Outline

Overview

This unit of study introduces the fundamental digital concepts upon which the design and operation of modern digital computers are based. A prime aim of the unit is to develop a professional view of, and a capacity for inquiry into, the field of computing. Topics covered include: data representation, basic computer organisation, the CPU, elementary gates and logic, machine language, assembly language and high level programming constructs.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	HSC Mathematics extension 1 or 2
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	David Boland, david.boland@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam	40%	Formal exam period	2 hours
Outcomes assessed:
Participation	Lab Completion Lab completion (group)	10%	Multiple weeks	3 hours
Outcomes assessed:
Assignment	Laboratory report	5%	Multiple weeks	n/a
Outcomes assessed:
Tutorial quiz	In-class Quiz Various	20%	Week 07 Due date: 16 Oct 2020 at 12:00	50 mins
Outcomes assessed:
Presentation	Project demonstration Project demonstration	18%	Week 12	n/a
Outcomes assessed:
Assignment	Project report Project Report	7%	Week 12	n/a
Outcomes assessed:
= hurdle task ? = group assignment ? = Type C final exam ?

Assessment summary

Lab Completion: Demonstrate all group members complete and understand work.
Midterm exam: Multiple choice question exam.
Laboratory report: Solve a problem requiring the use of hardware and embedded systems and then write a professional report describing the session (1 report during weeks 2 - 6).
Project report: Report describing how the project was implemented.
Project demonstration: Demonstrate the result of the project to the rest of the class.
Final exam: End of semester exam.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Course organization and computer system overview	Lecture and tutorial (4 hr)
Week 02	Encoding information in binary	Lecture and tutorial (4 hr)
Week 02	Arduino Programming	Computer laboratory (3 hr)
Week 03	Computer memory	Lecture and tutorial (4 hr)
Week 03	Lights and Buzzer	Computer laboratory (3 hr)
Week 04	Boolean algebra and combinational logic	Lecture and tutorial (4 hr)
Week 04	Temperature sensor and RGB LED	Computer laboratory (3 hr)
Week 05	Sequential circuit design	Lecture and tutorial (4 hr)
Week 05	Accelerometer and Servos	Computer laboratory (3 hr)
Week 06	Flexible resistor and DC motor	Computer laboratory (3 hr)
Week 07	AVR architecture	Lecture and tutorial (4 hr)
Week 07	Bot Assembly	Computer laboratory (3 hr)
Week 08	Instruction set architecture (AVR)	Lecture and tutorial (4 hr)
Week 08	Robot Vision, Communication	Computer laboratory (3 hr)
Week 09	Assembly programs	Lecture and tutorial (4 hr)
Week 09	Project	Computer laboratory (3 hr)
Week 10	Addressing modes	Lecture and tutorial (4 hr)
Week 10	Project	Computer laboratory (3 hr)
Week 11	High level programming constructs	Lecture and tutorial (4 hr)
Week 11	Project	Computer laboratory (3 hr)
Week 12	Subroutines and exam review	Lecture and tutorial (4 hr)
Week 12	Project Demo	Computer laboratory (3 hr)

Attendance and class requirements

You must participate in the lab (can be done online)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. write reports to present design specific information and results concisely and accurately
LO2. engage in team-based design, drawing on the knowledge, skills and creative talent of all members to deliver a solution to a particular engineering problem
LO3. appreciate the professional practice, standards and responsibilities in working with hardware and software to the limit afforded by lab sessions and exercises
LO4. apply concept, principles and techniques to configure a basic system
LO5. scope, build and test an engineering artefact
LO6. demonstrate proficiency in applying computer engineering knowledge in the design, construction and testing of commensurate solutions for specific engineering problems
LO7. demonstrate understanding of the concepts and principles of computer architecture, digital logic design and microprocessor assembly language
LO8. demonstrate understanding of the concepts, principles and relationship for computers, the internet and clients and servers
LO9. demonstrate fundamental knowledge of computer engineering issues.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Removed small assessments (unpopular) Added lab completion (boost general understanding for all students) Added online only option Changed project requirements to be more clear & challenging

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ELEC1601: Introduction to Computer Systems

Semester 2, 2021 [Normal day] - Remote

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links