IRS7 - COURSE:   

Stochastic Analysis for Engineers
UNITS  2 modules, 10 lectures
TYPE SE
LECTURER   Zhenyu Yang
TIME   Fall Semester 2006
LOCATION    A208

Some previous exam papers: 

A summary of the whole course: 6-slide per page

Objective  

Textbook

K. Sam Shanmugan and BArthur M. reipohl: "Random Signals - Detection, Estimation and Data Analysis ", John Wiley Sons, Inc., 1988.

SCHEDULES     (A Summary, see the self-study oppotunity)

MM.1  14.09.2006        kl. 12.30- 14:00

Contents:  Response of linear systems to random inputs

(a).  Review of what we learned in Stochastic Processes (Sem6)
(b). Response of continuous-time LTI systems
(c).  Response of discrete-time LTI systems


MM.2  21.09.2006        kl. 12.30- 14:00

Contents:  Dsicerete linear stochastic models

(a).  Autoregressive (AR) processes
(b). Moving Average (MA) processes 
(c).  Autoregressive Moving Average (ARMA) processes 


MM.3  28.09.2006        kl. 12.30- 14:00

Contents:  Detection of known signals (part one)

(a).  Hypothesis testing
(b). Decision rules
(c). Binary detection


MM.4  19.10.2005        kl. 12.30- 14:00

Contents:  Detection of known signals (part two)

(a).  Binary detection of discrete-time signals
(b). Binary detection of continuous-time signals
(c). M-ary detection
  • Reading Material:       Page 352-361, 366-370 of  the textbook 
  • Lecture slides:           See here     a print-oriented PDF file (6 slides per page)
  • Exercise:           see here    Solution


MM.5  26.10.2006        kl. 12.30- 14:00

Contents:  Minimum Mean Squared Error Estimation - Wiener filters (part one)  

(a).  Explain MM4 exercise
(b). Linear minimum mean squared error estimators
n
(c).Minimum mean squared error estimators
n
  • Reading Material:       Page 377-397 of  the textbook 
  • Lecture slides:                 See here     a print-oriented PDF file (6 slides per page)
  • Exercise:           see here    Solution


MM.6  02.11.2006        kl. 12.30- 14:00

Contents:  Discrete-time Wiener filters (part two)

(a).   Explain MM5 exercise
(b).  Noncausal Wiener filters
(c). Causal Wiener filters
  • Reading Material:       Page  406-419 of  the textbook 
  • Lecture slides:           See here     a print-oriented PDF file (6 slides per page)  
  • Exercise:          see here    Solution


MM.7  09.11.2005        kl. 12.30- 14:00

Contents:  Kalman filters (part one)

(a).  
Introduction
(b). 
An Intuitive Description of Kalman filter
(c).
7.3 Formal Description of Scale Kalman Filter


MM.8  16.11.2006        kl. 12.30- 14:00

Contents:  Kalman filters (part two)

(a).   Vector Kalman filter
(b).  explanation of exercises
(c).  
  • Reading Material:       Page  of  the textbook 
  • Lecture slides:           See here  
  • Exercise:       see here      Solution


MM.9  23.11.2006        kl. 12.30- 14:00

Contents:  Model-free and spectral estimation (part one)

(a).   Mean value estimation
(b).  autocorrelation function estimation
(c). Power spectral estimation
  • Reading Material:       Page  565-579 of  the textbook 
  • Lecture slides:           See here  
  • Exercise:         see here     Solution


MM.10  30.11. 2006        kl. 12.30- 14:00

Contents:  Model-free and spectral estimation (part two)

(a).   estimation of AR models
(b).  estimation of MA models
(c). estimation of ARMA models
  • Reading Material:       Page  584-605 of  the textbook 
  • Lecture slides:           See here   
  • Exercise:        see here   Solution
This page is maintained by Zhenyu Yang 
mailto: yang@cs.aue.auc.dk
Last modified 06.09.2006