ELEC ENG 7079 - Principles of Signal Processing
North Terrace Campus - Semester 2 - 2023
General Course Information
Course Code ELEC ENG 7079 Course Principles of Signal Processing Coordinating Unit School of Electrical & Electronic Engineering Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Incompatible ELEC ENG 3033, ELEC ENG 2104 Assumed Knowledge Undergraduate courses in electronic engineering and linear systems Course Description This course provides a foundation of digital signal processing techniques for further studies in electronic engineering. Topics include: Discrete time (DT) signals; DT Linear Shift Invariant (LSI) systems; Fourier transforms; Fourier analysis for discrete time systems: DT Fourier series, DT Fourier transform, discrete Fourier transform, spectral leakage, frequency resolution, non-parametric spectral estimation. Digital filtering principles; Digital filter design; Statistical signal processing fundamentals; Practical signal processing skills in MATLAB; Applications example of digital signal processing: digital radio techniques.
Course Coordinator: Associate Professor Brian Ng(Updated) Course coordinator and lecturer: Associate Professor Brian Ng
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1 Describe the process of sampling mathematically and articulate its benefits and limitations in modern engineering applications 2 Use and manipulate representations of discrete-time signals in both the time and frequency domains 3 Analyse and design discrete-time, linear shift-invariant (LSI) systems to manipulate discrete-time signals 4 Apply various techniques underpinned by z- and Fourier transforms for signal processing applications 5 Choose the most appropriate domain to perform processing, and switch fluidly between different domains 6 Describe the characteristics of stochastic signals and processes using statistical measures, and apply them to model real-world signals 7 Perform basic statistical spectrum analysis and apply them to the analysis of synthetic and real-world data in MATLAB 8 Write MATLAB code to perform signal processing functions in a team environment, to produce a high level product for real-world use
The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
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 2.4 3.1 3.2 3.3 3.4 3.5 3.6
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)
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.
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.
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.
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.
Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
Required ResourcesAll required material will be provided on MyUni.
Recommended ResourcesUseful textbooks for digital signal processing:
- Prandoni, Paolo and Vetterli, Martin, Signal Processing For Communications, EPFL Press, 2008.
- Proakis, John G. & Manolakis, Dimitris G. Digital Signal Processing, 4th edition, Prentice-Hall International, 2006, ISBN: 978-0-131-87374-2
Online LearningThis course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, strongly recommended that the students make intensive use of these resources for this course.
Link to MyUni login page: https://myuni.adelaide.edu.au
Learning & Teaching Activities
Learning & Teaching ModesThis course uses in-person workshops and tutorials to facilitate learning. Classes will take the form of two 2-hour workshops each week, combining short lecture-style presentation of materials with in-class exercises designed to build knowledge.
Material will be provided on MyUni. Communications will take place through announcements, interactive discussions on Piazza and emails. During the semester, questions will be answered within 1 business day.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.There will 30 contact hours in the course. Students are expected to spend approximately 120 hours of private study and to prepare for assessments. A guide is provided as follows.
Pre-class preparations: 1 hour each week for 12 weeks (12 hours)
Weekly workshops: 2 hours each week for 12 weeks (24 hours)
Post-class self-study: 5 hours per week for 12 weeks (60 hours)
Tutorials: 1 hour each fortnight (6 hours)
Assessment: preparation for 3 major assignments, 2 tests, 1 exam (50 hours)
Learning Activities Summary
Teaching and Learning Activities Frequency Course Learning Outcomes Workshops 1 per week 1-8 Tutorials 1 per fortinight 1-8
Specific Course RequirementsThere are no specific course requirements.
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 Task Weighting (%) Individual/ Group Formative/ Summative Due (week) Hurdle criteria Learning outcomes Active participation in workshops and tutorials 5 Individual Formative 1-12 1-8 Tests, open book, 45 minutes 20 Individual Summative 5,11 1-6 Assignments, mix of written solutions, Matlab code and outputs 30 Individual Both 6,9,12 1-8 Exam, open book, 2 hours 45 Individual Summative 1-6 Total 100
Assessment DetailThere 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 Matlab code fragments with numerical and graphical outputs. The question sheet will include marking rubric applicable to that assignment.
4. Exam: an open book of 2 hours duration will be scheduled at the University's standard examination period and venue.
Submission1. Active participation in workshops and tutorials: no submissions.
2. Tests: in person test, submission at the end of tests, scheduled for weeks 5 and 11.
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. Exam: in person exam, submission at the conclusion of exam.
Feedback on assignments and tests will be provided within 2 weeks of submission.
Extensions for assessment tasks can be granted. For details, consult the University's Assessment for Coursework Programs policy.
Late submissions on assignments will be penalised at a rate of 20% per day, unless you have applied for and received an extension as described in the Assessment for Coursework Programs policy.
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.Changes in response to SELT feedback are listed on MyUni.
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