ECE 315 - Signal Processing Methods

 

INSTRUCTION TEAM

Lectures: Prof. Marco F. Duarte, Marcus Hall 215I, mduarte@ecs.umass.edu
Office Hours: Tuesdays and Thursdays 10:30am-11:30am (or by e-mail appointment)

Teaching Assistant:
Shivakumar Valpady, svalpady@umass.edu: Office Hours TBA

COURSE FORMAT

Lectures: 10:10am-11:00am Monday, Wednesday, Friday @ 20 Goessman Lab.

DESCRIPTION

This course focuses on the study of discrete-time signals and linear discrete-time systems. It constitutes the basic theory behind a further study of digital communication theory and systems, digital control theory and systems, digital signal and image processing, networking, and almost all disciplines of electrical and computer engineering.

PREREQUISITES

ECE 213: Continuous-Time Signals and Systems.

ECE 214: Probability and Statistics.

TEXTBOOK

We will use two textbooks that are available online for download at no cost and in physical version at low cost:


  1. Signals and Systems: Theory and Applications” by F. Ulaby and A. Yagle (can be purchased for ~$70).

  2. Introduction to Probability, Statistics, and Random Processes” by H. Pishro-Nik (can be purchased for ~$30).

  3. H. Hsu, “Signals and Systems,” Schaum’s Outline Series, McGraw Hill, 2010: provides a significant number of examples and exercises for exam preparation at a low cost.


LECTURE SCHEDULE (TENTATIVE)


Week 1: Discrete-time signals; review of basic signals, signal operations and properties.

Week 2: Discrete-time system properties: causality, stability, linearity, time invariance. Eigenfunctions of discrete-time LTI systems.

Week 3: Time domain analysis of discrete-time systems: impulse response, convolution, difference equation representations.

Week 4: Z Transform. Properties and inversion. Partial fraction expansions.

Week 5: Filter design and stability. Frequency response.

Week 6: Fourier Series representations of periodic discrete-time signals. Discrete-Time Fourier Transform.

Week 7: Discrete Fourier Transform and its application to discrete periodic signals. Fast Fourier Transform.

Week 8: Applications to system analysis: filter classifications, causality, deconvolution.

Week 9: ROC, Stability, and Causality.

Week 10: FIR and IIR Filter Design

Week 11: Multirate signal processing. Upsampling, downsampling, and interpolation.

Week 12: Review of basic concepts in probability. Random processes. Wide-sense stationary processes.

Week 13: Power Spectral Density. Random processes through LTI systems. Noise models. Noise in electronic systems.