Project info
1—2 students per group | At most 1 groups from CS, 1 group from CDA (6 or 12 credits), 1 group from 2nd-major (6 credits) | Development project | Updated 2016-06-01[3]
1—2 students per group | At most 1 groups from CS, 1 group from CDA (6 or 12 credits), 1 group from 2nd-major (6 credits) | Development project | Updated 2016-06-01[3]
Natural science projects often involves recording the time and location particular species (e.g., birds, trees) or specimens (rocks) are recorded, as well as observation notes such as their behaviour (foraging, resting), measurements (juvenile bird, 5m tall tree) or conditions (weathered rocks). While the traditional way of keeping records on paper or cards is time-honoured and reliable, information such as the exact time location of records are not easy to keep.
This project is about making a system that would help surveyors to keep records in a convenient and reliable way.
Some resources that may make experimentation and implementation easier
1—2 students per group | At most 1 group from CS (12 credits), 1 group from CDA (6 or 12 credits), 2 groups for 2nd-major (6 credits) | Research and development project | Updated 2016-06-01[3]
Human beings are good at recognizing sounds of various types. On phone you can recognize it's the voice of your friend. When listening to music, you can distinguish that a piano, a violin and some drums are used.
The project is to design and implement algorithms for recognizing the instruments used in a piece of MP3/AAC music input by the user and showing the list of instruments used at different times of music play. Ideally, the pitches of individual notes of the instruments should be extracted as well.
Some resources that may make experimentation and implementation easier
1—2 students per group | At most 2 group from CS (12 credits), 2 groups from CDA (6 or 12 credits), 2 groups from 2nd-major (6 credits) | Research and development project | Updated 2016-06-01[3]
The Best Track Archive of Joint Typhoon Warning Center (JTWC) contains about 60 years of data of the tracks of tropical cyclones all over the world. The Severe Weather Information Centre and Meteoalarm contain current data for weather warning. These data are publicly available and can be analyzed for applications based on statistics.
The project is to use these data to answer some questions, such as
The student is expected to come up with questions similar to these above that makes sense meteorologically, and answer them by analysing data.
Some resources that may make experimentation and implementation easier
Although some resources here are Python libraries, there is no restriction on the languages and tools you use. Indeed, a good data analysis and machine learning project like this one often requires the use of multiple languages.
1—2 students per group | At most 2 groups from CS (12 credits), 1 from CDA (12 credits), 2 groups from 2nd-major (6 credits) | Development project | Updated 2016-09-14[3]
The open-sourced language Swift has been evolved to version 2.2 3, and is gaining popularity among iOS developers, as its library integration is getting better. The project is to develop an iOS application by look into the details of the language and taking advantage of its language features.
It is open to the student to design and implement a Swift 2-based application or application suite to be run on watchOS, iOS or OS X. i.e., it is the student who proposes what the app should be like. Your supervisor's job is to make sure its scale is that of a final year project, and oversees its progress.
Some resources that may make experimentation and implementation easier
1—2 students per group | At most 1 group from CS, 2 groups from CDA (12 credits only), 1 group from 2nd-major (6 credits) | Research and development project | Updated 2016-06-01[3]
Given the historical data of a number of stock or index prices at different points in time, design an algorithm that would predict their values in the future.
Some factors the algorithm can take into the consideration include the day in month, weekday of day, time of day, various financial indicators, correlations between data from different time series.
Time series of non-numerical data such as news articles, Tweeter feeds, or Facebook posts can be analysed to improve the accuracy of the prediction. Indeed, this has been proven to be quite effective in prior studies.
Time series data of at least 5 different types of time series, such as stocks, futures and/or indices, should be analyzed.
Note that the student is expected to build their own collection of training and testing data.
Be very careful about accuracy claims of better than 70% when you do literature research on how good their systems are, especially when the system uses historical numerical data or financial indicators only.
Some resources that may make experimentation and implementation easier
Although some resources here are Python libraries, there is no restriction on the languages and tools you use. Indeed, a good data analysis and machine learning project like this one often requires the use of multiple languages.