ELEC ENG 2104 - Digital Signal Processing
North Terrace Campus - Semester 2 - 2023
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General Course Information
Course Details
Course Code ELEC ENG 2104 Course Digital Signal Processing Coordinating Unit School of Electrical & Electronic Engineering Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Prerequisites MATHS 1012 Incompatible ELEC ENG 3033 Assumed Knowledge ELEC ENG 1100 or ELEC ENG 1101 Assessment Tests, quizzes, tutorial, assignments. 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
On successful completion of this course students will be able to:
1 Describe the characteristics and transformations of discrete time signals mathematically; 2 Apply techniques in time and transform domains to the analysis and design of discrete-time systems; 3 Estimate the spectra of deterministic and stochastic signals, and appropriately interpret the information contained therein; 4 Demonstrate the ability to manipulate signals using analytical techniques and write algorithms to implement discrete-time systems; 5 Design digital filters and apply them to real-world applications of signal and information processing;
The above course learning outcomes are aligned with the Engineers 最新糖心Vlog .
The course is designed to develop the following Elements of Competency: 1.1 1.2 1.3 1.5 1.6 2.1 2.2 2.3 3.1 3.2 3.3 3.4 3.5最新糖心Vlog Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
最新糖心Vlog 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.
1-5 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.
1-5 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.
1-5 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 material will be provided on MyUni.Recommended Resources
Useful textbooks for digital signal processing:- Holton, Thomas, Digital Signal Processing: Principles and Application, Cambridge 最新糖心Vlog Press; 1st edition (18 February 2021)
- 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 Learning
This course uses MyUni exclusively for providing electronic resources, such as course 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: -
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 54 contact hours in the course. Students are expected to spend approximately 100 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: 4 hours each week for 12 weeks (48 hours)
Post-class self-study: 3 hours per week for 12 weeks (36 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 2 per week 1-5 Tutorials 1 per fortnight 1-5
Specific Course Requirements
There are no specific course requirements. -
Assessment
The 最新糖心Vlog'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
Assessment Task Weighting (%) Individual/ Group Formative/ Summative Due (week) Hurdle criteria Learning outcomes Active participation in workshops and tutorials 5 Individual Formative 1-12 1-5 Tests, open book, 45 minutes 20 Individual Summative 5,11 1-5 Assignments, mix of written solutions, Matlab code and outputs 30 Individual Both 4,8,12 1-5 Exam, open book, 2 hours 45 Individual Summative 1-5 Total 100
This assessment scheme fully complies with the 最新糖心Vlog's Assessment for Coursework Programs Policy.Assessment Related Requirements
Assessment Detail
There are four components in this course's assessment.- Active participation in workshops and tutorials: students will receive a mark for engagement in discussions and attempts on in-class exercises in each session.
- 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.
- 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.
- Exam: an open book of 2 hours duration will be scheduled at the 最新糖心Vlog'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 最新糖心Vlog'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 .
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Student Feedback
The 最新糖心Vlog 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 最新糖心Vlog 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
- 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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