Proposal Archive


Proposal Archive

Ordered by semester and supervisor
Semesters
  1. Fall 2019
  2. Fall 2020
  3. Fall 2021
  4. Spring 2021
  5. Spring 2022

Semester: Fall 2019

Supervisors
  1. Aaron Duane
  2. Bethany Chamberlain
  3. Björn Þór Jónsson
  4. Dovile Juodelyte
  5. Ehsan Yousefzadeh-Asl-Miandoab
  6. Iman Elghandour
  7. Julian Priest
  8. Maria Astefanoaei
  9. Niclas Hedam
  10. Niels Ørbæk Chemnitz
  11. Paul Rosero
  12. Philippe Bonnet
  13. Pınar Tözün
  14. Sebastian Büttrich
  15. Veronika Cheplygina
  16. Zsolt István

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: Bethany Chamberlain


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

PROPOSAL

The index structure used for Exquisitor is eCP, a very scalable index for high-dimensional retrieval. While multimedia collections are typically constantly updated, however, the index is unfortunately static. The goal of this project is to implement and compare approaches for index maintenance. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: index maintenance, high-dimensional indexing

PROPOSAL

In this project, we propose to implement media server and media browser encapsulating a new data model for analysing media collections, called Multimedia Analytics Data Services (MADS). To validate the design, some scalability experiments should be performed. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability

PROPOSAL

Students at ITU have made a prototype version of the Exquisitor system for the Android mobile phone! The system is missing some of Exquisitor’s advanced functionality, such as search and indexing, and the goal is to add and evaluate this functionality. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, android

PROPOSAL

We propose to develop a new prototype for analysing large multimedia collections in Virtual Reality, using the new Valve Indexes. 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…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: virtual reality, multimedia analytics

PROPOSAL

The goal of this project is to integrate Exquisitor with other pieces of existing technology and turn into a competitor for a live video retrieval competition. The project is suitable for 3-4 well-qualified MSc students. The ​Video Browser Showdown​ (VBS) is a live competition​ for video search andretrieval, held at the International Conference on Multimedia Modeling (MMM). In VBS, the competition …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is ensure diversity in the relevance feedback results, to improve quality of the user experience. The project is suitable for 1-3 well-qualified MSc students. In many creative tasks, the designer will knowsome stock image is good for a design just stumbling upon the image. This “Aha!” moment requires browsing thousands of images by categories. In other words, it requires …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is to use state of the art in eye tracking to design, implement and evaluate different eye-tracking interfaces for Exquisitor. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is build a prototype of the Exquisitor system for mobile devices. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of those collections [1].Based on past research results [2], we …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019


Supervisor: Dovile Juodelyte

PROPOSAL

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: Spring 2022
Tags: transfer learning, deep learning, medical imaging


Supervisor: Ehsan Yousefzadeh-Asl-Miandoab


Supervisor: Iman Elghandour

PROPOSAL

It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Spark assumes that it executes its applications on a homogeneous cluster of similar nodes. However, it is becoming common that in-house clusters have heterogeneous compute re- sources and it is good to exploit all of them in the most efficient way. The objective of this master thesis is to extend the Spark scheduler to be resources- aware and to efficiently schedule Spark tasks on all the …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Distributed computing platforms such as Hadoop and Spark focus on addressing the fol- lowing challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

In the last few years, it became common to accelerate Hadoop and Spark by enabling them to execute tasks and jobs on accelerators such as GPUs and FPGAs. The objective of this master thesis is to study new approaches that efficiently predicts the execution time of Spark tasks and jobs executed on GPUs. Part of the work will be to build a performance prediction model for GPUs, which can be built …
Supervisor: Iman Elghandour
Semester: Fall 2019


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: Niclas Hedam

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC


Supervisor: Niels Ørbæk Chemnitz

PROPOSAL

With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data

PROPOSAL

How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: NILM, ML, IoT, Energy Data, Smart Meters, Python, Data Science, Time Series Data


Supervisor: Paul Rosero

PROPOSAL

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

PROPOSAL

TinyML is a new trend to deploy deep learning in tiny devices. Therefore, it is necessary to deploy several applications to understand the challenges and opportunities which tinyML brings us. In this scenario, any idea with embedded computer vision, voice recognition, and sensors are welcome.
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems, Computer vision, Voice recognition


Supervisor: Philippe Bonnet

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: Philippe Bonnet
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

PROPOSAL

Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, Nextflow), package managers (e.g., bioconda), and container systems (e.g., Singularity). However, validating their executions on high-performance computers remains an open issue. Indeed, there are many …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: ML, reproducibility, workflow, HPC

PROPOSAL

Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite for computational reproducibility is the availability of software and data. However, this is problematic for edge devices whose goal is to reduce the amount of data transferred to the backend. On …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Offloading processing to storage is a means to avoid data movement and thus deal efficiently with very large volumes of stored data. In the 90s, there were pioneering efforts to develop Processing-in-Memory as well as Active Disks. We are considering data stored on Open-Channel SSDs with a programmable storage controller (i.e., a Linux-based ARM processor) integrated into a network switch (e.g., …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Characterize the performance of commercial database systems on an NVIDIA Titan GPU, or Characterize the performance of DB2 PureScale on a cluster equipped with shared storage with a range of different benchmarks. Design and conduct experiment with a range of tuning strategies to measure their impact on performance and reliability.
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

In the context of the Orkney Cloud project, we are preparing the deployment of a decentralized cloud infrastructure on the archipelago. The infrastructure is composed of a collection of Pods (point of delivery) and a wireless core (5G + Wifi). Each Pod is equipped with storage, computing and communication components (so that it is connected to the core and to local endpoints). Each Pod is powered …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

New forms of Solid State Drives have interesting characteristics in terms of performance (10 to 100x faster than previous generations of SSDs) and in terms of functionalities (SSDs can now suspend the execution of writes or erase operations to minimize read latency). The performance characteristics of these devices is not well understood yet. The topic of this thesis is to design and conduct …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2019
Tags: FPGA, SSD


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

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

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC

PROPOSAL

The variety and complexity of data-intensive applications and systems have been increasing drastically the past decade. Tasks from a SQL-based big data analytics request running on Apache Spark can be very different from tasks from deep learning training using TensorFlow framework. Nevertheless, these data-intensive applications increasingly run on shared hardware resources in data centers or …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: hardware-software co-design

