Catalog description: Study of discrete-time signals and systems, including Z-transforms, fast Fourier transforms, and digital filter theory. Filter design and effects of finite register length, and applications to one-dimensional signals. (Lectures cover digital signal processing topics relevant to the Matlab exercises. The focus of the course is a series of lab-experiments, which provide practical knowledge in processing real signals, with examples from cardiology, speech, and audio).

Prerequisites: EE 3523 Signals and Systems II (Require grade of C or better)

Major prerequisite by topic:

1. Continuous and -discrete time signals and systems
2. Fourier series and Fourier transform.
3. Properties of discrete-time signals and systems
4. Convolution

Familiarity with Matlab (or willingness to learn!)

Textbook: (Required): Alan V. Oppenheim & Ronald W. Schafer, Discrete-Time Signal Processing, Prentice-Hall Second Edition, Prentice Hall, 1999 (ISBN 0-13-216292-X).

References:

1. S.Kay , Fundamentals of Statistical Signal Processing, Prentice-Hall 1999, (ISBN 0133457117)
2. R. Schilling &S. Harris, Fundamentals of Digital Signal Processing Using MATLAB w/CD_ROM Thomson_ Engineering, 2004, (ISBN 0534391508
3. A.Gonzalo, Nonlinear Signal Processing: a Statistical approach”, Wily-Interscience, 2004, (ISBN 04716762410)

• MATLAB Primer (Web)
• Hanselman & Littlefield, Prentice-Hall, 2001, MATLAB Manual, Signal Processing Toolbox
• The Mathworks, Inc., South Natick, MA. Exercises for Signal Processing using MATLAB 5 by J. McClellan et. al.

• http://sipi.usc.edu/services/database/Database.html
• http://www.library.uthscsa.edu/internet/ImageDatabases.cfm

• http://www.physionet.org/physiotour/3/
• http://sipi.usc.edu/database/

1. Oppenheimer and Schafer, Digital Signal Processing, Prentice-Hall, 1975. The classic graduate-level text. Has a bit unusual material on the Discrete Hilbert Transform, homomorphic processing, and power spectrum estimation.
2. Proakis, Rader, Ling, and Nikias, Advanced digital signal processing, Macmillan, 1992. Contains chapters on a number of advanced topics including algorithms for fast convolution, linear prediction and optimal filtering and least-squares methods, adaptive filters, and other topic.
3. Lim and Oppenheim, Advanced topics in signal processing, Prentice-Hall, 1988. A contributed book with chapters on parametric signal modeling, spectral estimation, multi-rate processing, efficient transform and convolution algorithms, adaptive signal processing, Fourier Transforms for signals whose frequency content changes rapidly in time, two-dimensional signal processing, etc.
4. S. J. Orfanidis, Introduction to Signal Processing, Prentice-Hall, 1996.
5. Proakis and Manolakis “DSP: Principles, Algorithms, and Applications,” 3rd Edition, Prentice Hall, 1996.
6. Ken Steiglitz, "DSP Primer: with applications to digital audio and computer music”
7. M. H. Hayes, “Digital Signal Processing,” Schaum’s Outlines, McGraw-Hill, 1999.
8. Sanjit K. Mitra, Digital Signal Processing McGraw-Hill (ISBN 0-07-232105-9)

Course objectives:

• What are the key DSP concepts and how do they relate to real applications?
• What are the methods of time domain and frequency domain implementation?
• What are typical characteristics of real DSP multirate systems?
• How can you use MATLAB to analyze and design DSP systems?

This course will also help the students to

• Provide a deeper understanding of the latest developments in the DSP research area.
• Present a comprehensive introduction to important emerging DSP technologies with a focus on wavelets/subband applications.
• Provide students with backgrounds for pursuing independent research in DSP with related applications.

Course Specific Objectives: Upon completion of this course student will be able to:

• Apply digital signal processing fundamentals.
• Understand the processes of analog-to-digital and digital-to-analog conversion.
• Master the representation of discrete-time signals in the frequency domain, using z-transform, discrete Fourier transform (DFT), and cosine transform.
• Understand the implementation of the DFT in terms of the FFT, as well as some of its applications (computation of convolution sums, spectral analysis).
• Learn the basic forms of FIR and IIR filters, and how to design filters with desired frequency responses.
• Appreciate relationships between first order low pass, and high pass filters, and between second-order Peaking and Notching filters. Design digital filters using Matlab.
• Use appropriate windows to diminish the effect of leakage.
• Demonstrate the effect of the time window length on the achievable spectral resolution.
• Learn the design procedures for filter bank.
• Do a time-frequency analysis of a signal.
• Become aware of some applications of digital signal processing.

