A dual-band antenna enabling improved quality of service in multi-radio wireless sensor applications in indoor environments

Science Foundation Ireland (CSET - Centre for Science, Engineering and Technology, Grant No. 07/CE/11147)

Can population-level variation in early childhood development be measured in a manner that supports service planning and intervention?

Background: The Early Development Instrument (EDI) is a population-level measure of five developmental domains at school-entry age. The overall aim of this thesis was to explore the potential of the EDI as an indicator of early development in Ireland. Methods: A cross-sectional study was conducted in 47 primary schools in 2011 using the EDI and a linked parental questionnaire. EDI (teacher completed) scores were calculated for 1,344 children in their first year of full-time education. Those scoring in the lowest 10% of the sample population in one or more domains were deemed to be 'developmentally vulnerable'. Scores were correlated with contextual data from the parental questionnaire and with indicators of area and school-level deprivation. Rasch analysis was used to determine the validity of the EDI. Results: Over one quarter (27.5%) of all children in the study were developmentally vulnerable. Individual characteristics associated with increased risk of vulnerability were being male; under 5 years old; and having English as a second language. Adjusted for these demographics, low birth weight, poor parent/child interaction and mother’s lower level of education showed the most significant odds ratios for developmental vulnerability. Vulnerability did not follow the area-level deprivation gradient as measured by a composite index of material deprivation. Children considered by the teacher to be in need of assessment also had lower scores, which were not significantly different from those of children with a clinical diagnosis of special needs. all domains showed at least reasonable fit to the Rasch model supporting the validity of the instrument. However, there was a need for further refinement of the instrument in the Irish context. Conclusion: This thesis provides a unique snapshot of early development in Ireland. The EDI and linked parental questionnaires are promising indicators of the extent, distribution and determinants of developmental vulnerability.

A next application prediction service using the BaranC framework

Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

A service-oriented user interaction analysis framework supporting adaptive applications

A comprehensive user model, built by monitoring a user's current use of applications, can be an excellent starting point for building adaptive user-centred applications. The BaranC framework monitors all user interaction with a digital device (e.g. smartphone), and also collects all available context data (such as from sensors in the digital device itself, in a smart watch, or in smart appliances) in order to build a full model of user application behaviour. The model built from the collected data, called the UDI (User Digital Imprint), is further augmented by analysis services, for example, a service to produce activity profiles from smartphone sensor data. The enhanced UDI model can then be the basis for building an appropriate adaptive application that is user-centred as it is based on an individual user model. As BaranC supports continuous user monitoring, an application can be dynamically adaptive in real-time to the current context (e.g. time, location or activity). Furthermore, since BaranC is continuously augmenting the user model with more monitored data, over time the user model changes, and the adaptive application can adapt gradually over time to changing user behaviour patterns. BaranC has been implemented as a service-oriented framework where the collection of data for the UDI and all sharing of the UDI data are kept strictly under the user's control. In addition, being service-oriented allows (with the user's permission) its monitoring and analysis services to be easily used by 3rd parties in order to provide 3rd party adaptive assistant services. An example 3rd party service demonstrator, built on top of BaranC, proactively assists a user by dynamic predication, based on the current context, what apps and contacts the user is likely to need. BaranC introduces an innovative user-controlled unified service model of monitoring and use of personal digital activity data in order to provide adaptive user-centred applications. This aims to improve on the current situation where the diversity of adaptive applications results in a proliferation of applications monitoring and using personal data, resulting in a lack of clarity, a dispersal of data, and a diminution of user control.