Project Proposals


Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive

Subjects
Supervisors
  1. Veronika Cheplygina
  2. Sebastian Büttrich
  3. Julian Priest
  4. Maria Astefanoaei
  5. Björn Þór Jónsson
  6. Aaron Duane
  7. Pınar Tözün

Supervisor: Veronika Cheplygina

PROPOSAL

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: Spring 2022
Tags: machine learning, data science, medical imaging

PROPOSAL

Machine learning is used extensively in different applications, including medical imaging and natural language processing. As different types of data are involved, it is reasonable to assume that different methods are needed for each application. However, there are also opportunities in translating a method successful in one application, to the other application where it is not widely used. The …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, natural language processing, medical imaging, literature review

PROPOSAL

Machine learning challenges hosted on platforms such as Kaggle (general) or grand-challenge.org (medical imaging) have attracted a lot of attention, both from academia and industry researchers. Challenge designs vary widely [1], including what type of data is available, how the algorithms are evaluated, and the rewards for the winners. In medical imaging, there is some evidence that challenges …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, data science, medical imaging


Supervisor: Sebastian Büttrich

PROPOSAL

The Danish Student Cubesat Program is an inter university collaboration that will launch 3 cubesats into Low Earth Orbit over the next 4 years. The satellites will be designed, operated, programmed and built by students and the project offers an opportunity for Master’s students to take part in a live satellite project. ITU is partnering with Aarhus University on DISCOSAT2 which will be an …
Supervisors: Sebastian Büttrich, Julian Priest
Semester: Fall 2021
Tags: Satellite, Cubesat, Image processing, Machine Learning, edge, constrained computing

PROPOSAL

Invasive bird species can be a serious problem in cities, towns and in agriculture. The common pigeon is a very unwelcome guest on many balconies, roofs, terraces. Conventional scarecrows often show no effect, as these birds are known to be quite intelligent, and capable of learning fast. The idea is to built a sensor/camera enhanced scarecrow that - can recognize birds present within its …
Supervisors: Sebastian Büttrich
Semester: Fall 2021
Tags: IoT, ML, machineLearning, sensors, security

PROPOSAL

For this project, you would be working with a partner company who are looking to re-establish wood as a building material for sustainable architecture, and thus are using sensors for quality control - to detect damages and deterioration in buildings. Wood such as timber may be analyzed by non-intrusive acoustic impact testing and subsequent waveform analysis, and the expectation is that machine …
Supervisors: Sebastian Büttrich
Semester: Fall 2021
Tags: IoT, sensors, machine learning, acoustics

PROPOSAL

Recent progress in LoRaWAN development has made a new generation of satellite communications offerings available to IoT devices. In these, the LoRaWAN gateway is satellite born, and collecting data from small inexpensive ground stations. So far, this is predominantly seen as a means of communciation for remote sensor data, e.g. in agriculture, logistics or wildlife monitoring. However, one can …
Supervisors: Sebastian Büttrich
Semester: Fall 2021
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, edge, security

PROPOSAL

There is currently a lot of progress in really small, yet powerful visual machine learning / computer vision, on hardware like the OpenMV Cam H7, Arduino Portenta Vision Shield, Luxonis LUX-ESP32, Himax WE-I Plus, Arducam Pico4ML, and Raspberry Pi, and on software platforms such as TinyML or OpenMV IDE. While many popular use cases stem from fields like traffic analysis, wildlife monitoring, we …
Supervisors: Sebastian Büttrich
Semester: Fall 2021
Tags: IoT, sensors, machine learning, computer vision

PROPOSAL

Deliberately scoped very wide, this group contains a number of projects in different possible directions, from Location services via LPWAN time-of-flight and GPS/GNSS, Vessel tracking and management in fisheries, tourism and logistcs Water quality anc chemistry sensing for Aquaculure, specifically Mariculture, Wave and tidal dynamics, e.g. in energy harvesting and variations/combinations of …
Supervisors: Sebastian Büttrich
Semester: Fall 2021
Tags: Satellite, Image processing, Machine Learning, edge, constrained computing, IoT, sensors, location


Supervisor: Julian Priest

PROPOSAL

The Danish Student Cubesat Program is an inter university collaboration that will launch 3 cubesats into Low Earth Orbit over the next 4 years. The satellites will be designed, operated, programmed and built by students and the project offers an opportunity for Master’s students to take part in a live satellite project. ITU is partnering with Aarhus University on DISCOSAT2 which will be an …
Supervisors: Sebastian Büttrich, Julian Priest
Semester: Fall 2021
Tags: Satellite, Cubesat, Image processing, Machine Learning, edge, constrained computing


Supervisor: Maria Astefanoaei

PROPOSAL

The idea behind “15-minutes cities” is that within a short walk or bike ride people should have access to all necessary facilities that constitute the essence of urban living, such as parks, shops, cafes, schools, hospitals. Initiatives to transform cities according to this paradigm are currently being implemented across the world, in an attempt to make urban spaces more liveable, …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, visualisation, Python, OSM data

PROPOSAL

As a response to increased traffic congestion and the need to reduce carbon emissions, cities consider ways to modernise, build and extend transit systems. Transit network design solutions can benefit from analysing the large amount of crowd-sourced location data available, which provides valuable insights into population mobility needs. Designing efficient metro lines, bicycle paths, or bus …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, network design, Python, OSM data

