EEL 6752 - Digital Signal Processing II

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 Pro-Copy, 5219 East Fowler Avenue, Tampa, (Tel: 988-5900; Web: www.pro-copy.com)
  Students can find some used books in the Bookstore or on the Internet

• 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, 2010.

• Office Phone: (813) 974-4769; Office Location: ENB 373
• Fax: (813) 974-5250
• E-mail: sankar@eng.usf.edu

Office Hours: Generally Open Door Policy;
Specific Hours: TR 11:00-12:00 pm (ENB 373); 5-6 pm (on-line and telephone)
You can also contact me by email (or) voice mail any time or by appointment

Course Homepages:

• USF Web Portal (secure): https://my.usf.edu (access my USF Online and Blackboard)

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)
  • Higher-Order Statistical Signal Processing (Class Handouts)
  • Self Similar Random Signal Models (Class Handouts)

Grades will be decided based on
Homework/Computer exercises using MATLAB (35%)
Project - MATLAB based Exercise (25%)
Final Exam (40%)
There will be three MATLAB based computer exercises (7 problems) and a project.
There will be NO MAKE-UP for a missed test without prior approval.

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.

Homework Policy:

• Assignment Submission Instructions: Homework/Computer Assignments are to be submitted by the due date. No late submissions will be accepted without prior permission. All students must submit the original hard copy of their assignments. Always keep a copy of all your submissions. As a backup students must upload a soft copy of their zipped file (report, MATLAB codes, etc) using digital drop box. Examples of reports (a good  template) illustrating requirements, style and formatting will be provided.
• Home Work problems 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.
• Computer Assignments (MATLAB codes and Reports) must be completed on your own (i.e., must be an individual effort) and only discussion of the concept is allowed.

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 sufficient advance notice given) except in the case of a documented medical emergency. All students must take the exams during regularly scheduled class or exam times either on campus or with an approved proctor. Any deviation from this policy MUST be pre-approved by the instructor in writing. 

Academic Dishonesty Policy: Students are reminded that University policies pertaining to academic dishonesty commonly found in both UG and G catalogs will be applied in this course.  Any form of cheating on exams or plagiarism on assigned homework and projects will result in an FF grade and further suspension or expulsion from the University with NO warnings given. Receiving or providing help on exams and not submitting individual work on assignments and project are forms of cheating; Submissions that are "identical" in any way are clear evidence of cheating. Copying materials from textbooks and papers without properly referencing them or not giving due credit are forms of plagiarism. It is the student's responsibility to review and understand USF and EE Department policies and procedures on Academic Conduct, Dishonesty, and Disruption. 

Attendance 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.

Students with Disabilities: Students in need of academic accommodations for a disability may consult with the office of Students with Disabilities Services to arrange appropriate accommodations. Students are required to give reasonable notice prior to requesting an accommodation.