PROPOSAL

Improvements in modern processor hardware does not automatically enable more complex and higher quality games, animations, and virtual reality applications. A paradigm shift is necessary when it comes to how we develop these applications in order to exploit the resources of modern hardware (i.e., main memory, multicores) effectively. Unity Technologies has recently developed Data-Oriented …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: memory hierarchy, concurrency

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

Spreading the computation of similar concurrent tasks that have a large instruction footprint over multiple cores via thread migration is shown to improve the instruction cache utilization drastically since it allows instruction re-use across the concurrent tasks. However, thread migrations are costly due to the context switching overhead. To reduce this overhead, recent work mainly proposed …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The computer architecture community is moving toward commoditization of hardware specialization instead of general purpose CPUs and more agile hardware development instead of years-long production cycles to enable faster, more energy-efficient, and more cost-effective hardware/software co-designs. This will lead to a disruption in the way we design and maintain the emerging data management systems …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The Transaction Processing Performance Council (TPC) is a non-profit IT organization founded to define database benchmarks and disseminate objective, verifiable performance data to the industry. TPC has standardized several new benchmarks (e.g., TPCx-HS and TPCx-BB), in recent years. Older popular benchmarks, like TPC-C (representing high-performance transaction processing) and TPC-H (representing …
Supervisor: Pınar Tözün
Semester: Fall 2019


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

In LoRaWAN networks such as the Things Network, long distance transmissions, well beyond the limitations of line of sight in terrestrial geometry, are frequently observed. Tropospheric effects are seen as responsible for bending or guiding radio waves around the earth curvature. As an example, under the right weather conditions, the LoRaWAN gateway at ITU may collect packets from northern Germany, …
Supervisors: Sebastian Büttrich
Semester: Spring 2021
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, troposphere, weather

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

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

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: Veronika Cheplygina

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 algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, medical imaging, crowdsourcing, similarity


Supervisor: Zsolt István

PROPOSAL

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

PROPOSAL

(This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Benchmarking, Distributed Systems

PROPOSAL

Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Blockchain, Data Management, Benchmarking

PROPOSAL

Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Hardware-software Co-design, Security and Privacy



Semester: Fall 2020

Supervisors
  1. Aaron Duane
  2. Bethany Chamberlain
  3. Björn Þór Jónsson
  4. Dovile Juodelyte
  5. Ehsan Yousefzadeh-Asl-Miandoab
  6. Iman Elghandour
  7. Julian Priest
  8. Maria Astefanoaei
  9. Niclas Hedam
  10. Niels Ørbæk Chemnitz
  11. Paul Rosero
  12. Philippe Bonnet
  13. Pınar Tözün
  14. Sebastian Büttrich
  15. Veronika Cheplygina
  16. Zsolt István

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: Bethany Chamberlain


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

PROPOSAL

The index structure used for Exquisitor is eCP, a very scalable index for high-dimensional retrieval. While multimedia collections are typically constantly updated, however, the index is unfortunately static. The goal of this project is to implement and compare approaches for index maintenance. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: index maintenance, high-dimensional indexing

PROPOSAL

In this project, we propose to implement media server and media browser encapsulating a new data model for analysing media collections, called Multimedia Analytics Data Services (MADS). To validate the design, some scalability experiments should be performed. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability

PROPOSAL

Students at ITU have made a prototype version of the Exquisitor system for the Android mobile phone! The system is missing some of Exquisitor’s advanced functionality, such as search and indexing, and the goal is to add and evaluate this functionality. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, android

PROPOSAL

We propose to develop a new prototype for analysing large multimedia collections in Virtual Reality, using the new Valve Indexes. 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…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: virtual reality, multimedia analytics

PROPOSAL

The goal of this project is to integrate Exquisitor with other pieces of existing technology and turn into a competitor for a live video retrieval competition. The project is suitable for 3-4 well-qualified MSc students. The ​Video Browser Showdown​ (VBS) is a live competition​ for video search andretrieval, held at the International Conference on Multimedia Modeling (MMM). In VBS, the competition …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is ensure diversity in the relevance feedback results, to improve quality of the user experience. The project is suitable for 1-3 well-qualified MSc students. In many creative tasks, the designer will knowsome stock image is good for a design just stumbling upon the image. This “Aha!” moment requires browsing thousands of images by categories. In other words, it requires …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is to use state of the art in eye tracking to design, implement and evaluate different eye-tracking interfaces for Exquisitor. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is build a prototype of the Exquisitor system for mobile devices. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of those collections [1].Based on past research results [2], we …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019


Supervisor: Dovile Juodelyte

PROPOSAL

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: Spring 2022
Tags: transfer learning, deep learning, medical imaging


Supervisor: Ehsan Yousefzadeh-Asl-Miandoab


Supervisor: Iman Elghandour

PROPOSAL

It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Spark assumes that it executes its applications on a homogeneous cluster of similar nodes. However, it is becoming common that in-house clusters have heterogeneous compute re- sources and it is good to exploit all of them in the most efficient way. The objective of this master thesis is to extend the Spark scheduler to be resources- aware and to efficiently schedule Spark tasks on all the …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Distributed computing platforms such as Hadoop and Spark focus on addressing the fol- lowing challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

In the last few years, it became common to accelerate Hadoop and Spark by enabling them to execute tasks and jobs on accelerators such as GPUs and FPGAs. The objective of this master thesis is to study new approaches that efficiently predicts the execution time of Spark tasks and jobs executed on GPUs. Part of the work will be to build a performance prediction model for GPUs, which can be built …
Supervisor: Iman Elghandour
Semester: Fall 2019


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: Niclas Hedam

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC


Supervisor: Niels Ørbæk Chemnitz

PROPOSAL

With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data

PROPOSAL

How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: NILM, ML, IoT, Energy Data, Smart Meters, Python, Data Science, Time Series Data


Supervisor: Paul Rosero

PROPOSAL

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

PROPOSAL

TinyML is a new trend to deploy deep learning in tiny devices. Therefore, it is necessary to deploy several applications to understand the challenges and opportunities which tinyML brings us. In this scenario, any idea with embedded computer vision, voice recognition, and sensors are welcome.
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems, Computer vision, Voice recognition


