Tech blog

Filtering noisy measurements can be an extremely difficult endeavor; take brain signals for example. While this is true, there are also many situations where the system is fairly simple and the focus lies on speed, online computation (i.e. in real time) and ease of use. Filtering distance measurements from a sonar sensor can be such a case. For these applications it is useful to have a simple, but effective, noise filtering algorithm in your toolkit....

A key problem (or challenge) within smart spaces is indoor localization: making estimates of users’ whereabouts. Without such information, systems are unable to react on the presence of users or, sometimes even more important, their absence. This can range from simply turning the lights on when someone enters a room to customizing the way devices interact with a specific user. Even more important for a system to know where users exactly are, is to know where users are relative to the devices it can control or use to sense the environment....

SLAC.js Indoor localisation using Javascript Simultaneously Localisation and Configuration (SLAC) of indoor Wireless Sensor Networks and their users built upon FastSLAM. With SLAC we aim to simultaneously locate both the user and the devices of a system deployed in an indoor environment. The algorithm is privacy-aware and is an online localisation method; i.e. localisation starts whenever a user starts moving inside a building. Moreover, for the SLAC system we focus on a solution that can be deployed in smart spaces without additional hardware requirements besides users’ mobile phones and the components of the space....

Lately we looked at GPy using some simple examples. Now we’ll dive deeper and look more closely to the inner workings of models. We’ll start with the basics and continue further by decomposing the Guassian Process Latent Variable model (GPLVM). Note: This post is fairly specific to GPy and the chosen model. Investigating parameters All GPy models extend the Parameterized class. This base class gives us several useful functions for interacting with the model parameters....

A random process, a collection of random variables, is said to be a Gaussian process (GP)1 if any finite number of these variables have a joint Gaussian distribution; i.e. the relation between variables follows a Gaussian distribution, this says something about the smoothness of functions generated by these processes. Guassian processes are used for many tasks in machine learning; from classification to regression and latent variable models. A lot of work on this subject is done by the machine learning group at the University of Sheffield which maintain and develop the GPy package: a framework, written in python, for GP’s....