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 (Assigned): Understanding handgrip strength of humans using light sensors. Handgrip strength is typically measured using a dynamometer. However, it is possible to envision the monitoring of handgrip strength using light sensors. In this project, a dataset that contains the handgrip strength of multiple participants is provided. The overall goal is to analyze the data, categorize handgrip strength and create a deep learning prediction model to identify individuals.
PROJECT 2: 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 3: A taxonomy of distributed and federated machine learning. In this project, a taxonomy of existing work about distributed and federated machine learning 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 4 (Assigned): Explainability of deep learning models using performance metrics. Deep learning models (aka AI-based reasoning) are black-box. However, by understanding the performance execution of a model, it could be possible to explain its behavior, which can then be correlated to the data used to train the model. In this project, given a static data set, a deep learning model is trained and configured. Later, the model is prone incrementally, such that performance metrics can be measured in each prone step. The main goal is to verify whether there is a correlation between a model configuration and its execution.
PROJECT 5: Perception of users towards autonomous vehicles invading public spaces Unmanned autonomous vehicles (UAV)s are automating many tasks, such as grocery and packet delivery. However, UAVs need to move autonomously through public urban spaces, which can cause problems with pedestrians. In this project, a survey with pictures of UAVs invading public spaces should be carried out. The survey should consider at least the opinions of 30 participants. Conclusions on how to address the problem should be given.
PROJECT 6: Detecting micro-plastics with thermal imaging and light reflectivity approaches. Micro-plastics are brought into the environment either by litter breaking apart that is disposed of over long periods of time or directly from the ocean carried by the atmosphere. In this project, we use thermal imaging to create a detection map which can be used to detect large size litter. Since micro-plastics are difficult to visualize with thermal imaging, then places in which there is not large litter detected, then can be analyzed using light reflectivity, such that micro-particles can be identified.
PROJECT 7 (Assigned): Estimating energy consumption of applications using thermal imaging. Nowadays, due to ergonomic optimization in designs, smart and IoT devices have batteries that cannot be detached. Thermal imaging running in smartphone cameras can be used to identify the level of heat in an area by looking at the invisible spectrum of light. In this project, we analyze whether it is possible to make a correlation between the energy consumption of applications running in devices and measurements obtained by thermal imaging when pointing the camera at the device.
PROJECT 8 (Assigned): A taxonomy of digital contact tracing. 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 9 (Assigned): User perception of litter classification using sensors. Separation of litter is a fundamental process to recycle reusable materials. Unfortunately, when materials get mixed with other waste objects, it is difficult to separate them - sometimes even impossible. As a result, litter classification approaches for early separation of litter waste are required. In this project, we will conduct a user study, in which several participants dispose of waste in trash bins using traditional visual inspection approaches, and then we compare with a recommendation system that suggests to users which trash bin is the right one (using light sensors in a glove).
PROJECT 10: 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.