Signals and Systems

Class Overview

Course aims to provide students with a rigorous treatment of the fundamentals of discrete- and continuous-time signals and systems. The course makes use of sophisticated tools such as vector spaces of signals (e.g. bounded, summable, and square-summable signals) and orthogonal expansions in Hilbert space in addition to covering standard material on time- and frequency-domain analysis of signals and systems, including discrete- and continuous-time convolution, Fourier series, continuous- and discrete-time Fourier transforms, sampling theory, the DFT and FFT, and spectrograms. Homework assignments include a computational component where appropriate.

Prerequisites: MATH 2930, MATH 2940, Basic knowledge of Python programming
Key Topics: Math, Python

Professor: Dr. David Delchamps

Semester(s): Fall

Difficulty: 2.5/5

Rating: 5/5

Assignments: Weekly problem sets (not super long) and bi-weekly python programming 'labs' (not super long either)

Exams: Two prelims and one final exam. Exams are mostly 'true/false' and are very fair.

Pros

Well-structured lectures and clear explanations of complex concepts.
Delchamps is an engaging lecturer

Cons

Largely theoretical/general/mathematical.

Tips for Success

For exams, make sure to pay attention to the "directionality" of implications proven in class (i.e. "implies") vs. "iff".