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Abstract. In this paper, we propose a kind of mobile learning paradigm learning by seamless migration, which has the capability that task for learning dynamically follows the learner from place to place and machine to machine without learner awareness or intervention. Our key idea is this capability can be achieved by architecture of component platform and agent-based migrating mechanism. In order to study this learning paradigm, a description of pervasive computing task for learning and migrating granularity of task of learning has been suggested, firstly. Then, the architecture for seamless migration has been proposed, a kind of mechanism of seamless migration has been adopted, including solving several sub-problems. Finally, the validity evaluation of this kind of mobile working paradigm is shown by an experimental demo. Suggested learning paradigm by seamless migration is convenient to learn during mobility and is useful for the busy or mobile learner.

Abstract. For supporting location-aware computing in indoor environments, the location sensing/positioning system not only need to provide objects' precise location, but also should own such characteristics as: isotropy and convenience for portability. In this paper, we present an indoor location sensing system, Cicada. This System is based on the TDOA (time difference of arrival) between Radiofrequency and ultrasound to estimate distance, and adopts a technology integrating Slide Window Filter (SWF) and Extended Kalman Filter (EKF) to calculate location. Consequently, it not only can determine the coordinate location within 5cm average deviation either for static objects or for mobile objects, but also owns a nearly omni-directional working area. Moreover, it is able to run independently, mini and light so that it is very easy to be portable and even embedded into people's paraphernalia.

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