One Minute Madness

1. EuroSSC 2009 One Minute Madness Poster & Demos One Minute Madness

2. EuroSSC 2009 One Minute Madness Poster & Demos Ontology based approach for data management Ilkka Niskanen

3. Ontology based approach for home data management • Sensors in smart homes • Provide various measurements from the surrounding environment • How to integrate the heterogeneous sensor data? • Ontologies • Provide efficient and machine readable way of representing and sharing knowledge • Enable automated context reasoning • VantagePoint - the ontology based home management approach • Visualizes semantic context information • Integrates heterogeneous sensor data into • contextual models • Experiments with sensor data • SimuContext virtual sensors emulate the behavior of life context sources • Carerider bed sensors collect versatile context data concerning the sleep of a person • This data can be utilized when creating informative diagrams

4. EuroSSC 2009 One Minute Madness Poster & Demos Home ReACT – a tool for real-time indoor environmental monitoring Tessa Daniel

5. HomeReACT – the Realtime Indoor Environmental Monitoring Tool Detect and track events Monitor faults Monitor comfort Analyse data Tessa Daniel : Cogent Computing Applied Research Centre, Coventry University

6. EuroSSC 2009 One Minute Madness Poster & Demos Towards semantic enablement for spatial data infrastructure Krzysztof Janowicz

8. EuroSSC 2009 One Minute Madness Poster & Demos A Hybrid MethodforIndoor User Localisation Milan Redžić

9. A Hybrid Method for Indoor User LocalisationMilan Redžić, Ciarán Ó Conaire, Conor Brennan, Noel O’Connor Image matching and localisationThe image matching uses the well-known SURF algorithm [2] which is implemented and installed on the N95 cell phone. An array of correct matches between given image and set of images is formed and max value indicates the image with most matches. AbstractIn this work we describe an approach to indoor user localisation by combining image-based and RF-based methods and compare this new approach to prior work [1]. This paper details a new algorithm for indoor user localisation, demonstrating more effective user localisation than prior approaches and therefore presents the next step in combining two different technologies for localisation in indoor type environments. System Overview Experiments and ResultsLocations scattered throughout DCU(3 floors). Camaignr software [3] was used. Photos from various angles, rotation, scale werecaptured. The hybrid technique is based on applying image matching on the smaller set of locations, which are generated by applying Bayesian analysis to the RF signal strength readings. RF localisation Table of Localisation results where Sirepresents a location, 1 ≤ i ≤ I Camaignr programme Camaignr SS data and Ojis observed signal strength data from access point j, where 1 ≤ j ≤ J References 1. C. O’Conaire, K. Fogarty, C. Brennan and N O’Connor: User Localisation using Visual Sensing and RF signal strength. in: The 6th ACM Conference on Embedded Networked Sensor Systems 2008, Raleigh, NC, 5-7 November 2008. 2. H. Bay, A. Ess, T. Tuytelaars and L. Van Gool: SURF: Speeded Up Robust Features. in: Computer Vision and Image Understanding (CVIU), Vol. 110, pp. 346-349, 2008. 3. http://wiki.urban.cens.ucla.edu/index.php?title=Campaignr This work is supported by Science Foundation Ireland under grant 07/CE/I1147

10. EuroSSC 2009 One Minute Madness Poster & Demos Semantic Rules for Context-Aware Geographical Information Retrieval Krzysztof Janowicz

12. EuroSSC 2009 One Minute Madness Poster & Demos Mobile Access to Smart Home Devices Safiyya Rusli

14. EuroSSC 2009 One Minute Madness Poster & Demos Energy-optimized sensor data processing Elena Chervakova

15. Energy-optimized Sensor Data Processing • ConSAS - Configurable Sensor and Actuator System • AnduIN: Data Stream Management System and In-Network Query Processor • Recognition of contexts detecting correlations and attributes in the measured data • Evaluating known correlations for the creation of “virtual sensors” • Query processing within sensor network or at a central instance

16. EuroSSC 2009 One Minute Madness Poster & Demos Tai Chi motion recognition using wearable sensors and Hidden Markov Model method Lars Widmer

17. Generated feature data: 1. Angles between Limbs 2. Limb-to-Limb orientation 3. Limb Positions HMMs Quantization Majority Vote Tai Chi Motion Recognition Using Wearable Sensors and Hidden Markov Model Method For 5 Tai Chi sub-movements, data was recorded and classification methods compared, indicating the superiority of using clustered limb positions as feature input.

18. EuroSSC 2009 One Minute Madness Poster & Demos Time-lag as limiting factor for indoor walking navigation Markus Straub

20. EuroSSC 2009 One Minute Madness Poster & Demos River Water-level Estimation Using Visual Sensing Edel O‘Connor

21. River Water-level Estimation Using Visual SensingE O’Connor, C O’Conaire, A. F. Smeaton, N. E. O’Connor, D. Diamond Image Data Examples of the challenging image data we are using, demonstrating disparate appearance due to varying rover conditions. Water management is an important part of monitoring the natural environment and includes monitoring the water quality of coastal and inland marine environments. Visual sensing can help to overcome some of the problems associated with in-situ wireless sensor networks and provide context to what is being sensed. The development of a smart multi-modal sensor network will lead to a more robust and effective environmental sensing system. We report on our initial work on using visual sensing to monitor a river environment.

22. EuroSSC 2009 One Minute Madness Poster & Demos Speed-dependent information retrieving for efficient navigation in large-scale sensor networks Kazumasa Ogawa

23. Speed-dependent Information Retrieving for Efficient Navigation in Large Scale Sensor Network Kazumasa Ogawa and Hiroki Saito Department of Information Systems and Multimedia Design, Tokyo Denki University, Japan User review User review User review User review A lot of traffic • Urban sensing systems enable us to obtain huge amount of environmental information. However, for using this system in navigation, vast information floods users’ understandability. • We focus on users’ mobility: • How to obtain suitable surrounding information based on users’ mobility. • How to query for appropriate range of area and how to obtain detailed suitable information. • We propose Speed-dependent information retrieving schema for mobile user navigation. • Our technical contribution is: Scaling search model, Priority-k method, and Map scale adjusting. • Please come to our poster for further content on our investigation. We focus users’ mobility. Our concept: Flooded Navigation Screen Managed by Mobility Speed-dependent information retrieving Map scale adjusting by speed Constant amount of information Scaling Search Model r θ Slow Navigation System for Mobile User VastEnvironmental Information Traffic, Weather, Accident, Event, … r What is necessary information for you? How do you get? Urban-scale Sensor Network Fast θ Direction

24. EuroSSC 2009 One Minute Madness Poster & Demos Mobile Context Toolbox Jakob Eg Larsen

25. Mobile Context Toolbox an extensible context framework for S60 mobile phonesJakob Eg Larsen and Kristian JensenTechnical University of Denmark{jel|krije}@imm.dtu.dk

26. EuroSSC 2009 One Minute Madness Poster & Demos Service and Content Presentation in Ubiquitous Environments Suparna De

27. Service/ Content Presentation in Ubiquitous EnvironmentsSuparna De, Abdelhak Attou, Klaus Moessner

28. EuroSSC 2009 One Minute Madness Poster & Demos That‘s it! Enjoy the Poster & Demo session!