Units
Signal Processing and Filtering
Unit code: ENB448
Contact hours: 4 per week
Credit points: 12
Information about fees and unit costs
This unit gives a comprehensive introduction to the representation and processing of signals distorted or corrupted by noise, and the systems needed to process them. Techniques to enhance, detect , classify and estimate useful information from the signals in the presence of noise and other distortions will be presented. The methods presented will be tested on real signals drawn from different engineering applications, such speech signals, image signals, biomedical signals and signals in communications systems.
Availability
| Semester | Available |
|---|---|
| 2013 Semester 2 | Yes |
Sample subject outline - Semester 2 2013
Note: Subject outlines often change before the semester begins. Below is a sample outline.
Rationale
Random signals are fundamental to many disciplines. Speech signals, image signals, biomedical signals and signals in communications systems can be regarded as random and are usually corrupted by noise during acquisition and transmission. Signals Processing engineers have to process these random signals to enhance, detect , classify and estimate useful information from the signals in the presence of noise and other distortions. This unit is placed in the final year of study, after introductory units in telecommunications, signals and systems, and mathematics are covered.
Aims
The aim of this unit is to provide you with a comprehensive introduction to the processing of random signals to enhance, detect, classify and estimate useful information from the signals.
Objectives
On the completion of this unit, you will be able to:
- Demonstrate knowledge of the theory behind selected key applications of the processing of random signals;
- Apply the principles of filtering, detection, classification and estimation to analyse random signals;
- Demonstrate the ability to apply the concepts learnt to process real world signals.
Content
1. Introduction to random variables
2. Statistics of multiple random variables
3. Filtering of signals including matched filtering, Wiener filters, Kalman filters and other adaptive filtering techniques and applications.
4. Detection of signals by feature extraction including Fourier, wavelet, Discrete Cosine Transform and other features.
5. Classification of signals using various approaches including modelling.
6. Estimation of useful information in signals.
7. Example applications to various real world signals
Approaches to Teaching and Learning
Teaching Mode: 4 hours per week
Lectures: 2 hrs per week
Tutorial: 2 hrs per week
Lectures will include application to several real world signals including, speech, image, telecommunication and other signals. Tutorial sessions are designed to integrate theory from lectures with practice. Tutorial tasks and assessments are problem based giving you the opportunity to assess your knowledge using worked solutions using Matlab software tools.
Assessment
Assessment in the unit consists of a number of short problem solving tasks, one group assignment and a final exam.
The problem solving tasks will involve Matlab-based exercises to enable students to acquire deeper understanding of various signal processing concepts. The assignment will illustrate the application of these concepts to real world signal processing problems and develop problem solving ability. The exam will be aimed at evaluating conceptual knowledge and application, as well as problem solving ability, for the topics presented throughout the semester.Assessment: A number of short problem solving tasks and a larger problem solving task.
Mode of feedback: Assessment results and written feedback will provided on your task reports.
Assessment: Final Exam
Mode of feedback: oral feedback will be given on final exam scripts up on student's request.
Assessment name:
Short Problem Solving Tasks
Description:
A number of short problem solving tasks involving Matlab based exercises. Regular submission of short tasks, all submissions to be completed by week 10.
Relates to objectives:
1 and 2
Weight:
30%
Internal or external:
Internal
Group or individual:
Individual
Due date:
Week 10
Assessment name:
Larger Problem Solving Task
Description:
Larger problem solving tasks requiring group effort.
Relates to objectives:
3
Weight:
30%
Internal or external:
Internal
Group or individual:
Group
Due date:
Week 13
Assessment name:
Examination (written)
Description:
You will answer questions and problems related to key concepts in signal processing and filtering covered in this unit during the semester. The exam will be aimed at evaluating conceptual knowledge and application, as well as problem solving ability, for the topics presented throughout the semester.
Relates to objectives:
1, 2, and 3
Weight:
40%
Internal or external:
Internal
Group or individual:
Individual
Due date:
Exam Period
Academic Honesty
QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.
A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.
Resource materials
Recommended Text:
To be advised
Risk assessment statement
You will undertake lectures and tutorials in the traditional classrooms and lecture theatres. As such, there are no extraordinary workplace health and safety issues associated with these components of the unit.
Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of semester.
Last modified: 20-Jun-2012