Supervisor: Philippe Bonnet

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: Philippe Bonnet
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

PROPOSAL

Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, Nextflow), package managers (e.g., bioconda), and container systems (e.g., Singularity). However, validating their executions on high-performance computers remains an open issue. Indeed, there are many …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: ML, reproducibility, workflow, HPC

PROPOSAL

Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite for computational reproducibility is the availability of software and data. However, this is problematic for edge devices whose goal is to reduce the amount of data transferred to the backend. On …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Offloading processing to storage is a means to avoid data movement and thus deal efficiently with very large volumes of stored data. In the 90s, there were pioneering efforts to develop Processing-in-Memory as well as Active Disks. We are considering data stored on Open-Channel SSDs with a programmable storage controller (i.e., a Linux-based ARM processor) integrated into a network switch (e.g., …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Characterize the performance of commercial database systems on an NVIDIA Titan GPU, or Characterize the performance of DB2 PureScale on a cluster equipped with shared storage with a range of different benchmarks. Design and conduct experiment with a range of tuning strategies to measure their impact on performance and reliability.
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

In the context of the Orkney Cloud project, we are preparing the deployment of a decentralized cloud infrastructure on the archipelago. The infrastructure is composed of a collection of Pods (point of delivery) and a wireless core (5G + Wifi). Each Pod is equipped with storage, computing and communication components (so that it is connected to the core and to local endpoints). Each Pod is powered …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

New forms of Solid State Drives have interesting characteristics in terms of performance (10 to 100x faster than previous generations of SSDs) and in terms of functionalities (SSDs can now suspend the execution of writes or erase operations to minimize read latency). The performance characteristics of these devices is not well understood yet. The topic of this thesis is to design and conduct …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2019
Tags: FPGA, SSD


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

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

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC

PROPOSAL

The variety and complexity of data-intensive applications and systems have been increasing drastically the past decade. Tasks from a SQL-based big data analytics request running on Apache Spark can be very different from tasks from deep learning training using TensorFlow framework. Nevertheless, these data-intensive applications increasingly run on shared hardware resources in data centers or …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: hardware-software co-design

PROPOSAL

Improvements in modern processor hardware does not automatically enable more complex and higher quality games, animations, and virtual reality applications. A paradigm shift is necessary when it comes to how we develop these applications in order to exploit the resources of modern hardware (i.e., main memory, multicores) effectively. Unity Technologies has recently developed Data-Oriented …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: memory hierarchy, concurrency

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

Spreading the computation of similar concurrent tasks that have a large instruction footprint over multiple cores via thread migration is shown to improve the instruction cache utilization drastically since it allows instruction re-use across the concurrent tasks. However, thread migrations are costly due to the context switching overhead. To reduce this overhead, recent work mainly proposed …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The computer architecture community is moving toward commoditization of hardware specialization instead of general purpose CPUs and more agile hardware development instead of years-long production cycles to enable faster, more energy-efficient, and more cost-effective hardware/software co-designs. This will lead to a disruption in the way we design and maintain the emerging data management systems …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The Transaction Processing Performance Council (TPC) is a non-profit IT organization founded to define database benchmarks and disseminate objective, verifiable performance data to the industry. TPC has standardized several new benchmarks (e.g., TPCx-HS and TPCx-BB), in recent years. Older popular benchmarks, like TPC-C (representing high-performance transaction processing) and TPC-H (representing …
Supervisor: Pınar Tözün
Semester: Fall 2019


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

In LoRaWAN networks such as the Things Network, long distance transmissions, well beyond the limitations of line of sight in terrestrial geometry, are frequently observed. Tropospheric effects are seen as responsible for bending or guiding radio waves around the earth curvature. As an example, under the right weather conditions, the LoRaWAN gateway at ITU may collect packets from northern Germany, …
Supervisors: Sebastian Büttrich
Semester: Spring 2021
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, troposphere, weather

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

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

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: Veronika Cheplygina

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 algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, medical imaging, crowdsourcing, similarity


Supervisor: Zsolt István

PROPOSAL

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

PROPOSAL

(This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Benchmarking, Distributed Systems

PROPOSAL

Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Blockchain, Data Management, Benchmarking

PROPOSAL

Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Hardware-software Co-design, Security and Privacy



Semester: Fall 2021

Supervisors
  1. Aaron Duane
  2. Bethany Chamberlain
  3. Björn Þór Jónsson
  4. Dovile Juodelyte
  5. Ehsan Yousefzadeh-Asl-Miandoab
  6. Iman Elghandour
  7. Julian Priest
  8. Maria Astefanoaei
  9. Niclas Hedam
  10. Niels Ørbæk Chemnitz
  11. Paul Rosero
  12. Philippe Bonnet
  13. Pınar Tözün
  14. Sebastian Büttrich
  15. Veronika Cheplygina
  16. Zsolt István

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: Bethany Chamberlain


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

PROPOSAL

The index structure used for Exquisitor is eCP, a very scalable index for high-dimensional retrieval. While multimedia collections are typically constantly updated, however, the index is unfortunately static. The goal of this project is to implement and compare approaches for index maintenance. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: index maintenance, high-dimensional indexing

PROPOSAL

In this project, we propose to implement media server and media browser encapsulating a new data model for analysing media collections, called Multimedia Analytics Data Services (MADS). To validate the design, some scalability experiments should be performed. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability

PROPOSAL

Students at ITU have made a prototype version of the Exquisitor system for the Android mobile phone! The system is missing some of Exquisitor’s advanced functionality, such as search and indexing, and the goal is to add and evaluate this functionality. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, android

PROPOSAL

We propose to develop a new prototype for analysing large multimedia collections in Virtual Reality, using the new Valve Indexes. 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…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: virtual reality, multimedia analytics

PROPOSAL

The goal of this project is to integrate Exquisitor with other pieces of existing technology and turn into a competitor for a live video retrieval competition. The project is suitable for 3-4 well-qualified MSc students. The ​Video Browser Showdown​ (VBS) is a live competition​ for video search andretrieval, held at the International Conference on Multimedia Modeling (MMM). In VBS, the competition …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is ensure diversity in the relevance feedback results, to improve quality of the user experience. The project is suitable for 1-3 well-qualified MSc students. In many creative tasks, the designer will knowsome stock image is good for a design just stumbling upon the image. This “Aha!” moment requires browsing thousands of images by categories. In other words, it requires …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is to use state of the art in eye tracking to design, implement and evaluate different eye-tracking interfaces for Exquisitor. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is build a prototype of the Exquisitor system for mobile devices. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of those collections [1].Based on past research results [2], we …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019


