EEL 6752 - Digital Signal Processing II

Prerequisites: EEL 6502 - Digital Signal Processing I (or) Consent of Instructor

Prerequisites by Topic:

Basics of Digital signal processing
Probability and Random signal theory

Fast algorithms, FFT, fast convolution; DCT, CZT, Random signals, Linear prediction, Application to speech coding, Spectrum estimation, Quantization effects, Pencil-of-functions method, Adaptive filtering and equalization. (3 credits)

To provide introduction to advanced topics in digital signal processing--linear estimation and prediction analysis, signal modeling, lattice filters, spectral estimation and adaptive filters; signal processing algorithms and techniques used in a broad range of applications.

• Optimum Signal Processing, An Introduction (2nd Ed.), S.J. Orfanidis, McGraw-Hill, 1988.

  The book is Out of Print but it is available from
  ProCopy, 5209 East Fowler Avenue, Tampa, (Tel: 988-5900)
  You can also find some used books in the USF Book Store.

• Statistical Digital Signal Processing and Modeling, M. H. Hayes, Wiley, 1996, ISBN 0-471-59431-8
• Computer Based Exercises for Signal Processing Using MATLAB 5, J. H. McClellan et al., Prentice-Hall, 1998, ISBN 0-13-789009-5
• Discrete-Time Signal Processing, A. V. Oppenheim and R. W. Schafer, Prentice Hall, 1989, ISBN 0-13-216292-X.

• Office Phone: (813) 974-4769; Office Location: ENB 368
• Fax: (813) 974-5250
• E-mail: sankar@eng.usf.edu

Topics:

• Review Basics of Signal Processing and Random Process
• Random Signals and Signal Models (Ch. 1; Skip Section 1.17)
• Some Signal Processing Applications (Ch. 2; Skip Section 2.5)
• Spectral Factorization (Ch. 3; Skip Sections 3.2, 3.3, and 3.4)
• Linear Estimation of Signals (Ch. 4; Skip Sections 4.6-4.9)
• Linear Prediction (Ch. 5; Skip Sections 5.6, 5.8, 5.9, 5.11, 5.13, 5.14)
• Selected Signal Processing Applications
  • Adaptive Filters (Ch. 7; Skip Sections 7.12-7.18)
  • Blind Deconvolution and Equalization (Class Handouts)
  • Self Similar Random Signal Models (Class Handouts)
  • Higher-Order Statistical Signal Processing (Class Handouts)

Grades will be decided based on
Mid-term Exam (30%) and Final Exam (30%)
Homework/Computer exercises using MATLAB (40%)
There will be four MATLAB based computer exercises.
There will be NO MAKE-UP for a missed test without prior approval.
The new grading system of PLUS/MINUS options for the letter grades
WILL NOT be used in this course.

Homework Policy: Homework Exercises will be assigned in the class but will not be collected. Everyone is recommended to do the homework earnestly since it will be a good preparation for the exam.

Exam Policy: All exams are closed books and notes. One page reference sheet for formulas and definitions is allowed but NO homework or any other worked out examples. There will be NO MAKE-UP for a missed exam without prior approval from the instructor (with sufficent advance notice given) except in the case of a documentable medical emergency.

Academic Dishonesty Policy: The first strike policy will be strictly adhered to with NO warnings given (any form of cheating on exams or plagiarism on assigned homework and projects will result in an F grade and further suspension or expulsion from the University). It is the student's responsibility to review USF and EE department policies and procedures on Acdemic Conduct, Dishonesty, and Disruption.

Attedance Policy: Students who anticipate the necessity of being absent from class due to the observation of a major religious observance must provide notice of the date(s) to the instructor, in writing, by the second class meeting.

Course Homepage