Matematikcentrum

Lund University

Examensarbetesförslag

  • Kors-spektrumanalys av hjärtfrekvens i samband med arbetsrelaterad stress
  • A number of recent studies report that decreased heart rate variability (HRV) power is related to cardivascular disease, depression, various anxiety disorders, and long-term work related stress or burnout. This project aims for classification of groups of patients with stress related diagnosis using a novel methodology for time-frequency analysis of locally stationary processes. The work includes analysis and evaluation on a novel set of HRV measurement data controlled by metronome guided respitation. Prerequisites: FMSF10/MASC04, FMSN35/MASM26
  • Phase and cross-spectral analysis for understanding the relations between sound and the ‘listening’ brain
  • We are remarkably good at focusing on only one talker in a scene consisting of multiple, spatially separated talkers, also known as the cocktail-party scenario. However, our knowledge of the brain’s ability in these situations is very limited. Phase and cross-spectral analysis of the sound and the brain responses should be investigated using different techniques, to find such relations. The project is performed in close collaboration with Eriksholm Research Centre, Oticon A/S, Denmark.
  • Songbird dialects
  • Quantification of similarity between complex songs recorded in noisy environments in the wild is a substantial challenge. Therefore, the goal of this project is to improve existing quantitative methods for assessing similarity between the songs of Spiza americana. The project is a collaboration with Timothy Parker, Dept of Biology, Whitham College, Walla Walla, USA Prerequisites: FMSF10/MASC04, (FMSN35/MASM26)
  • Respiratory and pulse monitoring
  • The goal of this project is to achieve respiratory and/or pulse monitoring using novel radar technology. The project will be done at Acconeer using their 60 GHz pulsed coherent radar which originates from research at LTH. Prerequisites: FMSF10/MASC04, (FMSN45/MASM17, FMSN35/MASM26)
  • Multiple-channel dolphin sonar beam characterization
  • The sonar beam of toothed whales contains several signal components and to accurately detect and localize the components in the time-frequency domain is essential to understand to what extent the signal can be controlled by the animal and what functions it serves. This project aims to studying and characterizing multiple-channel sonar beam measurements and also possibly develop and tailor the method using information from the multiple-channel structure. Prerequisites: FMSF10/MASC04, (FMSN35/MASM26)
  • How many persons are there in the room?
  • Minut AB makes a smart home alarm and monitoring system that is increasingly used as a way for short-stay hosts to monitor their properties. Our system has been designed from the ground up with privacy in mind, which makes it a good fit for people who want to make sure their properties are all right, without having a camera there. The reasons for why short-stay hosts might want to monitor their properties are many, one reason is that it's increasingly becoming a problem that guests are hosting parties in the rented properties, sometimes with devastating outcomes (https://www.bbc.com/news/world-us-canada-50276485). There are many ways in which a device like Minut could figure out whether there is a party ongoing in a property, the most telling signal would probably be the level of noise. Another possibility is to analyse the frequency content of the audio to get an understanding of what is happening, this is something we at Minut have done before by classifying the 'acoustic scene' as either a party or not. One factor that could determine whether there is a risk for a party is the number of people in the property. The number of people in the property is also useful for the host to know even if there is no party ongoing, since the billing is often based on the number of people that are staying over. We would like to explore the possiblity to use audio analysis to get an estimate of the number of people that are nearby, by counting the number of unique voices heard in an acoustic scene. We think the best approach for doing this is by training deep neural networks on some representation of the audio, but the student(s) are free to use which methods they want. One twist to this problem is that we would like to run the trained model on a small microcontroller, which puts serious contraints on the size and runtime memory usage by the model. The candidate(s) should preferably have a strong interest in machine learning and digital signal processing. We would be happy to see two students working on this, but you are welcome to apply alone too. If you are interested, send an email to colin@minut.com and tell us about your interests.