Supervisor: Dovile Juodelyte

PROPOSAL

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: Spring 2022
Tags: transfer learning, deep learning, medical imaging


Supervisor: Ehsan Yousefzadeh-Asl-Miandoab


Supervisor: Iman Elghandour

PROPOSAL

It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Spark assumes that it executes its applications on a homogeneous cluster of similar nodes. However, it is becoming common that in-house clusters have heterogeneous compute re- sources and it is good to exploit all of them in the most efficient way. The objective of this master thesis is to extend the Spark scheduler to be resources- aware and to efficiently schedule Spark tasks on all the …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Distributed computing platforms such as Hadoop and Spark focus on addressing the fol- lowing challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

In the last few years, it became common to accelerate Hadoop and Spark by enabling them to execute tasks and jobs on accelerators such as GPUs and FPGAs. The objective of this master thesis is to study new approaches that efficiently predicts the execution time of Spark tasks and jobs executed on GPUs. Part of the work will be to build a performance prediction model for GPUs, which can be built …
Supervisor: Iman Elghandour
Semester: Fall 2019


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: Niclas Hedam

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC


Supervisor: Niels Ørbæk Chemnitz

PROPOSAL

With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data

PROPOSAL

How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: NILM, ML, IoT, Energy Data, Smart Meters, Python, Data Science, Time Series Data


Supervisor: Paul Rosero

PROPOSAL

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

PROPOSAL

TinyML is a new trend to deploy deep learning in tiny devices. Therefore, it is necessary to deploy several applications to understand the challenges and opportunities which tinyML brings us. In this scenario, any idea with embedded computer vision, voice recognition, and sensors are welcome.
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems, Computer vision, Voice recognition


Supervisor: Philippe Bonnet

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: Philippe Bonnet
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

PROPOSAL

Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, Nextflow), package managers (e.g., bioconda), and container systems (e.g., Singularity). However, validating their executions on high-performance computers remains an open issue. Indeed, there are many …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: ML, reproducibility, workflow, HPC

PROPOSAL

Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite for computational reproducibility is the availability of software and data. However, this is problematic for edge devices whose goal is to reduce the amount of data transferred to the backend. On …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Offloading processing to storage is a means to avoid data movement and thus deal efficiently with very large volumes of stored data. In the 90s, there were pioneering efforts to develop Processing-in-Memory as well as Active Disks. We are considering data stored on Open-Channel SSDs with a programmable storage controller (i.e., a Linux-based ARM processor) integrated into a network switch (e.g., …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Characterize the performance of commercial database systems on an NVIDIA Titan GPU, or Characterize the performance of DB2 PureScale on a cluster equipped with shared storage with a range of different benchmarks. Design and conduct experiment with a range of tuning strategies to measure their impact on performance and reliability.
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

In the context of the Orkney Cloud project, we are preparing the deployment of a decentralized cloud infrastructure on the archipelago. The infrastructure is composed of a collection of Pods (point of delivery) and a wireless core (5G + Wifi). Each Pod is equipped with storage, computing and communication components (so that it is connected to the core and to local endpoints). Each Pod is powered …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

New forms of Solid State Drives have interesting characteristics in terms of performance (10 to 100x faster than previous generations of SSDs) and in terms of functionalities (SSDs can now suspend the execution of writes or erase operations to minimize read latency). The performance characteristics of these devices is not well understood yet. The topic of this thesis is to design and conduct …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2019
Tags: FPGA, SSD


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

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

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC

PROPOSAL

The variety and complexity of data-intensive applications and systems have been increasing drastically the past decade. Tasks from a SQL-based big data analytics request running on Apache Spark can be very different from tasks from deep learning training using TensorFlow framework. Nevertheless, these data-intensive applications increasingly run on shared hardware resources in data centers or …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: hardware-software co-design

PROPOSAL

Improvements in modern processor hardware does not automatically enable more complex and higher quality games, animations, and virtual reality applications. A paradigm shift is necessary when it comes to how we develop these applications in order to exploit the resources of modern hardware (i.e., main memory, multicores) effectively. Unity Technologies has recently developed Data-Oriented …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: memory hierarchy, concurrency

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

Spreading the computation of similar concurrent tasks that have a large instruction footprint over multiple cores via thread migration is shown to improve the instruction cache utilization drastically since it allows instruction re-use across the concurrent tasks. However, thread migrations are costly due to the context switching overhead. To reduce this overhead, recent work mainly proposed …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The computer architecture community is moving toward commoditization of hardware specialization instead of general purpose CPUs and more agile hardware development instead of years-long production cycles to enable faster, more energy-efficient, and more cost-effective hardware/software co-designs. This will lead to a disruption in the way we design and maintain the emerging data management systems …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The Transaction Processing Performance Council (TPC) is a non-profit IT organization founded to define database benchmarks and disseminate objective, verifiable performance data to the industry. TPC has standardized several new benchmarks (e.g., TPCx-HS and TPCx-BB), in recent years. Older popular benchmarks, like TPC-C (representing high-performance transaction processing) and TPC-H (representing …
Supervisor: Pınar Tözün
Semester: Fall 2019


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

In LoRaWAN networks such as the Things Network, long distance transmissions, well beyond the limitations of line of sight in terrestrial geometry, are frequently observed. Tropospheric effects are seen as responsible for bending or guiding radio waves around the earth curvature. As an example, under the right weather conditions, the LoRaWAN gateway at ITU may collect packets from northern Germany, …
Supervisors: Sebastian Büttrich
Semester: Spring 2021
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, troposphere, weather

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

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

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: Veronika Cheplygina

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 algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, medical imaging, crowdsourcing, similarity


Supervisor: Zsolt István

PROPOSAL

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

PROPOSAL

(This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Benchmarking, Distributed Systems

PROPOSAL

Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Blockchain, Data Management, Benchmarking

PROPOSAL

Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Hardware-software Co-design, Security and Privacy



