04/21/23 J. M. Sebeson

ECET-430

Advanced Digital Signal Processing

04/21/23 J. M. Sebeson

Important Information

Your professor: John SebesonPhone: (630)-652-8299Email: jsebeson@devry.edu or

sebeson@aol.comWeb page: http://jsebeson.webs.comThis course does not use the eCollege

shellOffice hours: see my faculty web page

04/21/23 J. M. Sebeson

Course Resources Web Page

Course syllabusAssignment calendarLab Exercises Homework coversheetOther important information and files

04/21/23 J. M. Sebeson

ECET-430 Advanced Digital Signal Processing

A sequel to ECET-350.Covers techniques used in many current

applications of DSP.Assumes concepts learned in ECET-350 (or

EET-350) and familiarity with MATLAB.Lab involves MATLAB computational

exercises Some homework assignments require

MATLAB.

04/21/23 J. M. Sebeson

Text and other requirements

J. Kronenburger and J. Sebeson, “Analog and Digital Signal Processing: An Integrated Computational Approach with MATLAB,” Thompson Delmar Learning (Cengage Learning), 2008. ISBN 1418041734

Scientific graphing calculator (TI-89 recommended)

MATLAB 7.0 or higher with Signal Processing Toolbox (available on Citrix)

04/21/23 J. M. Sebeson

Selected ECET-430 Topics

• Review of key DSP concepts• Discrete Fourier analysis of sampled signals• Phase distortion and linear phase filters• Impulse response of ideal filters• Linear-phase FIR filter design

• Windowed ideal response• Sampling method• Optimal Parks-McClellan method

• IIR filter design• Digital oscillators• Notch filters• Analog prototypes by the bilinear transformation

• Multi-rate techniques• Digital anti-aliasing • Decimation and interpolation• Noise power density and sampling• Noise-shaping, delta-sigma quantization

• Correlation and auto-correlation of signals• Adaptive filters• Image processing• Wavelets• Case Studies

04/21/23 J. M. Sebeson

ECET-430 Lab

Lab consists of 8 MATLAB computational exercises emulating the problems of a typical DSP engineer.

You are expected to complete the lab exercises on your own initiative, just as a DSP engineer would.

Labs are not “cookbook.” You may need to study lecture notes, textbook MATLAB user guides, and MATLAB help documentation to complete them.

Late labs will not receive credit. No labs are dropped.

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Grading Weights

Homework: 20%Quizzes: 20%Midterm Exam: 20%Final Exam: 20%Lab: 20%

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Grading Scale

Each element (and the total grade) is based on a point system (rounded to the nearest point) where: 90 to 100 = A (e.g. 89.51=90.0=A) 80 to 89.5 = B (e.g. 89.49 =89.0=B) 70 to 79.5 = C 60 to 69.5 = D Below 60 = F

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Why Learn DSP With MATLAB?

Digital Signal Processing is the dominant technology today, and into the future, for small-signal electronic systems (i.e., just about everything)

MATLAB has become one of the standard design environments for DSP engineering

Technology students need to be literate and skilled in this environment: knowledgeable in both DSP and MATLAB

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MATLAB in DSP Product Development

Develop and Test Algorithms in MATLAB

SIMULINK Simulation

Code Composer

DSP Processor Platform

MATLAB + PC = DSP Processor!! (just less efficient)

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My Background

Education: B. S. Physics, 1969, Michigan State University M. S. Electrical Engineering, 1971, Northwestern University M. S. Materials Science and Engineering, 1973, Northwestern

University Ph.D. Candidacy (ABT) Materials Science, 1978, Northwestern

University Professional Experience: (1969 to Present)

2000 – Present: Professor, ECET, DeVry University 1989 - 2000: Hardware Development Director, Switching and

Access Solutions, Lucent Technologies 1985 - 1989: Head, Computer Engineering Information

Department, AT&T Data Systems Group 1979 - 1985: Technical Manager, Data Switching Product

Engineering Group, Bell Laboratories 1969 - 1979: Member of Technical Staff, Bell Laboratories

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Areas of R&D

No. 5 Electronic Switching Systemtm

AT&T 3Btm Computers No. 2 Signal Transfer Point Common Channel Signaling (CCIS) 1A Processor (No. 1A ESStm and No. 4 ESStm ) Computer Aided Design Signaling link encryption systems Hybrid integrated circuit fabrication and testing Magnetic bubble memory devices Laser holographic mass memory systems Reliability theory Solid state surface physics Molecular kinetics