Posted: May 21st, 2019
SIT105 THINKING TECHNOLOGY AND DESIGN APPLIED PROJECTSIT105 THINKING TECHNOLOGY AND DESIGN APPLIED PROJECT
SIT105 Thinking Technology and Design – T1, 2019
SIT105 THINKING
TECHNOLOGY AND DESIGN APPLIED PROJECT
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DUE: MAY 20TH AT 5PM, 2019
UNIT LEARNING OUTCOMES
Two ‘Unit Learning Outcome (ULO)’ of this unit are (ULO2) develop strategies using generic and IT specific techniques to explore algorithms and (ULO3) Create algorithms using the input-processing-output model, defining diagrams and pseudocode to demonstrate simple program design.
This assignment requires you to design and develop algorithms using pseudocode. The assignment will indicate whether students can partially attain the associated Unit Learning Outcomes.
INSTRUCTIONS
Read the entire assignment sheet, the rubric and answer all the following tasks below.
Place your name, ID and answers in your document. Please note that only MS Word (docx) may be submitted. The word count is 1500 words max (upper limit), so be concise and efficient!
Submit your assignment document on CloudDeakin applied project dropbox.
INTRODUCTION
Jetstar has chosen Boston Dynamics as the vendor to produce the UAV (Unmanned Aerial Vehicle) passenger plane discussed in the critical thinking task. The project is now being broken down into segments (divide and conquer) by the project manager. They have outlined that one major part of this project is to develop the algorithms (artificial intelligence) to operate the UAV and ensure it does what it is designed to do safely and successfully. So, your first goal as the software developer is to create two algorithm modules in a pseudocode format. At a later stage these algorithms will be implemented into the UAV allowing it to perform specific tasks safely and accurately.
APPLIED PROJECT: DESIGN THE UAV PASSENGER PLANE ALGORITHM!
The purpose of this applied project is to begin to develop the overall algorithm for the UAV, however, this will begin with developing functionality for only two main operations, one easier and one more difficult described below (you will need a variety of modules).
Your algorithm should have a Main()+END where all sub-modules will be launched from. TASK 1 – FUNCTIONALITY: FACIAL RECOGNITION (BEGINNER / INTERMEDIATE)
1. The first functionality is focused around automated boarding of passengers. So, the approach is to use a facial recognition algorithm to verify the identity of passengers – E.g. board_passengers(passenger_database).
2. Focus on the steps involved to check the passenger’s passport photo vs. the characteristics of what the person’s face looks like on camera (think of a typical Australian airport procedure). If they positively match, the passenger can board but if they are different they will not be permitted on the UAV – E.g. Facial_recognition(passenger, passport).
Hint: You can see what a typical Australian passport looks like (sourced from Government website): https://www.usi.gov.au/sites/usi/files/inline img/15/10/passport.jpg
Hint: Check things like eye colour, hair colour, skin colour and nose/mouth shape.
? You need to submit your design (defining diagram) and algorithm written in Pseudocode.
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SIT105 Thinking Technology and Design – T1, 2019
TASK 2 – FUNCTIONALITY: SHORTEST FLIGHT PATH (ADVANCED)
I Refer to the map below and then develop algorithms (using pseudocode) which conduct the following:
1. Find the shortest path (kilometres) between destinations Node 7 (Tasmania) to Node 5 (Western Australia). – E.g. Find_all_paths(m,n) //m and n are two nodes (or cities)
· Inputs (the map):
o Set of nodes (or cities): {1,2,3,4,5,6,7,8}
o Set of links between nodes (or cities): {(1,2), (1,3), (1,8), (2,6), (3,5), (4,5), (4,6), (4,7), (5,8), (6,7)}.
· Output:
o List of all routes (without passing a node twice) from Node 7 to Node 5.
– E.g. 7 -) 4 -) 5
2. Find the geographical length (in KM) of each route by adding their distances. – E.g. Find_length(route) – Calculate the length of each possible route from Node 7 to Node 5.
3. Find the amount of time necessary to go through each route. – E.g. Find_time(route) – The UAV can travel at 250km per hour
4. The algorithm should finally print out the best route, the time it took, the length and fuel needed.
– E.g. Itinerary(route, time, length, fuel)
Supporting Information:
I The UAV holds 150,000 litres of fuel and uses 20 litres of fuel per kilometre (it will crash if you use too much fuel).
I Interpret nodes as cities, and the links as air-distance between those cities.
I The UAV can travel at 250km per hour, there is a 2-hour stopover time per intermediate nodes.
I When you are calculating the amount of time, you need to divide total distance by 250 (which is not a whole number). So,
you should upper estimate that. If it is one second more than 27 hours, it should be counted 28 hours.
I You need to design your algorithm to incorporate sequential cohesion and data coupling (check class notes). I You need to submit your algorithm sub-module written in pseudocode (no defining diagram required).
Source: Pixabay
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