University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Computing Perception from Sensor Data

Barnaghi, P, Ganz, F, Henson, C and Sheth, A (2012) Computing Perception from Sensor Data In: IEEE Sensors 2012, 2012-10-28 - 2012-10-31, Taipei, Taiwan.

[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf

Download (33kB)
[img]
Preview
PDF
PID2479545.pdf

Download (711kB)

Abstract

This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations in a knowledge-based model to create perceptions from sensor data.

Item Type: Conference or Workshop Item (Paper)
Additional Information:

Copyright 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research
Depositing User: Symplectic Elements
Date Deposited: 30 Nov 2012 12:03
Last Modified: 09 Jun 2014 13:15
URI: http://epubs.surrey.ac.uk/id/eprint/721671

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800