40000

An introduction to the analysis and representation of discrete-time signals. Explores aliasing, anti-aliasing filters, sampling continuous-time signals, quantization, and quantization noise.  Topics include Discrete-time convolution, difference equations, the z-transform, the Discrete-Time Fourier Transform, the Discrete Fourier Transform, the Fast Fourier Transform, and FIR and IIR filters. 

An introduction to the fundamentals of mechanical and electronic measurement and instrumentation.   Topics include instrument systems for measurements of pressure, temperature, displacement, force, strain, sound, vibration, and data collection and analysis to reinforce engineering concepts.  Prerequisite: EGR 30012 Engineering Lab I.

Basic concepts of the use of a microprocessor to control external devices is explored.  Topics include assembly language programming, digital logic, subroutines, stacks, input/output techniques, bus structure, sampling analog signals, A/D and D/A conversion, and digital filtering.  Prerequisites: EGR 40000 Digital Signal Processing and EGR 30012 Engineering Lab I; Co-requisite EGR 30003 Microprocessors.

Experience in major area of study. Arranged individually and taken after completion of major coursework.

An introduction to the principle concepts and methods of fluid motion.  Topics include pressure, control volume analysis, resistance of fluids, open-channel flow, fluid statics, and dimensional analysis. 

This course introduces the fundamental concepts and techniques used in the analysis and design of control systems. Topics include modeling of dynamic systems, transfer functions, block diagrams, and state-space representation. Students will explore time-domain and frequency-domain analysis, stability criteria (e.g., Routh-Hurwitz, Nyquist, Bode plots), and the design of controllers such as PID and state feedback. The course also covers system response to various inputs, including step, impulse, and sinusoidal signals, as well as the application of control strategies in real-world engineering systems. Laboratory work will provide practical experience with simulation tools (e.g., MATLAB/Simulink), system modeling, analysis, and controller design.

This course introduces undergraduate engineering students to the fundamental principles of probability and statistics, with a focus on their practical applications in engineering problems. Topics include probability theory, discrete and continuous random variables, probability distributions, statistical inference, hypothesis testing, regression analysis, and design of experiments.

Capstone course in which students apply the skills acquired to the development of a technical solution to an open-ended problem.  Topics include problem statement, specification, design process, building, implementation, testing, and documentation including a written report.

Capstone course in which students apply the skills acquired to the development of a technical approach to an open-ended problem. 

Capstone course in which students apply the skills acquired to the development of a technical solution to an open-ended problem. Topics include problem statement, specification, design process, building, implementation, testing, and documentation including a written report.