ELEC ENG 7112 - Signal Processing Applications PG
North Terrace Campus - Semester 1 - 2023
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General Course Information
Course Details
Course Code ELEC ENG 7112 Course Signal Processing Applications PG Coordinating Unit School of Electrical & Electronic Engineering Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Prerequisites ELEC ENG 7079 Incompatible ELEC ENG 7015 Assumed Knowledge Linear systems (discrete & continuous), linear algebra, probability theory, Fourier & Z transforms & Matlab Course Staff
Course Coordinator: Associate Professor Brian Ng
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
At the end of this course, the student would be able to- Describe and explain the basic models that underpin digital signal processing methods including discrete time linear systems and signals, digital filters and random processes.
- Choose and apply suitlable technique(s) to estimate the spectrum from a signal's time series.
- Explain the concept of an optimal (MMSE) filter, design and implement optimal filters for the problem of linear prediction.
- Articulate the motivation for adaptive filtering, and produce practical solutions such as Wiener and LMS filters.
- Describe the concept of multi-rate signal processing, its practical significance, incorporating aspects such as decimation and interpolation, multi-rate filters, perfect reconstruction, wavelet signal representations.
- Implement algorithms in Matlab and undertake computer-based experiments involving simulated and real data.
The above course learning outcomes are aligned with the .
The course is designed to develop the following Elements of Competency: 1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 3.2 3.3 3.4 3.5University 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) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1-6 Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
2-6 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
1-6 Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
1-6 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
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Learning Resources
Required Resources
All required resources are provided on MyUni.Recommended Resources
Recommended textbooks:
Proakis, John G. and Manolakis, Dimitris G. Digital Signal Processing, 4th edition (2007) Pearson Prentice Hall. ISBN-13: 9780131873742
Boashash, Boualem Time-Frequency Signal Analysis and Processing, 2nd edition (2016) Elsevier. ISBN-13: 978-0-12-398499-9.(Full e-book access from University library)
Gomes, Jonas and Velho, Luiz From Fourier Analysis to Wavelets (2015) Springer. eBook ISBN-13: 978-3-319-22075-8. (Full e-book access from University library)
Smith, Steven W. The Scientist and Engineer’s Guide to Digital Signal Processing. http://www.dspguide.comOnline Learning
This course uses a variety of online resources to support learning, including:
- slides, demo documents, example code and tutorial questions
- assessment tasks, including past material and/or exemplars
The use of the online discussion boards is strongly encouraged for questions related to course content.
The course gradebook will be used to return continuous assessment marks. Students should check the gradebook regularly and confirm their marks have been correctly entered. -
Learning & Teaching Activities
Learning & Teaching Modes
This course uses face-to-face workshops and tutorials, supplemented by online materials, to achieve its learning objectives.
There are pre-assigned readings each week, which students are expected to complete; key concepts and techniques are emphasised with written notes. Workshops involve short, class-wide discussions on the assigned reading, followed by small-group work on a variety of problems. These are typically completed in Matlab. There is a small assessment component for active participation in tutorials.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
There will be up to 54 contact hours throughout the course. Students are expected to spend approximately 100 hours of private study, preparing for tutorials, completing assignments and revising for tests.Learning Activities Summary
Teaching and Learning Activities Frequency Course Learning Outcomes Workshops 2 per week 1-6 Tutorials 1 per fortnight 1-6
Specific Course Requirements
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Assessment
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 Summary
Type Weighting (%) Weeks Workshops and Tutorials 10 1-12 Computer exercises 20 3,5,8,10 Assignments 40 6, 12 Tests 30 7, 11 Assessment Detail
There are four components in this course's assessment.
1. Active participation in workshops and tutorials: students will receive a mark for engagement in discussions and attempts on in-class exercises in each session.
2. Tests: the tests will be conducted during class, open book with a duration of 45 minutes. The questions will be a mix of short answers and calculations. Past tests are provided on MyUni to help students prepare.
3. Assignments: each assignment will consist of a set of questions, requiring written answers with explanations as appropriate, as well as design exercises. The question sheet will include marking rubric applicable to that assignment.
4. Computer Exercises: each CE will consist of a set of Matlab programming exercises, designed to develop practical signal processing skills. A common marking rubric applies to all CEs in this course.Submission
1. Active participation in workshops and tutorials: no submissions.
2. Tests: in person test, submission at the end of tests.
3. Assignments: electronic submission via MyUni; detailed instructions to be provided with each assignment. Usually a single pdf file but can require accompanying files of Matlab code. Turnitin may be used to detect collusion or plagiarism.
4. Computer Exercises: electronic submission via MyUni. Students must submit full package of Matlab code for each CE.Course Grading
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 .
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Student Feedback
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.
This course is offered for the first time in 2022. -
Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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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
- 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
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Fraud Awareness
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鈥檚 disciplinary procedures.
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