Rice University, Fall 2004
Instructor: Clay Scott
Classroom: Sewell 462
Office: Duncan Hall 2117
Campus extension: 3776
Email: cscott at rice dot edu
Office hour: Fri. 11-12
|Name||office hour||office number|
|Wai Lam (William) Chan||Tues. 2-3||wailam||DH 2046|
|Jyoti Uppuluri||Mon. 1-2||juppu||DH 2121|
Required text: none
Recommended texts: (on reserve at Fondren)
1) Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven Kay, 1993
2) Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven Kay, 1998
Another helpful text: (on reserve at Fondren)
1) Statistical Signal Processing, Louis Scharf, 1991
Basic probability: familiarity with densities, probability mass functions, expected value, mean and variance, independence, conditional distributions, characteristic function. ELEC 533 or equivalent taken concurrently.
Basic linear algebra: vector spaces, linear transformations, inner products, orthonormality. Familiarity with projections, the SVD, and least-squares problems will be useful, but not essential. We'll develop these concepts as necessary.
Digital signal processing: Nothing essential, but familiarity with filtering of linear time-invariant systems and the DFT may aid understanding.
Programming: Basic MATLAB skills (can learn these along the way). Here is a quick tutorial. There will be times when I provide you with MATLAB code to make your life easier.
If you feel underprepared, talk with me.
Class participation 5%
Midterm exam: 10%
Final project: 15%
Final exam: 20%
There will be in the range of 10-12 homeworks, about one per week. If you need to turn an assignment in late, please contact me in advance. Unexcused late homeworks will not be accepted.
I consider homeworks to be the most important part of the class. When writing up your problem sets, you are expected to
|The Linear Model
Adaptive Filtering (time permitting)
You may work together on homeworks, but you are required to write up/code your solutions by yourself. You may not refer to material from previous offerings of this course, including problem sets and solution sets. If you find a problem worked in a book or on the web, resist the temptation to copy it.
Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me during the first two weeks of class. All discussions will remain confidential. Students with disabilities should also contact Disabled Student Services in the Ley Student Center.
qinv.m (Inverse Q-function)
Files: fmri.mat, ref.mat, fmri.m.
A paper that may stimulate your thinking (note: this paper is more sophisticated that what I would expect you to do): Generalized likelihood ratio detection for fMRI using complex data , Nan, F.Y.; Nowak, R.D., IEEE Transactions on Medical Imaging, Volume: 18 Issue: 4 , April 1999 Page(s): 320 -329
2003 project description
Last summer Prashant made a nice applet for viewing and understanding different probability density and mass functions. This is a good resource for those of you who might be less familiar with certain distributions we'll be using. It allows you to play with the different parameter settings of a density/mass function and see how the shape of the function changes. The instructions for viewing the applet are listed below. You need a special program called LabVIEW, which you can download, in order to view the applet.
To run the applet, you first need to install the LabVIEW Run-Time Engine and the Connexions VI Runner (just one installer for the pair). Download and install setup_plus_rte.exe from http://cnx-246-91.ece.rice.edu/~prash/installers and you should be ready to go. If you're on a computer that's already got LabVIEW, you should install setup_minus_rte.exe. Once that's installed, go to http://cnx-246-91.ece.rice.edu/~prash/apps.html and click on the Common Probability Distributions link. If all goes well, the VI Runner should download and run the VI for you. The installation information is also posted at http://cnx.rice.edu/content/m11550/latest/