Projects
A number of projects are available on this page. Each project must be documented thoroughly using a Jupiter notebook or a GitHub repository. Send your GitHub id to huber DOT flores AT ut DOT ee
PROJECT 1: Ear sensing - understanding typing patterns of users on mobile screens through typing sound: Microphones are sensitive enough that can be used to capture the typing sound on the screen of a smartphone. In this project, a mobile application is given that captures sounds and gestures performed by a user on a mobile screen. The main goal of this project is to analyze the data and conduct a user study with at least 15 participants, such that it is possible to analyze whether patterns can be created with typing sounds.
PROJECT 2: Review Explainability methods for Artificial Intelligence: Several explainability methods are available to understand model execution and training. The goal of this project is to review existing methods for explainability. From this review, a particular method has to be studied in detail, e.g., permutation, occlusion, through examples and uses cases. A full descriptive report is the deliverable of this project.
PROJECT 3: Analyze whether is possible to characterize different types of drinks through WiFi. In this project, an application to collect WiFi signal between two phones is provided. The goal of the project is to analyze whether a WiFi fingerprint can be established for a drink (in a glass) as the drink is located between the phones. Moreover, as part of the analysis, it is also possible to analyze the effect of mixing different drinks.
PROJECT 4: Review about why digital contact tracing failed in pandemic times. In this project, a taxonomy of existing work about digital contact tracing should be created. Taxonomy is not a review, but it requires reviewing a certain amount of work. A good example of a taxonomy can be found here
PROJECT 5: Nutritional value of fruits and vegetables. In our previous project, we demonstrated that it is possible to capture the decomposition of fresh produce using sensors. In this project, we want to verify whether it is possible to link the nutritional value of produce (fruits and vegetables) to their decomposition state that was captured by the sensor.
More to be announced...