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 |
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Course description |
This course introduces the fundamental statistical tools that are required to analyse and described 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 M.Sc. course.
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Entry Requirements
Pre-requisites |
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Co-requisites |
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Prohibited Combinations |
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Other requirements |
None
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Additional Costs |
None |
Course Delivery Information
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Delivery period: 2010/11 Semester 1, Available to all students (SV1)
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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 |
First class information not currently available |
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 |
Please see Visiting Student Prospectus website for Visiting Student Assessment information |
Special Arrangements
Not entered |
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 |
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copyright 2010 The University of Edinburgh -
1 September 2010 6:25 am
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