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Ektara [18,75] EKTARA de nes a full framework for Context-Aware Wearable and Ubiquitous Computing Applications The EKTARA framework consists of the following components: Context-Aware Integration Manager (CAIM) This provides a uniform framework for interaction between applications and the user The primary goal of the CAIM is to minimize the demands on the user's time and attention while maximizing the relevance of the information provided The CAIM aims to achieve this goal by taking into account the HCI resources currently available to the user, important contextual factors making the user's ability to pay attention to the UWC system, and the actions of the user's applications Further, the criteria by which the CAIM makes these decisions must be understandable and controllable by the user.



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Ideally the CAIM should implicitly learn the user's preferences over time yet still be able to provide the user with an explicit description of its decision model, which becomes part of the user's personal pro le, which is always accessible wherever she goes (eg, by being stored on her person in a wearable computer) Contextual Information Service (CIS) This is a distributed database service, which provides UWC applications and services a uniform means of storing and retrieving contextual information Clients may query a server for all records matching a context template or subscribe to receive records when matching information is posted or expires CIS Servers may register selected contexts of other servers, allowing clients to discover other members of the CIS federation.





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def explanatory variables(self, t): if t < 0: raise RuntimeError, \ "attempting to call explanatory variables in the past" fill = self. model.fill() requestor = self. model.requestor() state = self. model.state().fill(t, requestor, self. env) exercise = self. model.exercise() return exercise(t, fill, state, requestor, self. env)

The CIS must support a range of context classi cations, including location, authorship, intended recipient of information, time of posting, time of relevance, time of expiry, deliverability (whether a record is ordinarily intended to be delivered once or multiple times), MIME document type, and an extensible mechanism to allow the uniform handling of unexpected or idiosyncratic contexts Perceptual Context Engine (PCE) This is a means of turning raw sensor data and information from other sources, such as a real-world description such as `Meeting in Espoo,' into symbolic context descriptions The PCE has a two-layer structure, with an inference system running over a perceptual context classi er system The Perceptual Context Classi er System (PCCS) is a signal-processing system, which converts the raw sensor data into a collection of probabilistic estimates.

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And by allowing for signi cant miniaturization, transistors serving as switches could be interconnected and assembled into ever more complex electronic systems thus enabling dramatic improvements in the ability to connect on off circuits..

[8] SD Glaser, Some Real-World Applications of Wireless Sensor Nodes, in Proceedings of SPIE Symposium on Smart Structures and Materials, March 2004 [9] C Grosse, F Finck, J Kurz and HW Reinhardt, Monitoring Techniques Based on Wireless AE Sensors for Large Structures in Civil Engineering, in EWGAE Symposium, 2004, 843 856 [10] D Guerri, M Lettere and R Fontanelli, Ambient Intelligence Overview: Vision, Evolution and Perspectives, in 1st GoodFood AmI Workshop, Florence, July 2004 [11] P Havinga, P Jansen, M Lijding and H Scholten, Smart Surroundings, in Proceedings of the 5th Progress Symposium on Embedded Systems, 2004 [12] http://corporatetraf ccom/ntdc/ [13] http://grvcuses/comets/ [14] http://grvcuses/cromat/ [15] http://meteoraucsdedu [16] http://nesleeuclaedu/projects/smartkg/ [17] http://roboticsuscedu [18] http://www roboticsuscedu [19] http://wwwascetcom [20] http://wwwaubade groupcom [21] http://wwwubisensenet/ [22] http://wwwukresearchattcom/bat/.

Conceptually, the classi er system allows the user to train event recognition functions, or classi ers, to recognize patterns in the sensory data and tag them as speci c events The mechanism by which this time-series recognition occurs is a multi-level HMM grammar that is capable of recognizing patterns over a range of timescales from seconds to days Inference System (IS) The job of the Inference System is to take the output of the classi cation system (and other sources of context such as a system clock or GPS.

[23] Rianne Huis in t Veld, Miguel Bruns Alonso, Aga Matysiak, Hermie Hermens, Frans Grobbe, Ana Ivanovic, Caroline Hummels, David Keyson, Albert Krohn, Maria Lijding, Joost Meijer, Klaas Sikkel and Felix Smits, Deliverable 51 Settings: The Future is Not What It has Been , Tech report, Smart Surroundings consortium, 2005 [24] Rianne Huis in t Veld, Miguel Bruns Alonso, Aga Matysiak, Hermie Hermens, Frans Grobbe, Ana Ivanovic, Caroline Hummels, David Keyson, Albert Krohn, Maria Lijding, Joost Meijer, Klaas Sikkel and Felix Smits, Deliverable 52 Requirements and Early Implementation of Prototypes, Tech report, Smart Surroundings consortium, 2005 [25] R Husler, Goodfood-Security, in 1st GoodFood AmI Workshop, Florence, July 2004 [26] RH in t Veld, M Vollenbroek and H Hermens, Personal Stress Training System: From Scenarios to Requirements, Tech report, Roessingh Research and Development [27] M Maroti, G Simon, A Ledeczi and J.

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