Semester: Spring 2021

Supervisors
  1. Aaron Duane
  2. Bethany Chamberlain
  3. Björn Þór Jónsson
  4. Dovile Juodelyte
  5. Ehsan Yousefzadeh-Asl-Miandoab
  6. Iman Elghandour
  7. Julian Priest
  8. Maria Astefanoaei
  9. Niclas Hedam
  10. Niels Ørbæk Chemnitz
  11. Paul Rosero
  12. Philippe Bonnet
  13. Pınar Tözün
  14. Sebastian Büttrich
  15. Veronika Cheplygina
  16. Zsolt István

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: Bethany Chamberlain


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

PROPOSAL

The index structure used for Exquisitor is eCP, a very scalable index for high-dimensional retrieval. While multimedia collections are typically constantly updated, however, the index is unfortunately static. The goal of this project is to implement and compare approaches for index maintenance. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: index maintenance, high-dimensional indexing

PROPOSAL

In this project, we propose to implement media server and media browser encapsulating a new data model for analysing media collections, called Multimedia Analytics Data Services (MADS). To validate the design, some scalability experiments should be performed. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability

PROPOSAL

Students at ITU have made a prototype version of the Exquisitor system for the Android mobile phone! The system is missing some of Exquisitor’s advanced functionality, such as search and indexing, and the goal is to add and evaluate this functionality. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, android

PROPOSAL

We propose to develop a new prototype for analysing large multimedia collections in Virtual Reality, using the new Valve Indexes. 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…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: virtual reality, multimedia analytics

PROPOSAL

The goal of this project is to integrate Exquisitor with other pieces of existing technology and turn into a competitor for a live video retrieval competition. The project is suitable for 3-4 well-qualified MSc students. The ​Video Browser Showdown​ (VBS) is a live competition​ for video search andretrieval, held at the International Conference on Multimedia Modeling (MMM). In VBS, the competition …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is ensure diversity in the relevance feedback results, to improve quality of the user experience. The project is suitable for 1-3 well-qualified MSc students. In many creative tasks, the designer will knowsome stock image is good for a design just stumbling upon the image. This “Aha!” moment requires browsing thousands of images by categories. In other words, it requires …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is to use state of the art in eye tracking to design, implement and evaluate different eye-tracking interfaces for Exquisitor. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is build a prototype of the Exquisitor system for mobile devices. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of those collections [1].Based on past research results [2], we …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019


Supervisor: Dovile Juodelyte

PROPOSAL

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: Spring 2022
Tags: transfer learning, deep learning, medical imaging


Supervisor: Ehsan Yousefzadeh-Asl-Miandoab


Supervisor: Iman Elghandour

PROPOSAL

It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Spark assumes that it executes its applications on a homogeneous cluster of similar nodes. However, it is becoming common that in-house clusters have heterogeneous compute re- sources and it is good to exploit all of them in the most efficient way. The objective of this master thesis is to extend the Spark scheduler to be resources- aware and to efficiently schedule Spark tasks on all the …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Distributed computing platforms such as Hadoop and Spark focus on addressing the fol- lowing challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

In the last few years, it became common to accelerate Hadoop and Spark by enabling them to execute tasks and jobs on accelerators such as GPUs and FPGAs. The objective of this master thesis is to study new approaches that efficiently predicts the execution time of Spark tasks and jobs executed on GPUs. Part of the work will be to build a performance prediction model for GPUs, which can be built …
Supervisor: Iman Elghandour
Semester: Fall 2019


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: Niclas Hedam

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC


Supervisor: Niels Ørbæk Chemnitz

PROPOSAL

With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data

PROPOSAL

How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: NILM, ML, IoT, Energy Data, Smart Meters, Python, Data Science, Time Series Data


Supervisor: Paul Rosero

PROPOSAL

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

PROPOSAL

TinyML is a new trend to deploy deep learning in tiny devices. Therefore, it is necessary to deploy several applications to understand the challenges and opportunities which tinyML brings us. In this scenario, any idea with embedded computer vision, voice recognition, and sensors are welcome.
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems, Computer vision, Voice recognition


Supervisor: Philippe Bonnet

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: Philippe Bonnet
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

PROPOSAL

Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, Nextflow), package managers (e.g., bioconda), and container systems (e.g., Singularity). However, validating their executions on high-performance computers remains an open issue. Indeed, there are many …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: ML, reproducibility, workflow, HPC

PROPOSAL

Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite for computational reproducibility is the availability of software and data. However, this is problematic for edge devices whose goal is to reduce the amount of data transferred to the backend. On …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Offloading processing to storage is a means to avoid data movement and thus deal efficiently with very large volumes of stored data. In the 90s, there were pioneering efforts to develop Processing-in-Memory as well as Active Disks. We are considering data stored on Open-Channel SSDs with a programmable storage controller (i.e., a Linux-based ARM processor) integrated into a network switch (e.g., …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Characterize the performance of commercial database systems on an NVIDIA Titan GPU, or Characterize the performance of DB2 PureScale on a cluster equipped with shared storage with a range of different benchmarks. Design and conduct experiment with a range of tuning strategies to measure their impact on performance and reliability.
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

In the context of the Orkney Cloud project, we are preparing the deployment of a decentralized cloud infrastructure on the archipelago. The infrastructure is composed of a collection of Pods (point of delivery) and a wireless core (5G + Wifi). Each Pod is equipped with storage, computing and communication components (so that it is connected to the core and to local endpoints). Each Pod is powered …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

New forms of Solid State Drives have interesting characteristics in terms of performance (10 to 100x faster than previous generations of SSDs) and in terms of functionalities (SSDs can now suspend the execution of writes or erase operations to minimize read latency). The performance characteristics of these devices is not well understood yet. The topic of this thesis is to design and conduct …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2019
Tags: FPGA, SSD


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

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

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC

PROPOSAL

The variety and complexity of data-intensive applications and systems have been increasing drastically the past decade. Tasks from a SQL-based big data analytics request running on Apache Spark can be very different from tasks from deep learning training using TensorFlow framework. Nevertheless, these data-intensive applications increasingly run on shared hardware resources in data centers or …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: hardware-software co-design

