Find watch anomalies:
* Bad connectivity and high power consumption causes
Instead of the customer contacting us can we proactively find watches
* Investigate if we can find watches deviating in connectivity and battery
* There are different kind of users e.g. early adapters, traditional watch users,
* To better support our customers can we get more understanding of how a
user is using the watch?
* Can also give us insights about our customers about future features and products.
* Divide the populations into different categories based on usage.
Watch SW (Firmware) quality
* Different release have different quality i.e. stability, connectivity, power
How can we by using watch analytics data quantify and measure the quality?
* Quantify the quality for different firmware releases, on primarily internal
users, before releasing to customers.
The world leading group in laser remote sensing located at Lund University collects hundreds of thousands insect observation per day from airborne insects using entomological lidar measurements. The measurements, where the signals are of time-varying character and suffer from the influence of varying noise and other disturbances, are used for classification of species, sex and age groups. Exploitation relies on robust estimation of the parameters of the time-varying signals and especially the fundamental tone. In this master thesis we suggest an investigation of a novel technique, matched reassignment, which is a mathematical method that relies on the phase of the Fourier transform to reassign the power of the spectrogram. The method has been shown to outperform other time-frequency techniques and it gives accurate estimates of the time- as well as the frequency locations.
Supervisors: Maria Sandsten, firstname.lastname@example.org; Mikkel Brydegaard and Samuel Jansson, Dept. Physics.
Prerequisites: FMSF10/MASC04, FMSN35/MASM26
Resistivity and time domain induced polarization
(DCIP) measurements is a great tool for mapping
properties of the subsurface. This master thesis project invloves analysis of
existiving DCIP data to find suitable machine learning
or signal processing apporaches to deal with non-stationary
disturbances from trams and metro.
Prerequisites: FMSF10/MASC04, (FMS051/MASM17), (FMSN35/MASM26)
Centre for Mathematical Sciences
Box 118, 221 00 LUND
Phone: 046-222 00 00 (växel)