ELEC ENG 7015 - Adaptive Signal Processing
North Terrace Campus - Semester 1 - 2014
General Course Information
Course Code ELEC ENG 7015 Course Adaptive Signal Processing Coordinating Unit School of Electrical & Electronic Engineering Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Assumed Knowledge Linear systems (discrete & continuous), linear algebra (matrices), probability theory, fourier & Z transforms & MATLAB Course Description Introductory and Preliminary material - Introduction to the concepts, key issues and motivating examples for adaptive filters; Discrete time linear systems and filters; Random variables and random processes, covariance matrices; Z transforms of stationary random processes. Optimum Linear Systems - Error surfaces and minimum mean square error; Optimum discrete time Wiener filter; Principle of orthogonality and canonical forms; Constrained optimisation; Method of steepest descent - convergence issues; Stochastic gradient descent LMS - convergence in the mean and misadjustment Case study. Least squares and recursive least squares. Linear Prediction - Forward and backward linear prediction; Levinson Durbin; Lattice filters.
Course Coordinator: Mr Matthew TrinkleCourse Coordinator and Lecturer: Mr Matthew Trinkle
Phone: 8313 4708
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesAfter completion of this course, students will be able to:
1. Use basic probability theory to model random signals in terms of Random Processes.
2. Use covariance matricis to describe the second order statics of Random Processes.
3. Derive the power spectrum of random signals.
4. Understand and derive the Wiener filter for signals with known second order statistics.
5. Formulate the Wiener filter as a constrained optimisation problem.
6. Use and understand the LMS algorithm for iteratively estimating the Wiener filter weights.
7. Determine suitable LMS step size to trade off convergence time and misadjustment.
8. Derive and apply the RLS algorithm for iteratively estimating the Wiener filter weights.
9. Be familiar with the prediction filter formulation and applications
10. Solve the Wiener filter weights for the prediction filter using the Levinson-Durbin algorithm
11. Be familiar with the Lattice filter implementation of the prediction filter.
12. Derive the Lattice filter architecture from the Levinson-Durbin algorithm
13. Apply a modified LMS algorithm to the lattice structure to improve convergence times.
14. Implement and understand basic neural networks involving multi-layer feed-forward networks.
15. Use Matlab to implement the Wiener filter, Least Squares, LMS and RLS algorithms.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-14 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 15 A proficiency in the appropriate use of contemporary technologies. 15 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 6,8,13
Required ResourcesA set of course notes, practice problems and other supporting materials will be available for downloading from the course web site.
Recommended ResourcesMain References
A. Poularikas, Z. Ramadan, Adaptive Filtering Primer with MATLAB®
S. Haykin Adaptive Filter Theory
C.W. Therrien Discrete Random Signals and Statistical Signal Processing
W.B. Davenport and W.L. Root An Introduction to the Theory of Random Signals and Noise
B Widrow and S.D. Stearns Adaptive Signal Processing
S.T. Alexander Adaptive Signal Processing - Theory and Applications
V Solo and X Kong Adaptive Signal Processing Algorithms
R.A. Monzingo and T.W. Miller Introduction to Adaptive Arrays
F Hsu and A.A. Giordano Least Squares Signal Processing
S.J. Orfanidis Optimum Signal Processing
G.C. Goodwin and K.S. Sim Adaptive Filtering, Prediction and Control
M.L. Honig and D.G. Messerschmidtt Advanced Signal Processing
B.D.O. Anderson and J.B. Moore Optimal Filtering
C.F.N. Cowan and P.M. Grant Adaptive Filters
Y. Bar Shalom Tracking and Data Association
P A Regalia Adaptive IIR Filtering in Signal Processing and Control
L.H. Sibul (Ed) Adaptive Signal Processing
M. G. Bellanger Adaptive Digital Filters and Signal Analysis Marcel Dekker 1987
Online LearningExtensive use will be made of the MyUni web site for this course:
Course notes, tutorial problems and solutions, laboratory exercises and practice problems will all be available for downloading from the web site. Where the lecture theatre facilities permit, audio or video recordings of lectures will also be available for downloading.There will be two on-line quizzes to be completed.
Learning & Teaching Activities
Learning & Teaching ModesThis course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. Practicals are used to provide hands-on experience for students to reinforce the theoretical concepts encountered in lectures. Continuous assessment activities provide the formative assessment opportunities for students to gauge their progress and understanding.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity No. Contact Hours Workload Hours Lectures 24 24 48 Tutorials 6 6 12 Homeworks 2 12 In class tests 2 2 12 Total 32 84
Learning Activities Summary
Activity Session Week Topic Lecture 1-2 1 Random Processes 3-4 2 Covariances of Random Processes 5-6 3 Z transform of Random Processes 7-8 4 Wiener Filter in time and frequency domain 9-10 5 Constrained Optimum Filtering 11-12 6 LMS Adaptive Algorithm 13-14 7 Least Squares Estimation 15-16 8 Prediction Filters 17-18 9 Lattice Filters 19-20 10 Neural Networks 21-22 11 Neural Networks 23-24 12 Revision Tutorial 1 2 Random Processes 2 4 Z Transform and Covariances of Random Processes 3 6 Wiener and Constrained Optimum Filtering 4 8 LMS Algorithm 5 10 Prediction Filters 6 12 Neural Networks and Revision In class quiz 1 3 Random Processes 2 9 Optimium and Adaptive Filters Homework 1 6 Random Processes and Optimum Wiener Filtering 2 12 Adaptive Algorithms and Prediction Filtering
Specific Course RequirementsNot applicable
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Activity Type Weighting Due Date Learning Objective Addressed In class quizzes* Summative 20% Weeks 5,9 All Homeworks Formative 20% Weeks 6,12 All Exam Summative - hurdle 60% End of semester All
Assessment Related RequirementsThe examination is a hurdle requirement. It is necessary to achieve at least 40% in the exam. If this is not achieved, the total course mark will be limited to a maximum of 49.
A hurdle requirement is defined by the University's Assessment for Coursework Programs policy as "...an assessment task mandating a minimum level of performance as a condition of passing the course.If a student fails to meet a hurdle requirement (normally no less than 40%),and is assigned a total mark for the course in the range of 45-49, then the student is entitled to an offer of additional assessment of some type. The type of assessment is to be decided by the School Assessment Review Committee when determining final results. The student’s final total mark will be entered at no more than 49% and the offer of an additional assessment will be specified eg. US01. Once the additional assessment has been completed, this mark will be included in the calculation of the total mark for the course and the better of the two results will apply. Note however that the maximum final result for a course in which a student has sat an additional assessment will be a “50 Pass” .
If a student is unable to meet a hurdle requirement related to an assessment piece (may be throughout semester or at semester’s end) due to medical or compassionate circumstances beyond their control, then the student is entitled to an offer of replacement assessment of some type. An interim result of RP will be entered for the student, and the student will be notified of the offer of a replacement assessment. Once the replacement assessment has been completed, the result of that assessment will be included in the calculation of the total mark for the course.
Assessment DetailDetails of individual assessment tasks will be provided during the semester.
SubmissionAll written submissions to formative assessment activities are to be submitted to designated boxes within the School of Electrical & Electronic Engineering by 3:00pm on the specified dated and must be accompanied by a signed cover sheet. Copies of blank cover sheets are available from the School office in Ingkarni Wardli 3.26.No late submissions will be accepted. All formative assessments will have a two week turn-around time for provision of feedback to students.
Full details can be found at the School policies website:
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- International Student Support
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
- YouX Student Care - Advocacy, confidential counselling, welfare support and advice
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.
The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.