PROPOSAL

Improvements in modern processor hardware does not automatically enable more complex and higher quality games, animations, and virtual reality applications. A paradigm shift is necessary when it comes to how we develop these applications in order to exploit the resources of modern hardware (i.e., main memory, multicores) effectively. Unity Technologies has recently developed Data-Oriented …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: memory hierarchy, concurrency

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

Spreading the computation of similar concurrent tasks that have a large instruction footprint over multiple cores via thread migration is shown to improve the instruction cache utilization drastically since it allows instruction re-use across the concurrent tasks. However, thread migrations are costly due to the context switching overhead. To reduce this overhead, recent work mainly proposed …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The computer architecture community is moving toward commoditization of hardware specialization instead of general purpose CPUs and more agile hardware development instead of years-long production cycles to enable faster, more energy-efficient, and more cost-effective hardware/software co-designs. This will lead to a disruption in the way we design and maintain the emerging data management systems …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The Transaction Processing Performance Council (TPC) is a non-profit IT organization founded to define database benchmarks and disseminate objective, verifiable performance data to the industry. TPC has standardized several new benchmarks (e.g., TPCx-HS and TPCx-BB), in recent years. Older popular benchmarks, like TPC-C (representing high-performance transaction processing) and TPC-H (representing …
Supervisor: Pınar Tözün
Semester: Fall 2019


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

In LoRaWAN networks such as the Things Network, long distance transmissions, well beyond the limitations of line of sight in terrestrial geometry, are frequently observed. Tropospheric effects are seen as responsible for bending or guiding radio waves around the earth curvature. As an example, under the right weather conditions, the LoRaWAN gateway at ITU may collect packets from northern Germany, …
Supervisors: Sebastian Büttrich
Semester: Spring 2021
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, troposphere, weather

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

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

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: Veronika Cheplygina

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 algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, medical imaging, crowdsourcing, similarity


Supervisor: Zsolt István

PROPOSAL

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

PROPOSAL

(This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Benchmarking, Distributed Systems

PROPOSAL

Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Blockchain, Data Management, Benchmarking

PROPOSAL

Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Hardware-software Co-design, Security and Privacy



Semester: Spring 2022

Supervisors
  1. Aaron Duane
  2. Bethany Chamberlain
  3. Björn Þór Jónsson
  4. Dovile Juodelyte
  5. Ehsan Yousefzadeh-Asl-Miandoab
  6. Iman Elghandour
  7. Julian Priest
  8. Maria Astefanoaei
  9. Niclas Hedam
  10. Niels Ørbæk Chemnitz
  11. Paul Rosero
  12. Philippe Bonnet
  13. Pınar Tözün
  14. Sebastian Büttrich
  15. Veronika Cheplygina
  16. Zsolt István

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: Bethany Chamberlain


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

PROPOSAL

The index structure used for Exquisitor is eCP, a very scalable index for high-dimensional retrieval. While multimedia collections are typically constantly updated, however, the index is unfortunately static. The goal of this project is to implement and compare approaches for index maintenance. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: index maintenance, high-dimensional indexing

PROPOSAL

In this project, we propose to implement media server and media browser encapsulating a new data model for analysing media collections, called Multimedia Analytics Data Services (MADS). To validate the design, some scalability experiments should be performed. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, scalability

PROPOSAL

Students at ITU have made a prototype version of the Exquisitor system for the Android mobile phone! The system is missing some of Exquisitor’s advanced functionality, such as search and indexing, and the goal is to add and evaluate this functionality. Read more…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: multimedia analytics, android

PROPOSAL

We propose to develop a new prototype for analysing large multimedia collections in Virtual Reality, using the new Valve Indexes. 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…
Supervisor: Björn Þór Jónsson
Semester: Fall 2020
Tags: virtual reality, multimedia analytics

PROPOSAL

The goal of this project is to integrate Exquisitor with other pieces of existing technology and turn into a competitor for a live video retrieval competition. The project is suitable for 3-4 well-qualified MSc students. The ​Video Browser Showdown​ (VBS) is a live competition​ for video search andretrieval, held at the International Conference on Multimedia Modeling (MMM). In VBS, the competition …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is ensure diversity in the relevance feedback results, to improve quality of the user experience. The project is suitable for 1-3 well-qualified MSc students. In many creative tasks, the designer will knowsome stock image is good for a design just stumbling upon the image. This “Aha!” moment requires browsing thousands of images by categories. In other words, it requires …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is to use state of the art in eye tracking to design, implement and evaluate different eye-tracking interfaces for Exquisitor. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019

PROPOSAL

The goal of this project is build a prototype of the Exquisitor system for mobile devices. The project is suitable for 1-3 well-qualified MSc students. Image and media collections are becoming a central information resource for a growing number of domains. This calls for very effective tools for interactive exploration of the contents of those collections [1].Based on past research results [2], we …
Supervisor: Björn Þór Jónsson
Semester: Fall 2019


Supervisor: Dovile Juodelyte

PROPOSAL

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: Spring 2022
Tags: transfer learning, deep learning, medical imaging


Supervisor: Ehsan Yousefzadeh-Asl-Miandoab


Supervisor: Iman Elghandour

PROPOSAL

It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Spark assumes that it executes its applications on a homogeneous cluster of similar nodes. However, it is becoming common that in-house clusters have heterogeneous compute re- sources and it is good to exploit all of them in the most efficient way. The objective of this master thesis is to extend the Spark scheduler to be resources- aware and to efficiently schedule Spark tasks on all the …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

Distributed computing platforms such as Hadoop and Spark focus on addressing the fol- lowing challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously …
Supervisor: Iman Elghandour
Semester: Fall 2019

PROPOSAL

In the last few years, it became common to accelerate Hadoop and Spark by enabling them to execute tasks and jobs on accelerators such as GPUs and FPGAs. The objective of this master thesis is to study new approaches that efficiently predicts the execution time of Spark tasks and jobs executed on GPUs. Part of the work will be to build a performance prediction model for GPUs, which can be built …
Supervisor: Iman Elghandour
Semester: Fall 2019


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: Niclas Hedam

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC


Supervisor: Niels Ørbæk Chemnitz

PROPOSAL

With the recent hunger for being “data driven”, many organizations are eager for integrating ML in there decision making process. Unfortunately, competent data scientists are still relatively scarce, and manual model development cannot keep up with the demand for magic AI solutions. This is no less true when it comes to forecasting. Knowing the future is extremely handy when making …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: AutoML, ML, Forecasting, Energy Data, Smart Meters, Python, Data Science, Time Series Data

