UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN

Department of Electrical and Computer Engineering

 

ECE 310: Digital Signal Processing

http://courses.ece.uiuc.edu/ece310

Spring 2016


Adminstrative Information

Annoucements

Text and References

Exams and Grading

Homework

 

Assoicated Lab Course:

ECE 311: Digital Signal Processing Lab

 

Lecture Times:

Lecture

D

9:00 AM - 9:50 AM

Mon./Wed./Fri.

1015 ECEB

Minh N.Do

Lecture

G

3:00 PM - 3:50 PM

Mon./Wed./Fri.

1015 ECEB

Zhi-Pei Liang

 

Instructors:

Prof. Minh N.Do

Prof. Zhi-Pei Liang

Office: 113 CSL

Office: 4257 Beckman Insititute

Email: minhdo@illinois.edu

Email: z-liang@illinois.edu

* Professors Do and Liang will alternate teaching both sections throughout the semester.

 

Teaching Assistants:

The Teaching Assistants for the course are  Zhiyuan Zheng and Aiyin Liu. The TAs will hold recitations, in which they will solve problems on the board and/or review course material, as well as office hours, during which they will answer specific questions from students.

 

Instructor Office Hour:

Covered by the instructor of the week before

Tuesday: 2:00 -3:00 PM

Location: 3081 ECEB

 

TA Office Hours:    

Mon., Tue. : 5:00 - 6:30 PM;

Wed.: 5:00 - 6:00 PM;

Location: 4034 ECEB

Thursday: 3:00 - 4:30 pm

Location: 5034 ECEB

Thursday: 5:00 - 6:30 pm

Location: 3034 ECEB

Recitation:

TA:Aiyin Liu

Wednesday: 6:00 - 7:00 pm

Location: 2013 ECEB

 

The TA email addresses are:  zheng55@illinois.edu, liu141@illinois.edu.

 

Integrity:

This course will operate under the following honor code: Students may collaborate on working through homework assignments, but each student must turn in his or her own work that has been worked out independently of any other student. Looking for solutions from prior year handouts or copying of other student's work is considered cheating and will not be permitted. All exams and quizzes are to be worked out independently without any aid from any person or device. By enrolling in this course and submitting HW assignments, quizzes, and exams for grading, each student implicitly accepts this honor code.

 

Course Objectives:

Upon completion of this course, you should be able to:

Syllabus:

#

Week

Reading

Concept matrix

Exam

Homework set

 

1/18

 

Martin Luther King Day

 

 

1

1/19 - 1/22

Ch 1

Appendix A

Appendix D

DSP overview;

Continuous-time (CT) and discrete-time (DT) signals;

Complex numbers;

Impulses

 

   

2

 1/25 - 1/29

Ch 2.1, 2.2, 2.3

2.42.5

Fourier Transform;

Discrete-time Fourier transform (DTFT); DTFT of sinusoid signals.

     H1

3

2/1 - 2/5

Ch 2.4, 2.5

Discrete-time Fourier transform (DTFT); Discrete Fourier transform (DFT)

 

   H2

4

2/8 - 2/12

Ch  2.5- 2.6

Ch  3.1-3.2

Discrete Fourier transform (DFT); DFT spectral analysis;

Sampling;

Ideal A/D (analog-to-digital) converter

 

   H3

5

2/15 - 2/19

 

 

Lecture Notes:Ch 3.3-3.9

Opponheim-Shafer: 2.2-2.4

Proakis-Manolakis: 2.2-2.3

 

 

Linear and shift invariant systems;

Convolution;

Impulse response

MT1 2/16

   H4

6

2/22 - 2/26

 

Lecture notes: Ch 4.1-4.5, 4.13

Opponheim-Shafer: 2.5, 3.1, 3.3.2, 3.4

Proakis-Manolakis: 2.4, 3.1-3.3, 3.4.3

 

z-transform (focus on right sided signals);

Difference equations;

Transfer function;

BIBO stability

 

   H5

7

2/29 - 3/4

 

Lecture notes: Ch 5.1, Ch 5.2

Opponheim-Shafer: 2.6, 2.7, 2.9, 5.1

Proakis-Manolakis: 4.2.3, 4.2.6, 5.1-5.2

 

Frequency representation of signals;

Frequency response of systems;

Magnitude and phase response

  

    H6

8

3/7 - 3/11

Lecture notes: Ch 6.3-6.4, Ch. 11(old note)

Opponheim-Shafer: 6.1, 7.2

Proakis-Manolakis: 2.5, 9.1, 10.1.2, 10.2.2

Digital filter structures;

FIR and IIR filters;

FIR filter design: window method

 

    H7

9

3/14 - 3/18

Ch 9

Analog frequency response of a digital processor;

Applications of DSP systems

  MT2 3/15

    H8

 

3/21 - 3/25

 

Spring Break

 

    

10

3/28 - 4/1

Introduction to Vector-Space Signal Processing: Part 1

Signal as vectors;

Signal representation and transformation;

Linear systems as matrices;

  

    H9

11

4/4 - 4/8

Introduction to Vector-Space Signal Processing: Part 2

LMS Demo in MATLAB

Singular Value Decomposition

Linear regression and least-squares filtering

SVD and principal component analysis

 

    H10

12

4/11 - 4/15

Ch 13

Downsampling and upsampling;

Oversampling A/D and D/A;

Digital interpolation

  MT3 4/12

    H11

13

4/18 - 4/22

Ch 14

Fast Fourier Transform (FFT);

Fast Convolution

 

    H12

14

4/25 - 4/29

Ch 12

IIR Filters: butterworth, Chebychev, Elliptical
Applications of digital filtering;

 

    H13

15

5/2 - 5/6

Ch 15

Review;

Applications

 MT4 5/2