Postgraduate Course: Statistical Signal Processing (PGEE11027)
Course Outline
| School |
School of Engineering |
College |
College of Science and Engineering |
| Course type |
Standard |
Availability |
Available to all students |
| Credit level (Normal year taken) |
SCQF Level 11 (Postgraduate) |
Credits |
20 |
| Home subject area |
Postgrad (School of Engineering) |
Other subject area |
None |
| Course website |
None
|
Taught in Gaelic? |
No |
| Course description |
This course introduces the fundamental statistical tools that are required to analyse and describe advanced signal processing algorithms. It provides a unified mathematical framework in which to describe random events and signals, and how to describe key characteristics of random processes. It investigates the affect of systems and transformations on time-series, and how they can be used to help design powerful signal processing algorithms. Finally, the course deals with the notion of representing signals using parametric models; it covers the broad topic of statistical estimation theory, which is required for determining optimal model parameters. Emphasis is placed on relating these concepts to state-of-the art applications and signals. This module provides the fundamental knowledge required for the advanced signal, image, and communication courses in the MSc. course. |
Entry Requirements
| Pre-requisites |
|
Co-requisites |
|
| Prohibited Combinations |
|
Other requirements |
None
|
| Additional Costs |
None |
Information for Visiting Students
| Pre-requisites |
None |
| Displayed in Visiting Students Prospectus? |
Yes |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 1, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | | 1-11 | | | | | 11:10 - 13:00 | | King's Buildings | Lecture | | 1-11 | 10:00 - 12:00 | | | | | | King's Buildings | Tutorial | | 1-11 | | | 09:00 - 10:50 | | |
| First Class |
Week 1, Monday, 10:00 - 12:00, Zone: King's Buildings. Classroom 6, Hudson Beare Building |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S1 (December) | Statistical Signal Processing | 2:00 | 16 sides | |
Summary of Intended Learning Outcomes
| At the end of this module, a student should be able to: define, understand, and manipulate scalar and multiple random variables using the theory of probability; explain the notion of characterising random variables using moments, and be able to manipulate them; explain, describe, and understand the notion of a random process and statistical time series; characterise random processes in terms of its statistical properties, including the notion of stationarity and ergodiciy; define, describe, and understand the notion of the power spectral density of stationary random processes; analyse and manipulate power spectral densities; analyse in both time and frequency the affect of transformations and linear systems on random processes, both in terms of the density functions and statistical moments; explain the notion of parametric signal models, and describe common regression-based signal models in terms of its statistical characteristics and in terms of its affect on random signals; discuss the principles of estimation theory, define basic properties of estimators, and be able to analyse and calculate the properties of a given estimator; apply least squares, maximum-likelihood, and Bayesian estimators to model based signal processing problems. |
Assessment Information
| 100% open-book formal written examination |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords |
Probability, scalar and multiple random variables, stochastic processes, power spectral densities, l |
Contacts
| Course organiser |
Dr James Hopgood
Tel: (0131 6)50 5571
Email: James.Hopgood@ed.ac.uk |
Course secretary |
Mrs Kim Orsi
Tel: (0131 6)50 5687
Email: Kim.Orsi@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
31 January 2011 8:05 am
|
University Homepage