Non Parametric Distributed Inference in Sensor Networks Using Box Particles Messages

This paper deals with the problem of inference in distributed systems where the probability model is stored in a distributed fashion. Graphical models provide powerful tools for modeling this kind of problems. Inspired by the box particle filter which combines interval analysis with particle filtering to solve temporal inference problems, this paper introduces a belief propagation-like message-passing algorithm that uses bounded error methods to solve the inference problem defined on an arbitrary graphical model. We show the theoretic derivation of the novel algorithm and we test its performance on the problem of calibration in wireless sensor networks. That is the positioning of a number of randomly deployed sensors, according to some reference defined by a set of anchor nodes for which the positions are known a priori. The new algorithm, while achieving a better or similar performance, offers impressive reduction of the information circulating in the network and the needed computation times

PLEIA: A Reconfigurable Platform for Evaluation of HCI acting

For people with severe motor disabilities, it is difficult or sometimes impossible to use standard interface devices (mouse, keyboards, joysticks, trackballs, etc …). The evaluation of their capabilities, the education or re education of abilities and the compensation of their deficiencies is essential. Nowadays, the performance of a person is only determinated by th

Optimal sizing and real-time EMS for low carbon emissions of a hybrid islanded microgrid

International audienc

Optimal sizing and real-time EMS for low carbon emissions of a hybrid islanded microgrid

International audienc