Les Eugene Atlas
E E 518
Digital representation of analog signals. Frequency domain and Z-transforms of digital signals and systems design of digital systems; IIR and FIR filter design techniques, fast Fourier transform algorithms. Sources of error in digital systems. Analysis of noise in digital systems. Prerequisite: knowledge of Fourier analysis techniques and graduate standing, or permission of instructor.
This class addresses the representation, analysis, and design of discrete time signals and systems. The major concepts covered include: Discrete-time processing and modeling of continuous-time signals and systems; decimation, interpolation, and sampling rate conversion; time-and frequency-domain design techniques for non-recursive (FIR) filters; prediction; discrete Fourier transforms, fast Fourier transform (FFT) algorithms and turning block into stream processing; short-time Fourier analysis and filter banks; multirate techniques; and various applications of these techniques. Some of the class homework will make use of MATLAB™ programs on computers within the UW or on your work or home computer. The course grade will be based upon weekly homework, a midterm exam, and the final exam.
Student learning goals
Knowledge of main fundamentals of modern discrete-time signal processing and the ubiquitous role of their formalisms in modern systems, such as wireless communications, machine learning, audio, image and moving-picture media.
Exposure to, knowledge of, and appreciation of example applications of some aspects of advanced signal processing, such as multi-rate signal processing.
Design and use of digital filtering, especially the most common finite impulse response (FIR) filtering approaches along with knowledge of the difference between the continuous-time and discrete-time Fourier transform, and the computational estimator--the discrete Fourier transform, and it's fast version, the fast Fourier transform (FFT), which can be used to implement FIR filters faster or much faster via the FFT.
Knowledge of key filter and digital signal processing design formalisms, such as steady-state frequency domain representations, including advanced phase response issues, such as phase delay, group delay, and minimum phase systems, including their role in equalization via inverse systems.
Understanding of definitions and properties of z-transforms and their use in applications such as multirate signal processing and as an operational calculus which extends steady state frequency domain concepts to damped or growing signal models and system responses.
Exposure to more advanced digital signal processing such as homomorphic and cepstral representations, along with examples of the wide range of their applications.
General method of instruction
Weekly lectures and problem discussion, along with students' weekly assigned work on both math-based problems and MATLAB programming example projects.
Prerequisites: A mathematical/quantitative undergraduate degree, preferably with knowledge of Fourier transforms, and some discrete math and linear algebra. This course presumes a solid understanding of linear time-invariant systems, discrete-time signals, basic sampling, with some background in Fourier transforms and bilateral z-transforms. Ability to use math-based simulation languages, such as MATLAB, is expected.
Class assignments and grading
Weekly homework is due in class on Tuesday, starting the 2nd Tuesday of the quarter.
The course grade will be based upon weekly homework, a midterm exam, and the final exam, with thse percentage contributions to the final grade: Homework 15% Midterm Exam (November) 35% (Open book and notes.) Final Exam (December) 50% (Open book and notes.) The final class grades are based upon a normalized curve.