ELEC ENG 4112 - Signal Processing Applications
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code ELEC ENG 4112 Course Signal Processing Applications Coordinating Unit School of Electrical & Electronic Engineering Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange Y Prerequisites ELEC ENG 2104 Assumed Knowledge Linear systems (discrete & continuous), linear algebra, probability theory, Fourier & Z transforms & Matlab Course Description This course builds on fundamental concepts in signal processing and teaches a range of more advanced techniques. The course focuses on the practical applications across multiple disciplines in electrical and electronic engineering. This is an elective course and requires completed prior studies in linear systems, frequency analysis, linear algebra and calculus. Course syllabus: Random variables and random processes; Array signal processing - models, beamforming, high resolution methods for directions-of-arrival estimation; Multi-rate signal processing ? rate changers, aliasing and imaging, polyphase representation, efficient algorithms for multi-rate systems, applications of multi-rate signal processing, interpolated FIR filters, wavelet transforms; Optimal filtering - MMSE FIR filters, linear prediction, Durbin-Levinson algorithm, lattice filters. Adaptive filtering ? motivation, key applications including interference cancellation and adaptive equalisation, Least-Mean-Square (LMS) adaptive filter, Recursive Least-Squares (RLS).
Course Coordinator: Associate Professor Brian Ng
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesAt 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.
- Describe and formulate the general problem of sensor array signal processing, including signal models, and solve using approaches such as beamforming and subspace methods.
- Implement algorithms in Matlab and undertake computer-based experiments involving simulated and real data.
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 3.5
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.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Required ResourcesAll required resources are provided on MyUni.
Recommended ResourcesRecommended 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.com
Online LearningThis 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 ModesThis 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.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.There will be up to 30 contact hours throughout the course. Students are expected to spend approximately 120 hours of private study, preparing for tutorials, completing assignments and revising for tests.
Learning Activities SummaryThe following lists the learning topics for the semester.
Week 1 Revision from prior studies
Week 2 Spectrum estimation. Non-parametric & parametric
Week 3 Spectrum estimation. Super-resolution
Week 4 Adaptive filters - fomulation, linear prediction
Week 5 Adaptive filters - solutions
Week 6 Time-frequency analysis. STFT/spectrograms, distributions, Gabor transforms
Week 7 Multi-rate filter banks. Rate changers, Nyquist, polyphase notation
Week 8 Multi-rate filter banks. Applications: interpolated FIR, transmux, subband coding
Week 9 Wavelets. CWT, PR filter banks, DWT
Week 10 Wavelet applications: subbing coding, image compression, M-channels
Week 11 Array signal processing. Beamforming basics, Direction-of-Arrival estimation.
Week 12 Summary and future studies
Specific Course RequirementsNone
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.
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 DetailDetails to be provided on MyUni.
SubmissionActive participation in workshops and tutorials are assessed in session. Written assignments and computer exercises are submitted online via MyUni. Tests are 1-hour open-book exercises, conducted under examination conditions.
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.This course is offered for the first time in 2022.
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