ECSE 512 Syllabus - Fall 2011

1 Information

Lectures

MW      1:05-2:25      MC 103

Tutorials

T      5:35-6:55      ENGTR 0070

Instructor

TA

2 Prerequisites

• ECSE 304 Signals and Systems 2, or equivalence.
  

• ECSE 305 Probability and Random Signal 1, or equivalence.
  

• This course is NOT open to students who have taken an undergraduate course in digital signal processing (equivalence to ECSE 412 Discrete-Time Signal Processing).
  

3 Assessment

• 20% homework

• 30% midterm exam

• 50% final exam
  
  We reserve the right to alter these percentages based on the performance of the entire class.
  
  

• Exams: The midterm exam is for 1.5 hour. The final will be a 3-hour exam administered according to the University's calendar.

• Homework: The homework are bi-weekly with both analysis problems and Matlab exercises. Homework sets are due by 5pm on the due date.
  For late homework without prior arrangement,we will deduct 10% for each late day and not accept after 3 late days.

4 Text and References

1. Textbook

   1. Oppenheim and Schafer, Discrete-time Signal Processing, 3rd ed., Prentice-Hall, 2010.

2. Recommended references

   1. Proakis and Manolakis, Digital Signal Processing, 4th ed., Pearson, 2007.

   2. D. Hanselman and B. Littlefield, Mastering MATLAB 6: A comprehensive tutorial and reference, Prentice-Hall, 2001.

5 Course Description

ECSE512 is a first-year graduate level class on digital signal processing. The course focuses on theoretical concepts, analysis methods and algorithms, while also exposing students to application and implementation issues through various examples and assignments. At the end of this course, students should be able to understand the basic principles and apply fundamental algorithms and methods to analyze and design discrete-time systems for modern DSP applications.

6 Syllabus

1. Review of signals and systems: linear time-invariant (LTI) systems, convolution sum, finite (FIR) and infinite (IIR) impulse responses, difference equations.

2. Transform analysis of LTI systems: The Z transform, pole-zero representation for rational systems, all-pass system, inverse system and minimum-phase system, generalized linear phase property.

3. Sampling: The sampling theorem, reconstruction formula, quantization, sampling rate conversion, ADC and DAC, oversampling and noise shaping.

4. Discrete Fourier Transform (DFT), trade-off between temporal and frequency resolutions. Computation of the DFT, FFT algorithms.

5. Structure for discrete-time systems: signal flow graph representation, FIR and IIR systems, effects of coefficient quantization and round-off noise.

6. Filter design: Filter specifications, discrete-time IIR filter design, FIR filter design: windowing, frequency sampling, linear-phase FIR filters.

7. Multirate signal processing: Sample-rate conversion, polyphase systems, subband filtering and filter banks.

8. Linear prediction and adaptive filters (as time permits).

7 Format

This course will have around 26 lectures covering 13 weeks. Each lecture is for 80 minute.