Electrical and Computer Engineering

Student-led special topic courses which vary by semester.

An introduction to the fundamental scientific principles governing information science and engineering. Topics include: definition of information; entropy; information representation in analog and digital forms; information transmission; spectrum and bandwidth; information transformation including data compression, filtering, encryption, and error correction; information storage and display; and large-scale information systems. Technologies for implementing information functions.

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.

This course introduces electrical engineering theory and its application to circuits containing active and passive circuit elements. Content includes fundamental concepts such as voltage, current, power, energy and Ohm's Law as well as circuit analysis techniques including node-voltage and mesh-current based on circuit laws and theorems such as Kirchhoff Laws, source superposition, and equivalent circuits. Prerequisite: Must have completed (APMA 1110 or MATH 1320) AND (ENGR 1624 or ENGR 1410 or ENGR 2595 Topic Engineering Foundations I or ENGR 1010)

Introduction to analysis and design of digital systems from switches to gates to components to CPU. Analysis and design of combinational and sequential components including multiplexers and demultiplexers, decoders and encoders, comparators, adders and ALU, registers and register files, counters and timers, RTL design, culminating in the design of a simple programmable processor. 10-12 studio design activities. Cross-listed as CS 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

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.

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.

Studies the modeling, analysis, design, computer simulation, and measurement of electrical circuits which contain non-linear devices such as junction diodes and field effect transistors. Includes the gain and frequency response of linear amplifiers, power supplies, and other practical electronic circuits. This course is taught in a studio style with mixed lecture and lab. Pre or Corequisite: APMA 2130 and ECE 2700 AND Prerequisite: (ECE 2300 or ECE 2501 Topic Applied Circuits (link 15599)

Electrical circuits with linear applications of passive and active elements; Kirchhoff's voltage and current laws to derive circuit equations; solutions for first- and second-order transient and DC steady-state responses; AC steady-state analysis; frequency and time domain signal representations; Fourier series; phasor methods; complex impedance; transfer functions; Thevenin/Norton equivalent models; controlled sources. Prerequisite: APMA 1110.

Studies the modeling, analysis, design, computer simulation, and measurement of electrical circuits which contain non-linear devices such as junction diodes, bipolar junction transistors, and field effect transistors. Includes the gain and frequency response of linear amplifiers, power supplies, and other practical electronic circuits. This course is taught in the studio mode with mixed lecture and lab. Prerequisite: ECE 2630, APMA 2130 co-requisite

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)

Analyzes the basics of band theory and atomic structure; charge-transport in solids; current voltage characteristics of semiconductor devices, including p-n junction diodes, bipolar transistors, Schottky diodes, and insulated-gate field-effect transistors; electron emission; and superconductive devices. Prerequisite: ECE 2300.

Analyzes the basic laws of electromagnetic theory, beginning with static electric and magnetic fields, and concluding with dynamic E&M fields; plane wave propagation in various media; Maxwell's Laws in differential and integral form; electrical properties of matter; transmission lines, waveguides, and elementary antennas. Prerequisite: APMA 2130, ECE 2300, and ECE 2200 or equivalent.

Analyzes the principles of electromechanical energy conversion; three-phase circuit analysis; magnetic circuits and nonlinearity; transformers; electromagnetic sensing devices; DC, synchronous, stepper, and induction machines; equivalent circuit models; power electronic control of machines, switching regulators, Class D amplification. Laboratory, computer, and design exercises complement coverage of fundamental principles. Prerequisite: ECE 2300 and PHYS 2415 or ECE 2200

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

An embedded computer is designed to efficiently interact directly with its physical environment. This lab-based course explores architecture and interface issues relating to the design, evaluation and implementation of embedded systems . Topics include hardware and software organization, power management, digital and analog I/O devices, memory systems, timing and interrupts. Prerequisites: (ECE 2300 or ECE 2630) AND ECE 2330 AND CS 2130 all with a grade of a C- or better.

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.

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.

Construction of electronic circuit design to specifications. Focuses on computer simulation, construction, and testing of designed circuits in the laboratory to verify predicted performance. Includes differential amplifiers, feedback amplifiers, multivibrators, and digital circuits. Three lecture and three laboratory hours. Prerequisite: ECE 2600 or ECE 2660

Develops tools for analyzing signals and systems operating in continuous-time, with applications to control, communications, and signal processing. Primary concepts are representation of signals, linear time-invariant systems, Fourier analysis of signals, frequency response, and frequency-domain input/output analysis, the Laplace transform, and linear feedback principles. Practical examples are employed throughout, and regular usage of computer tools (Matlab, CC) is incorporated. Students cannot receive credit for both this course and BIOM 3310. Prerequisite: ECE 2660, APMA 2130

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

The objective of this course is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and kinematics, control, sensing, localization, mapping, path and motion planning. Prerequisites: APMA 2130, APMA 3080, APMA 3100, and CS 2130 or equivalent. 

This class discusses solid state devices that are used for renewable energy application. While we will provide a general overview of most new and interesting technologies via lectures, discussions, and research presentations, we will focus on the detailed technical aspects of few devices namely: solar cells, thermionic devices, thermoelectric devices, solar thermal (CSPs), and batteries.

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.