PROPOSAL

How much does our smart meter readings disclose about us? Can we disentangle the oven from the washing machine from the kettle? Can we identify demographics and behavior patterns from the stream of electricity data? Most danish homes are now equipped so-called “smart meters” - networked electricity meters that report consumption and load at much higher rate than conventional meters. …
Supervisors: Niels Ørbæk Chemnitz
Semester: Spring 2021
Tags: NILM, ML, IoT, Energy Data, Smart Meters, Python, Data Science, Time Series Data


Supervisor: Paul Rosero

PROPOSAL

Outlier detection is carried out when the information is stored at the server. However, with the new IoT computational capabilities, outlier detection can be developed locally. Therefore, it is necessary to know how much RAM/Flash is needed for this step and which IoT brands can handle it. This project is divided into two parts. The first is implementing light-heavy ML algorithms in single points …
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems

PROPOSAL

TinyML is a new trend to deploy deep learning in tiny devices. Therefore, it is necessary to deploy several applications to understand the challenges and opportunities which tinyML brings us. In this scenario, any idea with embedded computer vision, voice recognition, and sensors are welcome.
Supervisor: Paul Rosero
Semester: Spring 2022
Tags: data analysis, IoT, Python, Embedded systems, Computer vision, Voice recognition


Supervisor: Philippe Bonnet

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: Philippe Bonnet
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

PROPOSAL

Reproducibility is a cornerstone of the scientific method. There are systems available today to build reproducible and sharable data and analysis pipelines including workflow engines (e.g., GWL, Nextflow), package managers (e.g., bioconda), and container systems (e.g., Singularity). However, validating their executions on high-performance computers remains an open issue. Indeed, there are many …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: ML, reproducibility, workflow, HPC

PROPOSAL

Reproducibility is a cornerstone of the scientific method. It is also a core element of compliance requirements for sensitive equipment, e.g., audit trails for medical equipment. Often, a prerequisite for computational reproducibility is the availability of software and data. However, this is problematic for edge devices whose goal is to reduce the amount of data transferred to the backend. On …
Supervisors: Philippe Bonnet
Semester: Fall 2020
Tags: reproducibility, edge

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Offloading processing to storage is a means to avoid data movement and thus deal efficiently with very large volumes of stored data. In the 90s, there were pioneering efforts to develop Processing-in-Memory as well as Active Disks. We are considering data stored on Open-Channel SSDs with a programmable storage controller (i.e., a Linux-based ARM processor) integrated into a network switch (e.g., …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Characterize the performance of commercial database systems on an NVIDIA Titan GPU, or Characterize the performance of DB2 PureScale on a cluster equipped with shared storage with a range of different benchmarks. Design and conduct experiment with a range of tuning strategies to measure their impact on performance and reliability.
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

In the context of the Orkney Cloud project, we are preparing the deployment of a decentralized cloud infrastructure on the archipelago. The infrastructure is composed of a collection of Pods (point of delivery) and a wireless core (5G + Wifi). Each Pod is equipped with storage, computing and communication components (so that it is connected to the core and to local endpoints). Each Pod is powered …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

New forms of Solid State Drives have interesting characteristics in terms of performance (10 to 100x faster than previous generations of SSDs) and in terms of functionalities (SSDs can now suspend the execution of writes or erase operations to minimize read latency). The performance characteristics of these devices is not well understood yet. The topic of this thesis is to design and conduct …
Supervisor: Philippe Bonnet
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2020
Tags: FPGA, SSD, computational storage

PROPOSAL

Field Programmable Gate Arrays are now an integral part of public cloud infrastructures. You can for example run customized FPGA instances on AWS. A project focuses on FPGA-based hardware acceleration at the level of an SSD Flash Translation Layer, at the level of a Database storage manager or at the level of the database client. You will be able to experiment with FPGAs in the lab and on AWS. We …
Supervisor: Philippe Bonnet
Semester: Fall 2019
Tags: FPGA, SSD


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

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

PROPOSAL

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC

PROPOSAL

The variety and complexity of data-intensive applications and systems have been increasing drastically the past decade. Tasks from a SQL-based big data analytics request running on Apache Spark can be very different from tasks from deep learning training using TensorFlow framework. Nevertheless, these data-intensive applications increasingly run on shared hardware resources in data centers or …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: hardware-software co-design

PROPOSAL

Improvements in modern processor hardware does not automatically enable more complex and higher quality games, animations, and virtual reality applications. A paradigm shift is necessary when it comes to how we develop these applications in order to exploit the resources of modern hardware (i.e., main memory, multicores) effectively. Unity Technologies has recently developed Data-Oriented …
Supervisor: Pınar Tözün
Semester: Fall 2020
Tags: memory hierarchy, concurrency

PROPOSAL

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking

PROPOSAL

Spreading the computation of similar concurrent tasks that have a large instruction footprint over multiple cores via thread migration is shown to improve the instruction cache utilization drastically since it allows instruction re-use across the concurrent tasks. However, thread migrations are costly due to the context switching overhead. To reduce this overhead, recent work mainly proposed …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The computer architecture community is moving toward commoditization of hardware specialization instead of general purpose CPUs and more agile hardware development instead of years-long production cycles to enable faster, more energy-efficient, and more cost-effective hardware/software co-designs. This will lead to a disruption in the way we design and maintain the emerging data management systems …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

Apache SystemML is an open-source platform to run machine learning tasks efficiently thanks to the hardware-conscious query compilation techniques it adopts. It can be run standalone or on top of Apache Spark. It is considered to be state-of-the-art when running machine learning tasks (i.e., in ACM SIGMOD 2017, there were ~5 papers that used SystemML as a comparison point). This project aims at …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The popularity of large-scale real-time analytics applications (real-time inventory/pricing, recommendations from mobile apps, fraud detection, risk analysis, IoT, etc.) keeps rising. These applications require distributed data management systems that can handle fast concurrent transactions (OLTP) and analytics on the recent data. Some of them even need running analytical queries (OLAP) as part of …
Supervisor: Pınar Tözün
Semester: Fall 2019

