MAT 202, 204, 217, or the equivalent.