ELEC5308: Intelligent Information Engineering Practice (2021 - Semester 2)
| Unit: | ELEC5308: Intelligent Information Engineering Practice (6 CP) |
| Mode: | Normal-Day |
| On Offer: | Yes |
| Level: | Postgraduate |
| Faculty/School: | School of Electrical & Information Engineering |
| Unit Coordinator/s: |
Dr Ouyang, Wanli
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| Session options: | Semester 2 |
| Versions for this Unit: |
| Campus: | Camperdown/Darlington |
| Pre-Requisites: | None. |
| Brief Handbook Description: | Intelligent Information Engineering is a new major of the electrical engineering and software engineering streams. This unit aims at the practising ability of students on utilizing intelligent information engineering techniques for solving practical problems in the latest applications of AI, e.g., Autopilot. Students will get programming skills for many tasks related to automatic driving, including lane detection, traffic sign detection, pedestrian detection, and path planning. Lane detection, traffic sign detection, pedestrian detection, and path planning are information processing techniques that will help students to learn how to use the Video Intelligence and Signal Understanding approaches for many practical problems. All students will be involved in designing mini-projects and a large project. The new unit is project-oriented. Students will run their programs on simulated environment. The course will be taught through lectures mainly on how to accomplish the goal for the mini-project and the final project. A specific lab design will be provided to students for hands-on design. Communication skills will be tested through several project presentations. Some teaching will be provided by intelligent information engineers working in the industry. |
| Assumed Knowledge: | Students must have a good understanding of Linear algebra and basic mathematics, Basic Programming skills in C, Python or Matlab. It is preferred that students have done ELEC5304/ELEC5306/ELEC5307 prior to this unit. |
| Timetable: | ELEC5308 Timetable | ||||||||||||||||||||
| Time Commitment: |
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| T&L Activities: | Independent Study: Students are expected to undertake the prescribed reading and work on homework exercises and assignments. Laboratory: Students will get programming skills for many tasks related to automatic driving, including lane detection, traffic sign detection, pedestrian detection, and path planning. Mini-cars will be available to students to run their cars automatically using the AI algorithms. |
Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.
Unassigned Outcomes
1. To be able to report results in a professional manner
2. To be able to use appropriate software platforms and programming tools for a given machine learning and control task
3. To be able to use the existing control and machine learning packages.
4. To be able to apply the control, machine learning, and computer vision techniques to solve real world applications.
5. To be able to understand the fundamental theory of machine learning, control, image processing, and computer vision algorithms.
| Assessment Methods: |
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| Assessment Description: |
Mini Project 1. Total weight: 15% (oral presentation+demo+report) Mini Project 2. Total weight: 25% (oral presentation+demo+report) Final Project. Total weight: 60% (oral presentation+demo+report) For group projects, both individual evaluations for reports and group evaluation for presentation and reports are used. Oral presentation, demo, and Q&A for the presentation will be used for checking academic integrity. [1]Text-based similarity detecting software (Turnitin) will be used for all text-based written assignments. [2]Late submission for lab reports: 1) There is no penalty for submissions until 11:59pm of the due day; 2) For submissions that are late than 11:59pm of the due day, 5% penalty will be applied for each day. Submissions that are late for two weeks will be given ZERO marks. |
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| Assessment Feedback: | Students will be given individual feedback on their participation during the tutor meetings (tutorials) with each group each week. Tutors will follow up with individuals if there is a problem identified. Students will also receive feedback on their demonstrations, presentations, individual and group reports. Feedback from clients through interactions, demonstrations and presentations should be useful for the students. |
| Prescribed Text/s: |
Note: Students are expected to have a personal copy of all books listed.
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| Recommended Reference/s: |
Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
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Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar https://web.timetable.usyd.edu.au/calendar.jsp
| Week | Description |
| Week 1 | Lecture: Introduction |
| Lab: Introduction to the system | |
| Week 2 | Lecture: Basics of image acquisition and control |
| Lab: Basics of image acquisition and mini-car control | |
| Week 3 | Lecture: Lane detection |
| Lab: Lane detection | |
| Week 4 | Lecture: Control using lane detection |
| Lab: Control using lane detection | |
| Week 5 | Lecture: Oral presentation and demo for project 1 |
| Lab: Oral presentation and demo for project 1 | |
| Assessment Due: Oral presentation and demo for project 1 | |
| Week 6 | Lecture: Traffic sign recognition |
| Lab: Traffic sign recognition | |
| Week 7 | Lecture: Obstacle recognition and control for avoiding obstacles |
| Lab: Obstacle recognition and control for avoiding obstacles | |
| Week 8 | Lecture: Introduction to object detection and pedestrian detection |
| Lab: Pedestrian detection | |
| Week 9 | Lecture: Pedestrian detection algorithm design |
| Lab: Project 2 lab | |
| Assessment Due: Oral presentation and demo for project 2 | |
| Week 10 | Lecture: Presentation and demo for project 2 |
| Lab: Pedestrian detection algorithm design and avoid pedestrians. | |
| Assessment Due: Mini Project 1 | |
| Week 11 | Lecture: Lidar data aquisition and usage |
| Lab: Lidar data aquisition and usage | |
| Week 12 | Lecture: Scene understanding and control using Lidar |
| Lab: Final Project lab | |
| Assessment Due: Mini Project 2 | |
| Week 13 | Lecture: Presentation and demo for final project |
| Lab: Presentation and demo for final project | |
| Assessment Due: Oral presentation for final project | |
| Assessment Due: Final large project (group) | |
| Assessment Due: Final large project (individual) |
Course Relations
The following is a list of courses which have added this Unit to their structure.
Course Goals
This unit contributes to the achievement of the following course goals:
| Attribute | Practiced | Assessed |
| Unit has not been assigned any attributes yet. | ||
These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.
