Introduces computer architecture and provides a foundation for the design of complex synchronous digital devices, focusing on: 1) Established approaches of computer architecture, 2) Techniques for managing complexity at the register transfer level, and 3) Tools for digital hardware description, simulation, and synthesis. Includes laboratory exercises. Prerequisites: ECE 2330 and CS 2130

Design, analysis and testing of an embedded computer system to meet specific needs, considering public health, safety and welfare as well as societal impacts. Tradeoff analysis and constraint satisfaction facilitated by the use of appropriate engineering analysis techniques. Semester-long team project develops physical prototype. Counts as major design experience for ECE students. Prerequisites (ECE 3430 or ECE 3502 ECR II) AND (ECE 3750 or ECE 2700) AND 4th year standing

Under faculty supervision, students plan a project of at least one semester's duration, conduct the analysis or design and test, and report on the results. If this work is to be the basis for an undergraduate thesis, the course should be taken no later than the seventh semester. Prerequisite: Instructor permission.

Digital CMOS circuit design and analysis: combinational circuits, sequential circuits, and memory. Second order circuit issues. Global design issues: clocking and interconnect. Use of Cadence CAD tools. Team design of a significant VLSI chip including layout and implementation. Prerequisites: ECE 2330 and (ECE 2660 or ECE 2600)

Digital CMOS circuit design and analysis: combinational circuits, sequential circuits, and memory. Second order circuit issues. Global design issues: clocking and interconnect. Use of Cadence CAD tools. Semester long team research project investigating new areas in circuit design. Prerequisites: ECE 2630, ECE 2330.

Learn about and experiment with machine learning algorithms using Python. Applications include image classification, removing noise from images, and linear regression. Students will collect and interpret data, learn machine learning theory, build systems-level thinking skills required to strategize how to break the problem down into various functions, and to implement, test and document those functions. Prerequisite: CS 111X

Develops tools for analyzing signals and systems in continuous and discrete-time, for controls, communications, signal processing and machine learning. Primary concepts are the representation of signals and linear systems in the time domain (convolution, differential equations, state-space representation) and in the frequency domain (Fourier/Laplace analysis) including practical programming examples. Co-requisite: APMA 2130 or MATH 3250, and Prerequisite: (ECE 2300 or ECE 2501 Topic: Applied Circuits)

This lab provides practical exposure and continuation of the topics covered in the lecture sections of ECE 3250. Topics include principles of measurement and analysis using computerized instrumentation. Co-requisite ECE 3250

A third-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.

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