EE254_S11_GreenSheet_bodyEE254_ClassSchedule_v1.pdfEE254, DSP II, Spring 2011 Page 1 of 5 San José State University College of Engineering, Electrical Engineering Department, EE254, Digital Signal Processing II, Spring 2011 Instructor: Prof. Essam Marouf Office Location: ENG 353 Telephone: (408) 924-3969 Email: [email protected] Office Hours: M&W 2:15-4:15 pm Class Days/Time: M&W 6:00-7:15 pm Classroom: IS 113 (may change) Prerequisites: Familiarity with digital filters, matrix algebra, and random signal analysis; EE253, EE250, or equivalent EE254 Website There will be a website for this course hosted by SJSU D2L (Desire-2-Learn), accessible through your account on http://sjsu.desire2learn.com. More detailed instructions will be provided in class. All handouts will be posted there (except for the Lecture Notes; available as a course reader at the university bookstore). Only officially registered students can access the website. Course Objectives After a brief review of basic results achieved in the DSP I course (EE253), the course proceeds to develop analytical and computational tools to study: 1- Decimation, interpolation and sample rate conversion. Efficient cascade and polyphase implementations. 2- Perfect reconstruction 2-channel filter banks. Quadrature-mirror, power-symmetric, and approximate implementations. Simple orthogonal and biorthogonal filter banks. 3- Binary tree-structured perfect-reconstruction filter banks. The discrete wavelet transform (DWT). Application to image compression and signal denoising. 4- Time and frequency domain characterization of random signals. Correlation and power-spectra of filtered white noise random signal models (AR, MA, and ARMA models). Whitening filters. Vector random signal models, correlation matrix, eigenvectors and eigenvalues properties. 5- Estimation of mean and autocorrelation function from random signal realizations. 6- Classical (non-parametric) & model-based (parametric) power spectrum estimation.EE254, DSP II, Spring 2011 Page 2 of 5 7- FIR & IIR Optimal (Wiener) filtering. Optimal system function and corresponding mean-square error. Application signal denoising and interference cancellation. 8- Forward and backward linear predictors as Weiner filters. The Levinson-Durbin algorithm and the lattice error-filters realization. Application to speech coding. The Burg algorithm for estimating the lattice filter coefficients. Lattice-ladder realization of the Wiener filter. 9- Adaptive implementation of Wiener filters. Geometry of the mean-square error surface. The steepest descent algorithm for searching the error surface. Conversion conditions, the learning curve, and speed of convergence. 10- The LMS adaptive algorithm and its close relatives. Performance measures (speed of convergence and the excess mean-square error). The basic RLS algorithm and comparison with the LMS algorithm. 11- Application to adaptive interference/noise cancellation, adaptive system identification, adaptive line enhancement, adaptive channel equalization, … . 12- Adaptive implementation of the linear-prediction error filters and general Wiener filters using the gradient adaptive lattice (GAL) implementation. Required & Recommended Texts/Software Textbook 1- Statistical Digital Signal Processing and Modeling, M. Hayes, Wiley,1996. Required. 2- Class Lecture Notes (available at reproduction cost at the bookstore). Required. 3- The Student Version of Matlab and the Signal Processing Toolbox (included in the Student Version Release 2007a or later), the Mathworks. Recommended. Other References 1- Statistical and Adaptive Signal Processing, Manolakis, Ingle, and Kogon, McGraw-Hill, 2000 2- Adaptive Filter Theory, 4th Ed., S. Haykin, Prentice-Hall, 2001. 3- Adaptive Filtering: Algorithms and Practical Implementation, 3nd Ed., P. Diniz, Springer, 2008. 4- Adaptive Signal Processing, Widrow and Stearns, Prentice-Hall, 1985. 5- Adaptive Filters:Theory and Applications, B. Farhang-Boroujeny, Wiley, 1998. 6- Optimal and Adaptive Signal Processing, P. Clarkson, CRC, 1993. 7- A Course in Digital Signal Processing, B. Porat, Wiley, 1997. 8- Digital Signal Processing, 4rd Edition, J. Proakis and D. Manolakis. Prentice-Hall, 2007. Software: Matlab & the Signal Processing Toolbox Matlab is used as the computational platform for class examples and homework problems. Matlab and many of its Toolboxes are available on the PCs in room ENG 387. The lab operates on an open door policy. Check availability times posted on the lab door. You may also consider purchasing the Student Version of Matlab (~$100) for private use at school and home. This is perhaps the most time flexible way to do the computational assignments and Project. The Student Version Release R2007a and after include the Signal Processing Toolbox. Check the web site http://www.mathworks.com/academia/ for more information. You may order the Matlab Student Version on the web or may purchase it directly from the Spartan Bookstore, Textbooks Department.EE254, DSP II, Spring 2011 Page 3 of 5 The Matlab m-files referenced in all textbooks can be downloaded from the websites below: 1) Hayes: http://users.ece.gatech.edu/~mhayes/stat_dsp/. 2) Mitra 3rd Ed.: http://www.mhhe.com/mitra/ (only Chs. 13 &14 are required) If you are not familiar with Matlab (you should be if you already took EE253), you should go through the tutorials in the Student Version manual immediately! A web- based introduction is at http://www.mathworks.com/access/helpdesk/help/techdoc/matlab.shtml. Electonic versions of all Matlab+Toolboxes manuals may be accessed at the same web site. Matlab has a good help facility that you should invoke to learn more about specific commands or functions. Dropping and Adding Students are responsible for understanding the policies and procedures about add/drops, academic renewal, etc. Information on add/drops are available at http://info.sjsu.edu/web-dbgen/narr/soc-fall/rec-298.html. Information about late drop is available at http://www.sjsu.edu/sac/advising/latedrops/policy/ . Students should be aware of the current deadlines and penalties for adding and dropping classes. Assignments and Grading Policy Grading: Homework 5% Midterm Exam #1 (Monday 03/07/11 ) 30% Midterm Exam #2 (Wednesday 04/20/11) 30% Optional Term Project (replaces the worst midterm) 30% Final Exam (Monday 5/23/11, 5:15-7:30 pm) 35% Exams & Term