This course provides an overview of the core design principles used in the holistic design of low power embedded computing systems. Modern embedded system design use co-design of both hardware and software subsystems to achieve energy efficiency. The content of this class builds on foundational ECE courses in circuits, low level software design, machine learning, and signal processing. Prior courses in in at least one of these areas is recommended as a prerequisite.

Embedded systems sense, actuate, compute, and communicate to accomplish tasks in domains such as medical, automotive, and industrial controls. Informal methods of hacking together embedded systems are at odds with the criticality of their applications. This course will introduce recent developments toward more rigorous modeling and verification of embedded and cyber-physical systems.

Physical and electronic structure of semiconductors, band theory, semiconductor statistics, scattering processes and carrier transport, optical properties, modern quantum electronic devices.

In-depth examination of semiconductor devices. The physics of semiconductors, p-n junction diodes, bipolar transistors, Schottky barriers, JFETs, MFSFETs, MIS diodes, CCDs, and MOSFETs. Prerequisite: E&C-Eng 344, or introductory semiconductor theory course.

Unified treatment of techniques for representation of signals and signal processing operations. Emphasis on physical interpre-tation of vector spaces, linear operators, transform theory, and digital signal processing with wavelet filter banks. Prerequisite: graduate standing.

Matrix analysis, state variables, state space techniques for continuous time systems, matrix fraction descriptions. Controllability, observability, realization theory. Feedback and observers. Stability analysis.

Machine learning is the practice of programming computers to learn and improve prediction through experience and data, and it is becoming pervasive in technology and science. This course will cover the mathematical underpinnings, algorithms, and practices that enable a computer to learn. Topics will include supervised learning, unsupervised learning, evaluation methodologies, and deep learning. The prerequisites of this course include introductory courses in linear algebra (e.g ECE 201 or Math 235), multivariate calculus (e.g., Math 233), and probability (e.g., ECE 214).

Not available at this time

This course will give students a deep exposure to cloud computing enabling technologies, its main building blocks, the design strategies behind scalable and fault tolerant cloud architectures, and an in-depth understanding through homework projects and exams. We learn about data center networks and their topologies, cloud transport layer, file systems, handling big data, enabling consistent data store, virtualization, and the softwarization trends in cloud computing.

This course introduces antennas and radiowave propagation for microwave frequency applications. Antenna topics include basic antenna parameters, antennas in communication and radar systems, wire antennas, and microstrip patch antennas. Propagation topics include direct transmission between a transmitter and a receiver, reflection and refraction, and propagation properties in ionosphere. In a team project, students will design and simulate an antenna for a communication or radar application of choice.