Complete trajectory reconstruction from sparse mobile phone data

Probabilistic models for mobile phone trajectory estimation

This dissertation is concerned with the problem of determining the track or trajectory of a mobile device — for example, a sequence of road segments on an outdoor map, or a sequence of rooms visited inside a building — in an energy-efficient and accurate manner. GPS, the dominant positioning technology today, has two major limitations. First, it consumes significant power on mobile phones, maki...

Daily Routine Classification from Mobile Phone Data

The automatic analysis of real-life, long-term behavior and dynamics of individuals and groups from mobile sensor data constitutes an emerging and challenging domain. We present a framework to classify people’s daily routines (defined by day type, and by group affiliation type) from real-life data collected with mobile phones, which include physical location information (derived from cell tower...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Image reconstruction from photon sparse data

We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the ...