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Revision as of 21:32, 20 July 2020
SMT483: Project Experience
Welcome to this course wiki for SMT483: Project Experience.
You can access the Project Groups page here, where you will write your group projects.
Please note that:
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Course Information
| Faculty | TAN Hwee Pink |
| Course | Project Experience |
| Course code | SMT483 |
| Term | AY2019/2020 Term 2 |
| Section | G1 |
| Teaching Assistant | Vinnie Chu |
Potential Projects
| No. | Sponsor | Project Topic | Project Description and Deliverable | Skill Requirements | Project Type |
|---|---|---|---|---|---|
| 1 | URA | Modelling vehicular flows into Pick-Up Drop-Off (PUDO) areas | With the increase in demand for private hire cars today, existing Pick-Up Drop-Off (PUDO) at various developments could have difficulty coping with increased flows, resulting in traffic spill overs. There are limited studies on the factors that influence vehicular flows into PUDOs. We are interested to quantify current and project future PUDO volumes to inform the future-proofing of PUDO design.
The objective of this project is to design a user-friendly model to predict vehicle flows and spill-overs for several typologies of PUDOs. |
Basic modelling skills, R/Python (Fieldwork may be necessary to collect additional data) | Type 1, 2 or 3 (Depending on meeting with sponsors) |
| 2 | URA | Developing a Video Analytics algorithm to detect conflict between active mobility users | As part of our move towards car-lite, active mobility modes are highly encouraged. Pedestrians, cyclists and PMD users often meet along shared paths and traffic junctions. Thus, there is a need to ensure that we provide comfortable and safe walkways for all users. An understanding of user interaction and conflict will be important to translate into useful applications.
Using footage collected along a footpath, the study would tag all interactions between active mobility users observed. It will then measure the angular change in direction and change in speed to derive some threshold values that determine conflicts. Applying machine learning, an algorithm should be designed to automatically pick out conflict cases in video and categorise these cases. |
Machine learning/Neural network, Python | Type 1, 2 or 3 (Depending on meeting with sponsors) |
| 3 | *Scape | Smart Toilet Management | *SCAPE is a 5 storey building with a total of is 6 individual toilets for those on wheel-chair, 6 common toilets for the male and 6 common toilets for the female with varying number of cubicles between 4 – 10 cubicles inside.
The toilets’ lights and fans are switched on, in most of the toilet, during the building operation hours from 8am to 12pm for levels 2-5. Except for 1 individual toilet for those on wheel-chair, 1 common toilet for the male and 1 common toilet for the female located on the 2nd level which is switched on 24 hours a day and 7 days a week. At times, during events, peak seasons and peak hours, there were several feedback that the toilet are not cleaned and supplies such as the toilet paper are not stock up. Estates team, sometimes, needs to visit to verify the feedback and or get the cleaners to attend to the feedback. Estates is exploring whether sensors can be deployed to:
Objectives of the project include:
|
IoT | Type 1 |
| 4 | SCDF | Fire Safety in Singapore | The Fire Safety Act requires buildings in Singapore to be equipped with Fire Alarm Systems, which are connected to smoke or heat detectors that will trigger the alarm in the presence of said by-products of fire.
However, often times the detectors do not get triggered until the fire has been well developed. This could be due to the slow rise of temperature, of if the smoke is emitting away from the detector. By the time the fire alarm rings, half the place might have been burnt down. The idea is to think of an innovative yet cost efficient way to detect the presence of a fire when it is still in its early stage. Currently there are solutions such as thermos cameras that can detect small fires but they cost in the thousands. Quite unlikely that companies would want to purchase these cameras. |
IoT | Type 1 |
| 5 | T3Each Global Ventures | Creating info / data / feedback app for wheelchair / Life Glider users | In general for those who have physical disabilities, it will be a definite advantage where there can be a structure to capture, share and offer useful information and feedback that can motivate them to develop certain desirable behavioural traits. Developing these traits should offer them a positive outlook to continuously attain higher levels of satisfaction (or expectation in some cases) to perform their daily physical lifestyle and activity routines within their limited capacities. Providing specific and relevant data that is focused in addressing concerns relating to their physical limitations within a realistic environment can be a way to develop these desired behavioural traits.
For this project, we propose to design and provide the ease of obtaining relevant data information, with the use of internet technology i.e. within an app. The objective is to help those with mobility issues that restrict or limit their movements from point to point. These subjects are identified to be constantly using some forms of mobility-assisting devices e.g. the wheelchair or with the revolutionary Life Glider (www.mylifeglider.com) to commute. Collation of this data information can offer valuable feedback to the subjects and the healthcare industry to continuously assess ways to maximize potentials of people with physical limitations. This will also drive the subjects to engage more effectively in their regular activities and discover new endeavours to participate in. Some non-validated thoughts on what the app features may provide as data information :-
|
Some knowledge in design thinking or other processes to conduct user requirements would be useful during the course of the project but not necessary; programming skills (e.g. in Python) to develop the necessary Android and/or iOS apps for the users with mobility issues. | Type 1 |
| 6 | Anly.io | Analysis of Educational Game Trends on Steam (or other platform) |
Anly.io has so far not created games that model on successful games in the market. We would like to find out what makes games successful while achieving the intended educational outcomes. From there, we would like to know how these findings can be contextualised to the local education sector, creating games that are fun for MOE school students.
|
Nil | Type 1 or 2 (Depending on meeting with sponsors) |
| 7 | Anly.io | Game Dashboard Enhancement | Anly.io produces games that weave data analysis skills into MOE curriculum. One such game is Housing Crisis, where students figure out how to provide enough housing for a population and build facilities optimally to keep people happy. For every game, we provide a dashboard on students' performance in the games for teachers to identify areas of improvement and the extent to which students understood certain concepts (eg. housing scarcity problem, models of town planning). How should this dashboard be effectively enhanced such that teachers get the info they need and can identify targeted areas of improvement to their teaching.
|
Nil | Type 1 or 2 (Depending on meeting with sponsors) |
| 8 | Anly.io | Citizen Engagement using Anly.io | Anly.io has so far only focused on creating games that incorporate data analytics skills into MOE curriculum. This is a rather niche scenario. We have some stakeholders from the urban planning side who are interested in how the platform can be leveraged to collect citizen opinions and engage communities in a gamified manner. An example could include games that mimic real-life scenarios such as family planning with a backend analytics engine to analyse in-game behaviour, which are then contextualised to inform policy planning.
|
Nil | Type 1 |