In addition:

1. The student will be either participates in a basic design experience or in a research experience through a team project.
2. The students will further there written and oral communication skills through the preparation of a written project report and a short oral presentation.

Topics covered:

1. Review of analog signals, discrete time signals, linear shift invariant systems, Impulse response, linear convolution, and Matrix formulations of a linear convolution.
2. The DTFT, The linear convolution theorem, DTFT of a rectangular window. Relation with the CTFT, The sampling theorem, Spectra of sampled signals, The Nyquist theorem. Aliasing, Signal reconstruction. Sample rate, decimation & interpolation. Quantization. Oversampling and noise shaping. A/D and D/A Converters. Quantization, finite word length effects.
3. DFT Properties, DFT of the rectangular window, Zero padding, DFT examples, Sampling of periodic signals and the DFT. The DFT matrix, Frequency resolution and windowing, Physical vs. computational resolution.
4. Factorizations of the DFT matrix. FFT algorithms: Decimation in frequency FFT. The FFT vs. the DFT. Circular convolution, The Circular convolution theorem, fast block convolution; Discrete Fourier and Cosine transforms; the spectrogram. Fast Cosine transform.
   (Project_1)
5. Z- Transform, Properties, Region of convergence, Causality and stability, Relation to the DTFT. The pole-zero pattern and its relation with the filter magnitude frequency response, Partial fraction expansion, Inverse Z- transform, Transform pairs.
6. Summary of equivalent description of digital filters (system function H(z), frequency response H(w), I/O difference equation, Pole zero pattern, Impulse response, I/O Convolution Equation). Difference equations and IIR filters. Sinusoidal response.
   Test
7. Develop the fundamentals of digital filter design techniques for Finite Impulse Response (FIR Fourier technique based digital filters design methods) and Develop Infinite Impulse Response filter types (IIR-Bilinear transformation, First order low pass, and high pass filters, second-order Peaking and Notching filters, higher order filter, including Butterworth and Chebyshev filters). Comparisons between FIR and IIR filters will be presented. MATLAB design examples will be also presented.
   (Project-2)
8. Multirate processing fundamentals of decimation and interpolation will be developed. Methods for optimizing processing throughput requirements via multirate designs will be developed. Multirate techniques in filter banks and spectrum analyzers and synthesizers will be developed.
9. Analog-to-Digital conversion errors will be studied. Quantization effects of finite arithmetic for common digital signal processing algorithms including digital filters and FFTs will be presented. Methods of calculating the noise at the digital system output due to arithmetic effects will be developed.
10. Several algorithms and associated applications need to be discussed based upon classical and recent papers/research: for example; audio signals reconstruction from phase, magnitude;.
    (Project_3)

Contribution of course to meet the professional component:
This course prepares students to work professionally in the area of digital signal processing.

Relationship to EE program objectives and outcomes:
This course primarily contributes to Electrical Engineering program outcomes that develop student abilities to:

1. Use the principles from, statistics, and mathematics in engineering applications. (A1)
2. Use computer-based tools for engineering applications. (B2)
3. Identify, formulate, and solve engineering problems. (B1)
   The secondarily contributes to Electrical Engineering program outcomes that develop student abilities to:
4. Have fundamental DSP’s algorithms design skills (A3).
5. Work effectively in multi-disciplinary teams (B4).
6. Present technical information clearly in both oral and written formats (C-2).
7. Develop creative and innovative designs that achieve desired performance criteria within specified objectives and constraints, understand the need for lifelong learning and continuing professional education (C-4)

Performance criteria: Objectives 1 through 11 will be evaluated by evaluation method 1.
Course content: Engineering Science: 2 credits (66.7 %); Engineering Design:1 credit (33.3%)

Academic Dishonesty: As an entity of The University of Texas at San Antonio, the Department of Electrical and Computer Engineering is committed to the development of its students and to the promotion of personal integrity and self-responsibility. The assumption that a student's work is a fair representation of the student's ability to perform forms the basis for departmental and institutional quality. All students within the Department are expected to observe appropriate standards of conduct. Acts of scholastic dishonesty such as cheating, plagiarism, collusion, the submission for credit of any work or materials that are attributable in whole or in part to another person, taking an examination for another person, any act designated to give unfair advantage to a student, or the attempt to commit such acts will not be tolerated. Any case involving academic dishonesty will be referred to the Office of Student Judicial Affairs who will investigate the charge and set a preliminary meeting with the student to discuss disposition. Consequences of academic dishonesty may be as severe as dismissal from the University.

