Digital Signal Processing I

Theory and algorithms for processing deterministic and stochastic signals. Topics include sampling theory, discrete-time signals, systems, and transforms, digital filtering, spectrum estimation, autoregressive modeling, efficient sampling rate alteration, perfect reconstruction filter banks, transmultiplexers, and Minimum Mean Square Error Estimation. Applications emphasized throughout including CD/DVD players, radar, 5G cellular communications, audio compression, wireless routers, and GPS signal processing for geolocation

ECE53800

Credit Hours:

3

Learning Objective:

To provide the student with a broad, yet strong background in techniques for processing digital signals for purposes of information transmission, information extraction, information compression, audio enhancement, noise suppression, interface compatibility, error correction, and understanding modern Analog-to-Digital Converters (ADC's) and Digital-to-Analog Converters (DACs,) wireless routers, GPS signal processing for geolocation, and high-resolution radar.

Description:

Theory and algorithms for processing deterministic and stochastic signals. Topics include sampling theory, discrete-time signals, systems, and transforms, digital filtering, spectrum estimation, autoregressive modeling, efficient sampling rate alteration, perfect reconstruction filter banks, transmultiplexers, and Minimum Mean Square Error Estimation. Applications emphasized throughout including CD/DVD players, radar, 5G cellular communications, audio compression, wireless routers, and GPS signal processing for geolocation

Topics Covered:

Review: Discrete-Time Signals, Systems & Transforms; Basic Sampling Theory and D/A Conversion; Z Transform; Discrete-Time Fourier Transform; Frequency Selective Linear Filtering; Sampling and Reconstruction; Multirate DSP; Applications to CD/DVD Players, Radar, GPS, Cellular Communications, Wireless Routers and Audio Compression; Digital Filter Design; Fast Fourier Transform Algorithms; Nonparametric methods of power spectrum estimation; Model-Based Spectrum Estimation; Autoregressive (AR) Modelling; Forward/Backward Linear Prediction,Levinson-Durbin Algorithm; Minimum Variance Method; Eigenstructure Methods; Perfect Reconstruction Filter Banks.

Prerequisites:

Undergraduate knowledge of: continuous-time signals and systems, Fourier transform theory, and linear algebra (basic concepts).

Applied / Theory:

50 / 50

Web Address:

https://engineering.purdue.edu/~ee538/

Web Content:

All class notes, past exams plus solutions, solutions to homeworks, and Matlab demos will be on the web site.

Homework:

Three homeworks involving the use of Matlab software package, plus bi-weekly assignments not collected but solutions posted on web.

Exams:

Three fifty-minute exams and one two hour final exam.

Textbooks:

Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.
Required--J.G. Proakis and D.G. Manolakis, "Digital Signal Processing: Principles, Algorithms, and Applications," 4th ed., Prentice-Hall, NJ, 2006, ISBN: 9780131873742

Computer Requirements:

ProEd minimum computer requirements; student version of Matlab

ProEd Minimum Requirements:

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