ELEC ENG 7079 - Principles of Signal Processing

North Terrace Campus - Semester 2 - 2024

Discrete time (DT) signals and systems are commonplace in engineering. It is essential for practicing electronic engineers to have a sound foundation in DT concepts and fluency in linear processing of DT signals. It is a core course in the Electronic specialisation, and an elective in the Electrical specialisation of the Master of Engineering program. Students will learn the fundamental principles and techniques of DT signals and systems, apply linear processing to solve practical problems in electronic, computer and information engineering. These skills will be developed concurrently in both time and transform (z and Fourier) Fourier domains. The course is delivered on campus, with an in-person component centred around workshops, and supported by tutorials. Learning activities include group discussions and individual written and Matlab exercises. Assessment activities include workshop and tutorial participation, individual assignments, tests and a final exam. The set of assessments is designed to allow students to demonstrate their knowledge in DSP and skills in solving problems with DSP techniques. Upon completion of the course, students will be able to apply linear digital processing methods to engineering analysis and problem solving. It will allow students to work or pursue further studies in areas including, but not limited to: control, telecommunications, biomedical engineering and machine learning.

  • General Course Information
    Course Details
    Course Code ELEC ENG 7079
    Course Principles of Signal Processing
    Coordinating Unit Electrical and 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 Discrete time (DT) signals and systems are commonplace in engineering. It is essential for practicing electronic engineers to have a sound foundation in DT concepts and fluency in linear processing of DT signals. It is a core course in the Electronic specialisation, and an elective in the Electrical specialisation of the Master of Engineering program.

    Students will learn the fundamental principles and techniques of DT signals and systems, apply linear processing to solve practical problems in electronic, computer and information engineering. These skills will be developed concurrently in both time and transform (z and Fourier) Fourier domains.

    The course is delivered on campus, with an in-person component centred around workshops, and supported by tutorials. Learning activities include group discussions and individual written and Matlab exercises.

    Assessment activities include workshop and tutorial participation, individual assignments, tests and a final exam. The set of assessments is designed to allow students to demonstrate their knowledge in DSP and skills in solving problems with DSP techniques.

    Upon completion of the course, students will be able to apply linear digital processing methods to engineering analysis and problem solving. It will allow students to work or pursue further studies in areas including, but not limited to: control, telecommunications, biomedical engineering and machine learning.
    Course Staff

    Course Coordinator: Associate Professor Brian Ng

    (Updated) Course coordinator and lecturer: Associate Professor Brian Ng
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On 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 Entry to Practice Competency Standard for the Professional Engineer. The course develops the following EA Elements of Competency to levels of introductory (A), intermediate (B), advanced (C):  
     
    1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.6
    B C C A B A B C B A B B B B B A
    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.

    1-8

    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.

    1-8

    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.

    8

    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.

    8

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    7-8
  • Learning Resources
    Required Resources
    All required material will be provided on MyUni.
    Recommended Resources
    Useful 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
    Other resources will be recommended on MyUni as they arise throughout the semester.

    Online Learning
    This 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 Modes
    This 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.
    Workload

    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 Requirements
    There are no specific course requirements.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    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 Min 40% 1-6
    Total 100
    This assessment scheme fully complies with the University's Assessment for Coursework Programs Policy.
    Assessment Related Requirements
    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.

    In this course the examination has a hurdle requirement. It is necessary to achieve at least 40% in the exam to successfully complete the course. If the exam hurdle requirement is not achieved, the total course mark will be limited to a maximum of 49.

    If a student fails to meet a hurdle requirement 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 e.g. 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 (maybe 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 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 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.
    Submission
    1. 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.
    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 Access Adelaide.

  • 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.

    Changes in response to SELT feedback are listed on MyUni.
  • Student Support
  • Policies & Guidelines
  • 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’s disciplinary procedures.

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