PROPOSAL

The idea behind “15-minutes cities” is that within a short walk or bike ride people should have access to all necessary facilities that constitute the essence of urban living, such as parks, shops, cafes, schools, hospitals. Initiatives to transform cities according to this paradigm are currently being implemented across the world, in an attempt to make urban spaces more liveable, …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, graph summaries, Python, OSM data

PROPOSAL

Musical genres are inherently ambiguous and difficult to define. Even more so is the task of establishing how genres relate to one another. Yet, genre is perhaps the most common and effective way of describing musical experience. The number of possible genre classifications (e.g. Spotify has over 4000 genre tags, LastFM over 500,000 tags) has made the idea of manually creating music taxonomies …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: scalable algorithms, hyperbolic embeddings, Python, Spotify data

PROPOSAL

The integration of wind power in the energy grid is dependent on accurate production forecasts. The power output curves between neighbouring wind farms are often correlated temporally and spatially, but currently, these spatiotemporal dependencies are under-utilised in prediction models. Graph neural networks allow for modelling these dependencies. In this project the student will implement a …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, graph neural networks, Python, timeseries data


Supervisor: Björn Þór Jónsson

PROPOSAL

In relevance feedback, the choice of images to present to the user is a difficult problem, as a naïve approach may present too many similar images. The challenge addressed in this project is to ensure diversity (aka “one of each”) as well as relevance. A particularly interesting project for students interested in efficient algorithms. Read more…
Supervisors: Björn Þór Jónsson
Semester: Fall 2021
Tags: multimedia analytics, scalability, diversity

PROPOSAL

In interactive learning systems, such as Exquisitor, the system presents potentially relevant images to users who label them as either relevant or irrelevant. Currently, Exquisitor uses a cluster-based index, which allows it to return results from a collection of 100 million images in 0.3 seconds. The goal of this project is to study the application of hash-based indexing to interactive learning …
Supervisors: Björn Þór Jónsson
Semester: Fall 2021
Tags: multimedia analytics, diversity

PROPOSAL

The goal of this project is to enhance PhotoCube as a competior for the Video Browser Showdown, an international video retrieval competition where competing systems are judged based on speed, accuracy and recall. We propose to develop new versions of the C++-based media server and JS-based media browser, to expand the data model to videos and improve the performance sufficiently to take part in …
Supervisors: Björn Þór Jónsson
Semester: Fall 2021
Tags: video search, multimedia analytics, photocube

PROPOSAL

We are actively developing a new prototype for analysing large multimedia collections in virtual reality, based on the ObjectCube data model. There are many ways in which students can contribute to the project, including work on the user interface and the back-end, and later on running large-scale user experiments. Read more…
Supervisors: Aaron Duane, Björn Þór Jónsson
Semester: Fall 2021
Tags: virtual reality, multimedia analytics


Supervisor: Aaron Duane

PROPOSAL

We are actively developing a new prototype for analysing large multimedia collections in virtual reality, based on the ObjectCube data model. There are many ways in which students can contribute to the project, including work on the user interface and the back-end, and later on running large-scale user experiments. Read more…
Supervisors: Aaron Duane, Björn Þór Jónsson
Semester: Fall 2021
Tags: virtual reality, multimedia analytics


Supervisor: Pınar Tözün

PROPOSAL

NVMe SSDs are not a uniform class of devices. IO software stack is not uniform either. Understanding the performance characteristics of new-generation SSDs and the impact of the IO stack on their performance is crucial while determining how to design data-intensive systems. In this project, we would like to characterize the performance of a range of NVMe SSDs (e.g., Samsung Z-SSD, Intel Optane, …
Supervisors: Pınar Tözün
Semester: Fall 2021
Tags: SSD, benchmarking

PROPOSAL

Disaggregated storage has gained acceptance in data centers. With disaggregated storage, storage resources are decoupled from compute resources, and made available through fabric. We are particularly interested in storage resources composed of an ARM-based smartNIC, which acts as fabric target as well as storage controller for a collection of SSDs. The performance characteristics of the storage …
Supervisors: Pınar Tözün
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

PROPOSAL

The work on running data-intensive applications on very powerful, expensive, and power-hungry server hardware is very popular thanks to the growing size of data centers and high-performance computing (HPC) platforms. However, with the rise of new generation internet of things (IoT) applications, the lower-power and lower-budget hardware devices that specifically target IoT, the edge platforms, …
Supervisors: Pınar Tözün
Semester: Fall 2021
Tags: edge, NVIDIA Jetson, Raspberry Pi, Odroid

PROPOSAL

A data science infrastructure orchestrates the execution of widely used machine learning frameworks (e.g., TensorFlow , PyTorch) on a heterogeneous set of processing units (e.g., CPU, GPU, TPU, FPGA) while powering an increasingly diverse and complex range of applications (e.g., fraud detection, healthcare, virtual assistance, automatic driving). Understanding the resource consumption …
Supervisor: Pınar Tözün
Semester: Fall 2021
Tags: benchmarking, hardware resource consumption, deep learning frameworks