Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embeded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Julian Priest, Sebastian Büttrich
Semester: Fall 2023
Tags: satellite, LoRa, cubesat, IoT, embeded, electronics
ITU is a partner of the Danish Student Cubesat Program, DISCOSAT. We launched our first satellite DISCO-1 into Low Earth Orbit in April 2023 and we will launch a second DISCO-2 in 2024. In this project you will gain experience with automating live satellite operations and communications, completing a groundstation at the Rued Langaards Vej site for use with both satellites.
The DISCO satellite …
Supervisors:
Julian Priest
Semester: Fall 2023
Tags: satellite, ground station, software defined radio, automation, csp
The DISCO-2 satellite is an Earth observation satellite in collaboration with the Arctic Research Center in Aarhus and is designed to complement ground based field studies in Greenland. The satellite instrument consists of 2 high quality visible light and 1 infrared cameras, as well as and attitude control system and coral TPU ML coprocessor.
In this project you will develop software to control …
Supervisors:
Julian Priest
Semester: Fall 2023
Tags: satellite, climate change, image processing, ML, csp, embedded, space
The DISCO-2 satellite will have accelerated machine learning capability based on the inclusion of a Coral TPU ML accelerator module. This will allow images taken by the satellite to be analaysed on satellite using a variety of ML models, with only select images sent back to Earth. This approach allows for more flexibility in image aquisition and saves downlink bandwidth which is very constrained …
Supervisors:
Julian Priest, Robert Bayer
Semester: Fall 2023
Tags: satellite, ground station, software defined radio, automation, csp
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embeded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Julian Priest, Sebastian Büttrich
Semester: Fall 2023
Tags: satellite, LoRa, cubesat, IoT, embeded, electronics
The DISCO-2 satellite will have accelerated machine learning capability based on the inclusion of a Coral TPU ML accelerator module. This will allow images taken by the satellite to be analaysed on satellite using a variety of ML models, with only select images sent back to Earth. This approach allows for more flexibility in image aquisition and saves downlink bandwidth which is very constrained …
Supervisors:
Julian Priest, Robert Bayer
Semester: Fall 2023
Tags: satellite, ground station, software defined radio, automation, csp
Geospatial data refers to information that is tied to specific geographic locations on the Earth’s surface. It includes both the location coordinates (such as latitude, longitude, and, potentially, altitude) and attribute data associated with those locations. Geospatial data is categorized into two types: raster and vector.
Vector data represents geographic features as points, lines, and …
Supervisors:
Eleni Tzirita Zacharatou
Semester: Fall 2023
Tags: spatial data analysis, data science, data loading, GIS file formats, geospatial data
In medical imaging, multi-task learning can be used to train a model that jointly predicts both a diagnosis, and other patient characteristics, such as demographic variables. Among others, this strategy has frequently been used for diagnosis of Alzheimer’s from brain MR scans, with age as an additional variable, see Zhang et al as an example. The idea is that both the disease, and age, …
Supervisors:
Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis, fairness
Machine learning models, especially larger models that are used in for example image or text datasets, can be expensive to train. During development models are usually trained multiple times for example to optimize hyperparameters, which can result in a large carbon footprint.
This project specifically focuses specifically on medical data. There are some recent efforts, for example by Selvan et …
Supervisors:
Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis, resource consumption
There have been several situations where machine learning classifiers, trained to diagnose a particular disease (for example, lung cancer from chest x-rays), overfit on hidden features within the data. Examples include gridlines, surgical markers or evidence of treatment or text present in the images (see references for examples). This causes the classifier to fail on other type of images. …
Supervisors:
Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, data science, medical imaging
Concept Bottleneck Models [1] are designed to leverage high-level concepts. They revisit the classic idea of first predicting concepts that are providing at training time, and then using these concepts to predict the label. By construction, it is possible to intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. …
Supervisors:
Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: machine learning, data science, medical imaging
A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is …
Supervisors:
Amelia Jiménez-Sánchez
Semester: Fall 2023
Tags: medical imaging, deep learning, machine learning, transfer learning, meta-learning
Are you interested in working with a big data open source project?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For a general overview …
Supervisors:
Zoi Kaoudi
Semester: Fall 2023
Tags: big data, database, cross-platform data processing, open source, Apache
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model (typically a regression model) that estimates the runtime of a query …
Supervisors:
Zoi Kaoudi
Semester: Fall 2023
Tags: machine learning, database, query optimization, ranking
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model that estimates the runtime of a plan. However, to build such a model …
Supervisors:
Zoi Kaoudi
Semester: Fall 2023
Tags: machine learning, training data, query optimizer
The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors:
Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA
The emergence of computational storage platforms like Delilah has transformed the data storage landscape, enabling new computing paradigms and facilitating data-intensive applications. Delilah is a cutting-edge computational storage platform developed by the IT University of Copenhagen. It runs on the Daisy OpenSSD and exposes an asynchronous computational storage protocol to the host, facilitated …
Supervisors:
Niclas Hedam, Philippe Bonnet
Semester: Fall 2023
Tags: Open Source, Testing, Computational Storage, Hardware, FPGA
Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such …
Supervisors:
Dovile Juodelyte
Semester: Fall 2023
Tags: transfer learning, deep learning, medical imaging