The DISCO-2 satellite is an Earth observation satellite in collaboration with the Arctic Research Center in Aarhus and is designed to complement ground based field studies in Greenland. The satellite instrument consists of 2 high quality visible light and 1 infrared cameras, as well as and attitude control system and coral TPU ML coprocessor. In this project you will develop software to control …
Supervisors: Julian Priest
Semester: Fall 2023
Tags: satellite, climate change, image processing, ML, csp, embedded, space

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 2022
Tags: IoT, ML, machineLearning, sensors, security

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

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

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