PROPOSAL

The Transaction Processing Performance Council (TPC) is a non-profit IT organization founded to define database benchmarks and disseminate objective, verifiable performance data to the industry. TPC has standardized several new benchmarks (e.g., TPCx-HS and TPCx-BB), in recent years. Older popular benchmarks, like TPC-C (representing high-performance transaction processing) and TPC-H (representing …
Supervisor: Pınar Tözün
Semester: Fall 2019


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

In LoRaWAN networks such as the Things Network, long distance transmissions, well beyond the limitations of line of sight in terrestrial geometry, are frequently observed. Tropospheric effects are seen as responsible for bending or guiding radio waves around the earth curvature. As an example, under the right weather conditions, the LoRaWAN gateway at ITU may collect packets from northern Germany, …
Supervisors: Sebastian Büttrich
Semester: Spring 2021
Tags: IoT, LoRaWAN, LPWAN, satellite, networks, troposphere, weather

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

The Things Network Stack v3 for LoRaWAN is an open source LoRaWAN network stack suitable for large, global and geo-distributed public and private networks as well as smaller networks. The architecture follows the LoRaWAN Network Reference Model for standards compliancy and interoperability. - https://github.com/TheThingsNetwork/lorawan-stack This stack, currently in pre-rollout testing, however …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

New types of networks such as LoRaWAN and Sigfox enable us to deploy inexpensive mobile Air Quality Sensors, e.g. on bicycles or vehicles, boats or trains, and help map and understand urban pollution. Such data would be valuable for e.g. the international citizen science project Luftdaten https://luftdaten.info/en/home-en/ in which we are participating. We have an ongoing collaboration with the …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Can we combine satellite data with terrestrial and marine sensor data, to study their correlation, and benefit our understanding of environmental processes, both in urban and agricultural/rural context? Potential collaboration with several organizations in Kenya and Orkney islands.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Not far from the IT University, citizens are buillding a “Small Smart City”, aiming to measure and analyze e.g. water and resource consumption. Help build the Small Smart City!
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Survey of potential sensor modalities (sounds, ultrasounds, vibrations) and related work (e.g., wind turbines) Starting with sound: Piezo contact mics/transducers, MEMS sensors Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Survey of potential sensor modalities (IR temp sensor, thermal imager) and related work Starting with consumer USB cams generate series of images or phone cams Characterization of state based on known signatures (classification problem) Characterization of state transitions (HMM) Experimentation on Coffee machine/Blender/3D printer at PitLab 1..k sensors; Local/cloud-based processing. …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Novo Nordisk

PROPOSAL

Most data collection in IoT does not critically depend on latency or speed from data collection to data analytics. Occasionally though we meet tasks that would benefit from near-realtime features, such as collection of wave and tidal dynamics around marine energy infrastructures. This project explores the limits of speed by bringing together a LoRa PHY, a LoRaWAN gateway, LoRaWAN stack, ultrafast …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

In collaboration with the IoT Lab at Computer Science Dept at Kathmandu University, Nepal, we are developing a potential service for tracking trekkers, i.e. offering a security service for tourists trekking the Himalayas, in particular Mt. Everest. This service very critically depends on having a robust hardware component, the actual GPS/GNSS tracker. Requirements with respect to battery life, …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019

PROPOSAL

Wind turbine electricity production data is sensitive for Energinet (and for the wind turbine producers). Energinet would like to publish wind turbine electricity production data sets that can be used to train relevant models and to develop innovative applications, without giving away sensitive data. The goal of the project is to explore various data publishing methods for that purpose.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, data publication

PROPOSAL

Energinet has a model that describes the electricity production of a given wind turbine given wind conditions. The current model based on kNN is trained with DMI weather data and historical electricity production data for the wind turbine. The goal of the project is to improve the current model with lifelong learning, extended weather data and different models for a range of different wind …
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Data Analysis

PROPOSAL

The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Forecasting, Machine Learning, Deep Learning

PROPOSAL

The goal of the project is to explore new ways of gathering data about wind turbines as well as local wind/weather conditions. To this end, sound/vibration-based and/or image-based instrumentation as well as innovative experiments such as balloons and light weight weather stations might be considered.
Supervisors: Philippe Bonnet, Sebastian Büttrich
Semester: Fall 2019
Tags: Wind Energy, Energinet, Instrumentation, Sensors

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: Veronika Cheplygina

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 algorithms for skin lesion classification typically learn from images which have been labeled as malignant (for example, melanoma) or not. Such tasks can still suffer from overfitting due to limited dataset size. In other computer vision tasks, crowdsourcing labels has been effective, but the average person typically does not have the background to classify skin lesions. However, …
Supervisors: Veronika Cheplygina
Semester: Fall 2021
Tags: machine learning, medical imaging, crowdsourcing, similarity


Supervisor: Zsolt István

PROPOSAL

(This project will be carried out in collaboration with Xilinx Research Labs in Dublin) Machine Learning operators are becoming increasingly commonly used in data management systems and, in this project, we will explore the challenges and benefits of integrating inference operators from FINN [1] within a so-called Smart Storage system [2]. Both the inference and data management aspects will be …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Data Management, MachineLearning

PROPOSAL

(This topic is going to be co-supervised by Bernardo Machado David [http://www.bmdavid.com/]) Database systems managing private data may leak sensitive information when queries are done in the clear, even if the data itself is encrypted. A recent line of research has looked into combining database engines supporting standard SQL queries with techniques for secure Multiparty Computation (MPC), …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Benchmarking, Distributed Systems

PROPOSAL

Given a private database that I can access only through specific queries, there is still a lot I can learn on its entries [1]. Differential Privacy (DP) tackles this: letting me learn the (approximate) result of complex queries on a database, but preventing me from learning much about its specific entries. The basic approach of DP often boils down to: “apply a privacy-preserving transformation T …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Theoretical Computer Science, Data Management, Security and Privacy

PROPOSAL

Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Blockchain, Data Management, Benchmarking

PROPOSAL

Modern data analytics systems are composed of two types of nodes: compute and storage (e.g., Amazon S3, Redis, MongoDB, etc.). The storage nodes typically offer a key-value interface and are often used to store data encoded in a columnar format (e.g., Parquet files). Due to growing data sizes in datacenters, there is an increasing interest in using specialized hardware devices, namely Field …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: FPGA, Hardware-software Co-design, Security and Privacy