Coordinator: Sos Agaian, Professor of Electrical Engineering
Persons who prepared this description: Sos Agaian, Ph.D, August 8, 2005

Signature  Part B- General Course Information and Policies Instructor: Sos Agaian, Ph.D., and Professor of Electrical Engineering.
Office: EB 3.04.44
Office Hours: Tuesday, Thursday, 3:30PM –5:30 PM, after class or by appointment
Phone: (210) 458-5939; E-mail: sagaian@utsa.edu
Lectures: Tuesday, Thursday, 5:30PM –6:45PM
Class schedule: 2 hours and 30 minutes of lectures per week.

Class Conduct: Students are expected to assist in maintaining a classroom environment that is conducive to learning. To assure all students have an opportunity to gain from time spent in class, students are prohibited from engaging in any form of distraction.

Class Policy: (The "Mathew Rule") You are responsible to know what transpires in class. In particular, even if you do not attend lectures, you are still responsible for knowing all announcements made during regular class hours.

Nonlinear Signal Processing Lab: Computing facilities are available for course projects in the Nonlinear Image Processing Lab (Juan Perez, 210 458 5594) and can set up accounts and arrange for access.

Attendance: Attendance is required and will be a part of the grade.

Folder: Each student is required to submit a notebook or folder containing all graded materials for the course (projects, etc.) at the end of semester.

Please bring a notebook (file) organized in the following format to the final exam/projects: 1. Course syllabus and the list of projects 2. Graded Projects Items 1-3 are required. Other materials such as your class notes could be added at the end (optional). If you don't submit a notebook containing all the required items, you will receive a grade of "IN"(incomplete) for the course which will be removed only when a completed notebook is turned in. Your actual grade will be based only for the work completed during semester.

Grading: The final grade will be assigned based on the following areas and weights: Homework: ……….10%
Project-1 …………….20%
Test 25%
Project 2 …..…………20%
Project-3 …………….35%
Total ............... 100 %

EE-5163- Digital Signal Processing , Fall 2005
Schedule for Exams, Holidays and other Events
Date	Event
September 25	First Project
October 2 or 25	Thursday-Test
October 28	Second Project
October	Last day to drop a course and receive an automatic grade of "W"
November 25	Third Project
November 24-26	Thanksgiving Holiday
December 5-6	Students Study Days. Classes do not meet

Homework (The first three):
HW #1: 2.3-2.6, 2.9, 2.13- 2.16, 2.28-2.33, due Thursday, September 15th at the beginning of class.
HW #2: 8.1-8.14, 9.2.- 9.16, due Thursday, September 22th at the beginning of class.
HW #3: 3..1- 3.20, 3.21- 3.25 due Thursday, October 24th at the beginning of class.

Also, you are of course welcome to hand the homework in early.

An important element of this course is the project where the students, working individually or as part of a team, will work on a problem in DSP. If one decides to do a hard project then it is not necessary to do homework.

There are three major steps in the project:

1. Design and implement your DSP routine in MATLAB based on the provided signals and ground truth data.
2. Test your routine with the training signals by the evaluation program.
3. Performance of your routine will be tested in the class presentation with a test signals by the same evaluation program.
   

The course project will consist of either a theoretical analysis of a DSP algorithm or the design, programming, and simulation of a DSP algorithm for a particular application (or some combination of the two).

Project Team: Students are encouraged to work in-group, but working alone is fine as well. Note, the project partners may share “Boiler plate” common material, but should emphasize the individual contributions in the report. The proposed work for each individual should be identified. For example, if a two-person team is working on lowpass filtering then each student can develop separate lowpass filtering algorithms and they can compare the performance. 

The project should be described in readable English in a report. There will also be a 15-minute oral presentation. The report should provide appropriate references to the literature and a comparative discussion with existing methods. Suggestions for projects will be handed out in class, but creativity in developing a topic will be considered in the grade. The projects can be developed for any available platform. The project grade will be based on the creativity and technical content of the project and on the quality of the presentation and participation in the discussion of the other project presentations.

Dr. Sos S. Agaian: 210-458-5939 (Phone) - 210-458-5547 (Fax) - Sos.Agaian@utsa.edu - BSE 1.540